光谱学与光谱分析 |
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Analysis of Spatial Distribution Characteristics of Dissolved Organic Matter in Typical Greenhouse Soil of Northern China Using Three Dimensional Fluorescence Spectra Technique and Parallel Factor Analysis Model |
PAN Hong-wei1,2,3, LEI Hong-jun2, HAN Yu-ping2, XI Bei-dou1,3*, HE Xiao-song3*, XU Qi-gong3, LI Dan3, SONG Cai-hong3 |
1. College of Water Conservancy and Architecture, Northeast Agricultural University, Harbin 150030, China 2. School of Water Conservancy, North China University of Water Conservancy and Electric Power, Zhengzhou 450011, China 3. Innovation Base of Ground Water & Environmental System Engineering, Chinese Research Academy of Environmental Science, Beijing 100012, China |
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Abstract The aim of the present work is to study the soil DOM characteristics in the vegetable greenhouse with a long-term of cultivation. Results showed that the soil DOM mainly consisted of three components, fulvic acid-like (C1), humic acid-like (C2) and protein-like (C3), with C1 as the majority one. The distribution of DOM in space was also studied. In vertical direction, C1 and C2 decreased significantly with the increase in soil depth, while C3 component decreased after increased. The humification coefficient decreased fast from 0~20 to 30~40 cm, and then increased from 30~40 to 40~50 cm. In the horizontal direction, the level of C2 component varied greatly in space, while that of C1 component changed little, and that of C3 component fell in between the above two. The change in the humification degree of each soil layer significantly varied spatially. Humification process of soil organic matter mainly occurred in the surface soil layer. In addition, the humification degree in space also changed significantly. The new ideas of this study are: (1) Analyze the composition and spatial heterogeneity of soil DOM in the vegetable greenhouse; (2) Use three dimensional fluorescence spectra technology and parallel factor analysis model successfully to quantify the components of soil DOM, which provides a new method for the soil DOM analysis.
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Received: 2014-01-12
Accepted: 2014-04-01
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Corresponding Authors:
XI Bei-dou
E-mail: xibeidou@263.net; hexs82@126.com
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[1] Hopkinson C S, Vallino J J. Nature, 2005, 433(7022): 142. [2] De Deyn G B, Quirk H, Yi Z, et al. Journal of Ecology, 2009, 97(5): 864. [3] Zeng F, Ali S, Zhang H, et al. Environmental Pollution, 2011, 159(1): 84. [4] Cleveland C C, Nemergut D R, Schmidt S K, et al. Biogeochemistry, 2007, 82(3): 229. [5] Fellman J B, D’Amore D V, Hood E, et al. Biogeochemistry, 2008, 88(2): 169. [6] Liu L, Song C, Yan Z, et al. Chemosphere, 2009, 77(1): 15. [7] Bro R, Vidal M E. Chemometrics and Intelligent Laboratory System, 2011, 106(1): 86. [8] Burke I C, Yonker C M, Parton W J, et al. Soil Science Society of America Journal, 1989, 53(3): 800. [9] Kalbitz K, Wennrich R. Science of the Total Environment, 1998, 209(1): 27. [10] Horváth Z, Ambrus , Mészáros L, et al. Journal of Environmental Science and Health, Part B, 2013, 48(8): 615. [11] Li M T, Liu J H, Zhao S J, et al. Journal of Environmental Science and Health, Part B, 2013, 48(10): 885. [12] Zhu Peng, Hua Zulin, Li Huimin. Spectroscopy and Spectral Analysis, 2012, 32(12): 3290. [13] He X S, Xi B D, Wei Z M, et al. Journal of Hazardous Materials, 2011, 190: 293. [14] Baghoth S A, Sharma S K, Amy G L. Water Research, 2011, 45(2): 797. [15] Wan S, Xi B, Xia X, et al. Bioresource Technology, 2012, 123: 439. |
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