Method for determining area soil remediation amount based on geological high background area
By acquiring soil sample data from areas with high geological background, and employing principal component analysis and logarithmic function models, the problem of uneven distribution of heavy metals was solved, enabling accurate calculation of soil remediation volume and differentiated remediation strategies.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GEOLOGICAL SURVEY INST OF GUANGXI ZHUANG AUTONOMOUS REGION
- Filing Date
- 2026-01-29
- Publication Date
- 2026-05-15
AI Technical Summary
Existing technologies do not fully consider the controlling role of geological background and the non-uniformity of vertical distribution of heavy metals in areas with high geological background, resulting in inaccurate estimation of heavy metal remediation amounts and difficulty in formulating differentiated remediation plans.
By deploying soil samples and soil profile samples, heavy metal content data were obtained. Principal component analysis and geological maps were used to identify the sources of heavy metals. A logarithmic function vertical distribution model was established. Statistical units were divided based on parent material, soil type, and land use pattern to calculate the potential remediation amount.
It improves the accuracy and operability of heavy metal remediation estimation, supports differentiated remediation strategies, and enables accurate determination of soil remediation volume in areas with high geological background.
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Figure CN122050587A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of soil testing technology, and in particular to a method for determining the amount of regional soil remediation based on high geological background areas. Background Technology
[0002] Heavy metal pollution in soil is a serious environmental problem, especially in areas with high geological background levels. Natural geological processes result in high background levels of heavy metals in soil, which, combined with the effects of human activities, easily lead to complex pollution, posing a potential threat to ecosystems and human health. Arsenic, as a carcinogenic heavy metal, is influenced by a combination of factors, including geological background, parent material, land use patterns, and soil physicochemical properties, in its accumulation and migration in soil.
[0003] Currently, methods for determining the amount of heavy metals to be remediated in soil mainly rely on empirical assessment methods based on location, spatial extrapolation methods based on statistical interpolation, and prediction methods based on migration and transformation models. However, existing technologies still have shortcomings, such as not fully considering the controlling role of geological background. In areas with high geological background, the spatial distribution of heavy metals is closely related to underlying lithology, structure, and mineralization. Vertical distribution models are too simplified, and existing technologies mostly assume that heavy metals are uniformly distributed or linearly varied in the profile, which does not match the actual situation. Sampling schemes and models lack representativeness. Existing estimation methods mostly give a total amount without calculating according to different influencing factors by region and category, making it difficult to formulate differentiated remediation technology solutions.
[0004] Therefore, there is a need for a method to determine the amount of regional soil remediation that simultaneously considers geological background and uneven vertical distribution of heavy metals. Summary of the Invention
[0005] The main objective of this invention is to provide a method for determining the amount of regional soil remediation based on geologically high background areas, aiming to solve the problems of insufficient consideration of the controlling role of geological background and overly simplified vertical distribution models in existing technologies.
[0006] To achieve the above objectives, this invention proposes a method for determining the amount of regional soil remediation based on geologically high background areas, comprising the following steps: Soil samples and soil profile samples were set up and collected from the area to be tested to obtain data on soil heavy metal content and its physicochemical indicators. Each sample weighed 1-2 kg and was sealed in a sampling bag. The coordinates and codes of the sampling points were recorded. Sample testing and data processing: obtaining principal component analysis and geological maps of soil samples and soil profiles; identifying the main source types of heavy metals in the soil based on principal component analysis and geological maps; and drawing spatial distribution maps. A vertical distribution model of soil heavy metal content with depth was established. The vertical distribution model was fitted with a logarithmic function, and a storage calculation formula was obtained based on the vertical distribution model to calculate the storage of soil heavy metals in the vertical direction. The statistical units are divided according to at least one of the parent material type, soil type and land use pattern, and the potential remediation amount of heavy metals in the soil within each statistical unit is calculated.
