A Spatiotemporal Prediction Model of PM2.5 Concentration Incorporating Geographic Environmental Anisotropy

PM2.5 concentration prediction plays a pivotal role in the prevention and control of air pollution. Traditional forecasting models, such as the Graph Convolutional Network (GCN) and other spatial-temporal prediction models, measure the spatial correlation of PM2.5 distribution primarily by monitoring the Euclidean distance between monitoring stations. However, these models often fail to account for the anisotropy effects of terrain, wind direction, and other factors that significantly influence the transport process of air pollutants. This oversight can result in lower accuracy of prediction results, especially in areas with complex terrain. This paper proposes a novel spatiotemporal convolutional network prediction model for PM2.5 concentration that takes into account the anisotropy of the geographical environment. The model first constructs the edges of the GCN by incorporating the anisotropic effects of terrain and wind direction on PM2.5 propagation between stations. It then models the station PM2.5 concentration, land use, and other meteorological factors as node characteristics of the GCN. The model employs GCN to extract the spatial characteristics of PM2.5 concentration and subsequently uses a Gate Recurrent Unit (GRU) to model and predict the temporal characteristics of PM2.5 concentration at the station. The model's performance was evaluated using hourly PM2.5 concentration records from the mountainous Guizhou province in 2017. It was compared against several spatiotemporal prediction baseline models, including Geographically and Temporally Weighted Regression (GTWR), Spatio-Temporal Support Vector Regression (STSVR), and a combined GCN+GRU model. The experimental results demonstrate that the model proposed in this paper significantly outperforms the baseline models. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) ratios of PM2.5 concentration predicted by the model are 10.047 and 6.848, respectively, which represent decreases of 11.29% and 12.16% compared to the baseline models. The R-squared value of 0.883 indicates an average improvement of 3.72% from the baseline. Furthermore, the analysis of the influence of different terrains on the correlation of PM2.5 concentration at different stations reveals that mountainous, gully, and other terrain features can significantly affect the correlation of PM2.5 concentration between stations. This, in turn, impacts the prediction results of PM2.5 concentration at different stations. The study concludes that fully considering the anisotropic effects of terrain and wind direction on PM2.5 propagation can substantially enhance the prediction accuracy of PM2.5 in mountain and gully terrain areas. By integrating the anisotropic characteristics of the geographical environment into the prediction model, this research contributes to the development of more accurate forecasting tools for PM2.5 concentrations. This development is expected to ensure accurate prediction of PM2.5 concentrations in areas with complex terrain and support the development of effective air pollution control strategies. © 2024 China Ship Scientific Research Center. All rights reserved.