A water pollutant discharge regulation method based on water environment quality zoning dynamic response

Abstract

This invention discloses a method for regulating water pollutant discharge based on dynamic response of water environment quality zones, comprising: Step S1, spatially dividing the receiving water body, identifying the hydrodynamic characteristics of different zones, and constructing a dynamic response function of pollutant concentration in each zone to the impact of discharge; Step S2, calculating the environmental capacity of each zone in reverse based on the water quality target value and background concentration of different areas, combined with the zone response function; Step S3, constructing a pollution impact matrix of discharge outlets and zones based on the pollution transmission effect of each discharge outlet on each zone; Step S4, allocating the environmental capacity to the permitted discharge volume of each discharge outlet to form differentiated discharge control instructions; Step S5, adjusting the wastewater discharge volume based on the discharge control instructions. This invention helps to identify highly sensitive areas before discharge permits are issued, avoiding the release of loads exceeding the carrying capacity into slow-response areas, thereby significantly reducing the risk of local water quality exceeding standards.

Description

Technical Field

[0001] This invention relates to the field of wastewater control and discharge technology, and in particular to a method for regulating water pollutant discharge based on dynamic response of water environment quality zones. Background Technology

[0002] Water environmental quality is a core element of ecological civilization construction, and its maintenance and improvement are crucial for the sustainable development of human society. During industrialization and urbanization, the discharge of water pollutants is a major factor affecting water environmental quality. Therefore, scientifically and accurately controlling water pollutant discharge to ensure compliance with water body functional zoning standards has become a key task in environmental management. Especially in river confluence areas, due to their complex hydrodynamic characteristics, such as the confluence of multiple water flows, variations in flow velocity and depth, and the resulting non-uniform flow field structures like main channels, backflow separation zones, and stagnant zones, the transport, diffusion, and dilution of pollutants in receiving water bodies exhibit high spatial heterogeneity and temporal dynamics. In such complex water bodies, accurately assessing the water body's response to pollutant discharge and formulating scientifically sound discharge control strategies accordingly is a major challenge currently facing water environment management.

[0003] When two rivers converge, such as the first river flowing into the second, the total amount of wastewater discharged into the second river is determined based on the flow rate of the first river flowing into the second river. However, due to the uneven spatial distribution of the flow field at the confluence, the response time of the receiving water body to the upstream discharge varies significantly. In backflow or stagnant areas, water renewal is slow and pollutants remain for a long time; while in the main channel area, water renewal is fast and pollutants flow downstream. If the total discharge is allocated only according to the flow rate without considering these differences in response time, it will lead to the inability to accurately distribute the total amount of wastewater to the effective dilution zone of the main channel, resulting in local accumulation, response lag, and dilution mismatch.

[0004] Therefore, it is necessary to establish a water pollutant discharge regulation method based on the dynamic response of water environment quality zones to solve the problems of uneven spatial distribution of water flow field in the confluence zone and significant differences in the response time of pollutants in different regions. By constructing a response function in the time domain, the dynamic dilution capacity and environmental capacity of each zone can be accurately quantified, and the pollutant discharge can be adjusted in real time and adaptively. Summary of the Invention

[0005] This invention overcomes the shortcomings of the prior art and provides a method for regulating water pollutant emissions based on dynamic response of water environment quality zones.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a method for regulating water pollutant emissions based on dynamic response of water environment quality zoning, comprising the following steps:

[0007] Step S1: Spatial partitioning of the receiving water body, identification of the hydrodynamic characteristics of different partitions, and construction of dynamic response functions of pollutant concentration in each partition to the impact of discharge.

[0008] Step S2: Based on the target water quality values ​​and background concentrations of different regions, and combined with the zoning response function, the environmental capacity of each zone is calculated in reverse.

[0009] Step S3: Based on the pollution transmission effect of each sewage outlet on each zone, construct the sewage outlet-zone pollution impact matrix;

[0010] Step S4: Allocate environmental capacity to the permitted discharge volume of each discharge outlet to form differentiated emission control instructions;

[0011] Step S5: Adjust the wastewater discharge volume based on emission control instructions.

[0012] In a preferred embodiment of the present invention, in step S1, spatial partitioning of the receiving water body includes:

[0013] Step S11: Extract calculation indicators, including flow velocity and gradient, vorticity, residence time, and turbulent diffusion coefficient;

[0014] Step S12: Use the obtained multiple calculation indicators as feature vectors to cover all grids in the entire calculation domain. Use machine learning clustering algorithm to automatically classify the multiple grids, which are classified into mainstream channel area, backflow stagnant water area and confluence turbulence area.

[0015] In a preferred embodiment of the present invention, in step S1, constructing a dynamic response function of pollutant concentration in each zone to emission impact includes:

[0016] Determine the causal link between pollutant input and output within the target response zone for each discharge outlet, and clarify the transmission path and impact channel of pollutants from the discharge point into each response zone;

[0017] Input the unit pollutant mass parameter at the designated discharge outlet, and continuously record the concentration change process over time in the target response zone to obtain the pollutant concentration curve and obtain the dynamic response function of each zone.

[0018] In a preferred embodiment of the present invention, the pollutant quality parameters include: effective exchange flow rate, water residence time, mixing intensity, response lag, and dilution index.

[0019] In a preferred embodiment of the present invention, step S2, reverse calculation of the environmental capacity of each partition, includes:

[0020] Obtain the target water quality value and background concentration for each response zone. The target water quality value is determined based on the functional zoning standards of the receiving water body or the compliance requirements of the control section. The background concentration is obtained based on the historical monitoring data of the zone and serves as the initial benchmark for future concentration changes.

[0021] Based on the water quality target value, the allowable range of target concentration variation for this zone is constructed, and the predicted concentration at any future time point is set not to exceed the water quality target value. The prediction relationship is as follows: ,in, Indicates partition Z in time The predicted concentration value, For the sewage outlet O in time Emissions, For the dynamic response function with time lag Concentration changes;

[0022] The constraints for solving environmental capacity can be expressed as: ;

[0023] Solving using the above constraints The maximum feasible solution yields the environmental capacity.

[0024] In a preferred embodiment of the present invention, in step S3, constructing the sewage outlet-zone pollution impact matrix includes:

[0025] Based on the partition response function, the pollution impact coefficient of sewage outlet O on partition Z is defined. This represents the average concentration impact per unit load on the partition within a set evaluation time window T. ,in, Let T represent the pollution response function of discharge outlet O to zone Z, and let T represent the response assessment time period. It reflects the intensity of the contribution of a unit mass of pollutant discharged from the discharge outlet O to the concentration of zone Z within the period T;

[0026] Arrange all sewage outlets and pollution impact coefficients of all response zones in a row-column correspondence to construct matrix N.

