A hybrid spatiotemporal model combining graph attention network and gated recurrent unit for regional composite air pollution prediction and collaborative control

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Machine learning (ML) models have been extensively applied in air quality prediction. However, many of these models often failed to unveil complex mechanisms and regional spatiotemporal variations of composite air pollution. This brings uncertainties in using ML models for effective composite air pollution control. The present study developed a novel hybrid spatiotemporal model framework combining Graph Attention Network (GAT) and Gated Recurrent Unit (GRU), namely the GAT-GRU model, to foresee composite air pollutions with a focus on PM2.5 and O3. By extracting attention matrices for PM2.5O3 composite pollution and applying the Louvain algorithm, the framework established effective community network divisions for coordinated control of PM2.5O3 composite pollution. The framework was applied and tested in China's “2 + 26″ cities, a city cluster with most heavy PM2.5 and O3 pollution and precursor emission sources. The results demonstrate that the framework successfully captured spatiotemporal evolution of combined PM2.5 and O3 pollution. The attention matrix is autonomously generated during course of the model learning process with the aim to interpret the complex interactions among “2 + 26″ cities. The framework provides a new perspective for the interpretability of artificial intelligence models and offers a methodological support and scientific evidence for formulating regional pollution cooperative governance strategies.