A method for detecting the extent of microplastic accumulation in bivalve molluscs in deep-sea extreme environments

Abstract

The present invention discloses a method for detecting the degree of microplastic enrichment of bivalve shellfish in deep-sea extreme environments. The method samples the community structure representing the biological bed layer through sampling of the biological community, and then performs morphological characterization statistics to classify the age stage data of shellfish monomers, and uses multivariate factor analysis to obtain the shellfish individuals with the greatest contribution in each classified age stage, and then extracts microplastics by tissue, restores the morphology of seabed microplastics, and takes into account the process identification of full-size microplastics. The continuous years of microplastic adsorption by each shellfish monomer are traced by carbon-14 dating, and the dynamic enrichment curve of seabed shellfish is constructed by splicing the survival years of each shellfish, and finally zoomed in to the entire shellfish bed to derive the rate and historical accumulation of microplastics enriched in the entire extreme ecosystem. This method provides a scientific basis and technical support for formulating effective marine environmental protection strategies and clarifying the source-sink relationship of marine microplastics.

Description

Technical field:

[0001] The present invention relates to the fields of ecological and biophysiological investigation of deep-sea extreme environments, extraction and detection of new pollutants, and migration and transformation technology of new pollutants in the body, and specifically to a method for detecting the degree of microplastic enrichment in bivalve molluscs in deep-sea extreme environments. Background technology:

[0002] Deep-sea extreme environments refer to seamounts, hydrothermal vents, cold springs and other extreme marine environments with a depth of less than 1000m. They are characterized by darkness, high pressure, low and stable temperature, and relatively scarce nutrients. Deep-sea methane seepage areas are a special type of deep-sea extreme environment. The formation of these areas comes from changes in seabed geological morphology or the decomposition of natural gas hydrates in sediments. Methane seepage fluids rich in methane and hydrogen sulfide gush out from cracks in seabed sediments, forming a special chemosynthetic ecosystem with methane as a carbon source. In this extreme environment, methane seepage microbial communities form a symbiotic relationship with bivalve organisms (such as mussels, white melon clams, and Phoenix clams). Most of these microorganisms coexist in the gills of bivalves, using nutrients released from cracks in the seabed for chemosynthesis to supply organic matter to shellfish, while the corresponding bivalves obtain particulate matter containing microorganisms by filtering seawater. These bivalves can not only survive in methane seepage areas, but also rely on compounds such as methane and hydrogen sulfide as energy sources, forming a unique ecological niche. When studying the age of deep-sea organisms or organic matter, researchers usually use carbon-14 dating to determine their growth history, that is, using the decay law of radioactive isotopes to determine age. This method has high accuracy and reliability. Bivalves, due to their benthic and filter-feeding habits and chitinous shells with adsorptive properties, are more likely to accumulate pollutants in water bodies than other species. Therefore, marine bivalves, especially mussels, are often used as model organisms to monitor marine pollution conditions and the absorption and impact of new pollutants.

[0003] Microplastics are a new type of environmental pollutant, mainly referring to plastic particles with a diameter of less than 5 mm. Because they are widely present around the world, they have attracted global attention. More than 10 million tons of plastic enter the ocean every year. After a long period of weathering, photolysis, and biodegradation, a large amount of microplastics are formed, posing a serious threat to the marine environment and marine life. There are interspecies differences in the impact of microplastics on marine life. Among them, benthic filter-feeding organisms are more sensitive to microplastics and can ingest more microplastics. According to the results of the current indoor simulation study on bivalve mussels, microplastics are often enriched in the gills, digestive tract, muscle tissue, gonads, adductors, adductor muscles and ventral feet of mussels, among which the digestive tract is the main enrichment site. When microplastics enter the digestive tract of mussels, they will undergo a series of physical and chemical changes including fragmentation, decomposition and possible biodegradation. However, due to its very slow degradation process, most of the microplastics in the mussels still maintain their original morphology and structure. Therefore, the potential for bivalve enrichment of microplastics in methane seepage areas is huge, and it can reflect the long-term history of microplastic pollution in the extreme environment of the seabed. Bivalve molluscs are biological indicators of the activity of seabed methane seepage areas. They can actively absorb microplastics in the surrounding seawater through feeding activities. By monitoring the content of microplastics in their living bodies and observing the size of their shells, we can assess the distribution, migration and ecological impact of microplastics in extreme marine environments, which indirectly reflects the degree of absorption of microplastics by deep-sea ecosystems and the environmental behavior of microplastics.

[0004] At present, in the field of characterizing the degree of microplastic absorption by ecosystems, domestic and foreign scholars mainly focus on the study of microplastics in organisms in estuaries and intertidal ecosystems. There are no reference cases for related research on indicator organisms in deep-sea extreme environments, especially in methane seepage areas. However, as an important part of the earth's ecosystem, the content and distribution of microplastics in organisms in deep-sea extreme environments are of great significance for assessing the pollution status of the entire marine environment. By studying the process of dynamic enrichment of microplastics by deep-sea organisms and quantifying the time scale for the deep sea to accept microplastic pollution since plastic production, we can reveal the distribution, migration and transformation of microplastics in extreme marine environments, and provide a scientific basis for formulating effective pollution prevention and control measures.

[0005] By reading a large number of literature on the extent of microplastic absorption by ecosystems and the extraction of microplastics from aquatic organisms, there is a lack of any patents involving the characterization of the ability of the entire ecosystem to enrich microplastics over a long period of time. Most scholars extract microplastics contained in a single medium environment (such as water / sediment / organisms) and simply calculate the pollution risk assessment index of microplastics to obtain the pollution of the ecological environment. They cannot withstand the test of sampling contingency and time constraints, and the results obtained are not a unified reference to the overall ecological environment. The experimental extraction of microplastics adsorbed by regional indicator organisms, plus the enrichment test of theoretical methods, is the core demand for steadily exploring the speed of microplastic adsorption by ecosystems. The current conventional method for extracting the content of microplastics in bivalve shellfish is to first use automated mechanical equipment and handheld knives to take organisms and dissect and sample, and then extract and detect microplastics indoors. The extraction process includes grinding biological tissue to obtain homogenate, releasing microplastics by alkaline digestion, and separating microplastic particles by density flotation and filtration. The detection process mainly uses Fourier infrared spectroscopy, Raman spectroscopy and other detection instruments to determine the type and abundance of microplastics. At present, the existing methods for extracting microplastics from bivalve shellfish have the following disadvantages: ① The process of extracting microplastics changes the morphology and abundance of microplastics too much instead of preserving their original state. In the pretreatment process, most patents often grind to obtain tissue homogenate to improve the efficiency of digesting organic matter, but this method will destroy the morphology of fibrous microplastics in organisms and affect the subsequent statistical results of the external characterization of microplastics in organisms. In the digestion process, whether it is acid-base digestion or strong oxidant digestion, it will dissolve specific types of microplastics when used alone, resulting in the in-situ microplastic absorption abundance obtained in the experiment deviating too much from the actual value. In extraction and separation experiments, density flotation is usually used to separate microplastics, but commonly used flotation agents have limitations and cannot meet the requirements of all experiments. ② Focus on the occurrence results of microplastics in a single medium carrier and ignore the absorption capacity of the entire extreme ecosystem. Most patents focus on a simple analysis of the abundance of extracted microplastics in water bodies / water areas / sediment media, and only judge the current pollution situation of the water area / sediment layer by counting the types, abundance, morphology, etc. of microplastics and calculating the ecological risk index. There is a lack of technical methods to evaluate the capacity of the entire environment to carry microplastics and the dynamic enrichment process of microplastics from the perspective of the ecosystem. ③ Focus on the static and instantaneous microplastic pollution situation and lack the dynamic enrichment analysis of regionalized microplastic colonization on a time scale. The only regional microplastic risk assessment method currently available mainly studies the abundance of microplastics contained in a single medium environment at the time and conducts a local limited-time assessment. It does not involve arbitrary characterization methods of the dynamic enrichment capacity of microplastics from birth to transmission to the deep seabed, or even the long-term dynamic enrichment capacity of extreme marine ecosystems on a time scale. Summary of the invention:

[0006] The present invention solves the problems of the existing technology in the experiments on extracting microplastics from seabed bivalve molluscs, the lack of the ability to characterize the spatial adsorption of microplastics by ecosystems, and the lack of long-term dynamic enrichment of microplastics by organisms. The present invention proposes a method for detecting the degree of microplastic enrichment in bivalve molluscs in deep-sea extreme environments. The method proposed in the present invention has carried out a series of adaptation and optimization in the community survey method of capturing seabed indicator organisms, multivariate factor analysis to find bivalve molluscs with community characteristics, bivalve microplastic enrichment experiments, and construction of dynamic adsorption curves for ecosystems to accommodate microplastics, so as to intuitively reflect the impact of deep-sea microplastic pollution. The purpose is to select bivalve molluscs of typical age groups based on the typical extreme environment of methane leakage on the seabed and classify them according to the morphological characteristics of the molluscs for microplastic extraction experiments, analyze the principle of community replacement of bivalve molluscs monomers in methane seepage areas, and improve the basic research on the degree of microplastic enrichment in indicator organisms in extreme ecosystems.

