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Record number 540804
Title Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment
Author(s) Marvin, H.J.P.; Bouzembrak, Y.; Janssen, E.M.; Zande, M. van der; Murphy, Finbarr; Sheehan, Barry; Mullins, Martin; Bouwmeester, H.
DOI http://dx.doi.org/10.6084/m9.figshare.4585144
Department(s) RIKILT - BU Toxicology Bioassays & Novel Foods
Business Economics
VLAG
Sub-department of Toxicology
Publication type Dataset
Publication year 2017
Keyword(s) Bayesian networks - metal nanomaterials - scenario studies - risk assessment - expert elicitation
Abstract In this study, a Bayesian Network (BN) was developed for the prediction of the hazard potential and biological effects with the focus on metal- and metal-oxide nanomaterials to support human health risk assessment. The developed BN captures the (inter) relationships between the exposure route, the nanomaterials physicochemical properties and the ultimate biological effects in a holistic manner and was based on international expert consultation and the scientific literature (e.g., in vitro/in vivo data). The BN was validated with independent data extracted from published studies and the accuracy of the prediction of the nanomaterials hazard potential was 72% and for the biological effect 71%, respectively. The application of the BN is shown with scenario studies for TiO2, SiO2, Ag, CeO2, ZnO nanomaterials. It is demonstrated that the BN may be used by different stakeholders at several stages in the risk assessment to predict certain properties of a nanomaterials of which little information is available or to prioritize nanomaterials for further screening.
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