Method for evaluating carbon distribution coefficient of phytoplankton based on FT-ICR MS technology and ocean carbon library prediction method

Abstract

The embodiment of the invention provides a method for evaluating a phytoplankton carbon distribution coefficient based on an FT-ICR MS technology and an ocean carbon library prediction method, and belongs to the technical field of ocean carbon circulation. The method comprises the following steps: arranging a phytoplankton culture; based on the phytoplankton culture, acquiring sample data of soluble organic carbon; according to the sample data of the soluble organic carbon, obtaining carbon distribution mode analysis data; according to the sample data of the soluble organic carbon, obtaining soluble organic carbon component characterization data based on FT-ICR MS; and acquiring phytoplankton carbon distribution coefficient evaluation information according to the carbon distribution mode analysis data and the soluble organic carbon component characterization data based on FT-ICR MS. According to the method, the dynamic change of the global ocean soluble carbon library can be reliably predicted.

Description

Technical Field

[0001] This application relates to the field of marine carbon cycle technology, and in particular to a method for assessing phytoplankton carbon allocation coefficients based on FT-ICR MS technology and a method for predicting marine carbon pools. Background Technology

[0002] In some related technologies, some marine phytoplankton models estimate the carbon allocation between DOC and particulate organic carbon (POC) using functional group model parameters. DOC is typically considered to account for 30% of total carbon, and this parameter is directly used to assess the contribution of phytoplankton to the global marine DOC pool and its dynamic changes. However, traditional particulate organic carbon (POC) analysis often excludes intracellular DOC (DOC) from the total carbon content. in ) and cell-bound exogenous DOC (b-DOC) ex This inclusion leads to an underestimation of the DOC portion (i.e., the existing parameter). in and b-DOC ex It will be gradually released and enter soluble DOC (s-DOC) as algae age and settle. ex ) stage, especially b-DOC ex This refers to exogenous polymeric substances (EPS) derived from algae, which are often overlooked due to their strong adhesion and difficulty in separation. This b-DOC... ex The composition of phytoplankton significantly affects the sedimentation efficiency of algal-derived phosphorus compounds (POCs), with its proportions fluctuating among different phytoplankton species and at different growth stages. Therefore, the differences in cellular composition among phytoplankton species and at different growth stages may introduce significant uncertainty into the prediction of the global carbon cycle. Summary of the Invention

[0003] The main objective of this application is to provide a method for evaluating phytoplankton carbon allocation coefficients and a method for predicting marine carbon pools based on FT-ICR MS technology.

[0004] The technical solution adopted in this invention is:

[0005] On one hand, embodiments of the present invention provide a method for evaluating the carbon allocation coefficient of phytoplankton based on FT-ICR MS technology, the method comprising the following steps:

[0006] Set up phytoplankton culture;

[0007] Based on the phytoplankton culture, sample data of dissolved organic carbon were obtained;

[0008] Based on the sample data of dissolved organic carbon, carbon partition pattern analysis data are obtained;

[0009] Based on the sample data of the dissolved organic carbon, obtain the component characterization data of the dissolved organic carbon based on FT-ICR MS;

[0010] Based on the carbon allocation pattern analysis data and the FT-ICR MS-based characterization data of dissolved organic carbon components, phytoplankton carbon allocation coefficient assessment information was obtained.

[0011] Furthermore, the setting of the phytoplankton culture includes the following steps:

[0012] Identify the target taxa of phytoplankton;

[0013] Based on the target taxonomic group, a culture system data is established; the culture system data includes culture medium preparation information and environmental parameter control information.

[0014] Acquire growth monitoring data; the growth monitoring data includes cell density and growth stage data;

[0015] Based on the target taxa, the culture system data, and the growth monitoring data, the phytoplankton culture was set up.

[0016] Furthermore, obtaining sample data on dissolved organic carbon based on the phytoplankton culture includes the following steps:

[0017] Based on the phytoplankton culture, the target sampling solution was obtained;

[0018] Based on the target sampling solution, sample data of dissolved organic carbon are obtained; the sample data of dissolved organic carbon includes extracellular soluble organic carbon data, extracellular bound organic carbon data, intracellular soluble organic carbon data, and particulate organic carbon data.

[0019] Further, obtaining carbon partition pattern analysis data based on the sample data of the dissolved organic carbon includes the following steps:

[0020] Based on the sample data of the dissolved organic carbon, an elemental analyzer was used to determine the concentration and detect the elemental content to obtain the organic carbon component concentration information; the organic carbon component concentration information includes the concentration of extracellular soluble organic carbon, the concentration of extracellular bound organic carbon, and the concentration of intracellular dissolved organic carbon.

[0021] The total amount of intracellular dissolved organic carbon and particulate organic carbon was obtained using an elemental analyzer, and the content data of particulate organic carbon was obtained by the difference calculation method.

[0022] Based on the organic carbon component concentration information and the particulate organic carbon content data, carbon distribution pattern analysis data are obtained.

[0023] Further, the step of obtaining FT-ICR MS-based characterization data of dissolved organic carbon components based on the sample data of the dissolved organic carbon includes the following steps:

[0024] Based on the sample data of dissolved organic carbon, solid-phase extraction was performed using a styrene-divinylbenzene polymer extraction column to obtain pretreated sample data;

[0025] Based on the preprocessed sample data, mass spectrometry data were acquired using FT-ICR MS.

[0026] Based on the sample data of the dissolved organic carbon, the mixture of extracellular bound organic carbon and intracellular dissolved organic carbon was compared with a pure extracellular bound organic carbon solution to obtain the molecular formula in which both extracellular bound organic carbon and intracellular dissolved organic carbon are present.

