光谱学与光谱分析, 2018, 38 (5): 1648, 网络出版: 2018-06-01
能量色散X射线荧光光谱检测土壤重金属砷、锌、铅和铬
Application of Energy-Dispersive X-Ray Fluorescence Spectrometry to the Determination of As, Zn,Pb and Cr in Soil
X射线荧光光谱 土壤重金属 二维相关同步光谱 偏最小二乘回归 X-ray fluorescence spectrometry Soil heavy metals Two-dimensional correlation spectroscopy Partial least squares regression
摘要
利用X射线荧光光谱检测土壤重金属砷、 锌、 铅和铬元素的含量。 通过分析仪器检出限和准确度, 得出仪器适用性良好。 然后, 利用二维相关同步光谱获得重金属元素的X射线荧光光谱能谱范围和变量数, 得出铅元素的能谱范围分别为10380~10740和12435~12900 keV,砷元素的能量范围是10380~10740和11610~11880 keV,铬元素的能量范围是5310~5520和5805~6015 keV, 锌元素的能量范围是8520~8805和9555~9630 keV, 铅、 砷、 铬和锌的变量数分别为57, 44, 30和26。 最后, 根据获得的能谱范围, 采用偏最小二乘回归方法建立重金属元素的X射线荧光光谱定量分析模型, 得出砷元素的模型性能最佳, 其次是铅、 锌和铬, 预测相关系数都高于092。 研究表明, 利用二维相关光谱获得的能谱范围有助于提高模型的预测性能和便携式X射线荧光光谱检测仪器适用于土壤重金属的原位监测。
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 (10380~10740 and 12435~12900 keV), As (10380~10740 and 11610~11880 keV), Cr (5310~5520 and 5805~6015 keV) and Zn (8520~8805 and 9555~9630 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 092. 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.
王世芳, 罗娜, 韩平. 能量色散X射线荧光光谱检测土壤重金属砷、锌、铅和铬[J]. 光谱学与光谱分析, 2018, 38(5): 1648. WANG Shi-fang, LUO Na, HAN Ping. Application of Energy-Dispersive X-Ray Fluorescence Spectrometry to the Determination of As, Zn,Pb and Cr in Soil[J]. Spectroscopy and Spectral Analysis, 2018, 38(5): 1648.