A method for predicting the spatial distribution of site soil pollutants based on multi-scale nesting

Abstract

The present invention relates to the field of site soil pollution investigation and remediation technology, specifically, a method for predicting the spatial distribution of site soil pollutants based on multi-scale nesting, establishing a prediction model from the perspective of multi-scale nesting, making up for the limitation that a single scale cannot reflect local spatial variation information; combining the formation mechanism of pollutants at each scale, breaking through the limitation of smoothing prediction results based only on mathematical models. Combining the multi-scale nested structure of pollutants and the formation mechanism of each structure, the spatial distribution results of each structure are formed, and then the prediction results of each structure are superimposed to obtain the multi-scale nested spatial distribution prediction results, thereby breaking through the limitation of previous research focusing on a single scale, improving the accuracy of spatial distribution prediction of site soil pollutants, and solving important problems that restrict site soil investigation and remediation work.

Description

Technical Field

[0001] The present invention relates to the technical field of site soil pollution investigation and remediation, and in particular to a method for predicting the spatial distribution of site soil pollutants based on multi-scale nesting. Background Art

[0002] Due to limited investigation costs, the number of samples taken during site soil pollution investigation is limited. In order to grasp the spatial distribution trend of site soil pollutant concentrations, it is necessary to simulate the discrete pollutant concentration data obtained by sampling into continuous surface data.

[0003] At present, the commonly used simulation methods are spatial interpolation methods, mainly including: ordinary kriging (OK), inverse distance weighting (IDW), radial basis function (RBF) and spline function (Spline). Among them, the OK model fits the attribute-distance change relationship based on the statistical relationship of the sample points; the IDW model uses the power value of the inverse distance as the weight for calculation, that is, the spatial gradient of the pollutant content is assumed to be an inverse distance function relationship; the RBF and Spline models use the mathematical function that minimizes the total curvature of the surface as the interpolation surface. The above interpolation methods use a single spatial gradient expression for the entire study area, that is, it is believed that the spatial distribution of pollutants in the entire study area follows a unified gradient law and pattern. These methods can approach the spatial gradient form of the research object with a smooth attribute change.

[0004] The soil pollutants on the site are usually caused by exhaust gas emissions, wastewater erosion during rainfall, and leakage at individual points. The pollution caused by exhaust gas emissions and wastewater erosion during rainfall can usually reach hundreds of kilometers, with a large spatial autocorrelation scale and a relatively uniform spatial distribution of pollutants. Figure 1 However, leakage is a short-distance migration method through soil solution, and the spatial autocorrelation scale is small, which will only cause high-concentration pollution in a small area and form extremely high values, such as Figure 1 The extremely high value area scale in the figure is shown in Figure 1. Multiple autocorrelation scales overlap with each other, resulting in a multi-scale nested spatial distribution structure of pollutants throughout the plant, and the presence of true extremely high values.

[0005] In order to solve the above problems, the commonly used spatial interpolation methods usually have a certain smoothing effect, that is, the real extreme value existing in the site is underestimated, so that it presents a smooth transition trend with the surrounding pollutant concentration, such as Figure 2 As shown in the figure, the spatial distribution prediction results only reflect the overall spatial distribution pattern, but cover up the local strong variation information, so it is impossible to provide detailed and accurate spatial distribution information for site soil pollution investigation and remediation.

[0006] Therefore, it is necessary to design a spatial distribution prediction method for site soil pollutants based on multi-scale nesting, combine the multi-scale nested structure of pollutants and the formation mechanism of each structure to form the spatial distribution results of each structure, and then superimpose the prediction results of each structure to obtain the multi-scale nested spatial distribution prediction results, breaking through the limitations of previous research focusing on a single scale, improving the accuracy of spatial distribution prediction of site soil pollutants, and solving important problems that restrict site soil investigation and remediation work. Summary of the invention

[0007] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method for predicting the spatial distribution of site soil pollutants based on multi-scale nesting, combining the multi-scale nested structure of pollutants and the formation mechanism of each structure to form the spatial distribution results of each structure, and then superimposing the prediction results of each structure to obtain the multi-scale nested spatial distribution prediction results, breaking through the limitations of previous research focusing on a single scale, improving the accuracy of spatial distribution prediction of site soil pollutants, and solving important problems that restrict site soil investigation and remediation work.

