A Method for Optimizing the Determination of Soil Remediation Volume for Contaminated Sites Based on Multi-Source Monitoring Data Fusion

Abstract

This invention relates to the field of soil remediation technology, specifically to a method for optimizing and determining the amount of soil remediation for contaminated sites based on multi-source monitoring data fusion. The method includes the following steps: acquiring borehole locations and in-situ data; extracting pollution boundaries; identifying low-lying areas and overlapping pollution zones; calculating cumulative pollution levels to classify remediation types; generating remediation load values ​​and extracting remediation ranking indicators; and determining remediation priorities. In this invention, by collecting in-situ concentration and apparent resistivity data, a unified depth-stitched structure is constructed to achieve spatial fusion of pollution information, improving identification accuracy. Abrupt change points are extracted and a three-dimensional mesh is generated, enhancing the integrity and adaptability of boundary reconstruction. Low-lying catchment areas are identified by combining elevation and aspect data to determine migration trends and accurately capture high-risk areas. Cumulative pollution levels are calculated based on pollution concentration and volume to clarify different pollution levels. Ranking indicators are extracted by integrating topographic and pollution load characteristics, optimizing resource allocation efficiency, and promoting the transformation of remediation towards precision governance.

Description

Technical Field

[0001] This invention relates to the field of soil remediation technology, and in particular to a method for optimizing and determining the amount of soil remediation for contaminated sites based on the fusion of multi-source monitoring data. Background Technology

[0002] Soil remediation technology involves the treatment and restoration of contaminated soil to reduce pollutant concentrations and restore soil ecological functions. Its core aspects include pollution identification, risk assessment, remediation target setting, remediation technology selection and implementation, and post-remediation evaluation. Commonly used soil remediation methods include in-situ remediation, ex-situ remediation, chemical remediation, bioremediation, and physical remediation. During implementation, a systematic remediation process must be formed by combining site investigation, monitoring data analysis, and remediation plan development. Traditional methods for determining soil remediation volume rely on spatial distribution data of pollutants obtained through a single monitoring method. This involves setting up a limited number of monitoring points to extract pollutant concentrations, combining regional geological information to estimate the pollution range and degree, and then estimating the volume of soil requiring remediation and the total amount of pollutants. This method depends on drilling sampling analysis, geochemical analysis, or geophysical exploration results as the basis for estimation, lacking the fusion of multi-source information and spatial precision control, making it difficult to achieve dynamic and accurate determination of remediation volume.

[0003] Traditional methods rely on a limited number of monitoring points to extract pollutant concentrations and extrapolate based on regional geological information. In sites with uneven spatial distribution of pollutants or significant topographic relief, this approach is prone to inaccurate estimations of the pollution extent. The lack of in-situ detection and deep fusion of multi-source data makes it difficult to identify the true morphology and changing trends of pollution boundaries, resulting in coarse delineation of remediation areas. The lack of systematic consideration of site micro-topographic features ignores the crucial role of low-lying areas in pollutant migration, potentially leading to inaccurate coverage of key pollution accumulation areas and affecting remediation effectiveness. For example, in typical hilly sites, pollutants may converge downwards to local depressions, but traditional estimation methods often fail to capture this runoff effect due to limited monitoring point distribution, resulting in underestimation of pollution levels. The lack of quantitative classification of pollution loads leads to a lack of priority guidance for resource allocation, potentially causing waste of remediation resources in low-pollution areas, prolonged remediation cycles, and reduced remediation benefits. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a method for optimizing and determining the amount of soil remediation for contaminated sites based on multi-source monitoring data fusion, comprising the following steps: To achieve the above objectives, the present invention adopts the following technical solution: a method for optimizing and determining the amount of soil remediation for contaminated sites based on multi-source monitoring data fusion, comprising the following steps: S1: Collect in-situ detection concentration data and apparent resistivity data at the borehole locations of the contaminated site, arrange them at uniform depth intervals, and combine the borehole coordinates and depths to construct a splicing structure to generate a multi-source profile splicing data table. S2: Based on the multi-source profile stitching data table, extract the locations of abrupt changes in pollution concentration and apparent resistivity in the vertical direction, extract pollution boundary points, interpolate boundary coordinates, construct a continuous spatial grid, and generate a three-dimensional pollution boundary grid dataset. S3: Based on the three-dimensional pollution boundary grid dataset, identify the location of the upper boundary of the grid, combine the elevation and slope aspect data to locate the low-lying area, and mark the overlap relationship with the pollution boundary grid to generate a set of pollution migration overlap blocks. S4: Based on the set of overlapping pollution migration blocks, extract the volume and pollution concentration data of the blocks, calculate the cumulative amount of pollutants, classify the types of remediation blocks, and generate a table of pollution load values ​​for remediation blocks. S5: Based on the pollution load value table of the remediation block, identify whether the block is located in a low-lying catchment area, assign a catchment area location identifier value, and extract ranking indicators in combination with the pollution load value to generate a remediation priority ranking.

[0005] As a further embodiment of the present invention, the multi-source profile stitching data table includes a borehole location information index, a unified depth sequence, a concentration sequence field, an apparent resistivity sequence field, and a coordinate reference system definition; the three-dimensional pollution boundary grid dataset includes a pollution boundary surface model, a grid coordinate set, and spatial interpolation parameters; the pollution migration overlapping block set includes a catchment low-lying area, spatial overlap identification results, and an overlapping block list; the remediation block pollution load value table includes block volume distribution, pollution accumulation value, and remediation category level; and the remediation priority ranking includes a catchment area location identifier value, a comprehensive priority index, and a ranking.

