Abstract: The simulation and prediction of regional pollutants generally use the traditional spatial interpolation method, which cannot obtain accurate results when the source data is not uniformly distributed. To address these problems, a method for simulation and prediction of regional pollutants based on the INLA-SPDE model was proposed. The interpolation model was based on a Bayesian hierarchical model where the spatial-component was represented through the stochastic partial differential equation (SPDE) approach, with a lag-1 temporal autoregressive component (AR1). In addition, the model included 10 spatial and spatio-temporal predictors such as meteorological variables. By building 12 models for each month with the integrated nested Laplace approximation (INLA), this research realized the spatio-temporal simulation and prediction of PM2.5 concentration at daily resolution in the Beijing-Tianjin-Hebei region in 2019. Experiments show that compared with traditional Kriging interpolation methods, the proposed model can yield a better prediction of air pollutants at regional scale. Particularly, the prediction accuracy of high-value pollutants was improved significantly, and air pollutants exceedance probabilities can also be generated. Furthermore, a system for regional PM2.5 concentration simulation and decision support was established, the system can provide support for the travel of ordinary people or the decision-making of government officials, and verify the practicability and value of the proposed model. 

Key words: PM2.5, spatio-temporal Bayesian hierarchical model, integrated nested Laplace approximation, simulation system, decision support