A method and system for identifying the main control factors of heavy metal storage in mangrove wetlands

Abstract

The present application relates to the technical field of data processing, in particular to a method and system for identifying main control factors of heavy metal storage in mangrove wetlands, which comprises the following steps: obtaining the content of heavy metals and the content of each sedimentary element at different depths of the mangrove wetlands; performing principal component analysis on the content of heavy metals and the content of each sedimentary element at different depths to identify the sedimentary elements affecting the heavy metal occurrence capacity; performing correlation analysis on the principal component scores and the heavy metal content to determine the principal components significantly related to the heavy metal occurrence capacity; determining the contribution proportion of the main sedimentary elements to the heavy metal occurrence capacity, and determining the sediment control factors of the heavy metal occurrence capacity of the mangrove wetland sediments based on the contribution proportion; and the present application can identify the main control factors of heavy metal storage in mangrove wetlands.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a method and system for identifying main control factors of heavy metal storage in mangrove wetlands. BACKGROUND

[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 ecological balance of the ocean. The existence of mangroves provides habitat for countless marine organisms, supports the development of fisheries, and also provides natural flood control and shore protection barriers for coastal communities.

[0003] However, mangroves are facing unprecedented threats due to human activities. The problem of heavy metal pollution in mangrove wetlands, if not effectively controlled, will seriously affect the stability of its ecological system and biodiversity. The occurrence of heavy metals in mangrove wetland sediments is influenced by multiple 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 control factors of heavy metal occurrence in mangrove wetlands, and to identify the main control factors of heavy metal storage in mangrove wetlands, which can provide important basis for scientific management and protection of mangroves. SUMMARY

[0005] The present application aims to provide a method and system for identifying main control factors of heavy metal storage in mangrove wetlands, which can identify the main control factors of heavy metal storage in mangrove wetlands.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a method for identifying main control factors of heavy metal storage in mangrove wetlands, which comprises the following steps:

[0008] S100, obtaining the content of heavy metals and the content of each sediment element at different depths of the mangrove wetland;

[0009] S200, performing principal component analysis on the content of heavy metals and the content of each sediment element at different depths, and identifying the sediment elements that affect the heavy metal occurrence capacity;

[0010] S300, performing correlation analysis on the principal component scores and the heavy metal content to determine the principal components that are significantly related to the heavy metal occurrence capacity;

[0011] S400, determining the contribution proportion of the main sediment elements to the heavy metal occurrence capacity, and determining the sediment control factors of the heavy metal occurrence capacity of the mangrove wetland sediment based on the contribution proportion.

[0012] Optionally, in S100, the content of heavy metals and the content of each sediment element at different depths of the mangrove wetland are obtained, including:

[0013] In S110, a columnar sediment drilled in the mangrove wetland is obtained, and a plurality of test samples are obtained from the columnar sediment along the longitudinal direction at equal intervals;

[0014] In S120, the content of heavy metals and the content of each sediment element in each test sample is determined, so as to obtain the content of heavy metals and the content of each sediment element in the columnar sediment at different depths.

[0015] Optionally, in S200, the content of heavy metals and the content of each sediment element at different depths are subjected to principal component analysis, and a sediment element affecting the heavy metal occurrence capacity is identified, including:

[0016] In S210, the content of heavy metals and the content of each sediment element at different depths are arranged into a matrix and subjected to standardization processing;

[0017] In S220, a correlation coefficient matrix calculation between variables is performed on the data after the standardization processing, characteristic values and characteristic vectors of the correlation coefficient matrix are determined, and the number of principal components is determined according to the characteristic values or cumulative variance contribution rate;

[0018] In S230, a factor loading matrix of the principal components is analyzed, the correlation between each principal component and a variable is identified, and a sediment element with the largest contribution to the principal component is extracted; the contribution of the variable to the principal component and the absolute value of the factor loading are positively correlated.

[0019] Optionally, in S300, the correlation between the principal component scores and the content of heavy metals is analyzed, and a principal component significantly related to the heavy metal occurrence capacity is determined, including:

[0020] In S310, the principal component scores of each test sample are obtained, and the heavy metal concentration corresponding to each principal component score is determined;

[0021] In S320, each variable is sorted individually and is assigned a rank, and the difference between the ranks of two variables is calculated;

[0022] In S330, a Pearson correlation coefficient between the principal component scores and the heavy metal concentration is calculated, and a principal component significantly related to the heavy metal occurrence capacity is determined based on the Pearson correlation coefficient.

