A soil heavy metal background data screening method based on cumulative analysis

Abstract

The application discloses a soil heavy metal background data screening method based on cumulative analysis, comprising the following steps: S1, obtaining soil heavy metal content data of multiple positions on a to-be-screened land block, and calculating a first cumulative coefficient of each heavy metal of the soil of each position; S2, removing outliers in the first cumulative coefficient to obtain multiple second cumulative coefficients of each heavy metal; S3, determining whether the corresponding dominant factor of each heavy metal is a soil type or a lithology type based on an SPSS decision tree model; S4, obtaining at least one third cumulative coefficient of each heavy metal by using a box plot method; taking the position corresponding to the third cumulative coefficient as a soil background position; and using the soil heavy metal content data of the soil background position to calculate a soil environmental background value of the to-be-screened land block; by removing outliers of the cumulative coefficient, accurate soil heavy metal content data can be obtained, and the accuracy of soil environmental background value calculation can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of environmental protection, in particular to a soil heavy metal background data screening method based on cumulative analysis. BACKGROUND

[0002] The soil environmental background content refers to the content of elements or compounds in the soil affected only by geochemical processes and non-point source inputs under certain conditions. The soil environmental background value is a statistical quantity representing the soil environmental background content in a certain statistical unit. These content values reflect the natural composition of the soil and are the basis for assessing the soil environmental quality. The soil environmental background value is used to develop soil environmental quality standards and study human health and agricultural issues. For example, by comparing the measured value of an element in the soil with the background value, it can be determined whether the soil is contaminated and the degree of contamination. In the prior art, the study of soil environmental background values covers the background values of 61 elements and the distribution characteristics and trends of these elements in the country.

[0003] However, due to the long-term accumulation of human activities and the development of modern industry and agriculture, the chemical composition and content level of the natural environment have changed, so the soil environmental background value is actually a relative concept. In reality, it is very difficult to find a soil environment that is completely unaffected by human activities, so part of the soil survey data is affected by the emissions and diffusion of various pollution sources (agricultural, industrial, etc. Pollutant production and activity location), which does not meet the connotation of the soil environmental background value, and these values need to be excluded, but in the prior art, the influence of the pollution source cannot be accurately excluded by experience, so that the obtained soil background value is not accurate, and the environmental protection evaluation and management are not scientific and reasonable. SUMMARY

[0004] To solve the above problems, the present application provides a soil heavy metal background data screening method based on cumulative analysis.

[0005] A soil heavy metal background data screening method based on cumulative analysis, comprising the following steps:

[0006] S1, obtaining soil heavy metal content data of multiple positions on a to-be-screened land block, and calculating a first cumulative coefficient of each heavy metal of the soil of each position;

[0007] S2, screening the first cumulative coefficient of each heavy metal of the soil of the multiple positions once, removing outliers, and obtaining multiple second cumulative coefficients of each heavy metal of the soil of the multiple positions;

[0008] S3, based on the SPSS decision tree model, taking the plurality of second accumulation coefficients of each heavy metal and the soil type and lithology type as independent variables, performing heavy metal accumulation dominant factor analysis to determine whether the dominant factor of the accumulation of each heavy metal is the soil type or the lithology type;

[0009] S4, according to the dominant factor of the accumulation of each heavy metal, using the box plot method to remove outliers in the second accumulation coefficient of each heavy metal, obtaining at least one third accumulation coefficient of each heavy metal; taking the soil heavy metal content data corresponding to the third accumulation coefficient as the soil heavy metal background data of the land plot to be screened, and the soil heavy metal background data is used to calculate the soil heavy metal environmental background value of the land plot to be screened.

[0010] Description: The above method ensures that the data used to calculate the background value is more accurate and reliable through multiple screening and outlier removal. The decision tree model is used to determine the dominant factor of the accumulation of each heavy metal (soil type or lithology type), which provides a scientific basis for subsequent environmental assessment and management, makes the finally screened soil background position better represent the uncontaminated soil environment, improves the accuracy of soil environmental background value calculation, and provides strong support for environmental management and protection.

[0011] Further, the first accumulation coefficient refers to the ratio of the heavy metal content in the surface soil to the heavy metal content in the lower layer soil; wherein the thickness of the surface soil is 0.1-20 cm from the ground surface downward; and the thickness of the lower layer soil is 80-120 cm from the ground surface downward.

