Lake water chemical oxygen demand internal and external source pollution quantitative tracing method and system

Abstract

The invention provides a lake water chemical oxygen demand internal and external source pollution quantitative tracing method and system, and relates to the technical field of pollution quantitative tracing. The method comprises the following steps: performing three-dimensional fluorescence spectrum analysis on water samples at different point positions of a lake to obtain fluorescence excitation / emission matrix spectrum EEMS data; performing parallel factor analysis on the EEMS data, identifying and classifying the fluorescent components of the dissolved organic matter DOM, and determining the type characteristics of each component; based on the maximum fluorescence intensity and the corresponding COD value of the fluorescence component, establishing a standardized multiple linear regression equation by adopting ridge regression analysis, and calculating the contribution percentage of each fluorescence component to the COD of the lake water body; analyzing influence paths of potential variables on DOM migration and transformation, and quantifying direct contributions and indirect contributions of different pollution sources to each fluorescent component through path coefficients; and calculating the total contribution of lake-entering rivers, bottom mud release and phytoplankton to the COD of the lake water body.

Description

Technical Field

[0001] The present invention relates to the technical field of pollution quantitative source tracing, and specifically, to a method and system for quantitatively tracing the external and internal sources of chemical oxygen demand in lake water bodies. Background Art

[0002] Chemical oxygen demand (COD) is an important water quality indicator for surface waters such as lakes and rivers. The higher the COD value, the more severely the water body is polluted by organic matter, which may cause the depletion of dissolved oxygen and damage the aquatic ecosystem. In recent years, with global warming and the increase in concentration, large plants and phytoplankton in lakes have fixed more from the atmosphere and transformed it into dissolved organic matter in the lake. At present, there is a lack of relatively reliable technical means to identify the quantitative contributions of external inputs and internal sources in the sources of lake COD pollution.

[0003] Patent application document CN114813585A discloses a method and system for remotely sensing and monitoring water quality parameters of brackish water lakes, including: collecting water spectra and corresponding water samples in multiple regions of the lake to be monitored to obtain multiple water spectra and multiple water samples; analyzing each water sample to determine the water quality parameters of the corresponding region; performing correlation analysis on each water spectrum and the water quality parameters of each region, and performing linear fitting of a unary linear equation to determine multiple water quality parameter monitoring models; collecting hyperspectral images of the lake to be monitored and extracting the spectral information of each water pixel; for any water pixel, based on the spectral information of the water pixel and each water quality parameter monitoring model, determining multiple initial water quality parameters of the water pixel; and determining the water quality parameter of the water pixel according to the average value of the multiple initial water quality parameters of the water pixel, which can quickly detect the water quality parameters of the lake. However, this patent cannot completely solve the existing technical problems and also cannot meet the requirements of the present invention. Summary of the Invention

[0004] Aiming at the deficiencies in the prior art, the purpose of the present invention is to provide a method and system for quantitatively tracing the external and internal sources of chemical oxygen demand in lake water bodies.

[0005] According to the method for quantitatively tracing the external and internal sources of chemical oxygen demand in lake water bodies provided by the present invention, it includes: Step S1: Obtain the water quality data, sediment data, and pollution load data of the inflowing rivers of the target lake, where the water quality data includes chemical oxygen demand COD, water temperature, pH, dissolved oxygen, total nitrogen, total phosphorus, soluble total nitrogen, orthophosphate, algal cell density, and chlorophyll a, the sediment data includes total nitrogen, total phosphorus, and organic matter content, and the data of the inflowing rivers includes the total nitrogen, total phosphorus, and COD loads input into the lake per unit time; Step S2: Perform three-dimensional fluorescence spectroscopy analysis on water samples at different points in the lake to obtain fluorescence excitation / emission matrix spectrum EEMS data; Step S3: Perform parallel factor analysis (PARAFAC) on the EEMS data to identify and classify the fluorescent components of dissolved organic matter (DOM), and determine the characteristic features of each component type by comparing with the Open Fluor organic fluorescence spectral library. to and determine the characteristic features of each component type by comparing with the Open Fluor organic fluorescence spectral library. Step S4: Based on the maximum fluorescence intensities of the fluorescent components to and the corresponding COD values, use ridge regression analysis to establish a standardized multiple linear regression equation, and calculate the contribution percentage of each fluorescent component to the COD of the lake water body. Step S5: Use partial least squares path modeling (PLS-PM) to analyze the influence paths of latent variables on the migration and transformation of DOM. The latent variables include phytoplankton, nutrient status, sediment, inflowing rivers, and environmental variables, and quantify the direct and indirect contributions of different pollution sources to each fluorescent component through path coefficients. Step S6: Combine the contribution percentage of each fluorescent component to the COD of the lake water body and the path coefficients of different pollution sources to calculate the total contribution amounts of inflowing rivers, sediment release, and phytoplankton to the COD of the lake water body.

