Articles | Volume 18, issue 6
https://doi.org/10.5194/gmd-18-1989-2025
© 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/gmd-18-1989-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks
Jens Peter Karolus Wenceslaus Frankemölle
CORRESPONDING AUTHOR
Belgian Nuclear Research Centre, SCK CEN, Boeretang 200, 2400 Mol, Belgium
Department of Mechanical Engineering, KU Leuven, Celestijnenlaan 300, 3000 Leuven, Belgium
Johan Camps
Belgian Nuclear Research Centre, SCK CEN, Boeretang 200, 2400 Mol, Belgium
Pieter De Meutter
Belgian Nuclear Research Centre, SCK CEN, Boeretang 200, 2400 Mol, Belgium
Johan Meyers
Department of Mechanical Engineering, KU Leuven, Celestijnenlaan 300, 3000 Leuven, Belgium
Related authors
No articles found.
Stijn Van Leuven, Pieter De Meutter, Johan Camps, Piet Termonia, and Andy Delcloo
Atmos. Chem. Phys., 25, 9199–9218, https://doi.org/10.5194/acp-25-9199-2025, https://doi.org/10.5194/acp-25-9199-2025, 2025
Short summary
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.
Stefan Ivanell, Warit Chanprasert, Luca Lanzilao, James Bleeg, Johan Meyers, Antoine Mathieu, Søren Juhl Andersen, Rem-Sophia Mouradi, Eric Dupont, Hugo Olivares-Espinosa, and Niels Troldborg
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-88, https://doi.org/10.5194/wes-2025-88, 2025
Preprint under review for WES
Short summary
Short summary
This study explores how the height of the atmosphere's boundary layer impacts wind farm performance, focusing on how this factor influences energy output. By simulating different boundary layer heights and conditions, the research reveals that deeper layers promote better energy recovery. The findings highlight the importance of considering atmospheric conditions when simulating wind farms to maximize energy efficiency, offering valuable insights for the wind energy industry.
Théo Delvaux and Johan Meyers
Wind Energ. Sci., 10, 613–630, https://doi.org/10.5194/wes-10-613-2025, https://doi.org/10.5194/wes-10-613-2025, 2025
Short summary
Short summary
The work explores the potential for wind farm load reduction and power maximization. We carried out a series of high-fidelity large-eddy simulations for a wide range of atmospheric conditions and operating regimes. Because of turbine-scale interactions and large-scale effects, we observed that maximum power extraction is achieved at regimes lower than the Betz operating point. Thus, we proposed three simple approaches with which thrust significantly decreases with only a limited impact on power.
Andrew Kirby, Takafumi Nishino, Luca Lanzilao, Thomas D. Dunstan, and Johan Meyers
Wind Energ. Sci., 10, 435–450, https://doi.org/10.5194/wes-10-435-2025, https://doi.org/10.5194/wes-10-435-2025, 2025
Short summary
Short summary
Traditionally, the aerodynamic loss of wind farm efficiency is classified into wake loss and farm blockage loss. This study, using high-fidelity simulations, shows that neither of these two losses is well correlated with the overall farm efficiency. We propose new measures called turbine-scale efficiency and farm-scale efficiency to better describe turbine–wake effects and farm–atmosphere interactions. This study suggests the importance of better modelling farm-scale loss in future studies.
Olivier Ndindayino, Augustin Puel, and Johan Meyers
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-6, https://doi.org/10.5194/wes-2025-6, 2025
Revised manuscript accepted for WES
Short summary
Short summary
Our aim is to understand the relationship between flow blockage and improved wind farm efficiency using large-eddy simulations, as well as developing an analytical model that shows promise for improving turbine power predictions under blockage. We found that blockage enhances turbine power and thrust by inducing a favourable pressure drop across the turbine row, while simultaneously inducing an unfavourable pressure increase downstream which has minimal direct impact on far wake development.
