Boosting Arctic Air Quality Forecasts with Deep Learning

Air pollution poses a critical risk to both human health and the environment, particularly in the Arctic and Northern Europe, where pollutants are primarily transported from mid-latitudes by atmospheric circulation. Also, local sources further contribute to pollution levels in Arctic communities. Accurate short-term forecasts of atmospheric pollutant concentrations are vital for enabling adaptive measures and protecting public health during pollution episodes.

The Copernicus Atmospheric Monitoring Service (CAMS) provides 96-hour forecasts for key pollutants across Europe using 11 state-of-the-art models and an ensemble approach. However, these forecasts exhibit significant errors in Northern Europe and the Arctic. To address this, we investigate the applicability of deep learning (Transformer-based) models for 48-hour PM₁₀ concentration forecasting at monitoring stations in Northern Europe. Our approach integrates in situ PM₁₀ observations with CAMS model outputs and forecasted meteorological parameters as input features. We evaluated four time-series specialized models—Informer, Autoformer, FEDformer, and Crossformer—to identify the most effective architecture for this task. The Crossformer model demonstrated superior performance, outperforming CAMS by 30% in Mean Squared Error (MSE) and 23% in Mean Absolute Error (MAE). It also surpassed the newly introduced CAMS Model Output Statistics (MOS), reducing MSE by 12% and MAE by 14%.

With its low computational complexity, fast execution time, and minimal resource requirements, the Crossformer presents a viable alternative to traditional numerical models for local-scale predictions. Future work will extend the forecasting window to 72 hours and incorporate additional pollutants, such as PM_2.5, NO₂, and O₃, to enhance predictive capabilities for Arctic and Northern European communities.