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| Hauptseite > Publikationsdatenbank > Impact of weather patterns and meteorological factors on PM 2.5 and O 3 responses to the COVID-19 lockdown in China |
| Hauptseite > Publikationsdatenbank > Impact of weather patterns and meteorological factors on PM 2.5 and O 3 responses to the COVID-19 lockdown in China |
| Journal Article | FZJ-2024-04645 |
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2024
EGU
Katlenburg-Lindau
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Please use a persistent id in citations: doi:10.5194/acp-24-6539-2024 doi:10.34734/FZJ-2024-04645
Abstract: Haze events in the North China Plain (NCP) and a decline in ozone levels in Southern Coast China (SC) from 21 January to 9 February 2020 during the COVID-19 lockdown have attracted public curiosity and scholarly attention. 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 approaches are employed. These approaches implemented the comparisons of observations in 2020 with climatology averaged over the years 2015–2019 with a novel structural SOM (self-organising map) model and with the prediction of the “business as usual” (hereafter referred to as BAU) emission strength by the GBM (gradient-boosting machine) model, respectively. The results reveal that the unexpected PM2.5 pollution and O3 decline from the climatology in NCP and SC could be effectively explained by the presence of a double-centre high-pressure system across China. Moreover, the GBM 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, as well as in the O3 change 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.
Keyword(s): Geosciences (2nd)
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