Ecological risk assessment method and system for heavy metal contaminated soil

Abstract

The invention provides an ecological risk assessment method and system for heavy metal contaminated soil, and relates to the technical field of soil pollution treatment. The method comprises the following steps: acquiring a soil sample set of a target area; constructing a space grid model of the target area according to the soil sample set; determining a comprehensive pollution index of the grid unit according to the concentration distribution information of the heavy metal; performing time sequence analysis on the space grid model, and predicting the dynamic migration trend of the heavy metal concentration corresponding to the grid unit; determining exposure threshold values of different ecological receptors in the grid units, and generating an ecological risk thermodynamic diagram of the target area according to the comprehensive pollution index, the dynamic migration trend and the exposure threshold values; and determining a risk position and a safety position from the target area according to the ecological risk thermodynamic diagram and a preset threshold value. Therefore, according to the scheme, the ecological risk of the target area can be evaluated more comprehensively and accurately, and the accuracy and reliability of risk evaluation are greatly improved.

Description

Technical Field

[0001] The present application relates to the technical field of soil pollution control, and particularly to an ecological risk assessment method and system for heavy metal contaminated soil. Background Art

[0002] In the current era of rapid industrialization and urbanization, the problem of soil heavy metal pollution is becoming increasingly severe. Heavy metal pollutants such as lead, mercury, cadmium, chromium, etc., due to their strong toxicity, difficult degradation, and easy enrichment in organisms, pose a huge threat to the soil ecosystem, human health, and the ecological environment. Currently, there are many limitations in the assessment of soil heavy metal pollution. The traditional single-factor pollution index method only considers the pollution situation of a single heavy metal and cannot comprehensively reflect the actual situation of multiple heavy metal combined pollution. Summary of the Invention

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

[0004] To this end, the first object of the present application is to propose an ecological risk assessment method for heavy metal contaminated soil to comprehensively and accurately evaluate the ecological risk of the target area.

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

[0006] To achieve the above object, an ecological risk assessment method for heavy metal contaminated soil according to the first aspect embodiment of the present application includes: obtaining a soil sample set of the target area, the soil sample set including a plurality of soil sample data, each soil sample data including a sampling location and concentration values of N heavy metals, where N is a positive integer; Constructing a spatial grid model of the target area according to the soil sample set, where the spatial grid model includes a plurality of grid cells, and each grid cell contains concentration distribution information of heavy metals; Determining the comprehensive pollution index of the grid cell according to the concentration distribution information of the heavy metals; Performing time series analysis on the spatial grid model to predict the dynamic migration trend of the heavy metal concentration corresponding to the grid cell; Determining the exposure threshold of different ecological receptors in the grid cell; Generating an ecological risk heat map of the target area for each grid cell according to the comprehensive pollution index, dynamic migration trend, and exposure threshold corresponding to the grid cell; Determining the risk positions and safe positions from the target area according to the ecological risk heat map of the target area and a preset threshold.

[0007] To achieve the above object, an embodiment of the second aspect of the present application provides an ecological risk assessment system for heavy metal contaminated soil, including: a data collection module for obtaining a soil sample set of a target area, the soil sample set including a plurality of soil sample data, each soil sample data including a sampling location and concentration values of N heavy metals, where N is a positive integer; a model construction module for constructing a spatial grid model of the target area according to the soil sample set, where the spatial grid model includes a plurality of grid cells, and each grid cell contains concentration distribution information of heavy metals; a pollution index determination module for determining a comprehensive pollution index of the grid cell according to the concentration distribution information of the heavy metals; a trend prediction module for performing time series analysis on the spatial grid model to predict the dynamic migration trend of the heavy metal concentration corresponding to the grid cell; an exposure threshold determination module for determining exposure thresholds of different ecological receptors in the grid cell; a heat map generation module for generating an ecological risk heat map of the target area for each grid cell according to the comprehensive pollution index, dynamic migration trend, and exposure threshold corresponding to the grid cell; a region division module for determining risk locations and safe locations from the target area according to the ecological risk heat map of the target area and a preset threshold.

[0008] The ecological risk assessment method and system for heavy metal contaminated soil provided by the present application obtain a soil sample set of a target area and construct a spatial grid model of the target area according to the soil sample set. According to 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 the heavy metal concentration corresponding to the grid cell is predicted. By determining the exposure thresholds of different ecological receptors in the grid cell, an ecological risk heat map of the target area is generated according to the comprehensive pollution index, dynamic migration trend, and exposure threshold, so that risk locations and safe locations can be determined from the target area according to the ecological risk heat map and the preset threshold. Thus, this solution can fully consider the spatial distribution characteristics of soil heavy metal pollution. By predicting the dynamic migration trend of heavy metal concentration, it is possible to master the development direction of heavy metal pollution in advance and provide a strong basis for formulating targeted pollution prevention and control measures. By evaluating the ecological risk of the target area from multiple dimensions such as the comprehensive pollution index, dynamic migration trend, and exposure threshold, it is possible to more comprehensively and accurately evaluate the ecological risk of different grid cells, greatly improving the accuracy and reliability of risk assessment.

[0009] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of embodiments in conjunction with the accompanying drawings, in which: Figure 1 is a schematic flowchart of an ecological risk assessment method for heavy metal contaminated soil provided by an embodiment of the present application; Figure 2 is a schematic flowchart of another ecological risk assessment method for heavy metal contaminated soil provided by an embodiment of the present application; Figure 3 is a schematic flowchart of the fuzzy estimation provided by an embodiment of the present application; Figure 4 is a schematic flowchart of determining the weight of heavy metals provided by an embodiment of the present application; Figure 5 is a schematic flowchart of another ecological risk assessment method for heavy metal contaminated soil provided by an embodiment of the present application; Figure 6 is a schematic flowchart of the prediction process of the dynamic migration trend provided by an embodiment of the present application; Figure 7 is a schematic flowchart of another ecological risk assessment method for heavy metal contaminated soil provided by an embodiment of the present application; Figure 8 is a schematic flowchart of assessing the ecological risk of heavy metal contaminated soil provided by an embodiment of the present application; Figure 9 is a schematic structural diagram of an ecological risk assessment system for heavy metal contaminated soil provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0011] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where 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 are intended to explain the present application and should not be construed as limiting the present application.

[0012] The ecological risk assessment method and system for heavy metal contaminated soil according to the embodiments of the present application will be described below with reference to the accompanying drawings.

