Accounting for the effect of concentration fluctuations on toxic load for gaseous releases of carbon dioxide

Gant, Simon; Kelsey, Adrian

doi:10.1016/j.jlp.2011.06.028

Cited by 10 publications

(6 citation statements)

References 40 publications

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“…Conversely, for the assessment of accidental hazards due to toxic or explosive airborne pollutants (inflammability and toxicity), knowledge of the mean concentration (Eq. 1) must be coupled with the probability of exceeding a specific concentration threshold and the expected mean time above the threshold (e.g., Gant et al 2011;Gant and Kelsey 2012;Gunatilaka et al 2014). Similarly, the impact of odours depends on instantaneous peak concentrations (e.g., Capelli et al 2013;Sommer-Quabach et al 2014), or concentrations averaged over the duration of one human breath (Mainland and Sobel 2006), to which the human nose is sensitive.…”

Section: Introductionmentioning

confidence: 99%

Concentration Fluctuations from Localized Atmospheric Releases

Cassiani

Bertagni

Marro

et al. 2020

Boundary-Layer Meteorol

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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.

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Section: Introductionmentioning

confidence: 99%

Concentration Fluctuations from Localized Atmospheric Releases

Cassiani

Bertagni

Marro

et al. 2020

Boundary-Layer Meteorol

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“…For example, Gant et al (2011) relate the likelihood of ignition of a flammable substance to the integral of the concentration PDF between the upper and lower flammability limits at any point. Gant and Kelsey (2012) relate instead the toxic load directly to the integral of the n-th power of the concentration times the concentration PDF (here n is the toxic load exponent, which Gant and Kelsey (2012) consider equal to two for chlorine and to eight for carbon dioxide). More generally, the knowledge of the concentration PDF is necessary but not sufficient to define the toxic load, which may also require the additional formulation of a model for the correlated concentration time series (e.g.…”

Section: Introductionmentioning

confidence: 99%

Dispersion of a Passive Scalar Fluctuating Plume in a Turbulent Boundary Layer. Part III: Stochastic Modelling

Marro

Salizzoni

Soulhac

et al. 2018

Boundary-Layer Meteorol

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We analyze the reliability of the Lagrangian stochastic micromixing method in predicting higher-order statistics of the passive scalar concentration induced by an elevated source (of varying diameter) placed in a turbulent boundary layer. To that purpose we analyze two different modelling approaches by testing their results against the wind-tunnel measurements discussed in Part I (Nironi et al., Boundary-Layer Meteorology, 2015, Vol.156, 415-446). The first is a probability density function (PDF) micromixing model that simulates the effects of the molecular diffusivity on the concentration fluctuations by taking into account the background particles. The second is a new model, named VPΓ , conceived in order to minimize the computational costs. This is based on the volumetric particle approach providing estimates of the first two concentration moments with no need for the simulation of the background particles. In this second approach, higher-order moments are computed based on the estimates of these two moments and under the assumption that the concentration PDF is a Gamma distribution. The comparisons concern the spatial distribution of the first four moments of the concentration and the evolution of the PDF along the plume centreline. The novelty of this work is twofold: i) we perform a systematic comparison of the results of micro-mixing Lagrangian models against experiments providing profiles of the first four moments of the concentration within an inhomogeneous and anisotropic turbulent flow, and ii) we show the reliability of the VPΓ model as an operational tool for the prediction of the PDF of the concentration.

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“…The 3-D concentration field is needed to evaluate the meandering and relative dispersion process in the three physical directions. An important point to recognize is that the production and dissipation of concentration fluctuation for a dispersion scalar are intimately linked to the process of relative dispersion of puffs and the related process of centre of mass meandering (Gifford, 1959;Csanady, 1973). Therefore, parameterized expressions of relative dispersion are used in defining simplified models of concentration fluctuations (e.g.…”

Section: Introductionmentioning

confidence: 99%

Observation of turbulent dispersion of artificially released SO<sub>2</sub> puffs with UV cameras

et al. 2018

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Abstract. In atmospheric tracer experiments, a substance is released into the turbulent atmospheric flow to study the dispersion parameters of the atmosphere. That can be done by observing the substance's concentration distribution downwind of the source. Past experiments have suffered from the fact that observations were only made at a few discrete locations and/or at low time resolution. The Comtessa project (Camera Observation and Modelling of 4-D Tracer Dispersion in the Atmosphere) is the first attempt at using ultraviolet (UV) camera observations to sample the three-dimensional (3-D) concentration distribution in the atmospheric boundary layer at high spatial and temporal resolution. For this, during a three-week campaign in Norway in July 2017, sulfur dioxide (SO2), a nearly passive tracer, was artificially released in continuous plumes and nearly instantaneous puffs from a 9 m high tower. Column-integrated SO2 concentrations were observed with six UV SO2 cameras with sampling rates of several hertz and a spatial resolution of a few centimetres. The atmospheric flow was characterised by eddy covariance measurements of heat and momentum fluxes at the release mast and two additional towers. By measuring simultaneously with six UV cameras positioned in a half circle around the release point, we could collect a data set of spatially and temporally resolved tracer column densities from six different directions, allowing a tomographic reconstruction of the 3-D concentration field. However, due to unfavourable cloudy conditions on all measurement days and their restrictive effect on the SO2 camera technique, the presented data set is limited to case studies. In this paper, we present a feasibility study demonstrating that the turbulent dispersion parameters can be retrieved from images of artificially released puffs, although the presented data set does not allow for an in-depth analysis of the obtained parameters. The 3-D trajectories of the centre of mass of the puffs were reconstructed enabling both a direct determination of the centre of mass meandering and a scaling of the image pixel dimension to the position of the puff. The latter made it possible to retrieve the temporal evolution of the puff spread projected to the image plane. The puff spread is a direct measure of the relative dispersion process. Combining meandering and relative dispersion, the absolute dispersion could be retrieved. The turbulent dispersion in the vertical is then used to estimate the effective source size, source timescale and the Lagrangian integral time. In principle, the Richardson–Obukhov constant of relative dispersion in the inertial subrange could be also obtained, but the observation time was not sufficiently long in comparison to the source timescale to allow an observation of this dispersion range. While the feasibility of the methodology to measure turbulent dispersion could be demonstrated, a larger data set with a larger number of cloud-free puff releases and longer observation times of each puff will be recorded in future studies to give a solid estimate for the turbulent dispersion under a variety of stability conditions.

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Accounting for the effect of concentration fluctuations on toxic load for gaseous releases of carbon dioxide

Cited by 10 publications

References 40 publications

Concentration Fluctuations from Localized Atmospheric Releases

Concentration Fluctuations from Localized Atmospheric Releases

Dispersion of a Passive Scalar Fluctuating Plume in a Turbulent Boundary Layer. Part III: Stochastic Modelling

Observation of turbulent dispersion of artificially released SO<sub>2</sub> puffs with UV cameras

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Accounting for the effect of concentration fluctuations on toxic load for gaseous releases of carbon dioxide

Cited by 10 publications

References 40 publications

Concentration Fluctuations from Localized Atmospheric Releases

Concentration Fluctuations from Localized Atmospheric Releases

Dispersion of a Passive Scalar Fluctuating Plume in a Turbulent Boundary Layer. Part III: Stochastic Modelling

Observation of turbulent dispersion of artificially released SO&lt;sub&gt;2&lt;/sub&gt; puffs with UV cameras

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Observation of turbulent dispersion of artificially released SO<sub>2</sub> puffs with UV cameras