Soil heavy metal background data screening method based on cumulative analysis

Abstract

The invention discloses a soil heavy metal background data screening method based on cumulative analysis, and the method comprises the steps: S1, obtaining soil heavy metal content data of a plurality of positions on a to-be-screened land parcel, and calculating a first accumulation coefficient of each heavy metal of the soil at each position; s2, outliers in the first accumulation coefficients are removed, and a plurality of second accumulation coefficients of each heavy metal are obtained; s3, based on an SPSS decision tree model, determining whether the dominant factor corresponding to each heavy metal is a soil type or a lithology type; s4, obtaining at least one third accumulation coefficient of each heavy metal by using a box plot method; taking a position corresponding to the third accumulation coefficient as a soil background position; the soil heavy metal content data of the soil background position is used for calculating a soil environment background value of the land parcel to be screened; according to the method, accurate soil heavy metal content data can be obtained by eliminating the outliers of the accumulation coefficients, and the accuracy of soil environment background value calculation can be improved.

Description

Technical Field

[0001] The present invention relates to the field of environmental protection technologies, and particularly to a method for screening soil heavy metal background data based on cumulative analysis. Background Art

[0002] The background content of the soil environment refers to the content of elements or compounds in the soil that is only affected by geochemical processes and non-point source inputs under certain time conditions. The background value of the soil environment is a statistic used to characterize the background content of the soil environment within a certain statistical unit. These content values reflect the natural composition of the soil and are the basis for evaluating the quality of the soil environment. The background value of the soil environment is used to formulate soil environmental quality standards and study problems in human health and agriculture. 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 polluted and the degree of pollution. In the prior art, the research on the background value of the soil environment covers the background values of 61 elements, as well as the distribution characteristics and changing trends of these elements across 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. Therefore, the background value of the soil environment is actually a relative concept. In reality, it has become very difficult to find a soil environment that is completely unaffected by human activities. Therefore, some soil survey data are affected by the emissions and diffusion effects of various pollution sources (locations where pollutants are generated in agriculture, industry, etc.) and do not meet the connotation of the background value of the soil environment, and these values need to be excluded. However, in the prior art, it is often determined by experience and the influence of pollution sources cannot be accurately excluded, resulting in inaccurate soil background values and making the environmental protection assessment and management less scientific and reasonable. Summary of the Invention

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

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

[0006] S1. Obtain the soil heavy metal content data at multiple positions on the plot to be screened, and calculate the first cumulative coefficient of each heavy metal in the soil at each of the positions;

[0007] S2. Perform a first screening on the first cumulative coefficient of each heavy metal in the soil at the multiple positions, exclude the outliers, and obtain multiple second cumulative coefficients of each heavy metal in the soil at the multiple positions;

[0008] S3. Based on the SPSS decision tree model, the multiple second accumulation coefficients of each heavy metal are used, and the soil type and lithology type are used as independent variables to analyze the dominant factors of heavy metal accumulation, and determine whether the dominant factor of each heavy metal accumulation is the soil type or the lithology type;

[0009] S4. According to the dominant factors of the accumulation of each heavy metal, the outliers in the second cumulative coefficient of each heavy metal are eliminated using the box plot method to obtain at least one third cumulative coefficient of each heavy metal; the soil heavy metal content data corresponding to the third cumulative 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.

[0010] Note: The above method ensures that the data used to calculate the background value is more accurate and reliable through multiple screening and elimination of outliers. The decision tree model is used to determine the dominant factor (soil type or lithology type) for the accumulation of each heavy metal, providing a scientific basis for subsequent environmental assessment and management. The final screened soil background position can better represent the uncontaminated soil environment, improve the accuracy of soil environmental background value calculation, and provide strong support for environmental management and protection.

[0011] Furthermore, 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 soil; wherein the thickness of the surface soil is 0.1 to 20 cm below the surface; and the thickness of the lower soil is 80 to 120 cm below the surface.

[0012] Furthermore, in S2, the first cumulative coefficient of each heavy metal is screened once to remove outliers, and the method for obtaining multiple second cumulative coefficients of each heavy metal includes:

[0013] 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 positions, and remove outliers, wherein the outliers are 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; after removing the outliers, obtain the remaining first cumulative coefficient;

[0014] S2-2. Determine the data distribution of the remaining first cumulative coefficients. If they conform to the normal distribution or the lognormal distribution, obtain multiple second cumulative coefficients for each heavy metal. If they do not conform to the normal distribution and the lognormal distribution, repeat the calculation of step S2-1 and eliminate outliers until the remaining first cumulative coefficients conform to the normal distribution or the lognormal distribution.