[0027] In a preferred embodiment of the present invention, in step S4, based on the environmental capacity of each zone calculated in reverse in step S2, the pollution impact matrix of the discharge outlet-zone constructed in step S3, and the dynamic dilution capacity weight of each zone, a multi-objective optimization model is constructed and solved, and the total environmental capacity is allocated to the permissible emission amount of each discharge outlet, thereby forming differentiated emission control instructions.

[0028] In a preferred embodiment of the present invention, the dynamic dilution capacity weight is calculated based on the partition dynamic response parameters, and is directly proportional to the effective exchange flow rate and mixing intensity, and inversely proportional to the water retention time;

[0029] The multi-objective optimization model takes ensuring that the water quality of all zones meets the standards as the main constraint, and maximizes the total allowable discharge and / or minimizes the discharge control cost as the optimization objectives.

[0030] The multi-objective optimization model is solved by using non-dominated sorting genetic algorithm II or multi-objective particle swarm optimization algorithm. The optimization allocation logic prioritizes allocating higher emission quotas to the sewage outlets corresponding to the partitions with higher weights.

[0031] In a preferred embodiment of the present invention, in step S5, the differentiated emission control command formed in step S4 is issued to the wastewater treatment facility or emission control system corresponding to each discharge outlet. By automatically adjusting the valve opening or the operating parameters of the wastewater treatment facility, the concentration and flow rate of the actual pollutants are precisely controlled within the permissible range. The actual emission data and water environment data are continuously monitored, and the deviation is evaluated by comparing with the model prediction results. This triggers the adaptive adjustment of model parameters and system optimization iteration, forming a closed-loop control.

[0032] In a preferred embodiment of the present invention, in step S1, data acquisition is achieved by deploying online hydrological monitoring stations in the confluence area of ​​rivers and upstream tributaries. The hydrological monitoring stations are equipped with acoustic Doppler current profilers for collecting flow velocity data, pressure sensors or ultrasonic sensors for collecting water depth data, and flow rate and water level are calculated and verified by combining cross-sectional geometric information and radar water level gauge data.

[0033] Multi-parameter online water quality monitoring buoys or shore-based stations deployed near hydrological monitoring points and in sensitive downstream areas can collect concentration data of dissolved oxygen, pH, ammonia nitrogen, chemical oxygen demand, and total phosphorus.

[0034] This invention addresses the shortcomings of the prior art and has the following beneficial effects:

[0035] (1) This invention provides a method for regulating the discharge of water pollutants based on the dynamic response of water environment quality zones. By spatially dividing the receiving water body and identifying the hydrodynamic characteristics of each zone, the traditional assumption that the receiving water body is regarded as a homogeneous response system can be broken. This enables the explicit expression of heterogeneous response areas such as the mainstream channel area and the backflow stagnant water area in the water body, and clarifies the acceptance capacity of different areas for pollutants. This allows the discharge behavior to be coupled with the differences in spatial transport within the water body, thereby avoiding the discharge management based on the flow ratio as a single factor, which leads to pollutants mistakenly entering the water body stagnant area, causing concentration accumulation and dilution mismatch. This helps to identify highly sensitive areas before discharge permits, avoid releasing loads exceeding the carrying capacity into the slow response area, and thus significantly reduce the risk of local water quality exceeding the standard.

[0036] (2) This invention provides a method for regulating water pollutant discharge based on dynamic response of water environment quality zones. By introducing water quality target values ​​and background concentrations as boundary conditions for capacity extrapolation, and combining the response function to back-calculate the environmental capacity of each zone, the discharge permit is no longer based on a capacity prediction model that is artificially set or proportionally allocated. Instead, it starts from the target and traces back to the source permit threshold, ensuring that the discharge permit behavior achieves total quantity control while ensuring water quality. Furthermore, it automatically compresses the environmental capacity of zones with high background concentrations or lagging responses, forming dynamic constraints. This helps to fully release the remaining capacity of water bodies while ensuring water quality compliance, improve the resource utilization efficiency of permit regulation, reduce ineffective and redundant constraints, and avoid the risk of overloading in local areas due to actual discharge. It also reduces the risk of pollutant accumulation and mutation in low-exchange areas, thereby prompting the discharge focus to shift to areas with self-purification and diffusion advantages, and optimizing the spatial allocation of pollutants.

[0037] (3) This invention provides a method for regulating water pollutant discharge based on dynamic response of water environment quality zones. By constructing a pollution impact matrix between discharge outlets and zones, the pollution transmission effect between discharge outlets and each zone is quantified, forming a structured impact weight relationship. This changes the coarse-grained regulation method in the previous permit management that used cross-sections or total amount as the only indicator. It can reflect the average concentration contribution intensity caused by unit discharge in different areas, greatly improving the system's coordination and scheduling capabilities. At the same time, it retains the spatial heterogeneity of water bodies and the characteristics of transmission paths, thus having a stronger response differentiation capability in complex water structures. This allows the pollution regulation process to flexibly configure the permit intensity according to the actual impact of the area, balancing the contradiction between the concurrent load of the area and the response capability of the receiving water body zones. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a schematic diagram of the dynamic response function construction process according to a preferred embodiment of the present invention;

[0040] Figure 2 This is a schematic diagram illustrating the reverse calculation and allocation of environmental capacity according to a preferred embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of the coordinated control of multiple sewage outlets according to a preferred embodiment of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Application Overview:

[0044] When different sources of wastewater discharge simultaneously into the same receiving water body, the transport paths of pollutants within the water body may overlap and couple. Because the exchange efficiency, turbulent diffusion capacity, and residence time within the water body vary across different areas, the impact of different discharge outlets on different areas is not linearly additive. Traditional management methods equate wastewater discharge with a homogenizing effect on the entire water body, neglecting the actual transport trajectory and dynamic diffusion response of pollutants within the water body. This results in discharge permits not matching the actual carrying capacity of the water body, leading to severe load accumulation in some areas while other areas still have unused dilution and self-purification margins, resulting in low overall environmental capacity allocation efficiency.

[0045] Solving the aforementioned contradictions does not rely on modifying a single parameter or raising emission standards. Instead, it requires breaking through the implicit assumption of homogeneous response in existing management logic and establishing an emission control mechanism that can truly reflect the spatial heterogeneous response and temporal dynamic evolution of receiving water bodies. The technological breakthrough of this invention lies in constructing a spatial zoning system based on hydrodynamic characteristic identification, and combining it with the dynamic response function of pollutant concentration in each region to the impact of emissions. This allows for the estimation of environmental capacity based on the actual absorption, dilution, and migration capacity of water bodies. Furthermore, by quantifying the correlation between the discharge outlet and the zoning through a pollution impact matrix, the total environmental capacity is allocated to different discharge outlets according to their contribution to the impact. This forms differentiated, dynamic, and response-oriented emission control commands, enabling emission behavior to be coupled with the actual carrying capacity of the water body and forming a closed-loop control mechanism on a time scale.

