Articles | Volume 9, issue 11
https://doi.org/10.5194/gmd-9-4297-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-4297-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
25 Nov 2016
Development and technical paper |
| 25 Nov 2016
LS-APC v1.0: a tuning-free method for the linear inverse problem and its application to source-term determination
Institute of Information Theory and Automation, Czech Academy of Sciences, Prague, Czech Republic
Václav Šmídl
Institute of Information Theory and Automation, Czech Academy of Sciences, Prague, Czech Republic
Radek Hofman
Institute of Information Theory and Automation, Czech Academy of Sciences, Prague, Czech Republic
Andreas Stohl
NILU: Norwegian Institute for Air Research, Kjeller, Norway
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34 citations as recorded by crossref.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- The rise of nonnegative matrix factorization: Algorithms and applications Y. Guo et al. https://doi.org/10.1016/j.is.2024.102379
- 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
- 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
- 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
- Bayesian Non-Negative Matrix Factorization With Adaptive Sparsity and Smoothness Prior O. Tichy et al. https://doi.org/10.1109/LSP.2019.2897230
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- An Adaptive Correlated Image Prior for Image Restoration Problems J. Sevcik et al. https://doi.org/10.1109/LSP.2018.2836964
- 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
- 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
- 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
- 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
- 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
- Source term determination with elastic plume bias correction O. Tichý et al. https://doi.org/10.1016/j.jhazmat.2021.127776
- 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
- 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
- Multi-scenario validation of the self-correction source term inversion method H. Tao et al. https://doi.org/10.1080/00223131.2025.2579627
- 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
- Impact of background representation on Bayesian radioxenon source estimation using IMS measurements O. Tichý et al. https://doi.org/10.1016/j.jenvrad.2026.108139
- 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
- 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
Saved (final revised paper)
Latest update: 16 Sep 2026
Short summary
Estimation of pollutant releases into the atmosphere is an important problem in the environmental sciences. We formulate a probabilistic model, where a full Bayesian estimation allows estimation of all tuning parameters from the measurements. The proposed algorithm is tested and compared with the state-of-the-art method on data from the European Tracer Experiment (ETEX), where advantages of the new method are demonstrated.
Estimation of pollutant releases into the atmosphere is an important problem in the...
