Ecological dynamic simulation method for emergency disposal of pollution events

Abstract

The invention discloses an ecological dynamic simulation method for emergency disposal of pollution events, which comprises the following steps of: constructing a dynamic water quality model by using detection data of a selected target water area ecological system over the years, setting a pollution influence coefficient according to pollution leakage time and position, and adding a pollutant influence item into the original dynamic water quality model, the method comprises the steps of establishing a pollution influence coefficient of a target water area, correcting a pollutant influence item according to the pollution influence coefficient to obtain a dynamic water quality model after pollution diffusion, establishing a convective diffusion model of the target water area, substituting the pollutant concentration output by the convective diffusion model into the dynamic water quality model in a point-to-point mode, and continuously updating various parameters of the dynamic water quality model. And finally obtaining a dynamic change process after the target water area ecological system is polluted, and evaluating a treatment result by utilizing the dynamic change process. According to the technical scheme, the influence of pollutant diffusion can be fully utilized to correct the dynamic water quality model, the accuracy of the dynamic water quality model is enhanced, and the accuracy of a final evaluation result is improved.

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 of aquatic ecosystems 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 process of pollutant diffusion in the water body is affected by many factors, including the convection diffusion characteristics of water flow, the physical and chemical properties of pollutants, and environmental changes. Traditional pollution and governance assessment methods include water quality monitoring, empirical methods, and analogy methods. These methods all require field visits to obtain assessment data, and are highly dependent on static monitoring data, manual processing, or field experiments. The assessment results usually have a large delay, making it difficult to provide fast and effective decision support at the beginning of a pollution incident. In addition, traditional pollution and governance assessment methods consume a lot of human resources, have a slow response speed, and are affected by equipment and climate. The amount of data measured is small and 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 disposal of pollution incidents, accurately simulate the dynamic change process of the target water ecosystem after pollution, and provide accurate data reference for emergency disposal of pollution incidents.

[0006] The technical solution of the present application is to provide an ecological dynamic simulation method for emergency disposal of pollution incidents, the method comprising:

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

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

[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 the dynamic water quality model after pollution for dynamic simulation, and obtain dynamic change data of various predetermined parameters of the target water ecosystem under the influence of pollutants;

[0010] Step 4: Conduct artificial intervention and treatment on 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 interaction between different predetermined parameters and the detection data of each predetermined parameter, which specifically includes:

[0013] Step 121, establishing relevant expressions according to the interaction relationship between different predetermined parameters, wherein the relevant expressions include an expression of a phytoplankton growth function, an expression of a phytoplankton respiration consumption function, an expression of a phytoplankton feeding function, and an expression of an organic debris settling function;

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

[0015] Step 123, bringing the historical detection data of the predetermined parameters into the dynamic water quality model, performing iterative operations on the model, optimizing the model parameters, and stopping the iteration after reaching a predetermined number of iterations.

[0016] Further, 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] In the formula, G pl is the growth rate of phytoplankton, C pl is the total amount of phytoplankton, the subscript pl is the label of phytoplankton, and 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, and 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] In the formula, m pl is the maximum respiration rate of phytoplankton at 0℃, μ pl is the exponential factor of phytoplankton growth changing with temperature, T 0 is the optimum temperature for phytoplankton;

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

[0023]

[0024] In the formula, 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 the growth of herbivorous zooplankton changing with temperature, and λ is the phytoplankton ingestion 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 death of phytoplankton, excretion of zooplankton, death of zooplankton and remineralization of organic debris by a simple linear relationship, combining the linear relationship with a related expression describing the interaction relationship between different predetermined parameters, and obtaining a dynamic water quality model:

[0029]

[0030] Where β is the assimilation rate of zooplankton feeding, 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 pollution source as reference, the 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] In the formula, 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 half-saturation constant of silicon absorption, 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] In the formula, K tox is the half-saturation constant of pollutant concentration;

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

[0044]

[0045] Step 223, the phytoplankton growth function after the pollution spreads, and the phytoplankton feeding function of herbivorous zooplankton after the pollution spreads are brought into the original dynamic water quality model, and the phytoplankton mortality rate and herbivorous zooplankton mortality rate are corrected according to the pollution impact coefficient and the toxic effect of the pollutant, so as to obtain the dynamic water quality model after the pollution spreads:

[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 where the dynamic water quality model is located, a convection diffusion model of the target water area is established, and the leakage time and leakage location of the pollutants are brought into the convection diffusion model to obtain the pollutant concentration distributed along the time axis at each spatial position. 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 leakage 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, the calculated pollutant concentrations at each spatial position are sequentially brought into the dynamic water quality model after pollution, and the changes of the predetermined parameters distributed along the time axis at each spatial position are obtained. Based on the changes of the predetermined parameters, the target water ecosystem is dynamically simulated according to the time axis to obtain the dynamic change process of the target water ecosystem under the influence of pollutants.

