Abstract
Water pollution is a globally significant issue. Water environment simulation is an important tool to study water pollution. In order to investigate the impact of hydrological periods, water quality indicators and cross-sections on the water environment capacity, we took the Luo River in Luoyang City as an example, and used MIKE21-coupled hydrodynamic and water quality model and the segmentation method to calculate the water environment capacity of COD, TP, and NH3-N in wet, normal, and dry periods under L1 (Gao Yazhai-Lilou), L2 (Li Lou-Baimasi), and L3 (Baimasi-G207 Highway Bridge) section. The results of the study showed that from the hydrological period, the water environmental capacity was the largest in the wet period due to the dilution effect caused by higher precipitation; from the water quality indexes, the COD concentration was the largest due to industrial pollution, but the higher degradation coefficients result in the largest water environment capacity for COD; from the cross-section, L1 has the longest channel, thus the highest capacity of the water environment was L1. This result can broaden ideas for the application of the MIKE21 model, provide a basis for the calculation of the dynamic water environment capacity and water pollution control in the river.
HIGHLIGHTS
Innovative hydrodynamic water quality model evaluates Luo River pollution across diverse flow scenarios.
Dry periods exhibit poorest water quality, while wet periods showcase the best.
Identified sections exceeding capacity emphasize pollution management during distinct hydrological conditions.
INTRODUCTION
In recent years, water pollution has emerged as a crucial global concern, presenting potential hazards to human health and economic progress, while also negatively impacting the balance of the ecological-social-system (Tang et al. 2022). The close relationship between water quality issues and hydrological as well as hydrodynamic processes is widely acknowledged. Hence, a comprehensive understanding of hydrological, hydrodynamic, and water quality processes, monitoring water pollutants, along with the establishment of a water environment model for assessing watershed water conditions, holds paramount importance for water management and ecological initiatives (Fatahi Nafchi et al. 2021; Sun et al. 2021). Approximately 85% of urban rivers in China are contaminated. The primary factors include household sewage, industrial wastewater, rainwater, and runoff (Zhao et al. 2019; Fu et al. 2023). River contamination will directly jeopardize human health and impede agricultural and fishery activities (Kumar et al. 2021). Additionally, water pollution can contribute to water scarcity. Thus, conducting water environment simulations for rivers, investigating their spatial and temporal distribution characteristics and evolutionary trends, and analyzing the underlying causes can furnish a theoretical foundation for managing water pollution in the environment and for the utilization and safeguarding of water resources (Żelazny et al. 2023).
Water environment simulation involves gathering hydrological, meteorological, pollution source, and runoff data, and utilizing specific numerical simulation techniques aligned with research objectives to achieve a more precise emulation of actual water flow and pollutant transport (García-Alba et al. 2019). This primarily encompasses hydrodynamic and water quality simulations. The natural water cycle constitutes an intricate interplay of physical and chemical processes influenced by a multitude of factors. Through river water quality monitoring and assessment, coupled with analysis of temporal-spatial pollutant distribution traits and evolutionary trends, a theoretical foundation for river basin water pollution management can be established (Deng et al. 2021). Given the expansive scale of natural rivers, studying them solely through direct measurements proves highly challenging. Recently, the fusion of simulation and measurement has gained prominence as the prevailing approach in contemporary research (Goliatt et al. 2021; Montazeri et al. 2023). Regan et al. (2019) utilized applications of the National Hydrologic Model (NHM) to offer insights for enhancing efficiency in water resources planning and management. Ghunowa et al. (2021) developed a spatial decision support system for Stream Power Index for Network (SPIN) toolbox, which can be used to evaluate the stability of urban rivers at the basin scale. Suriya & Mudgal (2012) took the Daya River Basin as an example to simulate the water environment and established a flood management system for the basin, which provided strategic support for water environment management in the basin. Son et al. (2020) used a combination of water quality indices (WQIs) and pollution indices (PIs) to assess riverine water quality in the Cau River to better understand water conditions in the river could be an option for water use decision-making. Research into river water environment simulation frequently demands substantial experimental data backing, and obtaining such data is restricted by diverse factors, including access permissions, data latency, and delays in analytical outcomes. The procurement of experimental data frequently entails substantial investments of human, material, and financial resources. Therefore, the goal of constructing a mathematical water environment model is to circumvent these challenges and minimize expenditure while attaining optimal economic gains.
