Prediction Model for Variation of Dissolved Oxygen Concentration in River Water Body, Its Construction Method and Application

Abstract

The present invention discloses a prediction model for the change of dissolved oxygen concentration in river water bodies, its construction method and application. The characteristics are that the dissolved oxygen prediction method includes: monitoring the target water body to be predicted to obtain the dissolved oxygen data samples of the target water body; performing PARAFAC analysis on the EEM data of dissolved organic matter in the target water body to obtain the result data of each fluorescent component; analyzing the result data of each component after PARAFAC analysis to determine the tryptophan component of high oxygen-consuming substances; constructing a new dissolved oxygen kinetic model based on the tryptophan component; using the dissolved oxygen prediction model to predict the dissolved oxygen in the water body. The advantages are that the testing and data processing methods of the model are simple, fast and efficient, making the classical model more practical and accurate, and can be used to predict the attenuation trend of dissolved oxygen in polluted rivers, providing reliable technical support for the supervision of abnormal fluctuations of dissolved oxygen in rivers.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality prediction, and in particular to a prediction model for the change of dissolved oxygen concentration in river water bodies, a construction method thereof, and an application thereof. Background Art

[0002] Dissolved oxygen (DO) is an indispensable component in aquatic ecosystems and is closely related to the quality and health of the aquatic environment. The dissolved oxygen level in a river can reflect the respiratory level, primary productivity, and environmental quality of aquatic organisms in that water area. Low dissolved oxygen conditions are conducive to denitrification, promoting the release of reactive phosphorus and toxic metals (such as arsenic and chromium) from contaminated sediments, thus causing endogenous pollution. The low dissolved oxygen conditions caused by eutrophication of river water bodies easily lead to dead zones in the water area. On the contrary, high dissolved oxygen conditions can promote the circulation of various elements, which play an important role in maintaining the water balance. Predicting the change of dissolved oxygen concentration in rivers is beneficial for early warning and risk optimization control of sudden hypoxia in the water environment, and improving the shock resistance ability of river water quality.

[0003] Currently, the methods applied to the prediction of dissolved oxygen concentration mostly focus on the calculation and estimation of deep learning models, or predicting the backend value of DO through the antecedent value of DO. After continuous exploration and improvement, these methods have certain advantages in the accuracy of dissolved oxygen prediction, but their large data requirements, complex model parameter tuning process, and the disadvantage of being unable to be popularized on a large scale still limit their practical applications. The presence of high oxygen-consuming substances in water bodies is the main influencing factor for the change of DO concentration in water bodies. By characterizing the effect of high oxygen-consuming substances on the dissolved oxygen concentration for effective prediction, not only can the amount of data required by the model be reduced, but also the output results of the model can be made more accurate. In the classical Streeter-Phelos (S-P) dissolved oxygen prediction model, BOD is used as an aerobic substance indicator parameter, but the BOD test procedure has the defects of a long cycle and low prediction accuracy. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an efficient, accurate, and convenient prediction model for the change of dissolved oxygen concentration in river water bodies, a construction method thereof, and an application thereof.

[0005] The technical solution adopted by the present invention to solve the above technical problem is: a prediction model for the change of dissolved oxygen concentration in river water bodies, and the prediction model is a tryptophan-DO model, and the formula is as follows:

[0006] In the formula, D t is the oxygen deficit at a certain moment, with the unit of mg·L -1; k1 is the first-order decay reaction rate constant of tryptophan-like components in the river, with the unit of d -1 ; k2 is the reoxygenation rate constant, with the unit of d -1 ; a′ is the effective factor; t is the time, with the unit of d.

[0007] Furthermore, the method for determining the first-order decay reaction rate constant k1 of tryptophan-like components in the river is as follows:

[0008] In the formula, CN t is the fluorescence intensity of tryptophan-like component CN in the water body at time t, with the unit of R.U.; CN0 is the initial fluorescence intensity of CN, with the unit of R.U.; t is the time, in d; within 0 - 20 h, the fluorescence intensity of component CN in the river is measured every 4 hours, and the 5 sets of time-series data are substituted into formula (2) for regression analysis to obtain the first-order decay reaction rate constant k1 of component CN in the river.