[0027] The relative abundance of each molecular formula was calculated using the difference method to obtain relative abundance data;

[0028] Based on the mass spectrometry data and the relative abundance data, characterization data of soluble organic carbon components based on FT-ICR MS were obtained.

[0029] Further, the step of obtaining phytoplankton carbon allocation coefficient assessment information based on the carbon allocation pattern analysis data and the FT-ICR MS-based dissolved organic carbon component characterization data includes the following steps:

[0030] Based on the carbon allocation pattern analysis data and the soluble organic carbon component characterization data based on FT-ICR MS, the changes in soluble organic carbon of different phytoplankton species were analyzed to obtain soluble organic carbon change data; the soluble organic carbon change data includes extracellular soluble organic carbon change data, extracellular bound organic carbon change data, and intracellular soluble organic carbon change data.

[0031] Based on the data on changes in dissolved organic carbon, the theoretical percentage contribution of phytoplankton-derived carbon to marine carbon sinks at different growth stages and the stage-specific differences in phytoplankton-derived carbon were analyzed. The theoretical percentage contribution includes the contribution percentage of degradable dissolved organic carbon, the contribution percentage of carbon-storing algal-derived organic matter, and the contribution percentage of particulate organic carbon. The stage-specific differences in phytoplankton-derived carbon include differences during the growth period and differences during the decay period.

[0032] Based on the changes in dissolved organic carbon, the theoretical contribution percentage information, and the stage-specific differences in phytoplankton-derived carbon, phytoplankton carbon allocation coefficient assessment information is obtained.

[0033] On the other hand, embodiments of the present invention also provide a method for predicting marine carbon pools, which is implemented by evaluating phytoplankton carbon allocation coefficients using the FT-ICR MS technology described above. The marine carbon pool prediction method includes the following steps:

[0034] The carbon allocation coefficient of phytoplankton, obtained by the aforementioned method based on FT-ICR MS technology, was used to determine the content of algal-derived organic matter in carbon storage.

[0035] Based on the content of algal-derived organic matter in the carbon storage, the recalcitrant organic carbon directly secreted by phytoplankton is obtained.

[0036] Furthermore, the marine carbon pool prediction method also includes:

[0037] The total organic carbon of phytoplankton, which is set at 0.4%, is converted into recalcitrant organic carbon by microorganisms, resulting in indirectly secreted recalcitrant organic carbon.

[0038] The theoretical monthly variation of marine dissolved organic carbon production is obtained based on the directly secreted recalcitrant organic carbon and the indirectly secreted recalcitrant organic carbon.

[0039] Furthermore, the formula used to obtain the theoretical monthly variation of marine dissolved organic carbon production based on the directly secreted recalcitrant organic carbon and the indirectly secreted recalcitrant organic carbon includes:

[0040] TheoreticalΔDOC production(mgC(L·M) -1 ) = RDOC direct (mgC(L·M) -1 )+RDOC indirect (mgC(L·M) -1 );

[0041] Among them, Theoretical ΔDOC production (mgC(L·M)) -1 ) represents the theoretical monthly variation in marine dissolved organic carbon production; RDCO direct (mgC(L·M) -1 ) represents the carbon content per liter of seawater per month that is directly secreted by phytoplankton; RDOC indirect (mgC(L·M) -1 This represents the amount of carbon in each liter of seawater per month that is indirectly converted into recalcitrant organic carbon by microorganisms from biodegradable dissolved organic carbon secreted by phytoplankton.

[0042] Furthermore, the formula used in the marine carbon pool prediction method also includes:

[0043] RDOC direct (mgC(L·M) -1 )=∑Phylum i (mgChla(L·M) -1 )×CRAM i (mgC·mg -1 Chla);

[0044] RDOC indirect (mgC(L·M) -1 )=∑Phylum i (mgChla(L·M) -1 )×TOC i (mgC·mg -1 Chla) × 0.4%;

[0045] Among them, Phylum i (mgChla(L·M) -1 () represents the chlorophyll a content of any phytoplankton taxa per liter of seawater per month; CRAM i (mgC·mg -1 CHla) represents the mass of algal-derived organic matter that can be produced by a unit of chlorophyll a in any phytoplankton taxa; TOC i (mgC·mg -1 Chla) represents the mass of total organic carbon that can be produced by the chlorophyll a content of any phytoplankton taxa unit.

[0046] The embodiments of this application include at least the following beneficial effects: This application provides a method for assessing phytoplankton carbon allocation coefficients and a method for predicting marine carbon pools based on FT-ICR MS technology. The steps of this invention include setting up a phytoplankton culture; acquiring sample data of dissolved organic carbon based on the phytoplankton culture; acquiring carbon allocation pattern analysis data based on the dissolved organic carbon sample data; acquiring FT-ICR MS-based characterization data of dissolved organic carbon components based on the dissolved organic carbon sample data; and acquiring phytoplankton carbon allocation coefficient assessment information based on the carbon allocation pattern analysis data and the FT-ICR MS-based characterization data of dissolved organic carbon components. This invention can reliably predict the dynamic changes of the global marine dissolved carbon pool. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of a method for evaluating phytoplankton carbon allocation coefficient based on FT-ICR MS technology provided in an embodiment of the present invention;

[0048] Figure 2(a) shows an image of phytoplankton culture and phytoplankton-derived organic carbon components (including s-DOC) provided in an embodiment of the present invention.ex b-DOC ex DOC in A schematic diagram of the separation method between POC and POC;

[0049] Figure 2(b) is a schematic diagram of the carbon allocation characteristics (the proportion of each organic carbon component to total organic carbon (TOC)) of each phytoplankton group at different growth stages provided in the embodiments of the present invention.

