Method for evaluating soil pollution hazard based on ecological risk index

Abstract

The invention discloses a method for evaluating soil pollution hazards based on ecological risk indexes in the technical field of ecological protection, which comprises the following steps: sampling soil in a target area to obtain sampled soil; the method comprises the following steps: collecting the concentration value of pollutants in sampled soil, and determining the basic toxicity coefficient of the pollutants by utilizing a toxicological experiment; constructing a basic toxicity coefficient dynamic correction model to obtain a dynamic toxicity response coefficient of the pollutant concentration value; and constructing an environment factor interaction model. According to the method, the multi-source data is composed of the concentration value and the pH value of the inorganic pollutant and the concentration value of the organic matter, and through fusion and nonlinear modeling of the multi-source data, the problems of weight solidification and neglect of spatial heterogeneity in a traditional method can be solved; the evaluation scientificity, accuracy and operability of the soil pollution risk in the target area can be improved, and the influence of the toxicity of the pollutants on the soil pollution hazard can be effectively evaluated by fusing the synergistic and antagonistic effects among the pollutants.

Description

Technical Field

[0001] The present invention relates to the field of ecological protection technology, and in particular to a method for assessing soil pollution hazards based on an ecological risk index. Background Art

[0002] The Ecological Risk Index (ERI) is a tool used to assess the risk of ecosystems facing external disturbances and internal vulnerability. It is constructed from the Disturbance Index (DI) and the Vulnerability Index (VI). The Disturbance Index measures the degree of external disturbance exposure to different landscape types, while the Vulnerability Index reflects the potential losses caused by ecological risks. The ERI has a wide range of applications, including but not limited to land consolidation, urban and watershed management, and agricultural management. By using the ERI, risks facing ecosystems can be more accurately identified and managed, leading to the implementation of appropriate conservation and restoration measures.

[0003] Existing soil pollution hazard assessment methods mostly use fixed pollutant toxicity coefficients and do not consider the dynamic effects of biological effectiveness, soil pH, and organic matter content in the environment. The factors used to assess soil pollution hazards are static weights, making it difficult to effectively assess the impact of pollutant toxicity on soil pollution hazards. On the other hand, existing assessment methods ignore the synergistic and antagonistic effects between pollutants, resulting in distorted risk values. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for assessing soil pollution hazards based on the ecological risk index. By adopting multi-source data consisting of inorganic pollutant concentration values, pH values ​​and organic matter concentration values, and through the fusion and nonlinear modeling of multi-source data, it can solve the problems of weight solidification and neglect of spatial heterogeneity in traditional methods, which is conducive to improving the scientificity, accuracy and operability of soil pollution risk assessment in the target area. By integrating the synergistic and antagonistic effects between pollutants, it can effectively assess the impact of pollutant toxicity on soil pollution hazards.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for assessing soil pollution hazards based on an ecological risk index, comprising:

[0007] Sampling the soil in the target area to obtain sampled soil;

[0008] Collect pollutant concentration values ​​in sampled soil and determine the basic toxicity coefficient of pollutants using toxicology experiments;

[0009] Construct a dynamic correction model for the basic toxicity coefficient to obtain the dynamic toxicity response coefficient of the pollutant concentration value;

[0010] Construct an environmental factor interaction model and use it to output the interaction effect factors of pollutants;

[0011] Calculate the spatial heterogeneity weights of pollutant distribution;

[0012] The final assessment model was constructed based on the dynamic correction model of basic toxicity coefficient, the environmental factor interaction model and the spatial heterogeneity weight of pollutant distribution;

[0013] The risks of soil pollution hazards are graded based on the final assessment model.

[0014] As a further solution of the present invention: the soil sampling of the target area includes:

[0015] Determine multiple soil sampling points and enter the spatial coordinates of each soil sampling point;

[0016] Collect equal amounts of soil samples from each soil sampling point and mix them in the same container;

[0017] The soil samples were free of bulk soil, plant debris and residual film, and the sample soil was obtained using the quartering method.

