Cumulative Environmental Risk Warning Analysis Method and System for Soil Heavy Metal Pollution

Abstract

The present invention discloses a cumulative environmental risk early warning analysis method and system for soil heavy metal pollution, specifically relating to the technical field of soil environmental monitoring, and is used to solve the problems of incomplete and inaccurate risk assessment of heavy metal pollution leakage into groundwater in the prior art. By measuring the heavy metal concentrations in soils at different depths, depth distribution data of heavy metal concentrations is obtained. According to this data, it is judged whether there is a heavy metal pollution risk affecting groundwater. When the risk exists, Gaussian process regression is used to analyze the dynamic influence of changes in the surface charge of soil particles on heavy metal adsorption and desorption behaviors, and at the same time, a random walk model is used to analyze the retention effect of heavy metals in the fracture network, and its delayed influence on the migration rate and path of heavy metals is evaluated. Finally, based on the generated heavy metal underground leakage risk assessment results, the groundwater pollution risk is classified, which can effectively improve the accuracy of groundwater pollution risk assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of soil environmental monitoring, and more specifically, to a method and system for early warning and analysis of the cumulative environmental risk of soil heavy metal pollution. Background Art

[0002] With the rapid development of industrialization and urbanization, the problem of soil heavy metal pollution has become increasingly serious. Especially in areas concentrated with industries such as mining, metallurgy, and chemical engineering, the heavy metal content in the soil shows a cumulative growth trend. These heavy metals have high toxicity, are difficult to degrade, and can migrate over long distances, posing a serious threat to the ecological environment and human health. Groundwater, as an important natural resource, its pollution problem deserves particular attention.

[0003] In the prior art, for the risk assessment of the migration of soil heavy metal pollution to groundwater, there is a lack of comprehensive analysis and overall consideration of the key influencing factors during the migration process of heavy metal pollution. This may lead to an underestimation or misjudgment of the groundwater pollution risk, affecting the accuracy of the assessment and the scientific nature of the treatment. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a method and system for early warning and analysis of the cumulative environmental risk of soil heavy metal pollution to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for early warning and analysis of the cumulative environmental risk of soil heavy metal pollution, comprising the following steps:

[0007] S1: Measure the concentration of heavy metals in the soil at each depth to obtain the depth distribution data of heavy metal concentrations;

[0008] S2: Based on the depth distribution data of heavy metal concentrations, determine whether there is a risk that heavy metal pollution affects groundwater;

[0009] S3: When there is a risk that heavy metal pollution affects groundwater, use Gaussian process regression to analyze the change of the surface charge of soil particles and evaluate its dynamic impact on the adsorption and desorption behavior of heavy metals; use a random walk model to analyze the retention effect of heavy metals in the fracture network and evaluate its delay impact on the migration rate and path of heavy metals;

[0010] S4: Based on the dynamic impact of the change of the surface charge of soil particles on the adsorption and desorption behavior of heavy metals and the delay impact of the retention effect of heavy metals in the fracture network on the migration rate and path of heavy metals, generate a risk assessment result of heavy metal leakage into groundwater;

[0011] S5: Classify the groundwater pollution risk according to the risk assessment result of heavy metal leakage into groundwater.

[0012] In a preferred embodiment, measure the concentration of heavy metals in the soil at each depth to obtain the heavy metal concentration depth distribution data. Specifically:

[0013] Collect soil samples at different depths, measure the concentration of heavy metals in each soil sample, and obtain the heavy metal concentration data at different depth positions to form the heavy metal concentration depth distribution data.

[0014] In a preferred embodiment, based on the heavy metal concentration depth distribution data, determine whether there is a risk that heavy metal pollution affects groundwater. Specifically:

[0015] Conduct statistical analysis on the heavy metal concentration data at different depths to determine the heavy metal concentration at each depth level;

[0016] According to the soil type and groundwater sensitivity, set the heavy metal concentration risk threshold for each soil layer;

[0017] Identify the soil layers that exceed the heavy metal concentration risk threshold, and based on the change trend of the soil heavy metal concentration in the soil layers that exceed the heavy metal concentration risk threshold, determine whether there is a risk that heavy metal pollution affects groundwater.

[0018] In a preferred embodiment, use Gaussian process regression to analyze the change of the surface charge of soil particles and evaluate its dynamic influence on the adsorption and desorption behavior of heavy metals. Specifically:

[0019] For soil samples at different depths, measure the surface charge density of soil particles in each sample;

[0020] Collect environmental parameters related to the change of the surface charge of soil particles. The environmental parameters include soil pH value, ionic strength, moisture content, and temperature;

[0021] Establish a Gaussian process regression model through the surface charge density of soil particles and environmental parameters to fit the charge change law;

[0022] Based on the Gaussian process regression model, calculate the adsorption and desorption influence evaluation value to quantify the dynamic influence of the change of the surface charge of soil particles on the adsorption and desorption behavior of heavy metals.

[0023] In a preferred embodiment, the expression of the adsorption and desorption influence evaluation value is: E = ∫ a b k(x, x′)·p(x)dx; where E is the adsorption and desorption influence evaluation value, p(x) represents the probability distribution function of environmental parameters, and a, b represent the integration range.

