Performance evaluation of dispersion parameterization schemes in the plume simulation of FFT-07 diffusion experiment

Pandey, Gavendra; Sharan, Maithili

doi:10.1016/j.atmosenv.2017.10.043

Cited by 10 publications

(6 citation statements)

References 30 publications

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“…On the contrary, the FAC2 that we found is worse. Concerning the unstable conditions, our results are worse with respect to those of [23] except for one of the parameterizations that they took into account.…”

Section: Discussioncontrasting

confidence: 89%

“…Comparing the results that we obtain for the mean concentrations using Hanna (Sa) parameterization with those estimated by [23], that evaluated the performance of a number of dispersion schemes for this data set, we can observed that in the case of stable conditions NMSE is of the order of the best values found in that paper, while FB is in the middle of the range of values obtained by [23]. On the contrary, the FAC2 that we found is worse.…”

Section: Discussionsupporting

confidence: 52%

“…Observations were taken by a set of 100 digital Photo Ionization Detector (PID) samplers, arranged in a rectangular staggered grid/array of area 475 m × 450 m in 10 rows and 10 columns as in Figure 1. Samplers height is the same as the one of the source and the probe grid was set to −25 • from the north direction in order to take advantage of the prevailing wind flow [23]. PIDs were set in the flattest, most uniform and most homogeneous terrain in order to reduce the effect of the ground level mechanical turbulence.…”

Section: The Fft-07 Experimentsmentioning

confidence: 99%

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Concentration Fluctuations of Single Particle Stochastic Lagrangian Model Assessment with Experimental Field Data

Ferrero

Maccarini

2021

Atmosphere

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A single particle Lagrangian Stochastic model has been developed and applied with the purpose of simulating the concentration fluctuations dispersion. This model treats concentration variance as a quantity whose motion is driven by an advection-diffusion process so that it can be studied by a single particle model. A parameterization for both velocity standard deviations and Lagrangian time-scales is required as input to the model. The paper is focused on the estimation of the best parameterization needed to simulate both mean and standard deviation concentrations in a case study. We consider the FFT-07 field experiment. The trials took place at Dugway Proving Ground, UTAH (USA) and consist of a dispersion analysis of a gas emitted from a point-like source in different atmospheric conditions with a continuous emission technique. The very small spatial scales (a few hundred meters) and short duration (about 10 min) that characterize the trials make the comparison with model results very challenging, since traditional boundary layer parameterizations fail in correctly reproducing the turbulent field and, as a consequence, the dispersion simulation yields unsatisfactorily results. We vary the coefficients of the turbulence parameterization to match the small-scale turbulence. Furthermore, we show that the parameterization for the variance dissipation time-scale, already tested in neutral conditions, can be used also in stable and unstable conditions and in low-wind speed conditions. The model gives good results as far as mean concentration is concerned and rather satisfactory results for the concentration standard deviations. Comparison between model results and observation is shown through both statistical and graphical analyses.

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Section: Discussioncontrasting

confidence: 89%

Section: Discussionsupporting

confidence: 52%

Section: The Fft-07 Experimentsmentioning

confidence: 99%

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Concentration Fluctuations of Single Particle Stochastic Lagrangian Model Assessment with Experimental Field Data

Ferrero

Maccarini

2021

Atmosphere

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“…These parameters provide useful information regarding the turbulent state of the atmospheric and are used as important inputs implicitly as well as explicitly to almost all dispersion models (Hanna et al, 1982;Sharan et al, 1999;Holmas and Morawska, 2006;Luhar, 2010;Kumar and Sharan, 2010;Kumar and Goyal, 2014;Pandey and Sharan, 2017). Thus, an accurate prescription of these parameters is required as input in the dispersion models for improved estimation of the concentration of air pollutants released from various sources.…”

Section: Takedownmentioning

confidence: 99%

Development of observation-based parameterizations of standard deviations of wind velocity fluctuations over an Indian region

Srivastava

Sharan²,

Kumar³

2019

Theor Appl Climatol

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“…A variety of dispersion models are in use, differing mainly by their mathematical formulation to the physics driving the transport and dispersion of pollutants in the atmosphere. Currently, the most frequently used dispersion models are based on the Gaussian, Lagrangian, and Eulerian descriptions [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Are Empirical Equations an Appropriate Tool to Assess Separation Distances to Avoid Odour Annoyance?

et al. 2020

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Annoyance due to environmental odour exposure is in many jurisdictions evaluated by a yes/no decision. Such a binary decision has been typically achieved via odour impact criteria (OIC) and, when applicable, the resultant separation distances between emission sources and residential areas. If the receptors lie inside the required separation distance, odour exposure is characterised with the potential of causing excessive annoyance. The state-of-the-art methodology to determine separation distances is based on two general steps: (i) calculation of the odour exposure (time series of ambient odour concentrations) using dispersion models and (ii) determination of separation distances through the evaluation of this odour exposure by OIC. Regarding meteorological input data, dispersion models need standard meteorological observations and/or atmospheric stability typically on an hourly basis, which requires expertise in this field. In the planning phase, and as a screening tool, an educated guess of the necessary separation distances to avoid annoyance is in some cases sufficient. Therefore, empirical equations (EQs) are in use to substitute the more time-consuming and costly application of dispersion models. Because the separation distance shape often resembles the wind distribution of a site, wind data should be included in such approaches. Otherwise, the resultant separation distance shape is simply given by a circle around the emission source. Here, an outline of selected empirical equations is given, and it is shown that only a few of them properly reflect the meteorological situation of a site. Furthermore, for three case studies, separation distances as calculated from empirical equations were compared against those from Gaussian plume and Lagrangian particle dispersion models. Overall, our results suggest that some empirical equations reach their limitation in the sense that they are not successful in capturing the inherent complexity of dispersion models. However, empirical equations, developed for Germany and Austria, have the potential to deliver reasonable results, especially if used within the conditions for which they were designed. The main advantage of empirical equations lies in the simplification of the meteorological input data and their use in a fast and straightforward approach.

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Performance evaluation of dispersion parameterization schemes in the plume simulation of FFT-07 diffusion experiment

Cited by 10 publications

References 30 publications

Concentration Fluctuations of Single Particle Stochastic Lagrangian Model Assessment with Experimental Field Data

Concentration Fluctuations of Single Particle Stochastic Lagrangian Model Assessment with Experimental Field Data

Development of observation-based parameterizations of standard deviations of wind velocity fluctuations over an Indian region

Are Empirical Equations an Appropriate Tool to Assess Separation Distances to Avoid Odour Annoyance?

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