Long-term urban air quality prediction with hierarchical attention loop network

Air pollution poses severe threats to human health, socioeconomic development, and natural environment, making it one of the most serious environmental issues. Accurate long-term regional air quality prediction plays a significant role in mitigating the severity of pollution events and effectively suppressing the intensification of air pollution, benefiting both the environment and society. In this paper, we propose a novel Hierarchical Attention Loop Network (HALN) comprising four key components for long-term prediction of PM _2.5 concentrations across regional multiple target stations, achieving effective prediction horizons of up to 120 h. Instead of using all monitoring stations within a region, the MIC-H selector selects station data exhibiting strong spatiotemporal correlations at each hierarchical level. HALN then injects additional spatial information through annular grid positional encoding. The hierarchical attention feature match layer refines the model's focus on data with a more substantial predictive impact. As the core integrative component of HALN, attention loop block reinforces the influence of historical outputs, enhancing the model's ability to capture long-term dependencies. Extensive real-world experimental results demonstrate that the proposed model exhibits exceptional robustness in regional predictions and achieves high accuracy in long-term forecasting. The coefficient of determination (R²) reaches 0.793 and 0.636 for 24-hour and 120-hour predictions, respectively.