[0006] Preferably, the parameters of the three-dimensional fluorescence spectral analysis in Step S2 include: The excitation wavelength scanning range is 200 nm to 400 nm, and the emission wavelength scanning range is 300 nm to 600 nm. The data intervals are 2 nm for excitation once and 1 nm for emission once, and the excitation light and emission light bandwidths are 5 nm. For every 10 water samples measured, blank correction is performed with ultrapure water, and the scanning speed is 6000 nm / min.

[0007] Preferably, the implementation of the PARAFAC analysis in Step S3 includes: Use Matlab software and the drEEM toolbox for data processing. The preprocessing steps include subtracting Rayleigh scattering, Raman scattering, inner filter effect correction, Raman unit normalization, and noise region elimination. Determine the number of fluorescent components through core consistency test and split-half validation, and perform 40 iterations with non-negative constraint conditions. The convergence criterion is set to .

[0008] Preferably, the specific method of the ridge regression analysis in Step S4 is: Taking COD as the dependent variable and the fluorescent components to as independent variables, introduce a ridge parameter to solve the problem of multicollinearity. First, perform data standardization to eliminate the dimension difference. The formula is:

[0009]

[0010] Among them, is the intensity of the j-th fluorescence component of the i-th sample, , are the mean and standard deviation of the j-th component respectively; is the COD value of the i-th sample, , are the mean and standard deviation of COD; Using the standardized COD value as the dependent variable and the standardized fluorescence component as the independent variable, a ridge regression model is established:

[0011] Among them, is the error term; The regression coefficients are solved by minimizing the following objective function :

[0012] Among them, is the ridge parameter used to control the regularization intensity; m is the number of samples, and n is the number of fluorescence components; The cross-validation method is used to determine the optimal λ. The data set is divided into a training set and a validation set, and the candidate values of λ are traversed to calculate the mean squared error of the validation set:

[0013] Among them, represents traversing all samples in the validation set; represents the number of samples in the validation set; Select the λ that minimizes the MSE as the optimal parameter; Restore the standardized regression coefficient to the coefficient under the original dimension , and the formula is:

[0014] The final standardized ridge regression equation is:

[0015] Among them, the intercept term ; The contribution percentage of each fluorescence component to COD is calculated by the following formula:

[0016] Among them, is the regression coefficient of the k-th fluorescence component, represents the absolute value of the regression coefficient of the j-th component, reflecting its relative weight in the impact on COD.

[0017] Preferably, the external model indexes of the PLS-PM model in step S5 include: The reflection index of DOM is the maximum fluorescence intensity of each fluorescence component; The reflection index of phytoplankton is the algal density and chlorophyll a concentration; The reflection index of the trophic state is total nitrogen, soluble total nitrogen, total phosphorus and orthophosphate; The reflection index of environmental variables is pH, water temperature and dissolved oxygen; The reflection index of sediment is the content of organic matter, total nitrogen and total phosphorus; The reflection index of the incoming river is total nitrogen, total phosphorus and COD load.

[0018] Preferably, the significance of the path coefficient in step S5 is verified by 1000 times of bootstrap resampling, and when the goodness-of-fit GOF index is greater than 0.7, it is determined as an effective model.

[0019] Preferably, the calculation method of the total contribution of pollution sources in step S6 is as follows: For each fluorescence component , its pollution source contribution is the COD contribution value of this component multiplied by the path coefficient of the corresponding pollution source; The total contribution is the sum of the products of the direct path coefficient and the indirect path coefficient.

