|
|
|
|
|
|
Study on Characteristics of Decomposition and Fluorescence Emission of PAHs in Soil by Pulsed Ultraviolet Laser |
HUANG Yao1,2,3, ZHAO Nan-jing1,3*, MENG De-shuo1,3, ZUO Zhao-lu1,2,3, CHEN Yu-nan1,2,3, CHEN Xiao-wei1,2,3, YIN Gao-fang1,3 |
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 Polycyclic aromatic hydrocarbons (PAHs) are a group of persistent organic pollutants (POPs) which are mutagenic, carcinogenic and teratogenic. They are widely distributed in air, water and soil. Once PAHs enter the soil, they remain in the soil for a long time. PAHs are concentrated in the soil. They can enter the human body in many ways and pose a threat to human health. Therefore, it is necessary to monitor PAHs in soil. Now, traditional detection methods are cumbersome and time-consuming, which is not conducive to the widely rapid detection of PAHs in the contaminated sites. The method based on laser-induced fluorescence spectroscopy can quickly identify and detect organic pollutants in the soil. However, PAHs are volatile and can be degraded by ultraviolet light, so the selection of UV laser energy is very important. In this work,a 266nm laser-induced fluorescence system is established in the laboratory. Anthracene, pyrene, and phenanthrene are used to investigate the decomposition and fluorescence spectra of PAHs under different laser energies. The results showed that when the energy density of the laser changed, the peak positions of the fluorescence center did not shift, but the relative standard deviations of the maximum intensity at the fluorescence peaks of three PAHs decreased firstly and increased then. When the energy density was 8.54 mJ·cm-2, the relative standard deviations of the three PAHs in 10 spectral measurements were the largest, and the relative standard deviations of the fluorescence peak intensities of anthracene, pyrene and phenanthrene reach the minimum value at 1.72,1.00 and 1.47 mJ·cm-2. The decomposition rates were 59.3%, 69.8% and 63.6% for anthracene, pyrene and phenanthrene at 100 s, respectively. At higher energies, three PAHs decompose rapidly. Compared with the other two PAHs, pyrene was more prone to photodegradation and thermal decomposition, and the relative standard deviation of fluorescence peak intensity was also higher than that of anthracene and phenanthrene. For anthracene, when the laser energy density was 1.72 mJ·cm-2, the decomposition rate was close to 0 at 10 s and 12.8% at 100 s, and the relative standard deviation of the fluorescence peak intensity was the lowest. When the laser energy density was reduced to 0.88 mJ·cm-2, the decomposition of anthracene in 100s was almost negligible. For pyrene, when the laser energy density dropped below 1.00 mJ·cm-2, the decomposition tended to be consistent, and the decomposition rate was 47.3%~47.4% at 100 s. For phenanthrene, when the energy density of the laser was lower than 1.47 mJ·cm-2, the decomposition rates no longer decreased, and the decomposition rates were 36.8%~38.6%. Pyrene and phenanthrene still decompose in low energy density.
|
Received: 2019-07-15
Accepted: 2019-12-02
|
|
Corresponding Authors:
ZHAO Nan-jing
E-mail: njzhao@aiofm.ac.cn
|
|
[1] Bi X, Luo W, Gao J, et al. Science of the Total Environment, 2016, 556: 12.
[2] Li G, Lang Y, Gao M, et al. Marine Pollution Bulletin, 2014, 84(1-2): 418.
[3] Suman S, Sinha A, Tarafdar A. Science of the Total Environment, 2016, 545: 353.
[4] Wang C, Wu S, Zhou S, et al. Pedosphere, 2017, 27(1): 17.
[5] Suman S, Sinha A, Tarafdar A. Science of the Total Environment, 2016, 545: 353.
[6] Amjadian K, Sacchi E, Mehr M R. Environmental Monitoring and Assessment, 2016, 188(11): 605.
[7] Humel S, Schmidt S N, Sumetzberger-Hasinger M, et al. Environmental Science & Technology, 2017, 51(14): 8017.
[8] Kusmierz M, Oleszczuk P, Kraska P, et al. Chemosphere, 2016, 146: 272.
[9] Wang X T, Miao Y, Zhang Y, et al. Science of the Total Environment, 2013, 447(1): 80.
