A method of soil analysis

Abstract

The application discloses a soil analysis and detection method and relates to the technical field of soil detection. Soil basic data corresponding to a plurality of soil sampling points in a detection area are acquired; the soil basic data at least include soil physicochemical indexes, pollutant contents and soil moisture contents of each soil sampling point at different sampling depths; based on the soil basic data, soil state characteristic values of each soil sampling point at different sampling depths are acquired; the soil state characteristic values are at least used for representing soil environment stability degrees of corresponding sampling depths. Through acquisition of soil basic data of a plurality of soil sampling points at different sampling depths in a detection area and acquisition of soil state characteristic values based on soil physicochemical indexes, pollutant contents and soil moisture contents, the application can quantitatively represent soil environment stability degrees at different sampling depths.

Description

Technical Field

[0001] This invention relates to the field of soil testing technology, specifically a soil analysis and testing method. Background Technology

[0002] Soil is a crucial component of the ecological environment. Its physicochemical properties, pollutant content, and moisture distribution directly impact land use safety, crop growth quality, and regional ecological stability. With the continuous increase in industrial production, agricultural fertilization, transportation, and domestic emissions, soil may accumulate pollutants such as heavy metals, nitrogen and phosphorus pollutants, petroleum hydrocarbons, and organic pollutants. Failure to analyze and detect soil pollution in a timely and accurate manner can easily lead to inaccurate assessment of the pollution extent, unreasonable delineation of remediation areas, and insufficient targeting of remediation measures.

[0003] Current soil testing methods typically involve setting up several sampling points within the testing area, collecting soil samples from different locations or depths, and then conducting experimental tests on the collected soil samples to obtain the pollutant content at each sampling point. However, in actual soil environments, pollutants are not fixed at a single sampling point or depth. Instead, they are affected by factors such as soil pore structure, water migration, soil adsorption capacity, topographic slope, and the pollution status of adjacent areas, resulting in migration and diffusion in both the vertical depth direction and the horizontal spatial direction.

[0004] In other words, the pollutant content detected at a single sampling point can only reflect the pollution status within a local area of ​​that sampling point and cannot fully characterize the overall distribution characteristics of pollutants in the area to be tested. Especially when there are differences in soil structure between different sampling depths and when pollutants may migrate laterally between adjacent sampling points, directly using the original detection value as the final soil analysis result can easily lead to a strong degree of randomness and locality in the detection results, thereby reducing the accuracy of soil pollution assessment.

[0005] Therefore, this application proposes a soil analysis and testing method. Summary of the Invention

[0006] The purpose of this invention is to provide a soil analysis and testing method to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a soil analysis and testing method, comprising:

[0008] Acquire basic soil data corresponding to multiple soil sampling points within the area to be tested; the basic soil data includes at least the soil physicochemical indicators, pollutant content, and soil moisture content of each soil sampling point at different sampling depths.

[0009] Based on the aforementioned basic soil data, soil state characterization values ​​are obtained for each soil sampling point at different sampling depths; these soil state characterization values ​​are used at least to characterize the soil environmental stability at the corresponding sampling depth.

[0010] Based on the soil state characterization values ​​at different sampling depths, the longitudinal migration influence coefficient corresponding to each soil sampling point is obtained; the longitudinal migration influence coefficient is at least used to characterize the magnitude of the impact of pollutants on the detection results when they migrate between different sampling depths.

[0011] Based on the positional relationship between each soil sampling point and the soil state characterization value, the lateral diffusion correction coefficient corresponding to each soil sampling point is obtained; the lateral diffusion correction coefficient is at least used to characterize the magnitude of the impact of pollutant diffusion between adjacent soil sampling points on the detection results.

[0012] Based on the longitudinal migration influence coefficient and the lateral diffusion correction coefficient, the pollutant content of each soil sampling point is corrected to obtain the corrected pollutant content of each soil sampling point.

[0013] Based on the corrected pollutant content corresponding to each soil sampling point, the soil analysis and testing results of the area to be tested are obtained.

[0014] As a specific solution to the technical solution of this application, the step of acquiring basic soil data corresponding to multiple soil sampling points in the area to be tested includes:

[0015] The area to be detected is divided into multiple first detection sub-regions;

[0016] Based on each first detection sub-region, a second detection sub-region is obtained; the second detection sub-region is any detection sub-region in each first detection sub-region where soil basic data collection has not been completed.

[0017] Based on the second detection sub-region, multiple soil samples are obtained at a preset sampling depth;

[0018] Each soil sample is tested to obtain basic soil data corresponding to each soil sampling point in the second testing sub-region.

[0019] As a specific embodiment of the technical solution of this application, the soil physicochemical indicators include at least one or more of the following: soil pH value, electrical conductivity, organic matter content, particle composition, and cation exchange capacity.

[0020] The pollutant content includes at least one or more of the following: heavy metal content, nitrogen and phosphorus pollutant content, petroleum hydrocarbon content, and organic pollutant content.

[0021] As a specific solution to the technical solution of this application, the step of obtaining soil state characterization values ​​at different sampling depths for each soil sampling point based on the soil baseline data includes:

[0022] Based on the soil physicochemical indicators, the soil adsorption and retention capacity at the corresponding sampling depth is obtained;

[0023] The soil adsorption and retention capacity is used at least to characterize the adsorption and fixation capacity of soil for pollutants at the corresponding sampling depth.

[0024] Based on the soil moisture content and particle composition, the pollutant migration support at the corresponding sampling depth is obtained; the pollutant migration support is used at least to characterize the soil's ability to support pollutant migration at the corresponding sampling depth.

[0025] The soil state characterization value is obtained based on the soil adsorption and retention capacity and the pollutant migration support.

[0026] As a specific solution to the technical solution of this application, the step of obtaining the soil adsorption and retention capacity at the corresponding sampling depth based on the soil physicochemical indicators includes:

[0027] The adsorption strength of pollutants is obtained based on the organic matter content and cation exchange capacity.

[0028] The degree of soil chemical stability is obtained based on the pH value and electrical conductivity.

[0029] The soil adsorption and retention capacity is obtained based on the adsorption intensity of the pollutants and the degree of soil chemical stability.

