Soil heavy metal pollution tracing method and system

Abstract

The invention discloses a soil heavy metal pollution traceability method and system, and belongs to the technical field of polluted soil investigation, the method comprises the following steps: arranging a plurality of soil sampling points in an investigation area for soil sampling to obtain a plurality of soil samples, and obtaining content data of various types of heavy metal elements in each soil sample; based on the content data of the heavy metal elements, screening out the heavy metal elements with spatial aggregation; selecting one of the heavy metal elements with spatial aggregation as a to-be-analyzed element, and screening out the heavy metal element having a significant correlation with the to-be-analyzed element; and carrying out isotopic tracing on the to-be-analyzed element, and determining the to-be-analyzed element and the source of the heavy metal element having the significant correlation with the to-be-analyzed element. By adopting the scheme, the purpose of quantitatively determining the heavy metal pollution source in the area can be achieved, the cost can be saved, and isotopic tracing of each heavy metal is avoided.

Description

Technical Field

[0001] This invention relates to the field of soil pollution investigation technology, and in particular to a method and system for tracing the source of heavy metal pollution in soil. Background Technology

[0002] Against the backdrop of continuous industrialization and urbanization, heavy metal pollution has become one of the most common environmental problems in industrial parks and sites. Heavy metals in site soil can originate from various industrial pollution sources such as coal combustion, steel smelting, and fuel oil consumption, and may also be influenced by geological background factors. After emission, various heavy metals migrate and accumulate in the soil through atmospheric deposition, underground pipeline leaks, and leachate from solid waste storage, gradually forming a complex pollution pattern. In this context, fully identifying the sources and formation mechanisms of heavy metal pollution has become a crucial prerequisite for the investigation and remediation of contaminated sites. Because the same site often has multiple emission sources, multiple migration pathways, and overlapping historical activities, relying solely on concentration distribution is insufficient to accurately determine the causes of pollution. Therefore, systematic causal analysis methods are needed to quantitatively or semi-quantitatively analyze the sources of pollutants.

[0003] Currently, a relatively mature methodological system has been established for the study of the causes of heavy metal pollution, including statistical methods such as principal component analysis (PCA), factor analysis (FA), and cluster analysis (CA). These belong to pattern recognition techniques and can only reveal the correlation structure and potential combination characteristics between elements, making it difficult to quantify the contribution of pollution sources. Isotope tracing techniques that use the isotope characteristics of elements such as Pb, Zn, and Cu for quantitative analysis have high accuracy, but they can usually only trace a single element and have limited applicability to complex sites where multiple heavy metals coexist.

[0004] In summary, all methods have certain limitations, and a single technology cannot fully reveal the multi-source characteristics of heavy metal pollution. Therefore, there is an urgent need for a new method to conduct intensive site surveys of soil pollution in industrial sites in order to solve the aforementioned problems. Summary of the Invention

[0005] This invention provides a method and system for tracing the source of heavy metal pollution in soil, in order to solve the technical problem that existing technologies are unable to fully reveal the multi-source characteristics of heavy metal pollution.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] On the one hand, the present invention provides a method for tracing the source of heavy metal pollution in soil, comprising:

[0008] Multiple soil sampling points were set up in the survey area to collect soil samples, and the content data of various heavy metal elements in each soil sample were obtained.

[0009] Based on the content data of heavy metal elements, heavy metal elements that exhibit spatial aggregation were screened out;

[0010] One heavy metal element exhibiting spatial aggregation is selected as the element to be analyzed, and heavy metal elements that have a significant correlation with the element to be analyzed are screened out;

[0011] Isotope tracing is performed on the element to be analyzed to determine its source, and the source of the element to be analyzed is used as the source tracing result of heavy metal elements that have a significant correlation with the element to be analyzed.

[0012] Furthermore, based on the content data of heavy metal elements, the heavy metal elements exhibiting spatial aggregation are screened out, including:

[0013] The Moran index for each heavy metal element is calculated based on its content data. The formula is as follows:

[0014] ;

[0015] Where N is the number of soil samples; For the heavy metal elements whose Moran index is to be calculated, in the first... The content in each soil sample; For the heavy metal elements whose Moran index is to be calculated, in the first... The content in each soil sample; The value represents the average content of heavy metal elements for which the Moran index is to be calculated in all soil samples. The first element in the spatial weight matrix The soil sample and the first The weight of each soil sample; , which is the sum of weights;

[0016] Based on the Moran index of each heavy metal element, heavy metal elements exhibiting spatial aggregation were screened out.