[0007] Furthermore, the step of deploying and collecting soil samples and soil profile samples from the area to be tested to obtain soil heavy metal content data and its physicochemical indicators also includes: Surface sampling was conducted in the area to be tested at a sampling depth of 0–20 cm and a sampling rate of 8–12 samples / km. 2 The average sample density was used for surface sampling, and surface sampling points were determined within a circumferential radiation range of 50-100m centered on the GPS location point of the area to be tested. The datasets of every 3-5 surface sampling points were combined into a surface sampling mixture. Deep sampling was conducted in the area to be tested at a sampling depth of 150-200 cm, every 4 km. 2 A deep sampling point is set up in a square area, and every four deep sampling points are combined to form a deep composite sample, that is, every 16km 2 Take a deep composite sample.
[0008] Furthermore, the step of equally dividing the sample point dataset into a single composite sample includes: If the sampling plot is approximately rectangular, then the sampling points should be arranged in an "S" shape. If the sampling plot is approximately square, then the sampling points are arranged in an "X" shape or a checkerboard pattern.
[0009] Furthermore, the step of deploying and collecting soil samples and soil profile samples from the area to be tested to obtain soil heavy metal content data and its physicochemical indicators also includes: Fifty-one representative profiles were set up, with sampling depths ranging from 0 to 200 cm. Ten samples were collected from each profile at 20 cm intervals.
[0010] Furthermore, the vertical distribution model is as follows: ; ; ; Wherein, d1 represents the center depth of the surface soil. Since the surface sampling is generally 0 to 0.2m, it is taken as 0.10m. d2 is the depth of the deep sampling point, and d2 is taken as 2m.
[0011] Furthermore, the potential remediation capacity of the heavy metals in the soil is as follows: , Where C represents the potential amount of heavy metals in the soil that needs to be remediated within a certain volume, c represents the heavy metal content in the soil, ρ represents the soil bulk density, x and y represent the horizontal two-dimensional space of the Earth's surface, and z represents the vertical space.
[0012] This invention constructs a vertical distribution model and uses a logarithmic function model to fit the non-uniform vertical distribution of heavy metals in geologically high background areas, significantly improving estimation accuracy. Furthermore, by overlaying principal component analysis with geological maps, the core data can be determined by obtaining the average heavy metal content at two key depths: the surface and the deep layer. This requires fewer parameters, and the data can be obtained from memory, making it highly operable. It enables the determination of regional soil remediation volume by simultaneously considering both geological background and the non-uniform vertical distribution of heavy metals. Attached Figure Description
[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the processes shown in these drawings without creative effort.
[0014] Figure 1 A flowchart illustrating a method for determining the amount of regional soil remediation based on a geologically high background area, provided in an embodiment of the present invention; Figure 2 This is a spatial distribution characteristic map of soil As in the area to be tested provided in an embodiment of the present invention; Figure 3 This is an area distribution map showing the graded arbitrariness of As element content in the surface soil of the test area, provided as an embodiment of the present invention. Figure 4 This is a principal component 2 factor score distribution diagram provided in an embodiment of the present invention; Figure 5 This is a principal component 3-factor score distribution diagram provided in an embodiment of the present invention; Figure 6 A vertical profile of deep soil As elemental composition provided in an embodiment of the present invention; Figure 7 This is a graph showing the variation of average As content with depth, provided as an embodiment of the present invention. Detailed Implementation
[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not 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 effort are within the scope of protection of the present invention.
[0016] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.
[0017] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.
[0018] It is understood that the heavy metals involved in this invention, taking arsenic (As) as an example, are carcinogenic inorganic substances that pose a serious threat to human health. The content and spatial distribution of arsenic in soil are closely related to human health. Arsenic migrates, accumulates, and is not easily degraded in soil. Therefore, calculating the potential remediation amount of arsenic in soil is beneficial for the remediation and treatment of arsenic-contaminated soil. Existing studies have shown that the As content in karst soil is related to many factors, including parent material, soil pH, soil organic matter, and soil adsorption and desorption capacity.
