An ecological dynamic simulation method for emergency response to pollution incidents

Abstract

The present application discloses an ecological dynamic simulation method for emergency disposal of pollution incidents, which uses historical detection data of a selected target water ecosystem to construct a dynamic water quality model, sets a pollution impact coefficient according to the time and location of the pollution leakage, adds a pollutant impact term to the original dynamic water quality model, and corrects the pollutant impact term according to the pollution impact coefficient to obtain a dynamic water quality model after the pollution spreads, and then establishes a convection diffusion model of the target water area, brings the pollutant concentration output by the convection diffusion model into the dynamic water quality model in a point-to-point manner, continuously updates various parameters of the dynamic water quality model, and finally obtains the dynamic change process of the target water ecosystem after pollution, and uses the dynamic change process to evaluate the treatment results; the technical solution of the present application can make full use of the impact of pollutant diffusion to correct the dynamic water quality model, thereby enhancing the accuracy of the dynamic water quality model and improving the accuracy of the final evaluation result.

Description

Technical Field

[0001] The present application relates to the technical field of environmental protection, and in particular to an ecological dynamic simulation method for emergency disposal of pollution incidents. Background Art

[0002] Ecological dynamic simulation of emergency response to pollution incidents refers to the use of computational models to simulate the dynamic changes in the aquatic ecosystem after a pollution incident, especially under the influence of pollutant diffusion and ecological environmental changes, to help relevant departments make scientific decisions and take effective emergency response measures.

[0003] When a pollution incident occurs, the diffusion of pollutants in water bodies is influenced by a variety of factors, including the convective diffusion characteristics of water flow, the physical and chemical properties of pollutants, and environmental changes. Traditional pollution and remediation assessment methods, including water quality monitoring, empirical methods, and analogy methods, require field visits to obtain assessment data and rely heavily on static monitoring data, manual processing, or field experiments. Their assessment results often have significant delays, making it difficult to provide rapid and effective decision-making support in the early stages of a pollution incident. Furthermore, traditional pollution and remediation assessment methods consume a large amount of human resources, have slow response times, and are affected by equipment and climate. The amount of measured data is small, and they cannot accurately simulate the changes in the ecosystem.

[0004] In addition, traditional dynamic water quality models usually only consider the changes in pollutant concentrations within the water body, but lack real-time integration with the pollution diffusion process, making it difficult to accurately predict the spread trend of pollutants in the water body and the dynamic response of the ecosystem. Moreover, existing simulation methods often ignore the dynamic factors of pollutant concentration changes over time and space, especially the environmental feedback effects after the diffusion of pollutants, which affects the accuracy and practicality of the model. Summary of the Invention

[0005] The purpose of this application is to provide an ecological dynamic simulation method for emergency response to pollution incidents, accurately simulate the dynamic changes of the target water ecosystem after pollution, and provide accurate data reference for emergency response to pollution incidents.

[0006] The technical solution of this application is to provide an ecological dynamic simulation method for emergency response to pollution incidents, which includes:

[0007] Step 1: Select a target water area, obtain historical test data of predetermined parameters in the target water area ecosystem from the database, and construct a dynamic water quality model based on the interactions between different predetermined parameters and the test data of each predetermined parameter;

[0008] Step 2: Set a pollution impact coefficient based on the time and location of the pollution leak, where the pollution impact coefficient decreases with time and distance from the pollution source. Add a pollutant impact term to the dynamic water quality model, and modify the added pollutant impact term based on the set pollution impact coefficient to obtain a dynamic water quality model after pollution diffusion.

[0009] Step 3: Establish a convection-diffusion model for the target waters. Calculate the concentration of pollutants at various spatial locations based on the convection-diffusion model. Bring the calculated pollutant concentrations at various spatial locations into a post-pollution dynamic water quality model for dynamic simulation to obtain dynamic change data for various predetermined parameters of the target water ecosystem under the influence of pollutants.

[0010] Step 4: Conduct artificial intervention and treatment of the polluted target water ecosystem, set a treatment monitoring cycle, measure the change data of the predetermined parameters after treatment based on the treatment monitoring cycle, use the data provided by the dynamic water quality model after pollution as the control group data, and calculate the health index of the target water ecosystem based on the change data of the predetermined parameters after treatment and the control group data;

[0011] Step 5: record the control group data of the target water ecosystem, the change data of the predetermined parameters after treatment, and the health index after treatment, and store them in the database.

[0012] Furthermore, in step 1, a dynamic water quality model is constructed based on the interactions between different predetermined parameters and the detection data of each predetermined parameter, specifically including:

[0013] Step 121: establishing relevant expressions based on the interaction relationships among different predetermined parameters, wherein the relevant expressions include an expression for a phytoplankton growth function, an expression for a phytoplankton respiration consumption function, an expression for a phytoplankton ingestion function, and an expression for an organic debris settling function.

[0014] Step 122 , establishing a dynamic water quality model based on the relevant expressions describing the interaction relationship between different predetermined parameters, as well as the process of phytoplankton mortality, zooplankton excretion, and organic debris remineralization;

[0015] Step 123 , the detection data of the predetermined parameters over the years are brought into the dynamic water quality model, and the model is iteratively calculated to optimize the model parameters until the predetermined number of iterations is reached and the iteration is stopped.

[0016] Furthermore, in step 121, the phytoplankton growth function R pl for:

[0017] R pl =G pl ·C pl=f T min[fN u ,f L ]C pl

[0018] Where G pl is the growth rate of phytoplankton, C pl is the total amount of phytoplankton, the subscript pl is the label of phytoplankton, f T is the exponential function of the effect of water temperature on phytoplankton growth, T is water temperature, f Nu is the limiting function of dissolved inorganic nutrients on phytoplankton, the subscript Nu is the label of dissolved inorganic nutrients, f L is the limiting function of light on phytoplankton growth, the subscript L is the label of light, and min[·] is the minimum value function;

[0019] Phytoplankton respiration consumption function B pl for:

[0020]

[0021] Where m pl is the maximum respiration rate of phytoplankton at 0℃, μ pl is the exponential factor of phytoplankton growth changing with temperature, T0 is the optimum temperature for phytoplankton;

[0022] The feeding function E of herbivorous zooplankton on phytoplankton an for:

[0023]

[0024] Where, E an is the feeding function of herbivorous zooplankton on phytoplankton, g an is the maximum growth rate of herbivorous zooplankton at 0℃, the subscript an is the label of herbivorous zooplankton, C an is the total amount of herbivorous zooplankton, μ an is the exponential factor of herbivorous zooplankton growth changing with temperature, and λ is the phytoplankton grazing rate;

[0025] Organic debris deposition function S or for:

[0026]

[0027] Where W or is the sedimentation rate of organic debris, the subscript or is the label of organic debris, C or is the total amount of organic debris, and h is the water depth.

