CN114136900B - Water quality detection method combining ultraviolet and visible light absorption spectrum technology - Google Patents
Water quality detection method combining ultraviolet and visible light absorption spectrum technology Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/314—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry with comparison of measurements at specific and non-specific wavelengths
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
- G01N1/34—Purifying; Cleaning
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/65—Raman scattering
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/314—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry with comparison of measurements at specific and non-specific wavelengths
- G01N2021/3155—Measuring in two spectral ranges, e.g. UV and visible
Abstract
The invention relates to a water quality detection method combining ultraviolet-visible light absorption spectrum technology, which designs a CFFA spectrum information extraction method to extract abundant water pollution information from three-dimensional fluorescence and ultraviolet-visible light absorption spectrum, and establishes Chemical Oxygen Demand (COD) Cr ) Permanganate index (COD) Mn ) Ammonia Nitrogen (NH) 3 -N), total Phosphorus (TP), total Nitrogen (TN) and five-day Biochemical Oxygen Demand (BOD) 5 ) The water quality index is inverted through the water spectrum information, the prediction result has good accuracy and precision, and a new solution is provided for efficient in-situ monitoring of surface water.
Description
The invention relates to the field of water quality index models and water quality grade rapid judging methods, in particular to a water quality detection method combining ultraviolet and visible light absorption spectrum technology.
Background
With the acceleration of the urban process and the aggravation of various resource pollution, the urban river water quality is continuously worsened, and the ecological system and human health are threatened. Real-time water quality monitoring is important for effectively managing urban water resources and rapidly identifying pollution sources. The main monitoring index of water quality comprises Chemical Oxygen Demand (COD) Cr ) Permanganate index (COD) Mn ) Ammonia Nitrogen (NH) 3 -N), total Phosphorus (TP), total Nitrogen (TN) and five-day Biochemical Oxygen Demand (BOD) 5 ). However, the traditional water quality index experiment is complex in operation, high in cost, long in time consumption and harmful to the environment, and is not suitable for mass sampling and real-time monitoring. In addition, these water quality indices do not provide any information about the structural composition and detailed source of water contaminants. Therefore, it is necessary to develop a more convenient, economical, efficient and environment-friendly method to overcome the shortcomings of the traditional monitoring technology and meet the requirements of real-time water quality monitoring.
At present, a water quality index prediction technology based on a three-dimensional fluorescence technology and an ultraviolet-visible absorption spectrum technology combined with a machine learning algorithm is reported, but the following problems exist:
1. the single spectrum technology is used, so that the method has the limitation that pollutant information in the water body cannot be comprehensively obtained;
2. the water quality samples are not accumulated for a long time and widely, the number of training set samples used by the prediction model is small, so that the generalization performance of the prediction model is weak, and the application range is narrow;
3. the water quality prediction model aims at single water quality index, and cannot comprehensively reflect the pollution condition of the water body;
4. the machine learning algorithm for building the prediction model is too simple (such as Multiple Linear Regression (MLR)), ignores the diversity and complexity of water pollution, and is difficult to cope with complex water quality conditions;
5. the method for extracting the pollutant information from the spectrum is single and one-sided, and some methods (such as parallel factor method PARAFAC) have long time consumption, complex calculation process, large result limitation and difficult application to actual water quality monitoring; some methods, such as the peak extraction method, are too simple, have unsatisfactory effects on complex spectrum processing, and cannot comprehensively reflect pollution conditions and pollution properties in water.