[0059] Furthermore, step 32 specifically includes:

[0060] Set the time interval Δt for updating the pollutant concentration in the dynamic water quality model after pollution 1 , according to the time interval Δt 1 The time axis is discretized, and the dynamically changing pollutant concentrations at each spatial position are sequentially brought into the post-pollution dynamic water quality model according to the arrangement order of each time point in the time axis, so as to obtain the predetermined parameter data at each spatial position that changes along the time axis.

[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 governance monitoring cycle Δt 2 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:

[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 ω 1 +ω 2 +…+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 the present 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 introduces 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 area ecosystem after pollution; the technical solution in the present 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 area 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 the present application obtains a dynamic water quality model after pollution diffusion by adding pollutant impact terms to the original dynamic water quality model, and also adds a pollution impact coefficient for correcting the pollutant impact terms, taking into account the two factors of time attenuation and space 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 easily understood in 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 disposal of pollution incidents according to an embodiment of the present application. DETAILED DESCRIPTION

[0070] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0071] In the following description, many specific details are elaborated 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 protection scope 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 disposal of pollution incidents, including:

[0073] Step 1: Select the target water area, obtain the historical detection data of the predetermined parameters in the target water area ecosystem from the database, and build a dynamic water quality model based on the interaction between different predetermined parameters and the detection data of each predetermined parameter, 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, wherein 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 (including the increase and loss of organic debris) and degradation rate; the detection data of environmental conditions include the 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 Detritusmodel, which is an ecological and water quality model used to describe the material cycle and ecological processes in water bodies, and is commonly used in the simulation and research of marine, 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 (depends on light intensity) and nutrient supply (i.e., dissolved inorganic nutrients, such as nitrogen, phosphorus and other nutrient concentrations). 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 interaction between different predetermined parameters and the detection data of each predetermined parameter, which specifically includes the following steps:

[0080] Step 121, establishing relevant expressions according to the interaction relationship between different predetermined parameters, wherein the relevant expressions include an expression of a phytoplankton growth function, an expression of a phytoplankton respiration consumption function, an expression of a phytoplankton feeding function, and an expression of an organic debris settling function;

[0081] 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; the expression of phytoplankton growth function is:

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

[0083] In the formula, 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, and 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, and 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 of temperature affecting phytoplankton growth is f T The expression is:

[0085]

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

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

[0088]

[0089] In the formula, C N is the total amount of nitrogen, kN 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 for phytoplankton growth L The expression is:

[0091]

[0092] Where I is the exponential function of light intensity changing with water depth, I 0 is the light intensity at the water surface, θ is the light attenuation coefficient, θ = 1.51 / transparency, h is the water depth, γ 1 and γ 2 is the light limitation coefficient for phytoplankton growth, where γ 1 and γ 2 are all positive real numbers, γ 1 It reflects the positive effect of low light intensity (i.e., the period before light increases to a certain level), that is, the initial stage of light-enhanced phytoplankton photosynthesis, γ 2 It reflects the inhibitory effect when the light intensity is too high and describes 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;

[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] In the formula, 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] In the formula, 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 the growth of herbivorous zooplankton changing with temperature, and λ is the phytoplankton ingestion 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] In the formula, S or is the organic debris deposition function, 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.

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

[0104] The death of phytoplankton, excretion of zooplankton, death of zooplankton and remineralization of organic debris are expressed by simple linear relationships to simplify the model. This linear relationship is combined with relevant expressions describing the interaction relationship 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 assimilation rate of zooplankton feeding, 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 It is 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, bringing the historical detection data of the predetermined parameters into the dynamic water quality model, performing iterative operations on the model, optimizing the model parameters, and stopping the iteration after reaching a predetermined number of iterations.

[0110] According to 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℃, optimum temperature and mortality rate, total amount of herbivorous zooplankton, maximum growth rate and mortality rate at 0℃, total amount of organic debris, sedimentation rate of organic debris, etc. These data are brought into the dynamic water quality model, and iterative calculations are performed to optimize the model parameters.

[0111] In this embodiment, the dynamic water quality model is initially set up, and 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, it is necessary to use a computer platform to bring the historical detection data of the predetermined parameters into the dynamic water quality model for fitting, continuously iterate the calculations, and optimize the various parameters until the dynamic water quality model reaches a predetermined number of iterations, then stop iterating and output the model.

