Volume 9 Issue 6
Nov.  2019
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YAN Zongcheng, GAO Hongjie, GUO Xujing, WANG Siyu, YU Huibin, XIN Xin. Study on UV-vis spectra of dissolved organic matter from sediment interstitial water in Moguhu Lake[J]. Journal of Environmental Engineering Technology, 2019, 9(6): 685-691. doi: 10.12153/j.issn.1674-991X.2019.05.160
Citation: YAN Zongcheng, GAO Hongjie, GUO Xujing, WANG Siyu, YU Huibin, XIN Xin. Study on UV-vis spectra of dissolved organic matter from sediment interstitial water in Moguhu Lake[J]. Journal of Environmental Engineering Technology, 2019, 9(6): 685-691. doi: 10.12153/j.issn.1674-991X.2019.05.160

Study on UV-vis spectra of dissolved organic matter from sediment interstitial water in Moguhu Lake

doi: 10.12153/j.issn.1674-991X.2019.05.160
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  • Corresponding author: Siyu WANG E-mail: wangsy@craes.org.cn
  • Received Date: 2019-02-27
  • Publish Date: 2019-11-20
  • The principal component analysis and cluster analysis were used to analyze the UV-vis spectra of sediment interstitial water in Moguhu Lake, to identify the spectral components and the main control factors, and to study the composition, humification degree and spatial variation of dissolved organic matter (DOM). Seven sampling points were selected from Moguhu Lake to extract DOM samples of sediment interstitial water. The research showed that DOM contained lignin and quinine, carboxylic acid groups, polyalkyl humus and so on. There were significant differences in the two regions of the periphery and deep lake area. Through the analysis of their UV-vis spectra, six spectral indices (Sr, SUVA254, E2/E3, E2/E4, E253/E203, A2/A1) with good correlation could be deduced to characterize the molecular weight and humification level of DOM, and the results indicated that the molecular weight and humification of DOM in the peripheral area were all higher than those in the deep lake area, and showed a decreasing trend with increasing water level in the area. The results of variable cluster analysis showed that the index (E253/E203, SUVA254, A2/A1), which was positively correlated with humification level, was more representative when characterizing DOM humification. Sample cluster analysis divided the sampling points into two categories, which were consistent with the principal component analysis results. Studies showed that the degree of spatial variability could be judged to some extent by the degree of DOM humification.

     

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