Objective identification and forecast method of PM2.5 pollution based on medium- and long-term ensemble forecasts in Beijing-Tianjin-Hebei region and its surrounding areas

Abstract

Accurate long-term forecasts of PM2.5 pollution are essential to mitigating health risks and formulating pollutant control strategies for decision-makers in China. In this study, an objective identification and forecast method for PM2.5 pollution (OIF-PM2.5) is developed based on medium- and long-term ensemble forecasts of PM2.5 in Beijing-Tianjin-Hebei region and its surrounding areas. The results show that the observed PM2.5 pollution ratio increases with the aggravating PM2.5 pollution. For example, the ratio of meteorological stations with heavy pollution is 4.4 times that of light pollution and 3.9 times that of moderate pollution. In addition, the correlation coefficients between observations and forecasts are above 0.60 for all forecast leading times. Statistical results show that the average accuracy for forecasts with the leading times of 1–3 days, 4–7 days, and 8–15 days are 74.1%, 81.3%, and 72.9% respectively, indicating that the OIF-PM2.5 method has a high reliability in forecasts with the leading times of 1–15 days. The OIF-PM2.5 method is further applied in a severe PM2.5 pollution episode in the December of 2021, and the average forecast precision in forecasts with the leading times of 6–8 days reaches as high as 100%, showing a certain reference value for PM2.5 forecasts.