Preprints
https://doi.org/10.5194/gmd-2024-137
https://doi.org/10.5194/gmd-2024-137
Submitted as: development and technical paper
 | 
19 Aug 2024
Submitted as: development and technical paper |  | 19 Aug 2024
Status: this preprint is currently under review for the journal GMD.

A Bayesian method for predicting background radiation at environmental monitoring stations

Jens Peter K. W. Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers

Abstract. Detector networks that measure environmental radiation serve as radiological surveillance and early warning networks in many countries across Europe and beyond. Their goal is to detect anomalous radioactive signatures that indicate the release of radionuclides to the environment. Often, the background H·*(10) is predicted using meteorological information. However, in dense detector networks the correlation between different detectors is expected to contain markedly more information. In this work, we investigate how the joint observations by neighbouring detectors can be leveraged to predict the background H·*(10). Treating it as a stochastic vector, we show that its distribution can be approximated as multivariate normal. We reframe the question of background prediction as a Bayesian inference problem including priors and likelihood. Finally, we show that the conditional distribution can be used to make predictions. To perform the inferences we use PyMC. All inferences are performed using real data for the nuclear sites in Doel and Mol, Belgium. We validate our calibrated model on previously unseen data. Application of the model to a case with known anomalous behaviour – observations during the operation of the BR1 reactor in Mol – highlights the relevance of our method for anomaly detection and quantification.

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Jens Peter K. W. Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2024-137', Anonymous Referee #1, 12 Sep 2024
  • RC2: 'Comment on gmd-2024-137', Anonymous Referee #2, 28 Nov 2024
Jens Peter K. W. Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Jens Peter K. W. Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers

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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.