Probabilistic model for assessing occupational risk during the handling of nanomaterials

• Published in: Nanotoxicology Vol. 14; no. 9; pp. 1258 - 1270
• Main Authors: Schmidt, José Renato Alves, Nogueira, Diego José, Nassar, Silvia Modesto, Vaz, Vitor Pereira, da Silva, Marlon Luiz Neves, Vicentini, Denice Schulz, Matias, William Gerson
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 20.10.2020; Taylor & Francis Ltd
• Subjects: Air Pollutants, Occupational - chemistry; Air Pollutants, Occupational - toxicity; Bayes Theorem; Bayesian analysis; Bayesian network; Control equipment; Decision making; Exposure; Handling; Hazards; Humans; Laboratories; Laboratories - standards; Laboratories - statistics & numerical data; Mathematical models; Models, Statistical; Nanomaterials; Nanostructures - chemistry; Nanostructures - toxicity; Nanotechnology; Occupational exposure; Occupational Exposure - adverse effects; Occupational Exposure - prevention & control; Occupational Exposure - statistics & numerical data; Occupational health; occupational health and safety; Probabilistic models; Protective Devices; Risk; Risk assessment; Risk Assessment - statistics & numerical data; Risk reduction; Safety Management; Working conditions; Workplace - standards; Workplace - statistics & numerical data; Risk; nanomaterials; occupational health and safety; Bayesian network
• DOI: 10.1080/17435390.2020.1815094
• ISSN: 1743-5390

Exposure to nanomaterials (NMs) can be considered as human, occupational or environmental. Occupational exposure may be experienced by the workers and/or researchers who develop and produce these products and the hazards inherent to exposure are not yet fully known. Quantitative and qualitative methods are available to estimate the occupational risks associated with the handling of NMs, however, both have limitations. In this context, the objective of this study was to create a Bayesian network (BN) that will allow an assessment of the occupational risk associated with the handling of NMs in research laboratories. The BN was developed considered variables related to exposure, the hazards associated with NMs and also the existing control measures in the work environment, such as collective protection equipment (CPE), administrative measures and personal protection equipment (PPE). In addition to assessing the occupational risk, simulations were carried out by the laboratory manager to obtain information on which actions should be taken to reduce the risk. The development of a BN to assess the occupational risk associated with the handling of NMs is a novel aspect of this study. As a distinctive feature, the BN has measurement control variables in addition to considering CPE, administrative measures and PPE. An advantage of this network in relation to other risk assessment models is that it allows the easy execution of simulations and provides a guide for a decision making by identifying which actions should be taken to minimize the risk.