|
|
|
|
|
|
Application of Energy-Dispersive X-Ray Fluorescence Spectrometry to the Determination of As, Zn,Pb and Cr in Soil |
WANG Shi-fang, LUO Na, HAN Ping* |
Beijing Research Center for Agriculture Standards and Testing, Beijing 100097, China |
|
|
Abstract Total concentrations of As, Zn, Pb and Cr were determined in soil samples by using X-ray fluorescence spectrometry. The instrument applicability was good by analyzing the detection limit and accuracy of the instrument. Then, the energy rangesand variable numbersof heavy metal elements were obtained by using two-dimensional correlation spectroscopy. The variable numbersof Pb (10.380~10.740 and 12.435~12.900 keV), As (10.380~10.740 and 11.610~11.880 keV), Cr (5.310~5.520 and 5.805~6.015 keV) and Zn (8.520~8.805 and 9.555~9.630 keV) were 57, 44, 30 and 26, respectively. Finally, X-ray fluorescence spectrometry analysis models for heavy metal elements were established based on selected energy ranges by using partial least-squares regression. The results showed that the model performance was best for As, followed by Pb, Zn and Cr, and Rp were higher than 0.92. The study indicated that the prediction performance of model is improved using optimal energy ranges and the PXRF analyzer is suitable for in-situ monitoring of heavy metals in soil.
|
Received: 2017-06-12
Accepted: 2017-12-22
|
|
Corresponding Authors:
HAN Ping
E-mail: hanping1016@163.com
|
|
[1] Peinado F M, Ruano S M, González M G B, et al. Geoderma, 2010, 159: 76.
[2] Deng Wenbo, Li Xuxiang. Journal of Earth Environment, 2015, 6: 219.
[3] Bernick M B, Kalnicky D J, Prince G, et al. Journal of Hazardous Materials, 1995, 43: 101.
[4] Chakraborty S, Weindorf D C, Michaelson G, et al. Pedosphere, 2016, 26: 549.
[5] Sacristán D, Rossel R A V, Recatalá L. Geoderma, 2016, 265: 6.
[6] Kallithrakaskontos N, Foteinis S, Paigniotaki K, et al. Environmental Monitoring & Assessment, 2016, 188: 120.
[7] Shaffer R E, Cross J O, Rose-Pehrsson S L, et al. Analytica Chimica Acta, 2001, 442: 295.
[8] Huang Qiting, Zhou Lianqing, Shi Zhou, et al. Spectroscopy and Spectral Analysis, 2009, 29(5): 1434.
[9] Li Fang, Wang Jihua, Lu Anxiang, et al. Spectroscopy and Spectral Analysis, 2015, 35(4): 1111.
[10] Zhang W, Zhang Y J, Chen D, et al. Advanced Materials Research, 2013, 705:70.
[11] Yeung Z L, Kwok R C, Yu K N. Applied Radiation and Isotopes, 2003, 58:339.
[12] Anjos M J D, Lopes R T, Jesus E F O D, et al. Spectrochimica Acta Part B Atomic Spectroscopy, 2000, 55:1189.
[13] Noda I. Applied Spectroscopy, 1993, 47:1329.
[14] Noda I. Vibrational Spectroscopy, 2004, 36:143.
[15] Hua R, Sun S Q, Zhou Q, et al. Journal of Pharmaceutical & Biomedical Analysis, 2003, 2:199.
[16] Sasic S, Sato H, Shimoyama M, et al. Analyst, 2005, 5:652.
[17] Song Haiyan, Cheng Xu. Spectroscopy and Spectral Analysis, 2014, 34(5): 1240.
[18] Wang Shifang, Cheng Xu, Song Haiyan. Spectroscopy and Spectral Analysis, 2016, 36(10): 3249.
[19] Siuly, Yin X X, Hadjiloucas S, et al. Computer Methods & Programs in Biomedicine, 2016, C:64.
[20] Sharma A, Weindorf D C, Man T, et al. Geoderma, 2014, s232-234:141.