[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 the pollution impact coefficient based on the time and location of the pollution leakage, where the pollution impact coefficient decreases with time and the distance from the pollution source. Add the pollutant impact term to the original dynamic water quality model, and modify the added pollutant impact term according to the set pollution impact coefficient to obtain the dynamic water quality model after the pollution spread, which specifically includes the following steps:

[0114] Step 21, taking the time point of pollution leakage and the location of the pollution source as reference, set the pollution impact coefficient related to time and space, and 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 decay and space decay are considered when setting the pollution impact coefficient. Among them, time decay refers to the gradual decrease in the concentration of pollutants over time. In this process, the degradation, volatilization, and sedimentation of pollutants will also occur, and their impact on the ecosystem will gradually decrease. Spatial decay refers to the gradual decrease in concentration due to the interaction between pollutants and water flow as the distance from the pollution source increases. In 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 the 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, adding pollutant impact items to the original dynamic water quality model, and correcting 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 entering the water, the pollutants will affect the growth of phytoplankton, among which the toxic effect of the pollutants will inhibit the growth of phytoplankton, and the nitrogen, phosphorus, and silicon elements contained in the pollutants will promote the growth of phytoplankton in the form of 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 effect of pollutants will increase the mortality rate of zooplankton; the organic matter produced after the death of phytoplankton and herbivorous zooplankton is eventually converted into organic debris, and pollutants will increase the mortality rate of phytoplankton and herbivorous zooplankton and indirectly increase the amount of organic debris.

[0120] Step 221, add the pollutant impact term to the original 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, and its expression is:

[0121]

[0122] In the formula, is the phytoplankton growth function after pollution diffusion, C poll is the concentration of pollutants, αN 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 tox The value is in the range of [0,1], indicating 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] In the formula, K tox is the half-saturation constant of the pollutant concentration.

[0125] Step 222, add the pollutant impact term to the original 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, which is expressed as:

[0126]

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

[0128] Step 223, the phytoplankton growth function after the pollution spread and the phytoplankton feeding function of herbivorous zooplankton after the pollution spread are brought into the original dynamic water quality model, and 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, and the dynamic water quality model after the pollution spread 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 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, pollutant concentration, etc. are obtained, and 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 positions according to the convection diffusion model, bring the calculated pollutant concentrations at various spatial positions into the dynamic water quality model after pollution for dynamic simulation, and obtain the 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), which specifically includes the following steps:

[0137] Step 31, based on the spatial coordinate system where the dynamic water quality model is located, a convection diffusion model of the target water area is established, and the leakage time and leakage position of the pollutants are brought into the convection diffusion model to obtain the pollutant concentration distributed along the time axis at each spatial position. 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 by using numerical methods (such as finite difference method, finite element method, finite volume method, etc.), and the leakage time and leakage position of 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:

[0141]

[0142] In this embodiment, according to the above convection diffusion model, when the location and time of the pollutant leakage are known, the change of pollutant concentration at each point in the spatial coordinate system of the target water area according to the time axis distribution can be calculated. The source term is a constant that changes with time, which 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)=Q 0 , Q 0 is the amount of pollutant emitted per unit time.

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

[0144] Set the time interval Δt for updating the pollutant concentration in the dynamic water quality model after pollution 1 , according to the time interval Δt 1 The time axis is discretized, and the dynamically changing pollutant concentrations at each spatial position are sequentially brought into the dynamic water quality model after pollution according to the arrangement order of each time point in the time axis, so as to obtain various predetermined parameter data that change along the time axis at each spatial position, 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, Vensim and other platforms).

[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. According to the diffusion and concentration levels of pollutants, dynamic water quality models 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 time period after pollution 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 pollution leakage, the middle stage is 6-20 days after the pollution leakage, and the late stage is 21-60 days after the pollution leakage. Among them, the early stage aims 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 middle stage aims 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 late stage aims 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 remaining 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 absorb 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) during algal blooms to prevent excessive growth of phytoplankton, especially harmful algae.

[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, help restore the living environment of herbivorous zooplankton, and reduce mortality.

[0151] Later artificial intervention 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 diffusion of pollutants through artificial control of water flow (such as adding water pumps, changing the direction of water flow, etc.); depending on 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 ecological restoration of the water body, 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 governance monitoring cycle Δt 2 The control group data is matched with the change data of the predetermined parameters after treatment 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 treatment. The health index is expressed as:

[0154]

[0155] Where H is the health index of the target water ecosystem, Δt 2 is the governance monitoring cycle, ω j is the weight, 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 ω 1 +ω 2 +…+ω j +ω poll =1;O in is the value of the pre-contamination parameter, the subscript in is the label before contamination, j,act is the value of the predetermined parameter measured in the field 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, Cin,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.