[0019] Based on this, such as Figure 1 As shown, this invention proposes a method for determining the amount of regional soil remediation based on a geologically high background area, comprising the following steps: Step S1: Set up and collect soil samples and soil profile samples of the area to be tested, obtain soil heavy metal content data and its physicochemical indicators, each sample weighs 1~2kg and is sealed in a sampling bag, and record the sampling point coordinates and codes. In detail, the sample collection process of this invention was carried out in accordance with relevant technical standards such as the "Specifications for Multi-Objective Regional Geochemical Survey (1:250000)" (DZT 0258-2014) issued by the China Geological Survey. The specific sample analysis methods are as follows: Table 1. Analytical methods for each element or index
[0020] To ensure precision and accuracy, one blank sample, four soil standard samples, and two replicates were added to every 50 samples for quality control during the experiment. The limits of detection (LODs) for As, B, Cd, Cl, Cr, Cu, F, Hg, I, Mn, Mo, N, Ni, P, Pb, S, Se, Zn, Co, and V were 0.2, 0.92, 0.02, 4, 2, 0.4, 50, 0.0002, 0.2, 3, 0.1, 20, 0.2, 5, 1.6, 20, 0.004, 1.1, 0.01, and 0.04 mg / kg, respectively. The LODs for MgO, CaO, K₂O, and Corg were 0.01, 0.01, 0.01, and 0.02%, respectively, and the LOD for pH was 0.1. The initial total reporting rate for all elements in the soil and rocks was 100%.
[0021] like Figures 2-3 As shown, Figure 2 This is a spatial distribution map of As in the soil of a region to be tested. Figure 3 This is an area distribution map showing the As content in the surface soil of the tested space, categorized by grade.
[0022] Furthermore, in accordance with the "Soil Environmental Quality Standard for Agricultural Land Soil Pollution Risk Control (Trial)" (GB15618-2018), this invention classifies soil pollution risk screening values and control values based on the total amount of As and pH value range in the soil for the classification and management of agricultural land soil. Referring to this standard, this invention uses its lower limit values (20 mg / kg to 100 mg / kg) to divide As content into three ranges: high, medium, and low: As content ≤ 20 mg / kg is low-grade; 20-100 mg / kg is medium-grade; and > 100 mg / kg is high-grade. Figure 3 It can be seen that the As content in the topsoil of the tested area is at a moderate level, and the area of soil with moderate As content is 1142.87 km². 2 This indicates that approximately 66.05% of the tested areas have soil As content at risk exceeding the screening value for agricultural land, and the data is relatively concentrated. The second largest area has soil with low As content, at 517.71 km². 2 The proportion was 29.92%, indicating a high As content but a relatively small soil area of 69.73 km². 2 The soil area accounts for 4.03%. Step S2, Sample testing and data processing: Obtain principal component analysis and geological maps of soil samples and soil profiles; Based on principal component analysis and geological maps, identify the main source types of metals in the soil and draw spatial distribution maps. In detail, geochemical parameter statistics were mainly performed using SPSS 20.0 and Excel 2010 for parameter statistics, significance testing, correlation analysis, and factor analysis; ArcGIS 10.2 was used to create maps such as the geographical distribution map of the area under test and the spatial distribution characteristic map of soil As; and Origin was used to create a vertical profile As content variation map. More specifically, statistical units were divided based on different parent rocks, soil types, and land use patterns, including: (1) Parent rocks of soil: Quaternary sediments, clastic rocks, carbonate rocks, intermediate rocks and acidic rocks; (2) Soil types: red soil, limestone soil, paddy soil, alluvial soil, purple soil; (3) Land use patterns: paddy fields and dry land.
[0023] Principal component analysis uses regression analysis to quantitatively analyze each variable, essentially recombining the original variables to form a new set of comprehensive variables. When dealing with a small number of variables, this method can yield a wealth of information; for example, principal component analysis of As can help infer the sources of As in soil.
