Probabilistic and Monte Carlo risk models for carbon nanomaterial production processes

Given considerable uncertainty surrounding the occupational, consumer, and environmental, health and safety (EHS) risks of various nanomaterial manufacturing processes, modeling their relative risks and benefits is especially important. This paper discusses possible risk assessment methods that might be useful in such applications and explores three probabilistic approaches in greater detail: Monte Carlo (MC) simulation models, Bayesian belief networks, and multi-criteria decision trees. These approaches are illustrated for single wall carbon nanotubes (SWNT) produced via high pressure carbon monoxide (HiPco) processes to study the impact of inherent uncertainties and early EHS standards decisions on predicting long-term manufacturing costs, occupational health (OH) exposure risks, and associated tradeoffs. Until research progresses on the EHS risks of nanomaterials, these types of analyses can provide useful information for private and regulatory decision-makers and for guiding research priorities.