Risk assessment method based on soil heavy metal pollution pathway analysis

Abstract

The invention discloses a risk assessment method based on soil heavy metal pollution pathway analysis, and particularly relates to the technical field of land risk assessment. Heavy metal pollutant data and geographical location information of a target area are collected to generate a basic data set; constructing a heavy metal migration model based on the basic data set, and predicting a pollutant migration path and concentration change in combination with the comprehensive characteristics of the regional environment to identify a first high-risk region; a second high-risk area is identified by analyzing retention and release behaviors of pollutants in the microfractures in combination with quantitative soil microfracture connectivity characteristics and pollution migration behaviors of a dynamic entropy model; union processing is carried out on the first high-risk area and the second high-risk area to generate a comprehensive high-risk area; heavy metal concentration and land utilization changes in different time periods of the high-risk area are analyzed and synthesized through a multi-dimensional data fusion algorithm, and cumulative risk distribution is generated; and comparing the accumulated risk distribution with enterprise land use planning, evaluating potential environmental responsibility and generating a risk evaluation report.

Description

Technical Field

[0001] The present invention relates to the technical field of land risk assessment, and more specifically, to a risk assessment method based on the analysis of soil heavy metal pollution pathways. Background Art

[0002] Currently, for the environmental assessment of land mergers or leases, it usually relies on shallow soil samples and limited pollutant concentration monitoring data for analysis. Such assessment methods are mostly based on static data, and it is difficult to cover the pollution status and migration potential of deep soil. In addition, the focus of environmental assessment mostly concentrates on the current pollution status of the land, while ignoring the dynamic migration laws of pollutants in the time and space dimensions. At the same time, the hidden and regional characteristics of soil pollution bring greater uncertainties to traditional assessment methods.

[0003] In the prior art, there is a lack of accurate assessment means based on the analysis of soil pollution pathways, and it is impossible to effectively realize the dynamic prediction and traceability analysis of pollution risks, thus affecting the accuracy of the environmental liability assessment of enterprises in land mergers or leases. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a risk assessment method based on the analysis of soil heavy metal pollution pathways 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 risk assessment method based on the analysis of soil heavy metal pollution pathways, comprising the following steps:

[0007] Collect heavy metal pollutant data and geographical location information of the target area, mark the pollution and spatial distribution characteristics of each plot, and generate a basic data set;

[0008] Based on the basic data set, construct a heavy metal migration model, use the comprehensive characteristics of the regional environment as dynamic variables, combine with the heavy metal migration model to predict the pollutant migration path and concentration change, and identify the first high-risk area;

[0009] Through the dynamic entropy model, quantitatively analyze the connectivity characteristics of soil microfractures and pollution migration behavior, and combine with the analysis of the retention and release behavior of pollutants in soil microfractures to identify the second high-risk area;

[0010] Perform a union operation on the first high-risk area and the second high-risk area to generate a comprehensive high-risk area;

[0011] Use a multi-dimensional data fusion algorithm to comprehensively analyze the heavy metal concentration data and land use changes at different times for the comprehensive high-risk area to generate an accumulated risk distribution;

[0012] Compare the cumulative risk distribution with the enterprise land use plan, evaluate the potential environmental liabilities in land annexation and lease, and generate a risk assessment report.

[0013] In a preferred embodiment, collect heavy metal pollutant data and geographical location information of the target area, mark the pollution and spatial distribution characteristics of each plot, and generate a basic data set, specifically including:

[0014] Set multiple sampling points in the target area, and collect heavy metal pollutant data of each sampling point, including the types of heavy metals, concentration values and their spatial distribution information;

[0015] Use spatial positioning technology to obtain the geographical location information of the sampling points, including the longitude, latitude, elevation of the sampling points and the topographic features of the area where they are located;

[0016] Associate the collected heavy metal pollutant data with the geographical location information of the sampling points to form a preliminary data set including the types of pollutants, concentrations and their distribution information;

[0017] Adopt an interpolation algorithm to generate a heavy metal pollution concentration distribution map of the target area;

[0018] Overlay the distribution map with the regional geographical information to form comprehensive data marked with pollution types, concentration distribution and spatial distribution characteristics, and organize the comprehensive data into a basic data set in a standardized format.

[0019] In a preferred embodiment, construct a heavy metal migration model based on the basic data set, use the comprehensive characteristics of the regional environment as dynamic variables, combine with the heavy metal migration model to predict the pollutant migration path and concentration change, and identify the first high-risk area, specifically including:

[0020] Extract the types of pollutants, concentration values and spatial distribution characteristics of the target area from the basic data set, and initialize the model input parameters;

[0021] Use the climate conditions, geological structure and land use patterns in the comprehensive characteristics of the regional environment as dynamic variables and associate them with the basic data set;

[0022] Establish a heavy metal migration model, and describe the diffusion, penetration and accumulation characteristics of pollutants in the soil through control equations;

[0023] Generate prediction data on the pollutant migration path and concentration distribution in the target area in combination with the calculation results of the heavy metal migration model;

[0024] According to the area range where the pollutant concentration exceeds the threshold in the concentration distribution prediction data, identify the first high-risk area in the target area.

[0025] In a preferred embodiment, a dynamic entropy model is used to quantitatively analyze the connectivity characteristics of soil microfractures and the pollution migration behavior. Combining the analysis of the retention and release behavior of pollutants in soil microfractures, the second high-risk area is identified, specifically including:

[0026] Based on the soil microfracture structure data, a microfracture connectivity network model is constructed, and the connection strength and path distribution characteristics of the nodes in the microfracture connectivity network are extracted;

[0027] The dynamic entropy model is used to quantify the change characteristics of the connectivity of the microfracture connectivity network, and the dynamic distribution of the network connectivity at different time periods is obtained;

[0028] Analyze the retention and release behavior of pollutants in soil microfractures, including the adsorption frequency, desorption frequency and their kinetic characteristics of pollutants, and generate pollutant dynamic retention distribution data;

[0029] Perform correlation analysis on the dynamic distribution of network connectivity at different time periods and the pollutant dynamic retention distribution data, and identify the overlapping part of the high connectivity area and the high retention concentration area;

[0030] Mark the area with the highest pollution risk in the soil according to the identification result, and identify it as the second high-risk area.

