Near-road air quality modelling that incorporates input variability and model uncertainty
2021
Wang, An | Xu, Junshi | Tu, Ran | Zhang, Mingqian | Adams, Matthew | Hatzopoulou, Marianne
Dispersion modelling is an effective tool to estimate traffic-related fine particulate matter (PM₂.₅) 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 PM₂.₅ probability distributions capturing the uncertainty in emissions and meteorological conditions. Traffic PM₂.₅ emissions from 7 a.m. to 6 p.m. were estimated at 3400 ± 117 g. Modelled PM₂.₅ levels were validated against measurements along a major arterial road in Toronto, Canada. We observe large overlapping areas between modelled and measured PM₂.₅ 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 PM₂.₅ 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 PM₂.₅ concentrations at twelve out of the eighteen locations.
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