Disclosure of Invention
In order to achieve the above purpose, the present invention provides the following technical solutions: a water quality detection method combining ultraviolet-visible light absorption spectrum technology comprises the following steps:
s1, sample collection:
collecting water body at deep position under water surface by using vertical sampler, standing to obtain supernatant, placing into sterile brown glass bottle, and transporting to laboratory;
s2, chemical analysis:
shaking the sample, standing for 30min, collecting supernatant, and detecting with the analysis item of Chemical Oxygen Demand (COD) Cr ) Permanganate index (COD) Mn ) Ammonia Nitrogen (NH) 3 -N), total Phosphorus (TP), total Nitrogen (TN) and five-day Biochemical Oxygen Demand (BOD) 5 ) The detection method refers to relevant national standards and industry standards;
s3, three-dimensional fluorescence spectrum measurement:
measuring the three-dimensional fluorescence spectrum of the water sample by using a Hitachi F4600 type fluorescence spectrophotometer, shaking the sample uniformly before measurement, standing to room temperature, diluting the sample by using ultrapure water if the fluorescence intensity of the sample exceeds the measuring range of the instrument, and keeping the time interval between the spectral analysis and chemical analysis of the same batch of samples to be no more than 24 hours;
s4, ultraviolet visible light absorption spectrum measurement:
firstly shaking the sample uniformly, standing to room temperature, measuring the absorption spectrum of the water quality sample by using a portable ultraviolet-visible spectrophotometer, diluting the sample by ultrapure water if the fluorescence intensity of the sample exceeds the measuring range of the instrument, and keeping the time interval between the spectral analysis and the chemical analysis of the same batch of samples to be no more than 24 hours;
s5, preprocessing a three-dimensional fluorescence spectrum:
before acquiring the data, the performance of the xenon lamp was verified by measuring the signal-to-noise ratio of the water raman peak at the 397nm emission wavelength, subtracting the spectrum of pure water from each three-dimensional fluorescence spectrum, setting the negative value generated to zero to remove the background spectrum, if the fluorescence intensity of the sample exceeds the measurement range of the spectrometer, diluting and re-measuring with ultrapure water, processing the three-dimensional fluorescence spectrum with an EEMscat kit to remove rayleigh scattering and raman scattering, interpolating the cut-off region, normalizing the three-dimensional fluorescence spectrum with the raman peak of pure water at the 350nm emission wavelength, the spectral intensity after processing being raman unit r.u., finally, eliminating the internal filtering effect with absorbance-based method, utilizing each pair of λ in the three-dimensional fluorescence spectrum Ex And lambda (lambda) Em Absorbance A measured at λ By means ofFluorescence intensity F to be observed obs Conversion to the corrected fluorescence intensity F corr ；
S6, ultraviolet-visible light absorption spectrum pretreatment:
taking ultrapure water as reference to offset the absorption of pure water and inorganic salt, reducing the influence of light scattering on the absorption spectrum by subtracting the average absorbance of 680-700 nm, spectrophotometryThe magnitude is converted into absorbance A with an optical path length of 1cm λ ；
S7, extracting CFFA spectrum information:
the CFFA method is composed of four kinds of spectrum information extraction results, respectively,
(1) CFPs of peak extraction method
The peak extraction method CFPs is a method for identifying components based on maximum intensity and corresponding excitation and emission wavelength pairs, and is characterized by extracting seven characteristic fluorescence peaks of fulvic acid, free tyrosine, humic acid, microbial byproducts, free tryptophan and tryptophan.
(2) FSIs (fluorescence Spectroscopy) method
The fluorescence spectroscopy FSIs extract pollution information with three fluorescence spectroscopy indices FI, BIX and HIX.
Wherein the fluorescence index FI is defined as lambda Em =470 nm and λ Ex Fluorescence intensity at 370nm λ Em =520 nm and λ Ex Ratio of fluorescence intensity at 370 nm.
The biological index BIX is determined as lambda Ex When=310 nm, λ Em Fluorescence intensity at =380 nm λ Em Ratio of maximum intensity values between 420nm and 435 nm.
The humification index HIX is defined as lambda Ex ＝255nm，λ Em Integral intensity between 435 and 480nm with lambda Em The ratio of the integrated fluorescence intensities in the 300-345 nm region.
(3) FRIs by fluorescence area integration
The fluorescence region integration method is a quantitative method for integrating the volume under the three-dimensional spectrum line, wherein the three-dimensional fluorescence spectrum is divided into five regions by drawing horizontal and vertical lines according to the FDOM fluorescence peak position of the soluble fluorescent substance, and the intensity integral value of each region is used for representing FDOM accumulated fluorescence response with similar properties.
Volume (Φ) below region i i ) Can pass throughCalculation is shown.
Delta of it Ex For excitation wavelength interval, delta Em For the emission wavelength interval, F is the fluorescence intensity at each excitation emission wavelength pair. Normalized fluorescence volume (Φ) i,n ) Represented as phi i,n ＝MF i ×Φ i Wherein MF is i For each region, the multiplication factor is equal to the inverse of the ratio of each projected excitation emission area to the total projected area.
