Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/22758
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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.date.accessioned2016-02-27T00:34:31Z-
dc.date.available2016-02-27T00:34:31Z-
dc.date.issued2016-03en_UK
dc.identifier.urihttp://hdl.handle.net/1893/22758-
dc.description.abstractRadium (226Ra) 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 226Ra 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 226Ra. 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, Principal Component Analysis and Machine Learning based algorithms to derive depth and activity estimates for 226Ra contamination. The approach was applied on spectra taken using two gamma-ray detectors (Lanthanum Bromide and Sodium Iodide), with the aim of identifying an optimised combination of detector and spectral processing routine. It was confirmed that, through a combination of Neural Networks and Lanthanum Bromide, the most accurate depth and activity estimates could be found. The advantage of the method was demonstrated by mapping depth and activity estimates at a case study site in Scotland. There the method identified significantly higher activity (<3Bqg−1) occurring at depth (>0.4m), that conventional gross counting algorithms failed to identify. It was concluded that the method could easily be employed to identify areas of high activity potentially occurring at depth, prior to intrusive investigation using conventional sampling techniques.en_UK
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.relationVarley A, Tyler A, Smith L, Dale P & Davies M (2016) Mapping the spatial distribution and activity of 226Ra at legacy sites through Machine Learning interpretation of gamma-ray spectrometry data. Science of the Total Environment, 545-546, pp. 654-661. https://doi.org/10.1016/j.scitotenv.2015.10.112en_UK
dc.rightsCopyright 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_UK
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_UK
dc.subjectRadium contaminated landen_UK
dc.subjectGamma-ray spectrometryen_UK
dc.subjectMachine Learningen_UK
dc.subjectContamination mappingen_UK
dc.titleMapping the spatial distribution and activity of 226Ra at legacy sites through Machine Learning interpretation of gamma-ray spectrometry dataen_UK
dc.typeJournal Articleen_UK
dc.identifier.doi10.1016/j.scitotenv.2015.10.112en_UK
dc.identifier.pmid26795756en_UK
dc.citation.jtitleScience of the Total Environmenten_UK
dc.citation.issn0048-9697en_UK
dc.citation.volume545-546en_UK
dc.citation.spage654en_UK
dc.citation.epage661en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.author.emaila.l.varley@stir.ac.uken_UK
dc.citation.date12/01/2016en_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.identifier.isiWOS:000369493000065en_UK
dc.identifier.scopusid2-s2.0-84953807467en_UK
dc.identifier.wtid580074en_UK
dc.contributor.orcid0000-0003-0604-5827en_UK
dc.contributor.orcid0000-0002-3716-8013en_UK
dc.date.accepted2015-10-22en_UK
dcterms.dateAccepted2015-10-22en_UK
dc.date.filedepositdate2016-01-20en_UK
rioxxterms.apcpaiden_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorVarley, Adam|en_UK
local.rioxx.authorTyler, Andrew|0000-0003-0604-5827en_UK
local.rioxx.authorSmith, Leslie|0000-0002-3716-8013en_UK
local.rioxx.authorDale, Paul|en_UK
local.rioxx.authorDavies, Mike|en_UK
local.rioxx.projectInternal Project|University of Stirling|https://isni.org/isni/0000000122484331en_UK
local.rioxx.freetoreaddate2016-01-20en_UK
local.rioxx.licencehttp://creativecommons.org/licenses/by/4.0/|2016-01-20|en_UK
local.rioxx.filenameVarley et al_Sci of Tot Env_2016.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source0048-9697en_UK
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