A method for tracing water pollution by fusing three-dimensional fluorescence and isotope tracking

Abstract

The present application relates to the technical field of pollution tracing, and specifically discloses a water pollution tracing method combining three-dimensional fluorescence and isotope tracing. The method comprises the following steps: collecting water samples in a river basin for three-dimensional fluorescence scanning, constructing a fluorescence spectrum dataset through component screening; performing fluorescence intensity and index analysis on the dataset to obtain the dynamic change trajectory of pollutants and the node response relationship; splitting the pollution logic stage based on the trajectory and response relationship, extracting the functional boundary and node dominant attribute; determining the unbalanced dominant object through input-output balance analysis, analyzing the competition and inhibition relationship of different unbalanced dominant objects when multiple source nodes coexist, and outputting the type. Through the synergistic combination of three-dimensional fluorescence and isotope tracing, combined with node attribute identification and competition and inhibition analysis, the present application realizes the analysis of pollution sources, migration paths and component competition relationships.

Description

Technical Field

[0001] This invention relates to the field of pollution source tracing technology, specifically to a water pollution source tracing method that integrates three-dimensional fluorescence and isotope tracing. Background Technology

[0002] While existing three-dimensional fluorescence spectroscopy can identify fluorescent components of DOM (Disseminated Oriented Pollutants), such as humic acids and autogenous pollutants, it fails to combine watershed hydrological connectivity and node functional attributes, and cannot determine the core role (release, transport, or accumulation) of different sampling points in pollution migration, resulting in ambiguity in pollution propagation path analysis. Secondly, when multiple pollution sources emit simultaneously within a watershed, existing technologies can only independently identify the component characteristics of each pollution source, ignoring the interaction between different pollution components during migration (such as the competitive inhibition between recalcitrant and readily degradable components), leading to pollution source tracing errors, inability to distinguish between migratory pollution peaks and local emission peaks, and consequently misjudgment of control targets.

[0003] Therefore, this invention provides a method for tracing the source of water pollution that integrates three-dimensional fluorescence and isotope tracking. Summary of the Invention

[0004] The purpose of this invention is to provide a method for tracing the source of water pollution that integrates three-dimensional fluorescence and isotope tracking, so as to solve the above-mentioned background problems.

[0005] The objective of this invention can be achieved through the following technical solutions: A method for tracing water pollution sources that integrates three-dimensional fluorescence and isotope tracing includes the following steps: Water samples from the basin were collected and subjected to three-dimensional fluorescence scanning. Fluorescence scanning data were obtained and the components were screened to obtain the optimal number of components. The fluorescence spectral characteristics of the optimal number of components were obtained and a fluorescence spectral dataset was constructed. Fluorescence intensity and exponential analysis were performed on the fluorescence spectral dataset to obtain the fluorescence intensity distribution characteristics and exponential characteristics of DOM at different sampling points. Source tracing analysis was then conducted to obtain the dynamic change trajectory of pollutant migration, and the response relationship between nodes in the dynamic change trajectory was identified. Based on dynamic change trajectories and node response relationships, the characteristic stages of watershed pollution are decomposed to obtain the logical stages of nodes, and the functional boundaries and dominant attributes of each logical stage are extracted. Based on the node logic stage, an input-output balance analysis is performed to obtain the unbalanced dominant object; the source node is extracted from the node attributes, and when multiple source nodes coexist, it is analyzed whether there is competition inhibition for different unbalanced dominant objects and the competition inhibition type is output.

[0006] As a further technical solution of the present invention, the method for screening the components is as follows: Different modeling group scores were set, and fluorescence scanning data matrices of N1 small water body samples were obtained for modeling and simulation processing; Extract the residual analysis and halving test results from the modeling simulation to determine the optimal group score.

[0007] As a further technical solution of the present invention, the method for performing the source tracing analysis is as follows: Obtain the maximum fluorescence intensity of all sampling points from the fluorescence intensity distribution characteristics; The maximum fluorescence intensity of all sampling points within the same watershed is normalized, and the high-value center corresponding to the maximum fluorescence intensity is extracted. Connect the physical locations corresponding to the high-value centers of maximum fluorescence intensity according to the sampling time axis to construct a dynamic change trajectory; Based on the dynamic trajectory, hysteresis response analysis is performed to obtain the nodal response relationship.

[0008] As a further technical solution of the present invention, the method for performing the hysteresis response analysis is as follows: Based on the dynamic trajectory, the sampling points corresponding to the high value center are divided according to the direction of water flow to obtain the upstream key nodes and downstream key nodes. Calculate the time point at which the maximum fluorescence intensity of the upstream key node reaches its peak. And the time point at which the maximum fluorescence intensity of downstream key nodes reaches its peak. ; Calculate two time points and The difference between the two is used to obtain the time lag. The node response relationship is obtained by comparing and analyzing the time lag difference.

[0009] As a further technical solution of the present invention, the method for decomposing the feature stage is as follows: Based on the maximum fluorescence intensity at different nodes in the dynamically changing trajectory, the temporal derivative of the maximum fluorescence intensity at the sampling interval is calculated using a first-order difference algorithm. Obtain the temporal derivative of each node across all sampling intervals and construct a derivative feature sequence; Simultaneously, the fluorescence intensity distribution characteristics and exponential characteristics of each node are acquired for cluster analysis, and the cluster labels of the nodes are output. Based on the clustering labels and derivative feature sequences of each node, the pollution of water bodies and watersheds is decomposed into logical stages, resulting in different logical stages and functional boundaries of pollution in different water bodies and watersheds.