Normalized fluorescence percentage (P i,n ) Can pass throughAnd (5) calculating. In this study, the five normalized fluorescence percentages and the average intensity of the three-dimensional fluorescence spectrum are collectively referred to as the fluorescence area integral index (FRIs)
(4) ASIs (enzyme-specific absorption spectroscopy) information extraction method
Absorbance at a specific wavelength (A λ ) Can reflect the concentration of dissolved organic carbon (DOM), can also infer the molecular weight, the aromatic humification degree and the source of DOM, and uses 10 absorbance at different wavelengths of 220-440nm and 4 absorbance ratios to represent the water pollution degree.
S8, establishing a model:
all data in the step S7 and 6 target output COD Cr 、COD Mn 、NH 3 -N、TP、TN、BOD 5 And uniformly linearly scaling to a range of 0-1 so as to increase the sparsity of spectrum data and improve the efficiency, and constructing six water quality index linear support vector regression prediction models by adopting MATLAB2019 (Mathworks, natick, MA, USA) software.
Further, in the step S1, the sample collection: in each collection, 5L of water sample is collected at a depth of 50-60 cm below the water surface by using a vertical sampler, and after standing for 30-40 min, the sample supernatant is filled into a 1L sterile brown glass bottle, and then stored at 3-4 ℃ and transported to a laboratory within 2 hours.
Further, in the step S2, chemical analysis: all samples were tested analytically within 1 week of sampling.
Further, in the step S3, three-dimensional fluorescence spectrum measurement: the Hitachi F4600 type fluorescence spectrophotometer is set to have excitation wavelength Ex of 220-400 nm and sampling interval of 5nm; the emission wavelength Em is 260-520 nm, and the sampling interval is 1nm; the slit width was 10nm, and the scanning speed was 12000 nm.min-1.
Further, in the step S4, the scanning range of the portable ultraviolet-visible spectrophotometer in the ultraviolet-visible light absorption spectrum measurement is 200-735 nm, the sampling interval is 2.5nm, and the scanning speed is 360nm/min.
Further, in the step S8, a water quality prediction model is built by using epsilon-SVR and Radial Basis Function (RBF) in LIBSVM-3.24 software package in MATLAB2019 (Mathworks, natick, mass., USA) software, a relaxation variable is introduced in epsilon-SVR, the function of the algorithm is shown in equation 1,
subject toω T φ(x i )+b-z i ≤ε+ξ i ,
where ω is the vector of model coefficients, ζ and ζ are relaxation variables representing the degree of sample outliers, boundary error ε is set to 0.01, b is the intercept, x i To input variable, z i For target output, n is the number of training samples, φ (x i ) Is a radial basis function by which x can be determined i Mapping to a high dimensional space, the penalty factor of the error term C represents tolerance to outliers, and the over-fitting of the model can be controlled, if C tends to ≡, the algorithm will over-fit, if C tends to 0, the algorithm will diverge.
Further, in the step S8, in the model establishment, C and γ in the RBF are optimized by a grid search and an internal cross-validation method, and the optimized model is applied to a test set of external cross-validation to evaluate the performance of the model. The k-fold cross-validation, where k is set to 5, can randomly divide the data into k subsets and take the kth subset as the test set to evaluate the model trained on the remaining k-1 subsets.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, a spectrum method is adopted to analyze the water sample, so that the use of chemical reagents is avoided, the analysis efficiency is improved, the method can be used for online analysis of water quality at a fixed site, and rapid, in-situ and mobile monitoring can be realized as an on-site analysis technology;
2. the three-dimensional fluorescence spectrum and the ultraviolet-visible light absorption spectrum of the water body are combined to more comprehensively reflect the water quality pollution condition and pollution sources;
3. the method has the advantages of absorbing various spectrum extraction methods, creating a simple, quick and practical spectrum extraction method, combining an advanced machine learning algorithm to carry out modeling and prediction, and ensuring that the prediction results of a training set and a test set have higher accuracy;
4. the training set of the prediction algorithm covers various water body types in Yangzhou markets through long-term data accumulation, changes of different seasons, water periods and surrounding environments can reflect the water quality conditions of Yangzhou more comprehensively. The generalization performance and the precision of the water quality prediction algorithm can be further improved along with the accumulation of water quality samples in different areas;
5. the invention can simultaneously predict COD Mn 、COD Cr 、TN、TP、BOD 5 And NH 3 N is 6 water quality indexes, and compared with other single index prediction technologies, the method has the characteristic of being more efficient.
Drawings
Fig. 1: correlation of model predictions with chemical analysis values.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making any inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention.