[0030] As a specific solution to the technical solution of this application, the step of obtaining the pollutant migration support at the corresponding sampling depth based on the soil moisture content and particle composition includes:

[0031] Based on the particle composition, the degree of soil pore connectivity is obtained; the degree of soil pore connectivity is used at least to characterize the ease with which pollutants move in the soil pores.

[0032] Based on the soil moisture content, the degree of water migration drive is obtained; the degree of water migration drive is used to characterize at least the magnitude of the driving ability of water on pollutant migration.

[0033] The pollutant migration support is obtained based on the soil pore connectivity and the water migration drive.

[0034] As a specific solution to the technical solution of this application, the step of obtaining the longitudinal migration influence coefficient corresponding to each soil sampling point based on the soil state characterization values ​​at different sampling depths includes:

[0035] For any soil sampling point, the soil state characterization values ​​corresponding to each sampling depth are sorted in order from shallow to deep to obtain the depth state sequence.

[0036] Based on the depth state sequence, a first state difference value is obtained between adjacent sampling depths; the first state difference value is the difference between soil state characterization values ​​corresponding to adjacent sampling depths.

[0037] Based on the first state difference value and the depth interval between adjacent sampling depths, the longitudinal migration influence value is obtained;

[0038] Based on each longitudinal migration impact value, the longitudinal migration impact coefficient corresponding to the soil sampling point is obtained.

[0039] As a specific solution of the technical solution of this application, the step of obtaining the lateral diffusion correction coefficient corresponding to each soil sampling point based on the positional relationship between each soil sampling point and the soil state characterization value includes:

[0040] Based on the location coordinates of each soil sampling point, a third soil sampling point is obtained; the third soil sampling point is any soil sampling point among the soil sampling points for which the lateral diffusion correction coefficient has not been obtained;

[0041] Based on the third soil sampling point, a fourth soil sampling point is obtained; the fourth soil sampling point is any soil sampling point whose distance from the third soil sampling point is less than a preset proximity distance.

[0042] The spatial proximity is obtained based on the sampling distance between the third and fourth soil sampling points;

[0043] Based on the soil state characterization values ​​of the third and fourth soil sampling points at the same sampling depth, the second state difference value is obtained.

[0044] Based on the spatial proximity and the second state difference value, the lateral diffusion correction coefficient corresponding to the third soil sampling point is obtained.

[0045] As a specific solution of this application, the step of correcting the pollutant content at each soil sampling point based on the longitudinal migration influence coefficient and the lateral diffusion correction coefficient to obtain the corrected pollutant content at each soil sampling point includes:

[0046] Based on the longitudinal migration influence coefficient, the pollutant content at different sampling depths of the same soil sampling point is longitudinally corrected to obtain the longitudinally corrected pollutant content.

[0047] Based on the lateral diffusion correction coefficient, the longitudinal correction pollutant content between adjacent soil sampling points is laterally corrected to obtain the corrected pollutant content.

[0048] As a specific solution to the technical solution of this application, the step of obtaining the soil analysis and testing results of the area to be tested based on the corrected pollutant content corresponding to each soil sampling point includes:

[0049] Based on the corrected pollutant content corresponding to each sampling point, the regional pollution evaluation value corresponding to each first detection sub-region within the area to be detected is obtained.

[0050] The pollution assessment values ​​of each region are compared with the preset pollution assessment thresholds to obtain the pollution level corresponding to each first detection sub-region.

[0051] Based on the pollution level corresponding to each first detection sub-region, soil analysis and detection results of the area to be detected are generated; the soil analysis and detection results include at least the spatial distribution information of pollutants, pollution level information, and abnormal sampling point information.

[0052] Compared with the prior art, the beneficial effects of the present invention are:

[0053] This application acquires basic soil data from multiple soil sampling points at different sampling depths within the testing area, and obtains soil state characterization values ​​based on soil physicochemical indicators, pollutant content, and soil moisture content, enabling a quantitative representation of soil environmental stability at different sampling depths. Furthermore, this application obtains vertical migration influence coefficients and horizontal diffusion correction coefficients, analyzing the impact of pollutant migration and diffusion on the detection results from the depth and spatial directions, respectively. The vertical migration influence coefficients and horizontal diffusion correction coefficients are used to correct for pollutant content, reducing the randomness and locality of detection results from a single sampling point or a single sampling depth, improving the accuracy of pollutant content information representation within the testing area, and thus enhancing the reliability of soil analysis and detection results. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of the overall process of the soil analysis and testing method of the present invention;

[0055] Figure 2 This is a schematic diagram of the process for collecting basic soil data and obtaining soil state characterization values ​​according to the present invention;

[0056] Figure 3 This is a schematic diagram illustrating the process of obtaining the vertical migration influence coefficient and the lateral diffusion correction coefficient of the present invention;

[0057] Figure 4 This is a schematic diagram of the pollutant content correction and detection result generation process of the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0059] The terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. For example, the first cluster and the second cluster mentioned below belong to different clusters. It should be understood that such names can be used interchangeably where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. The division of modules in the embodiments of this application is merely a logical division. In actual applications, there may be other division methods. For example, multiple modules may be combined into or integrated into another system, or some features may be ignored or not performed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interface, and the indirect coupling or communication connection between modules may be electrical or other similar forms. None of these are limited in the embodiments of this application. Furthermore, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed among multiple circuit modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of this application.

[0060] To address the technical problem mentioned in the background art that existing soil testing methods do not fully consider the effects of vertical migration and lateral diffusion of pollutants, resulting in low accuracy of soil analysis and testing results, this application proposes an embodiment of a soil analysis and testing method, such as... Figure 1 As shown, the soil analysis and testing method includes steps S100 to S600.

[0061] Step S100: Obtain basic soil data corresponding to multiple soil sampling points within the area to be tested.

[0062] In this implementation, the basic soil data includes at least the soil physicochemical indicators, pollutant content, and soil moisture content at different sampling depths for each soil sampling point.

[0063] In this embodiment, the area to be tested can be any area requiring soil analysis and testing, such as farmland, construction land, contaminated land, soil areas surrounding mining areas, soil areas surrounding industrial parks, or soil areas on both sides of river channels. Soil sampling points can be deployed according to the area of ​​the area to be tested, topography, historical pollution sources, and required testing accuracy. For example, multiple soil sampling points can be evenly distributed within the area to be tested using a grid layout; alternatively, the number of soil sampling points can be increased in locations with higher pollution risk based on the location of pollution sources, topographic slope, or historical monitoring data.