[0007] The purpose of the present invention is to provide a method for detecting the degree of microplastic accumulation in bivalve molluscs in deep-sea extreme environments, comprising the following steps:

[0008] S1. Collect and estimate the total amount of bivalve molluscs in the region:

[0009] S11. Search for live bivalve beds in the early and middle stages of methane seep development in deep-sea methane seep areas, measure the bed area, and find its equivalent diameter and geometric center;

[0010] S12, grabbing the bivalve mollusc bed several times, with the grabbing positions being located at the two ends and the midpoint of the radius of the equivalent circle, counting the number of live bivalve molluscs after grabbing them ashore, washing the bivalve molluscs, draining the water and storing them in a frozen state;

[0011] S13. Based on the number of bivalve molluscs captured in several times, roughly estimate the total amount of live molluscs in the entire bivalve mollusc bed;

[0012] S2. Mussel morphological measurement: The bivalve samples were thawed, and the morphological data were measured and weighed in batches. They were divided into four age groups: juvenile, young, middle-aged, and old according to the order of shell length;

[0013] S3. Multivariate factor analysis: Based on the morphological data obtained in step S2, the multivariate factor analysis statistical method is used to correlate the relationship between the bivalve molluscs of different ages on the seabed and their appearance length and wet weight, and the top five bivalve molluscs that can replace the characteristics of the four groups of each age group are found, and a total of 20 bivalve molluscs are obtained;

[0014] S4, microplastic enrichment and extraction: separate and extract the microplastics from each tissue of the four groups of typical bivalve samples of age groups selected by multivariate factor analysis in step S3, and finally obtain a microplastic purification filter membrane to facilitate subsequent observation and detection;

[0015] S5, instrument identification: The microplastics with a size of 0.001-5 mm in the microplastic purification filter membrane obtained in step S4 are identified in turn, and the occurrence state of microplastics in bivalve molluscs and the physical changes of microplastics after entering the deep sea are comprehensively analyzed;

[0016] S6. Carbon-14 dating method: Use carbon-14 dating method to determine the age nodes of the corresponding bivalve molluscs, so as to convert the survival years of the four-age-group molluscs into the time limit for the bivalve molluscs to absorb microplastics;

[0017] S7. Dynamic enrichment model of microplastics in bivalve molluscs: The adsorption kinetic model is used to construct the microplastic enrichment curve in bivalve molluscs, and the enrichment coefficient is calculated to indicate the degree of enrichment of microplastics in the sample relative to its concentration in the environment. A time series model is established based on the microplastic content contained in the top five shellfish monomers with typical alternative community characteristics selected from four age groups and their corresponding carbon-14 estimated ages. The temporal adsorption of microplastics by shellfish monomers obtained from all measurement data is inverted to the entire bivalve bed through sampling rules, and the rate of microplastic enrichment in the entire seabed extreme environment ecosystem and the total amount of microplastic accumulation in the system are approximately inferred.

[0018] The method proposed in the present invention obtains bivalves of various ages that can replace the characteristics of the entire community through multivariate factor analysis, selects typical bivalves of various ages, and then adopts a microplastic extraction scheme that does not involve homogenization, mixed solution digestion, and cancellation of density flotation operations to clarify the adsorption abundance of indicator organisms on microplastics in extreme seawater environments. Finally, the actual survival period of the selected bivalves is determined by carbon-14 dating. The purpose is to utilize the instantaneous microplastic concentration enriched in bivalves of various ages in the methane seepage area, and use the area approximate estimation method to convert it into a dynamically changing microplastic enrichment curve of extreme ecosystems on a scale of nearly 100 years, laying a foundation for further research on the migration and transformation process of deep-sea microplastics and its impact on organisms.

[0019] The present invention combines carbon-14 dating with the concentration of microplastics in bivalve molluscs to construct a microplastic enrichment curve for bivalve molluscs throughout the entire growth stage to reveal the long-term dynamic adsorption capacity of microplastics by extreme seabed ecosystems. This is the first integrated analysis scheme that uses time and space as entry points to measure the content of microplastics absorbed by ecosystems in methane seepage areas.

[0020] In step S1, the area approximation method is used to roughly estimate the total amount of live bivalve shellfish in the entire mussel bed. The specific steps are as follows: when using an unmanned submersible (ROV) to conduct resource exploration in the deep-sea methane seepage area, find the living bivalve bioaggregation bed in the early and middle stages of cold spring development, and then use a multi-beam bathymetric system to measure the area (A) of the seabed living bivalve shellfish bed, find its equivalent diameter (D, as shown in formula (1)) according to the area equivalence principle and calculate its geometric center by integration, record the latitude and longitude of the marking point, and set the grab area of ​​the lowered TV grab bucket to A. grab The bivalve bed was grabbed several times with a TV grab with a fixed grab area (three times are used as an example below). The grab positions were located at the two ends and the midpoint of the radius of the equivalent circle. After the grab was landed, the number of live bivalve molluscs carried by the grab was counted and the mud on the surface of the shellfish was cleaned with a cruise water device to remove the shell residues without flesh. After draining the water, they were packed separately and numbered. Shell seedlings with a shell length of less than 10 mm were not included in the statistics and were stored in a -20℃ horizontal refrigerator. The number of live bivalve molluscs grabbed by three cumulative statistics was set as N1, N2, and N3. By introducing the aggregation coefficient of live bivalve molluscs (C, the ratio of the variance of density to the square of the mean density, as shown in formula (2)), the total number of shells in the entire live bivalve bed (N 总 ) is roughly estimated as shown in the following formula (3):

[0021]

[0022] This invention is the first to numerically characterize the ability of deep-sea extreme ecosystems to enrich microplastics, breaking through the shackles of static microplastic pollution. Taking the survival age of shellfish as the starting point, it connects several time sections into a dynamic enrichment curve of microplastics throughout the shellfish life cycle, creating a precedent for expressing the enrichment rate of microplastics in ecosystems and filling a gap in this field. Deep-sea bivalve shellfish are typical indicator animals of microplastics. Exploring the long-term dynamic enrichment of microplastics in their bodies is of great significance for assessing the degree of microplastic pollution in deep-sea extreme environments, dynamically identifying the accumulation of microplastics enriched in extreme environments over a century, and their migration and transformation processes in the ocean.

[0023] The method proposed in the present invention first uses the area approximation estimation method to rationally sample and collect biological communities under extreme environments to maximize the representation of the community structure of the biological bed, and then performs shellfish morphological characterization statistics to simplify the classification of the age stage data of each shellfish monomer, and uses the multivariate factor analysis method to obtain the top five shellfish individuals with the greatest contribution in each classified age stage and the best representative of the entire age community. Subsequently, a tissue-based microplastic extraction optimization experiment is carried out to restore the morphology of seabed microplastics and a process-based identification scheme that takes into account full-size microplastics. With the help of carbon-14 dating, the continuous years of microplastic adsorption by each shellfish monomer is traced, and the dynamic enrichment curve of seabed shellfish is constructed by splicing the survival years of each shellfish, which is finally expanded to the entire shellfish bed to deduce the rate and historical accumulation of microplastics enriched in the entire extreme ecosystem. The present invention aims to depict the rate of enrichment of microplastics as specific cold seep indicator organisms in extreme ecosystems represented by methane seepage areas and to approximately estimate the time accumulation amount of microplastics that the entire seabed system can accommodate since the beginning of plastic production. This is a new method of assessing microplastic pollution that has been proposed by few people so far. It provides another way to quantify the damaging effects of human activities on extreme ecosystems, indirectly reflects the historical accumulation of microplastics in ecosystems and can infer future pollution trends. It provides a scientific basis and technical support for formulating effective marine environmental protection strategies and clarifying the source-sink relationship of marine microplastics.

[0024] Preferably, step S2 specifically comprises the following steps: performing morphological measurement and weighing in batches all bivalve mollusc samples accumulated from the seabed for several times, benchmarking three body length parts of the bivalve molluscs to obtain the shell length L, shell width W, and shell height H of each living mollusc, weighing the corresponding wet weight data of the molluscs, and setting four shell length ranges according to the body length standards corresponding to different age groups of general bivalve molluscs, which are 0<L<55mm, 55mm<L<80mm, 80mm<L<110mm, and 110mm<L, respectively, corresponding to the four age groups of childhood, youth, middle-aged, and elderly.

[0025] Step S2 aims to integrate the basic morphological data of each bivalve mollusc, and divide all the collected bivalve mollusc samples into four age groups: young, young, middle-aged and old. All microplastics adsorbed or swallowed by each bivalve from birth to survival and even the moment of grasping will be stored in the body or excreted. In this way, the state of microplastic enrichment in each growth cycle experienced by the local extreme ecosystem is indirectly characterized through the reorganized cross-sectional time nodes, and the dynamic microplastic adsorption curve of the methane seepage area in the past hundred years is connected in series.

[0026] Preferably, step S3 specifically comprises the following steps:

[0027] S31, based on the morphological data obtained in step S2, perform categorical sampling, add a new age classification array to the original four columns of basic data, use numbers 1, 2, 3, 4 to replace the corresponding age stage of each shellfish - young, young, middle-aged, old, and then set the age grouping as a categorical variable array, and the remaining four columns of morphological arrays are quantitative variable arrays;

[0028] S32, creating an indicator matrix for the existing age classification array, calculating its standardized contingency table and performing singular value decomposition, performing first singular value standardization according to the elements on the obtained diagonal matrix, and expanding the age classification array to a two-dimensional space display to obtain an MCA standardized coordinate matrix;

[0029] S33, normalize or center the remaining four quantitative arrays, calculate their covariance matrix, and find their eigenvalues ​​and eigenvectors, select the first k eigenvectors to form a matrix P, and project it into a new two-dimensional space to obtain the coordinate matrix after dimensionality reduction, from which the factor score coefficient matrix of each quantitative array after dimensionality reduction can be calculated;

[0030] S34, dividing the normalized or centered matrix of the four-column quantitative array by the square root of the first axis eigenvalue of its covariance matrix to obtain a factor loading matrix describing the linear combination relationship between the four-column quantitative array and the common factor, and merging this loading matrix with the first singular value standardized matrix of the age classification array to form a global factor loading T matrix;

[0031] S35, performing multivariate factor analysis on the global factor loading matrix T, and converting the matrix TTT into a projection matrix P, projecting the matrix T into the multivariate factor analysis model ranking diagram through the matrix P, so as to calculate the variable loading contribution rates of the five columns of morphological arrays to the global factor coordinate matrix respectively;

[0032] S36. Sort the variable load contribution rate of each array by size, and select the top five mussels in each age group to represent the microplastic enrichment characteristics of the community in the corresponding age group.