[0009] Furthermore, the method for determining the reoxygenation rate constant k2 and the effective factor a′ is as follows: within 0 - 20 h, the oxygen deficit in the river is measured every 4 hours, and the 5 sets of time-series data are substituted into formula (1) for quantitative regression analysis to obtain the reoxygenation coefficient constant k2 and the effective factor a′.

[0010] The present invention also provides a method for constructing a prediction model for the change in dissolved oxygen concentration in the above river channel water body, including the following steps:

[0011] (1) Monitor the target water body to be predicted, use a multi-parameter water quality analyzer to monitor the dissolved oxygen concentration at each sampling point, and simultaneously obtain the three-dimensional fluorescence spectral data corresponding to the dissolved oxygen data of the target water body;

[0012] (2) Analyze the three-dimensional fluorescence spectral data and ultraviolet spectral data obtained in step (1) using the parallel factor (PARAFAC) algorithm toolbox in Matlab software to obtain the fluorescence components and their fluorescence intensity data at each sampling point;

[0013] (3) Conduct quantitative regression analysis on the fluorescence intensity data of the fluorescence components at each sampling point obtained in step (2) and the corresponding dissolved oxygen data to determine that the key influencing factor component affecting the dissolved oxygen concentration level in the water body is the tryptophan-like component;

[0014] (4) Construct a tryptophan-like component-dissolved oxygen (tryptophan-DO) prediction model, specifically as follows: In the formula, D t is the oxygen deficit at a certain moment, with the unit of mg·L -1 ; k1 is the first-order decay reaction rate constant of tryptophan-like components in the river, with the unit of d-1 ; k2 is the reaeration rate constant, with the unit of d -1 ; a′ is the effective factor; t is the time, with the unit of d.

[0015] Furthermore, step (1) is specifically as follows: Monitor the urban river water body, measure the dissolved oxygen concentration of the target water body and the three-dimensional fluorescence spectra of the target water body and the blank water sample. Subtract the fluorescence data of the blank sample from the fluorescence data of the target water body to remove the Raman scattering effect. Set the fluorescence intensity of the Rayleigh scattered line as missing, and set the data in the triangular region with the emission wavelength less than the excitation wavelength on the fluorescence spectrum to zero to obtain the three-dimensional fluorescence spectrum data of the sample to be measured. The scanning conditions for three-dimensional fluorescence measurement are as follows: Hitachi F-4600 fluorescence photometer, excitation wavelength 220 - 450 nm, scanning interval 5 nm, emission wavelength 260 - 600 nm, scanning interval 1 nm, slit width 5 nm, scanning speed 2400 nm·min -1 .

[0016] The present invention also provides the application of the tryptophan component-dissolved oxygen prediction model constructed by the above method in predicting the dissolved oxygen in river water bodies, which is characterized as follows: Input the first-order decay reaction rate constant k1, the reaeration coefficient constant k2, the effective factor a′, and the initial fluorescence intensity of tryptophan CN0 of the component CN in the river into the tryptophan component-dissolved oxygen model, and calculate the oxygen deficit D at a certain moment t , and predict the change of the dissolved oxygen concentration in the river on the second and third days through the oxygen deficit D t .

[0017] The technical principle of the present invention: The method for realizing rapid prediction by the tryptophan component-dissolved oxygen (tryptophan-DO) model of the present invention is mainly based on the influence of high oxygen-consuming substances on the dissolved oxygen concentration. Analyze and simulate the main influencing factors of the change of dissolved oxygen concentration, so as to effectively predict the change trend of dissolved oxygen. For different dissolved organic matters in surface water, their specific chemical compositions form different fluorescence spectral characteristics. Using PARAFAC fluorescence components (CN) to replace BOD will improve the characterization accuracy of the independent variables of the model for aerobic organic matters in water bodies, making the analysis method more advanced, convenient and accurate. Based on this principle, the present invention establishes a quantitative regression model between the fluorescence intensity of high oxygen-consuming substances and the change of dissolved oxygen concentration level, so as to achieve the purpose of rapidly predicting the change trend of dissolved oxygen in surface water.

