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This article presents an innovative approach for developing an efficientreduced-order model to study the dispersion of urban air pollutants. The needfor real-time air quality monitoring has become increasingly important, giventhe rise in pollutant emissions due to urbanization and its adverse effects onhuman health. The proposed methodology involves solving the linearadvection-diffusion problem, where the solution of the Reynolds-averagedNavier-Stokes equations gives the convective field. At the same time, thesource term consists of an empirical time series. However, the computationalrequirements of this approach, including microscale spatial resolution,repeated evaluation, and low time scale, necessitate the use ofhigh-performance computing facilities, which can be a bottleneck for real-timemonitoring. To address this challenge, a problem-specific methodology wasdeveloped that leverages a data-driven approach based on Proper OrthogonalDecomposition with regression (POD-R) coupled with Galerkin projection (POD-G)endorsed with the discrete empirical interpolation method (DEIM). The proposedmethod employs a feedforward neural network to non-intrusively retrieve thereduced-order convective operator required for online evaluation. The numericalframework was validated on synthetic emissions and real wind measurements. Theresults demonstrate that the proposed approach significantly reduces thecomputational burden of the traditional approach and is suitable for real-timeair quality monitoring. Overall, the study advances the field of reduced ordermodeling and highlights the potential of data-driven approaches inenvironmental modeling and large-scale simulations.

A physics-based reduced order model for urban air pollution prediction