[0013] Figure 1 is a flowchart of an ecological risk assessment method for heavy metal contaminated soil provided by an embodiment of the present application. As Figure 1 shown, the ecological risk assessment method for heavy metal contaminated soil in the embodiments of the present application includes, but is not limited to, the following steps: S101. Obtain a set of soil samples for the target area. The set of soil samples 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] It should be noted that the execution subject of the ecological risk assessment method for heavy metal contaminated soil provided in the embodiments of this application is an electronic device, and this electronic device can be a terminal device. Optionally, the terminal device can be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device can be a mobile phone, a tablet computer, a laptop computer, a handheld computer, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), etc., and the non-mobile electronic device can be a personal computer (PC), a television, etc. The embodiments of this application do not make specific limitations.

[0015] In some embodiments, based on one or more sampling devices, soil samples at different positions in the target area can be collected and the soil samples can be detected to determine the N heavy metals contained in the soil samples and the concentration value of each heavy metal.

[0016] In some embodiments, a positioning device such as the Global Positioning System (GPS) can be used to determine the sampling locations of different sampling points in the target area, so as to collect soil samples at the sampling points, and use the sampling location and the concentration values of N heavy metals as soil sample data, and then generate a set of soil samples based on the soil sample data.

[0017] S102. According to the set of soil samples, construct a spatial grid model for the target area, where the spatial grid model includes multiple grid cells, and each grid cell contains concentration distribution information of heavy metals.

[0018] In some embodiments, the concentration distribution of heavy metals can be determined according to the sampling locations and the concentration values of N heavy metals in the set of soil samples, and then a heavy metal concentration distribution surface can be generated. By dividing the heavy metal concentration distribution surface into grid cells, the construction of the spatial grid model for the target area can be realized.

[0019] In some embodiments, an identifier can be assigned to each grid cell, and the corresponding position range of each grid cell can be determined. Based on the position 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 heavy metal concentration distribution vector.

[0020] In some embodiments, when dividing the heavy metal concentration distribution surface into grid cells, the heavy metal concentration distribution surface can be divided into a plurality of grid cells with equal intervals.

[0021] S103. Determine the comprehensive pollution index of the grid cell according to the heavy metal concentration distribution information.

[0022] In some embodiments, one or more heavy metals contained in the grid cell can be determined according to the heavy metal concentration distribution information, and the respective concentration values and weights of the heavy metals can be determined, so as to calculate the comprehensive pollution index of the grid cell according to the concentration values and weights.

[0023] In some embodiments, since the grid cell may contain multiple heavy metals, the pollution index of each heavy metal can be calculated separately, and the multiple pollution indexes can be summed up to obtain the comprehensive pollution index of the grid cell. For example, assume that grid cell A contains heavy metal 1, heavy metal 2, and heavy metal 3. By calculating the pollution index 1 of heavy metal 1 according to the concentration value and weight of heavy metal 1, calculating the pollution index 2 of heavy metal 2 according to the concentration value and weight of heavy metal 2, and calculating the pollution index 3 of heavy metal 3 according to the concentration value and weight of heavy metal 3, the comprehensive pollution index is pollution index 1 + pollution index 2 + pollution index 3.

[0024] S104. Perform time series analysis on the spatial grid model to predict the dynamic migration trend of the heavy metal concentration corresponding to the grid cell.

[0025] In some embodiments, a migration model of the heavy metal can be constructed, and by solving the migration model, the predicted value of the heavy metal concentration distribution of each grid cell can be obtained, so that the dynamic migration trend of the heavy metal concentration can be predicted according to the predicted value of the heavy metal concentration distribution of each grid cell.

[0026] In some embodiments, a migration model of the heavy metal can be constructed based on partial differential equations, where the migration model is related to time and the concentration of the heavy metal.

[0027] In some embodiments, according to the predicted value of the heavy metal concentration distribution of each grid cell, the change of the heavy metal concentration between different grid cells and the trend of the heavy metal concentration changing with time can be determined, so as to predict the dynamic migration trend of the heavy metal concentration in the time series.

[0028] S105. Determine the exposure thresholds of different ecological receptors in the grid cell.

[0029] It is understandable that an ecological receptor refers to an ecological entity exposed to environmental pollutants, which can be tissues and organs of organisms, or different levels of life organizations such as populations, communities, and ecosystems. The exposure threshold refers to the critical value of the exposure level of environmental pollutants that an ecological receptor can withstand. When the exposure level is lower than this threshold, the ecological receptor is usually not significantly negatively affected; while when the exposure level exceeds this threshold, the ecological receptor may be damaged, even leading to changes in the structure and function of the ecosystem.

[0030] In some embodiments, sensitivity data of different ecological receptors in the target area can be obtained from the ecological receptor sensitivity database, and environmental data of the target area can be collected, so as to simulate the dynamic change process of ecological receptors at different exposure levels according to the environmental data and sensitivity data, thereby determining the exposure threshold.

[0031] S106. For each grid cell, generate an ecological risk heat map of the target area according to the comprehensive pollution index, dynamic migration trend, and exposure threshold corresponding to the grid cell.

[0032] In some embodiments, for each grid cell, the risk level of each grid cell can be determined according to 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 according to the risk level.

[0033] In some embodiments, the spatial distribution of risk levels can be determined according to the position range of the grid cells, and based on visualization technology, the spatial distribution of risk levels can be generated into an ecological risk heat map to intuitively display the ecological risks at different positions in the target area.

[0034] In some embodiments, a multi-dimensional risk assessment matrix can be established according to the comprehensive pollution index, dynamic migration trend, and exposure threshold, and by solving the multi-dimensional risk assessment matrix, the risk level corresponding to the grid cell can be obtained.

[0035] S107. Determine the risk positions and safe positions in the target area according to the ecological risk heat map of the target area and a preset threshold.

[0036] In some embodiments, the risk levels at different positions in the target area can be determined according to the ecological risk heat map of the target area, and the risk levels can be compared with a preset threshold to determine the risk positions and safe positions in the target area.

[0037] In some embodiments, positions in the target area with a risk level greater than or equal to the preset threshold can be determined as risk positions according to the ecological risk heat map, and positions in the target area with a risk level less than the preset threshold can be determined as safe positions.

