Abstract:Three-dimensional fluorescence technology is attracting attention in detecting emergency drinking water pollution events. However, some unsolved problems remain, such as being easily affected by water environment fluctuations, low detection rate facing low-concentration organic pollutants, etc. Therefore, in response to the demand for online monitoring, this study proposed a time series double thresholds method for anomaly detection in drinking water using three-dimensional fluorescence. This method applied principal component analysis (PCA) to extract the feature spectrum of the detected samples and trained the linear autoregressive (AR) model to predict the principal component of the water samples in the future. The eigenvalue difference was then obtained by comparing the predicted and measured ones. At the same time, combined with the change rate of the measured eigenvalues, the double threshold for time series was set to finally determine the start and end points of the pollution event to determine the entire pollution event. The research validated the proposed method by simulating high-concentration pollution events, low-concentration pollution events, and fluctuations in water background. The experimental results show that this method maintains the detection accuracy for high-concentration pollution events. Moreover, compared with conventional methods, the proposed method improved the detection performance in low-concentration pollution events and low-concentration pollution in high-interference environments. The detection accuracy is increased by 9.4% and 20.7%, respectively.
Key words:Water pollution incident detection; Three dimensional fluorescence spectroscopy; Time series double threshold; Principal component analysis (PCA); Linear autoregression (AR)
[1] LIN Jing-xue, LI Bao-zhi, REN Da-sheng, et al(林景雪,李宝志,任达生,等). Chemical Analysis and Meterage(化学分析计量), 2017, 26(1): 118.
[2] TAN Li-feng, CHU Su-chun, HUI Gao-yun, et al(谈立峰, 褚苏春, 惠高云, 等). Journal of Environment and Health(环境与健康杂志), 2018, 35(9): 827.
[3] Liu Y H, Chen Y, Feng M J, et al. Environmental Science and Pollution Research, 2021, 28(31):42339.
[4] Vizioli B D, Hantao L W,Montagner C C. Environmental Science and Pollution Research, 2021, 28(25):32823.
[5] Wu M F, Wang X, Niu G H, et al. Analytical Chemistry, 2021, 93(29):10196.
[6] Quintana J B, Carpinteiro J, RodrGuez I, et al. Journal of Chromatography A, 2004, 1024(1-2): 177.
[7] WANG Xiao-xue, WU Ba-yi(王晓雪,吴八一). Resources Economization & Environmental Protection(资源节约与环保), 2014,(3): 91.
[8] JIANG Chuan(江 川). Resources Economization & Environmental Protection(资源节约与环保), 2014,(4): 59.
[9] Peiris R H, Hallé C, Budman H, et al. Water Research, 2010, 44(1): 185.
[10] Heibati M, Stedmon C A, Stenroth K, et al. Water Research,2017, 125:1.
[11] Yu J, Cao Y, Shi F, et al. Water,2021; 13(19): 2633.
[12] Huang P, Mao T, Yu Q, et al. Opt. Express,2019, 27: 5461.
[13] Yu J,Zhang X,Hou D, et al. Journal of Spectroscopy,2017, 2017:1485048.
[14] CHEN Fang, ZHANG Xiao-yan, HUANG Ping-jie, et al(陈 方,张晓燕,黄平捷,等). Journal of Zhejiang University Agriculture and Life Sciences(浙江大学学报·农业与生命科学版), 2016, 42(3): 368.
[15] YU Shao-hui, ZHANG Yu-jun, ZHAO Nan-jing(于绍慧, 张玉钧, 赵南京). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(6): 1624.
[16] National Standard of the People's Republic of China(中华人民共和国国家标准). GB 5749—2022 Standards for Drinking Water Quality(生活饮用水卫生标准). National Health Commission of the People's Republic of China(中华人民共和国国家卫生健康委员会),2022.
[17] Yu S, Xiao X, Xu G. Journal of Applied Spectroscopy, 2016, 83(5): 786.
[18] Bro R,Smilde A K. Analytical Methods,2014,6(9): 2812.