[0018] Compared with the prior art, the advantages of the present invention are as follows: A prediction model for the change in dissolved oxygen concentration in river water bodies, its construction method and application. Based on the strong correlation between highly oxygen-consuming organic substances and dissolved oxygen concentration levels in water bodies, as well as the powerful identification function of three-dimensional fluorescence spectroscopy for highly oxygen-consuming substances, it features simple operation, short detection period, and low cost. After obtaining the three-dimensional fluorescence spectral data of the sample, performing PARAFAC calculation and then substituting it into the tryptophan-DO model, the current oxygen deficit in the water body can be estimated and the change trend of dissolved oxygen in the future for a period of time can be predicted. The entire prediction process has a short cycle and is convenient. The prediction method of the present invention will provide a fast, efficient, and reliable method for predicting the dissolved oxygen concentration in surface water, and provide a theoretical basis and data support for surface water quality management and pollution early warning control.

[0019] In summary, the present invention provides a surface water dissolved oxygen change trend forecasting technology that integrates sampling, analysis, and prediction, which can be effectively applied to actual water bodies. It has the advantages of being inexpensive, fast, and reliable, with high accuracy of the forecast results. It can effectively solve the defects of large required data sets, complex and cumbersome parameter tuning processes when using deep learning models to predict dissolved oxygen in surface water, and provide a theoretical basis and data support for surface water quality management and pollution warning control. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flow chart of the steps for constructing the prediction model of the present invention;

[0021] Figure 2 is the excitation-emission diagram and loading diagram of five components (C1-C5) of EEM-PARAFAC;

[0022] Figure 3 is the dynamic change diagram of dissolved oxygen (a), PARAFAC representative component C2 (b), C5 (c), and DOC (d) during the cultivation process;

[0023] Figure 4 is the linear regression diagram between each water quality parameter, fluorescence component and dissolved oxygen concentration in the specific embodiment;

[0024] Figure 5 are the prediction results of the tryptophan-DO model and the Streeter-Phelos model. The solid line is the prediction result of the tryptophan-DO model, the dashed line is the prediction result of the Streeter-Phelos model, and the dots are the true values of Dt;

[0025] Figure 6 is the calculation result of the first-order decay reaction rate constant k1 of tryptophan-like in the application example;

[0026] Figure 7It is the calculation result of the reaeration coefficient k2 and the effective factor a' in the application example;

[0027] Figure 8 It is the prediction result of the dissolved oxygen content in the urban river channel based on the tryptophan-DO model in the application example. Detailed implementation manners

[0028] The present invention will be further described in detail below in conjunction with the embodiments with reference to the drawings. I. Specific embodiment

[0030] A prediction model for the change of dissolved oxygen concentration in river water body, and the prediction model is a tryptophan component-dissolved oxygen (tryptophan-DO) model, and the formula is as follows:

[0031] In the formula, D t is the oxygen deficit at a certain moment, and the unit is mg·L -1 ; k1 is the first-order decay reaction rate constant of the tryptophan component in the river, and the unit is d -1 ; k2 is the reaeration rate constant, and the unit is d -1 ; a' is the effective factor; t is the time, and the unit is d. The method for determining the first-order decay reaction rate constant k1 of the tryptophan component in the river is as follows: In the formula, CNt is the fluorescence intensity of the tryptophan component CN in the water body at the moment t, and the unit is R.U.; CN0 is the initial fluorescence intensity of CN, and the unit is R.U.; t is the time, d; within 0-20 h, the fluorescence intensity of the component CN in the river is measured every 4 hours, and the 5-time series data is substituted into formula (2) for regression analysis to obtain the first-order decay reaction rate constant k1 of the component CN in the river. The methods for determining the reaeration rate constant k2 and the effective factor a' are as follows: within 0-20 h, the oxygen deficit of the river is measured every 4 hours, and the 5-time series data is substituted into formula (1) for quantitative regression analysis to obtain the reaeration coefficient constant k2 and the effective factor a'.