Mathematical numerical models utilize fundamental equations to tackle a range of complex problems that are challenging to address through conventional means, making them prevalent in water environment simulation research (Paniconi & Putti 2015). The evolution of fluid dynamics mathematical models has undergone substantial refinement and enhancement, enabling them to accurately depict water body flow conditions across diverse operational circumstances (Ghidossi et al. 2006). By configuring hydraulic parameters and fine-tuning input model data, encompassing factors like rainfall, evaporation, tides, and wind patterns, the modeling process strives to closely emulate real-world scenarios (Michot et al. 2011). The hydrodynamic model employs numerical discretization techniques and governing equations to solve water flow, exploring distinct water pathways in varying settings. It significantly contributes to analyzing fluid motion patterns in rivers across different circumstances owing to its heightened accuracy and practicality (Hartmann et al. 2014). Water quality models are employed to depict the patterns of pollutant dispersion in rivers, frequently utilized in researching water pollution, self-purification capabilities, water quality prediction, pollution control, sewage management, and more (González et al. 2014). The MIKE model, a water environment simulation system devised by the Danish Institute for Water and Environment Research, integrates hydrodynamic and water quality modules to simulate and analyze one-dimensional, two-dimensional, and three-dimensional hydrodynamic and water quality states within a watershed (Ji et al. 2022). This model finds extensive application in water environment simulation.
In this study, the MIKE21 water environment model was employed to simulate hydrodynamic and water quality conditions in the Luo River of Luoyang City, serving as an illustrative case. Through refinement of input model boundary conditions, the simulation results exhibited reduced divergence from monitoring data. Subsequently, analysis of the spatial and temporal distribution of water quality variables was conducted. Optimization of pertinent parameters, including diffusion and degradation coefficients, was carried out, followed by an assessment of the plausibility of simulation outcomes. Ultimately, the water environment's capacity and the extent of reduction or remaining capacity in the Luo River across distinct hydrological periods were computed.
The objective of evaluating water environment capacity across varying hydrological phases is to gauge the water system's capability to absorb pollutants and uphold acceptable water quality benchmarks. Acquiring knowledge of water environment capacity empowers researchers to establish water quality thresholds or standards tailored to distinct hydrological periods. Simulating the spatial and temporal evolution of pollutants provides a scientific basis for the calculation of the water environmental capacity, and provides a new idea of thinking for the calculation of the environmental capacity of the river and the water pollution control of pollutants in different hydrological periods. At the same time, this information facilitates the establishment of pragmatic goals for pollutant reduction and offers direction in formulating strategies for water management.
MATERIALS AND METHODS
Study area
Map of the river system and hydrological station network in the study area.
Data sources
The climatic data such as daily precipitation, evaporation, wind direction, wind speed, discharge, and velocity of the Yiluo River Basin were obtained from the Yellow River Water Network (http://www.yrcc.gov.cn/). The topographic elevation data were obtained from the geospatial data cloud (https://www.gscloud.cn/) with a spatial resolution of 30 m. Monthly water quality data of the Luo River in Luoyang City, including COD, NH3-N, TP and DO, were obtained from the Luoyang Environmental Monitoring Monthly Report and the Henan Environmental Bulletin.
Model principle
Hydrodynamic model
The hydrodynamic module in MIKE21 utilizes terrain meshing and incorporates factors such as bed roughness, boundary conditions, precipitation, evaporation, wind patterns, ice coverage, eddy viscosity coefficients, and Coriolis force (Wood et al. 2023). These inputs are employed to simulate water levels, flow velocities, and various other elements of the flow field. This methodology finds extensive utility in simulating two-dimensional shallow water flows with open surfaces.














Water quality model



Model steps
The selection of the MIKE21 model is attributed to its widespread acknowledgment and extensive utilization in water resource management and environmental modeling endeavors. Its versatility, adaptability, and customizable attributes render it appropriate for a multitude of research objectives and site-specific circumstances. The model's robust numerical algorithms guarantee precise and efficient simulations, even when confronted with intricate and dynamic surroundings. Furthermore, the user-friendly interface and comprehensive post-processing tools simplify setup, visualization, and result analysis procedures. The substantial validation and endorsement from the scientific community additionally bolster its credibility. In summary, the MIKE21 model delivers reliability, adaptability, and computational efficiency, rendering it a suitable option for studying water quality dynamics and guiding decisions in water resource management.
MODEL ESTABLISHMENT OF THE LUOYANG CITY SECTION OF THE LUO RIVER
Establishment of the hydrodynamic model
Grid terrain
Boundary processing
Flow distribution from 1960 to 2020 at the Baimasi hydrological station.
Based on the multi-year long time series measured monthly flow data from 1960 to 2020 at Baimasi hydrological station, the P-III curve is drawn, and the measured flow data are allocated to determine a total of three calculation conditions, which are P = 10%, P = 50% and P = 90% of the annual allocation flow. The calculation conditions are shown in Table 1.
