## 1 Introduction
These data-driven approaches can forecast air pollution levels, assisting authorities in implementing timely measures when high pollutant concentrations are expected [8]. Furthermore, the hybrid models that combine deterministic and statistical techniques can leverage the strengths of both approaches, potentially offering more accurate and reliable pollution assessments and predictions [9]. Although several studies have used CFD, CMAQ, or AI-based models for urban air pollution assessment [6; 7; 8; 9], few integrate real-time meteorological data with high-resolution three-dimensional modeling tailored to a complex mountainous urban basin like Almaty. For instance, CMAQ models often simplify topography and focus on horizontal dispersion. Deep learning models such as Deep-AIR and Deep-MAPS provide temporal predictions but lack spatial resolution and physical interpretability. Our study is the first to apply a fully three-dimensional, physically grounded CFD model with Navier-Stokes-based pollutant transport over a finely meshed urban domain for Almaty, incorporating real meteorological measurements, urban morphology, and vertical pollution stratification. This offers a new level of detail and accuracy for local environmental planning. The deployment of low-cost sensor networks offers the potential to enhance the spatial resolution of air quality monitoring in Almaty. Despite their lower accuracy compared to reference-grade instruments, when properly calibrated and validated, these sensors can provide valuable data for identifying pollution hotspots and tracking pollution plumes [10]. This data, when combined with meteorological data such as wind speed, wind direction, temperature, and humidity, can help elucidate the transport and dispersion patterns of pollutants. These networks, along with weather data and particulate matter readings, can be used to train Long Short-Term Memory models to forecast and classify air quality, which has been shown to significantly reduce pollution in urban environments. The integration of data from various sources, including satellite remote sensing, ground-based monitoring stations, and meteorological models, can provide a comprehensive view of air quality and its relationship with meteorological conditions. The intricate interplay between meteorological factors and pollution levels underscores the necessity for sophisticated analytical approaches, where advancements in machine learning offer a potent tool for unraveling these complex relationships and developing predictive models that can inform effective pollution control strategies in Almaty [7,11]. Deep learning models, particularly those employing Convolutional LSTM-SDAE architectures, can effectively model temporal dependencies and correlations between pollutants and meteorological factors, improving prediction accuracy by addressing issues such as missing data [12]. One can employ deep learning models to develop air quality forecasting and classification systems by training LSTM models on sensor data, meteorology, aerosol optical depth, and particulate matter readings [13]. Furthermore, satellite data, with its global availability and frequent updates, can be integrated with deep learning models to estimate air pollution, even in areas with limited ground-based monitoring [14]. By fusing satellite information with pollution measurements, even simpler models can work effectively, allowing for accurate air quality predictions, which is applicable to locations where monitoring stations do not exist [10]. Mobile air pollution sensing, enhanced by machine learning frameworks, presents a cost-effective approach to obtain high-resolution air quality information across the city, even with sparse sensing coverage. By leveraging urban big data, these frameworks can generate insights into the potential causes of pollution, supporting evidence-based sustainable urban management [6].