The increase in atmospheric pollution has made it essential to develop accurate models for predicting pollutant concentrations. The current researches have faced challenges such as the neglect of significant information selection from local and neighboring stations, as well as insufficient attention to long-term historical data patterns. Therefore, this paper proposes a spatiotemporal prediction model called MGCGRU-SAN, which leverages long-term historical data to predict PM