A quantitative identification method for contribution of autochthonous organic carbon in surface water body

Abstract

The present application belongs to the technical field of environmental monitoring, and particularly relates to a quantitative identification method for contribution of autochthonous organic carbon of surface water. The present application constructs a water DOM source analysis method based on stable isotope ratio and three-dimensional fluorescence spectrum, comprehensively analyzes the source and characteristics of DOM, and identifies the characteristic information and contribution of autochthonous organic carbon. The three-dimensional fluorescence spectrum in the present application has the advantages of rapid detection, small sample consumption, etc., and can quickly qualitatively identify the source of DOM. The stable isotope ratio analysis in the present application can quantitatively calculate the different sources of DOM, and combined with the land use, hydrology and water environment and water quality parameters of the research area, the source and migration rule of water DOM can be more comprehensively determined. The quantitative identification method provided by the present application is more comprehensive for water DOM research, the identification system is more complete, and the tracing result is more reliable.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of environmental monitoring, and particularly relates to a quantitative identification method for contribution of autochthonous organic carbon in surface water. BACKGROUND

[0002] Dissolved organic matter (DOM) is soluble organic matter capable of passing through a 0.45 μm microporous filter, which is widely distributed in river, lake and groundwater environments, is the largest bioavailable carbon pool in the water environment, and participates in various biogeochemical processes. The source of DOM is divided into terrestrial (exogenous) and autochthonous (endogenous). Dissolved organic carbon (DOC) is a concentrated embodiment of carbon elements in DOM. Accordingly, DOC is divided into exogenous organic carbon and autochthonous organic carbon. Exogenous organic carbon is mainly derived from plant residues, wastewater discharged by human activities, and soil organic matter input into rivers through physical / chemical erosion. Autochthonous organic carbon is input by phytoplankton, microorganisms and release of organic carbon from riverbed sediment under the driving action of water flow.

[0003] In recent years, with the construction of water conservancy engineering facilities such as river damming and artificial water diversion trunk canals, the hydrological dynamic conditions of natural rivers have been changed by human activities, the transport, transformation and deposition patterns of DOM in rivers have been disturbed, the contribution of autochthonous components in rivers has increased, and the flux and structure of carbon transport to downstream, estuary and adjacent sea areas have changed. By analyzing the source of river organic carbon, especially identifying the contribution of autochthonous organic carbon, it is of great significance to reveal the changes in flux, migration and transformation of DOM in rivers and the ecological effects of human activities.

[0004] River DOM is complex and has significant spatial heterogeneity. At present, there are various technical means for analyzing and tracing the source of river DOM. Macroscopic means include element analysis and stable isotope technology, and microscopic means include ultraviolet-visible absorption spectroscopy (UV-vis), three-dimensional fluorescence spectroscopy (EEM) and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). These characterization methods are mainly used to characterize the composition, characteristics and structure information of DOM. However, single use of a certain characterization method cannot specifically analyze the source and contribution of autochthonous organic carbon, especially the influence of dammed rivers. SUMMARY

[0005] The purpose of the present application is to provide a quantitative identification method for contribution of autochthonous organic carbon in surface water. The quantitative identification method of the present application can specifically identify the characteristic information and contribution of autochthonous organic carbon.

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

[0007] The application provides a method for quantitatively identifying contribution of autochthonous organic carbon in surface water, comprising the following steps:

[0008] (1) pretreating the collected surface water sample and sediment respectively;

[0009] (2) using stable isotope mass spectrometry, ultraviolet-visible absorption spectroscopy and three-dimensional fluorescence spectroscopy respectively to determine the stable isotope ratio, ultraviolet-visible absorption spectrum and three-dimensional fluorescence spectrum of the pretreated water sample and sediment, so as to obtain carbon and nitrogen stable isotope ratio, ultraviolet-visible absorption spectrum data and three-dimensional fluorescence spectrum data;

[0010] (3) using a Bayesian isotope mixing model (MixSIAR) toolkit to constitute an isotope database of carbon and nitrogen stable isotope ratios of DOM from different sources, inputting the isotope database and water sample and sediment data into the Bayesian isotope mixing model, and using a Markov Chain Monte Carlo (MCMC) model algorithm to calculate, so as to obtain the average contribution rate of autochthonous organic matter to water body organic matter;

[0011] (4) calculating the spectral parameters of DOM, including ultraviolet spectral parameters SUVA 254 and E2 / E3, fluorescence spectral parameters HIX and BIX;

[0012] (5) using a DOMFluor toolkit to perform parallel factor analysis on the three-dimensional fluorescence excitation emission matrix data set obtained in step (2), so as to obtain the fluorescence peaks and characteristic components of the water sample;

[0013] (6) determining the characteristics of autochthonous DOM in the water body based on the fluorescence peaks and characteristic components of the water sample obtained in step (5) and the average contribution rate of autochthonous DOM to water body organic matter in step (3).

[0014] Preferably, the pretreatment of the water sample comprises the following steps: removing impurities from the water sample and then acidifying the water sample to a pH value of 2; and the pretreatment of the sediment comprises the following steps: sequentially drying the sediment, taking undersize of the primary sieve, crushing and taking undersize of the sieve.

[0015] Preferably, the stable isotope ratio determination of the water sample comprises stable isotope ratio mass spectrometry for determining δ 13 C-DOC value and stable isotope ratio mass spectrometry for determining δ 13 C-DIC value.

[0016] Preferably, the stable isotope ratio mass spectrometry for determining δ 13The C-DOC value comprises the following steps: mixing the water sample and pure phosphoric acid to decompose inorganic carbon into carbon dioxide and blow off to obtain an organic carbon water sample, mixing the organic carbon water sample and an oxidizing agent solution to perform an oxidation reaction to obtain carbon dioxide, injecting the carbon dioxide into a stable isotope ratio mass spectrometer to obtain δ 13 C-DOC value.

[0017] Preferably, the stable isotope ratio mass spectrometry determines δ 13 The C-DIC value comprises the following steps: mixing the water sample and pure phosphoric acid to perform an acidification reaction to obtain carbon dioxide, injecting the carbon dioxide into a stable isotope ratio mass spectrometer to detect the δ 13 C / 12 C value, and obtaining δ 13 C-DIC value.

[0018] Preferably, the stable isotope ratio determination of the sediment comprises the following steps: mixing the sediment and hydrochloric acid to remove inorganic carbon, then performing water washing, drying and grinding in sequence, and detecting δ 13 C and δ 15 N isotope values of organic matter of the sediment by using a stable isotope ratio mass spectrometer.

[0019] Preferably, the UV-visible absorption spectrum determination of the water sample comprises the following steps: rinsing a container, then performing absorbance determination by using a UV-visible spectrophotometer, correcting by using Milli-Q ultrapure water as a blank, and calculating to obtain SUVA 254 value.

