RESUMO
Communities located in near-road environments are exposed to traffic-related air pollution (TRAP), causing adverse health effects. While roadside vegetation barriers can help mitigate TRAP, their effectiveness to reduce TRAP is influenced by site-specific conditions. To test vegetation designs using direct field measurements or high-fidelity numerical simulations is often infeasible since urban planners and local communities often lack the access and expertise to use those tools. There is a need for a fast, reliable, and easy-to-use method to evaluate vegetation barrier designs based on their capacity to mitigate TRAP. In this paper, we investigated five machine learning (ML) methods, including linear regression (LR), support vector machine (SVM), random forest (RF), XGBoost (XGB), and neural networks (NN), to predict size-resolved and locationally dependent particle concentrations downwind of various vegetation barrier designs. Data from 83 computational fluid dynamics (CFD) simulations was used to train and test the ML models. We developed downwind region-specific models to capture the complexity of this problem and enhance the overall accuracy. Our feature space was composed of variables that can be feasibly obtained such as vegetation width, height, leaf area index (LAI), particle size, leaf area density (LAD) and wind speed at different heights. RF, NN, and XGB performed well with a normalized root mean square error (NRMSE) of 6-7% and an average test R2 value >0.91, while SVM and LR had an NRMSE of approximately 13% and an average test R2 value of 0.56. Using feature selection, vegetation dimensions and particle size had the highest influence in predicting pollutant concentrations. The ML models developed can help create tools to aid local communities in developing mitigation strategies to address TRAP problems.