A soil environmental pollutant risk prediction method and system

Abstract

The application provides a soil environmental pollutant risk prediction method and system, which comprises the following steps: determining toxic substances having an influence on plants and animals of a species in a soil environment, and establishing a species sensitivity curve; obtaining environmental factors affecting the cumulative exposure of the toxic substances in an industrial park, and determining main environmental factors affecting the cumulative exposure of the toxic substances based on the environmental factors; based on the main environmental factors, a time series evolution prediction model is established by combining a time series and a multivariate autoregressive model; based on the species sensitivity curve and the cumulative exposure of the toxic substances in the future soil environment predicted by the time series evolution prediction model, a risk prediction model is established by combining a joint probability density model, the risk prediction model is used at least for predicting the probability of the soil environment of the industrial park being polluted by the toxic substances in the future; and the probability prediction value of the soil environment being polluted by the toxic substances in the future is predicted based on the prediction model.

Description

Technical Field

[0001] This invention relates to the field of soil pollution prevention and control and environmental risk identification in the ecological and environmental protection industry, and in particular to a method and system for predicting the risk of soil environmental pollutants. Background Technology

[0002] Industrial parks are the main carriers of industrial development and an inevitable trend in industrialization. With the advancement of industrialization, industrial parks are gradually becoming concentrated areas of resource and energy consumption and environmental pollution emissions. Therefore, it is crucial to promptly grasp the characteristics of heavy metal accumulation in industrial park soils, accurately predict future trends and environmental risks associated with heavy metal accumulation, and establish a prevention-oriented system for heavy metal pollution control and risk management in industrial park soils. Strengthening environmental risk management in industrial parks and analyzing their ecological impacts have broad and urgent application needs in the current fields of industrial park management and ecological environmental protection.

[0003] According to Chinese patent publications, "industrial parks" have become a hot area and emerging direction for patent grants, as seen in patents such as "A Soil Pollution Analysis Method and System Based on Industrial Parks" (Publication No. CN114354892A), "Real-time Continuous Monitoring System for Soil and Groundwater in Industrial Parks" (Publication No. CN114441726B), and "An Environmental Data Monitoring System for Industrial Parks" (Publication No. CN213336231U). However, these patents mainly focus on methods for analyzing soil and groundwater in specific industrial parks, with fewer addressing the analysis of the ecological and environmental impacts of industrial parks. This makes it difficult to meet the application needs of industrial park status assessment, ecological risk identification, and environmental planning.

[0004] Currently, the following difficulties are commonly encountered in predicting soil ecological risks in industrial parks:

[0005] Soil heavy metal pollution is characterized by its concealment, lag, and cumulative nature, resulting in a delayed impact on ecological risks. Therefore, risk assessment methods used for water and air pollution cannot be simply applied. Current research primarily relies on the "Technical Guidelines for Risk Assessment of Soil Pollution in Construction Land" for environmental risk assessment. However, this method is suitable for small sites and not for regional-scale ecological risk assessment and prediction of heavy metal pollution in industrial parks. In fact, the ecological hazards of heavy metal pollution in industrial park soils are significant at the regional scale. From the perspective of pollutant risk management in industrial parks, it is necessary to fully consider the characteristics of soil ecology and establish a method suitable for predicting the probability of ecological risks in industrial parks. Specifically, this includes the following issues:

[0006] (1) Lack of quantitative ecological risk prediction and assessment. Existing methods for predicting environmental risks of heavy metals in soil mainly use qualitative methods such as the quotient method, which are not practical for risk management in industrial parks and lack quantitative model specifications for risk prediction.

[0007] (2) Lack of comprehensive consideration of environmental factors on soil cumulative exposure. Existing methods for predicting soil heavy metal cumulative exposure are mainly based on the accumulation rate or residual rate of heavy metals. They lack comprehensive consideration of environmental factors that affect soil heavy metal cumulative exposure, resulting in low model prediction accuracy. It is necessary to take into account both natural and anthropogenic factors that affect the accumulation of heavy metals in industrial park soils and assess their impact on future soil heavy metal accumulation. Summary of the Invention

[0008] This invention provides a method and system for predicting the pollution risk of pollutants in the soil environment in a fast, efficient and accurate manner.