[0033] Step S3 performs categorical sampling based on the morphological basic data obtained in step S2. A new age classification array is added to the original four columns of basic data, with numbers 1, 2, 3, and 4 replacing the full age stages of each shellfish - young, young, middle-aged, and old. Then, according to a variety of statistical methods, it is obtained which bivalve molluscs have the highest contribution rate to the shell length index under the four age groups, which best represents the characteristic information of the corresponding single age group. For example, the statistical method takes the selected multivariate factor analysis (MFA) as an example. First, the nature of each variable (categorical or quantitative) is determined in the five data columns, and then each variable column is standardized and characteristic decomposition is performed. Multiple correspondence analysis (MCA) is performed on the categorical variable column, and principal component analysis (PCA) is performed on the quantitative variable column. The eigenvalues ​​of each group of variables are projected on the MFA global ordination diagram, and arranged by the contribution rate of each shellfish monomer in the four age groups. The maximum explanation of each shellfish monomer in revealing the variance variation of the community morphological data set is displayed, and the eigenvalues ​​of which variable columns have a higher contribution to the MFA ordination space are evaluated, and which type of data columns dominate the dimensions of MFA. The purpose of this step is to select representative samples of four groups of shellfish communities of typical age groups, so as to reflect the enrichment of microplastics in the representative age group samples with the experimental results of a small number of typical samples, greatly reduce the number of samples to be tested, and improve research efficiency. The specific steps are as follows:

[0034] ① The obtained characteristic data variable columns are dimensionally standardized according to the data attributes. For categorical data sets such as age classification arrays, the data is standardized by the first singular value of each variable. To this end, it is first necessary to create an indicator matrix (Z0) for the existing age classification data. Its dimension is n×4, where n is the number of selected shellfish monomers. Each row of the indicator matrix (Z0) corresponds to an observation value, and each column corresponds to an age category. If the observation value belongs to this category, the corresponding element is 1, otherwise it is 0.

[0035] ② Calculate the standardized contingency table (BurtMatrix). The Burt matrix is ​​obtained by the indicator matrix (Z0) and its transposed matrix (Z T ), see the following formula (4).

[0036] Burt=Z·Z T (4)

[0037] Where Burt is a 4×4 matrix, and the elements of the matrix B ij It represents the number of times the i-th category and the j-th category appear together in the data set, and the diagonal elements of the Burt matrix represent the frequency of occurrence of a single age category in the data set.

[0038] ③ Singular value decomposition. Perform singular value decomposition on the Burt matrix to extract singular values ​​and corresponding singular vectors. The first singular value is the largest of these singular values, which captures the main variability in the data. First, the necessary centering is performed on the obtained Burt matrix to adjust the influence of each element on eliminating marginal frequencies. The centering formula is shown in the following formula (5).

[0039]

[0040] Where B ij is the original element in the Burt matrix, is the average value of the matrix corresponding to the i-th row, is the mean value corresponding to the jth column, and is the grand mean of the entire matrix.

[0041] The centralized Burt matrix is ​​then normalized to drive the sum of rows and columns to 1 to reflect the relative importance of different categories, as shown in the following equations (6)-(8).

[0042]

[0043] Where p i is the marginal sum of the i-th row divided by the sum of the matrix elements, p j is the marginal sum of the jth column divided by the sum of the matrix elements, where n is the dimension of the Burt matrix.

[0044] The processed Burt matrix is ​​subjected to singular value decomposition (SVD) as shown in equation (9).

[0045]

[0046] In the formula, the column vector of U is the projection of each age stage corresponding to the shellfish monomer on the dimension defined by the singular value, and the column vector of V represents the projection of the four-fold age stage on the new dimension. We arrange the diagonal elements of the diagonal matrix ∑ in descending order and select the first singular value.

[0047] ④ Standardization of the first singular value. Divide the frequency of each age class of each shellfish monomer by the first singular value (∑ 11 ).

[0048] The purpose of this step is to reduce the differences in age scales between different shellfish individuals, as shown in the following formula (10).

[0049]

[0050] ⑤ Extract MCA standardized coordinates. The row and column coordinates of the standardized categorical column vector can be obtained from the corresponding column data of U and V and Σ / Σ 11To express it, the data of the age classification array is projected into a two-dimensional space for visualization, and the interaction range and effect interval between different morphological variables in the age group are obtained, as shown in the following formulas (11)-(12).

[0051] F=U·Σ / Σ 11 (11)

[0052] G=V·Σ / Σ 11 (12)

[0053] Where F is the row coordinate matrix of age data, and each row of F i Indicates B ij Norm The standardized horizontal coordinate of the i-th age value in ; similarly, G is the corresponding column coordinate matrix, and each column of G j Indicates B ij Norm The normalized column coordinate of the j-th age value in .

[0054] ⑥ Then the remaining four columns of quantitative arrays are normalized or centered to eliminate the influence of different dimensions between continuous data. The expressions are as follows (13)-(14):

[0055]

[0056] Among them, Z is the processed data vector, x is the input shellfish monomer morphological observation value, x min is the smallest value in a single data column, x max is the largest value in the corresponding data column, x mean is the mean value of the corresponding data column, and σ is the variance of the data column.

[0057] ⑦ Calculate the covariance matrix of the normalized or centered quantitative array. The covariance matrix describes the degree of correlation between the morphological parameters in the quantitative array. For a data set with n variables, the covariance matrix is ​​an n×n matrix, in which the element (X, Y) represents the covariance of variable X and variable Y, see equations (15) and (16):

[0058]

[0059] In the formula, cov(X,Y) is the covariance matrix between the variables in each column of the quantitative array. It is the column vector average between two variables in the quantitative array.

[0060] ⑧ Calculate the eigenvalues ​​and eigenvectors of the covariance matrix in each column of the quantitative array. The eigenvalue represents the importance of the principal component corresponding to each eigenvector, while the eigenvector represents the direction of the principal component in the original variable space, as shown in formula (17):

[0061] (A-λE)x=0 (17)

[0062] Where A is the covariance matrix obtained from the above formula, λ is the eigenvalue corresponding to the covariance matrix, E is the unit matrix, and x is the eigenvector corresponding to the covariance matrix.

[0063] ⑨ Sort by the value of λ and select the eigenvalues ​​and their corresponding eigenvectors where λ>1. The number k of λ is the first k eigenvectors with larger eigenvalues.

[0064] The first k eigenvectors are combined into a matrix P and projected into the new space to obtain a new low-dimensional representation. The projection is shown in the following formula (18):

[0065] Z=XP (18)

[0066] Where Z is the data matrix after dimensionality reduction, X is the four quantitative data matrices, and P is the matrix composed of the first k eigenvectors.

[0067] Calculate the factor score coefficient matrix of each quantitative data matrix after dimension reduction, as shown in the following formulas (19)-(21):

[0068]

[0069] R xy = cov(X,Y) / (σ X ·σ Y ) (20)

[0070] A=ZC (21)

[0071] In the formula, σ X and σ Y Respectively represent the standard deviation of each pair of variables in each quantitative data group, R XY represents the correlation coefficient of the two variables in the corresponding array, cov(X,Y) is the covariance matrix of the two variables, Z is the data matrix after the dimensionality of various quantitative arrays is reduced to k dimensions, and C is the matrix of R in each array matrix. XY The correlation coefficient matrix composed of is, and A is the factor score coefficient matrix.

[0072] Calculate the normalized quantitative data set. Divide the normalized or centered quantitative morphological data set obtained in step ⑥ above by the singular values ​​of the first axis of the PCA in the quantitative array found in step ⑧ above to obtain a matrix describing the linear combination relationship between the variables and the extracted common factors. The specific calculation is shown in the following formula (22):

[0073]

[0074] Where, T n is the factor loading matrix of the 4 quantitative arrays, Z n is the normalized or centered quantitative morphological array, are the singular values ​​of the first axis of the PCA of each quantitative morphological array (i.e., the square root of the eigenvalue).

[0075] Normalize the contribution. The normalized quantitative data set obtained is combined with the age classification array after the first singular value standardization in step ④ to form a global factor loading T matrix.

[0076] Multivariate factor analysis was performed on the global factor loading matrix T to explore the global structure and pattern of the entire shellfish monomer array and reveal the global principal components and contribution rates in the age-morphology data set, which is equivalent to calculating the singular value decomposition of the global factor loading matrix and obtaining the shellfish monomers that can significantly replace the entire data set under the dominance of the shell length array. The calculation is shown in the following formulas (23)-(24):

[0077] T=UΔV T (twenty three)

[0078] U T U=V T V=I (24)

[0079] Where T is the global factor loading matrix converted from the categorical array and the quantitative array, U and V are the left and right singular vectors of the matrix T, Δ is the diagonal matrix of singular values, and I is the unit matrix.

[0080] The matrix TT composed of the global factor loading matrix T Converted into projection matrix P, it satisfies the following equation (25):

[0081]

[0082] Where M is an I×I diagonal matrix and P is the projection matrix.

[0083] The projection matrix P is used to project the global factor loading matrix onto the ordination diagram of the multivariate factor analysis model. The common structure and differences between the arrays are evaluated through the ordination diagram of objects and variables to obtain the typical shellfish monomer number. The calculation expression (26) is:

[0084] K n =D×(T n T n T )P (26)

[0085] In the formula, K n is the two-dimensional coordinate matrix of each shellfish feature array subset after projection, D is the number of groups in the shellfish feature array, T n is the column matrix of the global factor loading matrix for each shellfish feature array.