[0050] Figure 2(c) is a schematic diagram of the average proportion (percentage of TOC) of each organic carbon component in two growth stages for all phytoplankton taxa studied in the embodiments of the present invention.

[0051] Figure 2(d) is a schematic diagram showing the contribution of different phytoplankton groups to various organic carbon components during the growth and death phases provided in the embodiments of the present invention.

[0052] Figure 3(a) shows the FT-ICR MS features (i.e., protein-like, carbohydrate-like, fatty acid-like, CRAM, and other compounds) of various DOC components (including s-DOC) in different phytoplankton phyla provided in the embodiments of the present invention. ex b-DOC ex DOC in A schematic diagram illustrating the changes in )

[0053] Figure 3(b) is a schematic diagram of the theoretical contribution percentage of phytoplankton-derived carbon to marine carbon sinks at two different growth stages provided by the embodiments of the present invention, the stage-specific differences, the differences in the contribution percentage of these pathways among different phyla at each growth stage, and the CRAM content of each phylum at two different growth stages.

[0054] Figure 4 This is a schematic diagram of the model optimization of the global marine dissolved carbon pool based on carbon allocation coefficient optimization provided in the embodiments of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0056] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0057] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0059] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0060] 1) FT-ICR MS (Fourier Transform Ion Cyclotron Resonance Mass Spectrometry);

[0061] 2) DOC (Dissolved Organic Carbon);

[0062] 3) POC (Particulate Organic Carbon);

[0063] 4) DOC in (Initial DOC), intracellular dissolved organic carbon;

[0064] 5) b-DOC ex (Bioavailable DOC), extracellular bound organic carbon;

[0065] 6)s-DOC ex (Semi-labile DOC), extracellular soluble organic carbon;

[0066] 7) TOC (Total Organic Carbon);

[0067] 8) EPS (Extracellular Polymeric Substances);

[0068] 9) CRAM (Carboxyl-Rich Alicyclic Molecules), a type of carboxylic acid-rich alicyclic molecule;

[0069] 10) Erdschreiber, artificial seawater culture medium;

[0070] 11) Mini-Beadbeater 16, a model of mini bead mill crusher;

[0071] 12) Biospec, model number of biological sample processing equipment;

[0072] 13) Whatman, filter membrane / filter paper model;

[0073] 14) Carl Roth, organic solvents and standard products;

[0074] 15) ASI-V, the model number for the autosampler;

[0075] 16) Vario MAX cube CN, elemental analyzer model;

[0076] 17) Elementar, model of elemental analyzer;

[0077] 18) Hansell and Carlson's protocol, DOC / DOM parsing method;

[0078] 19) SPE (Solid-Phase Extraction);

[0079] 20) PPL, styrene-divinylbenzene polymer adsorbent;

[0080] 21) Agilent, a scientific instrument model;

[0081] 22) HPLC (High-Performance Liquid Chromatography);

[0082] 23) Sigma-Aldrich, models of standards, solvents and laboratory consumables;

[0083] 24) DOM (Dissolved Organic Matter), dissolved organic matter;

[0084] 25) ESI (Electrospray Ionization);

[0085] 26) The Gan and Guo protocol, an FT-ICR MS data acquisition method;

[0086] 27) Bruker Daltonics Data Analysis software package; Bruker mass spectrometry data analysis software;

[0087] 28) DBE (Double Bond Equivalent), the number of equivalent double bonds;

[0088] 29) RDOC (Refractory DOC), recalcitrant organic carbon;

[0089] 30) Chla (Chlorophyll a), chlorophyll a;

[0090] 31) BDOC (Biodegradable DOC), degradable dissolved organic carbon.

[0091] This invention considers that the marine dissolved organic carbon (DOC) pool is a crucial component of the global carbon cycle, containing approximately 662 pg of carbon, exceeding the total carbon storage in all marine and terrestrial biospheres. Phytoplankton are major contributors to the marine DOC pool, playing a key role in the marine carbon cycle. Understanding the phytoplankton DOC allocation mechanism is fundamental to accurately assessing the contributions of various phytoplankton species to the marine DOC pool. Unlike terrestrial organisms, phytoplankton allocate a significant amount of their carbon to the external environment in dissolved organic forms. Existing marine phytoplankton models estimate the carbon allocation between DOC and particulate organic carbon (POC) using functional group model parameters, typically assuming DOC accounts for 30% of total carbon, and directly use this parameter to assess the contribution of phytoplankton to the global marine DOC pool and its dynamic changes.

[0092] However, traditional particulate organic carbon (POC) analysis often excludes intracellular DOC (DOC). in ) and cell-bound exogenous DOC (b-DOC) ex This inclusion leads to an underestimation of the DOC portion (i.e., the existing parameters). in and b-DOC ex It will be gradually released and enter soluble DOC (s-DOC) as algae age and settle. ex ) stage, especially b-DOC ex This refers to exogenous polymeric substances (EPS) derived from algae, which are often overlooked due to their strong adhesion and difficulty in separation. This b-DOC...ex Composition significantly affects the sedimentation efficiency of algal-derived POCs, with their proportions fluctuating among different phytoplankton species and at different growth stages. This suggests that differences in cellular composition among phytoplankton species and at different growth stages may introduce considerable uncertainty into predictions of the global carbon cycle. Therefore, accurately separating the DOC components of phytoplankton at different growth stages and accurately characterizing their molecular features is of great significance for improving existing phytoplankton models.