[0064] In a specific embodiment of this application, step S100 involves acquiring basic soil data corresponding to multiple soil sampling points within the area to be tested, including steps S100 to S140.

[0065] Step S100: Divide the area to be detected into multiple first detection sub-regions.

[0066] In the embodiments of this application, the area to be detected can be divided into multiple first detection sub-regions in any reasonable manner. For example, if the terrain of the area to be detected is regular and the historical pollution sources are relatively evenly distributed, rectangular grids can be divided according to the boundaries of the area to be detected and a preset size, with each rectangular grid serving as a first detection sub-region. For example, for an area to be detected of 500m × 500m, it can be divided into 100 first detection sub-regions using 50m × 50m grids. In this way, the area of ​​each first detection sub-region can be made consistent, reducing sampling deviations caused by differences in area.

[0067] Alternatively, if there are obvious pollution sources in the area to be monitored, such as chemical production facilities, sewage outlets, solid waste dumps, or slag heaps, multiple annular zones can be divided around the pollution source with a preset radius. Then, considering the dominant risk factors (wind direction, water flow direction, or terrain slope), each annular zone can be further divided into multiple primary monitoring sub-zones. This method can more accurately reflect the spatial variation patterns of pollutants spreading from the pollution source to the surrounding area.

[0068] Alternatively, if the area to be tested is farmland, it can be divided into multiple first-level sub-regions based on field boundaries, irrigation ditch distribution, soil type distribution, or crop planting zones. This approach ensures that the soil use patterns and environmental conditions in each first-level sub-region are more similar, thus improving the representativeness of subsequent soil analysis results.

[0069] Step S120: Obtain the second detection sub-region based on each of the first detection sub-regions.

[0070] In this embodiment, the second detection sub-region is any detection sub-region in each of the first detection sub-regions where soil basic data collection has not been completed. That is to say, in this embodiment, the soil basic data collection method corresponding to any detection sub-region in each of the first detection sub-regions is the same as the soil basic data collection method corresponding to the second detection sub-region.

[0071] Step S130: Based on the second detection sub-region, acquire multiple soil samples according to a preset sampling depth.

[0072] In this embodiment, the preset sampling depth can be pre-set according to the soil type, pollutant type, and testing requirements of the area to be tested. For example, the preset sampling depth can be 0m to 0.2m, 0.2m to 0.5m, 0.5m to 1m, or a specific depth such as 0.5m, 1m, or 2m. For industrial contaminated sites that may have deep pollution, deeper sampling depths can be set, such as 3m, 5m, or even deeper. The above sampling depths are only illustrative and do not mean that the soil analysis and testing method proposed in this application is only applicable to the above depth ranges.

[0073] In the embodiments of this application, at least one soil sampling point can be set within the second detection sub-region, and multiple soil samples can be collected at each soil sampling point according to a preset sampling depth. For example, for a second detection sub-region, a soil sampling point can be set at its center, or soil sampling points can be set at its center and four corners respectively to obtain more representative soil samples.

[0074] Step S140: Test each soil sample to obtain the basic soil data corresponding to each soil sampling point in the second detection sub-region.

[0075] In this embodiment, any reasonable soil testing equipment or experimental method can be used to test each soil sample. For example, soil pH can be measured using a pH meter, soil conductivity can be measured using a conductivity meter, soil moisture content can be measured using a drying and weighing method, organic content can be measured using a potassium dichromate oxidation method or an elemental analyzer, particle composition can be measured using a sieve analysis or a laser particle size analyzer, and cation exchange capacity can be measured using an exchange method. For pollutant content, it can be obtained using atomic absorption spectrometry, inductively coupled plasma mass spectrometry, gas chromatography, liquid chromatography, or other applicable detection methods.

[0076] In this embodiment, soil physicochemical indicators include at least one or more of soil pH, electrical conductivity, organic matter content, particle size distribution, and cation exchange capacity. Pollutant content may include at least one or more of heavy metal content, nitrogen and phosphorus pollutant content, petroleum hydrocarbon content, and organic pollutant content. Heavy metals may include lead, cadmium, mercury, arsenic, chromium, copper, zinc, and nickel, etc.; nitrogen and phosphorus pollutants may include ammonia nitrogen, nitrate nitrogen, total nitrogen, available phosphorus, and total phosphorus, etc.; organic pollutants may include polycyclic aromatic hydrocarbons, organochlorine pesticides, volatile organic compounds, and semi-volatile organic compounds, etc.

[0077] Step S200: Based on the soil baseline data, obtain the soil state characterization values ​​of each soil sampling point at different sampling depths.

[0078] In this embodiment, the soil state characterization value is used at least to characterize the degree of soil environmental stability at the corresponding sampling depth.

[0079] It is important to note that in actual soil environments, the level of pollutants depends not only on the amount of pollutant input but also on the soil's own environmental stability. For example, soils with higher organic matter content and greater cation exchange capacity typically have a stronger capacity for adsorption and fixation of certain heavy metals; while soils with higher porosity and water content are more likely to form pollutant migration channels, allowing pollutants to migrate both vertically and horizontally. Therefore, relying solely on pollutant detection values ​​is insufficient to accurately reflect the true state of soil pollution; it is also necessary to combine soil physicochemical indicators and water content to obtain soil condition characterization values.

[0080] In one specific embodiment of this application, step S200 involves obtaining soil state characterization values ​​at different sampling depths for each soil sampling point based on the soil baseline data, including steps S210 to S230.

[0081] Step S210: Based on the soil physicochemical indicators, obtain the soil adsorption and retention capacity at the corresponding sampling depth.

[0082] In this embodiment, the soil adsorption and retention capacity is used at least to characterize the soil's ability to adsorb and fix pollutants at the corresponding sampling depth.

[0083] It is important to note that the stronger the soil's ability to adsorb and immobilize pollutants, the easier it is for the pollutants to remain in the soil at the corresponding sampling depth, and the lower the likelihood of the pollutants migrating to other depths or adjacent areas. Conversely, if the soil's ability to adsorb and immobilize pollutants is weak, the pollutants are more likely to migrate with water or through pore channels, resulting in the original pollutant content not accurately representing the long-term pollution status at that sampling depth.