[0012] Further, in S2, the method of screening and removing outliers for the first accumulation coefficient of each heavy metal to obtain a plurality of second accumulation coefficients of each heavy metal comprises:

[0013] S2-1, for each heavy metal, calculating the arithmetic mean x and the arithmetic standard deviation S of the first accumulation coefficients of the heavy metal at a plurality of positions, removing outliers greater than or equal to the sum of the arithmetic mean x and 2 times the arithmetic standard deviation S, or less than or equal to the difference between the arithmetic mean x and 2 times the arithmetic standard deviation S; obtaining the remaining first accumulation coefficients after removing the outliers;

[0014] S2-2, judging the data distribution of the remaining first accumulation coefficients, if it conforms to the normal distribution or the lognormal distribution, obtaining a plurality of second accumulation coefficients of each heavy metal, if it does not conform to the normal distribution or the lognormal distribution, repeating the calculation and outlier removal of step S2-1 until the remaining first accumulation coefficients meet the normal distribution or the lognormal distribution.

[0015] Note: The above method ensures that the data used to calculate the background value is more accurate and reliable through multiple screening and outlier removal. By judging the data distribution of the remaining cumulative coefficients and repeatedly removing outliers if necessary, the data is ensured to conform to the normal distribution or lognormal distribution, thereby improving the reliability of subsequent statistical analysis.

[0016] Further, in S3, the method of taking multiple second cumulative coefficients of each heavy metal as the dependent variable and taking soil type and lithology type as the independent variable to analyze the dominant factors of heavy metal accumulation includes:

[0017] By using the SPSS decision tree model, multiple cumulative coefficients of each heavy metal in soil type and lithology type are determined, and the cumulative coefficients of heavy metals in soil type are grouped into a first data set, and the cumulative coefficients of heavy metals in lithology type are grouped into a second data set. CHAID algorithm is used to perform chi-square test on the first data set and the second data set to obtain chi-square values. The soil type or lithology type with larger chi-square value is selected as the dominant factor corresponding to the heavy metal by comparing the chi-square values of the first data set and the second data set.

[0018] Note: The above method can scientifically and objectively identify the main factors affecting heavy metal accumulation, thereby providing more accurate basis for determination of soil environmental background value and prevention and control of soil pollution.

[0019] Further, in S4, according to the dominant factor of the accumulation of each heavy metal, the method of removing outliers in the second cumulative coefficient of each heavy metal by using the box plot method includes:

[0020] S4-1, according to the dominant factor of the accumulation of each heavy metal, the heavy metal accumulation coefficients at multiple positions are grouped according to the multiple soils in the soil type or the multiple lithologies in the lithology type, to obtain multiple groups, each group including at least four heavy metal accumulation coefficients;

[0021] S4-2, calculate the quartile and interquartile range of heavy metal accumulation coefficients in each group respectively; according to the quartile and interquartile range, determine the upper limit value of heavy metal accumulation coefficient and the lower limit value of heavy metal accumulation coefficient;

[0022] S4-3, remove the outliers of heavy metal accumulation coefficients that exceed the upper limit value and the lower limit value.

[0023] Note: The above method can effectively identify and exclude outliers in the data, thereby improving the accuracy and reliability of the data and ensuring the scientificity and effectiveness of the subsequent analysis results.

[0024] Further, S4 further comprises: sorting the third accumulation coefficients of the heavy metals in ascending order, and taking the third accumulation coefficients in the [75%~100%] range of the middle order in ascending order as outliers and removing the outliers, to obtain a plurality of fourth accumulation coefficients of each heavy metal.

[0025] Description: The above method can avoid the problem of inaccurate data caused by incomplete pollution source information and lack of some small and micro pollution sources in history, and delete the accumulation coefficients with large values in the data to make the soil background points more representative.

[0026] Further, in the outliers of the accumulation coefficients in S2 and the outliers of the accumulation coefficients in S4, the soil heavy metal content data corresponding to the outliers and less than or equal to 1 / 2 of the low background limit value are screened out; and the position of the soil heavy metal content data less than or equal to 1 / 2 of the low background limit value is taken as the soil background position.

[0027] Description: The above method can further improve the comprehensiveness of the data by retrieving the low-value points, prevent the false deletion of some points, more accurately reflect the soil environment that is not polluted, and provide a more reliable and representative data basis for calculating the soil environment background value.

