Variability and Uncertainty in Risk Assessment of Nanoparticles
Engineered nanoparticles (ENPs) are used everywhere and have large technological and economic potential. Insight into risks of nanotechnology and the use of nanoparticles is an essential condition for the societal acceptance and safe use of nanotechnology. Risk assessment of ENPs has been hampered by lack of knowledge about ENPs. This lack of knowledge results in uncertainty in the risk assessment. Besides the large uncertainty, another form of variation present in risk assessment is variability which is the natural inherent variation that is present in all natural processes and living organisms. In this thesis, I contributed to the methodology needed to understand potential environmental and human risk of ENPs in the presence of uncertainty and variability. With the probabilistic methods used and developed in this thesis, it is possible to identify the greatest sources of uncertainty. Based on such identification, research can be focused on those areas that need it most, thereby making large leaps in reducing the uncertainty that is currently hampering risk assessment of ENPs.