|
|
|
|
|
|
Study of the Retrieval and Adsorption Mechanism of Soil Heavy Metals Based on Spectral Absorption Characteristics |
WANG Hui-min1, 2, TAN Kun1, 2, 3*, WU Fu-yu1, 2, CHEN Yu1, 2, CHEN Li-han1, 2 |
1. NASG Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China
2. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
3. Key Laboratory of Geographic Information (Ministry of Education), East China Normal University, Shanghai 200241, China |
|
|
Abstract Heavy metals are scarce in soil, and it is difficult to identify their obvious characteristics in the soil spectrum. The previous soil heavy metal estimation methods have mostly applied statistical methods to find the characteristic bands, which cannot accurately explain the retrieval mechanism. It is therefore difficult to establish a universal model for soil heavy metal estimation. In order to investigate the influence of soil heavy metals in visible and near-infrared spectroscopy and analyze the retrieval mechanism of soil heavy metals, it is necessary to study the absorption characteristics of iron/manganese oxides, organic matter, clay minerals, etc. In this study, 80 soil samples were collected from the experimental field at Xuzhou, China. The spectra of the soil samples were measured with an Analytical Spectral Devices (ASD) field spectrometer. The soil heavy metal contents (Cr, Cd, Cu, Pb, and Zn) were determined by inductively coupled plasma-mass spectrometry. The soil spectra were processed by continuum removal. The absorption peaks related to heavy metals were around 480, 1 780, and 2 200 nm, which can be mainly attributed to iron/manganese oxides, organic matter, and clay minerals in the soil. The four spectral absorption characteristic parameters of Depth480, Depth1 780, Depth2 200, and Area2 200 were extracted at the positions of the absorption peaks. The variation trends of the parameters, along with the contents of the five heavy metals, were then analyzed. It was found that the four parameters were strongly correlated with the contents of the five heavy metals. Using a single variable to estimate the heavy metals, it was found that Depth480 had a higher estimation accuracy for Cr and Pb, and Area2 200 and Depth1 780 had a higher estimation accuracy for Cd, Cu, and Zn. The four spectral absorption characteristic parameters were implemented as independent variables, and the regression coefficients were obtained by ordinary least squares, ridge regression, and support vector regression. The heavy metal estimation model using the four spectral absorption characteristic parameters was stronger and more stable than those using only a single parameter. The best R2p (determination coefficient of prediction) values of the estimation models (Cr, Cd, Cu, Pb, and Zn) were 0.71, 0.84, 0.92, 0.80, and 0.89 respectively. The results suggest that Cr and Pb are easily adsorbed by iron/manganese oxides, while Cd, Cu, and Zn are more easily adsorbed by organic matter and clay minerals in this study area. The results of this study will provide a reference for researchers exploring the relationship between soil spectral characteristics and heavy metals.
|
Received: 2018-11-27
Accepted: 2019-04-11
|
|
Corresponding Authors:
TAN Kun
E-mail: tankuncu@gmail.com
|
|
[1] Li Qiurong, Luo Yuxing, Jin Leiyu, et al. Computing Techniques for Geophysical and Geochemical Exploration, 2017, (5): 705.
[2] Xu Liangji, Li Qingqing, Zhu Xiaomei, et al. Spectroscopy and Spectral Analysis, 2017, 37(12): 3839.
[3] Tan K, Wang H M, Zhang Q Q, et al. Journal of Soils & Sediments, 2018, 18(5): 1.
[4] Shi T Z, Wang J J, Chen Y Y, et al. International Journal of Applied Earth Observations & Geoinformation, 2016, 52: 95.
[5] Wang J J, Cui L J, Gao W X, et al. Geoderma, 2014, 216(4): 1.
[6] Xu Mingxing, Wu Shaohua, Zhou Shenglu, et al. Journal of Infrared and Millimeter Waves, 2011, 30(2): 109.
[7] Wei Jing, Ming Yanfang, Liu Fujiang. Earth Science-Journal of China University of Geosciences, 2015, (8): 1432.
[8] Jin Huining, Zhang Xinle, Liu Huanjun, et al. Acta Peologica Sinica, 2016, 53(3): 627.
[9] Choe E, Meer F V D, Ruitenbeek F V, et al. Remote Sensing of Environment, 2008, 112(7): 3222.