[0046] Example 1:

[0047] like Figure 1 , Figure 2 and Figure 3 As shown, a method for regulating water pollutant emissions based on dynamic response of water environment quality zones includes the following steps:

[0048] Step S1: Spatial partitioning of the receiving water body, identification of the hydrodynamic characteristics of different partitions, and construction of dynamic response functions of pollutant concentration in each partition to the impact of discharge.

[0049] Step S2: Based on the target water quality values ​​and background concentrations of different regions, and combined with the zoning response function, the environmental capacity of each zone is calculated in reverse.

[0050] Step S3: Based on the pollution transmission effect of each sewage outlet on each zone, construct the sewage outlet-zone pollution impact matrix;

[0051] Step S4: Allocate environmental capacity to the permitted discharge volume of each discharge outlet to form differentiated emission control instructions;

[0052] Step S5: Adjust the wastewater discharge volume based on emission control instructions.

[0053] Data acquisition is achieved by deploying multiple online monitoring units within the receiving water body. These online monitoring units include an acoustic Doppler current profiler for collecting water flow velocity profiles, a pressure or ultrasonic water level sensor for collecting water level data, and a multi-parameter online monitoring buoy or shore-based station for collecting water quality parameters.

[0054] The online monitoring units are deployed in areas including river confluences, upstream main control sections, downstream water quality sensitive areas, and stagnant water areas along the banks. By continuously collecting water flow velocity, water depth, water level, dissolved oxygen, pH value, ammonia nitrogen, chemical oxygen demand, total phosphorus, and auxiliary water quality parameters, a basic dataset is formed for constructing dynamic response functions and estimating environmental capacity.

[0055] The acoustic Doppler current profiler calculates the three-dimensional velocity components of water flow by emitting ultrasonic beams and receiving echo frequency shifts. Its data acquisition frequency is set to once per second, the vertical layer resolution is 0.2 meters, and the measurement depth range covers the entire wet section of the monitoring section.

[0056] The water level sensor obtains real-time water level data by measuring the static pressure of the water column or the propagation time of sound waves, and its sampling interval is set to once every 5 minutes.

[0057] The water quality monitoring device uses electrochemical or optical sensing methods for measurement, and the data is transmitted to a data processing server for real-time integration via a wireless communication module.

[0058] The collected hydrological and water quality data undergo quality control through a preprocessing module, including the identification and removal of abnormal abrupt changes, automatic calibration of sensor drift, time series interpolation of lost data points, and filtering and smoothing of noise signals.

[0059] The execution sequence of data quality control is as follows: First, detect time continuity by judging whether the time difference between adjacent sampling points exceeds the set upper limit; second, detect amplitude anomalies by setting a physical reasonable range for each parameter and using the sliding median method to identify instantaneous deviations; third, perform sensor drift correction by comparing multiple historical reference values ​​of the same monitoring point and combining the cross-sectional flow balance relationship to perform consistency correction on flow velocity or water depth data.

[0060] The preprocessed data is resampled at a uniform time step to align all parameters in time, so that they can be used in the spatial partitioning and response function construction process.

[0061] After data acquisition and preprocessing are completed, spatial partitioning of the receiving water body is performed according to the defined methodology. The spatial partitioning process includes: firstly, dividing the entire area of ​​the receiving water body into multiple discrete computational units, each of which is a regular or irregular grid. The grid size is set according to the river width, curvature, water depth variation, and hydrodynamic complexity.

[0062] In this embodiment, the average grid size is set to 20 meters, and the vertical layering is 5 layers to ensure that the main hydrodynamic structural features can be captured. For each grid cell, the flow velocity magnitude, flow velocity gradient, eddy current, turbulent diffusion coefficient, and water residence time are extracted from the hydrodynamic model or monitoring data.

[0063] The meanings of the above terms are as follows: Flow velocity refers to the instantaneous velocity amplitude of the water flow per unit time in the grid cell;

[0064] The velocity gradient refers to the rate of change of water velocity in space, and is used to reflect the degree of shear in the flow field;

[0065] Vorticity is a quantitative indicator of the rotational intensity of water flow, and it is calculated as the curl of the velocity vector.

[0066] The turbulent diffusion coefficient is used to characterize the mixing capacity caused by turbulence; the larger the value, the stronger the mixing.

[0067] Water retention time refers to the average length of time that water remains within a grid, and is used to reflect the potential for pollutant accumulation.

[0068] The five calculation indicators mentioned above are dimensionless to avoid cluster shift caused by differences in the dimensions and value ranges of different parameters.

[0069] The dimensionless method uses range standardization, which linearly maps each index to its maximum and minimum values ​​across the entire domain, unifying its numerical range to between 0 and 1.

[0070] The processed feature vectors are input into a machine learning clustering algorithm. In this embodiment, the K-means clustering algorithm is used for spatial partitioning, and the number of clusters K is set to 3, corresponding to three typical hydrodynamic structures: the main channel area, the backflow stagnant water area, and the confluence turbulent area.

[0071] The K-means algorithm execution process includes the following steps: First, three cluster centers are randomly initialized; then, each grid cell is assigned to the nearest cluster center according to the Euclidean distance principle; next, the average eigenvector of the cluster centers is recalculated based on the assignment results; the above steps are repeated until the cluster centers converge or the number of iterations reaches a preset upper limit. In this embodiment, the upper limit of iterations is set to 300 times, and the convergence criterion is that the change in the number of cluster centers is less than 10 to the power of -6.

[0072] After clustering, three spatial response zones were obtained within the receiving water body: the main channel zone, the backflow stagnant zone, and the confluence turbulent zone. The main channel zone is characterized by high flow velocity, low residence time, and high turbulent diffusion coefficient; the backflow stagnant zone is characterized by low flow velocity, high residence time, high vorticity, but low turbulent diffusion coefficient; and the confluence turbulent zone is composed of regions with high velocity gradients, high turbulent mixing, and unstable hydrodynamic structures. This spatial partitioning allows for a structured expression of the hydrodynamic differences within the receiving water body, providing a physical basis for subsequent dynamic response function construction and environmental capacity estimation.

[0073] After completing the spatial zoning of the receiving water body, the next step is to reverse-calculate the environmental capacity of each zone based on the water quality target values ​​and background concentrations of different areas, combined with the dynamic response function of the zone.

[0074] This step is executed by the capacity estimation module. Its purpose is to determine the maximum allowable pollutant discharge mass for each response zone within a future control time window ΔT, ensuring that the pollutant concentration in that zone does not exceed the corresponding water quality target value at any given time point. Its execution condition is that: the zone division results and the zone background concentration must already be obtained. Regional water quality target values And the dynamic response function; if any of the above parameters are missing, the system will automatically pause the capacity calculation process and maintain the previous emission control command unchanged in order to avoid instability or misjudgment.