[0028] Furthermore, step 122 specifically includes: expressing the process of phytoplankton death, zooplankton excretion, zooplankton death, and organic debris remineralization using a simple linear relationship, and combining the linear relationship with a related expression describing the interaction relationship between different predetermined parameters to obtain a dynamic water quality model:

[0029]

[0030] Where β is the zooplankton assimilation rate, m an is the maximum excretion rate of herbivorous zooplankton at 0℃, d pl is the natural mortality rate of phytoplankton, d an is the natural mortality rate of herbivorous zooplankton, ε is the remineralization rate of organic debris, is the operator of the dynamic water quality model; the horizontal plane in the target water area is taken as the xy plane and the vertical direction is taken as the z axis to establish a spatial coordinate system. The operator is expressed as:

[0031]

[0032] Where u is the flow velocity in the x-axis direction, v is the flow velocity in the y-axis direction, w is the flow velocity in the z-axis direction, and A x is the eddy diffusion coefficient in the x-axis direction, A y is the eddy diffusion coefficient in the y-axis direction, K m is the vertical eddy diffusion coefficient, and t is the time.

[0033] Furthermore, in step 2, the pollution impact coefficient is set based on the time and location of the pollution leakage, specifically including:

[0034] Taking the time point of pollution leakage and the location of the pollution source as reference, a pollution impact coefficient related to time and space is set. The pollution impact coefficient is expressed as:

[0035]

[0036] Where ω(t,r) is the pollution impact coefficient, t is time, r is the distance between the spatial point and the pollution source, η is the pollutant attenuation rate constant, and σ is the spatial attenuation exponent of the pollutant, σ∈[1,3].

[0037] Furthermore, the dynamic water quality model after the pollution diffusion is obtained in step 2, specifically including:

[0038] Step 221: Add the pollutant impact term to the phytoplankton growth function and use the pollution impact coefficient to correct the added pollutant impact term to obtain the phytoplankton growth function after pollution diffusion. for:

[0039]

[0040] Where g pl is the maximum growth rate of phytoplankton at 0℃, C poll is the concentration of pollutants, α N is the nitrogen content in the unit concentration of pollutants, k N is the nitrogen absorption half-saturation constant, α P is the phosphorus content in the unit concentration of pollutants, k P Phosphorus absorption half-saturation constant, α Si is the silicon content in the unit concentration of pollutants, k Si is the silicon absorption half-saturation constant, ρ tox is the toxicity factor of the pollutant, ρ tox belongs to [0,1], ρ tox (C poll ) is the toxic effect of the pollutant, which is expressed as:

[0041]

[0042] Where K tox is the half-saturation constant of pollutant concentration;

[0043] Step 222: Add the pollutant impact term to the grazing function of herbivorous zooplankton on phytoplankton, and use the pollution impact coefficient to correct the added pollutant impact term to obtain the grazing function of herbivorous zooplankton on phytoplankton after pollution diffusion. for:

[0044]

[0045] In step 223, the phytoplankton growth function after pollution diffusion and the phytoplankton feeding function after pollution diffusion are introduced into the original dynamic water quality model. The phytoplankton mortality rate and the herbivorous zooplankton mortality rate are corrected according to the pollution impact coefficient and the toxic effect of the pollutant, thereby obtaining the dynamic water quality model after pollution diffusion:

[0046]

[0047] in, is the natural mortality rate of phytoplankton after the pollution spreads, and its expression is:

[0048]

[0049] is the natural mortality rate of herbivorous zooplankton after the pollution spreads, and its expression is:

[0050]

[0051] Step 224, obtain the detection data of the predetermined parameters in the target water ecosystem after pollution, bring the detection data of the predetermined parameters after pollution into the dynamic water quality model after pollution, and iterate the model until the predetermined number of iterations is reached and the iteration is stopped.

[0052] Furthermore, step 3 specifically includes:

[0053] Step 31: Based on the spatial coordinate system of the dynamic water quality model, a convection diffusion model of the target water area is established. The leakage time and leakage location of the pollutants are introduced into the convection diffusion model to obtain the pollutant concentration distributed along the time axis at each spatial location. The convection diffusion model is:

[0054]

[0055] Where D is the diffusion coefficient, is the velocity vector of the water body, Q is the source term of the pollutant, is the gradient operator, is the spatial gradient of concentration, is the spatial second-order derivative of the pollutant;

[0056] The convection diffusion model is transformed, and the leakage time and location of the pollutants are brought into the transformed convection diffusion model to obtain the pollutant concentration distributed along the time axis at each spatial position. The transformed convection diffusion model is expressed as:

[0057]

[0058] Step 32, according to the time axis, sequentially bring the calculated pollutant concentrations at each spatial position into the dynamic water quality model after pollution, obtain the changes in the predetermined parameters distributed along the time axis at each spatial position, and dynamically simulate the target water ecosystem according to the time axis based on the changes in the predetermined parameters to obtain the dynamic change process of the target water ecosystem under the influence of pollutants.

[0059] Furthermore, step 32 specifically includes:

[0060] A time interval Δt1 for updating the pollutant concentration in the dynamic water quality model after pollution is set, and the time axis is discretized according to the time interval Δt1. According to the arrangement order of each time point in the time axis, the dynamically changing pollutant concentrations at each spatial position are sequentially brought into the dynamic water quality model after pollution, and the predetermined parameter data of each spatial position changing along the time axis are obtained.