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

[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 aquatic 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 a 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 according to a specific pollution category, and the artificial eco-boxes can be divided into a control group and an experimental group. The artificial eco-boxes are used to conduct pollutant leakage experiments to obtain final results, wherein the maximum difference in the data of 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, obtain the historical detection data of the predetermined parameters in the target water area ecosystem from the database, and build a dynamic water quality model based on the interaction between different predetermined parameters and the detection data of each predetermined parameter; Step 2: Set the pollution impact coefficient based on the time and location of the pollution leakage, where the pollution impact coefficient decreases with time and the distance from the pollution source. Add the pollutant impact term to the dynamic water quality model, and correct the added pollutant impact term according to the set pollution impact coefficient to obtain the dynamic water quality model after the pollution spreads. 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 the dynamic water quality model after pollution for dynamic simulation, and obtain dynamic change data of various predetermined parameters of the target water ecosystem under the influence of pollutants; Step 4: Conduct artificial intervention and treatment on 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 power simulation method for emergency disposal of pollution incidents according to claim 1, characterized in that: In step 1, a dynamic water quality model is constructed based on the interaction between different predetermined parameters and the detection data of each predetermined parameter, which specifically includes: Step 121, establishing relevant expressions according to the interaction relationship between different predetermined parameters, wherein the relevant expressions include an expression of a phytoplankton growth function, an expression of a phytoplankton respiration consumption function, an expression of a phytoplankton feeding function, and an expression of an organic debris settling function; Step 122, establishing a dynamic water quality model based on relevant expressions describing the interaction relationship between different predetermined parameters, and the process of phytoplankton death, zooplankton excretion, and organic debris remineralization; Step 123, bringing the historical detection data of the predetermined parameters into the dynamic water quality model, performing iterative operations on the model, optimizing the model parameters, and stopping the iteration after reaching a predetermined number of iterations.

3. The ecological power simulation method for emergency disposal of pollution incidents according to claim 2, characterized in that: In step 121, the phytoplankton growth function R pl for: R pl =G pl ·C pl =f T min[f Nu ,f L ]C pl In the formula, G pl is the growth rate of phytoplankton, C pl is the total amount of phytoplankton, the subscript p1 is the label of phytoplankton, and 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, and 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: In the formula, 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 of herbivorous zooplankton on phytoplankton E an for: In the formula, 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 the growth of herbivorous zooplankton changing with temperature, and λ is the phytoplankton ingestion 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, and h is the water depth.

4. The ecological power simulation method for emergency disposal of pollution incidents according to claim 3, characterized in that: The step 122 specifically includes: using a simple linear relationship to represent the death of phytoplankton, the excretion of zooplankton, the death of zooplankton, and the remineralization of organic debris, and combining the linear relationship with a related expression describing the interaction relationship between different predetermined parameters to obtain a dynamic water quality model: Where β is the assimilation rate of zooplankton feeding, 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: 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.

5. The ecological power simulation method for emergency disposal of pollution incidents according to claim 4, characterized in that: In step 2, the pollution impact coefficient is set based on the time and location of the pollution leakage, specifically including: Taking the time point of pollution leakage and the location of pollution source as reference, the 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 exponent of the pollutant, σ∈[1,3].

6. The ecological power simulation method for emergency disposal of pollution incidents according to claim 5, characterized in that: The dynamic water quality model after the pollution diffusion is obtained in step 2, specifically 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: In the formula, 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 half-saturation constant of silicon absorption, 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: In the formula, K tox is the half-saturation constant of pollutant concentration; Step 222, add the pollutant impact term to the feeding function of herbivorous zooplankton on phytoplankton, and use the pollution impact coefficient to correct the added pollutant impact term to obtain the feeding function of herbivorous zooplankton on phytoplankton after pollution diffusion. for: Step 223, the phytoplankton growth function after the pollution spreads, and the phytoplankton feeding function of herbivorous zooplankton after the pollution spreads are brought into the original dynamic water quality model, and the phytoplankton mortality rate and herbivorous zooplankton mortality rate are corrected according to the pollution impact coefficient and the toxic effect of the pollutant, so as to obtain the dynamic water quality model after the pollution spreads: 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, 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.

7. The ecological power simulation method for emergency disposal of pollution incidents according to claim 6, characterized in that: The step 3 specifically includes: Step 31, based on the spatial coordinate system where the dynamic water quality model is located, a convection diffusion model of the target water area is established, and the leakage time and leakage location of the pollutants are brought into the convection diffusion model to obtain the pollutant concentration distributed along the time axis at each spatial position. 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 leakage 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: Step 32, according to the time axis, the calculated pollutant concentrations at each spatial position are sequentially brought into the dynamic water quality model after pollution, and the changes of the predetermined parameters distributed along the time axis at each spatial position are obtained. Based on the changes of the predetermined parameters, the target water ecosystem is dynamically simulated according to the time axis to obtain the dynamic change process of the target water ecosystem under the influence of pollutants.

8. The ecological power simulation method for emergency disposal of pollution incidents according to claim 7, characterized in that: The step 32 specifically includes: A time interval Δt1 is set for updating the pollutant concentration in the dynamic water quality model after pollution, 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 to obtain various predetermined parameter data at each spatial position that change along the time axis.

9. 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 situ after contamination, 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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