GB2614769A - Method and apparatus for measuring concentration of dissolved organic carbon in water - Google Patents

Method and apparatus for measuring concentration of dissolved organic carbon in water Download PDF

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GB2614769A
GB2614769A GB2208462.8A GB202208462A GB2614769A GB 2614769 A GB2614769 A GB 2614769A GB 202208462 A GB202208462 A GB 202208462A GB 2614769 A GB2614769 A GB 2614769A
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substance
concentration
predetermined wavelength
water body
humus
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Huang Qi
Liu Lizhen
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Jiangxi Normal University
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Abstract

A method and apparatus for measuring the concentration of dissolved organic carbon (DOC) in water. The solution comprises: determining an optical absorption spectrum of a colored dissolved organic matter (CDOM) in water to be measured (102); calculating, on the basis of the optical absorption spectrum, an optical absorption coefficient afirst predetermined wavelength of the CDOM at a first predetermined wavelength, and a ratio M of absorbance of the CDOM at a second predetermined wavelength to absorbance of the CDOM at a third predetermined wavelength (104); determining a three-dimensional fluorescence spectrum of the CDOM in said water (106); analyzing relative content of fluorescent components in the three-dimensional fluorescence spectrum and ratios between the fluorescent components (108); and on the basis of the optical absorption coefficient afirst predetermined wavelength, the ratio M of the absorbance at the second predetermined wavelength to the absorbance at the third predetermined wavelength, and the relative content of the fluorescent components and the ratios between the fluorescent components, calculating the concentration of DOC in said water by using a trained random forest regression model (110). The technical solution can solve the problem in the concentration calculation of DOC in water having a large span of time and space, and the calculation efficiency is high.

Description

A METHOD AND DEVICE FOR DETERMINING THE CONCENTRATION OF
DISSOLVED ORGANIC CARBON IN THE WATER BODY
Technical field:
The application relates to the field of environmental monitoring and evaluation techniques for water bodies, particularly to a method and device for determining the concentration of dissolved organic carbon in the water body.
Background technology:
Dissolved organic carbon DOC represents the second largest bioactive carbon in the ocean, although storage capacity of DOG is uncertain in the inland water body, its effect on climate change and human beings in regional scale cannot be underestimated. The understanding of DOG storage, its migration and transformation can help better study the global carbon cycle and climate change, which requires DOG data on a large scale and a long time series. In current technologies, most conventional DOG measurement methods are limited to the analysis of indoor instruments, which produces waste water in the measurement analysis, resulting in environmental pollution, and it can only get limited and discrete data, which cannot acquire DOG content data on a long time series and a large scale space.
Therefore, how to provide an efficient method that can be applied to the calculation of DOG concentration in the water body with a large temporal and spatial span is a technical problem that needs to be solved.
The invention contents: This embodiment of this manual provides a method to determine the concentration of dissolved organic carbon in the water body, so as to solve the calculation problem of DOC concentration in the water body with a large temporal and spatial span.
To solve the above technical problem, the embodiment of this manual can be realized by the following ways: The embodiment of this manual provides a method for determining the concentration of dissolved organic carbon in the water body to be measured, which includes: Determining the optical absorption spectrum of chromophoric dissolved organic matter CDOM in the water body to be measured; Calculating the optical absorption coefficient (a the first predetermined wavelength) of chroinophoric dissolved organic matter CDOM at the first predetermined wavelength, the ratio Al of the absorbance at the second predetermined wavelength to the absorbance at the third predetermined wavelength, based on the optical absorption spectrum of the CDOM.
Determining the three-dimensional fluorescence spectrum of CDOM in the water body to be measured.
Analyzing the relative contents of all fluorescence components in the three-dimensional fluorescence spectrum and the ratios among all fluorescence components.
Calculating DOG concentration in the water body to be measured based on the optical absorption coefficient (a the first predetermined wavelength), the ratio M of the absorbance at the second predetermined wavelength to the absorbance at the third predetermined wavelength, the relative contents of all fluorescence components and the ratios among all fluorescence components, using a trained random forest regression model.
Preferably, the relative contents of all fluorescence components in the three-dimensional fluorescence spectrum include: the relative abundance Clr of the terrigenous humus-like substance, the relative abundance C2r of the the humus-like substance of microbial action, and the relative abundance Or of the proteinoid substance The ratios among all fluorescence components include: the ratio Cl/C2 of the fluorescence intensity Cl of the terrigenous humus-like substance to the fluorescence intensity C2 of the humus-like substance of microbial action, the ratio C2/C3 of the fluorescence intensity C2 of the humus-like substance of microbial action to the fluorescence intensity C3 of the proteinoid substance, the ratio CI /C3 of the fluorescence intensity C] of the terrigenous humus-like substance to the fluorescence intensity C3 of the proteinoid substance.
