Optimizing long-term monitoring of radiation air-dose rates after the Fukushima Daiichi Nuclear Power Plant.


Journal

Journal of environmental radioactivity
ISSN: 1879-1700
Titre abrégé: J Environ Radioact
Pays: England
ID NLM: 8508119

Informations de publication

Date de publication:
Sep 2020
Historique:
received: 22 01 2020
revised: 30 03 2020
accepted: 26 04 2020
entrez: 21 6 2020
pubmed: 21 6 2020
medline: 14 8 2020
Statut: ppublish

Résumé

Radiation air dose rates near the Fukushima Daiichi Nuclear Power Plant (FDNPP) have been steadily decreasing over the past eight years since the release of radioactive elements in March 2011. Currently, the radiation monitoring program is expected to transition to long-term monitoring after most of the remediation activities are completed. The main long-term monitoring objectives are to (1) confirm the continuing reduction of contaminant and hazard levels, (2) provide assurance for the public, (3) accumulate the basic datasets for scientific knowledge and future preparation, and (4) detect changes or anomalies in contaminant mobility (if they occur), or any unexpected processes or events. In this work, we have developed a methodology for optimizing the monitoring locations of radiation air dose-rate monitoring. Our approach consists of three steps in order to determine monitoring locations in a systematic manner: (1) prioritizing the critical locations, such as schools or regulatory requirement locations, (2) diversifying locations that cover the key environmental controls that are known to influence contaminant mobility and distributions, and (3) capturing the heterogeneity of radiation air-dose rates across the domain. For the second step, we use a Gaussian mixture model to identify the representative locations among multiple environmental variables, such as elevation and land-cover types. For the third step, we use a Gaussian process model to capture and estimate the heterogeneity of air-dose rates across the domain. Employing an integrated dose-rate map derived from Bayesian geostatistical methods as a reference map, we distribute the monitoring locations in such a way as to capture the heterogeneity of the reference map. Our results have shown that this approach allows us to select monitoring locations in a systematic manner such that the heterogeneity of air dose rates is captured by the minimal number of monitoring locations.

Identifiants

pubmed: 32560882
pii: S0265-931X(20)30018-7
doi: 10.1016/j.jenvrad.2020.106281
pii:
doi:

Substances chimiques

Air Pollutants, Radioactive 0
Cesium Radioisotopes 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

106281

Informations de copyright

Copyright © 2020 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

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.

Auteurs

Dajie Sun (D)

Department of Nuclear Engineering, University of California, Berkeley, CA, USA.

Haruko M Wainwright (HM)

Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; Department of Nuclear Engineering, University of California, Berkeley, CA, USA. Electronic address: hmwainwright@lbl.gov.

Carlos A Oroza (CA)

Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, UT, USA.

Akiyuki Seki (A)

Japan Atomic Energy Agency, Tokyo, Japan.

Satoshi Mikami (S)

Japan Atomic Energy Agency, Tokyo, Japan.

Hiroshi Takemiya (H)

Japan Atomic Energy Agency, Tokyo, Japan.

Kimiaki Saito (K)

Japan Atomic Energy Agency, Tokyo, Japan.

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Classifications MeSH