Articles | Volume 18, issue 6
https://doi.org/10.5194/gmd-18-1989-2025
https://doi.org/10.5194/gmd-18-1989-2025
Development and technical paper
 | 
27 Mar 2025
Development and technical paper |  | 27 Mar 2025

A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks

Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers

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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
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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.
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