Preferably, the trained random forest regression model Lis: DO(' concentration =. f (C] r. C2r, C3r, Cl/C2, C2/C3, Cl /C3, a the first predetermined wavelength, NI).
Preferably, calculating the concentration of dissolved organic carbon DOG in the water body to be measured using the trained random forest regression model also includes: Collecting several water body samples in typical hydrological seasons.
Determining multiple optical parameters of CDOA4 of the water body samples and the measured concentration of the dissolved organic carbon DOC; Multiple optical parameters mentioned include: the relative abundance Clr of the terrigenous humus-like substance, the relative abundance C2r of the the humus-like substance of microbial action, the relative abundance C3r of the proteinoid substance, the ratio Cl /C2 of the fluorescence intensity Cl of the terrigenous humus-like substance to the fluorescence intensity C2 of the humus-like substance of microbial action, the ratio C2/C3 of the fluorescence intensity C2 of the humus-like substance of microbial action to the fluorescence intensity C3 of the proteinoid substance, the ratio Cl/C3 of the fluorescence intensity Cl of the terrigenous humus-like substance to the fluorescence intensity C3 of the proteinoid substance, the optical absorption coefficient (a the first predctennined wavelength) and the ratio Al of the absorbance; Several water body samples mentioned for the measured concentration of the known DOG are divided into training samples and test samples by a preset proportion; Multiple optical parameters mentioned for the training samples and the measured concentration of the DOC are used to train the initial random forest regression model, and the trained random forest regression model is obtained.
Input multiple optical parameters for the test samples into the trained random forest regression model to obtain predicted DOG concentration, and compare the predicted DOG concentration with the measured DOG concentration of the test samples to obtain the accuracy rate Adjust the training parameters corresponding to the trained random forest regression model according to the accuracy rate until the accuracy rate meets the preset one to obtain the trained random forest regression model.
Preferably, the typical hydrological seasons includes: dry seasons, flooding seasons, recession seasons, and wet seasons. The random forest model used in the invention essentially is a machine learning model, which collects water body samples in typical hydrological seasons, so that the random forest model used in the invention can more accurately learn the relationship between multiple optical parameters of CD0/11 and the DOG in the water body to be measured, enabling the final random forest model to more accurately predict the concentration of the DOG in the water body to be measured.
Preferably, the analysis of the relative contents of all fluorescence components in the three-dimensional fluorescence spectrum is based on the peak selection method or the parallel factor method Preferably, the optical absorption coefficient at the first predetermined wavelength is calculated based on the absorbance value measured at the first predetermined wavelength after scattering correction.
The invention also provides a device for determining the concentration of dissolved organic carbon in lake water body, which includes.
Optical absorption spectrum determination module, used for measuring the optical absorption spectrum of chromophoric dissolved organic matter CDOM in the water body to be measured.
Optical absorption coefficient and absorbance determination module, used for calculating the optical absorption coefficient of chromophoric dissolved organic matter CDOM at the first predetermined wavelength, the absorbance at the second predetermined wavelength and the absorbance at the third predetermined wavelength, based on the optical absorption spectrum of the CDOM.
Three-dimensional fluorescence spectrum determination module, used for determining the three-dimensional fluorescence spectrum of the chromophoric dissolved organic matter in the water body to be measured Analysis module, used for analyzing the relative contents of all fluorescence components in the three-dimensional fluorescence spectrum and the ratios among all fluorescence components Concentration determination module for dissolved organic carbon DOG, used for calculating DOG concentration in the water body to be measured based on the optical absorption coefficient, the absorbance at the second predetermined wavelength, the absorbance at the third predetermined wavelength, the relative contents of all fluorescence components and the ratios among all fluorescence components, using a trained random forest regression model.
Preferably, the device also includes: the random forest regression model training module, used for collecting several water body samples in typical hydrological seasons, Determining multiple optical parameters of CDOM of the water body samples and the measured concentration of the dissolved organic carbon DOLT. Multiple optical parameters mentioned include: the relative abundance Clr of the terrigenous humus-like substance, the relative abundance C2r of the humus-like substance of microbial action, the relative abundance C3r of the proteinoid substance, the ratio Cl /C2 of the fluorescence intensity Cl of the terrigenous humus-like substance to the fluorescence intensity C2 of the humus-like submance of microbial action, the ratio C2/C3 of the fluorescence intensity C2 of the humus-like substance of microbial action to the fluorescence intensity C3 of the proteinoid substance, the ratio Cl /C3 of the fluorescence intensity Cl of the terrigenous humus-like substance to the fluorescence intensity C3 of the proteinoid substance, the optical absorption coefficient (a the first predetermined wavelength) and the ratio Al of the absorbance; Several water body samples mentioned for the measured concentration of the known DOG are divided into training samples and test samples by a preset proportion Multiple optical parameters mentioned for the training samples and the measured concentration of the DOG are used to train the initial random forest regression model, and the trained random forest regression model is obtained.
