Polluted site soil investigation and point distribution method and system based on risk unit and readable medium

Abstract

The invention belongs to the technical field of pollutant detection, and relates to a polluted site soil investigation and point distribution method and system based on a risk unit and a readable medium, and the method comprises the steps: collecting site soil pollution condition data; inputting the site soil pollution condition data into a pollution site space pollutant concentration prediction model to obtain site pollutant prediction concentration; determining a future utilization scene of the site through a future land planning scheme of the site; determining a risk reference value of the site according to a future utilization scene of the site; according to the site pollutant prediction concentration and the risk reference value, the ecological risk or health risk of the site is evaluated, the risk level of the site is judged, and the risk level area range is delimited; and laying grid point locations of the site according to the risk level area range of the site. According to the method, unnecessary sample collection can be reduced, time, resources and economic cost are saved, and meanwhile, the reliability and authenticity of soil pollution survey results are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to a risk unit-based contaminated site soil investigation site setting method, system and readable medium, belonging to the technical field of pollutant detection. BACKGROUND

[0002] With the development of economic society, the proportion of industrial production in the national economy is increasing, and the change of urban layout leads to the relocation of polluting enterprises and the change of land use nature. At present, various large and small cities in China are facing the problem of redeveloping the sites left by the relocation of enterprises. This requires soil pollution investigation of the site to understand the pollution status of the contaminated site, so as to better carry out site remediation and promote the reuse of the site. Due to the nature of soil as a solid, how to set up sampling points will have a crucial impact on risk control and soil remediation.

[0003] A reasonable contaminated site soil sampling site setting method is the core link of pollution investigation, risk assessment and subsequent remediation. A scientific sampling site setting method can accurately capture the soil pollution status of the site and provide a basis for subsequent soil environmental management. However, the soil and hydrogeological conditions of the contaminated site are complex and changeable, and the pollution is complex and has strong spatial and temporal heterogeneity, which makes it difficult to balance the sampling accuracy and cost saving.

[0004] In the actual operation of site soil pollution investigation, the commonly used sampling methods include systematic sampling, stratified sampling and random sampling. Each of these methods has its own unique advantages and disadvantages. Systematic sampling involves evenly dividing the site to be investigated and setting monitoring points in each region to achieve uniform coverage in space. This method is suitable for large-scale sites where pollution may not be evenly distributed, and can provide basic data for pollution detection under limited resources. However, systematic sampling has limitations in improving the accuracy of monitoring results, especially when the plot area is large, it may overlook some important pollution information, affecting the accuracy and authenticity of the overall investigation results. In contrast, stratified sampling is more targeted. This method involves careful division of the investigation area and targeted sampling based on specific pollution characteristics. This effectively improves the accuracy of pollution investigation in specific areas and ensures the authenticity of the investigation results. However, the reliability of stratified sampling is affected by the scientificity of regional division and the pollution characteristics reflected by the investigation data. If the regional division is unreasonable or the pollution characteristics are not obvious, the deviation of the investigation results may lead to mistakes in the treatment plan and affect the effectiveness of environmental governance. Random sampling is often used in cases where the site soil characteristics are similar and the spatial distribution of pollution is relatively uniform. Its advantage is to ensure that each potential sampling unit has an equal probability of being selected through random sampling, avoiding the influence of human subjective bias on sample representativeness. However, due to the concealment and complexity of soil pollution, randomly placed sample points may be concentrated in non-risk areas or miss low-concentration but important pollution areas, making it difficult to accurately reflect the risk distribution and reducing the practical value of the investigation results.