[0024] In a preferred embodiment, a random walk model is used to analyze the retention effect of heavy metals in the fracture network and evaluate its delaying effect on the migration rate and path of heavy metals, specifically as follows:

[0025] A fracture network model is established using three-dimensional geological data and fracture distribution information;

[0026] Parameters related to the migration of heavy metals in the fracture network are measured, and the parameters related to migration include diffusion coefficient, adsorption rate, and desorption rate;

[0027] Taking the fracture network model and migration-related parameters as inputs, the migration path of heavy metals in the fracture network is simulated based on the random walk model;

[0028] By analyzing the migration paths in the random walk model, the delaying effect of the retention effect on the migration rate and path of heavy metals is quantified.

[0029] In a preferred embodiment, the delaying effect of the retention effect on the migration rate and path of heavy metals is quantified based on the total migration delay time: Where T delayed represents the total migration delay time, P final (u) is the final position of the u-th path, P initial (u) is the initial position of the u-th path, U is the number of paths, and u is the path number.

[0030] In a preferred embodiment, based on the dynamic effect of the change in the surface charge of soil particles on the adsorption and desorption behavior of heavy metals and the delaying effect of the retention effect of heavy metals in the fracture network on the migration rate and path of heavy metals, a risk assessment result of heavy metal leakage into groundwater is generated, specifically as follows:

[0031] The adsorption / desorption effect evaluation value and the total migration delay time are normalized, weight coefficients are assigned to the normalized adsorption / desorption effect evaluation value and the total migration delay time respectively, and the heavy metal underground leakage coefficient is calculated.

[0032] In a preferred embodiment, according to the risk assessment result of heavy metal leakage into groundwater, the groundwater pollution risk is classified, specifically as follows:

[0033] A first threshold for heavy metal underground leakage and a second threshold for heavy metal underground leakage are set, where the first threshold for heavy metal underground leakage is less than the second threshold for heavy metal underground leakage; the heavy metal underground leakage coefficient is compared with the first threshold for heavy metal underground leakage and the second threshold for heavy metal underground leakage:

[0034] When the heavy metal underground leakage coefficient is less than the first threshold for heavy metal underground leakage, it is determined that the groundwater pollution risk is at a low risk level;

[0035] When the underground leakage coefficient of heavy metals is greater than or equal to the first threshold of underground leakage of heavy metals and less than or equal to the second threshold of underground leakage of heavy metals, the groundwater pollution risk is determined to be at the medium risk level;

[0036] When the underground leakage coefficient of heavy metals is greater than the second threshold of underground leakage of heavy metals, the groundwater pollution risk is determined to be at the high risk level.

[0037] On the other hand, the present invention provides a cumulative environmental risk early warning analysis system for soil heavy metal pollution, including a metal concentration measurement module, a pollution risk judgment module, an adsorption and desorption influence analysis module, a retention effect analysis module, a leakage risk assessment module, and a pollution risk grading module;

[0038] Metal concentration measurement module: Measuring the concentration of heavy metals in soils at various depths to obtain heavy metal concentration-depth distribution data;

[0039] Pollution risk judgment module: Judging whether there is a risk of heavy metal pollution affecting groundwater based on the heavy metal concentration-depth distribution data;

[0040] Adsorption and desorption influence analysis module: When there is a risk of heavy metal pollution affecting groundwater, using Gaussian process regression to analyze the change of surface charge of soil particles and evaluating its dynamic influence on the adsorption and desorption behavior of heavy metals;

[0041] Retention effect analysis module: When there is a risk of heavy metal pollution affecting groundwater, using a random walk model to analyze the retention effect of heavy metals in the fracture network and evaluating its delaying influence on the migration rate and path of heavy metals;

[0042] Leakage risk assessment module: Generating a risk assessment result of heavy metal leakage into groundwater based on the dynamic influence of the change of surface charge of soil particles on the adsorption and desorption behavior of heavy metals and the delaying influence of the retention effect of heavy metals in the fracture network on the migration rate and path of heavy metals;

[0043] Pollution risk grading module: Classifying the groundwater pollution risk according to the risk assessment result of heavy metal leakage into groundwater.

[0044] The technical effects and advantages of the cumulative environmental risk early warning analysis method and system for soil heavy metal pollution of the present invention:

[0045] 1. The present invention can effectively solve the problem in the prior art that it is impossible to comprehensively and accurately evaluate the risk of heavy metal pollution on groundwater leakage. By measuring the heavy metal concentrations in soils at different depths and combining Gaussian process regression and random walk models, it can dynamically analyze the changes in the surface charge of soil particles and the retention effect in the fracture network. It can not only quantify the potential threat of soil heavy metal pollution to groundwater leakage, but also provide accurate predictions for the migration process and behavior of heavy metals, effectively improving the accuracy and scientific nature of risk assessment.