Referring to fig. 1, the present invention provides the following technical solutions:
a water quality detection method combining ultraviolet-visible light absorption spectrum technology comprises the following steps:
s1, sample collection:
5L of water sample was collected at a depth of 50cm below the water surface using a vertical sampler, and after standing for 30min, the sample supernatant was filled into 1L sterile brown glass bottles, then stored at 4℃and transported to the laboratory within 2 hours
S2, chemical analysis:
shaking the sample, standing for 30min, collecting supernatant, and detecting with the analysis item of Chemical Oxygen Demand (COD) Cr ) Permanganate index (COD) Mn ) Ammonia Nitrogen (NH) 3 -N), total Phosphorus (TP), total Nitrogen (TN) and five-day Biochemical Oxygen Demand (BOD) 5 ) The detection method refers to the relevant national standard and industry standard, and all samples are subjected to analysis and test within 1 week of sampling;
s3, three-dimensional fluorescence spectrum measurement:
measuring the three-dimensional fluorescence spectrum of the water sample by using a Hitachi F4600 type fluorescence spectrophotometer, wherein the excitation wavelength Ex is 220-400 nm, and the sampling interval is 5nm; the emission wavelength Em is 260-520 nm, and the sampling interval is 1nm; the slit width is 10nm, the scanning speed is 12000 nm.min < -1 >, and before measurement, the water sample is uniformly shaken and then kept at room temperature. If the fluorescence intensity of the water sample exceeds the measuring range of the instrument, the water sample is diluted by ultrapure water, and the time interval between the spectral analysis and the chemical analysis of the same batch of water sample is not more than 24 hours. The method comprises the steps of carrying out a first treatment on the surface of the
S4, ultraviolet visible light absorption spectrum measurement:
firstly shaking a sample uniformly, standing to room temperature, and measuring an absorption spectrum of a water quality sample by using a portable ultraviolet-visible spectrophotometer (s: can, vienna, austria), wherein the scanning range is 200-735 nm, the sampling interval is 2.5nm, the scanning speed is 360nm/min, and the spectrometer is an in-situ monitoring instrument with an open optical path of 5cm, and can be directly measured by placing the instrument into water;
s5, preprocessing a three-dimensional fluorescence spectrum:
prior to data acquisition, the performance of the xenon lamp was verified by measuring the signal-to-noise ratio of the water raman peak at the 397nm emission wavelength of water. The spectrum of pure water was subtracted from each three-dimensional fluorescence spectrum, and the negative value generated was set to zero to remove the background spectrum. If the fluorescence intensity of the water sample exceeds the measurement range of the spectrometer, the water sample should be diluted with ultrapure water and measured again. Three-dimensional fluorescence spectra were processed using the EEMscat kit to remove rayleigh and raman scattering and interpolate the cut-out regions. The three-dimensional fluorescence spectrum was normalized using the raman peak of pure water at an emission wavelength of 350nm, and the spectral intensity after the treatment was raman unit (r.u.). Finally, an absorbance-based method is used to eliminate the internal filtering effect. By means of each pair lambda in the three-dimensional fluorescence spectrum Ex And lambda (lambda) Em Absorbance measured at (A) λ ) By means ofThe observed fluorescence intensity (F obs ) Converted into corrected fluorescence intensity (F corr )；
S6, ultraviolet-visible light absorption spectrum pretreatment:
using ultrapure water as a reference to offset the absorption of pure water and inorganic salts, the influence of light scattering on the absorption spectrum was reduced by subtracting the average absorbance at 680 to 700nm, and the spectrophotometer measurement was converted into absorbance (A λ )；
S7, extracting CFFA spectrum information:
the CFFA spectrum information extraction method is composed of the following four spectrum information extraction results.
(1) Peak extraction method
Peak extraction is a simple method of identifying components based on maximum intensity and corresponding excitation and emission wavelength pairs. The research extracts seven kinds of characteristic fluorescence peak (characteristic fluorescence peaks CFPs) information commonly existing in urban water body to represent water pollution conditions, and characteristic fluorescence peak parameters are shown in table 1.
TABLE 1 characteristic fluorescence peak spectral information
(2) Fluorescence spectrum index method
The fluorescence spectrum indexes (Fluorescence spectral indexes FSIs) in this study include three indexes of FI, BIX and HIX to characterize the water pollution condition.
Fluorescence index FI is lambda Em =470 nm and λ Ex Fluorescence intensity at 370nm λ Em =520 nm and λ Ex Ratio of fluorescence intensity at 370 nm.
