Articles | Volume 25, issue 16
https://doi.org/10.5194/acp-25-9199-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-9199-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Aug 2025
Research article |
| 25 Aug 2025
| 25 Aug 2025
Source reconstruction via deposition measurements of an undeclared radiological atmospheric release
Stijn Van Leuven
CORRESPONDING AUTHOR
Belgian Nuclear Research Centre, Mol, Belgium
Royal Meteorological Institute of Belgium, Brussels, Belgium
Department of Physics and Astronomy, Ghent University, Ghent, Belgium
Pieter De Meutter
Belgian Nuclear Research Centre, Mol, Belgium
Royal Meteorological Institute of Belgium, Brussels, Belgium
Johan Camps
Belgian Nuclear Research Centre, Mol, Belgium
Piet Termonia
Royal Meteorological Institute of Belgium, Brussels, Belgium
Department of Physics and Astronomy, Ghent University, Ghent, Belgium
Andy Delcloo
Royal Meteorological Institute of Belgium, Brussels, Belgium
Department of Physics and Astronomy, Ghent University, Ghent, Belgium
Related authors
Stijn Van Leuven, Pieter De Meutter, Johan Camps, Piet Termonia, and Andy Delcloo
Geosci. Model Dev., 16, 5323–5338, https://doi.org/10.5194/gmd-16-5323-2023, https://doi.org/10.5194/gmd-16-5323-2023, 2023
Short summary
Short summary
Precipitation collects airborne particles and deposits these on the ground. This process is called wet deposition and greatly determines how airborne radioactive particles (released routinely or accidentally) contaminate the surface. In this work we present a new method to improve the calculation of wet deposition in computer models. We apply this method to the existing model FLEXPART by simulating the Fukushima nuclear accident (2011) and show that it improves the simulation of wet deposition.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
Short summary
Short summary
To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
anomalous event.
Anouk Dierickx, Wout Dewettinck, Bert Van Schaeybroeck, Lesley De Cruz, Steven Caluwaerts, Piet Termonia, and Hans Van de Vyver
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-30, https://doi.org/10.5194/essd-2025-30, 2025
Preprint under review for ESSD
Short summary
Short summary
This study introduces the EURO-SUPREME dataset consisting of extreme precipitation events selected from a large ensemble of climate models over Europe. The dataset contains information on extreme precipitation events with a precipitation duration of 1 hour to 72 hours that can lead to flooding, high mortality rates and infrastructure damage. We highlight the usefulness of the dataset as a benchmark for improving high-resolution climate models for risk assessment of future extreme floods.
Stijn Van Leuven, Pieter De Meutter, Johan Camps, Piet Termonia, and Andy Delcloo
Geosci. Model Dev., 16, 5323–5338, https://doi.org/10.5194/gmd-16-5323-2023, https://doi.org/10.5194/gmd-16-5323-2023, 2023
Short summary
Short summary
Precipitation collects airborne particles and deposits these on the ground. This process is called wet deposition and greatly determines how airborne radioactive particles (released routinely or accidentally) contaminate the surface. In this work we present a new method to improve the calculation of wet deposition in computer models. We apply this method to the existing model FLEXPART by simulating the Fukushima nuclear accident (2011) and show that it improves the simulation of wet deposition.
Hugues Brenot, Nicolas Theys, Lieven Clarisse, Jeroen van Gent, Daniel R. Hurtmans, Sophie Vandenbussche, Nikolaos Papagiannopoulos, Lucia Mona, Timo Virtanen, Andreas Uppstu, Mikhail Sofiev, Luca Bugliaro, Margarita Vázquez-Navarro, Pascal Hedelt, Michelle Maree Parks, Sara Barsotti, Mauro Coltelli, William Moreland, Simona Scollo, Giuseppe Salerno, Delia Arnold-Arias, Marcus Hirtl, Tuomas Peltonen, Juhani Lahtinen, Klaus Sievers, Florian Lipok, Rolf Rüfenacht, Alexander Haefele, Maxime Hervo, Saskia Wagenaar, Wim Som de Cerff, Jos de Laat, Arnoud Apituley, Piet Stammes, Quentin Laffineur, Andy Delcloo, Robertson Lennart, Carl-Herbert Rokitansky, Arturo Vargas, Markus Kerschbaum, Christian Resch, Raimund Zopp, Matthieu Plu, Vincent-Henri Peuch, Michel Van Roozendael, and Gerhard Wotawa
Nat. Hazards Earth Syst. Sci., 21, 3367–3405, https://doi.org/10.5194/nhess-21-3367-2021, https://doi.org/10.5194/nhess-21-3367-2021, 2021
Short summary
Short summary
The purpose of the EUNADICS-AV (European Natural Airborne Disaster Information and Coordination System for Aviation) prototype early warning system (EWS) is to develop the combined use of harmonised data products from satellite, ground-based and in situ instruments to produce alerts of airborne hazards (volcanic, dust, smoke and radionuclide clouds), satisfying the requirement of aviation air traffic management (ATM) stakeholders (https://cordis.europa.eu/project/id/723986).