[0046] 2. By classifying the heavy metal pollution risk, it provides a more refined decision-making basis for groundwater pollution prevention and control. Traditional groundwater pollution risk assessment methods are usually relatively rough and cannot consider multiple influencing factors in the heavy metal migration process. The risk assessment method of the present invention can conduct multi-level and multi-dimensional risk assessments based on soil heavy metal concentrations, changes in the surface charge of soil particles, and the delay effect of heavy metal migration in the fracture network, so as to provide a more accurate groundwater pollution risk level, providing strong technical support for soil pollution treatment and helping to early warn of potential groundwater pollution risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the cumulative environmental risk early warning analysis method for soil heavy metal pollution of the present invention;

[0048] Figure 2 It is a schematic diagram of the structure of the cumulative environmental risk early warning analysis system for soil heavy metal pollution of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0050] Embodiment 1: Figure 1 The cumulative environmental risk early warning analysis method for soil heavy metal pollution of the present invention is given, which includes the following steps:

[0051] S1: Measure the heavy metal concentrations in soils at each depth to obtain heavy metal concentration depth distribution data.

[0052] S2: Based on the heavy metal concentration depth distribution data, determine whether there is a risk that heavy metal pollution affects groundwater.

[0053] S3: When there is a risk of heavy metal pollution affecting groundwater, Gaussian process regression is used to analyze the changes in the surface charge of soil particles and evaluate its dynamic impact on heavy metal adsorption and desorption behaviors. The random walk model is used to analyze the retention effect of heavy metals in the fracture network and evaluate its delaying impact on the migration rate and path of heavy metals.

[0054] S4: Based on the dynamic impact of the changes in the surface charge of soil particles on heavy metal adsorption and desorption behaviors and the delaying impact of the retention effect of heavy metals in the fracture network on the migration rate and path of heavy metals, a risk assessment result of heavy metal leakage into groundwater is generated.

[0055] S5: The groundwater pollution risk is classified according to the risk assessment result of heavy metal leakage into groundwater.

[0056] Measure the concentrations of heavy metals in the soil at each depth to obtain heavy metal concentration-depth distribution data, specifically:

[0057] Collect soil samples at different depths and measure the heavy metal concentrations in each soil sample to obtain heavy metal concentration data at different depth positions, so as to form heavy metal concentration-depth distribution data.

[0058] Among them, first select several sampling points within the study area. The selection of sampling points should cover typical areas where heavy metal pollution may exist, including different geographical locations, different soil types, and different environmental conditions. Each sampling point is stratified and collected according to the soil layers to ensure that the collected samples can represent the soil environment at different depths. The sampling depth range can be set according to the actual situation and generally can be divided into different layers such as 0-10 cm, 10-20 cm, 20-40 cm, 40-60 cm, etc. to ensure the representativeness of the samples.

[0059] Each collected soil sample should be properly preserved immediately to prevent the sample from getting damp or contaminated. The sample can be preserved by refrigeration or drying, and try to avoid changing its original properties during transportation. For wet soil samples, moisture should be removed quickly, and the solid components of the soil should be retained.

[0060] For each soil sample, use a standardized analysis method to determine the heavy metal concentration. Commonly used measurement techniques include, but are not limited to, atomic absorption spectrometry (AAS), inductively coupled plasma mass spectrometry (ICP-MS), X-ray fluorescence analysis (XRF), etc. These methods can accurately determine the concentrations of various heavy metal elements in the soil.

[0061] After the measurement, record the concentration data of each heavy metal element in the soil samples at each depth level and organize them into a table. The soil samples at each sampling point should correspond to different depth level data respectively. Through data statistics, obtain the variation of heavy metal concentrations in the soil at different depths, and further form the heavy metal concentration depth distribution data for each sampling point.

[0062] Based on the heavy metal concentration depth distribution data, judge whether there is a risk of heavy metal pollution affecting groundwater, specifically:

[0063] Conduct statistical analysis on the heavy metal concentration data at different depths to determine the heavy metal concentrations at each depth level:

[0064] Select statistical analysis methods, including mean, variance, and quantile calculations, to process the heavy metal concentration data at each depth level and determine the concentration distribution characteristics of each soil layer.

[0065] Use distribution fitting algorithms (such as Gaussian distribution fitting or normal distribution fitting) to verify the data distribution model to ensure the accuracy of the analysis.

[0066] Generate a depth-concentration distribution map based on the fitting results to clarify the variation trend of heavy metal concentrations at different depths and provide basic data for subsequent analysis.

[0067] Set the heavy metal concentration risk thresholds for each soil layer according to the soil type and groundwater sensitivity:

[0068] Collect soil type data in the study area, including physical properties such as soil texture, permeability, and porosity, and combine it with groundwater sensitivity data, such as groundwater depth, recharge rate, and rock formation characteristics, to evaluate the groundwater risk levels in different regions. Adopt the heavy metal concentration limit values specified by environmental standards or industry regulations, and combine the evaluation results to dynamically adjust the heavy metal concentration risk thresholds for each soil layer using a linear regression model to ensure that the thresholds are consistent with the environmental conditions.