[0006] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: After obtaining the borehole layout information, collect in-situ detection concentration data and apparent resistivity data at the borehole location, record the borehole coordinates, depth values, concentration values ​​and apparent resistivity values ​​point by point, and organize them according to the borehole location to generate a borehole monitoring dataset. S102: Based on the borehole monitoring dataset, the borehole coordinates and depth values ​​are used as a reference. The concentration values ​​and apparent resistivity values ​​are arranged according to a uniform depth interval, and the data in the same depth interval are layered accordingly to obtain a layered numerical table. S103: Based on the layered numerical table, call the borehole coordinates, depth values, concentration values ​​and apparent resistivity values, stitch together the data at the same depth and combine them in vertical order to generate a multi-source profile stitching data table.

[0007] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Based on the multi-source profile stitching data table, read the numerical sequence of contamination concentration and apparent resistivity in the depth direction in each borehole, identify the changing trend of adjacent intervals, extract the locations where abrupt changes occur and mark them as contamination boundary points, and obtain the contamination boundary point location set; S202: Call the pollution boundary point set, extract the three-dimensional coordinate information of the boundary points, perform interpolation processing according to the spatial interval, transform the discrete boundary points into a spatial continuous point sequence, and generate a boundary interpolation coordinate set; S203: Based on the boundary interpolation coordinate set, combine them in spatial distribution order, establish a grid connection for the three-dimensional coordinates of the interpolation points, form a continuous spatial grid structure, and establish a three-dimensional pollution boundary grid dataset.

[0008] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Based on the three-dimensional pollution boundary grid dataset, extract the upper boundary position of the grid in the vertical direction, identify the depth coordinates and lock the top boundary point to obtain the pollution upper boundary coordinate set; S302: Call the pollution upper boundary coordinate set, combine the site elevation and slope aspect point data, compare the elevation value and slope aspect direction, mark the low-lying areas with water catchment characteristics, and generate a set of water catchment low-lying blocks; S303: Based on the set of low-lying catchment areas, determine the spatial overlap with the coordinate set of the upper boundary of pollution, identify and integrate the overlapping parts, and establish a set of overlapping pollution migration areas.

[0009] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Based on the set of overlapping pollution migration blocks, read the block volume information and detect the corresponding pollution concentration data, match the volume value and concentration value of each block one by one, and generate a pollution volume concentration comparison set. S402: Call the pollution volume concentration reference set, perform cumulative calculation on the block volume value and concentration value to obtain the pollutant cumulative amount data set, and then establish a distribution based on the cumulative amount interval to obtain the pollution cumulative amount interval set; S403: Based on the set of pollution accumulation intervals, divide the remediation area block types according to the interval differences, and organize the division results with the accumulation data to establish a pollution load value table for the remediation blocks.

[0010] As a further aspect of the present invention, when the block volume value and concentration value are accumulated, the product of the block volume and the pollution concentration is used as the pollution amount, and in the process of establishing the cumulative amount distribution, the difference in the cumulative amount between adjacent intervals is set as a threshold basis to limit the division range of the pollution cumulative amount interval.

[0011] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Based on the pollution load value table of the remediation block, detect the spatial location parameters and topographic elevation values ​​of the block, determine whether it is located in a micro-topographic catchment low-lying area, assign catchment location identifier values ​​to blocks that meet the low-lying determination conditions, and generate a catchment identifier set for the blocks. S502: Call the block catchment identifier set, and perform one-by-one association processing based on the identifier value corresponding to the block and the pollution load value in the pollution load value table of the remediation block, and combine the identifier value and the pollution load value into a corresponding record to obtain the block pollution catchment combination value; S503: Based on the combined polluted water catchment values ​​of the blocks, extract ranking indicators that reflect the priority of remediation resource input, rank the blocks according to the indicator values, and establish a remediation priority ranking.

[0012] As a further aspect of the present invention, the in-situ detection concentration data is the pollutant concentration value directly obtained by the soil pollution monitoring equipment at the borehole or surface location. It originates from the in-situ detection device of the soil gas detector, ion-selective electrode or ultraviolet absorption probe, and reflects the actual content of pollutants in the soil at the target depth location, expressed in mg / kg or ppm. The apparent resistivity data refers to the ability of strata at different depths to conduct current in geophysical surveys using the resistivity method. It is derived from resistivity imagers or electrode deployment systems and reflects the electrical characteristics of the subsurface medium. The unit is Ω·m. The splicing structure refers to the combination and matching of borehole coordinates, depth values, in-situ detection concentration data and apparent resistivity data based on the same depth range. The pollution boundary point refers to the location in the vertical profile where the pollution concentration or apparent resistivity value changes abruptly, indicating the upper and lower boundary positions of the pollution layer. The identification criteria are significant jumps or abnormal rates of change in values ​​in continuous depth data. The spatial grid refers to a three-dimensional raster with uniform size and arrangement rules, generated by spatial interpolation based on the three-dimensional coordinates of pollution boundary points.

