Method and system for ecological risk assessment of heavy metal contaminated soil

Abstract

The application provides an ecological risk assessment method and system for heavy metal contaminated soil, and relates to the technical field of soil pollution control. The method comprises the following steps: obtaining a soil sample set of a target area; constructing a spatial grid model of the target area according to the soil sample set; determining a comprehensive pollution index of a grid unit according to concentration distribution information of heavy metals; performing time series analysis on the spatial grid model to predict a dynamic migration trend of the heavy metal concentration corresponding to the grid unit; determining an exposure threshold of different ecological receptors in the grid unit; generating an ecological risk heat map of the target area according to the comprehensive pollution index, the dynamic migration trend and the exposure threshold; and determining a risk position and a safe position in the target area according to the ecological risk heat map and a preset threshold. Therefore, the ecological risk of the target area can be more comprehensively and accurately evaluated, and the accuracy and reliability of risk assessment are greatly improved.

Description

Technical Field

[0001] This application relates to the field of soil pollution remediation technology, and in particular to an ecological risk assessment method and system for heavy metal contaminated soil. Background Technology

[0002] In today's era of rapid industrialization and urbanization, soil heavy metal pollution is becoming increasingly serious. Heavy metal pollutants such as lead, mercury, cadmium, and chromium, due to their high toxicity, reluctance to degrade, and tendency to accumulate in organisms, pose a significant threat to soil ecosystems, human health, and the ecological environment. Currently, the assessment of soil heavy metal pollution has many limitations. Traditional single-factor pollution index methods only consider the pollution status of a single heavy metal and cannot comprehensively reflect the actual situation of complex pollution from multiple heavy metals. Summary of the Invention

[0003] The purpose of this application is to at least partially solve one of the technical problems in the related art.

[0004] Therefore, the first objective of this application is to propose an ecological risk assessment method for heavy metal contaminated soil, so as to achieve a comprehensive and accurate assessment of the ecological risk of the target area.

[0005] The second objective of this application is to propose an ecological risk assessment system for heavy metal contaminated soil.

[0006] To achieve the above objectives, the first aspect of this application proposes an ecological risk assessment method for heavy metal contaminated soil, comprising: acquiring a soil sample set of a target area, wherein the soil sample set includes multiple soil sample data, and each soil sample data includes a sampling location and the concentration values ​​of N heavy metals, where N is a positive integer;

[0007] Based on the soil sample set, a spatial grid model of the target area is constructed, wherein the spatial grid model includes multiple grid cells, and each grid cell contains heavy metal concentration distribution information;

[0008] Based on the concentration distribution information of the heavy metals, the comprehensive pollution index of the grid unit is determined;

[0009] Time series analysis is performed on the spatial grid model to predict the dynamic migration trend of heavy metal concentrations corresponding to the grid cells;

[0010] Determine the exposure thresholds of different ecological receptors in grid cells;

[0011] For each grid cell, an ecological risk heat map of the target area is generated based on the comprehensive pollution index, dynamic migration trend, and exposure threshold corresponding to the grid cell.

[0012] Based on the ecological risk heat map of the target area and preset thresholds, risk locations and safe locations are determined from the target area.

[0013] To achieve the above objectives, a second aspect of this application proposes an ecological risk assessment system for heavy metal contaminated soil, comprising: a data acquisition module for acquiring a soil sample set of a target area, wherein the soil sample set includes multiple soil sample data, and each soil sample data includes a sampling location and concentration values ​​of N heavy metals, where N is a positive integer;

[0014] The model building module is used to construct a spatial grid model of the target area based on the soil sample set, wherein the spatial grid model includes multiple grid cells, and each grid cell contains the concentration distribution information of heavy metals;

[0015] The pollution index determination module is used to determine the comprehensive pollution index of the grid cell based on the concentration distribution information of the heavy metals.

[0016] The trend prediction module is used to perform time series analysis on the spatial grid model and predict the dynamic migration trend of heavy metal concentration corresponding to the grid cell.

[0017] The exposure threshold determination module is used to determine the exposure threshold of different ecological receptors in the grid cell;

[0018] The heat map generation module is used to generate an ecological risk heat map of the target area for each grid cell, based on the comprehensive pollution index, dynamic migration trend and exposure threshold corresponding to the grid cell.

[0019] The region division module is used to determine risk locations and safe locations in the target region based on the ecological risk heat map of the target region and preset thresholds.

[0020] The ecological risk assessment method and system for heavy metal contaminated soil provided in this application acquires a set of soil samples from a target area and constructs a spatial grid model of the target area based on the soil sample set. Based on the concentration distribution information of heavy metals contained in each grid cell, a comprehensive pollution index for the grid cell is determined, and the dynamic migration trend of heavy metal concentrations corresponding to the grid cell is predicted. By determining the exposure thresholds of different ecological receptors in the grid cells, an ecological risk heat map of the target area is generated based on the comprehensive pollution index, dynamic migration trend, and exposure thresholds. This allows for the identification of risk and safe locations within the target area based on the ecological risk heat map and preset thresholds. Therefore, this scheme can fully consider the spatial distribution characteristics of heavy metal pollution in soil. By predicting the dynamic migration trend of heavy metal concentrations, it can achieve early understanding of the development direction of heavy metal pollution, providing a strong basis for formulating targeted pollution prevention and control measures. By assessing the ecological risk of the target area through multi-dimensional factors such as the comprehensive pollution index, dynamic migration trend, and exposure thresholds, a more comprehensive and accurate assessment of the ecological risk of different grid cells can be achieved, greatly improving the accuracy and reliability of risk assessment.

[0021] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0022] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0023] Figure 1 A schematic flowchart illustrating an ecological risk assessment method for heavy metal contaminated soil provided in this application embodiment;

[0024] Figure 2 A flowchart illustrating another method for ecological risk assessment of heavy metal contaminated soil provided in this application embodiment;

[0025] Figure 3 A schematic diagram of the fuzzy estimation process provided in the embodiments of this application;

[0026] Figure 4 This is a schematic diagram of the process for determining the weight of heavy metals provided in an embodiment of this application;

[0027] Figure 5 A flowchart illustrating another method for ecological risk assessment of heavy metal contaminated soil provided in this application embodiment;

[0028] Figure 6 A flowchart illustrating the prediction process of dynamic migration trends provided in an embodiment of this application;

[0029] Figure 7 A flowchart illustrating another method for ecological risk assessment of heavy metal contaminated soil provided in this application embodiment;

[0030] Figure 8 This is a schematic diagram of the process for assessing the ecological risk of heavy metal contaminated soil provided in an embodiment of this application;

[0031] Figure 9 This is a schematic diagram of the structure of an ecological risk assessment system for heavy metal contaminated soil provided in an embodiment of this application. Detailed Implementation

[0032] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0033] The following describes, with reference to the accompanying drawings, an ecological risk assessment method and system for heavy metal contaminated soil according to embodiments of this application.