[0069] Identify the soil layers that exceed the heavy metal concentration risk thresholds, and based on the variation trend of the soil heavy metal concentrations in the soil layers that exceed the heavy metal concentration risk thresholds, judge whether there is a risk of heavy metal pollution affecting groundwater:

[0070] Compare the heavy metal concentration values of each soil layer with the set risk thresholds layer by layer to identify the soil layers that exceed the thresholds, mark the pollution levels for the soil layers that exceed the standards, and divide them into slightly, moderately, and severely polluted layers to accurately locate the high-risk layers in subsequent analysis.

[0071] Among them, by constructing a spatial distribution map of the exceeded standard layers and combining with the Geographic Information System (GIS) to mark the spatial positions of the soil layers that exceed the standards, it can provide spatial data support for subsequent trend analysis.

[0072] Conduct time series analysis on the heavy metal concentration data of the soil layer exceeding the standard, evaluate the dynamic trend of concentration changes, and determine whether the pollution is cumulative.

[0073] Combined with the depth position and change trend of the soil layer exceeding the standard, use a multi-layer propagation model (such as the finite difference method or the finite element method) to simulate the vertical migration behavior of heavy metals.

[0074] In the simulation results, determine whether heavy metals may affect groundwater through vertical migration, and generate a groundwater pollution risk assessment map.

[0075] Based on the above evaluation results, determine whether there is a risk that heavy metal pollution affects groundwater. If the vertical migration trend of heavy metals is obvious, it is determined that there is a risk that heavy metal pollution affects groundwater.

[0076] Use Gaussian process regression to analyze the change of surface charge on soil particles, and evaluate its dynamic impact on the adsorption and desorption behavior of heavy metals. Specifically:

[0077] For soil samples at different depths, measure the surface charge density of soil particles in each sample:

[0078] First, preprocess the samples, including drying, screening, and homogenization, to eliminate the influence of the external environment and ensure the accuracy and consistency of the measurement data.

[0079] Use electrophoresis experiments combined with zeta potential measurement techniques to measure the surface charge density of soil particles at different depths.

[0080] The steps of the electrophoresis experiment are as follows:

[0081] Dissolve the soil sample in a solution with a known ionic strength to form a suspension.

[0082] Apply an electric field through an electrophoresis instrument and record the migration speed of soil particles.

[0083] Use the zeta potential formula to calculate the surface charge density of soil particles: σ e =∈·μ e ; where σ e represents the surface charge density of soil particles, ∈ represents the dielectric constant, and μ e represents the electrophoretic mobility of particles.

[0084] Collect environmental parameters related to the change of surface charge on soil particles. The environmental parameters include soil pH value, ionic strength, moisture content, and temperature:

[0085] Record the environmental parameters of each sample, including the pH value, ionic strength, moisture content, and temperature of the soil. These parameters are important factors affecting the change of surface charge on soil particles.

[0086] Soil pH value: Measured by a pH meter. The soil sample is suspended in distilled water, and the pH value range of the solution is recorded.

[0087] Ionic strength: Measure the conductivity of the soil suspension by a conductivity meter, and calculate the ionic strength by combining with a known formula: where I represents the ionic strength, with the unit of mole per cubic meter; c i represents the concentration of the i-th ion, with the unit of mole per cubic meter; z i represents the charge number of the i-th ion; i represents the ion number.

[0088] Water content: Measured by the drying method, and record the proportional relationship between the wet weight and the dry weight of the soil sample.

[0089] Temperature: Use a digital thermometer to record the temperature of the measurement environment in real time.

[0090] Establish a Gaussian process regression model based on the surface charge density of soil particles and environmental parameters to fit the charge change law:

[0091] Take the measured surface charge density data and environmental parameters (including pH value, ionic strength, water content and temperature) as inputs to construct a training data set. The format of the training data is:

[0092] Input variables: Environmental parameters (pH value, ionic strength, water content, temperature).

[0093] Output variable: The surface charge density of soil particles.

[0094] Use the Gaussian process regression model to fit the non-linear relationship between the charge density and environmental parameters. The model expression is: where y(x) represents the surface charge density of soil particles, as the output variable; x represents the input variables, including environmental parameters (pH value, ionic strength, water content, temperature); m(x) represents the mean function, indicating the average trend of the surface charge density; k(x, x′) represents the covariance function, describing the correlation between two input variables; x′ represents a new input point; represents the Gaussian process.

[0095] where x′ is the input variable used for inference when making predictions in the regression model (i.e., the test point corresponding to the input variable x used in model training). In Gaussian process regression, x′ is the point for which we hope to predict its corresponding output through known data (such as the training set). is a non-parametric statistical method for regression analysis.

[0096] Using the aforementioned training data, the model parameters are optimized by the maximum likelihood estimation method to ensure the accuracy of model fitting. The model validation adopts the cross-validation method to evaluate the stability of the fitting results.

[0097] Based on the Gaussian process regression model, the adsorption and desorption influence evaluation value is calculated to quantify the dynamic influence of the change of soil particle surface charge on the heavy metal adsorption and desorption behavior:

[0098] Using the prediction results of the Gaussian process regression model, calculate the adsorption and desorption influence evaluation value: E = ∫ a b k(x, x′)·p(x)dx; where E is the adsorption and desorption influence evaluation value, which is used to quantify the dynamic influence of charge change on adsorption and desorption; p(x) represents the probability distribution function of environmental parameters; a, b represent the integration range, which are the minimum and maximum values of the parameters respectively.