[0093] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0094] On one hand, embodiments of the present invention provide a method for evaluating the carbon allocation coefficient of phytoplankton based on FT-ICR MS technology, with reference to Figure 1 The method for assessing the carbon allocation coefficient of phytoplankton based on FT-ICR MS technology includes the following steps:

[0095] S100, Set up phytoplankton culture;

[0096] S200, obtaining sample data of dissolved organic carbon based on phytoplankton culture;

[0097] S300. Based on the sample data of dissolved organic carbon, obtain carbon partition pattern analysis data;

[0098] S400. Based on the sample data of dissolved organic carbon, obtain the characterization data of dissolved organic carbon components based on FT-ICR MS;

[0099] S500, based on carbon allocation pattern analysis data and FT-ICR MS-based characterization data of dissolved organic carbon components, obtains phytoplankton carbon allocation coefficient assessment information.

[0100] S100 of this embodiment of the invention sets up a phytoplankton culture, including the following steps:

[0101] S110. Identify the target taxa of phytoplankton;

[0102] S120. Establish culture system data based on the target taxonomic group; the culture system data includes culture medium preparation information and environmental parameter control information;

[0103] S130. Obtain growth monitoring data; growth monitoring data includes cell density and growth stage data.

[0104] S140. Based on the target taxa, culture system data, and growth monitoring data, complete the setup of phytoplankton culture.

[0105] As an optional implementation, the embodiments of the present invention select representative phytoplankton species or groups, and select taxa (such as diatoms, dinoflagellates, and dinoflagellates) according to the research objectives, considering their abundance in the ecosystem, carbon allocation characteristics, or response to environmental changes.

[0106] The culture medium preparation information includes the selection of culture medium, such as artificial seawater culture medium (e.g., Erdschreiber).

[0107] Environmental parameter control information includes light intensity, light-dark cycle, temperature, pH, and salinity.

[0108] S200 of this embodiment of the invention obtains sample data of dissolved organic carbon based on phytoplankton culture, including the following steps:

[0109] S210. Obtain the target sampling solution based on phytoplankton culture;

[0110] S220. Based on the target sampling solution, obtain sample data of dissolved organic carbon; the sample data of dissolved organic carbon includes extracellular soluble organic carbon data, extracellular bound organic carbon data, intracellular soluble organic carbon data, and particulate organic carbon data.

[0111] S300 of this embodiment of the invention obtains carbon partition pattern analysis data based on sample data of dissolved organic carbon, including the following steps:

[0112] S310. Based on the sample data of dissolved organic carbon, use an elemental analyzer to determine the concentration and detect the elemental content to obtain the organic carbon component concentration information; the organic carbon component concentration information includes the concentration of extracellular soluble organic carbon, the concentration of extracellular bound organic carbon, and the concentration of intracellular dissolved organic carbon.

[0113] S320. Use an elemental analyzer to obtain the sum of intracellular dissolved organic carbon and particulate organic carbon, and obtain the content data of particulate organic carbon by the difference calculation method.

[0114] S330. Based on the organic carbon component concentration information and particulate organic carbon content data, carbon distribution pattern analysis data are obtained.

[0115] S400 of this embodiment of the invention obtains FT-ICR MS-based characterization data of dissolved organic carbon components based on sample data of dissolved organic carbon, including the following steps:

[0116] S410. Based on the sample data of dissolved organic carbon, solid-phase extraction was performed using a styrene-divinylbenzene polymer extraction column to obtain pretreated sample data.

[0117] S420. Based on the preprocessed sample data, mass spectrometry data is obtained by using FT-ICR MS.

[0118] S430. Based on the sample data of dissolved organic carbon, compare the mixture of extracellular bound organic carbon and intracellular dissolved organic carbon with a pure extracellular bound organic carbon solution to obtain the molecular formula in which both extracellular bound organic carbon and intracellular dissolved organic carbon are present.

[0119] S440. The relative abundance of each molecular formula is calculated using the difference method to obtain the relative abundance data;

[0120] S450. Based on mass spectrometry data and relative abundance data, characterization data of soluble organic carbon components based on FT-ICR MS were obtained.

[0121] S500 of this embodiment of the invention obtains phytoplankton carbon allocation coefficient evaluation information based on carbon allocation pattern analysis data and FT-ICR MS-based dissolved organic carbon component characterization data, including the following steps:

[0122] S510. Based on the carbon partitioning pattern analysis data and the soluble organic carbon component characterization data based on FT-ICR MS, analyze the changes in soluble organic carbon of different phytoplankton species to obtain soluble organic carbon change data; the soluble organic carbon change data includes extracellular soluble organic carbon change data, extracellular bound organic carbon change data, and intracellular soluble organic carbon change data.

[0123] S520. Based on the data on changes in dissolved organic carbon, analyze the theoretical percentage contribution of phytoplankton-derived carbon to marine carbon sinks at different growth stages and the stage-specific differences in phytoplankton-derived carbon. The theoretical percentage contribution includes the contribution of degradable dissolved organic carbon, the contribution of algal-derived organic matter for carbon storage, and the contribution of particulate organic carbon. The stage-specific differences in phytoplankton-derived carbon include differences during the growth period and differences during the decay period.