[0061] Furthermore, in step 4, the health index of the target water ecosystem is calculated based on the change data of the predetermined parameters after treatment and the control group data, specifically including:

[0062] According to the time of the governance monitoring cycle Δt2, the control group data is matched with the change data of the predetermined parameters after governance measured on the spot, and the health index of the target water ecosystem is calculated based on the difference between the control group data and the change data of the predetermined parameters after governance:

[0063]

[0064] Where H is the health index of the target water ecosystem, ω j is the weight, and its subscript j is the label of the predetermined parameter, ω poll is the weight of the pollutant, where ω1+ω2+…+ω j +ω poll =1;O in is the value of the predetermined parameter before pollution, O j,act is the value of the predetermined parameter measured in the field after pollution, O j,con is the value of the predetermined parameter of the control group after contamination, C act,poll is the average pollutant concentration of the selected target point after field measurement, C in,poll is the average pollutant concentration of the selected target point after field measurement, C con,poll is the average pollutant concentration corresponding to the selected target point in the control group.

[0065] The beneficial effects of this application are:

[0066] First, the technical solution in this application applies the dynamic water quality model of the ecosystem to the emergency response to pollution incidents, sets the dynamic water quality model and the convection-diffusion model of the water area in the same spatial coordinate system, and brings the constantly changing pollutant concentration output by the convection-diffusion model into the dynamic water quality model in a point-to-point manner, and continuously updates the various parameters that change along the time axis at different spatial positions in the dynamic water quality model to accurately simulate the dynamic change process of the target water ecosystem after pollution; the technical solution in this application can make full use of the influence of pollutant diffusion, dynamically optimize and update the dynamic water quality model, so that the dynamic change process of the target water ecosystem after pollution simulated by the dynamic water quality model is more accurate, and can provide accurate data reference for emergency response to pollution incidents.

[0067] Second, the technical solution in this application obtains a dynamic water quality model after pollution diffusion by adding pollutant impact terms to the original dynamic water quality model. At the same time, a pollution impact coefficient is added to correct the pollutant impact terms, taking into account the two factors of time attenuation and spatial attenuation of pollutant impact, so that the dynamic water quality model after pollution diffusion can reflect the changes in the size of pollutant impact in time and space, thereby enhancing the accuracy of the simulation of the dynamic water quality model after pollution diffusion and improving the accuracy of the final evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The advantages of the above and / or additional aspects of the present application will become apparent and readily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0069] Figure 1 It is a schematic flow chart of an ecological power simulation method for emergency response to pollution incidents according to an embodiment of the present application. DETAILED DESCRIPTION

[0070] In order to more clearly understand the above-mentioned objectives, features and advantages of the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features therein can be combined with each other in the absence of conflict.

[0071] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present application is not limited to the specific embodiments disclosed below.

[0072] like Figure 1 As shown, this embodiment provides an ecological power simulation method for emergency response to pollution incidents, including:

[0073] Step 1: Select the target water area and obtain historical test data of the predetermined parameters in the target water area ecosystem from the database. Based on the interactions between different predetermined parameters and the test data of each predetermined parameter, a dynamic water quality model is constructed, which specifically includes:

[0074] Step 11, obtaining historical detection data of predetermined parameters in the target water ecosystem, the predetermined parameters including dissolved inorganic nutrients, phytoplankton, herbivorous zooplankton, organic debris, and environmental conditions;

[0075] The target water area to be simulated is selected, and the historical detection data of predetermined parameters in the target water area ecosystem are extracted from the database of the target water area. Among them, the detection data of dissolved inorganic nutrients include the concentration change data of dissolved inorganic nutrients and the external input amount; the detection data of phytoplankton include the concentration change data, growth rate and mortality rate of phytoplankton; the detection data of herbivorous zooplankton include the concentration change data, feeding rate and mortality rate of herbivorous zooplankton; the detection data of organic debris include the concentration change data of organic debris (which includes the increase and loss of organic debris) and degradation rate; the detection data of environmental conditions include water temperature data and light data in the target water area ecosystem.

[0076] Step 12, based on the interaction between different predetermined parameters and the detection data of each predetermined parameter, construct a dynamic water quality model, specifically including:

[0077] In this embodiment, the NPZD model (Nutrient Phytoplankton Zooplankton Detritus model, an ecological and water quality model used to describe the material cycle and ecological processes in water bodies, commonly used in the simulation and research of ocean, lake and river ecosystems) is used to model the target water ecosystem through a set of differential equations to obtain its dynamic water quality model. The dynamic water quality model simulates the dynamic changes of the target water ecosystem in time and space by calculating these equations; the established dynamic water quality model can be run and visualized through platforms such as MATLAB / Simulink (Simulink itself is an extension module of MATLAB for graphical modeling and simulation), Python and Vensim.

[0078] It should be noted that the growth of phytoplankton is determined by photosynthesis (depending on light intensity) and nutrient supply (i.e., the concentration of dissolved inorganic nutrients, such as nitrogen, phosphorus and other nutrients). Zooplankton obtains energy by preying on phytoplankton. The feeding rate of zooplankton depends on the concentration of phytoplankton and the population size of zooplankton. The feeding rate of zooplankton is proportional to the density of phytoplankton. The death of phytoplankton and zooplankton, excretion after feeding and transmission in the food chain will eventually convert some organic matter (i.e., organic debris) into usable nutrients (dissolved inorganic nutrients). The degradation process of detrital matter will release nutrients and enter the water cycle. The organic matter produced after the death of phytoplankton and herbivorous zooplankton will eventually be converted into organic debris.

[0079] In this step, a dynamic water quality model is constructed based on the interactions between different predetermined parameters and the detection data of each predetermined parameter, which specifically includes the following steps:

[0080] Step 121: establishing relevant expressions based on the interaction relationships among different predetermined parameters, wherein the relevant expressions include an expression for a phytoplankton growth function, an expression for a phytoplankton respiration consumption function, an expression for a phytoplankton ingestion function, and an expression for an organic debris settling function.

[0081] The expression of the phytoplankton growth function is used to describe the process in which phytoplankton growth is affected by dissolved inorganic nutrients, water temperature, light, and its own population size; the expression of the phytoplankton growth function is:

[0082] R pl =G pl ·C pl =fT min[f Nu ,f L ]C pl

[0083] Where R pl is the phytoplankton growth function, G pl is the growth rate of phytoplankton, C pl is the total amount of phytoplankton, the subscript pl is the label of phytoplankton, f T is the exponential function of the effect of water temperature on phytoplankton growth, T is water temperature, f Nu is the limiting function of dissolved inorganic nutrients on phytoplankton, the subscript Nu is the label of dissolved inorganic nutrients, f L is the limiting function of light on phytoplankton growth, the subscript L is the label of light, and min[·] is the minimum value function.

