Early warning analysis method and system for accumulative environmental risk of soil heavy metal pollution

Abstract

The invention discloses a cumulative environmental risk early warning analysis method and system for soil heavy metal pollution, particularly relates to the technical field of soil environment monitoring, and is used for solving the problems that the existing risk assessment of leakage from heavy metal pollution to underground water is incomplete and insufficient in accuracy. Heavy metal concentration depth distribution data are obtained by measuring heavy metal concentrations in soil at different depths; judging whether a heavy metal pollution risk influencing underground water exists or not according to the data; when the risk exists, analyzing the dynamic influence of surface charge change of soil particles on heavy metal adsorption and desorption behaviors by adopting Gaussian process regression, analyzing the retention effect of the heavy metal in a fracture network through a random walk model, and evaluating the delay influence of the retention effect on the migration rate and path of the heavy metal; and finally, according to the generated heavy metal underground leakage risk assessment result, the groundwater pollution risk is graded, and the accuracy of groundwater pollution risk assessment can be effectively improved.

Description

Technical Field

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

[0002] With the rapid development of industrialization and urbanization, the problem of heavy metal pollution in soil is becoming increasingly serious, especially in areas where mining, metallurgy and chemical industries are concentrated, where the content of heavy metals in the soil shows a cumulative growth trend. These heavy metals are highly toxic, difficult to degrade and have long-distance migration, posing a serious threat to the ecological environment and human health. As an important natural resource, groundwater pollution is particularly worthy of attention.

[0003] In the existing technology, the risk assessment of the migration of heavy metal pollution in soil to groundwater lacks a comprehensive analysis and integrated consideration of the key influencing factors in the migration process of heavy metal pollution. This may lead to underestimation or misjudgment of groundwater pollution risks, affecting the accuracy of the assessment and the scientific nature of governance. Summary of the invention

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

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

[0006] The cumulative environmental risk early warning analysis method of soil heavy metal pollution includes 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 concentration;

[0008] S2: Determine whether there is a risk of heavy metal pollution affecting groundwater based on the depth distribution data of heavy metal concentrations;

[0009] 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;

[0010] 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;

[0011] S5: Grade the groundwater pollution risk based on the risk assessment results of heavy metal leakage into groundwater.

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

[0013] 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.

[0014] In a preferred embodiment, judging whether there is a risk of heavy metal pollution affecting groundwater is based on the heavy metal concentration depth distribution data, 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] Setting risk thresholds for heavy metal concentrations in each soil layer based on soil type and groundwater sensitivity;

[0017] 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.

[0018] In a preferred embodiment, Gaussian process regression is used to analyze the change in surface charge of soil particles and evaluate its dynamic effect on the adsorption and desorption behavior of heavy metals, specifically:

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

[0020] Collect environmental parameters related to changes in the surface charge of soil particles, including soil pH, ionic strength, moisture content, and temperature;

[0021] 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.

[0022] 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.

[0023] In a preferred embodiment, the expression of the adsorption / desorption impact evaluation 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, and a and 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 delayed effect on the migration rate and path of heavy metals, specifically:

[0025] Establish a fracture network model using 3D geological data and fracture distribution information;

[0026] Measure the migration-related parameters of heavy metals in the fracture network, including diffusion coefficient, adsorption rate and desorption rate;

[0027] 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.

[0028] 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.

[0029] In a preferred embodiment, the delayed effect of the retention effect on the migration rate and path of heavy metals is quantified based on the total delay time of migration: Among them, 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 delayed 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 to groundwater is generated, specifically:

[0031] 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.

[0032] In a preferred embodiment, the groundwater pollution risk is graded according to the risk assessment results of heavy metal leakage into groundwater, specifically:

[0033] 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:

[0034] 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;

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

[0036] 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.

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

[0038] 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;

[0039] 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;

[0040] Adsorption-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;

[0041] 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;

[0042] 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;

[0043] Pollution risk classification module: Classify the groundwater pollution risk based on the risk assessment results 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 are as follows:

[0045] 1. The present invention can effectively solve the problem that the existing technology cannot comprehensively and accurately assess the risk of heavy metal pollution to groundwater leakage. By measuring the heavy metal concentrations in soil at different depths and combining Gaussian process regression and random walk models, it can dynamically analyze the changes in 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 scientificity of risk assessment.

[0046] 2. By grading the heavy metal pollution risk, a more detailed decision-making basis is provided for groundwater pollution prevention and control. Traditional groundwater pollution risk assessment methods are usually rough and cannot take into account the 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 assessment based on soil heavy metal concentrations, changes in soil particle surface charge, and the delayed effect of heavy metal migration in the fracture network, thereby providing a more accurate groundwater pollution risk level, providing strong technical support for soil pollution control, and helping to provide early warning 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 of soil heavy metal pollution of the present invention;

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

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

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

[0051] S1: Measure the concentration of heavy metals in the soil at each depth to obtain the depth distribution data of heavy metal concentration.