[0034] Figure 1 This is a flowchart of an ecological risk assessment method for heavy metal contaminated soil provided in an embodiment of this application, as shown below. Figure 1 As shown, the ecological risk assessment method for heavy metal contaminated soil in this application includes, but is not limited to, the following steps:

[0035] S101, Obtain a set of soil samples from the target area. The set of soil samples includes multiple soil sample data. Each soil sample data includes the sampling location and the concentration values ​​of N heavy metals, where N is a positive integer.

[0036] It should be noted that the subject of the ecological risk assessment method for heavy metal contaminated soil provided in this application is an electronic device, which can be a terminal device. Optionally, the terminal device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be personal computers (PCs), televisions, etc. This application does not impose specific limitations.

[0037] In some embodiments, soil samples can be collected from different locations in the target area using one or more sampling devices, and the soil samples can be tested to determine the N heavy metals contained in the soil samples and the concentration value of each heavy metal.

[0038] In some embodiments, positioning devices such as Global Positioning System (GPS) can be used to determine the sampling locations of different sampling points within the target area, thereby collecting soil samples at the sampling points and using the sampling locations and the concentration values ​​of N heavy metals as soil sample data, and then generating a soil sample set based on the soil sample data.

[0039] S102. Based on the soil sample set, construct a spatial grid model of the target area. The spatial grid model includes multiple grid cells, and each grid cell contains information on the concentration distribution of heavy metals.

[0040] In some embodiments, the concentration distribution of heavy metals can be determined based on the sampling locations and concentration values ​​of N heavy metals in the soil sample set, thereby generating a heavy metal concentration distribution surface. By dividing the heavy metal concentration distribution surface into grid cells, a spatial grid model of the target area can be constructed.

[0041] In some embodiments, an identifier can be assigned to each grid cell, and the location range corresponding to each grid cell can be determined. Based on the location range of the grid cell and the concentration distribution of heavy metals, the concentration distribution information of heavy metals contained in each grid cell can be determined. Optionally, the concentration distribution information can be a concentration distribution vector of heavy metals.

[0042] In some embodiments, when dividing the heavy metal concentration distribution surface into grid cells, the heavy metal concentration distribution surface can be divided into multiple equally spaced grid cells.

[0043] S103, based on the concentration distribution information of heavy metals, determine the comprehensive pollution index of the grid unit.

[0044] In some embodiments, one or more heavy metals contained in a grid cell can be determined based on the concentration distribution information of heavy metals, and the concentration value and weight of each heavy metal can be determined, so as to calculate the comprehensive pollution index of the grid cell based on the concentration value and weight.

[0045] In some embodiments, since grid cells may contain multiple heavy metals, the pollution index of each heavy metal can be calculated separately, and the multiple pollution indices can be summed to obtain the comprehensive pollution index of the grid cell. For example, suppose grid cell A contains heavy metal 1, heavy metal 2, and heavy metal 3. By calculating the pollution index 1 of heavy metal 1 based on its concentration value and weight, the pollution index 2 of heavy metal 2 based on its concentration value and weight, and the pollution index 3 of heavy metal 3 based on its concentration value and weight, the comprehensive pollution index is pollution index 1 + pollution index 2 + pollution index 3.

[0046] S104 performs time series analysis on the spatial grid model to predict the dynamic migration trend of heavy metal concentrations corresponding to grid cells.

[0047] In some embodiments, a migration model of heavy metals can be constructed, and the concentration distribution prediction value of heavy metals in each grid cell can be obtained by solving the migration model. Thus, the dynamic migration trend of heavy metal concentration can be predicted based on the concentration distribution prediction value of heavy metals in each grid cell.

[0048] In some embodiments, a migration model of heavy metals can be constructed based on partial differential equations, wherein the migration model is related to time and the concentration of heavy metals.

[0049] In some embodiments, the changes in heavy metal concentration between different grid cells and the trend of heavy metal concentration over time can be determined based on the predicted concentration distribution of heavy metals in each grid cell, thereby predicting the dynamic migration trend of heavy metal concentration over time.

[0050] S105, determine the exposure thresholds of different ecological receptors in the grid cell.

[0051] As is understandable, an ecological receptor refers to an ecological entity exposed to environmental pollutants. This can be an organism's tissue or organ, or a different level of life structure such as a population, community, or ecosystem. The exposure threshold refers to the critical level of environmental pollutant exposure that an ecological receptor can tolerate. When the exposure level is below this threshold, the ecological receptor usually does not suffer significant negative effects; however, when the exposure level exceeds this threshold, the ecological receptor may be damaged, and it may even lead to changes in the structure and function of the ecosystem.

[0052] In some embodiments, sensitivity data of different ecological receptors in the target area can be obtained from an ecological receptor sensitivity database, and environmental data of the target area can be collected to simulate the dynamic changes of ecological receptors under different exposure levels based on the environmental data and sensitivity data, thereby determining the exposure threshold.

[0053] S106 generates an ecological risk heat map of the target area for each grid cell based on the comprehensive pollution index, dynamic migration trend, and exposure threshold corresponding to the grid cell.

[0054] In some embodiments, for each grid cell, the risk level of each grid cell can be determined based on the comprehensive pollution index, dynamic migration trend and exposure threshold corresponding to the grid cell, and then an ecological risk heat map of the target area can be generated based on the risk level.

[0055] In some embodiments, the spatial distribution of risk levels can be determined based on the location range of grid cells, and an ecological risk heat map can be generated based on visualization technology to intuitively display the ecological risks at different locations in the target area.

[0056] In some embodiments, a multidimensional risk assessment matrix can be established based on the comprehensive pollution index, dynamic migration trend, and exposure threshold. By solving the multidimensional risk assessment matrix, the risk level corresponding to the grid cell can be obtained.

[0057] S107. Based on the ecological risk heat map of the target area and preset thresholds, determine the risk locations and safe locations in the target area.

[0058] In some embodiments, the risk level of different locations in the target area can be determined based on the ecological risk heat map of the target area, and the risk level can be compared with a preset threshold to determine the risk location and safe location in the target area.

[0059] In some embodiments, locations with risk levels greater than or equal to a preset threshold in the target area can be determined as risk locations based on the ecological risk heat map, and locations with risk levels less than the preset threshold in the target area can be determined as safe locations.

[0060] The ecological risk assessment method for heavy metal contaminated soil provided in this application involves acquiring a set of soil samples from a target area and constructing a spatial grid model of the target area based on the soil sample set. Based on the concentration distribution information of heavy metals contained in each grid cell, a comprehensive pollution index for the grid cell is determined, and the dynamic migration trend of heavy metal concentrations corresponding to the grid cell is predicted. By determining the exposure thresholds of different ecological receptors in the grid cells, an ecological risk heat map of the target area is generated based on the comprehensive pollution index, dynamic migration trend, and exposure thresholds. This allows for the identification of risk and safe locations within the target area based on the ecological risk heat map and preset thresholds. Therefore, this method fully considers the spatial distribution characteristics of heavy metal pollution in soil. By predicting the dynamic migration trend of heavy metal concentrations, it enables early understanding of the development direction of heavy metal pollution, providing a strong basis for formulating targeted pollution prevention and control measures. By assessing the ecological risk of the target area through multi-dimensional factors such as the comprehensive pollution index, dynamic migration trend, and exposure thresholds, a more comprehensive and accurate assessment of the ecological risk of different grid cells can be achieved, greatly improving the accuracy and reliability of risk assessment.

