  This article presents an innovative approach for developing an efficient
reduced-order model to study the dispersion of urban air pollutants. The need
for real-time air quality monitoring has become increasingly important, given
the rise in pollutant emissions due to urbanization and its adverse effects on
human health. The proposed methodology involves solving the linear
advection-diffusion problem, where the solution of the Reynolds-averaged
Navier-Stokes equations gives the convective field. At the same time, the
source term consists of an empirical time series. However, the computational
requirements of this approach, including microscale spatial resolution,
repeated evaluation, and low time scale, necessitate the use of
high-performance computing facilities, which can be a bottleneck for real-time
monitoring. To address this challenge, a problem-specific methodology was
developed that leverages a data-driven approach based on Proper Orthogonal
Decomposition with regression (POD-R) coupled with Galerkin projection (POD-G)
endorsed with the discrete empirical interpolation method (DEIM). The proposed
method employs a feedforward neural network to non-intrusively retrieve the
reduced-order convective operator required for online evaluation. The numerical
framework was validated on synthetic emissions and real wind measurements. The
results demonstrate that the proposed approach significantly reduces the
computational burden of the traditional approach and is suitable for real-time
air quality monitoring. Overall, the study advances the field of reduced order
modeling and highlights the potential of data-driven approaches in
environmental modeling and large-scale simulations.