[0017] Furthermore, heavy metal elements that have a significant correlation with the element to be analyzed are screened out, including:

[0018] Calculate the correlation coefficient between each heavy metal element and the element to be analyzed;

[0019] Based on the calculated correlation coefficient between each heavy metal element and the element to be analyzed, a significance test is conducted to screen out heavy metal elements that have a significant correlation with the element to be analyzed.

[0020] Further, the correlation coefficient between each heavy metal element and the element to be analyzed is calculated, including:

[0021] Normality tests were performed on the content data of the elements to be analyzed in each soil sample.

[0022] If the data follows a normal distribution, calculate the Pearson correlation coefficient between each heavy metal element and the element to be analyzed.

[0023] If the data exhibits a non-normal distribution, calculate the Spearman rank correlation coefficient between each heavy metal element and the element to be analyzed.

[0024] Furthermore, after performing isotope tracing on the element to be analyzed to determine its origin, and using the obtained origin of the element to be analyzed as the source tracing result of heavy metal elements that are significantly correlated with the element to be analyzed, the method further includes:

[0025] Based on the spatial distribution characteristics of heavy metal pollution concentration in the soil of the surveyed area, the spatial layout of different functional zones and their spatial relationship with potential pollution sources were comprehensively analyzed to obtain spatial analysis results.

[0026] By coupling the source contribution results obtained from isotope tracing with the spatial analysis results, the relative contributions of different sources to the pattern of heavy metal pollution in soil at different functional zones and regional scales are quantitatively assessed.

[0027] On the other hand, the present invention also provides a soil heavy metal pollution tracing system, comprising:

[0028] The heavy metal content data acquisition module is used to acquire the content data of various heavy metal elements in each soil sample; the soil samples are obtained by sampling soil at multiple soil sampling points set up in the survey area.

[0029] The spatially clustered heavy metal screening module is used to screen out heavy metal elements that exhibit spatial clustering based on the content data of heavy metal elements.

[0030] A significantly correlated heavy metal screening module is used to select one heavy metal element as the element to be analyzed from the heavy metal elements that exhibit spatial aggregation, and to screen out the heavy metal elements that have a significant correlation with the element to be analyzed;

[0031] The source tracing module is used to perform isotope tracing on the element to be analyzed to determine its origin, and at the same time, the source of the element to be analyzed is used as the source tracing result of heavy metal elements that have a significant correlation with the element to be analyzed.

[0032] Furthermore, the spatially clustered heavy metal screening module is specifically used for:

[0033] The Moran index for each heavy metal element is calculated based on its content data. The formula is as follows:

[0034] ;

[0035] Where N is the number of soil samples; For the heavy metal elements whose Moran index is to be calculated, in the first... The content in each soil sample; For the heavy metal elements whose Moran index is to be calculated, in the first... The content in each soil sample; The value represents the average content of heavy metal elements for which the Moran index is to be calculated in all soil samples. The first element in the spatial weight matrix The soil sample and the first The weight of each soil sample; , which is the sum of weights;

[0036] Based on the Moran index of each heavy metal element, heavy metal elements exhibiting spatial aggregation were screened out.

[0037] Furthermore, the significantly correlated heavy metal screening module is specifically used for:

[0038] Calculate the correlation coefficient between each heavy metal element and the element to be analyzed;

[0039] Based on the calculated correlation coefficient between each heavy metal element and the element to be analyzed, a significance test is conducted to screen out heavy metal elements that have a significant correlation with the element to be analyzed.

[0040] Further, the correlation coefficient between each heavy metal element and the element to be analyzed is calculated, including:

[0041] Normality tests were performed on the content data of the elements to be analyzed in each soil sample.

[0042] If the data follows a normal distribution, calculate the Pearson correlation coefficient between each heavy metal element and the element to be analyzed.

[0043] If the data exhibits a non-normal distribution, calculate the Spearman rank correlation coefficient between each heavy metal element and the element to be analyzed.

[0044] Furthermore, the system also includes a comprehensive analysis module, used for:

[0045] Based on the spatial distribution characteristics of heavy metal pollution concentration in the soil of the surveyed area, the spatial layout of different functional zones and their spatial relationship with potential pollution sources were comprehensively analyzed to obtain spatial analysis results.

