|
|
|
|
|
|
Hyperspectral Inversion of Heavy Metal Content in Soils Reconstituted by Mining Wasteland |
SHEN Qiang1, ZHANG Shi-wen2*, GE Chang2, LIU Hui-lin2, ZHOU Yan3, CHEN Yuan-peng3, HU Qing-qing2, YE Hui-chun4*, HUANG Yuan-fang5 |
1. Faculty of Surveying and Mapping, Anhui University of Science and Technology,Huainan 232001, China
2. College of Earth and Environmental Science, Anhui University of Science and Technology,Huainan 232001, China
3. Land Consolidation and Rehabilitation Center of the Ministry of Natural Resources,Beijing 100035, China
4. Institute of Remote Sensing and Digital Earth Research, Chinese Academy of Sciences, Beijing 100094, China
5. College of Resource and Environment, China Agricultural University,Beijing 100193, China |
|
|
Abstract Mineral resources play an important role in the development of industry and national economy. However, with the expansion of mining scale, more and more abandoned mining land is formed due to resource depletion and poor management. Due to the prolonged mining impact, a large amount of heavy metal elements are present in the soils of mining wastelands. In such contaminated areas, high levels of heavy metals may have an impact on the environment and the human body. Land reclamation is an important method for remediation of contaminated and degraded soils. The detection of heavy metal content in the reconstructed soils is an important indicator of land reclamation efficiency and requires long-term follow-up and monitoring. The traditional chemical detection methods are inefficient and costly, and can not detect a wide range of heavy metals. Hyperspectral technology is a new technology with great potential for development and has a wide range of applications in environmental protection, resource utilization and regional sustainable development. After the rapid development in recent decades, the accuracy of instruments has been gradually increased, and the detection methods have gradually become mature, so as to realize the high efficiency of soil heavy metals. Easy detection provides a new way. Normal soil heavy metal content is generally relatively lower, and the use of spectral techniques to measure heavy metal content is more difficult, but mining iron ore mining area due to the soil more iron, will make the soil heavy metals in the form of existence and aggregation changes, impact the response of heavy metals to the spectra, and make the correlation between soil spectral reflectance and heavy metal content even more pronounced. The contents of heavy metal (As, Cr, Zn) in soils were obtained by sampling chemical detection method in the study area of reclamation mining area in Daye City, Hubei Province. The soil reflectance was obtained by means of FieldSpec4 spectrophotometer (350~2 500 nm) First-order differential, reciprocal logarithm, and continuous unmixing method were used to preprocess the reflectance curve respectively, and the spectral characteristic bands were extracted. The correlations between the three heavy metal elements and spectral features were analyzed and a stepwise regression model was established. The results showed that compared with the general soil, spectral data preprocessing could make spectral characteristic bands more obvious, of which the first-order differential and continuous removal were the most obvious. The characteristic bands of the three heavy metal elements were 495, 545, 675, 995, 1 425, 1 505, 1 935, 2 165, 2 205, 2 275 and 2 355 nm. Correlation analysis between soil heavy metal content and spectral characteristic bands showed that all the three heavy metals showed correlation with spectral curve, and most of the correlation coefficients reached above 0.5 and the maximum correlation coefficient was 0.663, and different heavy metal species and treatment methods led to significant differences in the correlation coefficients. Three heavy metal inversion models were established based on the characteristic bands with the highest correlation with heavy metals in soil. The optimal inversion model for each heavy metal was selected based on the size of inversion model r. Because of different selection of heavy metal species, Cr, Zn First-order differential step-by-step regression was the best inversion model, and heavy metal As continuous removal method gradually regression was the best inversion model. Through the test, Cr in the three kinds of heavy metals was the best, and RMSE is 2.67, followed by Zn, and As is the worst. Comparing the current different detection methods, we can see that hyperspectral inversion of soil heavy metal content spectrometer based on soil samples and spectral data pretreatment is ideal. The related research results can provide reference for the hyperspectral inversion of heavy metals in mining-abandoned soils.
|
Received: 2018-03-06
Accepted: 2018-08-12
|
|
Corresponding Authors:
ZHANG Shi-wen, YE Hui-chun
E-mail: mamin1190@126.com
|
|
[1] XIE Xian-li, SUN Bo, HAO Hong-tao, et al(解宪丽,孙 波,郝红涛). Acta Pedologica Sinica(土壤学报), 2007, 44(6): 982.
[2] GONG Shao-qi, WANG Xin, SHEN Run-ping, et al(龚绍琦,王 鑫,沈润平,等). Remote Sensing Technology and Application(遥感技术与应用), 2010, 25(2): 169.