[0005] In actual site soil pollution investigation, due to the complexity of geographical conditions, the diversity of soil pollution types, and the differences in pollution levels between different regions, the current sampling method is prone to produce inaccurate monitoring results, which cannot truly reflect the actual pollution situation. Therefore, in the sampling scheme design stage, it is easy to fall into the "overly cautious" dilemma. In order to avoid missing potential pollution areas, the actual number of sampling points is much higher than the actual demand, resulting in a large number of redundant sample points, wasting manpower, resources and resources, and may delay the progress of pollution prevention and remediation work. Based on the above problems, it is necessary to optimize and improve the site soil sampling method systematically to solve the problem of low accuracy of monitoring results and resource waste and work delay caused by "overly cautious" sampling point layout, improve the accuracy and scientificity of sampling layout, and better support site soil pollution investigation and subsequent governance work. SUMMARY

[0006] To solve the above problems, the application aims to provide a risk unit-based contaminated site soil investigation site layout method, system and readable medium, which can reduce unnecessary sample collection, save time, resources and economic cost, and also ensure the reliability and authenticity of the soil pollution investigation results.

[0007] To achieve the above-mentioned purposes, the application provides the following technical solutions: a risk unit-based contaminated site soil investigation site layout method, comprising the following steps: collecting site soil pollution condition data; inputting the site soil pollution condition data into a contaminated site spatial pollutant concentration prediction model to obtain site pollutant predicted concentration; determining a site future utilization scenario through a site future land planning scheme; determining a site risk benchmark value according to the site future utilization scenario; evaluating the ecological risk or health risk of the site according to the site pollutant predicted concentration and the risk benchmark value, and determining the risk level area range of the site; and laying out grid points of the site according to the risk level area range of the site.

[0008] Further, the site soil pollution condition data comprises site soil pollutant data, site spatial data, site climate data and site environmental data, the site spatial data comprises longitude, latitude and sampling depth; the site climate data comprises annual average rainfall, annual average air temperature and annual average wind speed; and the site environmental data comprises soil pH, soil organic matter content, water content, cation exchange capacity and soil type.

[0009] Further, the contaminated site spatial pollutant concentration prediction model mines the non-linear relationship between characteristic factors and pollutant concentration by different machine learning algorithms combined with Bayesian regression and geostatistical models, analyzes multi-factor interaction, realizes spatial distribution result prediction of site pollutant concentration, outputs pollutant predicted concentration, and the machine learning algorithm is one or several of random forest, artificial neural network, graph neural network, XGBoost, GBRT and PINN algorithm.

[0010] Further, the site future utilization mode comprises two scenarios of green land and construction land, the construction land is divided into one type of land and two types of land, the risk benchmark value of the site is determined according to different site future utilization scenarios, and the risk benchmark value of the site comprises a risk screening value and a risk control value.

[0011] Further, the site future utilization mode is the green land scenario, based on toxicity data of no observed effect concentration (NOEC) or lowest observed effect concentration (LOEC), the predicted no effect concentration (PNEC) for protecting 95% of soil ecological species is derived by a species sensitivity distribution (SSD) model as a site soil ecological risk screening value; based on half effect concentration (EC 50) or median lethal concentration (LC 50 The PNEC protecting 50% of soil ecological species is derived by an SSD model as a risk control value of the soil ecology of the site; the future use mode of the site is a construction land scenario, the construction land scenario is divided into a first-class land scenario and a second-class land scenario, and the risk screening value and the risk control value of the first-class land and the second-class land are determined by the risk screening value and the risk control value of the first-class land and the second-class land in the existing standard.

[0012] Further, the risk levels of the site are divided into a no-risk area range, a low-risk area range and a high-risk area range; the high-risk area includes a pollution core area and a pollution transition area, and the pollution core area range and the pollution transition area range are determined in combination with the original function area of the site in the high-risk area range.

[0013] Further, the method for determining the risk levels of the site is that if the predicted concentration / risk screening value is less than 1, it is considered that the detection point has no risk, and the no-risk area range of the site is determined; if the predicted concentration / risk screening value is greater than 1 but the predicted concentration / risk control value is less than 1, it is considered that the detection point belongs to low risk, and the low-risk area range of the site is determined; if the predicted concentration / risk control value is greater than 1, it is considered that the detection point belongs to high risk, and the high-risk area range of the site is determined.