[0009] To address the aforementioned technical problems, embodiments of the present invention provide a method for predicting the risk of soil environmental pollutants, comprising:

[0010] Obtain information on plant and animal species involved in the soil environment of the industrial park;

[0011] Identify toxic substances in the soil environment that affect the aforementioned plant and animal species;

[0012] Establish species sensitivity curves for the toxic substances;

[0013] To obtain the environmental factors in the industrial park that affect the cumulative exposure to the toxic substances;

[0014] The environmental factors were screened to identify the main environmental factors affecting the cumulative exposure of the toxic substances;

[0015] Based on the main environmental factors, a time-series evolution prediction model is established by combining time series and multivariate autoregressive models. The time-series evolution prediction model is used to predict the cumulative exposure of toxic substances in the soil environment of the industrial park in the future.

[0016] Based on the species sensitivity curve and the cumulative exposure of toxic substances in the future soil environment predicted by the time-series evolution prediction model, a risk prediction model is established by combining the joint probability density model. The risk prediction model is used at least to predict the probability that the soil environment of the industrial park will be polluted by the toxic substances in the future.

[0017] The prediction model predicts the probability that the soil environment will be polluted by the toxic substance in the future.

[0018] As an optional embodiment, establishing the species sensitivity curve of the toxic substance includes:

[0019] The probability density curve of the toxic substance is established based on the following formula, and the species sensitivity curve of the toxic substance is defined based on the probability density curve:

[0020]

[0021] Where f(x) represents the probability density curve of the toxic substance, x represents the toxic substance, μ represents the standard deviation, and γ represents the distribution parameter.

[0022] As an optional embodiment, the toxic substance is a heavy metal.

[0023] The screening of the environmental factors to determine the main environmental factors affecting the cumulative exposure to the toxic substances includes:

[0024] Obtain heavy metal content data from multiple sampling points in the soil environment;

[0025] The spatial stratification heterogeneity data of cumulative heavy metal exposure and environmental factors in the soil environment were calculated and determined according to the following formula:

[0026]

[0027] q h＝1-h＝2 The significance of spatial stratification variance is represented and defined as the spatial stratification heterogeneity data, Y. h n represents the mean of attributes within the stratified region h. h represents the number of samples within the stratified region h, and Var represents the variance;

[0028] The environmental factors are screened based on the hierarchical heterogeneity data to obtain the main environmental factors.

[0029] As an optional embodiment, the time series includes:

[0030]

[0031] Where T(t) is the time series function of the main environmental factors, a, b and c are fitting parameters, c is the environmental factor and t is time.

[0032] As an optional embodiment, it also includes:

[0033] The multivariate autoregressive model was adjusted by incorporating different environmental policies.

[0034] A time series evolution prediction model is established based on the time series and the corrected multivariate autoregressive model.

[0035] The corrected multivariate autoregressive model is as follows:

[0036]

[0037] Where ε1 and ε2 are constant terms, a n The coefficients of the controlling factor. The time series data are for the main control factors. For time series data of policy factors, a 政策 The coefficients of policy factors.

[0038] As an optional embodiment, it also includes:

[0039] Obtain a validation data sample set and validate the prediction accuracy of the time series evolution prediction model based on the following formula:

[0040] Mean absolute error:

[0041] Mean square error:

[0042] Average root mean square error of prediction:

[0043] Coefficient of determination:

[0044] Where n is the validation data sample set, y p y is the measured value. m These are predicted values.

[0045] As an optional embodiment, the toxic substance includes at least one or more of the following heavy metals:

[0046] As, Cd, Cu, Cr, Ni, Pb and Zn.

[0047] As an optional embodiment, the environmental factors include natural environmental factors and anthropogenic environmental factors. The natural environmental factors include at least one or more parameters selected from soil organic matter, soil pH, soil Eh, soil parent material, vegetation cover index, wind speed, rainfall, elevation, and slope. The anthropogenic environmental factors include at least one or more parameters selected from the distribution of enterprises in the industrial park, gross industrial output, PM10, PM2.5, industrial emissions, traffic flow, and population density.

[0048] As an optional embodiment, the prediction model is also used to predict the percentage probability that plant and animal species in the soil environment will be affected by the toxic substance and the time when the probability value of the future pollution risk of the soil environment exceeds the environmental risk threshold.

[0049] Another embodiment of the present invention also provides a soil environmental pollutant risk prediction system, comprising:

[0050] The module is used to obtain information on plant and animal species involved in the soil environment of the industrial park;

[0051] The first determining module is used to determine toxic substances in the soil environment that affect the species of plants and animals;

[0052] The first module is used to establish the species sensitivity curve of the toxic substance;

[0053] The acquisition module is used to acquire environmental factors in the industrial park that affect the cumulative exposure of the toxic substances.

[0054] The second determining module is used to screen the environmental factors and determine the main environmental factors that affect the cumulative exposure of the toxic substances.

