Soil from a historical uranium mining and metallurgical facility 226 Methods of Ra baseline survey

Abstract

This invention provides soil from historical uranium mining and metallurgical facilities. 226 The method of Ra baseline survey includes the following steps: (1) calculating soil... 226 Minimum sample size for Ra monitoring; (2) Monitoring soil in the baseline survey area 226 Ra activity concentration; (3) Remove monitoring data and select appropriate monitoring data from the remaining monitoring data; (4) Perform normal statistical analysis on the monitoring data; (5) Soil in the baseline survey area 226 The Ra activity concentration was characterized. This invention targets soil samples from which no such analysis has been performed. 226 The baseline survey of historical uranium mining and metallurgical facilities can scientifically and rationally determine the soil conditions of these facilities. 226 The Ra background value is used to guide the scientific and rational determination of soil conditions in the design of uranium mining and metallurgical decommissioning and remediation projects. 226 Ra decontamination management limits.

Description

Technical Field

[0001] This invention relates to a method for monitoring radioactive elements in environmental soil, specifically soil from historical uranium mining and metallurgical facilities. 226 Methods for Ra baseline surveys. Background Technology

[0002] Uranium mining and metallurgical facilities are production facilities for mining and smelting uranium ore, with natural uranium as the main product. Natural radioactive uranium series daughter bodies... 226 Ra is extremely toxic and has a long half-life of 1600 years. Its decay produces radon gas, and inhalation of large amounts can increase the risk of cancer in humans. Ra is an important indicator for radiation protection and environmental protection management in uranium mining and metallurgy.

[0003] In the process of uranium mining and metallurgical production, 226 Ra exists primarily in the form of ore, waste rock, tailings, and slag. Due to historical reasons, environmental protection awareness in uranium mining and metallurgy in my country was weak from the 1950s to the 1980s, resulting in improper storage of uranium mining and metallurgical waste, which was subject to long-term leaching by rainwater. 226 A small amount of Ra radionuclides were transferred into the bottom soil, causing localized soil contamination.

[0004] After uranium mining and smelting are completed, remedial measures are taken to treat the environmental pollution caused by uranium mining and smelting, and to protect public health and the environment. Key indicators for soil decontamination remediation include: 226 Ra.

[0005] In previous uranium mining and metallurgical decommissioning remediation efforts, soil... 226 The Ra decontamination treatment did not consider the environmental background levels, resulting in poor decontamination effectiveness in areas with high background levels. Some sites failed to meet the management limits set in the design, leading to poor operability. Therefore, soil conditions should be considered during decommissioning and remediation. 226Ra The background value, minus its influence.

[0006] Currently, the "Regulations on Radiation Protection and Environmental Protection in Uranium Mining and Metallurgy" (GB 23727-2020) stipulates that "after land decontamination and remediation, the radiation concentration in any soil layer within an average area of ​​100m² should be within a certain range." 226 "Ra specific activity, after deducting local background value, does not exceed 0.18 Bq / g, and can be opened without restriction," explicitly requiring deduction of soil... 226 Ra background value, while historical uranium mining and metallurgical facilities did not conduct environmental assessments of the surrounding area before construction. 226 Ra is being investigated, therefore a survey method is urgently needed to guide the decommissioning and remediation of uranium mines and metallurgical plants. Summary of the Invention

[0007] The purpose of this invention is to provide soil from historical uranium mining and metallurgical facilities. 226 Ra baseline survey methods for setting soil samples at uranium deposits 226A baseline survey of Ra (Ray) will guide the decommissioning and remediation of uranium mines and metallurgical plants.

