Abstract It is necessary to assess the radiological consequences of radioactive leakage accident in the planning and operation of a nuclear power plant, especially an atmospheric radioactive material spill that has a rapid and broad impact on public health. Uncertainty analysis of the assessment results will help to reduce the probability of making mistakes in the emergency response after accident. The Gaussian plume model is the most widely used computational model for atmospheric diffusion assessment. Based on this model, the FORTRAN computer language is used to compile Radionuclides Atmosphere Dispersion Codes (RADC). Calculation results based on RADC are compared with HotSpot Health Physics Codes to verify its calculation accuracy. Based on the Bayesian Markov Chain Monte Carlo method, uncertainty of the Gaussian plume model is analysed, and the influence of observation error on the confidence interval is calculated. The results show that the greater the air concentration of radioactivity, the wider the confidence interval; the observation error has a great impact on the confidence interval. Meanwhile, the small observation error will cause a large change in the width of the confidence interval.

中文翻译：

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基于高斯羽流模型的放射性核素大气扩散建模程序开发及不确定性分析

摘要 核电厂规划和运行过程中需要对放射性泄漏事故的放射性后果进行评估，尤其是对公众健康影响迅速而广泛的大气放射性物质泄漏事故。对评估结果进行不确定性分析，有助于降低事故发生后应急响应中出错的概率。高斯羽流模型是最广泛使用的大气扩散评估计算模型。基于该模型，使用 FORTRAN 计算机语言编写放射性核素大气扩散码（RADC）。将基于RADC的计算结果与HotSpot Health Physics Codes进行对比，验证其计算精度。基于贝叶斯马尔科夫链蒙特卡罗方法，分析了高斯羽流模型的不确定性，并计算观测误差对置信区间的影响。结果表明，空气中放射性浓度越大，置信区间越宽；观测误差对置信区间有很大影响。同时，小的观测误差会引起置信区间宽度的较大变化。