Demonstration of a modelling-based multi-criteria decision analysis procedure for prioritisation of occupational risks from manufactured nanomaterials. Issue 9 (20th October 2016)

• Record Type: Journal Article
• Authors: Hristozov, Danail; Zabeo, Alex; Alstrup Jensen, Keld; Gottardo, Stefania; Isigonis, Panagiotis; Maccalman, Laura; Critto, Andrea; Marcomini, Antonio
• Is Part Of: Nanotoxicology. Volume 10:Issue 9(2016)
• Page Start: 1215
• Page End: 1228
• Publication Date: 2016-10-20
• Subjects: Benchmark dose -- margin of exposure -- occupational exposure -- risk assessment; Toxicology -- Periodicals; 615.9
• DOI: 10.3109/17435390.2016.1144827
• Languages: English
• ISSNs: 1743-5390

Abstract

Abstract: Several tools to facilitate the risk assessment and management of manufactured nanomaterials (MN) have been developed. Most of them require input data on physicochemical properties, toxicity and scenario-specific exposure information. However, such data are yet not readily available, and tools that can handle data gaps in a structured way to ensure transparent risk analysis for industrial and regulatory decision making are needed. This paper proposes such a quantitative risk prioritisation tool, based on a multi-criteria decision analysis algorithm, which combines advanced exposure and dose-response modelling to calculate margins of exposure (MoE) for a number of MN in order to rank their occupational risks. We demonstrated the tool in a number of workplace exposure scenarios (ES) involving the production and handling of nanoscale titanium dioxide, zinc oxide (ZnO), silver and multi-walled carbon nanotubes. The results of this application demonstrated that bag/bin filling, manual un/loading and dumping of large amounts of dry powders led to high emissions, which resulted in high risk associated with these ES. The ZnO MN revealed considerable hazard potential in vivo, which significantly influenced the risk prioritisation results. In order to study how variations in the input data affect our results, we performed probabilistic Monte Carlo sensitivity/uncertainty analysis, which demonstrated that the performance of the proposed model is stable against changes in the exposure and hazard input variables.