[0046] By coupling the source contribution results obtained from isotope tracing with the spatial analysis results, the relative contributions of different sources to the pattern of heavy metal pollution in soil at different functional zones and regional scales are quantitatively assessed.

[0047] In another aspect, the present invention also provides an electronic device comprising a processor and a memory; wherein the memory stores at least one instruction, which is loaded and executed by the processor to implement the above-described method.

[0048] In another aspect, the present invention also provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the above method.

[0049] The beneficial effects of the technical solution provided by this invention include at least the following:

[0050] This invention proposes a method for tracing the source of heavy metal pollution in soil based on a combination of spatial geostatistics, traditional statistics, and isotope techniques, providing an effective solution for identifying the sources of heavy metal pollution in contaminated soil. The main feature of this technique is that it identifies heavy metals with common sources in the surveyed area based on the spatial autocorrelation of heavy metals in the soil. It then uses the correlation between heavy metals to screen for heavy metals correlated with a specific heavy metal, and combines this with isotope tracing techniques to determine the pollution source of that heavy metal. Furthermore, by combining these correlations with the functional zones of the surveyed area, it infers the pollution sources of other heavy metals. This method achieves the goal of quantitatively determining the sources of heavy metal pollution in a region while saving costs and avoiding the need for isotope tracing for every single heavy metal. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a schematic diagram of the execution flow of the soil heavy metal pollution source tracing method provided in the embodiments of the present invention;

[0053] Figure 2 This is a distribution map of heavy metal locations in the survey area provided in an embodiment of the present invention;

[0054] Figure 3 This is a schematic diagram illustrating the correlation between lead content and other heavy metal content in soil, provided in an embodiment of the present invention; wherein, This indicates a significant correlation (p<0.05). This indicates a significant correlation (p<0.01). This indicates a significant correlation (p<0.001);

[0055] Figure 4This is a schematic diagram of the 206Pb / 207Pb and 208Pb / 206Pb ratios of soil and its potential sources provided in an embodiment of the present invention; wherein, the error bars for isotope ratio determination are ± 1 SD and correspond to the symbols used.

[0056] Figure 5 This is a schematic diagram of the soil 1 / Pb to potential source ratio provided in an embodiment of the present invention; wherein, (a) is a schematic diagram of the soil 1 / Pb to 206Pb / 207Pb ratio, and (b) is a schematic diagram of the soil 1 / Pb to 208Pb / 206Pb ratio; wherein, the magnitude of the isotope ratio measurement error bar ± 1 SD is equivalent to the symbol used;

[0057] Figure 6 This is a system block diagram of the electronic device provided in the embodiments of the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0059] First, it should be noted that in the embodiments of the present invention, the words "exemplarily," "for example," etc., are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the term "exemplarily" is intended to present the concept in a specific manner. Furthermore, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either one or the other.

[0060] First Embodiment

[0061] This embodiment provides a method for tracing the source of heavy metal pollution in soil. This method can be implemented using electronic equipment, which can be a terminal or a server. The execution flow of this method is as follows: Figure 1 As shown, it includes the following steps:

[0062] S1. Multiple soil sampling points were set up in the survey area to collect soil samples, and the content data of various heavy metal elements in each soil sample were obtained.

[0063] Specifically, in this embodiment, the implementation process of S1 is as follows: Based on historical data such as the site's production processes, potential pollution sources, and functional zoning over the years, and with reference to relevant regulations and expert judgment, soil sampling points in the survey area are rationally deployed. Subsequently, laboratory testing of the soil samples is conducted to obtain heavy metal content data, while simultaneously collecting soil samples from typical pollution sources to support subsequent pollution source analysis.

[0064] S2, based on the content data of heavy metal elements, screens out heavy metal elements that exhibit spatial aggregation;

[0065] Specifically, in this embodiment, the implementation process of S2 is as follows:

[0066] Based on the heavy metal content of the soil, spatial autocorrelation analysis was conducted using Moran's I index. Moran's I is an indicator used to measure whether similar attribute values ​​in spatial data are clustered or dispersed spatially. It reflects the degree of autocorrelation of spatial variables in geographic space.

[0067] Positive value (>0): Spatial similarity values ​​cluster (high values ​​are near high values, and low values ​​are near low values), which shows positive autocorrelation.

[0068] Approaching zero (≈0): There is no obvious spatial correlation, and it exhibits a random distribution.