[0075] Calculate the background concentration for each response zone. The background concentration is defined as the baseline level of pollutant concentration in the zone under the condition of no new emission disturbance, in milligrams per liter. Its purpose is to serve as an initial benchmark for future concentration changes. The constraint is that it must be calculated based on real monitoring data and cannot use fixed empirical values.

[0076] In this embodiment, the background concentration is calculated using a sliding time window. The time window length is set to 72 hours, which means that the arithmetic mean of all valid observations of the monitoring points in the past 72 hours is taken as the background concentration Ca. When the coverage of valid data is less than 80% within 72 hours, the system automatically shortens the window to 48 hours. When the coverage conditions are still insufficient, spatial neighborhood substitution is performed, and the background concentration of the adjacent partition with the closest hydrodynamic characteristics to the partition is used as the substitution value. However, the substitution value must simultaneously meet the constraint that the deviation does not exceed ±10% to ensure that the background input does not shift.

[0077] After obtaining the background concentration, the water quality target determination unit acquires the water quality target value for each zone. The water quality target value is defined as the maximum allowable pollutant concentration limit under the regional functional requirements, expressed in milligrams per liter (mg / L). Its purpose is to serve as a capacity limit constraint, ensuring that the concentration does not exceed the national or regional standard limit. In this embodiment, ammonia nitrogen is used as the target pollutant. The water quality target value for the main channel zone is set to be no higher than 0.5 mg / L, the target value for the reflux stagnant water zone is set to be no higher than 0.3 mg / L, and the target value for the confluence turbulent zone is set to be no higher than 0.4 mg / L.

[0078] After obtaining the target water quality value and background concentration, the allowable concentration increment calculation unit calculates the allowable concentration increment at any future time point, specifically the difference between the target water quality value and the background concentration. When the concentration increment is negative, it indicates that the zone does not have environmental capacity under the current conditions, and the system automatically triggers the emission restriction mode, setting the corresponding sewage outlet discharge to 0 or the minimum guaranteed discharge value.

[0079] The dynamic response function is a response curve used to characterize the concentration change over time caused by a unit pollutant emission in the target zone. Its role is to establish the causal relationship between emission input and concentration output, so that subsequent environmental capacity estimation can quantify the future concentration change trend.

[0080] The execution method for constructing the response function includes: first, determining the transmission path between each discharge outlet and each response partition.

[0081] In this embodiment, a hydrodynamic diffusion model based on particle tracking is used to simulate the pollutant migration path. The particle tracking method involves injecting a large number of virtual tracer particles, per unit mass, into the water body from the discharge outlet. These particles move according to the hydrodynamic field velocity vector, with the particle movement time step matching the model's output time step. By recording the spatial trajectory distribution of the particles, the probability density distribution of their entry into different response zones at different times can be identified, thereby confirming the transmission path and transmission delay between the discharge outlet and each zone.

[0082] When constructing the response function, a unit mass of pollutant input is applied to the discharge outlet. In this embodiment, the unit mass is defined as 1 kg of pollutant added instantaneously or at a constant rate of 1 kg / hour. During the addition process, the pollutant concentration in the target zone is continuously recorded over time, with a sampling time step set to 1 hour. The recording period continues until the concentration change tends to stabilize or decreases to the background level before addition. In this embodiment, the recording period is set to 96 hours. The dynamic response function is obtained by fitting the concentration over time series.

[0083] In the process of constructing the response function, in order to avoid the deviation of the concentration curve caused by random disturbances or instantaneous hydrodynamic changes, a combination of moving average filtering and exponential smoothing is adopted.

[0084] The moving average window width is set to 3 time steps to smooth short-cycle oscillations; the exponential smoothing parameter α is set to 0.2 to preserve long-term trend information.

[0085] After processing, the concentration curve is normalized so that its peak corresponds to the maximum response amplitude per unit mass of injected pollutant. The dynamic response function uses time t as the independent variable and the concentration change ΔC(t) caused by a unit mass emission to the target zone as the dependent variable. This function reflects the input-output balance of pollutants within the zone, including key characteristics such as peak time, response lag, decay rate, and diffusion duration. The response function returns when the concentration change ΔC(t) decreases to within the background fluctuation range. In this embodiment, ±5% of the background concentration is used as the recovery criterion.

[0086] Specifically, regarding the construction of dynamic response functions for pollutant concentrations in each zone to emission impacts, a discrete impulse response kernel calibration method based on zoned water quality-hydraulic coupling is adopted. The discharge outlet-response zone is regarded as an input with an emission sequence and an output with a concentration sequence. By performing discrete convolution on the input-output relationship between measured emissions and concentration increments, a set of discrete-time response coefficients are identified to represent the contribution of unit emissions to the concentration increment of the zone at different lag times.

[0087] Unlike traditional approaches that directly assume an exponential decay function or use empirical dilution coefficients, this method does not predetermine the shape of the response curve. Instead, it combines hydraulic transport time and zone residence time to apply nonnegativity, unimodal decay, and finite memory constraints to the identified response coefficients, so that the dynamic response function simultaneously satisfies mathematical solvability and the physical rationality of the hydrodynamic process.

[0088] Compared with the traditional method of directly using empirical attenuation coefficients or setting a fixed dilution factor according to uniform hydraulic conditions, the above-mentioned constrained discrete impulse response identification method brings two technical effects: First, under the condition that the load of the same sewage outlet remains unchanged, the dynamic response functions of different zones can give completely different peak times and cumulative impacts, avoiding the problem of overestimation or underestimation of capacity caused by the traditional method of treating the mainstream channel area and the backflow stagnant water area the same, so that the subsequent environmental capacity back calculation can be differentiated according to the actual absorption capacity of the zone.

[0089] On the other hand, when upstream hydrodynamic conditions change, such as flood events, dam scheduling, or tidal reversal, which may lead to changes in transport and residence times, the response coefficients can be re-identified using a new round of input-output data to update the dynamic response function without rebuilding the entire water quality model. This enables rapid adaptation and dynamic updating to complex unsteady conditions.

[0090] The specific steps for constructing the dynamic response function of pollutant concentration in each zone to emission impact are as follows:

[0091] According to the information obtained from the sewage outlet O Emissions at any time The unit is kg / h, and the time step is... Set to 1h, and obtain partition Z in monitored concentration Smoothing and anomaly removal are then performed on it. Then, the concentration increment sequence is calculated. The execution relationship is as follows: ,in, This is represented as the background concentration in partition Z. This is the concentration increment value; the next step is allowed only if the duration of continuous valid data coverage is ≥48h and the missing rate is ≤20%.

[0092] Next, the transport calculation unit determines the shortest transport time from the discharge outlet O to zone Z. The execution relationship is as follows: ,in The shortest transport path length, For effective average flow velocity.

[0093] The lower limit constraint for transport time is: ;

[0094] And determine the maximum response lag time. : ;

[0095] in The average dwell time for each zone must meet the following conditions: That is, the maximum duration of the dynamic response cannot exceed 80% of the future prediction window.