[0061] Figure 2 This is a flowchart of an ecological risk assessment method for heavy metal contaminated soil provided in an embodiment of this application, as shown below. Figure 2 As shown, the ecological risk assessment method for heavy metal contaminated soil in this application includes, but is not limited to, the following steps:

[0062] S201, Obtain a set of soil samples from the target area. The set of soil samples includes multiple soil sample data. Each soil sample data includes the sampling location and the concentration values ​​of N heavy metals, where N is a positive integer.

[0063] S202. Based on the soil sample set, construct a spatial grid model of the target area. The spatial grid model includes multiple grid cells, and each grid cell contains information on the concentration distribution of heavy metals.

[0064] In the embodiments of this application, steps S201-S202 can be implemented in any of the embodiments of this application, and no limitation is made here, nor will it be described in detail.

[0065] S203, based on the concentration distribution information of heavy metals in the grid cells, determine the concentration value of each heavy metal in the grid cells.

[0066] In some embodiments, the heavy metals contained in a grid cell, and the concentration value of each heavy metal, can be determined from the heavy metal concentration distribution information of the grid cell.

[0067] In some embodiments, for the i-th heavy metal, it can be determined in advance whether the soil sample data contains the concentration value of the i-th heavy metal. In response to the soil sample data containing the concentration value of the i-th heavy metal, the concentration value of the i-th heavy metal can be directly obtained from the concentration distribution information of the heavy metal.

[0068] In some embodiments, in response to the absence of a concentration value for the i-th heavy metal in the soil sample data, a fuzzy estimation can be performed on the concentration value to determine the concentration value of the i-th heavy metal. Optionally, the concentration data of the i-th heavy metal in the target area can be obtained, and based on the concentration data, the minimum concentration value, average concentration value, and maximum concentration value of the heavy metal can be calculated. A fuzzy estimation can then be performed based on the minimum concentration value, average concentration value, and maximum concentration value to obtain the concentration value of the i-th heavy metal.

[0069] In other words, in response to the fact that the concentration value of the i-th heavy metal is not included in the soil sample data, the minimum, average and maximum concentration values ​​of the i-th heavy metal in the target area are obtained, and the minimum, average and maximum concentration values ​​of the i-th heavy metal are estimated by fuzzy estimation of the triangular membership function to obtain the concentration value of the i-th heavy metal in the soil sample data.

[0070] Alternatively, the formula for fuzzy estimation is as follows:

[0071]

[0072] in, C represents the concentration value of the i-th heavy metal. min C represents the minimum concentration value. avg C represents the average concentration value. max This indicates the maximum concentration value.

[0073] In some embodiments, by performing fuzzy estimation on the missing concentration values, a relatively reasonable ecological risk assessment can still be conducted even when data is missing, thereby reducing the impact of missing data on the assessment results.

[0074] Figure 3 The diagram shown is a flowchart of fuzzy estimation. Figure 3 As shown, by checking for missing data in the soil sample data, and when the concentration value of the i-th heavy metal is missing, the minimum, average, and maximum concentration values ​​of the i-th heavy metal in the target area are obtained. A fuzzy estimation is then performed using a triangular membership function to obtain the fuzzy estimation result as the concentration value of the i-th heavy metal. Furthermore, the comprehensive pollution index can be calculated based on the fuzzy estimation result. If no data is missing, the comprehensive pollution index can be calculated directly using complete data.

[0075] S204 determines the background value and weight of each heavy metal, and determines the pollution index of the heavy metal based on the concentration value, background value, and weight.

[0076] In some embodiments, the content of each heavy metal in the target area when it was not polluted can be obtained based on historical monitoring data of the soil in the target area, and this content can be used as the background value of the heavy metal.

[0077] In some embodiments, to ensure the reasonableness of calculating the pollution index of heavy metals, the sum of the weights of each heavy metal can be set to 1. Alternatively, the weights of heavy metals can be obtained by calculating the initial weights of the heavy metals and determining the correction factor to correct the initial weights, thereby enabling a more accurate determination of the weight of each heavy metal in the comprehensive pollution index.

[0078] In some embodiments, the initial weight of a heavy metal can be determined based on its toxicity response coefficient, migration rate, and bioaccumulation coefficient. The toxicity response coefficient reflects the relative ability of a heavy metal to exert a toxic effect on organisms; a higher value indicates stronger toxicity. The migration rate reflects the ability of a heavy metal to migrate in soil; a high migration rate means that the heavy metal is more likely to spread in the soil, potentially impacting a wider range of environments and organisms. The bioaccumulation coefficient measures the degree of accumulation of heavy metals in organisms; a higher bioaccumulation coefficient indicates a greater risk of heavy metal accumulation in the food chain.

[0079] Optionally, the formula for calculating the initial weights is as follows:

[0080]

[0081] in, T represents the initial weight of the i-th heavy metal. i M represents the toxicity response coefficient of the i-th heavy metal. i B represents the migration rate of the i-th heavy metal. i denoted as the bioaccumulation coefficient of the i-th heavy metal.

[0082] Furthermore, soil pH and organic matter content can be used as correction factors. By determining the soil pH and organic matter content, and then correcting the initial weights of heavy metals based on these factors, the weights of the heavy metals can be obtained. pH refers to the acidity or alkalinity of the soil, which significantly influences the form, migration, and transformation of heavy metals. For example, in acidic soils, the solubility of some heavy metals may increase, thereby enhancing their bioavailability and mobility. The level of organic matter content affects the adsorption and desorption processes of heavy metals in the soil, thus influencing their activity and toxicity.

[0083] Alternatively, the modified formula is as follows:

[0084]

[0085] Among them, W i denoted by , where α and β are empirical constants, pH represents the soil pH value, and OM represents the organic matter content.

[0086] In some embodiments, for each heavy metal, the pollution index of each heavy metal can be determined by obtaining the ratio of the concentration value of the heavy metal to the background value and multiplying the ratio by a weight.

[0087] Figure 4 This diagram illustrates the process for determining the weights of heavy metals. For example... Figure 4 As shown, for the i-th heavy metal, the toxicity response coefficient, migration rate, and bioaccumulation coefficient of the i-th heavy metal are obtained, and the initial weight is determined according to the above formula (2). By obtaining the soil pH value and organic matter content, and using the soil pH value and organic matter content as correction factors to correct the initial weight, the weight of the i-th heavy metal is obtained. Further, it is determined whether the weight of each heavy metal has been calculated, so as to obtain the weight of each heavy metal.

[0088] S205 sums the pollution indices of N heavy metals within a grid cell to obtain the comprehensive pollution index of the grid cell.

