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https://doi.org/10.5194/gmd-2023-173
https://doi.org/10.5194/gmd-2023-173
Submitted as: development and technical paper
 | 
27 Nov 2023
Submitted as: development and technical paper |  | 27 Nov 2023
Status: a revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

Generalized spatiotemporally-decoupled framework for reconstructing the source of non-constant atmospheric radionuclide releases

Yuhan Xu, Sheng Fang, Xinwen Dong, and Shuhan Zhuang

Abstract. Determining the source location and release rate are critical in assessing the environmental consequences of atmospheric radionuclide releases, but remain challenging because of the huge multi-dimensional solution space. We propose a generalized spatiotemporally-decoupled two-step framework to reduce the dimension of the solution space in each step and improves the reconstruction accuracy, which is applicable to non-constant releases. The decoupling process is conducted by applying a temporal sliding-window average filter to the observations, thereby reducing the influence of temporal variations in the release rate and ensuring that the features of the filtered data are dominated by the source location. A machine learning model is trained to link these features to the source location, enabling independent source localization. Then the release rate is determined using projected alternating minimization with the L1-norm and total variation regularization algorithm. Validation using SCK-CEN 41Ar experimental data demonstrates that the localization error is less than 1 %, and the temporal variations, peak release rate, and total release are reconstructed accurately. The proposed method exhibits higher accuracy and a smaller uncertainty range than the correlation-based and Bayesian methods. Furthermore, it achieves stable performance with different hyperparameters and produces low error levels even with only a single observation site.

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Yuhan Xu, Sheng Fang, Xinwen Dong, and Shuhan Zhuang

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-173', Anonymous Referee #1, 25 Dec 2023
    • AC1: 'Reply on RC1', Yuhan Xu, 23 Jan 2024
  • RC2: 'Comment on gmd-2023-173', Anonymous Referee #2, 05 Feb 2024
    • AC2: 'Reply on RC2', Yuhan Xu, 18 Feb 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-173', Anonymous Referee #1, 25 Dec 2023
    • AC1: 'Reply on RC1', Yuhan Xu, 23 Jan 2024
  • RC2: 'Comment on gmd-2023-173', Anonymous Referee #2, 05 Feb 2024
    • AC2: 'Reply on RC2', Yuhan Xu, 18 Feb 2024
Yuhan Xu, Sheng Fang, Xinwen Dong, and Shuhan Zhuang
Yuhan Xu, Sheng Fang, Xinwen Dong, and Shuhan Zhuang

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Short summary
Recent atmospheric radionuclide leakages from unknown sources have posed a new challenge for nuclear emergency. Reconstruction via environmental observations is the only feasible way to identify the source. However, simultaneous reconstruction of the source location and release rate yields high uncertainties. We propose a strategy of spatiotemporally decoupled reconstruction that avoids these uncertainties and outperforms state-of-art methods with respect to the accuracy and uncertainty range.