0647123 20260310073203.720260309235959.9 10.1016/j.jhazmat.2026.141523 17 s. P cav_un_epca*02571680304-3894506Elsevier Source inversion Bias correction Gaussian process prior Radionuclide emissions Hyper-parameter tuning cav_un_auth*0464277 Brožová Antonie UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101207 Šmídl Václav UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0267768 Tichý Ondřej UTIA-B Adaptivní systémy Department of Adaptive Systems AS AS Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0363740 Evangeliou N. NO https://library.utia.cas.cz/separaty/2026/AS/brozova-0647123.pdf https://www.sciencedirect.com/science/article/pii/S0304389426005017?via%3Dihub GA24-10400S GA ČR CZ cav_un_auth*0464279 101008004 EC XE cav_un_auth*0437505 SGS24/141/OHK4/3T/14 Studentska soutěž ČVUT CZ cav_un_auth*0483231 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 hyperparameter 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. WOS BB 10000 10100 10102 2027 4 RVO:67985556 https://hdl.handle.net/11104/0376749 S 141523 A https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5760363 ENVIRONMENTALSCIENCES|ENGINEERING.ENVIRONMENTAL 12.4 1.9 3.9 0.18877 1.88 239998 375 3.078 66.57 13.99 125960 Q1 10.100 3589 Q1 94.9 1.75 Q1 95.1 2026 SCOPUS PUBMED WOS cav_un_epca*0257168 Journal of Hazardous Materials 506 1 2026 0304-3894 1873-3336 Elsevier