Calculation conditions
Condition setting . | Type . | Flow rate under guarantee (m3/s) . | Typical representative year . |
---|---|---|---|
Condition 1 | Wet year (P = 10%) | 29.63 | 2010 |
Condition 2 | Normal water year (P = 50%) | 16.25 | 2013 |
Condition 3 | Dry year (P = 90%) | 6.42 | 2020 |
Condition setting . | Type . | Flow rate under guarantee (m3/s) . | Typical representative year . |
---|---|---|---|
Condition 1 | Wet year (P = 10%) | 29.63 | 2010 |
Condition 2 | Normal water year (P = 50%) | 16.25 | 2013 |
Condition 3 | Dry year (P = 90%) | 6.42 | 2020 |
Calibration results of model parameters
Status variables . | Parameter . | Value . |
---|---|---|
COD | Adsorption coefficient (/day) | 0.004 |
Semi-saturated oxygen concentration (mg/L) | 1.9 | |
20°C degradation rate coefficient (/day) | 0.003 | |
Temperature coefficient of degradation rate | 1.07 | |
NH3-N | Nitrogen uptake semi-saturation (mg/L) | 0.045 |
BOD decay release ammonia ratio (g NH3-N/g BOD) | 0.25 | |
Temperature coefficient | 1.092 | |
COD degradation of ammonia nitrogen ratio (g NH3-N/g COD) | 0.25 | |
DO | The sediment requires oxygen (/m2)/day | 0.4 |
Sediment aerobic temperature coefficient (/day) | 1.07 | |
The number of aerobic semi-saturated sediment in the sediment (mg/L) | 2 | |
The respiration rate of the plant (/m2) | 0.135 | |
TP | Phosphorus ingested by plants (g P/g DO) | 0.092 |
The phosphorus content of dissolved BOD (g P/g BDO) | 0.05 | |
Adsorption coefficient (/day) | 0.0002 | |
Phosphorus ingested by bacteria (g P/g DO) | 0.015 |
Status variables . | Parameter . | Value . |
---|---|---|
COD | Adsorption coefficient (/day) | 0.004 |
Semi-saturated oxygen concentration (mg/L) | 1.9 | |
20°C degradation rate coefficient (/day) | 0.003 | |
Temperature coefficient of degradation rate | 1.07 | |
NH3-N | Nitrogen uptake semi-saturation (mg/L) | 0.045 |
BOD decay release ammonia ratio (g NH3-N/g BOD) | 0.25 | |
Temperature coefficient | 1.092 | |
COD degradation of ammonia nitrogen ratio (g NH3-N/g COD) | 0.25 | |
DO | The sediment requires oxygen (/m2)/day | 0.4 |
Sediment aerobic temperature coefficient (/day) | 1.07 | |
The number of aerobic semi-saturated sediment in the sediment (mg/L) | 2 | |
The respiration rate of the plant (/m2) | 0.135 | |
TP | Phosphorus ingested by plants (g P/g DO) | 0.092 |
The phosphorus content of dissolved BOD (g P/g BDO) | 0.05 | |
Adsorption coefficient (/day) | 0.0002 | |
Phosphorus ingested by bacteria (g P/g DO) | 0.015 |
According to the corresponding guaranteed rate flow, the corresponding typical representative years are selected, and 2010 is chosen to represent the wet year, 2013 represents the normal year, and 2020 represents the dry year. According to the convergence experience of the model, the upstream boundary is given the flow condition, and the downstream boundary is given the water level condition, when the model is more likely to converge.
Parameter setting
The selection of the parameters will directly affect the accuracy of the model simulation. The following parameters are required for model calculations (Table 2).
Time step
The simulations are conducted for three hydrological scenarios, namely, wet year, average year, and dry year. The simulation time step for each scenario is one day, equivalent to 86,400 s, with a total of 365 time-steps representing 1 year of simulation.
CFL
Courant–Friedrichs–Lewy (CFL) is a stability criterion extensively employed in numerical techniques for solving partial differential equations (PDEs), notably within the realm of computational fluid dynamics (CFD). In numerical PDE-solving methodologies, equations are usually discretized onto a grid or mesh, while time is also divided into minute time intervals (Gnedin et al. 2018). The CFL criterion furnishes a prerequisite guaranteeing the stability of the numerical solution. Rooted in the concept that system information or disturbances ought not to disseminate excessively and swiftly in relation to the numerical method's resolution. It establishes a connection between the time step magnitude, spatial discretization, and the utmost speed at which perturbations can advance within the system. To summarize, the CFL criterion serves as a stability criterion, offering a directive for selecting a fitting time step magnitude within numerical methods geared towards solving CFD problems. This ensures the stability and precision of simulations. CFL is an important condition to judge the convergence of results in hydrodynamic calculations. In this study, CFL is 0.8.
Wind field conditions
Manning coefficient
Dry and wet water depth
Since the simulation area is in the alternating boundary area of dry and wet, it is necessary to set drying depth, flooding depth and wetting depth to avoid distortion in the operation of the model. According to the definition principle of dry and wet water depth, after several debuggings, the above three parameters are set to 0.005, 0.05, and 0.10 m, respectively.
Eddy viscosity
The eddy viscosity is a parameter to solve the problem of Reynolds additional stress. There are three ways to set, respectively, no eddy viscosity, constant eddy viscosity, and Smagorinsky formula method (Terziev et al. 2020). Eddy viscosity of the Smagorinsky formula setting method in the range of 0.25–1.00, after the model parameters for many times, the final determination of the study eddy viscosity to take the value of 0.28, at this time the minimum error of hydrodynamic simulation results.
