Concentration Fluctuations from Localized Atmospheric Releases

Abstract

We review the efforts made by the scientific community in more than seventy years to elucidate the behaviour of concentration fluctuations arising from localized atmospheric releases of dynamically passive and non-reactive scalars. Concentration fluctuations are relevant in many fields including the evaluation of toxicity, flammability, and odour nuisance. Characterizing concentration fluctuations requires not just the mean concentration but also at least the variance of the concentration in the location of interest. However, for most purposes the characterization of the concentration fluctuations requires knowledge of the concentration probability density function (PDF) in the point of interest and even the time evolution of the concentration. We firstly review the experimental works made both in the field and in the laboratory, and cover both point sources and line sources. Regarding modelling approaches, we cover analytical, semi-analytical, and numerical methods. For clarity of presentation we subdivide the models in two groups, models linked to a transport equation, which usually require a numerical resolution, and models mainly based on phenomenological aspects of dispersion, often providing analytical or semi-analytical relations. The former group includes: large-eddy simulations, Reynolds-averaged Navier–Stokes methods, two-particle Lagrangian stochastic models, PDF transport equation methods, and heuristic Lagrangian single-particle methods. The latter group includes: fluctuating plume models, semi-empirical models for the concentration moments, analytical models for the concentration PDF, and concentration time-series models. We close the review with a brief discussion highlighting possible useful additions to experiments and improvements to models.

Technical note: Interpretation of field observations of point-source methane plume using observation-driven large-eddy simulations

“…Finally, we will present concentration statistic in the absolute and relative coordinate systems. Additionally, we will compare these statistics to parametrizations that are commonly used in fluctuating plume models (Gailis et al, 2007;Marro et al, 2015;Cassiani et al, 2020). These fluctuating plume models have been validated against dispersion studies in laboratory channel flows, often by taking line transects through the plume (e.g.…”

“…One of the commonly used parameters to model the concentration pdf is the concentration fluctuation intensity, defined as i c = σc c (Gailis et al, 2007;Nironi et al, 2015;Cassiani et al, 2020), with c being the mean concentration (x of plumes averaged) and σ c its standard deviation. As pointed out in Marro et al (2015), the spatial evolution of this non-dimensional parameter is often assumed to depend only on the x-coordinate.…”

“…A large number of studies have found the Gamma distribution to be an appropriate description for the pdf of relative concentrations in the far-field (e.g. Dosio & de Arellano (2006), Nironi et al (2015), Marro et al (2015), Cassiani et al (2020)). In the far field, relative dispersion becomes the main mechanism that drives the plume fluctuations.…”

“…stationarity of the plume and the mean wind) that make their application challenging under the strongly transient conditions that often characterize the local atmospheric boundary. With the development in computer power in the past decades, high resolution models that are able to simultaneously resolve the turbulent velocity field and describe the transport of emitted tracers, large eddy simulations (LES), have been increasingly used for plume studies (Cassiani et al, 2020). LES explicitly resolve the largest eddies, which carry most of the energy, and parameterize the smallest scales using subgrid-scale models (e.g.…”

“…Finally, we will use LES to study the structure of the simulated plume in convective conditions and its behavior by analyzing the higher order statistical moments. We will build on the idealized studies Dosio & de Arellano (2006); Cassiani et al (2020) and apply the statistical analyses to simulations that represent realistic field conditions.…”