Title
Predicting As, Cd, Cu, Pb and Zn levels in grasses (**Agrostis** sp. and **Poa** sp.) and stinging nettle (**Urtica dioica**) applying soilplant transfer models Predicting As, Cd, Cu, Pb and Zn levels in grasses (**Agrostis** sp. and **Poa** sp.) and stinging nettle (**Urtica dioica**) applying soilplant transfer models
Author
Faculty/Department
Faculty of Sciences. Biology
Publication type
article
Publication
Amsterdam ,
Subject
Chemistry
Biology
Source (journal)
The science of the total environment. - Amsterdam
Volume/pages
493(2014) , p. 862-871
ISSN
0048-9697
ISI
000340312000094
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
The aim of this study was to derive regression-based soilplant models to predict and compare metal(loid) (i.e. As, Cd, Cu, Pb and Zn) concentrations in plants (grass Agrostis sp./Poa sp. and nettle Urtica dioica L.) among sites with a wide range of metal pollution and a wide variation in soil properties. Regression models were based on the pseudo total (aqua-regia) and exchangeable (0.01 M CaCl2) soil metal concentrations. Plant metal concentrations were best explained by the pseudo total soil metal concentrations in combination with soil properties. The most important soil property that influenced U. dioica metal concentrations was the clay content, while for grass organic matter (OM) and pH affected the As (OM) and Cu and Zn (pH). In this study multiple linear regression models proved functional in predicting metal accumulation in plants on a regional scale. With the proposed models based on the pseudo total metal concentration, the percentage of variation explained for the metals As, Cd, Cu, Pb and Zn were 0.56%, 0.47%, 0.59%, 0.61%, 0.30% in nettle and 0.46%, 0.38%, 0.27%, 0.50%, 0.28% in grass.
E-info
https://repository.uantwerpen.be/docman/iruaauth/84e6c8/37615cbe426.pdf
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