Spatiotemporal causal convolutional network for forecasting hourly PM2.5 concentrations in Beijing, China

Air pollution in Northeastern Asia is a serious environmental problem, especially in China where PM2.5 levels are quite high. Accurate PM2.5 predictions are significant to environmental management and human health. Recently, deep learning has received increasing attention from relevant researchers. In this work, a spatiotemporal causal convolutional neural network (ST-CausalConvNet) for short-term PM2.5 prediction is proposed. The distinguishing characteristics of the proposed model is that the convolutions in the model architecture are causal, where an output at a certain time step is convolved only with elements from the same or earlier time steps in the previous layer. Accordingly, no information leakage is induced from the future to the past in this model. The spatial dependence between multiple monitoring stations was also considered in the model. Spatiotemporal correlation analysis was performed to select relevant information from monitoring stations that have a high relationship with the target station. The information from the target and related stations were then employed as the inputs and fed into the model. A case study from May 1, 2014 to April 30, 2015 in Beijing, China was conducted. The next hour PM2.5 concentration was predicted by the proposed model by using historical air quality and meteorological data from 36 monitoring stations. Experimental results show that the trends of the predicted PM2.5 concentrations and the observed values were consistent. The proposed method achieved a better prediction performance than the other three comparative models, namely artificial neural network (ANN), gated recurrent unit (GRU), and long short-term memory (LSTM). Furthermore, the effects of the important parameters and the model transferability were also conducted. We conclude that the proposed ST-CausalConvNet is a potential effective model for air pollution forecasting.