[0028] Further, the method for calculating the soil environment background value from the soil heavy metal background data in S4 comprises:

[0029] judging the distribution of the soil heavy metal background data, wherein the distribution of the soil heavy metal background data includes normal distribution, lognormal distribution, and skew distribution;

[0030] when the distribution of the soil heavy metal background data is normal distribution, calculating the arithmetic mean z and the arithmetic standard deviation S of the soil heavy metal background data, and the soil environment background value is equal to the sum of the arithmetic mean z and 2 times the arithmetic standard deviation S;

[0031] when the distribution of the soil heavy metal background data is lognormal distribution, first taking the natural logarithm of the soil heavy metal background data to obtain log data, then calculating the geometric mean M and the geometric standard deviation D of the log data; and the soil environment background value is equal to the product of the geometric mean M and the square of the geometric standard deviation D. 2

[0032] when the distribution of the soil heavy metal background data is skew distribution, sorting the soil heavy metal background data in ascending order, and taking the quantile value at 95% of the number of the soil heavy metal background data as the soil environment background value.

[0033] ​The above method can ensure the accuracy and representativeness of the background value, can adapt to data with different distribution characteristics, and improves the scientificity and reliability of the determination of the soil environment background value.

[0034] The present application has the following advantages:

[0035] The present application ensures that the data used for calculating the background value is more accurate and reliable through multiple screening and outlier removal, determines the dominant factor of the accumulation of each heavy metal using a decision tree model, provides a scientific basis for subsequent environmental assessment and management, makes the finally screened soil background position better represent the uncontaminated soil environment, improves the accuracy of the calculation of the soil environment background value, and provides strong support for environmental management and protection. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 is a schematic diagram of the method flow of the embodiment of the present application;

[0037] Figure 2 is a data graph of the analysis result of the dominant factor of the Cd accumulation coefficient obtained based on the SPSS decision tree model of the embodiment of the present application;

[0038] Figure 3 is a data graph of the analysis result of the dominant factor of the As accumulation coefficient obtained based on the SPSS decision tree model of the embodiment of the present application;

[0039] Figure 4 is a box plot of the Cd accumulation coefficients of each soil type before the outlier removal of the embodiment of the present application;

[0040] Figure 5 is a box plot of the Cd accumulation coefficients of each soil type after the outlier removal of the embodiment of the present application. DETAILED DESCRIPTION

[0041] To further illustrate the manner of carrying out the present application and achieve the effects, the technical solutions of the present application will be described in detail below with reference to experiments.

[0042] Embodiment 1: A soil heavy metal background data screening method based on accumulation analysis, comprising the following steps:

[0043] S1, obtaining soil heavy metal content data of multiple positions on a to-be-screened land plot, and calculating a first accumulation coefficient of each heavy metal of the soil at each position;

[0044] The first accumulation coefficient refers to the ratio of the heavy metal content of the surface soil to the heavy metal content of the lower soil; wherein the thickness of the surface soil is 0.1-20 cm from the ground surface downward; and the thickness of the lower soil is 80-120 cm from the ground surface downward;

[0045] S2, screening the first accumulation coefficient of each heavy metal in the soil of the plurality of locations, and removing outliers to obtain a plurality of second accumulation coefficients of each heavy metal in the soil of the plurality of locations;

[0046] In S2, the method for screening the first accumulation coefficient of each heavy metal and removing outliers to obtain a plurality of second accumulation coefficients of each heavy metal includes:

[0047] S2-1, for each heavy metal, calculating the arithmetic mean x and the arithmetic standard deviation S of the first accumulation coefficients of the heavy metal in the plurality of locations, removing outliers greater than or equal to the sum of the arithmetic mean x and 2 times the arithmetic standard deviation S, or less than or equal to the difference between the arithmetic mean x and 2 times the arithmetic standard deviation S, and obtaining the remaining first accumulation coefficients after removing the outliers;

[0048] S2-2, judging the data distribution of the remaining first accumulation coefficients, if the data distribution conforms to the normal distribution or the lognormal distribution, obtaining a plurality of second accumulation coefficients of each heavy metal, if the data distribution does not conform to the normal distribution or the lognormal distribution, repeating the calculation and removal of outliers in step S2-1 until the remaining first accumulation coefficients meet the normal distribution or the lognormal distribution.