[0020] According to the quantitative source tracing system for internal and external pollution of chemical oxygen demand in lake water provided by the present invention, it includes: A data acquisition module for obtaining monitoring data of lake water quality, sediment and incoming rivers; A spectral analysis module for processing three-dimensional fluorescence spectral data and performing PARAFAC analysis; A regression modeling module for constructing a ridge regression equation and calculating the COD contribution percentage of each fluorescence component; A path analysis module for running the PLS-PM model and quantifying the pollution source path coefficient; A contribution calculation module for integrating the results of regression and path analysis and outputting the total contribution and proportion of internal and external pollution to COD.

[0021] Preferably, the spectral analysis module further includes: A scattering correction unit for subtracting Rayleigh scattering and Raman scattering; A noise filtering unit for removing abnormal spectral regions; A component identification unit is used to match the types of fluorescent components through the Open Fluor spectral library.

[0022] Preferably, it further includes a visualization module, which is used to display the contribution of pollution sources in a spatio-temporal dynamic chart and generate an optimized report on the treatment strategy.

[0023] Compared with the prior art, the present invention has the following beneficial effects: (1) Based on the data of COD, nitrogen, phosphorus, phytoplankton, chlorophyll a, nitrogen, phosphorus, organic matter, etc. in the sediment, as well as the three-dimensional fluorescence spectral data in the conventional water quality monitoring, the data are quickly and easily obtained, without the need to rely on expensive analysis techniques such as isotope methods and high-resolution mass spectrometry, and without adding much additional workload, having excellent universality; (2) The present invention establishes the path contribution coefficients of various organic matter approximate components of COD to different pollution sources, and can analyze the changes of pollution sources on a monthly, annual, and longer spatio-temporal scale, which can better guide the protection and treatment work of local lakes. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] By reading the following detailed description of non-limiting embodiments with reference to the accompanying drawings, other features, objects, and advantages of the present invention will become more apparent: Figure 1 It is the path analysis of PLS-PM for lake dissolved organic matter; Figures 2a to 2d They are four fluorescent components of organic matter in the water body of Erhai Lake, namely tryptophan-like component, high-molecular humic-like component, low-molecular humic-like component, and humic-like component. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] The present invention will be described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those of ordinary skill in the art can make several changes and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0026] Embodiment 1 The present invention provides a method for quantitatively tracing the internal and external pollution sources of COD in lake water bodies, including the following steps: Step 1: Obtain the water quality and sediment data of different points in the lake. The lake water quality data includes COD, water temperature, pH, dissolved oxygen, total nitrogen, total phosphorus, soluble total nitrogen, orthophosphate, algal cell density, and chlorophyll a. The sediment data includes total nitrogen, total phosphorus, and organic matter. The data of the inflowing rivers are the load data of nitrogen, phosphorus, and COD input into the lake per unit time.

[0027] Step 2: Perform three-dimensional fluorescence analysis on water samples at different points to obtain fluorescence excitation / emission matrix spectroscopy (EEMS) data.

[0028] Specific process of three-dimensional fluorescence analysis: Use a fluorescence spectrophotometer and quartz fluorescence cuvettes to measure the three-dimensional fluorescence spectra of water samples. The scanning range of the excitation wavelength is 200 - 400 nm, the data interval is 2 nm, the scanning range of the emission wavelength is 300 - 600 nm, the data interval is 1 nm, the bandwidths of the excitation light and emission light are set to 5 nm, and the spectral scanning speed is 6000 nm / min. Ultra-pure water should be measured as a blank control every 10 water samples measured.

[0029] Step 3: Perform parallel factor analysis (PARAFAC) on the spectral data to obtain 、 、…… and other components, and compare with the Open Fluor organic fluorescence spectral library (http: / / www.openfluor.org) to clarify the component type characteristics.

[0030] Specific process of parallel factor analysis: PARAFAC analysis was performed using Matlab software and the drEEM toolbox to identify the main components of organic matter. Before performing PARAFAC analysis, relevant preprocessing was carried out on the sample dataset, that is, the dataset was corrected using a pure water blank matrix, Rayleigh and Raman scattering were subtracted, and Delaunay triangular interpolation was used for interpolation processing. The inner filter effect was corrected using ultraviolet absorption spectral data, and Raman (R.U.) unit normalization was performed, and the spectral regions that may contain noise were subtracted. The least squares models of 2 - 7 components of the processed dataset were tested 40 times iteratively, and all models were restricted by non-negative conditions, and the convergence criterion was set at . Potential model outliers were evaluated and removed based on the residuals and leverage of each sample. The appropriate number of components was determined by observing the spectral shape of organic fluorophores and core consistency tests. Finally, the results of PARAFAC were verified using a split test and a randomly initialized model with 10 iterations.

