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Int. J. Mol. Sci. 2018, 19(3), 649; https://doi.org/10.3390/ijms19030649

Hazard Screening Methods for Nanomaterials: A Comparative Study

1
Department of Accounting and Finance, University of Limerick, V94PH93 Limerick, Ireland
2
Institute of Science and Technology for Ceramics (CNR-ISTEC), National Research Council of Italy, Via Granarolo 64, 48018 Faenza (RA), Italy
3
Department of Earth and Environmental Sciences, Particulate Matter and Health Risk (POLARIS) Research Centre, University of Milano Bicocca, 20126 Milano, Italy
*
Author to whom correspondence should be addressed.
Received: 30 January 2018 / Revised: 14 February 2018 / Accepted: 15 February 2018 / Published: 25 February 2018
(This article belongs to the Special Issue Nanotoxicology and Nanosafety)
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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 quantitative WoE 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 and WoE 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. View Full-Text
Keywords: nanomaterials; hazard assessment; Bayesian network; weight of evidence; multi-criteria decision analysis; human health hazard screening nanomaterials; hazard assessment; Bayesian network; weight of evidence; multi-criteria decision analysis; human health hazard screening
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Sheehan, B.; Murphy, F.; Mullins, M.; Furxhi, I.; Costa, A.L.; Simeone, F.C.; Mantecca, P. Hazard Screening Methods for Nanomaterials: A Comparative Study. Int. J. Mol. Sci. 2018, 19, 649.

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