[0084] Among them, the exponential function f of temperature affecting phytoplankton growth is T The expression is:

[0085]

[0086] Where g pl is the maximum growth rate of phytoplankton at 0℃, μ pl is the exponential factor of phytoplankton growth changing with temperature, T0 is the optimum temperature for phytoplankton, represents the factors affecting temperature on phytoplankton growth;

[0087] Limitation function f of dissolved inorganic nutrients on phytoplankton Nu The expression is:

[0088]

[0089] Where C N is the total amount of nitrogen, k N is the nitrogen absorption half-saturation constant, C P is the total amount of phosphorus, k P Phosphorus absorption half-saturation constant, C Si is the total amount of silicon, k Si is the half-saturation constant of silicon absorption; in this embodiment, the growth of phytoplankton is mainly limited by the element with the least content in the dissolved inorganic nutrients, so the minimum function needs to be adopted here.

[0090] Light limiting function f on phytoplankton growth L The expression is:

[0091]

[0092] Where I is the exponential function of light intensity varying with water depth, I0 is the light intensity at the water surface, θ is the light attenuation coefficient, θ = 1.51 / transparency, h is the water depth, γ1 and γ2 are the light limitation coefficients for phytoplankton growth, where both γ1 and γ2 are positive real numbers. γ1 reflects the positive effect of low light intensity (i.e., the period before light increases to a certain level), i.e., the initial stage of light-enhanced phytoplankton photosynthesis, and γ2 reflects the inhibitory effect when light intensity is too high, describing the inhibition stage of phytoplankton photosynthesis under strong light.

[0093] The expression of phytoplankton growth function is used to describe the process in which phytoplankton growth is affected by dissolved inorganic nutrients, water temperature, light and its own population size;

[0094] The respiratory consumption function of phytoplankton is used to describe the process in which the respiration of phytoplankton is affected by water temperature and its own population size. The expression of the respiratory consumption function of phytoplankton is:

[0095]

[0096] Where B pl is the respiratory consumption function of phytoplankton, m pl is the maximum respiration rate of phytoplankton at 0℃.

[0097] The feeding function of herbivorous zooplankton on phytoplankton is used to describe the process in which herbivorous zooplankton feeding is affected by its own growth rate, its own species population, zooplankton population, and water temperature. The expression of herbivorous zooplankton feeding function on phytoplankton is:

[0098]

[0099] Where, E an is the feeding function of herbivorous zooplankton on phytoplankton, g an is the maximum growth rate of herbivorous zooplankton at 0℃, the subscript an is the label of herbivorous zooplankton, C an is the total amount of herbivorous zooplankton, μ an is the exponential factor of herbivorous zooplankton growth changing with temperature, and λ is the phytoplankton grazing rate;

[0100] The organic debris sedimentation function is used to describe the sedimentation process of organic debris (including dead phytoplankton, zooplankton remains, etc.) in the water body. The expression of the organic debris sedimentation function is:

[0101]

[0102] Where S or is the organic debris deposition function, W oris the sedimentation rate of organic debris, the subscript or is the label of organic debris, C or is the total amount of organic debris.

[0103] Step 122 , establishing a dynamic water quality model based on the relevant expressions describing the interaction relationship between different predetermined parameters, as well as the process of phytoplankton mortality, zooplankton excretion, and organic debris remineralization;

[0104] The death of phytoplankton, excretion of zooplankton, death of zooplankton, and remineralization of organic debris are represented by a simple linear relationship to simplify the model. This linear relationship is combined with relevant expressions describing the interaction between different predetermined parameters to obtain a dynamic water quality model consisting of a phytoplankton dynamic change model, a herbivorous zooplankton dynamic change model, and an organic debris dynamic change model:

[0105]

[0106] Where β is the zooplankton assimilation rate, m an is the maximum excretion rate of herbivorous zooplankton at 0℃, d pl is the natural mortality rate of phytoplankton, d an is the natural mortality rate of herbivorous zooplankton, ε is the remineralization rate of organic debris, is the operator of the dynamic water quality model; taking the horizontal plane in the target water area as the xy plane and the vertical direction as the z axis, a spatial coordinate system is established, and it can be known that the operator Expressed as:

[0107]

[0108] Where u is the flow velocity in the x-axis direction, v is the flow velocity in the y-axis direction, w is the flow velocity in the z-axis direction, and A x is the eddy diffusion coefficient in the x-axis direction, A y is the eddy diffusion coefficient in the y-axis direction, K m is the vertical eddy diffusion coefficient, and t is the time.

[0109] Step 123 , the detection data of the predetermined parameters over the years are brought into the dynamic water quality model, and the model is iteratively calculated to optimize the model parameters until the predetermined number of iterations is reached and the iteration is stopped.

[0110] Based on the detection data of previous years with predetermined parameters, we obtain data such as the total amount of dissolved inorganic nutrients, water temperature, light intensity, total amount of phytoplankton, maximum growth rate and maximum respiration rate at 0°C, optimum temperature and mortality rate, total amount of herbivorous zooplankton, maximum growth rate and mortality rate at 0°C, total amount of organic debris, and sedimentation rate of organic debris. These data are then brought into the dynamic water quality model for iterative calculations to optimize the model parameters.

[0111] In this embodiment, when the dynamic water quality model is initially set up, the various parameter values ​​contained therein are all default values ​​preset by the system. After the modeling is completed, the dynamic water quality model needs to be fitted using the historical detection data of the predetermined parameters through a computer platform, and the various parameter values ​​of the model need to be continuously corrected to ultimately obtain a more accurate dynamic water quality model. Specifically, the computer platform needs to be used to bring the historical detection data of the predetermined parameters into the dynamic water quality model for fitting, and the calculations need to be iterated continuously to optimize the various parameters until the dynamic water quality model reaches a predetermined number of iterations, and the iteration is stopped and the model is output.

[0112] In this embodiment, the operator of the dynamic water quality model can calculate the predetermined parameter values ​​of each spatial position in the target water area at different times. By arranging these predetermined parameter values ​​according to the timeline, the complete dynamic change process of the target water area ecosystem can be obtained.

