Abstract:Heavy pollution weather is the key target of air pollution control during the 14th Five-Year Plan period. Accurate identification of risk sources during the period of heavy pollution weather paves the way for early warning in time, effective environmental pollution control, and prevention of the further aggravation of pollution incidents. On the basis of the data obtained by grid monitoring technology, this study proposes a deep learning model combining the residual network (ResNet), graph convolutional network (GCN), and gated recurrent unit (GRU) network, i.e., the ResGCN-GRU. This model is mainly used to identify risk sources during the period of heavy pollution weather. The risk sources of such weather are often regional and have salient spatiotemporal features. Therefore, this study starts by extracting the spatial features among monitoring points with the GCN and solving the problems of over-smoothing and gradient disappearance caused by the multi-layer GCN with the ResNet. Then, the GRU is used to extract the temporal features of risk sources. Finally, the spatiotemporal features fused by the fully connected layer are input into the Softmax activation function to obtain the binary classification probability, which is further used to obtain the classification result. To verify the performance of the proposed model, this study analyzes the data of 72 monitoring points in Shenyang and compares GCN, long short-term memory (LSTM), GRU, and GCN-GRU in accuracy, recall rate, and comprehensive evaluation indicators. The experimental results show that the classification accuracy of the ResGCN-GRU model is 16.9%, 4.3%, 3.1%, and 2.9% higher than that of the above four models, respectively, which proves that the model proposed in this study is more effective in identifying air risk sources, and it can accurately identify risk sources according to the spatiotemporal features of risk source data.