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.

翻译：本文提出了一种创新方法，用于开发高效的降阶模型以研究城市空气污染物的扩散。随着城市化导致污染物排放增加及其对人类健康的不利影响，实时空气质量监测的需求日益重要。所提出的方法涉及求解线性平流-扩散问题，其中雷诺平均纳维-斯托克斯方程的解提供对流场，而源项则由经验时间序列组成。然而，这一方法的计算需求（包括微尺度空间分辨率、重复评估及低时间尺度）迫使使用高性能计算设施，这成为实时监测的瓶颈。为解决这一挑战，本文开发了一种针对特定问题的方法，该方法利用基于本征正交分解与回归（POD-R）结合伽辽金投影（POD-G）并辅以离散经验插值法（DEIM）的数据驱动技术。所提方法采用前馈神经网络，以非侵入方式获取在线评估所需的降阶对流算子。数值框架在合成排放数据和真实风场测量上得到验证。结果表明，该方法显著降低了传统方法的计算负担，适用于实时空气质量监测。总体而言，本研究推进了降阶建模领域的发展，并凸显了数据驱动方法在环境建模和大规模模拟中的潜力。