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Preprints
https://doi.org/10.5194/egusphere-2022-1412
https://doi.org/10.5194/egusphere-2022-1412
02 Jan 2023
 | 02 Jan 2023

Global impact of COVID-19 lockdown on surface concentration and health risk of atmospheric benzene

Chaohao Ling, Lulu Cui, and Rui Li

Abstract. To curb the spread of COVID-19 pandemic, many countries around the world imposed an unprecedented lockdown producing reductions in pollutant emissions. Unfortunately, the lockdown-driven global ambient benzene changes still remained unknown. The ensemble machine-learning model coupled with the chemical transport models (CTMs) was applied to estimate global high-resolution ambient benzene levels. Afterwards, the XGBoost algorithm was employed to decouple the contributions of meteorology and emission reduction to ambient benzene. The change ratio (Pdew) of deweathered benzene concentration from pre-lockdown to lockdown period was in the order of India (−23.6 %) > Europe (−21.9 %) > United States (−16.2 %) > China (−15.6 %). The detrended change (P*) of deweathered benzene level (change ratio in 2020 – change ratio in 2019) followed the order of India (P* = −16.2 %) > Europe (P* = −13.9 %) > China (P* = −13.3 %) > United States (P* = −6.00 %). Substantial decreases of atmospheric benzene levels saved sufficient health benefits. The global average lifetime carcinogenic risks (LCR) and hazard index (HI) decreased from 4.89 × 10−7 and 5.90 × 10−3 and 4.51 × 10−7 and 5.40 × 10−3, respectively.

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Journal article(s) based on this preprint

16 Mar 2023
Global impact of the COVID-19 lockdown on surface concentration and health risk of atmospheric benzene
Chaohao Ling, Lulu Cui, and Rui Li
Atmos. Chem. Phys., 23, 3311–3324, https://doi.org/10.5194/acp-23-3311-2023,https://doi.org/10.5194/acp-23-3311-2023, 2023
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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The ensemble machine-learning model coupled with the chemical transport models (CTMs) was...
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