Majid Bastankhah, Marcus Becker, Matthew Churchfield, Caroline Draxl, Jay Prakash Goit, Mehtab Khan, Luis A. Martinez Tossas, Johan Meyers, Patrick Moriarty, Wim Munters, Asim Önder, Sara Porchetta, Eliot Quon, Ishaan Sood, Nicole van Lipzig, Jan-Willem van Wingerden, Paul Veers, and Simon Watson
Wind Energ. Sci., 9, 2171–2174, https://doi.org/10.5194/wes-9-2171-2024, https://doi.org/10.5194/wes-9-2171-2024, 2024
Short summary
Short summary
Dries Allaerts was born on 19 May 1989 and passed away at his home in Wezemaal, Belgium, on 10 October 2024 after battling cancer. Dries started his wind energy career in 2012 and had a profound impact afterward on the community, in terms of both his scientific realizations and his many friendships and collaborations in the field. His scientific acumen, open spirit of collaboration, positive attitude towards life, and playful and often cheeky sense of humor will be deeply missed by many.
Jérôme Neirynck, Jonas Van de Walle, Ruben Borgers, Sebastiaan Jamaer, Johan Meyers, Ad Stoffelen, and Nicole P. M. van Lipzig
Wind Energ. Sci., 9, 1695–1711, https://doi.org/10.5194/wes-9-1695-2024, https://doi.org/10.5194/wes-9-1695-2024, 2024
Short summary
Short summary
In our study, we assess how mesoscale weather systems influence wind speed variations and their impact on offshore wind energy production fluctuations. We have observed, for instance, that weather systems originating over land lead to sea wind speed variations. Additionally, we noted that power fluctuations are typically more significant in summer, despite potentially larger winter wind speed variations. These findings are valuable for grid management and optimizing renewable energy deployment.
Ruben Borgers, Marieke Dirksen, Ine L. Wijnant, Andrew Stepek, Ad Stoffelen, Naveed Akhtar, Jérôme Neirynck, Jonas Van de Walle, Johan Meyers, and Nicole P. M. van Lipzig
Wind Energ. Sci., 9, 697–719, https://doi.org/10.5194/wes-9-697-2024, https://doi.org/10.5194/wes-9-697-2024, 2024
Short summary
Short summary
Wind farms at sea are becoming more densely clustered, which means that next to individual wind turbines interfering with each other in a single wind farm also interference between wind farms becomes important. Using a climate model, this study shows that the efficiency of wind farm clusters and the interference between the wind farms in the cluster depend strongly on the properties of the individual wind farms and are also highly sensitive to the spacing between the wind farms.
Nick Janssens and Johan Meyers
Wind Energ. Sci., 9, 65–95, https://doi.org/10.5194/wes-9-65-2024, https://doi.org/10.5194/wes-9-65-2024, 2024
Short summary
Short summary
Proper wind farm control may vastly contribute to Europe's plan to go carbon neutral. However, current strategies don't account for turbine–wake interactions affecting power extraction. High-fidelity models (e.g., LES) are needed to accurately model this but are considered too slow in practice. By coarsening the resolution, we were able to design an efficient LES-based controller with real-time potential. This may allow us to bridge the gap towards practical wind farm control in the near future.
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.
Ishaan Sood, Elliot Simon, Athanasios Vitsas, Bart Blockmans, Gunner C. Larsen, and Johan Meyers
Wind Energ. Sci., 7, 2469–2489, https://doi.org/10.5194/wes-7-2469-2022, https://doi.org/10.5194/wes-7-2469-2022, 2022
Short summary
Short summary
In this work, we conduct a validation study to compare a numerical solver against measurements obtained from the offshore Lillgrund wind farm. By reusing a previously developed inflow turbulent dataset, the atmospheric conditions at the wind farm were recreated, and the general performance trends of the turbines were captured well. The work increases the reliability of numerical wind farm solvers while highlighting the challenges of accurately representing large wind farms using such solvers.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
Short summary
Short summary
Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
Johan Meyers, Carlo Bottasso, Katherine Dykes, Paul Fleming, Pieter Gebraad, Gregor Giebel, Tuhfe Göçmen, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, https://doi.org/10.5194/wes-7-2271-2022, 2022
Short summary
Short summary
We provide a comprehensive overview of the state of the art and the outstanding challenges in wind farm flow control, thus identifying the key research areas that could further enable commercial uptake and success. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight into control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design
(co-design).
Konstanze Kölle, Tuhfe Göçmen, Irene Eguinoa, Leonardo Andrés Alcayaga Román, Maria Aparicio-Sanchez, Ju Feng, Johan Meyers, Vasilis Pettas, and Ishaan Sood
Wind Energ. Sci., 7, 2181–2200, https://doi.org/10.5194/wes-7-2181-2022, https://doi.org/10.5194/wes-7-2181-2022, 2022
Short summary
Short summary
The paper studies wind farm flow control (WFFC) in simulations with variable electricity prices. The results indicate that considering the electricity price in the operational strategy can be beneficial with respect to the gained income compared to focusing on the power gain only. Moreover, revenue maximization by balancing power production and structural load reduction is demonstrated at the example of a single wind turbine.