[0052] S2: Determine whether there is a risk of heavy metal pollution affecting groundwater based on the depth distribution data of heavy metal concentrations.

[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 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 and evaluate its delayed effect on the migration rate and path of heavy metals.

[0054] S4: 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 delayed effects of the retention effect of heavy metals 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.

[0055] S5: Grade the groundwater pollution risk based on the risk assessment results of heavy metal leakage into groundwater.

[0056] The concentration of heavy metals in the soil at each depth was measured to obtain the depth distribution data of heavy metal concentration, specifically:

[0057] 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.

[0058] Among them, first select several sampling points in 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 layered and collected according to the soil layer 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 can generally be divided into different levels such as 0-10cm, 10-20cm, 20-40cm, 40-60cm, etc. to ensure the representativeness of the sample.

[0059] Each soil sample collected should be stored immediately and properly to prevent it from getting wet or contaminated. The sample can be stored in a refrigerator or dry place, and its original properties should be kept as little as possible during transportation. For moist soil samples, water should be removed quickly to retain the solid components of the soil.

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

[0061] After the measurement is completed, the concentration data of each heavy metal element in the soil samples at each depth level are recorded and organized into a table. The soil samples at each sampling point should correspond to different depth level data. Through data statistics, the changes in the concentration of heavy metals in the soil at different depths are obtained, and the depth distribution data of the heavy metal concentration at each sampling point is further formed.

[0062] 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:

[0063] Statistical analysis was performed on the heavy metal concentration data at different depths to determine the heavy metal concentration at each depth level:

[0064] Statistical analysis methods, including mean, variance, and quantile calculations, were used to process the heavy metal concentration data at each depth level to 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 and ensure the accuracy of the analysis.

[0066] A depth concentration distribution map is generated based on the fitting results to clarify the changing trend of heavy metal concentrations at different depths and provide basic data for subsequent analysis.

[0067] According to soil type and groundwater sensitivity, the heavy metal concentration risk thresholds are set for each soil layer:

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

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

[0070] The heavy metal concentration value of each layer of soil is compared with the set risk threshold layer by layer, and the soil layer exceeding the threshold is identified. The pollution level of the soil layer exceeding the threshold is marked, and the soil layer is divided into lightly, moderately and heavily polluted layers, so as to accurately locate the high-risk layer in subsequent analysis.

[0071] Among them, by constructing a spatial distribution map of the excessive layer and combining it with the geographic information system (GIS) to mark the spatial location of the excessive soil layer, spatial data support can be provided for subsequent trend analysis.

[0072] Conduct time series analysis on heavy metal concentration data in soil layers that exceed 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 excessive soil layer, a multi-layer propagation model (such as finite difference method or finite element method) is used to simulate the vertical migration behavior of heavy metals.

[0074] In the simulation results, it is determined whether heavy metals may affect groundwater through vertical migration, and a groundwater contamination risk assessment map is generated.

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

[0076] 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:

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

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

[0079] Electrophoresis experiments combined with zeta potential measurement technology were used to measure the surface charge density of soil particles at different depths.

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

[0081] The soil sample is dissolved in a solution of known ionic strength to form a suspension.

[0082] An electric field is applied through an electrophoresis instrument and the migration velocity of soil particles is recorded.

[0083] The surface charge density of soil particles is calculated using the zeta potential formula: σ e =∈·μ e ; Among them, σ e represents the surface charge density of soil particles, ∈ represents the dielectric constant, μ e Represents the electrophoretic mobility of the particles.

[0084] Collect environmental parameters related to changes in soil particle surface charge, including soil pH, ionic strength, moisture content, and temperature:

[0085] Environmental parameters were recorded for each sample, including soil pH, ionic strength, moisture content, and temperature, which are important factors affecting changes in the surface charge of soil particles.

[0086] Soil pH: Measured with a pH meter, suspend the soil sample in distilled water and record the pH range of the solution.

[0087] Ionic strength: The conductivity of the soil suspension is measured by a conductivity meter, and the ionic strength is calculated using the known formula: Where I represents the ionic strength, in moles per cubic meter; c i represents the concentration of the i-th ion in moles per cubic meter; z i represents the charge number of the i-th ion; i represents the number of the ion.

[0088] Moisture content: Measured by the drying method, recording the ratio of the wet weight to 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] A Gaussian process regression model was established based on the surface charge density of soil particles and environmental parameters to fit the charge change law:

[0091] The measured surface charge density data and environmental parameters (including pH value, ionic strength, water content and temperature) are used as input 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: Surface charge density of soil particles.