[0032] Its construction method is as Figure 1 shown, and includes the following steps:

[0033] 1. Monitor the target water body to be predicted. Use a multi-parameter water quality analyzer to monitor the dissolved oxygen concentration at each sampling point, and simultaneously obtain the three-dimensional fluorescence spectral data corresponding to the dissolved oxygen concentration data of the target water body. Specifically as follows: Water samples were collected from urban rivers in Ningbo, a coastal city in southeastern China, and the influent of the urban sewage treatment plant was collected for pollution load gradient experiments. In order to simulate the different degrees of influence of urban rivers by domestic sewage, we prepared four groups of samples, in which the ratios of filtered river water to sewage were 3:1, 1:1, 1:3, and 1:5 (v / v), respectively. These samples were denoted as R:S = 3:1, R:S = 1:1, R:S = 1:3, and R:S = 1:5, and three-stage culture experiments were carried out to obtain the dynamic change data of dissolved oxygen; the dissolved oxygen concentration of each river water sample was measured on-site when it was collected. After each water sample was filtered through a filter membrane, three-dimensional fluorescence was performed.

[0034] 2. Analyze the three-dimensional fluorescence spectral data obtained in step 1 using the parallel factor (PARAFAC) algorithm toolbox in Matlab software to obtain the fluorescence components and their fluorescence intensity data at each sampling point; import the three-dimensional fluorescence spectral data into the PARAFAC model, and finally obtain 5 PARAFAC fluorescence components (as Figure 2 shown in Table 1).

[0035] Table 1 Emission and excitation maxima and characteristics of each PARAFAC component

[0036]

[0037] 1 The excitation wavelength in parentheses represents the secondary peak.

[0038] The above PARAFAC algorithm is based on the trilinear decomposition theory, that is, assuming that at a certain excitation-emission wavelength, the fluorescence intensity of a certain component is a trilinear function of the concentration of this component and its specific absorption / emission spectral properties. The model is as follows:

[0039]

[0040] In the formula, F refers to the number of components in the model, which can be determined by the core consistency function; x ijk is the fluorescence intensity data of sample i at emission wavelength j and excitation wavelength k, that is, the constituent element of the cubic matrix X (I×J×K) of F; a if is the factor score, reflecting the percentage of the concentration of component f in sample i, that is, the constituent element of the component matrix A (I×F); b jf is the loading, which is linearly related to the fluorescence quantum yield of the component at emission wavelength j, that is, the constituent element of the component matrix E (J×F); c kfis the load, which is proportional to the specific absorption coefficient of component f at the excitation wavelength of k, that is, the constituent element of the component matrix C(K×F). ε ijk represents the residual, including noise and data signals not modeled. Its solution is to use the alternating least squares algorithm to reduce the sum of squared residuals (SSR). When SSR < 10 -6 , the model is considered to have converged.

[0041] 3. Perform quantitative regression analysis on the fluorescence intensity data of the fluorescent components at each sampling point obtained in step 2 and the corresponding dissolved oxygen data to determine that the key influencing factor component affecting the dissolved oxygen concentration level in the water body is the tryptophan-like component. The process analysis is as follows:

