Interpretable long-horizon air pollution forecasting using a transformer-based framework

Accurate and interpretable air pollution forecasting is essential for public health and proactive intervention. While deep learning models have achieved strong performance in long-horizon forecasting, they often require aligned historical sequences and provide limited interpretability. To address these limitations, we propose a novel Transformer-based framework that integrates historical observations with auxiliary forecasts to enhance predictive performance while providing built-in interpretability. The framework introduces two core components: a Structured Temporal Embedding Module (STEM) for preserving the semantic integrity of heterogeneous inputs, and an eXplainable Target-Oriented Cross-Attention (X2-Attention) mechanism that aligns future predictions with known inputs to yield fine-grained attribution for each forecasted timestep across both temporal and feature dimensions. Evaluated on diverse urban and suburban datasets in Stockholm, our model simultaneously delivers reliable interpretability and superior predictive accuracy. Compared to five baselines (TimeXer, Crossformer, iTransformer, Transformer and LSTM), it exhibits significantly lower Mean Squared Error (MSE) for NO X and competitive performance for PM 10 across horizons up to 720 hours. Ablation studies reveal that incorporating auxiliary forecasts reduces MSE by 29 % for NO X and 15 % for PM 10 . Moreover, the X2-Attention mechanism generates plausible feature attribution validated through perturbation analysis. Its attributions are consistent with GradientSHAP while avoiding prohibitive computational overhead, thereby facilitating efficient air quality forecasting.