[0038] In the ecological risk assessment method for heavy metal contaminated soil provided by the embodiments of the present application, a soil sample set of the target area is obtained, and a spatial grid model of the target area is constructed based on the soil sample set. According to 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 the heavy metal concentration corresponding to the grid cell is predicted. By determining the exposure thresholds of different ecological receptors in the grid cell, an ecological risk heat map of the target area is generated based on the comprehensive pollution index, the dynamic migration trend, and the exposure threshold, so that the risk locations and safe locations can be determined from the target area according to the ecological risk heat map and the preset threshold. Thus, this solution can fully consider the spatial distribution characteristics of soil heavy metal pollution. By predicting the dynamic migration trend of heavy metal concentrations, it is possible to master the development direction of heavy metal pollution in advance, providing a strong basis for formulating targeted pollution prevention and control measures. By evaluating the ecological risk of the target area through multi-dimensional factors such as the comprehensive pollution index, the dynamic migration trend, and the exposure threshold, it is possible to more comprehensively and accurately evaluate the ecological risks of different grid cells, greatly improving the accuracy and reliability of risk assessment.

[0039] Figure 2 is a flowchart of an ecological risk assessment method for heavy metal contaminated soil provided by the embodiments of the present application, as Figure 2 shown, the ecological risk assessment method for heavy metal contaminated soil of the embodiments of the present application includes, but is not limited to, the following steps: S201. Obtain a soil sample set of the target area. 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.

[0040] S202. Construct a spatial grid model of the target area according to the soil sample set, where the spatial grid model includes multiple grid cells, and each grid cell contains the concentration distribution information of heavy metals.

[0041] In the embodiments of the present application, the implementation manners of steps S201 - S202 can be respectively implemented by any one of the embodiments of the present application, and no limitation is made thereto here, nor will it be elaborated further.

[0042] S203. Determine the concentration value of each heavy metal in the grid cell according to the concentration distribution information of the heavy metals in the grid cell.

[0043] In some embodiments, the heavy metals contained in the grid cell and the concentration value of each heavy metal can be determined from the concentration distribution information of the heavy metals in the grid cell.

[0044] In some embodiments, for the i-th heavy metal, it can be pre-determined 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 metals.

[0045] In some embodiments, in response to the soil sample data not including the concentration value of the i-th heavy metal, the concentration value can be estimated fuzzily 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 this concentration data, the minimum concentration value, average concentration value, and maximum concentration value of the heavy metal can be calculated, and fuzzy estimation can 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.

[0046] That is to say, in response to the soil sample data not including 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, and fuzzy estimation of the minimum concentration value, average concentration value, and maximum concentration value of the i-th heavy metal is performed using a triangular membership function to obtain the concentration value of the i-th heavy metal in the soil sample data.

[0047] Optionally, the formula for fuzzy estimation is as follows:

[0048] Wherein, represents the concentration value of the i-th heavy metal, C min represents the minimum concentration value, C avg represents the average concentration value, C max represents the maximum concentration value.

[0049] In some embodiments, by performing fuzzy estimation on the missing concentration values, a more reasonable ecological risk assessment can still be carried out in the case of data missing, reducing the impact of data missing on the assessment results.

[0050] Figure 3 The flowchart of fuzzy estimation is shown. As Figure 3 shown, by checking whether there is data missing in the soil sample data, and when there is a missing 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, and fuzzy estimation is performed using a triangular membership function to obtain the result of fuzzy estimation as the concentration value of the i-th heavy metal. Further, the comprehensive pollution index can be calculated based on the result of fuzzy estimation. If there is no missing data, the comprehensive pollution index can be directly calculated using the complete data.

[0051] S204. For each heavy metal, determine the background value and weight of the heavy metal, and determine the pollution index of the heavy metal based on the concentration value, background value, and weight.

[0052] In some embodiments, the content of each heavy metal when the target area soil is not polluted can be obtained according to the historical monitoring data of the target area soil, and this content can be used as the background value of the heavy metal.

[0053] In some embodiments, to ensure the rationality of calculating the pollution index of heavy metals, the sum of the weights of each heavy metal can be set to 1. Optionally, the initial weight of the heavy metal can be calculated, and the correction factor can be determined to correct the initial weight to obtain the weight of the heavy metal, so that the weight of each heavy metal in the comprehensive pollution index can be determined more accurately.

[0054] In some embodiments, the initial weight of the heavy metal can be determined according to the toxicity response coefficient, mobility, and bioconcentration coefficient of the heavy metal. Among them, the toxicity response coefficient reflects the relative ability of the heavy metal to produce toxic effects on organisms. The larger the value, the stronger the toxicity of the heavy metal to organisms; the mobility reflects the migration ability of the heavy metal in the soil. A high mobility means that the heavy metal is more likely to diffuse in the soil and may affect a larger range of the environment and organisms; the bioconcentration coefficient measures the degree of accumulation of the heavy metal in organisms. The higher the bioconcentration coefficient, the greater the accumulation risk of the heavy metal in the food chain.

[0055] Optionally, the formula for calculating the initial weight is as follows:

[0056] Among them, represents the initial weight of the i-th heavy metal, T i represents the toxicity response coefficient of the i-th heavy metal, M i represents the mobility of the i-th heavy metal, B i represents the bioconcentration coefficient of the i-th heavy metal.

[0057] Furthermore, the soil pH value and organic matter content can be used as correction factors. By determining the soil pH value and organic matter content and correcting the initial weight of the heavy metal according to the soil pH value and organic matter content, the weight of the heavy metal can be obtained. Among them, the pH value is the acidity and alkalinity of the soil. The soil acidity and alkalinity have an important impact on the existence form and migration and transformation of heavy metals. For example, in acidic soils, the solubility of some heavy metals may increase, thereby increasing their bioavailability and mobility. The level of organic matter content will affect the adsorption, desorption, etc. of heavy metals in the soil, and thus affect the activity and toxicity of heavy metals.

[0058] Optionally, the correction formula is as follows:

[0059] Among them, W i represents the weight of the i-th heavy metal, α and β are empirical constants, pH represents the soil pH value, and OM represents the organic matter content.

[0060] 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 the weight.

[0061] Figure 4 The flowchart showing the determination of the weight of heavy metals is as follows. Figure 4 As shown, for the i-th heavy metal, obtain the toxicity response coefficient, mobility, and bioconcentration factor of the i-th heavy metal, and determine the initial weight 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 judged whether the weights of all heavy metals have been calculated to obtain the respective weights of each heavy metal.