[0031] In a preferred embodiment, the dynamic entropy model is used to quantify the change characteristics of the connectivity of the microfracture connectivity network, and the dynamic distribution of the network connectivity at different time periods is obtained, specifically as follows:

[0032] The formula of the dynamic entropy model is as follows: Among them, H(t) represents the dynamic entropy value of the microfracture connectivity network at time t, n is the total number of nodes in the microfracture connectivity network, and p i (t) represents the connectivity probability of the i-th node at time t;

[0033] Calculate the dynamic entropy value of the microfracture connectivity network in the target area hour by hour based on the formula of the dynamic entropy model, and generate a time series to reflect the dynamic change characteristics of the network connectivity.

[0034] In a preferred embodiment, the union processing of the first high-risk area and the second high-risk area is performed to generate a comprehensive high-risk area, specifically including:

[0035] Import the geographical marking data of the first high-risk area and the geographical marking data of the second high-risk area. The geographical marking data includes regional location coordinates, boundary ranges and pollutant concentration information;

[0036] Based on the geographic information system tool, perform spatial overlay on the geographical marking data of the first high-risk area and the second high-risk area, and identify the overlapping area and the adjacent area;

[0037] Perform boundary expansion and merging processing on the overlapping areas and adjacent areas in the spatial superposition result to generate a single boundary file for the comprehensive high-risk area.

[0038] In a preferred embodiment, use a multi-dimensional data fusion algorithm to comprehensively analyze the heavy metal concentration data and land use changes at different times for the comprehensive high-risk area to generate a cumulative risk distribution, specifically including:

[0039] Extract the heavy metal concentration data within the comprehensive high-risk area, and organize the concentration change information of each pollutant at different times in a time series.

[0040] Obtain the land use change data of the comprehensive high-risk area, and mark the spatial distribution characteristics of the land use types within each time period.

[0041] Use a multi-dimensional data fusion algorithm to jointly analyze the heavy metal concentration data and land use change data, and construct a dynamic risk calculation model.

[0042] Calculate the pollution accumulation characteristics of different time periods in the comprehensive high-risk area according to the dynamic risk calculation model to generate cumulative risk distribution data.

[0043] In a preferred embodiment, compare the cumulative risk distribution with the enterprise's land use plan, evaluate the potential environmental responsibilities in land merger and lease, and generate a risk assessment report, specifically including:

[0044] Import the cumulative risk distribution data and enterprise land use plan data of the comprehensive high-risk area. The land use plan data includes the planned area scope and land use.

[0045] Based on the geographic information system tool, conduct a spatial comparison of the cumulative risk distribution data and the enterprise land use plan data to identify the risk overlapping parts between the cumulative high-risk area and the planned area.

[0046] Evaluate the pollution risk level of the risk overlapping parts, and record the types of pollutants, concentration values, and area ranges of each risk overlapping part.

[0047] Comprehensively evaluate the suitability of the environmental risks of the risk overlapping parts and the land use, and generate an environmental responsibility analysis table.

[0048] Generate a risk assessment report containing detailed data of the high-risk area according to the environmental responsibility analysis table to support the enterprise's land use decision-making.

[0049] The technical effects and advantages of a risk assessment method based on the analysis of soil heavy metal pollution pathways of the present invention:

[0050] 1. By collecting heavy metal pollutant data and geographical location information of the target area, a basic dataset containing pollution distribution characteristics and spatial locations is generated, laying a comprehensive and accurate foundation for subsequent analysis. Combining the heavy metal migration model and the comprehensive characteristics of the regional environment, the dynamic prediction of pollutant migration paths and concentration changes is realized, effectively identifying the first high-risk areas where pollution may concentrate or spread. At the same time, through the dynamic entropy model, the connectivity characteristics of soil microfractures and pollution migration behaviors are quantitatively analyzed, and further combined with the dynamic characteristics of the retention and release behaviors of pollutants in soil microfractures, the deep-seated second high-risk areas are identified, providing technical support for the accurate identification of hidden soil pollution.

[0051] 2. By performing a union operation on the first high-risk area and the second high-risk area to generate a comprehensive high-risk area, the multi-dimensional data fusion algorithm is comprehensively used to jointly analyze the heavy metal concentrations and land use changes at different times, generating a cumulative risk distribution, providing a scientific basis for long-term pollution impact assessment. In addition, by comparing the cumulative risk distribution with the enterprise's land use plan, the suitability of high-risk areas and land uses is identified, the potential environmental responsibilities in land mergers or leases are systematically evaluated, and a detailed risk assessment report is generated, providing accurate support for enterprises in land management decisions, helping to reduce long-term environmental liability risks and improve the sustainability of land resource utilization. Brief Description of the Drawings

[0052] Figure 1 It is a schematic diagram of a risk assessment method based on the analysis of soil heavy metal pollution pathways according to the present invention. Detailed Embodiments

[0053] 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. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0054] Embodiment: Figure 1 A risk assessment method based on the analysis of soil heavy metal pollution pathways according to the present invention is given, which includes the following steps:

[0055] Collect heavy metal pollutant data and geographical location information of the target area, mark the pollution and spatial distribution characteristics of each plot, and generate a basic dataset.

[0056] Based on the basic dataset, construct a heavy metal migration model, and use the comprehensive characteristics of the regional environment as dynamic variables to combine with the heavy metal migration model to predict the pollutant migration paths and concentration changes, and identify the first high-risk areas.

