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, for instance, how humans are exposed to and affected by environmental stressors as they move through space and time in their daily activity patterns. Over time, urban air quality has seen significant improvements, yet hotspots of exposure to high levels of air pollutant concentrations have been insensitive to policy interventions. Likewise, impacts of air, water and soil pollution on ecosystem health have distinct temporal and spatial patterns. In the case of ecosystem impacts, historic pollution loads accumulating over time have contributed to current states of ecosystem functions. Their improvement or restoration are the objectives of current policy measures aimed at reducing emissions 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 between emissions, pollutant loads and impacts, dynamic and time-explicit modelling approaches are used. Most assessment models currently applied for policy support operate by integrating over comparatively long periods of time (e.g. annual average air quality) and often assume static representations of human and ecological receptors. In the view of global climate change and expected adaptations of human interactions with the environment (e.g. through ecosystem services), modelling of both effects and human responses to such changes need to be explicit in space and time. Finally, modelling environmental responses and adaptation to global change and how this adaptation process will affect ecosystem resilience and the provisioning of ecosystem services can give vital underpinning evidence for the design of robust policy strategies.
This session aims at attracting papers with a focus on modelling methods and model applications where spatio-temporal aspects of environmental pollution and its effects on human and ecosystem health are explicitly taken into account. Topics could cover, but are not limited to, air, water and soil pollution, as well as ecosystem function and ecosystem services. For human health impact assessment, modelling approaches addressing personal exposure modelling and human-environment interactions in space and time are welcome, explicitly including applications of environmental sensors, citizen science and the use of Big Data to solve challenging environmental health problems.