[0084] In a specific embodiment of this application, step S210, based on the soil physicochemical indicators, obtains the soil adsorption and retention capacity at the corresponding sampling depth, including steps S211 to S213.

[0085] Step S211: Based on the organic matter content and cation exchange capacity, obtain the adsorption strength of pollutants.

[0086] In this embodiment, both organic matter content and cation exchange capacity can reflect the soil's ability to adsorb and fix certain pollutants. Generally, the higher the organic matter content, the stronger the soil's ability to complex and adsorb certain organic pollutants and metal ions; the higher the cation exchange capacity, the stronger the soil's ability to adsorb and exchange cationic pollutants. Therefore, the adsorption intensity of pollutants can be obtained based on organic matter content and cation exchange capacity.

[0087] In this embodiment, any reasonable method can be used to obtain the pollutant adsorption intensity. For example, the organic matter content and cation exchange capacity can be normalized and then summed or weighted to obtain the pollutant adsorption intensity, and the calculation formula is as follows:

[0088]

[0089] in, Indicates the adsorption strength of pollutants; Indicates organic matter content; Indicates cation exchange capacity; This represents a normalization function used to normalize the values ​​within the brackets to the range [0,1]. and Each is labeled with a preset weight coefficient, and and All are positive numbers.

[0090] Step S212: Based on the pH value and electrical conductivity, obtain the degree of soil chemical stability.

[0091] In this study, soil pH affects the form and dissolution and migration capacity of pollutants in the soil. For example, under acidic conditions, some heavy metals are more likely to transform from an adsorbed state to a migratory state, while under alkaline conditions, some heavy metals may form precipitates or be adsorbed by soil colloids. Electrical conductivity reflects the soluble salt content and ionic strength in the soil. An abnormally high electrical conductivity may indicate enhanced ion migration capacity in the soil solution, thereby affecting the migration state of pollutants. Therefore, the degree of soil chemical stability can be obtained based on pH and electrical conductivity.

[0092] In this embodiment, the degree of soil chemical stability can be obtained using any reasonable method. For example, the degree of soil chemical stability can be obtained based on the deviation of the pH value from a preset suitable pH range and the deviation of the electrical conductivity from a preset electrical conductivity threshold. The closer the pH value is to the preset suitable pH range and the lower the electrical conductivity is than the preset electrical conductivity threshold, the higher the degree of soil chemical stability.

[0093] Step S213: Based on the adsorption intensity of the pollutants and the degree of soil chemical stability, obtain the soil adsorption and retention capacity.

[0094] In this embodiment, the soil adsorption and retention capacity can be obtained based on the pollutant adsorption intensity and soil chemical stability using any reasonable method. For example, the soil adsorption and retention capacity can be positively correlated with the pollutant adsorption intensity and soil chemical stability. The calculation formula is as follows:

[0095]

[0096] in, Indicates the soil's adsorption and retention capacity. Indicates the adsorption strength of pollutants. Indicates the degree of soil chemical stability. This represents a normalization function used to normalize the values ​​within the parentheses to the range [0, 1].

[0097] In this embodiment, if the soil adsorption and retention capacity is greater, it means that the soil at the corresponding sampling depth is more likely to adsorb and fix pollutants, and the pollutants are more difficult to migrate. If the soil adsorption and retention capacity is smaller, it means that the soil at the corresponding sampling depth has a weaker ability to retain pollutants, and the pollutants are more likely to migrate to other sampling depths or adjacent sampling points.

[0098] Step S220: Based on the soil moisture content and particle composition, obtain the pollutant migration support at the corresponding sampling depth.

[0099] In this embodiment, the pollutant migration support is used at least to characterize the extent to which the soil at the corresponding sampling depth supports pollutant migration.

[0100] It is important to note that pollutant migration in soil typically requires migration pathways and transport forces. Particle composition affects soil pore structure and connectivity. For example, soils with high gravel content usually have strong pore connectivity, making pollutant migration with water easier. Soils with high clay content typically have smaller pores and stronger adsorption capacity, limiting pollutant migration. Soil moisture content affects the driving force of water on pollutant migration; when moisture content is too low, pollutants are difficult to migrate with water, while when moisture content is high, pollutants are more likely to migrate and diffuse with soil moisture.

[0101] In a specific embodiment of this application, step S220, based on the soil moisture content and particle composition, obtains the pollutant migration support at the corresponding sampling depth, including steps S221 to S223.

[0102] Step S221: Based on the particle composition, obtain the degree of soil pore connectivity.

[0103] In this embodiment, the degree of soil pore connectivity is used at least to characterize the ease with which pollutants move within the soil pores.

[0104] In this embodiment, the particle composition may include sand content, silt content, and clay content. Generally, a higher sand content indicates a higher proportion of large pores in the soil and stronger pore connectivity, while a higher clay content indicates a higher proportion of micropores in the soil and more restricted pollutant migration channels. Therefore, the degree of soil pore connectivity can be obtained based on sand content, silt content, and clay content.

[0105] In this embodiment, the degree of soil pore connectivity can be obtained using any reasonable method. For example, the soil pore connectivity can be obtained by weighting the sand content, silt content, and clay content, and the calculation formula is as follows:

[0106]

[0107] in, Indicates the degree of connectivity of soil pores. Indicates sand content, Indicates particle content, Indicates clay content, , and Each is labeled with a preset weight coefficient, and , and All are positive numbers. This represents a normalization function used to normalize the values ​​within the parentheses to the range [0, 1].

[0108] Step S222: Based on the soil moisture content, obtain the degree of water migration drive.

[0109] In this embodiment, the degree of water migration drive is used at least to characterize the magnitude of water's ability to drive the migration of pollutants.

[0110] In this embodiment, the degree of water migration drive can be obtained based on soil moisture content using any reasonable method. For example, the soil moisture content can be normalized to obtain the degree of water migration drive. Alternatively, a preset suitable moisture content range can be set. When the soil moisture content is within this range, the degree of water migration drive remains at a moderate level; when the soil moisture content is higher than this range, the degree of water migration drive increases; and when the soil moisture content is lower than this range, the degree of water migration drive decreases.

