The simulation of in-situ groundwater detector response as a means of identifying beta emitting radionuclides by linear regression analysis

Turkington, G., Gamage, K. A.A. and Graham, J. (2021) The simulation of in-situ groundwater detector response as a means of identifying beta emitting radionuclides by linear regression analysis. Sensors, 21(17), 5732. (doi: 10.3390/s21175732)

Abstract

The in-situ characterisation of strontium-90 contamination of groundwater at nuclear decommissioning sites would represent a novel and cost-saving technology for the nuclear industry. However, beta particles are emitted over a continuous spectrum and it is difficult identify radionuclides due to the overlap of their spectra and the lack of characteristic features. This can be resolved by using predictive modelling to perform a maximum-likelihood estimation of the radionuclides present in a beta spectrum obtained with a semiconductor detector. This is achieved using a linear least squares linear regression and relating experimental data with simulated detector response data. In this case, by simulating a groundwater borehole scenario and the deployment of a cadmium telluride detector within it, it is demonstrated that it is possible to identify the presence of 90Sr, 90Y, 137Cs and 235U decay. It is determined that the optimal thickness of the CdTe detector for this technique is in the range of 0.1 to 1 mm. The influence of suspended solids in the groundwater is also investigated. The average and maximum concentrations of suspended particles found at Sellafield do not significantly deteriorate the results. It is found that applying the linear regression over two energy windows improves the estimate of 90Sr activity in a mixed groundwater source. These results provide validation for the ability of in-situ detectors to determine the activity of 90Sr in groundwater in a timely and cost-effective manner.

• Item Type: Articles
• Additional Information: This research was funded by the Nuclear Decommissioning Authority and School of Engineering, University of Glasgow.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Gamage, Professor Kelum and Turkington, Mr Graeme
• Authors: Turkington, G., Gamage, K. A.A., and Graham, J.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• ISSN (Online): 1424-8220
• Published Online: 25 August 2021
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in Sensors 21(17): 5732
• Publisher Policy: Reproduced under a Creative Commons License