Near-surface PM2.5 prediction combining the complex network characterization and graph convolution neural network

Massive studies focus on the prediction of main pollutants, to improve air quality by revealing the evolution of pollutants. However, existing prediction methods mostly emphasize the fitting analysis of time series, but ignore the spatial propagation effect among nearby places, resulting in a low prediction accuracy. To address this issue, this paper proposes a novel synthesis prediction method to simultaneously excavate the time series changing law and the spatial propagation effect. This method combines a characterization model named air quality spatial-temporal network (AQSTN) and a neural network model called graph convolution neural network (GCN). Firstly, by calculating three correlation coefficients, the time series of most related meteorological factors and aerosol data are gained for feature construction. The geographic distances between locations are computed to evaluate the spatial propagation cost. After that, AQSTN with locations as nodes and propagation relations as edges is constructed, compositing the temporal and spatial relationships. The network is regarded as graph data and input into GCN in chronological order. Secondly, GCN processing graph-structured data fits the optimal parameters in the training stage, simultaneously analyzes the spatial and temporal dimensions of the target site and its adjacent sites. And, the predicted PM2.5 concentration is gained in the test stage. The near-surface monitoring data of Beijing-Tianjin-Hebei area are adopted for experiment. Compared with the second-best model, the RMSE value of AQSTN-GCN is 6.85% lower, MAE value is 13.79% lower, MSE value is 13.23% lower, and MAPE value is 21.53% lower.