Tuhfe Göçmen, Filippo Campagnolo, Thomas Duc, Irene Eguinoa, Søren Juhl Andersen, Vlaho Petrović, Lejla Imširović, Robert Braunbehrens, Jaime Liew, Mads Baungaard, Maarten Paul van der Laan, Guowei Qian, Maria Aparicio-Sanchez, Rubén González-Lope, Vinit V. Dighe, Marcus Becker, Maarten J. van den Broek, Jan-Willem van Wingerden, Adam Stock, Matthew Cole, Renzo Ruisi, Ervin Bossanyi, Niklas Requate, Simon Strnad, Jonas Schmidt, Lukas Vollmer, Ishaan Sood, and Johan Meyers
Wind Energ. Sci., 7, 1791–1825, https://doi.org/10.5194/wes-7-1791-2022, https://doi.org/10.5194/wes-7-1791-2022, 2022
Short summary
Short summary
The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings, and model complexities for the (initial) assessment of wind farm flow control benefits. Here we present the first part of the benchmark results for three blind tests with large-scale rotors and 11 participating models in total, via direct power comparisons at the turbines as well as the observed or estimated power gain at the wind farm level under wake steering control strategy.
Koen Devesse, Luca Lanzilao, Sebastiaan Jamaer, Nicole van Lipzig, and Johan Meyers
Wind Energ. Sci., 7, 1367–1382, https://doi.org/10.5194/wes-7-1367-2022, https://doi.org/10.5194/wes-7-1367-2022, 2022
Short summary
Short summary
Recent research suggests that offshore wind farms might form such a large obstacle to the wind that it already decelerates before reaching the first turbines. Part of this phenomenon could be explained by gravity waves. Research on these gravity waves triggered by mountains and hills has found that variations in the atmospheric state with altitude can have a large effect on how they behave. This paper is the first to take the impact of those vertical variations into account for wind farms.
Thomas Haas, Jochem De Schutter, Moritz Diehl, and Johan Meyers
Wind Energ. Sci., 7, 1093–1135, https://doi.org/10.5194/wes-7-1093-2022, https://doi.org/10.5194/wes-7-1093-2022, 2022
Short summary
Short summary
In this work, we study parks of large-scale airborne wind energy systems using a virtual flight simulator. The virtual flight simulator combines numerical techniques from flow simulation and kite control. Using advanced control algorithms, the systems can operate efficiently in the park despite turbulent flow conditions. For the three configurations considered in the study, we observe significant wake effects, reducing the power yield of the parks.
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.
Luca Lanzilao and Johan Meyers
Wind Energ. Sci., 6, 247–271, https://doi.org/10.5194/wes-6-247-2021, https://doi.org/10.5194/wes-6-247-2021, 2021
Short summary
Short summary
This research paper investigates the potential of thrust set-point optimization in large wind farms for mitigating gravity-wave-induced blockage effects for the first time, with the aim of increasing the wind-farm energy extraction. The optimization tool is applied to almost 2000 different atmospheric states. Overall, power gains above 4 % are observed for 77 % of the cases.