[0069] Negative values ​​(<0): High values ​​are near low values, and low values ​​are near high values, which shows negative autocorrelation, that is, a spatially discrete pattern.

[0070] The formula for calculating Moran's I is as follows:

[0071]

[0072] in:

[0073] Number of soil samples;

[0074] : No. Heavy metal content of each soil sample;

[0075] Average heavy metal content in soil;

[0076] In the spatial weight matrix and Sample weights (e.g., adjacency or inverse distance);

[0077] : Total weights.

[0078] The significance of Moran's I is determined by the Z-score and p-value. If the calculated value is greater than the expected value, E[I] = − If the value is less than the expected value, it can be considered that there is significant spatial clustering; if it is less than the expected value, it can be considered that there is spatial dispersion.

[0079] Based on Moran's I calculations, heavy metals exhibiting spatial aggregation were identified, and the reasons for this spatial aggregation were analyzed in conjunction with functional zone analysis.

[0080] S3, Select one heavy metal element from the heavy metal elements that exhibit spatial aggregation as the element to be analyzed, and screen out the heavy metal elements that have a significant correlation with the element to be analyzed;

[0081] It should be noted that the above steps are for conducting correlation analysis among soil heavy metal elements based on soil heavy metal content data, focusing on identifying combinations of heavy metal elements that are significantly correlated with the isotope tracing research objects, and providing data support for heavy metal co-origin identification and subsequent isotope source tracing research.

[0082] Specifically, in this embodiment, the implementation process of S3 is as follows:

[0083] Based on the content data of various heavy metal elements in soil samples collected from the study area, the correlation relationships among different heavy metal elements were systematically analyzed. First, the normality of the heavy metal element content data was tested. Then, according to the data distribution characteristics, Pearson correlation coefficient (suitable for normally distributed data) or Spearman rank correlation coefficient (suitable for non-normally distributed data) was used to quantitatively characterize the pairwise correlations between heavy metal elements. Significance tests (p < 0.05 or p < 0.01) were used to identify significantly correlated pairs of heavy metal elements and clarify their cooperative variation characteristics.

[0084] During the correlation analysis, the focus was on the correlation between key heavy metal elements (such as Pb, Cd, and Cu) and other heavy metal elements in the proposed stable isotope tracing studies. Heavy metals showing a significant positive correlation with the target isotope tracing elements were screened. A significant positive correlation indicates that the related heavy metals share a high degree of consistency in spatial distribution patterns and enrichment characteristics, and may be controlled by the same or similar pollution sources or geochemical processes.

[0085] Based on this, a further classification analysis was conducted on heavy metal elements with significant positive correlations. Heavy metals that showed synergistic changes and significant correlations with isotope tracers were grouped into the same category, and their potential co-source characteristics were preliminarily identified. This classification result can provide an important basis for the rational selection and interpretation of source apportionment objects in stable isotope tracing studies.

[0086] Based on the comprehensive correlation analysis and heavy metal classification results, the co-source characteristics of heavy metals in the soil of the study area were systematically identified, laying a scientific data foundation and technical support for subsequent research on stable isotope tracing and pollution source apportionment of heavy metals. Then, combining the spatial autocorrelation and co-source heavy metals, one heavy metal was selected for isotope tracing.

[0087] S4, Isotope tracing is performed on the element to be analyzed to determine its source, and the source of the obtained element to be analyzed is used as the source tracing result of heavy metal elements that have a significant correlation with the element to be analyzed.

[0088] Specifically, in this embodiment, the implementation process of S4 is as follows:

[0089] Based on the heavy metal isotope fingerprint database of potential pollution sources constructed within the survey area, the system acquires the isotopic composition characteristics of target heavy metals in different types of pollution sources and soil samples. By comparing the matching relationship between soil samples and the isotopic fingerprints of each potential pollution source, a Bayesian isotope mixture model is introduced to quantitatively analyze the pollution sources of target heavy metals. In the model, the isotopic characteristics of different pollution sources are used as prior information, combined with isotopic observation data of soil samples, and the contribution ratio of each potential pollution source to soil heavy metal pollution is estimated using Bayesian inference methods. The uncertainty of the source analysis results is characterized by a posterior distribution, thereby achieving probabilistic and quantitative identification of heavy metal pollution sources.