[0062] S205, sum up the pollution indices of N heavy metals in the grid cell to obtain the comprehensive pollution index of the grid cell.

[0063] In some embodiments, the formula for calculating the comprehensive pollution index is as follows:

[0064] Among them, P represents the comprehensive pollution index, C i represents the concentration value of the i-th heavy metal, S i represents the background value of the i-th heavy metal, and W i represents the weight of the i-th heavy metal.

[0065] In some embodiments, in order to make the comprehensive pollution index better reflect the actual ecological risk, the comprehensive pollution index can be corrected according to the proportion of the bioavailable fraction of heavy metals to obtain the final comprehensive pollution index of the grid cell. Among them, the proportion of the bioavailable fraction of heavy metals reflects the proportion of heavy metals in the soil that can be absorbed and utilized by organisms.

[0066] In some embodiments, the proportion of the bioavailable fraction of heavy metals can be calculated based on the total concentration value 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 method, and the total concentration value 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.

[0067] Further, the bioavailable fraction of the i-th heavy metal can be determined based on the bioavailable concentration and total concentration value of the i-th heavy metal. Optionally, by calculating the ratio of the bioavailable concentration to the total concentration value, the bioavailable fraction of the i-th heavy metal can be determined, and the calculation formula is as follows:

[0068] where F i represents the bioavailable fraction of the i-th heavy metal, C bio represents the bioavailable concentration of the i-th heavy metal, and C total represents the total concentration value of the i-th heavy metal.

[0069] Further, the maximum bioavailable fraction is determined from the bioavailable fractions of the N heavy metals, and based on the maximum bioavailable fraction, the comprehensive pollution index of the grid cell is corrected to obtain the final comprehensive pollution index of the grid cell. The correction formula is as follows:

[0070] where represents the final comprehensive pollution index, and F represents the bioavailable fraction.

[0071] S206. Perform time series analysis on the spatial grid model to predict the dynamic migration trend of the heavy metal concentration corresponding to the grid cell.

[0072] S207. Determine the exposure thresholds of different ecological receptors in the grid cell.

[0073] S208. For each grid cell, generate an ecological risk heat map of the target area according to the comprehensive pollution index, dynamic migration trend, and exposure threshold corresponding to the grid cell.

[0074] S209. Determine the risk locations and safe locations from the target area according to the ecological risk heat map of the target area and the preset threshold.

[0075] In the embodiments of the present application, the implementation manners of steps S206 - S209 can be implemented by any one of the embodiments of the present application respectively. No limitation is made here and no further description is given.

[0076] In the ecological risk assessment method for heavy metal contaminated soil provided by the embodiments of the present application, the comprehensive pollution index is calculated based on the background value, weight, and concentration value of the heavy metal, fully considering the characteristics of different heavy metals and the influence of soil environmental factors on their pollution levels, so that the comprehensive pollution index can more objectively and accurately reflect the actual situation of the soil contaminated by multiple heavy metals.

[0077] Figure 5It is a flowchart of an ecological risk assessment method for heavy metal contaminated soil provided by an embodiment of the present application. As Figure 5 shown, the ecological risk assessment method for heavy metal contaminated soil in the embodiment of the present application includes but is not limited to the following steps: S501, Obtain a soil sample set of the target area. The soil sample set includes a plurality of 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.

[0078] In the embodiment of the present application, the implementation manner of step S501 can be implemented by any one of the embodiments of the present application respectively. No limitation is made here and it will not be elaborated further.

[0079] S502, Use the Kriging interpolation method to perform spatial interpolation on the sampling locations and the concentration values of heavy metals in the soil sample data to generate a heavy metal concentration distribution surface.

[0080] It can be understood that the Kriging interpolation method is a geostatistical interpolation method based on the theory of regionalized variables, which takes into account the spatial correlation between sampling locations. For example, in the target area, there are sampling locations of soil sample data, and the heavy metal concentration is Z(x i ,y i ), where (x i ,y i ) is the coordinate of the sampling location. By calculating the semi-variogram function γ(h) between sampling locations and using the fitted semi-variogram model, the heavy metal concentration at any unsampled sampling location in the target area can be interpolated to obtain the interpolation result. Among them, the calculation formula of the semi-variogram function is as follows:

[0081] where N(h) is the number of sampling location pairs with a distance of h.

[0082] That is to say, the semi-variogram function between the sampling locations of the soil sample data can be calculated, and based on the calculated semi-variogram function, it can be fitted according to the theoretical model to obtain a fitted semi-variogram model. By determining the unsampled sampling locations in the target area, according to this semi-variogram model and the concentration values of heavy metals, the concentration value of heavy metals at this sampling location is interpolated and calculated to obtain the interpolation result, that is, the concentration value of heavy metals at this sampling location. Further, the concentration value can be mapped onto the geographical space to generate a heavy metal concentration distribution surface.

[0083] S503, Divide the heavy metal concentration distribution surface into equally spaced grid cells to obtain a spatial grid model.

[0084] In some embodiments, the spatial grid model can be obtained by determining the side length of the grid cell and dividing the heavy metal concentration distribution surface according to the side length, so as to divide the heavy metal concentration distribution surface into equally spaced grid cells.

[0085] In some embodiments, when dividing the grid cells, a unique identification code can be assigned to each grid cell. For example, the sequential numbering method can be adopted, starting from the upper left corner, and numbered row by row from left to right and from top to bottom in sequence. At the same time, the coordinate range of each grid cell is clarified, which is represented by the coordinates of the lower left vertex (x min , y min ) and the upper right vertex (x max , y max ), so that each grid cell has a clear spatial identification, which is convenient for subsequent management and analysis of the heavy metal concentration distribution information in the grid cell, ensuring that each grid cell can accurately contain the heavy metal concentration distribution information and providing an accurate data basis for subsequent risk assessment.

[0086] S504. Determine the comprehensive pollution index of the grid cell according to the heavy metal concentration distribution information.

[0087] In the embodiments of the present application, the implementation manner of step S504 can be implemented by any one of the embodiments of the present application, and no limitation is made here and will not be elaborated.

[0088] S505. Perform time series analysis on the spatial grid model to predict the dynamic migration trend of the heavy metal concentration corresponding to the grid cell.

[0089] In some embodiments, the dynamic migration trend of the heavy metal concentration corresponding to the grid cell can be predicted by constructing a migration model related to the heavy metal concentration and performing time series analysis on the spatial grid model based on this model.

