Near-road air quality modelling that incorporates input variability and model uncertainty

Dispersion modelling is an effective tool to estimate traffic-related fine particulate matter (PM_2.5) concentrations in near-road environments. However, many sources of uncertainty and variability are associated with the process of near-road dispersion modelling, which renders a single-number estimate of concentration a poor indicator of near-road air quality. In this study, we propose an integrated traffic-emission-dispersion modelling chain that incorporates several major sources of uncertainty. Our approach generates PM_2.5 probability distributions capturing the uncertainty in emissions and meteorological conditions. Traffic PM_2.5 emissions from 7 a.m. to 6 p.m. were estimated at 3400 ± 117 g. Modelled PM_2.5 levels were validated against measurements along a major arterial road in Toronto, Canada. We observe large overlapping areas between modelled and measured PM_2.5 distributions at all locations along the road, indicating a high likelihood that the model can reproduce measured concentrations. A policy scenario expressing the impact of reductions in truck emissions revealed that a 30% reduction in near-road PM_2.5 concentrations can be achieved by upgrading close to 55% of the current trucks circulating along the corridor. A speed limit reduction of 10 km/h could lead to statistically significant increases in PM_2.5 concentrations at twelve out of the eighteen locations.