Model validation
In the simulation process, the lack of some data led to a not very good fit between the simulated water level and the actual data in the early stage, but as the model continued to simulate the downstream section of the river, the calculated values are more consistent with the actual measured values. The results show that the model has a high fit, the simulated value of water level has a consistent trend with the measured value, and the model simulation results are good. January–July belongs to the non-flood period, the water level basically remains the same or decreases, after July, the water level starts to rise rapidly, which is in line with the actual situation. From the error graph between the simulated and measured values, we can see that the largest relative error is June 30, the error is only 0.09 m, which is within the acceptable error range. The measured average water level at the Baimasi hydrological station is 113.42 m, and the simulated average water level value is 113.28 m, respectively. The relative error is 0.12%, R2 is 0.942, NSE is 0.928. The relative error is less than 1%, and the R2 and NSE are greater than 0.9, which meets the model calculation requirements.
Establishment of the water quality model
Parameter selection and calibration
Degradation coefficient k is an extraordinary quantity that varies with time and temperature and other influencing factors. Different water quality has different degradation capacity of pollutants, and usually, for the convenience of calculation, it is often considered that for pollutants in the same river section, the degradation capacity is certain and set as k value. The degradation coefficient can generally be determined based on the pollutant concentrations at different monitoring points in the simulation area. In the simulation process, the pollutant in the water body following the movement of the flow field has the phenomenon of diffusion, the degree of diffusion is expressed by the modeling of the given diffusion coefficient of this parameter to determine. The diffusion coefficient is finally determined through several model parameter rate determinations: E = 0.45 m2/s, KCOD = 0.18/day,KTP = 0.04/day, KDO = 0.10/day. After several simulations to verify the solution, the following are the results of water quality model parameter rate determination.
Simulated working conditions and model boundary conditions
In this study, the numerical water quality simulation conditions are set according to the three conditions of the hydrodynamic model. Based on the results of parameter rate determination of the above water environment model, the distribution of pollutant concentrations under different hydrological year assurance rate scenarios was analyzed. The measured time series data of Gao Yazhai hydrological station are selected upstream and the measured time series data of Baimasi hydrological station are selected downstream, as shown in Table 3.
Setting of different working conditions and boundary conditions
Condition . | Wet year (10%) . | Normal water year (50%) . | Dry year (90%) . | |
---|---|---|---|---|
Upstream boundary | Hydrology | Time series of measured flow in Gao Yazhai in 2010 | Time series of measured flow in Gao Yazhai in 2013 | Time series of measured flow in Gao Yazhai in 2020 |
Water quality | Time series of water quality concentration in Gao Yazhai in 2020 | |||
Downstream boundary | Hydrology | Time series of measured water levels in 2010 at Baimasi | Time series of measured water levels in 2013 at Baimasi | Time series of measured water levels in 2020 at Baimasi |
Water quality | Zero gradient boundary |
Condition . | Wet year (10%) . | Normal water year (50%) . | Dry year (90%) . | |
---|---|---|---|---|
Upstream boundary | Hydrology | Time series of measured flow in Gao Yazhai in 2010 | Time series of measured flow in Gao Yazhai in 2013 | Time series of measured flow in Gao Yazhai in 2020 |
Water quality | Time series of water quality concentration in Gao Yazhai in 2020 | |||
Downstream boundary | Hydrology | Time series of measured water levels in 2010 at Baimasi | Time series of measured water levels in 2013 at Baimasi | Time series of measured water levels in 2020 at Baimasi |
Water quality | Zero gradient boundary |
Model validation



Water quality model validation results
Section . | Indicator . | ![]() | R2 . | NSE . |
---|---|---|---|---|
Gao Yazhai monitoring section | COD | 2.40 | 0.89 | 0.87 |
DO | 2.66 | 0.90 | 0.88 | |
NH3-N | 3.63 | 0.87 | 0.85 | |
TP | 1.30 | 0.92 | 0.90 | |
Baimasi monitoring section | COD | 4.96 | 0.85 | 0.83 |
DO | 2.92 | 0.91 | 0.89 | |
NH3-N | 4.14 | 0.86 | 0.84 | |
TP | 1.01 | 0.93 | 0.90 |
Section . | Indicator . | ![]() | R2 . | NSE . |
---|---|---|---|---|
Gao Yazhai monitoring section | COD | 2.40 | 0.89 | 0.87 |
DO | 2.66 | 0.90 | 0.88 | |
NH3-N | 3.63 | 0.87 | 0.85 | |
TP | 1.30 | 0.92 | 0.90 | |
Baimasi monitoring section | COD | 4.96 | 0.85 | 0.83 |
DO | 2.92 | 0.91 | 0.89 | |
NH3-N | 4.14 | 0.86 | 0.84 | |
TP | 1.01 | 0.93 | 0.90 |
Broadly speaking, the model attains commendable outcomes in simulating pollutant concentrations. The deviations in simulated COD concentrations at both monitoring sections remain below 7%, with maximum, minimum, and average errors of 6.84, 0.35, and 3.68%, respectively. Simulation outcomes reveal that pollutant concentrations in each zone fall below actual monitoring data, suggesting the model's propensity to potentially underestimate pollutant concentrations. In certain instances of extreme values and abrupt transitions, simulation outcomes prove subpar, warranting further enhancements and optimization efforts. The statistical analysis depicted in Table 5 showcases the average R2 and NSE values derived through the execution of the water environment simulation model devised in this study. Notably, R2 values in both model sections surpass 0.85, denoting a robust correlation between observed and projected data. Moreover, the NSE values exceed 0.80, further bolstering the claim that the model demonstrates reliability in accurately prognosticating the pollution trends across various state variables.