Roeland Van Malderen, Dirk De Muer, Hugo De Backer, Deniz Poyraz, Willem W. Verstraeten, Veerle De Bock, Andy W. Delcloo, Alexander Mangold, Quentin Laffineur, Marc Allaart, Frans Fierens, and Valérie Thouret
Atmos. Chem. Phys., 21, 12385–12411, https://doi.org/10.5194/acp-21-12385-2021, https://doi.org/10.5194/acp-21-12385-2021, 2021
Short summary
Short summary
The main aim of initiating measurements of the vertical distribution of the ozone concentration by means of ozonesondes attached to weather balloons at Uccle in 1969 was to improve weather forecasts. Since then, this measurement technique has barely changed, but the dense, long-term, and homogeneous Uccle dataset currently remains crucial for studying the temporal evolution of ozone from the surface to the stratosphere and is also the backbone of the validation of satellite ozone retrievals.
Pieter De Meutter, Ian Hoffman, and Kurt Ungar
Geosci. Model Dev., 14, 1237–1252, https://doi.org/10.5194/gmd-14-1237-2021, https://doi.org/10.5194/gmd-14-1237-2021, 2021
Short summary
Short summary
Inverse atmospheric transport modelling is an important tool in several disciplines. However, the specification of atmospheric transport model error remains challenging. In this paper, we employ a state-of-the-art ensemble technique combined with a state-of-the-art Bayesian inference algorithm to infer point sources. Our research helps to fill the gap in our understanding of model error in the context of inverse atmospheric transport modelling.
Veerle De Bock, Alexander Mangold, L. Gijsbert Tilstra, Olaf N. E. Tuinder, and Andy Delcloo
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-425, https://doi.org/10.5194/amt-2020-425, 2020
Revised manuscript not accepted
Short summary
Short summary
The Absorbing Aerosol Height (AAH) is a new GOME-2 product representing the height of absorbing aerosol layers. In this paper the AAH is validated against the layer height detected by CALIOP. We concluded that the AAH often underestimates the height of volcanic layers, so it should be handled with care when using it for aviation safety purposes. Taking into account the uncertainties, the product can be considered as an important added value for near-real time monitoring of volcanic ash layers.
