Basin river channel outlet section pollutant flux tracing method

Abstract

The invention relates to a drainage basin river channel outlet section pollutant flux tracing method, which comprises the following steps of: firstly, decomposing pollutant flux into relatively stable point source flux and surface source flux which is obviously influenced by rainfall through an improved iterative algorithm by adopting a digital filtering technology; and then, performing further traceability analysis on point source and surface source flux obtained by decomposition by using absolute principal component score-multiple linear regression analysis, and identifying contribution proportions of different pollution sources such as an industrial point source, a living point source and an agricultural surface source. According to the method, by adjusting parameters of the digital filtering algorithm and the APCS-MLR analysis algorithm, mutual matching of results of the two algorithms is achieved, and the accuracy and reliability of traceability analysis are improved. The method is suitable for flux traceability of pollutant types such as permanganate index, ammonia nitrogen, total phosphorus and the like, and has important practical application value for drainage basin management and pollution control.

Description

Technical Field

[0001] This invention relates to the field of river environmental monitoring technology, and in particular to a method for tracing the source of pollutant flux at the outlet section of a river basin. Background Technology

[0002] With rapid industrialization and urbanization, water pollution has become increasingly serious. River outlet sections are a major pathway for pollutants to enter water bodies, making accurate source tracing of pollutant fluxes crucial for water environment management and pollution control. Traditional pollution source identification methods include field investigations and monitoring, data analysis and simulation, remote sensing, and the application of sensor technologies. However, these methods often suffer from high data acquisition costs, limited analytical accuracy, and the inability to quantitatively describe the impact of different pollution sources on water quality indicators.

[0003] In existing technologies, reliable data analysis models can be established by rationally collecting, organizing, and quality-controlling environmental monitoring data. Traditional statistical models, machine learning models, and deep learning models can all be used for predicting environmental monitoring data, each with its own advantages. This invention utilizes advanced information technology, communication technology, sensing technology, intelligent control technology, and other technical means to achieve the purposes of water quality monitoring, pollution source location, pollution incident early warning, and pollution source control. Summary of the Invention

[0004] The purpose of this invention is to provide a method for tracing pollutant fluxes at the outlet section of a river basin, enabling quantitative analysis and identification of different pollution sources within the basin, such as industrial point sources, residential point sources, and agricultural non-point sources. The aim is to accurately identify and quantify the contribution of different pollution sources within the basin to the pollutant fluxes at the river outlet section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for tracing pollutant fluxes at the outlet section of a river basin includes the following steps: S1. Perform flux segmentation on the collected pollutant flux data at the outlet section of the river in the basin; S2. Import pollutant flux data from the outlet section of the watershed into SPSS software, perform APCS-MLR analysis, output the results of multiple linear regression analysis, and calculate the contribution ratio of known and unknown sources. S3. Adjust the parameters of the flux segmentation module and the APCS-MLR analysis module to ensure consistency between the flux segmentation and APCS-MLR analysis methods in terms of the proportion of area source flux.

[0006] In some embodiments, the flux data includes time-series flow rates and corresponding pollutant concentration data.

[0007] In some embodiments, the flux splitting includes the following steps: The pollutant flux data were input into MATLAB software, and the filter coefficients and the number of iterations were defined. The data is divided into time vectors (TV) and raw pollutant flux data (hy). The point source flux array is initialized, and bf_p is set as the initial pollutant flux data. Iterative calculations are performed, with each iteration consisting of two processes: forward and reverse. In each iteration, the estimated value of the current point source flux is calculated using the filter coefficient fc based on the current and previous iteration point source flux values. Update bf_p to the bf value of the current iteration. The calculated bf is the point source flux, while sf = hy - bf is the surface source flux.

[0008] In some embodiments, the pollutant concentration data includes concentration data for permanganate index, ammonia nitrogen, and total phosphorus.

[0009] In some embodiments, the APCS-MLR analysis module analysis process includes the following steps: Import pollutant flux data from the outlet section of the watershed into SPSS software; The input data is standardized, then principal component analysis is performed, and factor extraction and rotation are carried out to obtain factor solutions with simple structures. A multiple linear regression equation was established to calculate the regression coefficients of pollution sources on water chemical factors, and the adjusted R², B value and APCS value of each factor were obtained.

[0010] In some embodiments, parameter adjustment includes the following steps: Adjust the filter coefficients in the digital filtering algorithm; Adjust the regression model parameters in the APCS-MLR analysis; By comparing the proportion of agricultural source flux obtained from the two algorithms, the above parameters are adjusted simultaneously to achieve consistency in the results.

