|
|
|
|
|
|
Study on the Characteristics of Laser Induced Fluorescence Signal of Machine Oil in Soil with Changing Excitation Light Energy |
ZUO Zhao-lu1, 2, 3, ZHAO Nan-jing1, 3*, MENG De-shuo1, 3, HUANG Yao1, 2, 3, YIN Gao-fang1, 3 , MA Ming-jun1, 3, LIU Jian-guo1, 3 |
1. Key Laboratory of Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
2. University of Science and Technology of China, Hefei 230026, China
3. Key Laboratory of Optical Monitoring Technology for Environment of Anhui Province, Hefei 230031, China |
|
|
Abstract The exploration and development of petroleum is spread all over the country, and the application of its products is inseparable from the industrial and agricultural production and the daily life of the people. In the use of petroleum and petroleum products, they leak into the soil and accumulate, which will destroy the ecological environment. Laser-Induced fluorescence (LIF) is an important method to detect petroleum hydrocarbon organic pollutants in soil. Laser pulse energy is an important experimental condition of LIF. It has a significant impact on detection sensitivity and stability. In order to explore the characteristics of LIF signal of petroleum hydrocarbons in soil with the pulse energy of excitation light, taking oil as an example, soil samples with machineoil concentration of 0.5%~6% were prepared in the laboratory. The Nd∶YAG laser was used as an excitation source with a wavelength of 266 nm. The fluorescence spectrum of the oily soil at different energy densities was obtained by changing the pulse energy of the 266 nm laser. The experimental results showed that the fluorescence intensity of the oil in the soil had a good linear relationship with its concentration at different energy densities. The fluorescence intensity of the machineoil in the soil itself and in the soil increased as the laser pulse energy increased. The experiment found that as the laser energy density decreased, the average relative error of the LIF system when measuring the oil first decreased first and then increased. The reason was that when the laser energy density was less than a certain range, the signal-to-noise ratio of the signal decreased. Therefore, the average relative error of the measurement gradually increased; when the laser energy density was larger than a certain range, although the signal-to-noise ratio of the signal increased, it had gradually exceeded the optimal measurement range of the system, so the average relative error of the measurement gradually increased. When the laser energy density wasin 2.4~4.0 mJ·cm-2, the fluorescence intensity of the oil in the soil increased linearly with the laser pulse energy density, and the measurement error of the machine oil concentration was less than 2.5%. At this time, the system limited the detection of machineoil to between 200~300 mg·kg-1. When the energy density was greater than 4.0 mJ·cm-2, the increase of the fluorescence intensity of the machineoil was significantly reduced, and the measurement error also increased. Therefore, taking into account the system to measure the average relative error of the machineoil in the soil and the measurement limit, the laser pulse energy was preferably 2.4~4.0 mJ·cm-2. In this paper, the characteristics of the fluorescence signal of the machine oil in the soil as a function of the excitation light energy were studied. The method could be extended to study the fluorescence signals of other petroleum hydrocarbons in soil. This paper provided a reference for the formation of LIF system to measure petroleum hydrocarbons in the site and select better laser energy conditions.
|
Received: 2019-01-24
Accepted: 2019-04-19
|
|
Corresponding Authors:
ZHAO Nan-jing
E-mail: njzhao@aiofm.ac.cn
|
|
[1] LI Dan, FENG Wei-wei, CHEN Ling-xin, et al(李 丹,冯巍巍,陈令新,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(2): 442.
[2] LI Ai-min,LIAN Zeng-yan,YANG Ren-jie, et al(李爱民,连增艳,杨仁杰,等). Environmental Chemistry(环境化学), 2018, 37(4): 910.
[3] Eshelman E, Daly M G, Slater G, et al. Planetary and Space Science, 2015, 4:119.
[4] Utkin A B, Felizardo R, Gameiro C, et al. Proc SPIE, 2014, 9286: 928609.
[5] WANG Yu-tian, ZHAO Xu, XU Jin, et al(王玉田,赵 旭,徐 进,等). Chinese Journal of Lasers(中国激光), 2016, 5(43): 515001-1.
[6] Kurata S, Fujitomi Y, Horioka Y, et al. Bunseki Kagaku, 2014, 63: 649.