The biological index BIX is lambda Ex When=310 nm, λ Em Fluorescence intensity at =380 nm λ Em Ratio of maximum intensity values between 420nm and 435 nm.
The humification index HI is lambda Ex ＝255nm，λ Em Integral intensity between 435 and 480nm with lambda Em The ratio of the integrated fluorescence intensities in the 300-345 nm region.
(3) Fluorescent region integration method
The fluorescence area integration method is a quantitative method for integrating the volume under the three-dimensional spectral line. The three-dimensional fluorescence spectrum is divided into five regions by plotting horizontal and vertical lines according to the soluble fluorescent organism (FDOM) fluorescence peak positions. The FDOM cumulative fluorescence response having similar properties is represented by the intensity integrated value of each region. Five zone definitions are shown in table 2 in combination with the parameter settings of the three-dimensional fluorescence spectrometer. Volume (Φ) below region i i ) Can pass throughAnd (5) calculating. Delta of it Ex For excitation wavelength interval (5 nm), Δ Em For the emission wavelength interval (1 nm), F is the fluorescence intensity at each excitation emission wavelength pair. Normalized fluorescence volume (Φ) i,n ) Represented asΦ i,n ＝MF i ×Φ i Wherein MF is i For each region, the multiplication factor is equal to the inverse of the ratio of each projected excitation emission area to the total projected area. The parameters of the fluorescence area integration method calculated in this work are shown in Table 2
TABLE 2 fluorescent region integration parameters
Normalized fluorescence percentage (P i,n ) Can pass throughAnd (5) calculating. In this study, the five normalized fluorescence percentages and the average intensity of the three-dimensional fluorescence spectrum are collectively referred to as the fluorescence area integral index (fluorescence regional integration indexes FRIIs).
(4) Method for extracting absorption spectrum information
Absorbance at a specific wavelength (A λ ) Can reflect the concentration of dissolved organic carbon (DOM), and can also infer the molecular weight, the degree of aromatic humification and the source of DOM. In this study, the water contamination level was characterized using 10 absorbance at different wavelengths from 220 to 440nm and 4 absorbance ratios. Absorbance at wavelengths greater than 440nm is ignored due to poor signal-to-noise ratio. Table 3 shows details of the 14 absorption spectrum indices (absorption spectral indexes ASIs).
TABLE 3 absorption spectrum index
S8, establishing a model:
COD was constructed using MATLAB2019 (Mathworks, natick, mass., USA) software Cr Six water quality indexesA support vector regression prediction model. And dividing the training set and the test set sample by adopting a k-fold Cross-Validation method. The k-fold cross-validation method randomly divides the data into k pieces, trains the model with (k-1) of the data therein, and the remaining 1 piece of data is used to evaluate the quality of the model, and loops the process over the k pieces of data in sequence. Since each data set is used as a test set during model adjustment, the overfitting problem can be avoided. Taking the CFFA spectrum information obtained in the step S7 as a prediction factor of the water quality index, forming a 33-dimensional vector as the input of the algorithm, taking the chemical analysis result of each water quality index as the target value of the algorithm, and respectively normalizing the input and output vectors of the algorithm to [0,1 ]]. And establishing a water quality prediction model by using an epsilon-support vector regression (epsilon-SVR) and a Radial Basis Function (RBF) in a LIBSVM-3.24 kit. The algorithm is shown in the formula (1),
subject toω T φ(x i )+b-z i ≤ε+ξ i ,
wherein x is i For algorithmic input, phi (x i ) Is a radial basis function, omega is a model weight coefficient, and xi * Epsilon is the marginal error (set to 0.01) for the relaxation variable, b is the intercept, and C is the penalty coefficient, indicating tolerance to outliers. In the modeling process, a grid point searching method and a cross validation method (GridSearchCV) are combined to build internal and external double-layer circulation cross validation, C and gamma in RBF cores are optimized, and an optimized model is applied to an external cross validation test set.
Model evaluation criterion
Using a decision coefficient R 2 And root mean square error RMSE as an evaluation index of the effect of the model of the present invention. The closer the training set decision coefficient and the test set decision coefficient are to 1, the higher the model correlation is, and the better the prediction effect is; training set root mean square error and measurementThe smaller the value of the root mean square error of the test set is, the higher the model precision is, the stronger the generalization capability is, the decision coefficients of the training set and the test set are calculated according to the formula (2), and the root mean square error of the training set and the test set is calculated according to the formula (3).