[0090] S5. Combining the spatial distribution characteristics of soil heavy metal pollution concentration in the survey area, we comprehensively analyze the spatial layout of different functional zones and their spatial relationship with potential pollution sources to obtain spatial analysis results. We couple the source contribution results obtained by isotope tracing with the spatial analysis results to quantitatively assess the relative contribution of different sources to the pattern of soil heavy metal pollution at different functional zones and regional scales.

[0091] Specifically, in this embodiment, the implementation process of S5 is as follows:

[0092] Based on isotopic tracing of the elements under analysis to obtain source tracing results, and combined with the spatial distribution characteristics of heavy metal pollution concentrations in the soil of the study area, this study comprehensively analyzes the spatial layout of different functional zones (such as water treatment zones, storage zones, and oil spill sites) and their spatial relationships with potential pollution sources. The source contribution results obtained from the Bayesian isotopic mixing model are coupled with the spatial analysis results to quantitatively assess the relative contributions of different sources to the pattern of heavy metal pollution in soil at different functional zones and regional scales. Through the comprehensive analysis of spatial distribution characteristics and Bayesian source tracing results, this study systematically reveals the formation mechanism and spatial heterogeneity of multi-source heavy metal pollution in the study area, providing a scientific basis for formulating zoned and differentiated strategies for the prevention, control, and remediation of heavy metal pollution in soil.

[0093] In summary, this embodiment proposes a method for tracing the source of heavy metal pollution in soil based on a combination of spatial geostatistics, traditional statistics, and isotope techniques, providing an effective solution for identifying the sources of heavy metal pollution in contaminated soil. The main feature of this technique is that it identifies heavy metals with common sources in the surveyed area based on the spatial autocorrelation of heavy metals in the soil. It then uses the correlation between heavy metals to screen for heavy metals correlated with a specific heavy metal, and combines this with isotope tracing techniques to determine its pollution source. Furthermore, it combines the correlation with the functional zones of the surveyed area to infer the pollution sources of other heavy metals. This approach achieves the goal of quantitatively determining the sources of heavy metal pollution in the area while saving costs and avoiding the need for isotope tracing for every single heavy metal.

[0094] Second Embodiment

[0095] This embodiment uses a practical application example to illustrate the implementation process and effects of the present invention. In this embodiment, the investigation area is a steel contaminated site, and the implementation process is as follows:

[0096] 1. Based on the analysis of historical data on industrial site production processes, pollution sources, and functional zoning, a total of 663 soil sampling points were set up, and 4267 soil samples were collected. Laboratory tests were conducted on the soil (arsenic, copper, lead, mercury, nickel, antimony, beryllium, cobalt, vanadium, thallium) to determine the heavy metal content of the soil.

[0097] The distribution of heavy metal sites in the surveyed area is as follows: Figure 2 As shown.

[0098] 2. Spatial autocorrelation analysis of heavy metals in the surveyed soil revealed that the p-values ​​for all heavy metals were less than 0.01, and all heavy metals in the surveyed area showed significant positive spatial autocorrelation, indicating a spatial clustering trend for similar concentrations. Pollutants with higher Moran indices (e.g., mercury I=0.49, beryllium I=0.47, lead I=0.41) exhibited the most pronounced spatial clustering characteristics, possibly influenced by regional production activities. The Moran indices for each heavy metal are shown in Table 1.

[0099] Table 1. Moran Index of Heavy Metals

[0100]

[0101] 3. Correlation analysis among heavy metals

[0102] Based on the spatial autocorrelation analysis results of heavy metals, lead was selected as a representative heavy metal for soil isotopic tracing. Therefore, the correlation analysis between lead and other heavy metals was conducted. The correlation between lead content and other heavy metal contents in the soil is as follows: Figure 3 As shown, Figure 3The data shows a significant positive correlation between soil lead content and heavy metals such as arsenic, copper, mercury, nickel, antimony, cobalt, and thallium. This symbiotic relationship means that lead isotope tracing technology can not only accurately indicate the source of lead, but also effectively trace the migration and fate of the above-mentioned heavy metal pollutants with the same origin.

