A dataset of 137Cs activity concentration and inventory in forests contaminated by the Fukushima accident

Radioactivity of anthropogenic and natural radionuclides in marine sediments of the Chaun Bay, East Siberian Sea

Natural radioactive isotopes serve as a useful proxy of geological and geochemical processes in marine environment, while radiocesium serves as an indicator of man-made contamination. Monitoring of natural and anthropogenic radioactivity under conditions of the climate changes in the Arctic region is of high importance in investigations of this natural system. For the first time, we report the data on spatial distribution of natural (232Th, 226Ra, 40K) and anthropogenic (137Cs) radionuclide activities in the marine sediments from Chaun Bay (East Siberian Sea). The measured activity concentrations varied in the range 23.7-77.9 (mean 39.2) Bq kg-1 for 232Th, 16.5-39.3 (mean 26.6) Bq kg-1 for 226Ra, 535-991 (mean 726) Bq kg-1 for 40K, and 0.5-4.7 (mean 2.0) Bq kg-1 for 137Cs. The radiocesium level in the sediments showed no local sources of anthropogenic pollution in the Chaun Bay, while the average activity concentration of 40K was 1.8 times higher than worldwide.

Estimation of spatio-temporal distribution of 137Cs concentrations in litter layer of forest ecosystems in Fukushima using FoRothCs model

The nuclear power plant accident in Fukushima had led to pollution of forest ecosystems with ¹³⁷Cs in 2011. In this study, we simulated the spatiotemporal distribution of ¹³⁷Cs concentrations of litter layer in the contaminated forest ecosystems in two decades from 2011, which is one of the key environmental components of ¹³⁷Cs migration in the environment due to the high bioavailability of ¹³⁷Cs in the litter. Our simulations showed that ¹³⁷Cs deposition is the most important factor in the degree of contamination of the litter layer but vegetation type (evergreen coniferous/deciduous broadleaf) and mean annual temperature are also important for changes over time. Deciduous broadleaf trees had higher initial concentrations in the litter layer due to the direct initial deposition on the forest floor. However, the concentrations remained higher than those in evergreen conifers after 10 years due to redistribution of ¹³⁷Cs by vegetation. Moreover, areas with lower average annual temperatures and lower litter decomposition activity retained higher ¹³⁷Cs concentrations in the litter layer. The results of the spatiotemporal distribution estimation of the radioecological model suggest that, in addition to ¹³⁷Cs deposition, elevation and vegetation distribution should also be considered in the long-term management of contaminated watersheds, which can be informative in identifying hotspots of ¹³⁷Cs contamination on a long-term scale.

Analysis of causal effects of 137Cs deposition on 137Cs concentrations in trees after the Fukushima accident using machine learning

Radioactive contamination of forests by long-lived radionuclides from nuclear accidents such as Chernobyl and Fukushima continues to be studied and quantitatively modeled. Whereas traditional statistical and machine learning (ML) techniques generate predictions by focusing on correlations between variables, quantification of causal effects of radioactivity deposition levels on contamination of plant tissues represents a more fundamental and relevant research goal. Modeling of cause-and-effect relationships is advantageous over standard predictive modeling, particularly by improving the generalizability of results to other situations, where the distributions of variables, including potential confounders, differ from those in the training data. Here we used the state-of-the-art causal forest (CF) algorithm to quantify the causal effect of ¹³⁷Cs land contamination after the Fukushima accident on ¹³⁷Cs activity concentrations in the wood of four common Japanese forest tree species: Hinoki cypress (Chamaecyparis obtusa), konara oak (Quercus serrata), red pine (Pinus densiflora), and Sugi cedar (Cryptomeria japonica). We estimated the average causal effect for the population, quantified how it was influenced by other environmental variables, and produced effect estimates at the individual level. The estimated causal effect was quite robust to various refutation methods, and was negatively influenced by high mean annual precipitation, elevation, and time after the accident. Wood subtype (e.g. sapwood, heartwood) and tree species made smaller contributions to the causal effect. We believe that causal ML techniques have promising potential in radiation ecology and can usefully expand the toolkit of modeling approaches available to researchers in this field.

Mode of Atmospheric Deposition in Forests Demonstrates Notable Differences in Initial Radiocesium Behavior

The March 2011 Fukushima Dai-ichi Nuclear Power Plant accident in Japan released 520 PBq of radionuclides compared to a total release of 5300 PBq from the Chornobyl Nuclear Power Plant accident. Both nuclear accidents resulted in deposition of radiocesium throughout the northern hemisphere, and a plethora of studies have been performed regarding radiocesium (¹³⁷Cs) behavior. However, few studies have assessed the impact of precipitation on ¹³⁷Cs deposition in forests. Wide-scale environmental measurements from 2011 and 2016 were used to determine the differences in ¹³⁷Cs deposition because of precipitation following the Fukushima accident. In areas where wet deposition processes were dominant, dense forests generally had lower ambient dose rates and levels of contamination on forest floors than other stands with fewer stems per hectare in 2011. Similar tendencies were not observed in areas that were primarily subject to dry deposition nor were any trends observed in 2016. ¹³⁷Cs was retained in dense forest canopies for an extended period regardless of the deposition mode. Additionally, it was found that the initial retention of radionuclides by forest canopies is in general higher for areas with predominantly dry deposition. Incorporation of radiocesium into wood tissues was the same for both wet and dry deposition.