[0086] Calculate the correlation between the two-dimensional coordinate matrix of each column of shellfish feature array and the global factor coordinate matrix to form the variable load contribution rate R nMFA , expression (27) is:

[0087]

[0088] In the formula, K ij and Kn ij The i-th row and j-th column elements of the two-dimensional coordinate matrix of the global factor coordinate matrix and each column of the shellfish feature array, respectively. and Represents the average value of all elements in the matrix. According to the obtained variable contribution rate, the top five samples in the four age groups were selected for enrichment extraction experiments to represent the microplastic enrichment of their age groups.

[0089] Preferably, step S4 specifically comprises the following steps:

[0090] S41, pretreatment: dissecting the bivalve molluscs into separate tissues and freeze-drying them;

[0091] S42, enzyme digestion: using trypsin solution to digest organic matter from the freeze-dried tissue to obtain an enzyme digestion solution;

[0092] S43, pH enhancement: adjusting the pH of the enzyme digestion solution to 7.5 to obtain a tissue digestion solution rich in filamentous condensates;

[0093] S44, progressive digestion with hydrogen peroxide: using hydrogen peroxide to completely eliminate the cells rich in filamentous condensates in the enzyme digestion solution, releasing the microplastics remaining in the intercellular spaces, and obtaining a hydrogen peroxide digestion solution;

[0094] S45, first membrane purification: vacuum filter the enzyme digestion solution and the hydrogen peroxide digestion solution, repeat several times, and the obtained microplastic purification membrane is ready for use;

[0095] S46. Second membrane purification: Leach out the microplastics on the purification membrane to remove the remaining organic matter on the membrane.

[0096] The freeze-drying pretreatment in step S41 is to remove moisture from the sample in the form of water vapor, retain its own substances, reduce the loss of microplastics in the sample, obtain the net dry weight of the sample, and also increase the subsequent digestion rate of organic matter.

[0097] Further preferably, the specific steps of the enzymatic digestion in step S42 are:

[0098] S421. Prepare trypsin solution: dissolve 6.80 g potassium dihydrogen phosphate in 500 mL water, add 0.1 mol / L potassium hydroxide solution to adjust the pH to 7.5, add 10.00 g trypsin, add water to dissolve, and dilute to 1 L;

[0099] S422. Enzyme digestion: add trypsin solution to the freeze-dried bivalve mollusc tissue at a ratio of 1 g of bivalve mollusc: 30 mL of trypsin solution, and shake to digest to obtain an enzyme digestion solution with filamentous coagulation bodies.

[0100] The present invention's scheme for extracting microplastics abandons the grinding, homogenization and flotation steps in previous patents, and instead uses a pH-phase enhanced enzyme-hydrogen peroxide mixed digestion method to build a microplastic extraction method that is completely suitable for bivalve molluscs in deep-sea extreme environments. This method uses the complete digestion ability of biochemical reactions to replace the traditional physical extraction steps, completely releasing microplastics in organisms and reducing the microplastic damage effects caused by chemical reagents, while taking into account future further microplastic aging test steps to achieve non-destructive detection requirements for microplastics adsorbed by organisms in deep-sea extreme environments.

[0101] Preferably, step S5 specifically comprises the following steps:

[0102] S51, stereo microscope observation: place the microplastic purification filter membrane obtained in step S4 under a stereo microscope, search for suspected microplastics larger than 100 μm, record their morphological parameters, and transfer them to a 25 mm glass fiber filter membrane;

[0103] S52, Microscopic infrared identification: Select representative suspected microplastics on the glass fiber filter membrane for qualitative analysis under μFTIR, and the library matching degree is set to be greater than 70%;

[0104] S53, Raman observation and identification: Place the purified filter membrane picked out by stereo microscope on a Raman spectrometer to observe and identify the morphological parameters of 1-20 μm microplastics on the membrane. Microplastics are identified when the library comparison rate is greater than 70%;

[0105] S54, laser infrared identification: the purified filter membrane identified by Raman spectrometer is extracted and concentrated with anhydrous ethanol, dripped onto high-reflective glass, and the composition is determined in a selected area under Agilent LDIR laser infrared spectrometer, with the matching degree set to >0.7;

[0106] S55. Abundance correction: After the process-based identification is completed, the abundance and types of microplastics obtained by the Raman spectrometer are subtracted from the microplastics measured by the laser infrared spectrometer, and the data measured by μFTIR are combined to obtain the full-size microplastic abundance of a certain tissue of the measured shellfish. At the same time, the sum of the microplastic abundances of all tissues is the abundance of microplastics enriched in the corresponding shellfish monomer.

[0107] In step S5, the size of the microplastics of 0.1-5 mm on the membrane can be measured using a microscope, such as a stereo microscope, and the microplastics are picked out and transferred to a new membrane. The new membrane is then placed on an infrared spectrometer to identify the type and degree of aging of the microplastics, such as a Fourier transform infrared spectrometer. The picked purified filter membrane is passed through a Raman spectrometer to identify the types of microplastics of 1-20 μm, and then after enrichment and extraction operations, the microplastics of 20-100 μm are identified using an infrared spectrometer, such as a laser infrared spectrometer (Agilent 8700LDIR). These three identification process schemes cover all microplastic sizes, making it easy to comprehensively analyze the occurrence state of microplastics in bivalve molluscs and the physical changes of microplastics after entering the deep sea.

[0108] The method proposed in the present invention uses the coupling relationship between the efficiency and total amount of microplastic adsorption by bivalves of different age groups, an indicator organism in deep-sea methane seepage areas, to replace the amount of microplastics absorbed by the entire extreme ecosystem, explore the long-term accumulation process of microplastics in the area, and fill the gap in the field of the capture and transformation process of microplastics by seabed ecosystems under extreme environments.

[0109] Preferably, step S6 specifically comprises the following steps:

[0110] S61, drilling and crushing: taking out the bivalve shells preserved during the dissection in step S4, washing them, finding the inorganic calcium carbonate prism interlayer in the middle layer of the shells under a micro-Raman spectrometer, and using a micro-drill to accurately drill samples for standby use;

[0111] S62, first pickling treatment: using a dilute hydrochloric acid solution to wash the powder obtained in step S61;

[0112] S63, second alkaline washing treatment: washing the obtained acid-washed powder with a NaOH solution;

[0113] S64, third pickling treatment: washing the obtained alkali-washed powder with dilute hydrochloric acid solution again, washing the remaining pickling solution, and drying for standby use;

[0114] S65, thermal decomposition: taking the dried fine powder and placing it in a thermal decomposition container for thermal decomposition;

[0115] S66, Carbon dioxide capture: The carbon dioxide generated during the thermal decomposition process is absorbed by a gas capture system containing 2M sodium hydroxide solution connected to the thermal decomposition furnace;

[0116] S67, purify carbon dioxide: add hydrochloric acid drop by drop to release the carbon dioxide in the absorbent until no bubbles are generated, pass the gas through the KOH solid absorption column, and then pass it into the gas chromatograph. If there is only a peak of m / z 44 on the mass spectrum displayed by the mass spectrometer MS detector, and the peaks of other values ​​do not exist or are very small, it means that the carbon dioxide sample is purified successfully and you can proceed to the next step;

[0117] S68, carbon fixation: in a reduction furnace, set a preheating temperature, place an excess of iron powder as a catalyst, introduce the remaining purified carbon dioxide gas under the condition of hydrogen as a barrier gas for reaction, and take out the solid carbon after the reduction furnace is cooled to room temperature;

[0118] S69, Carbon-14 determination: The prepared pure carbon powder was loaded into the sample chamber of the isotope mass spectrometer, and the thermal ionization mode was set. The ion source energy was set at 2000V, and the ion accelerator was set at 300WV. The ion currents of the carbon-14 and carbon-12 with specific mass / charge ratios were measured. The ion beam intensities I of carbon-14 and carbon-12 were measured. C-14 ,I C-12 , assuming the normalization factor is k, calculate the relative content R of carbon-14 and carbon-12, the expression is shown in formula (28):

[0119]

[0120] S610, Age estimation: The relative ratio of carbon-14 / carbon-12 measured in the shell of each bivalve monomer, the half-life of carbon-14 in the ocean and the carbon ratio of the modern water environment are entered into the formula of carbon-14 dating method. At the same time, CALIB software is used to correct the calculation results. The correction value for the marine reservoir effect is set to -178±50yr to obtain a relatively accurate survival age for each shell.

[0121] The method proposed in the present invention uses carbon-14 dating to identify the shells of indicative bivalve molluscs that record age information in extreme marine ecosystems, fits longitudinal enrichment curves of shells of different age groups, and deeply explores the amount of microplastics enriched by each bivalve mollusc from birth to death, forming a long-term microplastic enrichment curve for extreme marine ecosystems, revealing the dynamic migration and transformation process mechanism of microplastics from production to the deep sea to the extreme marine ecological environment.

[0122] Further preferably, the specific steps of step S610 are: according to the formula of carbon-14 dating method, the relative ratio of carbon-14 / carbon-12 measured in each bivalve mollusc sample is taken into account, and the decay of carbon-14 in the ocean is carried out according to the half-life of carbon-14 and the carbon ratio of modern water environment is about 1.176×10 -12The measurement results are corrected by CALIB software and expressed in calendar age (yrcal BP). The correction value for the marine reservoir effect is -178±50yr. Finally, the relative estimated age of each shell is obtained. According to the relative content R obtained, λ is the decay coefficient, R0 is the modern carbon standard value, and the data is substituted to calculate the accurate age t of the sample. The expression is shown in formula (29):

[0123]

[0124] Preferably, step S7 specifically comprises the following steps:

[0125] S71. Set standards: Set the concentration of microplastics in the surrounding seawater to a uniform and constant ideal condition, set the ratio of the abundance of microplastics in the lip tissue of bivalve molluscs to the concentration of microplastics in the environment to a constant feeding rate, set the abundance ratio of microplastics in the gills and visceral mass to the absorption coefficient of bivalve molluscs, and set the inverse ratio of the abundance of microplastics in the visceral mass and intestine to the exclusion coefficient of bivalve molluscs transferred to the body, and the microplastic accumulation rate of each mussel in the extreme environment can be obtained;

[0126] S72. Calculate the average microplastic accumulation rate of each age group based on the typical mussel population selected from each age group, and bring into the time nodes obtained by shell dating to approximately establish a time series model of microplastic enrichment in the entire cycle of a single mussel from birth to natural death in the methane seepage area;

[0127] S73. The average microplastic accumulation rate and amount of each age group are proportionally extrapolated to the entire ecosystem by collecting sampling statistical data, and the enrichment level of microplastics and potential pollution characteristics in the entire methane seepage area ecosystem are inferred.