[0010] As a further technical solution of the present invention, the method for decomposing the aforementioned logic stage is as follows: The derivative feature sequence is processed by feature recognition to obtain the stage boundary point; Divide continuous time intervals based on stage boundary points to identify independent logical stages; Based on independent logical stages, the start and end boundaries of each logical stage are extracted as functional boundaries.

[0011] As a further technical solution of the present invention: the method for extracting the dominant attribute of the node is as follows: Construct the criteria for determining the dominant attributes of nodes, combine the logical stages of watershed pollution, and extract the dominant attributes of nodes. The dominant attributes of a node include: sink node, source node, and channel node.

[0012] As a further technical solution of the present invention: the input-output balance analysis is performed as follows: Based on the derivative feature sequence of each node, the time derivatives in each logic stage are summed by difference to obtain the cumulative change in fluorescence intensity in the logic stage, and the cumulative change is marked as the node net flux. Accumulation-release state analysis is performed on the net flux of the node to obtain the accumulation-release state of the node; Based on the accumulation-release state of nodes, nodes in strong accumulation imbalance state and strong release imbalance state are filtered out and marked as imbalance state nodes. Component contribution analysis was performed on the nodes in the imbalanced state to obtain the variance contribution ratio; The fluorescent component with the highest variance contribution ratio is selected as the dominant object of imbalance at the state imbalance node.

[0013] As a further technical solution of the present invention, the method for performing the component contribution analysis is as follows: Obtain time-series data of fluorescence intensity of different fluorescent components of the imbalanced state node within the current imbalance logic stage; The variance contribution ratio was obtained by using the variance contribution rate analysis method to calculate the proportion of the intensity fluctuation variance of each fluorescent component in the total variance during the current imbalance stage.

[0014] As a further technical solution of the present invention: the method for outputting the competition suppression type is as follows: Extract the imbalanced state nodes whose dominant attribute is the source node, and select source nodes that are in the same logical stage, the same watershed sub-unit, and are all in a strong release imbalance state to form a coexisting source node group. Calculate the absolute difference of the lag time difference among the source nodes in the coexisting source node group to obtain the synchronization determination difference and determine whether there is synchronous emission. If there are simultaneous emissions, calculate the variance contribution ratio and decay rate of the dominant object of imbalance at each source node at the sink node; The competition inhibition is determined by combining the characteristics of the dominant object of imbalance, and the competition inhibition type is output.

[0015] The beneficial effects of this invention are as follows: 1. By using three-dimensional fluorescence scanning and component screening of water samples from the basin, the optimal number of fluorescent components and their corresponding spectral characteristics were identified, and a standardized fluorescence spectral dataset was constructed. Different types of fluorescent components (visible humic acid, marine humic acid, and humic acid-like substances) and their physicochemical properties were identified. The relative proportion of DOM was characterized by the maximum fluorescence intensity, and the source attributes and distribution differences of DOM at different sampling points were quantified by combining the fluorescence index (FI) and the biogenic index (BIX). Based on the extraction of high-value centers and the lag time difference, a dynamic trajectory of pollutant migration was constructed, the positive and negative lag response relationships between nodes were clarified, and the peak values ​​generated by pollution migration were distinguished from the peak values ​​of local independent pollution sources, which is conducive to the analysis of pollution transmission paths and the preliminary location of sources.

[0016] 2. Construct a time-series derivative feature sequence and combine it with Euclidean average clustering to realize the structured logical stage decomposition of pollution events, clarify the time boundaries and node clustering characteristics of each stage; define the dominant attributes of source nodes, channel nodes, and sink nodes through derivative features, fluorescence index, and response relationship, distinguish the core functions of nodes in the pollution process, which is conducive to improving the analysis of pollution processes.

[0017] 3. The net flux of nodes is obtained based on the definite integral of the time-series derivative, and nodes in an imbalanced state are screened. The dominant object of imbalance is located by combining the variance contribution rate analysis method, which is used as the core component of pollution imbalance. For coexisting source nodes in the same logical stage and the same watershed sub-unit, the emission time sequence is determined by the synchronization judgment difference. The competitive inhibition relationship between recalcitrant and easily degradable components is analyzed by combining the variance contribution ratio decay rate and the self-generated source index, which is conducive to realizing the priority division of watershed pollution source control and treatment. Attached Figure Description

[0018] The invention will now be further described with reference to the accompanying drawings.

[0019] Figure 1 This is a flowchart of a water pollution source tracing method that integrates three-dimensional fluorescence and isotope tracking according to the present invention; Figure 2 This is a distribution diagram of the excitation spectrum-residual sum of squares of different components of the three-dimensional fluorescence spectrum in this invention; Figure 3 This is a distribution diagram of the emission spectrum-residual sum of squares of different components of the three-dimensional fluorescence spectrum in this invention; Figure 4 This is an analytical chromatogram of visible humic acid in this invention, wherein... Figure 4 (a) is the excitation-emission spectrum of visible humic acid. Figure 4 (b) is a wavelength-load distribution diagram of visible humic acid; Figure 5 This is an analytical diagram of marine humic acid in this invention, wherein... Figure 5(a) is the excitation-emission spectrum of marine humic acid. Figure 5 (b) is a wavelength-load distribution diagram of marine humic acid; Figure 6 This is an analytical chromatogram of humic acid-like substances in this invention, wherein... Figure 6 (a) is the excitation-emission spectrum of humic acid-like substances. Figure 6 (b) is the wavelength-load distribution diagram of humic acid-like substances; Figure 7 This is a distribution map of the maximum fluorescence intensity of DOM fluorescent components in the water at each sampling point in this invention; Figure 8 This is a distribution diagram of the relative proportion of the maximum fluorescence intensity of DOM fluorescent components in the water at each sampling point in this invention; Figure 9 This is a distribution map of four fluorescence parameters of DOM in the water sampled at the sampling point in this invention; Figure 10 This is a correlation analysis diagram of fluorescent components and fluorescence parameters in this invention; Figure 11 This is a cluster analysis diagram of DOM fluorescent components in this invention; Figure 12 This is a functional module diagram of a water pollution source tracing method that integrates three-dimensional fluorescence and isotope tracking in this invention. Detailed Implementation