In the steps of 2 and 3,for the algorithm predictive value, y i For the chemical analysis result of each water quality index, +.>The average value of the true values of the water quality indexes is obtained, and N is the number of samples.
Example 1
Comparison of different algorithms input water quality prediction models
In order to evaluate the predictive performance of the epsilon-SVR model input by taking CFFA spectrum information as an algorithm, a water quality index predictive model input by taking CFFA, three-dimensional fluorescence spectrum (EEM), absorption spectrum (Abs) and Principal Component Analysis (PCA) results as the algorithm is respectively established, and R of a training set and a testing set is compared 2 And evaluating the water quality prediction model by the RMSE. When EEM and Abs are used as algorithm inputs, all three-dimensional fluorescence spectrum intensities and absorbance are used as predictors, respectively. When the PCA result is used as algorithm input, principal component analysis is carried out on EEM and Abs combined spectrum, the feature space dimension (the initial variance of 97% is reserved) is reduced, and the PCA input dimension of the six water quality index prediction models is between 7 and 10.
Table 4 compares the predicted results of the water quality index prediction model established by combining the four algorithm inputs with the epsilon-SVR. RMSE of CFFA input model is higher on all water quality datasets, especially on test setLow, R 2 Higher. This means that the CFFA input model has higher predictive performance. For organic pollution-related indicators, e.g. COD Cr 、COD Mn And BOD 5 The model of EEM input may provide good results on the training set, but the results on the test set are not satisfactory. In one aspect, the existing sample number may not contain enough information to support high-dimensional EEM inputs (9660 dimensions). On the other hand, EEM is used as algorithm input, but abundant FDOM spectrum information in the water body can be provided, so that the problem of overfitting is easy to cause, and the generalization capability of an image model is improved. For inorganic pollution related indicators such as TP, TN and NH 3 -N, abs input model has similar predictive performance as CFFA input model. Abs and NH because the uv-vis absorption spectrum is responsive to nitrate and phosphate ions in water 3 N, TN and TP have a good correlation, but these two ions do not respond in EEM. To balance the advantages of EEM and Abs input models in predicting organic and inorganic water quality metrics, the PCA results of the combined spectra of EEM and Abs were trained as algorithm inputs. The results indicate that the PCA input model has relatively low RMSE and high R 2 However, the performance of the model on the test set is far lower than that on the training set, indicating that the generalization performance of the model is poor.
Compared with the three input forms, the CFFA input has proper characteristic dimension and rich spectrum information, including spectrum information of fixed positions in three-dimensional fluorescence and absorption spectrum, area integral spectrum information and a series of empirical fluorescence indexes, so the CFFA input model has excellent prediction precision and generalization capability, as shown in table 4. In addition, the modeling time of the CFFA input model (the total time to build the six water quality index models) is comparable to that of PCA, much less than that of EEM and Abs input models. Since each dimension in the CFFA is associated with a particular water pollution characteristic, it can be used to determine the source of pollution and its nature in the body of water. In addition, the CFFA spectrum extraction method can be directly applied to the original spectrum data, and has good operability. In conclusion, the prediction model established by taking the CFFA as input has higher accuracy and precision, and simultaneously has higher modeling efficiency and operability, so that the method is more suitable for on-site water quality monitoring.
Table 4 Water quality model prediction results input by different algorithms
Example 2
In order to verify the predicting effect of the model on the water quality index, the epsilon-SVR model which takes CFFA spectrum information as algorithm input is used for predicting the water quality index of 42 unknown water samples collected in 9 th 2019. And carrying out correlation analysis on the model predicted value and the chemical analysis value by using Matlab2019 software. As can be seen from FIG. 1, COD Cr 、COD Mn 、BOD 5 、NH 3 The correlation coefficient R between the predicted value and the actual measured value of the six water quality indexes of N, TN and TP is 0.96, 0.97, 0.94, 0.95, 0.93 and 0.93 respectively, and the significance level test of p=0.05 shows that each water quality index result predicted by the CFFA support vector machine model has higher fitting degree with national standard and industry standard analysis results, and the feasibility of the three-dimensional fluorescence spectrum combined with ultraviolet-visible spectrum absorption spectrum technology in water quality pollution monitoring is proved.
It should be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the spirit and scope of the invention as defined by the appended claims and their equivalents.
