不同铅浓度土壤XRF光谱的频率域去噪及规律研究

doi:10.3964/j.issn.1000-0593(2020)09-2875-09

摘要
参考文献
相关文章 (15)

全文: PDF (6143 KB)
输出: BibTeX | EndNote (RIS)

摘要：近年来，全球工业快速发展和城市化的推进引发了一系列环境问题，其中土壤重金属铅（Pb）污染引起了科研人员的广泛关注。X射线荧光（XRF）光谱技术具有成本低、分析速度快、适合大面积监测等优势，已被广泛应用在土壤污染检测与生态环境保护等多个领域，发展前景广阔，因此充分挖掘土壤XRF光谱信息具有很强的现实意义，可为高效地进行土壤污染检测、土壤环境生态指标参量反演及矿区重金属污染早期预警等提供解决方案。目前关于XRF光谱研究大多集中在土壤重金属浓度测量结果的精度评价和环境质量评估，而对于土壤XRF光谱差异特征变化的深入分析研究较少。时频分析可将时域复杂信号变换到频率域空间，从频率域角度检测光谱、信号中存在的异常信息，是一种有效的谱差异特征变化检测方法，其中，谐波分析（HA）可用于电磁信号去噪；平滑伪魏格纳分布（SPWVD）可筛选合适的基函数，突出信号时频局域性细节特征。首先采用HA方法探究不同Pb浓度土壤XRF光谱的去噪效果，然后利用光谱的SPWVD研究实地采样土壤样品的去噪XRF光谱局部规律。研究结果表明：当谐波分解次数为400时，土壤XRF光谱去燥效果较好并节省了时间，且保留了光谱的特征。土壤样品Pb浓度与XRF光谱的SPWVD在400和600~700波段序列上的频率峰值的分布具有一定的规律性，根据此规律性可识别该地区土壤的Pb浓度超标：全部实地采样土壤中，能够识别75%的Pb浓度不超标的样品，在波段序列为400附近有1个较高的频率峰（频率小于400 Hz）或有2个很强的频率峰值（频率大于400 Hz）；全部实地采样土壤中，能够识别79.17%的Pb浓度超标的样品，在波段序列为400附近有1个很强的频率峰（频率大于400 Hz），且在600~700波段序列之间有3个层次明显的频率峰分布，据此推断该地区土壤Pb浓度超标的XRF光谱特征波段区间为6.42和9.42~10.92 keV。因此，研究通过引入时频分析法实现了对土壤XRF光谱频域甄析和可视化显现，为深度挖掘Pb污染谱规律特征及异常信息提供一种新思路。

关键词：重金属；土壤X射线荧光光谱；谐波分析；平滑伪维格纳-威尔分布

Abstract：In recent years, the rapid development of global industry and the advancement of urbanization have triggered a series of environmental problems, in which soil heavy metal lead (Pb) pollution has caused widespread concern among researchers. X-ray fluorescence (XRF) spectroscopy has the advantages of low cost, fast analysis speed and suitable for large-area monitoring. It has been widely used in many fields, such as soil pollution detection and ecological environment protection, and it has broad prospects for development. Therefore, it is of great practical significance to fully excavate the XRF spectral information of soil, which can provide solutions for the efficient detection and prevention of soil pollution, the inversion of soil environmental and ecological parameters, and the early warning of heavy metal pollution in mining areas. At present, most of the research on XRF spectroscopy focuses on the accurate evaluation of soil heavy metal concentration measurement and environmental quality assessment, while there are few in-depth studies on the variation of soil XRF spectral characteristics. Time-frequency analysis method can transform complex signals in the time domain into frequency domain space, and detect abnormal information in spectral signals from the angle of the frequency domain. It is an effective method for detecting the change of spectral difference features. Among them, harmonic analysis (HA) could be used for noise removal of electromagnetic signals; smoothed pseudo Wigner-Ville distribution (SPWVD) selects the appropriate basis function in advance, which can highlight the time-frequency local details of the original signal. This paper firstly used HA method to explore the denoising effect of soil XRF spectra with different Pb concentrations, and then used SPWVD of the spectrum to study the local law of denoising XRF spectra of soil samples sampled in the field. The results showed that when the number of harmonic decomposition was 400, soil XRF spectrum de-drying effect was better and saved time, and retained the characteristics of the spectrum. The Pb concentration of soil samples and the distribution of frequency peaks of SPWVD in the XRF spectrum on the 400, 600～700 band sequences had certain regularity. According to this regularity, the Pb concentration of soil in this area could be identified as exceeding the standard, in all in-situ sampled soil, 75% of samples with excessive Pb concentrations could be identified, there was a higher frequency peak near the band sequence of 400 (frequency less than 400 Hz) or 2 very strong frequency peaks (frequency greater than 400 Hz); in all in-situ sampled soil, 79.17% of samples with Pb concentration not exceeded the standard could be identified, there was a strong frequency peak (frequency greater than 400 Hz) near the band sequence of 400, and there were 3 distinct frequency peak distributions between the 600 and 700 band sequences. Accordingly, it was inferred that the XRF spectral characteristic band interval of soil Pb concentration exceeding the standard in this area was 6.42 and 9.42~10.92 keV. Therefore, by introducing the time-frequency analysis method, soil XRF spectral frequency domain analysis and visualization are realized, which provides a new idea for deep mining the characteristics of Pb pollution spectrum and abnormal information.

