Soil and groundwater ecological environment damage value quantification method taking monitoring of natural attenuation as recovery scheme

Abstract

The invention discloses a soil and groundwater ecological environment damage value quantification method taking monitoring of natural attenuation as a recovery scheme. The method comprises the following steps: establishing a numerical simulation model for monitoring natural attenuation and strengthening natural attenuation, exploring recovery rules and effects of polluted soil groundwater under natural conditions and different strengthening measures, and obtaining a model input sample set; based on the model input sample set, randomly sampling to obtain multiple groups of model input sample data, carrying out natural attenuation simulation prediction under different strengthening measures, and constructing a monitoring natural attenuation and strengthening natural attenuation prediction model; and coupling a theory treatment cost method and a resource value method, carrying out optimization analysis on the monitoring natural attenuation cost, the strengthening natural attenuation cost and the period damage based on a multi-objective optimization model, and selecting a monitoring natural attenuation and strengthening natural attenuation scheme with the lowest total cost. The problem that the damage value is difficult to quantify when natural attenuation is monitored and strengthened as a recovery scheme is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of soil and groundwater ecological environment damage value quantification, and in particular to a soil and groundwater ecological environment damage value quantification method using natural attenuation monitoring as a restoration plan. Background Art

[0002] Soil and groundwater play an important role in social development, resource utilization, and ecological protection. Quantifying the value of damage based on restoration plans is one of the keys to soil and groundwater control and remediation. The treatment and remediation of soil and groundwater pollution in production enterprises has its own particularity and difficulty. Long-term strong disturbance construction on the ground may affect the normal production of the enterprise, and pollution rebound is prone to occur after treatment and remediation. The monitoring of natural attenuation technology reduces the content of pollutants in soil and groundwater to an acceptable risk level based on the physical, chemical and biological effects of the site by implementing planned monitoring plans and strengthening measures. Based on the development trend of low-carbon remediation technology in the future and the good application prospects for the remediation of production enterprises, the quantification of the value of soil and groundwater ecological environment damage based on monitoring natural attenuation as a restoration plan is extremely important. At present, the quantification method of soil and groundwater ecological environment damage value in my country is mainly applicable to short-term restorable plans or long-term non-restorable plans, focusing on single basic damage quantification or single period damage quantification. However, the recovery period of monitoring natural attenuation technology has a large span, the strengthening measures are not unique, and the degree of damage is difficult to quantify. Therefore, a simple value quantification method cannot meet the calculation requirements.

[0003] Therefore, in order to solve the problems of long quantification cycle and unclear calculation method of soil and groundwater ecological environment damage value in natural attenuation monitoring scheme, a soil and groundwater ecological environment damage value quantification method with natural attenuation monitoring as the restoration scheme was proposed based on a multi-objective optimization model and coupling theoretical governance cost and period damage cost calculation method. The selection of natural attenuation monitoring and strengthening scheme with the lowest total cost under the condition of meeting human health risk standards is one of the important research trends in the field of soil and groundwater ecological environment damage value quantification technology. Summary of the invention

[0004] The purpose of the present invention is to propose a method for quantifying the value of soil and groundwater ecological environmental damage based on natural attenuation monitoring as a restoration plan. This method can optimize the selection of natural attenuation monitoring and strengthening plans for contaminated sites, and find a restoration plan with the lowest total cost under the constraint that human health risks meet the standards.

[0005] To achieve the above object, the technical solution of the present invention is:

[0006] The present invention provides a method for quantifying the value of soil and groundwater ecological environment damage using natural attenuation monitoring as a restoration scheme, comprising the following steps:

[0007] By establishing natural attenuation and enhanced natural attenuation numerical simulation models, the restoration rules and effects of contaminated soil groundwater under natural conditions and different enhancement measures are explored, and the model input sample set is obtained;

[0008] Monte Carlo method was used for random sampling to obtain multiple sets of model input sample data, and neural network method was used to simulate pollutant migration under different enhancement measures to build natural attenuation monitoring and enhanced natural attenuation prediction models;

[0009] By coupling the theoretical governance cost and the calculation method of the period damage cost, a multi-objective optimization model is constructed with the goal of minimizing the cost of monitoring and strengthening natural attenuation damage and the constraint of reducing the pollutant concentration to below the maximum acceptable risk to human health at the end of the restoration period.

