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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Interactive discussion

Status: closed

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
    • AC1: 'Reply on RC1', Jens Peter K.W. Frankemölle, 18 Dec 2024
  • RC2: 'Comment on gmd-2024-137', Anonymous Referee #2, 28 Nov 2024
    • AC2: 'Reply on RC2', Jens Peter K.W. Frankemölle, 18 Dec 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Jens Peter K.W. Frankemölle on behalf of the Authors (18 Dec 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (21 Dec 2024) by Dan Lu
RR by Anonymous Referee #2 (03 Jan 2025)
RR by Anonymous Referee #1 (04 Jan 2025)
ED: Publish as is (16 Jan 2025) by Dan Lu
AR by Jens Peter K.W. Frankemölle on behalf of the Authors (20 Jan 2025)
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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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