[0049] It can be understood that the following contents and points in the table refer to the positions on the land to be screened, and the removed points refer to the removal of the soil heavy metal content data at a certain position;

[0050] Table 1 Remaining points before and after one-time screening of each element

[0051]

[0052] S3, based on the SPSS decision tree model, taking the plurality of second accumulation coefficients of each heavy metal, the soil type and the lithology type as independent variables, performing heavy metal accumulation dominant factor analysis to determine whether the dominant factor of the accumulation of each heavy metal is the soil type or the lithology type;

[0053] In S3, the method for taking the plurality of second accumulation coefficients of each heavy metal as the dependent variable, taking the soil type and the lithology type as the independent variable, and performing heavy metal accumulation dominant factor analysis includes:

[0054] The SPSS decision tree model is used to determine the accumulation coefficients of each heavy metal in the soil type and the lithology type, and the accumulation coefficients of the heavy metals in the soil type are used to form a first data set, and the accumulation coefficients of the heavy metals in the lithology type are used to form a second data set; the CHAID algorithm is used to perform a chi-square test on the first data set and the second data set, and the chi-square values are obtained; the chi-square values of the first data set and the second data set are compared, and the soil type or the lithology type with a larger chi-square value is selected as the dominant factor corresponding to the heavy metal:

[0055] As shown in Figure 2 、 Figure 3 , the Cd accumulation coefficient (Cd-Ai) is mainly affected by the soil type, and the As accumulation coefficient (As-Ai) is mainly affected by the lithology.

[0056] It can be understood that if the dominant factor of a heavy metal is the soil type, the distribution characteristics and outlier range of the accumulation coefficient of the heavy metal may be different for different soil types (specifically, due to the influence of environment, climate, and other factors, the weathering process, biological enrichment, and other factors of different soil types are quite different, resulting in differences in the migration and enrichment of heavy metals in the soil layer). At this time, the box plot needs to be drawn according to the soil type, and the outliers in the group are removed. If the dominant factor is the lithology type (due to the composition of the lithology type and factors such as climate and environment, the accumulation characteristics of heavy metals are also different), the outliers need to be processed according to the lithology type. In summary, since the accumulation characteristics of heavy metals in soil and the accumulation characteristics of heavy metals in lithology are quite different, it is more reasonable to classify and group the outliers.

[0057] S4, according to the dominant factor of the accumulation of each heavy metal, outliers in the second accumulation coefficient of each heavy metal are removed by using the box plot method to obtain at least one third accumulation coefficient of each heavy metal; the soil heavy metal content data corresponding to the third accumulation coefficient is used as the soil heavy metal background data of the land plot to be screened, and the soil heavy metal background data is used to calculate the soil heavy metal environmental background value of the land plot to be screened;

[0058] The method for removing outliers in the second accumulation coefficient of each heavy metal according to the dominant factor of the accumulation of each heavy metal in S4 includes:

[0059] S4-1, according to the dominant factor of the accumulation of each heavy metal, the heavy metal accumulation coefficients of multiple positions are grouped according to the soil types or the lithology types to obtain multiple groups, and each group includes at least four heavy metal accumulation coefficients of the positions;

[0060] Specifically, taking soil types as an example, the grouping method includes: when the cumulative data of each soil type at multiple locations is 30 or more, each soil type is generally divided into a group, as shown in Figure 4 、 Figure 5 ; but when the cumulative data of each soil type at multiple locations is less than 30, multiple soil types are combined into one group and calculated, for example, grouped according to the nodes in Figure 2 ; in addition, when the cumulative data of an individual soil type is too small (for example, less than 5) or not meaningful, the soil type can not be used for grouping and subsequent outlier screening; similarly, lithology is also grouped according to the above method;

[0061] The above-mentioned multiple soil types include black soil, yellow-brown soil, yellow soil, red soil, red soil, coarse bone soil, laterite, lime soil, dry red soil, gray calcareous soil, volcanic ash soil, marshy soil, paddy soil, brown coniferous forest soil, brown soil, dark brown soil, new soil, mountain meadow soil; multiple lithologies include carbon salt rock, neutral crystalline rock, carbonate rock, basic group-containing argillaceous rock, carbon-containing argillaceous rock, acid crystalline rock, and quartz rock;

[0062] S4-2, calculate the quartiles (including lower quartile Q1 and upper quartile Q3) and interquartile range (IQR) of the heavy metal accumulation coefficient in each group; according to the quartiles and interquartile range, determine the upper limit value of the heavy metal accumulation coefficient and the lower limit value of the heavy metal accumulation coefficient;

[0063] S4-3, remove the outliers of the heavy metal accumulation coefficient that exceed the upper limit value (Q1-1.5*IQR) and the lower limit value (Q3+1.5*IQR).