Cited articles
Baklanov, A. and Sørensen, J. H.: Parameterisation of radionuclide deposition in atmospheric long-range transport modelling, Phys. Chem. Earth Pt. B, 26, 787–799, https://doi.org/10.1016/S1464-1909(01)00087-9, 2001. a
Davoine, X. and Bocquet, M.: Inverse modelling-based reconstruction of the Chernobyl source term available for long-range transport, Atmos. Chem. Phys., 7, 1549–1564, https://doi.org/10.5194/acp-7-1549-2007, 2007. a
De Meutter, P. and Hoffman, I.: Bayesian source reconstruction of an anomalous Selenium-75 release at a nuclear research institute, J. Environ. Radioactiv., 218, 106225, https://doi.org/10.1016/j.jenvrad.2020.106225, 2020. a, b, c, d
De Meutter, P., Camps, J., Delcloo, A., and Termonia, P.: Source localisation and its uncertainty quantification after the third DPRK nuclear test, Scientific Reports, 8, 10155, https://doi.org/10.1038/s41598-018-28403-z, 2018. a, b
De Meutter, P., Hoffman, I., and Hladun, N.: FREAR source code, Zenodo [code], https://doi.org/10.5281/zenodo.4588282, 2021. a
Devell, L., Guntay, S., and Powers, D.: The Chernobyl reactor accident source term: development of a consensus view, Technical report, Organisation for Economic Co-Operation and Development-Nuclear Energy Agency, NEA-CSNI-R–1995-24, 12–14, https://inis.iaea.org/records/ft02z-nym08 (last access: 19 August 2025), 1995. a
Dumont Le Brazidec, J., Bocquet, M., Saunier, O., and Roustan, Y.: Quantification of uncertainties in the assessment of an atmospheric release source applied to the autumn 2017 106Ru event, Atmos. Chem. Phys., 21, 13247–13267, https://doi.org/10.5194/acp-21-13247-2021, 2021. a, b
Dumont Le Brazidec, J., Bocquet, M., Saunier, O., and Roustan, Y.: Bayesian transdimensional inverse reconstruction of the Fukushima Daiichi caesium 137 release, Geosci. Model Dev., 16, 1039–1052, https://doi.org/10.5194/gmd-16-1039-2023, 2023. a, b, c
Eckhardt, S., Prata, A. J., Seibert, P., Stebel, K., and Stohl, A.: Estimation of the vertical profile of sulfur dioxide injection into the atmosphere by a volcanic eruption using satellite column measurements and inverse transport modeling, Atmos. Chem. Phys., 8, 3881–3897, https://doi.org/10.5194/acp-8-3881-2008, 2008. a
Eckhardt, S., Cassiani, M., Evangeliou, N., Sollum, E., Pisso, I., and Stohl, A.: Source–receptor matrix calculation for deposited mass with the Lagrangian particle dispersion model FLEXPART v10.2 in backward mode, Geosci. Model Dev., 10, 4605–4618, https://doi.org/10.5194/gmd-10-4605-2017, 2017. a, b, c, d
Eslinger, P. W. and Milbrath, B. D.: Source term estimation using noble gas and aerosol samples, J. Environ. Radioactiv., 280, 107544, https://doi.org/10.1016/j.jenvrad.2024.107544, 2024. a
Evangeliou, N., Hamburger, T., Talerko, N., Zibtsev, S., Bondar, Y., Stohl, A., Balkanski, Y., Mousseau, T. A., and Moller, A. P.: Reconstructing the Chernobyl Nuclear Power Plant (CNPP) accident 30 years after. A unique database of air concentration and deposition measurements over Europe, Environ. Pollut., 216, 408–418, https://doi.org/10.1016/j.envpol.2016.05.030, 2016. a
Evangeliou, N., Hamburger, T., Cozic, A., Balkanski, Y., and Stohl, A.: Inverse modeling of the Chernobyl source term using atmospheric concentration and deposition measurements, Atmos. Chem. Phys., 17, 8805–8824, https://doi.org/10.5194/acp-17-8805-2017, 2017. a, b
Evangeliou, N., Tichy, O., Eckhardt, S., Zwaaftink, C. G., and Brahney, J.: Sources and fate of atmospheric microplastics revealed from inverse and dispersion modelling: From global emissions to deposition, J. Hazard. Mater., 432, 128585, https://doi.org/10.1016/j.jhazmat.2022.128585, 2022. a
