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Incorporating terminal velocities into Lagrangian stochastic models of particle dispersal in the atmospheric boundary layer

Lagrangian stochastic models for simulation of tracer-particle trajectories in turbulent flows can be adapted for simulation of particle trajectories. This is conventionally done by replacing the zero-mean fall speed of a tracer-particle with the terminal speed of the particle. Such models have been...

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Autor principal: Reynolds, Andy M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6237984/
https://www.ncbi.nlm.nih.gov/pubmed/30442966
http://dx.doi.org/10.1038/s41598-018-34924-4
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author Reynolds, Andy M.
author_facet Reynolds, Andy M.
author_sort Reynolds, Andy M.
collection PubMed
description Lagrangian stochastic models for simulation of tracer-particle trajectories in turbulent flows can be adapted for simulation of particle trajectories. This is conventionally done by replacing the zero-mean fall speed of a tracer-particle with the terminal speed of the particle. Such models have been used widely to predict spore and pollen dispersal. Here I show that this modification predicts that particles become uniformly distributed throughout the air column, which is at variance with the seminal experimental studies of Hirst et al. (1967) that demonstrated spore concentrations (and pollen concentrations) declined exponentially with height in unstable air. This discrepancy arises because the terminal speed, which is a Lagrangian property of a particle, has always been treated as if it were an Eulerian property of an ensemble of particles. In this study models are formulated correctly. I show that the mean acceleration of a tracer-particle should be replaced by the mean acceleration of a particle. Model predictions for aerial density profiles then agreed with the observations of Hirst et al. (1967) and with observations of ground-level concentrations but differed significantly from predictions obtained using conventional models. In accordance with the results of numerical simulations, the models also predict that particles are moving downwind marginally more slowly than the wind itself. Finally, the new modelling approach can be extended to predict the dispersal of small insects with active flight behaviours.
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spelling pubmed-62379842018-11-23 Incorporating terminal velocities into Lagrangian stochastic models of particle dispersal in the atmospheric boundary layer Reynolds, Andy M. Sci Rep Article Lagrangian stochastic models for simulation of tracer-particle trajectories in turbulent flows can be adapted for simulation of particle trajectories. This is conventionally done by replacing the zero-mean fall speed of a tracer-particle with the terminal speed of the particle. Such models have been used widely to predict spore and pollen dispersal. Here I show that this modification predicts that particles become uniformly distributed throughout the air column, which is at variance with the seminal experimental studies of Hirst et al. (1967) that demonstrated spore concentrations (and pollen concentrations) declined exponentially with height in unstable air. This discrepancy arises because the terminal speed, which is a Lagrangian property of a particle, has always been treated as if it were an Eulerian property of an ensemble of particles. In this study models are formulated correctly. I show that the mean acceleration of a tracer-particle should be replaced by the mean acceleration of a particle. Model predictions for aerial density profiles then agreed with the observations of Hirst et al. (1967) and with observations of ground-level concentrations but differed significantly from predictions obtained using conventional models. In accordance with the results of numerical simulations, the models also predict that particles are moving downwind marginally more slowly than the wind itself. Finally, the new modelling approach can be extended to predict the dispersal of small insects with active flight behaviours. Nature Publishing Group UK 2018-11-15 /pmc/articles/PMC6237984/ /pubmed/30442966 http://dx.doi.org/10.1038/s41598-018-34924-4 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Reynolds, Andy M.
Incorporating terminal velocities into Lagrangian stochastic models of particle dispersal in the atmospheric boundary layer
title Incorporating terminal velocities into Lagrangian stochastic models of particle dispersal in the atmospheric boundary layer
title_full Incorporating terminal velocities into Lagrangian stochastic models of particle dispersal in the atmospheric boundary layer
title_fullStr Incorporating terminal velocities into Lagrangian stochastic models of particle dispersal in the atmospheric boundary layer
title_full_unstemmed Incorporating terminal velocities into Lagrangian stochastic models of particle dispersal in the atmospheric boundary layer
title_short Incorporating terminal velocities into Lagrangian stochastic models of particle dispersal in the atmospheric boundary layer
title_sort incorporating terminal velocities into lagrangian stochastic models of particle dispersal in the atmospheric boundary layer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6237984/
https://www.ncbi.nlm.nih.gov/pubmed/30442966
http://dx.doi.org/10.1038/s41598-018-34924-4
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