[0094] The Gaussian process regression model is used to fit the nonlinear relationship between charge density and environmental parameters. The model is expressed as: Among them, y(x) represents the surface charge density of soil particles, which is the output variable; x represents the input variables, including environmental parameters (pH value, ionic strength, moisture content, temperature); m(x) represents the mean function, which represents the average trend of the surface charge density; k(x, x′) represents the covariance function, which describes the correlation between two input variables; x′ represents a new input point; represents a Gaussian process.

[0095] Here, 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 whose corresponding output we want to predict given known data (e.g., the training set). It is a nonparametric statistical method used for regression analysis.

[0096] Based on the aforementioned training data, the maximum likelihood estimation method was used to optimize the model parameters to ensure the accuracy of model fitting. The model was validated using the cross-validation method to evaluate the stability of the fitting results.

[0097] 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:

[0098] Using the prediction results of the Gaussian process regression model, the adsorption-desorption impact assessment value is calculated: E = ∫ a b k(x, x′)·p(x)dx; where E is the adsorption-desorption impact assessment value, which is used to quantify the dynamic effect of charge change on adsorption and desorption; p(x) represents the probability distribution function of the environmental parameters; a and 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 the probability distribution form of environmental parameters such as soil pH, ionic strength, moisture content and temperature determined through statistical analysis. It usually adopts normal distribution, lognormal distribution and other forms to reflect the changing patterns of these parameters in different soil samples.

[0100] The larger the adsorption-desorption impact assessment value, the more significant the dynamic effect of changes in the surface charge of soil particles on the adsorption and desorption behavior of heavy metals, thereby increasing the migration rate of heavy metals in the soil; therefore, the larger the adsorption-desorption impact assessment value, the stronger the desorption behavior of heavy metals in the soil, and the greater the risk of heavy metal leakage into groundwater.

[0101] 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:

[0102] The fracture network model is established using 3D geological data and fracture distribution information:

[0103] Three-dimensional geological data include the structure of the strata, the geometry of the fractures, porosity, permeability, etc. Fracture distribution information includes the number, size, connectivity and other characteristics of the fractures, which directly affect the migration paths of groundwater and pollutants.

[0104] 3D geological data: Through geological exploration, the layered information of underground soil and rock is obtained by means of drilling, seismic waves, geological radar, etc. These data are used to describe the underground fracture system, including the distribution, trend, inclination, porosity, etc. of the fractures. With this information, a 3D 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 a fracture network model requires consideration of the spatial distribution and connectivity of fractures. According to the geometric shape, size, number and distribution of fractures, a fracture network model is constructed 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, including diffusion coefficient, adsorption rate and desorption rate:

[0107] Heavy metal migration related parameters 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 cracks and determine the intensity of 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 flow rate, and water temperature. This parameter is usually determined by experimental methods. In the experiment, a small-scale water permeability test can be used to record the diffusion behavior of heavy metals and obtain the corresponding diffusion coefficient value.

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

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

[0111] The fracture network model and migration-related parameters are used as input to 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 fracture geometry, porosity, and permeability needs to be input into the simulation. These data help determine the migration path of heavy metals, including the direction and rate of their movement in the fractures.

[0113] The random walk model assumes that heavy metal particles migrate in a random manner in the cracks, and each step of their movement is affected by factors such as the size, shape, and porosity of the cracks. By setting the migration step size and migration probability, the migration path of heavy metals can be simulated. The model adjusts the migration path according to the influence of parameters such as diffusion coefficient, adsorption rate, and desorption rate, thereby simulating the flow characteristics of heavy metals in the cracks.

[0114] During the model operation, each step of the random walk involves the state change of heavy metal particles (such as adsorption or desorption), as well as their diffusion or retention in the cracks. The mathematical expressions of 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, which is used to control the movement of the particles in each time step; Δt is the time step, that is, the time interval between 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 delayed effects of the retention effect on the migration rate and path of heavy metals were quantified:

[0117] By analyzing the migration path in the simulation results, the time required for heavy metal migration 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] Based on the simulation results, the residence time of heavy metals can be calculated, that is, the length of time they remain in the fracture network. The impact of this delay can be quantified by calculating parameters such as the average time of migration and the path length. Delayed migration of heavy metals in fractures increases the risk of groundwater contamination because retained heavy metals may be released into the water under the right conditions.