[0055] The second module is used to establish a time-series evolution prediction model based on the main environmental factors, combined with time series and multivariate autoregressive models. The time-series evolution prediction model is used to predict the cumulative exposure of toxic substances in the soil environment of the industrial park in the future.

[0056] The third module is used to establish a risk prediction model based on the species sensitivity curve and the cumulative exposure of toxic substances in the future soil environment predicted by the time-series evolution prediction model, and in combination with the joint probability density model. The risk prediction model is used at least to predict the probability that the soil environment of the industrial park will be polluted by the toxic substances in the future.

[0057] The prediction module is used to predict the probability that the soil environment will be polluted by the toxic substance in the future, based on the prediction model.

[0058] Based on the disclosure of the above embodiments, it can be understood that the beneficial effects of the embodiments of the present invention include: obtaining information on plant and animal species and toxic substances involved in the soil environment of industrial parks, establishing species sensitivity curves, then collecting sampling data from multiple sampling points in the soil environment, and performing spatial differentiation analysis on the toxic substance content data and environmental factors in the multiple sampling data to obtain the main environmental factors affecting the cumulative exposure of toxic substances. Then, based on this, a time series evolution model is established using time series and multivariate autoregressive models, and the parameters of the regression model are corrected by combining environmental protection policies under different scenarios to obtain the cumulative exposure of toxic substances in the soil of industrial parks in the future. Finally, the sensitivity curves obtained by comprehensive calculation and the predicted cumulative exposure of toxic substances in the future are combined with the joint probability density model to establish a risk prediction model, and finally, based on the risk prediction model, the predicted risk probability value of the soil environment of industrial parks being polluted by toxic substances in the future is predicted. The solution in this embodiment overcomes the technical deficiencies of existing technologies, such as the lack of quantitative ecological risk prediction and assessment, the lack of operability in practical guidance for risk management in industrial parks, and the lack of standardized quantitative models for risk prediction. It also solves the technical problem of low prediction accuracy caused by the lack of comprehensive consideration of the cumulative exposure of toxic substances in soil by environmental factors. The solution significantly improves the prediction accuracy of the risk prediction model, making the predicted values ​​more valuable and facilitating users' understanding of the actual soil environment in industrial parks, thus helping users effectively solve soil pollution problems.

[0059] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0060] The technical solution of this application will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0061] The accompanying drawings are provided to further illustrate the present application and form part of the specification. They are used together with the embodiments of the present application to explain the application and do not constitute a limitation thereof. In the drawings:

[0062] Figure 1 This is a flowchart of the soil environmental pollutant risk prediction method in an embodiment of the present invention.

[0063] Figure 2 This is a flowchart illustrating the application of a soil environmental pollutant risk prediction method in another embodiment of the present invention.

[0064] Figure 3 This is a schematic diagram of the sensitivity curve in an embodiment of the present invention.

[0065] Figure 4 This is the analysis result of the geographic detector in the embodiment of the present invention.

[0066] Figure 5 This is a schematic diagram of the probability density curves of different toxic substances in an embodiment of the present invention.

[0067] Figure 6 This is a structural block diagram of the soil environmental pollutant risk prediction system in an embodiment of the present invention. Detailed Implementation

[0068] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings, but these are not intended to limit the scope of the invention.

[0069] It should be understood that various modifications can be made to the embodiments disclosed herein. Therefore, the following description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope and spirit of this disclosure will be apparent to those skilled in the art.

[0070] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present disclosure and, together with the general description of the disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the disclosure.

[0071] These and other features of the invention will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0072] It should also be understood that although the invention has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of the invention, which have the features described in the claims and are therefore all within the scope of protection defined herein.

[0073] The above and other aspects, features and advantages of this disclosure will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0074] Specific embodiments of the present disclosure are described thereafter with reference to the accompanying drawings; however, it should be understood that the disclosed embodiments are merely examples of the present disclosure and can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the present disclosure. Therefore, the specific structural and functional details disclosed herein are not intended to be limiting, but merely to serve as the basis and representative basis for the claims to teach those skilled in the art to use the present disclosure in a variety of substantially any suitable detailed structures.

[0075] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in still another embodiment,” all of which may refer to one or more of the same or different embodiments according to this disclosure.

