An application of parametric and nonparametric models to the assessment of fluoride levels in vegetation exposed to stack emissions of an aluminum reduction plant in Greece

Various statistical models were developed for assessing airborne fluoride (F) levels in natural vegetation near an aluminum reduction plant using as predictor variables the distance from the emission source, the predominating wind, and characteristic topography-geomorphology parameters. Results revealed that F concentrations in vegetation showed a predictable response to both wind conditions and landscape features. The linear model was found to give good estimations, taking advantage of the relatively strong linear correlation between concentration and distance. A nonlinear relationship between the F concentration in vegetation and the other variables was also found, while interactions between the variables were found to be non-first-order. The nonlinear relationship hypothesis was supported by the improved results of various nonlinear models that also indicated the importance of the area's topography-geomorphology and meteorology in model predictions. The application of an artificial neural network (ANN) model showed the closest agreement between predicted and observed values with a correlation coefficient of 0.92. The improved reliability of the ANN and a regression tree model (RTM) also were indicated by the normal distribution of their residuals. The RTM and the ANN were further investigated and found to be capable of identifying the importance of the variables in vegetation exposure to air emissions.