[0111] Step S223: Based on the soil pore connectivity and the water migration drive, obtain the pollutant migration support.

[0112] In this embodiment, pollutant migration support can be obtained based on soil pore connectivity and water migration drive level using any reasonable method. For example, pollutant migration support can be positively correlated with soil pore connectivity and water migration drive level. The calculation formula is as follows:

[0113] in, Indicates support for pollutant migration. Indicates the degree of connectivity of soil pores. Indicates the degree of water migration drive. This represents a normalization function used to normalize the values ​​within the parentheses to the range [0, 1].

[0114] In this embodiment, the greater the pollutant migration support, the easier it is for the soil at the corresponding sampling depth to support pollutant migration; conversely, the smaller the pollutant migration support, the weaker the soil at the corresponding sampling depth is in supporting pollutant migration, and the more likely the pollutants are to remain in their original positions.

[0115] Step S230: Based on the soil adsorption and retention capacity and the pollutant migration support, obtain the soil state characterization value.

[0116] In this embodiment, soil state characterization values ​​can be obtained based on soil adsorption and retention capacity and pollutant migration support using any reasonable method. It should be noted that a greater soil adsorption and retention capacity generally indicates a more stable soil environment and less likely pollutants to migrate, while a greater heat-induced migration support generally indicates a more unstable soil environment and more likely pollutants to migrate. Therefore, soil state characterization values ​​can be positively correlated with soil adsorption and retention capacity and negatively correlated with pollutant migration support. The calculation formula is as follows:

[0117]

[0118] in, Indicates soil condition characterization values. Indicates the soil's adsorption and retention capacity. Indicates support for pollutant migration. This represents the zero-prevention coefficient, which is any positive number close to 0, for example, It can be 0.01 or 0.001. This represents a normalization function used to normalize the values ​​within the parentheses to the range [0,1].

[0119] In this embodiment, a larger soil condition characterization value indicates a higher degree of soil environmental stability at the corresponding sampling depth, and a less likely pollutants to migrate. Conversely, a smaller soil condition characterization value indicates a lower degree of soil environmental stability at the corresponding sampling depth, and a more likely pollutants to migrate and spread.

[0120] Step S300: Based on the soil state characterization values ​​at different sampling depths, obtain the longitudinal migration influence coefficient corresponding to each soil sampling point.

[0121] In this embodiment, the longitudinal migration influence coefficient is used at least to characterize the magnitude of the impact of pollutants on the detection results when they migrate between different sampling depths.

[0122] It is important to note that in actual soil environments, the physicochemical and moisture states of soils at different depths can vary significantly. For example, shallow soils are more affected by rainfall, evaporation, tillage, and human disturbance, and their pollutant content may fluctuate more dramatically. While deeper soils are less affected by external disturbances, they can become areas of pollutant leaching and accumulation. Therefore, if there are significant differences in soil conditions between different depths, the impact of pollutant migration along the depth direction becomes more noteworthy.

[0123] In a specific embodiment of this application, step S300 involves obtaining the longitudinal migration influence coefficient corresponding to each soil sampling point based on the soil state characterization values ​​at different sampling depths, including steps S310 to S340.

[0124] Step S310: For any soil sampling point, sort the soil state characterization values ​​at each sampling depth in order from shallow to deep to obtain a depth state sequence.

[0125] In this embodiment, the depth state sequence is used to represent the change in the stability of the soil environment at different sampling depths for the same soil sampling point. For example, if the sampling depths corresponding to a certain soil sampling point are 0.2m, 0.5m and 1m, the soil state characterization values ​​corresponding to the 0.2m depth, the 0.5m depth and the 1m depth can be arranged in order from shallow to deep to obtain the depth state sequence.

[0126] Step S320: Based on the depth state sequence, obtain the first state difference value between adjacent sampling depths.

[0127] In this embodiment, the first state difference value is the difference between the soil state characterization values ​​corresponding to adjacent sampling depths.

[0128] In this embodiment, the first state difference value can be used to characterize the difference in soil environmental stability between adjacent sampling depths. The larger the first state difference value, the more obvious the difference in soil environmental conditions between adjacent sampling depths, and the more complex the migration state of pollutants at that depth interface may be. The smaller the first state difference value, the more similar the soil environmental conditions between adjacent sampling depths, and the relatively stable impact of pollutant migration in the depth direction.

[0129] Step S330: Based on the first state difference value and the depth interval between adjacent sampling depths, obtain the longitudinal migration influence value.

[0130] In this embodiment, the longitudinal migration impact value can be obtained based on the first state difference value and the depth interval between adjacent sampling depths using any reasonable method. For example, the longitudinal migration impact value can be positively correlated with the first state difference value and negatively correlated with the depth interval. That is, when the distance between adjacent sampling depths is close and the soil state difference is large, pollutants are more likely to migrate between these adjacent depths, causing deviations in the detection results.

[0131] In this embodiment, the formula for calculating the vertical migration impact value is as follows:

[0132]

[0133] in, This represents the vertical migration influence value between the j-th sampling depth and the (j-1)-th sampling depth. This represents the soil state characterization value corresponding to the j-th sampling depth. This represents the soil state characterization value corresponding to the (j-1)th sampling depth. This represents the j-th sampling depth. This represents the (j-1)th sampling depth. This represents the zero-prevention coefficient, which is any positive number close to 0. The function represents the normalization function, used to normalize the values ​​within the parentheses to the range [0, 1]. || represents the absolute value.

[0134] Step S340: Based on each longitudinal migration influence value, obtain the longitudinal migration influence coefficient corresponding to the soil sampling point.

[0135] In this embodiment, the longitudinal migration influence coefficient corresponding to each soil sampling point can be obtained based on each reasonable longitudinal migration influence value using any reasonable method. For example, the longitudinal migration influence values ​​between adjacent sampling depths at the same soil sampling point can be averaged, summed, or weighted summed to obtain the longitudinal migration influence coefficient corresponding to that soil sampling point. The calculation formula is as follows:

[0136] in, This represents the vertical migration influence coefficient. This represents the j-th vertical migration impact value. This indicates the number of vertical migration impact values. This represents a normalization function used to normalize the values ​​within the parentheses to the range [0, 1].