[0010] Based on a multi-objective optimization model, an optimization analysis was conducted on the cost of monitoring natural attenuation, enhancing natural attenuation costs and period damage costs, and the natural attenuation monitoring and enhancing natural attenuation scheme with the lowest total cost was obtained.

[0011] Furthermore, in the present invention, by establishing a natural attenuation and enhanced natural attenuation numerical simulation model, the restoration law and effect of contaminated soil groundwater under natural conditions and different enhancement measures are explored, and a model input sample set is obtained, which specifically includes:

[0012] Investigate the status of natural attenuation of soil and groundwater in contaminated sites, including indicators such as pollutant concentrations, geochemical parameters, and hydrogeological conditions, as constant parameters for input into numerical simulation models;

[0013] Different degrees of enhanced natural attenuation measures are set as model input variables, and a numerical simulation model of soil and groundwater natural attenuation is constructed to simulate the changes in the scope and degree of pollution and diffusion at different times.

[0014] Furthermore, in the present invention, the Monte Carlo method is used for random sampling to obtain multiple groups of model input sample data, and a neural network method is used to simulate the migration of pollutants under different enhanced measures to construct a natural attenuation monitoring and enhanced natural attenuation prediction model, which specifically includes:

[0015] The sample data are extracted using the Monte Carlo random sampling method, and the obtained sample data are input into the numerical simulation model to obtain multiple sets of input and output sample data as training and testing data sets for the neural network method;

[0016] A neural network method is used to monitor natural attenuation and simulate enhanced natural attenuation. Different degrees of enhancement measures are used as input variables, and the residual concentration of pollutants, the volume of contaminated soil / groundwater, and the remediation time are used as output variables to construct a soil and groundwater natural attenuation prediction model.

[0017] Furthermore, in the present invention, the theoretical governance cost and the period damage cost calculation method are coupled to construct a multi-objective optimization model with the goal of monitoring and strengthening the minimum natural attenuation damage cost and the constraint condition of reducing the pollutant concentration to below the maximum acceptable risk to human health at the end of the recovery period, which specifically includes:

[0018] The cost of monitoring natural attenuation and enhancing natural attenuation is calculated based on the theoretical governance cost. The cost of monitoring natural attenuation mainly includes the cost of laying monitoring wells, the cost of monitoring well management and maintenance, and the cost of sampling and testing of various monitoring indicators. The cost of enhancing natural attenuation is the cost of enhanced measures, which is calculated specifically based on the enhanced measures adopted in the contaminated site restoration plan.

[0019] The calculation of the damage cost during the natural attenuation period is based on the resource value method, which mainly includes the value of damaged soil resources and damaged groundwater resources;

[0020] The carcinogenic risk and non-carcinogenic hazard quotient of the characteristic pollutants of the site should be calculated according to the formula in the Technical Guidelines for Soil Pollution Risk Assessment for Construction Land (HJ 25.3-2019), and the carcinogenic risk should be reduced to 10 -6 ~10 -4 Within the magnitude range, the non-carcinogen hazard quotient should be less than 1 to ensure that the human health risk of the evaluation factor is reduced to the maximum acceptable level at the end of the site restoration period.

[0021] Based on the above constraints, a multi-objective optimization model for quantifying the damage value with natural attenuation monitoring as the restoration plan was constructed, with the cost of monitoring natural attenuation, enhancing natural attenuation and period damage cost as the objective function, and the enhancement measures, restoration time, volume of contaminated soil and groundwater and residual concentration of pollutants as the decision variables.

[0022] Furthermore, in the present invention, based on a multi-objective optimization model, an optimization analysis is performed on the cost of monitoring natural attenuation, the cost of enhancing natural attenuation, and the cost of damage during the period, so as to obtain a natural attenuation monitoring and enhancing natural attenuation scheme with the lowest total cost, which specifically includes:

[0023] The particle swarm optimization algorithm is used to solve the multi-objective optimization model to obtain the optimal restoration plan set for monitoring and enhancing natural attenuation with the lowest cost. The data in the optimal solution set is analyzed, and the monitoring and enhancing natural attenuation plan with the lowest total cost is selected, and its damage value is quantified.

