Critical infrastructures are essential for modern societies, and protecting the population in case of disruptions is a matter of paramount importance. Thanks to its continuous conceptual and practical development in recent years, the digital twin (DT) framework became a very important analysis and forecasting tool. To gain a good virtual representation of the infrastructure, comprehensive knowledge of the physical system and its dynamic behavior is required. Physics-based descriptions are crucial for this, but due to their complexity, they quickly become costly and time-consuming to analyze. This becomes problematic in particular when meaningful real-time results are needed in crisis situations or when many different ”what-if” scenarios have to be played out in preparation for such. Model order reduction (MOR) methods can provide the necessary remedy here. The recent contribution in [1] addresses first steps towards a DT for real-time predictions of airborne contaminant transport in urban environments at moderate Reynolds numbers. The proposed framework contains the automatic generation of the computational domain from opensource geographic data as well as the simulation set-up featuring intrusive projection-based MOR to accelerate the computation of the wind field. To this aim, proper orthogonal decomposition (POD) is applied for dimensionality reduction and the discrete empirical interpolation method (DEIM) is used to treat the nonlinear convective term of the incompressible Navier-Stokes equations. This leads to a good approximation accuracy of the solution while gaining a significant speed-up for the complex built-up environments under consideration [1]. To enable even faster results, non-intrusive MOR methods like POD with interpolation are investigated providing an even higher speed-up. Eventually, a comparison to the applied intrusive approach is presented. REFERENCES [1] J. Bonari, L. Kühn, M. v. Danwitz, A. Popp, Towards real-time urban physics simulations with digital twins. doi:10.48550/arXiv.2405.10077.