[0020] Preferably, the three-dimensional fluorescence spectrum determination of the water sample comprises the following steps: obtaining three-dimensional fluorescence spectrum data of the water sample by using a three-dimensional fluorescence spectrophotometer, performing internal filter correction on the three-dimensional fluorescence spectrum data to remove blank fluorescence signals, and dividing fluorescence intensity by a Raman peak area of pure water at a set excitation wavelength to obtain normalized fluorescence intensity.

[0021] Preferably, in the input Bayesian isotope mixing model, the following steps are included: taking the isotope ratio and C / N ratio of the sediment as a tracer of organic matter source to construct a MixSIAR model; arranging the mean and standard deviation of the end-member isotope and C / N ratio of different pollution sources to generate a.csv format file named source; arranging the stable isotope and C / N ratio data of the sediment organic carbon in the basin to generate a.csv format file named consumer, and the row and column names of the file are consistent with those of the source file; arranging a blank correction file corresponding to the structure of the source file with data set to 0 to generate a.csv format file named discrimination, which is used for isotope bias (TEF) correction in the model; importing the arranged files into the MixSIAR model of RStudio software, and iteratively estimating the contribution rate of different end members to the DOM of the basin through Bayesian iteration.

[0022] Preferably, the parallel factor analysis includes the following steps: the obtained three-dimensional fluorescence spectrum data is deducted from the spectrum signal of the blank sample to eliminate background interference; then, the Raman peak normalization method is used to standardize the spectrum signal intensity to correct the signal deviation caused by instrument drift or batch difference, and ensure the consistency and comparability of the data; the processed three-dimensional fluorescence spectrum data is imported into the PARAFAC model for component number determination and model fitting, and the excitation-emission spectrum and relative concentration distribution of each fluorescence component are obtained by decomposition, and the fluorescence characteristic index is extracted; the established PARAFAC model is diagnosed and verified, and the core consistency index and residual distribution are evaluated to ensure the stability, interpretability and fitting effect of the model.

[0023] The present application provides a method for quantitatively identifying the contribution of autochthonous organic carbon in surface water. Based on stable isotope ratio and three-dimensional fluorescence spectrum, the present application constructs a method for analyzing the source of water DOM, comprehensively analyzes the source and characteristics of DOM, and specifically identifies the characteristic information and contribution of autochthonous organic carbon. The three-dimensional fluorescence spectrum in the present application has the advantages of rapid detection, small sample consumption, etc., and can quickly qualitatively identify the source of DOM. The stable isotope ratio analysis in the present application can quantitatively calculate the different sources of DOM, and combined with the land use, hydrology, water environment and water quality parameters of the research area, the source and migration rule of water DOM can be more comprehensively determined. The quantitative identification method provided by the present application is more comprehensive for water DOM research, the identification system is more complete, and the tracing result is more reliable. BRIEF DESCRIPTION OF DRAWINGS

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 Characteristics of organic carbon and nitrogen isotope ratios in Lancang River sediments (a) and contribution rates of carbon from different sources (b).

[0026] Figure 2 Fluorescent components from natural sections of the Lancang River;

[0027] Figure 3 The fluorescent components are from the reservoir section of the river.

[0028] Figure 4 This is a map showing the relative abundance distribution of fluorescent components at various sampling points along the Lancang River.

[0029] Figure 5 The distribution and characteristics of dissolved organic and inorganic carbon in the water bodies of the South-to-North Water Diversion Project (Middle Route) in different river sections (upper) and during the dry and rainy seasons (upper and lower);

[0030] Figure 6 Block map (top) and bar chart (bottom, a for spring, b for autumn) showing the characteristics and sources of carbon and nitrogen isotope ratios in sediments along the central route of the South-to-North Water Diversion Project.

[0031] Figure 7 Spatiotemporal distribution map of algal carbon contribution rate in the water bodies of the South-to-North Water Diversion Project (Middle Route). Detailed Implementation

[0032] This invention provides a method for quantitatively identifying the contribution of autogenic organic carbon in surface water bodies, comprising the following steps:

[0033] (1) The collected surface water samples and sediments were pretreated respectively;

[0034] (2) The isotope ratio, ultraviolet-visible absorption spectrum and three-dimensional fluorescence spectrum were used to determine the carbon-nitrogen stable isotope ratio, ultraviolet-visible absorption spectrum data and three-dimensional fluorescence spectrum data respectively.

[0035] (3) Using the Bayesian Isotope Mixing Model (MixSIAR) toolkit, the constituent isotope databases of DOMs from different sources are input into the Bayesian Isotope Mixing Model, and the Markov Chain Monte Carlo model algorithm is used to calculate the average contribution rate of autogenous organic matter to the organic matter in the water body.

[0036] (4) calculating the spectral parameters of DOM, including ultraviolet spectral parameter SUVA 254 and E2 / E3, fluorescence spectral parameters HIX and BIX;

[0037] (5) using the DOMFluor tool kit to perform parallel factor analysis on the three-dimensional fluorescence excitation emission matrix data set obtained in step (2) to obtain the fluorescence peaks and characteristic components of the water sample;

[0038] (6) determining the characteristics of the autochthonous DOM in the water body based on the fluorescence peaks and characteristic components of the water sample obtained in step (5) and the average contribution rate of the autochthonous DOM to the organic matter of the water body in step (3).

[0039] The collected surface water sample and sediment are pretreated respectively in the present application. In the present application, the water sample can be obtained by a water sampler; and the sediment can be grabbed by a Peterson mud grabber.

[0040] In the present application, the pretreatment of the water sample can include the following steps: removing impurities from the water sample and then acidifying the water sample to a pH value of 2; the impurity removal can be filtration; the filter membrane used for the filtration can have a pore size of 0.45 µm; and the reagent used for the acidification can be concentrated phosphoric acid, which can have a concentration of 14.63~14.75 mol / L.

[0041] In the present application, the pretreatment of the sediment can include the following steps: sequentially drying the sediment, taking undersize of the primary screening, crushing the sediment, and taking undersize of the screening; when the sediment is in a frozen state, the sediment can be thawed before the drying; and the thawing temperature can be room temperature.

[0042] In the present application, the drying can be air drying on sulfuric acid paper.

[0043] In the present application, the screen used for the primary screening can be a nylon screen; and the screen can have a pore size of 1 mm. The primary screening can remove stones and plant debris and other impurities in the sediment.

[0044] In the present application, the crushing can be grinding; and the equipment used for the grinding can be a wooden roller.

[0045] In the present application, the screen used for the screening can be a nylon screen; and the screen can have a pore size of 0.25 mm.

