|
|
|
|
|
|
Study on the Characteristics and Mechanism of Visible and Near Infrared Reflectance Spectra of Soil Heavy Metals |
CHENG Hang1,2, WAN Yuan3, CHEN Yi-yun2,4,5*, WAN Qi-jin1,6,7*, SHI Tie-zhu8, SHEN Rui-li9, GUO Kai2, HU Jia-meng2 |
1. School of Chemistry and Environmental Engineering,Wuhan Institute of Technology,Wuhan 430073, China
2. School of Resource and Environmental Science,Wuhan University,Wuhan 430079, China
3. College of Urban and Environmental Sciences,Hubei Normal University,Huangshi 435002, China
4. State Key Laboratory of Soil and Sustainable Agriculture,Nanjing 210008, China
5. Suzhou Institute of Wuhan University,Suzhou 215123, China
6. Key Laboratory for Green Chemical Process of Ministry of Education,Wuhan Institute of Technology, Wuhan 430073, China
7. Hubei Key Lab of Novel Reactor & Green Chemical Technology,Wuhan 430073, China
8. College of Life Sciences, Shenzhen University,Shenzhen 518060, China
9. Hubei Academy of Environmental Sciences,Wuhan 430072, China |
|
|
Abstract In this paper, the reflectance spectral features of chromium(CrCl3), copper chloride(CuCl2) and zinc chloride (ZnCl2) were measured by using visible and near-infrared reflectance spectroscopy (VNIRS). Thus, the spectra features integrating with the state of electrons arranged of heavy metal elements, and combined with the Crystal Field Theory were used to analyze where and why the wavebands of spectral features occurred. With the soil samples collected from Daye City, CrCl3, CuCl2 and ZnCl2 were added to them with different concentrations, and measured their VNIRS to study the impacts of different concentrations of heavy metals on soil reflectance spectra. Besides, the spectral data were transformed via different spectra pre-processing methods to explore the linear correlations between heavy metal concentrations and the reflectance spectra of the soil samples, and to study on where and why the Pearson’s significantly correlated wavebands (p<0.05) appeared and its potential mechanism. The results showed that the reflectance spectra in the range of visible and shortwave near-infrared of heavy metal compounds are related to the whether the 3d orbits of heavy metal elements filled by electron or not. The reflectance spectra of soil samples have been affected by the heavy metal compounds which were added, and negative correlations were revealed between the reflectance spectra data and the heavy metal concentrations. The maximum of the negative values of Pearson correlation coefficient were -0.788, -0.880, -0.824, respectively. There were some changes with the linear correlation between the heavy metal concentrations and the reflectance spectra, and the information of Pearson’s significantly correlated wavebands (p<0.05) became richer and more obvious after the reflectance spectra being pre-processed. This research indicated that the VNIRS of heavy metals are closely related to their electronic structure; the high concentrations of heavy metals in soils could be detected by the VNIRS technique, which has great potential in predicting soil heavy metal contents with rapid and efficient, non-destructive and cost-effective. Based on the VNIRS characteristics of some heavy metals, this paper integrated with the Crystal Field Theory to provide a theoretical basis and experimental reference for qualitative and quantitative analysis of reflectance spectroscopy of soil heavy metals.
|
Received: 2017-03-30
Accepted: 2017-07-21
|
|
Corresponding Authors:
CHEN Yi-yun, WAN Qi-jin
E-mail: chenyy@whu.edu.cn;qijinwan@wit.edu.cn
|
|
[1] GONG Yu-ling,FENG Yong-jun,LI Jin-tao,et al(巩玉玲,冯永军,李进涛,等). Journal of Hebei Agricultural Sciences(河北农业科学),2015,(5):74.
[2] Chen Yiyun,Liu Yaolin,Liu Yanfang,et al. International Journal of Environmental Research and Public Health,2012,9(5):1874.
[3] Shi Tiezhu,Liu Huizeng,Wang Junjie. Environmental Science & Technology,2014,48(11):6264.
[4] WANG Lu,LIN Qi-zhong,JIA Dong,et al(王 璐,蔺启忠,贾 东,等). Journal of Remote Sensing(遥感学报),2007,11(6):906.
[5] XU Bin-bin(徐彬彬). Soils(土壤),2000,32(6):281.
[6] HE Jun-liang,ZHANG Shu-yuan,ZHA Yong,et al(贺军亮,张淑媛,查 勇,等). Remote Sensing Technology and Application(遥感技术与应用),2015,30(3):407.
