Nuclear Technology and Radiation Protection (Jan 2022)

Analysis of 238U, 226Ra, and 210Pb transfer factors from soil to the leaves of broadleaf tree species

  • Vukašinović Ivana Ž.

DOI
https://doi.org/10.2298/NTRP2203219V
Journal volume & issue
Vol. 37, no. 3
pp. 219 – 228

Abstract

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This analysis of 238U, 226Ra and 210Pb transfer factors from the soil to the leaves of different native broadleaf trees at sites previously modified by uranium presence and at the site of background radioactivity levels, was conducted using data from a few available studies from the literature. The broadleaf tree species Quercus ilex, Quercus suber, Eucalyptus camaldulensis, Quercus pyrenaica, Quercus ilex rotundifolia, Populus sp. and Eucalyptus botryoides Sm. at the affected sites and Tilia spp. and Aesculus hippocastanum L. at the back ground site were in cluded in the study regardless of the deciduous or evergreen origins of the leaves. In the papers cited here, data about basic soil parameters: pH, total Ca [gkg-1], sand [%], and silt + clay [%] fractions were also available. All the collected data of activity concentration [Bqkg-1] dry weight in the soil (n=14) which was in the range: 22-6606 for 238U, 38-7700 for 226Ra, and 37-7500 for 210Pb, and the tree leaves in the range: 2.7-137.6 for 238U (n=10), 2.6-134.2 for 226Ra (n=14), and 27-77.2 for 210Pb (n=14), indicated that it was normally distributed after log-transformation. The present study was conducted under the hypothesis that biological differences between the examined broadleaf tree species have a lesser influence on the transfer factors of the investigated radionuclides from soil to tree leaves compared to the impact of the soil parameters and radionuclides activity concentrations in the soil. Consequently, it was examined whether 238U, 226Ra, and 210Pb soil-to-leaves transfer factor values for average broadleaf species could be predicted statistically in the first approximation based on their activity concentration in the soil and at least one basic soil parameter using multiple linear regression.

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