[0113] Step 2: Set a pollution impact coefficient based on the time and location of the pollution leak. The pollution impact coefficient decreases with time and distance from the pollution source. Add a pollutant impact term to the original dynamic water quality model and modify the added pollutant impact term based on the set pollution impact coefficient to obtain a dynamic water quality model after pollution diffusion. The specific steps include the following:

[0114] Step 21: Using the time point of the pollution leakage and the location of the pollution source as a reference, set the pollution impact coefficient related to time and space. The pollution impact coefficient is expressed as:

[0115]

[0116] Where ω(t,r) is the pollution impact coefficient, t is time, r is the distance between the spatial point and the pollution source, η is the pollutant attenuation rate constant, and σ is the spatial attenuation exponent of the pollutant. Both η and σ are used to control the rate of pollutant attenuation and are positive real numbers, σ∈[1,3].

[0117] In this embodiment, the two factors of time attenuation and space attenuation are taken into consideration when setting the pollution impact coefficient. Among them, time attenuation refers to the gradual decrease in the concentration of pollutants over time. During this process, the degradation, volatilization, and sedimentation of pollutants will also occur, and their impact on the ecosystem will gradually decrease. Spatial attenuation refers to the gradual decrease in concentration due to the interaction between pollutants and water flow as the distance from the pollution source increases. During this process, pollutants diffuse, settle or transform into other substances with less influence in the water body, and their impact on the ecosystem will gradually decrease. The diffusion of pollutants in the water body will accelerate the process of degradation, volatilization and sedimentation. In order to more accurately describe the impact process of the diffusion of pollutants in the water body, it is necessary to set pollution impact coefficients for these two attenuations to reflect the changes in pollutants in time and space, so as to accurately simulate the impact of pollution sources on different locations and times of the water body under the influence of these two factors.

[0118] Step 22: Add pollutant impact items to the original dynamic water quality model, and modify the added pollutant impact items according to the set pollution impact coefficient to obtain a dynamic water quality model after pollution diffusion.

[0119] In this embodiment, the impact of pollutants on the target water ecosystem is multifaceted; after pollutants enter the water, they will affect the growth of phytoplankton, among which the toxic effects of pollutants will inhibit the growth of phytoplankton, while the nitrogen, phosphorus, and silicon elements contained in the pollutants will promote the growth of phytoplankton by increasing the concentration of dissolved nutrients; after the phytoplankton growth function changes, the feeding function of herbivorous zooplankton on phytoplankton will also be affected, and the toxic effects of pollutants will increase the mortality rate of zooplankton; the organic matter produced after the death of phytoplankton and herbivorous zooplankton will eventually be converted into organic debris, and pollutants will increase the mortality rate of phytoplankton and herbivorous zooplankton, indirectly increasing the amount of organic debris.

[0120] In step 221, a pollutant impact term is added to the original phytoplankton growth function, and the added pollutant impact term is corrected using the pollution impact coefficient to obtain the phytoplankton growth function after pollution diffusion, which is expressed as follows:

[0121]

[0122] Where, is the phytoplankton growth function after pollution diffusion, C poll is the concentration of pollutants, α N is the nitrogen content in the unit concentration of pollutants, α P is the phosphorus content in the unit concentration of pollutants, α Si is the silicon content in the unit concentration of pollutants, ρ tox is the toxicity factor of the pollutant, ρ toxThe value is in the range of [0,1], which indicates the inhibitory effect of pollutant concentration on phytoplankton growth (0 means no effect, 1 means complete inhibition), ρ tox (C poll ) is the toxic effect of the pollutant, which is expressed as:

[0123]

[0124] Where K tox is the half-saturation constant of the pollutant concentration.

[0125] In step 222, the pollutant impact term is added to the original grazing function of herbivorous zooplankton on phytoplankton, and the added pollutant impact term is corrected using the pollution impact coefficient to obtain the grazing function of herbivorous zooplankton on phytoplankton after pollution diffusion, which is expressed as follows:

[0126]

[0127] Where, is the feeding function of herbivorous zooplankton on phytoplankton after pollution diffusion.

[0128] In step 223, the phytoplankton growth function after pollution diffusion and the phytoplankton feeding function after pollution diffusion are introduced into the original dynamic water quality model. The phytoplankton mortality rate and the herbivorous zooplankton mortality rate are corrected according to the pollution impact coefficient and the toxic effect of the pollutant. The dynamic water quality model after pollution diffusion is obtained, which is expressed as follows:

[0129]

[0130] in, is the natural mortality rate of phytoplankton after the pollution spreads, and its expression is:

[0131]

[0132] is the natural mortality rate of herbivorous zooplankton after the pollution spreads, and its expression is:

[0133]

[0134] Step 224, obtain the detection data of the predetermined parameters in the target water ecosystem after pollution, bring the detection data of the predetermined parameters after pollution into the dynamic water quality model after pollution, iterate the model, optimize the model parameters, and stop the iteration after reaching the predetermined number of iterations.

[0135] In this embodiment, based on the detection data of predetermined parameters after pollution, data such as the total amount of dissolved inorganic nutrients, water temperature, light intensity, total amount and mortality rate of phytoplankton, total amount and mortality rate of herbivorous zooplankton, total amount of organic debris, and pollutant concentration are obtained. These data are brought into the dynamic water quality model, and iterative calculations are performed to optimize the model parameters.

[0136] Step 3: Establish a convection-diffusion model for the target water area, calculate the concentration of pollutants at various spatial locations based on the convection-diffusion model, and bring the calculated pollutant concentrations at various spatial locations into the dynamic water quality model after pollution for dynamic simulation to obtain dynamic change data of various predetermined parameters of the target water ecosystem under the influence of pollutants (and the entire dynamic change process of the target water ecosystem under the influence of pollutants). Specifically, the steps include:

[0137] Step 31: Establish a convection diffusion model for the target water area based on the spatial coordinate system of the dynamic water quality model. Substitute the leakage time and leakage location of the pollutants into the convection diffusion model to obtain the pollutant concentration distributed along the time axis at each spatial location. The expression of the convection diffusion model is:

[0138]

[0139] Where D is the diffusion coefficient, is the velocity vector of the water body, representing the convection term, Q is the source term of the pollutant (representing the source of the pollutant), is the gradient operator, is the spatial gradient of concentration, indicating the spatial variation of pollutants, is the spatial second-order derivative of the pollutant, representing the diffusion term.

