Method and system for identifying main control factors of heavy metals stored in mangrove forest wetland

Abstract

The invention relates to the technical field of data processing, in particular to a method and system for identifying main control factors of heavy metal stored in mangrove forest wetland, and the method comprises the following steps: obtaining the content of heavy metal at different depths and the content of each deposition element in the mangrove forest wetland; carrying out principal component analysis on the contents of the heavy metals at different depths and the contents of the deposition elements, and identifying the deposition elements influencing the heavy metal occurrence capability; performing correlation analysis on the principal component score and the heavy metal content, and determining principal components significantly related to heavy metal occurrence; determining the contribution ratio of the main deposition elements to the heavy metal occurrence capability, and determining deposition control factors of the mangrove forest wetland sediment heavy metal occurrence capability based on the contribution ratio; the method can identify the main control factors of the heavy metals stored in the mangrove forest wetland.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method and system for identifying the main controlling factors of heavy metal storage in mangrove wetlands. Background Art

[0002] Mangrove wetlands play an irreplaceable role in global climate and biodiversity conservation. They are not only one of the most efficient carbon capture and storage systems on Earth, but also crucial for maintaining the marine ecological balance. The presence of mangroves provides habitats for countless marine organisms, supports the development of fisheries, and also provides natural flood control and shore protection barriers for coastal communities.

[0003] However, due to the impact of human activities, mangroves are facing unprecedented threats. The heavy metal pollution problem in mangrove wetlands, if not effectively controlled, will seriously affect the stability of its ecosystem and biodiversity. In the sediments of mangrove wetlands, the occurrence of heavy metals is affected by various factors, including sediment particle size, organic matter content, redox conditions, etc.

[0004] Therefore, it is necessary to provide a solution to conduct in-depth research on the main controlling factors of heavy metal occurrence in mangrove wetlands, be able to identify the main controlling factors of heavy metal storage in mangrove wetlands, and provide an important basis for the scientific management and protection of mangroves. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for identifying the main controlling factors of heavy metal storage in mangrove wetlands, aiming to be able to identify the main controlling factors of heavy metal storage in mangrove wetlands.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides a method for identifying the main controlling factors of heavy metal storage in mangrove wetlands, the method comprising the following steps:

[0008] S100, obtaining the contents of heavy metals at different depths and the contents of each sedimentary element in the mangrove wetland;

[0009] S200, performing principal component analysis on the contents of heavy metals at different depths and the contents of each sedimentary element to identify the sedimentary elements affecting the heavy metal occurrence ability;

[0010] S300, performing correlation analysis on the principal component scores and the heavy metal contents to determine the principal components significantly correlated with the heavy metal occurrence;

[0011] S400, determining the contribution ratio of the main sedimentary elements to the heavy metal occurrence ability, and based on the contribution ratio, determining the sedimentary control factors of the heavy metal occurrence ability in the sediments of the mangrove wetland.

[0012] Optionally, in S100, the obtaining of the contents of heavy metals at different depths and the contents of each sedimentary element in the mangrove wetland includes:

[0013] S110, obtaining columnar sediments drilled from the mangrove wetland, and obtaining a plurality of test samples at equal intervals longitudinally from the columnar sediments;

[0014] S120, measuring the contents of heavy metals and each sedimentary element in each test sample to obtain the contents of heavy metals and each sedimentary element in the columnar sediments at different depths.

[0015] Optionally, in S200, the principal component analysis of the contents of heavy metals at different depths and the contents of each sedimentary element to identify the sedimentary elements affecting the occurrence ability of heavy metals includes:

[0016] S210, organizing the heavy metal contents and the contents of each sedimentary element in the sediments at different depths into a matrix and performing standardization processing;

[0017] S220, calculating the correlation coefficient matrix between variables for the standardized data, determining the eigenvalues and eigenvectors of the correlation coefficient matrix, and determining the number of principal components according to the eigenvalues or the cumulative variance contribution rate;

[0018] S230, analyzing the factor loading matrix of the principal components, identifying the correlations between each principal component and the variables, and extracting the sedimentary elements that contribute the most to the principal components; the contribution of a variable to a principal component is positively correlated with the absolute value of the factor loading.