[0046] In the present application, the undersize obtained by the screening is in a powder form, which can be transferred to a plastic bottle for storage.

[0047] After the pretreatment, the present application respectively utilizes stable isotope ratio mass spectrometry, ultraviolet-visible absorption spectrum and three-dimensional fluorescence spectrum to determine the isotope ratio of the pretreated water sample and sediment, ultraviolet-visible absorption spectrum and three-dimensional fluorescence spectrum, and obtains carbon and nitrogen stable isotope ratio, ultraviolet-visible absorption spectrum data and three-dimensional fluorescence spectrum data.

[0048] In the present application, the isotope ratio determination of the water sample can include stable isotope ratio mass spectrometry determination of δ 13 C-DOC and stable isotope ratio mass spectrometry determination of δ 13 C-DIC.

[0049] In the present application, the stable isotope ratio mass spectrometry determination of δ 13 C-DOC can include the following steps: mixing and decomposing inorganic carbon into carbon dioxide after mixing the water sample and pure phosphoric acid, and then blowing off to obtain an organic carbon water sample, mixing the organic carbon water sample and an oxidizing agent solution to perform an oxidation reaction to obtain carbon dioxide, and injecting the carbon dioxide into a stable isotope ratio mass spectrometer (IRMS) to obtain δ 13 C-DOC value.

[0050] In the present application, the volume of the water sample can be 0.5-1 mL, and specifically can be 0.8 mL.

[0051] In the present application, the volume ratio of the water sample to pure phosphoric acid can be 1-2:1, and specifically can be 1.5:1.

[0052] In the present application, the mixing can be performed in a borosilicate glass sample bottle; the volume of the borosilicate glass sample bottle can be 12 mL.

[0053] In the present application, the gas used for blowing off carbon dioxide can be helium.

[0054] In the present application, the oxidizing agent in the oxidizing agent solution can include one or more of Na2S2O8 and K2S2O8.

[0055] In the present application, the concentration of the oxidizing agent solution can be 0.1 mol / L; the volume ratio of the water sample to the oxidizing agent solution can be 1-2:2, and specifically can be 1.5:2.

[0056] In the present application, the mixing of the organic carbon water sample and the oxidizing agent solution can adopt a syringe.

[0057] In the present application, the oxidation reaction can be performed in a water bath condition; the temperature of the oxidation reaction can be 100 ℃, and the incubation time can be 1 hour. The present application fully oxidizes the organic carbon through the oxidation reaction.

[0058] In the present application, the stable isotope ratio mass spectrometry determines δ 13 The C-DIC can comprise the following steps: mixing and heating the water sample and pure phosphoric acid to perform acidification reaction to obtain carbon dioxide, injecting the carbon dioxide into a stable isotope ratio mass spectrometer to detect the carbon dioxide 13 C / 12 C value to obtain δ 13 C-DIC value.

[0059] In the present application, the volume of the water sample can be 0.5-1 mL, and specifically can be 0.8 mL.

[0060] In the present application, the volume ratio of the water sample to pure phosphoric acid can be 1-2:1, and specifically can be 1.5:1.

[0061] In the present application, the mixing and heating can be performed in a borosilicate glass sample bottle; the volume of the borosilicate glass sample bottle can be 12 mL.

[0062] In the present application, the air in the borosilicate glass sample bottle can be blown off before the mixing and heating; the gas used for blowing off the air in the borosilicate glass sample bottle can be helium.

[0063] In the present application, the mixing and heating can use a syringe to mix the water sample and pure phosphoric acid.

[0064] In the present application, the acidification reaction can be performed in a water bath; the temperature of the acidification reaction can be not higher than 60 ℃, and specifically can be 50 ℃ or 55 ℃, and the incubation time can be 1 hour. The present application improves the release rate of carbon dioxide by heating.

[0065] In the present application, the acidification reaction can further comprise centrifugation.

[0066] In the present application, the determination of the isotope ratio of the sediment can comprise the following steps: mixing the sediment and hydrochloric acid to remove inorganic carbon, and then sequentially performing water washing, drying and grinding, and detecting the δ 13 C and δ 15 N isotope values of the organic matter of the sediment by using a stable isotope ratio mass spectrometer.

[0067] In the present application, the ratio of the mass of the sediment to the volume of the hydrochloric acid can be (1-10) g:75 mL, and specifically can be 3 g:75 mL, 5 g:75 mL or 7 g:75 mL.

[0068] In the present application, the concentration of the hydrochloric acid can be 0.5-1 mol / L, and specifically can be 0.7 mol / L.

[0069] In this invention, the time for removing inorganic carbon can be based on the absence of gas generation in the reaction system, and can be 2 to 48 hours, specifically 12 hours, 24 hours or 36 hours.

[0070] In this invention, the washing can be stopped after the product becomes neutral.

[0071] In this invention, the drying temperature can be 50~60 ℃, specifically 55 ℃.

[0072] In this invention, the determination of the ultraviolet-visible absorption spectrum of the water sample may include the following steps: rinsing the container and then measuring the absorbance using an ultraviolet-visible spectrophotometer, using Milli-Q ultrapure water as a blank for calibration, and calculating the SUVA. 254 Value. The SUVA 254 The ratio of DOM's UV absorbance at 254 nm to DOC concentration is directly proportional to the degree of aromatization of the DOM molecule.

[0073] In this invention, the wavelength range for measuring the ultraviolet-visible absorption spectrum of the water sample can be 200~550 nm.

[0074] In this invention, the three-dimensional fluorescence spectrum determination of the water sample may include the following steps: obtaining three-dimensional fluorescence spectrum data of the water sample using a three-dimensional fluorescence spectrophotometer, performing internal filtering correction on the three-dimensional fluorescence spectrum data to remove blank fluorescence signals, and dividing the fluorescence intensity by the Raman peak area of ​​pure water at a set excitation wavelength to obtain a standardized fluorescence intensity.

[0075] In this invention, the excitation wavelength (Ex) for the three-dimensional fluorescence spectroscopy determination of the water sample can be 200~550 nm, the scanning interval can be 2 nm, the emission wavelength (Em) can be 270~570 nm, the increment can be 2.3 nm, and the integration time can be 2 s.

[0076] In this invention, the three-dimensional fluorescence spectroscopy of the water sample can be performed using the Delaunay interpolation method to eliminate interference from Rayleigh and Raman scattering.

[0077] In the present application, the fluorescence spectrum data can include HIX and BIX. The HIX is calculated by the spectral area ratio of the emission wavelength 435~480 nm and 300~345 nm under the excitation wavelength 254 nm, and indicates the humification degree of organic matter, when the HIX increases in the range of 0~1, it indicates that the humification degree is stronger; the BIX is the ratio of the fluorescence intensity at the emission wavelength of 380 nm and 430 nm under the excitation wavelength of 310 nm, and can indicate the autochthonous source index of organic matter, and is used for determining the presence of beta fluorophore (representing the characteristics of the activity of protists in the sample), thus can be used for evaluating the relative contribution rate of the autochthonous DOM in the water sample, high BIX (>1) indicates higher autochthonous contribution rate, and low BIX (<0.8) indicates that the organic matter is mainly of terrestrial input.

