Abstract:With the development of environmental monitoring technology in China, the grid monitoring system of ambient air quality has received more attention from environmental workers. In order to solve the air quality prediction of small and miniature monitoring stations in the grid monitoring system of air pollution, we propose an air quality prediction model based on GCN and LSTM. First, GCN is applied to extract the spatial features between the small and miniature monitoring stations in the grid monitoring system. Then, LSTM is employed to extract the relevant temporal features. Finally, the linear regression layer is used to integrate the spatial and temporal features and get the prediction results of air quality. Furthermore, experiments are carried out on the air quality monitoring data from 14 small and miniature monitoring stations in Hunnan District, Shenyang, verifying the prediction effect of the proposed model. The experimental results show that the air quality prediction model based on GCN-LSTM is more accurate than the LSTM prediction model in terms of the prediction results on the small and miniature monitoring stations in the grid monitoring with strong spatial association.