Key words：Heavy metal; Soil XRF spectra; Harmonic analysis; Smoothed pseudo Wigner-Ville distribution

收稿日期: 2019-08-10 修订日期: 2019-12-18

中图分类号:

TP7

基金资助: 国家自然科学基金项目(41971401)，山东省自然科学基金面上项目(ZR2018MD008)，山东建筑大学2019年校内博士基金项目(X19081Z，X19010Z)资助

通讯作者: 刘浦东 E-mail: liupudong19@sdjzu.edu.cn

作者简介: 付萍杰，女，1989年生，山东建筑大学测绘地理信息学院讲师 e-mail: fpj890622@163.com

引用本文:

付萍杰，杨可明，刘浦东. 不同铅浓度土壤XRF光谱的频率域去噪及规律研究[J]. 光谱学与光谱分析, 2020, 40(09): 2875-2883.
FU Ping-jie, YANG Ke-ming, LIU Pu-dong. Frequency Domain Denoising and Regularity Study on XRF Spectra of Soils With Different Lead Concentrations. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(09): 2875-2883.

链接本文:

https://www.gpxygpfx.com/CN/10.3964/j.issn.1000-0593(2020)09-2875-09 或 https://www.gpxygpfx.com/CN/Y2020/V40/I09/2875

[1] Lal R. Science, 2004, 304(5677): 1623.
[2] Rial M, Cortizas A M, Rodríguez-Lado L. Science of the Total Environment, 2017, 609: 1411.
[3] Guan Q, Wang F, Xu C, et al. Chemosphere, 2018, 193: 189.
[4] Rasheed T, Bilal M, Nabeel F, et al. Science of the Total Environment, 2018, 615: 476.
[5] Kilbride C, Poole J, Hutchings T R. Environmental Pollution, 2006, 143(1): 16.
[6] HU Ming-qing(胡明情). Environmental Science & Technology(环境科学与技术), 2015, 38(S2): 269.
[7] Horta A, Malone B, Stockmann U, et al. Geoderma, 2015, 241(521): 180.
[8] ZHANG Shu-jie, WU Wen-qi, JIANG Tian-yi, et al(张术杰, 吴文琪, 蒋天怡, 等). Chinese Rare Earths(稀土), 2012, 33(4): 77.
[9] PENG Hong-liu, YANG Zhou-sheng, ZHAO Jie, et al(彭洪柳, 杨周生, 赵婕, 等). Journal of Agro-Environment Science(农业环境科学学报), 2018, 37(7): 1386.
[10] Kodom K, Preko K, Boamah D. Soil and Sediment Contamination: An International Journal, 2012, 21(8): 1006.
[11] KUANG Rong-xi, HU Wen-you, HE Yue, et al(邝荣禧, 胡文友, 何跃, 等). Soils(土壤), 2015, 47(3): 589.
[12] Salazar M J, Rodriguez J H, Cid C V, et al. Geoderma, 2016, 279: 97.
[13] Lintern A, Leahy P J, Heijnis H, et al. Water Research, 2016, 105: 34.
[14] YANG Gui-lan, SHANG Zhao-cong, LI Liang-jun, et al(杨桂兰, 商照聪, 李良君, 等). Applied Chemical Industry(应用化工), 2016, 45(8): 1586.
[15] Hu B, Chen S, Hu J, et al. PLoS ONE, 2017, 12(2), e0172438.
[16] WANG Qi-rui, CAI Qing-xiang, MA Cong-an, et al(王启瑞, 才庆祥, 马从安, 等). Coal Science and Technology(煤炭科学技术), 2006, 34(10): 72.
[17] YANG Yong, LIU Ai-jun, CHAO Lumengqiqige, et al(杨勇, 刘爱军, 朝鲁孟其其格, 等). Ecology and Environmental Sciences(生态环境学报), 2016, 25(5): 885.
[18] YU Tao, HAN Qing-kai, SUN Wei, et al(于涛, 韩清凯, 孙伟, 等). Journal of Northeastern University·Natural Science(东北大学学报·自然科学版), 2006, 27(5)：56.
[19] XU Yuan-nan, ZHAO Yuan, LIU Li-ping, et al(许元男, 赵远, 刘丽萍, 等). Acta Physica Sinica(物理学报), 2010, 59(2): 980.
[20] Jakubauskas M E, Legates D R, Kastens J H. Photogrammetric Engineering and Remote Sensing, 2001, 67(4): 461.
[21] YE Fei, WU Jia-quan, ZHANG Xin-yu, et al(叶飞, 吴加权, 张馨予, 等). Journal of Vibration and Shock(振动与冲击), 2018, 37(16): 234.
[22] Jakubauskas M E, Legates D R, Kastens J H. Computers and Electronics in Agriculture, 2002, 37(1-3): 127.
[23] Bradley B A, Jacob R W, Hermance J F, et al. Remote Sensing of Environment, 2007, 106(2): 137.
[24] Hu B, Chen S, Hu J, et al. PLoS ONE, 2017, 12(2)：e0172438.