[0024] Specifically, taking one embodiment of the present invention as an example: First, the KMO and Bartlett sphericity tests were performed on the As element in the soil of the area to be tested before analysis. The results showed that the KMO test statistic of the dataset was 0.833, which is suitable for factor analysis; the Bartlett test significance level was 0.00, which is less than 0.05, indicating that the selected indicators are suitable for factor analysis. Six principal components with eigenvalues greater than 1 after rotation were selected, among which principal components 2 and 3 both contain As elements.
[0025] like Figure 4 As shown, the elements with component loadings greater than 0.4 in principal component 2 are Zn, Co, Mn, Pb, Cd, As, Cr, pH, and B. There are highly significant positive correlations between any two elements (P < 0.01), with Co having the highest loading at 0.801. Cobalt generally comes from cobalt ore, which typically contains multiple metallic elements such as cobalt, copper, nickel, and iron.
[0026] The geological maps of the area under investigation show that: In the high-value areas, the lithology is mainly composed of carbonate rocks, acidic rocks, Quaternary sediments, and clastic rocks, with phosphate, placer gold, and manganese deposits. Soils are mainly lateritic red soil, brown limestone soil, shrub meadow soil, and gleyed paddy soil, with the geological background primarily coupled with the Quaternary, Carboniferous, and Permian systems. Land use types are mainly forest, cultivated land, and paddy fields. In the low-value areas, the main geological background is coupled with the Quaternary, Carboniferous, basic-ultrabasic intrusive rocks, Permian, and Triassic systems. The lithology is mainly Quaternary sediments, intermediate rocks, carbonate rocks, and clastic rocks, with copper, lead-zinc, tungsten, manganese, phosphate, and gold deposits. Soils are mainly lateritic red soil, gleyed paddy soil, and submerged paddy soil, with agricultural land use as the primary type.
[0027] Further verification is needed. Figure 5 The distribution map of factor scores in principal component 3 shows a similarity between the factor score distribution and the spatial distribution of As element in the soil of the tested area. The high-value areas exhibit a narrow, U-shaped band, corresponding to the two flanks of the Indosinian syncline. The geological background is mainly coupled with the Permian, Carboniferous, and Quaternary systems, with lithology primarily consisting of carbonate rocks and Quaternary sediments. The soil is mainly strongly acidic to acidic lateritic red soil, and the land use type is predominantly agricultural. Low-value areas are mainly located in the core of the syncline in the tested area. The geological background is mainly coupled with the Triassic, Cretaceous, and Carboniferous systems, with lithology primarily consisting of carbonate rocks, clastic rocks, Quaternary sediments, and acidic rocks. The soil type is mainly lateritic red soil, and the land use type is mainly orchards and forestry areas. Furthermore, the low-value areas are spatially linear, corresponding to the linear rivers in the tested area. Therefore, it can be concluded that the distribution of factor scores has a good spatial coupling relationship with strata, fold structures, rivers, etc., and shows obvious zoning characteristics, indicating that the spatial distribution of soil As is controlled by the geological background, and the soil As element mainly comes from the underlying strata.
[0028] Step S3: Establish a vertical distribution model of soil heavy metal content with depth. The vertical distribution model is fitted with a logarithmic function, and the storage calculation formula is obtained based on the vertical distribution model to calculate the storage of soil heavy metals in the vertical direction. Detailed, such as Figures 6-7 As shown, in the vertical profile deep soil As element analysis of the area to be tested, the following profile points were obtained after analysis and screening, such as... Figure 6 As shown; the average As content at different depths in each set of profile data is calculated to represent the overall vertical distribution trend of As elements, and a scatter plot is drawn and fitted as shown. Figure 7 As shown.