[0020] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments. Example 1

[0021] like Figure 1 As shown, a method for tracing water pollution sources that integrates three-dimensional fluorescence and isotope tracing includes the following steps: Step 1: Collect water samples from the watershed and perform three-dimensional fluorescence scanning to obtain fluorescence scanning data. Then, screen the components to obtain the optimal component number, acquire the fluorescence spectral characteristics of the optimal component number, and construct a fluorescence spectral dataset. The method for obtaining fluorescence scanning data by collecting water samples from the basin and performing three-dimensional fluorescence scanning is as follows: Preferably, N1 sampling points are set up in the watershed area of ​​the water body, such as farmland, forest, and watershed area where tributaries meet; Preferably, N1=35; By acquiring water samples from small water bodies at sampling points and filtering them, the filtered water samples are continuously scanned using a three-dimensional fluorescence divider to obtain the fluorescence intensity distribution matrix of each small water body sample from excitation wavelength Ex to emission wavelength Em, which serves as the fluorescence scanning data. Those skilled in the art will understand that a three-dimensional fluorescence spectrophotometer (F97Pro) was used to perform three-dimensional fluorescence spectral scanning on the filtered water sample. The spectral excitation wavelength Ex ranged from 200 to 500 nm with an interval of 3 nm; the emission wavelength Em ranged from 250 to 550 nm with an interval of 3 nm. Pure water was used as a blank reference to continuously scan the water sample in the small water body. Where Ex (Excitation Wavelength) represents the excitation wavelength of each small water sample, and Em (Emission Wavelength) represents the emission wavelength of each small water sample. The method for selecting the optimal number of components is as follows: Preferably, the fluorescence scanning data matrix of N1 small water samples is modeled using the MATLAB DOMfluor toolbox, and the number of modeling components is set to 2~4. like Figure 2 , Figure 3 As shown, simulations were performed on groups 2 to 4, the sum of squared residuals for different group numbers was calculated, and the reliability of the model was verified by a 50 / 50 test. Based on the results of residual analysis and the 50 / 50 test, the optimal group number was determined. It should be noted that, in combination Figure 2 and Figure 3 The visual representation is shown in the figure, with the horizontal axis corresponding to the excitation wavelength (Ex) and emission wavelength (Em), and the vertical axis corresponding to the sum of squared residuals of the model fit. The figure intuitively shows the error distribution under different component numbers: when the component number is 2, the residual curve has a significant high-level abrupt peak, indicating that the model is underfitting and has failed to fully capture the fluorescence characteristics; when the component number increases to 3, the sum of squared residuals decreases significantly; and when the component number further increases to 4, its curve highly overlaps with the curve of component number 3 and the overall trend tends to be flat (i.e., the marginal error improvement is minimal). Based on the objective principles of reducing algorithm complexity and preventing model overfitting, and combined with the fact that component number 3 strictly meets the preset quantitative screening criteria (sum of squared residuals ≤ 0.05 and correlation coefficient ≥ 0.95 in the half-test), the optimal component number for the PARAFAC model fit is finally objectively determined to be 3. The fluorescence scanning data of DOM of small water samples from N1 sampling points were analyzed by PARAFAC, and three types of fluorescence components were identified. A comparison table of fluorescence peak type, conventional peak value and peak value of each fluorescence component was established. It should be noted that PARAFAC (Parallel Factor Analysis) is a multidimensional data analysis method. Its core is to separate independent basic components from complex three-dimensional and above data and clarify their characteristics. It is mainly used to process three-dimensional fluorescence scanning data of water bodies. It can accurately separate different types of fluorescent components (such as visible humic acid, marine humic acid, etc.) from mixed fluorescence signals, and at the same time determine the excitation-emission spectral characteristics and relative content of each component, providing key basic data for subsequent analysis of DOM sources and pollutant migration trajectories. Table 1. Comparison of peak values ​​of the three fluorescent components of DOM in water.