[0128] Further preferably, the specific steps of step S7 are:

[0129] First, the concentration of microplastics in the surrounding seawater is set to a uniform and constant ideal condition (C water ), the feeding rate of a single mussel was set constant (E bei ), mussels set the absorption coefficient of microplastics from gills to cell tissues to be constant (S bei ), the exclusion coefficient of mussels transferring microplastics from tissue cells to the outside of the body was set constant (M bei ), then the accumulation rate of microplastics in the mussel body A(t) is shown in the following formula (30)-(31).

[0130]

[0131] Under steady-state conditions, when (t→t0), the accumulation rate A(t) tends to a stable value A(t0). At this time, the accumulation amount in the steady state can be expressed as the following formula (32):

[0132]

[0133] The enrichment coefficient (EC) is used to measure the degree of enrichment of microplastics in shellfish relative to their concentration in the environment. The EC calculation formula is as follows (33):

[0134]

[0135] A time series model was established based on the microplastic content of the top five shellfish monomers selected from the typical alternative community characteristics in each age group within the four layers and their corresponding carbon-14 estimated ages. The temporal adsorption of microplastics by shellfish monomers obtained from all measurement data was inverted to the entire bivalve bed through sampling rules. The average carbon-14 estimated age of the first five shellfish units in each age group, i = young, green, middle, old, and the rate of microplastic enrichment in the entire seafloor extreme environment ecosystem (v n ) and the total amount of microplastic accumulation in the system (T n ), as shown in equations (34)-(40):

[0136]

[0137] ν n =ν1·N 总 (39)

[0138]

[0139] Preferably, the bivalve molluscs include mussels, white clams and phoenix clams.

[0140] Compared with the prior art, the present invention has the following advantages:

[0141] 1. Different from the conventional fixed-point sampling and monomer pollution exploration, the present invention uses the sampling survey method to obtain only some shellfish groups with typical community characteristics, and then through statistical inspection and selection, large-scale community research and analysis can be realized, which greatly reduces the total number of samples required for the experiment, improves the detection efficiency, saves time, manpower and financial costs, and provides basic technical means for studying the degree of microplastic enrichment in the ecosystem. In addition, the present invention optimizes the conventional microplastic extraction experiment at multiple points, including non-homogenization, using mixed digestion method, removing flotation steps, etc., to improve the in-situ fidelity of microplastics and reduce the loss of microplastics, and ensure the accuracy and comprehensiveness of the experimental results. In addition, the microplastic process identification scheme adopted by the present invention realizes the identification of the full size of microplastics in turn, and after correcting the data, it can provide a comprehensive understanding of the abundance and types of microplastics enriched in the biological community. In the analysis stage, the present invention applies the carbon-14 dating method to the time series of microplastic adsorption by bivalve shellfish on the seabed for the first time, and uses this combination to show the long-term dynamic adsorption capacity of microplastics in the ecosystem of the methane seepage area, filling the gap in this field.

[0142] 2. The method proposed in the present invention is simple and easy to operate, and is suitable for exploring the characterization method of bivalve molluscs enriched in microplastics in deep-sea extreme ecosystems, and then deducing the time course and historical accumulation of microplastics absorbed by the entire extreme ecosystem, solving the problem of large-scale sampling, extraction and analysis in deep-sea extreme environments. The present invention can be used to systematically explore the dynamic adsorption evolution of microplastics by deep-sea bivalve molluscs in the past century, aiming to enrich the theoretical framework of the deep-sea fate behavior of microplastics and the ecological effects they produce, and provide a scientific test strategy for the impact of the history of microplastic pollution in specific sea areas. Description of the drawings:

[0143] Figure 1 It is a technical flow chart of the method proposed by the present invention;

[0144] Figure 2 It is the contribution ranking diagram of age-body length array in multivariate factor analysis;

[0145] Figure 3 This is the microplastic enrichment curve of mussels in the methane leakage area. Specific implementation method:

[0146] The following examples are provided to further illustrate the present invention, rather than to limit the present invention.

[0147] Unless otherwise defined, all professional terms used hereinafter have the same meaning as those generally understood by those skilled in the art. The professional terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the scope of protection of the present invention. Unless otherwise specified, the experimental materials and reagents herein are conventional commercial products in the field of this technology. The number of times of grabbing live bivalve molluscs can be adjusted according to actual conditions. The formula proposed in the present invention takes 3 times as an example, and other grabbing times are also within the scope of protection of the present invention.

[0148] The following instruments and equipment are used in the following examples:

[0149] Vernier caliper, metal scissors, stainless steel tweezers, sampling needle, scalpel, surgical tray, spring scissors, medicine spoon, glass rod, vacuum filtration device, freeze dryer, oven, electronic analytical balance, graphite electric heating plate, pH meter, magnetic stirrer, constant temperature shaker, laser infrared spectrometer, Fourier transform micro-infrared spectrometer, stereo microscope, high-resolution confocal micro-laser Raman spectrometer, unmanned submersible, multi-beam bathymetric system, TV grab, cruise water device, -20℃ refrigerator, 4℃ refrigerator, micro-hole drilling rig, thermal decomposition furnace, gas chromatograph, reduction furnace, thermal insulation pliers, isotope mass spectrometer.

[0150] The reagents and consumables used in the following examples are:

[0151] Potassium hydroxide, anhydrous ethanol solution, potassium dihydrogen phosphate, trypsin, sodium chloride, concentrated hydrochloric acid, sodium hydroxide, 30% hydrogen peroxide solution are all commercially available, 0.45 μm glass fiber filter membrane, 0.22 μm water-based microporous filter membrane, 2500-mesh steel membrane, 60 mm aluminum storage box, 10 mL glass syringe, sealing film, tin foil, 50 mL beaker, 2 L beaker, 50 mL stoppered conical flask, 100-mesh stainless steel sieve, glass culture dish, 5 g glass tube, absorption column, Fe powder, hydrogen, ziplock bag, ultrapure water, and high-reflective glass.

[0152] The software and versions involved in the analysis methods used in the following examples are:

[0153] The area approximate estimation method uses Office Excel for batch calculation.

[0154] Multivariate factor analysis was performed using R language version 4.1.2, mainly involving R packages FactoMineR, factoextra, and corrplot.

[0155] The adsorption kinetics model was run using the matplotlib library 3.5.2 with Python 3.10, and the visualization was performed using the R language version 4.1.2, involving the R package ggplot2.

[0156] Example 1

[0157] A method for large-scale detection of the degree of microplastics enrichment in deep-sea bivalve molluscs. The extreme environment - methane seepage area is used as the research environment, and its indicator organism - large top clam (hereinafter referred to as mussel) is selected. By extracting the average abundance of microplastics enriched in mussels of different ages and identifying their ages, the dynamic enrichment curve of microplastics adsorbed by the ecosystem of the seabed methane seepage area within a hundred years is inverted. The experimental samples are taken from the mussel beds near the small plume vents in the deep-sea methane seepage area, such as Figure 1 As shown, it includes the following steps:

[0158] S1. Collect and estimate the total amount of mussels in the area:

[0159] S11. Use an unmanned underwater vehicle (ROV) to search for live mussel beds in the early and middle stages of methane seep development in deep-sea methane seep areas, measure the bed area using a multibeam bathymetric system, and find its equivalent diameter and geometric center;

[0160] S12. Lower the TV grab to grab the mussel bed three times. The grabbing positions are located at the two ends and the midpoint of the equivalent circle radius. After grabbing the mussels ashore, count the number of living mussels (screen out residual mussels) and use a cruising water device to clean the silt on the surface of the mussels. After draining the water, pack them separately in sealed bags and store them in a -20° refrigerator.

[0161] S13. Based on the number of live mussels captured three times, the aggregation coefficient C of live mussels was introduced to roughly estimate the total amount of live shellfish in the entire mussel bed.

[0162] S2. Mussel morphology measurement: The captured mussel samples were taken out of the refrigerator and thawed, and morphological data were measured and weighed in batches.

[0163] S21. Use a vernier caliper to parallelly fit three parts of the body length of the mussel, read three measurements and take the average value. Use an electronic analytical balance to weigh the wet weight of the corresponding mussel. Record the obtained ecological morphological data such as the shell length L, shell width W, shell height H and wet weight M of a single mussel.

[0164] S22. Then, according to the different shell length-age standards of mussels, 0<L<55mm, 55mm<L<80mm, 80mm<L<110mm, 110mm<L, all the measured mussels are divided into four age groups, namely young, green, middle and old, in order of shell length, and the mussels in each age group are numbered and marked.

[0165] S3. Multivariate factor analysis: Based on the morphological data obtained, the multivariate factor analysis statistical method was used to correlate the relationship between mussels of different ages on the seabed and their appearance length and wet weight, to find the top five mussel monomers that can represent the characteristics of the four groups of each age group, and to obtain a total of twenty mussel groups.

