Moving from monitoring to real-time interventions for air quality: are low-cost sensor networks ready to support urban digital twins?

Modern cities now have an increasing multitude of Internet-of-Things data streams on urban phenomena, including transport, mobility, and meteorology. One area of development has been the use of low-cost sensors to complement (or in some cases, substitute for) regulatory monitoring of ambient air pollution. As part of a bigger integrated approach to monitoring cities, such as Urban Observatories, disparate live data streams can now readily be collated and disseminated via a platform to facilitate the use of hyperlocal data for real-time decision making whilst supporting longer term sustainable development goals. Urban digital twins are the next logical step on this journey and these are becoming increasingly popular as a tool, at least conceptually, to better interpret this data as well as better understand the consequences of management interventions. To date, there are few examples of true digital twins of environmental challenges with many limited to the ‘digital shadow’ stage of development, characterized by lack of bi-directional feedback between the digital model and physical world. Urban Observatories present an opportunity to change this by providing the often overlooked, but crucial, underpinning foundations of urban digital twins. This paper focuses on the utilization of live stream data and demonstrates that air quality applications can provide a realistic target given the density of observations available, which can routinely be combined with other urban datasets to provide the added value and insights needed for urban air pollution management. However, the availability and standardization of live streams of big data is a major challenge and there are issues with interoperability, metadata management, communicating uncertainty, network longevity, data ownership and transparency. This paper contributes insights concerning how to overcome these challenges and calls for common practice in generating and managing live streams of big data.