[0090] In some embodiments, a migration model of heavy metals can be constructed based on a partial differential equation model. Optionally, a migration model can be constructed according to the groundwater seepage velocity, diffusion coefficient and chemical reaction rate in the target area. Among them, the flow of groundwater will drive the migration of heavy metals in the soil. The faster the groundwater seepage velocity, the faster the migration speed of heavy metals will be; the diffusion coefficient reflects the diffusion ability of heavy metals in the soil and is related to factors such as the pore structure and water content of the soil. The larger the diffusion coefficient, the faster the diffusion speed 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 existence form and migration and transformation process of heavy metals in the soil.

[0091] In some embodiments, by determining the groundwater seepage velocity of 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.

[0092] Optionally, the migration model is as follows:

[0093] Where C is the concentration value 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.

[0094] Furthermore, the finite difference method can be used to discretely solve the migration model to obtain the predicted values of the heavy metal concentration distribution in different grid cells in the future time period. Using the finite difference method can discretize time and space, thereby accurately predicting the heavy metal concentration distribution in the grid cells in the future time period.

[0095] Exemplarily, assume that the time step is Δt, and the space steps are Δx and Δy. In space, for and the central difference scheme is used for approximation. For example, The discrete form in two-dimensional space can be expressed as , where C i,j represents the concentration value of heavy metals in the grid cell at the i-th row and j-th column. For the time derivative the forward difference scheme is used, that is, , where represents the concentration value of heavy metals in the grid cell at the i-th row and j-th column at the n-th moment. Substituting these discretized expressions into the above formula (8), an algebraic equation about can be obtained. By iteratively solving this algebraic equation, the predicted values of the heavy metal concentration distribution in the future time period can be obtained.

[0096] Furthermore, based on the predicted values of the heavy metal concentration distribution in the grid cells, the dynamic migration trend of the heavy metal concentration corresponding to the grid cells can be determined. Optionally, according to the predicted values of the heavy metal concentration distribution in each grid cell, the change of the heavy metal concentration between different grid cells and the trend of the heavy metal concentration changing with time can be determined, so as to determine the dynamic migration trend of the heavy metal concentration in the time series.

[0097] Figure 6 Shown is a schematic flow chart of the prediction process of the dynamic migration trend, as Figure 6As shown, by obtaining 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 is constructed, and the finite difference method is used to discretize the migration model. By setting parameters such as the time step and space step, and performing iterative calculations according to the discretized equations, the predicted values of the concentration distribution of heavy metals in the future time period can be obtained. By judging whether the predicted values of the concentration distribution meet the time requirements, and when the time requirements are met, the prediction of the dynamic migration trend is completed.

[0098] In the embodiment of the present application, by constructing a migration model of heavy metals, the migration process of heavy metals in the soil can be effectively simulated. By considering various factors such as the diffusion coefficient, groundwater seepage velocity, and chemical reaction rate, the prediction results are more in line with the actual situation.

[0099] S506. Determine the exposure thresholds of different ecological receptors in the grid cells.

[0100] S507. For each grid cell, generate an ecological risk heat map of the target area according to the comprehensive pollution index, dynamic migration trend, and exposure threshold corresponding to the grid cell.

[0101] S508. Determine the risk locations and safe locations in the target area according to the ecological risk heat map of the target area and the preset threshold.

[0102] In the embodiment of the present application, the implementation manners of steps S506 - S508 can be respectively implemented by any one of the embodiments of the present application, and no limitation is made herein and will not be elaborated further.

[0103] In the ecological risk assessment method for heavy metal - contaminated soil provided by the embodiment of the present application, the Kriging interpolation method is used to generate the heavy metal concentration distribution surface, which can more accurately reflect the actual distribution of heavy metals in the soil, avoid information omission caused by sample discreteness, and provide a solid data basis for subsequent accurate assessment.

[0104] Figure 7 is a flowchart of an ecological risk assessment method for heavy metal - contaminated soil provided by the embodiment of the present application. As Figure 7 shown, the ecological risk assessment method for heavy metal - contaminated soil in the embodiment of the present application includes but is not limited to the following steps: S701. Obtain a soil sample set of the target area, where 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, and N is a positive integer.

[0105] S702. According to the soil sample set, construct a spatial grid model of the target area, where the spatial grid model includes multiple grid cells, and each grid cell contains the concentration distribution information of heavy metals.

[0106] S703. Determine the comprehensive pollution index of the grid cell according to the concentration distribution information of heavy metals.

[0107] S704. Perform time series analysis on the spatial grid model to predict the dynamic migration trend of the heavy metal concentration corresponding to the grid cell.

[0108] S705. Determine the exposure threshold of different ecological receptors in the grid cell.

[0109] In the embodiments of the present application, the implementation manners of steps S701 - S705 can be respectively implemented by any one of the embodiments of the present application. No limitation is made here and no further elaboration is provided.

[0110] S706. For each grid cell, perform differential calculation on the dynamic migration trend of the grid cell to obtain the gradient value of the dynamic migration trend.

[0111] In some embodiments, when generating the ecological risk heat map of the target area according to 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 the ecological risk heat map is generated based on the risk level score.

[0112] Optionally, a multi - dimensional risk assessment matrix can be generated according to the comprehensive pollution index, dynamic migration trend and exposure threshold, and operations are performed on the multi - dimensional risk assessment matrix to obtain the risk level score.

[0113] In some embodiments, the change rate of the heavy metal concentration in space can be determined according to the dynamic migration trend of the heavy metal concentration corresponding to the grid cell. Optionally, by performing differential calculation on the dynamic migration trend, the gradient value of the dynamic migration trend can be determined as the change rate of the heavy metal concentration in space, and the gradient value of the dynamic migration trend is used as an element in the multi - dimensional risk assessment matrix.

[0114] For example, in one - dimensional space, the formula for calculating the gradient value of the dynamic migration trend is as follows:

[0115] Where, ΔC represents the gradient value of the dynamic migration trend, C i and C i+1 are the concentration values of heavy metals on adjacent grid cells, and Δx is the spacing of the grid cell.

[0116] S707. Obtain the ratio of the exposure threshold to the concentration value of the heavy metal.

[0117] In some embodiments, by calculating the ratio of the exposure threshold of each heavy metal in a grid cell to the concentration value of the heavy metal, and averaging multiple ratios, the ratio of the final exposure threshold of the grid cell to the concentration value of the heavy metal can be obtained, and this ratio is used as an element in the multi-dimensional risk assessment matrix.