[0076] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0077] like Figure 1 As shown, this embodiment of the invention provides a method for predicting the risk of soil environmental pollutants, including:

[0078] S100: Obtain information on plant and animal species involved in the soil environment of the industrial park;

[0079] S200: Identify toxic substances in the soil environment that have an impact on species of plants and animals;

[0080] S300: Establish species sensitivity curves for toxic substances;

[0081] S400: Acquire environmental factors that affect the cumulative exposure to toxic substances in industrial parks;

[0082] S500: Screening environmental factors to identify the main environmental factors affecting the cumulative exposure of toxic substances;

[0083] S600: Based on major environmental factors, a time-series evolution prediction model is established by combining time series and multivariate autoregressive models. The time-series evolution prediction model is used to predict the cumulative exposure of toxic substances in the soil environment of industrial parks in the future.

[0084] S700: Based on species sensitivity curves and the cumulative exposure of toxic substances in the future soil environment predicted by time-series evolution prediction models, a risk prediction model is established by combining a joint probability density model. The risk prediction model is used at least to predict the probability of the soil environment of industrial parks being polluted by toxic substances in the future.

[0085] S100: Predicted probability of soil environment being polluted by toxic substances in the future, based on a predictive model.

[0086] Based on the disclosure of the above embodiments, it can be understood that the beneficial effects of this embodiment include: obtaining information on plant and animal species and toxic substances involved in the soil environment of industrial parks, establishing species sensitivity curves, then collecting sampling data from multiple sampling points in the soil environment, and performing spatial differentiation analysis on the toxic substance content data and environmental factors in the multiple sampling data to obtain the main environmental factors affecting the cumulative exposure of toxic substances. Then, based on this, a time series evolution model is established using time series and multivariate autoregressive models, and the parameters of the regression model are corrected by combining environmental protection policies under different scenarios to obtain the cumulative exposure of toxic substances in the soil of industrial parks in the future. Finally, the sensitivity curve obtained by comprehensive calculation and the predicted cumulative exposure of toxic substances in the future are combined with the joint probability density model to establish a risk prediction model, and finally, based on the risk prediction model, the predicted risk probability value of the soil environment of industrial parks being polluted by toxic substances in the future is predicted. The solution in this embodiment overcomes the technical deficiencies of existing technologies, such as the lack of quantitative ecological risk prediction and assessment, the lack of operability in practical guidance for risk management in industrial parks, and the lack of standardized quantitative models for risk prediction. It also solves the technical problem of low prediction accuracy caused by the lack of comprehensive consideration of the cumulative exposure of toxic substances in soil by environmental factors. The solution significantly improves the prediction accuracy of the risk prediction model, making the predicted values ​​more valuable and facilitating users' understanding of the actual soil environment in industrial parks, thus helping users effectively solve soil pollution problems.

[0087] Furthermore, species sensitivity curves for toxic substances are established, including:

[0088] S301: Establish the probability density curve of the toxic substance based on the following formula, and define the species sensitivity curve of the toxic substance based on the probability density curve:

[0089]

[0090] Where f(x) represents the probability density curve of the toxic substance, x represents the toxic substance, μ represents the standard deviation, and γ represents the distribution parameter.

[0091] The toxic substances in this embodiment are heavy metals, including at least one or more of the following heavy metals: As, Cd, Cu, Cr, Ni, Pb, and Zn. Other types of heavy metals may also be included; the specific list is not unique. Furthermore, the acute toxicity data in this embodiment include at least the median lethal concentration (LC50) and the median effective concentration (EC50), and each type of concentration data includes at least 10 different concentration values.

[0092] Regarding the soil environment of the industrial park, the plant and animal species information involved in the industrial park soil ecosystem in this embodiment includes at least 3 phyla and 8 families of species. For example, soil plants and animals include, but are not limited to, red earthworms, nematodes, European red earthworms, nematodes, white tadpoles, wild ducks, radishes, wheat, lettuce, corn, eggplants and other soil organisms, and the specific species are not unique.

[0093] like Figure 2 As shown, in preparing the sensitivity curve, this embodiment matches and extracts the acute toxicity data of heavy metals in the soil environment, i.e. toxic substances, according to the plant and animal species involved in the soil ecosystem of the industrial park. Then, outliers in the toxicity data are removed by box plot. The optimal species sensitivity curve of the industrial park soil ecosystem is established based on the curve fitting determination coefficient and logistic equation. The specific formula for establishing the sensitivity curve can be referred to the above formula.

[0094] Furthermore, environmental factors affecting the cumulative exposure of toxic substances in industrial parks are obtained. In this embodiment, these environmental factors include both natural and anthropogenic factors. Natural environmental factors include at least one or more parameters selected from soil organic matter, soil pH, soil Eh, soil parent material, vegetation cover index, wind speed, rainfall, elevation, and slope. Anthropogenic environmental factors include at least one or more parameters selected from the distribution of enterprises in the industrial park, gross industrial output, PM10, PM2.5, industrial emissions, traffic flow, and population density. Of course, other types of natural and anthropogenic environmental factors may also be included, as the environmental factors differ depending on the toxic substance. In practical applications, environmental factor data affecting the cumulative exposure of heavy metals in soil in industrial parks can be obtained through methods including, but not limited to, web scraping, literature review, and prior knowledge.