[0099] Among them, the probability distribution function of environmental parameters refers to determining the probability distribution form of environmental parameters such as soil pH value, ionic strength, moisture content, and temperature through statistical analysis. Usually, normal distribution, lognormal distribution, etc. are adopted to reflect the variation laws of these parameters in different soil samples.

[0100] The larger the adsorption and desorption influence evaluation value, usually means that the change of soil particle surface charge has a more significant dynamic influence on the heavy metal adsorption and desorption behavior, resulting in an increase in the migration rate of heavy metals in the soil; therefore, the larger the adsorption and desorption influence evaluation value, usually indicates that the desorption behavior of heavy metals in the soil is stronger, and the risk of heavy metals leaking into groundwater is greater.

[0101] The random walk model is used to analyze the retention effect of heavy metals in the fracture network and evaluate its delay effect on the heavy metal migration rate and path. Specifically:

[0102] Using three-dimensional geological data and fracture distribution information to establish a fracture network model:

[0103] The three-dimensional geological data includes the structure of the strata, the geometric shape of the fractures, porosity, permeability, etc. The fracture distribution information includes the number, size, connectivity and other characteristics of the fractures, which directly affect the migration path of groundwater and pollutants.

[0104] Three-dimensional geological data: Through geological exploration work, means such as drilling, seismic waves, and ground penetrating radar are used to obtain the stratification information of underground soil and rocks. These data are used to describe the underground fracture system, including the distribution, strike, dip, porosity, etc. of the fractures. Through this information, a three-dimensional model of the fractures can be established, and then the flow path of heavy metals in the underground fracture network can be simulated.

[0105] The establishment of the fracture network model needs to consider the spatial distribution and connectivity of fractures. According to the geometric shape, size, quantity and distribution pattern of fractures, the fracture network model is constructed by using the finite element method or other network modeling techniques. This network model can reflect the flow law of heavy metal pollutants in fractures and provide a basis for subsequent migration simulation.

[0106] Measure the migration-related parameters of heavy metals in the fracture network. The migration-related parameters include diffusion coefficient, adsorption rate and desorption rate:

[0107] The migration-related parameters of heavy metals are the basis for migration simulation, mainly including diffusion coefficient, adsorption rate and desorption rate. These parameters directly affect the migration behavior of heavy metals in fractures and determine the intensity of the retention effect.

[0108] The diffusion coefficient is a parameter that describes the diffusion rate of pollutants in fracture water. The diffusion coefficient is affected by factors such as fracture porosity, pore water velocity and water temperature. This parameter is usually determined by experimental methods. In the experiment, small-scale water infiltration tests can be used to record the diffusion behavior of heavy metals, and then the corresponding diffusion coefficient values can be obtained.

[0109] The adsorption rate refers to the binding strength of heavy metals to the surface of soil or rock particles, reflecting the adsorption ability of heavy metals. The adsorption rate is closely related to factors such as the surface charge of the soil, mineral composition, pH value, etc. The adsorption rate in different soil samples can be measured through static adsorption experiments.

[0110] The desorption rate describes the rate at which heavy metals adsorbed on soil particles are desorbed into the aqueous phase. The desorption rate is related to the properties of the soil, heavy metal concentration, environmental conditions, etc. The desorption rate of heavy metals is usually measured through leaching experiments. In the experiment, the desorption rate is obtained by simulating the heavy metal release process under different environmental conditions.

[0111] Taking the fracture network model and migration-related parameters as inputs, simulate the migration path of heavy metals in the fracture network based on the random walk model:

[0112] When inputting the fracture network model, information such as the geometric structure, porosity, permeability, etc. of the fractures needs to be input into the simulation. These data help to determine the migration path of heavy metals, including the direction and rate of its movement in the fractures, etc.

[0113] The random walk model assumes that heavy metal particles migrate in a random way in fractures, and each step of its movement is affected by factors such as the size, shape, porosity of the fractures, etc. By setting the migration step size and migration probability, the migration path of heavy metals can be simulated. In the model, the migration path will be adjusted according to the influence of parameters such as diffusion coefficient, adsorption rate, desorption rate, etc., so as to simulate the flow characteristics of heavy metals in fractures.

[0114] During the operation of the model, each step of the random walk involves changes in the state of heavy metal particles (such as adsorption or desorption), as well as their diffusion or retention in the fractures. The mathematical expressions for these processes are usually: P(t + 1) = P(t) + δ·Δt; where P(t + 1) represents the position of the heavy metal particles at time t + 1; P(t) represents the position of the heavy metal particles at time t; δ is the migration step length, which is used to control the amount of movement of the particles in each time step; and Δt is the time step length, that is, the time interval for each iteration.

[0115] Through multiple random walks, a series of migration paths are obtained.