[21] Weindorf D C, Zhu Y, Mcdaniel P, et al. Geoderma, 2012, s189-190:268.
[22] Zhu Y D, Weindorf D C, Zhang W T. Geoderma, 2011, 167:167.
[23] Schneider A R, Cancès B, Breton C, et al. Journal of Soils and Sediments, 2016, 16:1.
[24] Weindorf D C, Bakr N, Zhu Y D, et al. Pedosphere, 2014, 24:1.
[25] Bastos R O, Melquiades F L, Biasi G E V. X-Ray Spectrometry, 2012, 41:304. |
[1] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[2] |
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. |
[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] |
LI Xiao-li1, WANG Yi-min2*, DENG Sai-wen2, WANG Yi-ya2, LI Song2, BAI Jin-feng1. Application of X-Ray Fluorescence Spectrometry in Geological and
Mineral Analysis for 60 Years[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 2989-2998. |
[5] |
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. |
[6] |
WU Yong-qing1, 2, TANG Na1, HUANG Lu-yao1, CUI Yu-tong1, ZHANG Bo1, GUO Bo-li1, ZHANG Ying-quan1*. Model Construction for Detecting Water Absorption in Wheat Flour Using Vis-NIR Spectroscopy and Combined With Multivariate Statistical #br#
Analyses[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2825-2831. |
[7] |
LIU Rui-min, YIN Yong*, YU Hui-chun, YUAN Yun-xia. Extraction of 3D Fluorescence Feature Information Based on Multivariate Statistical Analysis Coupled With Wavelet Packet Energy for Monitoring Quality Change of Cucumber During Storage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2967-2973. |
[8] |
ZHOU Qing-qing1, LI Dong-ling1, 2, JIANG Li-wu1, 3*, WAN Wei-hao1, ZENG Qiang4, XUE Xin4, WANG Hai-zhou1, 2*. Quantitative Statistical Study on Dendritic Component Distribution of Single Crystal Blade Based on Microbeam X-Ray Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2112-2118. |
[9] |
ZHANG Hai-liang1, XIE Chao-yong1, TIAN Peng1, ZHAN Bai-shao1, CHEN Zai-liang1, LUO Wei1*, LIU Xue-mei2*. Measurement of Soil Organic Matter and Total Nitrogen Based on Visible/Near Infrared Spectroscopy and Data-Driven Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2226-2231. |
[10] |
DU Zhi-heng1, 2, 3, HE Jian-feng1, 2, 3*, LI Wei-dong1, 2, 3, WANG Xue-yuan1, 2, 3, YE Zhi-xiang1, 2, 3, WANG Wen1, 2, 3. A New EDXRF Spectral Decomposition Method for Sharpening Error Wavelets[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1719-1724. |
[11] |
LIN Jing-tao, XIN Chen-xing, LI Yan*. Spectral Characteristics of “Trapiche-Like Sapphire” From ChangLe, Shandong Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1199-1204. |
[12] |
WU Lei1, LI Ling-yun2, PENG Yong-zhen1*. Rapid Determination of Trace Elements in Water by Total Reflection
X-Ray Fluorescence Spectrometry Using Direct Sampling[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 990-996. |
[13] |
XU Wei-xuan1, CHEN Wen-bin2, 3*. Determination of Barium in Purple Clay Products for Food Contact by
Energy Dispersive X-Ray Fluorescence Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 475-483. |
[14] |
CHEN Ji-wen, YANG Zhen, ZHANG Shuai, CUI En-di, LI Ming*. Fast Resolution Algorithm for Overlapping Peaks Based on Multi-Peak Synergy and Pure Element Characteristic Peak Area Normalization[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 151-155. |
[15] |
JIA Wen-bao1, LI Jun1, ZHANG Xin-lei1, YANG Xiao-yan2, SHAO Jin-fa3, CHEN Qi-yan1, SHAN Qing1*LING Yong-sheng1, HEI Da-qian4. Study on Sample Preparation Method of Plant Powder Samples for Total Reflection X-Ray Fluorescence Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 169-174. |
|
|
|
|