[0064] For example, if it is found that the Q1 of the lead accumulation coefficient of clay type is 10 and the Q3 is 30, then the IQR is 20. The range of outliers will be 10-1.5*20=-5 (negative number is unreasonable, so the lower limit is 0) and 30+1.5*20=60. Any clay sample with an accumulation coefficient exceeding 60 will be considered an outlier and removed from the data set.

[0065] The results are shown in Figure 4 、 Figure 5 and Table 2 below. On the box plot, observe and mark the points that exceed the above defined range, which are the outliers;

[0066] Table 2: Remaining points after secondary screening and removal S4 further comprises, the third cumulative coefficient of heavy metals is sorted from small to large, the third cumulative coefficient in the middle of the sorting from small to large is [75%~100%] range as an outlier and is eliminated, and a plurality of fourth cumulative coefficients of each heavy metal is obtained, and the result is shown in Table 3:

[0067] Table 3: Remaining point positions after three screenings of each element

[0068]

[0069] In the outliers of the cumulative coefficient in S2 and the outliers of the cumulative coefficient in S4, the soil heavy metal content data corresponding to the outliers less than or equal to 1 / 2 low background limit value is screened out; and the position of the soil heavy metal content data less than or equal to 1 / 2 low background limit value is taken as the soil background position; the result is shown in Table 2;

[0070] Table 4: Final background point positions of each element

[0071]

[0072]

[0073] The method for calculating the soil environmental background value of the soil heavy metal background data in S4 comprises:

[0074] The distribution of the soil heavy metal background data is determined, and the distribution of the soil heavy metal background data comprises normal distribution, lognormal distribution and skew distribution;

[0075] When the distribution of the soil heavy metal background data is normal distribution, the arithmetic mean z and the arithmetic standard deviation S of the soil heavy metal background data are calculated, and the soil environmental background value is equal to the sum of the arithmetic mean z and 2 times the arithmetic standard deviation S;

[0076] When the distribution of the soil heavy metal background data is lognormal distribution, the natural logarithm of the soil heavy metal background data is first taken to obtain the logarithmic data, and then the geometric mean M and the geometric standard deviation D of the logarithmic data are calculated; the soil environmental background value is equal to the product of the geometric mean M and the square of the geometric standard deviation D 2 ;

[0077] When the distribution of the soil heavy metal background data is skew distribution, the soil heavy metal background data is sorted from small to large, and the quantile value located at 95% of the number of soil heavy metal background data is taken as the soil environmental background value.

Claims

1. A method for screening soil heavy metal background data based on cumulative analysis, characterized in that, Includes the following steps: S1. Obtain soil heavy metal content data at multiple locations on the plot to be screened, and calculate the first cumulative coefficient of each heavy metal in the soil at each location; S2. The first accumulation coefficient of each heavy metal in the soil at the multiple locations is screened once to remove outliers, and multiple second accumulation coefficients of each heavy metal in the soil at the multiple locations are obtained. S3. Based on the SPSS decision tree model, using multiple second accumulation coefficients for each heavy metal and soil type and lithology as independent variables, conduct a heavy metal accumulation dominance factor analysis to determine whether soil type or lithology is the dominant factor for the accumulation of each heavy metal. The methods for analyzing the dominant factors of heavy metal accumulation include: Using the SPSS decision tree model, multiple accumulation coefficients for each heavy metal in soil type and lithology were determined. The accumulation coefficients of heavy metals in soil type were used to form the first dataset, and the accumulation coefficients of heavy metals in lithology were used to form the second dataset. The chi-square test was performed on the first and second datasets using the CHAID algorithm to obtain the chi-square values. The chi-square values ​​of the first and second datasets were compared, and the soil type or lithology with the larger chi-square value was selected as the dominant factor corresponding to the heavy metal. S4. According to the dominant factors of each heavy metal accumulation, outliers in the second accumulation coefficient of each heavy metal are removed using the box plot method to obtain at least one third accumulation coefficient for each heavy metal; the soil heavy metal content data corresponding to the third accumulation coefficient is used as the soil heavy metal background data of the plot to be screened, and the soil heavy metal background data is used to calculate the soil heavy metal environmental background value of the plot to be screened.

