The Mathematics of Atmospheric Dispersion Modeling

Stockie, John M.

doi:10.1137/10080991x

Cited by 290 publications

(183 citation statements)

References 31 publications

Supporting

Mentioning

161

Contrasting

Unclassified

Order By: Relevance

“…The tradeoff between spatial and temporal sampling of diffusion field to recover point sources on a discrete grid has been investigated by Ranieri et al in [5]. A very comprehensive overview of the mathematical modeling of atmospheric dispersions is given in [6].…”

Section: Previous Workmentioning

confidence: 99%

“…The substance concentration is a spatio-temporal physical field that can be mathematically modeled by partial differential equations (PDEs) [6]. We assume we know the locations of the smokestacks…”

Section: Assumptions and Problem Statementmentioning

confidence: 99%

“…Note that turbulent diffusion is usually modeled using Navier-Stokes equations, that are non-linear and therefore prevent the use of the Green's function. However, for the specific case of atmospheric dispersion, the advection-diffusion equation and the Gaussian plume approximation are considered sufficiently accurate by environmental engineers [6]. Throughout the paper, we do not specify the particular Green's function, to keep the WIND Fig.…”

Section: Assumptions and Problem Statementmentioning

confidence: 99%

See 2 more Smart Citations

Sampling and reconstruction of time-varying atmospheric emissions

Ranieri

Chebira

Vetterli

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

We study the spatio-temporal sampling of physical fields representing the dispersion of a substance in the atmosphere. We consider the following setup: N sensors are deployed at ground level and measure the concentration of a particular substance, while M smokestacks are located in the same area and emit a time-varying amount of the substance. To recover the emission rates of the smokestacks with a limited number of spatio-temporal samples, we consider time varying emissions rates lying in two specific lowdimensional subspaces. We propose efficient algorithms and sufficient conditions to recover the emission rates of the smokestacks from the local measurements collected by the sensor network.

show abstract

Section: Previous Workmentioning

confidence: 99%

Section: Assumptions and Problem Statementmentioning

confidence: 99%

Section: Assumptions and Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

Sampling and reconstruction of time-varying atmospheric emissions

Ranieri

Chebira

Vetterli

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

show abstract

“…The possibility of replacing these models at the near-range by a more accurate non-Gaussian model must represented [9] and the comparison is therefore being investigated. Among a non-Gaussian model, in which wind speed and turbulence are not constant with height and depending on a general performance for solving the advection-diffusion equation a comparison were hold [10,11]. For both models, the solution is forced to represent real situations by means of empirical parameters, referred to as "sigmas".…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Gaussian and Non-Gaussian Models in Evaluation of Pollutant Dispersion

Mubarak¹

2018

Arab Journal of Nuclear Sciences and Applications

View full text Add to dashboard Cite

Different degrees of accuracy for Gaussian and non-Gaussian models were analyzed for the evaluation of dispersion processes with homogeneous or spatial dependent dispersion coefficients that were described by different sigma schemes. The aim of this study is to present and investigate a comparison between Gaussian and non-Gaussian models for simulation of pollutant dispersion in the Planetary Boundary Layer (PBL), considering the effect of meteorological parameters. Downwind concentrations of I-131 were measured through five experiments at different meteorological conditions. Observed data were compared with that predicted using Gaussian and non-Gaussian calculations. Models performances were evaluated using different sigma schemes estimation. The results show that nonGaussian calculations perform much better than Gaussian as Gaussian models have shown to be unreliable at closer range, i.e. at few hundred meters away from the source. At high wind speed, all approaches in case of non-Gaussian calculations perform much better than Gaussian. Power law function methods show reasonable estimates within factors of 1.2 to 2.4 in case of Gaussian and 0.25 to 0.86 in non-Gaussian application. In a moderate wind speed, Brigg's formula (in non-Gaussian) provides reasonable estimates of downwind concentration and has been shown to be accurate to within factors of 0.24 to 1.76 when compared observed data. Although Gaussian models works reasonably not good during weak and variable wind conditions, split sigma shows equitable estimates within factors of 0.5 to 1.08 in low wind speed with Gaussian application. In general, uncertainty increases as going downwind far from the source and decreases with increasing atmospheric stability.

show abstract

“…Mathematical air pollution modeling represents an essential tool to control and predict atmospheric pollution [12]. Gaussian plume can be used to determine pollutant dispersion in the environment [13][14][15][16]). Moreover, the Gaussian plume model is a standard approach for studying the transport of airborne contaminants due to turbulent diffusion and advection by the wind [17].…”

Section: Introductionmentioning

confidence: 99%