[0116] By analyzing the migration paths in the random walk model, the delay effects of the retention effect on the migration rate and path of heavy metals are quantified:

[0117] By analyzing the migration paths in the simulation results, the time required for the migration of heavy metals and the total migration distance are calculated. The retention effect is mainly reflected in the delay of the migration path and the slowdown of the migration rate.

[0118] According to the simulation results, the retention time of heavy metals can be calculated, that is, the length of time they are retained in the fracture network. This delay effect can be quantified by calculating parameters such as the average migration time and path length. The migration delay of heavy metals in the fractures will increase the risk of groundwater pollution because the retained heavy metals may be released into the water under appropriate conditions.

[0119] Calculate the total migration delay time: where T delayed represents the total migration delay time, P final (u) is the final position of the u-th path, P initial (u) is the initial position of the u-th path, U is the number of paths, and u is the path number.

[0120] The larger the total migration delay time, the slower the migration speed of heavy metals in the fracture network, the stronger the influence of the retention effect on the migration process, and the more significant the delay effect of the retention effect on the migration rate and path of heavy metals. This will lead to a slower migration rate and a more delayed migration path of heavy metals, resulting in a potentially longer and more extensive impact on groundwater or the environment. This also means that the penetration and diffusion processes of heavy metals are more strongly inhibited, increasing the risk of pollution diffusion.

[0121] Based on the dynamic effects of changes in the surface charge of soil particles on the adsorption and desorption behavior of heavy metals and the delay effects of the retention effect of heavy metals in the fracture network on the migration rate and path of heavy metals, a risk assessment result of heavy metal leakage into groundwater is generated, specifically:

[0122] Normalize the desorption influence evaluation value and the total migration delay time, assign weight coefficients to the normalized desorption influence evaluation value and the total migration delay time respectively, and calculate the heavy metal underground leakage coefficient, whose expression is: L leak = w E ·E′ + w T ·T′ delayed ; where L leak is the heavy metal underground leakage coefficient, E′ is the normalized desorption influence evaluation value, T′ delayed is the normalized total migration delay time, w E and w T are the weight coefficients of the desorption influence evaluation value and the total migration delay time respectively, and both w E and w T are greater than 0.

[0123] The weight coefficients of the desorption influence evaluation value and the total migration delay time should be set according to the characteristics of the actual research object and application scenario. Usually, the weight coefficients can be determined by the expert evaluation method, sensitivity analysis or historical data analysis. Specifically, the setting of the weight coefficients should consider the contribution degree of each factor to the heavy metal underground leakage risk, and ensure that the weight coefficients reflect the relative importance of each factor in the risk assessment. Generally speaking, if a certain factor has a greater impact on the leakage risk, its weight coefficient should be appropriately increased.

[0124] The larger the heavy metal underground leakage coefficient, the more significant the comprehensive effect of the desorption influence evaluation value and the total migration delay time, resulting in an increase in the risk of heavy metal leakage into groundwater. Specifically, when the dynamic influence of the adsorption and desorption behavior of heavy metals is stronger (i.e., the desorption influence evaluation value is higher) or the retention effect during the migration process is more significant (i.e., the total migration delay time is longer), the probability and degree of heavy metal leakage will increase. Therefore, the larger the heavy metal underground leakage coefficient, the greater the risk of heavy metal leakage into groundwater, and the greater the potential threat of groundwater pollution.

[0125] According to the risk assessment results of heavy metal leakage into groundwater, classify the groundwater pollution risk, specifically:

[0126] Set the first threshold of heavy metal underground leakage and the second threshold of heavy metal underground leakage, where the first threshold of heavy metal underground leakage is less than the second threshold of heavy metal underground leakage.

[0127] Compare the heavy metal underground leakage coefficient with the first threshold of heavy metal underground leakage and the second threshold of heavy metal underground leakage:

[0128] When the underground leakage coefficient of heavy metals is less than the first threshold of underground leakage of heavy metals, the groundwater pollution risk is determined to be at a low risk level; this indicates that the risk of heavy metal leakage is relatively low, the possibility of groundwater pollution is small, and the adsorption capacity of heavy metals in the soil is strong or the migration rate is slow. In this case, emergency intervention is not required, but regular monitoring is still necessary.

[0129] When the underground leakage coefficient of heavy metals is greater than or equal to the first threshold of underground leakage of heavy metals and less than or equal to the second threshold of underground leakage of heavy metals, the groundwater pollution risk is determined to be at a medium risk level; this means that there is a certain leakage risk, the possibility of groundwater pollution increases significantly, and monitoring and assessment should be strengthened, and appropriate preventive measures should be taken.

[0130] When the underground leakage coefficient of heavy metals is greater than the second threshold of underground leakage of heavy metals, the groundwater pollution risk is determined to be at a high risk level; this indicates that the risk of heavy metal leakage is very high and the risk of groundwater pollution is great. In this case, measures should be taken immediately to reduce the spread of pollution and conduct soil and water quality restoration.

[0131] Among them, the setting of the first threshold and the second threshold of underground leakage of heavy metals should be based on the type of soil, the properties of heavy metals, local environmental conditions, and historical monitoring data. The first threshold should be set according to the migration characteristics of heavy metals and the adsorption capacity of the soil to ensure effective determination of low-risk areas; the second threshold needs to be set according to the possible maximum pollution risk, considering the potential pollution accumulation effect and the protection requirements of groundwater.