[0140] The convection-diffusion model is transformed using numerical methods (such as finite difference method, finite element method, finite volume method, etc.). The leakage time and location of the pollutants are brought into the transformed convection-diffusion model to obtain the pollutant concentration distributed along the time axis at each spatial location. The transformed convection-diffusion model is expressed as:

[0141]

[0142] In this embodiment, based on the above-mentioned convection diffusion model, when the location and time of the pollutant leakage are known, the change in pollutant concentration at each point in the spatial coordinate system of the target water area along the time axis can be calculated. The source term is a constant that changes with time and can be set according to the specific pollutant source. For example, when the pollution source is stable in a certain period of time, Q(C poll ,t)=Q0, Q0 is the amount of pollutant emitted per unit time.

[0143] Step 32, according to the time axis, sequentially bring the calculated pollutant concentrations at each spatial position into the dynamic water quality model after pollution, obtain the changes in the predetermined parameters distributed along the time axis at each spatial position, and dynamically simulate the target water ecosystem according to the time axis based on the changes in the predetermined parameters to obtain the dynamic change process of the target water ecosystem under the influence of pollutants.

[0144] A time interval Δt1 for updating the pollutant concentration in the dynamic water quality model after pollution is set, and the time axis is discretized according to the time interval Δt1. According to the arrangement order of each time point in the time axis, the dynamically changing pollutant concentrations at each spatial position are sequentially brought into the dynamic water quality model after pollution, and various predetermined parameter data that change along the time axis at each spatial position are obtained, including dissolved inorganic nutrient concentration change data, phytoplankton concentration change data, growth rate and mortality rate, herbivorous zooplankton concentration change data, feeding rate and mortality rate, and organic debris concentration change data and degradation rate.

[0145] In this embodiment, the dynamic change process of the target water ecosystem under the influence of pollutants can be visualized through a computer software platform (such as MATLAB / Simulink, Python, and Vensim).

[0146] In this embodiment, by dynamically simulating the target water ecosystem under the influence of pollutants, it is possible to predict the time when pollutants reach specific sensitive areas (such as drinking water sources and fishery areas) and the extent of the impact of pollutants on these areas. Based on the diffusion and concentration levels of pollutants, the dynamic water quality model can be used for environmental risk assessment.

[0147] Step 4: Conduct artificial intervention and treatment on the target water ecosystem after pollution, set a treatment monitoring cycle, measure the change data of the predetermined parameters after treatment based on the treatment monitoring cycle, use the data provided by the dynamic water quality model after pollution as the control group data, and calculate the health index of the target water ecosystem based on the change data of the predetermined parameters after treatment and the control group data.

[0148] The post-pollution time period is divided into early, middle and late stages, and different measures are used for artificial intervention and control in each period. The early stage is 0-5 days after the pollutant leakage, the middle stage is 6-20 days after the pollutant leakage, and the late stage is 21-60 days after the pollutant leakage. The main goals of the early stage are to quickly reduce the concentration of pollutants, prevent excessive accumulation of pollutants, slow down the death of phytoplankton, curb the outbreak of algal blooms, and prevent water hypoxia; the main goals of the middle stage are to further restore the healthy growth of phytoplankton, promote self-purification of water quality, reduce the mortality rate of herbivorous zooplankton, strengthen the degradation of organic matter, and avoid further deterioration of eutrophication; the main goals of the late stage are to restore the ecological balance of water bodies, ensure the dynamic balance of ecological elements such as phytoplankton, herbivorous zooplankton, and organic debris, avoid continuous eutrophication, further remove residual pollutants, and restore the self-purification function of the ecosystem.

[0149] Early artificial intervention measures include but are not limited to: adding chemical sedimentation agents (such as aluminum salts, iron salts, etc.) to help absorb and settle pollutants in the water and reduce their concentration in the water (especially nutrients such as nitrogen and phosphorus); using adsorption materials (such as activated carbon, seaweed, adsorption resins, etc.) to adsorb harmful substances in the water (such as heavy metals, nitrogen, phosphorus, etc.), especially pollutants that are toxic to the growth of phytoplankton in the water body; installing oxygenators or using aeration equipment to increase the dissolved oxygen concentration in the water body to help maintain the living conditions of aquatic organisms, slow down the oxygen consumption process during the decomposition of bottom organic matter, and prevent water hypoxia; using specific phytoplankton inhibitors (such as algaecides) to prevent excessive growth of phytoplankton, especially harmful algae, during algal blooms.

[0150] Medium-term artificial intervention measures include but are not limited to: releasing microorganisms or enzymes that can decompose pollutants to help degrade organic matter, especially organic pollutants that may be contained in pollution sources (such as oil, pesticides, etc.); controlling nitrogen and phosphorus concentrations in the water by adjusting fertilization or reducing the input of nutrient sources, reducing excessive eutrophication, and avoiding algal blooms and algae outbreaks; introducing exogenous phytoplankton populations: if necessary, while controlling algal blooms, some beneficial phytoplankton populations can be released to restore the productivity and ecological balance of the water body; improving the bottom oxygen supply: taking measures to improve the oxygen conditions of the bottom water body, help restore the living environment of herbivorous zooplankton, and reduce mortality.

[0151] Later artificial intervention and control measures include but are not limited to: adjusting the pH value and other physical and chemical properties of the water body to create conditions conducive to the growth of phytoplankton and aquatic organisms, helping to restore the stability of the ecosystem; reducing the retention of pollutants, promoting water flow and the spread of pollutants through artificial control of water flow (such as adding water pumps, changing the direction of water flow, etc.); according to the water conditions, the number of specific types of phytoplankton can continue to be controlled to avoid the continued expansion of certain harmful algae; in the later stages of water ecological restoration, herbivorous zooplankton, such as Daphnia, rotifers, etc., can be introduced to help control the number of phytoplankton and restore the food chain; continue to strengthen the oxygen supply to the bottom of the water body, reduce organic pollutants in the bottom sediments, and restore the healthy environment of the water body.

[0152] The dynamic water quality model after pollution is used to simulate the dynamic changes of the target water ecosystem after pollution occurs, and the predetermined parameter simulation data output by the model is obtained. The predetermined parameter simulation data is used as the control group data. The control group data includes dissolved inorganic nutrient concentration change data, phytoplankton concentration change data, growth rate and mortality rate, herbivorous zooplankton concentration change data, feeding rate and mortality rate, organic debris concentration change data and degradation rate data, etc. Among them, the water temperature and light data in the dynamic water quality model after pollution are set according to the actual measured values.