Table of pollutant degradation coefficients in the river section of the study area
River section number . | Cross-section . | Hydrological condition . | Degradation coefficient (K) . | ||
---|---|---|---|---|---|
TP . | COD . | NH3-N . | |||
L1 | Gao Yazhai-Li Lou | Wet year | 0.1428 | 0.1517 | 0.0824 |
Normal year | 0.1107 | 0.0632 | 0.0632 | ||
Dry year | 0.0858 | 0.0387 | 0.0414 | ||
L2 | Li Lou-Baimasi | Wet year | 0.0905 | 0.2114 | 0.0588 |
Normal year | 0.0839 | 0.1829 | 0.0453 | ||
Dry year | 0.0531 | 0.0652 | 0.0327 | ||
L3 | Baimasi-G207 Highway Bridge | Wet year | 0.1208 | 0.1452 | 0.0619 |
Normal year | 0.1128 | 0.1085 | 0.0531 | ||
Dry year | 0.0990 | 0.0648 | 0.0399 |
River section number . | Cross-section . | Hydrological condition . | Degradation coefficient (K) . | ||
---|---|---|---|---|---|
TP . | COD . | NH3-N . | |||
L1 | Gao Yazhai-Li Lou | Wet year | 0.1428 | 0.1517 | 0.0824 |
Normal year | 0.1107 | 0.0632 | 0.0632 | ||
Dry year | 0.0858 | 0.0387 | 0.0414 | ||
L2 | Li Lou-Baimasi | Wet year | 0.0905 | 0.2114 | 0.0588 |
Normal year | 0.0839 | 0.1829 | 0.0453 | ||
Dry year | 0.0531 | 0.0652 | 0.0327 | ||
L3 | Baimasi-G207 Highway Bridge | Wet year | 0.1208 | 0.1452 | 0.0619 |
Normal year | 0.1128 | 0.1085 | 0.0531 | ||
Dry year | 0.0990 | 0.0648 | 0.0399 |
Calculation of the water environment capacity


RESULTS AND DISCUSSION
Spatiotemporal variations of water quality
Wet year
Pollutant concentration at each monitoring point during the wet year.
Normal year
Pollutant concentration at each monitoring point during the normal year.
Dry year
Pollutant concentration at each monitoring point during the dry year.
In summary, from the perspective of hydrological year, the overall water environment quality is ranked from good to bad in the order of wet year, normal year, and dry year. The large amount of precipitation in wet year has a diluting effect on pollutants, and good hydrodynamic conditions and faster flow rates increase the self-purification capacity of the river, thus leading to the best water quality in that hydrological year. From the point of view of the concentration of water quality indicators, the overall water quality concentration in descending order is COD, NH3-N, TP, and DO. Because there are a large number of wastewater treatment plants in the downstream, such as Luonan Wastewater Treatment Plant, Jiandong Wastewater Treatment Plant, Luoning Wastewater Treatment Plant, and a large number of sewage outfalls, such as the Luolonglu outfall, the Huarun Power industrial outfall, which discharges a large amount of industrial wastewater, leading to an increase in the concentration of COD. Farmland surface source pollution and residential sewage discharge lead to increased concentrations of NH3-N and TP. In terms of sections, the overall water quality of the sections is ranked from good to bad in the order of A1, A2, and A3. The water quality of section is poorer in the downstream due to the presence of a large number of wastewater treatment plants and outfalls in the downstream, and the deterioration of water quality in the downstream is aggravated by the cumulative effect of water quality in the upstream to that in the downstream.
Spatiotemporal variations of water environment capacity
In this paper, the typical representative factors COD, NH3-N, and TP are selected as the control indexes of the water environment capacity of Luoyang city section of the Luo River. We examined the water environment capacity of river sections under three hydrological periods: wet, normal, and dry. The one-dimensional steady-state model is used to calculate the water environment capacity. The calculation results are shown in Tables 6–8.