[0029] from Figure 7As can be seen, the average content of As in the vertical profile generally increases with depth, and there is a significant positive correlation between the surface and deep As content, further verifying that the As originates from the geological background. The distribution pattern of As in the soil vertical profile of the tested area is consistent with publicly available research. However, existing research methods involve fewer profiles and do not cover different geological backgrounds, soil types, and land use types, lacking representativeness. Therefore, they cannot calculate the potential remediation amount of soil arsenic, but can only summarize the variation of soil As content with depth. In contrast, this invention uses a large number of profile sampling points, collecting 51 sets of profile data across different geological backgrounds, soil types, and land use types. The data is representative, thus demonstrating that the fitting curve of the soil profile in the tested area follows a logarithmic pattern.
[0030] Because the distribution of As in the topsoil is influenced by different soil use types, soil types, and parent rocks, As enrichment occurs in the topsoil. Therefore, the potential remediation amount of As in the soil is calculated separately for each type of influencing factor. The total potential remediation amount of As in the soil is the sum of the potential remediation amounts of deep As, mid-layer As, and topsoil As. Mathematically, calculating the potential remediation amount of As in the soil is essentially an integral over the three-dimensional (three-directional) product of the As content and soil bulk density. This can be expressed mathematically as:
[0031] Where C represents the potential amount of heavy metals in the soil that needs to be remediated within a certain volume, c represents the heavy metal content in the soil, ρ represents the soil bulk density, x and y represent the horizontal two-dimensional space of the Earth's surface, and z represents the vertical space.
[0032] The essence of calculating soil As density is to integrate the soil profile along the vertical z-direction, which can be expressed by the following formula:
[0033] When the soil bulk density in the profile is assumed to be constant.
[0034] Where A is the area enclosed by the soil As content curve and the coordinate axis in the profile.
[0035] Step S4: Divide the soil into statistical units based on at least one of parent material type, soil type and land use pattern, and calculate the potential remediation amount of heavy metals in the soil within each statistical unit.
[0036] Furthermore, this invention employs a logarithmic model to calculate the potential remediation requirements of As in the soil for each factor type in the tested area. Assuming that As decreases from the surface to the deeper layers of the soil profile according to the logarithmic model y = algebranx + b, the curve must pass through the point (As...). 表 ,d1) and (As 深 d1 represents the center depth of the surface soil, which is typically 0.0–0.2 m, so we take 0.10 m. d2 is the depth of the deep sampling point, which is taken as 2 m. Substituting the coordinates of the two points into the logarithmic model formula and solving the equation yields:
[0037]
[0038] Logarithmic curve segment (As) 表 ,d1)—(As 深 The area enclosed by d2 and the x and y coordinate axes is:
[0039] When calculating the potential remediation amount of As in the soil from the surface to a depth of 1m, if the calculation depth is d3, then calculate the logarithmic curve segment (As). 表 The area enclosed by (d1) - (t3, d3) and the x and y coordinate axes is:
[0040] In the formula, t3 is an unknown, which can be obtained by substituting the point (t3, d3) into the logarithmic equation:
[0041] After substituting, we get:
[0042] Wherein, soil As density = S × 10 4 ×ρ, where 10 4 Here is the unit conversion factor, and ρ is the soil bulk density (t / m³). 3 S can be either S1 or S2 depending on the depth range required.
[0043] Furthermore, since existing technologies lack relevant data on soil bulk density, this invention employs a regression model of soil bulk density and organic carbon content: y = 1.377 * e -0.0048 *SOC (R²=0.7870, p<0.001, n=4765), where SOC represents soil organic carbon, in percentage (%). This invention uses this as a model for estimating soil bulk density and calculates its average value. The following are the methods for calculating the As density of each layer: (1) Calculation of As density in deep soil. The calculation formula is: As density in deep soil = As × D × 10４ ×ρ, where: D represents the sampling depth (2m); 10 ４ Here is the unit conversion factor; ρ is the soil bulk density (t·m³). -3 As represents the calculated deep average As content, and the calculation formula is:
[0044] In the formula, As 表 As content in topsoil 深 The As content in deep soil is expressed as a percentage (d1) at a depth of 0.1m in the middle of the surface soil layer, and d2 at a depth of 2m. (2) Calculation of As density in the middle layer soil. The calculation formula is: As density in the middle layer = As × D × 10 ４ ×ρ, where D represents the sampling depth (1m), and As is calculated using the following formula:
[0045] In the formula, d3 = 1.0m, and other parameters are the same as before.