[0008] This invention is achieved as follows: a soil from a historical legacy uranium mining and metallurgical facility. 226 The Ra baseline survey method includes the following steps:

[0009] (1) Estimating soil 226 Ra monitors the minimum sample size N, and the steps are as follows:

[0010] a. Let the sample estimation formula be:

[0011]

[0012] Where m is the acceptable relative deviation, and m is a value between 20% and 30%; n is the degrees of freedom, and n = 1, 2, 3, ...; t n C represents the confidence level for n degrees of freedom. v The coefficient of variation;

[0013] Let the historical monitoring data within the baseline survey area be X = {X1, X2, X3, ...}, and the coefficient of variation be C. v Then it is calculated using the following formula:

[0014]

[0015] Wherein, δ represents the historical soil concentration in the baseline survey area. 226 Standard deviation of Ra activity concentration; For historical monitoring of soil in the baseline survey area 226 The average Ra activity concentration;

[0016] When there is no historical monitoring data in the baseline survey area, then take C. v The value is between 10% and 30%.

[0017] b. Let N1 = 1, N n =N n-1 +1, starting from N1, sequentially input into formula (1) to calculate t. n n = 1, 2, 3, ...;

[0018] c. Query the relationship between degrees of freedom and confidence scores. The confidence score for each degree of freedom (n) in the table is denoted as T. n ;

[0019] d. According to the convergence condition, minΔt=|T n -t n |, to obtain the n value that satisfies the condition, the n value is the minimum sample size N to be found;

[0020] (2) Monitoring soil samples within the baseline survey area226 Ra activity concentration: The baseline survey area is divided into grids with a grid number greater than the minimum sample size N. Sampling points are set up in each grid according to national or industry regulations for monitoring and sampling.

[0021] (3) Collect soil samples from sampling points 226 Ra activity concentration monitoring data were used to remove suspicious data. From the remaining data, N sampling points were selected and denoted as Y = {Y1, Y2, ..., Y}. N};

[0022] (4) Perform normal statistical analysis on the selected monitoring data, as follows:

[0023] a. Calculate the skewness SK of the monitoring data or the logarithm of the monitoring data obtained from N sampling points in step (3):

[0024]

[0025] Among them, Y i The data is the monitoring data for the i-th sampling point, where i = 1 to N; ε is the average value of the monitoring data from all sampling points; ε is the standard deviation of the monitoring data from all sampling points or the logarithm of the monitoring data.

[0026] b. Calculate the kurtosis BK of the monitoring data of the N sampling points obtained in step (3):

[0027]

[0028] C. Based on the normal distribution condition, determine whether the monitoring data or the logarithm of the monitoring data collected in step (3) follows a normal distribution; if the normal distribution condition is met, then the monitoring data or the logarithm of the monitoring data of the N sampling points follows a normal distribution; if the normal distribution condition is not met, then the monitoring data of the N sampling points follows a skewed distribution.

[0029] The conditions for a normal distribution are as follows:

[0030]

[0031] (5) Soil in the baseline survey area 226 Ra activity concentration characterization:

[0032] When the monitoring data Y = {Y1, Y2, ... Y} N When the soil in the survey area follows a normal distribution, 226 The background value of Ra activity concentration Y0 is calculated using the following formula:

[0033]

[0034] When the monitoring data Y = {Y1, Y2, ... Y} N When the logarithm of} follows a normal distribution, the soil in the baseline survey area 226 The background value of Ra activity concentration Y0 is calculated using the following formula:

[0035]

[0036] When the monitoring data Y = {Y1, Y2, ... Y} N When the soil in the survey area follows a skewed distribution condition, 226 The background value of Ra activity concentration Y0 is calculated using the following formula:

[0037] Y0 = M e ±T (8)

[0038] in,

[0039] When n is odd

[0040] When n is even

[0041] In step (3), suspicious data is removed through the following steps:

[0042] a. Calculate the soil samples collected in step (3) at the sampling points. 226 The Grobbs coefficient of Ra activity concentration monitoring data is calculated using the following formula:

[0043]

[0044] Among them, G j Let Grubbs be the coefficient of the j-th sampling point, where j = 1 to N;

[0045] b. Compare the Grubbs coefficients of the sampling points obtained in the previous step with the corresponding critical values ​​in the Grubbs coefficient critical table. Discard the monitoring data of sampling points that exceed the critical value, and retain them otherwise.

