Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 6
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.
https://doi.org/10.5194/gmd-18-1989-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 6
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

Viewed

Total article views: 2,981 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,418 478 85 2,981 79 170
  • HTML: 2,418
  • PDF: 478
  • XML: 85
  • Total: 2,981
  • BibTeX: 79
  • EndNote: 170
Views and downloads (calculated since 19 Aug 2024)
Cumulative views and downloads (calculated since 19 Aug 2024)

Viewed (geographical distribution)

Total article views: 2,981 (including HTML, PDF, and XML) Thereof 2,881 with geography defined and 100 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Jan 2026
Download
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.
Read more
Share