[0118] For example, assume that 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 to the concentration value of the heavy metal, 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 ratio 1, ratio 2, and ratio 3 to obtain the ratio of the final exposure threshold to the concentration value of the heavy metal.

[0119] S708. Construct a multi-dimensional risk assessment matrix based on the comprehensive pollution index, the gradient value of the dynamic migration trend, and the ratio.

[0120] S709. Calculate the multi-dimensional risk assessment matrix through a pre-calibrated linear combination to obtain the risk level score of the grid cell.

[0121] In some embodiments, the multi-dimensional risk assessment matrix is composed of the comprehensive pollution index, the gradient value of the dynamic migration trend, and the ratio. By calculating the multi-dimensional risk assessment matrix through a linear combination, multiple dimensions of data are integrated into a comprehensive score through weighted summation, thereby obtaining the risk level score.

[0122] Optionally, the calculation formula is as follows:

[0123] Among them, R s 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 the heavy metal, and a, b, and c are normalization coefficients.

[0124] S710. Generate an ecological risk heat map of the target area based on the risk level score.

[0125] In some embodiments, in order 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 cell, soil physical and chemical property parameters, and meteorological data, so that the final risk level covers the impacts of soil environment and external factors on heavy metal pollution and ecological risks.

[0126] In some embodiments, the risk level score can be optimized through machine learning. Optionally, by training a random forest model and using the random forest model to optimize the risk level score.

[0127] In some embodiments, a random forest model can be trained using historical pollution data, input parameters of grid cells can be obtained, and the input parameters are input into the random forest model to obtain the risk level classification probability of the grid cells. Among them, the input parameters at least include the concentration distribution information of heavy metals in the grid cells, soil physical and chemical property parameters, and meteorological data.

[0128] Optionally, the risk level classification probability refers to the classification probability of each risk level score corresponding to each parameter among the concentration distribution information of heavy metals, soil physical and chemical property parameters, and meteorological data.

[0129] By performing weighted fusion on the risk level score and the risk level classification probability, the final risk level of the grid cell is obtained. For example, let the risk level classification probability be P rf , and the risk level score be R s , the weighted fusion formula can be expressed as:

[0130] Among them, R final represents the final risk level, and w1 and w2 represent weight coefficients.

[0131] Furthermore, an ecological risk heat map of the target area can be generated according to the final risk level of the grid cells. The spatial distribution of the final risk level can be determined according to the position range of the grid cells, and based on visualization technology, the spatial distribution of the final risk level is used to generate an ecological risk heat map to intuitively display the ecological risks at different positions in the target area.

[0132] S711. Determine the risk positions and safe positions from the target area according to the ecological risk heat map of the target area and a preset threshold.

[0133] In the embodiments of the present application, the implementation manner of step S711 can be implemented by any one of the embodiments of the present application respectively, and no limitation is made here and no further description is given.

[0134] In the ecological risk assessment method for heavy metal contaminated soil provided by the embodiments of the present application, through establishing a multi-dimensional risk assessment matrix for operation, various factors such as the comprehensive pollution index, dynamic migration trend, and exposure threshold are comprehensively considered, and the risk levels of different grid cells can be evaluated more comprehensively and accurately, improving the accuracy and reliability of the risk assessment. Moreover, by using machine learning to optimize the steps, through training a random forest model and performing weighted fusion with the results of the multi-dimensional risk assessment matrix, the accuracy of the risk assessment is further improved, and it can better adapt to the complex and changeable soil pollution situation.

[0135] Figure 8 The figure shows a schematic flow chart for evaluating the ecological risk of heavy metal contaminated soil, as Figure 8As shown, by obtaining the soil sample set of the target area, constructing a spatial grid model of the target area according to the sampling positions in the soil sample set, and enabling each grid cell to contain the concentration distribution information of heavy metals. According to the weights of the heavy metals, calculate the comprehensive pollution index of each grid cell, and perform time series analysis on the spatial grid model to predict the dynamic migration trend of the heavy metal concentration corresponding to the grid cell. Further, in combination with the ecological receptor sensitivity database, determine the exposure thresholds of different ecological receptors in the grid cell, so as to establish a multi-dimensional risk assessment matrix, perform matrix calculations on the comprehensive pollution index, dynamic migration trend, and exposure threshold, obtain the risk level of each grid cell, and generate an ecological risk heat map according to the spatial distribution of the risk levels. Through the ecological risk heat map, judge whether the risk levels at different positions in the target area exceed the preset threshold to determine the risk positions and safe positions in the target area. If the risk level exceeds the preset threshold, determine it as a risk position, otherwise it is a safe position.

[0136] Corresponding to the ecological risk assessment methods for heavy metal contaminated soil proposed in the above several embodiments, an embodiment of the present 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 the embodiment of the present application corresponds to the ecological risk assessment methods for heavy metal contaminated soil proposed in the above several embodiments, the implementation manners 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 the embodiment of the present application, and will not be described in detail in the following embodiments.

[0137] To implement the above embodiments, the present application also proposes an ecological risk assessment system for heavy metal contaminated soil.

[0138] Figure 9 It is a schematic structural diagram of an ecological risk assessment system for heavy metal contaminated soil provided by an embodiment of the present application.

[0139] As Figure 9 shown, the ecological risk assessment system 900 for heavy metal contaminated soil includes: A data acquisition module 901, configured to obtain a soil sample set of the target area. The soil sample set includes a plurality of soil sample data, and each soil sample data includes a sampling position and the concentration values of N heavy metals, where N is a positive integer.

[0140] A model construction module 902, configured to construct a spatial grid model of the target area according to the soil sample set. The spatial grid model includes a plurality of grid cells, and each grid cell contains the concentration distribution information of heavy metals.

[0141] A pollution index determination module 903, configured to determine the comprehensive pollution index of the grid cell according to the concentration distribution information of the heavy metals.

[0142] A trend prediction module 904 for performing time series analysis on the spatial grid model to predict the dynamic migration trend of the heavy metal concentration corresponding to the grid cell.

[0143] An exposure threshold determination module 905 for determining the exposure thresholds of different ecological receptors in the grid cell.

[0144] A heat map generation module 906 for generating 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.

[0145] A region division module 907 for determining risk positions and safe positions from the target area according to the ecological risk heat map of the target area and a preset threshold.