2. The method for screening soil heavy metal background data based on cumulative analysis as described in claim 1, characterized in that, The first accumulation coefficient refers to the ratio of the heavy metal content in the topsoil to the heavy metal content in the lower soil layer; wherein the thickness of the topsoil layer is 0.1 to 20 cm below the ground surface; and the thickness of the lower soil layer is 80 to 120 cm below the ground surface.

3. The method for screening soil heavy metal background data based on cumulative analysis as described in claim 1, characterized in that, In step S2, the method for filtering out outliers from the first cumulative coefficient of each heavy metal to obtain multiple second cumulative coefficients for each heavy metal includes: S2-1. For each heavy metal, calculate the arithmetic mean x and arithmetic standard deviation S of the first cumulative coefficient of the heavy metal at multiple locations, and remove outliers. The outliers are greater than or equal to the sum of the arithmetic mean x and twice the arithmetic standard deviation S, or less than or equal to the difference between the arithmetic mean x and twice the arithmetic standard deviation S. After removing the outliers, the remaining first cumulative coefficients are obtained. S2-2. Determine the data distribution of the remaining first cumulative coefficients. If it conforms to a normal distribution or a log-normal distribution, then obtain multiple second cumulative coefficients for each heavy metal. If it does not conform to a normal distribution or a log-normal distribution, repeat step S2-1 to calculate and remove outliers until the remaining first cumulative coefficients satisfy a normal distribution or a log-normal distribution.

4. The method for screening soil heavy metal background data based on cumulative analysis as described in claim 1, characterized in that, The method for removing outliers from the second accumulation coefficient of each heavy metal in S4, based on the dominant factors of accumulation for each heavy metal, using box plots, includes: S4-1. According to the dominant factors of each heavy metal accumulation, the heavy metal accumulation coefficients at multiple locations are grouped according to multiple soil types or multiple lithologies in soil types to obtain multiple groups, and each group includes heavy metal accumulation coefficients at at least four locations. S4-2. Calculate the quartiles and interquartile ranges of the heavy metal accumulation coefficient in each group; determine the upper limit and lower limit of the heavy metal accumulation coefficient based on the quartiles and interquartile ranges. S4-3. Remove outliers in the heavy metal accumulation coefficient that exceed the upper limit or the lower limit of the heavy metal accumulation coefficient.

5. The method for screening soil heavy metal background data based on cumulative analysis as described in claim 1, characterized in that, S4 also includes sorting the third accumulation coefficients of heavy metals in ascending order, and removing the third accumulation coefficients in the range of [75%~100%] from the ascending order as outliers, thereby obtaining multiple fourth accumulation coefficients for each heavy metal.

6. The method for screening soil heavy metal background data based on cumulative analysis as described in claim 1, characterized in that, Among the outliers of the cumulative coefficient in S2 and S4, soil heavy metal content data corresponding to the outliers that are less than or equal to 1 / 2 of the low background limit are selected; and soil heavy metal content data less than or equal to 1 / 2 of the low background limit are used as soil heavy metal background data.

7. The method for screening soil heavy metal background data based on cumulative analysis as described in claim 1, characterized in that, The method for calculating soil environmental background values ​​from soil heavy metal background data as described in S4 includes: Determine the distribution of soil heavy metal background data, wherein the distribution of soil heavy metal background data includes normal distribution, log-normal distribution, and skewed distribution; When the distribution of the soil heavy metal background data is normal, calculate the arithmetic mean z and the arithmetic standard deviation S of the soil heavy metal background data. The soil environmental background value is equal to the sum of the arithmetic mean z and twice the arithmetic standard deviation S. When the distribution of the soil heavy metal background data follows a log-normal distribution, the natural logarithm of the soil heavy metal background data is first taken to obtain logarithmic data. Then, the geometric mean M and geometric standard deviation D of the logarithmic data are calculated. The soil environmental background value is equal to the geometric mean M multiplied by the square of the geometric standard deviation D. 2 ; When the distribution of the soil heavy metal background data is skewed, the soil heavy metal background data is sorted from smallest to largest, and the 95th percentile value of the soil heavy metal background data is taken as the soil environmental background value.

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