[0015] Description: The above method ensures more accurate and reliable data for calculating background values by screening and removing outliers multiple times. By judging the data distribution of the remaining cumulative coefficients and repeating the removal of outliers when necessary, it ensures that the data conforms to a normal distribution or a lognormal distribution, thereby improving the reliability of subsequent statistical analysis.

[0016] Further, in S3, the method for analyzing the main factors leading to heavy metal accumulation, with the multiple second cumulative coefficients of each heavy metal as the dependent variable and soil type and lithology type as the independent variables, includes:

[0017] Using the SPSS decision tree model, determine the multiple cumulative coefficients of each heavy metal in soil type and lithology type respectively. Form the first data set with the cumulative coefficients of heavy metals in soil type and the second data set with the cumulative coefficients of heavy metals in lithology type. Use the CHAID algorithm to perform a chi-square test on the first data set and the second data set to obtain the chi-square value. Compare the chi-square values of the first data set and the second data set, and select the soil type or lithology type with the larger chi-square value as the main factor corresponding to the heavy metal.

[0018] Description: The above method can scientifically and objectively identify the main factors affecting heavy metal accumulation, thereby providing a more accurate basis for determining soil environmental background values and preventing and controlling soil pollution.

[0019] Further, in S4, the method for removing outliers in the second cumulative coefficients of each heavy metal using the box plot method according to the main factors of each heavy metal accumulation includes:

[0020] S4-1. According to the main factors of each heavy metal accumulation, group the heavy metal cumulative coefficients at multiple locations according to multiple soils in soil type or multiple lithologies in lithology type to obtain multiple groups, and each group includes at least four heavy metal cumulative coefficients at different locations;

[0021] S4-2. Calculate the quartiles and interquartile ranges of the heavy metal cumulative coefficients in each group respectively; determine the upper limit value of the heavy metal cumulative coefficient and the lower limit value of the heavy metal cumulative coefficient according to the quartiles and interquartile ranges;

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

[0023] Description: 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 subsequent analysis results.

[0024] Further, S4 also includes sorting the third accumulation coefficients of heavy metals from smallest to largest, taking the third accumulation coefficients in the range of [75% - 100%] in the sorting from smallest to largest as outliers and removing them, to obtain multiple fourth accumulation coefficients of each heavy metal.

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

[0026] Further, among the outlier values of the accumulation coefficients in S2 and the outlier values of the accumulation coefficients in S4, screen out the soil heavy metal content data corresponding to the outlier values that are less than or equal to 1 / 2 of the low background limit; and take the position where the soil heavy metal content data less than or equal to 1 / 2 of the low background limit is located as the soil background position.

[0027] Explanation: The above method can further improve the comprehensiveness of the data through retrieving low-value points, prevent the misdeletion of some points in the foregoing part, more accurately reflect the uncontaminated soil environment, and provide a more reliable and representative data basis for calculating the soil environmental background value.

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

[0029] Judge the distribution of the soil heavy metal background data, and the distribution of the soil heavy metal background data includes normal distribution, lognormal distribution, and skewed distribution;

[0030] When the distribution of the soil heavy metal background data is a normal distribution, calculate the arithmetic mean z and arithmetic standard deviation S of the soil heavy metal background data, and the soil environmental 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 a lognormal distribution, first take the natural logarithm of the soil heavy metal background data to obtain logarithmic data, and then calculate the geometric mean M and geometric standard deviation D of the logarithmic data; the soil environmental background value is equal to the geometric mean M multiplied by the square of the geometric standard deviation D 2 ;

[0032] When the distribution of the soil heavy metal background data is a skewed distribution, sort the soil heavy metal background data from smallest to largest, and take the quantile value at 95% of the soil heavy metal background data quantity as the soil environmental background value.

[0033] Description: The above method can ensure the accuracy and representativeness of background values, adapt to data with different distribution characteristics, and improve the scientificity and reliability of determining soil environmental background values.