[0024] The present invention has the following beneficial effects:

[0025] The present invention discloses a method for quantifying the value of soil and groundwater ecological environment damage using natural attenuation monitoring as a restoration scheme. Aiming at the problem that the damage value of the restoration scheme using natural attenuation monitoring as a restoration scheme is difficult to be accurately quantified in the quantification of the value of soil and groundwater damage in contaminated sites, a natural attenuation and enhanced natural attenuation numerical simulation model and a neural network prediction model are established, and the theoretical governance cost method and the resource value method are coupled. Based on a multi-objective optimization model, the cost of monitoring natural attenuation, the cost of enhancing natural attenuation and the damage during the period are optimized and analyzed, and the monitoring natural attenuation and enhanced natural attenuation scheme with the lowest total cost is obtained. The existing value quantification method focuses on the calculation of a single basic damage or a single period of damage, and is only applicable to the quantification of the damage value of a short-term specific restoration scheme or a long-term unrecoverable scheme. In addition, the natural attenuation scheme has a long restoration period, a wide range of solute migration, and high temporal and spatial differentiation, and the damage during the period is difficult to be accurately quantified. The present invention solves the above problems, realizes the quantification of the value of soil and groundwater environmental damage in contaminated sites under the monitoring and enhanced natural attenuation schemes, and the minimization analysis of the restoration cost under the condition of human health risk as a restriction, and provides a reference basis for the determination of the natural attenuation restoration scheme for contaminated sites and the quantification of their damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the embodiments or the drawings of the related technical solutions in the prior art will be introduced below. It should be understood that the drawings introduced below are only for the convenience of clearly expressing some embodiments of the technical solutions of the present invention. For ordinary technicians in this field, without paying creative work, drawings of other embodiments can be obtained based on these drawings.

[0027] Figure 1 The present invention is a flow chart of a method for quantifying the value of soil and groundwater ecological environmental damage using natural attenuation monitoring as a restoration solution. DETAILED DESCRIPTION

[0028] In order to more clearly understand the above-mentioned purposes, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. It should be noted that the embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0029] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0030] In view of this, the present invention provides a method for calculating the cost of soil and groundwater ecological environmental damage based on natural attenuation monitoring and enhanced natural attenuation as a restoration plan. The method can quantify the value of soil and groundwater ecological environmental damage based on natural attenuation monitoring as a restoration plan, and calculate the monitoring and enhancement plan with the lowest total cost under the limiting condition that human health risks meet the standards.

[0031] like Figure 1 As shown, the present invention provides a method for quantifying the value of soil and groundwater ecological environmental damage using natural attenuation monitoring as a restoration scheme, comprising the following steps:

[0032] By establishing natural attenuation and enhanced natural attenuation numerical simulation models, the restoration rules and effects of contaminated soil groundwater under natural conditions and different enhancement measures are explored, and the model input sample set is obtained;

[0033] Monte Carlo method was used for random sampling to obtain multiple sets of model input sample data, and neural network method was used to simulate pollutant migration under different enhancement measures to build natural attenuation monitoring and enhanced natural attenuation prediction models;

[0034] By coupling the theoretical governance cost and the calculation method of the period damage cost, a multi-objective optimization model is constructed with the goal of minimizing the cost of monitoring and strengthening natural attenuation damage and the constraint of reducing the pollutant concentration to below the maximum acceptable risk to human health at the end of the restoration period.

[0035] Based on a multi-objective optimization model, an optimization analysis was conducted on the cost of monitoring natural attenuation, enhancing natural attenuation costs and period damage costs, and the natural attenuation monitoring and enhancing natural attenuation scheme with the lowest total cost was obtained.

[0036] The present invention solves the problem that the damage value of the restoration plan based on monitoring natural attenuation is difficult to quantify, realizes the quantification of the damage value of the natural attenuation monitoring and strengthening plan, and the minimization analysis of the restoration cost under the restriction of human health risk, and provides a reference basis for the determination of the natural attenuation restoration plan for soil and groundwater in contaminated sites and the quantification of its damage.