Claims (7)
1. The water quality detection method combining the ultraviolet-visible light absorption spectrum technology comprises the following steps:
s1, sample collection:
collecting water body at 50cm below water surface by using a vertical sampler, standing for 30min, taking supernatant, placing into a sterile brown glass bottle, and transporting to a laboratory;
s2, chemical analysis:
shaking the sample uniformly and standing for 30min, and then taking supernatant to detect, wherein analysis items are chemical oxygen demand, permanganate index, ammonia nitrogen, total phosphorus, total nitrogen and five-day biochemical oxygen demand, and the detection method refers to relevant national standards and industry standards;
s3, three-dimensional fluorescence spectrum measurement:
measuring the three-dimensional fluorescence spectrum of the water sample by using a Hitachi F4600 type fluorescence spectrophotometer, shaking the sample uniformly before measurement, standing to room temperature, diluting the sample by using ultrapure water if the fluorescence intensity of the sample exceeds the measuring range of the instrument, and keeping the time interval between the spectral analysis and chemical analysis of the same batch of samples to be no more than 24 hours;
s4, ultraviolet visible light absorption spectrum measurement:
firstly shaking the sample uniformly, standing to room temperature, measuring the absorption spectrum of the water quality sample by using a portable ultraviolet-visible spectrophotometer, diluting the sample by ultrapure water if the fluorescence intensity of the sample exceeds the measuring range of the instrument, and keeping the time interval between the spectral analysis and the chemical analysis of the same batch of samples to be no more than 24 hours;
s5, preprocessing a three-dimensional fluorescence spectrum:
before spectral testing, the performance of the xenon lamp was verified by measuring the signal-to-noise ratio of the water raman peak at 397nm emission wavelength of water, subtracting the spectrum of pure water from the three-dimensional fluorescence spectrum of each water sample, setting the negative value generated to zero to remove the background spectrum, diluting and re-measuring the three-dimensional fluorescence spectrum with ultrapure water if the fluorescence intensity of the sample exceeds the measurement range of the spectrometer, processing the three-dimensional fluorescence spectrum with an EEMscat kit to remove rayleigh and raman scattering, interpolating the cut-off region, interpolating the three-dimensional fluorescence spectrum with the raman peak of pure water at 350nm emission wavelengthPerforming standardization treatment, wherein the spectrum intensity after treatment is Raman unit R.U., and finally, adopting an absorbance-based method to eliminate the internal filtering effect and utilizing each pair of lambda in the three-dimensional fluorescence spectrum Ex And lambda (lambda) Em Absorbance A measured at λ By means ofFluorescence intensity F to be observed obs Conversion to the corrected fluorescence intensity F corr ；
S6, ultraviolet-visible light absorption spectrum pretreatment:
the absorption spectrum measurement value is converted into absorbance A with an optical path length of 1cm by subtracting the average absorbance of 680-700 nm to reduce the influence of light scattering on the absorption spectrum by taking ultrapure water as a reference to cancel the absorption effect of the pure water and inorganic salt λ ；
S7, extracting CFFA spectrum information:
the CFFA method is composed of four kinds of spectrum information extraction results, respectively,
(1) CFPs of peak extraction method
The peak extraction method CFPs is a method for extracting spectral information based on the maximum spectral intensity and the corresponding excitation and emission wavelength ranges, and water pollution information is represented by extracting seven characteristic fluorescence peaks of fulvic acid, free tyrosine, humic acid, microbial byproducts, free tryptophan and tryptophan;
(2) FSIs (fluorescence Spectroscopy) method
The fluorescence spectrum index method FSIs uses three fluorescence spectrum indexes: extracting water pollution information by using a fluorescence index FI, a biological index BIX and a humification index HIX;
wherein the fluorescence index FI is lambda Em =470 nm and λ Ex Fluorescence intensity at 370nm λ Em =520 nm and λ Ex Ratio of fluorescence intensities at 370 nm;
the biological index BIX is lambda Ex When=310 nm, λ Em Fluorescence intensity at =380 nm λ Em Maximum intensity value between 420nm and 435nmRatio of;
the humification index HIX is lambda Ex ＝255nm，λ Em Integral intensity between 435 and 480nm with lambda Em The ratio of the integrated fluorescence intensities in the 300-345 nm region;
(3) FRIs by fluorescence area integration
The fluorescence region integration method is a quantitative method for integrating the volume under the three-dimensional spectrum line, wherein the three-dimensional fluorescence spectrum is divided into five regions by drawing horizontal lines and vertical lines according to the FDOM fluorescence peak position of the soluble fluorescent substance, and the intensity integral value of each region is used for representing FDOM accumulated fluorescence response with similar properties;