[0078] The present application uses the Bayesian isotope mixing model (MixSIAR) toolkit to construct the isotope database of DOM of different sources, inputs the isotope database and water sample and sediment data into the Bayesian isotope mixing model, and calculates by using the Markov chain Monte Carlo model algorithm to obtain the average contribution of autochthonous organic matter to water body organic matter.

[0079] In the present application, the input into the Bayesian isotope mixing model can include the following steps:

[0080] The isotope ratio and C / N ratio of the sediment are used as the tracing index of the source of sediment organic matter to construct the MixSIAR model;

[0081] The mean value and standard deviation of the end member isotope and C / N ratio of different pollution sources are arranged to generate a.csv format file, named source; the stable isotope and C / N ratio data of the sediment organic carbon in the basin are arranged to generate a.csv format file, named consumer, and the row and column names of the file are consistent with the source file; a blank correction file corresponding to the structure of the source file is arranged into a.csv format, named discrimination, which is used for isotope bias (TEF) correction in the model; and the arranged files are imported into the MixSIAR model of the RStudio software, and the contribution rate of different end members to the basin DOM is estimated by Bayesian iteration. The present application tests the isotope ratio of sediment, inputs the isotope data into the Bayesian isotope mixing model for calculation, obtains the average contribution rate of different sources, and further quantitatively analyzes the contribution of the autogenic source of DOM. When the carbon-nitrogen ratio is less than 12, it indicates that the organic carbon is mainly derived from endogenous aquatic plants, and when the carbon-nitrogen ratio is greater than 12, it is considered that the river organic carbon is mainly derived from the terrestrial ecosystem. Different biogeochemical processes have different stable isotope composition ranges, and accordingly the source of organic carbon can be identified. The δ 13 C of typical C3 plants is -35‰~-20‰, the δ 13 C of C4 plants is -19‰~-9‰, and the δ 13 C of soil organic matter is -25‰~-22‰.

[0082] The present application calculates the spectral parameters of DOM, including the ultraviolet spectral parameters SUVA 254 and E2 / E3, the fluorescence spectral parameters HIX and BIX. The E2 / E3 is the ratio of ultraviolet absorbance at wavelengths of 250 nm and 365 nm, respectively, and represents the degree of humification of DOM, and the ratio size is inversely proportional to the degree of humification.

[0083] The present application uses the DOMFluor toolkit to perform parallel factor analysis on the three-dimensional fluorescence excitation emission matrix data set obtained in step (2) to obtain the fluorescence peaks and characteristic components of the water sample.

[0084] In the present application, the parallel factor analysis can comprise the following steps: determining the obtained three-dimensional fluorescence spectrum data to deduct the spectrum signal of the blank sample, so as to eliminate the background interference; then, the spectrum signal intensity is standardized by using the Raman peak normalization method, so as to correct the signal deviation caused by instrument drift or batch difference, and ensure the consistency and comparability of the data; the processed three-dimensional fluorescence spectrum data is introduced into the PARAFAC model, the component number is determined and the model fitting is carried out, the excitation-emission spectrum and the relative concentration distribution of each fluorescence component are obtained by decomposition, and the fluorescence characteristic index is extracted; the established PARAFAC model is diagnosed and verified, the core consistency index and the residual distribution are evaluated, so as to ensure the stability, interpretability and fitting effect of the model. Through the three-dimensional fluorescence detection of the surface water sample, the fluorescence spectrum of the water sample is obtained, the dissolved organic matter with fluorescence in the water body is quickly identified, and the characteristic component corresponding to the water sample is obtained based on the parallel factor analysis.

[0085] In the present application, the PARAFAC analysis can be carried out by using the DOMFluor toolbox in Matlab software; the PARAFAC analysis can comprise the following steps: eliminating the influence of Raman scattering and Rayleigh scattering on the EEM data of each site water sample; determining the abnormal sample by analyzing the central lever value of each sample, and the sample with a lever value obviously higher than that of other samples is regarded as an abnormal sample and is removed; residual analysis is carried out on the sample, and when the factor number is changed to the factor number at which the residual value is minimum and no longer obviously changes, the factor number is regarded as the suspected optimal factor number; the model is further verified by using the halving analysis method and random initialization assignment; the sample database is randomly divided into two sub-databases by halving analysis for verification, and the reasonable factor number should make the results simulated by the two sub-database models consistent; after random assignment, the model is more optimized, and finally the optimal factor number is determined and the optimal model is obtained.

[0086] Based on the fluorescence peak and the characteristic component of the water sample obtained in step (5) and the average contribution rate of the autochthonous DOM to the organic matter in the water body in step (3), the characteristics of the autochthonous DOM in the water body are determined.

[0087] In the present application, the stable isotope ratio data, the ultraviolet-visible absorption spectrum data and the three-dimensional fluorescence spectrum data determined are analyzed and processed, the source, composition and spectral characteristics of the organic matter in the surface water body are obtained, the influence of different end members on the organic matter in the watershed is quantified based on the constructed Bayesian isotope mixing model (MixSIAR), and the contribution of the autochthonous organic carbon is quantitatively analyzed.

[0088] The application qualitatively and quantitatively identifies the characteristics of the autochthonous component in the surface water DOM on the basis of UV-vis and EEM spectrum analysis, and further quantifies the relative contribution of the autochthonous DOM to the watershed DOM by using the MixSIAR model, solves the problem that the prior art mainly analyzes the whole composition, characteristics and structure information of DOM, and cannot reveal the contribution and characteristics of the autochthonous DOM.

[0089] In the application, UV-vis is mainly based on the strong light absorption of the aqueous solution of the compound containing the π-π conjugated system in the DOM in the ultraviolet region of the spectrum, and the UV-vis parameter can reflect the source of the DOM while describing the properties of the DOM; the characteristic ultraviolet absorbance (SUVA 254 ) can reflect the input of the colored dissolved organic matter (CDOM) from the terrestrial vascular plants to a certain extent, and the SUVA 254 of the CDOM in the inland water body can reflect the input of the colored dissolved organic matter (CDOM) from the terrestrial vascular plants to a certain extent. -1 m -1 The EEM is used to track the source of the DOM by using the correspondence between different fluorescent components and different source organic species, and further combined with the humification index (HIX) and the biological index (BIX) parameters.