[0022] It should be noted that the three-dimensional fluorescence spectra and maximum Ex and Em loadings of the three fluorescent components are as follows: Figure 4 (a) Figure 4 (b) Figure 5 (a) Figure 5 (b) Figure 6 (a) Figure 6 As shown in (b); in, Figure 4 (a) Figure 4 (b) is the visible humic acid component of component C1; Figure 5 (a) Figure 5 (b) is the marine humic acid component of component C2; Figure 6 (a) Figure 6 (b) is the humic acid-like component of component C3; The fluorescence peak of component C1 (Ex / Em=360nm / 457nm) shows visible humic acid, corresponding to the C peak. It belongs to typical terrestrial humus, which is mostly a decomposition product of plant and soil organic matter. It is characterized by large relative molecular weight, high aromaticity, strong stability, and is not easily biodegraded and utilized. The fluorescence peak of component C2 (Ex / Em=320nm / 410nm) indicates marine humic acid substances, corresponding to the M peak. It is mostly authigenic DOM, produced by the metabolism of microorganisms in water bodies. It is commonly found in water bodies such as oceans, lakes, and rainwater. Due to its relatively small molecular weight and relatively simple structure, it is relatively easy to be biodegraded and utilized. The fluorescence peak of component C3 (Ex / Em=410nm / 510nm) is similar to that of humic acid, corresponding to the F peak. It has a longer excitation and emission wavelength and its properties are similar to those of component C1. Simultaneously, isotope analysis (such as δ¹²) was performed on water samples from small water bodies after filtration. 18 O, δ 2 H or nitrogen isotope δ 15N) detection is used to obtain isotope characteristic data, which are then combined with fluorescence spectral data (excitation and emission spectral characteristics of the optimal components of all N1 small and micro water samples) to construct a basic dataset for source tracing analysis. Step 2: Perform fluorescence intensity and exponential analysis on the fluorescence spectrum dataset to obtain the fluorescence intensity distribution characteristics and exponential characteristics of DOM at different sampling points, and conduct source tracing analysis to obtain the dynamic change trajectory of pollutant migration, while identifying the response relationship between nodes in the dynamic change trajectory. The method for obtaining the fluorescence intensity distribution characteristics and exponential characteristics of DOM at different sampling points by performing fluorescence intensity and exponential analysis on the fluorescence spectral dataset is as follows: Preferably, the maximum fluorescence intensity of each component is extracted using PARAFAC based on the fluorescence spectroscopy dataset. The relative proportion of DOM in small water bodies at each sampling point is characterized; the unit of the maximum fluorescence intensity is any unit (au), which is based on the relative photoelectric signal obtained under the same three-dimensional fluorescence spectrophotometer and the same scanning parameters, and is used to objectively and quantitatively compare the relative abundance of DOM between different sampling points. like Figure 7 , Figure 8 The maximum fluorescence intensity of each component at each sampling point shown ( ) and relative proportions, sampling points S5-S8, S28-S33, S34-S35 Too large; S1-S4 sampling points At a moderate level, sampling points S9-S27 Relatively small; It should be noted that visible humic acid had the largest relative proportion in all three components, marine humic acid had a relatively large proportion in S1-S4, and marine humic acid had a relatively small proportion in S5-S8. Those skilled in the art will understand that agricultural activities release large amounts of DOM at sampling points S5-S8. The main reason for the larger size is that the river water collects from multiple sources including upstream forests, farmland, and villages, making the S34-S35 sampling points larger. The concentration is relatively high. The weak connection between S9-S27 and the main terrestrial source areas, as well as the short water retention time, prevent DOM from accumulating sufficiently. Preferably, four fluorescence parameters are obtained by fluorescence spectroscopy calculation: fluorescence index (FI), biogenic index (BIX), humification index (HIX), and freshness index (β:α). The fluorescence index FI is calculated as follows: The ratio of fluorescence intensity at 470 nm to 520 nm emission wavelengths at an excitation wavelength Ex of 370 nm is used as the fluorescence index FI. The source of DOM was determined based on the fluorescence index FI: FI < 1.4 indicates that the DOM in water mainly comes from terrestrial organic matter, and FI > 1.9 indicates that the metabolic products of microbial decomposition in water contribute significantly to the DOM. If the fluorescence index FI of all N1 sampling points is between 1.6 and 1.9, it indicates that the fluorescent organic matter in the sample is generated by both terrestrial input and endogenous release. The method for calculating the Biological Origin Index (BIX) is as follows: At a spectral excitation wavelength of 310 nm (Ex), the ratio of fluorescence intensity at emission wavelengths of 380 nm to 430 nm (Em) is used as the biogenic index (BIX) to reflect the contribution of biogenic sources. Further differentiation of sources based on the autobiographic index: BIX < 0.8 indicates that the water body DOM is mainly from terrestrial sources, with no significant contribution from autobiography; BIX > 1 indicates that the water body DOM is mainly from algae or bacteria, with obvious autobiographic characteristics. If the BIX of all N1 sampling points is less than 1, it indicates that the fluorescent organic matter in the sample is mainly from land sources, with poor water activity and no obvious self-origin characteristics, which is consistent with the conclusions obtained from the FI analysis above. The BIX values ​​of sampling points S2, S4, S9, S10, S17, S22, S23, and S27 were greater than 0.8, while the BIX values ​​of the remaining samples were less than 0.8. The BIX values ​​of S5-S7 were between 0.60 and 0.68, which is consistent with the results of the analysis of the relative proportion of marine humus. The method for calculating the humification index (HIX) is as follows: At a spectral excitation wavelength of 255 nm, the ratio of the average Em fluorescence intensity of 435-480 nm to that of 300-345 nm is calculated to obtain the humification index, which is used to reflect the degree of humification. Based on the humification index to distinguish the degree of humification, the preferred result is: when HIX < 3, it indicates that the degree of DOM humification in the water body is weak and DOM exists in the form of simple small molecules. When HIX is between 3 and 6, it indicates that the water body is