[0057] Quantitatively analyze the connectivity characteristics of soil microfractures and pollution migration behavior through a dynamic entropy model, and identify the second highest risk area by combining the analysis of the retention and release behavior of pollutants in soil microfractures.

[0058] Perform a union operation on the first highest risk area and the second highest risk area to generate a comprehensive high risk area.

[0059] Use a multi-dimensional data fusion algorithm to comprehensively analyze the heavy metal concentration data and land use changes at different times in the comprehensive high risk area to generate an accumulated risk distribution.

[0060] Compare the accumulated risk distribution with the enterprise's land use plan, evaluate the potential environmental liabilities in land merger and lease, and generate a risk assessment report.

[0061] Collect heavy metal pollutant data and geographical location information of the target area, mark the pollution and spatial distribution characteristics of each plot, and generate a basic data set, specifically including:

[0062] Set multiple sampling points in the target area, and collect heavy metal pollutant data at each sampling point, including the types of heavy metals, concentration values, and their spatial distribution information:

[0063] Set a number of sampling points in the target area according to the area, terrain characteristics, and expected pollution distribution characteristics of the area. The layout of these sampling points follows the sampling spacing requirements in the national soil environmental monitoring technical specifications to ensure that the representative areas of the entire target area can be covered.

[0064] At each sampling point, use a high-precision portable heavy metal detector to collect heavy metal pollutant data in the soil at that point. The data includes the following:

[0065] Types of heavy metals: such as lead, cadmium, mercury, arsenic, copper, etc.

[0066] Heavy metal concentration values: Determine the heavy metal content in the soil per unit mass through colorimetry or spectral analysis techniques, and the results are expressed in milligrams per kilogram.

[0067] Spatial distribution information of heavy metals: Record the soil profile stratification of the sampling point and mark the depth of pollutant distribution (such as 0 - 20 cm, 20 - 40 cm, etc.).

[0068] During sampling, strictly follow the standard operating procedures to avoid cross-contamination of samples, and number and store the collected samples for subsequent analysis.

[0069] Use spatial positioning technology to obtain the geographical location information of the sampling point, including the longitude, latitude, elevation of the sampling point, and the terrain characteristics of the area where it is located:

[0070] Latitude and longitude coordinates: The specific location of each sampling point, expressed in degrees, minutes, and seconds, with the positioning accuracy controlled at the centimeter level.

[0071] Elevation information: The terrain height data of the sampling points is collected through GPS devices, and after calibration with the regional elevation benchmark, accurate elevation values are generated.

[0072] Regional terrain characteristics: By combining Geographic Information System (GIS) software, the slope, aspect, and relative height of the area where the sampling points are located are described to form a comprehensive geographical information record for each sampling point.

[0073] The geographical location information of the sampling points and the heavy metal pollutant data are collected and recorded synchronously to ensure the accuracy and timeliness of the data.

[0074] Associate the collected heavy metal pollutant data with the geographical location information of the sampling points to form a preliminary data set including pollutant types, concentrations, and their distribution information:

[0075] Import all pollutant data and geographical information into the database according to a unified template, ensuring that the data fields include sampling point numbers, latitude and longitude, elevation, heavy metal types, concentration values, depth stratification, etc.

[0076] By comparing the pollutant data with the geographical information, exclude abnormal data (such as data points with concentration values exceeding the reasonable range).

[0077] Store the cleaned data in a relational database and generate a preliminary dot distribution map of pollutants to visually display the heavy metal pollution concentration and distribution of the sampling points.

[0078] Use interpolation algorithms to generate a heavy metal pollution concentration distribution map within the target area:

[0079] Use the Inverse Distance Weighting (IDW) method as the interpolation algorithm to predict the heavy metal pollution concentration at unsampled points within the target area.

[0080] Determine the interpolation calculation range according to the boundary information of the target area. The interpolation results are output in the form of a regular grid, with the side length of each grid not exceeding 10 meters; set the weight index of the IDW algorithm (such as 2.0) to ensure that neighboring sampling points have a greater impact on the interpolation results; calculate the heavy metal concentration values of each grid within the target area, and compare the interpolation results with the actual concentration values of the sampling points to verify the prediction accuracy, ensuring that the error is controlled within 10%; finally, generate a heavy metal pollution concentration distribution map including all grids within the target area.

[0081] Overlay the distribution map with the regional geographical information to form comprehensive data marked with pollution types, concentration distribution, and spatial distribution characteristics, and organize the comprehensive data into a basic data set in a standardized format:

[0082] Use GIS software to perform georegistration on the heavy metal pollution concentration distribution map and combine it with the digital elevation model (DEM) and topographic data of the target area.

[0083] Mark the pollution types, concentration values, spatial distribution depths, and geographical features of each grid in the overlay map and generate a comprehensive map containing multi-level information.

[0084] Output the superimposed comprehensive data in a standardized format, ensure that the file format is a common vector file (such as.shp or.geojson), and generate tabular statistical data for subsequent analysis.

[0085] Organize the comprehensive data into a basic dataset. The data format includes the following: a comprehensive map file in vector data format for spatial analysis; an attribute table of heavy metal pollutants in database format, with fields including sampling point numbers, pollutant types, concentration values, spatial distribution characteristics, and corresponding geographical location information; a metadata file in text format recording information such as the time, method, and parameter settings of data collection.

[0086] Build a heavy metal migration model based on the basic dataset. Incorporate the comprehensive characteristics of the regional environment as dynamic variables into the heavy metal migration model to predict the pollutant migration paths and concentration changes, and identify the first high-risk area, specifically including:

[0087] Extract the pollutant types, concentration values, and spatial distribution characteristics of the target area from the basic dataset and initialize the model input parameters:

[0088] Extract pollutant-related data of the target area from the generated basic dataset, including pollutant types, concentration values, and spatial distribution characteristics.

