Wang Ying, Gan Tingting, Zhao Nanjing, Yin Gaofang, Li Tanghu, Li Xingchi, Tan Xiaoxuan
School of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei 230026, Anhui, China.
Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China.
Anal Methods. 2025 Jun 26;17(25):5186-5202. doi: 10.1039/d5ay00249d.
To clarify the impact of soil type variability on the quantitative analysis of heavy metals using X-ray fluorescence (XRF), the feasibility of establishing XRF quantitative analysis curves based on 15 different soil types was investigated. Pearson's correlation coefficient was employed to analyze the relationship between the XRF results and soil matrix constituents. The analysis was focused on four specific soil types: grey fluvo-aquic soil, fluvo-aquic soil, purple soil, and rice soil. The differences in the XRF quantitative analysis curves for heavy metals across these soil types were assessed by examining the overlap of the 95% confidence intervals and the cosine distances between the curves. The accuracy of heavy metal content determinations in grey fluvo-aquic soil was evaluated using the quantitative analysis curves derived from the four soil types. It was found that the linear coefficients of determination for the XRF quantitative analysis curves of heavy metals (Zn, Pb, Ni, Cu, Cr, and Cd) established from the 15 soil types were all below 0.2, indicating a poor fit and rendering them unsuitable for accurate analysis. This highlights that variability in soil types, attributed to differences in soil matrix compositions, significantly affects the accuracy of heavy metal quantification by XRF. When the quantitative analysis curves from fluvo-aquic soil, purple soil, and rice soil were applied to assess heavy metal concentrations (Cr, Ni, Cu, Zn, Pb, Cd, As, and Hg) in grey fluvo-aquic soil, significant increases in the average relative errors were noted. Specifically, these errors rose from 9.89%, 8.56%, 13.51%, 7.10%, 9.86%, 26.19%, 6.71%, and 30.97% to the following ranges: 29.74% to 34.80% (minimum 29.74%, maximum 34.80%), 59.82% to 96.34%, 41.12% to 78.33%, 25.33% to 32.64%, 16.92% to 70.36%, 24.07% to 68.79%, 48.91% to 128.98%, and 130.29% to 238.70%, increasing as much as 0.11 to 18.22 times. Such increases indicate that variability among soil types greatly impacts the accuracy of heavy metal quantitative analysis using XRF. This study establishes an important foundation for the precise quantitative detection of heavy metals XRF across various soil types.