[0037] Furthermore, in the present invention, by establishing a natural attenuation and enhanced natural attenuation numerical simulation model, the restoration law and effect of contaminated soil groundwater under natural conditions and different enhancement measures are explored, and a model input sample set is obtained, which specifically includes:

[0038] Investigate the status of natural attenuation of soil and groundwater in contaminated sites, including indicators such as pollutant concentrations, geochemical parameters, and hydrogeological conditions, as constant parameters for input into numerical simulation models;

[0039] It should be noted that the pollutant concentration index mainly refers to the concentration of site characteristic pollutants, such as total petroleum hydrocarbons, benzene series, volatile phenols and ammonia nitrogen; geochemical parameter indicators include pH, dissolved oxygen content, total dissolved solids content, redox potential, permanganate index and temperature; hydrogeological condition indicators include groundwater depth, groundwater flow rate, hydraulic gradient, permeability, dispersion, net recharge and aquifer medium;

[0040] It should be noted that the specific input indicators can be adjusted according to the actual situation of the contaminated site. This method only lists the common pollutant concentrations, geochemical parameters and hydrogeological conditions indicators;

[0041] Different degrees of enhanced natural attenuation measures were set as model input variables to construct a numerical simulation model of soil and groundwater natural attenuation to simulate the changes in the scope and degree of pollution and diffusion at different times;

[0042] It should be noted that enhanced natural attenuation measures mainly include biological enhancement measures, chemical oxidation enhancement measures, air injection enhancement measures and hydrodynamic enhancement measures, which are determined according to the contaminated site restoration plan;

[0043] It should be noted that different degrees of enhanced measures refer to changes in pollutant attenuation rates as the dosage of added biological agents, the dosage of added chemical agents, aeration volume, and hydraulic gradient change, that is, changes in the pollutant chemical attenuation coefficient, biological attenuation coefficient, and key hydrogeological parameters in the numerical simulation model, including the maximum degradation rate of pollutants, the half-saturation constant of pollutants, the number of microorganisms, the activity of microorganisms, the permeability coefficient, and the diffusion coefficient.

[0044] Furthermore, in the present invention, the Monte Carlo method is used for random sampling to obtain multiple groups of model input sample data, and a neural network method is used to simulate the migration of pollutants under different enhanced measures to construct a natural attenuation monitoring and enhanced natural attenuation prediction model, which specifically includes:

[0045] The Monte Carlo random sampling method is used to extract input data, and the obtained sample data is input into the numerical simulation model to obtain multiple sets of input and output sample data as training and testing data sets for the neural network method;

[0046] It should be noted that the use of the Monte Carlo method to extract sample data mainly includes the steps of establishing a probability model, determining the variable distribution of the model, and implementing random sampling; first, clarify the mathematical model and objective function of the problem, as well as the variables or parameters to be solved, and establish a mathematical model that can express the relationship between the target variable and multiple variables and the probability distribution of each variable in the model; further, select a suitable random number generation method to generate random samples; further, determine its sampling method based on the probability distribution of the variable, and perform sampling based on the generated random numbers;

[0047] It should be noted that the input variables to be optimized in the numerical simulation model in this method are the intensity of the strengthening measures, and the output variables are the remediation time, the residual concentration of pollutants, and the volume of contaminated soil and groundwater;

[0048] A natural attenuation monitoring and enhanced natural attenuation prediction model was established using a neural network method, with key parameters of enhanced measures as input variables, and remediation time, residual concentration of pollutants, and volume of contaminated soil and groundwater as output variables to predict the natural attenuation of soil and groundwater.

[0049] It should be noted that this method uses BP neural network to construct the prediction model, inputs the training data set to complete the natural attenuation monitoring and strengthen the establishment of the natural attenuation neural network prediction model, substitutes the test data set into the constructed model for simulation prediction, and calculates its approximation accuracy;

[0050] It should be noted that in the process of constructing the BP neural network prediction model, the calculation steps mainly include initializing the neural network parameters, forward propagation calculation output, calculation error, back propagation update weights and biases, repeated iterative training, etc. First, complete the initialization of the neural network parameters, determine the number of neurons in the input layer, hidden layer and output layer of the neural network prediction model, initialize the weights and bias parameters, set the error function, calculation accuracy value and maximum number of learning times, etc.; further, pass the sample data into the input layer neurons, and in the forward propagation process, the input data passes through the input layer, hidden layer and output layer in turn, and the output value is calculated according to the weight and bias parameters; further, compare the output value of the neural network with the output value of the numerical simulation model, and calculate the error between the prediction ability of the network and the actual target; further, in the back propagation, the neural network updates the weights and biases in the network according to the error between the predicted value and the true value through the gradient descent algorithm; further, judge whether the network error meets the requirements. If the error reaches the preset accuracy or the number of learning times reaches the maximum number, the training process ends, otherwise, continue to select the next learning sample and the corresponding expected output sample, and repeat the above steps for training;