[0013] As a further aspect of the present invention, the elevation and slope point data refer to the elevation values ​​and slope direction information of the site surface collected by topographic surveying, which are derived from digital elevation models (DEM), UAV lidar scanning, or ground mapping data. The low-lying area refers to the area in the site where the terrain is lower, the catchment area or depression area calculated based on elevation data, and has the characteristics of the endpoint of pollutant deposition or migration. The pollution migration overlap block set refers to the closed area formed by the overlap of the pollution boundary grid and the low-lying area in space, representing the location where pollutants stay or accumulate on potential migration paths. The pollution load value refers to the product of the pollutant concentration and the volume of the area within a specified spatial block, expressed in mg. The catchment area location identifier value refers to the classification and identification value used to mark whether a block of area is located in a low-lying catchment area. It is derived from the elevation data analysis results and is labeled using a Boolean identifier. The remediation priority ranking refers to the ranking index obtained by sorting the different regional blocks according to the correlation between pollution load value and catchment area location identifier value.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by collecting in-situ detection concentration and apparent resistivity data, a unified depth stitching structure is constructed to achieve spatial fusion of pollution information, improve pollution identification accuracy, extract concentration and resistivity abrupt change points and generate a three-dimensional mesh, enhance the integrity and dynamic adaptability of pollution boundary reconstruction, identify low-lying catchment areas by combining elevation and slope aspect data, determine pollution migration trends, accurately capture high-risk blocks, calculate the cumulative amount based on pollution concentration and volume, identify areas with different pollution levels, and extract ranking indicators by integrating topographic and pollution load characteristics to optimize resource allocation efficiency and promote the transformation of remediation work towards precision governance. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] Please see Figure 1 This invention provides a method for optimizing and determining the amount of soil remediation for contaminated sites based on the fusion of multi-source monitoring data, comprising the following steps: S1: Obtain borehole layout information for contaminated site profiles, collect in-situ detection concentration data and apparent resistivity data at borehole locations, arrange the data according to a uniform depth interval, call borehole coordinates, depth values, concentration values ​​and apparent resistivity values ​​to construct a splicing structure, and generate a multi-source profile splicing data table. In-situ detection concentration data are pollutant concentration values ​​obtained directly from boreholes or surface points by soil pollution monitoring equipment. They are derived from in-situ detection devices such as soil gas detectors, ion-selective electrodes, or ultraviolet absorption probes, and reflect the actual content of pollutants in the soil at the target depth location, expressed in mg / kg or ppm. Apparent resistivity data refers to the ability of strata at different depths to conduct current in geophysical surveys using the resistivity method. It is derived from resistivity imagers or electrode deployment systems, reflects the electrical characteristics of the subsurface medium, and is measured in Ω·m. It is used to help determine the distribution of pollutants and the differences between layers. The splicing structure refers to the combination and matching of borehole coordinates, depth values, in-situ detected concentration data and apparent resistivity data according to the same depth range to form a composite data table for three-dimensional analysis, which is used to describe the correspondence between multi-source monitoring data under pollution profile. S2: Based on the multi-source profile stitching data table, read the vertical trend of pollution concentration and apparent resistivity in each borehole, extract the locations of abrupt changes as pollution boundary points, interpolate the three-dimensional coordinates of all boundary points, organize them into a continuous spatial grid structure, and generate a three-dimensional pollution boundary grid dataset. The pollution boundary point refers to the location in the vertical profile where the pollution concentration or apparent resistivity value changes abruptly, indicating the upper and lower boundary positions of the pollution layer. The identification criteria are significant jumps or abnormal rates of change in values ​​in continuous depth data. The spatial grid structure refers to a three-dimensional grid with uniform size and arrangement rules generated by spatial interpolation based on the three-dimensional coordinates of pollution boundary points, used to divide the spatial range of pollution areas; S3: Based on the 3D pollution boundary grid dataset, obtain the upper boundary position of the pollution boundary grid in the vertical direction. Combine the site elevation and slope aspect data to mark the low-lying areas with water catchment characteristics, determine whether there is spatial overlap between the low-lying areas and the pollution boundary grid, identify the overlapping areas, and generate a set of pollution migration overlapping blocks. Elevation and aspect point data refers to the elevation values ​​and slope direction information of the site surface collected through topographic surveying methods. These data are derived from digital elevation models (DEMs), UAV lidar scanning, or ground mapping data and are used to describe micro-topographic changes and water flow path trends. Low-lying areas refer to areas in the site where the terrain is lower than the surrounding area. They are catchment areas or depressions calculated based on elevation data and have the characteristics of pollutant deposition or migration endpoints. The set of overlapping pollution migration blocks refers to the closed area formed by the overlap between the pollution boundary grid and the low-lying area in space. It represents the location where pollutants stay or accumulate on potential migration paths and is used to guide the priority area delineation in remediation strategies. S4: Based on the set of overlapping pollution migration blocks, read the volume information and pollution concentration data of each block, perform pollutant accumulation calculation, classify the remediation block types according to the pollution accumulation level, and generate a pollution load value table for remediation blocks; The pollution load value refers to the product of pollutant concentration and area volume within a specified spatial block. It is used to measure the degree of pollution at a location, and the unit is mg. It is a basic indicator for estimating remediation volume. S5: Based on the pollution load value table of the remediation blocks, determine whether each block is located in a micro-topographic low-lying catchment area, assign a corresponding catchment area identification value to the block, and perform correlation processing with the identification value and pollution load value to extract a ranking index that reflects the priority of remediation resource investment and generate a remediation priority ranking. The catchment area location identifier value refers to the classification and identification value used to mark whether a block is located in a low-lying catchment area. It is derived from the elevation data analysis results and is labeled with a Boolean identifier to help determine the priority relationship of the restoration tasks. The remediation priority ranking refers to the ranking index obtained by sorting the different regional blocks according to the correlation between pollution load value and catchment area location identifier value, which is used to reflect the priority of remediation areas in resource allocation or governance process.

[0023] The multi-source profile stitching data table includes borehole location information index, unified depth sequence, concentration sequence field, apparent resistivity sequence field, and coordinate reference system definition. The three-dimensional pollution boundary grid dataset includes pollution boundary surface model, grid coordinate set, and spatial interpolation parameters. The pollution migration overlapping block set includes catchment low-lying area, spatial overlap identification results, and overlapping block list. The remediation block pollution load value table includes block volume distribution, pollution accumulation value, and remediation category level. The remediation priority ranking includes catchment area location identifier value, comprehensive priority index, and ranking.

