Establishment and evaluation of a quantitative analysis model for potentially toxic metals in wet soil samples by LIBS

EUROPEAN JOURNAL OF SOIL SCIENCE(2022)

Cited 4|Views22
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Abstract
Fast on-site detection of potentially toxic metals in soils is crucial for soil remediation and contamination monitoring. The moisture in soils is a challenge for quantitative analysis of potentially toxic metals. Here, we used laser-induced breakdown spectroscopy (LIBS) and partial least squares regression (PLSR) for fast on-site quantitative analysis of potentially toxic metals in wet soil samples. The model offered direct measurement of the moisture content in soil samples: the coefficient of determination (R-2) value and the root-mean-square error of prediction (RMSEP) of predicted moisture content were 0.98% and 1.3%, respectively. The correlation between laser ablation factor and moisture content of the samples was determined by analysing the influence of sample moisture content on laser ablation. Quantitative analysis of wet soil samples based on ablation factor was established with an R-2 of 0.97. Potentially toxic elemental Cu and Cr in sample 4# were verified by the quantitative analysis model through weighing and direct measurement. The relative error of concentration was within 10%, and the accuracy was improved by over 80%. The preliminary results show that the quantitative analysis model of potentially toxic metals in wet soil samples based on LIBS technology can detect potentially toxic metals in wet soil samples quickly and accurately on-site. This has important guiding significance for real-time and on-site monitoring of contaminated soils. Highlights Fast on-site quantitative analysis of potentially toxic metals in wet soil samples Quantitative analysis of wet soil samples based on ablation factor was established The relative error of concentration was within 10%, and the accuracy was improved by over 80% The model can detect potentially toxic metals in wet soil samples quickly and accurately on-site
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Key words
ablation factor, coefficient of determination, contamination monitoring, laser-induced breakdown spectroscopy, moisture content, on-site, partial least squares regression
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