[0019] Optionally, in S300, the correlation analysis of the principal component scores and the heavy metal contents to determine the principal components significantly correlated with the occurrence of heavy metals includes:

[0020] S310, obtaining the principal component scores of each test sample and determining the heavy metal concentrations corresponding to each principal component score;

[0021] S320, sorting each variable separately, assigning ranks, and calculating the difference in ranks between the two variables;

[0022] S330, calculating the Pearson correlation coefficient between the principal component scores and the heavy metal concentrations, and determining the principal components significantly correlated with the occurrence ability of heavy metals based on the Pearson correlation coefficient as the principal components significantly correlated with the occurrence of heavy metals.

[0023] Optionally, in S400, the determining of the contribution ratio of the main sedimentary elements to the occurrence ability of heavy metals and the determination of the sedimentary control factors for the ability of the mangrove wetland sediments to store heavy metals based on the contribution ratio includes:

[0024] S410. Perform multiple linear regression modeling on the heavy metal occurrence capacity and each sedimentary element to obtain a multiple linear model.

[0025] S410. Determine the coefficients of each sedimentary element in the multiple linear model, and determine the contribution ratio of the corresponding sedimentary element to the heavy metal occurrence capacity according to the absolute value of the coefficient.

[0026] S410. Use at least one sedimentary element with a larger contribution ratio as the sedimentary control factor for the heavy metal occurrence capacity of mangrove wetland sediments.

[0027] In a second aspect, an embodiment of the present invention provides a system for identifying the main control factors of heavy metal storage in mangrove wetlands. The system includes:

[0028] At least one processor;

[0029] At least one memory for storing at least one program;

[0030] When the at least one program is executed by the at least one processor, the at least one processor implements the method described in any one of the above.

[0031] The beneficial effects of the present invention are as follows: The present invention obtains the heavy metal content and the content of each sedimentary element at different depths in mangrove wetlands; performs principal component analysis on the heavy metal content and the content of each sedimentary element at different depths to identify the sedimentary elements affecting the heavy metal occurrence capacity; through correlation analysis and multiple linear regression modeling, accurately quantify the contribution of each sedimentary element to the heavy metal occurrence capacity, and then determine the main control factors, providing a scientific basis for the treatment of heavy metal pollution in mangrove wetlands. By performing correlation analysis on the principal component scores and the heavy metal content, determine the principal components significantly correlated with the heavy metal occurrence; determine the contribution ratio of the main sedimentary elements to the heavy metal occurrence capacity, and based on the contribution ratio, determine the sedimentary control factors for the heavy metal occurrence capacity of mangrove wetland sediments. The present invention can effectively identify and quantify the main control factors of heavy metal occurrence in mangrove wetlands, providing an accurate scientific basis for the treatment of heavy metal pollution. Description of the Drawings

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0033] Figure 1 It is a flowchart of the method for identifying the main control factors of heavy metal storage in mangrove wetlands in an embodiment of the present invention;

[0034] Figure 2 It is a framework diagram for identifying the main controlling factors of heavy metal storage in mangrove wetlands in the embodiments of the present invention;

[0035] Figure 3 It is a structural schematic diagram of a system for identifying the main controlling factors of heavy metal storage in mangrove wetlands in the embodiments of the present invention. Detailed implementation manners

[0036] The following will clearly and completely describe the concept, specific structure and technical effects generated by the present invention in combination with embodiments and drawings, so as to fully understand the purpose, solution and effects of the present invention. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0037] Refer to Figure 1 , the present invention provides a method for identifying the main controlling factors of heavy metal storage in mangrove wetlands, and the method includes the following steps:

[0038] S100, obtaining the contents of heavy metals at different depths in the mangrove wetland and the contents of each sedimentary element;

[0039] S200, performing principal component analysis on the contents of heavy metals at different depths and the contents of each sedimentary element to identify the sedimentary elements affecting the occurrence ability of heavy metals;

[0040] S300, performing correlation analysis on the principal component scores and the heavy metal contents to determine the principal components significantly correlated with the occurrence of heavy metals;

[0041] S400, determining the contribution ratio of the main sedimentary elements to the occurrence ability of heavy metals, and determining the sedimentary control factors of the heavy metal occurrence ability of the mangrove wetland sediments based on the contribution ratio.