[0024] Please see Figure 2 The specific steps of S1 are as follows: S101: After obtaining the borehole layout information, collect in-situ detection concentration data and apparent resistivity data at the borehole location, record the borehole coordinates, depth values, concentration values ​​and apparent resistivity values ​​point by point, and organize them according to the borehole location to generate a borehole monitoring dataset. After obtaining the borehole layout information, it is necessary to specify the borehole number, coordinate location, and design depth in the exploration area. For example, 10 boreholes are laid out in a 200m × 200m area. Each borehole is recorded with three elements: east coordinate X, north coordinate Y, and design depth H. Subsequently, in-situ detection concentration data and apparent resistivity data are collected at the borehole locations. Gas detectors and resistivity measuring devices are used to obtain pollutant concentration and apparent resistivity values, respectively. For example, borehole A is located at coordinates X = 100.5m, Y = 50.3m. At a depth of 5m, the benzene concentration is measured to be 18mg / L, and the apparent resistivity is 25Ω·m. At a depth of 10m, the benzene concentration is measured to be 11mg / L, and the apparent resistivity is 25Ω·m. The limit is 30 Ω·m. The measured data needs to be recorded point by point and stored in the form of fields. Each data entry includes the borehole number, coordinates, depth, concentration, and apparent resistivity. The data is organized in order of depth within the same borehole. For example, the dataset for borehole A consists of two sets of values ​​at depths of 5m and 10m. The dataset for borehole B consists of a concentration of 14 mg / L and a resistivity of 27 Ω·m at a depth of 5m, and a concentration of 9 mg / L and a resistivity of 34 Ω·m at a depth of 10m. All data are stored in a uniform unit, with concentration expressed in mg / L and resistivity in Ω·m. If the measured value is lower than the detection limit, it is recorded as 0 and noted as ND. The data from each borehole are organized by location to form a borehole monitoring dataset.

[0025] S102: Based on the borehole monitoring dataset, the borehole coordinates and depth values ​​are used as a reference. The concentration values ​​and apparent resistivity values ​​are arranged according to a uniform depth interval, and the data in the same depth interval are layered accordingly to obtain a layered numerical table. Based on the borehole monitoring dataset, borehole coordinates and depth values ​​need to be used as a benchmark to standardize the data for each borehole depth. For example, all borehole data can be divided into 2-meter intervals. Data from borehole A at 5m is assigned to the 4-6m interval, with a concentration of 18 mg / L and apparent resistivity of 25 Ω·m recorded within this interval. Data from borehole A at 10m is assigned to the 9-11m interval, with a concentration of 11 mg / L and apparent resistivity of 30 Ω·m recorded within this interval. If data is missing from borehole B at a depth of 8m, it can be obtained from existing data at 5m and 10m. Interpolation calculations yielded a concentration of approximately 11 mg / L and a resistivity of approximately 31 Ω·m at a depth of 8 m. All calculated data were grouped into corresponding intervals, and the concentrations and resistivities of boreholes within the same depth interval were then grouped together. For example, in the 4 to 6 m interval, borehole A (concentration 18 mg / L, resistivity 25 Ω·m), borehole B (concentration 14 mg / L, resistivity 27 Ω·m), and borehole C (concentration 12 mg / L, resistivity 22 Ω·m) were grouped into the same row. In the 9 to 11 m interval, the corresponding data were grouped into another row, resulting in a stratified numerical table.

[0026] S103: Based on the layered numerical table, call the borehole coordinates, depth values, concentration values ​​and apparent resistivity values, stitch together the data at the same depth and combine them in vertical order to generate a multi-source profile stitching data table. Based on the hierarchical arrangement of numerical tables, borehole coordinates, depths, concentrations, and apparent resistivity need to be retrieved. Data at the same depth need to be stitched together. For example, in the 4 to 6 m range, borehole A has a concentration of 18 mg / L and a resistivity of 25 Ω·m, borehole B has a concentration of 14 mg / L and a resistivity of 27 Ω·m, and borehole C has a concentration of 12 mg / L and a resistivity of 22 Ω·m. These data need to be combined in coordinate order to form a horizontal data row at that depth. In the 9 to 11 m range, borehole A has a concentration of 11 mg / L and a resistivity of 30 Ω·m, borehole B has a concentration of 9 mg / L and a resistivity of 34 Ω·m, and borehole C has a concentration of 7 mg / L and a resistivity of 28 Ω·m. All depth ranges are arranged sequentially and connected vertically to form a continuous dataset. Each row represents the monitoring value of different boreholes within a certain depth range, and each column represents the concentration and resistivity combination of a borehole, generating a multi-source profile stitched data table.

