Mapping the spatial distribution and activity of (226)Ra at legacy sites through Machine Learning interpretation of gamma-ray spectrometry data

The Science of the Total Environment
Adam VarleyMike Davies

Abstract

Radium ((226)Ra) contamination derived from military, industrial, and pharmaceutical products can be found at a number of historical sites across the world posing a risk to human health. The analysis of spectral data derived using gamma-ray spectrometry can offer a powerful tool to rapidly estimate and map the activity, depth, and lateral distribution of (226)Ra contamination covering an extensive area. Subsequently, reliable risk assessments can be developed for individual sites in a fraction of the timeframe compared to traditional labour-intensive sampling techniques: for example soil coring. However, local heterogeneity of the natural background, statistical counting uncertainty, and non-linear source response are confounding problems associated with gamma-ray spectral analysis. This is particularly challenging, when attempting to deal with enhanced concentrations of a naturally occurring radionuclide such as (226)Ra. As a result, conventional surveys tend to attribute the highest activities to the largest total signal received by a detector (Gross counts): an assumption that tends to neglect higher activities at depth. To overcome these limitations, a methodology was developed making use of Monte Carlo simulations, Princip...Continue Reading

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Citations

Jul 24, 2020·Environmental Science and Pollution Research International·Hamid GholamiAdrian L Collins
Mar 13, 2017·The Science of the Total Environment·Nicola DeluigiMikhail Kanevski

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