[0089] Pollutant type extraction: According to the pollutant type information recorded in the basic dataset, extract heavy metal elements such as lead, cadmium, and mercury, and record the presence of each pollutant in the target area.

[0090] Concentration value extraction: Read the concentration values of each heavy metal pollutant recorded in the basic dataset, with the unit of milligrams per kilogram.

[0091] Spatial distribution characteristic extraction: Obtain the spatial distribution characteristics of the pollutants from the basic dataset, including the geographical location coordinates and distribution depth information of each heavy metal pollutant.

[0092] Initialize the input parameters of the heavy metal migration model through the extracted pollutant-related data to provide support for subsequent calculations.

[0093] Take the climate conditions, geological structure, and land use patterns in the comprehensive regional environment characteristics as dynamic variables and associate them with the basic dataset:

[0094] After initializing the model input parameters, take the comprehensive regional environment characteristics of the target area as dynamic variables and associate them with the basic dataset.

[0095] Climate conditions: Extract the temperature, rainfall, and humidity data of the target area. These parameters will be used as dynamic variables to describe the pollutant migration characteristics under different climate conditions.

[0096] Geological structure: Combine the geological exploration data to analyze the porosity, permeability, and fracture connectivity of the soil in the target area, and quantify the impact of the soil on the diffusion and infiltration of pollutants.

[0097] Land use patterns: Based on the land use data of the target area, mark the distribution of farmland, building areas, and wasteland in the area, and consider the impact of different land use types on pollutant accumulation.

[0098] After associating the comprehensive regional environment characteristics of the target area with the basic dataset, uniformly import them into the model input module to ensure the consistency of all data in the spatial and temporal dimensions.

[0099] Establish a heavy metal migration model to describe the diffusion, infiltration, and accumulation characteristics of pollutants in the soil through governing equations:

[0100] The mathematical expression of the heavy metal migration model is as follows: Among them, C represents the concentration of pollutants in the soil, with the unit of milligrams per kilogram; t represents time, with the unit of seconds; D represents the diffusion coefficient, with the unit of square meters per second, which is used to describe the diffusion ability of pollutants in the soil, and its value is calculated based on the soil porosity and the molecular weight of pollutants; represents the second-order spatial derivative of the pollutant concentration, which is used to describe the spatial variation of diffusion; v represents the flow velocity vector field in the soil, with the unit of meters per second, which depends on the groundwater flow velocity and soil permeability; represents the first-order spatial derivative of the pollutant concentration, which reflects the influence of the concentration gradient on the migration direction; R represents the accumulation rate of pollutants, with the unit of milligrams per kilogram per second, considering the combined effects of adsorption, desorption, and chemical reactions.

[0101] For example, the diffusion coefficient can be calculated by the following formula Among them, d is the soil porosity, dimensionless; k is the Boltzmann constant, with the unit of joules per kelvin; T is the soil temperature, with the unit of kelvin; η is the viscosity of the soil pore liquid, with the unit of pascal seconds; r is the hydrodynamic radius of the pollutant molecule, with the unit of meters.

[0102] The heavy metal migration model calculates the dynamic distribution characteristics of pollutants in the target area through numerical solutions, discretizes the governing equations using the finite difference method, and adopts an iterative solution method to simulate the concentration changes of pollutants at different time points.

[0103] Generate the migration path and concentration distribution prediction data of pollutants in the target area by combining the calculation results of the heavy metal migration model:

[0104] Draw a path map of the diffusion of pollutants from the high-concentration area to the low-concentration area according to the model calculation results. The path map marks the length and main migration direction of each migration path. Output the prediction results in the form of a spatial grid. Each grid cell contains the pollutant type and its concentration value, which is used to display the concentration distribution of pollutants in the target area.

[0105] The generation process of the migration path and concentration distribution prediction data is associated with the comprehensive characteristics of the regional environment. Different combinations of dynamic variables will affect the spatio-temporal distribution characteristics of the prediction results.

[0106] Identify the first high-risk area in the target area according to the range of the area where the pollutant concentration exceeds the threshold in the concentration distribution prediction data:

[0107] Set the risk threshold corresponding to the pollutant concentration, screen out the grid areas where the pollutant concentration exceeds its corresponding risk threshold, perform geographical marking on the screened grid areas, and generate a high-risk area distribution map to display the specific location and range of each high-risk area. Associate the high-risk area marking information with the basic data set and the migration path prediction data to form a comprehensive information table of the first high-risk area.

[0108] Among them, the setting of the risk threshold corresponding to the pollutant concentration refers to the national environmental standards or the environmental sensitivity requirements of the target area.

[0109] Quantitatively analyze the connectivity characteristics of soil microfractures and pollution migration behavior through the dynamic entropy model, and combine the analysis of the retention and release behavior of pollutants in soil microfractures to identify the second high-risk area, specifically including:

[0110] Construct a microfracture connectivity network model based on the soil microfracture structure data, and extract the connection strength and path distribution characteristics of the nodes in the microfracture connectivity network:

[0111] Collection of soil microfracture structure data: Obtain the three-dimensional structure data of soil microfractures of the soil samples in the target area through high-resolution CT scanning technology. The data includes the width, length, shape and distribution of the fractures.

[0112] In the connectivity network model, define the intersection points of soil microfractures as nodes and the fracture channels as edges. The attributes of nodes and edges include their geometric and physical properties, such as channel width, length and flow velocity parameters.

[0113] Use graph theory algorithms to generate a complete microfracture connectivity network model, and calculate the connection strength of nodes (defined as the number of edges around the node) and the path distribution characteristics (defined as the number of shortest paths from the node to other nodes). These parameters are used for subsequent dynamic entropy analysis.