[0008] In order to achieve the above object, the present invention provides a method for predicting the spatial distribution of site soil pollutants based on multi-scale nesting, comprising the following steps:

[0009] S1, based on ArcGIS cluster analysis and Laida criterion method 3δ, the data greater than μ+3σ were screened out as outliers, and the rest of the data were non-outliers, μ was the mean, and σ was the standard deviation;

[0010] S2, calculate the average value of non-outlier values ​​as the background value of the entire plant scale;

[0011] S3, subtract the background value at the plant scale from the outlier in S1 to obtain the pollutant concentration generated by leakage at the extremely high value area; subtract the background value at the plant scale from the non-outlier to obtain the pollutant concentration generated by chimney emissions at the flat area, wastewater erosion during rainfall, etc.

[0012] S4. For the spatial distribution of pollutant concentrations caused by leakage in the extremely high-value area, the gravity gradient caused by the terrain is mainly considered. The location of the extremely high value is taken as the source of pollutant release. The contour lines of the area where the extremely high value point is located are obtained based on the digital elevation model DEM data of the study area. The direction of solute migration at the extremely high value point is determined, and the solute migration rate is calculated based on the following formula:

[0013]

[0014] in, is the migration rate; k is the permeability coefficient, which is obtained through geotechnical experiments;

[0015] θ is the volumetric moisture content, which is obtained through geotechnical experiments;

[0016] i is the gravity gradient, and its calculation formula is:

[0017] i = ΔDEM / ΔL;

[0018] Among them, ΔDEM is the variable of the elevation between the release source and a point downstream, in meters m, and ΔL is the horizontal distance between the release source and a point downstream, in meters m;

[0019] S5, calculate the effective diffusion coefficient D of pollutants at the extremely high value area P :

[0020] logD P =0.82logk-2.56; where k is the permeability coefficient;

[0021] S6, based on the two-dimensional continuity equation of solute transport, the spatial distribution results of pollutants caused by the solute transport of extremely high value point pollutants are obtained;

[0022] S7, for the spatial distribution of pollutant concentrations at the flat zone scale, first collect the potential driving factors related to the spatial distribution of pollutant concentrations;

[0023] S8, based on SPSS multiple linear regression analysis, construct multiple linear regression equations of pollutants and main influencing factors;

[0024] S9, based on the raster calculator of ArcGIS software, input the multivariate linear regression equation to obtain the spatial distribution results of pollutants at the flat zone scale;

[0025] S10, for the background value at the plant scale, based on ArcGIS, a raster layer with the same raster resolution as the extremely high value area and the flat area is generated to obtain the spatial distribution results of pollutants at the plant scale;

[0026] S11, based on the ArcGIS software raster calculator, the spatial distribution results of pollutants at the extremely high value area scale, the flat area scale and the plant area scale were added together to obtain the spatial distribution map of soil pollutants for the entire site.

[0027] The potential driving factors in S7 include basic information of the plant area, physical and chemical properties of soil in the plant area and diffusion pathways.

[0028] Basic information of the factory area includes: distribution of functional areas, topography, slope, distance to the workshop, and distance to the chimney.

[0029] The physical and chemical properties of the soil in the factory area include: pH value, soil texture, moisture content, organic matter, soil density, soil porosity and soil saturation.

[0030] Dispersion pathways include stack dispersion data and surface runoff paths.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] A prediction model is established from the perspective of multi-scale nesting to make up for the limitation that a single scale cannot reflect local spatial variation information; the formation mechanism of pollutants is combined at each scale to break through the limitation of smooth prediction results based only on mathematical models. The spatial distribution results of each structure are formed by combining the multi-scale nested structure of pollutants and the formation mechanism of each structure, and then the prediction results of each structure are superimposed to obtain the multi-scale nested spatial distribution prediction results, thus breaking through the limitation of previous research focusing on a single scale, improving the accuracy of the spatial distribution prediction of site soil pollutants, and solving important problems that restrict site soil investigation and remediation work. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the three-dimensional spatial distribution of soil pollutants in the background technology of the present invention.