[0046] This invention uses monitoring point layout and discrete data statistical analysis as its basic methods, targeting soil samples that have not been previously monitored. 226 The baseline survey of historical uranium mining and metallurgical facilities scientifically and rationally determined the soil conditions of these facilities. 226 Ra background value can solve the soil environmental background of historical uranium mining and metallurgical facilities. 226 The issue of missing Ra. Attached Figure Description

[0047] Figure 1 This is a flowchart of the present invention.

[0048] Figure 2 A baseline survey map of a uranium mining and metallurgical facility in southern China, showing the distribution of monitoring points in the area. Detailed Implementation

[0049] like Figure 1 As shown, the soil from the historical uranium mining and metallurgical facilities of this invention 226 The method for Ra baseline surveys includes the following steps:

[0050] S1. First, estimate the soil conditions within the baseline survey area. 226 The minimum sample size N for Ra monitoring is estimated using the following steps:

[0051] a. Sample estimation is calculated using the following formula:

[0052]

[0053] Where m is the acceptable relative deviation; n is the degree of freedom, taken as n = 1, 2, 3, ...; t n The confidence level is given when there are n degrees of freedom; Cv is the coefficient of variation.

[0054] Assume the historical monitoring data within the baseline survey area is X = {X1, X2, X3, ...}, and the coefficient of variation is C. v Then it is calculated using the following formula:

[0055]

[0056] Wherein, δ represents the historical soil concentration in the baseline survey area. 226 Standard deviation of Ra activity concentration; For historical monitoring of soil in the baseline survey area 226 The average Ra activity concentration;

[0057] When there is no historical monitoring data in the baseline survey area, option C is selected based on experience. v The value is between 10% and 30%.

[0058] b. Let N1 = 1, N n =N n-1 +1, starting from N1, sequentially input into formula (1) to calculate t. n n = 1, 2, 3, ...;

[0059] c. Look up the confidence level T corresponding to the degrees of freedom n in Table 1. n value;

[0060] Table 1. Relationship between degrees of freedom and confidence level

[0061]

[0062] d. According to the convergence condition, minΔt=|T n -t n |, to obtain the n value that satisfies the condition, the n value is the minimum sample size N to be found;

[0063] S2. Detect soil samples within the baseline survey area. 226 Ra activity concentration: Divide the baseline survey area into grids with a number of grids greater than the minimum sample size N, and divide the area into as many grids as possible. This way, after removing suspicious points, the remaining sampling data can be greater than or equal to the minimum sample size N. Set up sampling points in each grid according to national or industry regulations for monitoring and sampling.

[0064] S3. Collect soil samples from the sampling points. 226 Ra activity concentration monitoring data were used to remove suspicious data. From the remaining data, N sampling points were selected and denoted as Y = {Y1, Y2, ..., Y}. N};

[0065] The following methods were used to remove suspicious data:

[0066] Calculate the soil samples collected in this step. 226 The Grobbs coefficient of Ra activity concentration monitoring data is calculated using the following formula:

[0067]

[0068] Among them, G j Let Grubbs be the coefficient of the j-th sampling point, where j = 1 to N;

[0069] b. Compare the Grubbs coefficients of the sampling points obtained in the previous step with the corresponding critical values ​​in the Grubbs coefficient critical table. Table 2 is the Grubbs coefficient critical table. The monitoring data of the sampling points that exceed the critical value are removed, and the data that do not exceed the critical value are retained. When too much data is removed, additional sampling points can be added to supplement the monitoring sample data of the baseline survey area.

[0070] Table 2 Critical values ​​of the Grubbs coefficient

[0071]

[0072] S4. Perform normal statistical analysis on the selected monitoring data. The analysis steps are as follows:

[0073] a. Calculate the skewness SK of the monitoring data or the logarithm of the monitoring data obtained from the N sampling points in step S3:

[0074]

[0075] Among them, Y iThe data is the monitoring data for the i-th sampling point, where i = 1 to N; ε is the average value of the monitoring data from all sampling points; ε is the standard deviation of the monitoring data from all sampling points or the logarithm of the monitoring data.

