Journal
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Volume 19, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/ijms19030649
Keywords
nanomaterials; hazard assessment; Bayesian network; weight of evidence; multi-criteria decision analysis; human health hazard screening
Funding
- European Union's Horizon 2020 research and innovation programme [720851]
- H2020 Societal Challenges Programme [720851] Funding Source: H2020 Societal Challenges Programme
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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.
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