[0018] At the same time, the sample soil is ground and sieved to obtain uniform and fine soil particles. The pollutant concentration values ​​of the treated soil particles are accurately measured using soil testing equipment. To ensure the accuracy and reliability of the data, some soil samples need to be tested repeatedly to verify the stability and consistency of the test results.

[0019] As a further solution of the present invention: the concentration value of the pollutants includes the concentration value of inorganic pollutants, pH value and the concentration value of organic matter.

[0020] Among them, the concentration values ​​of inorganic pollutants mainly include the concentration values ​​of heavy metal ions, non-metallic ions and other inorganic salts; the pH value is used to evaluate the acidity and alkalinity of the soil, which has an important impact on soil microbial activity and plant growth; the concentration value of organic matter covers the content of harmful organic matter such as pesticide residues, petroleum hydrocarbons, organochlorine compounds in the soil. The determination of these concentration values ​​provides key data support for subsequent pollution risk assessment.

[0021] As a further solution of the present invention: the construction of the basic toxicity coefficient dynamic correction model to obtain the dynamic toxicity response coefficient of the pollutant concentration value includes:

[0022] DTC i =T base,i×(1+α·f(pH)+β·f(OM));

[0023] f(pH)=|pH-pH opt |;

[0024]

[0025] Where, T base,i is the basic toxicity coefficient of pollutant i, f(pH) is the absolute value function of pH value deviating from the optimal biological activity range, f(OM) is the inhibition function of organic pollutants (OM) on pollutant activity, α, β and k are the first fitting parameter, the second fitting parameter and the third fitting parameter respectively, OM0 is the basic toxicity coefficient of organic pollutants (OM), pH opt The optimal biological activity value of pH is obtained by training historical data using machine learning methods. The first fitting parameter, the second fitting parameter and the third fitting parameter are obtained. The machine learning method includes a random forest algorithm. The dynamic toxicity response coefficient of the pollutant concentration value is obtained by constructing a dynamic correction model of the basic toxicity coefficient. It can quantify the dynamic impact of the environmental conditions in the target area on the toxicity of the pollutant, and improve the biological rationality of the soil pollution hazard assessment. By quantifying the comprehensive effect of these environmental factors on the toxicity of the pollutant, the dynamic toxicity response coefficient can be further adjusted and optimized. At the same time, the soil pollution ecological risk index is obtained in combination with the pollutant concentration value, and the degree of soil pollution hazard is evaluated. This method not only improves the accuracy of the assessment, but also enhances the biological rationality of the assessment, and provides a scientific basis for soil pollution management and remediation.

[0026] As a further solution of the present invention: the construction of the environmental factor interaction model includes:

[0027]

[0028] Where C i is the measured concentration of pollutant i, C j is the measured concentration of pollutant j, C s,i is the soil environmental quality standard value of pollutant i, C s,j is the soil environmental quality standard value of pollutant j, r ij is the synergistic or antagonistic effect intensity coefficient, δ ij is the effect nonlinearity index.

[0029] When r ij When r is the synergistic effect intensity coefficient, ij Take positive value, when r ij When r is the antagonistic effect intensity coefficient, ij Taking a negative value, the synergistic or antagonistic effect strength coefficient is determined by laboratory microcosm experiments, δ ijThe value is 0.5 to 2. By constructing an environmental factor interaction model based on the dynamic toxicity response coefficient, the hypothetical linear superposition method can be avoided, thereby truly reflecting the risk of soil complex pollution.

[0030] As a further solution of the present invention: the calculation of the spatial heterogeneity weight of pollutant distribution includes:

[0031] Combining the GIS spatial difference algorithm and the Kriging algorithm, the spatial heterogeneity weight value of the pollutant distribution is calculated. First, the spatial difference algorithm in the geographic information system (GIS) is used. This algorithm can infer the pollutant concentration distribution in the entire study area based on the known sampling point data. Secondly, combined with the Kriging algorithm, the Kriging algorithm is an optimal unbiased estimation method based on statistics, which is used to further improve the accuracy of spatial interpolation. Through the combination of these two algorithms, the spatial heterogeneity weight value of the pollutant distribution can be calculated, thereby more accurately evaluating the distribution of pollutants in different regions.