[0089] In some embodiments, the formula for calculating the comprehensive pollution index is as follows:

[0090]

[0091] Where P represents the comprehensive pollution index, C i S represents the concentration value of the i-th heavy metal. i Let W represent the background value of the i-th heavy metal. i This represents the weight of the i-th heavy metal.

[0092] In some embodiments, to make the comprehensive pollution index more reflective of actual ecological risks, it can be modified according to the proportion of bioavailable heavy metals to obtain the final comprehensive pollution index for the grid cell. The proportion of bioavailable heavy metals reflects the percentage of heavy metals in the soil that can be absorbed and utilized by organisms.

[0093] In some embodiments, the bioavailable percentage of heavy metals can be calculated based on the total concentration and bioavailable concentration of heavy metals in the target area. Optionally, for the i-th heavy metal, the bioavailable concentration of the i-th heavy metal can be determined by chemical extraction, and the total concentration of the i-th heavy metal in the target area can be determined by measuring the total content of the i-th heavy metal in the soil of the target area.

[0094] Furthermore, the bioavailable percentage of the i-th heavy metal can be determined based on its bioavailable concentration and total concentration. Optionally, the bioavailable percentage of the i-th heavy metal can be determined by calculating the ratio of its bioavailable concentration to its total concentration, as shown in the following formula:

[0095]

[0096] Among them, F i C represents the bioavailable percentage of the i-th heavy metal. bio C represents the bioavailable concentration of the i-th heavy metal. total This represents the total concentration of the i-th heavy metal.

[0097] Furthermore, the maximum bioavailable percentage is determined from the bioavailable percentages of the N heavy metals, and the comprehensive pollution index of the grid cell is corrected based on the maximum bioavailable percentage to obtain the final comprehensive pollution index of the grid cell. The correction formula is shown below:

[0098]

[0099] in, This represents the final comprehensive pollution index, where F represents the percentage of bioavailable substances.

[0100] S206 performs time series analysis on a spatial grid model to predict the dynamic migration trend of heavy metal concentrations corresponding to grid cells.

[0101] S207, determine the exposure thresholds of different ecological receptors in grid cells.

[0102] S208 generates an ecological risk heat map of the target area for each grid cell based on the comprehensive pollution index, dynamic migration trend, and exposure threshold corresponding to the grid cell.

[0103] S209. Based on the ecological risk heat map of the target area and preset thresholds, determine the risk locations and safe locations in the target area.

[0104] In the embodiments of this application, steps S206-S209 can be implemented in any of the embodiments of this application, and no limitation is made here, nor will it be described in detail.

[0105] The ecological risk assessment method for heavy metal contaminated soil provided in this application calculates a comprehensive pollution index based on the background value, weight, and concentration value of heavy metals. This fully considers the characteristics of different heavy metals and the influence of soil environmental factors on their pollution level, enabling the comprehensive pollution index to more objectively and accurately reflect the actual situation of soil pollution by multiple heavy metals.

[0106] Figure 5 This is a flowchart of an ecological risk assessment method for heavy metal contaminated soil provided in an embodiment of this application, as shown below. Figure 5 As shown, the ecological risk assessment method for heavy metal contaminated soil in this application includes, but is not limited to, the following steps:

[0107] S501, Obtain a set of soil samples from the target area. The set of soil samples includes multiple soil sample data. Each soil sample data includes the sampling location and the concentration values ​​of N heavy metals, where N is a positive integer.

[0108] In the embodiments of this application, step S501 can be implemented in any of the ways described in the various embodiments of this application. This is not limited here and will not be described in detail.

[0109] S502 uses Kriging interpolation to spatially interpolate the sampling locations and heavy metal concentrations of soil samples, generating a heavy metal concentration distribution surface.

[0110] Understandably, Kriging interpolation is a geostatistical interpolation method based on the theory of regionalized variables, which considers the spatial correlation between sampling locations. For example, within the target region, there are... The sampling locations for each soil sample are given by the heavy metal concentration Z(x). i ,y i ), where (x i ,y i Let be the coordinates of the sampling location. By calculating the semivariance function γ(h) between sampling locations and using the fitted semivariance model, the heavy metal concentration at any unsampled location within the target area can be interpolated to obtain the interpolation result. The formula for calculating the semivariance function is shown below:

[0111]

[0112] Where N(h) is the number of sampling position pairs at a distance of h.

[0113] In other words, by calculating the semivariance function between sampling locations of soil sample data, and fitting a theoretical model based on the calculated semivariance function, a well-fitted semivariance model can be obtained. By identifying unsampled locations in the target area, and using this semivariance model and the concentration values ​​of heavy metals, interpolation calculations can be performed on the heavy metal concentration values ​​at those sampling locations to obtain the interpolated result, which is the heavy metal concentration value at that sampling location. Furthermore, the concentration values ​​can be mapped onto geographic space to generate a heavy metal concentration distribution surface.

[0114] S503 divides the heavy metal concentration distribution surface into equally spaced grid cells to obtain a spatial grid model.

[0115] In some embodiments, the heavy metal concentration distribution surface can be divided into equally spaced grid cells by determining the side length of the grid cells and dividing the heavy metal concentration distribution surface according to the side length, thereby obtaining a spatial grid model.

[0116] In some embodiments, when dividing the grid into cells, a unique identifier can be assigned to each grid cell. For example, sequential numbering can be used, starting from the top left corner and numbering row by row from left to right and top to bottom. Simultaneously, the coordinate range of each grid cell is defined, using the coordinates of the bottom left vertex (x...). min ,y min ) and the coordinates of the top right vertex (x max ,y max The use of ) to represent each grid cell gives each grid cell a clear spatial identifier, which facilitates the management and analysis of the concentration distribution information of heavy metals within the grid cell. This ensures that each grid cell accurately contains the concentration distribution information of heavy metals, providing an accurate data foundation for subsequent risk assessment.

[0117] S504 determines the comprehensive pollution index of grid units based on the concentration distribution information of heavy metals.

[0118] In the embodiments of this application, step S504 can be implemented in any of the ways described in the various embodiments of this application. This is not limited here and will not be elaborated further.

[0119] S505 performs time series analysis on a spatial grid model to predict the dynamic migration trend of heavy metal concentrations corresponding to grid cells.

[0120] In some embodiments, by constructing a migration model related to heavy metal concentration and performing time series analysis on a spatial grid model based on the model, the dynamic migration trend of heavy metal concentration corresponding to the grid cell can be predicted.

[0121] In some embodiments, a migration model for heavy metals can be constructed based on a partial differential equation model. Optionally, the migration model can be constructed based on the groundwater seepage velocity, diffusion coefficient, and chemical reaction rate in the target area. Groundwater flow drives the migration of heavy metals in the soil; the faster the groundwater seepage velocity, the faster the heavy metal migration. The diffusion coefficient reflects the diffusion capacity of heavy metals in the soil and is related to factors such as soil pore structure and moisture content; the larger the diffusion coefficient, the faster the diffusion rate of heavy metals in the soil. The chemical reaction rate describes the speed of chemical reactions related to heavy metals in the soil, and the chemical reaction rate affects the form and migration / transformation process of heavy metals in the soil.

