Accurate ozone concentration forecasting is crucial for effective air quality management and environmental protection. However, most of the existing forecasting models focus on short-term (24–72 h) forecasts and lack validation for extended horizons (weeks to months). Current understanding of how deep learning models bridge the temporal scale gap between short-term ozone fluctuations and long-term concentration trends is limited. This study presents a comprehensive analysis of the ozone-meteorological relationships and multi-horizon forecasting in Shanghai, a megacity in China during 2014–2021. We systematically evaluate six state-of-the-art deep learning models (Transformer, Informer, FEDformer, Autoformer, NS-Transformer, and iTransformer) for ozone concentrations forecasting across six temporal scales (96–1440 h). The correlation analysis identifies a significant positive correlation between ozone concentration and temperature and a significant negative correlation with relative humidity. Comparative model evaluation reveals distinct temporal performance patterns: Informer achieves superior accuracy for sub-two-week forecasts, while NS-Transformer demonstrates optimal performance for extended forecasting windows beyond 2 weeks. The incorporation of meteorological covariates as dynamic exogenous variables enhances the accuracy of the model, especially the monthly recalibration provides additional robustness. These results demonstrate the necessity of selecting temporal-scale-adapted models for ozone forecasting and the crucial role of meteorological characteristic engineering in long-term air quality forecasting. The physics-constrained machine learning model proposed in this study provides a scientific basis for sub-seasonal ozone prediction on a 14–60 day horizon.
Zheng et al. (Tue,) studied this question.