[7] MU Tao-tao, CHEN Si-ying, ZHANG Yin-chao, et al(牟涛涛,陈思颖,张寅超,等). Analytical Letters,2016, 49(8):1233.
[8] Fan Z, Schroeder O, Krahl J, et al. Land Bauforschung,2015, 65(1): 1.
[9] YANG Ren-jie, DONG Gui-mei, YANG Yan-rong, et al(杨仁杰,董桂梅,杨延荣). Optics and Precision Engineering(光学精密工程), 2016, 11: 2665.
[10] WU Wei-xing(吴维兴). Journal of Anhui Agri. Sci.(安徽农业科学),2014,42(25):8563. |
[1] |
ZHENG Hong-quan, DAI Jing-min*. Research Development of the Application of Photoacoustic Spectroscopy in Measurement of Trace Gas Concentration[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 1-14. |
[2] |
CHENG Jia-wei1, 2,LIU Xin-xing1, 2*,ZHANG Juan1, 2. Application of Infrared Spectroscopy in Exploration of Mineral Deposits: A Review[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 15-21. |
[3] |
FAN Ping-ping,LI Xue-ying,QIU Hui-min,HOU Guang-li,LIU Yan*. Spectral Analysis of Organic Carbon in Sediments of the Yellow Sea and Bohai Sea by Different Spectrometers[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 52-55. |
[4] |
LI Jie, ZHOU Qu*, JIA Lu-fen, CUI Xiao-sen. Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 125-133. |
[5] |
BAI Xi-lin1, 2, PENG Yue1, 2, ZHANG Xue-dong1, 2, GE Jing1, 2*. Ultrafast Dynamics of CdSe/ZnS Quantum Dots and Quantum
Dot-Acceptor Molecular Complexes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 56-61. |
[6] |
XU Tian1, 2, LI Jing1, 2, LIU Zhen-hua1, 2*. Remote Sensing Inversion of Soil Manganese in Nanchuan District, Chongqing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 69-75. |
[7] |
YANG Cheng-en1, 2, LI Meng3, LU Qiu-yu2, WANG Jin-ling4, LI Yu-ting2*, SU Ling1*. Fast Prediction of Flavone and Polysaccharide Contents in
Aronia Melanocarpa by FTIR and ELM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 62-68. |
[8] |
WANG Fang-yuan1, 2, HAN Sen1, 2, YE Song1, 2, YIN Shan1, 2, LI Shu1, 2, WANG Xin-qiang1, 2*. A DFT Method to Study the Structure and Raman Spectra of Lignin
Monomer and Dimer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 76-81. |
[9] |
LIU Zhen1*, LIU Li2*, FAN Shuo2, ZHAO An-ran2, LIU Si-lu2. Training Sample Selection for Spectral Reconstruction Based on Improved K-Means Clustering[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 29-35. |
[10] |
YANG Chao-pu1, 2, FANG Wen-qing3*, WU Qing-feng3, LI Chun1, LI Xiao-long1. Study on Changes of Blue Light Hazard and Circadian Effect of AMOLED With Age Based on Spectral Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 36-43. |
[11] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[12] |
ZHENG Pei-chao, YIN Yi-tong, WANG Jin-mei*, ZHOU Chun-yan, ZHANG Li, ZENG Jin-rui, LÜ Qiang. Study on the Method of Detecting Phosphate Ions in Water Based on
Ultraviolet Absorption Spectrum Combined With SPA-ELM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 82-87. |
[13] |
XU Qiu-yi1, 3, 4, ZHU Wen-yue3, 4, CHEN Jie2, 3, 4, LIU Qiang3, 4 *, ZHENG Jian-jie3, 4, YANG Tao2, 3, 4, YANG Teng-fei2, 3, 4. Calibration Method of Aerosol Absorption Coefficient Based on
Photoacoustic Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 88-94. |
[14] |
LI Xin-ting, ZHANG Feng, FENG Jie*. Convolutional Neural Network Combined With Improved Spectral
Processing Method for Potato Disease Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 215-224. |
[15] |
XING Hai-bo1, ZHENG Bo-wen1, LI Xin-yue1, HUANG Bo-tao2, XIANG Xiao2, HU Xiao-jun1*. Colorimetric and SERS Dual-Channel Sensing Detection of Pyrene in
Water[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 95-102. |
|
|
|
|