highly humified and the DOM structure becomes more complex, characterized by high aromaticity and large relative molecular weight. Those skilled in the art will understand that as the HIX value increases, the degree of DOM humification in the water body also increases accordingly. The HIX values ​​of samples S28-S33 and S34 are higher than other samples, while the HIX values ​​of samples S9-S27 are relatively lower. The observed results were consistent: S9-S27 showed weaker humification, while S28-S33, etc. Larger sites show a higher degree of humification; The freshness index (β:α) is calculated as follows: At a spectral excitation wavelength of 310 nm (Ex), the ratio of fluorescence intensity at emission wavelength of 380 nm (Em) to the maximum fluorescence intensity in the 420-435 nm range is calculated to obtain the freshness index (β:α), which is used to measure biological activity, i.e., the proportion of recently generated DOM to the total DOM in the water body. It should be noted that a freshness index (β:α) > 1 indicates that the proportion of recently generated, biodegradable DOM in the water body is high, and the bioavailability is strong. When the freshness index (β:α) is less than 1, it indicates that ancient, non-degradable terrestrial DOM dominates the water body, and the microorganisms are relatively inert. If the freshness index (β:α) of the sampling points is between 0.6 and 1, it indicates that the microbial activity in the water is mainly anaerobic, the degradation rate is slow, and the DOM tends to age. This is consistent with the results obtained from the fluorescence component analysis and fluorescence index analysis above. like Figure 9 The figure shows the distribution of four fluorescence parameters of dissolved organic matter (DOM) in the water sampled at the sampling points in this invention. The four fluorescence parameters include fluorescence index (FI), biogenic index (BIX), humification index (HIX), and freshness index (β:α). By intuitively and quantitatively analyzing the parameter distribution of 35 sampling points in the same coordinate system, it was found that the FI of all sampling points was between 1.6 and 1.9, and the BIX was less than 1. The HIX was higher than other samples at S28-S33 and S34, and the β:α was between 0.6 and 1. This objectively characterizes the physical properties of dissolved organic matter (DOM) in the entire watershed, which is mainly input from land and has no obvious biogenic characteristics. This provides basic parameter support for subsequent pollution stage dismantling and source node attribute identification. Those skilled in the art will understand that the consistent FI and BIX results indicate that the DOM is dominated by land-source input, and the HIX distribution is consistent with... Consistently, the degree of humification is related to the degree of DOM accumulation, and the β:α range indicates that recalcitrant terrestrial DOM dominates in water bodies; Maximum fluorescence intensity ( As fluorescence intensity distribution characteristics, four fluorescence parameters—fluorescence index (FI), autogenicity index (BIX), humification index (HIX), and freshness index (β:α)—are used as index features. Correlation analysis was performed based on fluorescence intensity distribution characteristics and index characteristics, and the source of DOM in the water body was further determined based on the correlation analysis results; For example, such as Figure 10 As shown, Pearson correlation analysis was performed on the fluorescent components and fluorescence index of the sample DOM. Figure 10The values ​​in the matrix cells represent Pearson correlation coefficients, the size of the circles and the intensity of their colors indicate the strength of the correlation, and the asterisks indicate the confidence level. The analysis results show that components C1, C2 and C3 are all highly significantly positively correlated with each other (Pearson correlation coefficient r>0.9, confidence level p<0.001), which objectively proves that the fluorescent substances detected in each water body have homology or similar characteristics, that is, the main components are all humic acids, thus verifying the internal logical correctness of constructing a standard fluorescence spectrum dataset using these three independent basic components. All three components showed a highly significant positive correlation with HIX (r>0.8, p<0.001), indicating that these components mainly originated from organic matter with a high degree of humification and increased synchronously during the humification process. All three components were significantly negatively correlated with BIX and freshness index (β:α) (r<-0.4, p<0.01) and significantly positively correlated with FI (r>0.4, p<0.05), indicating that the DOM in the water sample was mainly terrestrial humic matter. The method for conducting source tracing analysis to obtain the dynamic trajectory of pollutant migration and identify node response relationships is as follows: Preferably, the maximum fluorescence intensity of all N1 sampling points is obtained from the fluorescence intensity distribution characteristics. ), and the fluorescence index FI of the sampling points as the sampling interval changes; The maximum fluorescence intensity of all sampling points within the same watershed was normalized, and the high-value centers corresponding to the maximum fluorescence intensity were extracted (e.g., sampling points S5-S8 and S34-35). Connect the physical locations corresponding to the high-value centers of maximum fluorescence intensity according to the sampling time axis to construct a dynamic change trajectory; Based on the dynamic trajectory, the sampling points corresponding to the high value center are divided according to the direction of water flow to obtain the upstream key nodes and downstream key nodes. Calculate the time point at which the maximum fluorescence intensity of the upstream key node reaches its peak. And the time point at which the maximum fluorescence intensity of downstream key nodes reaches its peak. ; Calculate two time points and The difference yields the time lag. ; If the lag time difference (i.e. positive lag), and if the exponential characteristics of the upstream key node and the downstream key node are similar, then it is determined that there is a pollutant migration response relationship between the two nodes as a node response relationship, that is, the pollution peak in the downstream is generated by upstream migration and not locally generated. If the lag time difference If the response is negative or zero hysteresis, it is determined that the two nodes are synchronous or have no direct migration response relationship, indicating that the pollution peak of the downstream key node is caused by local independent pollution source input or sudden endogenous release, and is not directly generated by the upstream node.