[0046] (3) Calculation of As density in topsoil. The calculation method is: Topsoil As density = As × D × 10 4 ×ρ, where D represents the sampling depth (0.2m), and other parameters are the same as before.
[0047] Table 2 Potential remediation requirements of soil as particulate matter (As) under different parent material types, soil types, and land use patterns.
[0048] The calculation results show that the total area of the region to be measured is 1730.31 km². 2 The average bulk density of each soil layer was 1.37 t·m³. -3 Table 2 shows the potential remediation amount of As (As) for different influencing factors. Among different parent rocks, the potential remediation amount of As in Quaternary sediments is 119,306.43 t, and that in carbonate rocks is 116,386.5 t. Mudstone and sandstone have too small an area to be included in the statistics. Among different soil use types, the total potential remediation amount of As in dry land is 166,112.66 t, and that in paddy fields is 49,142.52 t. Among different soil types, red soil has the largest total potential remediation amount of As at 189,545.48 t. The potential remediation amounts of As in paddy soil, limestone soil, mesosol, and purple soil are 74,090.34 t, 26,677.57 t, 5,354.08 t, and 3,223.97 t, respectively. The sample size of marsh soil is too small to be included in the statistics.
[0049] This invention combines the vertical profile distribution pattern of soil arsenic content in the tested area to fit the soil profile. The fitted curve follows a logarithmic law, and a formula for the potential remediation amount of soil arsenic is derived. Analysis of 51 sets of profile data collected by the system confirms the formula's rationality. Existing technologies have limited research on soil arsenic in karst areas, lacking representativeness due to the limited number of profiles and the absence of profiles across different geological backgrounds, soil types, and land use types. This makes it impossible to calculate the potential remediation amount of soil arsenic, only summarizing the variation of soil arsenic content with depth. This invention, however, uses numerous profile sampling points, collecting 51 sets of profile data across different geological backgrounds, soil types, and land use types. This data is representative, and therefore the formula disclosed in this invention is also representative. Furthermore, existing technologies do not calculate the potential remediation amount of soil arsenic in karst areas; therefore, this formula is advanced and generalizable, and has significant research value for the remediation and control of soil arsenic in karst areas.
[0050] Furthermore, in step S1, the steps of deploying and collecting soil samples and soil profile samples from the area to be tested, and obtaining soil heavy metal content data and its physicochemical indicators, also include: Step S11, surface sampling, in the area to be tested at a sampling depth of 0~20cm and 8~12 samples / km. 2 The average sample density was used for surface sampling, and surface sampling points were determined within a circumferential radiation range of 50-100m centered on the GPS location point of the area to be tested. The datasets of every 3-5 surface sampling points were combined into a surface sampling mixture. Step S12, deep sampling, in the area to be tested, at a sampling depth of 150~200cm, every 4km 2 A deep sampling point is set up in a square area, and every four deep sampling points are combined to form a deep composite sample, that is, every 16km 2 Take a deep composite sample.
[0051] In this embodiment, the step S11 of dividing the sample point dataset into equal parts and combining them into a mixed sample includes: Step S11A: If the sampling plot is approximately rectangular, then the sampling points are arranged in an "S" shape. In step S11B, if the sampling plot is approximately square, then the sampling points are arranged in an "X" shape or a checkerboard pattern.
