Publikasjonsdetaljer
Tidsskrift: Journal of Hazardous Materials, vol. 506, 141523, 2026
Doi: doi.org/10.1016/j.jhazmat.2026.141523
Arkiv: hdl.handle.net/11250/5509637
Arkiv: nva.sikt.no/registration/019db8dc3b11-12a9df3a-7ab4-4bcd-8b32-b1cefc262bc4
Sammendrag:
Precise estimation of atmospheric pollutant releases is crucial for assessing the impact of environmental accidents. Atmospheric inversion typically relies on a linear model with a source–receptor sensitivity (SRS) matrix, which may contain significant errors or even completely fail to capture the real magnitude of the event. We propose a correction of the SRS matrix formulated as slight shifts in the observation locations, effectively warping the sensitivity field. To constrain these shifts and ensure data-driven corrections, we model them using a Gaussian process prior. This prior not only enforces smoothness and sparsity, but also enables posterior prediction of shifts at previously unseen locations. This key feature provides a mechanism for hyper-parameter tuning: the predicted shift field can be visualized on a map and assessed by an expert. We present a user-friendly framework that combines a Bayesian inversion model with correction and a tuning algorithm based on L-curve-like plots and the maps of predicted shifts. The proposed method is demonstrated on three case studies: the ETEX-I experiment, the 137Cs emissions during the 2020 Chernobyl wildfires, and the 106Ru release in 2017.