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Hazard screening methods for nanomaterials: a comparative study

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dc.contributor.author Sheehan, Barry
dc.contributor.author Murphy, Finbarr
dc.contributor.author Mullins, Martin
dc.contributor.author Furxhi, Irini
dc.contributor.author Costa, Anna L.
dc.contributor.author Simeone, Felice C.
dc.contributor.author Mantecca, Paride
dc.date.accessioned 2018-03-13T10:10:48Z
dc.date.available 2018-03-13T10:10:48Z
dc.date.issued 2018
dc.identifier.uri http://hdl.handle.net/10344/6644
dc.description peer-reviewed en_US
dc.description.abstract Hazard identification is the key step in risk assessment and management of manufactured nanomaterials (NM). However, the rapid commercialisation of nano-enabled products continues to out-pace the development of a prudent risk management mechanism that is widely accepted by the scientific community and enforced by regulators. However, a growing body of academic literature is developing promising quantitative methods. Two approaches have gained significant currency. Bayesian networks (BN) are a probabilistic, machine learning approach while the weight of evidence (WoE) statistical framework is based on expert elicitation. This comparative study investigates the efficacy of quantitativeWoE and Bayesian methodologies in ranking the potential hazard of metal and metal-oxide NMs—TiO2, Ag, and ZnO. This research finds that hazard ranking is consistent for both risk assessment approaches. The BN andWoE models both utilize physico-chemical, toxicological, and study type data to infer the hazard potential. The BN exhibits more stability when the models are perturbed with new data. The BN has the significant advantage of self-learning with new data; however, this assumes all input data is equally valid. This research finds that a combination of WoE that would rank input data along with the BN is the optimal hazard assessment framework. en_US
dc.language.iso eng en_US
dc.publisher MDPI en_US
dc.relation.ispartofseries International Journal of Molecular Sciences;19, 649
dc.relation.uri http://dx.doi.org/10.3390/ijms19030649
dc.subject nanomaterials en_US
dc.subject hazard assessment en_US
dc.subject bayesian network en_US
dc.subject weight of evidence en_US
dc.subject multi-criteria decision analysis en_US
dc.subject human health hazard screening en_US
dc.title Hazard screening methods for nanomaterials: a comparative study 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.identifier.doi 10.3390/ijms19030649
dc.contributor.sponsor ERC en_US
dc.relation.projectid 720851 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 2738511


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