[0122] In some embodiments, by determining the groundwater seepage velocity in the target area, as well as the diffusion coefficient and chemical reaction rate of heavy metals, a migration model of heavy metals can be constructed based on the groundwater seepage velocity, diffusion coefficient, and chemical reaction rate.

[0123] Alternatively, the transfer model is as follows:

[0124]

[0125] Where C is the concentration of heavy metals, D is the diffusion coefficient, v is the groundwater seepage velocity, k is the chemical reaction rate, and t is the time variable.

[0126] Furthermore, the finite difference method can be used to discretize and solve the migration model, obtaining predicted values ​​of heavy metal concentration distribution in different grid cells over future time periods. The finite difference method discretizes time and space, thus accurately predicting the heavy metal concentration distribution in grid cells over future time periods.

[0127] For example, assume the time step is Δt and the spatial steps are Δx and Δy. Spatially, for and Approximation is performed using a central difference scheme, for example... The discrete form in two-dimensional space can be represented as: , where C i,j This represents the concentration value of the heavy metal in the grid cell at row i and column j, with respect to time derivative. Using the forward difference scheme, i.e. ,in, This represents the concentration of heavy metals in the grid cell at row i and column j at time n. From these discretized expressions in formula (8) above, we can obtain a value related to... By iteratively solving this algebraic equation, we can obtain the predicted concentration distribution of heavy metals in the future time period.

[0128] Furthermore, the dynamic migration trend of heavy metal concentration corresponding to a grid cell can be determined based on the predicted concentration distribution of heavy metals in that grid cell. Optionally, the variation of heavy metal concentration between different grid cells and the trend of heavy metal concentration over time can be determined based on the predicted concentration distribution of heavy metals in each grid cell, thereby determining the dynamic migration trend of heavy metal concentration over time.

[0129] Figure 6 The diagram shown illustrates the process of predicting dynamic migration trends. Figure 6 As shown, by acquiring the groundwater seepage velocity, diffusion coefficient, and chemical reaction rate of heavy metals in the target area, a heavy metal migration model is constructed. The migration model is then discretized using the finite difference method. By setting parameters such as the time step and spatial step, and performing iterative calculations based on the discretized equations, the predicted concentration distribution of heavy metals over a future time period can be obtained. The prediction of the dynamic migration trend is then completed by determining whether the predicted concentration distribution meets the time requirements and, if so, by doing so.

[0130] In this embodiment, by constructing a heavy metal migration model, the migration process of heavy metals in soil can be effectively simulated. By considering multiple factors such as diffusion coefficient, groundwater seepage velocity, and chemical reaction rate, the prediction results are more consistent with reality.

[0131] S506, determine the exposure thresholds of different ecological receptors in grid cells.

[0132] S507 generates an ecological risk heat map of the target area for each grid cell based on the comprehensive pollution index, dynamic migration trend, and exposure threshold corresponding to the grid cell.

[0133] S508, based on the ecological risk heat map of the target area and preset thresholds, determines the risk locations and safe locations in the target area.

[0134] In the embodiments of this application, steps S506-S508 can be implemented in any of the embodiments of this application, and no limitation is made here, nor will it be described in detail.

[0135] The ecological risk assessment method for heavy metal contaminated soil provided in this application uses Kriging interpolation to generate a heavy metal concentration distribution surface, which can more accurately reflect the actual distribution of heavy metals in the soil, avoid information omissions caused by sample dispersion, and provide a solid data foundation for subsequent accurate assessment.

[0136] Figure 7 This is a flowchart of an ecological risk assessment method for heavy metal contaminated soil provided in an embodiment of this application, as shown below. Figure 7As shown, the ecological risk assessment method for heavy metal contaminated soil in this application includes, but is not limited to, the following steps:

[0137] S701, Obtain a set of soil samples from the target area. The set of soil samples includes multiple soil sample data. Each soil sample data includes the sampling location and the concentration values ​​of N heavy metals, where N is a positive integer.

[0138] S702. Based on the soil sample set, construct a spatial grid model of the target area. The spatial grid model includes multiple grid cells, and each grid cell contains information on the concentration distribution of heavy metals.

[0139] S703 determines the comprehensive pollution index of grid units based on the concentration distribution information of heavy metals.

[0140] S704 performs time series analysis on a spatial grid model to predict the dynamic migration trend of heavy metal concentrations corresponding to grid cells.

[0141] S705, determine the exposure thresholds of different ecological receptors in grid cells.

[0142] In the embodiments of this application, steps S701-S705 can be implemented in any of the embodiments of this application, and no limitation is made here, nor will it be described in detail.

[0143] S706: For each grid cell, the gradient value of the dynamic migration trend is obtained by differential calculation of the dynamic migration trend of the grid cell.

[0144] In some embodiments, when generating an ecological risk heat map of a target area based on the comprehensive pollution index, dynamic migration trend, and exposure threshold corresponding to the grid cell, matrix operations can be performed based on the comprehensive pollution index, dynamic migration trend, and exposure threshold to determine the risk level score of the grid cell, and an ecological risk heat map can be generated based on the risk level score.

[0145] Optionally, a multidimensional risk assessment matrix can be generated based on the comprehensive pollution index, dynamic migration trend, and exposure threshold, and the risk level score can be obtained by calculating the multidimensional risk assessment matrix.

[0146] In some embodiments, the rate of change of heavy metal concentration in space can be determined based on the dynamic migration trend of heavy metal concentration corresponding to the grid cell. Optionally, the gradient value of the dynamic migration trend can be determined as the rate of change of heavy metal concentration in space by performing differential calculation on the dynamic migration trend, and the gradient value of the dynamic migration trend can be used as an element in the multidimensional risk assessment matrix.

[0147] For example, the formula for calculating the gradient value of a dynamic migration trend in one-dimensional space is as follows:

[0148]

[0149] Where ΔC represents the gradient value of the dynamic migration trend, C i and C i+1 Δx represents the concentration of heavy metals in adjacent grid cells, and Δx represents the spacing between grid cells.

[0150] S707, obtain the ratio of the exposure threshold to the concentration value of heavy metals.

[0151] In some embodiments, by calculating the ratio of the exposure threshold to the concentration value of each heavy metal in the grid cell and averaging multiple ratios, the final ratio of the exposure threshold to the concentration value of the heavy metal in the grid cell can be obtained, and this ratio can be used as an element in the multidimensional risk assessment matrix.

[0152] For example, suppose grid cell A contains heavy metal 1, heavy metal 2 and heavy metal 3. By calculating the ratio 1 of the exposure threshold corresponding to heavy metal 1 and the concentration value of heavy metal 1, as well as the ratio 2 corresponding to heavy metal 1 and the ratio 3 corresponding to heavy metal 3, the average value can be calculated based on the ratios 1, 2 and 3 to obtain the final ratio of the exposure threshold to the concentration value of heavy metal.

