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dc.contributor.authorVarley, Adamen_UK
dc.contributor.authorTyler, Andrewen_UK
dc.contributor.authorSmith, Leslieen_UK
dc.contributor.authorDale, Paulen_UK
dc.contributor.authorDavies, Mikeen_UK
dc.description.abstractThe 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.en_UK
dc.relationVarley A, Tyler A, Smith L, Dale P & Davies M (2015) Remediating radium contaminated legacy sites: Advances made through machine learning in routine monitoring of "hot" particles. Science of the Total Environment, 521-522, pp. 270-279.
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.en_UK
dc.subjectRadium remediationen_UK
dc.subjectGamma spectroscopyen_UK
dc.subject“Hot” particlesen_UK
dc.subjectMachine learningen_UK
dc.subjectMonte Carloen_UK
dc.subjectSodium iodideen_UK
dc.subjectLanthanum bromideen_UK
dc.titleRemediating radium contaminated legacy sites: Advances made through machine learning in routine monitoring of "hot" particlesen_UK
dc.typeJournal Articleen_UK
dc.rights.embargoreason[Varley et al_Science of the Total Environment_2015.pdf] The publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.en_UK
dc.citation.jtitleScience of the Total Environmenten_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.affiliationBiological and Environmental Sciencesen_UK
dc.contributor.affiliationBiological and Environmental Sciencesen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationBiological and Environmental Sciencesen_UK
dc.contributor.affiliationNuvia Limiteden_UK
dc.description.refREF Compliant by Deposit in Stirling's Repositoryen_UK
Appears in Collections:Biological and Environmental Sciences Journal Articles

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