[0042] According to biodegradation kinetics, the dynamic changes of dissolved oxygen during the cultivation process can be analyzed. The change of dissolved oxygen in the water body is based on the balance between microbial oxygen consumption and air reoxygenation processes. Dividing the instantaneous dissolved oxygen concentration by the initial dissolved oxygen concentration at this stage to normalize the dissolved oxygen concentration at each stage can more intuitively reveal the dynamic changes of the dissolved oxygen concentration (as Figure 3 shown in a). The dynamic changes of each component of PARAFAC during the cultivation process can be used to reveal the essence of the biodegradation process. Figure 3 Figures b and 3c show the dynamic changes of two representative components of PARAFAC (i.e., C2 and C5) during the cultivation period. Among the five components, the tryptophan-like component C2 shows an obvious downward trend, indicating that the tryptophan-like component is the main contributor to biodegradable organic carbon (BDOC) and an important driving factor for biodegradation activity. Figure 3 Figure d shows the dynamic changes of chemical indicators during the entire cultivation process. The dissolved organic carbon (DOC) generally shows a downward trend during the cultivation process. The degradation rate of DOC in the first stage accounts for 50.2%-80.3% of the total degradation rate, indicating that microorganisms preferentially utilize soluble DOC at this stage. In the second and third stages, since the remaining DOC is more difficult to degrade, the DOC concentration slowly decreases and tends to be stable, which corresponds to the dynamic changes in the content of the tryptophan-like component C2.

[0043] An important driving factor for biodegradation, that is, the tryptophan-like component C2 in the sewage, has been determined through cultivation experiments. If the content of the tryptophan-like component C2 can be used to simulate the dynamic changes of the dissolved oxygen content, this will be an important breakthrough in predicting the dissolved oxygen consumption mechanism during the biodegradation process. On this basis, a linear regression analysis is performed on the water quality parameters, fluorescence intensity, and dissolved oxygen of the water sample, as Figure 4As shown, dissolved oxygen is strongly negatively correlated with tryptophan-like component C2 (0.719), while the negative correlation with terrestrial humic-like component C5 is weaker (0.104). This indicates that tryptophan-like component C2 from domestic sewage is the main factor determining the changing trend of river dissolved oxygen concentration. Due to its own biological recalcitrance, humic substances have no significant relationship with the change in dissolved oxygen concentration.

[0044] To further analyze the correlation between dissolved oxygen and chemical indices, the following correlation studies were conducted in this research, as Figure 4 shown, the relationships between dissolved oxygen and conductivity, TDN, TDP, and DOC are weak, with R 2 Adj. values of 0.369, 0.376, 0.262, and 0.128 respectively. Conductivity is mainly affected by the ionic strength of Na + , Mg 2+ , Ca 2+ , Cl-, and SO4 2- in the river and is not completely dominated by microbial activities. Nutrients such as TDN and TDP may not be the main consumption and limiting factors for the biodegradation process driven by protein-like substances. As for DOC, although DOC shows a rapid decline trend at the initial stage of cultivation, DOC also contains some organic carbon that cannot be biodegraded. Dissolved oxygen has a moderate relationship with ammonia nitrogen, with R 2 Adj. = 0.533, which may be because ammonia nitrogen can reflect the input of domestic sewage to a certain extent. In addition, there is no direct relationship between dissolved oxygen concentration and turbidity, with R 2 Adj = -0.042. This is because under natural water body conditions, turbidity is affected by various factors such as soil particles, residues, organisms, and colloids. Therefore, there is no direct internal connection between dissolved oxygen and turbidity in the river. Thus, tryptophan-like components are determined to be the key influencing factors for dissolved oxygen.

[0045] 4. Based on the data of tryptophan-like components in the time series, a tryptophan-DO (tryptophan-dissolved oxygen) prediction model was constructed, and the formula is as follows:

[0046]

[0047] In the formula, D t is the oxygen deficit at a certain moment, with the unit of mg·L -1 ; k1 is the first-order decay reaction rate constant of tryptophan-like components in the river, with the unit of d -1 ; k2 is the reoxygenation rate constant, with the unit of d -1 ; a′ is the effective factor; t is the time, with the unit of d. The derivation process of the prediction model is as follows:

[0048] (1) The culture experiment was divided into three stages. After aerating the target water body in the river channel to the maximum dissolved oxygen concentration, it was placed in an incubator for observation of the change in dissolved oxygen concentration. This was the first stage, which lasted from 0 to 88 hours. After 88 hours, the river water was aerated again to the maximum dissolved oxygen concentration, and then the change in dissolved oxygen concentration was observed. This was the second stage, which lasted for 72 hours. After the end of the second stage, the river water was aerated for the third time to the maximum dissolved oxygen concentration, and the change in dissolved oxygen concentration was observed. This was the third stage, which lasted for 64 hours.