[0095] Once the environmental factors are obtained, they are screened to identify the main environmental factors affecting the cumulative exposure of toxic substances, including:

[0096] S401: Obtain heavy metal content data from multiple sampling points in the soil environment;

[0097] S402: Calculate and determine the spatial stratification heterogeneity data of cumulative heavy metal exposure and environmental factors in the soil environment according to the following formula:

[0098]

[0099] q h＝1-h＝2 The significance of spatially stratified variance is represented and defined as spatially stratified heterogeneous data, Y. h n represents the mean of attributes within the stratified region h. h represents the number of samples within the stratified region h, and Var represents the variance;

[0100] Environmental factors were screened based on stratified heterogeneous data to obtain the main environmental factors.

[0101] For example, data on heavy metal content in soil sampling points at least 10 different regions in the soil environment can be obtained, and combined with the geographic detector model and formula (specifically as above), the spatial stratification heterogeneity data of cumulative exposure of heavy metals in soil and environmental factors can be identified. Then, based on the spatial stratification heterogeneity data, multiple environmental factors can be screened to obtain the main environmental factors.

[0102] Continue to combine Figure 2 As shown, after identifying the main environmental factors, a time-series evolution prediction model (also known as an autoregressive coupled model) is established based on the main environmental factors and heavy metal content data in the soil, combined with time series and multivariate autoregressive models. This time-series evolution prediction model is used to predict the cumulative exposure of toxic substances in the soil environment of the industrial park in the future.

[0103] The time series includes:

[0104]

[0105] Where T(t) is the time series function of the main environmental factors, a, b and c are fitting parameters, c is the environmental factor and t is time.

[0106] For multivariate autoregressive models, the method in this embodiment further includes:

[0107] S900: Adjusting multivariate autoregressive models by incorporating different environmental policies.

[0108] In other words, it is necessary to combine environmental policies under different scenarios, and adjust the corresponding parameters in the regression model based on the policy to make the model more accurate, thus laying the foundation for the subsequent preparation of prediction models.

[0109] Once the corrected regression model is obtained, a time series evolution prediction model can be established based on the time series and the corrected multivariate autoregressive model.

[0110] The corrected multivariate autoregressive model is as follows:

[0111]

[0112] Where ε1 and ε2 are constant terms, a n The coefficients of the controlling factor. The time series data are for the main control factors. For time series data of policy factors, a 政策 The coefficients of policy factors.

[0113] To ensure the accuracy of the obtained time-series evolution prediction model, its accuracy can be verified. Specific methods include:

[0114] Obtain a validation data sample set and validate the prediction accuracy of the time series evolution prediction model based on the following formula:

[0115] Mean absolute error:

[0116] Mean square error:

[0117] Average root mean square error of prediction:

[0118] Coefficient of determination:

[0119] Where n is the validation data sample set, y p y is the measured value. m These are predicted values.

[0120] In other words, the parameters of the model are calculated and determined based on the above formulas and data in the validation data sample set, and the prediction accuracy of the time series evolution model is verified based on the obtained parameters.

[0121] After validating the time-series evolution model, the cumulative exposure to toxic substances in the future soil environment can be predicted based on this model. Once this cumulative exposure is obtained, a risk prediction model can be established based on the species sensitivity curve and the cumulative exposure to toxic substances in the future soil environment predicted by the time-series evolution model, combined with a joint probability density model (specifically shown below), to predict the probability of future soil environment pollution by toxic substances. Moreover, based on the prediction model, the percentage probability of plant and animal species in the soil environment being affected by toxic substances and the time when the probability of future soil environment pollution risk exceeds the environmental risk threshold can also be specifically predicted.

[0122] The combined joint probability density model is as follows:

[0123]

[0124] In the formula, f(u) represents the probability density of soil ecological species sensitivity in industrial parks, and f(v) represents the cumulative exposure of toxic substances in the soil environment in the future.

[0125] Specifically, to better illustrate the method of this embodiment, the following description is provided in conjunction with specific embodiments:

[0126] Information on animal and plant species involved in the soil ecosystem of the industrial park was collected and screened. Acute toxicity data of heavy metals were further collected from the US EPA ECOTOX database and publicly published literature / reports both domestically and internationally. Outliers were identified and removed from the obtained data using box plots; SPSS 22.0 was a suitable tool for this purpose. Species sensitivity curves were then calculated and fitted using the aforementioned formulas; Origin 2022b was a suitable tool for this purpose. The process of constructing the sensitivity curves can be found in [reference needed]. Figure 3 As shown.

