A Hybrid GCN–PCA–LSTM Framework for Accurate Spatiotemporal Prediction of PM2.5 Concentrations

PM2.5 pollution has become a critical environmental issue affecting air quality and public health. Accurate concentration prediction is of great significance for pollution early warning and control. Considering that PM2.5 concentration variations exhibit both temporal dependence and spatial correlation, along with the presence of redundant features in multi-source data, a spatiotemporal prediction model integrating a Graph Convolutional Network (GCN), Principal Component Analysis (PCA), and a Lo