[0027] Please see Figure 3 The specific steps of S2 are as follows: S201: Based on the multi-source profile stitching data table, read the numerical sequence of contamination concentration and apparent resistivity in the depth direction in each borehole, identify the changing trend of adjacent intervals, extract the locations of abrupt changes and mark them as contamination boundary points, and obtain the contamination boundary point location set; Based on the multi-source profile stitched data table, it is necessary to read the pollution concentration and apparent resistivity sequence of each borehole along the depth direction. First, the monitoring values ​​of each borehole are arranged in order of depth. For example, borehole A records the concentration and resistivity every 2m within the depth range of 0 to 20m. The resulting concentration sequence may be 3mg / L, 5mg / L, 4mg / L, 12mg / L, and 13mg / L, corresponding to resistivities of 40Ω·m, 42Ω·m, 38Ω·m, 25Ω·m, and 24Ω·m. Then, the changing trend of adjacent intervals is judged. If the concentration difference between two adjacent layers is within 2m, the pollutant concentration and resistivity sequence is determined. A concentration within mg / L is considered normal fluctuation. A difference exceeding 5 mg / L is considered a sudden change. Similarly, a resistivity difference between 5 and 10 Ω·m is considered normal, while a difference exceeding 10 Ω·m is considered a sudden change. For example, in borehole A, the concentration increases from 4 mg / L to 12 mg / L between depths of 6m and 8m, and the resistivity decreases from 38 Ω·m to 25 Ω·m. Both changes exceed the threshold, so the 8m position is recorded as a pollution boundary point. This process needs to be repeated in all boreholes, and the resulting set of boundary points can fully reflect the sudden changes in depth direction within each borehole.

[0028] S202: Call the pollution boundary point set, extract the three-dimensional coordinate information of the boundary points, perform interpolation processing according to the spatial interval, transform the discrete boundary points into a spatial continuous point sequence, and generate the boundary interpolation coordinate set; After calling the pollution boundary point set, it is necessary to extract the three-dimensional coordinate information of each boundary point, including the east coordinate, north coordinate and depth value. For example, the boundary point of borehole A is X=100m, Y=50m, Z=8m, and the boundary point of borehole B is X=120m, Y=60m, Z=10m. After organizing these discrete points according to their spatial distribution, interpolation is performed according to the set spatial interval. If the horizontal distance between adjacent points is greater than 10m or the vertical distance is greater than 2m, one or more interpolation points need to be generated. For example, a new point X=110m, Y=55m, Z=9m can be generated in the middle position between A and B to reduce the spatial interval. If the difference is even greater, interpolation points are added at the 1 / 3 and 2 / 3 positions to ensure continuity. All the generated new points and the original boundary points together form the interpolation coordinate sequence. After sorting, the boundary interpolation coordinate set is obtained and used for the subsequent construction of the spatial structure.

[0029] S203: Based on the boundary interpolation coordinate set, combine them in spatial distribution order, establish gridded connections for the three-dimensional coordinates of the interpolation points, form a continuous spatial grid structure, and establish a three-dimensional pollution boundary grid dataset. Based on the boundary interpolation coordinate set, all points need to be combined in spatial order. For example, within the same profile, the boundary points are X=100m, Y=50m, Z=8m, X=110m, Y=55m, Z=9m, and X=120m, Y=60m, Z=10m. These points need to be connected one by one to form continuous line segments, and then connected with the corresponding points in the upper and lower layers to form surface units. Multiple surface units are further combined into volume units. For example, four adjacent points are connected to form prism units. If the point distribution is triangular, triangular pyramid units are generated. When all interpolation points and boundary points are combined in sequence, a regular spatial grid structure is formed, forming a continuous three-dimensional pollution boundary grid dataset to reflect the spatial distribution of the boundary at different depths and coordinates.

[0030] Please see Figure 4 The specific steps of S3 are as follows: S301: Based on the three-dimensional pollution boundary grid dataset, extract the upper boundary position of the grid in the vertical direction, identify the depth coordinates and lock the top boundary point to obtain the pollution upper boundary coordinate set; Based on a 3D pollution boundary grid dataset, it is necessary to extract the upper boundary positions in the vertical direction from each grid point. First, the Z values ​​of all grid points are scanned and compared, and the point with the maximum Z value in the vertical direction for each grid is selected as the candidate top-level boundary point. For example, in a 10×10×10 grid dataset, the Z value range of a certain column is 0m to 20m. Through layer-by-layer comparison, the point at Z=20m in that column is determined to be the upper boundary point. Then, the upper boundary points of all columns need to be uniformly organized into a coordinate set. During this process, it is necessary to... Repeated comparisons are performed to prevent the influence of outliers. For example, if the upper boundary points of adjacent columns are 20m and 15m, and the difference exceeds the set threshold of 5m, it is necessary to determine whether it is a measurement error. If the error is valid, the average value of adjacent columns of 17.5m is used instead. If the difference is reasonable, the original value is retained. The threshold of 5m can be determined based on the site exploration accuracy or the soil settlement range. For example, in a contaminated site, 5m is used as the anomaly judgment threshold. The coordinates of each top boundary point, including X, Y, and Z, are organized into a set to obtain the coordinate set of the contaminated upper boundary.

[0031] S302: Call the pollution upper boundary coordinate set, combine the site elevation and slope aspect point data, compare the elevation value and slope aspect direction, mark the low-lying areas with water catchment characteristics, and generate a set of water catchment low-lying blocks. After retrieving the coordinate set of the pollution upper boundary, it is necessary to compare and mark it with the site elevation and slope aspect data. First, the elevation value of each boundary point needs to be extracted and the difference calculated with the surface elevation data. For example, if the coordinates of boundary point A are X=100m, Y=50m, Z=18m, and the site surface elevation is 20m, then the difference is 2m. Next, the slope aspect data is compared. If the slope aspect angle is within the range of 5° to 15° and the elevation of this point is more than 3m lower than the average elevation of surrounding points, it is marked as a low-lying area. If the elevation of a certain point is 17m and the average elevation of the surrounding area is 21m, the difference is 4m, which meets the criteria for low-lying areas. The threshold of 3m can be determined based on the standard of site topographic relief. 3m is used in plain areas and 5m can be used in hilly areas. All points that meet the criteria are grouped into a low-lying area block set. If multiple low-lying points are less than 20m apart in the horizontal range, they are automatically merged into one block. For example, if the horizontal distance between points A and B is 15m and both meet the low-lying criteria, they are merged into one block, forming a catchment low-lying area block set.