[0034] Figure 2 It is a schematic diagram of the prediction results of the commonly used spatial interpolation method in the background technology of the present invention.

[0035] Figure 3 It is a schematic diagram of the technical route of the present invention.

[0036] Figure 4 This is a schematic diagram of the spatial distribution results of site soil pollutants achieved by the present invention. DETAILED DESCRIPTION

[0037] The present invention is now further described in conjunction with the accompanying drawings. The present invention provides a method for predicting the spatial distribution of site soil pollutants based on multi-scale nesting:

[0038] See also Figures 3-4 The present invention provides a method for predicting the spatial distribution of soil pollutants on a site based on multi-scale nesting:

[0039] The following steps are involved:

[0040] S1, based on ArcGIS cluster analysis and Laida criterion method 3δ, the data greater than μ+3σ were screened out as outliers, and the rest of the data were non-outliers, μ was the mean, and σ was the standard deviation;

[0041] S2, calculate the average value of non-outlier values ​​as the background value of the entire plant scale;

[0042] S3, subtract the background value at the plant scale from the outlier in S1 to obtain the pollutant concentration generated by leakage at the extremely high value area; subtract the background value at the plant scale from the non-outlier to obtain the pollutant concentration generated by chimney emissions at the flat area, wastewater erosion during rainfall, etc.

[0043] S4. For the spatial distribution of pollutant concentrations caused by leakage in the extremely high-value area, the gravity gradient caused by the terrain is mainly considered. The location of the extremely high value is taken as the source of pollutant release. The contour lines of the area where the extremely high value point is located are obtained based on the digital elevation model DEM data of the study area. The direction of solute migration at the extremely high value point is determined, and the solute migration rate is calculated based on the following formula:

[0044]

[0045] in, is the migration rate; k is the permeability coefficient, which is obtained through geotechnical experiments;

[0046] θ is the volumetric moisture content, which is obtained through geotechnical experiments;

[0047] i is the gravity gradient, and its calculation formula is:

[0048] i = ΔDEM / ΔL;

[0049] Among them, ΔDEM is the variable of the elevation between the release source and a point downstream, in meters m, and ΔL is the horizontal distance between the release source and a point downstream, in meters m;

[0050] S5, calculate the effective diffusion coefficient D of pollutants at the extremely high value area P :

[0051] logD P =0.82logk-2.56; where k is the permeability coefficient;

[0052] S6, based on the two-dimensional continuity equation of solute transport, the spatial distribution results of pollutants caused by the solute transport of extremely high value point pollutants are obtained;

[0053] S7, for the spatial distribution of pollutant concentrations at the flat zone scale, first collect the potential driving factors related to the spatial distribution of pollutant concentrations;

[0054] S8, based on SPSS multiple linear regression analysis, construct multiple linear regression equations of pollutants and main influencing factors;

[0055] S9, based on the raster calculator of ArcGIS software, input the multivariate linear regression equation to obtain the spatial distribution results of pollutants at the flat zone scale;

[0056] S10, for the background value at the plant scale, based on ArcGIS, a raster layer with the same raster resolution as the extremely high value area and the flat area is generated to obtain the spatial distribution results of pollutants at the plant scale;

[0057] S11, based on the ArcGIS software raster calculator, the spatial distribution results of pollutants at the extremely high value area scale, the flat area scale and the plant area scale were added together to obtain the spatial distribution map of soil pollutants for the entire site.

[0058] The potential driving factors in S7 include basic information of the plant area, physical and chemical properties of soil in the plant area and diffusion pathways.

[0059] Basic information of the factory area includes: distribution of functional areas, topography, slope, distance to the workshop, and distance to the chimney.

[0060] The physical and chemical properties of the soil in the factory area include: pH value, soil texture, moisture content, organic matter, soil density, soil porosity and soil saturation.