[0023] Example 2 Please see Figure 1 As shown, a method for tracing water pollution sources that integrates three-dimensional fluorescence and isotope tracing includes the following steps: Step 3: Based on the dynamic change trajectory and node response relationship, the characteristic stage of water body pollution is decomposed to obtain the logical stage of the node, and the functional boundary and node dominant attribute of each logical stage are extracted. Based on the maximum fluorescence intensity at different nodes in the dynamically changing trajectory ( The temporal derivative of the maximum fluorescence intensity at the sampling interval is calculated using a first-order difference algorithm. Obtain the temporal derivative of each node across all sampling intervals and construct a derivative feature sequence; It should be noted that the sampling interval is matched with the watershed hydrological cycle; Simultaneously, the fluorescence intensity distribution characteristics and exponential characteristics of each node are acquired for cluster analysis, and the cluster labels of the nodes are output. It will be understood by those skilled in the art that, as Figure 11 As shown, the cluster analysis uses the Euclidean average clustering algorithm; Figure 11 The tree diagram on the left represents the node clustering topology based on Euclidean distance. The vertical axis on the right represents 35 representative sampling points, and the horizontal axis represents three fluorescent components (C1, C2, C3). The color intensity of the cells represents the relative fluorescence intensity after standardization. The input data for the algorithm are the standardized fluorescence intensity distribution characteristics and exponential characteristics. The number of clusters is determined by the elbow rule. The algorithm objectively classifies the 35 sampling point nodes into three categories based on spatial distance aggregation: highly humic substance accumulation (e.g., S28, S32, S34), maximum humic substance stability (e.g., S5-S7), and balanced component ratio (the remaining samples), thus realizing the structured classification of watershed pollution events. It should be noted that the three components S28, S32, and S34 have similar compositions and their overall DOM content is higher than that of other samples. Among them, the visible humic acid content is high, followed by marine humic acid, which represents a type of water body with high humic accumulation. The visible humic acid content of S5-S7 is significantly higher than that of the other two components, which represents a type of water body with the greatest humic stability. The proportions of the three components in the remaining samples are relatively similar. Based on the clustering label and derivative feature sequence of each node, the pollution of water bodies and watersheds is decomposed into logical stages to obtain different logical stages and functional boundaries of pollution in different water bodies and watersheds. The preferred method for breaking down logical stages is as follows: A1. Perform feature recognition processing on the derivative feature sequence to obtain the stage boundary points; Preferably, the derivative feature sequence is smoothed (using the moving average method with a window size of 3 sampling intervals), the second derivative of the smoothed sequence is calculated, and the critical point where the second derivative changes from positive to negative or from negative to positive is determined as the stage boundary point, while eliminating pseudo boundary points caused by single sampling fluctuations. It should be noted that the threshold for determining the pseudo-boundary point is set as follows: the maximum change in fluorescence intensity between two adjacent samples is less than or equal to 5% of the arithmetic mean of the maximum fluorescence intensity of all sampling points in the entire watershed, and this is taken as the pseudo-boundary point. A2. Divide continuous time intervals based on stage boundary points to identify independent logical stages; Preferably, continuous time intervals are divided with the stage boundary point as the boundary, and the clustering labels of all nodes in each time interval are statistically analyzed; If ≥80% of the nodes in a certain interval have the same clustering label (80% is verified based on 10 sets of measured data from the watershed, and 80% is the critical threshold; if it is lower than this value, the stage division is not statistically significant), or if there is a clear positive lag in the node response relationship, then the time interval is determined as an independent logical stage. A3. Based on independent logical stages, extract the start and end boundaries of each logical stage as functional boundaries; Preferably, the functional boundaries of each logical stage are based on the time axis, with the starting boundary being the sampling time corresponding to the first dividing point and the ending boundary being the sampling time corresponding to the next dividing point. If it is the first stage, the starting boundary is the time when the maximum fluorescence intensity anomaly (maximum fluorescence intensity ≥ 1.2 times the background value) is first detected in the water body basin pollution. If it is the last stage, the ending boundary is the time when the maximum fluorescence intensity recovers to within ±5% of the background value. The start boundary and the end boundary are used as the functional boundaries of each logical stage; Construct the criteria for determining the dominant attributes of nodes, combine the logical stages of watershed pollution, and extract the dominant attributes of nodes. The dominant attributes of a node include: sink node, source node, and channel node; Preferably, the method for determining sink nodes, source nodes, and channel nodes is as follows: The criteria for determining the source node are as follows: 1. There is a positive derivative interval with ≥3 consecutive sampling intervals in the derivative feature sequence, and the cluster label is the class with the greatest humic stability; 2. The fluorescence index FI < 1.4 or BIX > 1, and the maximum fluorescence intensity continues to increase; 3. The node is an upstream key node in the node response relationship, and no other upstream nodes produce a positive hysteresis response to it. If any of the criteria for a source node is met, then it is determined to be a source node; The criteria for determining sink nodes are as follows: 1. If there are zero derivative intervals for ≥3 consecutive sampling intervals in the derivative feature sequence, and the cluster label is "high accumulation of humic substances"; 2. The humification index HIX>3 (high degree of humification), and the node response relationship is a downstream key node with ≥2 upstream nodes generating positive hysteresis responses to it. It should be noted that the three sampling rooms are based on the typical pollutant release cycle (5-7 days) in the watershed, and the three sampling intervals (3 days / time) can effectively capture continuous release behavior and exclude accidental fluctuations; If a node satisfies any of the criteria for a sink node, it is determined to be a sink node. The criteria for determining a channel node are as follows: it does not meet the criteria for determining a source node or sink node, and meets the following criteria: 1. The cluster label is a balanced component ratio class; 2. There is a clear positive hysteresis in the node response relationship. If the criteria for any channel node are met, then it is determined to be a channel node.