[0103] 4. Based on the lead isotope ratios measured from collected soil pollution source samples, an isotope fingerprint database for various pollution sources was constructed. The analysis results show that samples from different sources... 206 Pb / 207 Pb and 208 Pb / 206 The Pb binary graph clearly occupies different regions (e.g.) Figure 4 (As shown in the image). When the lead isotope data of representative soil samples from each functional zone were plotted on this fingerprint map, the vast majority of sample points fell on the connecting line between the three pollution sources: iron and steel smelting, coal combustion, and backfill soil. They were also densely clustered towards the iron and steel smelting source, clearly indicating that iron and steel smelting activities are the dominant source of pollution in this area. This further confirms the spatial aggregation of heavy metals. It is noteworthy that two sampling points, AX13-0.7 and BQ12-0.2, significantly deviated from the main area, their locations closely matching the range of the backfill soil source. Lead isotope data indicate that these two specific anomalous sampling points were significantly influenced by the backfill soil source.

[0104] From 1 / Pb and 206 Pb / 207 Pb ratio, 1 / Pb and 208 Pb / 206 Pb ratio relationship ( Figure 5 Analysis showed that the vast majority of sample points were mainly distributed within a triangular area formed by steel smelting sources, coal combustion sources, and backfill slag sources, which further confirmed that the above three sources are the main sources of heavy metal pollution in the area.

[0105] Based on the accurately identified pollution sources, a Bayesian mixture model was further used to quantify the contribution of each pollution source to soil lead. The results showed that iron and steel smelting was the dominant contributing source, with an average contribution rate of 46.2±10.2%, followed by coal combustion, with an average contribution rate of 26.0±5.3%. The contribution from fuel sources was relatively stable, averaging 11.0±3.9%, while the contribution from natural sources was the lowest, averaging less than 5%, indicating a weak regional background influence. Locally, backfill soil sources accounted for over 60% at some locations, which could explain the locally dispersed high values, possibly related to backfill soil.

[0106] In summary, by combining the spatial autocorrelation of heavy metals and the correlation between lead and other heavy metals, quantitative tracing of common heavy metals in the surveyed area can be achieved through lead isotope analysis.

[0107] Third Embodiment

[0108] This embodiment provides a soil heavy metal pollution tracing system, which includes the following modules:

[0109] The heavy metal content data acquisition module is used to acquire the content data of various heavy metal elements in each soil sample; the soil samples are obtained by sampling soil at multiple soil sampling points set up in the survey area.

[0110] The spatially clustered heavy metal screening module is used to screen out heavy metal elements that exhibit spatial clustering based on the content data of heavy metal elements.

[0111] A significantly correlated heavy metal screening module is used to select one heavy metal element as the element to be analyzed from the heavy metal elements that exhibit spatial aggregation, and to screen out the heavy metal elements that have a significant correlation with the element to be analyzed;

[0112] The source tracing module is used to perform isotope tracing on the element to be analyzed to determine its source, and at the same time, the source of the element to be analyzed is used as the source tracing result of heavy metal elements that have a significant correlation with the element to be analyzed.

[0113] The comprehensive analysis module is used to combine the spatial distribution characteristics of soil heavy metal pollution concentration in the survey area, comprehensively analyze the spatial layout of different functional zones and their spatial relationship with potential pollution sources, and obtain spatial analysis results. The source contribution results obtained by isotope tracing are coupled with the spatial analysis results to quantitatively assess the relative contribution of different sources to the pattern of soil heavy metal pollution at different functional zones and regional scales.

[0114] It should be noted that the soil heavy metal pollution tracing system of this embodiment corresponds to the soil heavy metal pollution tracing method of the first embodiment described above; the functions implemented by each functional module in the soil heavy metal pollution tracing system of this embodiment correspond one-to-one with the process steps in the soil heavy metal pollution tracing method of the first embodiment described above; therefore, they will not be described again here.

[0115] Fourth embodiment

[0116] This embodiment provides an electronic device, such as... Figure 6 As shown, the electronic device includes a processor and a memory; wherein the processor and the memory can be connected via a communication bus; the memory stores at least one instruction, which is loaded and executed by the processor to implement the method of the first embodiment described above. Furthermore, the electronic device may also include a transceiver, the processor and the transceiver can be connected via a communication bus, and the transceiver is used to communicate with other devices.

[0117] Below, in conjunction with Figure 6A detailed introduction to each component of this electronic device is provided below:

[0118] The processor is the control center of the electronic device. The electronic device may include multiple processors, each of which can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The term "processor" can refer to a single processor or a collective term for multiple processing elements. For example, a processor can be one or more central processing units (CPUs), other general-purpose processors, application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), one or more field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor can perform various functions of the electronic device by running or executing software programs stored in memory and by calling data stored in memory.

