A dense gas dispersion model based on revised meteorological parameters and its performance evaluation

He, Jinbo; Liu, Lei; Li, Angang; Ma, Ye; Dong-mei, Zhou; Zhou, Qiyong; Zhan, Yonghua

doi:10.1016/j.atmosenv.2020.117953

Cited by 11 publications

(6 citation statements)

References 49 publications

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“…The release of radioactive noble gas 133 Xe and particulate 137 Cs was estimated using the SRS matrix generated by the Lagrangian particle model FLEXPART and the measurements of activity concentrations as well as the deposition of 137 Cs [221] based on the regularized least squares method. The preliminary guess of release rates based on fuel inventories and information about the events in the accident were utilized as a priori knowledge in inverse modeling [221] .…”

Section: Regularized Regression Inverse Methodsmentioning

confidence: 99%

“…These effects lead to a wider and lower cloud near the ground than neutrally buoyant gas [130] . As a result, denser gas stays near the ground and increases the risk [133][134][135] .…”

Section: Denser-and Lighter-than-air Substancesmentioning

confidence: 99%

“…Copyright (2017) Elsevier B.V. Noble gasses, for example, 133 Xe, are first released into the ambient environment if the reactors are damaged. It was estimated that most of the 133 Xe was released over short periods of time by the first venting of the reactors in the Fukushima accident, but other particulate radionuclides, such as 137 Cs, were released over an extended period and only a certain part of the inventory was released [221] . Five representative radionuclides were utilized to approximate the source terms of nuclear accidents, including 131 I, 137 Cs, 132 Te, and 140 La for aerosols and 133 Xe for noble gasses [21] .…”

Section: Source Term and Particle Size Distributionmentioning

confidence: 99%

“…It was estimated that most of the 133 Xe was released over short periods of time by the first venting of the reactors in the Fukushima accident, but other particulate radionuclides, such as 137 Cs, were released over an extended period and only a certain part of the inventory was released [221] . Five representative radionuclides were utilized to approximate the source terms of nuclear accidents, including 131 I, 137 Cs, 132 Te, and 140 La for aerosols and 133 Xe for noble gasses [21] . Iodine can exist in both gaseous and particulate phases, thereby strongly influencing the dry deposition and scavenging of radionuclides [222] .…”

Section: Source Term and Particle Size Distributionmentioning

confidence: 99%

See 3 more Smart Citations

Atmospheric dispersion of chemical, biological, and radiological hazardous pollutants: Informing risk assessment for public safety

Zhang

Wang

2022

Journal of Safety Science and Resilience

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Section: Regularized Regression Inverse Methodsmentioning

confidence: 99%

“…These effects lead to a wider and lower cloud near the ground than neutrally buoyant gas [130] . As a result, denser gas stays near the ground and increases the risk [133][134][135] .…”

Section: Denser-and Lighter-than-air Substancesmentioning

confidence: 99%

Section: Source Term and Particle Size Distributionmentioning

confidence: 99%

Section: Source Term and Particle Size Distributionmentioning

confidence: 99%

See 2 more Smart Citations

Atmospheric dispersion of chemical, biological, and radiological hazardous pollutants: Informing risk assessment for public safety

Zhang

Wang

2022

Journal of Safety Science and Resilience

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“…Hourly ambient mass concentrations of surface pollutants, including O3 and nitrogen dioxide (NO2), in Sichuan Province for a 7-year (2015-2021) The meteorological data for surface air temperature, relative humidity (RH), precipitation, wind speed (WS) and cloud fraction were acquired from CLDAS data for a 4-year (2018)(2019)(2020)(2021) period, which had a temporal resolution of 1 hr and spatial resolution of 0.05° × 0.05° (approximately 5 km × 5 km). The atmospheric mixing layer height (MLH) was calculated from the temperature, RH, WS and cloud fraction data according to the Nozaki method (He et al, 2021).…”

Section: Study Area and Datamentioning

confidence: 99%

A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone Pollution

Cao

Zhao²,

et al. 2023

Aerosol Air Qual. Res.

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Ozone pollution is harmful to human health and ecosystem, which occurs in ecosystems and has occurred frequently in China in recent years, especially during the warm seasons.Meteorological conditions are among the important factors affecting the occurrence of ozone pollution. In this study, a classification method for meteorological conditions of ozone pollution levels based on a back propagation (BP) neural network was proposed to reflect the impact of meteorological conditions on the occurrence of ozone pollution. Ozone pollution was divided into three levels according to surface hourly ozone (O3) concentrations and thus into three groups of meteorological conditions. The input physical parameters for the BP neural network were determined by evaluating the relationship between surface O3 concentrations and meteorological parameters and precursors, including relative humidity, temperature, mixing layer height, precipitation, and nitrogen dioxide (NO2) concentrations. The study area focused on 21 cities in Sichuan Province in southwestern China, which was divided into 12 BP classifiers according to the urban geographical location and sample number of each city, and a single BP classifier was trained for 21 cities. The classification results of the trained BP classifiers were verified by comparison to the observations. With 12 individual BP classifiers, the classification accuracy of all 21 cities was more than 60%, of which 18 cities were more than 70%, and 9 cities were more than 80%. With the single BP classifier, the classification accuracy of 20 cities was more than 60%, of which 18 cities were more than 70%, and 14 cities were more than 80%. Overall, the classification performance of the trained single model was better than trained 12 individual models. The classification method can comprehensively reflect the impact of meteorological conditions on the occurrence of ozone pollution.

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Diffusion simulation and risk assessment model establishment of chlorine gas leakage based on terrain conditions

Ren

Liu

et al. 2023

Environ Sci Pollut Res

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A dense gas dispersion model based on revised meteorological parameters and its performance evaluation

Cited by 11 publications

References 49 publications

Atmospheric dispersion of chemical, biological, and radiological hazardous pollutants: Informing risk assessment for public safety

Atmospheric dispersion of chemical, biological, and radiological hazardous pollutants: Informing risk assessment for public safety

A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone Pollution

Diffusion simulation and risk assessment model establishment of chlorine gas leakage based on terrain conditions

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