[0146] In a possible implementation manner of the embodiment of the present application, the pollution index determination module 903 is further configured to: determine the concentration value of each heavy metal in the grid cell according to the concentration distribution information of the heavy metals in the grid cell; for each heavy metal, determine the background value and weight of the heavy metal, and determine the pollution index of the heavy metal according to the concentration value, background value, and weight; sum the pollution indices of N heavy metals in the grid cell to obtain the comprehensive pollution index of the grid cell.

[0147] In a possible implementation manner of the embodiment of the present application, the pollution index determination module 903 is further configured to: determine the initial weight of the heavy metal according to the toxicity response coefficient, mobility, and bioconcentration factor of the heavy metal; determine the soil pH value and organic matter content, and correct the initial weight of the heavy metal according to the soil pH value and organic matter content to obtain the weight of the heavy metal.

[0148] In a possible implementation manner of the embodiment of the present application, the model construction module 902 is further configured to: perform spatial interpolation on the sampling positions and heavy metal concentration values of the soil sample data by using the Kriging interpolation method to generate a heavy metal concentration distribution surface; divide the heavy metal concentration distribution surface into equally spaced grid cells to obtain a spatial grid model.

[0149] In a possible implementation manner of the embodiment of the present application, the trend prediction module 904 is further configured to: determine the groundwater seepage velocity of the target area, as well as the diffusion coefficient and chemical reaction rate of the heavy metal; construct a migration model of the heavy metal according to the groundwater seepage velocity, diffusion coefficient, and chemical reaction rate; perform discrete solution on the migration model by using the finite difference method to obtain the predicted values of the heavy metal concentration distribution in different grid cells in the future time period; determine the dynamic migration trend of the heavy metal concentration corresponding to the grid cell based on the predicted values of the heavy metal concentration distribution in the grid cell.

[0150] In a possible implementation manner of the embodiment of the present application, the heat map generation module 906 is further configured to: for each grid cell, perform differential calculation on the dynamic migration trend of the grid cell to obtain the gradient value of the dynamic migration trend; obtain the ratio of the exposure threshold to the concentration value of the heavy metal; construct a multi-dimensional risk assessment matrix according to the comprehensive pollution index, the gradient value of the dynamic migration trend, and the ratio; calculate the multi-dimensional risk assessment matrix through a pre-calibrated linear combination to obtain the risk level score of the grid cell; generate an ecological risk heat map of the target area according to the risk level score.

[0151] In a possible implementation manner of the embodiment of the present application, the heat map generation module 906 is further configured to: train a random forest model using historical pollution data; obtain the input parameters of the grid cell and input the input parameters into the random forest model to obtain the risk level classification probability of the grid cell, where the input parameters at least include the concentration distribution information of heavy metals in the grid cell, soil physical and chemical property parameters, and meteorological data; perform weighted fusion on the risk level score and the risk level classification probability to obtain the final risk level of the grid cell; generate an ecological risk heat map of the target area according to the final risk level of the grid cell.

[0152] In a possible implementation manner of the embodiment of the present application, the pollution index determination module 903 is further configured to: in response to the concentration value of the i-th heavy metal not being included in the soil sample data, 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 triangular membership function on the minimum concentration value, average concentration value, and maximum concentration value of the i-th heavy metal to obtain the concentration value of the i-th heavy metal in the soil sample data.

[0153] In a possible implementation manner of the embodiment of the present application, the pollution index determination module 903 is further configured to: determine the bioavailable concentration of the i-th heavy metal; determine the total concentration value of the i-th heavy metal in the target area; determine the bioavailable proportion of the i-th heavy metal according to the bioavailable concentration and the total concentration value of the i-th heavy metal; determine the maximum bioavailable proportion from the bioavailable proportions of N heavy metals; correct the comprehensive pollution index of the grid cell based on the maximum bioavailable proportion to obtain the final comprehensive pollution index of the grid cell.

[0154] In the ecological risk assessment system for heavy metal contaminated soil provided by the embodiments of the present application, a soil sample set of the target area is obtained, and a spatial grid model of the target area is constructed based on the soil sample set. According to 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 the heavy metal concentration corresponding to the grid cell is predicted. By determining the exposure thresholds of different ecological receptors in the grid cell, an ecological risk heat map of the target area is generated based on the comprehensive pollution index, the dynamic migration trend, and the exposure thresholds, so that the risk locations and safe locations can be determined from the target area according to the ecological risk heat map and the preset thresholds. Thus, this solution can fully consider the spatial distribution characteristics of soil heavy metal pollution. By predicting the dynamic migration trend of heavy metal concentration, it is possible to master the development direction of heavy metal pollution in advance, providing a strong basis for formulating targeted pollution prevention and control measures. By evaluating the ecological risk of the target area through multi-dimensional factors such as the comprehensive pollution index, the dynamic migration trend, and the exposure threshold, it is possible to more comprehensively and accurately evaluate the ecological risk of different grid cells, greatly improving the accuracy and reliability of the risk assessment.

[0155] It should be noted that the foregoing explanation of the embodiments 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 elaborated here.

[0156] To implement the above embodiments, the present application also proposes an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided in the foregoing embodiments. To implement the above embodiments, the present application also proposes a computer-readable storage medium storing computer-executable instructions, where the computer-executable instructions are used to implement the method provided in the foregoing embodiments when executed by a processor.

[0157] To implement the above embodiments, the present application also proposes a computer program product including a computer program, where the computer program implements the method provided in the foregoing embodiments when executed by a processor.

[0158] The collection, storage, use, processing, transmission, provision, and application of the user's personal information involved in the present application all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0159] It should be noted that personal information from users should be collected for legal and reasonable purposes and not shared or sold outside of such legal uses. In addition, such collection / sharing should be carried out after obtaining the informed consent of the user, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization including authorizing relevant user information before the user uses the function. In addition, any necessary steps should be taken to defend and safeguard access to such personal information data and ensure that others with access to the personal information data comply with their privacy policies and procedures.

[0160] This application is expected to provide embodiments in which users can selectively block the use or access of personal information data. That is, this application is expected to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, the risk can be minimized by restricting data collection and deleting the data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of the user.

[0161] In the description of the foregoing embodiments, the descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0162] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this application, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically and clearly defined.

[0163] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of this application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in the reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of this application belong.

