33 citations as recorded by crossref.

1. Sensitivity of Bayesian source reconstruction to likelihood functions: analyses based on ETEX field experiments Y. Zhao et al. https://doi.org/10.1016/j.pnucene.2025.105909
2. MCMC methods applied to the reconstruction of the autumn 2017 Ruthenium-106 atmospheric contamination source J. Dumont Le Brazidec et al. https://doi.org/10.1016/j.aeaoa.2020.100071
3. A comparison of sparse Bayesian regularization methods on computed tomography reconstruction J. Ševčík & V. Šmídl https://doi.org/10.1088/1742-6596/1047/1/012013
4. Bayesian transdimensional inverse reconstruction of the Fukushima Daiichi caesium 137 release J. Dumont Le Brazidec et al. https://doi.org/10.5194/gmd-16-1039-2023
5. On the tuning of atmospheric inverse methods: comparisons with the European Tracer Experiment (ETEX) and Chernobyl datasets using the atmospheric transport model FLEXPART O. Tichý et al. https://doi.org/10.5194/gmd-13-5917-2020
6. Adaptive center constraint for joint release rate estimation and model correction: Multi-scenario validation against wind tunnel experiments X. Li et al. https://doi.org/10.1016/j.pnucene.2024.105413
7. Atmospheric dispersion of chemical, biological, and radiological hazardous pollutants: Informing risk assessment for public safety X. Zhang & J. Wang https://doi.org/10.1016/j.jnlssr.2022.09.001
8. The rise of nonnegative matrix factorization: Algorithms and applications Y. Guo et al. https://doi.org/10.1016/j.is.2024.102379
9. The Environmental Effects of the April 2020 Wildfires and the Cs-137 Re-Suspension in the Chernobyl Exclusion Zone: A Multi-Hazard Threat R. Baró et al. https://doi.org/10.3390/atmos12040467
10. Inversion of 137Cs emissions following the fukushima accident with adaptive release recovery for temporal absences of observations S. Fang et al. https://doi.org/10.1016/j.envpol.2022.120814
11. Source reconstruction for atmospheric radionuclide leakage: Recent advances in decoding information from atmospheric transport physics Y. Xu et al. https://doi.org/10.1016/j.jhazmat.2025.139534
12. Bayesian Non-Negative Matrix Factorization With Adaptive Sparsity and Smoothness Prior O. Tichy et al. https://doi.org/10.1109/LSP.2019.2897230
13. Bayesian inverse modeling and source location of an unintended 131I release in Europe in the fall of 2011 O. Tichý et al. https://doi.org/10.5194/acp-17-12677-2017
14. Quantification of uncertainties in the assessment of an atmospheric release source applied to the autumn 2017 106Ru event J. Dumont Le Brazidec et al. https://doi.org/10.5194/acp-21-13247-2021
15. Source term estimation of multi‐specie atmospheric release of radiation from gamma dose rates O. Tichý et al. https://doi.org/10.1002/qj.3403
16. Robust source reconstruction of atmospheric radionuclides from observations of different sparsity with spatial preselection and non-smooth constraints Y. Xu et al. https://doi.org/10.1016/j.jhazmat.2024.136919
17. Unchanged PM2.5 levels over Europe during COVID-19 were buffered by ammonia N. Evangeliou et al. https://doi.org/10.5194/ar-3-155-2025
18. Determination of radiological background fields designated for inverse modelling during atypical low wind speed meteorological episode P. Pecha et al. https://doi.org/10.1016/j.atmosenv.2020.118105
19. Intuitively tuned elastic bias correction of atmospheric inversion using Gaussian process prior: Application to accidental radioactive emissions A. Brožová et al. https://doi.org/10.1016/j.jhazmat.2026.141523
20. Oscillation-free source term inversion of atmospheric radionuclide releases with joint model bias corrections and non-smooth competing priors S. Fang et al. https://doi.org/10.1016/j.jhazmat.2022.129806
21. An Adaptive Correlated Image Prior for Image Restoration Problems J. Sevcik et al. https://doi.org/10.1109/LSP.2018.2836964
22. Sources and fate of atmospheric microplastics revealed from inverse and dispersion modelling: From global emissions to deposition N. Evangeliou et al. https://doi.org/10.1016/j.jhazmat.2022.128585
23. Objective inversion of the continuous atmospheric 137Cs release following the Fukushima accident X. Dong et al. https://doi.org/10.1016/j.jhazmat.2023.130786
24. An inverse method to estimate the source term of atmospheric pollutant releases J. Wang et al. https://doi.org/10.1016/j.atmosenv.2021.118554
25. Decreasing trends of ammonia emissions over Europe seen from remote sensing and inverse modelling O. Tichý et al. https://doi.org/10.5194/acp-23-15235-2023
26. Real-time measurement of radionuclide concentrations and its impact on inverse modeling of 106Ru release in the fall of 2017 O. Tichý et al. https://doi.org/10.5194/amt-14-803-2021
27. Source term determination with elastic plume bias correction O. Tichý et al. https://doi.org/10.1016/j.jhazmat.2021.127776
28. Stable Outlier-Robust Signal Recovery Over Networks: A Convex Analytic Approach Using Minimax Concave Loss M. Tillmann & M. Yukawa https://doi.org/10.1109/TSIPN.2024.3451992
29. Efficient Bayesian source reconstruction and uncertainty quantification of atmospheric radionuclide releases by replacing release rate sampling with Maximum-A-Posteriori estimation of time-varying release rates Y. Xu et al. https://doi.org/10.1016/j.jhazmat.2025.138171
30. Multi-scenario validation of the self-correction source term inversion method H. Tao et al. https://doi.org/10.1080/00223131.2025.2579627
31. Spatial-temporal source term estimation using deep neural network prior and its application to Chernobyl wildfires A. Brožová et al. https://doi.org/10.1016/j.jhazmat.2025.137510
32. Multi-scenario validation of the robust inversion method with biased plume range and values X. Dong et al. https://doi.org/10.1016/j.jenvrad.2023.107363
33. Uncertainty quantification of pollutant source retrieval: comparison of Bayesian methods with application to the Chernobyl and Fukushima Daiichi accidental releases of radionuclides Y. Liu et al. https://doi.org/10.1002/qj.3138