1. Near-road air quality modelling that incorporates input variability and model uncertainty.
- Author
-
Wang, An, Xu, Junshi, Tu, Ran, Zhang, Mingqian, Adams, Matthew, and Hatzopoulou, Marianne
- Subjects
AIR quality ,UNCERTAINTY ,PARTICULATE matter ,DISTRIBUTION (Probability theory) ,SPEED limits - Abstract
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. [Display omitted] • A traffic-emission-dispersion model is developed within the R-LINE framework. • Trucks emit half of traffic PM 2.5 and contribute most to emission uncertainty. • Modelled PM 2.5 levels match measurements at locations least affected by background. • Traffic variability is the main driver of uncertainty. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF