Reply on RC2

Abstract

Exposure to high levels of concentration of fine particle matters with diameter ≤ 2.5 μm (PM2.5) can lead to great threats to human health in east of China. Air pollution control has greatly reduced the PM2.5 concentration and entered a crucial stage that required supports like fine seasonal prediction. In this study, we analysed 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 of 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 × 10 km) in east of China was trained by integrating of emission and climate predictors. The area-averaged percentage of same sign was 81.8 % (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 0.88 (Pearl River Delta) during 2001–2019 and the root-mean-square errors were 6.5, 4.1 and 4.6 μg/m3. More important, the significant decrease in PM2.5 concentration, resulted from 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.