the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A Bayesian method for predicting background radiation at environmental monitoring stations
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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RC1: 'Comment on gmd-2024-137', Anonymous Referee #1, 12 Sep 2024
The authors address the interesting topic by developing a Bayesian method aimed at the prediction of background in atmospheric monitoring. The paper is well-written and the proposed methodology is clear. What I see as the main drawback of the paper is that it focuses mainly on local monitoring networks, limiting the application on a larger scale to zero. I suggest the authors should reformulate the title/abstract/introduction of the paper so that it clearly states that the aim is the local networks. This limitation is logical since the method is purely statistical with no additional information from the environment such as terrain or dispersion modeling, as also mentioned by the authors in the conclusion which is appreciated by this reviewer. Also, the authors made several strong assumptions that need further clarification, see the following comments.
Major comments:
line 72: the first paragraph of Section 2.1. motivates the paper regarding data and monitoring networks. Are these networks rather standard in other facilities or are they Belgium-specific? It should be somehow undestanded from the very beginning of the abstract/introduction.
line 97: without any further comments, the authors selected specific periods of data, however, it is not clear why the authors made this choice. Do the periods cover "standard" periods or do they have specific reasons? Please, clarify these choices.
line 160: The assumption of independence between S and R seems wrong for the defined model. The matrix R is defined based on \sigma_l (line 137) while the matrix S has \sigma_l elements on its diagonal (line 138). Hence, this assumption is not justified and should be either reformulated or omitted. However, this assumption seems crucial for further estimation. Please, comment on this or reformulate the estimation procedure.
line 222: I understand the assumption of Gaussianity on line 129 due to the tractability of the model. However, the assumption on line 222 is quite difficult to follow and accept since it seems very strong to the reviewer considering the complexity of the atmospheric environment. The errors may be huge and to accept such an assumption seems (maybe) possible for concrete and very compact networks, but it is not general.Minor comments:
line 3: the notation H*(10) is used in the abstract but it is defined later. Please, define it here or remove it from the abstract.
line 21: ...what a normal really means. - add a
line 64: ...how how the Bayesian... - remove how
line 134: Subsubsect. --> Sect.
eq. (3): here, \Sigma_{lm} is defined using R_{lm} which is defined, again, using \Sigma_{lm}. Please, clarify.
line 144: I suggest stating that the \mathcal{M} is the vector here for clarity and better understanding.
eq. (5, 7, 8): please, use brackets together with exp function for clarity.
line 171: I suggest defining LKJ correlation distribution since it is not standard and general knowledge. Also, its property related to the choice of \eta equal to 1 should be discussed.
line 186: I suggest removing the computer specifics since they are not of interest, computational complexity is not studied here.
eq. (10): use \widehat instead of the \hat symbol.Citation: https://doi.org/10.5194/gmd-2024-137-RC1 -
RC2: 'Comment on gmd-2024-137', Anonymous Referee #2, 28 Nov 2024
The study proposes a new Bayesian inference method for predicting background radiation. This method considers the correlation between different detectors. The authors use observations from two nuclear sites in Belgium to validate and verify the new method and investigate its potential application in radiation anomaly detection and quantification. The manuscript is well-organized, but the authors could improve the language further. I also have some detailed comments below.
Line 3: Please define H*(10) at its first appearance, the same as BR1 in Line 11.
Lines 21-22 and 34-36: Do you mean the normal equals background radiation? According to the descriptions in the second paragraph, precipitation and cosmic radiation, which are background factors, can suddenly change the dose equivalent rate much. Are they considered normal or abnormal? Please clarify the concepts (normal, background, and anomalous).
Line 64: Delete the second “how”
Line 98: Delete “for”
Line 149: Which four?
Lines 155: This assumption limits the application of the method to situations with significant temporal variations, which is consistent with the statement in Line 165. But Figure 2 shows the excellent performance of the method even if apparent temporal variations exist. Please explain it.
Lines 206-208: This is what I am very concerned about. Under such an application, we must know which detectors are influenced by the local radiation source. As the example shown in Section 5, the results will be entirely different if you select different sectors to construct the Bayesian model. Please discuss the limitations.
Line 274: What do you mean by an order of magnitude more uncertainty? Compared to even-numbered stations? Does Figure 4 show that?
Line 287: Could you please provide some statistics to verify it (variance is overestimated)?
Citation: https://doi.org/10.5194/gmd-2024-137-RC2
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