[0076] b. Calculate the kurtosis BK of the monitoring data from the N sampling points obtained in step S3:

[0077]

[0078] S5. Based on the normal distribution condition, determine whether the monitoring data or the logarithm of the monitoring data collected in step S3 follows a normal distribution; if the normal distribution condition is met, then the monitoring data or the logarithm of the monitoring data of the N sampling points follows a normal distribution; if the normal distribution condition is not met, then the monitoring data of the N sampling points follows a skewed distribution.

[0079] The conditions for a normal distribution are as follows:

[0080]

[0081] (5) Soil in the baseline survey area 226 Ra activity concentration characterization:

[0082] When the monitoring data Y = {Y1, Y2, ... Y} N When the soil in the survey area follows a normal distribution, 226 The background value of Ra activity concentration Y0 is calculated using the following formula:

[0083]

[0084] When the monitoring data Y = {Y1, Y2, ... Y} N The logarithm of} is lnY = {lnY1, lnY2, ..., lnY} N When the soil in the baseline survey area follows a normal distribution, 226 The background value of Ra activity concentration Y0 is calculated using the following formula:

[0085]

[0086] When the monitoring data Y = {Y1, Y2, ... Y} N When the soil in the survey area follows a skewed distribution condition, 226 The background value of Ra activity concentration Y0 is calculated using the following formula:

[0087] Y0 = M e ±T (8)

[0088] in,

[0089] When n is odd

[0090] When n is even

[0091] The following section uses the baseline investigation method of this invention to investigate the soil before the construction of a uranium mining facility in southern China. 226 Ra background data.

[0092] First, the minimum sample size is estimated according to step S1. Since historical data is lacking in this survey area, C in this embodiment... v Based on experience, a maximum value of 30% is taken, and an acceptable relative deviation is conservatively considered to be 20%. Iterative calculations show that when n=22, the convergence requirement minΔt=|T is met. n -t n If |=0.00794, then the minimum sample size is 22; then, following step S2, the baseline survey area within 5km is divided into 25 grids, and sampling points are set up in each grid according to national or industry regulations. The layout results are as follows: Figure 2 As shown in Table 3, the monitoring results are as follows.

[0093] The Grobbes coefficient of the monitoring data collected in Table 3 is calculated, and the calculation structure is shown in Table 4.

[0094] By consulting the critical table of Grubbs coefficients, it was found that the critical value of Grubbs coefficient for a sample of 25 sampling points is 2.663. Therefore, the Grubbs coefficient values ​​of monitoring points 1 to 25 in this embodiment are all less than or equal to the critical value of 2.663. Thus, 22 points can be selected from the 1 to 25 sampling points. In this embodiment, the monitoring data of sampling points 1 to 22 are selected.

[0095] The SK and BK calculated according to step S4 are 0.0879 and 2.573, respectively. The normal distribution of SK calculated according to step S5...

[0096] Table 3 Soil 226 Ra content monitoring results

[0097]

[0098] Table 4 Soil samples from the baseline survey area 226 Ra Grubbs coefficient

[0099]

[0100] The distribution condition is between [-0.979, 0.979], and the normal distribution condition for BK is between [1.040, 4.959]. Since the values ​​of SK and BK in this embodiment are both within the normal distribution condition, the sample data distribution type is normal distribution.

[0101] As can be seen from step S5, the soil in this embodiment 226 The Ra background value is characterized by the average value of the monitoring data and twice the standard deviation, which is [79.8±7.7] Bq / kg.