[0014] Further, according to the risk level area range of the site, the method for arranging the grid point positions of the site is that a small number of sampling points are randomly arranged in the no-risk area by using a random point arrangement method; the 40 m*40 m grid point positions are arranged in the low-risk area by using a systematic point arrangement method; in the high-risk area, the 20 m*20 m grid point positions are arranged in the pollution core area by using the systematic point arrangement method, and the 10 m*10 m grid point positions are arranged in the pollution transition area by using the systematic point arrangement method.

[0015] The application further discloses a pollution site soil investigation point arrangement system based on a risk unit, which comprises: a data acquisition module for acquiring site soil pollution condition data; a pollutant concentration prediction module for inputting the site soil pollution condition data into a pollution site spatial pollutant concentration prediction model to obtain site pollutant predicted concentration; a future use scenario determination module for determining a site future use scenario through a site future land planning scheme; a risk benchmark value determination module for determining a site risk benchmark value according to the site future use scenario; a risk level determination module for evaluating a site ecological risk or health risk according to the site pollutant predicted concentration and the risk benchmark value, determining a site risk level, and determining a site risk level area range; and a grid point position arrangement module for arranging site grid point positions according to the site risk level area range.

[0016] The application further discloses a computer readable storage medium, and the computer readable storage medium stores a computer program.

[0017] The technical scheme of the application has at least the following technical effects or advantages: the scheme provides intelligent support for sampling site design, opens up a new direction for soil pollution investigation, reduces unnecessary sample collection, saves time, resources and economic cost, and ensures reliability and authenticity of soil pollution investigation results. The risk unit-based soil investigation site design method is not only a technical problem in soil pollution investigation, but also relates to social responsibility of environmental protection and resource management. A scientific and efficient site design method can provide strong support for soil pollution treatment. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 FIG. 1 is a schematic diagram of a risk unit-based soil investigation site design method in an embodiment of the application. DETAILED DESCRIPTION

[0019] In order for those skilled in the art to better understand the technical scheme of the application, the application is described in detail through specific embodiments. However, it should be understood that the specific embodiments are provided only for better understanding of the application, and they should not be understood as a limitation on the application. In the description of the application, it should be understood that the terms used are only for the purpose of description, and cannot be understood as indicating or implying relative importance.

[0020] When sampling points are laid out according to the existing sampling method, the accuracy of the monitoring result is not high, and the actual pollution condition cannot be truly reflected. Therefore, in the sampling scheme design stage, it is easy to fall into the dilemma of 'overcautiousness'. In order to avoid missing potential pollution areas, the number of actually laid sampling points is far more than the actual demand, resulting in a large number of redundant sampling points, causing waste of manpower, material resources and resources, and may delay the progress of pollution prevention and remediation work. Based on the above problems, it is urgent to carry out systematic optimization and improvement on the site soil sampling point layout method to solve the problem of resource waste and work delay caused by overcautiousness, improve the scientificity and efficiency of sampling point layout, and better support the site soil pollution investigation and subsequent governance work. In order to solve the problems that the accuracy of the monitoring result of the site soil is not high, the sampling points are redundant, the pollution characteristic identification and risk boundary delineation are not accurate, and it is difficult to effectively respond to the actual pollution risk, the present application provides a pollution site soil investigation point layout method, system and readable medium based on a risk unit, which uses a machine learning method, a Bayesian regression and a geostatistical model to construct a pollution site spatial pollutant concentration prediction model according to site preliminary soil pollution investigation data, and predicts the site spatial pollutant concentration. The future utilization scenario is determined by the future land use mode of the site, and the risk reference value is determined according to the future utilization scenario. The ecological risk or health risk of the site is evaluated according to the predicted concentration of the site spatial pollutant and the determined risk reference value, the risk level is determined, and the risk boundary is delineated. Different point layout methods are used for different risk level areas. The present application can reduce unnecessary sample collection, save time, resources and economic cost, and also ensure the reliability and authenticity of the soil pollution investigation result. The present application will be described in detail below with reference to the drawings.

[0021] Embodiment one The present embodiment discloses a pollution site soil investigation point layout method based on a risk unit, as shown in the following steps: Figure 1 S1, collecting site soil pollution condition data.

