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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 R2 value on the order of 0.9. The lowest R2 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 R2 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. [ABSTRACT FROM AUTHOR]

Predictive modeling techniques are applied to investigate their potential usefulness in providing first order estimates on atmospheric emission flux of gaseous soil mercury and in identifying those parameters most critical in controlling such emissions. Predicted data by simulation and statistical techniques are compared to previously published observational data. Results showed that simulation techniques using air/soil coupling may provide a plausible description of mercury flux trends with a RMSE of 24.4 ng m[sup -2] h[sup -1] and a mean absolute error of 10.2 ng m[sup -2] h[sup -1] or 11.9%. From the statistical models, two linear models showed the lowest predictive abilities (R²=0.76 and 0.84, respectively) while the Generalized Additive model showed the closest agreement between estimated and observational data (R²=0.93). Predicted values from a Neural Network model and the Locally Weighted Smoother model showed also very good agreement to measured values of mercury flux (R² =0.92). A Regression Tree model demonstrated also a satisfactory predictability with a value of R² = 0.90. Sensitivities and statistical analyses showed that surface soil mercury concentrations, solar radiation and, to a lesser degree, temperature are important parameters in predicting airborne Hg flux from terrestrial soils. These findings are compatible with results from recent experimental studies. Considering the uncertainties associated with mercury cycling and natural emissions, it is concluded, that predictions based on simple modeling techniques seem quite appropriate at present; they can be useful tools in evaluating the role of terrestrial emission sources as part of mercury modeling in local and regional airsheds. [ABSTRACT FROM AUTHOR]

Elaborates the 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. Advantages of using a linear statistical model for the assessment of airborne fluoride levels in natural vegetation; Evaluation of results obtained as a result of use of artificial neural networks on the same experiment; Comparative benefits of linear models over multimedia-based simulations.

Presents a model analysis of elemental mercury airborne emissions from soils in various terrestrial settings. Quantitative estimates of elemental mercury; Role of terrestrial settings in reducing Hg.