[0124] S530. Based on the data on changes in dissolved organic carbon, the theoretical contribution percentage information, and the stage-specific differences in carbon from phytoplankton sources, the phytoplankton carbon allocation coefficient assessment information is obtained.

[0125] On the other hand, embodiments of the present invention also provide a method for predicting marine carbon pools, which is implemented by the method described above for evaluating phytoplankton carbon allocation coefficients based on FT-ICR MS technology. The marine carbon pool prediction method includes the following steps:

[0126] S600. Using the phytoplankton carbon allocation coefficient assessment information obtained by the previous method based on FT-ICR MS technology, the content of algal-derived organic matter for carbon storage is determined.

[0127] S700: Based on the content of algal-derived organic matter in carbon storage, obtain the recalcitrant organic carbon directly secreted by phytoplankton.

[0128] The marine carbon pool prediction method of this invention also includes:

[0129] S800, with a preset total organic carbon content of 0.4% from phytoplankton, is converted into recalcitrant organic carbon by microorganisms to obtain indirectly secreted recalcitrant organic carbon;

[0130] S900. Based on directly secreted and indirectly secreted recalcitrant organic carbon, obtain the theoretical monthly variation of marine dissolved organic carbon production.

[0131] In embodiment S900 of the present invention, the theoretical monthly variation of marine dissolved organic carbon production is obtained based on directly secreted and indirectly secreted recalcitrant organic carbon, and the formula used includes:

[0132] TheoreticalΔDOC production(mgC(L·M) -1 )=

[0133] RDOC direct (mgC(L·M) -1 )+RDOC indirect (mgC(L·M) -1 );

[0134] Among them, Theoretical ΔDOC production (mgC(L·M)) -1 ) represents the theoretical monthly variation in marine dissolved organic carbon production; RDOC direct (mgC(L·M) -1 ) represents the carbon content per liter of seawater per month that is directly secreted by phytoplankton; RDOC indirect (mgC(L·M) -1 This represents the amount of carbon in each liter of seawater per month that is indirectly converted into recalcitrant organic carbon by microorganisms from biodegradable dissolved organic carbon secreted by phytoplankton.

[0135] The marine carbon pool prediction method of this invention further includes the following formulas:

[0136] RDOC direct (mgC(L·M) -1 )=∑Phylum i (mgChla(L·M) -1 )×CRAM i (mgC·mg -1 Chla);

[0137] RDOC indirect (mgC(L·M) -1 )=∑Phylum i (mgChla(L·M) -1 )×TOC i (mgC·mg -1 Chla) × 0.4%;

[0138] Among them, Phylum i (mgChla(L·M) -1 () represents the chlorophyll a content of any phytoplankton taxa per liter of seawater per month; CRAM i (mgC·mg -1 Chla) represents the mass of algal-derived organic matter that can be produced by a unit of chlorophyll a in any phytoplankton taxa; TOC i (mgC·mg -1 Chla) represents the mass of total organic carbon that can be produced by the chlorophyll a content of any phytoplankton taxa unit.

[0139] As an optional implementation, this invention proposes a novel method for separating DOC components produced by phytoplankton, based on traditional phytoplankton monoclonal culture technology and the emerging FT-ICR MS technology. Through high-precision molecular characterization analysis, it solves the problem of unclear allocation coefficients of algal-derived DOCs in existing methods. Using this method, the carbon allocation coefficients of DOCs from different phytoplankton species can be more accurately assessed, and by incorporating these coefficients into existing global carbon cycle models, the dynamic changes of the global marine dissolved carbon pool can be predicted more reliably.

[0140] The technical solution of the present invention includes:

[0141] 1. By culturing different taxa of phytoplankton and isolating DOC, the DOC components of different taxa were obtained;

[0142] 2. Obtain the DOC component characteristics and allocation ratios of different taxa based on FT-ICR MS technology;

[0143] 3. Incorporate the obtained carbon allocation ratios into the global ocean dissolved carbon pool model;

[0144] 4. Apply the optimized model to practical applications such as the assessment of the global ocean dissolved carbon pool and climate change prediction.

[0145] This invention not only provides the allocation coefficients of DOC components within the same phytoplankton taxa, but also clarifies the differences in these allocation coefficients between different phytoplankton taxa at two growth stages. Finally, it offers a novel method for predicting marine carbon pools, which has significant scientific and practical value.

[0146] As an optional implementation, embodiments of the present invention include:

[0147] Example 1: Carbon Allocation Pattern Analysis: s-DOC ex b-DOC ex DOC in Preparation and analysis of POC:

[0148] Referring to Figure 2(a) images of phytoplankton culture and methods for separating phytoplankton-derived organic carbon components (including s-DOCex, b-DOCex, DOCin, and POC); Figure 2(b) carbon allocation characteristics of each phytoplankton group at different growth stages (the proportion of each organic carbon component in total organic carbon (TOC)); Figure 2(c) the average proportion of each organic carbon component (as a percentage of TOC) of all studied phytoplankton taxa in two growth stages; Figure 2(d) the contribution of different phytoplankton groups to each organic carbon component during the growth and decay periods.