Input multiple optical parameters for the test samples into the trained random forest regression model to obtain predicted DOG concentration, and compare the predicted DOG concentration with the measured DOG concentration of the test samples to obtain the accuracy rate.
Adjust the training parameters corresponding to the trained random forest regression model according to the accuracy rate until the accuracy rate meets the preset one to obtain the trained random forest regression model.
Preferably, the device also includes a calculation module for the relative contents of fluorescence components, which is used to analyze the relative contents of all fluorescence components in the three-dimensional fluorescence spectrum based on the peak selection method or the parallel factor method.
At least one embodiment in this manual can achieve the following beneficial effects: 1) Compared with the traditional DOG test analysis, the method in the invention is environmentally friendly, without adding any chemical reagents or producing any waste liquid.
2) The determination and pretreatment of the optical absorption spectrum and fluorescence spectrum of CDOM in the technical scheme of the invention only requires simple filtration, which is easy and cheap. Multiple optical parameters determination of the ('DOM selected into the random forest model integrates easily with sensors sold in the market for determination, so a large number of data sets can be obtained by real-time and on-line monitoring. Because there are plenty of data in the data set obtained, the random forest model can be better trained to get a more accurate estimation method of DOG in the water body, which is helpful to deepen the basic theoretical research of inland carbon cycle.
3) The technical scheme of the invention is based on the random forest regression model, and uses plenty of optical parameters of CDOM in the water body to be measured to estimate the concentration of the dissolved organic carbon DOC. Wherein, the chromophoric dissolved organic matter CDOM is an important optical substance component of dissolved organic DOG, and its optical properties can be obtained by field online monitoring or inversion from satellite remote sensing data, which is efficient and environment-friendly.
Introductions of attached drawings
In order to better explain the embodiments of this manual or the technical schemes in current technologies, the followings are brief introductions of the embodiments or attached drawings used in descriptions of current technologies. Obviously, the attached drawings in the descriptions below are only some embodiments recorded in this manual. For ordinary technicians in the field, additional attached drawings can be obtained without making creative labor.
Figure 1 is a schematic diagram for a procedure of a method for determining the concentration of the dissolved organic carbon in the water body provided by the embodiment of this manual; Figure 2 is a schematic diagram of the structure of a device for determining the concentration of the dissolved organic carbon in the water body provided by the embodiment of this manual corresponding to Figure 1; Figure 3 is a schematic diagram of the sampling site distribution of Poyang Lake in different hydrological seasons in the embodiment of this manual.
Figure 4 is a schematic diagram of three fluorescent components Cl, C2 and C3 obtained by analyzing the three-dimensional fluorescence spectrum of CD0/14 based on the parallel factor analysis in the embodiment of this manual.
Figure 5 is a schematic diagram of the comparative relationship between the calculated DOG concentration according to the method in the invention and the measured DOG concentration.
Specific embodiment methods: In order to make the purposes, technical schemes and advantages of one or more embodiments of this manual more clear, the followings are clear and complete descriptions for the technical schemes of one or more embodiments of this manual in combination with the specific embodiments of this manual and corresponding attached drawings. Obviously, the described embodiments are only some embodiments in the manual, not all embodiments. Based on the embodiments in the manual, all other embodiments obtained by ordinary technicians in the field without making creative labor are in the scope of protection of one or more embodiments in the manual.
Lakes are one of the most important freshwater reservoirs on the earth. As key nodes of all elements interaction in the land surface system, lakes are closely related to the process of global change and are sensitive recorders of regional response to global change, playing an important role in recording regional environmental change, regulating regional climate, maintaining regional ecological balance and increasing biodiversity. In recent years, under the influence of both natural and man-made factors, the water environment of lakes in China, especially those in the middle and lower reaches of the Yangtze River, is faced with many problems, such as the deterioration of water quality, the degradation of ecological function, the decline of flood regulation and storage capacity and so on, which have seriously affected the sustainable development of the social economy in lake basins, increasingly becoming the focus of governments and the public. With the increasingly prominent eco-environmental problems of lakes and the in-depth study of the global carbon cycle, there is an urgent need to understand the reserves and laws of organic carbon in inland water bodies.