[0042] In the embodiments provided by the present invention, by integrating the results of principal component analysis, correlation analysis and multiple linear regression, the main sedimentary control factors of the heavy metal occurrence ability of the mangrove wetland sediments are determined. The specific steps are as follows: First, determine the number of principal components through principal component analysis, and extract the sedimentary elements with the greatest contribution to the principal components. Secondly, perform correlation analysis on the principal component scores and the heavy metal contents to determine which principal components are significantly correlated with the occurrence of heavy metals. Finally, clarify the contribution degree of the relevant sedimentary elements to the heavy metal occurrence ability through multiple linear regression, and determine the main sedimentary control factors of the heavy metal occurrence ability of the mangrove wetland sediments.

[0043] Through in-depth analysis of the heavy metal occurrence mechanism in mangrove wetlands, and through comprehensive consideration of factors such as sediment particle size, organic matter content, redox conditions, etc., it reveals how they affect the occurrence of heavy metals. A comprehensive assessment and analysis of the heavy metal content in the sediments of mangrove wetlands and its main controlling factors is carried out. The main controlling factors for the storage of heavy metals in mangrove wetlands can be identified.

[0044] In some embodiments, in S100, the obtaining of the heavy metal content at different depths and the content of each sedimentary element in the mangrove wetland includes:

[0045] S110, obtaining columnar sediments drilled from the mangrove wetland, and taking a plurality of test samples at equal intervals longitudinally from the columnar sediments;

[0046] S120, measuring the heavy metal and the content of each sedimentary element in each test sample, and obtaining the heavy metal content and the content of each sedimentary element in the columnar sediments at different depths.

[0047] First, drill columnar sediments in the mangrove wetland and obtain test samples at equal intervals. Secondly, measure the longitudinal content of heavy metals in the columnar sediments to clarify the heavy metal occurrence content in the sediments at different depths of the mangrove wetland; at the same time, measure the content of each sedimentary element in the columnar sediments. The sedimentary elements include particle size elements, clay minerals, organic matter, total microorganisms, pH, salinity, and redox potential, and determine the content of each sedimentary element in the sediments at different depths of the mangrove wetland.

[0048] Then, on the basis of the above measurements, perform a principal component analysis on the heavy metal content and the content of each sedimentary element in the sediments at different depths, systematically identify the sedimentary elements that mainly affect the heavy metal occurrence ability in the sediments of the mangrove wetland. On the basis of the principal component analysis results, perform a correlation analysis between the principal component scores and the heavy metal content to determine which principal components are significantly correlated with the heavy metal occurrence.

[0049] In some embodiments, in S200, the performing of a principal component analysis on the heavy metal content and the content of each sedimentary element at different depths to identify the sedimentary elements that affect the heavy metal occurrence ability includes:

[0050] S210, organizing the heavy metal content in the sediments at different depths and the content of each sedimentary element into a matrix and performing a standardization process;

[0051] S220, calculating the correlation coefficient matrix between variables for the data after the standardization process, determining the eigenvalues and eigenvectors of the correlation coefficient matrix, and determining the number of principal components according to the eigenvalues or the cumulative variance contribution rate;

[0052] S230. Analyze the factor loading matrix of the principal components, identify the correlations between the principal components and the variables, and extract the sedimentary elements that contribute the most to the principal components. The contribution of a variable to a principal component is positively correlated with the absolute value of the factor loading.

[0053] Specifically, organize the heavy metal contents of sediments at different depths and the contents of each sedimentary element into a matrix and perform standardization processing. Calculate the correlation coefficient matrix between variables for the standardized data to evaluate the correlations between variables. Calculate the eigenvalues and eigenvectors of the correlation coefficient matrix, and determine the number of principal components according to the eigenvalues (usually select the principal components with eigenvalues ≥ 1) or the cumulative variance contribution rate (such as the cumulative contribution rate ≥ 80%). Analyze the factor loading matrix of the principal components to identify the correlations between the principal components and the original variables. The larger the absolute value of the factor loading, the greater the contribution of the variable to the principal component. Name the principal components according to the variables with larger factor loadings, and extract the sedimentary elements that contribute the most to the principal components based on the factor loading matrix.

