An Adaptive Spatiotemporal Graph Transformer for Multi-Site PM2.5 Multi-Step Forecasting with Non-stationary and Sparse-Aware Method.
TL;DR
The results demonstrate the model's effectiveness, accuracy, and robustness across different datasets, highlighting its generalization capability under irregular spatial distributions, and offering a reliable solution for multi-site multi-step air quality prediction and refined environmental management.
Abstract
Fine particulate matter (PM2.5) concentration is a critical indicator of air quality and is closely related to human health and ecological environments. Accurate multi-site PM2.5 forecasting still faces considerable challenges: PM2.5 exhibits non-stationary distribution drift, monitoring sites are unevenly distributed, and temporal evolution is tightly coupled with spatial interactions. To tackle these problems, this paper proposes an Adaptive Spatiotemporal Graph Transformer (AST-GT) framework for multi-site PM2.5 multi-step forecasting. The framework incorporates five core components: adaptive non-stationary normalization to alleviate time-varying distribution drift and feature scale mismatch; Transformer-based temporal representation learning to capture long-range temporal dependencies; multi-source context encoding to fuse geographic location, meteorological conditions, and co-pollutant information; graph-based dual-scale spatial attention and Temporal-Spatial Cooperative Attention mechanism to jointly model spatial correlations and spatiotemporal interactions, particularly under uneven site distribution. Extensive experiments are conducted on multi-site datasets from Beijing and India to validate the performance of the proposed AST-GT. The results demonstrate the model's effectiveness, accuracy, and robustness across different datasets, highlighting its generalization capability under irregular spatial distributions, and offering a reliable solution for multi-site multi-step air quality prediction and refined environmental management.