## 1 Introduction  
The Metropolitan Area of the Valley of Mexico is 2240 m above sea level, located in a terrain of significant complexity, surrounded by mountains with an average height ranging between 600 and 800 m above the valley floor. This geographical setting makes it a region of great interest due to the substantial influence of meteorological variables on the dispersion of pollutants in the atmosphere. Furthermore, it is considered one of the largest cities in the world, characterized by a high population density, rendering it a focal point for the study of pollutant dispersion phenomena in the air. It played a pivotal role in the MILAGRO campaign (Megacity Initiative: Local And Global Research Observations) [1], in which nearly 150 institutions collaborated, supported by 450 researchers from various parts of the world. This extensive effort involved deploying diverse equipment in March 2006 to collect extensive data on pollutants and meteorological information, aiming to gain a deeper understanding of pollutant dispersion phenomena in the atmosphere of a megacity.  
The air quality issue has garnered international priority, with studies conducted in Mexico City and major cities worldwide. In response to this challenge, governmental agencies have enacted regulations governing air quality levels to reduce pollutant concentrations. For instance, restrictions on the use of leaded gasoline were implemented due to the more contaminating and particularly toxic emissions from vehicles, which could reach hazardous concentrations in urban environments, posing risks to the health of residents.  
Pollution poses a significant impact on public health, with the World Health Organization (WHO) estimating approximately 7 million premature deaths annually, equivalent to 800 deaths every hour or 13 per minute. Numerous studies have been conducted to assess the adverse effects resulting from prolonged exposure to various pollutants. For example, the study by [PERSON] et al. [2] on the lung development issues in children in Mexico City due to prolonged exposure to air pollutants concluded, after three years of follow-up that the children involved in the study exhibited adverse effects on lung development. This implies long-term health risks associated with an increased likelihood of developing heart-related conditions. As mentioned by [PERSON] et al. [3], the established regulations have succeeded in reducing air pollutant concentrations, except ozone (O\({}_{3}\)), which has shown a global upward trend, making its impact on health a priority. This is evident in studies such as [PERSON] et al. [4], which conducted a review of 55 medical articles from 1980 to 2014 on the health impacts of ozone exposure, and [PERSON] et al. [5], where the results indicate that prolonged exposure to ozone (O\({}_{3}\)) significantly affects cardiac mortality in China. Given its substantial health impact, understanding the formation and dispersion of ozone has become critically important, as well as the ability to forecast its concentrations to enable timely control measures.  
Currently, this process is accomplished through conventional techniques such as mathematical models to simulate pollutant dispersion and statistical tools to comprehend and infer the behavior of this phenomenon. The advancement of technology and the capacity for massive data processing through machine learning have opened up new avenues of research to address this issue. Section 2 explores some research endeavors that apply machine learning to air quality.