The mixed layer modified radionuclide atmospheric diffusion based on Gaussian model

Frontiers in Public Health

Public Health

Jie Cheng¹  Ting Li¹  Jie Liu¹  Shengpeng Yu²  Xiaolei Zheng²  Jin Wang³ 

[1] Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, China;University of Science and Technology of China, Hefei, Anhui, China;International Academy of Neutron Science, Qingdao, Shandong, China;International Academy of Neutron Science, Qingdao, Shandong, China;China Three Gorges University, Yichang, Hubei, China;

关键词: radionuclide;    atmospheric diffusion;    inversion temperature;    mixed layer;    Gaussian diffusion model;   

DOI  :  10.3389/fpubh.2022.1097643

 received in 2022-11-18, accepted in 2022-12-08,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundAtmospheric diffusion is often accompanied by complex meteorological conditions of inversion temperature.MethodsIn response to the emergency needs for rapid consequence assessment of nuclear accidents under these complex meteorological conditions, a Gaussian diffusion-based model of radionuclide is developed with mixed layer modification. The inhibition effect of the inversion temperature on the diffusion of radionuclides is modified in the vertical direction. The intensity of the radionuclide source is modified by the decay constant.ResultsThe results indicate that the enhancement effect of the mixed layer on the concentration of radionuclides is reflected. The shorter the half-life of the radionuclide, the greater the effect of reducing the diffusion concentration. The Kincaid dataset validation in the Model Validation Kit (MVK) shows that, compared to the non-modified model, predictions of the modified model have an enhancement effect beyond 5 km, modulating the prediction values to be closer to the observation values.ConclusionsThis development is consistent with the modification effects of the mixed layer. The statistical indicators show that the criteria of the modified model meet the criteria of the recommended model.

【 授权许可】

Unknown   
Copyright © 2023 Li, Zheng, Yu, Wang, Cheng and Liu.