| 光谱学与光谱分析 |
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| Characterization and Identification of Spilled Oils Using Synchronous Fluorescence Spectroscopy of Concentrated Solutions |
| ZHU Li-li1,2,3, ZHANG Qian-qian1,2*, AN Wei4, WANG Chun-yan5 |
1. College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao 266100, China
2. Key Laboratory of Marine Chemistry Theory and Technology of the Ministry of Education, Ocean University of China, Qingdao 266100, China
3. Shouguang Environmental Monitoring Station, Weifang 262700, China
4. China Offshore Environmental Service LTD., Tianjin 300452, China
5. College of Physics and Electronics, Weifang College, Weifang 261061, China |
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Abstract In the present paper, a tentative study was made to identify spilled oils using synchronous fluorescence spectrum (SFS). Seventeen crude oil samples from different areas in China were studied. SFS of oil solutions with three concentrations, which were 5, 500 and 5 000 mg·L-1, were analyzed. The number and position of SFS peaks were different for the different concentrations of oil solutions. Oil solutions of 5, 500 and 5 000 mg·L-1 had characteristic peaks in excitation wavelength 260～460, 290～550 and 400～850 nm, respectively. In order to study the weathering effect on SFS, four crude oils were also set outdoors for weathering experiment and SFS of weathered oils after 1, 3, 5, 10, 15 and 35 d were determined. All the SFS of original and weathered oils were analyzed using principal component analysis (PCA). The principal component scores charts showed that the SFS of the oil solution of 500 mg·L-1 had better distinguishing ability than the other two concentrations. Thus SFS of 500 mg·L-1 between 290 and 600 nm were selected as spectrum feature of crude oils and used to build oil fingerprint data base for identifying crude oils. Taking one oil sample with unknown source among the seventeen crude oils as a case study, SFS were analyzed by PCA to find the origin of the unknown crude oil. The results draw a conclusion that SFS of high concentration solution (500 mg·L-1) may become a useful means in spilled oils identification.
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Received: 2010-05-15
Accepted: 2010-08-18
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Corresponding Authors:
ZHANG Qian-qian
E-mail: qqzhang@ouc.edu.cn
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[1] Wang Z, Fingas M. Journal of Chromatography A, 1995, 712(2): 321.
[2] API. Methods for Determination of Petroleum Hydrocarbons in Soil, Reversion 1, American Petroleum Institute, Washington DC, 1992.
[3] Wang Z, Fingas M. Journal of Chromatography A, 1997, 774: 51.
[4] Malmquist L M V, Olsen R R. Journal of Chromatography A, 2007, 1164: 262.
[5] Wang Z, Fingas M, Lambert P. Journal of Chromatography A, 2002, 1038: 201.
[6] Wang Z, Fingas M. Environment Forensics, 2002, 3: 251.
[7] Saravanabhavan G, Helferty A, Hodson P V, et al. Journal of Chromatography A, 2007, 1156: 124.
[8] PANG Shi-ping, ZHENG Xiao-ling, HE Ying, et al(庞士平，郑晓玲，何 鹰，等). Advances In Marine Science(海洋科学进展), 2007, 25(1): 91.
[9] WANG Li, HE Ying, WANG Yan-ping, et al(王 丽，何 鹰，王颜萍，等). Marine Environmental Science(海洋环境科学), 2004, 23(2): 58.
[10] Sikorska E, Gorecki T, Khmelinskii I V. Food Chemistry, 2005, 89: 217.
[11] ZHAO Yun-ying, MA Yong-an(赵云英，马永安). Marine Environmental Science(海洋环境科学), 1997, 16(2): 29.
[12] Li J, Fuller S, Cattle J, et al. Analytica Chimica Acta, 2004, 514: 51.
[13] Christensen J H, Tomasi G. Journal of Chromatography A, 2007, 1169: 1.
[14] CHEN Shu-mei, ZHAO Yun-ying(陈淑梅，赵云英). Journal of Dalian Fisheries University(大连水产学院学报), 2000, 15(1): 35.
[15] ZHAO Yun-ying, YANG Qing-xiao(赵云英，杨庆霄). Marine Environmental Science(海洋环境科学), 1997, 16(1): 45.
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