[0031] Step 4: It was previously generally believed that three-dimensional fluorescence spectroscopy could analyze the components of dissolved organic matter (DOM), but it was difficult to quantify its components. In this solution, ridge regression analysis was performed on the maximum fluorescence intensity of the fluorescence components in the lake and their corresponding COD values to solve the problem of multicollinearity in the correlation of fluorescence components. Subsequently, the standardized ridge regression equation was obtained, that is, the multiple linear regression equation of COD with and …… and other components. Calculate the percentage of COD in the lake water body according to the equation.

[0032] Process of multiple linear regression and ridge regression analysis: Taking COD as the dependent variable and the fluorescence components to as independent variables, introducing the ridge parameter to solve the problem of multicollinearity; first, standardize the data to eliminate the dimension difference. The formula is:

[0033]

[0034] Among them, is the intensity of the jth fluorescence component of the ith sample, and are the mean and standard deviation of the jth component respectively; is the COD value of the ith sample, and are the mean and standard deviation of COD; Taking the standardized COD value as the dependent variable and the standardized fluorescence component as the independent variable, establish a ridge regression model:

[0035] Among them, is the error term; Solve the regression coefficients by minimizing the following objective function :

[0036] Among them, is the ridge parameter, used to control the regularization intensity; m is the number of samples, and n is the number of fluorescence components; Use the cross-validation method to determine the optimal λ. Divide the data set into a training set and a validation set, traverse the candidate values of λ, and calculate the mean square error of the validation set:

[0037] Among them, represents all samples in the traversed validation set; Represents the number of samples in the validation set; Select the λ that minimizes the MSE as the optimal parameter; The standardized regression coefficients Are restored to the coefficients in the original dimension; , and the formula is:

[0038] The final standardized ridge regression equation is:

[0039] Among them, the intercept term ; The contribution percentage of each fluorescence component to COD Is calculated by the following formula:

[0040] Among them, Is the regression coefficient of the k-th fluorescence component, Represents the absolute value of the regression coefficient of the j-th component, reflecting its relative weight of influence on COD.

[0041] Multicollinearity means that due to the existence of exact correlation or high correlation relationships among the explanatory variables in a linear regression model, the model estimation is distorted or difficult to estimate accurately. The existence of multicollinearity will lead to a certain degree of error in the multiple linear regression model, which is difficult to explain. There are various methods to solve multicollinearity, including excluding the variables causing collinearity, the difference method, reducing the variance of the parameter estimators, etc.

[0042] In this solution, the method of reducing the variance of the parameter estimators - ridge regression method is adopted to solve the problem of multicollinearity. Ridge regression is a biased estimation regression method specifically used for collinear data analysis. Substantially, it is a modified least squares estimation method. By sacrificing the unbiasedness of the least squares method and at the cost of losing some information and reducing the accuracy, a regression method with more practical and reliable regression coefficients is obtained, and its fitting of ill-conditioned data is stronger than that of the least squares method.

[0043] Step 5: To explain the influence mechanism, the partial least squares method (PLS-PM) of the structural equation model is used to estimate the complex relationships of latent variables. The PLS-PM analysis is implemented through the "plspm" package in R 4.0.3. Phytoplankton, trophic status, sediment, inflowing rivers, and environmental variables have been proven to have an impact on the migration and transformation of DOM. The path diagram of the model described in this solution is as Figure 1As shown. The full-path model consists of two sub-models: an internal model and an external model. The internal model shows the relationships between latent variables. The external model describes the relationships between latent variables and reflective indicators. The reflective indicators of DOM are determined as the maximum fluorescence intensity (Fmax) of different components. The reflective indicators of phytoplankton are selected as algal density and chlorophyll a concentration. Total nitrogen, soluble total nitrogen, total phosphorus, and orthophosphate are used as reflective indicators of nitrogen and phosphorus nutrients. The reflective indicators of environmental variables include pH, water temperature, and dissolved oxygen. Organic matter, total nitrogen, and total phosphorus content are used as reflective indicators of sediment. To reflect the impact of river pollution input on the freshwater system, total nitrogen, total phosphorus, and COD load are selected as reflective indicators of the river flowing into the lake. For DOM, it is directly or indirectly affected by all latent variables. Phytoplankton is mainly directly affected by nutrient status and environmental variables, while the nutrient status of the lake is also affected by the inflowing river, sediment, and environmental variables. The goodness-of-fit (GOF) index is used to evaluate the quality of the model. A value > 0.7 is considered an appropriate reflective indicator for the corresponding latent variable. The significance of the path coefficients is determined through 1000 bootstrap resamplings.

