Characterizing polycyclic aromatic hydrocarbon build-up processes on urban road surfaces.

Reliable prediction models are essential for modeling pollutant build-up processes on urban road surfaces. Based on successive samplings of road deposited sediments (RDS), this study presents empirical models for mathematical replication of the polycyclic aromatic hydrocarbon (PAH) build-up processes on urban road surfaces. The contaminant build-up behavior was modeled using saturation functions, which are commonly applied in US EPA's Stormwater Management Model (SWMM). Accurate fitting results were achieved in three typical urban land use types, and the applicability of the models was confirmed based on their acceptable relative prediction errors. The fitting results showed high variability in PAH saturation value and build-up rate among different land use types. Results of multivariate data and temporal-based analyses suggested that the quantity and property of RDS significantly influenced PAH build-up. Furthermore, pollution sources, traffic parameters, road surface conditions, and sweeping frequency could synthetically impact the RDS build-up and RDS property change processes. Thus, changes in these parameters could be the main reason for variations in PAH build-up in different urban land use types.