[0153] S708 constructs a multidimensional risk assessment matrix based on the comprehensive pollution index, gradient values ​​and ratios of dynamic migration trends.

[0154] S709 calculates the risk level score of the grid cell by using a pre-calibrated linear combination to calculate the multidimensional risk assessment matrix.

[0155] In some embodiments, the multidimensional risk assessment matrix consists of a comprehensive pollution index, gradient values ​​and ratios of dynamic migration trends. By linearly combining the multidimensional risk assessment matrix, the data from multiple dimensions are integrated into a comprehensive score through weighted summation, thereby obtaining a risk level score.

[0156] Alternatively, the calculation formula is as follows:

[0157]

[0158] Among them, R s ΔC represents the risk level score, P represents the comprehensive pollution index, ΔC represents the gradient value of the dynamic migration trend, E represents the ratio of the exposure threshold to the concentration value of heavy metals, and a, b, and c are normalization coefficients.

[0159] S710 generates an ecological risk heat map of the target area based on the risk level score.

[0160] In some embodiments, to improve the accuracy of the final risk level, the risk level score can be optimized based on the concentration distribution information of heavy metals in the grid cells, soil physicochemical property parameters, and meteorological data, so that the final risk level covers the impact of soil environment and external factors on heavy metal pollution and ecological risks.

[0161] In some embodiments, risk level scores can be optimized using machine learning. Optionally, a random forest model can be trained and used to optimize the risk level scores.

[0162] In some embodiments, a random forest model can be trained using historical pollution data, and input parameters of grid cells can be obtained. The input parameters are then input into the random forest model to obtain the risk level classification probability of the grid cells. The input parameters include at least the concentration distribution information of heavy metals in the grid cells, soil physicochemical property parameters, and meteorological data.

[0163] Optionally, the risk level classification probability refers to the classification probability of each risk level score corresponding to each parameter in the information on the concentration distribution of heavy metals, soil physicochemical properties, and meteorological data.

[0164] The final risk level of a grid cell is obtained by weighting and fusing the risk level score and the risk level classification probability. For example, let the risk level classification probability be P. rf The risk level is rated R. s The weighted fusion formula can be expressed as:

[0165]

[0166] Among them, R final The final risk level is indicated by w1 and w2, which represent weighting coefficients.

[0167] Furthermore, an ecological risk heat map of the target area can be generated based on the final risk level of the grid cells. The spatial distribution of the final risk level can be determined based on the location range of the grid cells, and an ecological risk heat map can be generated based on visualization technology to intuitively display the ecological risk at different locations in the target area.

[0168] S711, based on the ecological risk heat map of the target area and preset thresholds, determines the risk locations and safe locations in the target area.

[0169] In the embodiments of this application, step S711 can be implemented in any of the ways described in the various embodiments of this application. This is not limited here and will not be described in detail.

[0170] The ecological risk assessment method for heavy metal contaminated soil provided in this application establishes a multi-dimensional risk assessment matrix for calculation. This matrix comprehensively considers factors such as the overall pollution index, dynamic migration trends, and exposure thresholds, enabling a more comprehensive and accurate assessment of the risk levels of different grid units, thus improving the accuracy and reliability of the risk assessment. Furthermore, by utilizing machine learning optimization steps, a random forest model is trained and weighted and fused with the results of the multi-dimensional risk assessment matrix, further enhancing the accuracy of the risk assessment and better adapting to complex and ever-changing soil pollution conditions.

[0171] Figure 8 The diagram shown illustrates the process for assessing the ecological risk of heavy metal-contaminated soil. Figure 8 As shown, a soil sample set of the target area is obtained, and a spatial grid model of the target area is constructed based on the sampling locations in the soil sample set, with each grid cell containing heavy metal concentration distribution information. Based on the weights of the heavy metals, the comprehensive pollution index of each grid cell is calculated, and time series analysis is performed on the spatial grid model to predict the dynamic migration trend of heavy metal concentrations corresponding to the grid cells. Furthermore, by combining an ecological receptor sensitivity database, the exposure thresholds of different ecological receptors in the grid cells are determined, thereby establishing a multidimensional risk assessment matrix. The comprehensive pollution index, dynamic migration trend, and exposure thresholds are matrix-calculated to obtain the risk level of each grid cell, and an ecological risk heat map is generated based on the spatial distribution of the risk levels. Using the ecological risk heat map, it is determined whether the risk level of different locations within the target area exceeds a preset threshold, thus identifying risky and safe locations within the target area. If the risk level exceeds the preset threshold, it is identified as a risky location; otherwise, it is considered a safe location.

[0172] Corresponding to the ecological risk assessment methods for heavy metal contaminated soil proposed in the above embodiments, an embodiment of this application also proposes an ecological risk assessment system for heavy metal contaminated soil. Since the ecological risk assessment system for heavy metal contaminated soil proposed in this application corresponds to the ecological risk assessment methods for heavy metal contaminated soil proposed in the above embodiments, the implementation methods of the above ecological risk assessment methods for heavy metal contaminated soil are also applicable to the ecological risk assessment system for heavy metal contaminated soil proposed in this application, and will not be described in detail in the following embodiments.

[0173] To achieve the above embodiments, this application also proposes an ecological risk assessment system for heavy metal contaminated soil.

[0174] Figure 9 This is a schematic diagram of the structure of an ecological risk assessment system for heavy metal contaminated soil provided in an embodiment of this application.

[0175] like Figure 9As shown, the ecological risk assessment system 900 for heavy metal contaminated soil includes:

[0176] The data acquisition module 901 is used to acquire a set of soil samples from the target area. The set of soil samples includes multiple soil sample data, and each soil sample data includes the sampling location and the concentration values ​​of N heavy metals, where N is a positive integer.

[0177] The model building module 902 is used to build a spatial grid model of the target area based on the soil sample set. The spatial grid model includes multiple grid cells, and each grid cell contains the concentration distribution information of heavy metals.

[0178] The pollution index determination module 903 is used to determine the comprehensive pollution index of the grid cell based on the concentration distribution information of heavy metals.

[0179] The trend prediction module 904 is used to perform time series analysis on the spatial grid model and predict the dynamic migration trend of heavy metal concentrations corresponding to the grid cells.

[0180] The exposure threshold determination module 905 is used to determine the exposure threshold of different ecological receptors in the grid cell.

[0181] The heat map generation module 906 is used to generate an ecological risk heat map of the target area for each grid cell, based on the comprehensive pollution index, dynamic migration trend and exposure threshold of the corresponding grid cell.

[0182] The region division module 907 is used to determine the risk location and safe location in the target region based on the ecological risk heat map of the target region and preset thresholds.

[0183] In one possible implementation of this application embodiment, the pollution index determination module 903 is further configured to: determine the concentration value of each heavy metal in the grid cell based on the concentration distribution information of heavy metals in the grid cell; determine the background value and weight of each heavy metal, and determine the pollution index of the heavy metal based on the concentration value, background value and weight; and sum the pollution indices of N heavy metals in the grid cell to obtain the comprehensive pollution index of the grid cell.