[0034] The beneficial effects of the present invention are as follows:

[0035] Through multiple screenings and eliminations of outliers, the present invention ensures that the data used for calculating background values is more accurate and reliable. The decision tree model is adopted to determine the dominant factors for the accumulation of each heavy metal, providing a scientific basis for subsequent environmental assessment and management. As a result, the finally selected soil background locations can better represent the unpolluted soil environment, improve the accuracy of calculating soil environmental background values, and provide strong support for environmental management and protection work. Brief Description of the Drawings

[0036] Figure 1 is a schematic flowchart of the method in the embodiment of the present invention;

[0037] Figure 2 is a data graph of the analysis results of the dominant factors of the Cd accumulation coefficient obtained from the SPSS decision tree model in the embodiment of the present invention;

[0038] Figure 3 is a data graph of the analysis results of the dominant factors of the As accumulation coefficient obtained from the SPSS decision tree model in the embodiment of the present invention;

[0039] Figure 4 is a box plot of the cadmium accumulation coefficients of each soil type before elimination in the embodiment of the present invention;

[0040] Figure 5 is a box plot of the Cd accumulation coefficients of each soil type after eliminating outliers in the embodiment of the present invention. Detailed Embodiments

[0041] To further elaborate on the methods adopted and the effects achieved by the present invention, the technical solutions of the present invention will be clearly and completely described below in conjunction with experiments.

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

[0043] S1. Obtain the soil heavy metal content data at multiple positions on the plot to be screened, and calculate the first accumulation coefficient of each heavy metal in the soil at each of the positions;

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

[0045] S2. For each heavy metal in the soil at the multiple locations, perform a screening on the first accumulation coefficient of each heavy metal to eliminate outliers, and obtain multiple second accumulation coefficients of each heavy metal in the soil at the multiple locations;

[0046] In S2, the method for performing a screening on the first accumulation coefficient of each heavy metal to eliminate outliers and obtaining multiple second accumulation coefficients of each heavy metal includes:

[0047] S2-1. For each heavy metal, calculate the arithmetic mean x and the arithmetic standard deviation S of the first accumulation coefficients of the heavy metal at multiple locations, and eliminate outliers, where the outliers are 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; after eliminating the outliers, obtain the remaining first accumulation coefficients;

[0048] S2-2. Judge the data distribution of the remaining first accumulation coefficients. If it conforms to a normal distribution or a lognormal distribution, obtain multiple second accumulation coefficients of each heavy metal. If it does not conform to a normal distribution and a lognormal distribution, repeat the calculation in step S2-1 and eliminate outliers until the remaining first accumulation coefficients satisfy a normal distribution or a lognormal distribution.

[0049] It can be understood that the points mentioned in the following content and the table refer to the locations on the plot to be screened. Eliminating a point means eliminating the soil heavy metal content data at a certain location;

[0050] Table 1 Remaining points before and after the first screening and elimination of each element

[0051]

[0052] S3. Based on the SPSS decision tree model, use the multiple second accumulation coefficients of each heavy metal, with soil type and lithology type as independent variables, to perform an analysis of the dominant factors of heavy metal accumulation, and determine whether the dominant factor of each heavy metal accumulation is soil type or lithology type;

[0053] In S3, the method for performing an analysis of the dominant factors of heavy metal accumulation with the multiple second accumulation coefficients of each heavy metal as the dependent variable and soil type and lithology type as the independent variables includes:

[0054] Using the SPSS decision tree model, determine multiple accumulation coefficients of each heavy metal in soil types and lithology types respectively. Compose the accumulation coefficients of heavy metals in soil types into a first data set, and compose the accumulation coefficients of heavy metals in lithology types into a second data set. Use the CHAID algorithm to perform a chi-square test on the first data set and the second data set to obtain a chi-square value. Compare the chi-square values of the first data set and the second data set, and select the soil type or lithology type with the larger chi-square value as the dominant factor corresponding to the heavy metal:

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

[0056] It can be understood that if the dominant factor of a certain heavy metal is the soil type, the distribution characteristics and outlier ranges of its accumulation coefficients may vary due to different soil types (specifically, due to the influence of environment, climate, etc., there are large differences in weathering processes, biological enrichment, etc. of different soil types, resulting in differences in the migration and enrichment of heavy metals in soil layers). At this time, box plots need to be drawn by grouping according to soil types, and the outliers within the group are removed. If the dominant factor is the lithology type (due to the composition of the lithology type itself and factors such as climate environment, the accumulation characteristics of heavy metals also vary), then outlier processing needs to be carried out by grouping according to the lithology type. In summary, since the accumulation characteristics and mechanisms of heavy metals in soil and lithology are quite different, it is reasonable to classify and group them to screen out outliers.

