光谱学与光谱分析 |
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On-Site Analysis of Heavy Metals in Water with Handheld X-Ray Fluorescence and Pre-Concentration Device without External Power Supply |
JIAO Ju1, ZHAN Xiu-chun1*, ZHAI Lei1, FAN Xing-tao1, WEN Hong-li1, YUAN Ji-hai1, LIU Xiao1, GUO Shu1, 2 |
1. National Research Center for Geoanalysis, Beijing 100037, China 2. Faculty of Material Science and Chemistry, China University of Geosciences (Wuhan), Wuhan 430074, China |
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Abstract Currently, increasing importance has been attached to heavy metal pollution of water environment. In order to help geologist and environmentalist to obtain on-site experimental data, there is an urgent need to develop portable analysis techniques of water quality, which can be used to give an timely and accurate assessment of water quality of the contaminated area with low cost and to monitor water environment conveniently in daily preventing and controlling work of water pollution. Based on adsorption with Purolite S930 chelating resin at pH 4 and thin-film sample preparation technique, a rapid and simple on-site-oriented analysis method of heavy metal ions, namely Mn,Fe,Ni,Cu,Zn and Pb in aqueous solutions, was developed utilizing a handheld energy-dispersive X-ray fluorescence in this paper. The correlation coefficients of the calibration curves were greater than 0.992 for all six metal ions, the lower limits of detection were between 5.8 and 18.6 μg·L-1. Precision tests carried out on multi-element mixed solutions showed that the relative standard deviations (RSD, n=10) were better than 15%. The method has been used on-site to analysis 21 water samples collected from different river or well sites near a mining area northeast China. Out of the five underground water samples,one was found contaminated by Cu and Zn (Class Ⅲ), one by Zn and Fe (Class Ⅱ), and one by Fe(in between Class Ⅱ to Ⅲ). Surface water samples showed better qualities. But two out of the sixteen were found to be contaminated by Mn heavily (Class Ⅳ). Laboratory ICPMS was used to analyze the same samples. The results for Cu,Ni and Pb were coincident with the on-site data in general. But for Zn and Mn with higher concentrations, on-site data were lower than that by ICPMS. The reason for that might be the inclusion of the fine suspending particles in the samples by ICPMS. We conclude that the on-site data were effective. With the described method, an overall data acquisition time, including sampling, processing and measuring, can be within 12 hours for a batch of 10 samples. The pre-concentration device and the XRF instrument are both small, light, portable and can operate without external power supply. So, the method is suitable for on-site water sample analysis, especially in remote areas.
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Received: 2015-02-08
Accepted: 2015-06-11
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Corresponding Authors:
ZHAN Xiu-chun
E-mail: zhanxiuchun2012@126.com
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[1] West M,Ellis A T,Potts P J,et al. Journal of Analytical Atomic Spectrometry,2013,28(10): 1544. [2] West M,Ellis A T,Potts P J,et al. Journal of Analytical Atomic Spectrometry,2014,29(9): 1516. [3] Bosco G L. Trends in Analytical Chemistry,2013,45: 121. [4] JIA Wen-bao,ZHANG Yan,HEI Da-qian,et al(贾文宝,张 焱,黑大千,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2014,34(11): 3123. [5] Marguí E,Hidalgo M,Queralt I,et al. Spectrochimica Acta Part B: Atomic Spectroscopy,2012,67: 17. [6] GE Liang-quan(葛良全). Rock and Mineral Analysis(岩矿测试),2013,32(2): 203. [7] Bagur-González M G,Estepa-Molina C,Martín-Peinado F,et al. Journal of Soils and Sediments,2011,11(2): 281. [8] Weindorf D C,Zhu Y,Chakraborty S,et al. Environmental Monitoring and Assessment,2012,184(1): 217. [9] JI Ang,ZHUO Shang-jun,LI Guo-hui(吉 昂,卓尚军,李国会). Energy Dispersive X-Ray Fluorescence Spectra(能量色散X射线荧光光谱). Beijing: Science Press(北京:科学出版社),2011. 350. [10] Marguí E,Van Grieken R,Fontàs C,et al. Applied Spectroscopy Reviews,2010,45(3): 179. [11] Marguí E,Zawisza B,Sitko R. Trends in Analytical Chemistry,2014,53: 73. [12] ZHAI Lei,ZHAN Xiu-chun(翟 磊,詹秀春). Rock and Mineral Analysis(岩矿测试),2015, 34(1): 118. [13] Peng Y,Huang Y,Yuan D,et al. Chinese Journal of Analytical Chemistry,2012,40(6): 877. [14] Kocot K,Sitko R. Spectrochimica Acta Part B: Atomic Spectroscopy,2014,94-95: 7. [15] Marguí E,Zawisza B,Skorek R,et al. Spectrochimica Acta Part B: Atomic Spectroscopy,2013,88: 192. [16] Al-Ameri S A H,Al-Jibouri M N A,Musa T M D. Journal of Saudi Chemical Society,2014,18(6): 802. [17] Kuz’Min V I,Kuz’Min D V. Hydrometallurgy,2014,141: 76. |
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