[0184] In one possible implementation of this application, the pollution index determination module 903 is further configured to: determine the initial weight of heavy metals based on the toxicity response coefficient, migration rate and bioaccumulation coefficient of heavy metals; determine the soil pH value and organic matter content, and correct the initial weight of heavy metals based on the soil pH value and organic matter content to obtain the weight of heavy metals.

[0185] In one possible implementation of this application embodiment, the model building module 902 is further configured to: use Kriging interpolation to spatially interpolate the sampling location and heavy metal concentration values ​​of soil sample data to generate a heavy metal concentration distribution surface; and divide the heavy metal concentration distribution surface into equally spaced grid cells to obtain a spatial grid model.

[0186] In one possible implementation of this application embodiment, the trend prediction module 904 is further configured to: determine the groundwater seepage velocity, the diffusion coefficient, and the chemical reaction rate of heavy metals in the target area; construct a migration model of heavy metals based on the groundwater seepage velocity, diffusion coefficient, and chemical reaction rate; use the finite difference method to discretize and solve the migration model to obtain the predicted concentration distribution of heavy metals in different grid cells over a future time period; and determine the dynamic migration trend of the heavy metal concentration corresponding to the grid cell based on the predicted concentration distribution of heavy metals in the grid cell.

[0187] In one possible implementation of this application embodiment, the heat map generation module 906 is further configured to: perform differential calculation on the dynamic migration trend of each grid cell to obtain the gradient value of the dynamic migration trend; obtain the ratio of the exposure threshold to the concentration value of heavy metals; construct a multidimensional risk assessment matrix based on the comprehensive pollution index, the gradient value of the dynamic migration trend, and the ratio; calculate the risk level score of the grid cell by using a pre-calibrated linear combination on the multidimensional risk assessment matrix; and generate an ecological risk heat map of the target area based on the risk level score.

[0188] In one possible implementation of this application embodiment, the heat map generation module 906 is further configured to: train a random forest model using historical pollution data; obtain input parameters for grid cells and input the input parameters into the random forest model to obtain the risk level classification probability of the grid cells, wherein the input parameters include at least the concentration distribution information of heavy metals, soil physicochemical properties parameters, and meteorological data of the grid cells; perform weighted fusion of the risk level score and the risk level classification probability to obtain the final risk level of the grid cells; and generate an ecological risk heat map of the target area based on the final risk level of the grid cells.

[0189] In one possible implementation of this application embodiment, the pollution index determination module 903 is further configured to: in response to the soil sample data not including the concentration value of the i-th heavy metal, obtain the minimum concentration value, average concentration value and maximum concentration value of the i-th heavy metal in the target area; perform fuzzy estimation of the minimum concentration value, average concentration value and maximum concentration value of the i-th heavy metal using triangular membership functions to obtain the concentration value of the i-th heavy metal in the soil sample data.

[0190] In one possible implementation of this application embodiment, the pollution index determination module 903 is further configured to: determine the bioavailable concentration of the i-th heavy metal; determine the total concentration of the i-th heavy metal in the target area; determine the bioavailable proportion of the i-th heavy metal based on the bioavailable concentration and the total concentration; determine the maximum bioavailable proportion from the bioavailable proportions of the N heavy metals; and correct the comprehensive pollution index of the grid unit based on the maximum bioavailable proportion to obtain the final comprehensive pollution index of the grid unit.

[0191] The ecological risk assessment system for heavy metal contaminated soil provided in this application acquires a set of soil samples from a target area and constructs a spatial grid model of the target area based on the soil sample set. Based on the concentration distribution information of heavy metals contained in each grid cell, the comprehensive pollution index of the grid cell is determined, and the dynamic migration trend of heavy metal concentrations corresponding to the grid cell is predicted. By determining the exposure thresholds of different ecological receptors in the grid cells, an ecological risk heat map of the target area is generated based on the comprehensive pollution index, dynamic migration trend, and exposure thresholds. Thus, risk locations and safe locations in the target area can be determined based on the ecological risk heat map and preset thresholds. Therefore, this scheme can fully consider the spatial distribution characteristics of heavy metal pollution in soil. By predicting the dynamic migration trend of heavy metal concentrations, it can achieve early understanding of the development direction of heavy metal pollution, providing a strong basis for formulating targeted pollution prevention and control measures. By assessing the ecological risk of the target area through multi-dimensional factors such as the comprehensive pollution index, dynamic migration trend, and exposure thresholds, a more comprehensive and accurate assessment of the ecological risk of different grid cells can be achieved, greatly improving the accuracy and reliability of risk assessment.

[0192] It should be noted that the explanation of the above-mentioned embodiment of the ecological risk assessment method for heavy metal contaminated soil also applies to the ecological risk assessment system for heavy metal contaminated soil in this embodiment, and will not be repeated here.

[0193] To implement the above embodiments, this application also proposes an electronic device, including: a processor and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0194] To implement the above embodiments, this application also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.

[0195] To implement the above embodiments, this application also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.

[0196] The collection, storage, use, processing, transmission, provision, and application of user personal information involved in this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0197] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0198] This application is intended to provide an implementation scheme for users to selectively prevent the use or access to their personal information data. Specifically, this application is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information is de-identified to protect user privacy.

[0199] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0200] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0201] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0202] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0203] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0204] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0205] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0206] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. An ecological risk assessment method for heavy metal contaminated soil, characterized in that, include: Obtain a set of soil samples from the target area. The set of soil samples includes multiple soil sample data. Each soil sample data includes the sampling location and the concentration values ​​of N heavy metals, where N is a positive integer. Based on the soil sample set, a spatial grid model of the target area is constructed, wherein the spatial grid model includes multiple grid cells, and each grid cell contains heavy metal concentration distribution information; Based on the concentration distribution information and weights of the heavy metals, the comprehensive pollution index of the grid unit is determined, and the comprehensive pollution index is corrected according to the proportion of bioavailable heavy metals. Determining the weights of the heavy metals includes: determining the initial weights of the heavy metals based on their toxicity response coefficients, migration rates, and bioaccumulation coefficients; determining the soil pH and organic matter content, and correcting the initial weights of the heavy metals based on the soil pH and organic matter content to obtain the final weights of the heavy metals. Time series analysis is performed on the spatial grid model to predict the dynamic migration trend of heavy metal concentrations corresponding to the grid cells; Determine the exposure thresholds of different ecological receptors in grid cells; For each grid cell, an ecological risk heat map of the target area is generated based on the comprehensive pollution index, dynamic migration trend, and exposure threshold corresponding to the grid cell. Based on the ecological risk heat map of the target area and preset thresholds, risk locations and safe locations are determined from the target area; The step of performing time series analysis on the spatial grid model to predict the dynamic migration trend of heavy metal concentrations corresponding to the grid cells includes: Determine the groundwater seepage velocity in the target area, as well as the diffusion coefficient and chemical reaction rate of the heavy metal; Based on the groundwater seepage velocity, the diffusion coefficient, and the chemical reaction rate, a migration model for heavy metals is constructed, which is expressed as follows: Where C is the concentration of heavy metals, D is the diffusion coefficient, v is the groundwater seepage velocity, k is the chemical reaction rate, and t is the time variable; The migration model is discretely solved using the finite difference method to obtain predicted concentration distribution values ​​of heavy metals in different grid cells over future time periods. The discrete solution process includes: Assume the time step is Δt, and the spatial steps are Δx and Δy; In terms of space, and An approximation is made using the central difference scheme. The discrete form in two-dimensional space is represented as: Among them, C i,j This represents the concentration value of heavy metals in the grid cell at row i and column j. time derivative Using the forward difference scheme, it is represented as: based on , , The discrete expression is used to update the transfer model, resulting in information about... Algebraic equations; Iterative solution of about The algebraic equations are used to obtain the predicted concentration distribution of heavy metals in the future time period; Based on the predicted concentration distribution of heavy metals in the grid cells, the dynamic migration trend of heavy metal concentrations corresponding to the grid cells is determined.

