A study of the atmospheric dispersion of a high release of krypton-85 above a complex coastal terrain, comparison with the predictions of Gaussian models (Briggs, Doury, ADMS4)

Leroy, C.; Maro, D.; Hébert, D.; Solier, L.; Rozet, M.; Cavelier, S. Le; Connan, O.

doi:10.1016/j.jenvrad.2010.06.011

Cited by 18 publications

(17 citation statements)

References 16 publications

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“…Under several scenarios referred to hereafter as scenarios of good model performance, the range of variation of performance indicators reveals a good agreement between simulated and observed data, which is consistent with recent ADMS4 dispersion simulations of krypton-85 (Leroy et al, 2010) and total (Carruthers et al, 2008). For example, FB value approached 0 for NO x simulation at receptor location 2 (S10, S35, S60) and SO 2 simulation at PM 10 simulation at receptor location 3 (S15, S40, S55).…”

Section: Model Performance Using Best Emission Factorssupporting

confidence: 85%

“…ADMS4 is an advanced steady-state Gaussian dispersion model that can simulate the atmospheric dispersion of multiple industrial, domestic, and traffic releases (CERC, 2007;Carruthers, 2010;Leroy et al, 2010). The model employs a meteorological preprocessor to calculate the boundary layer height (h) and the Monin-Obukhov length (L MO ) used to define the vertical profiles of the wind and turbulence.…”

Section: Model Characteristicsmentioning

confidence: 99%

“…Thus, appropriate similarity scaling factors can be used to simulate the boundary layer and to calculate a vertical profile of the corresponding turbulence (Carruthers, 2010;Leroy et al, 2010;Righi et al, 2009). These models can define the atmosphere as a continuum with meteorological parameters and turbulence fluctuations allowed to vary with height, leading to a more physically realistic description of the atmosphere.…”

Section: Introductionmentioning

confidence: 99%

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Simulating industrial emissions using Atmospheric Dispersion Modeling System: Model performance and source emission factors

El‐Fadel

Abi-Esber

2012

Journal of the Air & Waste Management Association

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In this paper, the Gaussian Atmospheric Dispersion Modeling System (ADMS4) was coupled with field observations of surface meteorology and concentrations of several air quality indicators (nitrogen oxides (NO X ), carbon monoxide (CO), fine particulate matter (PM 10 ) and sulfur dioxide (SO 2 )) to test the applicability of source emission factors set by the European Environment Agency (EEA) and the United States Environmental Protection Agency (USEPA) at an industrial complex. Best emission factors and data groupings based on receptor location, type of terrain and wind speed, were relied upon to examine model performance using statistical analyses of simulated and observed data. The model performance was deemed satisfactory for several scenarios when receptors were located at downwind sites with index of agreement 'd' values reaching 0.58, fractional bias 'FB' and geometric mean bias 'MG' values approaching 0 and 1, respectively, and normalized mean square error 'NMSE' values as low as 2.17. However, median ratios of predicted to observed concentrations 'Cp/Co' at variable downstream distances were 0.01, 0.36, 0.76 and 0.19 for NO X , CO, PM 10 and SO 2 , respectively, and the fraction of predictions within a factor of two of observations 'FAC2' values were lower than 0.5, indicating that the model could not adequately replicate all observed variations in emittant concentrations. Also, the model was found to be significantly sensitive to the input emission factor bringing into light the deficiency in regulatory compliance modeling which often uses internationally reported emission factors without testing their applicability.Implications: In the absence of site-specific source emission factors, the use of internationally reported emission factors without testing their validity may generate significant errors. Instead, recorded field measurements and meteorological data may be combined with atmospheric transport and dispersion models to better estimate source emissions, particularly in regulatory compliance studies. In this context, lower model performance is expected at higher wind speeds for most indicators such as CO, PM 10 , and SO 2 .

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Section: Model Performance Using Best Emission Factorssupporting

confidence: 85%

Section: Model Characteristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simulating industrial emissions using Atmospheric Dispersion Modeling System: Model performance and source emission factors

El‐Fadel

Abi-Esber

2012

Journal of the Air & Waste Management Association

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“…The following equation gives the concentration of a gaseous pollutant emitted from a point source (El-Gamal et al, 2013;Enkeleida and John, 2010;Leroy et al, 2010):…”

Section: Point Sources (Ventilation Towers)mentioning

confidence: 99%

“…Their results showed that; with the aid of several statistical measures, the performances of models such as GPM could be ranked differently on the basis of single parameters and so the choice of the correct parameter could be made keeping in mind the purpose of the application of the model. Leroy et al (2010) compared the experimental results of krypton-85 dispersion, discharged from a 100 m high stack with calculations obtained from three Gaussian plume models: ADMS 4.0, Briggs and Doury. They concluded that; only the Briggs model correctly reproduced the measured values for the width of the plume, whereas the ADMS 4.0 model overestimated it and the Doury model underestimated it.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Gaussian Plume Model against CFD Simulations through the Estimation of CO and NO Concentrations in an Urban Area

Bady

2017

American Journal of Environmental Sciences

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Dispersion of air pollutants, Carbon monoxide (CO) and Nitrogen oxide (NO), due to three point sources and a high-density traffic road in an urban area located near the center of Tokyo, Japan was studied using two different approaches. The first is an analytical approach using Gaussian Plume Model (GPM) while the second is a numerical approach using Computational Fluid Dynamics (CFD) simulations. In the first approach, the study area was divided into a fine grid and the traffic road was treated as a number of continuous adjacent stacks. The GPM was applied upon each stack and the concentration at each receptor was calculated using the principle of superposition. CFD simulations were carried out where steady state analysis was adopted and the standard k-ε turbulence model was used. The concentrations calculated with the two approaches were compared together. The study results show that the agreement between the concentrations estimated by the two approaches was good in low-density built-up locations within the study area while significant deviation was obtained for high-density locations. Such result reflects the fact that the GPM is poor in predicting the actual concentrations in cases of densely urban areas.

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A 3D Visualization Framework for Real-time Distribution and Situation Forecast of Atmospheric Chemical Pollution

Wang

Mai

Song

et al. 2013

Communications in Computer and Information Science

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A study of the atmospheric dispersion of a high release of krypton-85 above a complex coastal terrain, comparison with the predictions of Gaussian models (Briggs, Doury, ADMS4)

Cited by 18 publications

References 16 publications

Simulating industrial emissions using Atmospheric Dispersion Modeling System: Model performance and source emission factors

Simulating industrial emissions using Atmospheric Dispersion Modeling System: Model performance and source emission factors

Evaluation of Gaussian Plume Model against CFD Simulations through the Estimation of CO and NO Concentrations in an Urban Area

A 3D Visualization Framework for Real-time Distribution and Situation Forecast of Atmospheric Chemical Pollution

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