Flesch, T. K., Wilson, J. D., and Yee, E.: Backward-Time Lagrangian Stochastic Dispersion Models and Their Application to Estimate Gaseous Emissions, J. Appl. Meteorol., 34, 1320–1332, https://doi.org/10.1175/1520-0450(1995)034<1320:BTLSDM>2.0.CO;2, 1995. a
Gueibe, C., Kalinowski, M. B., Bare, J., Gheddou, A., Krysta, M., and Kusmierczyk-Michulec, J.: Setting the baseline for estimated background observations at IMS systems of four radioxenon isotopes in 2014, J. Environ. Radioactiv., 178, 297–314, https://doi.org/10.1016/j.jenvrad.2017.09.007, 2017. a
Henne, S., Brunner, D., Oney, B., Leuenberger, M., Eugster, W., Bamberger, I., Meinhardt, F., Steinbacher, M., and Emmenegger, L.: Validation of the Swiss methane emission inventory by atmospheric observations and inverse modelling, Atmos. Chem. Phys., 16, 3683–3710, https://doi.org/10.5194/acp-16-3683-2016, 2016. a
Houweling, S., Baker, D., Basu, S., Boesch, H., Butz, A., Chevallier, F., Deng, F., Dlugokencky, E. J., Feng, L., Ganshin, A., Hasekamp, O., Jones, D., Maksyutov, S., Marshall, J., Oda, T., O'Dell, C. W., Oshchepkov, S., Palmer, P. I., Peylin, P., Poussi, Z., Reum, F., Takagi, H., Yoshida, Y., and Zhuravlev, R.: An intercomparison of inverse models for estimating sources and sinks of CO using GOSAT measurements, J. Geophys. Res.-Atmos., 120, 5253–5266, https://doi.org/10.1002/2014jd022962, 2015. a
Katata, G., Chino, M., Kobayashi, T., Terada, H., Ota, M., Nagai, H., Kajino, M., Draxler, R., Hort, M. C., Malo, A., Torii, T., and Sanada, Y.: Detailed source term estimation of the atmospheric release for the Fukushima Daiichi Nuclear Power Station accident by coupling simulations of an atmospheric dispersion model with an improved deposition scheme and oceanic dispersion model, Atmos. Chem. Phys., 15, 1029–1070, https://doi.org/10.5194/acp-15-1029-2015, 2015. a
Kristiansen, N. I., Stohl, A., Prata, A. J., Richter, A., Eckhardt, S., Seibert, P., Hoffmann, A., Ritter, C., Bitar, L., Duck, T. J., and Stebel, K.: Remote sensing and inverse transport modeling of the Kasatochi eruption sulfur dioxide cloud, J. Geophys. Res.-Atmos., 115, D00L16, https://doi.org/10.1029/2009JD013286, 2010. a
Laloy, E. and Vrugt, J. A.: High-dimensional posterior exploration of hydrologic models using multiple-try DREAM(ZS) and high-performance computing, Water Resour. Res., 48, W01526, https://doi.org/10.1029/2011WR010608, 2012. a
Masson, O., Steinhauser, G., Zok, D., Saunier, O., Angelov, H., Babic, D., Beckova, V., Bieringer, J., Bruggeman, M., Burbidge, C. I., Conil, S., Dalheimer, A., Geer, L. E., Ott, A. D., Eleftheriadis, K., Estier, S., Fischer, H., Garavaglia, M. G., Leonarte, C. G., Gorzkiewicz, K., Hainz, D., Hoffman, I., Hyza, M., Isajenko, K., Karhunen, T., Kastlander, J., Katzlberger, C., Kierepko, R., Knetsch, G. J., Konyi, J. K., Lecomte, M., Mietelski, J. W., Min, P., Moller, B., Nielsen, S. P., Nikolic, J., Nikolovska, L., Penev, I., Petrinec, B., Povinec, P. P., Querfeld, R., Raimondi, O., Ransby, D., Ringer, W., Romanenko, O., Rusconi, R., Saey, P. R. J., Samsonov, V., Silobritiene, B., Simion, E., Söderström, C., Sostaric, M., Steinkopff, T., Steinmann, P., Sykora, I., Tabachnyi, L., Todorovic, D., Tomankiewicz, E., Tschiersch, J., Tsibranski, R., Tzortzis, M., Ungar, K., Vidic, A., Weller, A., Wershofen, H., Zagyvai, P., Zalewska, T., Garcia, D. Z., and Zorko, B.: Airborne concentrations and chemical considerations of radioactive ruthenium from an undeclared major nuclear release in 2017, P. Natl. Acad. Sci. USA, 116, 16750–16759, https://doi.org/10.1073/pnas.1907571116, 2019. a, b, c, d, e, f, g, h, i, j, k