[0052] This invention constructs a vertical distribution model and uses a logarithmic function model to fit the non-uniform vertical distribution of heavy metals in geologically high background areas, significantly improving estimation accuracy. Furthermore, by overlaying principal component analysis with geological maps, the core data only requires the average heavy metal content at two key depths: the surface and deep layers. This minimizes parameter requirements, leverages readily available data, and enhances operability. Additionally, this invention establishes a multi-dimensional remediation volume calculation system based on parent material, soil type, and land use, supporting differentiated and precise remediation strategies. This facilitates the determination of the required human intervention, enabling the simultaneous consideration of geological background and the non-uniform vertical distribution of heavy metals to determine regional soil remediation volumes.
[0053] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.
Claims
1. A method for determining the amount of regional soil remediation based on a geologically high background zone, characterized in that, Includes the following steps: Soil samples and soil profile samples were set up and collected from the area to be tested to obtain data on soil heavy metal content and its physicochemical indicators. Each sample weighed 1-2 kg and was sealed in a sampling bag. The coordinates and codes of the sampling points were recorded. Sample testing and data processing: obtaining principal component analysis and geological maps of soil samples and soil profiles; identifying the main source types of heavy metals in the soil based on principal component analysis and geological maps; and drawing spatial distribution maps. A vertical distribution model of soil heavy metal content with depth was established. The vertical distribution model was fitted with a logarithmic function, and a storage calculation formula was obtained based on the vertical distribution model to calculate the storage of soil heavy metals in the vertical direction. The statistical units are divided according to at least one of the parent material type, soil type and land use pattern, and the potential remediation amount of heavy metals in the soil within each statistical unit is calculated.
2. The method for determining the amount of regional soil remediation based on a geologically high background area as described in claim 1, characterized in that, The steps of deploying and collecting soil samples and soil profile samples from the area to be tested, and obtaining soil heavy metal content data and its physicochemical indicators, also include: Surface sampling was conducted in the area to be tested at a sampling depth of 0–20 cm and a sampling rate of 8–12 samples / km. 2 The average sample density was used for surface sampling, and surface sampling points were determined within a circumferential radiation range of 50-100m centered on the GPS location point of the area to be tested. The datasets of every 3-5 surface sampling points were combined into a surface sampling mixture. Deep sampling was conducted in the area to be tested at a sampling depth of 150-200 cm, every 4 km. 2 A deep sampling point is set up in a square area, and every four deep sampling points are combined to form a deep composite sample, that is, every 16km 2 Take a deep composite sample.
3. The method for determining the amount of regional soil remediation based on a geologically high background area as described in claim 2, characterized in that, The step of combining the equally divided sample point dataset into a single mixed sample includes: If the sampling plot is approximately rectangular, then the sampling points should be arranged in an "S" shape. If the sampling plot is approximately square, then the sampling points are arranged in an "X" shape or a checkerboard pattern.
4. The method for determining the amount of regional soil remediation based on a geologically high background area as described in claim 3, characterized in that, The steps of deploying and collecting soil samples and soil profile samples from the area to be tested, and obtaining soil heavy metal content data and its physicochemical indicators, also include: Fifty-one representative profiles were set up, with sampling depths ranging from 0 to 200 cm. Ten samples were collected from each profile at 20 cm intervals.
5. The method for determining the amount of regional soil remediation based on a geologically high background area as described in claim 1, characterized in that, The vertical distribution model is as follows: ； ； ； Wherein, d1 represents the center depth of the surface soil. Since the surface sampling is generally 0 to 0.2m, it is taken as 0.10m. d2 is the depth of the deep sampling point, and d2 is taken as 2m.
6. The method for determining the amount of regional soil remediation based on a geologically high background area as described in claim 1, characterized in that, The potential remediation capacity for the heavy metals in the soil is: ， Where C represents the potential amount of heavy metals in the soil that needs to be remediated within a certain volume, c represents the heavy metal content in the soil, ρ represents the soil bulk density, x and y represent the horizontal two-dimensional space of the Earth's surface, and z represents the vertical space.