[0096] After determining the hysteresis range, the discrete structure of the response kernel is constructed using the response kernel initialization unit: Furthermore, in order to obtain the dynamic response value of unit emissions from the discharge outlet to the concentration in the zone, Where k is the lag step number, Initial settings It equals 0.

[0097] The input-output correlation unit then calculates the initial response value corresponding to each lag, and the calculation relationship is as follows: N is the total number of time steps. The hysteresis response value is generated only when the number of valid samples is not less than 20. After obtaining the initial response sequence, the physical constraint correction unit applies nonnegativity, unimodality and tail cutoff constraints in sequence.

[0098] And after a peak lag, it decreases with the lag, when When the response value is less than 1% of the maximum response value, all subsequent hysteresis terms are set to 0. After the shaping is completed, the morphology adaptation unit corrects the response structure according to the partition type.

[0099] An exponential decay pattern is adopted in the mainstream channel region. , where α is 0.15-0.35.

[0100] A two-stage superposition method is adopted in the reflux stagnant water zone. Where A1 + A2 = 1, A1 is the weighting coefficient of the first decay component, corresponding to the proportion of the slow accumulation phase, and A2 is the weighting coefficient of the second decay component, corresponding to the proportion of the subsequent release and decay phases. This is the decay rate parameter for the first stage, representing the rate at which pollutants decay during the initial accumulation phase in the stagnant water zone. This is the decay rate parameter for the second stage, representing the rapid decay behavior of the stagnant zone as it enters the release phase after accumulation. .

[0101] An exponential response with a re-diffusion term is adopted in the confluence turbulence region. ,in A value of 0.1-0.4 indicates the re-diffusion enhancement factor. ≥0.05 indicates the re-diffusion decay rate.

[0102] Forming a dynamic response function This is used for subsequent environmental capacity back-calculation and the construction of the sewage outlet-zone pollution impact matrix.

[0103] After obtaining the dynamic response function, the environmental capacity of each zone is calculated in reverse based on the water quality target value and background concentration of different regions and the zone response function.

[0104] The meaning of reverse calculation of environmental capacity is the maximum cumulative discharge allowed within a set time window in the future, provided that the concentration of pollutants in a zone does not exceed the target value for water quality.

[0105] The execution method includes the following steps: First, obtain the water quality target value for each response zone. The water quality target value is determined based on the functional zoning standards of the receiving water body, such as drinking water source areas, landscape water bodies, and fishery water areas, which have different target values ​​for ammonia nitrogen, total phosphorus, chemical oxygen demand, or dissolved oxygen.

[0106] In this embodiment, ammonia nitrogen is used as the target pollutant, and its water quality target value is set to be no higher than 0.5 mg / L. Subsequently, the background concentration is obtained. The background concentration is calculated by the moving average of the online monitoring data within the zone over the past 72 hours to represent the current water environment baseline state.

[0107] At each time step, the acceptable concentration increment for that partition is calculated. The dynamic response function is coupled with the concentration increment to solve for the maximum allowable emission sequence within the future time window ΔT. The future time window ΔT is set to 24 hours, the time step Δt is 1 hour, and the total number of steps NT is 24.

[0108] For each time step, the convolution relationship between the discharge volume of the sewage outlet and the response function is calculated to ensure that the predicted concentration values ​​at future time points do not exceed the target values.

[0109] The convolutional relationship for predicting future concentration changes is: .in, Indicates partition Z in time The predicted concentration value, For the sewage outlet O in time Emissions, For the dynamic response function with time lag Concentration changes.

[0110] The constraints for solving environmental capacity can be expressed as: .

[0111] Solving using the above constraints The maximum feasible solution yields the environmental capacity. This embodiment employs a successive approximation solution method. It first assumes that the discharge outlet maintains a constant discharge rate throughout the entire time window. The future concentration change curve is predicted by substituting the input into the response function, and it is determined whether the curve meets the requirements for all time points. If the constraints are not met, emissions are gradually reduced proportionally until they are satisfied; if they are satisfied, emissions are increased incrementally until the maximum feasible value is approached. By combining binary search with gradient adjustment, the solution process converges to the optimal solution.

[0112] After calculating the environmental capacity of each zone, a pollution impact matrix of discharge outlets and zones is constructed based on the pollution transmission effect of each discharge outlet on each zone.

[0113] The discharge outlet-zone pollution impact matrix is ​​used to quantify the pollution contribution of each discharge outlet to each zone, providing a structured basis for discharge allocation. The implementation method includes: firstly, converting the dynamic response function into a unit impact coefficient. The unit impact coefficient is defined as the average concentration contribution of each kilogram of pollutant discharged from discharge outlet O into the water body to zone Z within the assessment time window T, specifically: ,in, Let T represent the pollution response function of discharge outlet O to zone Z, and let T represent the response assessment time period. It reflects the concentration change of a unit mass of pollutant in zone Z within period T after it is discharged from outlet O;

[0114] Arrange all sewage outlets and pollution impact coefficients of all response zones in a row-column correspondence to construct matrix N.

[0115] After constructing the pollution impact matrix of discharge outlets and zones, the environmental capacity is allocated to the permitted discharge volume of each discharge outlet to form differentiated emission control instructions.

[0116] This embodiment employs a multi-objective optimization model for emission allocation. The optimization objectives include maximizing the total allowable emission and minimizing regional water quality risk. Constraints include: the predicted concentration of all zones within future time windows must not exceed the water quality target value; the emission volume of each discharge outlet must be non-negative; and the rate of change of emission volume must not exceed a set threshold between adjacent time steps to avoid system regulation oscillations.

[0117] After obtaining the optimal discharge scheme for the sewage outlets, the next step is to adjust the wastewater discharge volume based on discharge control commands. In this embodiment, the permissible discharge sequence obtained from the optimization solution is used as the control input, and it is issued to the corresponding discharge control devices for each sewage outlet. The discharge control devices include automatic regulating valves and a wastewater treatment facility operation control system.

[0118] Automatic regulating valves adjust discharge flow by changing valve opening through electric actuators. The operation is as follows: when the permissible discharge increases, the control system instructs the valve opening to increase proportionally; when the permissible discharge decreases, the valve opening decreases proportionally to maintain consistency between the actual discharge and the permissible discharge. The wastewater treatment facility's operation control system adjusts operating parameters such as aeration rate, return ratio, sludge discharge rate, and reagent dosage to change the pollutant concentration in the discharged water.

[0119] When the permitted discharge load increases, the operation control system increases the processing capacity to maintain the effluent concentration within the permitted range; when the permitted discharge load decreases, the operation control system reduces the operating intensity to reduce energy consumption and operating costs.

[0120] During the operation of the emission control device, actual emission data and water environment data are continuously monitored. The emission data includes the instantaneous flow rate of the discharge outlet and the concentration of pollutants discharged, which are collected in real time through online water quality monitoring equipment and flow meters, and uploaded to the data processing server once per minute.

