Increased air pollution (with traffic and industrial emissions being of primary concern) is now a significant problem in overpopulated and increasingly pressurised urban areas throughout the world. The key question addressed by this research is how to develop a sustainable environment for these areas of increasing intensity. The research uses novel methodologies to mitigate pollution-related problems that impact on densely populated areas by considering not only isolated aspects of the urban environment but by creating a Virtual City; i.e. an interactive landscape designed to test and evaluate new ideas and concepts for city/buildings planning and management.
The Virtual City (Fig. 1) will be fully integrated with a network of sensors designed to obtain critical environmental data in real time (pollution concentration, wind velocity, temperature etc.).
This, in turn, will promote effective local and city-wide passive and managed adaptation to changes in both pollution levels (accidental, traffic-induced or otherwise, Fig. 2) and weather conditions.
A system that is able to operate quickly (in real-time) and effectively in local and city-scale scenarios, and can be used to optimize emergency responses (either pollution or weather related) will result in both safer and healthier cities.
In this current Programme we further propose to create an intelligent virtual design and management system that is a combination of advanced and ultra-fast computer models with state-of-the-art wind-tunnel measurements and real-time monitoring of environmental conditions (pollution concentrations, air flows and thermal conditions) that continuously feedback into the model framework. Finally, local planning and regulatory authorities, as well as urban architects, already involved with the project from its concept, will participate in an operational exercise.
The methodologies proposed in this programme will include beyond state-of-the-art computational fluid dynamics in combination with advanced physical modelling and sensor monitoring in a controlled environment. This work will be undertaken in collaboration with Cambridge University and Surrey University.