[0022] The site soil pollution condition data includes site soil pollutant data, site spatial data, site climate data and site environmental data, etc. The site spatial data includes longitude, latitude and sampling depth, etc. The site climate data includes annual average rainfall, annual average temperature and annual average wind speed, etc. The site environmental data includes soil pH, soil organic matter content, water content, cation exchange capacity and soil type, etc.

[0023] S2, inputting the site soil pollution condition data into a pollution site spatial pollutant concentration prediction model to obtain the site pollutant predicted concentration.

[0024] ​The model uses soil pollutant data, site spatial data, site climate data, and site environmental data as input variables for predicting spatial pollutant concentrations at contaminated sites. This model employs various machine learning algorithms, combining Bayesian regression and geostatistical models, to uncover the complex nonlinear relationships between feature factors and pollutant concentrations. It analyzes multi-factor interactions and adapts to noisy data to predict the spatial distribution of pollutant concentrations, outputting the predicted pollutant concentrations. The machine learning algorithms include one or more of the following: Random Forest, Artificial Neural Networks, Graph Neural Networks, XGBoost (eXtreme Gradient Boosting), GBRT (Gradient Boosting Regression Tree), and PINN (Physics-Informed Neural Networks).

[0025] S3 determines the future use scenarios of the site through the site's future land planning scheme.

[0026] The future use of the site includes two main scenarios: green space and construction land. Construction land is further divided into Class I and Class II land use. Based on different future use scenarios, the protected recipients and ecological processes are determined, the site's risk baseline value is established, and site monitoring points are strategically located. The site's risk baseline value includes risk screening values ​​and risk control values.

[0027] In the scenario where the site is to be used as green space in the future, the ecological risks are the primary consideration. The Species Sensitivity Distribution (SSD) model is used. With the goal of protecting 95% of soil ecological species, the predicted no-effect concentration (PNEC) for protecting 95% of soil ecological species is derived from toxicity data based on no-observed-effect concentration (NOEC) or lowest-observed-effect concentration (LOEC) using the SSD model as the screening value for site soil ecological risk. With the goal of protecting 50% of soil ecological species, the half-maximum effect concentration (EC50) is used as the screening value. 50 ) or median lethal concentration (LC50) 50 The toxicity data of the soil were used to derive the PNEC (Protective Potential Ecosystem Scale) for protecting 50% of soil ecological species through the SSD model, which was then used as the site soil ecological risk control value.

[0028] The future use of the site is as Class I land for construction, which mainly considers the health risks of exposure during childhood and adulthood. The risk screening and control values ​​for health risks are selected from the current "Soil Environmental Quality Standard for Soil Pollution Risk Control of Construction Land (Trial)" (GB 36600-2018) for Class I land for construction.

[0029] The future use of the site is as Class II land for construction, which mainly considers the health risks of exposure to the population during adulthood. The risk screening and control values ​​for health risks are selected from the current "Soil Environmental Quality Standard for Soil Pollution Risk Control of Construction Land (Trial)" (GB 36600-2018) for Class II land for construction.

[0030] S4 assesses the ecological or health risks of a site based on the predicted concentrations of pollutants and risk benchmarks, determines the risk level of the site, and delineates the risk level zone.

[0031] The site's risk level is divided into risk-free areas, low-risk areas, and high-risk areas; high-risk areas include the core pollution area and the transitional pollution area.

[0032] The method for determining the risk level of a site is as follows: if the predicted concentration / risk screening value is <1, the monitoring point is considered to have no risk, and a risk-free zone is defined within the site; if the predicted concentration / risk screening value is >1, but the predicted concentration / risk control value is <1, the monitoring point is considered to be low-risk, and a low-risk zone is defined within the site; if the predicted concentration / risk control value is >1, the monitoring point is considered to be high-risk, and a high-risk zone is defined within the site. In high-risk areas, the original functional zones of the site are analyzed to determine the core pollution area and the pollution transition area.

[0033] S5 sets up grid points for the site based on the risk level range of the site.