[0166] S31. Add a new age classification array to the original four columns of basic data, and use the numbers 1, 2, 3, and 4 to represent the age stages of each shellfish, namely, infant, youth, middle-aged, and old. Then set the age group as the classification variable array, and the remaining four columns of morphology arrays as quantitative variable arrays;

[0167] S32, creating an indicator matrix for the existing age classification array, calculating its standardized contingency table and performing singular value decomposition, performing first singular value standardization according to the elements on the obtained diagonal matrix, and expanding the age classification array to a two-dimensional space display to obtain an MCA standardized coordinate matrix;

[0168] S33, normalize or center the remaining four quantitative arrays, calculate their covariance matrix, and find their eigenvalues ​​and eigenvectors, select the first k eigenvectors to form a matrix P, and project it into a new two-dimensional space to obtain the coordinate matrix after dimensionality reduction, from which the factor score coefficient matrix of each quantitative array after dimensionality reduction can be calculated;

[0169] S34, dividing the normalized or centered matrix of the four-column quantitative array by the square root of the first axis eigenvalue of its covariance matrix to obtain a factor loading matrix describing the linear combination relationship between the four-column quantitative array and the common factor, and merging this loading matrix with the first singular value standardized matrix of the age classification array to form a global factor loading T matrix;

[0170] S35, perform multivariate factor analysis on the global factor loading matrix T, and the matrix TT T Convert it into a projection matrix P, and project the matrix T into the ordination diagram of the multivariate factor analysis model through the matrix P to calculate the variable load contribution rate of the five columns of morphological arrays to the global factor coordinate matrix;

[0171] S36. Sort the variable load contribution rate of each array by size, and select the top five mussels in each age group to represent the community enrichment characteristics of microplastics in the corresponding age group, see Appendix Figure 2 ;

[0172] S4. Microplastic enrichment and extraction experiment: The selected typical mussel populations were divided into tissues for microplastic enrichment and extraction experiments.

[0173] S41. Freeze-drying pretreatment: The mussel monomers are dissected and pre-treated for freeze-drying. The moisture in the mussel tissues is removed in the form of water vapor, and the microplastics in the mussel body are dried non-destructively.

[0174] S411, thawing: select the required mussels from the -20°C refrigerator and place them in a 4°C refrigerator to thaw for 1-2 hours;

[0175] S412, Dissection: Take a whole mussel and use spring scissors to dissect the viscera, gills, lips, intestines, feet, adductor muscles, and mantle. The dissected tissues need to be cleaned. The entire operating environment is handled in a ventilated environment on the dissection table. Before dissection, use filtered ultrapure water to soak and clean all the utensils and dissection tools used, and use tin foil to wrap the dissection table top;

[0176] S413, cleaning: weigh 35.03g of sodium chloride, add 1L of water, shake and dissolve to prepare a saline cleaning solution, and filter it three times using a 0.45μm glass fiber filter. Use the saline cleaning solution to clean the dissected tissues. After cleaning three times, let it stand to drain the water and place it in a 60mm aluminum box;

[0177] S414, weighing wet weight: placing the aluminum box containing each mussel tissue on an analytical balance, weighing the wet weight of each tissue in the tare mode, and recording the data;

[0178] S415. Cryopreservation: Place each weighed aluminum box in a -80°C refrigerator and attach a wet weight label;

[0179] S416, freeze drying: slightly unscrew each sealed aluminum box, and when the temperature of the freeze dryer cold trap reaches -60°C, cover the vacuum cover and start to evacuate the box to make the vacuum below 100 Pa. Under this condition, freeze dry each tissue for 24 hours;

[0180] S417, weigh dry weight: take out each freeze-dried mussel tissue from the freeze dryer, place it on a 1 / 10,000 analytical balance to weigh it, obtain the dry weight of the corresponding gills, mantle, adductor muscle, foot, byssus, visceral mass, intestine and other tissues, and record the data.

[0181] S42. Trypsin digestion: Use trypsin solution to digest the organic matter of the freeze-dried mussel tissue.

[0182] S421. Prepare trypsin solution: weigh 6.80 g of potassium dihydrogen phosphate, add 500 mL of water and shake to dissolve it, then adjust the pH to 7.5 with 0.1 mol / L potassium hydroxide solution, add 10.00 g of trypsin, add water to dissolve it, dilute to 1 L, and filter three times using a 0.45 μm glass fiber filter membrane;

[0183] S422. Enzyme digestion: Place the freeze-dried tissues into a 50-mL ground-mouth conical flask with a stopper, add trypsin solution at a ratio of 1 g mussel: 30 mL digestion solution, and place the tissue digestion solution in a constant temperature shaker at 37°C and shake at 100 rpm for 24 hours. Take it out every 3 hours and place it under a magnetic stirrer for accelerated shaking to obtain an enzyme digestion solution with more filamentous coagulants.

[0184] S43. Progressive digestion with hydrogen peroxide: Use progressive digestion to completely eliminate cell clusters rich in filamentous aggregates and release microplastics remaining in the intercellular spaces.

[0185] S431. Prepare pH modulating solution: weigh 0.68 g of potassium dihydrogen phosphate, add 50 mL of water to dissolve it, filter it through a 0.45 μm glass fiber filter and shake it three times; weigh 2.81 g of potassium hydroxide, add 50 mL of water to dissolve it, filter it through a 0.45 μm glass fiber filter and shake it three times, and bottle the prepared pH modulating solution for later use;

[0186] S432, adjust the solution pH: After 24 hours of enzyme digestion, take out the conical flask containing the trypsin digestion solution, measure its pH with a pH meter, draw the pH adjustment solution with a clean 10mL glass syringe, maintain the pH value of the enzyme digestion solution at 7.5, clean the pH meter with excess enzyme digestion solution, and put it back into the constant temperature shaker after the adjustment is completed. Repeat this operation every 12 hours for a total of four times;

[0187] S433. Hydrogen peroxide digestion: Use tweezers to pick out the filamentous coagulation in the conical flask and put it into a new conical flask, add 30mL of 30% hydrogen peroxide digestion solution (GR), and clean the tweezers. Digest the residual cell adhesions in the sample on a graphite electric heating plate at 60°C for 3 hours until no bubbles are generated in the digestion solution, indicating that the digestion is complete.

[0188] S44, first membrane purification: add enzyme digestion solution and hydrogen peroxide digestion solution into the filtration bottle, perform vacuum filtration, repeat the filtration three times, and rinse the inner wall of the filter with ultrapure water. Place the purified filter membrane in a 60mm aluminum box and store it and mark it.

[0189] The filter membrane uses a 2500-mesh steel membrane to avoid interference from traditional glass fiber membranes. The use of steel membranes can also improve the effectiveness of subsequent infrared spectroscopy in identifying microplastics.

[0190] S45. Second membrane purification: Use anhydrous ethanol solution to extract the microplastics on the membrane and remove the remaining organic matter on the membrane.

[0191] S451. Place the purified filter membrane after the first treatment upright in a 50mL beaker, add about 15mL of anhydrous ethanol solution until the liquid surface completely submerges the surface of the filter membrane, seal the beaker with cleaned tin foil, secure it with a rubber band, and place it in a shaker at 90rpm for 12h to completely release the microplastic particles on the filter membrane into the solution.

[0192] S452. Anhydrous ethanol cleaning: Take out the ethanol solution filter membrane after extraction, and repeatedly clean the filter membrane with anhydrous ethanol until no obvious particles remain on the filter membrane.

[0193] S453, filtration and membrane preparation: vacuum filter the anhydrous ethanol cleaning solution again, filter three times, and finally wash the cup wall and carrier multiple times with anhydrous ethanol to enrich all microplastics on the filter membrane. Finally, place the obtained second purification filter membrane in a 60mm aluminum box and dry it in an oven at 60℃ for 4h.

[0194] S5. Identification of full-size microplastics: Use a streamlined solution to identify microplastics of 0.001-5 mm on the membrane in batches to obtain the corresponding type, shape, size and color information.

[0195] S51. Stereo microscope observation (>100μm): Place the dried second purification filter membrane under a stereo microscope, observe the stainless steel membrane surface in a "Z" shape, use the measuring bar tool to find suspected microplastics larger than 100μm, record their morphological parameters, and use a sampling needle to transfer them to a 25mm glass fiber filter membrane;

[0196] S52, Microscopic infrared identification (>100μm): Select representative suspected microplastics on the glass fiber filter membrane for qualitative analysis under μFTIR, and the library matching degree is set to be greater than 70%;

[0197] S53, Raman observation and identification (1-20 μm): Place the purified filter membrane picked out by stereo microscope on the stage of high-resolution confocal laser Raman spectrometer, perform "Z" scanning, observe and identify the morphological parameters of 1-20 μm microplastics on the membrane, and those with a library comparison rate greater than 70% are microplastics;

[0198] S54, laser infrared identification (20-100μm): the purified filter membrane identified by Raman spectrometer is extracted again with anhydrous ethanol and concentrated into a 100μL suspension, which is then dripped onto the cleaned high-reflective glass. The composition is determined in a selected area under the Agilent LDIR laser infrared spectrometer, and the matching degree is set to >0.7;

[0199] S55. Abundance correction: After the process-based identification is completed, the abundance and types of microplastics obtained by the Raman spectrometer are subtracted from the microplastics measured by the laser infrared spectrometer, and the data measured by μFTIR are combined to obtain the full-size microplastic abundance of a certain tissue of the measured shellfish. At the same time, the sum of the microplastic abundances of all tissues is the abundance of microplastics enriched in the corresponding shellfish monomer.

[0200] S6. Carbon-14 dating method: Use the carbon-14 dating method to determine the age node of the survival of the corresponding mussel monomer.

