LSTM model for multi-day forecasting of Air Quality Index in urban areas

Air quality forecasting is crucial for assessing population exposure to pollutants and avoid health complications. The Air Quality Index (AQI) is a scale of air pollution that indicates how clean is the air and helps to evaluate these complications. However, predictive models for air quality in European urban environments remain limited.

This index is calculated from air quality monitored data related to different pollutants such as PM_2.5, PM₁₀, CO, NO₂ and O₃ and there are six categories for the AQI: Good, Moderate, Fair, Poor, Very Poor and Hazardous. This work focuses on the implementation and evaluation of AQI forecast models for Spanish metropolitan areas such as Granada, Madrid and Barcelona. This study provides one of the first applications of LSTM-based AQI forecasting with extended horizons in a southern European environment.

The input data used are pollutant concentrations (PM_2.5, PM₁₀, CO, NO₂ and O₃) from an urban background station and meteorological variables (T, RH, P, wind direction, wind velocity and precipitation) from the nearest available station. Missing values were imputed to address short-term gaps, and all input variables were scaled to ensure stable training. The dataset was then split into 70/15/15 for training, validation and testing, respectively. The model used is a Long Short-Term Memory (LSTM) neural network, implemented to forecast AQI levels for 1 to 3-day horizons. Two AQI formulations were tested: a continuous index and the discrete version defined in national guidelines. Both were used to evaluate the model, and the continuous AQI consistently outperformed the discrete one. Thus, the continuous AQI was selected for 1-, 2- and 3-day forecasts. In order to improve model performance, additional features were included to capture temporal patterns, and backtesting was applied to obtain a robust performance estimate.

Preliminary conducted for the city of Granada and hourly AQI of PM10 have shown an accuracy in the range of 0.86 to 0.73 for horizons of 1 to 3 days, decreasing with the forecast horizon. The model reproduces the main AQI variability and most index transitions, although its performance is limited by the lack of high AQI levels (<1 %). In sum, these results highlight the potential of LSTM models to support air-quality forecasting in Spanish urban environments, enabling synergistic work with local authorities for early warnings. Future work will focus on improving the 3-day forecast, extending it to other cities with transfer learning.

Acknowledgements:

This work is part of the project funded by the 2024 Leonardo Grant for Researchers and Cultural Creators from the BBVA Foundation and grant JDC2022-048231-I, funded by MICIU/AEI/10.13039/501100011033 and the EU NextGenerationEU/PRTR. The authors also acknowledge the Junta de Andalucía for providing the air quality data.