Comprehensive Scale Fusion Networks with High Spatiotemporal Feature Correlation for Air Quality Prediction

Abstract

The escalation of industrialization has worsened air quality, underscoring the essential need for accurate forecasting to inform policies and protect public health. Current research has primarily emphasized individual spatiotemporal features for prediction, neglecting the interconnections between these features. To address this, we proposed the generative Comprehensive Scale Spatiotemporal Fusion Air Quality Predictor (CSST-AQP). The novel dual-branch architecture combines multi-scale spatial correlation analysis with adaptive temporal modeling to capture the complex interactions in pollutant dispersion and enhanced pollution forecasting. Initially, a fusion preprocessing module based on localized high-correlation spatiotemporal features encodes multidimensional air quality indicators and geospatial data into unified spatiotemporal features. Then, the core architecture employs a dual-branch collaborative framework: a multi-scale spatial processing branch extracts features at varying granularities, and an adaptive temporal enhancement branch concurrently models local periodicities and global evolutionary trends. The feature fusion engine hierarchically integrates spatiotemporally relevant features at individual and regional scales while aggregating local spatiotemporal features from related sites. In experimental results across 14 Chinese regions, CSST-AQP achieves state-of-the-art performance compared to LSTM-based networks with RMSE 6.11–9.13 μg/m3 and R2 0.91–0.93, demonstrating highly robust 60 h forecasting capabilities for diverse pollutants.

Keywords

• spatiotemporal convolution
• air quality prediction
• generative CNN
• multi-scale fusion
• feature correlation