MEXT (Ministry of Education, Culture, Sports, Science and Technology): Results of the 2nd Airborne Monitoring by the Ministry of Education Culture Sports Science and Technology and the U.S. Department of Energy, https://radioactivity.nra.go.jp/cont/en/results/land/airborne/survey-results/1304797_0616e.pdf (last access: 12 August 2025), 2011. a
Pisso, I., Sollum, E., Grythe, H., Kristiansen, N. I., Cassiani, M., Eckhardt, S., Arnold, D., Morton, D., Thompson, R. L., Groot Zwaaftink, C. D., Evangeliou, N., Sodemann, H., Haimberger, L., Henne, S., Brunner, D., Burkhart, J. F., Fouilloux, A., Brioude, J., Philipp, A., Seibert, P., and Stohl, A.: The Lagrangian particle dispersion model FLEXPART version 10.4, Geosci. Model Dev., 12, 4955–4997, https://doi.org/10.5194/gmd-12-4955-2019, 2019a. a, b
Pisso, I., Sollum, E., Grythe, H., et al.: FLEXPART 10.4, Zenodo [code], https://doi.org/10.5281/zenodo.3542278, 2019b. a
Pudykiewicz, J. A.: Application of adjoint tracer transport equations for evaluating source parameters, Atmos. Environ., 32, 3039–3050, https://doi.org/10.1016/S1352-2310(97)00480-9, 1998. a, b
Ramebäck, H., Söderström, C., Granström, M., Jonsson, S., Kastlander, J., Nylén, T., and Ågren, G.: Measurements of 106Ru in Sweden during the autumn 2017: Gamma-ray spectrometric measurements of air filters, precipitation and soil samples, and in situ gamma-ray spectrometry measurement, Appl. Radiat. Isotopes, 140, 179–184, https://doi.org/10.1016/j.apradiso.2018.07.008, 2018. a
Saunier, O., Didier, D., Mathieu, A., Masson, O., and Le Brazidec, J. D.: Atmospheric modeling and source reconstruction of radioactive ruthenium from an undeclared major release in 2017, P. Natl. Acad. Sci. USA, 116, 24991–25000, https://doi.org/10.1073/pnas.1907823116, 2019. a, b, c, d, e, f, g, h, i
Seibert, P. and Frank, A.: Source-receptor matrix calculation with a Lagrangian particle dispersion model in backward mode, Atmos. Chem. Phys., 4, 51–63, https://doi.org/10.5194/acp-4-51-2004, 2004. a, b, c, d
Seinfeld, J. H. and Pandis, S. N.: Atmospheric chemistry and physics: from air pollution to climate change, A Wiley Inter-science Publication, Wiley John Wiley distributor, Hoboken, N.J. Chichester, 2nd edn., ISBN 0471720186(pbk), 0471720178 (cloth.), 0471720186, 2006. a
Shershakov, V. M., Borodin, R. V., and Tsaturov, Y. S.: Assessment of Possible Location Ru-106 Source in Russia in September-October 2017, Russ. Meteorol. Hydrol., 44, 196–202, https://doi.org/10.3103/S1068373919030051, 2019. a, b
Sørensen, J. H.: Method for source localization proposed and applied to the October 2017 case of atmospheric dispersion of Ru-106, J. Environ. Radioactiv., 189, 221–226, https://doi.org/10.1016/j.jenvrad.2018.03.010, 2018. a, b
Stohl, A., Forster, C., Frank, A., Seibert, P., and Wotawa, G.: Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2, Atmos. Chem. Phys., 5, 2461–2474, https://doi.org/10.5194/acp-5-2461-2005, 2005. a
Stohl, A., Seibert, P., Arduini, J., Eckhardt, S., Fraser, P., Greally, B. R., Lunder, C., Maione, M., Mühle, J., O'Doherty, S., Prinn, R. G., Reimann, S., Saito, T., Schmidbauer, N., Simmonds, P. G., Vollmer, M. K., Weiss, R. F., and Yokouchi, Y.: An analytical inversion method for determining regional and global emissions of greenhouse gases: Sensitivity studies and application to halocarbons, Atmos. Chem. Phys., 9, 1597–1620, https://doi.org/10.5194/acp-9-1597-2009, 2009. a
Stohl, A., Seibert, P., Wotawa, G., Arnold, D., Burkhart, J. F., Eckhardt, S., Tapia, C., Vargas, A., and Yasunari, T. J.: Xenon-133 and caesium-137 releases into the atmosphere from the Fukushima Dai-ichi nuclear power plant: determination of the source term, atmospheric dispersion, and deposition, Atmos. Chem. Phys., 12, 2313–2343, https://doi.org/10.5194/acp-12-2313-2012, 2012. a, b