[0032] S303: Based on the set of low-lying catchment blocks and the set of coordinates of the upper boundary of pollution, determine the spatial overlap, identify and integrate the overlapping parts, and establish a set of overlapping pollution migration blocks. Based on the set of low-lying catchment areas and the coordinate set of the upper boundary of pollution, it is necessary to determine the overlap of spatial locations. First, the horizontal range of each low-lying area is compared with the coordinates of the upper boundary point. For example, if the horizontal range of the low-lying area is X=100 to 120m and Y=50 to 70m, while the upper boundary point is X=110m, Y=60m, and Z=18m, then the point is within the horizontal range of the low-lying area and is determined to be an overlapping point. If the same area contains multiple upper boundary points, each point needs to be counted. If the proportion of points exceeds 30%, the entire area is marked as a pollution migration overlapping area. This 30% threshold can be set according to the site risk assessment standard. For example, if there are 10 monitoring points in an area, and 4 of them are located in low-lying areas, then the proportion is 40%, which is greater than 30%. Therefore, the area is identified as a pollution migration overlapping area. If the proportion is insufficient, the points are marked individually instead of the whole area. After all the markings are completed, a set of pollution migration overlapping areas is formed.

[0033] Please see Figure 5 The specific steps of S4 are as follows: S401: Based on the set of overlapping pollution migration blocks, read the block volume information and detect the corresponding pollution concentration data. Match the volume value and concentration value of each block one by one to generate a pollution volume concentration comparison set. Based on a set of overlapping pollution migration zones, it is necessary to read the volume information of each zone and detect the corresponding pollution concentration data. First, a number and boundary parameters are established for each zone in the set. The volume is calculated step-by-step based on the length, width, and height values. For example, if a zone has a boundary length of 20m, a width of 15m, and a height of 10m, its volume is 3000m³. Then, the concentration value of the monitoring point corresponding to that zone is retrieved. For example, if the detected value is 150mg / L, it is recorded as a pair of entries: volume 3000m³ and concentration 150mg / L. This operation is repeated across all zones. The process involves obtaining multiple corresponding entries. During this process, it is necessary to consider the correction of abnormal concentrations. When the concentration value exceeds 500 mg / L, it is marked as abnormal. This threshold can be set with reference to the groundwater pollution standard. For example, if the concentration detected in a certain block is 600 mg / L, while the average concentration of adjacent blocks is 400 mg / L, the difference between the two is 200 mg / L, which is greater than the preset deviation threshold of 100 mg / L. Therefore, the concentration of this block can be corrected by using 500 mg / L as the recorded value. The volume and concentration of all blocks are matched and summarized to form a pollution volume concentration control set.

[0034] S402: Call the pollution volume concentration reference set, perform cumulative calculation on the block volume value and concentration value to obtain the pollutant cumulative amount data set, and then establish the distribution according to the cumulative amount interval to obtain the pollution cumulative amount interval set; After calling the pollution volume concentration reference set, the volume and concentration of each block need to be accumulated. First, the volume and concentration of each block are matched to calculate the cumulative amount of pollutants. For example, if a block has a volume of 3000 m³ and a concentration of 150 mg / L, the accumulated amount can be assigned to the corresponding set. The same calculation is performed on other blocks in sequence, and the total amount is obtained by accumulating them one by one. Then, different intervals are divided according to the size of the total amount, for example, less than 1 × 10⁻⁶. 8 mg represents the low range, between 1×10⁻⁶. 8 mg and 5×10 8 The range between mg is the middle range, and the range is greater than 5 × 10. 8 mg represents the high-risk range. These ranges are defined based on pollutant concentrations and site risk classification standards. For example, a certain block may have a cumulative concentration of 4.5 × 10⁻⁶ mg / L. 8 mg falls within the middle range, while the cumulative amount in the other block is 0.8 × 10. 8 mg belongs to the low interval, completing the interval assignment of all blocks and forming a set of pollution accumulation intervals.

[0035] S403: Based on the set of pollution accumulation intervals, classify the remediation area block types according to the interval differences, and organize the classification results with the accumulation data to establish a pollution load value table for remediation blocks; Based on the set of pollution accumulation intervals, remediation area types need to be classified according to the differences between the intervals. First, the lower intervals correspond to Class I remediation areas, the middle intervals to Class II remediation areas, and the higher intervals to Class III remediation areas. In actual processing, each block is classified individually. For example, a block with an accumulation of 4.5 × 10⁻⁶... 8 mg was classified as a Class II repair zone, while the cumulative amount in another block was 0.8 × 10⁻⁶. 8 mg was classified as a Class I remediation area. After all blocks were classified, they needed to be integrated with the original cumulative data. The number, volume, concentration, cumulative amount and corresponding category of each block were recorded to gradually form a complete comparison data table. After all classification results were uniformly sorted, the pollution load value table of remediation blocks was obtained.

