|
|
|
|
|
|
On-Line Monitoring and Analysis Method of Three-Dimensional Fluorescence Spectrum in Urban Domestic Sewage Treatment Process |
YANG Jin-qiang1, 2, 4, ZHAO Nan-jing1, 4*, YIN Gao-fang1, 4 , YU Zhi-min2, GAN Ting-ting1, 4, WANG Xiang1, 3, 4, CHEN Min1, 3, 4, FENG Chun1, 3, 4 |
1. Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optic and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
2. Department of Biological and Environmental Engineering, Hefei University, Hefei 230601, China
3. University of Science and Technology of China, Hefei 230026, China
4. Key Laboratory of Optical Monitoring Technology for Environment, Hefei 230031, China |
|
|
Abstract Three-dimensional fluorescence spectrum (3D-EEMs) and principal component analysis (PCA) were used. The three-dimensional fluorescence spectrum of urban sewage was divided into four spectral regions: aromatic proteins, microbial metabolites, humic acids and fulvic acids. Determine the regional principal component contribution rate of the lambda λ. Calculate the value of the first principal component area of each area, to establish it with water chemical oxygen demand (COD) and total nitrogen (TN), studies the urban sewage treatment effect rapid analysis and evaluation method.The results show that the urban sewage fluorescent material is mainly composed of aromaticity protein material, microbial metabolites, humic acid and fulvic acid material, regional fluorescent distribution is different, the material in the process of sewage treatment aromaticity protein material area spectral changes obviously, and the microorganism metabolites humic acid and fulvic acid material area spectral changes smaller, Spectral regions the value of the first principal component area and water body has a good correlation between COD and TN, aromaticity of protein material spectrum coefficient of the value of the first principal component areas related to the COD reached 97.63%, aromaticity protein material and the sum of the value of the first principal component area microbial metabolites and humic acid and fulvic acid material ratio of the sum of the value of the first principal component area (Yp/Yf) and TN correlation coefficient reached 94.02%.By combining the three-dimensional fluorescence spectrum of water with the principal component analysis method, the dimensionless extraction of fluorescence spectrum information of each process of sewage treatment is realized, the overlapping of fluorescence peaks and redundancy of spectral information of each substance are avoided. Through the spectral characteristics of each substance in the water, the spectrum is divided into different material regions, the first principal component region value of each region is obtained, which improves the accuracy of substance identification and effectively solves the problem of spectral information identification of each substance. By using the correlation analysis of the first principal component area value of aromatic protein spectrum and Yp/Yf and the conventional water quality indexes COD and TN, it provides a real-time and effective method for monitoring the quality of domestic sewage and solves the problem that the sewage treatment process is difficult to accurately monitor in real-time. Therefore, the three-dimensional fluorescence spectrum combined with principal component analysis method can be used for fast discrimination of urban domestic sewage treatment process, providing a new fast on-line monitoring and analysis method for water quality monitoring, process optimization and treatment effect evaluation in the sewage treatment process.
|
Received: 2019-07-03
Accepted: 2019-11-20
|
|
Corresponding Authors:
ZHAO Nan-jing
E-mail: njzhao@aiofm.ac.cn
|
|
[1] SHI Jun, WANG Zhi-gang, XIAO Yong-hui, et al(施 俊, 王志刚, 肖永辉, 等). Journal of Atmospheric and Environmental Optics(大气与环境光学学报), 2012, 7(1): 31.
[2] LI Lu-lu, JIANG Tao, LU Song, et al(李璐璐, 江 韬, 卢 松, 等). Environmental Sciences(环境科学), 2014, 35(9): 3408.
[3] OUYANG Er-ming, ZHANG Xi-hui, WANG Wei, et al(欧阳二明, 张锡辉, 王 伟,等). Water Resources Protection(水资源保护), 2007, (3): 56.
[4] Jacquin C, Lesage G, Traber J, et al. Water Res., 2017, 118: 82.
[5] Saadi I, Borisover M, Armon R, et al. Chemosphere, 2006, 63(3): 530.
[6] Zhou Jie, Wang Junjian, Antoine Baudon, et al. Journal of Environmental Quality, 2013, 42(3):925.