As recorded in the background technology above, dissolved organic carbon DOG represents the second largest bioactiye carbon in the ocean. Although storage capacity of DOG is uncertain in the inland water body, its effect on climate change and human beings in regional scale can not be underestimated. The understanding of DOG storage, its migration and transformation can help better study the global carbon cycle and climate change, which requires DOG data on a large scale and a long time series. In current technologies, some conventional DOG measurement methods are limited to the analysis of indoor instruments, which produces waste water in the measurement analysis, resulting in environmental pollution, and it can only get limited and discrete data, which cannot acquire DOG content data on a long time series and a large scale space. Some methods of DOG concentration estimation indirectly estimate DOC concentration of the water body to be measured by using the salinity value of the water body to be measured based on the correlation between the measured salinity of the water body and the DOG concentration of the water body to be measured. However, this method is only applicable to the lake water body with a wide range of salinity changes. For the local water body of the lake with relatively stable salinity, or the obvious changing range of DOG concentration with seasonal changes, it is not appropriate to estimate DOG concentration indirectly based on the salinity of the water body.
Considering that in the water body, the DOG is closely related to the chromophor c dissolved organic matter ('DM/with light attenuation, the embodiment in the manual provides a universal method for the concentration determination of the dissolved organic carbon in the water body. Based on the random forest model, the DOG concentration in the water body can be estimated by using measured multiple optical parameters of the chromophoric dissolved organic matter CDOM, which can be applied to the estimation of DOG concentration in high water level variable amplitude lakes with a large temporal and spatial variation and different turbidity distributions.
The technical scheme provided by all embodiments of this application is described in detail below in combination with the attached drawings.
Figure 1 is a schematic diagram for a procedure of a method for determining the concentration of the dissolved organic carbon in the water body provided by the embodiment of this manual. From the perspective of program execution, the execution subjects of the procedure can be programs or application clients installed on the application server.
As shown in Figure 1, the procedure can include the following steps: Step 102: Determining the optical absorption spectrum of the chromophoric dissolved organic matter CDOAI in the water body to be measured.
In the embodiment, after collecting the water body to be measured, the water body to be measured is filtered by the filter membrane to obtain the filtered liquid, and then the filtered liquid is divided into the first filtered liquid and the second filtered liquid by a preset proportion. The optical absorption spectrum of the chromophoric dissolved organic matter CDOM in the first filtered liquid is determined using a UV-Vis spectrophotometer.
Step 104: Calculating the optical absorption coefficient (a the first predetermined wavelength) of the chromophoric dissolved organic matter CDOM at the first predetermined wavelength, the ratio M of the absorbance at the second predetermined wavelength to the absorbance at the third predetermined wavelength, based on the optical absorption spectrum of the CDOM The invention determines the concentration of the dissolved organic carbon DOC in the water body based on multiple optical parameters of CDOM in the water body to be measured. Specifically, multiple optical parameters mentioned include: the calculated optical absorption coefficient (a the fast predetermined wavelength) Of CDOM, based on the optical absorption spectrum obtained in Step 102, and the ratio Al of the absorbance at the second predetermined wavelength to the absorbance at the third predetermined wavelength, the ratio M reflects CDOM information regarding relative molecular weight.
Step 106: Determining the three-dimensional fluorescence spectrum of the chromophoric dissolved organic matter CDOM in the water body to be measured.
In this step, a fluorophotometer is used for determining the second filtered liquid obtained in Step 102 to obtain the three-dimensional fluorescence spectrum of the chromophoric dissolved organic matter (DOA' in the water body to be measured. Compared with the traditional analysis method, the three-dimensional fluorescence spectrum, with higher sensitivity, is 2:3 orders of magnitude higher than the general analysis method. The three-dimensional fluorescence spectrum has good selectivity and low sample consumption, without destroying the sample structure.
Step 108: Analyzing the relative contents of all fluorescence components in the three-dimensional fluorescence spectrum and the ratios among all fluorescence components Dissolved organic carbon DOG belongs to dissolved organic matter DOM, and humus is the main component of the dissolved organic carbon DOC.
The dissolved organic carbon DOG in the water body to be measured is analyzed by the sampling spectral technology in the embodiment. Wherein, the mechanism of the three-dimensional fluorescence spectral technology is that the light is absorbed by the dissolved organic carbon DOG in the process of irradiation, and the energy of the incident light is transferred to organic molecules. When organic molecules are excited, some electrons around the nucleus will transition from orbits of lower energy level to orbits of higher energy level, that is, from the ground state to the excited state. The whole process lasts for about 10' seconds, and the energy difference between the two energy levels is the energy absorbed. Because the excited state is unstable, it will recover to the ground state by radiation transition (emitting fluorescence) or non-radiation transition (vibration relaxation, internal conversion and inter-system crossing), so that when electrons recover to the ground state from the excited state, they are, accompanied by the energy, released in the form of electromagnetic radiation, thus fluorescence is produced. This is the mechanism that the three-dimensional fluorescence spectrum technology can analyze the dissolved organic carbon DOG.
