Predicting gridded winter PM2.5 concentration in the east of China

Exposure to high concentration levels of fine particle matter with diameter <= 2.5 mu m (PM2.5) can lead to great threats to human health in the east of China. Air pollution control has greatly reduced the PM2.5 concentration and entered a crucial stage that required support like fine seasonal prediction. In this study, we analyzed the contributions of emission predictors and climate variability to seasonal prediction of PM2.5 concentration. The socioeconomic PM2.5, isolated by atmospheric chemical models, could well describe the gradual increasing trend of PM2.5 during the winters of 2001-2012 and the sharp decreasing trend since 2013. The preceding climate predictors have successfully simulated the interannual variability in winter PM2.5 concentration. Based on the year-to-year increment approach, a model for seasonal prediction of gridded winter PM2.5 concentration (10 km x 10 km) in the east of China was trained by integrating emission and climate predictors. The area-averaged percentage of same sign was 81.4 % (relative to the winters of 2001-2019) in the leave-one-out validation. In three densely populated and heavily polluted regions, the correlation coefficients were 0.93 (North China), 0.95 (Yangtze River Delta) and 0.87 (Pearl River Delta) during 2001-2019, and the root-mean-square errors were 6.8, 4.2 and 4.7 mu g m(-3). More important, the significant decrease in PM2.5 concentration, resulting from the implementation of strict emission control measures in recent years, was also reproduced. In the recycling independent tests, the prediction model developed in this study also maintained high accuracy and robustness. Furthermore, the accurate gridded PM2.5 prediction had the potential to support air pollution control on regional and city scales.

Automated summary

This study analyzed the contributions of emission predictors and climate variability to seasonal prediction of PM2.5 concentration in the east of China and developed a model for winter PM2.5 concentration prediction. The model showed high accuracy and robustness in cross-validation and independent tests and successfully reproduced the significant decrease in PM2.5 concentration resulting from strict emission control measures in recent years.