[0149] Representative cell samples and surrounding water samples were collected from algal cultures. As shown in Figure 2(a), the sequential separation method for DOC components is as follows: s-DOC ex The solution was obtained by acidifying the supernatant after centrifuging (9,000 rpm, 5 minutes) 10 mL of algal culture. For b-DOC ex The centrifuged algal pellet was immediately resuspended in 10 mL of Erdschreiber medium (without soil extract) and vortexed for 5 minutes. Then, 0.06 mL of 37% formalin solution was added, and the mixture was shaken for 1 hour at room temperature using a bead mill (Mini-Beadbeater 16, Biospec, USA). Afterward, 4 mL of 1M NaOH solution was added, and shaking continued for 3 hours. The mixture was then centrifuged at 9000 rpm for 15 minutes, and the supernatant was collected as b-DOC. ex The algal precipitate was gently resuspended and filtered through a 0.7-μm GF / F pre-burned (400°C, 4 hours) glass fiber filter (Whatman). The filtrate was stored in pre-burned 20 mL glass vials and sealed with acid-washed Teflon caps (Wheaton). Immediately after filtration, the sample was acidified to pH 2 with HCl (25%, analytical grade, Carl Roth) and then washed with deionized water (ddH2O) to pH 7. The acidified and dried algal precipitate was weighed for POC and DOC assessment.in The sum of s-DOC ex and b-DOC ex The carbon content was analyzed using a Shimadzu (Japan) TOC-VCPH / CPN total organic carbon analyzer equipped with an ASI-V autosampler via high-temperature catalytic oxidation. POC and DOC were also analyzed. in The sum was analyzed using an elemental analyzer (Vario MAX cube CN, Elementar, Germany). DOC in The determination was made by measuring algal precipitates (containing β-DOC) that had not been treated with formalin-NaOH. ex The contents released from these cells were thoroughly ground and lysed after treatment with NaOH. This measurement method is consistent with s-DOC. ex The determination method is the same. Finally, algal particles (excluding DOC) in The POC content of DOC was calculated using the difference method. in From POC and DOC in The result is obtained by subtracting from the sum.

[0150] Example 2: Characterization of DOC components based on FT-ICR MS:

[0151] Referring to Figure 3(a), it can be seen that FT-ICR MS characteristics (i.e., protein-like, carbohydrate-like, fatty acid-like, CRAM and other compounds) are present in different phytoplankton phyla for various DOC components (including s-DOC). ex b-DOC ex DOC in The changes in phytoplankton-derived carbon in marine carbon sinks at two different growth stages are shown in Figure 3(b). This includes the theoretical percentage contribution of phytoplankton-derived carbon to marine carbon sinks at two different growth stages, and the stage-specific differences, including BDOC (degradable dissolved organic carbon, which can be converted into recalcitrant organic carbon by microorganisms), CRAM (directly produced by algae and contributed to RDOC ponds), POC (particulate organic carbon contributed to carbon sinks through sedimentation), and other DOCs; the differences in the percentage contribution of these pathways among different phyla at each growth stage; and the CRAM content (mg / mg) of each phylum at the two different growth stages. -1 Chl-a).

[0152] DOC fraction samples characterized by molecular features were obtained as described above. After filtration through a 0.7-μm pore size GF / F glass fiber filter (Whatman), the filtrate was acidified to pH 2.0 with HCl (25%, analytical grade, Carl Roth) and then extracted by solid-phase extraction (SPE, styrene-divinylbenzene polymer (PPL) extraction column, 3 mL, 200 mg, Agilent) according to the Hansell and Carlson protocol. Prior to extraction, the extraction column was soaked in methanol [HPLC grade, Sigma-Aldrich] [35,36]. The concentrated DOC sample prepared for FT-ICR MS measurement had a concentration of 20 mg DOC L⁻¹. The average recovery of DOM during pretreatment was 41%. Mass spectrometry acquisition was performed using a 7T FT-ICR-MS (Bruker SolariX) equipped with a negative ion mode electrospray ionization (ESI) source, according to the Gan and Guo protocol. Each measurement was accumulated through 500 scans, with a mass range of 150 to 2000 Daltons. The spectra were internally calibrated using the Bruker Daltonics Data Analysis software package to ensure that the mass error of all samples was less than 0.06 ppm. Peaks with a signal-to-noise ratio (S / N) less than 4 were excluded; additionally, minor peaks with an S / N ratio less than 20 and appearing in less than 20% of the samples were also excluded. A total of 36 samples from 6 algal species at two growth stages (growth and decline) were analyzed, derived from three DOC fractions (s-DOC). ex b-DOC ex DOC in The sample order is random. This invention is based on the algal cell growth and reproduction curve. Generally, a standard growth and reproduction curve is an S-shaped growth curve. This invention defines the logarithmic phase as the growth phase and the plateau phase and subsequent periods as the decline phase. This results in the acquisition of pure DOC. in Due to the difficulty of obtaining samples, this invention will use b-DOC ex and DOC in The mixture with pure β-DOC ex Solutions were compared. For molecular formulas where both were present, the present invention used the difference method to calculate the relative abundance of each molecular formula, while only in DOC solutions... ex and DOC in The molecular formulas found in the mixture are retained and their relative abundance is calculated, as follows:

[0153]

[0154] in, ) indicates pure DOC in The relative abundance of any molecular formula in the sample. Indicates b-DOCex and DOC in The relative abundance of any molecular formula in a mixture, Indicates b-DOC ex The relative abundance of any molecular formula in the formula.

[0155] Molecules are classified according to the stoichiometric ratios of their molecular formulas. Molecules are classified as CRAM when the double bond equivalence (DBE) / C ratio is between 0.30 and 0.68, the DBE / H ratio is between 0.20 and 0.95, and the DBE / O ratio is between 0.77 and 1.75 [38,39]. When 1.7 ≤ H / C ≤ 2.2, the molecule is defined as a lipid-like compound; when 1.5 ≤ H / C ≤ 2.2 and 0.2 ≤ O / C ≤ 0.6, the molecule is defined as a protein-like compound; when 1.5 ≤ H / C ≤ 2.2 and 0.6 ≤ O / C ≤ 1.2, the molecule is defined as a carbohydrate-like compound [40,41].