[0049] Since the microbial activity in the first stage of the culture was relatively strong, the change in dissolved oxygen concentration was representative. Therefore, a dynamic dissolved oxygen model was established using the dissolved oxygen change data in the first stage. The Streeter-Phelos (S-P) model is a classic river model that has been around for hundreds of years. It is based on the following three assumptions: (a) The oxygen consumption in the river is caused by the decay of BOD, and the dissolved oxygen in the river comes from atmospheric reoxygenation; (b) The decay of BOD and the reoxygenation process in the river are first-order reactions; (c) The reaction rate is constant. Therefore, the decay amount of BOD can be calculated by the following formula:

[0050]

[0051] In the formula: L t refers to the BOD concentration at a certain moment in the river, with the unit of mg·L -1 ; L0 refers to the initial BOD concentration in the river, with the unit of mg·L -1 ; k1 refers to the decay rate coefficient of BOD in the river, with the unit of d -1 ; t is the time, with the unit of d.

[0052] (2) The fluorescence intensity of the tryptophan-like component C2 is proportional to the amount of organic matter. Since the oxygen consumption for the complete oxidation of organic matter is proportional to the amount of organic matter, BOD can be calculated by the following formula:

[0053] L t = aC2 t (4),

[0054] In the formula, C2 t refers to the fluorescence intensity of C2 within a certain period of time, with the unit of R.U.,.

[0055] Substituting formula (3) and L0 = aC20 into formula (4), the following formula can be obtained:

[0056]

[0057] Among them, C20 refers to the initial fluorescence intensity of C2 (the same meaning as CN in formula (2)), with the unit of R.U..

[0058] Using formula (5), quantitative regression was performed on the C2 fluorescence intensity change data of the four groups in Stage I to determine the velocity coefficient k1. The obtained k1 values were between 0.10 and 0.12, which were very close. Therefore, in this study, k1 was taken as 0.1.

[0059] (3) According to the S-P model, the change rate of river hypoxia value is the sum of the oxygen consumption rate and the reoxygenation rate. The dynamic model of dissolved oxygen can be expressed by the formula:

[0060]

[0061] where k2 refers to the reoxygenation velocity coefficient, with the unit d -1 ; D t is the oxygen deficit at a certain moment, with the unit mg·L -1 . Since the water samples were fully aerated at the beginning of each stage, we assumed that the dissolved oxygen concentration was saturated after each aeration. Therefore, the hypoxia value was calculated as follows:

[0062] D t = DO0 - DO t (7), where DO0 refers to the initial dissolved oxygen concentration after aeration, with the unit mg·L -1 ; DO t refers to the dissolved oxygen concentration at a certain moment, with the unit mg·L -1 . Integrating equation (6) gives the following equation:

[0063]

[0064] where D0 refers to the initial hypoxia value, with the unit mg·L -1 . According to the above assumption, D0 = 0. Therefore, formula (8) can be simplified as follows: Regression analysis was performed on the tryptophan-like component C2 data of the four groups using equation (1), as Figure 5 shown. The obtained k2 values for each group were 0.75, 0.98, 0.86, and 0.82, and the R 2 Adj. values were between 0.798 and 0.989, and the effective factor a' values were 2.481, 3.296, 2.616, and 2.426, respectively. As Figure 5 shown by the solid line, the R 2 Adj.The values are 0.968, 0.988, 0.882, and 0.840 respectively, indicating that the tryptophan-like component can be used to effectively predict the change of dissolved oxygen during the entire biodegradation process. The test and analysis method of the tryptophan-like component-dissolved oxygen kinetic model is simple, fast, and efficient, making the model more practical and accurate. This model can be used to predict the lack of dissolved oxygen in the river after sewage discharge, providing technical support for the abnormal fluctuation of dissolved oxygen in the river.