[0127] Next, sampling points can be set up in the industrial park using a grid method (100m×100m), with at least 10 sampling points required to collect soil samples. The collected soil samples will then be analyzed for heavy metal accumulation. Afterwards, environmental factor data affecting soil heavy metal accumulation can be obtained through web scraping, literature review, and prior knowledge (see the table below for details). ArcGIS 12.0 software will be used to vectorize elevation, slope, soil type, wind speed, and rainfall to generate numerical variable data usable by the geographic detector.

[0128]

[0129]

[0130] like Figure 4 As shown, the analysis results obtained from the geographic detector revealed that PM2.5, PM10, gross industrial product, and enterprise distribution are the main factors influencing the accumulation of Cd, Cu, Pb, and Zn in the soil, while soil parent material and soil pH are the main factors influencing the accumulation of As, Cr, and Ni in the soil. These main factors are thus identified as the primary environmental factors.

[0131] Based on the identification of key environmental factors, predictive models for heavy metals As, Cd, Cr, Cu, Ni, Pb, and Zn in industrial park soil environments are constructed using the aforementioned time series and multivariate autoregressive equations. The accuracy of the models is validated using the following formula. Once validated, the cumulative exposure of heavy metals in industrial park soils over the next 5 years or other timeframes can be obtained based on the predictive models. For details, please refer to [reference needed]. Figure 5 As shown.

[0132] The specific calculation process of the formula involved in the verification is as follows:

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140] The verification results are shown in the table below:

[0141]

[0142]

[0143] Subsequently, based on the joint probability density model, the species sensitivity curve and the cumulative exposure of heavy metals in the industrial park soil after 5 years were calculated to obtain a probability prediction model that can at least predict the probability of the soil environment of the industrial park being polluted by toxic substances in the future. Based on this model, predictive analysis was performed to obtain the probability curve of heavy metal ecological risk in the industrial park soil for the next 5 years. This includes the soil ecological risk probabilities of As, Cd, Cr, Cu, Ni, Pb and Zn under future exposure conditions as 62.35%, 56.71%, 46.54%, 28.26%, 22.74%, 5.16% and 5.48%, respectively.

[0144] like Figure 6 As shown, another embodiment of the present invention also provides a soil environmental pollutant risk prediction system 600, comprising:

[0145] The module is used to obtain information on plant and animal species involved in the soil environment of the industrial park;

[0146] The first determining module is used to determine toxic substances in the soil environment that affect the species of plants and animals;

[0147] The first module is used to establish the species sensitivity curve of the toxic substance;

[0148] The acquisition module is used to acquire environmental factors in the industrial park that affect the cumulative exposure of the toxic substances.

[0149] The second determining module is used to screen the environmental factors and determine the main environmental factors that affect the cumulative exposure of the toxic substances.

[0150] The second module is used to establish a time-series evolution prediction model based on the main environmental factors, combined with time series and multivariate autoregressive models. The time-series evolution prediction model is used to predict the cumulative exposure of toxic substances in the soil environment of the industrial park in the future.

[0151] The third module is used to establish a risk prediction model based on the species sensitivity curve and the cumulative exposure of toxic substances in the future soil environment predicted by the time-series evolution prediction model, and in combination with the joint probability density model. The risk prediction model is used at least to predict the probability that the soil environment of the industrial park will be polluted by the toxic substances in the future.

[0152] The prediction module is used to predict the probability that the soil environment will be polluted by the toxic substance in the future, based on the prediction model.

[0153] As an optional embodiment, establishing the species sensitivity curve of the toxic substance includes:

[0154] The probability density curve of the toxic substance is established based on the following formula, and the species sensitivity curve of the toxic substance is defined based on the probability density curve:

[0155]

[0156] Where f(x) represents the probability density curve of the toxic substance, x represents the toxic substance, μ represents the standard deviation, and γ represents the distribution parameter.

[0157] As an optional embodiment, the toxic substance is a heavy metal.

[0158] The screening of the environmental factors to determine the main environmental factors affecting the cumulative exposure to the toxic substances includes:

[0159] Obtain heavy metal content data from multiple sampling points in the soil environment;

[0160] The spatial stratification heterogeneity data of cumulative heavy metal exposure and environmental factors in the soil environment were calculated and determined according to the following formula:

[0161]

[0162] q h＝1-h＝2 The significance of spatial stratification variance is represented and defined as the spatial stratification heterogeneity data, Y. h n represents the mean of attributes within the stratified region h. h represents the number of samples within the stratified region h, and Var represents the variance;

[0163] The environmental factors are screened based on the hierarchical heterogeneity data to obtain the main environmental factors.