[0034] Based on the risk level range of the site, the method for setting up grid points for the site is as follows: in the risk-free area, a small number of sampling points are randomly set up using the random point method, and it is recommended to collect 0-0.5m surface soil samples.

[0035] In low-risk areas, a systematic sampling method was used to establish a 40 m × 40 m grid of sampling points. Deep soil samples were collected in accordance with the requirements of the "Technical Guidelines for Soil Pollution Status Investigation of Construction Land" (HJ 25.1-2019) and the "Technical Guidelines for Risk Control and Remediation Monitoring of Soil Pollution in Construction Land" (HJ 25.2-2019). For surface soil, samples were collected from 0 to 0.5 m depths. In deeper soil layers (0.5-6 m), it is recommended that soil sampling intervals not exceed 2 m, or that at least one soil sample be collected from each soil type.

[0036] For high-risk areas, the sampling method should be determined based on the original functional zones of the site. If the original functional zones of the site are production areas, storage areas, waste dumping areas, or other locations with a high probability of concentrated pollutant production and leakage, it is identified as a core pollution area, and a systematic sampling method should be used to set up 20 m × 20 m sampling points within this area. If the area is any other area within the original functional zones of the site besides those with a high probability of concentrated pollutant production and leakage, it is identified as a pollution transition area, and a systematic sampling method should be used to set up 10 m × 10 m sampling points within this area. Deep soil samples should be collected in accordance with the requirements of the "Technical Guidelines for Soil Pollution Status Investigation of Construction Land" (HJ 25.1-2019) and the "Technical Guidelines for Risk Control and Remediation Monitoring of Soil Pollution in Construction Land" (HJ 25.2-2019). For surface soil, samples should be collected from 0-0.5 m depths. In the 0.5-6 m deep soil layer, it is recommended that the soil sampling interval not exceed 2 m, and at least one soil sample should be collected for each soil type.

[0037] Example 2 Based on the same inventive concept, this embodiment discloses a soil survey site layout system for contaminated sites based on risk units, including: The data acquisition module is used to collect data on the soil pollution status of the site. The pollutant concentration prediction module is used to input site soil pollution status data into the contaminated site spatial pollutant concentration prediction model to obtain the predicted concentration of site pollutants. The future use scenario determination module is used to determine the future use scenario of the site through the future land planning scheme of the site. The risk baseline determination module is used to determine the risk baseline value of the site based on the future use scenarios of the site. The risk level determination module is used to assess the ecological or health risks of a site based on the predicted concentration of pollutants and risk benchmark values, determine the risk level of the site, and delineate the risk level area. The grid point layout module is used to lay out grid points on the site according to the risk level range of the site.

[0038] Example 3 Based on the same inventive concept, this embodiment discloses a computer-readable storage medium storing a computer program, which is executed by a processor to implement the above-mentioned method for the layout of soil survey points for contaminated sites based on risk units.

[0039] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0040] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0041] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0042] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0043] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific embodiments of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention. The above content is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the protection scope of the claims.

Claims

1. A method for surveying and identifying locations for contaminated sites based on risk units, characterized in that, Includes the following steps: Collect data on soil pollution status at the site; The soil pollution status data of the site is input into the spatial pollutant concentration prediction model of the polluted site to obtain the predicted concentration of pollutants at the site. Determine the future use scenarios of the site through future land planning schemes; Based on the future use scenarios of the site, determine the risk baseline value of the site; Based on the predicted concentration of pollutants at the site and the risk benchmark value, assess the ecological or health risks of the site, determine the risk level of the site, and delineate the risk level area. Based on the risk level range of the site, grid points are set up for the site.

2. The method for surveying and identifying locations for contaminated sites based on risk units as described in claim 1, characterized in that, The site soil pollution status data includes soil pollutant data, site spatial data, site climate data, and site environmental data. The site spatial data includes longitude, latitude, and sampling depth; the site climate data includes annual average rainfall, annual average temperature, and annual average wind speed; and the site environmental data includes soil pH, soil organic matter content, moisture content, cation exchange capacity, and soil type.