[0121] The water environment data includes concentration changes at regional water quality monitoring points, collected by online water quality monitoring buoys and updated every 10 minutes. The data processing server compares the actual observed values ​​with the model predictions to calculate the deviation. When the deviation exceeds a set threshold (±10% in this embodiment), an adaptive adjustment mechanism is triggered.

[0122] The adaptive adjustment mechanism includes the following actions: First, the dynamic response function is updated by correcting its parameters using Kalman filtering and recursive least squares algorithm to align it with the latest observed behavior; second, the environmental capacity is recalculated, and the allowable discharge from the sewage outlet is updated; finally, the optimization model is resolved to obtain new emission control commands. Through this closed-loop control mechanism, the present invention can adapt to changes in hydrodynamic and water quality conditions, achieving dynamic self-adaptation in emission management.

[0123] This implementation is fully realized through steps including data acquisition, spatial partitioning, response function construction, capacity estimation, pollution impact matrix construction, optimization solution, and emission control. It couples emission behavior with the actual carrying capacity of the water body, achieving differentiated and dynamic emission control. This embodiment provides the most basic implementation path, meeting the minimum requirements for full disclosure, but there is still room for further improvement. For example, when hydrodynamic conditions change abruptly, there are interconnected effects between discharge outlets, or the background concentration of water quality fluctuates abnormally within a short period, relying solely on a single response function or fixed partitioning may be insufficient to reflect system changes in real time.

[0124] Therefore, in Example 2, the core steps will be further refined and enhanced based on Example 1, so that the method has higher robustness and adaptability under complex working conditions.

[0125] Example 2 further improves the process of constructing the dynamic response function and calculating environmental capacity based on Example 1. This example does not change the method flow defined in claim 1, but achieves further technical effects by improving the execution method without adding extra steps.

[0126] In Example 2, the process of constructing the dynamic response function in Example 1 is further refined.

[0127] Example 1 uses a single-time-scale response function, with the input being the unit mass emission and the output being the concentration versus time curve. However, in water bodies with tidal reverse flow, flood peak processes, or reservoir regulation interventions, pollutant response behavior exhibits significant multi-scale characteristics, including short-period surging diffusion and long-period retention accumulation.

[0128] To address this issue, this embodiment employs a decomposition-based response function modeling method. The process includes: first, decomposing the concentration response curve into several intrinsic mode components using an empirical mode decomposition (EMD) method, where each mode corresponds to a response behavior at different time scales; then, fitting each mode component to obtain a short-period response function ΔCs(t), a medium-period response function ΔCm(t), and a long-period response function ΔCl(t); finally, superimposing the three types of response functions in a weighted manner to form a comprehensive response function ΔC(t) = wsΔCs(t) + wmΔCm(t) + wlΔCl(t).

[0129] Where ws, wm, and wl are weighting coefficients. In this embodiment, the weights are determined based on energy contribution rate allocation, that is, the proportion of energy of each mode to the total energy. By modeling the multi-scale response, both instantaneous turbulent diffusion and long-term retention effects can be incorporated into the response framework, ensuring that the prediction results remain accurate under complex changing conditions.

[0130] After adopting a multi-scale response function, in order to further improve the stability of the dynamic response function during the real-time update process, this embodiment introduces a parameter adaptive correction mechanism based on recursive Bayesian update.

[0131] The execution method includes: first, setting the initial parameter set of the dynamic response function, including peak amplitude, peak arrival time, decay constant, and duration parameters;

[0132] Subsequently, in each monitoring period, the error between the actual observed concentration sequence and the concentration predicted by the response function is calculated; then, the posterior distribution of the parameters is updated by recursive Bayesian formula, and a new parameter estimate is obtained; after substituting each new parameter set into the response function, the response curve is regenerated to make the prediction results consistent with the observed behavior.

[0133] The recursive update mechanism described above ensures that the response function is no longer fixed, but evolves continuously with changes in hydrodynamic and water quality conditions, thereby avoiding model lag or underestimation of peak risk.

[0134] In this embodiment, the parameter update frequency is set to once every 6 hours, and the update weight is determined by the error variance. When the error increases, the update magnitude increases, and when the error decreases, the update magnitude decreases to ensure system stability.

[0135] After improving the dynamic response function, Example 2 further enhances the spatial partitioning execution method. The spatial partitioning in Example 1 is a static clustering result, meaning that the partitions remain unchanged after the initial clustering. However, in tidal estuaries, regulated river sections, or areas affected by heavy rainfall, the hydrodynamic structure may change significantly in a short period of time, rendering the original partitions invalid.

[0136] This embodiment introduces a dynamic partitioning update mechanism to solve this problem. The execution method includes: first, refreshing the feature vector of each grid cell in real time, with the refresh frequency set to once every 3 hours; then calculating the distance difference between the new feature vector and the original cluster center. When the difference exceeds a set threshold, which is set to 0.15 in this embodiment, local re-clustering is triggered.

[0137] Local re-clustering is performed only on the changed regions, rather than recalculating the entire domain, to reduce the computational burden; the updated partitions replace the original partitions in the response function and capacity estimation chain. Through the dynamic partitioning mechanism, this invention can adapt to rapidly changing hydrodynamic conditions, avoiding misjudgments of capacity or incorrect allocation of discharge during abrupt changes in water structure.

[0138] After improving the dynamic response function and dynamic zoning, Example 2 further refines the environmental capacity estimation process. This example introduces uncertainty propagation analysis to ensure the robustness of the estimation results. The execution method includes: firstly, setting uncertainty distributions for background concentration, water quality target, and response function, for example, setting the background concentration as a normal distribution of mean and standard deviation; setting the response function output as a probability distribution range of 95% confidence interval; then performing Monte Carlo simulations of the capacity estimation process within the future time window, with the number of simulations set to 5000; randomly sampling parameters and recalculating the maximum allowable emission sequence in each simulation; finally, sorting the 5000 simulation results by quantiles, using the 5th quantile as the conservative capacity, the 50th quantile as the median capacity, and the 95th quantile as the maximum capacity for management selection. Through uncertainty analysis, emission control commands do not rely on a single deterministic value, but are selected within a risk range, thereby avoiding capacity misjudgment and regional overload risks under extreme water quality fluctuations or sudden emission disturbances.

[0139] Considering time delay and cumulative effects, this embodiment introduces a time-weighted influence coefficient to improve the matrix's expressive power. The execution method includes: firstly, dividing the dynamic response function into segments according to its time decay characteristics, defining the initial impact coefficient kOZ,0, the mid-term diffusion coefficient kOZ,1, and the late-term retention coefficient kOZ,2 respectively; then, superimposing the three types of influence coefficients according to time weights w0, w1, and w2 to obtain the comprehensive influence coefficient.

