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Hazard screening methods for nanomaterials: a comparative study

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journal contribution
posted on 2018-03-13, 10:10 authored by Barry SheehanBarry Sheehan, Finbarr MurphyFinbarr Murphy, Martin MullinsMartin Mullins, Irini Furxhi, Anna L. Costa, Felice C. Simeone, Paride Mantecca
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.

Funding

Study on Aerodynamic Characteristics Control of Slender Body Using Active Flow Control Technique

Japan Society for the Promotion of Science

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History

Publication

International Journal of Molecular Sciences;19, 649

Publisher

MDPI

Note

peer-reviewed

Other Funding information

ERC

Language

English

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