[10] Jin Jian, Zhou Xianping. Journals of Inner Mongolia university of Science and Technology, 2017, (3): 280.
[11] Liu Ming. Statistics and Decision, 2012, (4): 90.
[12] Yu Changkun, Song Wenbo, Wu Cifang, et al. Areal Research and Development, 2015, 34(1): 155. |
[1] |
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. |
[2] |
ZHAO Jian-ming, YANG Chang-bao, HAN Li-guo*, ZHU Meng-yao. The Inversion of Muscovite Content Based on Spectral Absorption
Characteristics of Rocks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 220-224. |
[3] |
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. |
[4] |
JING Xia1, ZHANG Jie1, 2, WANG Jiao-jiao2, MING Shi-kang2, FU You-qiang3, FENG Hai-kuan2, SONG Xiao-yu2*. Comparison of Machine Learning Algorithms for Remote Sensing
Monitoring of Rice Yields[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1620-1627. |
[5] |
HE Shao-fang1, SHEN Lu-ming1, XIE Hong-xia2*. Hyperspectral Estimation Model of Soil Organic Matter Content Using Generative Adversarial Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1905-1911. |
[6] |
CHEN Ying1, YANG Hui1, XIAO Chun-yan2, ZHAO Xue-liang1, 3, LI Kang3, PANG Li-li3, SHI Yan-xin3, LIU Zheng-ying1, LI Shao-hua4. Research on Prediction Model of Soil Heavy Metal Zn Content Based on XRF-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 880-885. |
[7] |
WU Xi-jun1, ZHANG Jie1, XIAO Chun-yan2, ZHAO Xue-liang1, 3, LI Kang3, PANG Li-li3, SHI Yan-xin3, LI Shao-hua4. Study on Inversion Model of Soil Heavy Metal Content Based on NMF-PLS Water Content[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 271-277. |
[8] |
ZHOU Mo1, 2, ZOU Bin1, 2*, TU Yu-long1, 2, XIA Ji-pin1, 2. Hyperspectral Modeling of Pb Content in Mining Area Based on Spectral Feature Band Extracted from Near Standard Soil Samples[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(07): 2182-2187. |
[9] |
LI Hang-fei, TU Liang-ping*, HU Yu-han, LIU Hao, ZHAO Jian. Automatic Measurement of Stellar Atmospheric Physical Parameters Based on Kernel Ridge Regression Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(04): 1297-1303. |
[10] |
YUAN Zi-ran, WEI Li-fei*, ZHANG Yang-xi, YU Ming, YAN Xin-ru. Hyperspectral Inversion and Analysis of Heavy Metal Arsenic Content in Farmland Soil Based on Optimizing CARS Combined with PSO-SVM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(02): 567-573. |
[11] |
WANG Jin-feng1, 2, 5, WANG Shi-jie2, 3, BAI Xiao-yong2, 3*, LIU Fang1, LU Qian1, 2, TIAN Shi-qi2, 4, WANG Ming-ming2. Prediction Soil Heavy Metal Zinc Based on Spectral Reflectance in Karst Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(12): 3873-3879. |
[12] |
WANG Teng-jun1, 2, ZHAO Ming-hai3, YANG Yun1*, ZHANG Yang2, 4, CUI Qin-fang1, LI Long-tong1. Inversion of Heavy Metals Content in Soil Using Multispectral Remote Sensing Imagery in Daxigou Mining Area of Shaanxi[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(12): 3880-3887. |
[13] |
Yasenjiang Kahaer1, 2, Rukeya Sawut1, 2, Nijat Kasim1, 2, Nigara Tashpolat1, 2*, ZHANG Fei1, 2*, Abdugheni Abliz2, 3, SHI Qing-dong1, 2, 3. Estimation of Heavy Metal Contents in Soil Around Open Pit Coal Mine Area Based on Optimized Spectral Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(08): 2486-2494. |
[14] |
TAO Chao1, CUI Wen-bo1, WANG Ya-jin1, ZOU Bin1, 2*, ZOU Zheng-rong1. Soil Heavy Metal Qualitative Classification Model Based on Hyperspectral Measurements and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(08): 2602-2607. |
[15] |
XUE Ren-zheng1, CHEN Shu-xin1*, HUANG Hong-ben2. Line Index of A-Type Stellar Astronomical Spectrum Predict Effective Temperature by Ridge Regression Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(08): 2624-2629. |
|
|
|
|