[0201] S61, drilling and crushing: take out the mussel shells sealed with ultrapure water during the previous dissection, clean them with ultrapure water to remove pollutants, find the inorganic calcium carbonate prism interlayer in the middle layer of the shell under a micro-Raman spectrometer, and use a micro-drill to accurately drill out about 1g of the sample, and place it in a glass tube for later use;

[0202] S62, first pickling treatment: using 1M dilute hydrochloric acid solution to clean the fine powder obtained by grinding under a 100-mesh stainless steel sieve, slightly etching the surface of the shell powder to remove possible secondary calcium carbonate;

[0203] S63, second alkaline washing treatment: using 1M NaOH solution to wash the acid-washed powder obtained in the previous step to remove most of the residual organic impurities;

[0204] S64, third pickling treatment: wash the obtained alkali-washed powder again with 1M dilute hydrochloric acid solution, then wash the remaining pickling solution with ultrapure water, and dry it in a 70° oven for 12 hours to ensure that no impurities are introduced into the oven to interfere with the carbon component of the fine powder;

[0205] S65, thermal decomposition: take 200 mg of the dried fine powder and put it in a thermal decomposition furnace, set the parameters to 850°C, heating rate 10°C / min, holding time 2h, cooling time 3h;

[0206] S66, Carbon dioxide capture: The carbon dioxide generated during the thermal decomposition process is absorbed by a gas capture system containing 2M sodium hydroxide solution connected to the thermal decomposition furnace;

[0207] S67, purify carbon dioxide: add 5M hydrochloric acid drop by drop to release the carbon dioxide in the absorbent until no bubbles are generated, pass 1mL of gas through the KOH solid absorption column, and pass it into the gas chromatograph. If there is only a peak of m / z 44 on the mass spectrum displayed by the mass spectrometer MS detector, and the peaks of other values ​​do not exist or are very small, it means that the carbon dioxide sample is purified successfully and you can proceed to the next step;

[0208] S68, carbon fixation: in a reduction furnace, set the temperature to 800°C for preheating for 2 hours, place excess iron powder as a catalyst, introduce about 60 mL of the remaining purified carbon dioxide gas under the condition of hydrogen as a barrier gas, set the reaction time to 2 hours, and after the reduction furnace is cooled to room temperature, use pliers to take out the fixed solid carbon;

[0209] S69, Carbon-14 dating: Load the prepared pure carbon powder into the sample chamber of the isotope mass spectrometer, set the thermal ionization mode, set the ion source energy at 2000V, and the ion accelerator at 300WV, measure the ion flow of -14 and -12 with specific mass / charge ratios, and obtain the content and relative ratio of carbon-14 and carbon-12 in the shell sample;

[0210] S610, Age estimation: The relative ratio of carbon-14 / carbon-12 measured in the shell of each mussel monomer, the half-life of carbon-14 in the ocean (about 5568 years), and the carbon ratio of the modern water environment are about 1.176×10 -12 Substitute the carbon-14 dating formula and use the international 14 The calculation results were corrected using the common method recommended by Committee C (CALIB software), and the correction value for the marine reservoir effect was set to -178±50yr to obtain a relatively accurate survival age for each shell.

[0211] S7. Dynamic model of long-term adsorption of microplastics in extreme ecosystems: Calibrate environmental conditions, and build a microplastic enrichment curve in mussels based on the abundance of microplastics enriched in mussel tissues and adsorption dynamics model. By combining the survival age of each mussel with the sampling pattern, the rate and accumulation of microplastics in the seabed ecosystem can be inferred.

[0212] S71. Set standards: Set the concentration of microplastics in the surrounding seawater to a uniform and constant ideal condition, set the ratio of the abundance of microplastics in the mussel's lip tissue to the concentration of microplastics in the environment to a constant feeding rate, set the abundance ratio of microplastics in the gills and visceral mass to the absorption coefficient of the mussel, and set the inverse ratio of the abundance of microplastics in the visceral mass and intestine to the exclusion coefficient of the mussel transferred to the body. The formula can be used to calculate the microplastic accumulation rate of each mussel in an extreme environment;

[0213] S72. Based on the typical mussel populations selected from each age group, the average microplastic accumulation rate of each age group was calculated. By inserting the time nodes obtained by shell dating, a time series model of microplastic enrichment in the whole cycle of a single mussel from birth to natural death in the methane seepage area can be approximately established. Figure 3 ;

[0214] S73. The average microplastic accumulation rate and amount of each age group are proportionally extrapolated to the entire ecosystem by collecting sampling statistical data, and the enrichment level of microplastics and potential pollution characteristics in the entire methane seepage area ecosystem in the past hundred years are inferred.

[0215] The experimental data on the enrichment of microplastics in the above-mentioned ecosystems are shown in Table 1 below.

[0216] Table 1

[0217]

[0218]

[0219]

[0220] Existing technologies have not yet explored the spatial enrichment capacity of microplastics in extreme environments. Currently, there is only one method for identifying the spatial distribution of microplastic-contaminated waters based on remote sensing, which can be used to identify the spatial distribution characteristics of microplastics, but does not involve the temporal enrichment capacity of ecosystems. It mainly includes the following steps:

[0221] (1) Water sample collection: Collect water samples in the target water area, record the latitude and longitude of the sampling point, filter it through a filter, and rinse the residue on the filter and transfer it to a glass bottle;

[0222] (2) Pretreatment: Use hydrogen peroxide solution to digest the natural organic matter in the water sample in the glass bottle, and use a glass fiber filter membrane to vacuum filter the digestion solution to enrich the microplastics in the water onto the membrane;

[0223] (3) Identification statistics: qualitatively judge the microplastics on the film by visual inspection under a stereo microscope, and record the color, quantity, size and shape of the microplastics;

[0224] (4) Remote sensing database construction: A circular area with a radius of 100 m centered on each collection point is set as the research area. The remote sensing impact spectral curve characteristics of each research area are extracted. The microplastic information obtained previously is compared with the spectral characteristic curve. Figure 1 One by one, establish a spectral library of microplastic polluted waters;

[0225] (5) Pollution identification: Obtain hyperspectral data of unknown waters, match and identify their spectral characteristic curves with the database, and quickly identify microplastic pollution areas in surface waters.

[0226] Compared with this embodiment 1, the prior art only involves the identification of the spatial distribution of microplastics in shallow waters or the pollution assessment, and does not involve the dynamic rate characterization of microplastic adsorption in extreme ecosystems. In addition, most studies focus on the spatial pollution of microplastics formed instantly, and there are not many strategies for expressing the temporal progressive pollution or enrichment capacity of microplastics. In order to solve the gap in existing technologies, different from ordinary water bodies or sediment carrier media, the present invention provides a new technical solution for expressing the spatiotemporal enrichment of microplastics. It uses extreme ecosystem indicator organisms as research objects, extracts microplastics in their tissues, and uses carbon-14 dating to develop an adsorption curve of the biological temporal enrichment rate of microplastics, thereby applying statistical principles to deduce it to the enrichment law of microplastics in extreme ecosystems on the seabed, so as to evaluate the spatiotemporal distribution and enrichment trend of microplastics in special environments.

[0227] The description of the above embodiments is only used to help understand the technical solution and core ideas of the present invention. It should be pointed out that for technicians in this technical field, several improvements and modifications can be made to the present invention without departing from the principles of the present invention. These improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A method for detecting the degree of microplastic accumulation in bivalve molluscs in deep-sea extreme environments, characterized in that: The steps include: S1. Collect and estimate the total amount of bivalve molluscs in the region: S11. Search for live bivalve beds in the early and middle stages of methane seep development in deep-sea methane seep areas, measure the bed area, and find its equivalent diameter and geometric center; S12, grabbing the bivalve mollusc bed several times, with the grabbing positions being located at the two ends and the midpoint of the radius of the equivalent circle, counting the number of live bivalve molluscs after grabbing them ashore, washing the bivalve molluscs, draining the water and storing them in a frozen state; S13. Based on the number of bivalve molluscs captured in several times, roughly estimate the total amount of live molluscs in the entire bivalve mollusc bed; S2. Mussel morphological measurement: The bivalve samples were thawed, and the morphological data were measured and weighed in batches. They were divided into four age groups: juvenile, young, middle-aged, and old according to the order of shell length; S3. Multivariate factor analysis: Based on the morphological data obtained in step S2, the multivariate factor analysis statistical method is used to correlate the relationship between the bivalve molluscs of different ages on the seabed and their appearance length and wet weight, and the top five bivalve molluscs that can replace the characteristics of the four groups of each age group are found, and a total of 20 bivalve molluscs are obtained; S4, microplastic enrichment and extraction: separate and extract the microplastics from each tissue of the four groups of typical bivalve samples of age groups selected by multivariate factor analysis in step S3, and finally obtain a microplastic purification filter membrane to facilitate subsequent observation and detection; S5, instrument identification: The microplastics with a size of 0.001-5 mm in the microplastic purification filter membrane obtained in step S4 are identified in turn, and the occurrence state of microplastics in bivalve molluscs and the physical changes of microplastics after entering the deep sea are comprehensively analyzed; S6. Carbon-14 dating method: Use carbon-14 dating method to determine the age nodes of the corresponding bivalve molluscs, so as to convert the survival years of the four-age-group molluscs into the time limit for the bivalve molluscs to absorb microplastics; S7. Dynamic enrichment model of microplastics in bivalve molluscs: An adsorption kinetic model is used to construct a microplastic enrichment curve in bivalve molluscs, and the enrichment coefficient is calculated to indicate the degree of enrichment of microplastics in the sample relative to its concentration in the environment. A time series model is established based on the microplastic content contained in the top five shellfish monomers with typical alternative community characteristics selected from four age groups and their corresponding carbon-14 estimated ages. The temporal adsorption of microplastics by shellfish monomers obtained from all measurement data is inverted to the entire bivalve bed through sampling rules, and the rate of microplastic enrichment in the entire seabed extreme environment ecosystem and the total amount of microplastic accumulation in the system are approximately inferred.

