ABSTRACT
The present study demonstrates the efficiency of applying statistical models to estimate airborne pollutant concentrations in urban vegetation by using as predictor variables readily available or easily accessible data. Results revealed that airborne cadmium concentrations in vegetation showed a predictable response to wind conditions and to various urban landscape features such as the distance between the vegetation and the adjacent street, the mean height of the adjacent buildings, the mean density of vegetation between vegetation and the adjacent street and the mean height of vegetation. An artificial neural network (ANN) model was found to have superiority in terms of accuracy with an R(2) value on the order of 0.9. The lowest R(2) value (on the order of 0.7) was associated with the linear model (SMLR model). The linear model with interactions (SMLRI model) and the tree regression (RTM) model gave similar results in terms of accuracy with R(2) values on the order of 0.8. The improvement of the results with the use of the non-linear models (RTM and ANN) and the inclusion of interaction terms in the SMLRI model implied the nonlinear relationships of pollutant concentrations to the selected predictors and showed the importance of the interactions between the various predictor variables. Despite the limitations of the models, some of them appear to be promising alternatives to multimedia-based simulation modeling approaches in urban areas with vegetation, where the application of typical deposition models is sometimes limited.
