Predicting As, Cd, Cu, Pb and Zn levels in grasses (Agrostis sp. and Poa sp.) and stinging nettle (Urtica dioica) applying soil-plant transfer models

The aim of this study was to derive regression-based soil-plant 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.