[0061] Dispersion pathways include stack dispersion data and surface runoff paths.

[0062] The above are only preferred embodiments of the present invention, which are only used to help understand the method and core idea of ​​the present application. The protection scope of the present invention is not limited to the above embodiments. All technical solutions under the idea of ​​the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

[0063] In order to improve the accuracy of spatial distribution prediction of site soil pollutants and solve important problems that restrict site soil investigation and remediation work, the present invention needs to combine the multi-scale nested structure of pollutants and the formation mechanism of each structure to form the spatial distribution results of each structure, and then superimpose the prediction results of each structure to obtain multi-scale nested spatial distribution prediction results, breaking through the limitation of previous research focusing on a single scale.

Claims

1. A method for predicting the spatial distribution of site soil pollutants based on multi-scale nesting, characterized in that: The following steps are involved: S1, based on ArcGIS cluster analysis and Laida criterion method 3δ, the data greater than μ+3σ were screened out as outliers, and the remaining data were non-outliers, where μ was the mean and σ was the standard deviation; S2, calculate the average value of non-outlier values ​​as the plant scale background value; S3, subtract the plant scale background value from the outlier in S1 to obtain the pollutant concentration generated by leakage in the extremely high value area; subtract the plant scale background value from the non-outlier to obtain the pollutant concentration generated by chimney emissions in the flat area and wastewater flushing during rainfall; S4. For the spatial distribution of pollutant concentrations caused by leakage in the extremely high value area, the gravity gradient caused by the terrain is considered. The location of the extremely high value is taken as the source of pollutant release. The contour lines of the area where the extremely high value point is located are obtained based on the digital elevation model (DEM) data of the study area. The direction of solute migration at the extremely high value point is determined, and the solute migration rate is calculated based on the following formula: ； in, is the migration rate; k is the permeability coefficient, which is obtained through geotechnical experiments; is the volumetric moisture content, which is obtained through geotechnical experiments; The i is the gravity gradient, and its calculation formula is: ； in, is the variable of the elevation between the release source and a point downstream, in meters. is the horizontal distance between the release source and a point downstream, in meters; S5, calculate the effective diffusion coefficient of pollutants in the extremely high value area : ； S6, based on the two-dimensional continuity equation of solute transport, the spatial distribution results of pollutants caused by the solute transport of extremely high value point pollutants are obtained; S7, for the spatial distribution of pollutant concentrations at the flat zone scale, first collect the potential driving factors related to the spatial distribution of pollutant concentrations; S8, based on SPSS multiple linear regression analysis, construct multiple linear regression equations of pollutants and main influencing factors; S9, based on the raster calculator of ArcGIS software, input the multivariate linear regression equation to obtain the spatial distribution results of pollutants at the flat zone scale; S10, for the background value at the plant scale, based on ArcGIS, a raster layer with the same raster resolution as the extremely high value area and the flat area is generated to obtain the spatial distribution results of pollutants at the plant scale; S11, based on the ArcGIS software raster calculator, the spatial distribution results of pollutants at the extremely high value area scale, the flat area scale and the plant area scale were added together to obtain the spatial distribution map of soil pollutants for the entire site.

2. The method for predicting the spatial distribution of site soil pollutants based on multi-scale nesting according to claim 1 is characterized in that: The potential driving factors in S7 include basic information of the plant area, physical and chemical properties of soil in the plant area and diffusion pathways.

3. The method for predicting the spatial distribution of site soil pollutants based on multi-scale nesting according to claim 2 is characterized in that: The basic information of the factory area includes: distribution of functional areas, topography, slope, distance to the workshop, and distance to the chimney.

4. The method for predicting the spatial distribution of site soil pollutants based on multi-scale nesting according to claim 2 is characterized in that: The physical and chemical properties of the soil in the factory area include: pH value, soil texture, moisture content, organic matter, soil density, soil porosity and soil saturation.

5. The method for predicting the spatial distribution of site soil pollutants based on multi-scale nesting according to claim 2 is characterized in that: The diffusion pathway includes a chimney diffusion path and a surface runoff path.

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