[0011] Compared with the prior art, the present invention has the following beneficial effects: This invention achieves mutual matching of the results from the digital filtering algorithm and the APCS-MLR analysis algorithm by adjusting the parameters of the two algorithms, thereby improving the accuracy and reliability of source tracing analysis. The method of this invention is applicable to flux source tracing of pollutants such as permanganate index, ammonia nitrogen, and total phosphorus, and has significant practical application value for watershed management and pollution control. Attached Figure Description Figure 1 This is a schematic diagram of the flux data for indicators such as permanganate index in this invention. Figure 2This is a schematic diagram of the flux segmentation calculation results of the present invention; Figure 3 This is a schematic diagram of the pollutant source tracing analysis results of the present invention. Detailed Implementation

[0012] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0013] Example 1: Please see Figures 1-3 This invention provides a method for tracing pollutant fluxes at river outlet sections, aiming to accurately identify and quantify the contributions of different pollution sources within the watershed to pollutant fluxes at the river outlet sections. This method is particularly suitable for tracing the sources of key pollutants such as permanganate index, ammonia nitrogen, and total phosphorus, and is of great significance for implementing effective water environment management and pollution control strategies.

[0014] The technical solution of the present invention includes the following steps: 1. Flux partitioning: Collect pollutant flux data at the outlet sections of the river basin, including time-series flow (hy) and corresponding pollutant concentration data.

[0015] Organize this data into a format that MATLAB can process, such as loading the data file 'BRDS ce.txt', which contains time and pollutant flux data.

[0016] In the MATLAB environment, the filter coefficient fc = 0.992 and the number of iterations pass = 4 are defined.

[0017] Call the separatebaseflow function, with input parameters including pollutant flux data hy, filter coefficients fc, and iteration number pass.

[0018] Inside the function, the data is first divided into a time vector TV and raw pollutant flux data hy.

[0019] Initialize the point source flux array bf to NaN to store the calculation results, and set bf_p to the initial pollutant flux data, representing the point source flux of the previous iteration.

[0020] Iterative calculations are performed, with each iteration including both forward and reverse processes to ensure the comprehensiveness of flux partitioning.

[0021] In each iteration, the estimated value of the current point source flux is calculated using the filter coefficient fc, based on the point source flux values ​​of the current and previous iterations.

[0022] The intermediate value tmp is calculated using the formula tmp = fc*bf(i-incr) + (1-fc)*(bf_p(i)+bf_p(i-incr)) / 2; then the minimum value of tmp and bf_p(i) is taken as the current point source flux value, i.e., bf(i) = min([tmp,bf_p(i)]);.

[0023] Update bf_p to the bf value of the current iteration to prepare for the next iteration.

[0024] After a set number of iterations, the calculated bf is the point source flux, and sf = hy - bf is the surface source flux.

[0025] 2. APCS-MLR analysis: Import pollutant flux data from the river outlet section into SPSS software, including concentration data for indicators such as permanganate index, ammonia nitrogen, and total phosphorus. Modify the variable names in the variable view for easier identification and processing.

[0026] Data standardization. The original data was standardized using SPSS's "Analyze - Description - Select All Variables - Check 'Save standardized values ​​as variables'" function to obtain the Z-score variable.

[0027] Principal Component Analysis (PCA). In SPSS, select "Analyze - Dimensionality Reduction - Factorial Analysis," and choose the standardized Z-score variable for PCA. Perform KMO and Bartlett's sphericity tests to ensure the data is suitable for PCA.

[0028] Factor extraction and rotation. In the extraction stage, select "Extract - Continue" and extract components with eigenvalues ​​greater than 1 according to the Kaiser criterion. In the rotation stage, select "Rotate - Select 'Maximum Variance Method' - Continue" to obtain factor solutions with simple structures.

[0029] Save the scores. During the scoring phase, check "Save as variable" to save the normalized factor scores and eigenvectors obtained from the principal component analysis.

[0030] APCS score calculation. The APCS score is calculated based on the PCA score saved in SPSS. The formula for calculating the APCS score is: Calculate the APCS score and its average value, and save the APCS score as a new variable, such as "APCS1", "APCS2", etc.

[0031] Multiple linear regression analysis. Select the APCS data and perform an "Analyze-Regression-Linear" analysis, choosing the desired dependent variable (pollutant concentration) and independent variable (APCS score). Establish a multiple linear regression equation and calculate the regression coefficients of the pollution source on the water chemical factors.

[0032] Results Output and Interpretation. Outputs the results of the multiple linear regression analysis, including adjusted R², B-values, and corresponding APCS values ​​for each factor. Based on the regression coefficients and APCS scores, calculate the contribution ratios of known and unknown sources.

[0033] 3. Parameter adjustment and optimization: Flux segmentation module parameter adjustment Adjusting the filter coefficient (fc). The filter coefficient `fc` is a key parameter affecting the separation of point sources and area sources. In the MATLAB code, `fc=0.992` is a preset value, but it may need to be adjusted in practical applications. By comparing the flux segmentation results with the area source contribution ratio of APCS-MLR analysis, gradually adjust the value of `fc` until the area source ratios of the two modules tend to be consistent.

