Prediction of PM2.5 Concentration in Yangtze River Economic Belt Based on Graph Neural Network.

Based on the monitoring data of PM2.5 concentration in 99 cities along the Yangtze River Economic Belt, the article constructed a spatial interaction network of air pollution in the Belt using transfer entropy, and analyzed the pollutant transmission direction and transmission intensity from both the overall and local perspectives. Then, in order to make full use of the spatial correlation information of urban air pollution, the article used the spatial interaction network of air pollution to improve the graph structure of T-GCN, and constructed a prediction model based on the T-GCN_TE to predict the pollutant concentrations in 99 cities of the Belt. It is found that the air pollution in each city shows strong compactness, and the information transfer of the overall network is dominated by inter-regional information transfer. Moreover, T-GCN_TE can capture the spatio-temporal dependence and the influence direction of the air pollution, and better results can be obtained. Based on the above conclusions, the article provides suggestions for the development of collaborative governance system of air pollution in the Belt, strengthening industrial cooperation and improving the ecological compensation mechanism. [ABSTRACT FROM AUTHOR]