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

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 into the environment. Often, the background ambient dose equivalent rate 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 Belgian Reactor 1 (BR1) in Mol – highlights the relevance of our method for anomaly detection and quantification.