[0137] In this embodiment, if the longitudinal migration influence coefficient is larger, it indicates that the pollutant migration between different sampling depths of the soil sampling point has a greater impact on the detection results, and a stronger longitudinal correction of the pollutant content of the soil sampling point is needed subsequently. If the longitudinal migration influence coefficient is smaller, it indicates that the pollutant migration between different sampling depths of the soil sampling point has a weaker impact.

[0138] Step S400: Based on the positional relationship between each sampling point and the soil state characterization value, obtain the lateral diffusion correction coefficient corresponding to each soil sampling point.

[0139] In this embodiment, the lateral diffusion correction coefficient is used at least to characterize the magnitude of the impact of pollutant diffusion between adjacent soil sampling points on the detection results.

[0140] It is important to note that in actual soil testing, adjacent sampling points are not completely independent. Pollutants may be affected by factors such as surface runoff, groundwater flow direction, soil pore channels, topographic slope, and human disturbance, resulting in lateral migration between adjacent sampling points. If a sampling point is close to an adjacent sampling point and the soil condition characterization values ​​of the two at the same sampling depth are significantly different, the pollutant content of that sampling point may be affected by the pollution status of the adjacent sampling point. Therefore, it is necessary to further obtain the lateral diffusion correction coefficient.

[0141] In a specific embodiment of this application, step S400 involves obtaining the lateral diffusion correction coefficient corresponding to each soil sampling point based on the positional relationship between each soil sampling point and the soil state characterization value, including steps S410 to S450.

[0142] Step S410: Obtain the third soil sampling point based on the location coordinates of each soil sampling point.

[0143] In this embodiment, the third soil sampling point is any soil sampling point among the various soil sampling points for which the lateral diffusion correction coefficient has not been obtained. That is to say, in this embodiment, the method for obtaining the lateral diffusion correction coefficient corresponding to any soil sampling point among the various soil sampling points is the same as the method for obtaining the lateral diffusion correction coefficient corresponding to the third soil sampling point.

[0144] Step S420: Based on the third soil sampling point, obtain the fourth soil sampling point.

[0145] In this embodiment, the fourth soil sampling point is any soil sampling point whose distance from the third soil sampling point is less than a preset proximity distance. The preset proximity distance can be pre-set based on the area to be tested, the density of sampling points, the soil type, and the migration characteristics of pollutants. For example, the preset proximity distance can be 10m, 20m, 50m, or other values.

[0146] Step S430: Based on the sampling distance between the third soil sampling point and the fourth soil sampling point, obtain the spatial proximity.

[0147] In this embodiment, spatial proximity can be obtained based on sampling distance using any reasonable method. For example, spatial proximity can be negatively correlated with sampling distance; that is, the smaller the sampling distance, the higher the spatial proximity between the third and fourth soil sampling points, and the larger the sampling distance, the lower the spatial proximity between them. The calculation formula is as follows:

[0148]

[0149] in, Indicates spatial proximity. This indicates the sampling distance between the third and fourth soil sampling points. This represents the zero-prevention coefficient, which is any positive number close to 0.

[0150] Step S440: Based on the soil state characterization values ​​of the third soil sampling point and the fourth soil sampling point at the same sampling depth, obtain the second state difference value.

[0151] In this embodiment, the second state difference value is used to characterize the difference in soil environmental stability between adjacent soil sampling points at the same sampling depth. The larger the second state difference value, the more obvious the difference in soil state between adjacent sampling points at the corresponding sampling depth. This may lead to a larger detection deviation when pollutants diffuse laterally between the two. Conversely, the smaller the second state difference value, the more similar the soil state between adjacent sampling points is. This results in a relatively smaller detection deviation caused by the lateral migration of pollutants.

[0152] Step S450: Based on the spatial proximity and the second state difference value, obtain the lateral diffusion correction coefficient corresponding to the third soil sampling point.

[0153] In this embodiment, the lateral diffusion correction coefficient corresponding to the third soil sampling point can be obtained using any reasonable method based on spatial proximity and the difference between the second state. For example, the lateral diffusion correction coefficient can be positively correlated with spatial proximity and the difference between the second state, and its calculation formula is as follows:

[0154] in, This represents the lateral diffusion correction factor. This indicates the spatial proximity between the third soil sampling point and the r-th fourth soil sampling point. This represents the difference in the second state between the third soil sampling point and the r-th fourth soil sampling point at the same sampling depth. This indicates the number of the fourth soil sampling point. This represents a normalization function used to normalize the values ​​within the parentheses to the range [0, 1].

[0155] In this embodiment, the larger the lateral diffusion correction coefficient, the more significant the impact of pollutant diffusion between adjacent soil sampling points on the detection results, and the stronger the lateral correction of the pollutant content at the corresponding soil sampling point is required. Conversely, the smaller the lateral diffusion coefficient, the weaker the impact of pollutant diffusion from adjacent soil sampling points on the soil sampling point.

[0156] Step S500: Based on the longitudinal migration influence coefficient and the lateral diffusion correction coefficient, the pollutant content of each soil sampling point is corrected to obtain the corrected pollutant content corresponding to each soil sampling point.

[0157] In this embodiment, the corrected pollutant content is used to characterize the pollutant content corresponding to each soil sampling point after considering the effects of vertical migration and horizontal diffusion. Compared with the original pollutant content, the corrected pollutant content can reduce the randomness of the detection value of a single sampling point or a single sampling depth, making the detection results closer to the true distribution of pollutants in the area to be detected.

[0158] In a specific embodiment of this application, step S500 involves correcting the pollutant content at each soil sampling point based on the longitudinal migration influence coefficient and the lateral diffusion correction coefficient, thereby obtaining the corrected pollutant content at each soil sampling point, including steps S510 to S520.

[0159] Step S510: Based on the longitudinal migration influence coefficient, the pollutant content at different sampling depths of the same soil sampling point is longitudinally corrected to obtain the longitudinally corrected pollutant content.

[0160] In this embodiment, any reasonable method can be used for longitudinal correction. For example, for the same soil sampling point, the pollutant content at different sampling depths can be weighted and corrected based on the longitudinal migration lateral coefficient corresponding to that sampling point. The calculation formula is as follows:

[0161]

[0162] in, This represents the longitudinally corrected pollutant content corresponding to the j-th sampling depth. This represents the original pollutant content corresponding to the depth at the j-th sampling point. This represents the longitudinal migration influence coefficient corresponding to the soil sampling point. This represents the depth correction weight corresponding to the j-th sampling depth. It can be preset based on sampling depth, differences in pollutant content at adjacent depths, or depth state sequences.

