A method that uses the ultraviolet-visible (UV-Vis) spectrum to detect organic contamination events in water distribution systems exhibits the advantages of rapid detection, low cost, and no need for reagents. The speed, accuracy, and comprehensive analysis of such a method meet the requirements for online water quality monitoring. However, the UV-Vis spectrum is easily disturbed by environmental factors that cause fluctuations of the spectrum and result in false alarms. This study proposes an adaptive method for detecting organic contamination events in water distribution systems that uses the UV-Vis spectrum based on a semi-supervised learning model. This method modifies the baseline using dynamic orthogonal projection correction and adjusts the support vector regression model in real time. Thus, an adaptive online anomaly detection model that maximizes the use of unlabeled data is obtained. Experimental results demonstrate that the proposed method is adaptive to baseline drift and exhibits good performance in detecting organic contamination events in water distribution systems.

Most organic pollutants analysis and detection in water using three-dimensional fluorescence spectroscopy usually rely on the comparison of new samples with a given set of benchmark pollutants. In our real world, however, tested samples collected from in-situ monitoring system may contain unknown organic pollutants. The conventional classification approaches were forced to choose from one of the benchmarks, which may lead to poor detection performance. In this paper, an open-set recognition approach was proposed using the wavelength-coding feature extraction network incorporated into extreme value machine (EVM) to overcome the shortages. Convolutional neural network (CNN) coupled with wavelength-coding module of spectra was designed to obtain the feature vectors from excitation-emission matrices (EEMs) data. The probability distribution for each known class can be achieved by training the EVM with the extracted feature vectors. New samples then can be identified as known or unknown according to the threshold. An online simulation pilot device was set up to simulate the in-situ monitoring conditions and verify the effectiveness of the proposed method. Twelve kinds of organic pollutants were collected and tested. Compared with conventional methods and other open-set methods, the experimental results showed that the proposed method achieved more precision regardless of whether the unknown pollutants appeared or not. The concept of open-set recognition in water quality detection and the proposed method described in this work can be applied to the online identification system with the increasing categories of pollutants, which has great potential to ensure the safety of drinking water.

Emergent contamination warning systems are critical to ensure drinking water supply security. After detecting the existence of contaminants, identifying the types of contaminants is conducive to taking remediation measures. An online classification method for contaminants, which explored abnormal fluctuation information and the correlation between 12 water quality indicators adequately, is proposed to realize comprehensive and accurate discrimination of contaminants. Firstly, the paper utilized multi-fractal detrended fluctuation analysis (MF-DFA) to select indicators with abnormal fluctuation, used multi-fractal detrended cross-correlation analysis (MF-DCCA) to measure the cross-correlation between indicators. Subsequently, the algorithm fused the abnormal probability of each indicator and constructed the abnormal probability matrix to further judge the abnormal fluctuation of indicators using D–S evidence theory. Finally, the singularity index of the cross-correlation function and the selected indicators were used to classification by cosine distance. Experiments of five chemical contaminants at three concentration levels were implemented, and analysis results show the method can weaken disturbance of water quality background noise and other interfering factors. It effectively improved the classification accuracy at low concentrations compared with another three methods, including methods using triple standard deviation threshold and single indicator fluctuation analysis-only methods without fluctuation analysis. This can be applied to water quality emergency monitoring systems to reduce contaminant misclassification.

In recent years, optical methods have been proven to be a powerful tool for m onitoring dissolved organic carbon (DOC) in natural waters. However, the effectiveness of this method in marine systems with low DOC concentrations remains to be shown. Herein, a new method based on fluorescence excitation–emission matrix spectroscopy for seawater DOC quantification is proposed. Pre-processing method is investigated to achieve a high signal to noise ratio. Peak-picking operation is then performed to obtain feature peaks. In order to combine the information from sparsely located feature peaks, sparse principal component analysis is applied to identifying important variables used in the following regression procedure. Under these conditions the result of regression analysis can be obtained readily in a given data set coupling with boosting regression tree. The method was tested on samples collected from the East China Sea. Compared to the parallel factor analysis–multivariate linear regression method, experimental results show that the proposed method achieved a more consistent regression output and indicate that the boosting regression tree has potential for DOC quantification even at low concentrations.