[0057] S4. According to the dominant factor of the accumulation of each heavy metal, use the box plot method to remove the outliers in the second accumulation coefficient of each heavy metal to obtain at least one third accumulation coefficient of each heavy metal; use the soil heavy metal content data corresponding to the third accumulation coefficient 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;

[0058] The method for removing the outliers in the second accumulation coefficient of each heavy metal by using the box plot method 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, group the heavy metal accumulation coefficients at multiple locations according to multiple soils in soil types or multiple lithologies in lithology types to obtain multiple groups, and each group includes at least four heavy metal accumulation coefficients at locations;

[0060] Specifically, taking soil types as an example, the grouping methods include: when the cumulative data of each soil type at multiple locations is 30 or more, each soil type is usually divided into a group as shown in Figure 4 , Figure 5 ; however, when the cumulative data of each soil type at multiple locations is less than 30, multiple soil types are combined into a group for calculation. For example, grouping is carried out in the manner of each node in Figure 2 ; in addition, when the cumulative data of individual soil types is too small (for example, less than 5) or has no reference significance, such soil types can be excluded from grouping and subsequent outlier screening; similarly, lithology is grouped in the same way as above;

[0061] The above-mentioned multiple soil types include black felt soil, yellow brown soil, yellow soil, lateritic red soil, red soil, skeletal soil, laterite, calcareous soil, red ferralitic soil, sierozem, volcanic ash soil, swamp soil, paddy soil, brown coniferous forest soil, brown soil, dark brown soil, newly accumulated soil, mountain meadow soil; multiple lithologies include carbonate rock, intermediate crystalline rock, carbonate rock, argillaceous rock containing basic components outside the group, carbonaceous argillaceous rock, acidic crystalline rock quartzite, basic crystalline rock;

[0062] S4-2. Calculate the quartiles (including the lower quartile Q1 and the upper quartile Q3) and the interquartile range (the interquartile range IQR is the difference between the upper quartile and the lower quartile) of the heavy metal accumulation coefficient for each group; determine the upper limit value and the lower limit value of the heavy metal accumulation coefficient based on the quartiles and the interquartile range;

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

[0065] The results are as 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, and these points are the outliers;

[0066] Table 2 Remaining points after secondary screening and removal of each element S4 also includes sorting the third cumulative coefficients of heavy metals in ascending order, taking the third cumulative coefficients in the range of [75% - 100%] in the ascending order as outliers and removing them, to obtain multiple fourth cumulative coefficients of each heavy metal. The results are shown in Table 3:

[0067] Table 3 Remaining points after three screenings and removals of each element

[0068]

[0069] Among the outliers of the cumulative coefficients in S2 and the outliers of the cumulative coefficients in S4, screen out the soil heavy metal content data corresponding to the outliers that are less than or equal to 1 / 2 of the low background limit; and take the position where the soil heavy metal content data less than or equal to 1 / 2 of the low background limit is located as the soil background position. The results are shown in Table 2;

[0070] Table 4 Final background points of each element

[0071]

[0072]

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

[0074] Judge the distribution of the soil heavy metal background data, and the distribution of the soil heavy metal background data includes normal distribution, lognormal distribution, and skewed distribution;

[0075] When the distribution of the soil heavy metal background data is a normal distribution, calculate the arithmetic mean z and arithmetic standard deviation S of the soil heavy metal background data, 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 a lognormal distribution, first take the natural logarithm of the soil heavy metal background data to obtain logarithmic data, and then calculate the geometric mean M and geometric standard deviation D of the logarithmic data; the soil environmental background value is equal to the geometric mean M multiplied by the square of the geometric standard deviation D 2 ;

[0077] When the distribution of the soil heavy metal background data is a skewed distribution, sort the soil heavy metal background data in ascending order, and take the 95% quantile value of the soil heavy metal background data quantity as the soil environmental background value.

Claims

1. A method for screening soil heavy metal background data based on cumulative analysis, characterized in that, It includes the following steps: S1. Obtain the soil heavy metal content data at multiple positions on the plot to be screened, and calculate the first cumulative coefficient of each heavy metal in the soil at each of the positions; S2. Perform a first screening on the first cumulative coefficient of each heavy metal in the soil at the multiple positions, eliminate the outliers, and obtain multiple second cumulative coefficients of each heavy metal in the soil at the multiple positions; S3. Based on the SPSS decision tree model, using the multiple second cumulative coefficients of each heavy metal, and taking the soil type and lithology type as independent variables, conduct an analysis of the dominant factors of heavy metal accumulation to determine whether the dominant factor of each heavy metal accumulation is the soil type or the lithology type; S4. According to the dominant factor of each heavy metal accumulation, use the box plot method to eliminate the outliers in the second cumulative coefficient of each heavy metal, and obtain at least one third cumulative coefficient of each heavy metal; use the soil heavy metal content data corresponding to the third cumulative coefficient 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 according to claim 1, wherein The first cumulative coefficient refers to the ratio of the heavy metal content in the surface soil to the heavy metal content in the underlying soil; wherein the thickness of the surface soil is 0.1 - 20 cm downward from the ground surface; the thickness of the underlying soil is 80 - 120 cm downward from the ground surface.

