Reconstructing the deposition environment and long-term fate of Chernobyl 137Cs at the floodplain scale through mobile gamma spectrometry

Abstract

Cs-137 is considered to be the most significant anthropogenic contributor to human dose and presents a particularly difficult remediation challenge after a dispersal following nuclear incident. The Chernobyl Nuclear Power Plant meltdown in April 1986 represents the largest nuclear accident in history and released over 80 PBq of Cs into the environment. As a result, much of the land in close proximity to Chernobyl, which includes the Polessie State Radioecology Reserve in Belarus, remains highly contaminated with Cs to such an extent they remain uninhabitable. Whilst there is a broad scale understanding of the depositional patterns within and beyond the exclusion zone, detailed mapping of the distribution is often limited. New developments in mobile gamma spectrometry provide the opportunity to map the fallout ofCs and begin to reconstruct the depositional environment and the long-term behaviour of Cs in the environment. Here, full gamma spectrum analysis using algorithms based on the peak-valley ratio derived from Monte Carlo simulations are used to estimate the totalCs deposition and its depth distribution in the soil. The results revealed a pattern of Cs distribution consistent with the deposition occurring at a time of flooding, which is validated by review of satellite imagery acquired at similar times of the year. The results were also consistent with systematic burial of the falloutCs by annual flooding events. These results were validated by sediment cores collected along a transect across the flood plain. The true merit of the approach was confirmed by exposing new insights into the spatial distribution and long term fate of Cs across the floodplain. Such systematic patterns of behaviour are likely to be fundamental to the understanding of the radioecological behaviour ofCs whilst also providing a tracer for quantifying the ecological controls on sediment movement and deposition at a landscape scale.

Environmental Remediation of the difficult-to-return zone in Tomioka Town, Fukushima Prefecture

“…Real-time maps with color-scaled ambient dose rates and gamma-ray energy spectra can be output. The detected energy peaks of radiocesium ( 134 Cs and 137 Cs) registered in the nuclear library (i.e., detected net count values) and their associated confidence intervals were obtained for the region of interest (with levels 1-10 used as reference values) 7,39 .…”

“…Long-term environmental monitoring as well as further decontamination efforts should continue around the FDNPS, including Tomioka Town. On the other hand, the external exposure level and the decontamination effects on landscape within the difficult-to-return zone have not been evaluated concretely, although data from the literature, databases and websites have been reported by the national and local governments [4][5][6][7][8][9]12,13 . Especially, recent reports on the decontamination effect on landscape are not sufficiently published 14 .…”

Rapid in situ assessment of radiocesium wood contamination using field gamma-ray spectroscopy to optimise felling

“…Small scale variation in contamination distribution is particularly important, as more recent studies have demonstrated that orders of magnitude of change can be observed in areas closer to the Chernobyl nuclear power plant (Golosov, 2003;Golosov et al, 2000;Adam Varley et al, 2017;Vitaly et al, 2019Vitaly et al, , 2018. In part this variation is due to remobilisation, but mainly it is down to finer scale deposition patterns dictated by localised meteorological events in 1986 (Varley et al, 2018). Therefore, basing wood activities for an entire forest stand from TR derived from limited soil samples leaves a study particularly vulnerable to very large uncertainties on final estimates.…”

Nonintrusive Depth Estimation of Buried Radioactive Wastes Using Ground Penetrating Radar and a Gamma Ray Detector

“…However, traditional methods of depth estimation such as core sampling and logging are slow and have limited spatial sampling extent because of their intrusive nature. Furthermore, the nonintrusive methods reported in [5,[7][8][9][10][11][12][13][14] are either based on regressional models whose parameters typically have no physical significance or are limited to specific radioactive sources. Also, other nonintrusive methods reported in [15,16] use specialised shielding and collimator arrangements while those that employ machine learning [17][18][19] require significant amount of data to train the algorithms.…”