[0051] It should be noted that this method uses three indicators, namely, the coefficient of certainty, the mean absolute error and the mean relative error, to evaluate the approximation accuracy of the BP neural network prediction model. Suppose the input sample data of the model is x i (i=1,2,3,……,m), the output data is y i (i=1,2,3,……,m), the calculation formula of the above index is:

[0052]

[0053]

[0054]

[0055] It should be noted that, n is the total number of input samples; y i Output sample data for numerical simulation models; Output sample data for the neural network prediction model; is the average value of the output sample data of the numerical simulation model;

[0056] It should be noted that for these three indicators, when the value of the certainty coefficient is larger (close to 1), and the values ​​of the mean absolute error and the mean relative error are smaller, it means that the fitting effect of the BP neural network prediction model is better, and the approximation accuracy of the numerical simulation model is higher;

[0057] Furthermore, in the present invention, the theoretical governance cost and the period damage cost calculation method are coupled to construct a multi-objective optimization model with the goal of minimizing the cost of monitoring and strengthening natural attenuation and the constraint condition of reducing the pollutant concentration to below the maximum acceptable risk to human health at the end of the recovery period, which specifically includes:

[0058] The cost of monitoring natural attenuation and strengthening natural attenuation is calculated based on the theoretical governance cost. Among them, the cost of monitoring natural attenuation mainly includes the cost of laying monitoring wells, the cost of monitoring well management and maintenance, and the cost of sampling and testing of various monitoring indicators; the cost of strengthening natural attenuation is the cost of strengthening measures, which is calculated specifically according to the strengthening measures adopted in the contaminated site restoration plan;

[0059] It should be noted that the calculation formula for monitoring natural attenuation cost is y1 = ∑K j ×f×t+n×(W d +t×W m ). Among them, j is the monitoring index, K j is the sampling and testing fee of the indicator (yuan), f is the sampling frequency (times / year), t is the recovery period (year), n is the number of monitoring wells, W d is the well layout cost (yuan), W mis the maintenance cost of monitoring wells (yuan / year);

[0060] It should be noted that the specific strengthening measures are determined according to the contaminated site restoration plan. The formula for calculating the strengthening natural attenuation cost is y2 = Q m ×P m +Q b ×P b +V g ×P g +P f +P h Here is a list of commonly used cost calculation formulas for strengthening measures: The cost of chemical oxidation strengthening is Q m ×P m , Q m is the chemical dosage (kg), P m is the price of the reagent (yuan / kg); the cost of chemical strengthening is Q b ×P b , Q b is the biological bacteria dose (kg), P b is the unit price of the microbial agent (yuan / kg); the air injection enhancement cost is V g ×P g , V g Aeration volume (m 3 ), P g is the energy consumption cost per unit volume of gas (yuan / m 3 ); the cost of hydrodynamic enhancement is P f +P h , P f is the well group control system equipment and installation cost (yuan), P h Energy cost for artificially extracting groundwater or injecting water into aquifers (yuan);

[0061] The calculation of the damage cost during the natural attenuation period is based on the resource value method, which mainly includes the value of damaged soil resources and damaged groundwater resources;

[0062] It should be noted that the value of damaged soil and groundwater resources is calculated according to the resource value method in the Technical Guidelines for the Identification and Assessment of Ecological and Environmental Damage Environmental Elements Part 1: Soil and Groundwater (GB / T 39792.1-2020), and the formula is y3=V r ×V p , V r =V b ×γ. Among them, y3 is the period damage cost (yuan); V r is the value of damaged soil / groundwater resources (yuan / m 3 );V b is the non-use benchmark value of soil / groundwater resources (yuan / m 3 );V pis the volume of damaged soil / groundwater (m 3 ); γ is the adjustment coefficient, which is related to the maximum excess concentration of pollutants in soil and groundwater;

[0063] Refer to the formula in the Technical Guidelines for Soil Pollution Risk Assessment for Construction Land (HJ 25.3-2019) to calculate the carcinogenic risk and non-carcinogenic hazard quotient of the site's characteristic pollutants, where the carcinogenic risk should be reduced to 10 -6 ~10 -4 Within the magnitude range, the non-carcinogen hazard quotient should be less than 1, ensuring that the human health risk of each evaluation factor is reduced to the maximum acceptable level at the end of the site restoration period;

