Environmental models are widely used for policy decision support, in particular for the assessment of policy options to improve human and ecosystem health. In this context, the explicit representation of temporal and spatial domains is a key requirement to accurately model how, for instance, human receptors are exposed to environmental stressors as they move through space and time in their daily activity patterns. In addition, impacts of air, water and soil pollution on ecosystem health have distinct temporal patterns. Historic pollution loads accumulating over time are met by current reduction plans and thus future changes in deposition, potentially leading to recovery of ecosystem functions and reduced biodiversity loss. To account for such intricate spatio-temporal relationships, dynamic and time-explicit modelling approaches exist, but most assessment models currently applied for policy support operate by integrating over comparatively long periods of time (e.g. annual average air quality values) and often assume static representations of receptors. In the view of global climate change and other projected changes of human interaction with the environment (e.g. ecosystem services and biodiversity), modelling of both effects and human responses to such changes need to be space and time explicit. Finally, modelling environmental responses and adaptation to global change and how this adaptation process will affect e.g. supporting and provisioning ecosystem services can give vital underpinning evidence for the design of policy strategies.