[10] Wang C, Wu S, Zhou S, et al. Pedosphere, 2017, 27(1): 17.
[11] Suman S, Sinha A, Tarafdar A. Science of the Total Environment, 2016, 545: 353.
[12] YANG Ren-Jie, SHANG Li-Ping, BAO Zhen-Bo, et al. Spectroscopy and Spectral Analysis, 2011, 31(8): 2148.
[13] He Jun, Shang Liping, Deng Hu, et al. Opto-Electronic Engineering, 2014, 41(9): 51.
[14] Okparanma R N, Mouazen A M. Applied Spectroscopy Reviews, 2013, 48(6): 458.
[15] Wang S T, Zheng Y N, Wang Z F, et al. Actaphotonicasinica, 2017, 46(9):75.
[16] Abdel-Shafy H I, Mansour M S M. Egyptian Journal of Petroleum, 2016, 25(1): 107.
[17] Schade W, Bublitz J. Environmental Science & Technology, 1996, 30(5): 1451.
[18] Miller J S, Olejnik D. Water Research, 2001, 35(1): 233.
[19] Sanches S, Leitao C, Penetra A, et al. Journal of Hazardous Materials, 2011, 192(3): 1458.
[20] Xu C, Dong D, Meng X, et al. Journal of Environmental Sciences, 2013, 25(3): 569. |
[1] |
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. |
[2] |
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. |
[3] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
[4] |
HAN Xue1, 2, LIU Hai1, 2, LIU Jia-wei3, WU Ming-kai1, 2*. Rapid Identification of Inorganic Elements in Understory Soils in
Different Regions of Guizhou Province by X-Ray
Fluorescence Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 225-229. |
[5] |
MENG Shan1, 2, LI Xin-guo1, 2*. Estimation of Surface Soil Organic Carbon Content in Lakeside Oasis Based on Hyperspectral Wavelet Energy Feature Vector[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3853-3861. |
[6] |
LI Qi-chen1, 2, LI Min-zan1, 2*, YANG Wei2, 3, SUN Hong2, 3, ZHANG Yao1, 3. Quantitative Analysis of Water-Soluble Phosphorous Based on Raman
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3871-3876. |
[7] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
[8] |
QI Guo-min1, TONG Shi-qian1, LIN Xu-cong1, 2*. Specific Identification of Microcystin-LR by Aptamer-Functionalized Magnetic Nanoprobe With Laser-Induced Fluorescence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3813-3819. |
[9] |
XIE Peng, WANG Zheng-hai*, XIAO Bei, CAO Hai-ling, HUANG Yi, SU Wen-lin. Hyperspectral Quantitative Inversion of Soil Selenium Content Based on sCARS-PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3599-3606. |
[10] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[11] |
AN Bai-song1, 2, WANG Xue-mei1, 2*, HUANG Xiao-yu1, 2, KAWUQIATI Bai-shan1, 2. Hyperspectral Estimation of Soil Lead Content Based on Random Frog Band Selection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3302-3309. |
[12] |
KONG De-ming1, LIU Ya-ru1, DU Ya-xin2, CUI Yao-yao2. Oil Film Thickness Detection Based on IRF-IVSO Wavelength Optimization Combined With LIF Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2811-2817. |
[13] |
DENG Yun1, 2, NIU Zhao-wen1, 2, FENG Qi-yao1, 2, WANG Yu1, 2*. A Novel Hyperspectral Prediction Model of Organic Matter in Red Soil Based on Improved Temporal Convolutional Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2942-2951. |
[14] |
CAI Hai-hui1, ZHOU Ling2, SHI Zhou3, JI Wen-jun4, LUO De-fang1, PENG Jie1, FENG Chun-hui5*. Hyperspectral Inversion of Soil Organic Matter in Jujube Orchard
in Southern Xinjiang Using CARS-BPNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2568-2573. |
[15] |
XIA Chen-zhen1, 2, 3, JIANG Yan-yan4, ZHANG Xing-yu1, 2, 3, SHA Ye5, CUI Shuai1, 2, 3, MI Guo-hua5, GAO Qiang1, 2, 3, ZHANG Yue1, 2, 3*. Estimation of Soil Organic Matter in Maize Field of Black Soil Area Based on UAV Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2617-2626. |
|
|
|
|