[0114] Adopt a dynamic entropy model to quantify the connectivity change characteristics of the microfracture connectivity network and obtain the dynamic distribution of network connectivity at different time periods:

[0115] The dynamic entropy model is used to describe the change of network connectivity in the time dimension, and the formula is as follows: Among them, H(t) represents the dynamic entropy value of the microfracture connectivity network at time t; n is the total number of nodes in the microfracture connectivity network; p i (t) represents the connectivity probability of the i-th node at time t, which is calculated by the ratio of its connection strength to the total connection strength of the network.

[0116] Based on the formula of the dynamic entropy model, calculate the dynamic entropy value of the microfracture connectivity network in the target area hour by hour, and generate a time series (the dynamic distribution of network connectivity at different time periods), which reflects the dynamic change characteristics of network connectivity. The time period with a higher dynamic entropy value corresponds to a more complex connectivity state, which may lead to rapid pollutant migration.

[0117] Among them, p i (t) is determined by calculating the ratio of the connection strength of this node to the total connection strength of all nodes in the network. Specifically, first count the connection strength of each node at time t, that is, the number of channels directly connected to this node, and consider the weight of the channels (such as the comprehensive characteristics of channel width or flow rate). Then, accumulate the connection strengths of all nodes at time t to obtain the total connection strength of the network. Finally, by dividing the connection strength of this node by the total connection strength, the connectivity probability of this node in the network can be obtained.

[0118] Analyze the retention and release behavior of pollutants in soil microfractures, including pollutant adsorption frequency, desorption frequency and their kinetic characteristics, and generate pollutant dynamic retention distribution data:

[0119] On the basis of the connectivity network analysis, further conduct dynamic analysis on the retention and release behavior of pollutants in microfractures, specifically including:

[0120] Based on experimental determination of the adsorption kinetic parameters of pollutants on the surface of microfractures in soil, including adsorption rate constant and maximum adsorption capacity. The adsorption kinetic formula is as follows: Among them, q t represents the adsorption amount at time t; Q m represents the maximum adsorption capacity, with the unit of milligram per gram; ka represents the adsorption rate constant, with the unit of per second.

[0121] The rate of pollutant release from the fracture surface is described by the desorption kinetics formula as follows: where, C d (t) represents the desorption concentration at time t; C 0 represents the initial desorption concentration, with the unit of milligram per liter; k d represents the desorption rate constant, with the unit of per second.

[0122] By fitting the adsorption and desorption models with experimental data, the dynamic retention distribution data of pollutants in microfractures in the target area are generated for subsequent analysis.

[0123] Perform correlation analysis on the dynamic distribution of network connectivity and the dynamic retention distribution data of pollutants in different time periods to identify the overlapping parts of high connectivity areas and high retention concentration areas:

[0124] Synchronize the dynamic distribution of network connectivity and the dynamic retention distribution data of pollutants in different time periods to ensure that the two are correlated based on the same time node. For example, extract the dynamic entropy values of all nodes in the network and the retention concentration values of the corresponding nodes at each time point.

[0125] Based on the spatial dimension, perform geographical matching on the dynamic distribution of network connectivity and the distribution data of pollutant retention concentration. For each grid area, calculate whether its connectivity value exceeds the set threshold, and combine the distribution data of retention concentration to identify the overlapping areas that simultaneously meet high connectivity and high retention concentration.

[0126] Assign risk weights to the identified overlapping areas. The weights are calculated based on the comprehensive indicators of connectivity values and retention concentrations, and priority is given to areas with stronger connectivity and higher pollutant retention concentrations.

[0127] Finally, generate a comprehensive risk distribution map of the target area, which marks the spatial range of the overlapping areas and their comprehensive risk weights for the next step of identifying the second highest risk area.

[0128] Mark the area with the highest pollution risk in the soil according to the identification results and identify it as the second highest risk area:

[0129] For each overlapping area, calculate the risk value based on the comprehensive indicators of its connectivity value and retention concentration value. The grading standard of the risk value can refer to the environmental sensitivity requirements of the target area. For example, an area where both high connectivity and high retention concentration exceed the threshold is defined as a high risk area.

[0130] According to the comprehensive risk value, the target area is divided into multiple risk levels (such as high risk, medium risk, low risk). After classification, the areas with the top-ranked comprehensive risk values that exceed the high-risk threshold are marked as the second-high-risk areas.

[0131] Generate a geographic information file marked with the second-high-risk areas, which contains information such as the specific location, scope, connectivity characteristics, and retention concentration characteristics of each high-risk area; at the same time, output a statistical table, recording in detail the area, pollutant types, and related risk data of the second-high-risk areas.

[0132] Perform a union operation on the first-high-risk areas and the second-high-risk areas to generate comprehensive high-risk areas, specifically including:

[0133] Import the geographic marker data of the first-high-risk areas and the second-high-risk areas. The geographic marker data includes regional location coordinates, boundary scope, and pollutant concentration information:

[0134] Regional location coordinates: The longitude and latitude coordinate data of each high-risk area, accurately marking the specific location range of the area within the target area; Boundary scope: A polygon boundary file based on the spatial vector data format, describing the shape and spatial coverage of the area; Pollutant concentration information: Record the types and concentration values of heavy metal pollutants in the high-risk areas, such as the concentrations of heavy metals such as lead and cadmium, expressed in milligrams per kilogram.

[0135] The import process is implemented through geographic information system tools to ensure the integrity and consistency of the imported data, and establish a data structure to support subsequent analysis operations.

[0136] Based on geographic information system tools, perform a spatial overlay of the geographic marker data of the first-high-risk areas and the second-high-risk areas to identify the overlapping areas and adjacent areas:

[0137] According to the imported geographic marker data, identify the spatial overlapping part between the two high-risk areas. The overlapping area is defined as the spatial area with overlapping boundaries between the two, and the spatial coordinates of the overlapping part are recorded in a newly generated polygon file.