[0119] In a specific implementation, as one example, the processor may include one or more CPUs, for example... Figure 6 CPU0 and CPU1 shown are, of course, merely illustrative examples.

[0120] The memory is used to store the software program that executes the solution of the present invention, and the processor controls its execution. For specific implementation methods, please refer to the above method embodiments, which will not be repeated here.

[0121] Optionally, the memory may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory may be integrated with the processor or exist independently, and may be accessed through the interface circuit of the electronic device ( Figure 6 (Not shown in the image) is coupled to the processor; however, this embodiment of the invention does not impose specific limitations on this.

[0122] The transceiver may include a receiver and a transmitter. Figure 6 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function. The transceiver can be integrated with the processor or exist independently, and can be connected through the interface circuit of the electronic device (…). Figure 6 (Not shown in the image) is coupled to the processor, and this embodiment of the invention does not specifically limit this.

[0123] In addition, it should be noted that, Figure 6 The structure of the electronic device shown is not intended to limit the device. Actual devices may include more or fewer components than shown, or combine certain components, or have different component arrangements. Furthermore, the technical effects achieved by this electronic device when performing the method of the first embodiment described above can be referenced to the technical effects described in the first embodiment; therefore, they will not be repeated here.

[0124] Fifth embodiment

[0125] This embodiment provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the method of the first embodiment described above. The computer-readable storage medium may be a ROM, random access memory, CD-ROM, magnetic tape, floppy disk, or optical data storage device, etc. The instruction stored therein can be loaded and executed by a processor in a terminal.

[0126] Furthermore, it should be noted that the present invention can be provided as a method, apparatus, or computer program product. Therefore, embodiments of the present invention can take the form of a completely or partially hardware embodiment, a completely or partially software embodiment, or an embodiment combining software and hardware aspects. Moreover, when implemented in software, embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any usable medium accessible to a computer or a data storage device such as a server or data center containing one or more sets of usable media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive (SSD).

[0127] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0128] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0129] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element. Furthermore, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Additionally, the character " / " in this text generally indicates an "or" relationship between the preceding and following objects, but it can also indicate an "AND / OR" relationship. Please refer to the context for specific interpretations. "At least one" refers to one or more items, while "more than" refers to two or more items. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can be represented as: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0130] Furthermore, it is understood that in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0131] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0132] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of functional modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs. Additionally, the functional units in the various embodiments of this invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0133] If the method is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0134] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention. It should be pointed out that although preferred embodiments of the present invention have been described, those skilled in the art, once they understand the basic inventive concept of the present invention, can make several improvements and modifications without departing from the principles described herein. These improvements and modifications should also be considered within the scope of protection of the present invention. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

Claims

1. A method for tracing the source of heavy metal pollution in soil, characterized in that, include: Multiple soil sampling points were set up in the survey area to collect soil samples, and the content data of various heavy metal elements in each soil sample were obtained. Based on the content data of heavy metal elements, heavy metal elements that exhibit spatial aggregation were screened out; One heavy metal element exhibiting spatial aggregation is selected as the element to be analyzed, and heavy metal elements that have a significant correlation with the element to be analyzed are screened out; Isotope tracing is performed on the element to be analyzed to determine its source, and the source of the element to be analyzed is used as the source tracing result of heavy metal elements that have a significant correlation with the element to be analyzed.

2. The method for tracing the source of heavy metal pollution in soil as described in claim 1, characterized in that, The heavy metal elements, based on their content data, were screened to identify those exhibiting spatial aggregation, including: The Moran index for each heavy metal element is calculated based on its content data. The formula is as follows: ； Where N is the number of soil samples; For the heavy metal elements whose Moran index is to be calculated, in the first... The content in each soil sample; For the heavy metal elements whose Moran index is to be calculated, in the first... The content in each soil sample; The value represents the average content of heavy metal elements for which the Moran index is to be calculated in all soil samples. The first element in the spatial weight matrix The soil sample and the first The weight of each soil sample; , which is the sum of weights; Based on the Moran index of each heavy metal element, heavy metal elements exhibiting spatial aggregation were screened out.

3. The method for tracing the source of heavy metal pollution in soil as described in claim 1, characterized in that, The heavy metal elements that are significantly correlated with the element to be analyzed include: Calculate the correlation coefficient between each heavy metal element and the element to be analyzed; Based on the calculated correlation coefficient between each heavy metal element and the element to be analyzed, a significance test is conducted to screen out heavy metal elements that have a significant correlation with the element to be analyzed.