[0032] As a further solution of the present invention: the combination of GIS spatial difference algorithm and Kriging algorithm includes:

[0033]

[0034] Where δ(x,y) is the standard deviation of the pollutant concentration at the point (x,y), is the average standard deviation of pollutant concentration in the target area, D(x,y) is the Euclidean distance from the point (x,y) to the nearest sensitive target, and D max The maximum distance threshold of the target area is calculated by combining the GIS spatial difference algorithm and the Kriging algorithm to calculate the spatial heterogeneity weight value of the pollutant distribution. The pollution intensity and spatial sensitivity values ​​of the pollutants can be weighted at the same time, which is conducive to improving the accurate zoning of the pollution severity in the target area.

[0035] As a further solution of the present invention, the proximal sensitive targets include the coordinates of water sources and residential areas. Water sources, as the primary source of drinking water, are extremely sensitive to pollutants, and any contamination can severely impact water quality. Residential areas, on the other hand, are densely populated, with residents living there for extended periods of time. Soil contamination can have long-term effects on human health through the food chain, air inhalation, or direct contact. Therefore, incorporating water sources and residential areas as proximal sensitive targets into the assessment system allows for a more comprehensive consideration of the impacts of pollutants on the ecological environment and human health, ensuring the accuracy and practicality of the assessment results.

[0036] As a further solution of the present invention: the final evaluation model is constructed based on the basic toxicity coefficient dynamic correction model, the environmental factor interaction model and the spatial heterogeneity weight of pollutant distribution, including:

[0037]

[0038] Where CERI is the comprehensive ecological risk index.

[0039] As a further solution of the present invention: the risk classification of soil pollution hazards based on the final assessment model includes:

[0040] When CERI < 50, the risk level of soil pollution hazard is classified as low risk;

[0041] When 50≤CERI<150, the risk level of soil pollution hazard is classified as medium risk;

[0042] When CERI ≥ 150, the risk level of soil pollution hazard is classified as high risk.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. The present invention adopts multi-source data consisting of inorganic pollutant concentration values, pH values ​​and organic matter concentration values. Through the fusion and nonlinear modeling of multi-source data, it can solve the problems of weight solidification and neglect of spatial heterogeneity in traditional methods, which is conducive to improving the scientificity, accuracy and operability of soil pollution risk assessment in the target area. By integrating the synergistic and antagonistic effects between pollutants, it can effectively evaluate the impact of pollutant toxicity on soil pollution hazards.

[0045] 2. The present invention obtains the first fitting parameter, the second fitting parameter and the third fitting parameter by training historical data using a machine learning method. The machine learning method includes a random forest model. By constructing a dynamic correction model of the basic toxicity coefficient, the dynamic toxicity response coefficient of the pollutant concentration value is obtained, which can quantify the dynamic impact of the environmental conditions in the target area on the toxicity of the pollutant and improve the biological rationality of soil pollution hazard assessment.

[0046] 3. By constructing an environmental factor interaction model based on the dynamic toxicity response coefficient, the present invention can avoid the hypothetical linear superposition method, thereby truly reflecting the risk of complex soil pollution. By combining the GIS spatial difference algorithm and the Kriging algorithm to calculate the spatial heterogeneity weight value of the pollutant distribution, the pollution intensity and spatial sensitivity values ​​of the pollutants can be weighted simultaneously, which is conducive to improving the accurate zoning of the pollution severity of the target area.

