title: Remediating radium contaminated legacy sites: Advances made through machine learning in routine monitoring of "hot" particles.
abstract: The extensive use of radium during the 20th century for industrial, military and pharmaceutical purposes has led to a large number of contaminated legacy sites across Europe and North America. Sites that pose a high risk to the general public can present expensive and long-term remediation projects. Often the most pragmatic remediation approach is through routine monitoring operating gamma-ray detectors to identify, in real-time, the signal from the most hazardous heterogeneous contamination (hot particles); thus facilitating their removal and safe disposal. However, current detection systems do not fully utilise all spectral information resulting in low detection rates and ultimately an increased risk to the human health. The aim of this study was to establish an optimised detector-algorithm combination. To achieve this, field data was collected using two handheld detectors (sodium iodide and lanthanum bromide) and a number of Monte Carlo simulated hot particles were randomly injected into the field data. This allowed for the detection rate of conventional deterministic (gross counts) and machine learning (neural networks and support vector machines) algorithms to be assessed. The results demonstrated that a Neural Network operated on a sodium iodide detector provided the best detection capability. Compared to deterministic approaches, this optimised detection system could detect a hot particle on average 10cm deeper into the soil column or with half of the activity at the same depth. It was also found that noise presented by internal contamination restricted lanthanum bromide for this application.
authors.affiliation: Department of Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA, United Kingdom. Electronic address: a.l.varley@stir.ac.uk.
authors.forename: Adam
authors.initials: A
authors.lastname: Varley
authors.affiliation: Department of Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA, United Kingdom. Electronic address: a.n.tyler@stir.ac.uk.
authors.forename: Andrew
authors.initials: A
authors.lastname: Tyler
authors.affiliation: Department of Computing Science and Mathematics, University of Stirling, Stirling FK9 4LA, United Kingdom. Electronic address: l.s.smith@cs.stir.ac.uk.
authors.forename: Leslie
authors.initials: L
authors.lastname: Smith
authors.affiliation: Scottish Environmental Protection Agency, Radioactive Substances, Strathallan House, Castle Business Park, Stirling FK9 4TZ, United Kingdom. Electronic address: paul.dale@sepa.org.uk.
authors.forename: Paul
authors.initials: P
authors.lastname: Dale
authors.affiliation: Nuvia Limited, The Library, Eight Street, Harwell Oxford, Didcot, Oxfordshire OX11 0RL, United Kingdom. Electronic address: Mike.Davies@nuvia.co.uk.
authors.forename: Mike
authors.initials: M
authors.lastname: Davies
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D052918:Environmental Restoration and Remediation / Q000379:methods*; D005060:Europe; D064371:Hazardous Waste Sites; D008956:Models, Chemical; D009656:North America; D011834:Radiation Monitoring; D011850:Radioactive Waste / Q000032:analysis* / Q000706:statistics & numerical data; D011883:Radium / Q000032:analysis*; D012987:Soil
chemical_list: D011850:Radioactive Waste; D012987:Soil; D011883:Radium
keywords: Gamma spectroscopy; Lanthanum bromide; Machine learning; Monte Carlo; Radium remediation; Sodium iodide; “Hot” particles