[0054] In some embodiments, in S300, the correlation analysis of the principal component scores and the heavy metal contents to determine the principal components significantly related to the occurrence of heavy metals includes:

[0055] S310. Obtain the principal component scores of each test sample and determine the heavy metal concentrations corresponding to the respective principal component scores.

[0056] S320. Sort each variable separately, assign ranks, and calculate the difference in ranks between the two variables.

[0057] S330. Calculate the Pearson correlation coefficient between the principal component scores and the heavy metal concentrations, and determine the principal components significantly related to the heavy metal occurrence ability based on the Pearson correlation coefficient as the principal components significantly related to the occurrence of heavy metals.

[0058] Specifically, after completing the principal component analysis, obtain the principal component scores of each test sample. Then, perform data organization and filling to ensure that the data are paired principal component scores (such as PC1, PC2) and heavy metal concentrations (such as Cd, Pb) without missing values. Sort each variable (such as PC1 score, Cd concentration) separately, assign ranks, and calculate the difference in ranks between the two variables. Calculate the Pearson correlation coefficient and perform a significance test to determine which principal components are significantly related to the heavy metal occurrence ability.

[0059] In some embodiments, in S400, the determination of the contribution ratio of the main sedimentary elements to the heavy metal occurrence ability and the determination of the sedimentary control factors for the heavy metal occurrence ability of mangrove wetland sediments based on the contribution ratio include:

[0060] S410. Perform multiple linear regression modeling on the heavy metal occurrence ability and each sedimentary element to obtain a multiple linear model.

[0061] S410. Determine the coefficients of each sedimentary element in the multiple linear model, and determine the contribution ratio of the corresponding sedimentary element to the heavy metal occurrence ability according to the absolute value of the coefficient.

[0062] S410. Use at least one sedimentary element with a relatively large contribution ratio as the sedimentary control factor for the heavy metal occurrence ability of mangrove wetland sediments.

[0063] Specifically, perform multiple linear regression modeling to determine the contribution ratio of the main sedimentary elements to the heavy metal occurrence ability, and conduct relevant environmental explanations to determine the main sedimentary control factors for the heavy metal occurrence ability of mangrove wetland sediments. If the regression equation shows: Y = 0.5X organic matter + 0.3X pH - 0.2X salinity, then the main sedimentary elements are considered to be organic matter (the largest contribution, positive correlation), pH (the second largest contribution, positive correlation), and salinity (inhibiting heavy metal occurrence, negative correlation).

[0064] This embodiment also adopts the method of repeated sampling and redundancy verification; verify the stability of the principal component results by repeated sampling during the principal component analysis process; the heavy metal occurrence ability may be affected by the interaction of multiple factors, and redundancy analysis can be combined for further verification. This embodiment adopts the method of cross-validation; during the process of multiple linear regression analysis, the data can be divided into a training set and a test set according to a certain proportion for the reliability of the model, and the prediction ability of the cross-validation model for the location data can be verified.

[0065] corresponding to Figure 1 the method of Figure 3 and referring to

[0066] at least one processor;

[0067] at least one memory for storing at least one program;

[0068] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0069] It can be seen that the content in the above method embodiments is applicable to the system embodiments of the present invention. The functions specifically implemented by the system embodiments of the present invention are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method embodiments.

[0070] In addition, an embodiment of the present invention also discloses a computer program product or a computer program. The computer program product or the computer program is stored in a computer-readable storage medium. A processor of a computer device can read the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device executes the above method. Similarly, the content in the above method embodiments is applicable to this storage medium embodiment. The functions specifically implemented by this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0071] Those of ordinary skill in the art can understand that all or some of the methods and systems disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or can be implemented as hardware, or can be implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include a computer storage medium (or a non-transitory medium) and a communication medium (or a transitory medium). As is well known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassette, tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, as is well known to those of ordinary skill in the art, a communication medium typically includes computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium.