[0163] In this embodiment, the larger the longitudinal migration influence coefficient, the more obvious the influence of pollutant migration between different sampling depths, and the greater the longitudinal correction of pollutant content. Conversely, the smaller the longitudinal migration influence coefficient, the weaker the longitudinal migration influence, and the smaller the longitudinal correction of pollutant content.

[0164] Step S520: Based on the lateral diffusion correction coefficient, the longitudinal correction pollutant content between adjacent soil sampling points is laterally corrected to obtain the corrected pollutant content.

[0165] In this embodiment, any reasonable method can be used for lateral correction. For example, for any soil sampling point, the longitudinal correction pollutant content of that soil sampling point can be corrected again based on its lateral diffusion correction coefficient and the longitudinal correction pollutant content of adjacent soil sampling points. The calculation formula is as follows:

[0166]

[0167] in, This indicates a correction of pollutant levels. This indicates the longitudinally corrected pollutant content corresponding to the third soil sampling point. This represents the longitudinally corrected pollutant content corresponding to the r-th fourth soil sampling point. This represents the lateral diffusion correction factor. This indicates the number of the fourth soil sampling point. This represents a normalization function used to normalize the values ​​within the parentheses to a preset range.

[0168] In this embodiment, the larger the lateral diffusion correction coefficient, the more significant the impact of pollutant diffusion between adjacent soil sampling points on the detection results, and the greater the lateral correction range of pollutant content. Conversely, the smaller the lateral diffusion correction coefficient, the weaker the lateral diffusion effect, and the smaller the lateral correction range of pollutant content.

[0169] Step S600: Based on the corrected pollutant content corresponding to each soil sampling point, obtain the soil analysis and testing results of the area to be tested.

[0170] In this embodiment, the soil analysis and testing results include at least the spatial distribution information of pollutants, the pollution level information, and the information on abnormal sampling points. The spatial distribution information of pollutants can be used to represent the distribution of pollutants at different locations and depths within the area to be tested. The pollution level information can be used to represent the degree of pollution corresponding to each sub-region of the test. The information on abnormal sampling points is used to indicate sampling points where the pollutant content is abnormally high or the soil condition is abnormally changed.

[0171] In a specific embodiment of this application, step S600 involves obtaining the soil analysis and testing results of the area to be tested based on the corrected pollutant content corresponding to each soil sampling point, including steps S610 to S630.

[0172] Step S610: Based on the corrected pollutant content corresponding to each soil sampling point, obtain the regional pollution assessment value corresponding to each first detection sub-region within the area to be detected.

[0173] In this embodiment, any reasonable method can be used to obtain the regional pollution assessment value. For example, the adjusted pollutant content corresponding to each soil sampling point within the same first detection sub-region can be averaged, maximized, or weighted and summed to obtain the regional pollution assessment value corresponding to that first detection sub-region. If there are multiple pollutant types within a certain first detection sub-region, the pollution assessment score corresponding to each pollutant type can be obtained separately, and the scores of each pollutant can be comprehensively processed to obtain the regional pollution assessment value.

[0174] Step S620: Compare the pollution assessment values ​​of each area with the preset pollution assessment thresholds to obtain the pollution level corresponding to each first detection sub-area.

[0175] In this embodiment, the preset pollution assessment threshold can be determined based on relevant soil pollution assessment standards, land use type of the area to be tested, or preset testing requirements. For example, a first pollution assessment threshold, a second pollution assessment threshold, and a third pollution assessment threshold can be set. When the regional pollution assessment value is less than the first pollution assessment threshold, the corresponding first testing sub-area can be determined to be at a low pollution level. When the regional pollution assessment value is greater than or equal to the first pollution assessment threshold and less than the second pollution assessment threshold, it can be determined to be at a medium pollution level. When the regional pollution assessment value is greater than or equal to the second pollution assessment threshold, it can be determined to be at a high pollution level. The above pollution level classification method is only an example and is not limited in this application.

[0176] Step S630: Based on the pollution level corresponding to each first detection sub-region, generate the soil analysis results of the area to be detected.

[0177] In this embodiment, a pollution level distribution map, pollutant concentration distribution table, or list of abnormal sampling points for the area to be tested can be generated based on the pollution level corresponding to each first detection sub-region. For example, the first detection sub-region with a higher pollution level can be marked as a key treatment area, the first detection sub-region with a medium pollution level can be marked as a continuous monitoring area, and the first detection sub-region with a lower pollution level can be marked as a routine management area. In this way, data can be provided for subsequent soil pollution quality, ecological restoration, risk management, and sampling verification.

[0178] In summary, the soil analysis and testing method proposed in this application, by acquiring basic soil data and calculating a soil state table, can quantitatively analyze the stability of the soil environment at different sampling depths. By acquiring the vertical migration influence coefficient, it can analyze the impact of pollutant migration in the depth direction on the test results. By acquiring the lateral diffusion correction coefficient, it can analyze the impact of pollutant diffusion between adjacent sampling points on the test results. By performing vertical and lateral corrections on pollutant content, it can reduce the randomness and randomness of local sampling data, improve the accuracy of pollutant content representation in the area to be tested, and thus improve the accuracy and reliability of soil analysis and testing results.

[0179] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended embodiments and their equivalents.

Claims

1. A soil analysis and testing method, characterized in that, include: Acquire basic soil data corresponding to multiple soil sampling points within the area to be tested; The basic soil data includes at least the soil physicochemical properties, pollutant content, and soil moisture content at different sampling depths for each soil sampling point. Based on the aforementioned basic soil data, soil state characterization values ​​are obtained for each soil sampling point at different sampling depths; these soil state characterization values ​​are used at least to characterize the soil environmental stability at the corresponding sampling depth. Based on the soil state characterization values ​​at different sampling depths, the longitudinal migration influence coefficient corresponding to each soil sampling point is obtained; the longitudinal migration influence coefficient is at least used to characterize the magnitude of the impact of pollutants on the detection results when they migrate between different sampling depths. Based on the positional relationship between each soil sampling point and the soil state characterization value, the lateral diffusion correction coefficient corresponding to each soil sampling point is obtained. The lateral diffusion correction coefficient is used at least to characterize the magnitude of the impact of pollutant diffusion between adjacent soil sampling points on the detection results; Based on the longitudinal migration influence coefficient and the lateral diffusion correction coefficient, the pollutant content of each soil sampling point is corrected to obtain the corrected pollutant content of each soil sampling point. Based on the corrected pollutant content corresponding to each soil sampling point, the soil analysis and testing results of the area to be tested are obtained.

