Modern cities are increasingly equipped with a wealth of data from Internet-of-Things (IoT) devices, including those monitoring air quality using low-cost sensors. The concept of urban digital twins, virtual representations of urban environments, has emerged as a promising tool to interpret this data and understand the impact of interventions. These digital twins hold the potential to move beyond mere monitoring towards real-time, automatic solutions to environmental challenges like air pollution. However, current efforts often prioritize technological development, sometimes at the expense of addressing fundamental societal needs and achieving seamless integration with digital twin technologies.

The deployment of low-cost sensor networks has indeed revolutionized air quality monitoring by providing data at much higher spatial & temporal resolutions than traditional regulatory sites. This densification of observations, facilitated by IoT, offers a greater understanding of pollution sources and dispersion. Smart city initiatives further integrate various data streams onto online platforms, theoretically enabling real-time decision-making. However, the development of fully integrated smart city infrastructure remains rare, with many applications focusing on single aspects like air quality and often struggling to address community needs, being more driven by technological deployment. Moreover, a significant number of projects do not progress beyond the demonstration stage due to funding limitations, highlighting a potential disconnect between technological advancement and sustained societal benefit.