[0024] Step 4: Perform input-output balance analysis based on the node logic stage to obtain the imbalanced dominant object; extract the source node from the node attributes, and analyze whether there is competition inhibition for different imbalanced dominant objects when multiple source nodes coexist, and output the competition inhibition type. The method for obtaining the unbalanced dominant object by performing input-output balance analysis based on the dominant attributes of nodes in the logical stage is as follows: Based on the derivative feature sequence of each node, the time derivatives in each logic stage are summed by difference to obtain the cumulative change in fluorescence intensity in the logic stage, and the cumulative change is marked as the node net flux. It should be noted that the differential summation operation is performed by multiplying the temporal derivative of each sampling interval by the sampling interval duration, and then summing the results of multiplying all sampling intervals to obtain the cumulative change in fluorescence intensity within the logic stage. Accumulation-release state analysis is performed on the net flux of the node to obtain the accumulation-release state of the node; The method for parsing the input release state is as follows: The node net flux is compared with a preset balance threshold. Perform a comparison; If the node's net flux The node is determined to be in a state of strong cumulative imbalance. If the node's net flux The node is determined to be in a state of strong release imbalance; If net flux is at Between these points, it is determined to be a dynamic equilibrium state, and no dominant object identification is performed; It should be noted that the balance threshold It was set at 10% of the average net flux of all nodes in the basin, and determined based on the background values ​​of 35 sampling points. Based on the accumulation-release state of nodes, nodes in strong accumulation imbalance state and strong release imbalance state are filtered out and marked as imbalance state nodes. Component contribution analysis is performed on the imbalanced state nodes to obtain the variance contribution ratio of each fluorescent component in the current imbalanced logic stage of the imbalanced state node. Preferably, the method for performing component contribution analysis is as follows: Obtain the time series data of fluorescence intensity of three fluorescent components (C1, C2, C3) of the imbalanced state node in the current imbalance logic stage. Use the variance contribution rate analysis method to calculate the proportion of the intensity fluctuation variance of each fluorescent component in the current imbalance stage to the total variance, and obtain the variance contribution ratio. The fluorescent component with the highest variance contribution ratio is selected as the dominant object of imbalance at the state imbalance node. Among them, the method for analyzing whether there is competitive inhibition among different imbalanced dominant objects when multiple source nodes coexist is as follows: Extract the unbalanced state nodes identified in step three as the dominant attributes of the source nodes, and select source nodes that are in the same logical stage, the same watershed sub-unit, and are all in a strong release unbalanced state to form a coexisting source node group (the number of source nodes in the group is ≥2). It should be noted that the watershed sub-units within the coexisting source node group are divided according to the sampling point distribution and topographic data in step one, and there is no water flow obstruction between nodes (good hydrological connectivity). Obtain the imbalance dominant object type of each source node in the coexisting source node group, the imbalance dominant object variance contribution ratio of each source node in the current logic stage, and the variance contribution ratio of the three fluorescent components of the downstream nearest sink node corresponding to each source node in the same logic stage. At the same time, extract the lag time difference between each source node and the sink node. Calculate the absolute difference of the lag time difference of each source node to obtain the synchronization judgment difference. If the synchronization judgment difference is ≤ 1 / 2 of the sampling interval, it is determined to be synchronous emission (with the condition of competition suppression). If the synchronization difference is greater than the sampling interval, then it is asynchronous emission, i.e., there is no competition suppression; If there are simultaneous emissions, calculate the variance contribution ratio and decay rate of the dominant object of imbalance at each source node at the sink node; It should be noted that the variance contribution ratio decay rate is the ratio obtained by dividing the difference between the variance contribution ratio of the source node and the variance contribution ratio of the corresponding component of the sink node by the variance contribution ratio of the source node. Competition inhibition is determined by combining the characteristics of the dominant object in the imbalance (components C1 and C3 are recalcitrant components, while component C2 is readily degradable) and the variance contribution ratio decay rate: Those skilled in the art will understand that, due to the degradation characteristics of humic acid components (such as components C1 and C3, which are more difficult to degrade than component C2 due to their high aromaticity and large molecular weight), components C1 and C3 are identified as recalcitrant components, while component C2 is identified as readily degradable. The competitive inhibition stems from the fact that recalcitrant components (C1 and C3) preferentially occupy microbial metabolic sites during transport, resulting in a decrease in the biodegradation efficiency of readily degradable components (C2). If the dominant objects of imbalance in the coexisting source nodes are recalcitrant and readily degradable components, and the decay rate of readily degradable components is not less than 30%, the variance contribution ratio decay rate of recalcitrant components does not exceed 15%, and the self-generating index BIX of the sink node does not exceed 0.8, it is determined to be competitive inhibition of readily degradable dominant objects by recalcitrant dominant objects. If the dominant object of the imbalance in the coexisting source node is two types of recalcitrant components, and the difference in the variance contribution ratio of the two types of components does not exceed 10%, it is determined to be a non-competitive inhibition. Output the competition suppression type of the coexisting source node group, the dominant imbalance object involved, and key judgment parameters, including: synchronization judgment difference, decay rate of the two types of components, and sink node BIX; It should be noted that the core of competitive inhibition stems from the fact that the recalcitrant components (C1, C3) occupy the microbial metabolic sites during transport, leading to a decrease in the degradation efficiency of the easily degradable component (C2), which is manifested as a significant difference in decay rate. Synchronous emission is a prerequisite for competitive inhibition. During asynchronous emission, the component transport does not overlap in time and does not cause mutual interference.