[0090] The parallel factor analysis method is a multi-component analysis method, which can distinguish different components in the sample, and obtain the spectrum and score value of a single component. The method assumes that the behavior of the fluorescent group mixture meets the Lambert-Beer law, and the detected fluorescence value is the comprehensive contribution of each fluorescent group, and the concentration of the mixture only affects the fluorescence intensity and does not change the shape of the excitation and emission spectrum. According to the principle of the alternating least squares iteration method, a large number of three-dimensional matrices of EEM are decomposed into three load matrices with actual physical meaning: a three-dimensional linear matrix X of the emission wavelength number (I) x the excitation wavelength number (J) x the sample number (F) and a residual matrix E, and the specific mathematical expression is as follows:

[0091] (1)

[0092] Wherein x ijk is an element in the matrix X, indicating the fluorescence intensity under the condition that the emission and excitation wavelengths of the kth sample are i and j respectively; f represents the number of fluorescent components, and F represents the optimal number of fluorescent components; a if is an element in the emission matrix A (I x F), indicating the emission wavelength of the fth fluorescent component in the kth sample, and bif is an element in the excitation matrix B (J x F) representing the excitation wavelength of the fth fluorescent component in the kth sample, c kf is an element in the relative concentration matrix C (K x F) representing the fluorescence intensity (relative concentration) of the fth fluorescent component in the kth sample, e ijf is an element in the residual matrix E (I x J x K) representing the residual value under different excitation and emission wavelengths (usually caused by instrument noise and other non-model change factors).

[0093] The alternating least squares iterative calculation mainly includes the following steps:

[0094] (1) Determine the sample factor number F;

[0095] (2) Initialize the matrices A and B, and calculate the matrix C:

[0096] (2);

[0097] (3) Calculate the matrix B:

[0098] (3);

[0099] (4) Calculate the matrix A:

[0100] (4);

[0101] (5) Normalize the A and B matrices column by column in the iterative process, repeat steps (2) to (4), and stop the iteration when the convergence condition is met:

[0102] (5).

[0103] In order to further illustrate the present application, the schemes of the present application are described in detail below in conjunction with the drawings and examples, but they should not be understood as limiting the scope of protection of the present application.

[0104] Example 1

[0105] (1) Use a water sampler to collect the water sample, add concentrated phosphoric acid to acidify to pH 2 after passing through a 0.45 µm filter, and complete the pretreatment of the water sample; the sediment sample is thawed at room temperature, dried on sulfuric acid paper, and passed through a 1 mm aperture nylon screen to remove stones and plant debris, then the screen undersize is crushed with a wooden roller, and then passed through a 0.25 mm aperture nylon screen, and the obtained powdered sample is transferred to a plastic bottle for storage.

[0106] (2) The δ 13C-DOC, inject filtered water sample into 12 mL borosilicate glass vials, add pure H3PO4 to remove inorganic carbon, inject high purity helium to blow off the generated carbon dioxide, then inject 0.1 M Na2S2O8 oxidant with a syringe, heat in water bath for 1 h to fully oxidize organic carbon, inject the CO2 gas generated by the conversion of organic carbon into IRMS to obtain δ 13 C-DOC value;

[0107] Determine δ 13 C-DIC, take 12 mL borosilicate glass sample vials, after purging with helium to remove residual air in the vials, inject 0.5 mL of pure H3PO4 with a syringe, heat in a 60℃ water bath for 60 minutes to generate CO2 gas, after centrifugation, detect the δ 13 C / 12 C value, i.e. δ 13 C-DIC value;

[0108] Take 5 g of sediment sample, add 75 mL of 1 mol / L HCl to remove inorganic carbon, after fully reacting for 48 h, wash off the excess HCl to neutral pH, dry and grind at 50℃, then use IRMS to detect δ 13 C and δ 15 N isotope value.

[0109] Determine the pretreated water sample by UV-visible absorption spectrum: after rinsing the container, determine it by UV-visible spectrophotometer, and use Milli-Q ultrapure water as blank for correction.

[0110] Determine the pretreated water sample by three-dimensional fluorescence spectrum, including the following steps: obtain the three-dimensional fluorescence spectrum data of the water sample by three-dimensional fluorescence spectrophotometer; correct the three-dimensional fluorescence spectrum data by internal filtering, remove the blank fluorescence signal, and divide the fluorescence intensity by the Raman peak area of pure water at the set excitation wavelength to obtain the standardized fluorescence intensity, complete the determination of the water sample.

[0111] (3) Use the MixSIAR toolkit to input the carbon and nitrogen ratios of different sources and water samples and sediments into the MixSIAR model for calculation to obtain the average contribution rate of authigenic organic matter to water body organic matter: quantify the influence of different end members on the DOM of the watershed, including the following steps:

[0112] Use the isotope ratio and C / N ratio of sediments as tracers of sediment organic matter sources to construct the MixSIAR model;

[0113] The mean and standard deviation of the end-member isotopes and C / N ratio of different pollution sources are arranged to generate a.csv format file named source; the stable isotope characteristics and C / N ratio data of the basin sediments are arranged to generate a.csv format file named consumer, and the row and column names of the file are consistent with those of the source file; a blank correction file corresponding to the structure of the source file is arranged into a.csv format with data set to 0, named discrimination, which is used for isotope bias (TEF) correction in the model; and the arranged files are imported into the MixSIAR model of the RStudio software, and the contribution rate of different end members to the basin DOM is estimated by Bayesian iteration.

[0114] (4) Calculate the spectral parameters of DOM, including ultraviolet spectral parameters SUVA 254 and E2 / E3, fluorescence spectral parameters HIX and BIX;

[0115] (5) Use the DOMFluor toolkit to analyze and process the measured three-dimensional fluorescence spectral data, including the following steps:

[0116] Subtract the spectral signal of the blank sample from the obtained three-dimensional fluorescence spectral data to eliminate background interference; then, use the Raman peak normalization method to standardize the spectral signal intensity to correct the signal deviation caused by instrument drift or batch difference, and ensure the consistency and comparability of the data.

[0117] Import the processed three-dimensional fluorescence spectral data into the PARAFAC model to determine the component number and model fitting, decompose to obtain the excitation-emission spectrum and relative concentration distribution of each fluorescence component, and extract the fluorescence characteristic index. Diagnose and verify the established PARAFAC model, evaluate the core consistency index and residual distribution, and ensure the stability, interpretability and fitting effect of the model.

[0118] (6) Based on the fluorescence peaks and characteristic components of the water sample in step S5 and the average contribution rate of the authigenic DOM in step S3, determine the characteristics of the authigenic DOM in the water body.

[0119] Example 2 - Contribution of DOM authigenic components in the cascade reservoir reach of the Lancang River

[0120] The specific test steps of this example are carried out with reference to Example 1.