[0023] Optionally, in S400, the contribution proportion of the main sediment element to the heavy metal occurrence capacity is determined, and a sediment control factor of the heavy metal occurrence capacity of the sediment in the mangrove wetland is determined based on the contribution proportion, including:

[0024] S410, multiple linear regression modeling is performed on the heavy metal occurrence ability and each deposition element to obtain a multiple linear model;

[0025] S420, coefficients of each deposition element in the multiple linear model are determined, and a contribution proportion of the corresponding deposition element to the heavy metal occurrence ability is determined according to an absolute value of the coefficient;

[0026] S430, at least one deposition element with a larger contribution proportion is taken as a deposition control factor of the heavy metal occurrence ability of the mangrove wetland sediment.

[0027] In a second aspect, an embodiment of the present application provides a system for identifying a main control factor of heavy metal storage in a mangrove wetland, and the system comprises:

[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 according to any one of the above.

[0031] The present application has the following beneficial effects: The present application obtains the content of heavy metals at different depths and the content of each deposition element in a mangrove wetland, performs principal component analysis on the content of heavy metals at different depths and the content of each deposition element, identifies deposition elements affecting the heavy metal occurrence ability, accurately quantifies the contribution of each deposition element to the heavy metal occurrence ability through correlation analysis and multiple linear regression modeling, and further determines the main control factor, thereby providing a scientific basis for heavy metal pollution control in the mangrove wetland. The present application determines the principal components significantly related to the heavy metal occurrence ability through correlation analysis of the principal component scores and the content of heavy metals, determines the contribution proportion of the main deposition elements to the heavy metal occurrence ability, and determines the deposition control factor of the heavy metal occurrence ability of the mangrove wetland sediment based on the contribution proportion. The present application can effectively identify and quantify the main control factor of heavy metal occurrence in the mangrove wetland, and provides accurate scientific basis for heavy metal pollution control. BRIEF DESCRIPTION OF DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0033] Figure 1 is a flowchart of the method for identifying the main control factor of heavy metal storage in a mangrove wetland in the embodiments of the present application;

[0034] Figure 2 is the framework of identifying the main control factors of heavy metal storage in mangrove wetlands in the embodiments of the present application;

[0035] Figure 3 is the structural schematic diagram of the system for identifying the main control factors of heavy metal storage in mangrove wetlands in the embodiments of the present application. DETAILED DESCRIPTION

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

[0037] Referring to Figure 1 The present application provides a method for identifying the main control factors of heavy metal storage in mangrove wetlands, which comprises the following steps:

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

[0039] S200, performing principal component analysis on the content of heavy metals at different depths and the content of each sedimentary element, and identifying the sedimentary elements affecting the heavy metal occurrence capacity;

[0040] S300, performing correlation analysis on the principal component scores and the heavy metal content, and determining the principal components significantly related to the heavy metal occurrence capacity;

[0041] S400, determining the contribution proportion of the main sedimentary elements to the heavy metal occurrence capacity, and determining the sediment control factors of the heavy metal occurrence capacity of the mangrove wetland sediment based on the contribution proportion.

[0042] In the embodiments provided by the present application, the results of principal component analysis, correlation analysis and multiple linear regression are combined to determine the main sediment control factors of the heavy metal occurrence capacity of the mangrove wetland sediment. The specific steps are as follows: first, the number of principal components is determined by principal component analysis, and the sedimentary elements with the largest contribution to the principal components are extracted; second, the correlation analysis between the principal component scores and the heavy metal content is performed to determine which principal components are significantly related to the heavy metal occurrence; and finally, the contribution degree of the related sedimentary elements to the heavy metal occurrence capacity is determined by multiple linear regression, and the main sediment control factors of the heavy metal occurrence capacity of the mangrove wetland sediment are determined.

[0043] This invention provides an in-depth analysis of the heavy metal storage mechanism in mangrove wetlands. By comprehensively considering factors such as sediment particle size, organic matter content, and redox conditions, it reveals how these factors influence heavy metal storage. A comprehensive assessment and analysis of the heavy metal content and its main controlling factors in mangrove wetland sediments is conducted. The main controlling factors for heavy metal storage in mangrove wetlands can be identified.

[0044] In some embodiments, S100, obtaining the content of heavy metals and various sedimentary elements at different depths in mangrove wetlands includes:

[0045] S110, Obtain columnar sediments from mangrove wetlands, and obtain multiple test samples from the columnar sediments at equal intervals along the longitudinal direction;

[0046] S120, the content of heavy metals and various sedimentary elements in each test sample was determined to obtain the content of heavy metals and various sedimentary elements in columnar sediments at different depths.