[0064] It should be noted that the formula for calculating the carcinogenic risk of characteristic pollutants in soil and groundwater is CR = ER × c t ×SF, where ER is the pollutant exposure (kg / kg·d), c t is the concentration of pollutants in soil / groundwater (mg / kg, mg / L), SF is the carcinogenic slope factor (kg·d / mg); the non-carcinogenic hazard quotient calculation formula is Where RfD is the reference dose of pollutants absorbed by the human body through different pathways (mg / kg·d), and WAF is the reference dose allocation ratio for exposure to groundwater, which is dimensionless;

[0065] Based on the above constraints, a multi-objective optimization model for quantifying the damage value with natural attenuation monitoring as the restoration plan was constructed, with the cost of monitoring natural attenuation, the cost of enhancing natural attenuation and the cost of damage during the period as the objective function, and the enhancement measures, restoration time, the volume of contaminated soil and groundwater and the residual concentration of pollutants as the decision variables.

[0066] It should be noted that in actual engineering, the optimization scheme that only meets a single indicator often cannot meet the actual overall optimization needs. In this context, a multi-objective optimization problem is generated. Multi-objective optimization is to find the optimal solution under certain constraints so that a set of objective function values ​​can reach the minimum value (or maximum value) at the same time as much as possible, and the comprehensive effect achieved is the best. The multi-objective optimization problem of the present invention can be expressed as the following formula:

[0067]

[0068] It should be noted that in the multi-objective optimization design process of the damage value quantification with monitoring and strengthening natural attenuation as the restoration plan of the present invention, the objective functions are to minimize the cost of monitoring natural attenuation, minimize the cost of strengthening natural attenuation and minimize the cost of damage during the period, and the human health carcinogenic risk and non-carcinogenic hazards reach an acceptable level as the constraint condition, thus forming a multi-objective optimization model for the damage value quantification with monitoring natural attenuation as the restoration plan;

[0069] Furthermore, in the present invention, based on a multi-objective optimization model, an optimization analysis is performed on the cost of monitoring natural attenuation, the cost of enhancing natural attenuation, and the cost of damage during the period, and a natural attenuation monitoring and enhanced natural attenuation restoration scheme with the lowest total cost is selected, specifically including:

[0070] The particle swarm optimization algorithm is used to solve the multi-objective optimization model to obtain the optimal restoration plan set for monitoring and enhancing natural attenuation with the lowest cost. The data in the optimal solution set is analyzed, and the monitoring and enhancing natural attenuation plan with the lowest total cost is selected, and its damage value is quantified.

[0071] It should be noted that the steps of the particle swarm optimization algorithm mainly include initializing particle swarm parameters, calculating particle fitness, updating particle speed and position, judging whether the termination condition is met, and outputting the optimal value. First, initialize the particle swarm parameters, mainly including setting the swarm size (N) and the number of iterations, and randomly initialize the position of each particle (x i ) and speed (v i ), set the individual best position of each particle and the global best position of the entire group, etc.; further, calculate the fitness value (F it ), update the individual optimal position (p best ); further, find the individual optimal positions of all particles and update the global optimal position (g best ); further, according to the individual optimal position and the global optimal position, update the particle speed (v i ) and position (x i ); further, determine whether the termination condition is met. If the termination condition is met (the error is small enough or the preset maximum number of iterations is reached), the algorithm ends and outputs the solution corresponding to the global best position as the optimal solution to the optimization problem. Otherwise, return to the second step to continue iterating.

[0072] It should be noted that after the optimal solution set is calculated using the particle swarm optimization algorithm, the sum of the monitoring natural attenuation cost, natural attenuation enhancement cost and period damage of each group in the optimal solution set is calculated, and the restoration plan with the lowest total cost is obtained after sorting.

[0073] In summary, the soil and groundwater ecological environmental damage value quantification method based on natural attenuation monitoring as a restoration plan in the present invention has wide applicability and is easy to implement. It can address the limitations of existing value quantification methods, which focus on calculating a single basic damage or a single period of damage and are only applicable to short-term specific restoration plans or long-term unrecoverable plans, as well as the problem that damage during natural attenuation plans is difficult to accurately quantify. It quantifies the damage value and minimizes the restoration cost of monitoring and strengthening natural attenuation plans, provides a reference for determining natural attenuation restoration plans for contaminated sites and quantifying their damage, and has good application prospects.