3. The method for soil survey and sampling points at contaminated sites based on risk units as described in claim 1, characterized in that, The contaminated site spatial pollutant concentration prediction model uses different machine learning algorithms, combined with Bayesian regression and geostatistical models, to mine the nonlinear relationship between feature factors and pollutant concentration, analyze the interaction of multiple factors, and realize the prediction of the spatial distribution of site pollutant concentration, outputting the predicted pollutant concentration. The machine learning algorithm is one or more of the following algorithms: random forest, artificial neural network, graph neural network, XGBoost, GBRT, and PINN.

4. The method for soil survey and sampling points at contaminated sites based on risk units as described in claim 1, characterized in that, The future use of the site includes two main scenarios: green space and construction land. The construction land is divided into Class I land use and Class II land use. The risk benchmark value of the site is determined according to the different future use scenarios. The risk benchmark value of the site includes risk screening value and risk control value.

5. The method for soil survey and sampling points at contaminated sites based on risk units as described in claim 4, characterized in that, In the scenario where the future use of the site is green space, based on the toxicity data of No Observed Effect Concentration (NOEC) or Lowest Observed Effect Concentration (LOEC), the predicted No Observed Effect Concentration (PNEC) that protects 95% of the soil ecological species is derived through the Species Sensitivity Distribution (SSD) model as the site soil ecological risk screening value. Based on the half-maximal effect concentration (EC5) 50 ) or median lethal concentration (LC50) 50 The toxicity data of the soil were used to derive the PNEC (Protective Energy Conservation Capacity) for protecting 50% of soil ecological species through the SSD (Soil Ecological Risk Control) model, which was then used as the site soil ecological risk control value. The future use of the site was to be construction land, which was divided into Class I and Class II land use scenarios. The risk screening values ​​and risk control values ​​for Class I and Class II land use were determined by the current standards for Class I and Class II land use.

6. The method for soil survey and sampling of contaminated sites based on risk units as described in claim 4, characterized in that, The site is classified into three risk levels: no-risk area, low-risk area, and high-risk area. The high-risk area includes a core pollution area and a transitional pollution area. Within the high-risk area, the core pollution area and the transitional pollution area are determined based on the original functional zones of the site.

7. The method for soil survey and sampling points at contaminated sites based on risk units as described in claim 6, characterized in that, The method for determining the risk level of the site is as follows: If the predicted concentration / risk screening value is less than 1, the testing site is considered to be without risk, and a risk-free area is defined within the site. If the predicted concentration / risk screening value is >1, but the predicted concentration / risk control value is <1, the testing site is considered to be low-risk, and a low-risk area is defined within the site. If the predicted concentration / risk control value is greater than 1, the testing site is considered to be high-risk, and a high-risk area is delineated within the site.

8. The method for surveying and identifying locations for contaminated sites based on risk units as described in claim 7, characterized in that, Based on the risk level range of the site, the method for deploying grid points is as follows: in risk-free areas, a random deployment method is used to randomly deploy a small number of sampling points; in low-risk areas, a systematic deployment method is used to deploy 40 m × 40 m grid points; in high-risk areas, for the core pollution area, a systematic deployment method is used to deploy 20 m × 20 m grid points; and for the pollution transition area, a systematic deployment method is used to deploy 10 m × 10 m grid points.

9. A soil survey and sampling system for contaminated sites based on risk units, characterized in that, include: The data acquisition module is used to collect data on the soil pollution status of the site. The pollutant concentration prediction module is used to input the site soil pollution status data into the site spatial pollutant concentration prediction model to obtain the site pollutant prediction concentration. The future use scenario determination module is used to determine the future use scenario of the site through the future land planning scheme of the site. The risk benchmark value determination module is used to determine the risk benchmark value of the site based on the future use scenario of the site. The risk level determination module is used to assess the ecological or health risks of the site based on the predicted concentration of pollutants at the site and the risk benchmark value, determine the risk level of the site, and delineate the risk level area. The grid point layout module is used to lay out grid points for the site according to the risk level range of the site.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is executed by a processor to implement the risk unit-based soil survey site layout method for contaminated sites as described in any one of claims 1-8.

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