Operational Ozone Forecasting System in Mexico City - A Machine Learning Framework Integrating Forecasted Weather and Historical Ozone Data

Abstract

Mexico City, a densely populated urban area, experiences multiple episodes of elevated air pollution almost every year. To mitigate the impact of these pollution episodes on the population, it is important to improve forecast systems that allow government authorities to take preventive actions, reducing the exposure of vulnerable groups. This study introduces a pilot operational ozone forecasting system based on machine learning. The proposed system is trained using historical data from a long-standing governmental air quality and atmospheric monitoring network, and with meteorological reanalysis data from a regional implementation of the Weather Research and Forecasting (WRF) model for Mexico. Additional input features are incorporated, including cyclical time encoding for the day of the week, time of day, and day of the year, to improve system accuracy. Once trained, the system utilizes real-time data from multiple atmospheric stations and regional meteorological forecasts to predict ozone levels for the following 24 h. This study evaluates the impact of data augmentation and the advantages of integrating meteorological forecast information into the model. The model achieves a mean absolute error (MAE) of 9.81 ppb and an index of agreement of 0.91 across all stations. For the top 10 stations, the MAE falls below 8.7 ppb, and the index of agreement exceeds 0.93. The system’s performance is comparable to similar systems in other large metropolitan areas and represents an improvement over the existing systems in Mexico City. This operational system is available at https://aire.atmosfera.unam.mx/.

Cite

@article{zavalaromero2025operational,
  title = {Operational Ozone Forecasting System in Mexico City - A Machine Learning Framework Integrating Forecasted Weather and Historical Ozone Data},
  author = {Zavala-Romero, Olmo and Segura, Pedro and Camacho, Pablo and Zavala-Hidalgo, Jorge and Garcia, Agustin R. and Oropeza-Alfaro, Pavel and Romero-Centeno, Rosario and Gomez-Ramos, Octavio},
  year = {2025},
  doi = {10.1016/j.atmosenv.2024.121017},
  url = {https://doi.org/10.1016/j.atmosenv.2024.121017},
}

Related projects

• AI-Driven Air Pollution Forecasting and Early Warning Systems

Topics

• Atmosphere
• Deep Learning
• SciML