[0121] (1) Isotope method

[0122] Firstly, the organic carbon content and carbon isotope ratio of the water body and sediments of the Lancang River were determined, and the contribution of different sources of DOM was quantitatively calculated by combining the Bayesian isotope mixing model. The isotope ratio of the organic carbon of the sediments of the Lancang River was between-20.2‰ and-32.07‰, with an average of-30.52‰. The carbon-nitrogen ratio ranged from 5.9 to 39.13, with an average of 14.25. From Figure 1 It can be seen from (a) in FIG. 1 that the sediments of the Lancang River mainly fall within the ranges of soil, phytoplankton and C3 plants, indicating that the organic matter of the sediments is a combination of the three. The average organic carbon isotope of the Lancang River cascade reservoir reach was-28.43‰, and the average C / N ratio was 13.5, indicating that the organic carbon may mainly come from C3 plants.

[0123] The Bayesian isotope mixing model was used to quantify the contribution rate of potential carbon sources to the organic matter of the sediments. MixSIAR is used to estimate the probability distribution of the contribution of different sources to the mixture, which is suitable for n isotopes and >n+1 sources, and takes into account the uncertainty of the data (standard deviation, fractionation coefficient, etc.), improving the accuracy of the calculation. The uncertainty of the end member value of different sources and the error of the model will be included in the model calculation, and the MCMC parameter "long run length" is selected, and the variables in the Gelman diagnosis are all less than 1.05. The Bayesian isotope mixing model was run using R language, and the detailed Bayesian isotope mixing model code was obtained from https: / / github.com / brianstock / MixSIAR The C / N ratio and δ 13 C were used to quantitatively analyze the sources of organic carbon in the Lancang River. It was assumed that there were mainly four sources: 1) C3 plants; 2) C4 plants; 3) soil organic carbon; and 4) phytoplankton, and the isotope values of each source are shown in Table 1 and Figure 1 .

[0124] Table 1 δ 13 C and C / N ratio of typical sources of organic matter in the Lancang River

[0125]

[0126] According to Figure 1The average contribution rates of C3 plants, C4 plants, phytoplankton and soil organic carbon in natural river section C3 were 22.40%, 18.80%, 27.40% and 31.40%, respectively, and the contribution of soil organic carbon to river sediment organic matter was the largest; while in the reservoir section, the average contribution rates of C3 plants, C4 plants, phytoplankton and soil organic carbon were 20.10%, 14.50%, 35.00% and 30.40%, respectively, and the phytoplankton represented by algae contributed the most to the river sediment organic matter. By calculating the contribution rate of different source carbon in the sediment of Lancang River, it can be seen that compared with the natural river section, the contribution of C3 plants, C4 plants and soil organic carbon to river sediment organic carbon decreased in the cascade reservoir river section, while the contribution of algae increased by 7.6%. This shows that the construction of cascade reservoirs makes the source of organic carbon gradually transition from soil source to typical phytoplankton source in the reservoir area.

[0127] (2) Spectroscopy

[0128] The three-dimensional fluorescence spectra (EEM) of water samples were determined using an Aqualog fluorescence spectrophotometer (Horiba, Japan). The excitation wavelength (Ex) was 200-550 nm, the scanning interval was 2 nm, the emission wavelength (Em) was 270-570 nm, the increment was 2.3 nm, the integration time was 2 s, and ultra-pure water prepared using a Milli-Q water purification device was used as a blank sample. The interference of Rayleigh and Raman scattering was eliminated using the Delaunay interpolation method. At the same time, the ultraviolet-visible absorption spectrum was obtained in the scanning range of 200-550 nm, and SUVA 254 , HIX and BIX were calculated from the three-dimensional fluorescence spectrum and ultraviolet-visible absorption spectrum. In addition, parallel factor analysis (PARAFAC) was performed using the DOMFluor toolbox in Matlab software, and based on the results of split-half analysis and residual analysis, independent fluorescence components were extracted, and the relative abundance of different fluorescence components was calculated from the Fmax value (unit: R.U.) in DOMFluor.

[0129] PARAFAC analysis was performed using the DOMFluor toolbox in Matlab software, and the specific operation steps included:

[0130] (1) Eliminate the influence of Raman scattering and Rayleigh scattering on the EEM data of water samples at each site;

[0131] (2) Determine abnormal samples by calculating the centering leverage value of each sample, and samples with significantly higher leverage values than other samples are considered abnormal and are excluded;

[0132] (3) Perform residual analysis on the samples, change the factor number, and when the residual value is minimal and no longer changes significantly, the factor number is tentatively determined as the suspected optimal factor number;

[0133] (4) Further verify the model by half analysis and random initialization assignment. Half analysis randomly divides the sample database into two sub-databases for verification, and the reasonable factor number should make the results of the two sub-database models consistent. Random initialization is to verify that the obtained model is the result of least square rather than local minimum. Random assignment will make the model more optimized, finally determine the optimal factor number and obtain the optimal model.

[0134] Since the parallel factor calculation finally obtains the relative fluorescence intensity of each component, the fluorescence intensity I f , the total fluorescence intensity I T and the proportion P f of each component are calculated according to the following formula:

[0135] (6);

[0136] (7);

[0137] (8);

[0138] Wherein, Score f represents the relative fluorescence intensity value of the fth component, E xf (λ max ) represents the maximum excitation load of the fth component, E mf (λ max ) represents the maximum emission load of the fth component.

[0139] Through EEM-PARAFAC method, four fluorescence components are obtained in the natural river section of Lancang River, including three humic components C1 (328 nm / 425 nm), C2 (274 nm (370) / 453 nm), C3 (330 (464) nm / 518 nm) and one protein component C4 (286 nm / 340 nm). Five fluorescence components are obtained in the cascade reservoir river section, which are humic components C1 (342 nm / 444 nm), C2 (278 (382) nm / 472 nm) and protein component C4 (282 nm / 329 nm), soil fulvic acid C3 (300 (466) nm / 527 nm), and C5 component (332 nm / 527 nm), as shown in Table 1 and Table 2. Figures 2~4

[0140] Table 2 Fluorescence peak position and indication significance of surface water body in Lancang River

[0141]

[0142] ​Among them, the natural river section C1 component and the cascade reservoir river section C1 component are soil organic matter; the natural river section C2 component and the cascade reservoir river section C2 component are mainly terrigenous output humus; the natural river section C3 component and the cascade reservoir C3 component are similar to peak E (455 nm / 521 nm), which is mainly derived from soil fulvic acid; the natural river section C4 component and the cascade reservoir C4 component of the Lancang River are a combination of peak B and peak T, which are protein-like components as endogenous and fresh inputs, mainly derived from the metabolism of algae, phytoplankton and microorganisms. C5 is a unique component of the cascade reservoir river section, and its maximum fluorescence intensity is close to M peak or N peak, wherein the M peak is terrigenous organic matter, and the N peak is generated by the degradation of phytoplankton.

