Probabilistic model for assessing occupational risk during the handling of nanomaterials

Abstract

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.

Keywords:

• Risk
• nanomaterials
• Bayesian network
• occupational health and safety

Acknowledgments

The authors thank the experts who contributed to the development of the Bayesian network.

Disclosure statement

The authors report no conflicts of interest. The authors are solely responsible for the content and writing of this article.

Additional information

Funding

This work was funded by the National Council for Scientific and Technological Development (CNPq) and Coordination of Improvement of Higher Education Personnel (CAPES).