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Reusing soils near abandoned mines requires the assessment of soil quality, which includes determining potentially toxic elements (PTEs), such as As, Cd, Co, Cr, Cu, Fe, Mo, Ni, Pb, Sr, Zn and Zr. Levels of PTEs in soil can be harmful. Hence, the measurement of their concentrations is crucial to assess whether soil properties are reusable or it represents a potential environmental risk. Field techniques such as X-ray fluorescence (XRF) imprinting may be an option for rapid PTEs monitoring. Still, due to low sensitivity and selectivity, the partially obtained results by XRF software can be biased. This study presents an alternative solution for soil PTEs monitoring based on the advantages of multivariate analysis (MVA) principally partial least square (PLS) regression applied to orthogonally signal-corrected (OSC) XRF spectroscopic data. The developed PLS models were applied to soil samples from two regions of adjacent abandoned coal mines, in NW Portugal. High correlation coefficients obtained for As, Fe, Pb, Sr and Zn validation models (R 2 = 0.79–0.99) pointed to the improved accuracy of their monitoring (compared to directly obtained XRF results) in this regional soil environment. The other PTEs (Co, Cr, Cu, Ni and Zr) showed good PLS models at local environment (R 2 = 0.84–0.98). The test of these models in the contaminated regions reinforces their effectiveness in monitoring contaminated soils toward the reuse of environments near abandoned mines. • XRF spectra imprints reflect the chemical composition of the soil around coal mines. • PLS applied to XRF spectra provided advanced models for monitoring contaminants. • PLS models for As, Fe, Pb, Sr and Zn levels were validated at the regional level. • Co, Cr, Cu, Ni and Zr showed accurate PLS models in the local coal mine environment. • PLS model testing reinforces effectiveness in soil exploration near abandoned mines.
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Milinović et al. (2024) studied this question.
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