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Evaluation of fast atmospheric dispersion models in a regular street network

Evaluation of fast atmospheric dispersion models in a regular street network
Evaluation of fast atmospheric dispersion models in a regular street network
The need to balance computational speed and simulation accuracy is a key challenge in designing atmospheric dispersion models that can be used in scenarios where near real-time hazard predictions are needed. This challenge is aggravated in cities, where models need to have some degree of building-awareness, alongside the ability to capture effects of dominant urban flow processes. We use comprehensive large-eddy simulation (LES) and wind-tunnel data of flow and dispersion in an idealised urban canopy to highlight important dispersion processes and evaluate how these are reproduced by representatives of the most prevalent modelling approaches: (1) a Gaussian plume model, (2) a Lagrangian stochastic model and (3) street-network dispersion models. The poorest model performances were associated with mean over-predictions of concentrations of approximately a factor of 2 and with a relative scatter larger than a factor of 4 of the mean, corresponding to cases where the mean plume centreline deviated significantly from the LES and wind-tunnel observations. This was linked to a low accuracy of the underlying flow models/parameters that resulted in a misrepresentation of pollutant channelling along streets and of the uneven plume branching observed in intersections. Model performances greatly improved by increasing the accuracy of the driving flow field. When provided with a limited set of representative velocity parameters, the comparatively simple street-network models performed equally well or better compared to the Lagrangian model run on full 3D wind fields. The study showed that network models capture the dominant building-induced dispersion processes in the canopy layer through parametrisations of horizontal advection and vertical exchange processes at scales of practical interest. At the same time, computational costs and computing times associated with the network approach are ideally suited for emergency-response applications.
Pollutant dispersion, Urban environment, Street-network model, Gaussian plume model, Lagrangian stochastic model, Model validation
1567-7419
1-38
Hertwig, Denise
3e9f6800-a1ad-42ca-bec0-3e455fa8033a
Soulhac, Lionel
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Fuka, Vladimir
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Auerswalda, Torsten
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Hayden, Paul
b5662acc-6375-4f86-afee-04c779155d5f
Robins, Alan
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Xie, Zhengtong
98ced75d-5617-4c2d-b20f-7038c54f4ff0
Coceal, Omduth
c89d9de1-c311-4203-b6ea-b72eb48fa4f9
Hertwig, Denise
3e9f6800-a1ad-42ca-bec0-3e455fa8033a
Soulhac, Lionel
9e07e70c-837c-4b0c-b22d-82dc4bdfbb11
Fuka, Vladimir
88c0044a-1c97-44cf-9238-785603e752a9
Auerswalda, Torsten
745ecf80-86f4-4641-892c-ca163bdecfcd
Hayden, Paul
b5662acc-6375-4f86-afee-04c779155d5f
Robins, Alan
fae9b9ac-6c19-4f4a-b265-a26cdd675585
Xie, Zhengtong
98ced75d-5617-4c2d-b20f-7038c54f4ff0
Coceal, Omduth
c89d9de1-c311-4203-b6ea-b72eb48fa4f9

Hertwig, Denise, Soulhac, Lionel, Fuka, Vladimir, Auerswalda, Torsten, Hayden, Paul, Robins, Alan, Xie, Zhengtong and Coceal, Omduth (2018) Evaluation of fast atmospheric dispersion models in a regular street network. Environmental Fluid Mechanics, 1-38. (doi:10.1007/s10652-018-9587-7).

Record type: Article

Abstract

The need to balance computational speed and simulation accuracy is a key challenge in designing atmospheric dispersion models that can be used in scenarios where near real-time hazard predictions are needed. This challenge is aggravated in cities, where models need to have some degree of building-awareness, alongside the ability to capture effects of dominant urban flow processes. We use comprehensive large-eddy simulation (LES) and wind-tunnel data of flow and dispersion in an idealised urban canopy to highlight important dispersion processes and evaluate how these are reproduced by representatives of the most prevalent modelling approaches: (1) a Gaussian plume model, (2) a Lagrangian stochastic model and (3) street-network dispersion models. The poorest model performances were associated with mean over-predictions of concentrations of approximately a factor of 2 and with a relative scatter larger than a factor of 4 of the mean, corresponding to cases where the mean plume centreline deviated significantly from the LES and wind-tunnel observations. This was linked to a low accuracy of the underlying flow models/parameters that resulted in a misrepresentation of pollutant channelling along streets and of the uneven plume branching observed in intersections. Model performances greatly improved by increasing the accuracy of the driving flow field. When provided with a limited set of representative velocity parameters, the comparatively simple street-network models performed equally well or better compared to the Lagrangian model run on full 3D wind fields. The study showed that network models capture the dominant building-induced dispersion processes in the canopy layer through parametrisations of horizontal advection and vertical exchange processes at scales of practical interest. At the same time, computational costs and computing times associated with the network approach are ideally suited for emergency-response applications.

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Submitted date: April 2017
Accepted/In Press date: 1 March 2018
e-pub ahead of print date: 10 March 2018
Keywords: Pollutant dispersion, Urban environment, Street-network model, Gaussian plume model, Lagrangian stochastic model, Model validation
Organisations: Aeronautics, Astronautics & Comp. Eng, Aerodynamics & Flight Mechanics Group

Identifiers

Local EPrints ID: 408703
URI: http://eprints.soton.ac.uk/id/eprint/408703
ISSN: 1567-7419
PURE UUID: 4ba5a3c6-c3bc-4b28-911b-c04768276710
ORCID for Zhengtong Xie: ORCID iD orcid.org/0000-0002-8119-7532

Catalogue record

Date deposited: 26 May 2017 04:03
Last modified: 07 Oct 2020 05:53

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Contributors

Author: Denise Hertwig
Author: Lionel Soulhac
Author: Vladimir Fuka
Author: Torsten Auerswalda
Author: Paul Hayden
Author: Alan Robins
Author: Zhengtong Xie ORCID iD
Author: Omduth Coceal

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