Digital Twins of Urban Air Quality: Opportunities and Challenges - Topping, David; Bannan, Thomas J.; Coe, Hugh; Evans, James; Jay, Caroline; Murabito, Ettore; Robinson, Niall (Frontiers in Sustainable Cities, 2021)
The increasing amount of data collected about the environment brings tremendous potential to create digital systems that can predict the impact of intended and unintended consequences of change. With growing interest in the construction of Digital Twins across multiple sectors, combined with rapid changes to where we spend our time and the nature of pollutants we are exposed to, we find ourselves at a crossroads of opportunity with regards to air quality mitigation in cities. With this in mind, we briefly discuss the interplay between available data and state of the science on air quality, infrastructure needs and areas of opportunities that should drive subsequent planning of the digital twin ecosystem and associated components. Data driven modelling and digital twins are promoted as the most efficient route to decision making in an evolving atmosphere. However, following the diverse data streams on which these frameworks are built, they must be supported by a diverse community. This is an opportunity to build a collaborative space to facilitate closer working between instrument manufacturers, data scientists, atmospheric scientists, regional and national policy makers alike.