[0156] Example 3: Establishment of a global ocean dissolved carbon pool prediction model:

[0157] refer to Figure 4 It can be seen that the model optimization of the global marine dissolved carbon pool based on carbon allocation coefficient optimization, the model prediction results of the global marine dissolved carbon pool without using carbon allocation coefficient during the growth and decay phases, and the model prediction results of the global marine dissolved carbon pool using carbon allocation coefficient during the growth and decay phases are as follows.

[0158] FT-ICR MS-based characterization data of dissolved organic carbon (DOC) components were used to allocate RDOC (recurrently degradable organic carbon) production capacity to each phytoplankton taxa. Theoretically, the expansion of the marine DOC pool should result from both direct and indirect contributions of RDOC produced by phytoplankton. Direct production, defined as primary production, refers to the ability to directly release RDOC, which we quantify by observing CRAM (carbon-storage algal-derived organic matter) content (mg / mg). -1 Chla) is used to quantify this capacity. The indirect pathway refers to the conversion of bioactive DOCs (such as lipids, proteins, and carbohydrate-like compounds) into RDOCs by microorganisms. It has been reported that 0.4% of primary productivity is converted into RDOCs. Therefore, in this embodiment of the invention, 0.4% of TOC represents the RDOCs produced by each phytoplankton taxonomy through microbial conversion. In summary, the theoretical monthly variation in marine DOC production can be expressed as:

[0159] TheoreticalΔDOC production(mgC(L·M) -1 ) = RDOC direct +RDOC indect

[0160] Among them, RDOCdirect and RDOC indirect The calculation formula is as follows:

[0161]

[0162] Among them, RDOC direct (mgC(L·M) -1 This indicates the carbon content of RDOC directly secreted by phytoplankton per liter of seawater per month; Phylum i (mgChla(L·M) -1 This indicates the chlorophyll a content of any phytoplankton taxa per liter of seawater per month; CRAM i (mgC·mg -1 Chla) represents the mass (mg) of CRAM that can be produced from a unit of chlorophyll a in any phytoplankton taxa. RDOC indirect (mgC(L·M) -1 TOC represents the amount of carbon in each liter of seawater per month that is indirectly converted into RDOC by microorganisms from BDOC secreted by phytoplankton. i (mgC·mg -1 Chla) represents the mass (mg) of total organic carbon (TOC) that can be produced by any phytoplankton taxa unit of chlorophyll a content; 0.4% is the proportion of algal BDOC converted into RDOC by microorganisms as reported in the literature.

[0163] On the other hand, embodiments of the present invention also provide an apparatus for predicting marine carbon pools, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for evaluating phytoplankton carbon allocation coefficients based on FT-ICR MS technology and the method for predicting marine carbon pools as described above.

[0164] The processor and memory can be connected via a bus or other means. Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0165] On the other hand, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the aforementioned method for evaluating phytoplankton carbon allocation coefficients and marine carbon pool prediction method based on FT-ICR MS technology.

[0166] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0167] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for evaluating the carbon allocation coefficient of phytoplankton based on FT-ICR MS technology, characterized in that, The method for evaluating phytoplankton carbon allocation coefficients based on FT-ICR MS technology includes the following steps: Set up phytoplankton culture; Based on the phytoplankton culture, sample data of dissolved organic carbon were obtained; Based on the sample data of dissolved organic carbon, carbon partition pattern analysis data are obtained; Based on the sample data of the dissolved organic carbon, obtain the component characterization data of the dissolved organic carbon based on FT-ICR MS; Based on the carbon allocation pattern analysis data and the FT-ICR MS-based characterization data of dissolved organic carbon components, phytoplankton carbon allocation coefficient assessment information was obtained.

2. The method for evaluating phytoplankton carbon allocation coefficients based on FT-ICR MS technology according to claim 1, characterized in that, The process of setting up a phytoplankton culture includes the following steps: Identify the target taxa of phytoplankton; Based on the target taxonomic group, a culture system data is established; the culture system data includes culture medium preparation information and environmental parameter control information. Acquire growth monitoring data; the growth monitoring data includes cell density and growth stage data; Based on the target taxa, the culture system data, and the growth monitoring data, the phytoplankton culture was set up.

3. The method for evaluating phytoplankton carbon allocation coefficients based on FT-ICR MS technology according to claim 1, characterized in that, The process of obtaining dissolved organic carbon sample data based on the phytoplankton culture includes the following steps: Based on the phytoplankton culture, the target sampling solution was obtained; Based on the target sampling solution, sample data of dissolved organic carbon are obtained; the sample data of dissolved organic carbon includes extracellular soluble organic carbon data, extracellular bound organic carbon data, intracellular soluble organic carbon data, and particulate organic carbon data.

4. The method for evaluating phytoplankton carbon allocation coefficients based on FT-ICR MS technology according to claim 1, characterized in that, The step of obtaining carbon partition pattern analysis data based on the sample data of dissolved organic carbon includes the following steps: Based on the sample data of the dissolved organic carbon, an elemental analyzer was used to determine the concentration and detect the elemental content to obtain the organic carbon component concentration information; the organic carbon component concentration information includes the concentration of extracellular soluble organic carbon, the concentration of extracellular bound organic carbon, and the concentration of intracellular dissolved organic carbon. The total amount of intracellular dissolved organic carbon and particulate organic carbon was obtained using an elemental analyzer, and the content data of particulate organic carbon was obtained by the difference calculation method. Based on the organic carbon component concentration information and the particulate organic carbon content data, carbon distribution pattern analysis data are obtained.

