Preprints
https://doi.org/10.5194/acp-2022-248
https://doi.org/10.5194/acp-2022-248
 
25 Apr 2022
25 Apr 2022
Status: this preprint is currently under review for the journal ACP.

Predicting gridded winter PM2.5 concentration in east of China

Zhicong Yin1,2,3, Mingkeng Duan1, Yuyan Li1, Tianbao Xu1, and Huijun Wang1,2,3 Zhicong Yin et al.
  • 1Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, 210044, China
  • 2Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, 519080, China
  • 3Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China

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.

Zhicong Yin et al.

Status: open (until 17 Jun 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Zhicong Yin et al.

Zhicong Yin et al.

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Short summary
The PM2.5 concentration has been greatly reduced in recent years in China, and has entered a crucial stage that required fine seasonal prediction. However, there is still no study aimed to predict gridded PM2.5 concentration. A model for seasonal prediction of gridded winter PM2.5 concentration in east of China was developed by analyzing the contributions of emission and climate variability, which could provide scientific supports for air pollution control at the regional and city levels.
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