The dissolved organic carbon DOC includes several humus, and the relative abundance of these humus and the ratios among all relative abundance are analyzed in Step 108.
Step 110: Calculating DOG concentration in the water body to be measured based on the optical absorption coefficient (a the first predetermined wavelength), the ratio AI of the absorbance at the second predetermined wavelength to the absorbance at the third predetermined wavelength, the relative contents of all fluorescence components and the ratios among all fluorescence components, using a trained random forest regression model.
Since the principle of the invention is to determine the concentration of the dissolved organic carbon DOG in the water body based on multiple optical parameters of the CD0A1 in the water body to be measured, after obtaining multiple optical parameters of the CD0A1 in the water body to be measured in previous steps, the concentration of the dissolved organic carbon DOG in the water body to be measured can be calculated using multiple optical parameters mentioned and based on the trained random forest regression model. Wherein, because the random forest model can be used for regression analysis, the regression analysis is a statistical analysis method to determine the interdependent quantitative relationship between two or more variables. According to the number of variables involved, the regression analysis method can be divided into the univariate regression analysis and the multiple regression analysis. The random forest regression model in this Step is a multiple regression analysis method, which can refer to multiple optical parameters of CDOM in the water body in which the concentration of the known dissolved organic carbon DOG is determined in advance, and the relationship between multiple optical parameters mentioned and the concentration of the dissolved organic carbon DOG is fitted based on the established random forest model to obtain the trained random forest regression model. Wherein, multiple optical parameters of CDOM in the water body to be measured should be ensured to be measured parameters under the same measurement conditions (including the same first predetermined wavelength, the second predetermined wavelength and the third predetermined wavelength) as described in Steps 102 to Step 108 above, where the concentration of the dissolved organic carbon DOG can be measured by the total organic carbon analyzer.
Based on the method in Figure 1, the embodiment of this manual also provides some specific embodiment methods, which are described below.
Optionally, the relative contents of all fluorescence components in the three-dimensional fluorescence spectrum described in Step 108 include: relative abundance Clr of terrigenous humus-like substance, the relative abundance C2r of the humus-like substance of microbial action, and the relative abundance C3r of the proteinoid substances The ratios among all fluorescent components include: the ratio Cl/C2 of the fluorescence intensity Cl of the terrigenous humus-like substance to the fluorescence intensity C2 of the humus-like substance of microbial action, the ratio C2/C3 of the fluorescence intensity C2 of the humus-like substance of microbial action to the fluorescence intensity C3 of the proteinoid substance, the ratio C1/C3 of the fluorescence intensity Cl of the terrigenous humus-like substance to the fluorescence intensity C3 of the proteinoid substance.
Optionally, the trained random forest regression modelldescr bed in Step 110 is as follows: Do(' concentration =.ie (C1 r, C2r, C3r, Cl/C2, C2/C3, Cl /C3, a the fast predeteinmied wavelength, Ni), Wherein, because the technical scheme of the embodiment is based on multiple optical parameters of CDOM in the water body to be measured, the trained random forest regression model is used to calculate the concentration of the dissolved organic carbon DOG in the water body to be measured, so the random forest model used should also be trained heretofore, which specially includes: Collecting several water body samples in typical hydrological seasons. Wherein, typical hydrological seasons include dry seasons, flooding seasons, recession seasons and wet seasons.
Determining multiple optical parameters of CDOM of the water body samples and the measured concentration of the dissolved organic carbon DOG.
Multiple optical parameters mentioned include: the relative abundance Clr of the terrigenous humus-like substance, the relative abundance C2r of the humus-like substance of microbial action, the relative abundance C3r of the proteinoid substance, the ratio C1/C2 of the fluorescence intensity Cl of the terrigenous humus-like substance to the fluorescence intensity C2 of the humus-like substance of microbial action, the ratio C2/C3 of the fluorescence intensity C2 of the humus-like substance of microbial action to the fluorescence intensity C3 of the proteinoid substance, the ratio Cl /C3 of the fluorescence intensity Cl of the terrigenous humus-like substance to the fluorescence intensity C3 of the proteinoid substance, the optical absorption coefficient (a the first predetermined wavelength) and the ratio Al of the absorbance.
Several water body samples mentioned for the measured concentration of the known DOG are divided into training samples and test samples by a preset proportion.
Multiple optical parameters mentioned for the training samples and the measured concentration of the DOG are used to train the initial random forest regression model, and the trained random forest regression model is obtained.
Input multiple optical parameters for the test samples into the trained random forest regression model to obtain predicted DOG concentration, and compare the predicted DOG concentration with the measured DOG concentration of the test samples to obtain the accuracy rate.