Cited articles
Abril-Pla, O., Andreani, V., Carroll, C., Dong, L., Fonnesbeck, C. J., Kochurov, M., Kumar, R., Lao, J., Luhmann, C. C., Martin, O. A., Osthege, M., Vieira, R., Wiecki, T., and Zinkov, R.: PyMC: a modern, and comprehensive probabilistic programming framework in Python, PeerJ Computer Science, 9, e1516, https://doi.org/10.7717/peerj-cs.1516, 2023. a
Agentschap Digitaal Vlaanderen: Orthofotomozaïek, grootschalig, winteropnamen, kleur, 2013–2015, Vlaanderen, https://www.vlaanderen.be/datavindplaats/catalogus/
orthofotomozaiek-grootschalig-winteropnamen-kleur-2013-2015-vlaanderen, (last access: 25 March 2025), 2016. a
Arahmane, H., Dumazert, J., Barat, E., Dautremer, T., Carrel, F., Dufour, N., and Michel, M.: Statistical approach for radioactivity detection: A brief review, J. Environ. Radioactiv., 272, 107358, https://doi.org/10.1016/j.jenvrad.2023.107358, 2024. a
Bergan, T. D.: Radioactive fallout in Norway from atmospheric nuclear weapons tests, J. Environ. Radioactiv., 60, 189–208, https://doi.org/10.1016/S0265-931X(01)00103-5, 2002. a
Breitkreutz, H., Mayr, J., Bleher, M., Seifert, S., and Stöhlker, U.: Identification and quantification of anomalies in environmental gamma dose rate time series using artificial intelligence, J. Environ. Radioactiv., 259–260, 107082, https://doi.org/10.1016/j.jenvrad.2022.107082, 2023. a, b
Brennan, S., Mielke, A., and Torney, D.: Radioactive source detection by sensor networks, IEEE T. Nucl. Sci., 52, 813–819, https://doi.org/10.1109/TNS.2005.850487, 2005. a
Chernyavskiy, P., Kendall, G., Wakeford, R., and Little, M.: Spatial prediction of naturally occurring gamma radiation in Great Britain, J. Environ. Radioactiv., 164, 300–311, https://doi.org/10.1016/j.jenvrad.2016.07.029, 2016. a
Elson, P., Sales de Andrade, E., Lucas, G., May, R., Hattersley, R., Campbell, E., Comer, R., Dawson, A., Little, B., Raynaud, S., scmc72, Snow, A. D., Igolston, Blay, B., Killick, P., Ibdreyer, Peglar, P., Wilson, N., Andrew, Szymaniak, J., Berchet, A., Bosley, C., Davis, L., Filipe, Krasting, J., Bradbhury, M., stephenworsley, and Kirkham, D.: SciTools/cartopy: REL: v0.24.1, Zenodo [code], https://doi.org/10.5281/zenodo.13905945, 2024. a
European Commission, Directorate-General for Research and Innovation, De Cort, M., Dubois, G., Fridman, S., Germenchuk, M., Izrael, Y., Janssens, A., Jones, A., Kelly, G., Kvasnikova, E., Matveenko, I., Nazarov, I., Pokumeiko, Y., Sitak, V., Stukin, E., Tabachny, L., Tsaturov, Y., and Avdyushin, S.: Atlas of caesium deposition on Europe after the Chernobyl accident, Publications Office of the European Union, https://op.europa.eu/publication-detail/-/publication/110b15f7-4df8-49a0-856f-be8f681ae9fd (last access: 25 March 2025), 1998. a
Evensen, G., Vossepoel, F. C., and van Leeuwen, P. J.: Kalman Filters and 3DVar, in: Data Assimilation Fundamentals: A Unified Formulation of the State and Parameter Estimation Problem, Springer International Publishing, Cham, 63–71, https://doi.org/10.1007/978-3-030-96709-3_6, 2022. a
Federal Agency for Nuclear Control: Telerad, https://www.telerad.be (last access: 25 March 2025), 2024. a
Folly, C. L., Konstantinoudis, G., Mazzei-Abba, A., Kreis, C., Bucher, B., Furrer, R., and Spycher, B. D.: Bayesian spatial modelling of terrestrial radiation in Switzerland, J. Environ. Radioactiv., 233, 106571, https://doi.org/10.1016/j.jenvrad.2021.106571, 2021. a
Frankemölle, J. P. K. W., Camps, J., De Meutter, P., Antoine, P., Delcloo, A., Vermeersch, F., and Meyers, J.: Near-range atmospheric dispersion of an anomalous selenium-75 emission, J. Environ. Radioactiv., 255, 107012, https://doi.org/10.1016/j.jenvrad.2022.107012, 2022a. a