volume Φ under region i i Can pass throughPerforming public calculation;
delta of it Ex For excitation wavelength interval, delta Em For the emission wavelength interval, F is the fluorescence intensity at each excitation emission wavelength pair; normalized fluorescence volume Φ i,n Denoted as F i,n ＝MF i ×F i Wherein MF is i A multiplication factor for each region equal to the inverse of the ratio of each projected excitation emission area to the total projected area;
normalized fluorescence percentage P i,n Can pass throughCalculating; in this study, the five normalized fluorescence percentages and the average intensity of the three-dimensional fluorescence spectrum are collectively referred to as fluorescence area integral indexes (FRIs);
(4) ASIs (enzyme-specific absorption spectroscopy) information extraction method
Absorbance A at a specific wavelength λ The concentration of dissolved organic carbon can be reflected, the molecular weight, the aromatic humification degree and the DOM source can be deduced, and the water pollution degree can be represented by using 10 absorbance at different wavelengths of 220-440nm and 4 absorbance ratios;
s8, establishing a model:
will be the same in step S7With data and 6 target output COD Cr 、COD Mn 、NH 3 -N、TP、TN、BOD 5 Unified linear scaling is carried out to a range of 0-1 so as to increase the sparsity of spectrum data and improve the efficiency, and MATLAB2019 software is adopted to construct six water quality index linear support vector regression prediction models.
2. The water quality detection method combined with ultraviolet-visible light absorption spectrum technology according to claim 1, wherein the water quality detection method is characterized in that: in the step S1, the sample is collected: in each collection, 5L of water sample is collected at a depth of 50-60 cm below the water surface by using a vertical sampler, and after standing for 30-40 min, the sample supernatant is filled into a 1L sterile brown glass bottle, and then stored at 3-4 ℃ and transported to a laboratory within 2 hours.
3. The water quality detection method combined with ultraviolet-visible light absorption spectrum technology according to claim 1, wherein the water quality detection method is characterized in that: in the step S2, in the chemical analysis: all of the samples were tested analytically within 1 week of sampling.
4. The water quality detection method combined with ultraviolet-visible light absorption spectrum technology according to claim 1, wherein the water quality detection method is characterized in that: in the step S3, three-dimensional fluorescence spectrum measurement: the Hitachi F4600 type fluorescence spectrophotometer is set to have excitation wavelength Ex of 220-400 nm and sampling interval of 5nm; the emission wavelength Em is 260-520 nm, and the sampling interval is 1nm; the slit width was 10nm, and the scanning speed was 12000 nm.min-1.
5. The water quality detection method combined with ultraviolet-visible light absorption spectrum technology according to claim 1, wherein the water quality detection method is characterized in that: and in the step S4, the scanning range of the portable ultraviolet-visible spectrophotometer in the ultraviolet-visible light absorption spectrum measurement is 200-735 nm, the sampling interval is 2.5nm, and the scanning speed is 360nm/min.
6. The water quality detection method combined with ultraviolet-visible light absorption spectrum technology according to claim 1, wherein the water quality detection method is characterized in that: in the step S8, the model is built
A water quality prediction model is established by utilizing epsilon-SVR and radial basis function RBF in LIBSVM-3.24 software package in MATLAB2019 software, a relaxation variable is introduced into epsilon-SVR, the function of the model is shown in the following equation,
subject toω T φ(x i )+b-z i ≤ε+ξ i ,
where ω is the vector of model coefficients, ζ and ζ are relaxation variables representing the degree of sample outliers, boundary error ε is set to 0.01, b is the intercept, x i To input variable, z i For target output, n is the number of training samples, φ (x i ) Is a radial basis function by which x can be determined i Mapping to a high dimensional space, the penalty factor of the error term C represents tolerance to outliers, and the over-fitting of the model can be controlled, if C tends to ≡, the algorithm will over-fit, if C tends to 0, the algorithm will diverge.
7. The method for detecting water quality by combining ultraviolet-visible light absorption spectrum technology according to claim 6, wherein the method comprises the following steps: and in the step S8, optimizing C and gamma in RBF through grid search and an internal cross validation method in model establishment, applying the optimized model to a test set of external cross validation to evaluate the performance of the model, wherein the k-fold cross validation can randomly divide data into k subsets, and takes the k subset as the test set to evaluate the model obtained by training the rest k-1 subsets, wherein k in the internal and external cross validation is set to be 5.
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