[0138] On the basis of the overlapping analysis, further identify the adjacent parts of the two high-risk areas. The adjacent area refers to the area range where the boundary distance between the two high-risk areas is less than the set threshold, and the value of the threshold is based on the regional environmental characteristics (such as 10 meters or less).

[0139] Output the data results of the overlapping areas and adjacent areas in vector data format for subsequent merger analysis.

[0140] Perform boundary expansion and merging on the overlapping and adjacent regions in the spatial superposition result to generate a single boundary file for the comprehensive high-risk region:

[0141] Expand the boundaries of the overlapping and adjacent regions according to the set spatial buffer rules. The radius of the buffer is set to 10 meters to ensure that the adjacent relationships between regions can be connected through the buffer. The expanded region boundaries are represented by newly generated polygon files.

[0142] Merge the boundaries of the expanded overlapping and adjacent regions into a single comprehensive region. The merging process is based on the polygon merging algorithm of the geographic information system to achieve the merging of overlapping parts and the integration of adjacent regions, generating a new boundary file for the comprehensive high-risk region.

[0143] Integrate all attribute information within the merged region, including pollutant types, concentration values, and spatial distribution characteristics, and merge all relevant attributes into the attribute table of the comprehensive high-risk region to ensure data integrity and consistency.

[0144] Generate a single boundary file for the comprehensive high-risk region based on the results of boundary expansion and merging.

[0145] Export the boundary file of the comprehensive high-risk region in a standard format supported by the geographic information system (such as.shp file or.geojson file). The file records the spatial extent, boundary shape, and location coordinates of the comprehensive region.

[0146] Generate a statistical data file for the comprehensive high-risk region, including information such as the total area of the region, types of pollutants, and maximum concentration values. The statistical data file is stored in tabular form and corresponds one-to-one with the boundary file.

[0147] Use the multi-dimensional data fusion algorithm to comprehensively analyze the heavy metal concentration data and land use changes at different times for the comprehensive high-risk region to generate a cumulative risk distribution, specifically including:

[0148] Extract the heavy metal concentration data within the comprehensive high-risk region and organize the concentration change information of each pollutant at different times in a time series:

[0149] Import the heavy metal monitoring data within the comprehensive high-risk region. The monitoring data is from the results of multi-time sampling, including the concentration values of heavy metal pollutants such as lead, cadmium, and mercury, with the unit of milligrams per kilogram.

[0150] According to the monitoring time points, organize the concentration values of each heavy metal pollutant in chronological order to form a concentration change sequence at different times. For example, associate the concentration value of a certain heavy metal pollutant at each monitoring point with the corresponding time information and store it in a two-dimensional table form.

[0151] Match the concentration data with the geographical location data of the comprehensive high-risk area to ensure that each concentration value corresponds to its specific spatial location, and generate a comprehensive data table containing time, space, and concentration information.

[0152] Obtain the land use change data of the comprehensive high-risk area and mark the spatial distribution characteristics of land use types in each period:

[0153] Import the remote sensing images and geographical information data of each period within the comprehensive high-risk area. These data are obtained through remote sensing monitoring or drone photography and record the distribution of land use types such as farmland, construction areas, and wasteland.

[0154] Use the supervised classification method to process the remote sensing images. Combine the existing land use classification standards (such as farmland, forest land, construction areas, wasteland, etc.) to classify and mark the image data, and generate a land use classification map.

[0155] According to the remote sensing data of different periods, mark the change characteristics of land use types within the comprehensive high-risk area. For example, for the areas where farmland is converted into construction areas or wasteland, record their time and space ranges.

[0156] Align the marked land use change data with the spatial coordinate system of the comprehensive high-risk area to form a comprehensive data table containing time, space, and land use types.

[0157] Use the multi-dimensional data fusion algorithm to jointly analyze the heavy metal concentration data and land use change data, and construct a dynamic risk calculation model:

[0158] Format the heavy metal concentration data and land use change data according to the time and space dimensions to ensure the consistency of the two types of data in time and space.

[0159] Adopt a weighted fusion model to fuse and calculate the heavy metal concentration data and land use change data. The expression of the model is as follows: R(x,y,t) = W c ·C(x,y,t) + W l ·L(x,y,t); where, R(x, y, t) represents the dynamic risk value at the coordinate (x, y) at time t; C(x, y, t) represents the concentration value of heavy metal pollutants; L(x, y, t) represents the land use change factor at the coordinate (x, y) at time t, which is used to quantify the influence degree of the land use type at this location on the pollution accumulation risk; W c and W l respectively represent the weight coefficients of the heavy metal concentration data and land use change data, and the value range is from 0 to 1, and the sum of the weight coefficients is 1.

[0160] Calculate the dynamic risk value of each spatio-temporal point within the target area through a multi-dimensional data fusion algorithm and generate risk distribution data.

[0161] Calculate the pollution accumulation characteristics of different time periods in the comprehensive high-risk area according to the dynamic risk calculation model and generate cumulative risk distribution data:

[0162] Accumulate the dynamic risk values according to the time series, and the accumulation formula is: Among them, R cum (x, y) represents the cumulative risk value at the coordinate (x, y), and Z represents the total time period of the time series.

[0163] Output the calculation result of the cumulative risk value in the form of a raster map to show the cumulative risk magnitude of each location within the comprehensive high-risk area.

[0164] Generate a cumulative risk distribution map of the comprehensive high-risk area, mark the cumulative risk value of each grid cell, and overlay it with the data layer of the geographic information system to form an intuitive risk distribution map.

