## 1 Introduction  
Almaty is the largest megacity of Kazakhstan, which annually ranks among the cities with the highest level of air pollution (see Figure 1) in the region. The geographical location in the foothill basin and high density of buildings contribute to the accumulation of harmful impurities, which is confirmed by monitoring data: average annual concentrations of PM\({}_{2.5}\) and NO\({}_{2}\) exceed WHO permissible standards 2-3 times (see Figure 2). In 2024, the number of days with clean air when the daily average PM\({}_{2.5}\) concentration remained within the WHO guidelines (daily average of 15 ug/m\({}^{3}\)) increased compared to 2023. While in 2023 there were only 137 such days, in 2024 there were 164. Existing air quality assessment methods are mainly based on two-dimensional models or point measurements, which do not allow taking into account the complex structure of air flows and vertical stratification of pollutants. As a result, it is not possible to accurately predict and effectively manage air quality. In this work, for the first time for Almaty, three-dimensional numerical modelling of pollutant distribution with regard to real meteorological and topographical conditions has been implemented. This makes it possible to identify spatial patterns of pollution distribution and propose new approaches to air quality monitoring and management. Figure 1: Annual average concentrations of PM\({}_{2.5}\) in atmospheric air in Almaty (Circular markers on the map indicate geographical coordinates of stationary air quality monitoring stations. These measuring points represent the initial values of PM\({}_{2.5}\) concentrations.). Figure 2: Number of days with exceedances of the WHO daily standard (15 ug/m\({}^{3}\)) in 2023 and 2024. Traditional methods of air quality monitoring, which rely on fixed, high-end monitoring stations, offer accurate measurements but are limited by their high deployment and maintenance costs, leading to sparse spatial coverage and the need for interpolation models to estimate air quality in unsampled areas [1]. An effective method for evaluating the environmental safety of urban areas involves analyzing the risks posed by various environmental pollution factors, providing a basis for environmental and sanitary measures to protect the population [2]. Advancements in artificial intelligence, particularly artificial neural networks, have ushered in new possibilities for tackling air quality and climate change-related problems, offering solutions to address the rising concerns about air pollution and its effects on human health and the environment [3; 4]. This has led to the development of data-driven models that leverage big data from sources like traffic management and air pollutant concentration monitoring systems, enabling better urban planning, traffic route optimization, and the formulation of policies to mitigate carbon footprints [4]. These models, including machine learning algorithms, can predict air pollution levels across a city with high spatial and temporal resolution, addressing the limitations of traditional methods [5]. One promising avenue involves integrating meteorological and environmental parameters through sophisticated modeling techniques that can offer a more holistic understanding of pollution dynamics in Almaty. Conventional +bottom-up\({}^{\triangleright}\) models, such as Computational Fluid Dynamic Models, Chemical Transport Models, and CMAQ models, offer high model fidelity pertaining to the physical and chemical processes of air pollution [6]. However, these models necessitate detailed, high-resolution data on pollutant sources, topography, and meteorological conditions, which can be difficult and expensive to acquire for a city like Almaty. Statistical approaches, including machine learning methods like adaptive boosting, random forests, and support vector machines, have shown promising results in air quality index level predictions, using long-term datasets to account for seasonal and other factors [7].