04 Dec 2023
 | 04 Dec 2023

Impact of Weather Patterns and Meteorological Factors on PM2.5 and O3 during the Covid-19 Lockdown in China

Fuzhen Shen, Michaela I. Hegglin, and Yue Yuan

Abstract. The abnormal haze event in NCP (North China Plain) and the decline in ozone levels in SC (Southern China) from 21st January to 9th February 2020 have attracted public curiosity and scholarly attention during the COVID-19 lockdown. Most previous studies focused on the impact of atmospheric chemistry processes associated with anomalous weather elements in these cases, but fewer studies quantified the impact of various weather elements within the context of a specific weather pattern. To identify the weather patterns responsible for inducing this unexpected situation and to further quantify the importance of different meteorological factors during the haze event, two scenarios are employed. These scenarios compared observations to climatology averaged over the years 2015–2019 and the ‘Business As Usual’ (hereafter referred to as BAU) emission strength, using a novel structural SOM (Self-Organising Map) and ML (Machine Learning) models. The results reveal that the unexpected PM2.5 pollution and O3 decline from the climatology in NCP, North East China (NEC), and SC could be effectively explained by the presence of a double-centre high-pressure system. Moreover, the ML results provided a quantitative assessment of the importance of each meteorological factor in driving the predictions of PM2.5 and O3 under the specific weather system. These results indicate that temperature played the most crucial role in the haze event in NCP and NEC, as well as in the O3 decline in SC. This valuable information will ultimately contribute to our ability to predict air pollution under future emission scenarios and changing weather patterns that may be influenced by climate change.

Fuzhen Shen, Michaela I. Hegglin, and Yue Yuan

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2425', Anonymous Referee #1, 15 Dec 2023
  • RC2: 'Comment on egusphere-2023-2425', Anonymous Referee #2, 24 Jan 2024
Fuzhen Shen, Michaela I. Hegglin, and Yue Yuan
Fuzhen Shen, Michaela I. Hegglin, and Yue Yuan


Total article views: 337 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
232 90 15 337 32 6 13
  • HTML: 232
  • PDF: 90
  • XML: 15
  • Total: 337
  • Supplement: 32
  • BibTeX: 6
  • EndNote: 13
Views and downloads (calculated since 04 Dec 2023)
Cumulative views and downloads (calculated since 04 Dec 2023)

Viewed (geographical distribution)

Total article views: 317 (including HTML, PDF, and XML) Thereof 317 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 29 Feb 2024
Short summary
We attempt to use a novel structural Self-Organising Map and Machine Learning models to identify a weather system and quantify the importance of each meteorological factor in driving the unexpected PM2.5 and O3 changes under the specific weather system during the COVID-19 lockdown in China. The result highlights temperature under the double-centre high-pressure system plays the most crucial role in abnormal events.