[0047] 4. The present invention combines the geographic information system (GIS) to model the heterogeneity of pollution distribution, which is conducive to improving the assessment accuracy of regionalized risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 11 is a flowchart of the method steps of the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] Example 1:

[0051] See also Figure 1 In an embodiment of the present invention, a method for assessing soil pollution hazards based on an ecological risk index comprises the following steps:

[0052] S1: Sampling the soil in the target area to obtain sampled soil;

[0053] S2: Collect the pollutant concentration values ​​in the sampled soil and determine the basic toxicity coefficient of the pollutants using toxicology experiments;

[0054] S3: Construct a dynamic correction model for the basic toxicity coefficient to obtain the dynamic toxicity response coefficient of the pollutant concentration value;

[0055] S4: Construct an environmental factor interaction model and use the environmental factor interaction model to output the interaction effect factors of pollutants;

[0056] S5: Calculate the spatial heterogeneity weight of pollutant distribution;

[0057] S6: Construct the final assessment model based on the dynamic correction model of the basic toxicity coefficient, the environmental factor interaction model and the spatial heterogeneity weight of pollutant distribution;

[0058] S7: Classify the risk of soil pollution hazards based on the final assessment model.

[0059] Preferably, soil sampling of the target area includes:

[0060] Determine multiple soil sampling points and enter the spatial coordinates of each soil sampling point;

[0061] Collect equal amounts of soil samples from each soil sampling point and mix them in the same container;

[0062] The soil samples were free of bulk soil, plant debris and residual film, and the sample soil was obtained using the quartering method.

[0063] Preferably, sampling the soil in the target area further comprises:

[0064] The sample soil is ground and sieved to obtain uniform and fine soil particles. The pollutant concentration values ​​of the treated soil particles are accurately measured using soil testing equipment. To ensure the accuracy and reliability of the data, some soil samples need to be tested repeatedly to verify the stability and consistency of the test results.

[0065] Preferably, the target area is the soil heavy metal pollution hazard level assessment around a mining area.

[0066] Preferably, the concentration values ​​of pollutants include the concentration values ​​of inorganic pollutants, pH values ​​and concentration values ​​of organic matter, among which the concentration values ​​of inorganic pollutants mainly include the concentration values ​​of heavy metal ions, non-metallic ions and other inorganic salts; the pH value is used to evaluate the acidity and alkalinity of the soil, which has an important impact on soil microbial activity and plant growth; the concentration value of organic matter covers the content of harmful organic matter such as pesticide residues, petroleum hydrocarbons, organochlorine compounds, etc. in the soil. The determination of these concentration values ​​provides key data support for subsequent pollution risk assessment.

[0067] Preferably, a basic toxicity coefficient dynamic correction model is constructed to obtain a dynamic toxicity response coefficient of the pollutant concentration value, including:

[0068] DTC i =T base,i ×(1+α·f(pH)+β·f(OM));

[0069] f(pH)=|pH-pH opt |;

[0070]

[0071] Where, T base,i is the basic toxicity coefficient of pollutant i, f(pH) is the absolute value function of pH value deviating from the optimal biological activity range, f(OM) is the inhibition function of organic pollutants (OM) on pollutant activity, α, β and k are the first fitting parameter, the second fitting parameter and the third fitting parameter respectively, OM0 is the basic toxicity coefficient of organic pollutants (OM), pH optThe optimal biological activity value of pH is obtained by training historical data using machine learning methods. The first fitting parameter, the second fitting parameter and the third fitting parameter are obtained. The machine learning method includes a random forest algorithm. The dynamic toxicity response coefficient of the pollutant concentration value is obtained by constructing a dynamic correction model of the basic toxicity coefficient. It can quantify the dynamic impact of the environmental conditions in the target area on the toxicity of the pollutant, and improve the biological rationality of the soil pollution hazard assessment. By quantifying the comprehensive effect of these environmental factors on the toxicity of the pollutant, the dynamic toxicity response coefficient can be further adjusted and optimized. At the same time, the soil pollution ecological risk index is obtained in combination with the pollutant concentration value, and the degree of soil pollution hazard is evaluated. This method not only improves the accuracy of the assessment, but also enhances the biological rationality of the assessment, and provides a scientific basis for soil pollution management and remediation.

[0072] Preferably, constructing an environmental factor interaction model includes:

[0073]

[0074] Where C i is the measured concentration of pollutant i, C j is the measured concentration of pollutant j, C s,i is the soil environmental quality standard value of pollutant i, C s,j is the soil environmental quality standard value of pollutant j, r ij is the synergistic or antagonistic effect intensity coefficient, δ ij is the effect nonlinearity index.

