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Variability in radiocesium activity concentration in growing hardwood shoots in Fukushima, Japan

Author

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  • Hiroki Itô
  • Satoru Miura
  • Masabumi Komatsu
  • Tsutomu Kanasashi
  • Junko Nagakura
  • Keizo Hirai

Abstract

The radiocesium contamination caused by the Fukushima Daiichi Nuclear Power Plant accident has made it difficult to use coppice woods as bed logs for mushroom cultivation. Evaluating the variability in the radiocesium activity concentration of logs is necessary in order to predict how many coppice woodlands are available for producing mushroom bed logs. To clarify the variability in radiocesium activity concentrations and to estimate the sample size required to estimate these concentrations with sufficient accuracy, we modeled the log-transformed radiocesium activity concentrations in growing shoots of hardwoods. We designed two models: (1) a model with mean concentrations that varied among stands with a standard deviation that was the same among stands, and (2) a model with varying means and standard deviations. We fit the data pertaining to only Quercus serrata to both models and calculated the widely applicable information criterion values. Consequently, we adopted the simpler model (1). Applying the selected model to data for all species, we examined the relationship between the number of measurement individuals and the predictive distribution of the expected concentration. Based on previous recommendations and measurement costs, we proposed that five individuals would be appropriate for estimating radiocesium activity concentration in a stand.

Suggested Citation

  • Hiroki Itô & Satoru Miura & Masabumi Komatsu & Tsutomu Kanasashi & Junko Nagakura & Keizo Hirai, 2023. "Variability in radiocesium activity concentration in growing hardwood shoots in Fukushima, Japan," PLOS ONE, Public Library of Science, vol. 18(12), pages 1-15, December.
  • Handle: RePEc:plo:pone00:0293166
    DOI: 10.1371/journal.pone.0293166
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    References listed on IDEAS

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    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
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