Optimizing long-term monitoring of radiation air-dose rates after the Fukushima Daiichi Nuclear Power Plant.
Dose rate
Fukushima
Long-term monitoring
Machine learning
Network optimization
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
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
106281Informations 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.