Newly developed materials such as engineered nanomaterials are produced in increasing amounts and applied in a growing number of products. Once released to the environment, they can pose a hazard to ecosystems and human health. To assess potential risks, the exposure of the material to humans and the environment has to be determined. For many materials such as engineered nanomaterials, a quantitative measurement of environmental concentrations is not feasible. Material flow models can be used to determine these concentrations indirectly by predicting material flows in the environment. Several modelling approaches can be applied to represent existing knowledge about the flows of materials into and between environmental media or compartments and to consider the uncertainty and variability of the input parameters. In this study we evaluate four existing approaches with regard to their capabilities for indirect exposure assessment, focusing on their ability to treat uncertainty. We first explain how we preselected the four most promising modelling approaches: material flow analysis, system dynamics, material flow networks, and probabilistic material flow modelling. We then define a set of evaluation criteria based on the requirements of environmental exposure assessment and develop a simplified example system that is designed to test these criteria. Based on the comparative modelling and implementation of the example system, we discuss the capabilities and limitations of the approaches and indicate what is missing for a reliable environmental exposure prediction using material flow modelling.