[0065] To test the superiority of the tryptophan-like component-dissolved oxygen kinetic model in characterizing aerobic organic matter in water, the BOD index was used as the input variable and input into the S-P model for prediction calculation. Four groups of Dt prediction curves under different domestic sewage loads were obtained, as Figure 5 shown by the dotted line. By comparing the model prediction values with the true DO values, it can be found that the gap between the theoretical concentration and the actual concentration of DO is relatively obvious, and it does not play a good predictive effect on the dissolved oxygen content in the water body. This also proves the scientificity, rationality, and irreplaceability of selecting the tryptophan-like component to predict the DO concentration.

[0066] II. Application Example

[0067] To verify the model, random sampling was also carried out on the inner rivers in Ningbo. For the river sections with the characteristics of this area and without devices (such as aeration equipment and waterwheels) that interfere with the dissolved oxygen in the water body, positioning and sampling were carried out. Sampling of the river water samples was carried out every 4 hours. The filtered samples (a total of 5 samples) on the first day of sampling were added to a quartz cuvette and placed in a Hitachi F-4600 fluorescence photometer. The excitation wavelength was set to 220 - 450 nm, the scanning interval was 5 nm, the emission wavelength was 260 - 600 nm, the scanning interval was 1 nm, the slit width was 5 nm, and the scanning speed was 2400 nm·min -1 . The ultrapure water was treated in the same way, and the samples and blank samples were scanned to obtain the fluorescence data of the samples and blanks. In Matlab, the PARAFAC algorithm toolbox was used to analyze the spectral data of the calibration sample set and the samples. The tryptophan-like component was selected, and the corresponding 5 groups of fluorescence data intensity time series data were exported and substituted into to obtain the first-order decay reaction rate constant k1 of tryptophan as 0.45, as Figure 6 shown.

[0068] Substitute the first-order decay reaction rate constant k1 of tryptophan and the 5 groups of oxygen deficit time series data on the first day into the tryptophan-like component-dissolved oxygen (tryptophan-DO) model (1), and calculate to obtain the reaeration rate coefficient k2 of the river water body as 2.15 and the effective factor a' as 8.8, as Figure 7 shown. Therefore, the established tryptophan-like component-dissolved oxygen (tryptophan-DO) model is

[0069] The dissolved oxygen concentration prediction performance of the present invention was further verified using the tryptophan component-dissolved oxygen model. As Figure 8 shown, it is a comparison of the predicted results and the true values of the dissolved oxygen concentration on the second and third days. The predicted graphs of the dissolved oxygen concentration in the water body on the second and third days have the following characteristics: (1) The slope is close to 1; (2) The intercept is close to 0; (3) R 2 > 0.85. This indicates that the tryptophan-DO model has a high prediction accuracy in actual river channels.

[0070] Thus, it can be seen that the surface water dissolved oxygen change trend forecasting technology developed by the present invention, which integrates sampling, analysis, and prediction, can be effectively applied to actual water bodies. It has the advantages of being inexpensive, fast, and reliable, with a high accuracy of the forecasting results. It can effectively solve the defects that the deep learning model requires a large dataset and the parameter tuning process is complex and cumbersome when predicting the dissolved oxygen in surface water, providing a theoretical basis and data support for surface water quality management and pollution warning control.

[0071] The above description is not a limitation of the present invention, nor is the present invention limited to the above examples. Changes, modifications, additions, or substitutions made by those of ordinary skill in the art within the essence of the present invention shall also fall within the protection scope of the present invention.

