This model is very useful for the exposure assessment of products containing nanomaterials during shredding (end-of-life), a part of the life cycle where there is little data available. With a Bayesian probabilistic nature in its core, it uses subjective judgement when data is unavailable or scarce while being able to adapt and update risk forecasts as new information becomes available. Its novelty lies on a simplistic approach which combines the material and process variables of the system to determine the probability of number, size, mass and composition of released particles. It is applicable to the shredding of a wide range of nano-enabled products and it aims to reduce the nanomaterial release by using the safe(r)-by-design approach.