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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- Atmospheric dispersion of chemical, biological, and radiological hazardous pollutants: Informing risk assessment for public safety X. Zhang & J. Wang 10.1016/j.jnlssr.2022.09.001
- The rise of nonnegative matrix factorization: Algorithms and applications Y. Guo et al. 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. 10.3390/atmos12040467
- Inversion of 137Cs emissions following the fukushima accident with adaptive release recovery for temporal absences of observations S. Fang et al. 10.1016/j.envpol.2022.120814
- Bayesian Non-Negative Matrix Factorization With Adaptive Sparsity and Smoothness Prior O. Tichy et al. 10.1109/LSP.2019.2897230
- Bayesian inverse modeling and source location of an unintended <sup>131</sup>I release in Europe in the fall of 2011 O. Tichý et al. 10.5194/acp-17-12677-2017
- Quantification of uncertainties in the assessment of an atmospheric release source applied to the autumn 2017 <sup>106</sup>Ru event J. Dumont Le Brazidec et al. 10.5194/acp-21-13247-2021
- Source term estimation of multi‐specie atmospheric release of radiation from gamma dose rates O. Tichý et al. 10.1002/qj.3403
- Determination of radiological background fields designated for inverse modelling during atypical low wind speed meteorological episode P. Pecha et al. 10.1016/j.atmosenv.2020.118105
- Oscillation-free source term inversion of atmospheric radionuclide releases with joint model bias corrections and non-smooth competing priors S. Fang et al. 10.1016/j.jhazmat.2022.129806
- An Adaptive Correlated Image Prior for Image Restoration Problems J. Sevcik et al. 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. 10.1016/j.jhazmat.2022.128585
- Objective inversion of the continuous atmospheric 137Cs release following the Fukushima accident X. Dong et al. 10.1016/j.jhazmat.2023.130786
- An inverse method to estimate the source term of atmospheric pollutant releases J. Wang et al. 10.1016/j.atmosenv.2021.118554
- Decreasing trends of ammonia emissions over Europe seen from remote sensing and inverse modelling O. Tichý et al. 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. 10.5194/amt-14-803-2021
- Source term determination with elastic plume bias correction O. Tichý et al. 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 10.1109/TSIPN.2024.3451992
- Multi-scenario validation of the robust inversion method with biased plume range and values X. Dong et al. 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. 10.1002/qj.3138
Latest update: 23 Nov 2024
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...
