Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/38721
Title: Modelling the effectiveness of urban trees and grass on PM<inf>2.5</inf> reduction via dispersion and deposition at a city scale
Authors: Jeanjean, Antoine P. R.
Monks, Paul S.
Leigh, Roland J.
First Published: 19-Sep-2016
Publisher: Elsevier
Citation: Atmospheric Environment, 2016, 147, pp. 1-10
Abstract: Green infrastructure can reduce PM2.5 traffic emissions on a city scale, by a combination of dispersion by trees and deposition on buildings, trees and grass. Simulations of PM2.5 concentrations were performed using a validated CFD model. A 2 × 2 km area has been reconstructed as a 3D representation of Leicester (UK) city centre which is on a scale larger than most of the other CFD studies. Combining both the effects of tree aerodynamics and the deposition capabilities of trees and grass is also something that has not yet been modelled at this scale. During summer time in Leicester City, the results show that the aerodynamic dispersive effect of trees on PM2.5 concentrations result in a 9.0% reduction. In contrast, a decrease of PM2.5, by 2.8% owing to deposition on trees (11.8 t year−1) and 0.6% owing to deposition on grass (2.5 t year−1), was also observed. Trees and grass are shown to have greater effects locally, as smaller decreases in PM2.5 were found when considering reduction across the whole boundary layer. Densely built areas like Leicester City centre have relatively less vegetation and subsequently have a smaller effect on PM2.5 concentration. It was found that particle deposition on buildings was negligible with less than 0.03%. An empirical equation was derived to describe the changes in PM2.5 based on ground surface fraction of trees and grass, and their deposition velocities.
DOI Link: 10.1016/j.atmosenv.2016.09.033
ISSN: 1352-2310
eISSN: 1873-2844
Links: http://www.sciencedirect.com/science/article/pii/S1352231016307336
http://hdl.handle.net/2381/38721
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Appears in Collections:Published Articles, College of Science and Engineering

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