Mathematical model for atmospheric dispersion in low winds with eddy diffusivities as linear functions of downwind distance

Sharan, Maithili; Singh, Madan Pal; Yadav, Anil Kumar

doi:10.1016/1352-2310(95)00368-1

Cited by 95 publications

(55 citation statements)

References 12 publications

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“…3.15b) (1983). Essa diferença pode estar relacionada à baixa velocidade horizontal do vento considerada nas simulações, o que faz com que o transporte vertical de poluente seja mais efetivo [132]; [57] Quantitativamente, os resultados obtidos pela metodologia euleriana para estudos de dispersão de poluentes na CLA altamente convectiva reproduziram o comportamento esperado da pluma para fontes de emissão localizadas em alturas elevadas (z s > 0, 1z i ). Portanto, espera-se que os padrões de dispersão de um poluente emitido por fonte área contínua localizada na superfície sejam representativos do comportamento da dispersão de CO na RMSP (Seção 3.5).…”

Section: Dispersão Euleriana Para Fontes Pontuais Contínuasunclassified

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2005

Notas Em Matemática Aplicada

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Section: Dispersão Euleriana Para Fontes Pontuais Contínuasunclassified

Observar e Pensar antes de Modelar.

2005

Notas Em Matemática Aplicada

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“…Steady retroplumes, drawn with four colors, for configuration 2 of DYCE experiment (focus around the source position). The Gaussian dispersion model proposed by Sharan et al [1996] in the forward mode is utilized here for the computation of these sensitivity functions by rotating the wind direction by 180 and assuming an unit release at the FFIDs' locations [Singh et al, 2013].…”

Section: Estimate Of the Visible Part Of The Sourcementioning

confidence: 99%

Reconstructing source terms from atmospheric concentration measurements: Optimality analysis of an inversion technique

Turbelin

Singh

Issartel³

2014

J Adv Model Earth Syst

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In the event of an accidental or intentional contaminant release in the atmosphere, it is imperative, for managing emergency response, to diagnose the release parameters of the source from measured data. Reconstruction of the source information exploiting measured data is called an inverse problem. To solve such a problem, several techniques are currently being developed. The first part of this paper provides a detailed description of one of them, known as the renormalization method. This technique, proposed by Issartel (2005), has been derived using an approach different from that of standard inversion methods and gives a linear solution to the continuous Source Term Estimation (STE) problem. In the second part of this paper, the discrete counterpart of this method is presented. By using matrix notation, common in data assimilation and suitable for numerical computing, it is shown that the discrete renormalized solution belongs to a family of well-known inverse solutions (minimum weighted norm solutions), which can be computed by using the concept of generalized inverse operator. It is shown that, when the weight matrix satisfies the renormalization condition, this operator satisfies the criteria used in geophysics to define good inverses. Notably, by means of the Model Resolution Matrix (MRM) formalism, we demonstrate that the renormalized solution fulfils optimal properties for the localization of single point sources. Throughout the article, the main concepts are illustrated with data from a wind tunnel experiment conducted at the Environmental Flow Research Centre at the University of Surrey, UK.

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“…We are aware of the existence of analytical solutions in the literature, but for specific and particular problems. Among them we mention the works [1][2][3][4][5][6]. In fact, all these solutions are valid for very specialized practical situations with restrictions on wind and eddy diffusivities vertical profiles.…”

Section: Introductionmentioning

confidence: 99%

A closed form solution for pollutant dispersion in atmosphere considering nonlocal closure of the turbulent diffusion

et al. 2012

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Atmospheric air pollution turbulent fluxes can be assumed proportional to the mean concentration gradient. This assumption, along with the equation of continuity, leads to the advection-diffusion equation. Moreover, large eddies are able to mix scalar quantities in a manner that is counter to the local gradient. In this work we present an analytical solution of the three-dimensional steady state advection-diffusion equation, considering nonlocal turbulence closure using the Integral Transform Technique (GILTT). Numerical results and statistical comparisons with experimental data are presented.

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Mathematical model for atmospheric dispersion in low winds with eddy diffusivities as linear functions of downwind distance

Cited by 95 publications

References 12 publications

Observar e Pensar antes de Modelar.

Observar e Pensar antes de Modelar.

Reconstructing source terms from atmospheric concentration measurements: Optimality analysis of an inversion technique

A closed form solution for pollutant dispersion in atmosphere considering nonlocal closure of the turbulent diffusion

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