[3] Nachiyar K S K, Annadurai B, et al. Asian Journal of Microbiology Biotechnology & Environmental Sciences, 2013, 15(4): 709.
[4] Dai Zhixiu. Sci-Tech Innovation and Productivity, 2016, 6: 214.
[5] Kemper T, Sommer S. Environmental Science & Technology, 2002, 36(12): 2742.
[6] GUO Ying, GUO Shao-xing, LIU Jia,et al(郭 颖,郭绍兴,刘 佳). Remote Sensing Information(遥感信息), 2005,(3): 10.
[7] XU Liang-ji, LI Qing-qing, ZHU Xiao-mei, et al(徐良骥,李青青,朱小美). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(12): 3839.
[8] HE Jun-liang, ZHANG Shu-yuan, ZHA Yong, et al(贺军亮,张淑媛,查 勇). Remote Sensing Technology and Application(遥感技术与应用), 2015, 30(3): 407. |
[1] |
LIANG Ye-heng1, DENG Ru-ru1, 2*, LIANG Yu-jie1, LIU Yong-ming3, WU Yi4, YUAN Yu-heng5, AI Xian-jun6. Spectral Characteristics of Sediment Reflectance Under the Background of Heavy Metal Polluted Water and Analysis of Its Contribution to
Water-Leaving Reflectance[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 111-117. |
[2] |
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. |
[3] |
LIU Hong-wei1, FU Liang2*, CHEN Lin3. Analysis of Heavy Metal Elements in Palm Oil Using MP-AES Based on Extraction Induced by Emulsion Breaking[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3111-3116. |
[4] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
[5] |
CHENG Fang-beibei1, 2, GAN Ting-ting1, 3*, ZHAO Nan-jing1, 4*, YIN Gao-fang1, WANG Ying1, 3, FAN Meng-xi4. Rapid Detection of Heavy Metal Lead in Water Based on Enrichment by Chlorella Pyrenoidosa Combined With X-Ray Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2500-2506. |
[6] |
ZHANG Xia1, WANG Wei-hao1, 2*, SUN Wei-chao1, DING Song-tao1, 2, WANG Yi-bo1, 2. Soil Zn Content Inversion by Hyperspectral Remote Sensing Data and Considering Soil Types[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2019-2026. |
[7] |
JIANG Chuan-li1, ZHAO Jian-yun1, 2*, DING Yuan-yuan1, ZHAO Qin-hao1, MA Hong-yan1. Study on Soil Water Retrieval Technology of Yellow River Source Based on SPA Algorithm and Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1961-1967. |
[8] |
TANG Quan1, ZHONG Min-jia2, YIN Peng-kun2, ZHANG Zhi3, CHEN Zhen-ming1, WU Gui-rong3*, LIN Qing-yu4*. Imaging of Elements in Plant Under Heavy Metal Stress Based on Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1485-1488. |
[9] |
ZHANG Chao1*, SU Xiao-yu1, XIA Tian2, YANG Ke-ming3, FENG Fei-sheng4. Monitoring the Degree of Pollution in Different Varieties of Maize Under Copper and Lead Stress[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1268-1274. |
[10] |
LAI Si-han1, LIU Yan-song1, 2, 3*, LI Cheng-lin1, WANG Di1, HE Xing-hui1, LIU Qi1, SHEN Qian4. Study on Hyperspectral Inversion of Rare-Dispersed Element Cadmium Content in Lead-Zinc Ores[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1275-1281. |
[11] |
CHEN Ping-yun1, KANG Xiu-tang1, GUO Liang-qia2*. Study of Emission Characteristics of Particulate Arsenic, Cadmium, Copper and Lead Derived From Burning of Tibetan Incenses by
ICP-OES Method With Microwave Digestion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 419-425. |
[12] |
ZHU Zhao-zhou1, YAN Wen-rui1, 2, ZHANG Zi-jing1, 2. Research of Pollution Characteristics, Ecological and Health Risks of Heavy Metals in PM2.5 From Fireworks by Inductively Coupled
Plasma-Mass Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 644-650. |
[13] |
TANG Ju1, 2, DAI Zi-yun2*, LI Xin-yu2, SUN Zheng-hai1*. Investigation and Research on the Characteristics of Heavy Metal Pollution in Children’s Sandpits Based on XRF Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3879-3882. |
[14] |
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. |
[15] |
JUMAHONG Yilizhati1, 2, TAN Xi-juan1, 2*, LIANG Ting1, 2, ZHOU Yi1, 2. Determination of Heavy Metals and Rare Earth Elements in Bottom Ash of Waste Incineration by ICP-MS With High-Pressure Closed
Digestion Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3168-3173. |
|
|
|
|