[0165] Compare the cumulative risk distribution with the enterprise land use plan, evaluate the potential environmental liability in land merger and lease, and generate a risk assessment report, specifically including:

[0166] Import the cumulative risk distribution data of the comprehensive high-risk area and the enterprise land use plan data. The land use plan data includes the planned area scope and land use:

[0167] Import the generated cumulative risk distribution map of the comprehensive high-risk area. This data is stored in a spatial raster format and includes the cumulative risk value of each grid cell and its corresponding geographic coordinate information;

[0168] Import the enterprise-provided land use plan data. This data is stored in a polygon vector file format and includes the spatial scope of the planned area, land use classification (such as farmland, construction land, forest land, etc.), and the relevant land use time plan.

[0169] When importing the data, perform projection conversion using a unified coordinate system to ensure that the spatial scope and geographic reference system of the two types of data are consistent, providing a basis for subsequent comparative analysis.

[0170] Based on the geographic information system tool, conduct a spatial comparison between the cumulative risk distribution data and the enterprise land use plan data to identify the risk overlapping part between the cumulative high-risk area and the planned area:

[0171] Through the overlay analysis function, spatially overlay the cumulative risk distribution map with the polygon file of the land use plan to generate a data layer of the overlapping area; in the spatial overlay result, identify the overlapping part of the grid cells with a cumulative risk value greater than the set threshold and the planned area, and mark it as the risk overlapping part; add attribute fields to each risk overlapping part, including its geographical location, cumulative risk value, and the corresponding land use classification, providing input data for subsequent risk assessment.

[0172] Among them, the steps to obtain the cumulative high-risk area are as follows: Based on the cumulative risk distribution data generated by the multi-dimensional data fusion algorithm, extract the cumulative risk value of each grid cell according to the spatial location. According to the environmental assessment criteria or specific application requirements, set the threshold of the cumulative risk value. For example, define the area with a cumulative risk value exceeding a certain set value as the high-risk area. Screen the grid cells in the cumulative risk distribution data, extract the grid cells with a cumulative risk value exceeding the threshold, and mark them as high-risk areas.

[0173] Evaluate the pollution risk level of the risk overlapping parts, and record the types of pollutants, concentration values, and area ranges of each risk overlapping part:

[0174] According to the attribute fields in the cumulative risk distribution map, extract the types and concentration values of the main pollutants in each overlapping part, and calculate the average concentration value of each pollutant in the overlapping area; combine the concentration value of the pollutant with the land use classification, and use the preset risk level standard (such as high risk, medium risk, low risk) to classify the risk of each overlapping area; calculate the area of each risk overlapping part through the geographic information system tool, record the area data in the attribute table, and form a complete risk assessment data table.

[0175] Comprehensively evaluate the suitability of the environmental risk of the risk overlapping parts and the land use, and generate an environmental liability analysis table:

[0176] According to the functional requirements of different land uses (such as the sensitivity of farmland to soil pollution and the tolerance of construction land to soil pollution), set the suitability rules for environmental risk and land use; conduct a suitability analysis of the environmental risk level and land use of each risk overlapping part, mark the unsuitable land use as the environmental high-risk area, and record the specific reasons; organize the analysis results into an environmental liability analysis table, recording the types of pollutants, concentration values, area ranges, risk levels, and suitability assessment results of each overlapping part.

[0177] According to the environmental liability analysis table, generate a risk assessment report containing detailed data of high-risk areas to support the enterprise's land use decision-making:

[0178] Organize all the data of the risk assessment in the form of chapters, including the spatial comparison results of the cumulative risk distribution and land use planning, the pollution assessment results of the risk overlap part, and the conclusions of the suitability analysis; for the overlapping parts marked as high environmental risks, list in detail their geographical locations, pollutant types, cumulative risk values, land use classifications, and recommended improvement measures; store the risk assessment report in the form of documents and spreadsheets for easy use by enterprises in land merger or lease decisions, and at the same time generate a high-risk area marking file compatible with the geographic information system (such as.shp or.geojson format).

[0179] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters and threshold selections in the formulas are set by those skilled in the art according to the actual situation.

[0180] 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 by wire (such as infrared, wireless, microwave, etc.). 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.

[0181] Those of ordinary skill in the art can realize that the modules and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0182] Those skilled in the art can clearly understand that for the convenience and conciseness 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.

[0183] In 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 merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. 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 couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0184] The modules described as separate components may or may not be physically separated. 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.

[0185] In addition, in each embodiment of the present application, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0186] If the function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present 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 to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0187] As described above, this is only the specific implementation manner of the present application. However, the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

[0188] Finally: The above are only the preferred embodiments of the present invention and are 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 within the protection scope of the present invention.

Claims

1. A risk assessment method based on soil heavy metal pollution pathway analysis, characterized in that: The steps include: Collect heavy metal pollutant data and geographic location information of the target area, mark the pollution and spatial distribution characteristics of each block, and generate a basic data set; A heavy metal migration model was constructed based on the basic data set. The comprehensive characteristics of the regional environment were used as dynamic variables and combined with the heavy metal migration model to predict the migration path and concentration changes of pollutants, and identify the first high-risk area. The second highest risk area was identified by quantitatively analyzing the connectivity characteristics of soil microcracks and the migration behavior of pollution through a dynamic entropy model, combined with the analysis of the retention and release behavior of pollutants in soil microcracks; The first high-risk area and the second high-risk area are combined to generate a comprehensive high-risk area; For comprehensive high-risk areas, a multidimensional data fusion algorithm is used to comprehensively analyze heavy metal concentration data and land use changes at different time periods to generate a cumulative risk distribution; Compare the cumulative risk distribution with the enterprise's land use plan, evaluate the potential environmental liabilities in land merger and leasing, and generate a risk assessment report.