Frankemölle, J. P. K. W., Camps, J., De Meutter, P., and Meyers, J.: Near-range Gaussian plume modelling for gamma dose rate reconstruction, in: 21st International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, 27–30 September 2022, Aveiro, Portugal, https://www.harmo.org/Conferences/Proceedings/_Aveiro/publishedSections/00514_172_h21-023-jens-peter-frankemolle.pdf (last access: 25 March 2025), 2022b. a, b
Frankemölle, J. P. K. W., Camps, J., De Meutter, P., and Meyers, J.: Accompanying dataset for: “A Bayesian Method for predicting background radiation at environmental monitoring stations”, Zenodo [data set], https://doi.org/10.5281/zenodo.12581795, 2024a. a, b
Frankemölle, J. P. K. W., Camps, J., De Meutter, P., and Meyers, J.: Accompanying software for: “A Bayesian Method for predicting background radiation at environmental monitoring stations”, Zenodo [code], https://doi.org/10.5281/zenodo.12644422, 2024b. a
Gelman, A.: Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper), Bayesian Anal., 1, 515–534, https://doi.org/10.1214/06-BA117A, 2006. a
Gelman, A. and Rubin, D. B.: Inference from Iterative Simulation Using Multiple Sequences, Stat. Sci., 7, 457–472, https://doi.org/10.1214/ss/1177011136, 1992. a
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B.: Bayesian Data Analysis, in: 3rd Edn., Chapman and Hall/CRC, https://doi.org/10.1201/b16018, 2013. a
Hoffman, M. D. and Gelman, A.: The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo, J. Mach. Learn. Res., 15, 1593–1623, 2014. a
Hogg, R., McKean, J., and Craig, A.: Introduction to Mathematical Statistics, ib: 8th Edn., Pearson, ISBN 9780134686998, 2018. a
Holt, W. and Nguyen, D.: Essential Aspects of Bayesian Data Imputation, https://doi.org/10.2139/ssrn.4494314, 2023. a, b, c
Howarth, D., Miller, J. K., and Dubrawski, A.: Analyzing the Performance of Bayesian Aggregation Under Erroneous Environmental Beliefs, IEEE T. Nucl. Sci., 69, 1257–1266, https://doi.org/10.1109/TNS.2022.3169990, 2022. a
ICRP: Dose coefficients for external exposures to environmental sources. ICRP Publication 144, Ann. ICRP, https://www.icrp.org/publication.asp?id=ICRP Publication 144 (last access: 25 March 2025), 2020. a
Kumar, R., Carroll, C., Hartikainen, A., and Martin, O.: ArviZ a unified library for exploratory analysis of Bayesian models in Python, Journal of Open Source Software, 4, 1143, https://doi.org/10.21105/joss.01143, 2019. a
Lewandowski, D., Kurowicka, D., and Joe, H.: Generating random correlation matrices based on vines and extended onion method, J. Multivariate Anal., 100, 1989–2001, https://doi.org/10.1016/j.jmva.2009.04.008, 2009. a, b
Liu, Z. and Sullivan, C. J.: Prediction of weather induced background radiation fluctuation with recurrent neural networks, Radiat. Phys. Chem., 155, 275–280, https://doi.org/10.1016/j.radphyschem.2018.03.005, 2019. a
Liu, Z., Abbaszadeh, S., and Sullivan, C. J.: Spatial-temporal modeling of background radiation using mobile sensor networks, PLoS One, 13, 1–14, https://doi.org/10.1371/journal.pone.0205092, 2018. a
Livesay, R., Blessinger, C., Guzzardo, T., and Hausladen, P.: Rain-induced increase in background radiation detected by Radiation Portal Monitors, J. Environ. Radioactiv., 137, 137–141, https://doi.org/10.1016/j.jenvrad.2014.07.010, 2014. a, b
Martin, O. A., Hartikainen, A., Abril-Pla, O., Carroll, C., Kumar, R., Naeem, R., Arroyuelo, A. Gautam, P., rpgoldman, Banerjea, A., Pasricha, N., Sanjay, R., Gruevski, P., Axen, S., Rochford, A., Mahweshwari, U., Kazantsev, V., Zinkov, R., Phan, D., Matamoros, A. A., Arunava, Shekhar, M., Andorra, A., Carrera, E., Osthege, M., Munoz, H., Gorelli, M. E., Capretto, T., Kunanuntakij, T., and Sarina: ArviZ (v0.18.0), Zenodo [code], https://doi.org/10.5281/zenodo.10929056, 2024. a