[0025] Example 3 Please see Figure 12 As shown, a water pollution tracing system integrating three-dimensional fluorescence and isotope tracing includes the following modules: Water body acquisition module: used to collect water samples from the watershed for three-dimensional fluorescence scanning, obtain fluorescence scanning data, screen components to obtain the optimal component number, obtain the fluorescence spectral characteristics of the optimal component number, and construct a fluorescence spectral dataset; Response Analysis Module: Used to perform fluorescence intensity and exponential analysis on fluorescence spectral datasets, obtain fluorescence intensity distribution characteristics and exponential characteristics of DOM at different sampling points, perform source tracing analysis, obtain the dynamic change trajectory of pollutant migration, and identify the response relationship between nodes in the dynamic change trajectory; Stage decomposition module: Based on dynamic change trajectory and node response relationship, the characteristic stage decomposition of water body watershed pollution is performed to obtain the logical stage of the node, and the functional boundary and node dominant attribute of each logical stage are extracted. Competition Analysis Module: Used to perform input-output balance analysis based on the node logic stage to obtain the unbalanced dominant object; extract the source node from the node attributes, and analyze whether there is competition inhibition for different unbalanced dominant objects when multiple source nodes coexist, and output the competition inhibition type.

[0026] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A method for tracing the source of water pollution by integrating three-dimensional fluorescence and isotope tracing, characterized in that: Includes the following steps: Three-dimensional fluorescence scanning was performed on water samples collected from the watershed to obtain fluorescence scanning data. The optimal component number was obtained by screening the components. The fluorescence spectral characteristics of the optimal component number were obtained and a fluorescence spectral dataset was constructed. Simultaneously, isotope detection was performed on water samples from small water bodies after filtration to obtain isotope characteristic data. This data was then combined with the fluorescence spectral data to construct a basic dataset for source tracing analysis. Fluorescence intensity and exponential analysis were performed on the fluorescence spectral dataset to obtain the fluorescence intensity distribution characteristics and exponential characteristics of DOM at different sampling points. Source tracing analysis was then conducted to obtain the dynamic change trajectory of pollutant migration, and the response relationship between nodes in the dynamic change trajectory was identified. Based on dynamic change trajectories and node response relationships, the characteristic stages of watershed pollution are decomposed to obtain the logical stages of nodes, and the functional boundaries and dominant attributes of each logical stage are extracted. The method for decomposing the feature stages to obtain the logical stages of nodes and extracting functional boundaries is as follows: Based on the maximum fluorescence intensity at different nodes in the dynamically changing trajectory, the temporal derivative of the maximum fluorescence intensity at the sampling interval is calculated using a first-order difference algorithm. Obtain the temporal derivative of each node across all sampling intervals and construct a derivative feature sequence; simultaneously, obtain the fluorescence intensity distribution characteristics and exponential characteristics of each node for cluster analysis and output the cluster labels of the nodes; Feature recognition processing is performed on the derivative feature sequence to obtain the stage boundary point; based on the stage boundary point, continuous time intervals are divided to identify independent logical stages; Based on independent logical stages, the start and end boundaries of each logical stage are extracted as functional boundaries. The method for extracting the node dominant attributes is as follows: construct the judgment conditions for the node dominant attributes, combine the logical stages of watershed pollution, and extract the node dominant attributes; wherein, the node dominant attributes include: sink node, source node, and channel node; Based on the node logic stage, an input-output balance analysis is performed to obtain the imbalanced dominant object; the source node is extracted from the node attributes, and when multiple source nodes coexist, it is analyzed whether there is competition inhibition for different imbalanced dominant objects and the competition inhibition type is output. The method for obtaining the unbalanced dominant object through the input-output balance analysis is as follows: Based on the derivative feature sequence of each node, the time derivatives in each logic stage are summed by difference to obtain the cumulative change in fluorescence intensity in the logic stage, and the cumulative change is marked as the node net flux. Accumulation-release state analysis is performed on the net flux of the node to obtain the accumulation-release state of the node; Based on the accumulation-release state of nodes, nodes in strong accumulation imbalance state and strong release imbalance state are filtered out and marked as imbalance state nodes. Obtain time-series data of fluorescence intensity of different fluorescent components of the imbalanced state node within the current imbalance logic stage; The variance contribution ratio was obtained by using the variance contribution rate analysis method to calculate the proportion of the intensity fluctuation variance of each fluorescent component in the total variance during the current imbalance stage. The fluorescent component with the highest variance contribution ratio is selected as the dominant object of imbalance at the state imbalance node. Among them, the method for analyzing whether competition inhibition exists among different imbalanced dominant objects and outputting the type of competition inhibition when multiple source nodes coexist is as follows: Extract the imbalanced state nodes whose dominant attribute is the source node, and select source nodes that are in the same logical stage, the same watershed sub-unit, and are all in a strong release imbalance state to form a coexisting source node group. Calculate the absolute difference of the lag time difference among the source nodes in the coexisting source node group to obtain the synchronization determination difference and determine whether there is synchronous emission. If there are simultaneous emissions, calculate the variance contribution ratio and decay rate of the dominant object of imbalance at each source node at the sink node; The competition inhibition is determined by combining the characteristics of the dominant object of imbalance, and the competition inhibition type is output.

2. The water pollution source tracing method integrating three-dimensional fluorescence and isotope tracking according to claim 1, characterized in that: The method for screening the components is as follows: Different modeling group scores were set, and fluorescence scanning data matrices of multiple small water body samples were obtained for modeling and simulation processing. Extract the residual analysis and halving test results from the modeling simulation to determine the optimal group score.

3. The water pollution source tracing method integrating three-dimensional fluorescence and isotope tracking according to claim 1, characterized in that: The method for conducting the source tracing analysis is as follows: Obtain the maximum fluorescence intensity of all sampling points from the fluorescence intensity distribution characteristics; The maximum fluorescence intensity of all sampling points within the same watershed is normalized, and the high-value center corresponding to the maximum fluorescence intensity is extracted. Connect the physical locations corresponding to the high-value centers of maximum fluorescence intensity according to the sampling time axis to construct a dynamic change trajectory; Based on the dynamic trajectory, hysteresis response analysis is performed to obtain the nodal response relationship.

4. The water pollution source tracing method integrating three-dimensional fluorescence and isotope tracking according to claim 3, characterized in that: The method for performing the hysteresis response analysis is as follows: Based on the dynamic trajectory, the sampling points corresponding to the high value center are divided according to the direction of water flow to obtain the upstream key nodes and downstream key nodes. Calculate the time point at which the maximum fluorescence intensity of the upstream key node reaches its peak. And the time point at which the maximum fluorescence intensity of downstream key nodes reaches its peak. ; Calculate two time points and The difference between them yields the time lag. The node response relationship is obtained by comparing and analyzing the time lag difference.

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