Claims

1. A prediction model for the change in dissolved oxygen concentration in river water bodies, characterized in that The prediction model described above is a tryptophan-like component-dissolved oxygen model, and the formula is as follows: where D t is the oxygen deficit at a certain moment, with the unit of mg·L -1 ; k1 is the first-order decay reaction rate constant of tryptophan-like components in the river, with the unit of d -1 ; k2 is the reoxygenation rate constant, with the unit of d -1 ; a' is the effective factor; t is the time, with the unit of d. The method for determining the first-order decay reaction rate constant k1 of tryptophan-like components in the river is as follows: wherein, CN t is the fluorescence intensity of the tryptophan-like component CN in the water body at time t, with the unit of R.U.; CN0 is the initial fluorescence intensity of CN, with the unit of R.U.; t is the time, in d; within 0 - 20 h, the fluorescence intensity of the component CN in the river is measured every 4 hours, and the 5 sets of sequential data are substituted into formula (2) for regression analysis to obtain the first-order decay reaction rate constant k1 of the component CN in the river. The determination methods of the reaeration rate constant k2 and the effective factor a' are as follows: within 0 - 20 h, the dissolved oxygen deficit in the river is measured every 4 hours, and the 5 sets of sequential data are substituted into formula (1) for quantitative regression analysis to obtain the reaeration coefficient constant k2 and the effective factor a'.

2. A method for constructing a prediction model of the change in dissolved oxygen concentration in river water bodies according to claim 1, characterized in that It includes the following steps: (1) Monitor the water body of the prediction target. Use a multi-parameter water quality analyzer to monitor the dissolved oxygen concentration at each sampling point, and at the same time obtain the three-dimensional fluorescence spectrum data corresponding to the dissolved oxygen data of the target water body; (2) Analyze the three-dimensional fluorescence spectrum data and ultraviolet spectrum data obtained in step (1) using the parallel factor (PARAFAC) algorithm toolbox in Matlab software to obtain the fluorescence components and their fluorescence intensity data at each sampling point; (3) Conduct a quantitative regression analysis on the fluorescence intensity data of the fluorescence components at each sampling point obtained in step (2) and the corresponding dissolved oxygen data to determine that the key influencing factor component affecting the dissolved oxygen concentration level in the water body is the tryptophan-like component; (4) Construct a tryptophan-like component-dissolved oxygen prediction model, specifically as follows: where D t is the oxygen deficit at a certain moment, with the unit of mg·L -1 ; k1 is the first-order decay reaction rate constant of tryptophan-like components in the river, with the unit of d -1 ; k2 is the reoxygenation rate constant, with the unit of d -1 ; a' is the effective factor; t is the time, with the unit of d.

3. The method for constructing a prediction model for the change in dissolved oxygen concentration in river water bodies according to claim 2, characterized in that Step (1) is specifically as follows: Monitor the water body of urban river channels, measure the dissolved oxygen concentration of the target water body and the three-dimensional fluorescence spectra of the target water body and blank water samples. Subtract the fluorescence data of the blank sample from the fluorescence data of the target water body to remove the Raman scattering effect. Set the fluorescence intensity of the Rayleigh scattering line to be missing, and set the data in the triangular region where the emission wavelength on the fluorescence spectrum is less than the excitation wavelength to zero to obtain the three-dimensional fluorescence spectrum data of the sample to be measured. The scanning conditions for three-dimensional fluorescence measurement are as follows: Hitachi F-4600 fluorescence photometer, excitation wavelength 220 - 450 nm, scanning interval 5 nm, emission wavelength 260 - 600 nm, scanning interval 1 nm, slit width 5 nm, scanning speed 2400 nm·min -1 .

4. Application of the tryptophan-like component-dissolved oxygen prediction model constructed by the method according to claim 2 in predicting the dissolved oxygen in river water, characterized in that The specific steps are as follows: Input the first-order decay reaction rate constant k1 of component CN in the river, the reoxygenation coefficient constant k2, the effective factor a', and the initial fluorescence intensity of tryptophan-like CN0 into the tryptophan-like component-dissolved oxygen model to calculate the oxygen deficit D at a certain moment. t , and through the oxygen deficit D t predict the changes in the dissolved oxygen concentration in the river channel on the second and third days.

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