[0143] According to Figure 4 As shown in the figure, the proportion of the natural river section C1 component is 45.94%~49.74%, and the average value is 47.92±0.99% (n=24); the proportion of the C2 component is 26.88%~30.21%, and the average value is 28.64±0.98% (n=24); the proportion of the C3 component is 12.73%~16.56%, and the average value is 14.82±1.32% (n=24); the proportion of the C4 component is 6.98%~15.73%, and the average value is 9.39±2.26% (n=24). The proportion of the cascade reservoir river section C1 component is 42.05%~47.12%, and the average value is 44.69±1.45% (n=28); the proportion of the C2 component is 20.81%~26.54%, and the average value is 23.49±1.57% (n=28); the proportion of the C3 component is 15.10%~18.67%, and the average value is 16.77±1.08% (n=28); the proportion of the C4 component is 6.03%~9.11%, and the average value is 7.82±0.72% (n=28); the proportion of the C5 component is 4.37%~12.30%, and the average value is 7.23±2.04% (n=28).

[0144] It can be seen that the DOM of the Lancang River is mainly composed of terrigenous humus, among which the proportion of humus-like components (C1~C3) in the natural river section is 91.24±0.10%, and the proportion of humus-like components in the reservoir river section is 84.95±0.20% (P<0.01). Figure 4 The proportion of the autochthonous component in the reservoir river section is significantly higher than that in the natural river section. The fluorescence intensity of the unique component C5 of the cascade reservoir gradually increases along the flow direction. Therefore, the construction of the reservoir not only increases the content of DOM, but also makes the composition of DOM more complex.

[0145] Example 3—Characteristics and source analysis of organic carbon isotope ratio of the middle route of the South-to-North Water Diversion Project

[0146] The specific test steps of this example are carried out with reference to Example 1.

[0147] Isotope ratio analysis was performed on dissolved organic carbon in the water bodies of the South-to-North Water Diversion Project's central route. The results are as follows: Figure 5 As shown, the δ of organic carbon in water 13 The C-DOC range was -11.56‰ to -31.60‰, with an average of -22.60±3.55‰. The δ of the water body in autumn... 13 The mean C-DOC was -20.31 ± 3.02‰, and the δ of the water body in spring was... 13 The mean C-DOC value was -24.89 ± 2.38‰, which is close to the DOC isotope ratio of typical surface water bodies. From the perspective of distribution along the course, the δ¹⁸O values ​​of the water body in autumn... 13 C-DOC showed three peaks at Lanhebei, Houxiaotunxi, and Liujiazuo, while other stations remained at similar levels; except for the δ¹⁸O₂ in the Danjiangkou Reservoir area, water bodies in spring... 13 Apart from the lower C-value, the spatial differences at other sites were not significant. Generally speaking, the δ-value of algae... 13 C-values ​​range from -23‰ to -40‰, and the δ¹⁴⁻ of terrestrial C3 plants... 13 The C value is -23‰ to -40‰, and the δ of C4 plants is... 13 The carbon isotope value ranges from -9‰ to -19‰. Analysis of carbon isotope values ​​reveals that the organic carbon δ¹⁹ in the water bodies of the South-to-North Water Diversion Project's central route is... 13 The C value falls between algal carbon and terrestrial carbon (C3 plants, C4 plants, and soil), indicating a combination of endogenous and exogenous factors.

[0148] Carbon and nitrogen isotope ratios play an important role in tracing the origin of organic matter. Stable carbon and nitrogen isotope ratio analysis was performed on sediments from the South-to-North Water Diversion Project to explore the origin of organic matter. For example... Figure 6 As shown, the δ-value of organic carbon in autumn sediments from the South-to-North Water Diversion Project's central route. 13 The C ratio ranged from -10.16‰ to -28.53‰, with an average of -14.57 ± 4.59‰. δ 15 The nitrogen ratio ranged from 0.21‰ to 5.66‰, with an average of 3.06±1.49‰; the δ¹⁴ of organic carbon in spring sediments... 13 The C value (range -12.54‰ to -21.28‰, average -16.49±2.73‰) is slightly lower than in autumn, δ 15 The N value (range 0.01‰~6.50‰, average 2.91±1.95‰) is comparable to that in autumn. Endmember values ​​from different potential sources were input into the MixSIAR model (as shown in Table 3) to obtain the relative contribution rates of each source to the organic matter in the sediments along the South-to-North Water Diversion Project's central route. MixSIAR calculations show that terrestrial components contribute 55%~72% to the sediment organic carbon content, while algal carbon contributes 16%~20%. Sediment organic carbon is mainly due to terrestrial carbon, with algal carbon contributing relatively little.

[0149] Table 3. Sources of organic matter in sediments along the central route of the South-to-North Water Diversion Project (δ) 13 C and δ 15 N-terminal value distribution

[0150]

[0151] Furthermore, the inorganic carbon isotope ratios in the water were analyzed, revealing the sources and transformation characteristics of carbon in the water. For example... Figure 5 As shown, the δ of the water body in the central route of the South-to-North Water Diversion Project 13 The C-DIC range is -6.61‰ to -17.66‰, with an average of -10.98±1.58‰. The δ¹⁸O value of the water body in autumn is [not specified in the original text]. 13 The mean C-DIC was -10.07 ± 0.71‰, with the maximum value at Tuanchenghu station (-8.70‰) and the minimum value at Houzhuang station (-11.79‰). δ¹⁸O in spring water... 13 The mean C-DIC value was -11.90±1.69‰, with the maximum value occurring at the Shahe South Station in the Henan section, at -6.61‰. Along the route, the dissolved inorganic carbon isotope values ​​generally increased in spring and autumn. The impoundment of reservoirs along the South-to-North Water Diversion Project enhanced photosynthesis by phytoplankton and other organisms in the surface water, leading to CO2 absorption and a corresponding increase in water δ¹⁸O. 13 C-DIC is significantly positive.

[0152] Comparison of δ values ​​between the two seasons during the monitoring period 13 C-DOC and δ 13 According to the results of the C-SOC study, δ 13 A more negative C-DOC bias indicates that, besides C4 plants, there are other sources (such as algal carbon). Generally, algae preferentially utilize lighter carbon from DIC (e.g., carbon from algae). 12 C), leading to carbon isotope fractionation between algae and DIC. Autumn δ¹⁸ 13 C-DIC is significantly more positive than in spring, indicating strong algal photosynthesis in autumn. Based on algal origin and terrestrial input, δ¹⁸O₂... 13 The C-terminal value, further calculated using a binary endmember model, shows the contribution of algae to the DOC of the South-to-North Water Diversion Project's central route. Figure 7 As shown, the algal contribution rate of DOC in the middle route of the South-to-North Water Diversion Project ranges from 7.22% to 95.52%, with an average of 56.22%. The algal contribution increases along the route in autumn and decreases downstream, with an average contribution of 72.9%. The algal contribution in spring also shows the same trend, with an average of 39.53%, which is significantly lower than that in autumn.

