Classification and regression trees (CARTs) for modelling the sorption and retention of heavy metals by soil

Abstract

The sorption and retention of mixtures of heavy metals by soil is a complex process that depends on both soil properties and competition between metals for sorption sites. In this study, the sorption and retention of mixtures of Cd, Cr, Pb, Cu, Zn and Ni by a representative sample of soils from Galicia (N.W. Spain) was reproduced considerably more precisely by binary decision-tree regression models constructed using the CART algorithm than by linear regression models. Of the six metals competing for sorption sites in these experiments, Pb, Cu and Cr were sorbed and retained to a greater extent than Cd, Ni and Zn. Non-linear tree regression models constructed with CART fitted the data better than linear models, especially for Cd, Ni and Zn; and with both kinds of model the data for Pb, Cu and Cr were fitted better than those for Cd, Ni and Zn (the difference being much more marked for linear models), suggesting that the influence of soil properties on the sorption and retention of the latter three metals was limited by the preferential binding of the former three.