[0075] When r ij When r is the synergistic effect intensity coefficient, ij Take positive value, when r ij When r is the antagonistic effect intensity coefficient, ij Taking negative values, the synergistic or antagonistic effect strength coefficient is determined by laboratory microcosm experiments.

[0076] Preferably, δ ij The value of is 1.2.

[0077] By constructing an environmental factor interaction model based on the dynamic toxicity response coefficient, the hypothetical linear superposition method can be avoided, thereby truly reflecting the risk of complex soil pollution.

[0078] Preferably, calculating the spatial heterogeneity weight of pollutant distribution includes:

[0079] Combining the GIS spatial difference algorithm and the Kriging algorithm, the spatial heterogeneity weight value of the pollutant distribution is calculated. First, the spatial difference algorithm in the geographic information system (GIS) is used. This algorithm can infer the pollutant concentration distribution in the entire study area based on the known sampling point data. Secondly, combined with the Kriging algorithm, the Kriging algorithm is an optimal unbiased estimation method based on statistics, which is used to further improve the accuracy of spatial interpolation. Through the combination of these two algorithms, the spatial heterogeneity weight value of the pollutant distribution can be calculated, thereby more accurately evaluating the distribution of pollutants in different regions.

[0080] Preferably, the GIS spatial difference algorithm is combined with the Kriging algorithm, including:

[0081]

[0082] Where δ(x,y) is the standard deviation of the pollutant concentration at the point (x,y), is the average standard deviation of pollutant concentration in the target area, D(x,y) is the Euclidean distance from the point (x,y) to the nearest sensitive target, and D max The maximum distance threshold of the target area is calculated by combining the GIS spatial difference algorithm and the Kriging algorithm to calculate the spatial heterogeneity weight value of the pollutant distribution. The pollution intensity and spatial sensitivity values ​​of the pollutants can be weighted at the same time, which is conducive to improving the accurate zoning of the pollution severity in the target area.

[0083] Preferred nearest sensitive targets include the coordinates of water sources and residential areas. These sensitive targets are selected based on their sensitivity to pollutant exposure and potential environmental risks. Water sources, as the primary source of drinking water, are extremely sensitive to pollutants, and any contamination can severely impact water quality. Residential areas, on the other hand, are densely populated, with residents living there for extended periods of time. If soil contamination is present, it can have long-term effects on human health through the food chain, air inhalation, or direct contact. Therefore, incorporating water sources and residential areas as nearest sensitive targets into the assessment system allows for a more comprehensive consideration of the impacts of pollutants on the ecological environment and human health, ensuring the accuracy and practicality of the assessment results.

[0084] Preferably, the final assessment model is constructed based on the basic toxicity coefficient dynamic correction model, the environmental factor interaction model and the spatial heterogeneity weight of pollutant distribution, including:

[0085]

[0086] Where CERI is the comprehensive ecological risk index.

[0087] Preferably, the risk of soil pollution hazards is graded based on the final assessment model, including:

[0088] When CERI < 50, the risk level of soil pollution hazard is classified as low risk;

[0089] When 50≤CERI<150, the risk level of soil pollution hazard is classified as medium risk;

[0090] When CERI ≥ 150, the risk level of soil pollution hazard is classified as high risk.

[0091] Soil samples were collected from 100 locations around a mining area to obtain the sampled soil. Toxicology experiments were used to determine the concentrations of As and Cd, as well as the pH and OM values. The As concentration was 120 mg / kg, the Cd concentration was 8 mg / kg, the pH was 5.2, and the OM was 1.5%.

[0092] The random forest model was trained using historical data, and the values ​​of α, β, and k were determined as follows: α = 0.15, β = 0.23, and k = 0.5;

[0093] The r of As-Cd was measured using a laboratory microcosm experiment. ij =0.8,δ ij =1.2;

[0094] The calculation result is: CERI = 182, which means that the hazard level of heavy metal pollution in the soil around the mining area is high risk.