[0140] In this embodiment, the time weights are set to w0=0.5, w1=0.35, and w2=0.15 to reflect the dominant effect of the initial shock on concentration changes. Simultaneously, when the zone is a reflux stagnant zone, the later retention weight is increased, allowing w2 to reach a maximum of 0.4 to reflect cumulative risk. After constructing the time-weighted influence matrix, the optimized allocation is re-executed to give emission control commands time sensitivity and zone specificity, thereby further improving the accuracy of emission control.

[0141] To ensure computational efficiency during dynamic updates, an adaptive iteration termination mechanism is introduced. The execution method includes: firstly, calculating the difference between the previous period's optimal solution and the current initial solution before each optimization iteration begins; if the difference between the previous period's optimal solution and the current initial solution is lower than the threshold of 0.02 kg / h set in this embodiment, the previous period's optimal solution is directly inherited without resolving.

[0142] When the difference between the current optimal solution and the current initial solution exceeds a threshold, local optimization is performed instead of global optimization. This achieves rapid convergence by reducing the search range of variables and narrowing constraints. Global optimization is triggered only after three consecutive local optimization failures. Through an adaptive termination mechanism, the optimization model maintains real-time executability even with high dynamic monitoring and update frequencies, making it suitable for automated operation in engineering settings.

[0143] Introduce robust enforcement strategies to the emission control directive execution process to ensure that actual emission behavior can consistently meet permit requirements even with equipment delays, valve lags, or fluctuations in treatment facility load.

[0144] This embodiment changes the emission execution process from a single command-following mode to a tolerance-tracking mode. The execution methods include:

[0145] First, an allowable deviation band is set for each discharge outlet, which is defined in this embodiment as ±8% of the allowable discharge amount; when the actual discharge amount falls within the allowable deviation band, the control system only performs status monitoring and does not perform adjustment actions;

[0146] When the actual emissions exceed the permitted emission limit, the control system gradually reduces the valve opening using a proportional-integral method to avoid sudden closure that could cause instantaneous pressure fluctuations.

[0147] When the actual discharge falls below the permissible discharge limit, the system gradually increases the valve opening or decreases the wastewater treatment intensity to restore the discharge to the permissible range. This tolerance tracking method avoids equipment fatigue, valve vibration, or system over-response caused by frequent adjustments, thereby improving the stability of discharge control execution.

[0148] To accommodate the potential overlap of pollutant migration among multiple discharge outlets, this embodiment introduces a collaborative suppression mechanism for discharge outlets. When two or more discharge outlets have high influence coefficients on the same sensitive zone, traditional independent optimization may lead to alternating overloads or mutual cancellation risks. This embodiment achieves joint constraints through a collaborative penalty coefficient for discharge outlets.

[0149] The execution method includes: first, calculating the coupling index γO1O2,Z of sewage outlets O1 and O2 to partition Z, which is defined as the normalized value of the product of the influence coefficients of the two sewage outlets; when γO1O2,Z exceeds the threshold of 0.6 set in this embodiment, a collaborative penalty term is added to the optimization model. Wherein, P is the penalty factor, ranging from 0.1 to 0.5; in this embodiment, P=0.3. When two discharge outlets cause a cumulative effect on the same area, the optimization solution automatically reduces the emissions of both, prioritizing the allocation of emission quotas to discharge outlets with a lower impact on the area, thereby reducing the risk of regional pollution accumulation. Through the discharge outlet collaborative inhibition mechanism, emission management is transformed from single-point control to system coordinated control, which is suitable for multi-source complex pollution scenarios.

[0150] This embodiment also introduces an extreme hydrodynamic scenario prediction module to ensure that emission control remains controllable under emergencies. Extreme scenarios include typhoon storm surges, heavy rainfall peaks, sudden dam releases, and tidal backwater.

[0151] The execution method includes: firstly, pre-setting four typical extreme working condition boundary conditions through the hydrodynamic model; when the monitoring data triggers the early warning threshold, such as when the water level rise rate exceeds 0.05 m / h, automatically switching to the corresponding scenario mode;

[0152] Subsequently, the dynamic zoning is recalculated, the response function is updated, and the environmental capacity is recalculated based on extreme scenarios. Finally, emergency emission control orders are issued, including emission restrictions, staggered emission periods, or emission suspensions, to ensure that pollution accumulation in sensitive areas can be prevented from getting out of control during the rapid rise in environmental risks.

[0153] Example 3, without changing the basic methodological structure defined in Example 1, provides another different execution path for emission control under conditions of cross-basin scheduling, cross-regional emission coordination, or long-distance transport. The core technical feature of this example is that it extends the regional response from a point-type expression to a distributed field expression, so that emission control is not limited to discrete regions, but can cover the continuous response process of the entire river segment.

[0154] In Example 3, the receiving water body is first transformed from a discrete grid into a continuous distributed river segment model. The process includes: dividing the river centerline into several cross-sections along its length; calculating the cross-sectional average velocity, retention index, and turbulent diffusion coefficient for each cross-section; and then establishing a pollutant transport model using a one-dimensional convection-diffusion equation. .

[0155] in, The pollutant concentration at location x along the river and at time t. For the distribution of river flow velocity, The longitudinal diffusion coefficient is... This is the source term function for the sewage outlet.

[0156] By solving the aforementioned convection-diffusion equations, the predicted pollutant concentration at any location within a future timeframe can be obtained. Subsequently, based on the concentration predictions and water quality target constraints, the continuous capacity curve of the river segment is calculated in reverse, so that discharge permits no longer depend on fixed zones but are achieved based on continuous hydrodynamic response.

[0157] After completing the capacity estimation of continuous river segments, Example 3 further constructs an optimized allocation model for sewage outlets, enabling coordinated execution of emission control across regional scales. This example does not employ a discrete matrix structure but instead quantifies emission impact by constructing a continuous influence kernel function. The execution method includes: firstly, defining an emission influence kernel K(xO,xZ,t) to represent the concentration contribution of sewage outlet location xO at time t to river segment location xZ; the influence kernel is calculated using the Green's function solution of the convection-diffusion equation. ;

[0158] in Let be the equivalent diffusion coefficient, u be the average flow velocity, and t be time. The concentration response contribution of any discharge outlet at any location and time point in any river segment can be obtained by integrating the influence kernel. The influence kernel is then discretized into a numerical form and combined with water quality target constraints to form a continuous optimization model. The optimization objective is to maximize the total allowable discharge along the entire river segment while ensuring that the concentration at all locations does not exceed the water quality target value. This embodiment employs a piecewise finite element method, transforming the continuous optimization problem into a solvable linear constraint optimization problem, making emission control applicable to long-distance transport and cross-administrative region discharge coordination scenarios.

[0159] To adapt to the needs of cross-regional and multi-departmental collaborative management, an emission priority scheduling mechanism has also been introduced.

[0160] The implementation method includes: first, setting priority weights W for different discharge outlets, with the weights determined based on factors such as industry type, environmental sensitivity, emission stability, and historical compliance rate.

