How parked cars affect pollutant dispersion at street level in an urban street canyon? A CFD modelling exercise assessing geometrical detailing and pollutant decay rates

摘要

Computational fluid dynamics (CFD) is a recognised modelling tool for simulating pollutant dispersion in urban street canyons. These studies require accurate street geometry, sufficient modelling experience and access to software to provide reliable outputs. Parked cars are known to act as a passive barrier and affects pollutant dispersion in an urban street canyon. This modelling study examines the impact of accurate geometrical detailing and suitable mesh sizing on air pollution concentrations measured at street level. Mean steady state concentrations of carbon monoxide (CO) were measured in a reference scenario (without parked cars) and three parked car scenarios. A comparison of the results indicated that individual rectangular blocks and generic car scenarios overestimated pollutant concentrations on the footpaths by up to 25% when compared to the results from the five distinct car designs scenario, with the parked cars acting as a passive barrier in each scenario under different wind conditions. The most notable finding demonstrated how the five distinct car designs scenario presented an increase in CO concentrations on the leeward footpath as opposed to a reduction in the results in the other coarser geometrically detailed scenarios. The parked cars trapped pollutants in each scenario with low wind speeds for perpendicular wind conditions, as higher concentrations were measured on the footpaths. A further examination of pollutant decay rates demonstrated how the geometrical detailing of the parked cars also impacted upon the duration taken for pollutants to escape from the footpath zones. These findings demonstrate how parked cars affects pollutant dispersion and concentrations measured on the footpaths at street level, and the adoption of a suitable meshing scheme is vital to capture results in the zone of interest in an urban street canyon. (C) 2018 Elsevier B.V. All rights reserved.