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An accurate and ultrafast method for estimating three-dimensional radiological dose rate fields from arbitrary atmospheric radionuclide distributions

Author:
Li, Xinpeng, Xiong, Wei, Hu, Xiaofeng, Sun, Sida, Li, Hong, Yang, Xingtuan, Zhang, Qijie, Nibart, Maxime, Albergel, Armand, Fang, Sheng
Source:
Atmospheric environment 2019 v.199 pp. 143-154
ISSN:
1352-2310
Subject:
air, atmospheric chemistry, bioactive properties, biological hazards, field experimentation, hazard characterization, models, radionuclides, tissues
Abstract:
The three-dimensional (3D) distribution of radiological dose rate is important for assessing the biological hazard of atmospheric radionuclides in the environment. Because of the complexity of such scenarios, existing methods only estimate one- or two-dimensional dose rates, and trade accuracy and generality for acceptable speed. The lack of efficient 3D estimation methods prevents the 3D biological effect assessment of atmospheric radionuclides. This paper presents a 3D dose rate field estimation method that accelerates the computation by several orders of magnitude without loss of accuracy or generality. This method reformulates the time-consuming 3D integral in the dose rate model as a convolution and uses a fast Fourier transform to accelerate its solution. The convolution form provides a new receptor-oriented insight into dose rate estimation that can flexibly describe the radiological response of biological tissues. The proposed method makes no approximations or assumptions, so it is accurate and applicable to arbitrary atmospheric dispersion models and radionuclide distributions. Our approach is validated by both simulations and a field experiment. The results show that the proposed method is accurate and fast in both simple and highly complex air dispersion scenarios, and provides better quantitative and qualitative agreement with the experimental data than RIMPUFF's tabulated method. This method bridges the long-standing gap between the refined 3D atmospheric radionuclide transport modelling and the corresponding 3D biological hazard evaluation.