[0161] In this embodiment, the highest priority discharge outlet is the domestic sewage treatment plant, with W set to 1.5; the industrial discharge outlet has W set to 1.0; and the rainwater overflow outlet has W set to 0.5. During optimization, priority weights are added to the objective function so that high-priority discharge outlets can still guarantee necessary discharge capacity under limited capacity conditions, while low-priority discharge outlets are given priority to reduce discharge when water quality risks increase.

[0162] By employing an emission priority scheduling mechanism, this invention not only possesses technical rationality but also management feasibility, making it applicable to cross-departmental and cross-regional joint emission management systems.

[0163] Based on the preferred embodiments of the present invention described above, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for regulating water pollutant emissions based on dynamic response of water environment quality zones, characterized in that, Includes the following steps: Step S1: Spatial partitioning of the receiving water body, identification of the hydrodynamic characteristics of different partitions, and construction of dynamic response functions of pollutant concentration in each partition to the impact of discharge. Step S2: Based on the target water quality values ​​and background concentrations of different regions, and combined with the zoning response function, the environmental capacity of each zone is calculated in reverse. Step S3: Based on the pollution transmission effect of each sewage outlet on each zone, construct the sewage outlet-zone pollution impact matrix; Step S4: Allocate environmental capacity to the permitted discharge volume of each discharge outlet to form differentiated emission control instructions; Step S5: Adjust the wastewater discharge volume based on emission control instructions.

2. The method for regulating water pollutant discharge based on dynamic response of water environment quality zones according to claim 1, characterized in that: In step S1, spatial partitioning of the receiving water body includes: Step S11: Extract calculation indicators, including flow velocity and gradient, vorticity, residence time, and turbulent diffusion coefficient; Step S12: Use the obtained multiple calculation indicators as feature vectors to cover all grids in the entire calculation domain. Use machine learning clustering algorithm to automatically classify the multiple grids, which are classified into mainstream channel area, backflow stagnant water area and confluence turbulence area.

3. The method for regulating water pollutant discharge based on dynamic response of water environment quality zones according to claim 1, characterized in that: In step S1, the dynamic response function of pollutant concentration in each zone to emission impact is constructed, including: Determine the causal link between pollutant input and output within the target response zone for each discharge outlet, and clarify the transmission path and impact channel of pollutants from the discharge point into each response zone; Input the unit pollutant mass parameter at the designated discharge outlet, and continuously record the concentration change process over time in the target response zone to obtain the pollutant concentration curve and obtain the dynamic response function of each zone.

4. The method for regulating water pollutant discharge based on dynamic response of water environment quality zoning according to claim 1, characterized in that: The pollutant quality parameters include: effective exchange flow rate, water residence time, mixing intensity, response lag, and dilution index.

5. The method for regulating water pollutant discharge based on dynamic response of water environment quality zoning according to claim 1, characterized in that: In step S2, the environmental capacity of each partition is calculated in reverse, including: Obtain the target water quality value and background concentration for each response zone. The target water quality value is determined based on the functional zoning standards of the receiving water body or the compliance requirements of the control section. The background concentration is obtained based on the historical monitoring data of the zone and serves as the initial benchmark for future concentration changes. Based on the water quality target value, the allowable range of target concentration variation for this zone is constructed, and the predicted concentration at any future time point is set not to exceed the water quality target value. The prediction relationship is as follows: ,in, Indicates partition Z in time The predicted concentration value, For the sewage outlet O in time Emissions, For the dynamic response function with time lag Concentration changes; The constraints for solving environmental capacity can be expressed as: ; Solving using the above constraints The maximum feasible solution yields the environmental capacity.

6. The method for regulating water pollutant discharge based on dynamic response of water environment quality zones according to claim 1, characterized in that: In step S3, the pollution impact matrix of the discharge outlet-zone is constructed, including: Based on the partition response function, the pollution impact coefficient of sewage outlet O on partition Z is defined. This represents the average concentration impact per unit load on the partition within a set evaluation time window T. ,in, Let T represent the pollution response function of discharge outlet O to zone Z, and let T represent the response assessment time period. It reflects the intensity of the contribution of a unit mass of pollutant discharged from the discharge outlet O to the concentration of zone Z within the period T; Arrange all sewage outlets and pollution impact coefficients of all response zones in a row-column correspondence to construct matrix N.

7. The method for regulating water pollutant discharge based on dynamic response of water environment quality zones according to claim 1, characterized in that: In step S4, based on the environmental capacity of each zone calculated in reverse in step S2, the pollution impact matrix of the discharge outlet-zone constructed in step S3, and the dynamic dilution capacity weight of each zone, a multi-objective optimization model is constructed and solved, and the total environmental capacity is allocated to the permissible emission amount of each discharge outlet, thereby forming differentiated emission control instructions.

8. The method for regulating water pollutant discharge based on dynamic response of water environment quality zones according to claim 7, characterized in that: The dynamic dilution capacity weight is calculated based on the dynamic response parameters of the zone and is directly proportional to the effective exchange flow rate and mixing intensity, and inversely proportional to the water retention time. The multi-objective optimization model takes ensuring that the water quality of all zones meets the standards as the main constraint, and maximizes the total allowable discharge and / or minimizes the discharge control cost as the optimization objectives. The multi-objective optimization model is solved by using non-dominated sorting genetic algorithm II or multi-objective particle swarm optimization algorithm. The optimization allocation logic prioritizes allocating higher emission quotas to the sewage outlets corresponding to the partitions with higher weights.

9. The method for regulating water pollutant discharge based on dynamic response of water environment quality zoning according to claim 1, characterized in that: In step S5, the differentiated emission control command generated in step S4 is issued to the wastewater treatment facilities or emission control systems corresponding to each discharge outlet. By automatically adjusting the valve opening or the operating parameters of the wastewater treatment facilities, the concentration and flow rate of the actual pollutants are precisely controlled within the permissible range. The actual emission data and water environment data are continuously monitored, and the deviation is evaluated by comparing them with the model prediction results. This triggers the adaptive adjustment of model parameters and system optimization iteration, forming a closed-loop control.

10. The method for regulating water pollutant discharge based on dynamic response of water environment quality zones according to claim 1, characterized in that: In step S1, data is collected by online hydrological monitoring stations deployed in the confluence of rivers and upstream tributaries. The hydrological monitoring stations are equipped with acoustic Doppler current profilers for collecting flow velocity data, pressure sensors or ultrasonic sensors for collecting water depth data, and flow rate and water level are calculated and verified by combining cross-sectional geometric information and radar water level gauge data. Multi-parameter online water quality monitoring buoys or shore-based stations deployed near hydrological monitoring points and in sensitive downstream areas can collect concentration data of dissolved oxygen, pH, ammonia nitrogen, chemical oxygen demand, and total phosphorus.

Images