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A CFD study on the effectiveness of trees to disperse road traffic emissions at a city scale

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posted on 2015-09-02, 11:30 authored by Antoine P. R. Jeanjean, G. Hinchliffe, W. A. McMullan, Paul S. Monks, Roland J. Leigh
This paper focuses on the effectiveness of trees at dispersing road traffic emissions on a city scale. CFD simulations of air-pollutant concentrations were performed using the OpenFOAM software platform using the k-ε model. Results were validated against the CODASC wind tunnel database before being applied to a LIDAR database of buildings and trees representing the City of Leicester (UK). Most other CFD models in the literature typically use idealised buildings to model wind flow and pollution dispersion. However, the methodology used in this study uses real buildings and trees data from LIDAR to reconstruct a 3D representation of Leicester City Centre. It focuses on a 2 × 2 km area which is on a scale larger than those usually used in other CFD studies. Furthermore, the primary focus of this study is on the interaction of trees with wind flow dynamics. It was found that in effect, trees have a regionally beneficial impact on road traffic emissions by increasing turbulence and reducing ambient concentrations of road traffic emissions by 7 % at pedestrian height on average. This was an important result given that previous studies generally concluded that trees trapped pollution by obstructing wind flow in street canyons. Therefore, this study is novel both in its methodology and subsequent results, highlighting the importance of combining local and regional scale models for assessing the impact of trees in urban planning.

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Citation

Atmospheric Environment, 2015, 120, pp. 1-14

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Chemistry

Version

  • VoR (Version of Record)

Published in

Atmospheric Environment

Publisher

Elsevier

issn

1352-2310

Acceptance date

2015-08-03

Copyright date

2015

Available date

2015-09-02

Publisher version

http://www.sciencedirect.com/science/article/pii/S135223101530248X

Language

en

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