2. The method according to claim 1, characterized in that The specific steps of step S2 are: all bivalve mollusc samples accumulated from the seabed for several times are measured and weighed in batches for morphological measurement, and three body length parts of the bivalve molluscs are benchmarked to obtain the shell length L, shell width W, and shell height H of each living mollusc, and four shell length ranges are set according to the body length standards corresponding to different age groups of general bivalve molluscs, which are 0<L≤55mm, 55mm<L≤80mm, 80mm<L≤110mm, and 110mm<L, which are divided into four age groups: infant, youth, middle-aged, and elderly.

3. The method according to claim 1, characterized in that The specific steps of step S3 are: S31, based on the morphological data obtained in step S2, perform categorical sampling, add a new age classification array to the original four columns of basic data, use numbers 1, 2, 3, 4 to replace the corresponding age stage of each shellfish - young, young, middle-aged, old, and then set the age grouping as a categorical variable array, and the remaining four columns of morphological arrays are quantitative variable arrays; S32, creating an indicator matrix for the existing age classification array, calculating its standardized contingency table and performing singular value decomposition, performing first singular value standardization according to the elements on the obtained diagonal matrix, and expanding the age classification array to a two-dimensional space display to obtain an MCA standardized coordinate matrix; S33, normalize or center the remaining four quantitative arrays, calculate their covariance matrix, and find their eigenvalues ​​and eigenvectors, select the first k eigenvectors to form a matrix Q, and project it into a new two-dimensional space to obtain the coordinate matrix after dimensionality reduction, from which the factor score coefficient matrix of each quantitative array after dimensionality reduction can be calculated; S34, dividing the normalized or centered matrix of the four-column quantitative array by the square root of the first axis eigenvalue of its covariance matrix to obtain a factor loading matrix describing the linear combination relationship between the four-column quantitative array and the common factor, and merging this loading matrix with the first singular value standardized matrix of the age classification array to form a global factor loading matrix T; S35, perform multivariate factor analysis on the global factor loading matrix T, and the matrix TT T Convert it into a projection matrix P, and project the matrix T into the ordination diagram of the multivariate factor analysis model through the matrix P to calculate the variable load contribution rate of the five columns of morphological arrays to the global factor coordinate matrix; S36. Sort the variable load contribution rate of each array by size, and select the top five mussels in each age group to represent the microplastic enrichment characteristics of the community in the corresponding age group.

4. The method according to claim 1, characterized in that: The specific steps of step S4 are: S41, pretreatment: dissecting the bivalve molluscs into separate tissues and freeze-drying them; S42, enzyme digestion: using trypsin solution to digest organic matter from the freeze-dried tissue to obtain an enzyme digestion solution; S43, pH enhancement: adjusting the pH of the enzyme digestion solution to 7.5 to obtain a tissue digestion solution rich in filamentous condensates; S44, progressive digestion with hydrogen peroxide: using hydrogen peroxide to completely eliminate the cells rich in filamentous condensates in the enzyme digestion solution, releasing the microplastics remaining in the intercellular spaces, and obtaining a hydrogen peroxide digestion solution; S45, first membrane purification: vacuum filter the enzyme digestion solution and the hydrogen peroxide digestion solution, repeat several times, and the obtained microplastic purification membrane is ready for use; S46. Second membrane purification: Leach out the microplastics on the purification membrane to remove the remaining organic matter on the membrane.

5. The method according to claim 4, characterized in that The specific steps of step S42 enzyme digestion are: S421. Prepare trypsin solution: dissolve 6.80 g potassium dihydrogen phosphate in 500 mL water, add 0.1 mol / L potassium hydroxide solution to adjust the pH to 7.5, add 10.00 g trypsin, add water to dissolve, and dilute to 1 L; S422. Enzyme digestion: add trypsin solution to the freeze-dried bivalve mollusc tissue at a ratio of 1 g of bivalve mollusc: 30 mL of trypsin solution, and shake to digest to obtain an enzyme digestion solution with filamentous coagulation bodies.

6. The method according to claim 1, characterized in that The specific steps of step S5 are: S51, stereo microscope observation: place the microplastic purification filter membrane obtained in step S4 under a stereo microscope, search for suspected microplastics larger than 100 μm, record their morphological parameters, and transfer them to a 25 mm glass fiber filter membrane; S52, Microscopic infrared identification: Select representative suspected microplastics on the glass fiber filter membrane for qualitative analysis under μFTIR, and the library matching degree is set to be greater than 70%; S53, Raman observation and identification: Place the purified filter membrane picked out by stereo microscope on a Raman spectrometer to observe and identify the morphological parameters of 1-20 μm microplastics on the membrane. Microplastics are identified when the library comparison rate is greater than 70%; S54, laser infrared identification: the purified filter membrane identified by Raman spectrometer is extracted and concentrated with anhydrous ethanol, dripped onto high-reflective glass, and the composition is determined in a selected area under Agilent LDIR laser infrared spectrometer, with the matching degree set to >0.7; S55. Abundance correction: After the process-based identification is completed, the abundance and types of microplastics measured by the laser infrared spectrometer are subtracted from the abundance and types of microplastics obtained by the Raman spectrometer, and the data measured by μFTIR are combined to obtain the full-size microplastic abundance of a certain tissue of the measured shellfish monomer. At the same time, the sum of the microplastic abundances of all tissues is the abundance of microplastics enriched in the corresponding shellfish monomer.

7. The method according to claim 1, characterized in that The specific steps of step S6 are: S61, drilling and crushing: taking out the bivalve shells preserved during the dissection in step S4, washing them, finding the inorganic calcium carbonate prism interlayer in the middle layer of the shells under a micro-Raman spectrometer, and using a micro-drill to accurately drill samples for standby use; S62, first pickling treatment: using a dilute hydrochloric acid solution to wash the powder obtained in step S61; S63, second alkaline washing treatment: washing the obtained acid-washed powder with a NaOH solution; S64, third pickling treatment: washing the obtained alkali-washed powder with dilute hydrochloric acid solution again, washing the remaining pickling solution, and drying for standby use; S65, thermal decomposition: taking the dried fine powder and placing it in a thermal decomposition container for thermal decomposition; S66, Carbon dioxide capture: The carbon dioxide generated during the thermal decomposition process is absorbed by a gas capture system containing 2M sodium hydroxide solution connected to the thermal decomposition furnace; S67, purify carbon dioxide: add hydrochloric acid drop by drop to release the carbon dioxide in the absorbent until no bubbles are generated, pass the gas through the KOH solid absorption column, and then pass it into the gas chromatograph. If there is only a peak of m / z 44 on the mass spectrum displayed by the mass spectrometer MS detector, and the peaks of other values ​​do not exist or are very small, it means that the carbon dioxide sample is purified successfully and you can proceed to the next step; S68, carbon fixation: in a reduction furnace, set a preheating temperature, place an excess of iron powder as a catalyst, introduce the remaining purified carbon dioxide gas under the condition of hydrogen as a barrier gas for reaction, and take out the solid carbon after the reduction furnace is cooled to room temperature; S69, Carbon-14 determination: The prepared pure carbon powder was loaded into the sample chamber of the isotope mass spectrometer, and the thermal ionization mode was set. The ion source energy was set at 2000V, and the ion accelerator was set at 300WV. The ion currents of the carbon-14 and carbon-12 with specific mass / charge ratios were measured. The ion beam intensities I of carbon-14 and carbon-12 were measured. C-14 ,I C-12 , assuming the normalization factor is k, calculate the relative content R of carbon-14 and carbon-12, the expression is shown in formula (28): S610, Age estimation: The relative ratio of carbon-14 / carbon-12 measured in the shell of each bivalve monomer, the half-life of carbon-14 in the ocean and the carbon ratio of the modern water environment are entered into the formula of carbon-14 dating method. At the same time, CALIB software is used to correct the calculation results. The correction value for the marine reservoir effect is set to -178±50yr to obtain a relatively accurate survival age for each shell.

8. The method according to claim 7, characterized in that The specific steps of step S610 are as follows: according to the formula of carbon-14 dating, the relative ratio of carbon-14 / carbon-12 measured in each bivalve sample is taken into account, and the decay of carbon-14 in the ocean is carried out according to the half-life of carbon-14 and the carbon ratio of the modern water environment is about 1.176×10 -12 The measurement results were corrected by CALIB software and expressed in calendar age yr cal BP. The correction value for the marine reservoir effect was -178±50yr. Finally, the relative estimated age of each shell was obtained. According to the relative content R obtained, λ was set as the decay coefficient and R0 was set as the modern carbon standard value. The accurate age t of the sample was calculated by substituting the data into the expression as shown in formula (29):

9. The method according to claim 1, characterized in that: The specific steps of step S7 are: S71. Set standards: Set the concentration of microplastics in the surrounding seawater to a uniform and constant ideal condition, set the ratio of the abundance of microplastics in the lip tissue of bivalve molluscs to the concentration of microplastics in the environment to a constant feeding rate, set the abundance ratio of microplastics in the gills and visceral mass to the absorption coefficient of bivalve molluscs, and set the inverse ratio of the abundance of microplastics in the visceral mass and intestine to the exclusion coefficient of bivalve molluscs transferred to the body, and the microplastic accumulation rate of each mussel in the extreme environment can be obtained; S72. Calculate the average microplastic accumulation rate of each age group based on the typical mussel population selected from each age group, and bring into the time nodes obtained by shell dating to approximately establish a time series model of microplastic enrichment in the entire cycle of a single mussel from birth to natural death in the methane seepage area; S73. The average microplastic accumulation rate and amount of each age group are proportionally extrapolated to the entire ecosystem by collecting sampling statistical data, and the enrichment level of microplastics and potential pollution characteristics in the entire methane seepage area ecosystem are inferred.

10. The method according to claim 1, characterized in that The bivalve molluscs include mussels, white melon clams and phoenix clams.

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