Pre- and post-accident environmental transfer of radionuclides in Japan: lessons learned in the IAEA MODARIA II programme

An international review of radioecological data derived after the accident at the Fukushima Daiichi nuclear power plant was an important component of activities in working group 4 of the IAEA Models and data for radiological impact assessment, phase II (MODARIA II) programme. Japanese and international scientists reviewed radioecological data in the terrestrial and aquatic environments in Japan reported both before and after the accident. The environmental transfer processes considered included: (a) interception and retention radionuclides by plants, (b) loss of radionuclides from plant and systemic transport of radionuclides in plants (translocation), (c) behaviour of radiocaesium in soil, (d) uptake of radionuclides from soil by agricultural crops and wild plants, (e) transfer of radionuclides from feedstuffs to domestic and wild animals, (f) behaviour of radiocaesium in forest trees and forest systems, (g) behaviour of radiocaesium in freshwater systems, coastal areas and in the ocean, (h) transport of radiocaesium from catchments through rivers, streams and lakes to the ocean, (i) uptake of radiocaesium by aquatic organisms, and (j) modification of radionuclide concentrations in food products during food processing and culinary preparation. These data were compared with relevant global data within IAEA TECDOC-1927 'Environmental transfer of radionuclides in Japan following the accident at the Fukushima Daiichi Nuclear Power Plant'. This paper summarises the outcomes of the data collation and analysis within MODARIA II work group 4 and compares the Japan-specific data with existing radioecological knowledge acquired from past and contemporary radioecological studies. The key radioecological lessons learned are outlined and discussed.

Machine learning analysis of 137Cs contamination of terrestrial plants after the Fukushima accident using the random forest algorithm

Radioactive contamination of terrestrial plants was extensively investigated and quantitatively modeled after the Fukushima nuclear power plant accident. This phenomenon, which is important for ecosystem functioning and protection of human health, is influenced by multiple factors, including plant species, time after the accident, and climate. Machine learning algorithms such as random forests (RF) have a record of strong performance on large multi-dimensional data sets, but, to our knowledge, combined data on post-Fukushima plant contamination with radionuclides were not yet subjected to a machine learning analysis. Here we performed such analysis on two large published data sets: (1) ¹³⁷Cs activity concentrations in four common Japanese forest tree species. (2) Plant/soil ¹³⁷Cs concentration ratios in multiple perennial plant species. The goal was to show the usefulness of machine learning for identifying and quantifying the main trends of ¹³⁷Cs contamination in terrestrial plants. Each data set was split randomly into training and testing parts, RF was fitted and tuned on the training parts, and its performance was assessed on the testing parts by three metrics: coefficient of determination (R²), root mean squared error, and mean absolute error. Synthetic noise variables and the Boruta algorithm were used in a customized procedure to identify the most important predictor variables, which consistently outperformed random noise. Good agreement between observations and RF predictions (e.g. R²∼0.9 on testing data) was obtained on both data sets. The effects of the most important predictors (e.g. time after the accident, ¹³⁷Cs land contamination level, and plant species) and interactions between them were quantified by partial dependence plots. These results of machine learning analyses of large data collections can help to complement previous modeling efforts, and to clarify the patterns of ¹³⁷Cs contamination of plants after the Fukushima accident.

Radiocaesium Contamination of Mushrooms at High- and Low-Level Chernobyl Exposure Sites and Its Consequences for Public Health

We compare the specific activities of 137Cs and 40K in stipes and caps of three different common mushroom species (Xerocomus badius, Russula ochroleuca and Armillariella mellea) measured at the Czech Chernobyl hot spot in the Opava area (Silesia) and at a low-exposed site at the Beskydy mountains in 2011. The highest values of 137Cs were found in caps of Xerocomus badius and Russula ochroleuca in the Opava area (11.8 and 8.77 kBq/kg, respectively). The source of 137Cs was verified by the measurement of the 134Cs/137Cs ratio. Based on our results, we estimate an effective dose per year due to radiocaesium intake in the two investigated areas for Xerocomus badius, one of the most popular edible mushrooms in the Czech Republic. In 2011, the effective dose reached the maximum value of 0.102 mSv in the Opava area and 0.004 mSv at the low-exposed site at the Beskydy mountains. Therefore, it does not represent a significant risk for public health.

Dynamics of radiocaesium within forests in Fukushima-results and analysis of a model inter-comparison

Forests cover approximately 70% of the area contaminated by the Fukushima Daiichi Nuclear Power Plant accident in 2011. Following this severe contamination event, radiocaesium (¹³⁷Cs) is anticipated to circulate within these forest ecosystems for several decades. Since the accident, a number of models have been constructed to evaluate the past and future dynamics of ¹³⁷Cs in these forests. To explore the performance and uncertainties of these models we conducted a model inter-comparison exercise using Fukushima data. The main scenario addressed an evergreen needleleaf forest (cedar/cypress), which is the most common and commercially important forest type in Japan. We also tested the models with two forest management scenarios (decontamination by removal of soil surface litter and forest regeneration) and, furthermore, a deciduous broadleaf forest (konara oak) scenario as a preliminary modelling study of this type of forest. After appropriate calibration, the models reproduced the observed data reliably and the ranges of calculated trajectories were narrow in the early phase after the fallout. Successful model performances in the early phase were probably attributable to the availability of comprehensive data characterizing radiocaesium partitioning in the early phase. However, the envelope of the calculated model end points enlarged in long-term simulations over 50 years after the fallout. It is essential to continue repetitive verification/validation processes using decadal data for various forest types to improve the models and to update the forecasting capacity of the models.