[0074] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope provided by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for quantifying the value of soil and groundwater ecological environmental damage based on monitoring natural attenuation as a restoration plan, characterized by: The following steps are involved: 1) To establish a natural attenuation monitoring and enhanced natural attenuation numerical simulation model, explore the recovery rules and effects of contaminated soil groundwater under natural conditions and different enhancement measures, and obtain the model input sample set; 2) Use the Monte Carlo method for random sampling to obtain multiple sets of model input sample data, use the neural network method to simulate and predict natural attenuation under different enhancement measures, and build a natural attenuation monitoring and enhanced natural attenuation prediction model; Use BP neural network to build the prediction model, input the training data set to complete the natural attenuation monitoring and strengthen the establishment of the natural attenuation neural network prediction model, substitute the test data set into the built model for simulation prediction, and calculate its approximation accuracy; In the process of building the BP neural network prediction model, the calculation steps mainly include initializing the neural network parameters, forward propagation calculation output, calculation error, back propagation update weights and biases, and repeated iterative training: First, complete the initialization of neural network parameters, determine the number of neurons in the input layer, hidden layer and output layer of the neural network prediction model, initialize weights and bias parameters, set error function, calculation accuracy value and maximum number of learning times; The sample data is passed into the input layer neurons. During the forward propagation process, the input data passes through the input layer, hidden layer, and output layer in sequence, and the output value is calculated based on the weight and bias parameters; Compare the output of the neural network with the output of the numerical simulation model and calculate the error between the prediction ability of the network and the actual target; In back propagation, the neural network updates the weights and biases in the network through the gradient descent algorithm according to the error between the predicted value and the true value; Determine whether the network error meets the requirements. If the error reaches the preset accuracy or the number of learning times reaches the maximum number, the training process ends. Otherwise, continue to select the next learning sample and the corresponding expected output sample, and repeat the above steps for training; 3) Couple the theoretical governance cost and the calculation method of the period damage cost, and construct a multi-objective optimization model with the goal of minimizing the damage cost of natural attenuation monitoring and strengthening natural attenuation, and the constraint condition of reducing the pollutant concentration to below the maximum acceptable risk to human health at the end of the recovery period; 4) Based on the multi-objective optimization model, the cost of monitoring natural attenuation, the cost of enhancing natural attenuation and the cost of damage during the period are optimized to obtain the natural attenuation monitoring and enhanced natural attenuation plan with the lowest total cost; The particle swarm optimization algorithm is used to solve the multi-objective optimization model to obtain the optimal set of natural attenuation monitoring and enhanced natural attenuation restoration solutions with the lowest costs. The data in the optimal solution set is analyzed to obtain the natural attenuation monitoring and enhanced natural attenuation restoration solution with the lowest total cost, thereby quantifying its damage value.

2. The method for quantifying the value of soil and groundwater ecological environmental damage based on monitoring natural attenuation as a restoration plan according to claim 1 is characterized in that: The specific steps of step 1) are as follows: Investigate the status of natural attenuation of soil and groundwater in contaminated sites, including pollutant concentrations, geochemical parameters and hydrogeological condition indicators, as constant parameters for input into numerical simulation models; Different degrees of enhanced natural attenuation measures are set as model input variables, and a numerical simulation model of soil and groundwater natural attenuation is constructed to simulate the changes in the scope and degree of pollution and diffusion at different times.

3. The method for quantifying the value of soil and groundwater ecological environmental damage based on monitoring natural attenuation as a restoration plan according to claim 2 is characterized in that: Enhanced natural attenuation measures mainly include biological enhancement measures, chemical oxidation enhancement measures, air injection enhancement measures and hydrodynamic enhancement measures; Different degrees of enhanced measures refer to changes in pollutant attenuation rate as the dosage of biological agents added, the dosage of chemical agents added, the aeration volume and the hydraulic gradient change, that is, changes in the pollutant chemical attenuation coefficient, biological attenuation coefficient and key hydrogeological parameters in the numerical simulation model, specifically including the maximum degradation rate of pollutants, pollutant half-saturation constant, microbial number, microbial activity, permeability coefficient and diffusion coefficient.