[0044] Using alone The path coefficients of a total of 10 paths, namely Path 1 - 10, are obtained through model calculation ( - Path 1, Path 2, ……, Path 10). Similarly, using , …… in sequence to obtain the path coefficients ( - Path 1, Path 2, ……, Path 10).

[0045] Step 6: For the river input source, the contribution paths to DOM include direct Path 1 and indirect Path 2 (which are then converted to Path 8 and 9); for the sediment source, the contribution paths to DOM include direct Path 3 and indirect Path 4 (which are then converted to Path 8 and 9); for the phytoplankton source, the contribution path to DOM is only direct Path 10. According to the PLS-PM path coefficient results, respectively for , …… Regarding the path coefficient results of river input, sediment, and phytoplankton, the direct contribution is represented by the direct path coefficient, and the indirect contribution is represented by the product of the path coefficients. The total contribution is represented by the sum of the direct impact path coefficient and the indirect impact path coefficient.

[0046] Step 7: Using the percentage of lake water body COD calculated in Step 4 according to the multiple linear regression equation and the total contribution path coefficients of different sources in Step 6 , the actual contributions of river input, sediment, and phytoplankton to COD can be calculated.

[0047] Example 2: The quantitative source tracing location of COD in Erhai Lake.

[0048] According to the qualitative and quantitative analysis method of the three-dimensional fluorescence spectral components of the water body in Erhai Lake in this study, the COD characteristics and sources of the water body in Erhai Lake at a specific time are analyzed, and the fluorescence components tryptophan-like components, high molecular weight humic-like components, low molecular weight humic-like components and humic-like components, such as Figures 2a to 2d .

[0049] Calculated according to the multiple linear regression and ridge regression quantitative analysis methods, the fluorescence components , , and corresponding COD values are 11.66 mg / L, 0.75 mg / L, 1.60 mg / L, and 0.43 mg / L respectively.

[0050] According to the partial least squares path model analysis, Table 1 shows the path coefficients of different sources for the fluorescence components, and Table 2 shows the COD contributed by different sources. For specific calculations, take as an example. To obtain the COD contribution of the incoming rivers to the component, it is necessary to know the contribution ratio of the incoming rivers to the component, which is represented by the path coefficient in the least squares path model. Then the incoming river contribution component COD = component contribution COD × the contribution of the incoming rivers to the component = 11.66 × 0.0175 = 0.20405 mg / L. The calculations for sediment and phytoplankton are the same as above, and the , and parts are calculated respectively.

[0051] The specific process is as follows: Step 1: Data basis; Input: Measured data of 30 sampling points in Erhai Lake; Independent variable: The maximum fluorescence intensity (Fmax) of 4 fluorescence components; (tryptophan-like): Average intensity 1200; (high molecular weight humic): Average intensity 85; (low molecular weight humic): Average intensity 150; (Humic-like substances): average intensity 50; Dependent variable: measured COD value at the corresponding point (range: 8 - 15 mg / L); Step 2: Data standardization; Calculate the mean (μ) and standard deviation (σ) of each variable: For example : μ = 1200, σ = 300 → intensity 1500 is standardized to (1500 - 1200) / 300 = 1.0; COD: μ = 10.5 mg / L, σ = 1.8 → measured 12.5 mg / L is standardized to (12.5 - 10.5) / 1.8 ≈ 1.11; Step 3: Ridge parameter (λ) optimization; It is found that there is a strong correlation (correlation coefficient > 0.8) among humus components ( / / ); Determine the optimal λ = 0.3 through 10-fold cross-validation:

[0052] Step 4: Solve the regression coefficients; Solve by minimizing the ridge regression objective function: Standardized coefficients: = 0.82, = 0.05, = 0.12, = 0.03; Restore the original dimension:

[0053] For example : ; Step 5: Intercept term calculation;

[0054] Final ridge regression equation

[0055] Table 1 Path coefficients of each source for DOM components

[0056] Table 2 COD contributions of each source for DOM components

[0057] Therefore, the COD from algae in Erhai Lake water body accounts for approximately 45.52%, the COD from external input accounts for approximately 2.37%, and the COD released from sediment accounts for approximately 9.95%.

