"Oscillation-free source term inversion of atmospheric radionuclide releases with joint model bias corrections and non-smooth competing priors" (vol 440, 129806, 2022)

The source term of atmospheric radionuclide releases is essential for the hazardous consequence assessment and emergency response. However, the artificial release oscillations in the source term estimate remain a funda-mental challenge and may deliver misleading information, because of the unavoidable model biases and observation uncertainties. We propose a new method that removes oscillations while recovering the release details. This method explicitly corrects the model biases using the joint correction model and compensates the observation uncertainties through non-smooth competing priors that involve two rival functions. The new priors better model the unsteady feature of the radionuclide releases and distinguish the true releases from oscillations, enabling release-preserving oscillation removal. We extend the projected alternating minimization algorithm for an efficient solution. The method achieves oscillation-free and nearly perfect profiles for real releases of the Perfluoro-Methyl-Cyclo-Hexane on continental and regional scales, and the radionuclide 41Ar on a local scale, outperforming state-of-the-art and very recent methods. The sensitivities to model inputs and key parameters are also investigated. Robust performance is exhibited under emissions of both radioactive and non-radioactive substances, different meteorological inputs and numbers of observations, paving the way for identifying dy-namic atmospheric radionuclide releases at multiple scales, especially when the release status is unknown.