[0034] Adjusting the number of iterations (pass). The number of iterations `pass` determines the number of times the filtering process is repeated, affecting the stability of point source flux and the volatility of area source flux. Increase or decrease the `pass` value and observe the effect on the matching degree to find the optimal number of iterations.

[0035] APCS-MLR Analysis Module Parameter Adjustment Factor rotation method selection. The rotation method (such as the "maximum variance method") affects the interpretability of factor loadings. Different rotation methods may result in different APCS scores. Try different rotation methods and evaluate their impact on the match.

[0036] Match evaluation and iterative optimization Match assessment. Define a match metric, such as absolute percentage error or correlation coefficient, to quantify the consistency between throughput splitting and APCS-MLR analysis results. Use this metric to assess the match under the current parameter settings.

[0037] Validation and feedback. After each parameter adjustment, the throughput splitting and APCS-MLR analysis need to be rerun to verify the effect of the parameter adjustment. Based on the validation results, the parameters are adjusted again until the results of the two modules match.

[0038] By adjusting and optimizing the above-mentioned parameters, the consistency of flux segmentation and APCS-MLR analysis methods in terms of the proportion of area source flux can be ensured, thereby improving the accuracy and reliability of source tracing analysis.

[0039] Pollution source contribution ratio identification: Based on the above analysis results, the proportion of pollutant flux from industrial point sources, residential point sources and agricultural non-point sources in the watershed outlet section was finally determined.

[0040] This step provides a direct scientific basis for watershed management and pollution control.

[0041] In a specific embodiment, such as Figure 1 As shown, using the flow and water quality data of the Minjiang River Basin outlet section from March 2021 to March 2024, flux data of indicators such as permanganate index were obtained by multiplying flow rate by water quality concentration.

[0042] like Figure 2 As shown, the flux segmentation algorithm is used to perform signal segmentation calculations on the flux time series data, and the segmentation results in point source flux (including industrial and residential sources) and agricultural non-point source flux.

[0043] like Figure 3 As shown, the APCS-MLR method was used to quantify the contribution of industrial, residential, and agricultural non-point sources to pollutant fluxes. Parameters such as the filter coefficients, iteration count, and factor rotation method in the flux segmentation algorithm were adjusted to ensure consistency between the agricultural non-point source proportions of the two methods. The final matched proportions were 49% for agricultural non-point sources, 20% for industrial sources, and 31% for residential sources, enabling source tracing analysis of pollutant fluxes at the watershed outlet section.

Claims

1. A method for tracing the source of pollutant fluxes at the outlet section of a river basin, characterized in that, Includes the following steps: S1. Perform flux segmentation on the collected pollutant flux data at the outlet section of the river in the basin; S2. Import pollutant flux data from the outlet section of the watershed into SPSS software, perform APCS-MLR analysis, output the results of multiple linear regression analysis, and calculate the contribution ratio of known and unknown sources. S3. Adjust the parameters of the flux segmentation module and the APCS-MLR analysis module to ensure consistency between the flux segmentation and APCS-MLR analysis methods in terms of the proportion of area source flux.

2. The method for tracing pollutant fluxes at the outlet section of a river basin according to claim 1, characterized in that, The flux data includes time-series flow rates and corresponding pollutant concentration data.

3. The method for tracing pollutant fluxes at the outlet section of a river basin according to claim 1, characterized in that, The flux splitting includes the following steps: The pollutant flux data were input into MATLAB software, and the filter coefficients and the number of iterations were defined. The data is divided into time vectors (TV) and raw pollutant flux data (hy). The point source flux array is initialized, and bf_p is set as the initial pollutant flux data. Iterative calculations are performed, with each iteration consisting of two processes: forward and reverse. In each iteration, the estimated value of the current point source flux is calculated using the filter coefficient fc based on the current and previous iteration point source flux values. Update bf_p to the bf value of the current iteration. The calculated bf is the point source flux, while sf = hy - bf is the surface source flux.

4. The method for tracing pollutant fluxes at the outlet section of a river basin according to claim 2, characterized in that, The pollutant concentration data includes the concentration data of permanganate index, ammonia nitrogen, and total phosphorus.

5. The method for tracing pollutant fluxes at the outlet section of a river basin according to claim 1, characterized in that, The APCS-MLR analysis module analysis process includes the following steps: Import pollutant flux data from the outlet section of the watershed into SPSS software; The input data is standardized, then principal component analysis is performed, and factor extraction and rotation are carried out to obtain factor solutions with simple structures. A multiple linear regression equation was established to calculate the regression coefficients of pollution sources on water chemical factors, and the adjusted R², B value and APCS value of each factor were obtained.

6. The method for tracing pollutant fluxes at the outlet section of a river basin according to claim 1, characterized in that, Parameter adjustment includes the following steps: Adjust the filter coefficients in the digital filtering algorithm; Adjust the regression model parameters in the APCS-MLR analysis; By comparing the proportion of agricultural source flux obtained from the two algorithms, the above parameters are adjusted simultaneously to achieve consistency in the results.

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