COD water environment capacity accounting results of the study area river section
River section number . | Cross-section . | Calculation type . | COD water environment capacity . | ||
---|---|---|---|---|---|
Wet period . | Normal period . | Dry period . | |||
L1 | Gao Yazhai-Li Lou | Dilution capacity | 2,874.614 | 1,880.779 | 738.132 |
Self-cleaning capacity | 3,942.566 | 2,438.966 | 1,301.870 | ||
Environmental capacity | 6,817.180 | 4,319.746 | 2,040.002 | ||
L2 | Li Lou-Baimasi | Dilution capacity | 1,846.793 | 564.621 | 176.887 |
Self-cleaning capacity | 1,384.567 | 1,091.814 | 361.241 | ||
Environmental capacity | 3,231.360 | 1,656.434 | 538.127 | ||
L3 | Baimasi -G207 Highway Bridge | Dilution capacity | 2,311.425 | 1,008.067 | 395.064 |
Self-cleaning capacity | 2,326.899 | 1,587.613 | 681.432 | ||
Environmental capacity | 4,638.323 | 2,595.679 | 1,076.496 |
River section number . | Cross-section . | Calculation type . | COD water environment capacity . | ||
---|---|---|---|---|---|
Wet period . | Normal period . | Dry period . | |||
L1 | Gao Yazhai-Li Lou | Dilution capacity | 2,874.614 | 1,880.779 | 738.132 |
Self-cleaning capacity | 3,942.566 | 2,438.966 | 1,301.870 | ||
Environmental capacity | 6,817.180 | 4,319.746 | 2,040.002 | ||
L2 | Li Lou-Baimasi | Dilution capacity | 1,846.793 | 564.621 | 176.887 |
Self-cleaning capacity | 1,384.567 | 1,091.814 | 361.241 | ||
Environmental capacity | 3,231.360 | 1,656.434 | 538.127 | ||
L3 | Baimasi -G207 Highway Bridge | Dilution capacity | 2,311.425 | 1,008.067 | 395.064 |
Self-cleaning capacity | 2,326.899 | 1,587.613 | 681.432 | ||
Environmental capacity | 4,638.323 | 2,595.679 | 1,076.496 |
Accounting results of TP water environment capacity in the study area river section
River section number . | Cross-section . | Calculation type . | TP water environment capacity . | ||
---|---|---|---|---|---|
Wet period . | Normal period . | Dry period . | |||
L1 | Gao Yazhai-Li Lou | Dilution capacity | 363.727 | 204.965 | 80.542 |
Self-cleaning capacity | 49.620 | 35.045 | 21.007 | ||
Environmental capacity | 413.347 | 240.010 | 101.549 | ||
L2 | Li Lou-Baimasi | Dilution capacity | 262.822 | 135.354 | 51.486 |
Self-cleaning capacity | 7.975 | 6.747 | 4.817 | ||
Environmental capacity | 270.797 | 142.101 | 56.302 | ||
L3 | Baimasi -G207 Highway Bridge | Dilution capacity | 307.408 | 171.449 | 66.779 |
Self-cleaning capacity | 25.913 | 21.991 | 11.087 | ||
Environmental capacity | 333.321 | 193.440 | 77.866 |
River section number . | Cross-section . | Calculation type . | TP water environment capacity . | ||
---|---|---|---|---|---|
Wet period . | Normal period . | Dry period . | |||
L1 | Gao Yazhai-Li Lou | Dilution capacity | 363.727 | 204.965 | 80.542 |
Self-cleaning capacity | 49.620 | 35.045 | 21.007 | ||
Environmental capacity | 413.347 | 240.010 | 101.549 | ||
L2 | Li Lou-Baimasi | Dilution capacity | 262.822 | 135.354 | 51.486 |
Self-cleaning capacity | 7.975 | 6.747 | 4.817 | ||
Environmental capacity | 270.797 | 142.101 | 56.302 | ||
L3 | Baimasi -G207 Highway Bridge | Dilution capacity | 307.408 | 171.449 | 66.779 |
Self-cleaning capacity | 25.913 | 21.991 | 11.087 | ||
Environmental capacity | 333.321 | 193.440 | 77.866 |
Accounting results of NH3-N water environment capacity of the river section in the study area
River section number . | Cross-section . | Calculation type . | NH3-N water environment capacity . | ||
---|---|---|---|---|---|
Wet period . | Normal period . | Dry period . | |||
L1 | Gao Yazhai-Li Lou | Dilution capacity | 1,131.073 | 620.051 | 238.568 |
Self-cleaning capacity | 162.920 | 102.024 | 61.404 | ||
Environmental capacity | 1,293.993 | 722.076 | 299.971 | ||
L2 | Li Lou-Baimasi | Dilution capacity | 553.803 | 265.552 | 95.835 |
Self-cleaning capacity | 39.877 | 29.813 | 12.135 | ||
Environmental capacity | 593.680 | 295.365 | 107.970 | ||
L3 | Baimasi-G207 Highway Bridge | Dilution capacity | 835.398 | 462.783 | 177.906 |
Self-cleaning capacity | 122.734 | 61.341 | 36.221 | ||
Environmental capacity | 958.132 | 524.123 | 214.127 |
River section number . | Cross-section . | Calculation type . | NH3-N water environment capacity . | ||
---|---|---|---|---|---|
Wet period . | Normal period . | Dry period . | |||
L1 | Gao Yazhai-Li Lou | Dilution capacity | 1,131.073 | 620.051 | 238.568 |
Self-cleaning capacity | 162.920 | 102.024 | 61.404 | ||
Environmental capacity | 1,293.993 | 722.076 | 299.971 | ||
L2 | Li Lou-Baimasi | Dilution capacity | 553.803 | 265.552 | 95.835 |
Self-cleaning capacity | 39.877 | 29.813 | 12.135 | ||
Environmental capacity | 593.680 | 295.365 | 107.970 | ||
L3 | Baimasi-G207 Highway Bridge | Dilution capacity | 835.398 | 462.783 | 177.906 |
Self-cleaning capacity | 122.734 | 61.341 | 36.221 | ||