[0153] As can be seen from the above embodiments, the quantitative identification method provided by the present invention can comprehensively analyze the source and characteristics of DOM, and specifically identify the characteristic information and contribution of autogenous organic carbon.

[0154] Although the above embodiments have been described in detail, they are only some embodiments of the present application, not all embodiments, and other embodiments can be obtained under the premise of not being creative according to the above embodiments, and these embodiments all belong to the protection scope of the present application.

Claims

1. A method for quantitatively identifying the contribution of endogenous organic carbon in surface water bodies, characterized in that, Includes the following steps: (1) The collected surface water samples and sediments were pretreated respectively; (2) Stable isotope ratios, ultraviolet-visible absorption spectrometry and three-dimensional fluorescence spectrometry were used to determine the stable isotope ratios, ultraviolet-visible absorption spectrometry and three-dimensional fluorescence spectrometry of the pretreated water samples and sediments, respectively, to obtain carbon and nitrogen stable isotope ratios, ultraviolet-visible absorption spectral data and three-dimensional fluorescence spectral data. (3) Using the Bayesian isotope mixing model toolkit, the carbon-nitrogen ratio of DOM from different sources is used to form an isotope database. The isotope database and water sample and sediment data are input into the Bayesian isotope mixing model and the Markov chain Monte Carlo model algorithm is used to calculate the average contribution rate of autogenous organic matter to water organic matter. (4) Calculate the spectral parameters of the DOM, including the ultraviolet spectral parameter SUVA. 254 And E2 / E3, fluorescence spectral parameters HIX and BIX; (5) Use the DOMFluor toolkit to perform parallel factor analysis on the three-dimensional fluorescence excitation and emission matrix dataset obtained in step (2) to obtain the fluorescence peaks and characteristic components of the water sample; (6) Based on the fluorescence peaks and characteristic components of the water sample obtained in step (5), and the average contribution rate of the autogenic DOM to the organic matter in the water body in step (3), the characteristics of the autogenic DOM in the water body are determined. The input Bayesian isotope mixture model includes the following steps: The isotopic ratios and C / N ratios of sediments were used as tracers of the source of sediment organic matter to construct a MixSIAR model; The mean and standard deviation of end-member isotopes and C / N ratios from different pollution sources were compiled into a .csv file named "source". The stable isotope and C / N ratio data of organic carbon in watershed sediments were compiled into a .csv file named "consumer", with the row and column names of the consumer file being consistent with those of the source file. The blank correction file corresponding to the structure of the source file was set to 0 and compiled into a .csv file named "discrimination" for isotope bias correction in the model. The compiled files were imported into the MixSIAR model in RStudio software, and the contribution rate of different endmembers to watershed organic carbon was estimated through Bayesian iteration.

2. The quantitative identification method according to claim 1, characterized in that, The pretreatment of the water sample includes the following steps: The water sample was purified and then acidified to a pH of 2. The pretreatment of the sediment includes the following steps: drying the sediment in sequence, initial screening to collect the undersize material, crushing, and sieving to collect the undersize material again.

3. The quantitative identification method according to claim 1, characterized in that, The determination of the stable isotope ratio of the water sample includes the determination of δ¹⁸O⁻ by stable isotope ratio mass spectrometry. 13 C-DOC value and stable isotope ratio mass spectrometry determination of δ 13 C-DIC value.

4. The quantitative identification method according to claim 3, characterized in that, The stable isotope ratio mass spectrometry method for determining δ 13 The C-DOC value includes the following steps: A water sample was mixed with pure phosphoric acid to decompose inorganic carbon into carbon dioxide, which was then blown away to obtain an organic carbon water sample. This organic carbon water sample was then mixed with an oxidant solution for oxidation to produce carbon dioxide. The carbon dioxide was injected into a stable isotope ratio mass spectrometer to obtain δ¹⁸O₂. 13 C-DOC value.

5. The quantitative identification method according to claim 3, characterized in that, The stable isotope ratio mass spectrometry method for determining δ 13 The C-DIC value includes the following steps: A water sample and pure phosphoric acid were heated and mixed to undergo an acidification reaction, yielding carbon dioxide. The carbon dioxide was then injected into a stable isotope ratio mass spectrometer for detection. 13 C / 12 The value of C is used to obtain δ. 13 C-DIC value.

6. The quantitative identification method according to claim 1 or 2, characterized in that, The determination of the isotopic ratios of the sediments includes the following steps: After removing inorganic carbon by mixing the sediment with hydrochloric acid, the sediment was successively washed with water, dried, and ground. The δ¹⁸O₅ of the organic matter in the sediment was then detected using a stable isotope ratio mass spectrometer. 13 C and δ 15 N isotope value.

7. The quantitative identification method according to claim 1 or 3, characterized in that, The ultraviolet-visible absorption spectroscopy determination of the water sample includes the following steps: After rinsing the container, the absorbance was measured using a UV-Vis spectrophotometer. Milli-Q ultrapure water was used as a blank for calibration, and the SUVA was calculated. 254 value.

8. The quantitative identification method according to claim 1 or 3, characterized in that, The three-dimensional fluorescence spectroscopy determination of the water sample includes the following steps: The three-dimensional fluorescence spectrum data of the water sample were measured using a three-dimensional fluorescence spectrophotometer. The three-dimensional fluorescence spectrum data were then subjected to internal filtering correction to remove blank fluorescence signals. The fluorescence intensity was then divided by the Raman peak area of ​​pure water at the set excitation wavelength to obtain the standardized fluorescence intensity.

9. The quantitative identification method according to claim 1, characterized in that, The parallel factor analysis includes the following steps: The obtained three-dimensional fluorescence spectral data were subtracted from the spectral signal of the blank sample to eliminate background interference. Subsequently, the Raman peak normalization method was used to standardize the spectral signal intensity to correct the signal deviation caused by instrument drift or batch differences, and to ensure the consistency and comparability of the data. The processed three-dimensional fluorescence spectral data were imported into the PARAFAC model to determine the number of components and fit the model. The excitation-emission spectra and relative concentration distribution of each fluorescent component were obtained, and the fluorescence characteristic index was extracted. The established PARAFAC model was diagnosed and validated, and the core consistency index and residual distribution were evaluated to ensure the stability, interpretability and fitting effect of the model.

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