Articles | Volume 16, issue 20
https://doi.org/10.5194/gmd-16-5729-2023
https://doi.org/10.5194/gmd-16-5729-2023
Model description paper
 | 
17 Oct 2023
Model description paper |  | 17 Oct 2023

QES-Plume v1.0: a Lagrangian dispersion model

Fabien Margairaz, Balwinder Singh, Jeremy A. Gibbs, Loren Atwood, Eric R. Pardyjak, and Rob Stoll

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Cited articles

Archambeau, F., Méchitoua, N., and Sakiz, M.: Code Saturne: A Finite Volume Code for the computation of turbulent incompressible flows – Industrial Applications, International Journal on Finite Volumes, 1, https://hal.science/hal-01115371 (last access: 26 September 2023), 2004. a
Aylor, D.: Aerial Dispersal of Pollen and Spores, The American Phytopathological Society, St. Paul, Minnesota, USA, https://doi.org/10.1094/9780890545430, 2017. a
Aylor, D. E.: Spread of plant disease on a continental scale: role of aerial dispersal of pathogens, Ecology, 84, 1989–1997, https://doi.org/10.1890/01-0619, 2003. a
Bahlali, M. L., Dupont, E., and Carissimo, B.: A hybrid CFD RANS/Lagrangian approach to model atmospheric dispersion of pollutants in complex urban geometries, Int. J. Environ. Pollut., 64, 74–89, https://doi.org/10.1504/ijep.2018.099150, 2018. a
Bahlali, M. L., Dupont, E., and Carissimo, B.: Atmospheric dispersion using a Lagrangian stochastic approach: Application to an idealized urban area under neutral and stable meteorological conditions, J. Wind Eng. Ind. Aerod., 193, 103976, https://doi.org/10.1016/j.jweia.2019.103976, 2019. a
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The Quick Environmental Simulation (QES) tool is a low-computational-cost fast-response framework. It provides high-resolution wind and concentration information to study complex problems, such as spore or smoke transport, urban pollution, and air quality. This paper presents the particle dispersion model and its validation against analytical solutions and wind-tunnel data for a mock-urban setting. In all cases, the model provides accurate results with competitive computational performance.
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