[0047] First, columnar sediments were drilled in the mangrove wetland, and test samples were obtained at equal intervals. Second, the longitudinal content of heavy metals in the columnar sediments was determined to clarify the heavy metal content in sediments at different depths in the mangrove wetland. At the same time, the content of various sedimentary elements in the columnar sediments was measured, including grain size, clay minerals, organic matter, total microbial content, pH, salinity, and redox potential, to determine the content of each sedimentary element in sediments at different depths in the mangrove wetland.

[0048] Then, based on the aforementioned measurements, principal component analysis was performed on the heavy metal content and the content of various sedimentary elements in sediments at different depths. The sedimentary elements that mainly affect the heavy metal occurrence capacity in mangrove wetland sediments were systematically identified. Based on the principal component analysis results, the principal component scores were correlated with the heavy metal content to determine which principal components were significantly correlated with heavy metal occurrence.

[0049] In some embodiments, S200, the principal component analysis of the content of heavy metals at different depths and the content of various sedimentary elements to identify sedimentary elements affecting the occurrence capacity of heavy metals includes:

[0050] S210 organizes the heavy metal content of sediments at different depths and the content of various sedimentary elements into a matrix and performs standardization processing.

[0051] S220: Calculate the correlation coefficient matrix between variables for the standardized data, determine the eigenvalues ​​and eigenvectors of the correlation coefficient matrix, and determine the number of principal components based on the eigenvalues ​​or cumulative variance contribution rate.

[0052] S230, analyze the factor loading matrix of the principal components, identify the correlation between each principal component and the variable, and extract the sedimentary element that contributes most to the principal component; the contribution of the variable to the principal component and the absolute value of the factor loading are positively correlated.

[0053] Specifically, the heavy metal content of the sediment at different depths and the content of each sedimentary element are arranged into a matrix and standardized; the correlation coefficient matrix between variables is calculated for the standardized data to evaluate the correlation between variables; the eigenvalues and eigenvectors of the correlation coefficient matrix are calculated, and the number of principal components is determined according to the eigenvalues (usually the principal components with eigenvalues ≥1 are selected) or the cumulative variance contribution rate (such as the cumulative contribution rate ≥80%); analyze the factor loading matrix of the principal components, identify the correlation between each principal component and the original variable, and the larger the absolute value of the factor loading, the greater the contribution of the variable to the principal component; name the principal component according to the variable with larger factor loading, and extract the sedimentary element that contributes most to the principal component according to the factor loading matrix.

[0054] In some embodiments, in S300, the correlation analysis between the principal component score and the heavy metal content is performed to determine the principal component that is significantly correlated with the heavy metal occurrence capacity, including:

[0055] S310, obtaining the principal component score of each test sample, and determining the heavy metal concentration corresponding to each principal component score;

[0056] S320, sorting each variable separately and assigning a rank, and calculating the difference between the ranks of two variables;

[0057] S330, calculating the Pearson correlation coefficient between the principal component score and the heavy metal concentration, and determining the principal component that is significantly correlated with the heavy metal occurrence capacity based on the Pearson correlation coefficient.

[0058] Specifically, after completing the principal component analysis, the principal component score of each test sample is obtained; then the data is arranged and completed to ensure that the data is paired principal component scores (such as PC1, PC2) and heavy metal concentrations (such as Cd, Pb) without missing values; each variable (such as PC1 score, Cd concentration) is sorted separately and assigned a rank, and the difference between the ranks of two variables is calculated; the Pearson correlation coefficient is calculated, the significance test is performed, and it is determined which principal component is significantly correlated with the heavy metal occurrence capacity.

[0059] In some embodiments, in S400, the contribution proportion of the main sedimentary element to the heavy metal occurrence capacity is determined, and the sediment control factor of the heavy metal occurrence capacity of the mangrove wetland sediment is determined based on the contribution proportion, including:

[0060] S410, performing multiple linear regression modeling on the heavy metal occurrence capacity and each sedimentary element to obtain a multiple linear model;

[0061] S420, determining the coefficients of each deposition element in the multiple linear model, and determining the contribution proportion of the corresponding deposition element to the heavy metal occurrence ability according to the absolute value of the coefficient;

[0062] S430, taking at least one deposition element with a larger contribution proportion as a deposition control factor of the heavy metal occurrence ability of the mangrove wetland sediment.