[0119] Calculate the total migration delay time: Among them, 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 longer the total migration delay time is, the slower the migration speed of heavy metals in the fracture network is, the more strongly the migration process is affected by the retention effect, and the more significant the delay effect of the retention effect on the migration rate and path of heavy metals is. This will lead to a slower migration rate of heavy metals and a more delayed migration path, and the impact on groundwater or the environment may be longer-lasting and more extensive. This also means that the penetration and diffusion process of heavy metals is more strongly inhibited, increasing the risk of pollution spread.

[0121] 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:

[0122] The adsorption and decomposition impact assessment value and the total migration delay time are normalized, and the normalized adsorption and decomposition impact assessment value and the total migration delay time are respectively assigned weight coefficients to calculate the underground leakage coefficient of heavy metals, and its expression is: L leak =w E ·E′+w T ·T′ delayed Among them, L leak is the underground leakage coefficient of heavy metals, E′ is the normalized adsorption and decomposition impact assessment value, T′ delayed is the normalized total migration delay time, w E and w T are the weight coefficients of the adsorption / desorption impact assessment value and the total migration delay time, and w E and w T Both are greater than 0.

[0123] The weight coefficients of the adsorption-desorption impact assessment 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 expert evaluation, sensitivity analysis or historical data analysis. Specifically, the setting of the weight coefficients should take into account the contribution of each factor to the underground leakage risk of heavy metals to ensure that the weight coefficients reflect the relative importance of each factor in the risk assessment. Generally speaking, if a factor has a greater impact on the leakage risk, its weight coefficient should be appropriately increased.

[0124] The larger the underground leakage coefficient of heavy metals, the more significant the combined effect of the adsorption-desorption impact assessment value and the total migration delay time, which increases the risk of heavy metal leakage into groundwater. Specifically, when the dynamic impact of the adsorption and desorption behavior of heavy metals is stronger (i.e., the adsorption-desorption impact assessment value is higher) or the retention effect during migration 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 underground leakage coefficient of heavy metals, the greater the risk of heavy metal leakage into groundwater, and the potential threat of groundwater pollution also increases accordingly.

[0125] According to the risk assessment results of heavy metal leakage into groundwater, the groundwater pollution risk is graded as follows:

[0126] 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.

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

[0128] When the heavy metal underground leakage coefficient is less than the first heavy metal underground leakage threshold, the groundwater pollution risk is judged to be a low risk level; this indicates that the risk of heavy metal leakage is 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. At this time, no emergency intervention is required, but regular monitoring is still required.

[0129] When the heavy metal underground leakage coefficient is greater than or equal to the first heavy metal underground leakage threshold, and the heavy metal underground leakage coefficient is less than or equal to the second heavy metal underground leakage threshold, the groundwater pollution risk is judged to be a medium risk level; it means that there is a certain leakage risk, and the possibility of groundwater pollution has increased significantly, and monitoring and evaluation should be strengthened, and appropriate preventive measures should be taken.

[0130] When the heavy metal underground leakage coefficient is greater than the second heavy metal underground leakage threshold, the groundwater pollution risk is judged to be a high-risk level; this indicates that the risk of heavy metal leakage is very high and the risk of groundwater pollution is very high. At this time, measures should be taken immediately to reduce the spread of pollution and carry out soil and water quality remediation.

[0131] The first and second thresholds for underground heavy metal leakage should be set based on soil type, the nature of heavy metals, local environmental conditions and historical monitoring data. The first threshold should be set based on the migration characteristics of heavy metals and the adsorption capacity of soil to ensure effective judgment of low-risk areas; the second threshold should be set based on the possible maximum pollution risk, taking into account the potential cumulative effects of pollution and groundwater protection requirements.

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

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

[0134] 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.

[0135] Pollution risk assessment module: Determine whether there is a risk of heavy metal pollution affecting groundwater based on the depth distribution data of heavy metal concentrations.

[0136] Adsorption-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;

[0137] 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.

[0138] Leakage risk assessment module: Based on the dynamic influence of changes in soil particle surface charge on the adsorption and desorption behavior of heavy metals and the delayed effect of the retention effect of heavy metals 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.

[0139] Pollution risk classification module: Classify the groundwater pollution risk based on the risk assessment results of heavy metal leakage into groundwater.

[0140] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[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 process or function described in the embodiment of the present application is 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 computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

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

[0144] In the several embodiments provided in the present 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 only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be 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, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0146] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0147] If the 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 the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0148] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0149] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should 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 the adsorption-desorption impact evaluation value is: Where E is the adsorption-desorption impact assessment value, p(x) represents the probability distribution function of environmental parameters, and a and b represent the integration range.

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 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.

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 seepage coefficient of heavy metals is greater than or equal to the first threshold of underground seepage of heavy metals, and the underground seepage coefficient of heavy metals is less than or equal to the second threshold of underground seepage 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-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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