TY - JOUR
T1 - Near-road air quality modelling that incorporates input variability and model uncertainty
AU - Wang, An
AU - Xu, Junshi
AU - Tu, Ran
AU - Zhang, Mingqian
AU - Adams, Matthew
AU - Hatzopoulou, Marianne
N1 - Funding Information:
This study was funded by a grant from the X-Seed program at the University of Toronto , jointly held by Professors Matthew Adams and Marianne Hatzopoulou.
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/9/1
Y1 - 2021/9/1
N2 - 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.
AB - 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.
KW - Computer vision
KW - Fine particulate matter
KW - Monte-carlo simulation
KW - MOVES
KW - Near-road dispersion modelling
KW - RLINE
KW - Short-term fixed measurement
KW - Uncertainty analysis
UR - http://www.scopus.com/inward/record.url?scp=85107158310&partnerID=8YFLogxK
U2 - 10.1016/j.envpol.2021.117145
DO - 10.1016/j.envpol.2021.117145
M3 - Journal article
C2 - 33910134
AN - SCOPUS:85107158310
SN - 0269-7491
VL - 284
JO - Environmental Pollution
JF - Environmental Pollution
M1 - 117145
ER -