[0164] As an optional embodiment, the time series includes:

[0165]

[0166] Where T(t) is the time series function of the main environmental factors, a, b and c are fitting parameters, c is the environmental factor and t is time.

[0167] As an optional embodiment, it also includes:

[0168] The correction module is used to correct the multivariate autoregressive model in conjunction with different environmental policies;

[0169] The second module is also used to establish a time series evolution prediction model based on the time series and the corrected multivariate autoregressive model.

[0170] The corrected multivariate autoregressive model is as follows:

[0171]

[0172] Where ε1 and ε2 are constant terms, a n The coefficients of the controlling factor. The time series data are for the main control factors. For time series data of policy factors, a 政策 The coefficients of policy factors.

[0173] As an optional embodiment, it also includes:

[0174] The second acquisition module is used to acquire the verification data sample set;

[0175] The verification module is used to verify the prediction accuracy of the time series evolution prediction model according to the following formula:

[0176] Mean absolute error:

[0177] Mean square error:

[0178] Average root mean square error of prediction:

[0179] Coefficient of determination:

[0180] Where n is the validation data sample set, y p y is the measured value. m These are predicted values.

[0181] As an optional embodiment, the toxic substance includes at least one or more of the following heavy metals:

[0182] As, Cd, Cu, Cr, Ni, Pb and Zn.

[0183] As an optional embodiment, the environmental factors include natural environmental factors and anthropogenic environmental factors. The natural environmental factors include at least one or more parameters selected from soil organic matter, soil pH, soil Eh, soil parent material, vegetation cover index, wind speed, rainfall, elevation, and slope. The anthropogenic environmental factors include at least one or more parameters selected from the distribution of enterprises in the industrial park, gross industrial output, PM10, PM2.5, industrial emissions, traffic flow, and population density.

[0184] As an optional embodiment, the prediction model is also used to predict the percentage probability that plant and animal species in the soil environment will be affected by the toxic substance and the time when the probability value of the future pollution risk of the soil environment exceeds the environmental risk threshold.

[0185] like Figure 6 As shown, another embodiment of the present invention also provides an electronic device, including:

[0186] One or more processors;

[0187] Memory, configured to store one or more programs;

[0188] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-described air self-alignment method for the integrated navigation system.

[0189] Furthermore, one embodiment of the present invention also provides a storage medium storing a computer program, which, when executed by a processor, implements the soil environmental pollutant risk prediction method as described above. It should be understood that the various solutions in this embodiment have the corresponding technical effects in the above-described method embodiments, and will not be repeated here.

[0190] Furthermore, embodiments of the present invention also provide a computer program product, which is tangibly stored on a computer-readable medium and includes computer-readable instructions that, when executed, cause at least one processor to perform a soil environmental pollutant risk prediction method such as the embodiments described above.

[0191] It should be noted that the computer storage medium in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. Computer-readable media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access storage media (RAM), read-only storage media (ROM), erasable programmable read-only storage media (EPROM or flash memory), optical fibers, portable compact disk read-only storage media (CD-ROM), optical storage media, magnetic storage media, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program configured for use by or in connection with an instruction execution system, system, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, antenna, optical fiber, RF, etc., or any suitable combination thereof.

[0192] Furthermore, those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0193] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0194] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0195] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0196] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

[0197] The above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the present invention. The scope of protection of the present invention is defined by the claims. Those skilled in the art can make various modifications or equivalent substitutions to the present invention within its spirit and scope of protection, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of the present invention.