2. The soil analysis and testing method according to claim 1, characterized in that, The acquisition of basic soil data corresponding to multiple soil sampling points within the area to be tested includes: The area to be detected is divided into multiple first detection sub-regions; Based on each first detection sub-region, a second detection sub-region is obtained; the second detection sub-region is any detection sub-region in each first detection sub-region where soil basic data collection has not been completed. Based on the second detection sub-region, multiple soil samples are obtained at a preset sampling depth; Each soil sample is tested to obtain basic soil data corresponding to each soil sampling point in the second testing sub-region.

3. The soil analysis and testing method according to claim 2, characterized in that, The soil physicochemical indicators include at least one or more of the following: soil pH, electrical conductivity, organic matter content, particle composition, and cation exchange capacity. The pollutant content includes at least one or more of the following: heavy metal content, nitrogen and phosphorus pollutant content, petroleum hydrocarbon content, and organic pollutant content.

4. The soil analysis and testing method according to claim 1, characterized in that, The process of obtaining soil state characterization values ​​at different sampling depths for each soil sampling point based on the aforementioned basic soil data includes: Based on the soil physicochemical indicators, the soil adsorption and retention capacity at the corresponding sampling depth is obtained; The soil adsorption and retention capacity is used at least to characterize the adsorption and fixation capacity of soil for pollutants at the corresponding sampling depth. Based on the soil moisture content and particle composition, the pollutant migration support at the corresponding sampling depth is obtained; the pollutant migration support is used at least to characterize the soil's ability to support pollutant migration at the corresponding sampling depth. The soil state characterization value is obtained based on the soil adsorption and retention capacity and the pollutant migration support.

5. The soil analysis and testing method according to claim 1, characterized in that, The step of obtaining the soil adsorption and retention capacity at the corresponding sampling depth based on the soil physicochemical indicators includes: The adsorption strength of pollutants is obtained based on the organic matter content and cation exchange capacity. The degree of soil chemical stability is obtained based on the pH value and electrical conductivity. The soil adsorption and retention capacity is obtained based on the adsorption intensity of the pollutants and the degree of soil chemical stability.

6. The soil analysis and testing method according to claim 4, characterized in that, The process of obtaining pollutant migration support at the corresponding sampling depth based on the soil moisture content and particle composition includes: Based on the particle composition, the degree of soil pore connectivity is obtained; the degree of soil pore connectivity is used at least to characterize the ease with which pollutants move in the soil pores. Based on the soil moisture content, the degree of water migration drive is obtained; the degree of water migration drive is used to characterize at least the magnitude of the driving ability of water on pollutant migration. The pollutant migration support is obtained based on the soil pore connectivity and the water migration drive.

7. The soil analysis and testing method according to claim 1, characterized in that, The method of obtaining the longitudinal migration influence coefficient corresponding to each soil sampling point based on the soil state characterization values ​​at different sampling depths includes: For any soil sampling point, the soil state characterization values ​​corresponding to each sampling depth are sorted in order from shallow to deep to obtain the depth state sequence. Based on the depth state sequence, a first state difference value is obtained between adjacent sampling depths; the first state difference value is the difference between soil state characterization values ​​corresponding to adjacent sampling depths. Based on the first state difference value and the depth interval between adjacent sampling depths, the longitudinal migration influence value is obtained; Based on each longitudinal migration impact value, the longitudinal migration impact coefficient corresponding to the soil sampling point is obtained.

8. The soil analysis and testing method according to claim 1, characterized in that, The step of obtaining the lateral diffusion correction coefficient corresponding to each soil sampling point based on the positional relationship between each soil sampling point and the soil state characterization value includes: Based on the location coordinates of each soil sampling point, a third soil sampling point is obtained; the third soil sampling point is any soil sampling point among the various soil sampling points for which the lateral diffusion correction coefficient has not been obtained; Based on the third soil sampling point, a fourth soil sampling point is obtained; the fourth soil sampling point is any soil sampling point whose distance from the third soil sampling point is less than a preset proximity distance. The spatial proximity is obtained based on the sampling distance between the third and fourth soil sampling points; Based on the soil state characterization values ​​of the third and fourth soil sampling points at the same sampling depth, the second state difference value is obtained. Based on the spatial proximity and the second state difference value, the lateral diffusion correction coefficient corresponding to the third soil sampling point is obtained.

9. A soil analysis and testing method according to claim 1, characterized in that, The process of correcting the pollutant content at each soil sampling point based on the longitudinal migration influence coefficient and the lateral diffusion correction coefficient to obtain the corrected pollutant content at each soil sampling point includes: Based on the longitudinal migration influence coefficient, the pollutant content at different sampling depths of the same soil sampling point is longitudinally corrected to obtain the longitudinally corrected pollutant content. Based on the lateral diffusion correction coefficient, the longitudinal correction pollutant content between adjacent soil sampling points is laterally corrected to obtain the corrected pollutant content.

10. A soil analysis and testing method according to any one of claims 1 to 9, characterized in that, The process of obtaining soil analysis and testing results for the area to be tested based on the corrected pollutant content corresponding to each soil sampling point includes: Based on the corrected pollutant content corresponding to each sampling point, the regional pollution evaluation value corresponding to each first detection sub-region within the area to be detected is obtained. The pollution assessment values ​​of each region are compared with the preset pollution assessment thresholds to obtain the pollution level corresponding to each first detection sub-region. Based on the pollution level corresponding to each first detection sub-region, soil analysis and detection results of the area to be detected are generated; the soil analysis and detection results include at least the spatial distribution information of pollutants, pollution level information, and abnormal sampling point information.

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