Air quality in urban settings and environmental disruptions associated with traffic operations significantly impact public well-being and sustainable development of cities. The current research focuses on the interdependence between emissions of particulate matter and characteristics of traffic activity in terms of frequency distributions, especially the interaction patterns observed at different frequency levels and vertical dispersal properties. A set of data related to particulate matter concentrations and frequency-specific indicators were processed via the random forest analysis approach in order to detect non-linear associations between the considered factors including the height of measuring point, the type of pavement, and frequency characteristics. The findings prove that the height of measuring point is the key predictor of the concentrations of PM₁ and PM₂.₅. At the same time, model performance drops when estimating PM₄.₂₅ and PM₁₀ values.
Mak et al. (Mon,) studied this question.