4. The method for tracing the source of heavy metal pollution in soil as described in claim 3, characterized in that, The calculation of the correlation coefficient between each heavy metal element and the element to be analyzed includes: Normality tests were performed on the content data of the elements to be analyzed in each soil sample. If the data follows a normal distribution, calculate the Pearson correlation coefficient between each heavy metal element and the element to be analyzed. If the data exhibits a non-normal distribution, calculate the Spearman rank correlation coefficient between each heavy metal element and the element to be analyzed.

5. The method for tracing the source of heavy metal pollution in soil as described in claim 1, characterized in that, After performing isotope tracing on the element to be analyzed to determine its origin, and using the obtained origin of the element to be analyzed as the source tracing result of heavy metal elements that are significantly correlated with the element to be analyzed, the method further includes: Based on the spatial distribution characteristics of heavy metal pollution concentration in the soil of the surveyed area, the spatial layout of different functional zones and their spatial relationship with potential pollution sources were comprehensively analyzed to obtain spatial analysis results. By coupling the source contribution results obtained from isotope tracing with the spatial analysis results, the relative contributions of different sources to the pattern of heavy metal pollution in soil at different functional zones and regional scales are quantitatively assessed.

6. A soil heavy metal pollution source tracing system, characterized in that, include: The heavy metal content data acquisition module is used to acquire the content data of various heavy metal elements in each soil sample; the soil samples are obtained by sampling soil at multiple soil sampling points set up in the survey area. The spatially clustered heavy metal screening module is used to screen out heavy metal elements that exhibit spatial clustering based on the content data of heavy metal elements. A significantly correlated heavy metal screening module is used to select one heavy metal element as the element to be analyzed from the heavy metal elements that exhibit spatial aggregation, and to screen out the heavy metal elements that have a significant correlation with the element to be analyzed; The source tracing module is used to perform isotope tracing on the element to be analyzed to determine its origin, and at the same time, the source of the element to be analyzed is used as the source tracing result of heavy metal elements that have a significant correlation with the element to be analyzed.

7. The soil heavy metal pollution source tracing system as described in claim 6, characterized in that, The spatially aggregated heavy metal screening module is specifically used for: The Moran index for each heavy metal element is calculated based on its content data. The formula is as follows: ； Where N is the number of soil samples; For the heavy metal elements whose Moran index is to be calculated, in the first... The content in each soil sample; For the heavy metal elements whose Moran index is to be calculated, in the first... The content in each soil sample; The value represents the average content of heavy metal elements for which the Moran index is to be calculated in all soil samples. The first element in the spatial weight matrix The soil sample and the first The weight of each soil sample; , which is the sum of weights; Based on the Moran index of each heavy metal element, heavy metal elements exhibiting spatial aggregation were screened out.

8. The soil heavy metal pollution source tracing system as described in claim 6, characterized in that, The significantly correlated heavy metal screening module is specifically used for: Calculate the correlation coefficient between each heavy metal element and the element to be analyzed; Based on the calculated correlation coefficient between each heavy metal element and the element to be analyzed, a significance test is conducted to screen out heavy metal elements that have a significant correlation with the element to be analyzed.

9. The soil heavy metal pollution source tracing system as described in claim 8, characterized in that, The calculation of the correlation coefficient between each heavy metal element and the element to be analyzed includes: Normality tests were performed on the content data of the elements to be analyzed in each soil sample. If the data follows a normal distribution, calculate the Pearson correlation coefficient between each heavy metal element and the element to be analyzed. If the data exhibits a non-normal distribution, calculate the Spearman rank correlation coefficient between each heavy metal element and the element to be analyzed.

10. The soil heavy metal pollution source tracing system as described in claim 6, characterized in that, The system also includes a comprehensive analysis module, used for: Based on the spatial distribution characteristics of heavy metal pollution concentration in the soil of the surveyed area, the spatial layout of different functional zones and their spatial relationship with potential pollution sources were comprehensively analyzed to obtain spatial analysis results. By coupling the source contribution results obtained from isotope tracing with the spatial analysis results, the relative contributions of different sources to the pattern of heavy metal pollution in soil at different functional zones and regional scales are quantitatively assessed.

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