2. The method according to claim 1, characterized in that, The step of determining the comprehensive pollution index of the grid unit based on the concentration distribution information of the heavy metals includes: Based on the concentration distribution information of heavy metals in the grid cell, determine the concentration value of each heavy metal in the grid cell; For each heavy metal, a background value and weight are determined, and a pollution index for the heavy metal is determined based on the concentration value, the background value, and the weight. The pollution indices of N heavy metals within the grid cell are summed to obtain the comprehensive pollution index of the grid cell.

3. The method according to claim 1, characterized in that, The step of constructing a spatial grid model of the target area based on the soil sample set includes: Using the Kriging interpolation method, spatial interpolation is performed on the sampling location of the soil sample data and the concentration value of the heavy metal to generate a heavy metal concentration distribution surface; The heavy metal concentration distribution surface is divided into equally spaced grid cells to obtain the spatial grid model.

4. The method according to any one of claims 1-3, characterized in that, The step of generating an ecological risk heat map of the target area based on the comprehensive pollution index, dynamic migration trend, and exposure threshold corresponding to the grid unit includes: For each grid cell, the gradient value of the dynamic migration trend is obtained by differential calculation of the dynamic migration trend of the grid cell; Obtain the ratio of the exposure threshold to the concentration value of the heavy metal; A multidimensional risk assessment matrix is ​​constructed based on the comprehensive pollution index, the gradient value of the dynamic migration trend, and the ratio. The risk level score of the grid cell is obtained by calculating the multidimensional risk assessment matrix through a pre-calibrated linear combination. Based on the risk level score, an ecological risk heat map of the target area is generated.

5. The method according to claim 4, characterized in that, The step of generating an ecological risk heat map of the target area based on the risk level score includes: Train a random forest model using historical pollution data; The input parameters of the grid cell are obtained and input into the random forest model to obtain the risk level classification probability of the grid cell. The input parameters include at least the concentration distribution information of heavy metals, soil physicochemical properties and meteorological data of the grid cell. The risk level score and the risk level classification probability are weighted and fused to obtain the final risk level of the grid cell; An ecological risk heat map of the target area is generated based on the final risk level of the grid cells.

6. The method according to any one of claims 1-3, characterized in that, The method further includes: In response to the fact that the soil sample data does not include the concentration value of the i-th heavy metal, the minimum concentration value, average concentration value, and maximum concentration value of the i-th heavy metal in the target area are obtained; The minimum, average, and maximum concentration values ​​of the i-th heavy metal are estimated using triangular membership functions to obtain the concentration value of the i-th heavy metal in the soil sample data.

7. The method according to any one of claims 1-3, characterized in that, After determining the comprehensive pollution index of the grid unit based on the heavy metal concentration distribution information, the method further includes: Determine the bioavailable concentration of the i-th heavy metal; Determine the total concentration of the i-th heavy metal within the target area; Based on the bioavailable concentration and total concentration of the i-th heavy metal, determine the bioavailable percentage of the i-th heavy metal; The maximum bioavailable percentage is determined from the bioavailable percentages of the N heavy metals. Based on the maximum bioavailable percentage, the comprehensive pollution index of the grid cell is corrected to obtain the final comprehensive pollution index of the grid cell.

8. An ecological risk assessment system for heavy metal contaminated soil, characterized in that, include: The data acquisition module is used to acquire a set of soil samples from the target area. The set of soil samples includes multiple soil sample data, and each soil sample data includes the sampling location and the concentration values ​​of N heavy metals, where N is a positive integer. The model building module is used to construct a spatial grid model of the target area based on the soil sample set, wherein the spatial grid model includes multiple grid cells, and each grid cell contains the concentration distribution information of heavy metals; The pollution index determination module is used to determine the comprehensive pollution index of the grid cell based on the concentration distribution information and weight of the heavy metals, and to correct the comprehensive pollution index based on the bioavailable proportion of the heavy metals. Determining the weight of the heavy metals includes: determining the initial weight of the heavy metals based on their toxicity response coefficient, migration rate, and bioaccumulation coefficient; determining the soil pH and organic matter content, and correcting the initial weight of the heavy metals based on the soil pH and organic matter content to obtain the final weight of the heavy metals. The trend prediction module is used to perform time series analysis on the spatial grid model and predict the dynamic migration trend of heavy metal concentration corresponding to the grid cell. The exposure threshold determination module is used to determine the exposure threshold of different ecological receptors in the grid cell; The heat map generation module is used to generate an ecological risk heat map of the target area for each grid cell, based on the comprehensive pollution index, dynamic migration trend and exposure threshold corresponding to the grid cell. The region division module is used to determine risk locations and safe locations in the target region based on the ecological risk heat map of the target region and preset thresholds; The step of performing time series analysis on the spatial grid model to predict the dynamic migration trend of heavy metal concentrations corresponding to the grid cells includes: Determine the groundwater seepage velocity in the target area, as well as the diffusion coefficient and chemical reaction rate of the heavy metal; Based on the groundwater seepage velocity, the diffusion coefficient, and the chemical reaction rate, a migration model for heavy metals is constructed, which is expressed as follows: Where C is the concentration of heavy metals, D is the diffusion coefficient, v is the groundwater seepage velocity, k is the chemical reaction rate, and t is the time variable; The migration model is discretely solved using the finite difference method to obtain predicted concentration distribution values ​​of heavy metals in different grid cells over future time periods. The discrete solution process includes: Assume the time step is Δt, and the spatial steps are Δx and Δy; In terms of space, and An approximation is made using the central difference scheme. The discrete form in two-dimensional space is represented as: Among them, C i,j This represents the concentration value of heavy metals in the grid cell at row i and column j. time derivative Using the forward difference scheme, it is represented as: based on , , The discrete expression is used to update the transfer model, resulting in information about... Algebraic equations; Iterative solution of about The algebraic equations are used to obtain the predicted concentration distribution of heavy metals in the future time period; Based on the predicted concentration distribution of heavy metals in the grid cells, the dynamic migration trend of heavy metal concentrations corresponding to the grid cells is determined.

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