[0164] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion having one or more wirings (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then storing it in a computer memory.

[0165] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0166] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0167] In addition, each functional unit in various embodiments of the present application may be integrated into a processing module, or each unit may exist physically alone, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0168] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. An ecological risk assessment method for heavy metal contaminated soil, characterized in that, Including: Obtain a set of soil samples from the target area, where the set of soil samples includes multiple soil sample data, and each soil sample data includes a sampling location and concentration values of N heavy metals, and N is a positive integer; Construct a spatial grid model of the target area according to the set of soil samples, where the spatial grid model includes multiple grid cells, and each grid cell contains concentration distribution information of heavy metals; Determine the comprehensive pollution index of the grid cell according to the concentration distribution information of the heavy metals; Perform time series analysis on the spatial grid model to predict the dynamic migration trend of the heavy metal concentration corresponding to the grid cell; Determine the exposure thresholds of different ecological receptors in the grid cell; For each grid cell, generate an ecological risk heat map of the target area according to the comprehensive pollution index, dynamic migration trend and exposure threshold corresponding to the grid cell; Determine the risk locations and safe locations from the target area according to the ecological risk heat map of the target area and a preset threshold.

2. The method according to claim 1, characterized in that The determining the comprehensive pollution index of the grid cell according to the concentration distribution information of the heavy metals includes: Determine the concentration value of each heavy metal in the grid cell according to the concentration distribution information of the heavy metals in the grid cell; For each heavy metal, determine the background value and weight of the heavy metal, and determine the pollution index of the heavy metal according to the concentration value, the background value and the weight; Sum the pollution indices of the N heavy metals in the grid cell to obtain the comprehensive pollution index of the grid cell.

3. The method according to claim 2, wherein The process of determining the weight of the heavy metal includes: Determine the initial weight of the heavy metal according to the toxicity response coefficient, mobility and bioaccumulation coefficient of the heavy metal; Determine the soil pH value and organic matter content, and correct the initial weight of the heavy metal according to the soil pH value and organic matter content to obtain the weight of the heavy metal.

4. The method according to claim 1, wherein The constructing the spatial grid model of the target area according to the set of soil samples includes: Use Kriging interpolation method to perform spatial interpolation on the sampling locations of the soil sample data and the concentration values of the heavy metals to generate a heavy metal concentration distribution surface; Divide the heavy metal concentration distribution surface into equally spaced grid cells to obtain the spatial grid model.

5. The method according to any one of claims 1-4, characterized in that, The performing time series analysis on the spatial grid model to predict the dynamic migration trend of the heavy metal concentration corresponding to the grid cell includes: Determine the groundwater seepage velocity of the target area, as well as the diffusion coefficient and chemical reaction rate of the heavy metal; Construct a migration model of the heavy metal according to the groundwater seepage velocity, the diffusion coefficient and the chemical reaction rate; Use the finite difference method to discretely solve the migration model to obtain the predicted values of the heavy metal concentration distribution in different grid cells in the future time period; Based on the predicted values of the heavy metal concentration distribution of the grid cell, determine the dynamic migration trend of the heavy metal concentration corresponding to the grid cell.

6. The method according to any one of claims 1-4, characterized in that, The generating the ecological risk heat map of the target area according to the comprehensive pollution index, dynamic migration trend and exposure threshold corresponding to the grid cell includes: For each grid cell, perform differential calculation on the dynamic migration trend of the grid cell to obtain the gradient value of the dynamic migration trend; Obtain the ratio of the exposure threshold to the concentration value of the heavy metal; Construct a multi-dimensional risk assessment matrix based on the comprehensive pollution index, the gradient value of the dynamic migration trend, and the ratio; Calculate the multi-dimensional risk assessment matrix through a pre-calibrated linear combination to obtain the risk level score of the grid cell; Generate an ecological risk heat map of the target area according to the risk level score; 7. The method according to claim 6, characterized in that, The generating the ecological risk heat map of the target area according to the risk level score includes: Train a random forest model using historical pollution data; Obtain the input parameters of the grid cell and input the input parameters into the random forest model to obtain the risk level classification probability of the grid cell, where the input parameters at least include the concentration distribution information of heavy metals, soil physical and chemical property parameters, and meteorological data of the grid cell; Perform weighted fusion on the risk level score and the risk level classification probability to obtain the final risk level of the grid cell; Generate an ecological risk heat map of the target area according to the final risk level of the grid cell; 8. The method according to any one of claims 1-4, characterized in that, The method further includes: In response to the concentration value of the i-th heavy metal not being included in the soil sample data, 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 triangular membership function on the minimum concentration value, average concentration value, and maximum concentration value of the i-th heavy metal to obtain the concentration value of the i-th heavy metal in the soil sample data; 9. The method according to any one of claims 1-4, characterized in that After determining the comprehensive pollution index of the grid cell according to the concentration distribution information of the heavy metal, further include: Determine the bioavailable concentration of the i-th heavy metal; Determine the total concentration value of the i-th heavy metal in the target area; Determine the bioavailable ratio of the i-th heavy metal according to the bioavailable concentration and total concentration value of the i-th heavy metal; Determine the maximum bioavailable ratio from the bioavailable ratios of the N heavy metals; Based on the maximum bioavailable ratio, correct the comprehensive pollution index of the grid cell to obtain the final comprehensive pollution index of the grid cell; 10. An ecological risk assessment system for heavy metal contaminated soil, characterized in that, Includes: A data acquisition module for obtaining a soil sample set of the target area, the soil sample set including a plurality of soil sample data, each soil sample data including a sampling location and the concentration values of N heavy metals, where N is a positive integer; A model construction module for constructing a spatial grid model of the target area according to the soil sample set, where the spatial grid model includes a plurality of grid cells, and each grid cell contains the concentration distribution information of heavy metals; A pollution index determination module for determining the comprehensive pollution index of the grid cell according to the concentration distribution information of the heavy metal; A trend prediction module for performing time series analysis on the spatial grid model to predict the dynamic migration trend of the heavy metal concentration corresponding to the grid cell; An exposure threshold determination module for determining the exposure thresholds of different ecological receptors in grid cells; A heat map generation module for generating an ecological risk heat map of the target area for each grid cell according to the comprehensive pollution index, dynamic migration trend, and exposure threshold corresponding to the grid cell; A region division module for determining risk locations and safe locations from the target area according to the ecological risk heat map of the target area and a preset threshold.

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