[0153] According to the treatment monitoring period Δt2, the control group data is matched with the change data of the predetermined parameters after treatment measured on the spot. The health index of the target water ecosystem is calculated based on the difference between the control group data and the change data of the predetermined parameters after treatment. The health index is expressed as:

[0154]

[0155] Where H is the health index of the target water ecosystem, Δt2 is the governance monitoring period, ω j is the weight, and its subscript j is the label of the predetermined parameter (the value of j is equal to the number of predetermined parameters), ω poll is the weight of the pollutant, where ω1+ω2+…+ω j +ω poll =1;O in is the value of the predetermined parameter before pollution, the subscript in is the label before pollution, j,act is the value of the predetermined parameter measured on site after pollution, the subscript act is the label after pollution, j,con is the value of the predetermined parameter of the control group after contamination, the subscript con is the label of the control group, C act,poll is the average pollutant concentration of the selected target point after field measurement, C in,poll is the average pollutant concentration of the selected target point after field measurement, C con,pollis the average pollutant concentration corresponding to the selected target point in the control group.

[0156] In this embodiment, the closer the value of the health index is to 1, the closer the aquatic ecosystem is to its pre-pollution health state.

[0157] Step 5: record the control group data of the target water ecosystem, the change data of the predetermined parameters after treatment, and the health index after treatment, and store them in the database.

[0158] In this embodiment, after the treatment is completed, the control group data of the target water ecosystem, the change data of the predetermined parameters after treatment, and the health index after treatment are recorded according to the treatment monitoring cycle, the recorded data are stored in the database, and a report is compiled according to the treatment process to provide a basis for subsequent evaluation.

[0159] In this embodiment, polluted waters can be selected as target waters, or artificial eco-boxes can be set up to simulate the ecosystem of a predetermined water area based on the specific pollution. The artificial eco-boxes can be divided into a control group and an experimental group. Pollutant leakage experiments can be conducted using the artificial eco-boxes to obtain final results, wherein the maximum difference in various data among all artificial eco-boxes does not exceed 5%.

[0160] The steps in this application can be adjusted in order, combined, and deleted according to actual needs.

[0161] The units in the device of the present application can be combined, divided and deleted according to actual needs.

[0162] Although the present application is disclosed in detail with reference to the accompanying drawings, it should be understood that these descriptions are merely exemplary and are not intended to limit the application of the present application. The scope of protection of the present application is defined by the appended claims and may include various modifications, alterations and equivalents made to the invention without departing from the scope and spirit of the present application.

Claims

1. An ecological dynamic simulation method for emergency disposal of pollution incidents, characterized in that: The method includes: Step 1: Select the target water area and obtain historical test data of the predetermined parameters in the target water area ecosystem from the database. Based on the interactions between different predetermined parameters and the test data of each predetermined parameter, a dynamic water quality model is constructed, which specifically includes: Step 121: establishing relevant expressions based on the interaction relationships among different predetermined parameters, wherein the relevant expressions include an expression for a phytoplankton growth function, an expression for a phytoplankton respiration consumption function, an expression for a phytoplankton ingestion function, and an expression for an organic debris settling function. Phytoplankton growth function R pl for: R pl =G pl ·C pl =f T min[f Nu ,f L ]C pl Where G pl is the growth rate of phytoplankton, C pl is the total amount of phytoplankton, the subscript pl is the label of phytoplankton, f T is the exponential function of the effect of water temperature on phytoplankton growth, T is water temperature, f Nu is the limiting function of dissolved inorganic nutrients on phytoplankton, the subscript Nu is the label of dissolved inorganic nutrients, f L is the limiting function of light on phytoplankton growth, the subscript L is the label of light, and min[·] is the minimum value function; Phytoplankton respiration consumption function B pl for: Where m pl is the maximum respiration rate of phytoplankton at 0℃, μ pl is the exponential factor of phytoplankton growth changing with temperature, T0 is the optimum temperature for phytoplankton; The feeding function E of herbivorous zooplankton on phytoplankton an for: Where, E an is the feeding function of herbivorous zooplankton on phytoplankton, g an is the maximum growth rate of herbivorous zooplankton at 0℃, the subscript an is the label of herbivorous zooplankton, C an is the total amount of herbivorous zooplankton, μ an is the exponential factor of herbivorous zooplankton growth changing with temperature, and λ is the phytoplankton grazing rate; Organic debris deposition function S or for: Where W or is the sedimentation rate of organic debris, the subscript or is the label of organic debris, C or is the total amount of organic debris, h is the water depth; Step 122: Based on the relevant expressions describing the interaction between different predetermined parameters, and the processes of phytoplankton death, zooplankton excretion, and organic debris remineralization, a dynamic water quality model is established. Specifically, the process of phytoplankton death, zooplankton excretion, zooplankton death, and organic debris remineralization is expressed using a simple linear relationship, and the linear relationship is combined with the relevant expressions describing the interaction between different predetermined parameters to obtain the dynamic water quality model: Where β is the zooplankton assimilation rate, m an is the maximum excretion rate of herbivorous zooplankton at 0℃, d pl is the natural mortality rate of phytoplankton, d an is the natural mortality rate of herbivorous zooplankton, ε is the remineralization rate of organic debris, is the operator of the dynamic water quality model; Step 123, bringing the predetermined parameter detection data over the years into the dynamic water quality model, performing iterative calculations on the model to optimize the model parameters until the predetermined number of iterations is reached and the iteration is stopped; Step 2: Set a pollution impact coefficient based on the time and location of the pollution leak, where the pollution impact coefficient decreases with time and distance from the pollution source. Add a pollutant impact term to the dynamic water quality model, and modify the added pollutant impact term based on the set pollution impact coefficient to obtain a dynamic water quality model after pollution diffusion. The pollution impact coefficient is set based on the time and location of the pollution leak, including: Taking the time point of pollution leakage and the location of the pollution source as reference, a pollution impact coefficient related to time and space is set. The pollution impact coefficient is expressed as: Where ω(t,r) is the pollution impact coefficient, t is time, r is the distance between the spatial point and the pollution source, η is the pollutant attenuation rate constant, and σ is the spatial attenuation index of the pollutant, σ∈[1,3]; The dynamic water quality model after pollution diffusion is obtained, including: Step 221: Add the pollutant impact term to the phytoplankton growth function and use the pollution impact coefficient to correct the added pollutant impact term to obtain the phytoplankton growth function after pollution diffusion. for: Where g pl is the maximum growth rate of phytoplankton at 0℃, C poll is the concentration of pollutants, α N is the nitrogen content in the unit concentration of pollutants, k N is the nitrogen absorption half-saturation constant, α P is the phosphorus content in the unit concentration of pollutants, k P Phosphorus absorption half-saturation constant, α Si is the silicon content in the unit concentration of pollutants, k Si is the silicon absorption half-saturation constant, ρ tox is the toxicity factor of the pollutant, ρ tox belongs to [0,1], ρ tox (C poll ) is the toxic effect of the pollutant, which is expressed as: Where K tox is the half-saturation constant of pollutant concentration; Step 222: Add the pollutant impact term to the grazing function of herbivorous zooplankton on phytoplankton, and use the pollution impact coefficient to correct the added pollutant impact term to obtain the grazing function of herbivorous zooplankton on phytoplankton after pollution diffusion. for: In step 223, the phytoplankton growth function after pollution diffusion and the phytoplankton feeding function after pollution diffusion are introduced into the original dynamic water quality model. The phytoplankton mortality rate and the herbivorous zooplankton mortality rate are corrected according to the pollution impact coefficient and the toxic effect of the pollutant, thereby obtaining the dynamic water quality model after pollution diffusion: in, is the natural mortality rate of phytoplankton after the pollution spreads, and its expression is: is the natural mortality rate of herbivorous zooplankton after the pollution spreads, and its expression is: Step 224: Acquire detection data of predetermined parameters in the target water ecosystem after pollution, apply the detection data of the predetermined parameters after pollution to the dynamic water quality model after pollution, and iterate the model until a predetermined number of iterations is reached and the iteration is stopped; Step 3: Establish a convection-diffusion model for the target waters. Calculate the concentration of pollutants at various spatial locations based on the convection-diffusion model. Bring the calculated pollutant concentrations at various spatial locations into a post-pollution dynamic water quality model for dynamic simulation to obtain dynamic change data for various predetermined parameters of the target water ecosystem under the influence of pollutants. Step 4: Conduct artificial intervention and treatment of the polluted target water ecosystem, set a treatment monitoring cycle, measure the change data of the predetermined parameters after treatment based on the treatment monitoring cycle, use the data provided by the dynamic water quality model after pollution as the control group data, and calculate the health index of the target water ecosystem based on the change data of the predetermined parameters after treatment and the control group data; Step 5: record the control group data of the target water ecosystem, the change data of the predetermined parameters after treatment, and the health index after treatment, and store them in the database.