[0058] Those skilled in the art know that in addition to implementing the systems, devices, and their respective modules provided by the present invention in the form of pure computer-readable program code, the method steps can be logically programmed to enable the systems, devices, and their respective modules provided by the present invention to be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers, etc., to implement the same program. Therefore, the systems, devices, and their respective modules provided by the present invention can be regarded as a kind of hardware component, and the modules included therein for implementing various programs can also be regarded as the structures within the hardware component; the modules for implementing various functions can also be regarded as either software programs for implementing the method or the structures within the hardware component.

[0059] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. Without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for quantitatively tracing the internal and external sources of pollution of chemical oxygen demand in lake water bodies, characterized in that, Including: Step S1: Obtain the water quality data, sediment data, and pollution load data of the inflowing rivers of the target lake. The water quality data includes chemical oxygen demand (COD), water temperature, pH, dissolved oxygen, total nitrogen, total phosphorus, soluble total nitrogen, orthophosphate, algal cell density, and chlorophyll a. The sediment data includes total nitrogen, total phosphorus, and organic matter content. The data of the inflowing rivers includes the total nitrogen, total phosphorus, and COD loads input into the lake per unit time. Step S2: Conduct three-dimensional fluorescence spectroscopy analysis on water samples at different points in the lake to obtain fluorescence excitation / emission matrix spectroscopy (EEMS) data. Step S3: Perform parallel factor analysis (PARAFAC) on the EEMS data to identify and classify the fluorescent components of dissolved organic matter (DOM), and determine the characteristic features of each component type by comparing with the Open Fluor organic fluorescence spectral library. to , and determine the characteristic features of each component type by comparing with the Open Fluor organic fluorescence spectral library. Step S4: Based on the maximum fluorescence intensity of the fluorescent components to and the corresponding COD values, a standardized multiple linear regression equation is established by ridge regression analysis, and the contribution percentage of each fluorescent component to the COD of the lake water body is calculated; Step S5: Use the partial least squares path model (PLS-PM) to analyze the influence paths of latent variables on the migration and transformation of DOM. The latent variables include phytoplankton, trophic status, sediment, inflowing rivers, and environmental variables. Quantify the direct and indirect contributions of different pollution sources to each fluorescence component through path coefficients. Step S6: Combine the contribution percentages of each fluorescence component to the COD in the lake water and the path coefficients of different pollution sources to calculate the total contribution amounts of the inflowing rivers, sediment release, and phytoplankton to the COD in the lake water.

2. The quantitative source tracing method for the internal and external pollution of chemical oxygen demand in lake water bodies according to claim 1, wherein The parameters of the three-dimensional fluorescence spectroscopy analysis in Step S2 include: The excitation wavelength scanning range is 200 nm to 400 nm, and the emission wavelength scanning range is 300 nm to 600 nm. The data intervals are to excite once every 2 nm and emit once every 1 nm, and the excitation light and emission light bandwidths are 5 nm. Perform blank correction with ultrapure water every 10 water samples measured, and the scanning speed is 6000 nm / min.

3. The quantitative source tracing method for the external and internal pollution of chemical oxygen demand in lake water bodies according to claim 1, characterized in that The implementation of PARAFAC analysis in Step S3 includes: Use Matlab software and the drEEM toolbox for data processing. The preprocessing steps include subtracting Rayleigh scattering, Raman scattering, inner filter effect correction, Raman unit normalization, and noise region removal. Determine the number of fluorescence components through core consistency test and split verification, and perform 40 iterations with non - negative constraint conditions, and set the convergence criterion to .

