Spectroscopy and Spectral Analysis, Volume. 44, Issue 9, 2494(2024)
Simultaneous Quantitative Analysis of Soil Heavy Metals As and Pb Based on Energy Dispersive XRF Spectroscopy
This paper proposes a simultaneous quantitative analysis method for Pb and As in soil based on the spectral peak characteristics of Lα and Lβ of the Pb element and Kα and Kβ of the As element, aiming at the difficulty of accurately quantifying Pb and As in soil simultaneously by energy-dispersive XRF spectrometry. The method constructs the relationship between the characteristic peak intensities of Lα and Lβ of Pb element at different concentrations, accurately analyzes the characteristic peak intensities of Lα of Pb element and Kα of As element according to the overlapping peak intensity information of Lα of Pb element and Kα of As element in the measured sample, and realizes the accurate back calculation of Pb and As in the soil sample based on the established quantitative analysis curve. Through quantitative detection of Pb and As coexisting soil samples with different concentrations, compared with the results measured by ICP-MS, the relative error of this method for detecting 20.25~844.84 mg·kg-1 heavy metal Pb is between 0.86% and 11.09%, with an average relative error of 6.17%; the relative error for detecting 26.99~825.93 mg·kg-1 heavy metal As is between 0.10% and 14.72%, with an average relative error of 8.11%. This method achieves the simultaneous accurate quantitative analysis of Pb and As contents in soil coexisting with Pb and As, and provides a method for developing rapid XRF precision detection equipment for soil heavy metals on site.
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YE Zi-qi, GAN Ting-ting, WU Wen-tao, ZHAO Nan-jing, YIN Gao-fang, FANG Li, YUE Zheng-bo, WANG Jin, SHENG Ruo-yu, WANG Ying, LI Tang-hu. Simultaneous Quantitative Analysis of Soil Heavy Metals As and Pb Based on Energy Dispersive XRF Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2024, 44(9): 2494
Received: Aug. 3, 2023
Accepted: --
Published Online: Sep. 10, 2024
The Author Email: Ting-ting GAN (ttgan@aiofm.ac.cn)