2. The ecological dynamic simulation method for emergency disposal of pollution incidents according to claim 1, characterized in that: The step 122 further includes: establishing a spatial coordinate system with the horizontal plane in the target water area as the xy plane and the vertical direction as the z axis, and the operator Expressed as: Where u is the flow velocity in the x-axis direction, v is the flow velocity in the y-axis direction, w is the flow velocity in the z-axis direction, and A x is the eddy diffusion coefficient in the x-axis direction, A y is the eddy diffusion coefficient in the y-axis direction, K m is the vertical eddy diffusion coefficient, and t is the time.

3. The ecological dynamic simulation method for emergency disposal of pollution incidents according to claim 2, characterized in that: The step 3 specifically includes: Step 31: Based on the spatial coordinate system of the dynamic water quality model, a convection diffusion model of the target water area is established. The leakage time and leakage location of the pollutants are introduced into the convection diffusion model to obtain the pollutant concentration distributed along the time axis at each spatial location. The convection diffusion model is: Where D is the diffusion coefficient, is the velocity vector of the water body, Q is the source term of the pollutant, is the gradient operator, is the spatial gradient of concentration, is the spatial second-order derivative of the pollutant; The convection diffusion model is transformed, and the leakage time and location of the pollutants are brought into the transformed convection diffusion model to obtain the pollutant concentration distributed along the time axis at each spatial position. The transformed convection diffusion model is expressed as: Where D x is the diffusion coefficient in the x-axis direction, D y is the diffusion coefficient in the y-axis direction, D z is the diffusion coefficient in the z-axis direction; Step 32, according to the time axis, sequentially bring the calculated pollutant concentrations at each spatial position into the dynamic water quality model after pollution, obtain the changes in the predetermined parameters distributed along the time axis at each spatial position, and dynamically simulate the target water ecosystem according to the time axis based on the changes in the predetermined parameters to obtain the dynamic change process of the target water ecosystem under the influence of pollutants.

4. The ecological dynamic simulation method for emergency disposal of pollution incidents according to claim 3, characterized in that: The step 32 specifically includes: A time interval Δt1 for updating the pollutant concentration in the dynamic water quality model after pollution is set, and the time axis is discretized according to the time interval Δt1. According to the arrangement order of each time point in the time axis, the dynamically changing pollutant concentrations at each spatial position are sequentially brought into the dynamic water quality model after pollution, and the predetermined parameter data of each spatial position changing along the time axis are obtained.

5. The ecological power simulation method for emergency disposal of pollution incidents according to claim 1, characterized in that: In step 4, the health index of the target water ecosystem is calculated based on the change data of the predetermined parameters after treatment and the control group data, specifically including: According to the time of the governance monitoring cycle Δt2, the control group data is matched with the change data of the predetermined parameters after governance measured on the spot, and the health index of the target water ecosystem is calculated based on the difference between the control group data and the change data of the predetermined parameters after governance: Where H is the health index of the target water ecosystem, ω j is the weight, and its subscript j is the label of the predetermined parameter, ω poll is the weight of the pollutant, where ω1+ω2+…+ω j +ω poll =1;O in is the value of the predetermined parameter before pollution, O j,act is the value of the predetermined parameter measured in the field after pollution, O j,con is the value of the predetermined parameter of the control group after contamination, C act,poll is the average pollutant concentration of the selected target point after field measurement, C in,poll is the average pollutant concentration of the selected target point after field measurement, C con,poll is the average pollutant concentration corresponding to the selected target point in the control group.

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