[0036] Please see Figure 6 The specific steps of S5 are as follows: S501: Based on the pollution load value table of the remediation block, detect the spatial location parameters and topographic elevation values ​​of the block, determine whether it is located in a micro-topographic catchment low-lying area, assign catchment location identifier values ​​to blocks that meet the low-lying criteria, and generate a catchment identifier set for the blocks. Based on the pollution load value table of the remediation blocks, it is necessary to detect the spatial location parameters and topographic elevation values ​​of the blocks. First, the plane coordinates and elevation values ​​of the center point of each block are recorded. For example, the center position of a block is 120m in the X direction, 80m in the Y direction, and the elevation is 10m. Then, the elevation of this block is compared with the average elevation of the surrounding blocks. If the average elevation of the surrounding blocks is 15m and the elevation of this block is 10m, the difference is 5m. When the preset low-lying threshold is 3m, the block is determined to be a low-lying area. The setting of this threshold can be determined according to the topographic relief range and drainage capacity of the area. Blocks that meet the conditions are assigned a water catchment indicator value, which is recorded as 1 to indicate that it is in a low-lying area and 0 to indicate that it is not in a low-lying area. For example, the above block is recorded as 1. After the detection is completed for each block, a block water catchment indicator set is formed.

[0037] S502: Call the block catchment identifier set, and perform one-by-one association processing based on the identifier value corresponding to the block and the pollution load value in the pollution load value table of the repair block. Combine the identifier value and the pollution load value into a corresponding record to obtain the block pollution catchment combination value. After calling the block catchment identifier set, it is necessary to match the identifier value of each block with the pollution load value in the remediation block pollution load value table. For example, if the identifier value of a certain block is 1, the pollution load value is 3.2 × 10⁻⁶. 8 If the value is mg, then the two are paired and recorded as the combined polluted catchment value for that block. If the identification value of the other block is 0, the pollution load value is 1.5 × 10 mg. 8 mg is recorded as another combined value. All blocks are processed in this way to establish a correspondence between the spatial location attributes of each block and the pollution load characteristics, thus obtaining a complete set of combined values ​​for the pollution catchment of each block.

[0038] S503: Based on the combined values ​​of polluted water catchment in the blocks, extract ranking indicators that reflect the priority of remediation resource input, rank the blocks according to the indicator values, and establish a remediation priority ranking. Based on the combined pollution load and catchment area values ​​of each block, a ranking index that reflects the order of remediation resource input needs to be extracted to prioritize each block. The ranking index is set as a weighted result of the pollution load value and the catchment area indicator, with a weight ratio of 70% for pollution load and 30% for catchment area indicator. For example, if the pollution load of a certain block is 3.2 × 10⁻⁶, the ranking index is determined by the following criteria: 8 If the value is mg and the label value is 1, then the weighted index value is relatively large, and the pollution load of another block is 1.5 × 10. 8 If mg is a value of 0, the index value is relatively small. The index values ​​of each block are compared and sorted. The larger the index value, the higher the ranking. The sorting results of all blocks are sorted to obtain the repair priority ranking.

[0039] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing the determination of soil remediation volume for contaminated sites based on multi-source monitoring data fusion, characterized in that, Includes the following steps: S1: Collect in-situ detection concentration data and apparent resistivity data at the borehole locations of the contaminated site, arrange them at uniform depth intervals, and combine the borehole coordinates and depths to construct a splicing structure to generate a multi-source profile splicing data table. S2: Based on the multi-source profile stitching data table, extract the locations of abrupt changes in pollution concentration and apparent resistivity in the vertical direction, extract pollution boundary points, interpolate boundary coordinates, construct a continuous spatial grid, and generate a three-dimensional pollution boundary grid dataset. S3: Based on the three-dimensional pollution boundary grid dataset, identify the location of the upper boundary of the grid, combine the elevation and slope aspect data to locate the low-lying area, and mark the overlap relationship with the pollution boundary grid to generate a set of pollution migration overlap blocks. The specific steps of S3 are as follows: S301: Based on the three-dimensional pollution boundary grid dataset, extract the upper boundary position of the grid in the vertical direction, identify the depth coordinates and lock the top boundary point to obtain the pollution upper boundary coordinate set; S302: Call the pollution upper boundary coordinate set, combine the site elevation and slope aspect point data, compare the elevation value and slope aspect direction, mark the low-lying areas with water catchment characteristics, and generate a set of water catchment low-lying blocks; S303: Based on the set of low-lying catchment areas, determine the spatial overlap with the set of upper boundary coordinates of pollution, identify and integrate the overlapping parts, and establish a set of overlapping pollution migration areas. S4: Based on the set of overlapping pollution migration blocks, extract the volume and pollution concentration data of the blocks, calculate the cumulative amount of pollutants, classify the types of remediation blocks, and generate a table of pollution load values ​​for remediation blocks. S5: Based on the pollution load value table of the remediation block, identify whether the block is located in a low-lying catchment area, assign a catchment area location identifier value, and extract ranking indicators in combination with the pollution load value to generate a remediation priority ranking. The specific steps of S5 are as follows: S501: Based on the pollution load value table of the remediation block, detect the spatial location parameters and topographic elevation values ​​of the block, determine whether it is located in a micro-topographic catchment low-lying area, assign catchment location identifier values ​​to blocks that meet the low-lying determination conditions, and generate a catchment identifier set for the blocks. S502: Call the block catchment identifier set, and perform one-by-one association processing based on the identifier value corresponding to the block and the pollution load value in the pollution load value table of the remediation block, and combine the identifier value and the pollution load value into a corresponding record to obtain the block pollution catchment combination value; S503: Based on the combined polluted water catchment values ​​of the blocks, extract ranking indicators that reflect the priority of remediation resource input, rank the blocks according to the indicator values, and establish a remediation priority ranking.

