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Study on LIF Emission Characteristics of Petroleum Pollutants in Different Soil Physical Properties |
WANG Xiang1, 2, ZHAO Nan-jing1*, YU Zhi-min2, MENG De-shuo1, 3, XIAO Xue1, MA Ming-jun1, 3, YANG Rui-fang1, HUANG Yao1, LIU Jian-guo1, 3 |
1. Key Laboratory of Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Heifei 230031, China
2. Department of Biological and Environmental Engineering, Hefei University, Hefei 230601, China
3. Wan Jiang New Industry Technology Development Center, Tongling 244000, China |
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Abstract The petroleum pollutants in soil can be rapidly detected with laser induced fluorescence technique. However, the fluorescence emission characteristic of each pollutant would be different under different soil physical properties. In order to prepare suitable soil samples rapidly in the field, the relationship between fluorescence intensity and spectral stability of soil organic pollutants, soil bulkiness, particle size and moisture was studied in this paper. The stability of the fluorescence spectra of the soil samples was better when the pressure was greater than 2 MPa, and the relative standard deviation of the fluorescence intensity of the nine soil samples with different porosity was 3.51%. The fluorescence intensity difference of the oil soil samples with different particle sizes was small, and the fluorescence spectrum RSD of 100-mesh soil samples was 2.25%. The results shows that the fluorescence spectrum is stable when the surface of the soil sample is flat and clean. The bulkiness and particle size of soil have little influence on fluorescence emission. When the soil moisture is less than 10%, the changes of fluorescence intensity is less significant , but when the humidity range is greater than 10%, the fluorescence intensity changes greatly. This paper provides references for the rapid and effective pretreatment and accurate measurement of petroleum contaminated soil samples in the field.
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Received: 2017-11-02
Accepted: 2018-03-20
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Corresponding Authors:
ZHAO Nan-jing
E-mail: njzhao@aiofm.ac.cn
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[1] Hu Wenyou, Lu Yonglong, Wang Tieyu, et al. Environmental Geochemistry and Health, 2010, 32(2): 84.
[2] Insam Heribert,Seewald Martin S A. Biology and Fertility of Soils, 2010, 46(3): 199.
[3] YANG Yong, ZHANG Jiang-wei, CHEN Kai, et al(杨 勇, 张蒋维, 陈 恺, 等). Chinese Journal of Environmental Engineering(环境工程学报), 2016, 10(1): 427.
[4] WANG Ru-gang, WANG Min, NIU Xiao-wei, et al(王如刚, 王 敏, 牛晓伟, 等). Chinese Journal of Analytical Chemistry(分析化学), 2010, 38(3): 417.
[5] HU Xiao-ming, PAN Zi-hong(胡小明, 潘自红). Applied Chemical Industry(应用化工), 2012, 41(4): 708.
[6] ZHANG Ya-nan, YANG Xing-lun, BIAN Yong-rong, et al(张亚楠, 杨兴伦, 卞永荣, 等). Chinese Journal of Analytical Chemistry(分析化学), 2016, (10): 1514.
[7] CHEN Ye, XU Xiu-yan, WANG Chao, et al(陈 烨, 许秀艳, 王 超, 等). Chinese Journal of Analytical Chemistry(分析化学), 2015, (7): 1009.
[8] Khosroshahi, Mohamad E, Rahmani M, et al. Journal of Fluorescence, 2012, 22(1): 281.
[9] Taketani F, Kanaya Y, Nakamura T, et al. Journal of Aerosol Science, 2013, 58: 1.
[10] Meng F D, Chen S Y, Zhang Y C, et al. Analytical Letters,2015, 48(13): 2090.
[11] HE Jun, DENG Hu, WU Zhi-xiang , et al(何 俊, 邓 琥, 武志翔, 等). Opto-Electronic Engineering(光电工程), 2011, 6: 105.
[12] YANG Ren-jie, DONG Gui-mei, YANG Yan-rong, et al(杨仁杰, 董桂梅, 杨延荣, 等). Optics and Precision Engineering(光学精密工程), 2016, 24(11): 2665.
[13] Ko E J, Kim K W, Park K, et al. Sensors,2010, 10(4): 3868. |
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