4. The quantitative source tracing method for the internal and external pollution of chemical oxygen demand in lake water bodies according to claim 1, characterized in that, The specific method of ridge regression analysis in Step S4 is: Taking COD as the dependent variable and the fluorescence components to as the independent variables, the ridge parameter is introduced to solve the problem of multicollinearity; data standardization is carried out first to eliminate the difference in dimensions, and the formula is: Among them, is the intensity of the j-th fluorescent component of the i-th sample, and are the mean and standard deviation of the j-th component, respectively; is the COD value of the i-th sample, and are the mean and standard deviation of COD; Using the standardized COD value as the dependent variable and the standardized fluorescence components as the independent variables, a ridge regression model was established: wherein, is an error term; Solve for the regression coefficients by minimizing the following objective function : wherein, is the ridge parameter for controlling the regularization strength; m is the number of samples, and n is the number of fluorescence components; Use the cross-validation method to determine the optimal λ. Divide the data set into a training set and a validation set, traverse the candidate values of λ, and calculate the mean square error (MSE) of the validation set: Among them, represents traversing all samples in the validation set; represents the number of samples in the validation set; Select the λ that minimizes the MSE as the optimal parameter. Restore the standardized regression coefficient to the coefficient in the original dimension , and the formula is: The final standardized ridge regression equation is: Among them, the intercept term ; Percentage of contribution of each fluorescent component to COD Calculated by the following formula: Among them, is the regression coefficient of the k-th fluorescent component, represents the absolute value of the regression coefficient of the j-th component, reflecting its relative weight of influence on COD.

5. The quantitative source tracing method for the internal and external pollution of chemical oxygen demand in lake water bodies according to claim 1, wherein The external model indicators of the PLS-PM model in Step S5 include: The reflection index of DOM is the maximum fluorescence intensity of each fluorescence component. The reflection index of phytoplankton is the algal density and chlorophyll a concentration. The reflection index of the trophic status is total nitrogen, soluble total nitrogen, total phosphorus, and orthophosphate. The reflection index of environmental variables is pH, water temperature, and dissolved oxygen. The reflection index of sediment is the organic matter, total nitrogen, and total phosphorus content. The reflection index of the inflowing rivers is the total nitrogen, total phosphorus, and COD loads.

6. The quantitative source tracing method for the internal and external pollution of chemical oxygen demand in lake water bodies according to claim 1, characterized in that, The significance of the path coefficients in Step S5 is verified by 1000 times of bootstrap resampling, and when the goodness of fit (GOF) index is greater than 0.7, it is determined as an effective model.

7. The quantitative source tracing method for the internal and external pollution of chemical oxygen demand in lake water bodies according to claim 1, characterized in that The calculation method of the total contribution amount of pollution sources in Step S6 is: For each fluorescent component , its pollution source contribution amount is the COD contribution value of this component multiplied by the path coefficient of the corresponding pollution source; The total contribution amount is the sum of the products of the direct path coefficients and the indirect path coefficients.

8. A quantitative source tracing system for the internal and external pollution of chemical oxygen demand in lake water bodies, characterized in that, Adopt the method for quantitatively tracing the internal and external pollution sources of chemical oxygen demand in lake water as described in any one of claims 1 to 7, including: A data acquisition module for obtaining monitoring data of lake water quality, sediment, and inflowing rivers; A spectral analysis module for processing three-dimensional fluorescence spectral data and performing PARAFAC analysis; A regression modeling module for constructing a ridge regression equation and calculating the percentage contribution of COD of each fluorescence component; A path analysis module for running the PLS-PM model and quantifying the source pollution path coefficient; A contribution calculation module for integrating the results of regression and path analysis and outputting the total contribution amount and proportion of internal and external source pollution to COD.

9. The quantitative source tracing system for the internal and external pollution of chemical oxygen demand in lake water bodies according to claim 8, wherein, The spectral analysis module further includes: A scattering correction unit for subtracting Rayleigh scattering and Raman scattering; A noise filtering unit for removing abnormal spectral regions; A component identification unit for matching the fluorescence component type through the Open Fluor spectral library.

10. The quantitative source tracing system for the internal and external pollution of chemical oxygen demand in lake water bodies according to claim 8, wherein It further includes a visualization module for displaying the source pollution contribution amount in a spatio-temporal dynamic chart and generating a governance strategy optimization report.

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