[0063] Specifically, multiple linear regression modeling is performed to determine the contribution proportion of the main deposition element to the heavy metal occurrence ability, and relevant environmental interpretation is performed to determine the main deposition control factor of the heavy metal occurrence ability of the mangrove wetland sediment. If the regression equation shows Y=0.5X organic matter+0.3X pH-0.2X salinity, it is considered that the main deposition elements are organic matter (the largest contribution, positive correlation), pH (the second largest contribution, positive correlation), and salinity (inhibiting heavy metal occurrence, negative correlation).

[0064] The embodiment also adopts the way of repeated sampling and redundancy verification; the stability of the principal component result is verified by repeated sampling in the principal component analysis process; the heavy metal occurrence ability may be influenced by the interaction of multiple factors, and can be further verified in combination with redundancy analysis. The embodiment adopts the way of cross verification; in the process of multiple linear regression analysis, the data can be divided into a training set and a test set in proportion for the reliability of the model, and the cross verification model is in the prediction ability of the position data.

[0065] Corresponding to the method of Figure 1 , with reference to Figure 3 , the embodiment of the present application provides a system for identifying main control factors of heavy metals stored in a mangrove wetland, comprising:

[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 contents in the above method embodiment are all applicable to the present system embodiment, the system embodiment specifically implements the same functions as the above method embodiment, and achieves the same beneficial effects as the above method embodiment.

[0070] Furthermore, embodiments of the present invention also disclose a computer program product or computer program 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, causing the computer device to perform the described method. Similarly, the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in 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 achieved in the above method embodiments.

[0071] It will be understood by those skilled in the art that all or some of the methods and systems disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media 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 disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0072] The above is a detailed description of the preferred embodiments of this disclosure. However, this disclosure is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this disclosure. All such equivalent modifications or substitutions are included within the scope defined by the claims of this disclosure.

Claims

1. A method for identifying the main controlling factors of heavy metal storage in mangrove wetlands, characterized in that, The method comprises the following steps: S100, acquiring the content of heavy metals at different depths of the mangrove wetland and the content of each sediment element; S200, performing principal component analysis on the content of heavy metals at different depths and the content of each sediment element to identify the sediment elements affecting the heavy metal occurrence capacity; S300, performing correlation analysis on the principal component scores and the heavy metal content to determine the principal components significantly related to the heavy metal occurrence capacity; S400, determining the contribution proportion of the main sediment elements to the heavy metal occurrence capacity, and determining the sediment control factor of the heavy metal occurrence capacity of the mangrove wetland sediment based on the contribution proportion; S200 specifically comprises: S210, arranging the heavy metal content of the sediment at different depths and the content of each sediment element into a matrix and performing standardization processing; S220, calculating the correlation coefficient matrix between variables of the data after standardization processing, determining the eigenvalue and eigenvector of the correlation coefficient matrix, and determining the number of principal components according to the eigenvalue or cumulative variance contribution rate; S230, analyzing the factor loading matrix of the principal components, identifying the correlation between the principal components and the variables, and extracting the sediment element with the largest contribution to the principal components; the contribution of the variable to the principal component and the absolute value of the factor loading are positively correlated; S400 specifically comprises: S410, performing multiple linear regression modeling on the heavy metal occurrence capacity and each sediment element to obtain a multiple linear model; S420, determining the coefficients of each sediment element in the multiple linear model, and determining the contribution proportion of the corresponding sediment element to the heavy metal occurrence capacity according to the absolute value of the coefficient; S430, taking at least one sediment element with a larger contribution proportion as the sediment control factor of the heavy metal occurrence capacity of the mangrove wetland sediment.

2. The method of claim 1, wherein, In S100, the acquisition of the content of heavy metals at different depths of the mangrove wetland and the content of each sediment element comprises: S110, obtaining a columnar sediment drilled on the mangrove wetland, and obtaining a plurality of test samples from the columnar sediment along the longitudinal direction at equal intervals; S120, determining the content of heavy metals and each sediment element in each test sample to obtain the content of heavy metals and each sediment element in the columnar sediment at different depths.

3. The method of claim 1, wherein, In S300, the correlation analysis on the principal component scores and the heavy metal content to determine the principal components significantly related to the heavy metal occurrence capacity comprises: S310, obtaining the principal component scores of each test sample, and determining the heavy metal concentration corresponding to each principal component score; S320, sorting each variable separately and assigning a rank, and calculating the difference between the ranks of two variables; S330, calculating the Pearson correlation coefficient of the principal component scores and the heavy metal concentration, and determining the principal components significantly related to the heavy metal occurrence capacity based on the Pearson correlation coefficient.

4. A system for identifying the main controlling factors of heavy metal storage in mangrove wetlands, characterized by, The system comprises: 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, the at least one processor implements the method of any one of claims 1 to 3.

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