Abstract

The Chernobyl nuclear power meltdown that took place in 1986 has left a radioactive contamination legacy that currently severely limits the economic potential of impacted regions including the Polessie State Radioecology Reserve in Southern Belarus. Extensive areas of forested land could potentially become economically viable for firewood and building materials if radioactive contamination, notably 137Cs, could be characterised faster, whilst closely adhering to regulatory limits. Currently, laboursome tree coring and unreliable transfer factors derived from limited soil sampling data are routinely employed in felling decision making, which has financial repercussions owed to the large amounts of waste produced and unnecessary transportation costs. In this study, it is demonstrated that a combination of targeted mobile gamma-ray spectrometry and a newly developed, lead shielded, in situ gamma-ray spectrometry method can significantly speed up the process of characterisation of 137Cs wood activity in the field. For the in situ method, Monte Carlo calibration routines were developed alongside spectral processing procedures to unfold spectra collected in the field allowing for separation of ground and tree spectral components. Isolated contributions from the tree could then be converted to activity. The method was validated at a test facility and then demonstrated at three separate sites with differing contamination levels. This technique showed that single trees could be measured within approximately 20% of the activity compared to conventional tree core data. However, some discrepancies were found which were attributed to under sampling using the tree corer and low count rates at the lowest activity site, prompting the need for further data collection to optimise the method. It was concluded that this real-time approach could be a valuable tool for management of contaminated forested areas, releasing valuable timber and ultimately reducing the risk associated with living and working in these areas.

Full Text
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