[7] Yang J, Zhang D, Frangi A F, et al. IEEE Trans PAMI, 2004 , 26(1): 131.
[8] ZHAO Nan-jing, LIU Wen-qing, CUI Zhi-cheng, et al(赵南京, 刘文清, 崔志成, 等). Acta Optica Sinica(光学学报), 2005, 25(5): 687.
[9] Green S A, Blough N V. Limnology and Oceanography, 1994, 39(8): 1903.
[10] YAO Lu-lu, TU Xiang, YU Hui-bin,et al(姚璐璐,涂 响,于会彬,等). Chinese Journal of Environmental Engineering(环境工程学报),2013,7(2): 411.
[11] CHEN Mao-fu, WU Jing, LÜ Yan-li, et al(陈茂福, 吴 静, 律严励, 等). Acta Optica Sinica(光学学报), 2008, 28(3): 578.
[12] Ohno T, Bro R. Soil Science Society of America Journal, 2006, 70: 2028.
[13] Cory R M, Mcknight D M. Environmental Science & Technology, 2005, 39: 8142.
[14] Stedmon C A, Markager S. Estuarine Coastal and Shelf Science, 2003, 57(5): 973. |
[1] |
LEI Hong-jun1, YANG Guang1, PAN Hong-wei1*, WANG Yi-fei1, YI Jun2, WANG Ke-ke2, WANG Guo-hao2, TONG Wen-bin1, SHI Li-li1. Influence of Hydrochemical Ions on Three-Dimensional Fluorescence
Spectrum of Dissolved Organic Matter in the Water Environment
and the Proposed Classification Pretreatment Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 134-140. |
[2] |
GU Yi-lu1, 2,PEI Jing-cheng1, 2*,ZHANG Yu-hui1, 2,YIN Xi-yan1, 2,YU Min-da1, 2, LAI Xiao-jing1, 2. Gemological and Spectral Characterization of Yellowish Green Apatite From Mexico[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 181-187. |
[3] |
WANG Cai-ling1,ZHANG Jing1,WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
[4] |
SONG Yi-ming1, 2, SHEN Jian1, 2, LIU Chuan-yang1, 2, XIONG Qiu-ran1, 2, CHENG Cheng1, 2, CHAI Yi-di2, WANG Shi-feng2,WU Jing1, 2*. Fluorescence Quantum Yield and Fluorescence Lifetime of Indole, 3-Methylindole and L-Tryptophan[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3758-3762. |
[5] |
YANG Ke-li1, 2, PENG Jiao-yu1, 2, DONG Ya-ping1, 2*, LIU Xin1, 2, LI Wu1, 3, LIU Hai-ning1, 3. Spectroscopic Characterization of Dissolved Organic Matter Isolated From Solar Pond[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3775-3780. |
[6] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[7] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[8] |
FANG Zheng, WANG Han-bo. Measurement of Plastic Film Thickness Based on X-Ray Absorption
Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3461-3468. |
[9] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[10] |
JIA Zong-chao1, WANG Zi-jian1, LI Xue-ying1, 2*, QIU Hui-min1, HOU Guang-li1, FAN Ping-ping1*. Marine Sediment Particle Size Classification Based on the Fusion of
Principal Component Analysis and Continuous Projection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3075-3080. |
[11] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[12] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
[13] |
JIA Hao1, 3, 4, ZHANG Wei-fang1, 3, LEI Jing-wei1, 3*, LI Ying-ying1, 3, YANG Chun-jing2, 3*, XIE Cai-xia1, 3, GONG Hai-yan1, 3, DING Xin-yu1, YAO Tian-yi1. Study on Infrared Fingerprint of the Classical Famous
Prescription Yiguanjian[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3202-3210. |
[14] |
CAO Qian, MA Xiang-cai, BAI Chun-yan, SU Na, CUI Qing-bin. Research on Multispectral Dimension Reduction Method Based on Weight Function Composed of Spectral Color Difference[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2679-2686. |
[15] |
JIA Yu-ge1, YANG Ming-xing1, 2*, YOU Bo-ya1, YU Ke-ye1. Gemological and Spectroscopic Identification Characteristics of Frozen Jelly-Filled Turquoise and Its Raw Material[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2974-2982. |
|
|
|
|