Claims

1. A method of predicting the risk of a soil environmental pollutant, characterized by, include: Obtain information on plant and animal species involved in the soil environment of the industrial park; Identify toxic substances in the soil environment that affect the aforementioned plant and animal species; Establish species sensitivity curves for the toxic substances; To obtain the environmental factors in the industrial park that affect the cumulative exposure to the toxic substances; The environmental factors were screened to identify the main environmental factors affecting the cumulative exposure of the toxic substances; Based on the main environmental factors, a time-series evolution prediction model is established by combining time series and multivariate autoregressive models. The time-series evolution prediction model is used to predict the cumulative exposure of toxic substances in the soil environment of the industrial park in the future. Based on the species sensitivity curve and the cumulative exposure of toxic substances in the future soil environment predicted by the time-series evolution prediction model, a risk prediction model is established by combining the joint probability density model. The risk prediction model is used at least to predict the probability that the soil environment of the industrial park will be polluted by the toxic substances in the future. Based on the prediction model, the predicted probability value of the soil environment being polluted by the toxic substance in the future is predicted; The toxic substance is a heavy metal; The screening of the environmental factors to determine the main environmental factors affecting the cumulative exposure to the toxic substances includes: Obtain heavy metal content data from multiple sampling points in the soil environment; The spatial stratification heterogeneity data of cumulative heavy metal exposure and environmental factors in the soil environment were calculated and determined according to the following formula: The significance of spatial stratification variance is represented and defined as the spatial stratification heterogeneity data. This represents the mean of the attribute within the stratified region h. The value of Var represents the number of samples within the stratified region h. and These represent the mean values ​​of the cumulative exposure of heavy metals in the soil within the first and second soil layers, respectively. and These represent the number of samples in the first and second layer regions, respectively. The environmental factors are screened based on the hierarchical heterogeneity data to obtain the main environmental factors; Also includes: The multivariate autoregressive model was adjusted by incorporating different environmental policies. A time series evolution prediction model is established based on the time series and the corrected multivariate autoregressive model. The corrected multivariate autoregressive model is as follows: wherein, , is a constant term, a n is a coefficient of a master factor, is master factor time series data, is policy factor time series data, a 政策 is a coefficient of a policy factor.

2. The soil environmental pollutant risk prediction method according to claim 1, characterized by, The establishment of the species sensitivity curve for the toxic substance includes: The probability density curve of the toxic substance is established based on the following formula, and the species sensitivity curve of the toxic substance is defined based on the probability density curve: where f(x) represents a probability density curve of the toxic substance, x represents a toxic substance, represents a standard deviation, represents a distribution parameter.

3. The soil environmental pollutant risk prediction method according to claim 1, characterized by, The time series includes: Where T(t) is the time series function of the main environmental factors, a, b and c are fitting parameters, c is the environmental factor and t is time.

4. The soil environmental pollutant risk prediction method according to claim 1, characterized by, Also includes: Obtain a validation data sample set and validate the prediction accuracy of the time series evolution prediction model based on the following formula: Mean Absolute Error: Mean Square Error: Mean square root error of prediction: Determination of the coefficient of determination: wherein n is the validation data sample set, is the measured value, is the predicted value.

5. The soil environmental pollutant risk prediction method according to claim 1, characterized by, The toxic substance includes at least one or more of the following heavy metals: As, Cd, Cu, Cr, Ni, Pb and Zn.

6. The soil environmental pollutant risk prediction method according to claim 1, characterized by, The environmental factors include natural environmental factors and anthropogenic environmental factors. The natural environmental factors include at least one or more parameters selected from soil organic matter, soil pH, soil Eh, soil parent material, vegetation cover index, wind speed, rainfall, elevation, and slope. The anthropogenic environmental factors include at least one or more parameters selected from the distribution of enterprises in the industrial park, gross industrial output, PM10, PM2.5, industrial emissions, traffic flow, and population density.

7. The soil environmental pollutant risk prediction method according to claim 1, characterized by, The prediction model is also used to predict the percentage probability that plant and animal species in the soil environment will be affected by the toxic substances and the time when the probability value of the future pollution risk of the soil environment exceeds the environmental risk threshold.

8. A soil environmental pollutant risk prediction system for implementing a soil environmental pollutant risk prediction method according to claim 1, characterized by, include: The module is used to obtain information on plant and animal species involved in the soil environment of the industrial park; The first determining module is used to determine toxic substances in the soil environment that affect the species of plants and animals; The first module is used to establish the species sensitivity curve of the toxic substance; The acquisition module is used to acquire environmental factors in the industrial park that affect the cumulative exposure of the toxic substances. The second determining module is used to screen the environmental factors and determine the main environmental factors that affect the cumulative exposure of the toxic substances. The second module is used to establish a time-series evolution prediction model based on the main environmental factors, combined with time series and multivariate autoregressive models. The time-series evolution prediction model is used to predict the cumulative exposure of toxic substances in the soil environment of the industrial park in the future. The third module is used to establish a risk prediction model based on the species sensitivity curve and the cumulative exposure of toxic substances in the future soil environment predicted by the time-series evolution prediction model, and in combination with the joint probability density model. The risk prediction model is used at least to predict the probability that the soil environment of the industrial park will be polluted by the toxic substances in the future. The prediction module is used to predict the probability that the soil environment will be polluted by the toxic substance in the future, based on the prediction model.

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