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New predictions of (137)Cs dynamics in forests after the Fukushima nuclear accident
Most of the area contaminated by the Fukushima Daiichi Nuclear Power Plant accident is covered by forest. In this paper, we updated model predictions of temporal changes in the (137)Cs dynamics using the latest observation data and newly provided maps of the predicted (137)Cs activity concentration...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6949301/ https://www.ncbi.nlm.nih.gov/pubmed/31913315 http://dx.doi.org/10.1038/s41598-019-56800-5 |
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author | Hashimoto, Shoji Imamura, Naohiro Kaneko, Shinji Komatsu, Masabumi Matsuura, Toshiya Nishina, Kazuya Ohashi, Shinta |
author_facet | Hashimoto, Shoji Imamura, Naohiro Kaneko, Shinji Komatsu, Masabumi Matsuura, Toshiya Nishina, Kazuya Ohashi, Shinta |
author_sort | Hashimoto, Shoji |
collection | PubMed |
description | Most of the area contaminated by the Fukushima Daiichi Nuclear Power Plant accident is covered by forest. In this paper, we updated model predictions of temporal changes in the (137)Cs dynamics using the latest observation data and newly provided maps of the predicted (137)Cs activity concentration for wood, which is the most commercially important part of the tree body. Overall, the previous prediction and latest observation data were in very good agreement. However, further validation revealed that the migration from the soil surface organic layer to the mineral soil was overestimated for evergreen needleleaf forests. The new prediction of the (137)Cs inventory showed that although the (137)Cs distribution within forests differed among forest types in the first 5 years, the difference diminished in the later phase. Besides, the prediction of the wood (137)Cs activity concentrations reproduced the different trends of the (137)Cs activity concentrations for cedar, oak, and pine trees. Our simulation suggests that the changes of the wood (137)Cs activity concentration over time will slow down after 5–10 years. Although the model uncertainty should be considered and monitoring and model updating must continue, the study provides helpful information on the (137)Cs dynamics within forest ecosystems and the changes in wood contamination. |
format | Online Article Text |
id | pubmed-6949301 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-69493012020-01-13 New predictions of (137)Cs dynamics in forests after the Fukushima nuclear accident Hashimoto, Shoji Imamura, Naohiro Kaneko, Shinji Komatsu, Masabumi Matsuura, Toshiya Nishina, Kazuya Ohashi, Shinta Sci Rep Article Most of the area contaminated by the Fukushima Daiichi Nuclear Power Plant accident is covered by forest. In this paper, we updated model predictions of temporal changes in the (137)Cs dynamics using the latest observation data and newly provided maps of the predicted (137)Cs activity concentration for wood, which is the most commercially important part of the tree body. Overall, the previous prediction and latest observation data were in very good agreement. However, further validation revealed that the migration from the soil surface organic layer to the mineral soil was overestimated for evergreen needleleaf forests. The new prediction of the (137)Cs inventory showed that although the (137)Cs distribution within forests differed among forest types in the first 5 years, the difference diminished in the later phase. Besides, the prediction of the wood (137)Cs activity concentrations reproduced the different trends of the (137)Cs activity concentrations for cedar, oak, and pine trees. Our simulation suggests that the changes of the wood (137)Cs activity concentration over time will slow down after 5–10 years. Although the model uncertainty should be considered and monitoring and model updating must continue, the study provides helpful information on the (137)Cs dynamics within forest ecosystems and the changes in wood contamination. Nature Publishing Group UK 2020-01-08 /pmc/articles/PMC6949301/ /pubmed/31913315 http://dx.doi.org/10.1038/s41598-019-56800-5 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Hashimoto, Shoji Imamura, Naohiro Kaneko, Shinji Komatsu, Masabumi Matsuura, Toshiya Nishina, Kazuya Ohashi, Shinta New predictions of (137)Cs dynamics in forests after the Fukushima nuclear accident |
title | New predictions of (137)Cs dynamics in forests after the Fukushima nuclear accident |
title_full | New predictions of (137)Cs dynamics in forests after the Fukushima nuclear accident |
title_fullStr | New predictions of (137)Cs dynamics in forests after the Fukushima nuclear accident |
title_full_unstemmed | New predictions of (137)Cs dynamics in forests after the Fukushima nuclear accident |
title_short | New predictions of (137)Cs dynamics in forests after the Fukushima nuclear accident |
title_sort | new predictions of (137)cs dynamics in forests after the fukushima nuclear accident |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6949301/ https://www.ncbi.nlm.nih.gov/pubmed/31913315 http://dx.doi.org/10.1038/s41598-019-56800-5 |
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