Claims

1. A soil of a historical legacy uranium mining and metallurgy facility 226 A method for Ra background investigation, characterized by, Comprising the following steps: (1) Estimate the soil 226 Ra monitors the minimum sample size N, as follows: a. Let the sample estimation formula be: wherein m is an acceptable relative deviation, m is a value between 20% and 30%; n is a degree of freedom, taken n = 1, 2, 3,... ; t n is a confidence level for a degree of freedom n; C v is a coefficient of variation; Let the historical monitoring data in the background investigation area be X = {X1, X2, X3, …}, the coefficient of variation C v is calculated by the following formula: wherein δ is the average of the Ra activity concentrations in the soils historically monitored in the background investigation area 226 the standard deviation of the Ra activity concentrations; is the average of the Ra activity concentrations in the soils historically monitored in the background investigation area 226 the average of the Ra activity concentrations; When there is no historical monitoring data within the background investigation region, then take C v is a value between 10% and 30%; b、 Let N1 = 1, N n = N n-1 + 1, sequentially into the formula (1) from N1, calculated t n , n = 1, 2, 3,...; c. query the table of relationship between the degree of freedom and the confidence, and the confidence of the degree of freedom n is recorded as T n ; d、 According to the convergence condition minΔt = |T n -t n |, get the n value that meets the condition, n value is the minimum sample size N. (2) In the background investigation area, monitor the soil in 226 Ra active concentration: The background investigation area is divided into grids, the number of grids is greater than the minimum sample size N, and sampling points are set in each grid according to national or industry regulations for monitoring sampling; (3) Collecting the soil in the sampling points 226 Ra activity concentration monitoring data, eliminating suspicious monitoring data, selecting N sampling point monitoring data from the remaining monitoring data, and recording as Y = {Y1, Y2, … Y N} (4) The selected monitoring data are subjected to normal statistical analysis, and the steps are as follows: a. Calculate the skewness SK of the monitoring data or the logarithm of the monitoring data of the N sampling points obtained in step (3): wherein Y i is the monitoring data of the i-th sampling point, i = 1, 2, 3, …, N; is the average value of the monitoring data of all sampling points; ε is the standard deviation of the monitoring data or the logarithm of the monitoring data of all sampling points; b. Calculate the kurtosis BK of the monitoring data of the N sampling points obtained in step (3): c. According to the normal distribution condition, it is determined whether the monitoring data or the logarithm of the monitoring data of the N sampling points collected in step (3) obeys the normal distribution; if the normal distribution condition is met, it is determined that the monitoring data or the logarithm of the monitoring data of the N sampling points obeys the normal distribution, and if the normal distribution condition is not met, it is determined that the monitoring data of the N sampling points obeys the skewness distribution; The normal distribution condition is as follows: (5) Soil of the background survey area 226 Ra activity concentration characterization: When the monitoring data Y = {Y1, Y2,... Yn} obeys the normal distribution condition, the soil of the investigation area N Ra activity concentration background value Y0 is calculated by the following formula: 226 Ra activity concentration background value Y0 is calculated by the following formula: When the log of the monitoring data Y = {Y1, Y2,... Yn} obeys a normal distribution, the background investigation area soil N Ra activity concentration background value Y0 is calculated by the following formula: 226 Ra activity concentration background value Y0 is calculated by the following formula: When the monitoring data Y = {Y1, Y2,... Yn} obeys the skew distribution condition, the soil of the investigation area N Ra activity concentration background value Y0 is calculated by the following formula: 226 Ra activity concentration background value Y0 is calculated by the following formula: Y0 = M e ±T (8) wherein when n is odd, when n is even, 2. The method of claim 1, wherein the step of In step (3), the suspicious data are removed by the following steps: a. Calculate the soil in the collected sampling points 226 The Grubbs coefficient of Ra activity concentration monitoring data is calculated as follows: wherein G j is the Grubbs coefficient for the jth sampling point, j = 1, 2, 3, …, N; b. Compare the Grubbs coefficient of the sampling point obtained in the above step with the corresponding critical value in the Grubbs coefficient critical table, and remove the monitoring data of the sampling point exceeding the critical value, and otherwise keep it.

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