Adjust the training parameters corresponding to the trained random forest regression model according to the accuracy rate until the accuracy rate meets the preset one to obtain the trained random forest regression model.
Wherein, when collecting water body samples, water body samples at different locations of the water body to be measured can be collected, so that the water body samples obtained are more representative, which enables the final random forest model to have strong generalization ability for accurately estimating the DO(' concentration for the samples to be measured in different locations in the actual measurement Specifically, the analysis of the relative contents of all fluorescence components in the three-dimensional fluorescence spectrum is based on the peak selection method or the parallel factor method Specifically, the optical absorption coefficient at the first predetermined wavelength is calculated based on the absorbance value measured at the first predetermined wavelength after scattering correction.
Based on the same idea, the device corresponding to the above method is also provided in the embodiment of the manual.
Figure 2 is a schematic diagram of the structure of a device for determining the concentration of the dissolved organic carbon in the water body provided by the embodiment of this manual corresponding to Figure 1 As shown in Figure 2, the device can include Optical absorption spectrum determination module, used for measuring the optical absorption spectrum of chromophoric dissolved organic matter CDOM in the water body to be measured.
Optical absorption coefficient and absorbance determination module, used for calculating the optical absorption coefficient of chromophoric dissolved organic matter CDOM at the first predetermined wavelength, the absorbance at the second predetermined wavelength and the absorbance at the third predetermined wavelength, based on the optical absorption spectrum of the CDOA1.
Three-dimensional fluorescence spectrum determination module, used for determining the three-dimensional fluorescence spectrum of the chromophoric dissolved organic matter in the water body to be measured..
Analysis module, used for analyzing the relative contents of all fluorescence components in the three-dimensional fluorescence spectrum and the ratios among all fluorescence components.
Concentration determination module for dissolved organic carbon DOG, used for calculating DOG concentration in the water body to be measured, based on the optical absorption coefficient, the absorbance at the second predetermined wavelength, the absorbance at the third predetermined wavelength, the relative contents of all fluorescence components and the ratios among all fluorescence components, using a trained random forest regression model.
Compared with the traditional DOG test analysis, the method in the invention is environmentally friendly, without adding any chemical reagents or producing any waste liquid. The determination and pretreatment of the optical absorption spectrum and fluorescence spectrum of CD0/4 in the technical scheme of the invention only requires simple filtration, which is easy and cheap. Multiple optical parameters determination of the C'DOM selected into the random forest model integrates easily with sensors sold in the market for determination, so a large number of data sets can be obtained by real-time and on-line monitoring. Because there are plenty of data in the data set obtained, the random forest model can be better trained to get a more accurate estimation method of DOG in the water body, which is helpful to deepen the basic theoretical research of inland carbon cycle. At the same time, the technical scheme of the invention is based on the random forest regression model, arid uses plenty of optical parameters of CDOM in the water body to be measured to estimate the concentration of the dissolved organic carbon DOG. Wherein, the chromophoric dissolved organic matter CD0/14 is an important optical substance component of dissolved organic DOG, and its optical properties can be obtained by field online monitoring or inversion from satellite remote sensing data, which is efficient and environment-friendly.
The technical scheme of the invention is described below in combination with specific embodiments.
Water samples are collected proactively from the water body 0.5 meters below the surface of Poyang Lake, the largest freshwater lake in China, in four typical hydrological seasons (including dry seasons, flooding seasons, recession seasons arid wet seasons). The specific distribution of the sampling points is shown in Figure 3. Due to the sharp increase in water area during wet seasons, the number of sampling points has been increased, with the total number of 117.
The collected water sample are filtered by 0.2pm filter membrane to get filtered liquid. And then the optical absorption spectrum of CDONI in the water samples is determined by a UV-vis spectrophotometer, and the three-dimensional fluorescence spectrum of CDOM in the water samples is measured by a fluorophotometer. Then the three-dimensional fluorescence spectrum is analyzed based on the parallel factor analysis. As shown in Figure 4, three fluorescent components of CDOM in Poyang Lake are obtained, in which component Cl is the terrigenous humus-like substance, C2 is humus-like substance of microbial action, C3 is the proteinoid substances. Also, Ex represents the excitation wavelength, Em represents the emission wavelength. At the same time, the concentration of the dissolved organic carbon DOG in the water body from the sampling sites is measured by the total organic carbon analyzer, and the random forest model DOC _ _ _ concentration =f (CIL C2r, C3r. Cl/C2, C2/C3, Cl/Cl a the that predetermined wavelength, ND.
sampling sites (that is, the optical parameters of the model mentioned above DOC (C1r, C2r, C3r, CUC2, C2/C3, Cl /C3, elo._Tja Al) and the measured concentrations of the dissolved organic carbon DOG in the water body from the sampling sites, so as to obtain the trained random forest model.
