ST-CCN-PM_2.5: fine-grained PM_2.5 concentration prediction via spatial-temporal causal convolution network

PM_2.5 concentration is an important evaluation index of urban air quality. Improving the prediction and early warning ability of PM_2.5 concentration is of great significance for guiding residents' real-time protection and efficient control of air pollution. A large number of studies have shown that the temporal and spatial correlation between PM_2.5 concentration of surrounding stations and target stations directly affects the prediction accuracy of the model. There is still a lack on how to select strongly correlated and representative temporal and spatial information, and the selection methods are somewhat subjective. In this paper, air pollutants and meteorological factors are taken as the influencing factors, and a PM_2.5 prediction model (ST-CCN-PM_2.5) based on causal convolution network is proposed to improve the performance of PM_2.5 concentration prediction at a fine-grained spatial-temporal scale. We use spatial attention mechanism to fuse spatial correlation information, and optimize threshold parameters. Based on time attention mechanism, the sliding window size is adjusted to dilate the time-series input. Finally, the hourly records of air pollutants and meteorological factors monitored by 95 monitoring stations in Haikou city are employed, and the performance is compared with baseline. Compared with AR and ARMA models, the MSE values decrease by 38.9% and 40.9%, and R² values increase by 2.3% and 2.6%, respectively. Compared with GRU, SVR, LSTM and ANN models, the MSE value of ST-CCN-PM_2.5 decreases by 44.8%, 45.4%, 46.5% and 49.0%, respectively, and its R² value increases by 3.6%. The final results show that the prediction performance of ST-CCN-PM_2.5 model is significantly improved compared with the baseline, which proves that the model has stronger reflection ability and prediction generalization ability for dynamic nonlinear system, and proves the potential of the model in the prediction of fine particle PM_2.5 concentration.