2. A risk assessment method based on soil heavy metal pollution pathway analysis according to claim 1, characterized in that: Collect heavy metal pollutant data and geographic location information of the target area, mark the pollution and spatial distribution characteristics of each block, and generate a basic data set, including: Set up multiple sampling points in the target area and collect heavy metal pollutant data at each sampling point, including heavy metal types, concentration values ​​and spatial distribution information; Use spatial positioning technology to obtain the geographical location information of the sampling points, including the latitude and longitude, elevation and terrain characteristics of the sampling points; The collected heavy metal pollutant data are associated with the geographical location information of the sampling points to form a preliminary data set including pollutant types, concentrations and their distribution information; An interpolation algorithm is used to generate a distribution map of heavy metal pollution concentration in the target area; The distribution map is superimposed with the regional geographic information to form comprehensive data marked with pollution types, concentration distribution and spatial distribution characteristics, and the comprehensive data is organized into a basic data set in a standardized format.

3. A risk assessment method based on soil heavy metal pollution pathway analysis according to claim 1, characterized in that: A heavy metal migration model is constructed based on the basic data set. The comprehensive characteristics of the regional environment are used as dynamic variables and combined with the heavy metal migration model to predict the migration path and concentration changes of pollutants, and identify the first high-risk area, including: Extract pollutant types, concentration values ​​and spatial distribution characteristics of the target area based on the basic data set, and initialize the model input parameters; The climate conditions, geological structure and land use patterns in the comprehensive characteristics of regional environment are taken as dynamic variables and associated with the basic data set; Establish a heavy metal migration model to describe the diffusion, penetration and accumulation characteristics of pollutants in soil through control equations; Combine the calculation results of the heavy metal migration model to generate the migration path and concentration distribution prediction data of pollutants in the target area; According to the area range where the pollutant concentration exceeds the threshold in the concentration distribution prediction data, the first high-risk area in the target area is identified.

4. A risk assessment method based on soil heavy metal pollution pathway analysis according to claim 1, characterized in that: The dynamic entropy model is used to quantify the connectivity characteristics of soil microcracks and the migration behavior of pollution. Combined with the analysis of the retention and release behavior of pollutants in soil microcracks, the second highest risk area is identified, including: A microcrack connectivity network model was constructed based on soil microcrack structure data, and the connection strength and path distribution characteristics of nodes in the microcrack connectivity network were extracted. The dynamic entropy model is used to quantify the connectivity change characteristics of the microfracture network and obtain the dynamic distribution of network connectivity in different time periods. Analyze the retention and release behavior of pollutants in soil microcracks, including pollutant adsorption frequency, desorption frequency and its dynamic characteristics, and generate dynamic retention and distribution data of pollutants; Correlate the dynamic distribution of network connectivity in different time periods with the dynamic retention distribution data of pollutants to identify the overlap between high connectivity areas and high retention concentration areas; Based on the identification results, the area with the highest risk of soil contamination is marked and identified as the second highest risk area.

5. A risk assessment method based on soil heavy metal pollution pathway analysis according to claim 4, characterized in that: The dynamic entropy model is used to quantify the connectivity change characteristics of the microfracture network and obtain the dynamic distribution of network connectivity in different time periods. Specifically: The dynamic entropy model formula is as follows: Where H(t) represents the dynamic entropy value of the microcrack connectivity network at time t, n is the total number of nodes in the microcrack connectivity network, and p i (t) represents the connectivity probability of the i-th node at time t; The dynamic entropy value of the microcrack connectivity network in the target area is calculated hourly based on the formula of the dynamic entropy model, and a time series is generated to reflect the dynamic change characteristics of the network connectivity.

6. A risk assessment method based on soil heavy metal pollution pathway analysis according to claim 1, characterized in that: The first high-risk area and the second high-risk area are combined to generate a comprehensive high-risk area, including: Importing geographical tag data of the first high-risk area and geographical tag data of the second high-risk area, the geographical tag data including regional location coordinates, boundary range and pollutant concentration information; Spatially overlay the geo-tagged data of the first high-risk area and the second high-risk area based on GIS tools to identify overlapping areas and adjacent areas; The overlapping areas and adjacent areas in the spatial overlay results are expanded and merged to generate a single boundary file for the comprehensive high-risk areas.

7. A risk assessment method based on soil heavy metal pollution pathway analysis according to claim 1, characterized in that: For comprehensive high-risk areas, a multi-dimensional data fusion algorithm is used to comprehensively analyze heavy metal concentration data and land use changes in different periods to generate cumulative risk distribution, including: Extract the heavy metal concentration data in the comprehensive high-risk area and organize the concentration change information of each pollutant in different periods of time according to time series; Obtain land use change data for comprehensive high-risk areas and mark the spatial distribution characteristics of land use types in each period; Use multi-dimensional data fusion algorithm to jointly analyze heavy metal concentration data and land use change data to build a dynamic risk calculation model; Based on the dynamic risk calculation model, the pollution accumulation characteristics of comprehensive high-risk areas in different periods are calculated to generate cumulative risk distribution data.

8. A risk assessment method based on soil heavy metal pollution pathway analysis according to claim 1, characterized in that: Compare the cumulative risk distribution with the enterprise's land use plan, evaluate the potential environmental liabilities in land merger and leasing, and generate a risk assessment report, including: Import the cumulative risk distribution data of comprehensive high-risk areas and the enterprise land use planning data, which includes the planning area scope and land use; Using geographic information system tools, spatially compare the cumulative risk distribution data with the enterprise land use planning data to identify the overlapping risk areas between the cumulative high-risk areas and the planned areas; Conduct pollution risk level assessment on the overlapping risk areas, and record the pollutant types, concentration values ​​and area ranges of each overlapping risk area; Comprehensively assess the environmental risks and suitability of land use in the overlapping risk areas and generate an environmental responsibility analysis table; Based on the environmental responsibility analysis table, a risk assessment report containing detailed data on high-risk areas is generated to support corporate land use decisions.

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