4. The method for quantifying the value of soil and groundwater ecological environmental damage based on monitoring natural attenuation as a restoration plan according to claim 1 is characterized in that: The specific steps of step 2) are as follows: The sample data are extracted using the Monte Carlo random sampling method, and the obtained sample data are input into the numerical simulation model to obtain multiple sets of input and output sample data as the training data set and the test data set of the neural network model; A neural network model for monitoring natural attenuation and enhancing natural attenuation simulation was constructed. Different degrees of enhancement measures were used as input variables, and the residual concentration of pollutants, the volume of contaminated soil and groundwater, and the restoration time were used as output variables to construct a soil and groundwater natural attenuation prediction model. The three indicators of certainty coefficient, mean absolute error and mean relative error are used to evaluate the approximation accuracy of the BP neural network prediction model. Suppose the input sample data of the model is x i (i=1,2,3,……,m), the output data is y i (i=1,2,3,……,m), the calculation formula of the above index is: Where n is the total number of input samples; y i Output sample data for numerical simulation models; Output sample data for the neural network prediction model; is the average value of the output sample data of the numerical simulation model; For these three indicators, when the value of the certainty coefficient is larger, that is, closer to 1, and the values ​​of the mean absolute error and the mean relative error are smaller, it means that the fitting effect of the BP neural network prediction model is better and the approximation accuracy of the numerical simulation model is higher.

5. The method for quantifying the value of soil and groundwater ecological environment damage based on monitoring natural attenuation as a restoration plan according to claim 1 is characterized in that: The specific steps of step 3) are as follows: The natural attenuation monitoring is carried out based on the theoretical governance cost, and the calculation of the enhanced natural attenuation cost is carried out. The natural attenuation monitoring cost mainly includes the cost of laying monitoring wells, the cost of monitoring well management and maintenance, and the cost of sampling and testing of various monitoring indicators. The cost of enhanced natural attenuation is the cost of enhanced measures, which is calculated based on the enhanced measures adopted in the contaminated site restoration plan; The calculation of the damage cost during the natural attenuation period is based on the resource value method, which mainly includes the value of damaged soil resources and damaged groundwater resources; Refer to the formula in the Technical Guidelines for Soil Pollution Risk Assessment for Construction Land (HJ 25.3-2019) to calculate the carcinogenic risk and non-carcinogenic hazard quotient of the site's characteristic pollutants, where the carcinogenic risk should be reduced to 10 -6 ~10 -4 Within the magnitude range, the non-carcinogen hazard quotient should be less than 1, ensuring that the human health risk of each evaluation factor is reduced to the maximum acceptable level at the end of the site restoration period; Based on the above constraints, a multi-objective optimization model for quantifying the damage value with natural attenuation monitoring as the restoration plan was constructed, with the cost of monitoring natural attenuation, enhancing natural attenuation and period damage cost as the objective function, and the enhancement measures, restoration time, volume of contaminated soil and groundwater and residual concentration of pollutants as the decision variables.

6. The method for quantifying the value of soil and groundwater ecological environment damage based on monitoring natural attenuation as a restoration plan according to claim 1 is characterized in that: The specific steps of step 4) are as follows: The particle swarm optimization algorithm mainly includes initializing particle swarm parameters, calculating particle fitness, updating particle speed and position, judging whether the termination condition is met, and outputting the optimal value; first, the particle swarm parameters are initialized, mainly including setting the swarm size (N) and the number of iterations, and randomly initializing the position of each particle (x i ) and speed (v i ), set the individual best position of each particle and the global best position of the entire group; Furthermore, the fitness value (F it ), update the individual optimal position (p best ); Furthermore, find the individual optimal positions of all particles and update the global optimal position (g best ); Furthermore, according to the individual optimal position and the global optimal position, the particle velocity (v i ) and position (x i ); Further, it is determined whether the termination condition is met. If the termination condition is met (the error is small enough or the preset maximum number of iterations is reached), the algorithm ends and outputs the solution corresponding to the global best position as the optimal solution to the optimization problem. Otherwise, it returns to the second step to continue iterating. After calculating the optimal solution set using the particle swarm optimization algorithm, the sum of the monitoring natural attenuation cost, natural attenuation enhancement cost and period damage of each group in the optimal solution set is calculated, and the restoration plan with the lowest total cost is obtained after sorting.

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