Environmental capacity | 958.132 | 524.123 | 214.127 |
Water environment capacity variations in different hydrological periods
The dynamic changes of water environment capacity in different hydrological periods can be seen from the above three tables. The water environment capacity of COD is the largest, NH3-N is the second largest and TP is the smallest in the section of Luoyang city, where the water environment capacity of COD is 26,913.35 kg/d, NH3-N is 5,009.44 kg/d, and TP is 1,828.73 kg/d. The water environment capacity of the three pollution indicators is the largest in the wet period, the second largest in the normal water period and the smallest in the dry period, in which the water environment capacity of COD is 14,686.86; 8,571.86; and 3,654.63 kg/d in the wet, normal, and dry water periods, respectively, the water environment capacity of NH3-N is 2,845.80; 1,541.56; and 622.07 kg/d in wet, normal, and dry water periods, respectively. The water environment capacity of TP is 1,017.47; 575.55; and 235.72 kg/d in wet, normal, and dry water periods, respectively. This is mainly because COD, NH3-N and TP in the Luoyang City section of the Luo River mainly originate from point source pollution. The poor hydrodynamic conditions of the river during the dry period and the decrease in flow rate, coupled with the growth of industrial and urban residents' domestic water demand, result in the growth of urban industrial and domestic wastewater discharge. It can be seen that there is a non-uniformity in the water environment capacity of the Luoyang city section of the Luo River in time, and calculating it only with a certain guarantee rate will inevitably lead to the waste of water environment capacity, which will in turn restrict the economic development of the region. Studying the water environment capacity of the river section from the perspective of different hydrological periods is more conducive to socio-economic development and regional water resources protection.
Water environment capacity variations in different river sections
For COD, the longest L1 river section corresponds to the largest COD water environment capacity, which is 6,817.18; 4,319.75; and 2,040.00 t/a in the wet, normal, and dry periods, respectively. L3 is the second longest, which corresponds to the smaller COD water environment capacity, which is 4,638.32; 2,595.68; and 1,076.50 t/a in the wet, normal, and dry periods, respectively. L2 is the shortest river section and has the smallest water environment capacity, which is 3,231.36; 1,656.43; and 538.13 t/a in wet, normal, and dry periods, respectively, indicating that the longer the length of the river, the larger the water environment capacity of COD. Of course, the size of the water environmental capacity should also have a great relationship with the water quality objectives, but the water quality objectives on the river section of this study are all class I. Therefore, the difference of the water environmental capacity of this study is mainly reflected in the length of the river.
For NH3-N, under the conditions of different hydrological periods, the water environmental capacity of each river section of the Luoyang city section of the Luo River is the largest during the wet period, slightly smaller than that during the normal period, and the worst flow conditions during the dry period lead to the smallest water environmental capacity. The water environmental capacity of L1 is the largest, and the water environmental capacity during the wet, normal, and dry periods are 1,293,99, 722.08, and 299.97 t/a, respectively. The corresponding water environmental capacity of L3 is the second shortest and corresponds to a smaller water environmental capacity of NH3-N, which is 958.13, 524.12, 214.13 t/a during the wet, normal, and dry periods, respectively. L2 is the shortest and has the smallest water environmental capacity, which is 93.68, 295.36, 107.97 t/a, further indicating that the longer the length of the river, the greater the NH3-N water environmental capacity.
For TP, the flow conditions of each section of the Luo River Luoyang city are the best during the wet period resulting in the largest water environment capacity, followed by the second in the normal period and the smallest in the dry period. L1 has the largest water environment capacity, which is 413.35, 240.01, 101.55 t/a in the wet, normal, and dry periods, respectively. L3 has the second largest, corresponding to the smallest water environment capacity of TP, which is 333.32, 193.44, 77.87 t/a in the wet, normal, and dry water periods, respectively. L2 was the shortest and had the smallest water environment capacity, with the TP water environment capacity in wet, normal, dry periods being 270.80; 142.10; and 56.30 t/a, respectively.