3. The method for screening soil heavy metal background data based on cumulative analysis according to claim 1, wherein In S2, the method of performing a first screening on the first cumulative coefficient of each heavy metal, eliminating the outliers, and obtaining multiple second cumulative coefficients of 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 positions, and eliminate the outliers, where the outliers are 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; after eliminating the outliers, obtain the remaining first cumulative coefficients; S2-2. Judge the data distribution of the remaining first cumulative coefficients. If it conforms to the normal distribution or lognormal distribution, obtain multiple second cumulative coefficients of each heavy metal. If it does not conform to the normal distribution and lognormal distribution, repeat the calculation in step S2-1 and eliminate the outliers until the remaining first cumulative coefficients satisfy the normal distribution or lognormal distribution.

4. A method for screening soil heavy metal background data based on cumulative analysis according to claim 1, characterized in that, In S3, the method of using the multiple second cumulative coefficients of each heavy metal as the dependent variable and taking the soil type and lithology type as independent variables to conduct an analysis of the dominant factors of heavy metal accumulation includes: Through the SPSS decision tree model, determine the multiple cumulative coefficients of each heavy metal in the soil type and lithology type respectively, form a first data set with the cumulative coefficients of the heavy metal in the soil type, form a second data set with the cumulative coefficients of the heavy metal in the lithology type, use the CHAID algorithm to conduct a chi-square test on the first data set and the second data set, obtain the chi-square value, compare the chi-square values of the first data set and the second data set, and select the soil type or lithology type with the larger chi-square value as the dominant factor corresponding to the heavy metal.

5. The method for screening soil heavy metal background data based on cumulative analysis according to claim 1, characterized in that The method for removing outliers in the second accumulation coefficient of each heavy metal by using the box plot method according to the dominant factor of the accumulation of each heavy metal in S4 includes: S4-1. According to the dominant factor of the accumulation of each heavy metal, group the heavy metal accumulation coefficients at multiple locations according to multiple soil types in the soil type or multiple lithology types in the lithology type to obtain multiple groups, and each group includes the heavy metal accumulation coefficients at at least four locations; S4-2. Calculate the quartiles and interquartile ranges of the heavy metal accumulation coefficients in each group respectively; determine the upper limit value of the heavy metal accumulation coefficient and the lower limit value of the heavy metal accumulation coefficient according to the quartiles and interquartile ranges; S4-3. Remove the outliers of the heavy metal accumulation coefficients that exceed the upper limit value of the heavy metal accumulation coefficient and the lower limit value of the heavy metal accumulation coefficient.

6. The method for screening soil heavy metal background data based on cumulative analysis according to claim 1, wherein, S4 further includes sorting the third accumulation coefficients of the heavy metals from small to large, and taking the third accumulation coefficients in the range of [75% - 100%] in the sorting from small to large as outliers and removing them to obtain multiple fourth accumulation coefficients of each heavy metal.

7. The method for screening soil heavy metal background data based on cumulative analysis according to claim 6, wherein Among the outliers of the accumulation coefficients in S2 and the outliers of the accumulation coefficients in S4, screen out the soil heavy metal content data corresponding to the outliers that are less than or equal to 1 / 2 of the low background limit value; and take the soil heavy metal content data less than or equal to 1 / 2 of the low background limit value as the soil heavy metal background data.

8. The method for screening soil heavy metal background data based on cumulative analysis according to claim 1, wherein, The method for calculating the soil environmental background value from the soil heavy metal background data in S4 includes: Judge the distribution of the soil heavy metal background data, and the distribution of the soil heavy metal background data includes normal distribution, lognormal distribution and skewed distribution; When the distribution of the soil heavy metal background data is a normal distribution, calculate the arithmetic mean z and arithmetic standard deviation S of the soil heavy metal background data, and the soil environmental background value is equal to the sum of the arithmetic mean z and 2 times the arithmetic standard deviation S; When the distribution of the soil heavy metal background data is log-normal distribution, first take the natural logarithm of the soil heavy metal background data to obtain logarithmic data, and then calculate the geometric mean M and geometric standard deviation D of the logarithmic data; 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 a skewed distribution, sort the soil heavy metal background data from small to large, and take the quantile value at 95% of the number of the soil heavy metal background data as the soil environmental background value.

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