Application of Excitation-Emission Matrix (EEM) Fluorescence Combined With Linear SVM in Organic Pollution Monitoring of Water
DAI Yuan1, XIE Ji-zheng1, YUAN Jing1, SHEN Wei1, GUO Hong-da1, SUN Xiao-ping1, WANG Zhi-gang2*
1. Jiangsu Province Yangzhou Environmental Monitoring Center, Yangzhou 225100, China
2. College of Environmental Science and Engineering, Yangzhou University, Yangzhou 225009, China
Abstract：In view of the increasingly serious organic pollution of urban waterbodies, this paper proposes a water quality indexes prediction model based on excitation-emission matrix (EEM) fluorescence technology and a method for quickly judging the water quality category. In this study, a large number of diversified surface waters around Yangzhoucity were taken as the training sample of the model. Based on the EEM spectrum of water and linear support vector regression (LIBLINEAR), the prediction models of six water quality indexes were established, including chemical oxygen demand (CODCr) and permanganate index (CODMn) , ammonia nitrogen (NH3-N), total phosphorus (TP), total nitrogen (TN) and five-day biochemical oxygen demand (BOD5). The test results show that the determination coefficient R2 of the training set and the test set of the six index prediction models are both greater than 73%, while the correlation coefficient between the predicted value and analysis results by the national standard and industry-standard methods is greater than 0.9. Base on the prediction results of the water quality index, the water quality category could be the further judge. The recognition rate of black-odor waterbody reached 86%, and the classification accuracy rate of water bodies above category Ⅲ was 60%. The results show that the method has good accuracy and precision in predicting the water quality index through the three-dimensional fluorescence spectrum information of the waterbodies, which provides a solution for the efficient in-situ monitoring and rapid classification of water quality of urban and surrounding surface water.
Key words：EEM spectrum; Linear support vector regression; Water quality indexes; Water quality grade; In situ monitoring
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