Maurer, C., Baré, J., Kusmierczyk-Michulec, J., Crawford, A., Eslinger, P. W., Seibert, P., Orr, B., Philipp, A., Ross, O., Generoso, S., Achim, P., Schoeppner, M., Malo, A., Ringbom, A., Saunier, O., Quèlo, D., Mathieu, A., Kijima, Y., Stein, A., Chai, T., Ngan, F., Leadbetter, S. J., De Meutter, P., Delcloo, A., Britton, R., Davies, A., Glascoe, L. G., Lucas, D. D., Simpson, M. D., Vogt, P., Kalinowski, M., and Bowyer, T. W.: International challenge to model the long-range transport of radioxenon released from medical isotope production to six Comprehensive Nuclear-Test-Ban Treaty monitoring stations, J. Environ. Radioactiv., 192, 667–686, https://doi.org/10.1016/j.jenvrad.2018.01.030, 2018. a
Mercier, J.-F., Tracy, B., d'Amours, R., Chagnon, F., Hoffman, I., Korpach, E., Johnson, S., and Ungar, R.: Increased environmental gamma-ray dose rate during precipitation: a strong correlation with contributing air mass, J. Environ. Radioactiv., 100, 527–533, https://doi.org/10.1016/j.jenvrad.2009.03.002, 2009. a, b
Michaud, I. J., Schmidt, K., Smith, R. C., and Mattingly, J.: A hierarchical Bayesian model for background variation in radiation source localization, Nucl. Instrum. Meth. A, 1002, 165288, https://doi.org/10.1016/j.nima.2021.165288, 2021. a
Natural Earth: Free vector and raster map data, https://www.naturalearthdata.com (last access: 25 March 2025), 2024. a
Nomura, S., Tsubokura, M., Hayano, R., Furutani, T., Yoneoka, D., Kami, M., Kanazawa, Y., and Oikawa, T.: Comparison between Direct Measurements and Modeled Estimates of External Radiation Exposure among School Children 18 to 30 Months after the Fukushima Nuclear Accident in Japan, Environ. Sci. Technol., 49, 1009–1016, https://doi.org/10.1021/es503504y, 2015. a
Park, S. Y. and Bera, A. K.: Maximum entropy autoregressive conditional heteroskedasticity model, J. Econometrics, 150, 219–230, https://doi.org/10.1016/j.jeconom.2008.12.014, 2009. a
Querfeld, R., Hori, M., Weller, A., Degering, D., Shozugawa, K., and Steinhauser, G.: Radioactive Games? Radiation Hazard Assessment of the Tokyo Olympic Summer Games, Environ. Sci. Technol., 54, 11414–11423, https://doi.org/10.1021/acs.est.0c02754, 2020. a
Sangiorgi, M., Hernández-Ceballos, M. A., Jackson, K., Cinelli, G., Bogucarskis, K., De Felice, L., Patrascu, A., and De Cort, M.: The European Radiological Data Exchange Platform (EURDEP): 25 years of monitoring data exchange, Earth Syst. Sci. Data, 12, 109–118, https://doi.org/10.5194/essd-12-109-2020, 2020. a
Sonck, M., Desmedt, M., Claes, J., and Sombré, L.: TELERAD: the radiological surveillance network and early warning system in Belgium, in: 12th Congress of the International Radiation Protection Association (IRPA12): Proceedings of a Conference Held in Buenos Aires, Argentina, 19–24 October, 2008, Proceedings Series, International Atomic Energy Agency, Vienna, https://www.iaea.org/publications/8450/12th-congress-of-the-international-radiation-protection-association-irpa12 (last access: 22 November 2021), 2010. a, b, c
Sportisse, B.: A review of parameterizations for modelling dry deposition and scavenging of radionuclides, Atmos. Environ., 41, 2683–2698, https://doi.org/10.1016/j.atmosenv.2006.11.057, 2007. a
Vehtari, A., Gelman, A., Simpson, D., Carpenter, B., and Bürkner, P.-C.: Rank-Normalization, Folding, and Localization: An Improved for Assessing Convergence of MCMC (with Discussion), Bayesian Anal., 16, 667–718, https://doi.org/10.1214/20-BA1221, 2021. a
Wiecki, T., Salvatier, J., Vieira, R., Kochurov, M., Patil, A., Osthege, M., Willard, B. T., Engels, B., Martin, O. A., Carroll, C., Seyboldt, A., Rochford, A., Paz, L., rpgoldman, Meyer, K., Coyle, P., Abril-Pla, O., Gorelli, M. E., Andreani, V., Kumar, R., Lao, J., Yoshioka, T., Ho, G., Kluyver, T., Andorra, A., Beauchamp, K., Pananos, D., Spaak, E., and larryshamalama: pymcdevs/pymc: v5.13.1, Zenodo [code], https://doi.org/10.5281/zenodo.10973000, 2024. a
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.
To detect anomalous radioactivity in the environment, it is paramount that we understand the...