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Radon progeny monitoring outdoors using multiple NaI detectors.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Radon progeny monitoring outdoors using multiple NaI detectors.
Συγγραφείς: Kanoutos K; Nuclear Engineering Laboratory, National Technical University of Athens, Athens, 15780, Greece. Electronic address: kkanoutos@mail.ntua.gr., Petropoulos NP; Nuclear Engineering Laboratory, National Technical University of Athens, Athens, 15780, Greece., Anagnostakis MJ; Nuclear Engineering Laboratory, National Technical University of Athens, Athens, 15780, Greece.
Πηγή: Journal of environmental radioactivity [J Environ Radioact] 2026 Aug; Vol. 298, pp. 108099. Date of Electronic Publication: 2026 Jul 06.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Elsevier Applied Science Publishers Country of Publication: England NLM ID: 8508119 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-1700 (Electronic) Linking ISSN: 0265931X NLM ISO Abbreviation: J Environ Radioact Subsets: MEDLINE
Imprint Name(s): Original Publication: Barking, Essex : Elsevier Applied Science Publishers
Ιατρικοί όροι (MeSH): Radiation Monitoring*/methods , Radiation Monitoring*/instrumentation , Air Pollutants, Radioactive*/analysis , Radon Daughters*/analysis , Radon*/analysis, Sodium Iodide/chemistry
Περίληψη: Radon progeny may be used as tracers of environmental processes. In this work, a methodology involving two NaI(Tl) detector systems for the continuous monitoring of short-lived γ-emitting radon progeny activity in atmospheric air is presented. The primary detector focuses on the γ-rays of radon progeny accumulated on an air filter during aerosol sampling, while the secondary detector monitors changes in the γ-background of the measurement site. The methodology yields measurements regarding the 214Pb and 214Bi atmospheric activity variability at regular and short intervals. Efficiency calibration of the measurement set-up allows for activity concentration estimations. Changes in the correlation between total γ-measurements recorded from the two detectors indicate events such as precipitation impulses, difficult to be otherwise identified, that lead to intense radionuclide deposition. Moreover, total -γ deposition modeling along with machine learning can be used to predict the γ-activity in air during intense scavenging events.
(Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributed Indexing: Keywords: Environmental monitoring; Gamma spectroscopy; NaI; Radon progeny
Substance Nomenclature: 0 (Air Pollutants, Radioactive)
0 (Radon Daughters)
F5WR8N145C (Sodium Iodide)
Q74S4N8N1G (Radon)
Entry Date(s): Date Created: 20260706 Date Completed: 20260806 Latest Revision: 20260806
Update Code: 20260807
DOI: 10.1016/j.jenvrad.2026.108099
PMID: 42407301
Βάση Δεδομένων: MEDLINE
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  Data: Radon progeny may be used as tracers of environmental processes. In this work, a methodology involving two NaI(Tl) detector systems for the continuous monitoring of short-lived γ-emitting radon progeny activity in atmospheric air is presented. The primary detector focuses on the γ-rays of radon progeny accumulated on an air filter during aerosol sampling, while the secondary detector monitors changes in the γ-background of the measurement site. The methodology yields measurements regarding the <superscript>214</superscript>Pb and <superscript>214</superscript>Bi atmospheric activity variability at regular and short intervals. Efficiency calibration of the measurement set-up allows for activity concentration estimations. Changes in the correlation between total γ-measurements recorded from the two detectors indicate events such as precipitation impulses, difficult to be otherwise identified, that lead to intense radionuclide deposition. Moreover, total -γ deposition modeling along with machine learning can be used to predict the γ-activity in air during intense scavenging events.<br /> (Copyright © 2026 The Authors. Published by Elsevier Ltd.. All rights reserved.)
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  Data: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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        Value: 10.1016/j.jenvrad.2026.108099
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        Text: English
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        StartPage: 108099
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        Type: general
      – SubjectFull: Radiation Monitoring methods
        Type: general
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        Type: general
      – SubjectFull: Radon Daughters analysis
        Type: general
      – SubjectFull: Radon analysis
        Type: general
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      – TitleFull: Radon progeny monitoring outdoors using multiple NaI detectors.
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