[7] WU Yong-feng,DONG Yi-wei,HU Xin,et al(武永峰,董一威,胡 新,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2015,35(12):3416.
[8] Liu Yaolin,Jiang Qinghu,Shi Tiezhu,et al. Acta Agriculturae Scandinavica,Section B (Soil & Plant Science), 2014,64(3):267.
[9] Liu Yaolin,Chen Yiyun. International Journal of Remote Sensing,2012,33(18):5954.
[10] XIE Xian-li,SUN Bo,HAO Hong-tao(解宪丽,孙 波,郝红涛). Acta Pedologica Sinica (土壤学报),2007,44 (6):982.
[11] Liu Yaolin,Chen Yiyun. Soil and Sediment Contamination:An International Journal,2012,21(8):951.
[12] YANG Ya-na,PAN Tao,LI Min-miao,et al(杨亚娜,潘 涛,李敏妙,等). Science Technology and Engineering(科学技术与工程),2014,14(4):150.
[13] Wu Y Z, Chen J, Ji J F, et al. Soil Science Society of America Journal, 2007, 71(3): 918.
[14] HU Xue-yu,SUN Hong-fa,CHEN De-lin (胡学玉,孙宏发,陈德林). Ecology and Environment(生态环境),2007,16(5):1421.
[15] Haubrock S N,Chabrillat S,Lemmnitz C,et al. International Journal of Remote Sensing,2008,29 (1):3. |
[1] |
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. |
[2] |
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. |
[3] |
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. |
[4] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[5] |
PU Shan-shan, ZHENG En-rang*, CHEN Bei. Research on A Classification Algorithm of Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2446-2451. |
[6] |
JIN Cheng-liang1, WANG Yong-jun2*, HUANG He2, LIU Jun-min3. Application of High-Dimensional Infrared Spectral Data Preprocessing in the Origin Identification of Traditional Chinese Medicinal Materials[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2238-2245. |
[7] |
LI Hao-dong1, 2, LI Ju-zi1*, CHEN Yan-lin1, HUANG Yu-jing1, Andy Hsitien Shen1*. Establishing Support Vector Machine SVM Recognition Model to Identify Jadeite Origin[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2252-2257. |
[8] |
LIU Yu-juan1, 2, 3 , LIU Yan-da1, 2, 3, SONG Ying1, 2, 3*, ZHU Yang1, 2, 3, MENG Zhao-ling1, 2, 3. Near Infrared Spectroscopic Quantitative Detection and Analysis Method of Methanol Gasoline[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1489-1494. |
[9] |
HU Zheng1, ZHANG Yan1, 2*. Effect of Dimensionality Reduction and Noise Reduction on Hyperspectral Recognition During Incubation Period of Tomato Early Blight[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 744-752. |
[10] |
WANG Rui, SHI Lan-lan, WANG Yu-rong*. Rapid Prediction of Bending Properties of Catalpa Bungei Wood by
Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 557-562. |
[11] |
XIA Tong, LIU Yi-wei, GAO Yuan, CHENG Jie*, YIN Jian. Model-Fitting Methods for Mineral Raman Spectra Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 583-589. |
[12] |
CAI Yu1, 2, ZHAO Zhi-fang3, GUO Lian-bo4, CHEN Yun-zhong1, 2*, JIANG Qiong4, LIU Si-min1, 2, ZHANG Cong-zi4, KOU Wei-ping5, HU Xiu-juan5, DENG Fan6, HUANG Wei-hua7. Research on Origin Traceability of Rhizoma Dioscoreae Based on LIBS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 138-144. |
[13] |
LIU Hong-jun1, NIU Teng1, YU Qiang1*, SU Kai2, YANG Lin-zhe1, LIU Wei1, WANG Hui-yuan1. Inversion and Estimation of Heavy Metal Element Content in Peach Forest Soil in Pinggu District of Beijing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(11): 3552-3558. |
[14] |
YAN Peng-cheng1, 2, ZHANG Xiao-fei2*, SHANG Song-hang2, ZHANG Chao-yin2. Research on Mine Water Inrush Identification Based on LIF and
LSTM Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3091-3096. |
[15] |
XU Lu1, CHEN Yi-yun1, 2, 3*, HONG Yong-sheng1, WEI Yu1, GUO Long4, Marc Linderman5. Estimation of Soil Organic Carbon Content by Imaging Spectroscopy With Soil Roughness[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2788-2794. |
|
|
|
|