[0072] The above is a specific description of the preferred embodiments of the present disclosure, but the present disclosure is not limited to the above embodiments. Those skilled in the art can make various equivalent deformations or substitutions without departing from the spirit of the present disclosure, and these equivalent deformations or substitutions are all included within the scope defined by the claims of the present disclosure.

Claims

1. A method for identifying the main controlling factors of heavy metal storage in mangrove wetlands, characterized in that, The method includes the following steps: S100, obtaining the contents of heavy metals at different depths in the mangrove wetland and the contents of various sedimentary elements; S200, performing principal component analysis on the contents of heavy metals at different depths and the contents of various sedimentary elements to identify the sedimentary elements affecting the occurrence ability of heavy metals; S300, performing correlation analysis between the principal component scores and the heavy metal contents to determine the principal components significantly correlated with the occurrence of heavy metals; S400, determining the contribution ratio of the main sedimentary elements to the occurrence ability of heavy metals, and determining the sedimentary control factors for the ability of mangrove wetland sediments to store heavy metals based on the contribution ratio.

2. The method according to claim 1, characterized in that In S100, the obtaining the contents of heavy metals at different depths in the mangrove wetland and the contents of various sedimentary elements includes: S110, obtaining columnar sediments drilled from the mangrove wetland, and obtaining a plurality of test samples at equal intervals longitudinally from the columnar sediments; S120, measuring the contents of heavy metals and various sedimentary elements in each test sample to obtain the contents of heavy metals and various sedimentary elements in the columnar sediments at different depths.

3. The method according to claim 1, characterized in that In S200, the performing principal component analysis on the contents of heavy metals at different depths and the contents of various sedimentary elements to identify the sedimentary elements affecting the occurrence ability of heavy metals includes: S210, organizing the heavy metal contents and the contents of various sedimentary elements in sediments at different depths into a matrix and performing standardization processing; S220, calculating the correlation coefficient matrix between variables for the data after standardization processing, determining the eigenvalues and eigenvectors of the correlation coefficient matrix, and determining the number of principal components according to the eigenvalues or the cumulative variance contribution rate; S230, analyzing the factor loading matrix of the principal components, identifying the correlations between each principal component and the variables, and extracting the sedimentary elements with the greatest contribution to the principal components; the contribution of a variable to a principal component is positively correlated with the absolute value of the factor loading.

4. The method according to claim 1, characterized in that, In S300, the performing correlation analysis between the principal component scores and the heavy metal contents to determine the principal components significantly correlated with the occurrence of heavy metals includes: S310, obtaining the principal component scores of each test sample and determining the heavy metal concentrations corresponding to the principal component scores; S320, sorting each variable separately and assigning ranks, and calculating the difference in ranks between the two variables; S330, calculating the Pearson correlation coefficient between the principal component scores and the heavy metal concentrations, and determining the principal components significantly correlated with the occurrence ability of heavy metals based on the Pearson correlation coefficient as the principal components significantly correlated with the occurrence of heavy metals.

5. The method according to claim 1, characterized in that, In S400, the determining the contribution ratio of the main sedimentary elements to the occurrence ability of heavy metals and determining the sedimentary control factors for the ability of mangrove wetland sediments to store heavy metals based on the contribution ratio includes: S410, performing multiple linear regression modeling on the occurrence ability of heavy metals and each sedimentary element to obtain a multiple linear model; S410, determining the coefficients of each sedimentary element in the multiple linear model, and determining the contribution ratio of the corresponding sedimentary element to the occurrence ability of heavy metals according to the absolute value of the coefficient; S410, taking at least one sedimentary element with a larger contribution ratio as the sedimentary control factor for the ability of mangrove wetland sediments to store heavy metals.

6. A system for identifying the main controlling factors of heavy metal storage in mangrove wetlands, characterized in that, The system includes: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor such that the at least one processor implements the method according to any one of claims 1 to 5.

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