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dc.contributor.authorCrowley, Quentin
dc.date.accessioned2023-09-22T13:45:25Z
dc.date.available2023-09-22T13:45:25Z
dc.date.issued2023
dc.date.submitted2023en
dc.identifier.citationBanríon, M., Cobelli, M., Crowley, Q.G, Applying machine learning to model radon using topsoil geochemistry, Applied Geochemistry, 158, 2023, 105790-en
dc.identifier.otherY
dc.identifier.urihttp://hdl.handle.net/2262/103918
dc.descriptionPUBLISHEDen
dc.description.abstractRadon is classified as a Class 1 carcinogen, being the leading cause of lung cancer in non-smokers. Understanding the prominent sources of radon helps to mitigate against the adverse effects of radon exposure. Considering soil-gas radon is the main contributor to indoor radon, it is possible that soil geochemistry can be used as a proxy for the soil radon emanation potential or geogenic radon classes for a particular location. This paper investigates the relationship between soil geochemistry and geogenic radon. A large area of 17,983 km2 from the West, Midlands and East of Ireland was selected to represent a range of geology types and radon categories. A rigorous assessment is presented to investigate the relationship of geogenic radon and topsoil geochemistry; using univariate processes (i.e. r2, Pearson r and heatmaps) and multivariate techniques (i.e. principle component analysis (PCA) and machine learning (ML) algorithms including Gaussian process regression, logistic regression and random forest). Here, PCA and ML techniques were used to test the utility of soil geochemistry to predict geogenic radon classes. Gaussian Process Regression yielded the highest accuracy (74%) and f1-score (0.74) of all models. The feature importance (i.e. highest ranking elements for predicting geogenic radon class) from the ML models outputs elements including [Y, Tl, Mn, Cr, Co, Be, Sc and Rb]. The PCA biplot demonstrates that these elements cluster in conjunction with higher geogenic radon categories. Multivariate data analysis reveals that certain elements important for predicting higher geogenic radon classes, also covary together within topsoil samples; here these are termed “radon-prone elements”. Spatial covariance of radon-prone elements permits soil geochemistry to be used as a tool for understanding the distribution of geogenic radon. The methodology presented in this paper provides a comprehensive geo-statistical approach to investigate the relation between topsoil geochemistry and geogenic radon. This approach could be applied as a diagnostic tool to assist radon mitigation measures, hence adding value to legacy soil geochemistry datasets.en
dc.format.extent105790en
dc.language.isoenen
dc.relation.ispartofseriesApplied Geochemistry;
dc.relation.ispartofseries158;
dc.rightsYen
dc.subjectGeogenic radon potentialen
dc.subjectRadonen
dc.subjectTopsoil geochemistryen
dc.subjectMachine learningen
dc.subjectPrincipal component analysisen
dc.titleApplying machine learning to model radon using topsoil geochemistryen
dc.typeJournal Articleen
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/crowleyq
dc.identifier.rssinternalid258843
dc.identifier.doihttps://doi.org/10.1016/j.apgeochem.2023.105790
dc.rights.ecaccessrightsopenAccess
dc.subject.TCDThemeCanceren
dc.subject.TCDThemeSmart & Sustainable Planeten
dc.subject.TCDTagGEOCHEMISTRYen
dc.subject.TCDTagMACHINE LEARNINGen
dc.subject.TCDTagRADONen
dc.subject.TCDTagSOIL CHEMISTRYen
dc.identifier.orcid_id0000-0001-9254-6657
dc.status.accessibleNen


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