[0132] Example 2: The difference between Example 2 and Example 1 of the present invention is that this example introduces the cumulative environmental risk early warning analysis system for soil heavy metal pollution.

[0133] Figure 2 The structural schematic diagram of the cumulative environmental risk early warning analysis system for soil heavy metal pollution of the present invention is given. The cumulative environmental risk early warning analysis system for soil heavy metal pollution includes a metal concentration measurement module, a pollution risk judgment module, an adsorption and desorption influence analysis module, a retention effect analysis module, a leakage risk assessment module, and a pollution risk grading module.

[0134] Metal concentration measurement module: Measure the concentration of heavy metals in the soil at each depth to obtain the heavy metal concentration depth distribution data.

[0135] Pollution risk judgment module: Based on the heavy metal concentration depth distribution data, judge whether there is a risk of heavy metal pollution affecting groundwater.

[0136] Adsorption and desorption influence analysis module: When there is a risk of heavy metal pollution affecting groundwater, use Gaussian process regression to analyze the change of the surface charge of soil particles and evaluate its dynamic influence on the adsorption and desorption behavior of heavy metals;

[0137] Retention effect analysis module: When there is a risk of heavy metal pollution affecting groundwater, a random walk model is used to analyze the retention effect of heavy metals in the fracture network and evaluate its delaying effect on the migration rate and path of heavy metals.

[0138] Leakage risk assessment module: Based on the dynamic influence of the change in the surface charge of soil particles on the adsorption and desorption behavior of heavy metals and the delaying effect of the retention effect of heavy metals in the fracture network on the migration rate and path of heavy metals, a risk assessment result of heavy metal leakage into groundwater is generated.

[0139] Pollution risk grading module: The groundwater pollution risk is graded according to the risk assessment result of heavy metal leakage into groundwater.

[0140] The above formulas are all dimensionless and take their numerical values for calculation. The formula is a formula obtained by collecting a large amount of data for software simulation to approximate the real situation as closely as possible. The preset parameters and threshold selection in the formula are set by those skilled in the art according to the actual situation.

[0141] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0142] Those of ordinary skill in the art will realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0143] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0144] In several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of the devices or modules can be in electrical, mechanical, or other forms.

[0145] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0146] In addition, the functional modules in each embodiment of this application can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0147] If the above functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0148] As described above, the above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0149] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A cumulative environmental risk early warning analysis method for soil heavy metal pollution, characterized in that: The steps include: S1: Measure the concentration of heavy metals in the soil at each depth to obtain the depth distribution data of heavy metal concentration; S2: Determine whether there is a risk of heavy metal pollution affecting groundwater based on the depth distribution data of heavy metal concentrations; S3: When there is a risk of heavy metal contamination affecting groundwater, Gaussian process regression is used to analyze the changes in the surface charge of soil particles to evaluate its dynamic impact on the adsorption and desorption behavior of heavy metals; the random walk model is used to analyze the retention effect of heavy metals in the fracture network to evaluate its delayed effect on the migration rate and path of heavy metals; S4: Generate risk assessment results of heavy metal leakage to groundwater based on the dynamic effects of soil particle surface charge changes on heavy metal adsorption and desorption behaviors and the delayed effects of heavy metal retention effects in fracture networks on heavy metal migration rates and pathways; S5: Grade the groundwater pollution risk based on the risk assessment results of heavy metal leakage into groundwater.

2. The cumulative environmental risk early warning analysis method for soil heavy metal pollution according to claim 1 is characterized in that: The concentration of heavy metals in the soil at each depth was measured to obtain the depth distribution data of heavy metal concentration, specifically: Soil samples at different depths are collected, and the heavy metal concentration in each soil sample is measured to obtain heavy metal concentration data at different depths to form heavy metal concentration depth distribution data.

3. The cumulative environmental risk early warning analysis method for soil heavy metal pollution according to claim 2 is characterized in that: Based on the depth distribution data of heavy metal concentration, it is judged whether there is a risk of heavy metal pollution affecting groundwater, specifically: Conduct statistical analysis on the heavy metal concentration data at different depths to determine the heavy metal concentration at each depth level; Setting risk thresholds for heavy metal concentrations in each soil layer based on soil type and groundwater sensitivity; Identify soil layers that exceed the heavy metal concentration risk threshold, and based on the changing trend of soil heavy metal concentration in the soil layers that exceed the heavy metal concentration risk threshold, determine whether there is a risk of heavy metal pollution affecting groundwater.

4. The cumulative environmental risk early warning analysis method for soil heavy metal pollution according to claim 3 is characterized in that: Gaussian process regression was used to analyze the changes in the surface charge of soil particles and evaluate their dynamic effects on the adsorption and desorption behavior of heavy metals. Specifically: For soil samples at different depths, the surface charge density of soil particles in each sample was measured; Collect environmental parameters related to changes in the surface charge of soil particles, including soil pH, ionic strength, moisture content, and temperature; A Gaussian process regression model was established based on the surface charge density of soil particles and environmental parameters to fit the charge variation law. The adsorption-desorption impact assessment value was calculated based on the Gaussian process regression model to quantify the dynamic effect of changes in soil particle surface charge on the adsorption and desorption behavior of heavy metals.