5. The method for evaluating phytoplankton carbon allocation coefficients based on FT-ICR MS technology according to claim 1, characterized in that, The step of obtaining FT-ICR MS-based characterization data of dissolved organic carbon components based on the sample data of the dissolved organic carbon includes the following steps: Based on the sample data of dissolved organic carbon, solid-phase extraction was performed using a styrene-divinylbenzene polymer extraction column to obtain pretreated sample data; Based on the preprocessed sample data, mass spectrometry data were acquired using FT-ICR MS. Based on the sample data of the dissolved organic carbon, the mixture of extracellular bound organic carbon and intracellular dissolved organic carbon was compared with a pure extracellular bound organic carbon solution to obtain the molecular formula in which both extracellular bound organic carbon and intracellular dissolved organic carbon are present. The relative abundance of each molecular formula was calculated using the difference method to obtain relative abundance data; Based on the mass spectrometry data and the relative abundance data, characterization data of soluble organic carbon components based on FT-ICR MS were obtained.

6. The method for evaluating phytoplankton carbon allocation coefficients based on FT-ICR MS technology according to claim 1, characterized in that, The process of obtaining phytoplankton carbon allocation coefficient assessment information based on the carbon allocation pattern analysis data and the FT-ICR MS-based dissolved organic carbon component characterization data includes the following steps: Based on the carbon allocation pattern analysis data and the soluble organic carbon component characterization data based on FT-ICR MS, the changes in soluble organic carbon of different phytoplankton species were analyzed to obtain soluble organic carbon change data; the soluble organic carbon change data includes extracellular soluble organic carbon change data, extracellular bound organic carbon change data, and intracellular soluble organic carbon change data. Based on the data on changes in dissolved organic carbon, the theoretical percentage contribution of phytoplankton-derived carbon to marine carbon sinks at different growth stages and the stage-specific differences in phytoplankton-derived carbon were analyzed. The theoretical percentage contribution includes the contribution percentage of degradable dissolved organic carbon, the contribution percentage of carbon-storing algal-derived organic matter, and the contribution percentage of particulate organic carbon. The stage-specific differences in phytoplankton-derived carbon include differences during the growth period and differences during the decay period. Based on the changes in dissolved organic carbon, the theoretical contribution percentage information, and the stage-specific differences in phytoplankton-derived carbon, phytoplankton carbon allocation coefficient assessment information is obtained.

7. A method for predicting marine carbon pools, implemented by assessing phytoplankton carbon allocation coefficients using FT-ICR MS technology as described in any one of claims 1 to 6, characterized in that, The marine carbon pool prediction method includes the following steps: The carbon allocation coefficient assessment information of phytoplankton obtained by the method of assessing phytoplankton carbon allocation coefficient based on FT-ICR MS technology as described in any one of claims 1 to 6 is used to determine the content of algal-derived organic matter in carbon storage; Based on the content of algal-derived organic matter in the carbon storage, the recalcitrant organic carbon directly secreted by phytoplankton is obtained.

8. The marine carbon pool prediction method according to claim 7, characterized in that, The marine carbon pool prediction method also includes: The total organic carbon of phytoplankton, which is set at 0.4%, is converted into recalcitrant organic carbon by microorganisms, resulting in indirectly secreted recalcitrant organic carbon. The theoretical monthly variation of marine dissolved organic carbon production is obtained based on the directly secreted recalcitrant organic carbon and the indirectly secreted recalcitrant organic carbon.

9. The marine carbon pool prediction method according to claim 8, characterized in that, The formula used to obtain the theoretical monthly variation of marine dissolved organic carbon production based on the directly secreted recalcitrant organic carbon and the indirectly secreted recalcitrant organic carbon includes: TheoreticalΔDOC production(mgC(L·M) -1 )=RDOC dirrct (mgC(L·M) -1 )+RDOC indirect (mgC(L·M) -1 ); Among them, Theoretical ΔDOC production (mgC(L·M)) -1 ) represents the theoretical monthly variation in marine dissolved organic carbon production; RDOC direct (mgC(L·M) -1 ) represents the carbon content per liter of seawater per month that is directly secreted by phytoplankton; RDOC indirect (mgC(L·M) -1 This represents the amount of carbon in each liter of seawater per month that is indirectly converted into recalcitrant organic carbon by microorganisms from biodegradable dissolved organic carbon secreted by phytoplankton.

10. The marine carbon pool prediction method according to claim 8, characterized in that, The formulas used in the marine carbon pool prediction method also include: RDOC direct (mgC(L·M) -1 )=∑Phylum i (mgChla(L·M) -1 )×CRAM i (mgC·mg -1 (Chl); RDOC indirect (mgC(L·M) -1 )=∑Phylum i (mgChla(L·M) -1 )×TOC i (mgC·mg -1 (Chla)×0.4%; Among them, Phylum i (mgChla(L·M) -1 () represents the chlorophyll a content of any phytoplankton taxa per liter of seawater per month; CRAM i (mgC·mg -1 Chla) represents the mass of algal-derived organic matter that can be produced by a unit of chlorophyll a in any phytoplankton taxa; TOC i (mgC·mg -1 Chla) represents the mass of total organic carbon that can be produced by the chlorophyll a content of any phytoplankton taxa unit.

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