[0095] The above are only preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for assessing soil pollution hazards based on an ecological risk index, characterized in that: include: Sampling the soil in the target area to obtain sampled soil; Collect pollutant concentration values ​​in sampled soil and determine the basic toxicity coefficient of pollutants using toxicology experiments; Construct a dynamic correction model for the basic toxicity coefficient to obtain the dynamic toxicity response coefficient of the pollutant concentration value; Construct an environmental factor interaction model and use it to output the interaction effect factors of pollutants; Calculate the spatial heterogeneity weights of pollutant distribution; The final assessment model was constructed based on the dynamic correction model of basic toxicity coefficient, the environmental factor interaction model and the spatial heterogeneity weight of pollutant distribution; The risks of soil pollution hazards are graded based on the final assessment model.

2. The method for assessing soil pollution hazards based on ecological risk index according to claim 1, characterized in that: The soil sampling of the target area includes: Determine multiple soil sampling points and enter the spatial coordinates of each soil sampling point; Collect equal amounts of soil samples from each soil sampling point and mix them in the same container; The soil samples were free of bulk soil, plant debris and residual film, and the sample soil was obtained using the quartering method.

3. The method for assessing soil pollution hazards based on ecological risk index according to claim 1, characterized in that: The concentration values ​​of pollutants include the concentration values ​​of inorganic pollutants, pH values ​​and the concentration values ​​of organic matter.

4. The method for assessing soil pollution hazards based on ecological risk index according to claim 3, characterized in that: The basic toxicity coefficient dynamic correction model is constructed to obtain the dynamic toxicity response coefficient of the pollutant concentration value, including: DTC i =T base,i ×(1+α·f(pH)+β·f(OM)); f(pH)=|pH-pH opt |; Where, T base,i is the basic toxicity coefficient of pollutant i, f(pH) is the absolute value function of pH value deviating from the optimal biological activity range, f(OM) is the inhibition function of organic pollutants (OM) on pollutant activity, α, β and k are the first fitting parameter, the second fitting parameter and the third fitting parameter respectively, OM0 is the basic toxicity coefficient of organic pollutants (OM), pH opt pH is the optimal biological activity value.

5. The method for assessing soil pollution hazards based on ecological risk index according to claim 4, characterized in that: The construction of the environmental factor interaction model includes: Where C i is the measured concentration of pollutant i, C j is the measured concentration of pollutant j, C s,i is the soil environmental quality standard value of pollutant i, C s,j is the soil environmental quality standard value of pollutant j, r ij is the synergistic or antagonistic effect intensity coefficient, δ ij is the effect nonlinearity index.

6. The method for assessing soil pollution hazards based on ecological risk index according to claim 5, characterized in that: The calculation of the spatial heterogeneity weight of pollutant distribution includes: The spatial heterogeneity weight value of pollutant distribution is calculated by combining GIS spatial difference algorithm and Kriging algorithm.

7. The method for assessing soil pollution hazards based on ecological risk index according to claim 6, characterized in that: The combination of GIS spatial difference algorithm and Kriging algorithm includes: Where δ(x,y) is the standard deviation of the pollutant concentration at the point (x,y), is the average standard deviation of pollutant concentration in the target area, D(x,y) is the Euclidean distance from the point (x,y) to the nearest sensitive target, and D max is the maximum distance threshold of the target area.

8. The method for assessing soil pollution hazards based on ecological risk index according to claim 7, characterized in that: The nearest sensitive targets include the coordinates of water sources and residential areas.

9. The method for assessing soil pollution hazards based on ecological risk index according to claim 1, characterized in that: The final assessment model is constructed based on the basic toxicity coefficient dynamic correction model, the environmental factor interaction model and the spatial heterogeneity weight of pollutant distribution, including: Where CERI is the comprehensive ecological risk index.

10. The method for assessing soil pollution hazards based on ecological risk index according to claim 9, characterized in that: The risk classification of soil pollution hazards based on the final assessment model includes: When CERI < 50, the risk level of soil pollution hazard is classified as low risk; When 50≤CERI<150, the risk level of soil pollution hazard is classified as medium risk; When CERI ≥ 150, the risk level of soil pollution hazard is classified as high risk.

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