5. The cumulative environmental risk early warning analysis method for soil heavy metal pollution according to claim 4 is characterized in that: The expression of adsorption-desorption impact assessment value is: E = ∫ a b k(x, x′)·p(x)dx; where E is the adsorption / desorption impact assessment value, p(x) represents the probability distribution function of the environmental parameters, a and b represent the integration range, k(x, x′) represents the covariance function, x represents the input variable; x′ represents a new input point.

6. The cumulative environmental risk early warning analysis method for soil heavy metal pollution according to claim 5 is characterized in that: The random walk model was used to analyze the retention effect of heavy metals in the fracture network and evaluate its delayed impact on the migration rate and path of heavy metals, specifically: Establish a fracture network model using 3D geological data and fracture distribution information; Measure the migration-related parameters of heavy metals in the fracture network, including diffusion coefficient, adsorption rate and desorption rate; The fracture network model and migration-related parameters were used as input to simulate the migration path of heavy metals in the fracture network based on the random walk model. By analyzing the migration paths in the random walk model, the delayed effects of the retention effect on the migration rate and path of heavy metals were quantified.

7. The cumulative environmental risk early warning analysis method for soil heavy metal pollution according to claim 6 is characterized in that: in, The delayed effect of the retention effect on the migration rate and path of heavy metals is quantified based on the total migration delay time: Among them, T delayed represents the total delay time of migration, P final (u) is the final position of the u-th path, P initial (u) is the initial position of the u-th path, U is the number of paths, and u is the path number.

8. The cumulative environmental risk early warning analysis method for soil heavy metal pollution according to claim 7 is characterized in that: Based on the dynamic effect of soil particle surface charge changes on the adsorption and desorption behavior of heavy metals and the delayed effect of heavy metal retention in the fracture network on the migration rate and path of heavy metals, the risk assessment results of heavy metal leakage to groundwater are generated, specifically: The adsorption and decomposition impact assessment value and the total migration delay time are normalized, and weight coefficients are assigned to the normalized adsorption and decomposition impact assessment value and the total migration delay time, respectively, to calculate the underground leakage coefficient of heavy metals.

9. The cumulative environmental risk early warning analysis method for soil heavy metal pollution according to claim 8, characterized in that: According to the risk assessment results of heavy metal leakage into groundwater, the groundwater pollution risk is graded as follows: A first threshold value for underground heavy metal leakage and a second threshold value for underground heavy metal leakage are set, wherein the first threshold value for underground heavy metal leakage is less than the second threshold value for underground heavy metal leakage; and a heavy metal underground leakage coefficient is compared with the first threshold value for underground heavy metal leakage and the second threshold value for underground heavy metal leakage: When the underground seepage coefficient of heavy metals is less than the first threshold of underground seepage of heavy metals, the groundwater pollution risk is judged to be at a low risk level; When the underground leakage coefficient of heavy metals is greater than or equal to the first threshold of underground leakage of heavy metals, and the underground leakage coefficient of heavy metals is less than or equal to the second threshold of underground leakage of heavy metals, the groundwater pollution risk is judged to be a medium risk level; When the heavy metal underground leakage coefficient is greater than the second heavy metal underground leakage threshold, the groundwater pollution risk is determined to be a high risk level.

10. A system for early warning analysis of the cumulative environmental risk of heavy metal pollution in soil, used to implement the method for early warning analysis of the cumulative environmental risk of heavy metal pollution in soil according to any one of claims 1 to 9, characterized in that: It includes metal concentration measurement module, pollution risk judgment module, adsorption and desorption impact analysis module, retention effect analysis module, leakage risk assessment module and pollution risk classification module; Metal concentration measurement module: measures the concentration of heavy metals in the soil at each depth to obtain the depth distribution data of heavy metal concentration; Pollution risk assessment module: Based on the depth distribution data of heavy metal concentration, determine whether there is a risk of heavy metal pollution affecting groundwater; Adsorption and desorption impact analysis module: When there is a risk of heavy metal pollution affecting groundwater, Gaussian process regression is used to analyze the changes in the surface charge of soil particles to evaluate its dynamic impact on the adsorption and desorption behavior of heavy metals; Retention effect analysis module: When there is a risk of heavy metal pollution affecting groundwater, the random walk model is used to analyze the retention effect of heavy metals in the fracture network and evaluate its delayed impact on the migration rate and path of heavy metals; Leakage risk assessment module: Generates risk assessment results of heavy metal leakage to groundwater based on the dynamic effects of changes in soil particle surface charge on the adsorption and desorption behavior of heavy metals and the delayed effects of the retention effect of heavy metals in the fracture network on the migration rate and path of heavy metals; Pollution risk classification module: Classify the groundwater pollution risk based on the risk assessment results of heavy metal leakage into groundwater.

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