Short time PM2.5 prediction model for Beijing-Tianjin-Hebei region based on Generalized Space Time Autoregressive (GSTAR)

Abstract

In recent years, with the rapid development of China’s economy and urbanization, the irrational industrial structure, large energy consumption, and increased car ownership have made China’s air pollution increasingly serious, especially in Beijing-Tianjin-Hebei, Yangtze River Delta, and the Pearl River Delta. As the primary pollutant affecting the air quality in the Beijing-Tianjin-Hebei region, the accurate prediction of PM2.5 concentration is of great significance for predicting heavy pollution weather, formulating the start-up mechanism of emergency plan and optimizing the production of enterprises. This study used the online hourly data of conventional pollutants from 80 national control stations in Beijing, Tianjin and Hebei from May 1, 2015 to May 31, 2019. Considering the influence of time and space, the GSTAR model of PM2.5 prediction is established. Comparing the prediction results of GSTAR model with ARMA and STAR, the validity of GSTAR model is verified by applying relevant accuracy evaluation indicators.