Water environment management
In summary, in terms of hydrological periods, the capacity of the water environment is ranked from largest to smallest as the wet period, the normal period, and the dry period. The higher precipitation is in the wet period, which has a higher dilution capacity for pollutants, and better hydrodynamic conditions, with higher flow rates and better pollutant transport performance. From the point of view of water quality indicators, the capacity of the water environment from large to small is in the order of COD, NH3-N, TP. The degradation coefficient of COD is larger compared to NH3-N and TP, and affected by the discharge of industrial wastewater, the flow of COD discharged into the river is higher, which leads to the larger water environment capacity for COD. From the point of view of the river section, the capacity of the water environment is ranked from large to small as L1, L3, L2. The length of the river section plays a key role in the size of the capacity of the water environment, and the longer the river section, the larger the capacity of the water environment.
Considering the field conditions of the Luoyang urban section of the Luo River, the following control suggestions are proposed. Based on the characteristics of pollutants in the river channel of the Luoyang urban section, targeted cultivation of microbial communities is recommended for pollutant decomposition. These microbial communities can then be transplanted into the river channel to ensure favorable growth conditions. Measures such as artificial aeration and oxygenation should be implemented to support the survival of the microbial communities, significantly increasing their survival time and degradation effectiveness. It is important to note that throughout the entire stage of applying water ecological restoration techniques, priority should be given to selecting diverse and abundant plant species that can better complement each other in order to improve water quality through degradation effects. During the low-water period, pollutant discharge restrictions should be implemented, while water flow control measures should be taken during the high-water period. In the Luoyang urban section of the Luo River, a portion of COD, NH3-N, and TP pollutants originates from non-point source pollution. During the high-water period with increased rainfall and higher flow rates, non-point source pollutants are more likely to enter the river through stormwater runoff, leading to a decrease in water environmental capacity. During the low-water period, when water flow is reduced and industrial water supply to the riverside industrial enterprises in the Luoyang section decreases, it is recommended for the city of Luoyang to establish a pollution prevention and control system. This system should include implementing temporary shutdowns and production restrictions for key pollution source enterprises, as well as strengthening supervision to ensure compliance with pollutant emissions standards.
CONCLUSIONS
In order to explore the impact of hydrological periods, water quality indicators, and river sections on the water environment capacity, this paper took the Luo River in Luoyang city as an example, we used MIKE21-coupled hydrodynamic and water quality model and the segmentation method to calculate the water environment capacity of COD, TP, and NH3-N in wet, normal, and dry periods under L1, L2, and L3 sections. The following conclusions were shown.
During the hydrodynamic model process, the average value of the measured water level is 113.42 m and the average value of the simulated water level is 113.28 m, with a relative error of 0.12%. R2 is 0.942 and NSE is 0.928. During the water quality model process, the R2 reached more than 0.85 and the mean value of NSE of the two monitoring sections reached above 0.80. Thus, the model is reliable and can accurately predict the migration change of each water quality index.
From the perspective of hydrological year, the water quality is best in the wet year, which is due to the fact that the increase of precipitation has a diluting effect on the pollutants, the faster flow rate can also promote the water purification capacity, and the largest water environment capacity during the wet period. In terms of the concentration of water quality indexes, the concentration of COD is the largest, but the higher degradation coefficients result in the largest water environment capacity for COD. Therefore, the government needs to strengthen the management of COD, and control the discharge of industrial wastewater upstream and the domestic sewage discharge of coastal residents. In terms of river sections, the water quality in the downstream is worse than the upstream due to the diffusion and accumulation of pollutants from the upstream to the downstream, and the presence of a large number of wastewater treatment plants in the downstream exacerbates the deterioration of water quality. Additionally, the longer river section results in the largest water environment capacity in L1.
This paper can achieve the organic linkage between the management of the water quality objectives and the control of the total amount of pollution and provide scientific guidance to the improvement of water quality in the Luo River. This study also broadens new ideas for the application of MIKE 21 and promoting refined management of river water environments in wet, normal, and dry periods. However, there are some shortcomings in this paper. For example, this paper only selected the main pollutant exceedance factors COD, DO, TP, NH3-N. There are still some important pollutants not considered, such as biochemical oxygen demand and Escherichia coli. Thus, further research is needed. The hydrodynamic water quality coupling model and water environment capacity model can be considered in the future with a three-dimensional model, so that the vertical flow and pollution dispersion are also taken into account.
FUNDING
This work was supported by the North China University of Water Resources and Electric Power Innovation Ability Improvement Project for Postgraduates [grant number NCWUYC-2023006] and [grant number NCWUYC-2023027].
AUTHOR CONTRIBUTION
All authors contributed to the study conception and design. Writing and editing by Y.W. Preliminary data collection by Y.W. All authors read and approved the final manuscript.
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.