Managing uncertainties in risk assessment of activities involving nanomaterials: the case of control banding-based methodologies

The risk assessment of Engineered Nanomaterials takes place under continuous conditions of data scarcity, mainly induced by the fast-paced development of new materials and the absence of sufficient toxicological and exposure data. Control banding-based risk assessment tools for activities involved with nanomaterials manage uncertainties by combining qualitative hazard banding with exposure estimation and by implementing precautionary principles that account for data gaps and variability. These tools categorize nanomaterials into risk bands based on available physicochemical and toxicological data, while acknowledging that uncertainties stem from incomplete safety data, limited standardized toxicological parameters, and variability in process conditions. However, the reliability and consistency of these tools are challenged by uncertainties arising from data gaps, user variability, and methodological limitations, necessitating robust strategies for uncertainty management within CB methodologies. So, advanced approaches have been proposed to incorporate Adaptive Testing Strategies, Bayesian Networks, Monte Carlo simulations and fuzzy inference systems that quantify the uncertainty in input parameters and improve decision-making. This integrated approach allows CB tools to provide pragmatic risk prioritization and guidance for exposure control measures even in the face of significant uncertainties.