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Environmental Fluid Mechanics

, Volume 18, Issue 4, pp 1007–1044 | Cite as

Evaluation of fast atmospheric dispersion models in a regular street network

  • Denise Hertwig
  • Lionel Soulhac
  • Vladimír Fuka
  • Torsten Auerswald
  • Matteo Carpentieri
  • Paul Hayden
  • Alan Robins
  • Zheng-Tong Xie
  • Omduth Coceal
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 a combination of high-resolution large-eddy simulation (LES) and wind-tunnel data of flow and dispersion in an idealised, equal-height 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. Concentration data from the LES, validated against the wind-tunnel data, were averaged over the volumes of streets in order to provide a high-fidelity reference suitable for evaluating the different models on the same footing. For the particular combination of forcing wind direction and source location studied here, the strongest deviations from the LES reference were associated with mean over-predictions of concentrations by 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 also deviated significantly from the LES. This was linked to 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. The agreement of model predictions with the LES (which explicitly resolves the turbulent flow and dispersion processes) greatly improved by increasing the accuracy of building-induced modifications 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 street-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.

Keywords

Pollutant dispersion Urban environment Street-network model Gaussian plume model Lagrangian stochastic model Model inter-comparison 

Notes

Acknowledgements

The DIPLOS project is funded by the UK’s Engineering and Physical Sciences Research Council Grants EP/K04060X/1 (Southampton), EP/K040731/1 (Surrey) and EP/K040707/1 (Reading). The EnFlo wind tunnel is an NCAS facility and we gratefully acknowledge ongoing NCAS support. We are grateful for comments and ongoing discussions with other colleagues at Surrey, Southampton and elsewhere. We thank Michael Brown and Eric Pardyjak for providing access to the QUIC dispersion modelling system and helpful discussions throughout this study. Stephen Belcher and Elisa Goulart are gratefully acknowledged for their development of and support with the University of Reading Street-Network Model (UoR-SNM). The wind-tunnel data measured in the DIPLOS project are available from the University of Surrey (DOI: https://doi.org/10.6084/m9.figshare.5297245). The LES data analysed in this study can be obtained from the University of Southampton (DOI: https://doi.org/10.5258/SOTON/D0314).

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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Denise Hertwig
    • 1
  • Lionel Soulhac
    • 2
  • Vladimír Fuka
    • 3
  • Torsten Auerswald
    • 1
  • Matteo Carpentieri
    • 4
  • Paul Hayden
    • 4
    • 5
  • Alan Robins
    • 4
  • Zheng-Tong Xie
    • 3
  • Omduth Coceal
    • 1
    • 5
  1. 1.Department of MeteorologyUniversity of ReadingReadingUK
  2. 2.Laboratoire de Mécanique des Fluids et d’AcoustiqueÉcole Centrale de LyonÉcullyFrance
  3. 3.Faculty of Engineering and the EnvironmentUniversity of SouthamptonSouthamptonUK
  4. 4.EnFlo, Department of Mechanical Engineering SciencesUniversity of SurreyGuildfordUK
  5. 5.National Centre for Atmospheric ScienceLeedsUK

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