After obtaining the trained random forest model, if the concentration of DOG in the water body at a certain point in Poyang Lake needs to be measured, multiple optical parameters of C130/14 in the water body at the sampling point can be input into the trained random forest model. Thus, the concentration of the dissolved organic carbon DOG in the water body at the sampling point can be estimated. Wherein, multiple optical parameters mentioned are multiple optical c DOC' concentration -.)" (C ir, C2r, C3r, Cl/C2, C2/C3, C1 /C3, a the first precletermined wawelength, Ni). ied above (DOC, =f (Clr, C2r, (T3r, Cl /C2, C2/C3, Cl /C3, Al)).
Specifically, in the embodiment, the first predetermined wavelength is 254 nm, the second predetermined wavelength is 250 nm, and the third predetermined wavelength is 365 nm.
Although only more than 100 sample points of Poyang Lake are used in the embodiment, these data cover typical hydrological seasons of the Lake, namely dry seasons, flooding seasons, recession seasons and wet seasons. As the largest freshwater lake in China, Poyang Lake has large internal differences in its vast waters. Also, as the largest typical river-connected lake in China, Poyang Lake has the characteristic of "An area of wet place, a line of dry place", containing different types of water body. Therefore, such water body can better represent the optical properties for water bodies of lakes in the middle and lower reaches of the Yangtze River, which ensures that the developed model has good applicability.
As shown in Figure 5, the DOG concentration estimated according to the method in the invention and the measured DOG concentration are fitted and analyzed, and the linear fitting determination coefficient R2 between the two is as high as 0.75, the root mean square error RIVISE is 0.23mg1, and the mean relative error N1RE is 8.26%. This fully shows that the method in the invention can accurately estimate the concentration of the dissolved organic carbon in the water body to be measured.
The above embodiments are illustrative only of the principle and efficacy of the invention, and are not intended to limit the invention. The above embodiments may be modified or altered by any person familiar with the technology without violation of the spirit and scope of the invention. Therefore, all equivalent modifications or alterations made by persons with general knowledge in the field of the technology to which they belong, without being detached from the spiritual and technical ideas revealed by the invention, shall still be covered by the Claims of the invention.

Claims (10)

  1. CLAIMS1. A method for determining the concentration of dissolved organic carbon in the water body, which are characterized by the following items: determining the optical absorption spectrum of chromophoric dissolved organic matter CDOVI in the water body to be measured; calculating the optical absorption coefficient (a the first predetermined wavelength) of the chromophoric dissolved organic matter CDOA1 at the first predetermined wavelength, and the ratio Al of the absorbance at the second predetermined wavelength to the absorbance at the third predetermined wavelength, based on the optical absorption spectrum of the CD0/14; determining the three-dimensional fluorescence spectrum of CDOM in the water body to be measured; analyzing the relative contents of all fluorescence components in the three-dimensional fluorescence spectrum and the ratios among all fluorescence components calculating the concentration of dissolved organic carbon DOC in the water body to be measured based on the optical absorption coefficient (a the first predetermined wavelength), the ratio Al of the absorbance at the second predetermined wavelength to the absorbance at the third predetermined wavelength, the relative contents of all fluorescence components and the ratios among all fluorescence components, using a trained random forest regression model.
  2. 2. According to the method described in Claims 1, the characteristic is that the relative contents of all fluorescence components in the three-dimensional fluorescence spectrum include: the relative abundance CI,' of terrigenous humus-like substance, the relative abundance (72r of humus-like substance of microbial action and the relative abundance C3r of proteinoid substances; the ratios among all fluorescence components include: the ratio Cl/C2 of the fluorescence intensity Cl of the terrigenous humus-like substance to the fluorescence intensity C2 of the humus-like substance of microbial action, the ratio C2/C3 of the fluorescence intensity C2 of the humus-like substance of microbial action to the fluorescence intensity 0 of the proteinoid substance, and the ratio C 1/C3 of the fluorescence intensity Cl of the terrigenous humus-like substance to the fluorescence intensity C3 of the proteinoid substance.
  3. 3. According to the method described in Claims 2, the characteristic is that the trained random forest regression model/is as follows: DOC concentration (C1r, C2r, C3r, Cl/C2, 02/03, 01/03 a the picdctermined wavelength, M).
