Attention-Based LSTM Forecasting of O3, NO2, and NO Concentrations in Eastern Santiago, Chile

In Santiago, Chile, the 8 h average atmospheric ozone (O3) concentration frequently exceeds the national air quality standard of 61 ppbv (parts per billion by volume). Due to local meteorological and topographic conditions, the highest ozone levels are observed at the Las Condes station in eastern Santiago. To provide a reliable tool for forecasting adverse air quality events, we developed two predictive models for the daily maximum 8 h average concentration: an XGBoost decision tree model and an attention-based Long Short-Term Memory (LSTM) deep neural network. Given the photochemical coupling between ozone and nitrogen oxides, models were also implemented to forecast daily concentrations of nitric oxide (NO) and nitrogen dioxide (NO2). Models were trained on data spanning 2007–2022 and evaluated on a strictly out-of-sample period (January 2023 to July 2024). The attention-based LSTM achieved symmetric mean absolute percentage errors (sMAPE) of 8.2%, 21%, and 17% for O3, NO, and NO2, respectively. It outperformed XGBoost across all eight statistical metrics evaluated, while both models demonstrated substantial improvements over a previous-day persistence baseline.