2. The method for optimizing and determining the amount of soil remediation for contaminated sites based on multi-source monitoring data fusion as described in claim 1, characterized in that, The multi-source profile stitching data table includes a borehole location information index, a unified depth sequence, a concentration sequence field, an apparent resistivity sequence field, and a coordinate reference system definition. The three-dimensional pollution boundary grid dataset includes a pollution boundary surface model, a grid coordinate set, and spatial interpolation parameters. The pollution migration overlapping block set includes a catchment low-lying area, spatial overlap identification results, and an overlapping block list. The remediation block pollution load value table includes block volume distribution, cumulative pollution value, and remediation category level. The remediation priority ranking includes a catchment area location identifier value, a comprehensive priority index, and a ranking.

3. The method for optimizing and determining the amount of soil remediation for contaminated sites based on multi-source monitoring data fusion as described in claim 1, characterized in that, The specific steps of S1 are as follows: S101: After obtaining the borehole layout information, collect in-situ detection concentration data and apparent resistivity data at the borehole location, record the borehole coordinates, depth values, concentration values ​​and apparent resistivity values ​​point by point, and organize them according to the borehole location to generate a borehole monitoring dataset. S102: Based on the borehole monitoring dataset, the borehole coordinates and depth values ​​are used as a reference. The concentration values ​​and apparent resistivity values ​​are arranged according to a uniform depth interval, and the data in the same depth interval are layered accordingly to obtain a layered numerical table. S103: Based on the layered numerical table, call the borehole coordinates, depth values, concentration values ​​and apparent resistivity values, stitch together the data at the same depth and combine them in vertical order to generate a multi-source profile stitching data table.

4. The method for optimizing and determining the amount of soil remediation for contaminated sites based on multi-source monitoring data fusion according to claim 3, characterized in that, The specific steps of S2 are as follows: S201: Based on the multi-source profile stitching data table, read the numerical sequence of contamination concentration and apparent resistivity in the depth direction in each borehole, identify the changing trend of adjacent intervals, extract the locations where abrupt changes occur and mark them as contamination boundary points, and obtain the contamination boundary point location set; S202: Call the pollution boundary point set, extract the three-dimensional coordinate information of the boundary points, perform interpolation processing according to the spatial interval, transform the discrete boundary points into a spatial continuous point sequence, and generate a boundary interpolation coordinate set; S203: Based on the boundary interpolation coordinate set, combine them in spatial distribution order, establish a grid connection for the three-dimensional coordinates of the interpolation points, form a continuous spatial grid structure, and establish a three-dimensional pollution boundary grid dataset.

5. The method for optimizing and determining the amount of soil remediation for contaminated sites based on multi-source monitoring data fusion according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Based on the set of overlapping pollution migration blocks, read the block volume information and detect the corresponding pollution concentration data, match the volume value and concentration value of each block one by one, and generate a pollution volume concentration comparison set. S402: Call the pollution volume concentration reference set, perform cumulative calculation on the block volume value and concentration value to obtain the pollutant cumulative amount data set, and then establish a distribution based on the cumulative amount interval to obtain the pollution cumulative amount interval set; S403: Based on the set of pollution accumulation intervals, divide the remediation area block types according to the interval differences, and organize the division results with the accumulation data to establish a pollution load value table for the remediation blocks.

6. The method according to claim 5, characterized in that, When accumulating the block volume value and the concentration value, the product of the block volume and the pollution concentration is used as the pollution amount. In the process of establishing the cumulative amount distribution, the difference in the cumulative amount between adjacent intervals is set as a threshold to limit the division range of the pollution cumulative amount interval.

7. The method for optimizing and determining the amount of soil remediation for contaminated sites based on multi-source monitoring data fusion according to claim 1, characterized in that, The in-situ detection concentration data are pollutant concentration values ​​obtained directly from boreholes or surface locations by soil pollution monitoring equipment. They originate from in-situ detection devices such as soil gas detectors, ion-selective electrodes, or ultraviolet absorption probes, and reflect the actual content of pollutants in the soil at the target depth location, expressed in mg / kg or ppm. The apparent resistivity data refers to the ability of strata at different depths to conduct current in geophysical surveys using the resistivity method. It is derived from resistivity imagers or electrode deployment systems and reflects the electrical characteristics of the subsurface medium. The unit is Ω·m. The splicing structure refers to the combination and matching of borehole coordinates, depth values, in-situ detection concentration data and apparent resistivity data based on the same depth range. The pollution boundary point refers to the location in the vertical profile where the pollution concentration or apparent resistivity value changes abruptly, indicating the upper and lower boundary positions of the pollution layer. The identification criteria are significant jumps or abnormal rates of change in values ​​in continuous depth data. The spatial grid refers to a three-dimensional raster with uniform size and arrangement rules, generated by spatial interpolation based on the three-dimensional coordinates of pollution boundary points.

8. The method for optimizing and determining the amount of soil remediation for contaminated sites based on multi-source monitoring data fusion according to claim 1, characterized in that, The elevation and slope point data refer to the elevation values ​​and slope direction information of the site surface collected through topographic surveying, which are derived from digital elevation models (DEM), UAV lidar scanning, or ground mapping data. The low-lying area refers to the area in the site where the terrain is lower, the catchment area or depression area calculated based on elevation data, and has the characteristics of the endpoint of pollutant deposition or migration. The pollution migration overlap block set refers to the closed area formed by the overlap of the pollution boundary grid and the low-lying area in space, representing the location where pollutants stay or accumulate on potential migration paths. The pollution load value refers to the product of the pollutant concentration and the volume of the area within a specified spatial block, expressed in mg. The catchment area location identifier value refers to the classification and identification value used to mark whether a block of area is located in a low-lying catchment area. It is derived from the elevation data analysis results and is labeled using a Boolean identifier. The remediation priority ranking refers to the ranking index obtained by sorting the different regional blocks according to the correlation between pollution load value and catchment area location identifier value.

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