  4. 4. According to the method described in Claims 3, apart from the concentration of dissolved organic carbon DOC in the water body to be measured calculated by the trained random forest regression model, the following items are also included: collecting several water body samples in typical hydrological seasons; determining multiple optical parameters of CDOM of the water body samples and the measured concentration of the dissolved organic carbon DOC; multiple optical parameters include: the relative abundance Clr of terrigenous humus-like substance, the relative abundance C2r of the humus-like substance of microbial action, the relative abundance C3r of the proteinoid substance, the ratio C 1/C2 of the fluorescence intensity Cl of the terrigenous humus-like substance to the fluorescence intensity C2 of the humus-like substance of microbial action, the ratio (C2/C3) of the fluorescence intensity C2 of the humus-like substance of microbial action to the fluorescence intensity C3 of the proteinoid substance, and the ratio (C1/C3) of the fluorescence intensity Cl of the terrigenous humus-like substance to the fluorescence intensity C3 of the proteinoid substance, the optical absorption coefficient (a the fast predetermined wavelength) and the ratio M of the absorbance; several water body samples mentioned for the measured concentration of the known DOC are divided into training samples and test samples by a preset proportion; multiple optical parameters mentioned for the training samples and the measured concentration of the DOC are used to train the initial random forest regression model, and the trained random forest regression model is obtained; input multiple optical parameters for the test samples into the trained random forest regression model to obtain predicted DOC concentration, and compare the predicted DOC concentration with the measured DOC concentration of the test samples to obtain the accuracy rate; adjust the training parameters corresponding to the trained random forest regression model according to the accuracy rate until the accuracy rate meets the preset one to obtain the trained random forest regression model.
  5. 5. According to the method described in Claims 1, the characteristic is that the typical hydrological seasons include: dry seasons, flooding seasons, recession seasons and wet seasons.
  6. 6. According to the method described in Claims I, the characteristic is that the analysis of the relative contents of all fluorescence components in the three-dimensional fluorescence spectrum is based on the peak selection method or the parallel factor method.
  7. 7. According to the method described in Claims I, the characteristic is that the optical absorption coefficient at the first predetermined wavelength is calculated based on the absorbance value measured at the first predetermined wavelength after scattering correction.
  8. 8. A device for determining the concentration of dissolved organic carbon in lake water body, its characteristics include: optical absorption spectrum determination model, used for measuring the optical absorption spectrum of chromophoric dissolved organic matter (DOM-in the water body to be measured; optical absorption coefficient and absorbance determination model, used for calculating the optical absorption coefficient of chromophoric dissolved organic matter CDOM at the first predetermined wavelength, the absorbance at the second predetermined wavelength and the absorbance at the third predetermined wavelength, based on the optical absorption spectrum of the ( DOM; three-dimensional fluorescence spectrum determination model, used for determining the three-dimensional fluorescence spectrum of the chromophoric dissolved organic matter in the water body to be measured; analysis module, used for analyzing the relative contents of all fluorescence components in the three-dimensional fluorescence spectrum and the ratios among all fluorescence components; concentration determination model for dissolved organic carbon DOG, used for calculating DO(' concentration in the water body to be measured based on the optical absorption coefficient, the absorbance at the second predetermined wavelength, the absorbance at the third predetermined wavelength, the relative contents of all fluorescence components and the ratios among all fluorescence components, using a trained random forest regression model.
  9. 9. According to the device described in Claims 8, the characteristics also include: random forest regression model training module, used for collecting several water body samples in typical hydrological seasons; determining multiple optical parameters of the water body samples and the measured concentration of the dissolved organic carbon DOC; multiple optical parameters mentioned include: the relative abundance Cl r of the terrigenous humus-like substance, the relative abundance C2r of the the humus-like substance of microbial action, the relative abundance C3r of the proteinoid substance, the ratio Cl/C2 of the fluorescence intensity C I of the terrigenous humus-like substance to the fluorescence intensity C2 of the humus-like substance of microbial action, the ratio C2/C3 of the fluorescence intensity C2 of the humus-like substance of microbial action to the fluorescence intensity C3 of the proteinoid substance, the ratio C1/C3 of the fluorescence intensity Cl of the terrigenous humus-like substance to the fluorescence intensity C3 of the proteinoid substance, the optical absorption coefficient (a the first predetermined wavelength) and the ratio N4 of the absorbance; several water body samples mentioned for the measured concentration of the known DOG are divided into training samples and test samples by a preset proportion; multiple optical parameters mentioned for the training samples and the measured concentration of the DOG are used to train the initial random forest regression model, and the trained random forest regression model is obtained.input multiple optical parameters for the test samples into the trained random forest regression model to obtain predicted DOG concentration, and compare the predicted DOG concentration with the measured DOG concentration of the test samples to obtain the accuracy rate.adjust the training parameters corresponding to the trained random forest regression model according to the accuracy rate until the accuracy rate meets the preset one to obtain the trained random forest regression model.
  10. 10. According to the device described in Claims 8, the characteristics also include: calculation module for the relative contents of fluorescence components, used for analyzing the relative contents of all fluorescence components in the three-dimensional fluorescence spectrum based on the peak selection method or the parallel factor method.
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