Thomson, D. J.: Criteria for the Selection of Stochastic-Models of Particle Trajectories in Turbulent Flows, J. Fluid Mech., 180, 529–556, https://doi.org/10.1017/S0022112087001940, 1987. a
Tichý, O., Hýža, M., Evangeliou, N., and Šmídl, V.: Real-time measurement of radionuclide concentrations and its impact on inverse modeling of 106Ru release in the fall of 2017, Atmos. Meas. Tech., 14, 803–818, https://doi.org/10.5194/amt-14-803-2021, 2021. a
Tipka, A., Haimberger, L., and Seibert, P.: Flex_extract v7.1.2 – a software package to retrieve and prepare ECMWF data for use in FLEXPART, Geosci. Model Dev., 13, 5277–5310, https://doi.org/10.5194/gmd-13-5277-2020, 2020. a
Tølløse, K. S., Kaas, E., and Sørensen, J. H.: Probabilistic Inverse Method for Source Localization Applied to ETEX and the 2017 Case of Ru-106 including Analyses of Sensitivity to Measurement Data, Atmosphere, 12, 1567, https://doi.org/10.3390/atmos12121567, 2021. a, b
Van Leuven, S.: FREAR: adjusted R scripts for deposition, Version v1, Zenodo [code], https://doi.org/10.5281/zenodo.14525978, 2024. a
Van Leuven, S.: Data for “Source reconstruction via deposition measurements of an undeclared radiological atmospheric release”, Version v2, Zenodo [data set], https://doi.org/10.5281/zenodo.15594250, 2025. a
Van Leuven, S., De Meutter, P., Camps, J., Termonia, P., and Delcloo, A.: An optimisation method to improve modelling of wet deposition in atmospheric transport models: applied to FLEXPART v10.4, Geosci. Model Dev., 16, 5323–5338, https://doi.org/10.5194/gmd-16-5323-2023, 2023. a
Western, L. M., Millington, S. C., Benfield-Dexter, A., and Witham, C. S.: Source estimation of an unexpected release of Ruthenium-106 in 2017 using an inverse modelling approach, J. Environ. Radioactiv., 220, 106304, https://doi.org/10.1016/j.jenvrad.2020.106304, 2020. a, b
Winiarek, V., Bocquet, M., Duhanyan, N., Roustan, Y., Saunier, O., and Mathieu, A.: Estimation of the caesium-137 source term from the Fukushima Daiichi nuclear power plant using a consistent joint assimilation of air concentration and deposition observations, Atmos. Environ., 82, 268–279, https://doi.org/10.1016/j.atmosenv.2013.10.017, 2014. a, b, c
Wotawa, G., De Geer, L.-E., Denier, P., Kalinowski, M., Toivonen, H., D’Amours, R., Desiato, F., Issartel, J.-P., Langer, M., Seibert, P., Frank, A., Sloan, C., and Yamazawa, H.: Atmospheric transport modelling in support of CTBT verification–overview and basic concepts, Atmos. Environ., 37, 2529–2537, https://doi.org/10.1016/S1352-2310(03)00154-7, 2003. a
Yee, E.: Inverse Dispersion for an Unknown Number of Sources: Model Selection and Uncertainty Analysis, International Scholarly Research Notices, 2012, 465320, https://doi.org/10.5402/2012/465320, 2012. a
Yee, E., Lien, F.-S., Keats, A., and D’Amours, R.: Bayesian inversion of concentration data: Source reconstruction in the adjoint representation of atmospheric diffusion, J. Wind Eng. Ind. Aerod., 96, 1805–1816, https://doi.org/10.1016/j.jweia.2008.02.024, 2008. a
Short summary
We use deposition measurements to trace the source of the radioactive isotope 106Ru released into the atmosphere in 2017, which led to detections in Europe and other parts of the Northern Hemisphere. Most frequently, measurements of air concentration are used for such purposes. Our research shows that, while air concentration data can provide more precise results, deposition measurements can still effectively pinpoint the release location, offering a less costly and more versatile alternative.
We use deposition measurements to trace the source of the radioactive isotope 106Ru released...
Altmetrics
Final-revised paper
Preprint