Multi-pollutant air quality forecasting using bidirectional attention and multi-scale temporal networks

Abstract

Introduction: Accurate multi-pollutant forecasting is vital for urban governance and public health. Existing deep models struggle to capture multi-scale temporal dynamics and synergistic cross-pollutant relations. Methods: We propose an Enhanced Bidirectional Attention Multi-scale Temporal Network (EBAMTN) that combines a multi-scale TCN with linear attention, a two-layer BiLSTM augmented by multi-head self-attention, and a gated fusion layer. Under a multi-task paradigm, the backbone jointly learns shared temporal representations and outputs PM2.5 and PM10 via task-specific heads. Results: Using hourly data from Guangzhou, Beijing, and Chengdu, EBAMTN achieved R2 > 0.94 for both pollutants while maintaining low errors (e.g., PM2.5 MAE≈2.03, RMSE≈2.94; PM10 MAE≈3.44, RMSE≈4.99). Confidence-interval analyses and scatter plots indicate strong trend tracking and robustness, with remaining challenges mainly at sharp peaks. Discussion: The integration of multi-scale convolutions, bidirectional memory, attention, and gated fusion improves accuracy, interpretability, and generalization. The lightweight design (≈2.1M parameters; ∼ 13.2 ms/sample) supports real-time and edge deployment. Overall, EBAMTN offers a scalable, interpretable solution for multi-pollutant forecasting in complex urban settings.