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A tractable method for measuring nanomaterial risk using Bayesian Networks

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dc.contributor.author Murphy, Finbarr
dc.contributor.author Sheehan, Barry
dc.contributor.author Mullins, Martin
dc.contributor.author Bouwmeester, Hans
dc.contributor.author Marvin, Hans J.P.
dc.contributor.author Bouzembrak, Yamine
dc.contributor.author Costa, Anna L.
dc.contributor.author Das, Rasel
dc.contributor.author Stone, Vicki
dc.contributor.author Tofail, Syed A.M.
dc.date.accessioned 2017-01-25T11:20:21Z
dc.date.available 2017-01-25T11:20:21Z
dc.date.issued 2016
dc.identifier.citation Murphy, F,Sheehan, B,Mullins, M,Bouwmeester, H,Marvin, HJP,Bouzembrak, Y,Costa, AL,Das, R,Stone, V,Tofail, SAM (2016) 'A Tractable Method for Measuring Nanomaterial Risk Using Bayesian Networks'. Nanoscale Research Letters, 11 . en_US
dc.identifier.uri http://hdl.handle.net/10344/5472
dc.description peer-reviewed en_US
dc.description.abstract While control banding has been identified as a suitable framework for the evaluation and the determination of potential human health risks associated with exposure to nanomaterials (NMs), the approach currently lacks any implementation that enjoys widespread support. Large inconsistencies in characterisation data, toxicological measurements and exposure scenarios make it difficult to map and compare the risk associated with NMs based on physicochemical data, concentration and exposure route. Here we demonstrate the use of Bayesian networks as a reliable tool for NM risk estimation. This tool is tractable, accessible and scalable. Most importantly, it captures a broad span of data types, from complete, high quality data sets through to data sets with missing data and/or values with a relatively high spread of probability distribution. The tool is able to learn iteratively in order to further refine forecasts as the quality of data available improves. We demonstrate how this risk measurement approach works on NMs with varying degrees of risk potential, namely, carbon nanotubes, silver and titanium dioxide. The results afford even non-experts an accurate picture of the occupational risk probabilities associated with these NMs and, in doing so, demonstrated how NM risk can be evaluated into a tractable, quantitative risk comparator. en_US
dc.language.iso eng en_US
dc.publisher SpringerOpen en_US
dc.relation 604305 en_US
dc.relation.ispartofseries Nanoscale Research Letters;11:503
dc.relation.uri http://dx.doi.org/10.1186/s11671-016-1724-y
dc.subject risk assessment en_US
dc.subject control banding en_US
dc.subject Bayesian en_US
dc.title A tractable method for measuring nanomaterial risk using Bayesian Networks en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.date.updated 2017-01-25T10:27:09Z
dc.description.version PUBLISHED
dc.identifier.doi 10.1186/s11671-016-1724-y
dc.contributor.sponsor ERC en_US
dc.relation.projectid 604305 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 2693288
dc.internal.copyrightchecked Yes
dc.identifier.journaltitle Nanoscale Research Letters
dc.description.status peer-reviewed


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