Abstract

Abstract. To curb the spread of the 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 remain unknown. An ensemble machine-learning model coupled with chemical transport models (CTMs) was applied to estimate global high-resolution ambient benzene levels. Afterwards, the extreme gradient boosting (XGBoost) algorithm was employed to decouple the contributions of meteorology and emission reduction to ambient benzene. The change ratio (Pdew) of the deweathered benzene concentration from the pre-lockdown to lockdown period was in the order of India (−23.6 %) > Europe (−21.9 %) > the United States (−16.2 %) > China (−15.6 %). The detrended change (P∗) of the 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 %) > the United States (P∗=-6.00 %). Emission reductions derived from industrial activities and transportation were major drivers for the benzene decrease during the lockdown period. The highest decreasing ratio of ambient benzene in India might be associated with local serious benzene pollution during the business-as-usual period and restricted transportation after lockdown. Substantial decreases in atmospheric benzene levels had significant health benefits. The global average lifetime carcinogenic risk (LCR) and hazard index (HI) decreased from 4.89×10-7 and 5.90×10-3 to 4.51×10-7 and 5.40×10-3, respectively. China and India showed higher health benefits due to benzene pollution mitigation compared with other countries, highlighting the importance of benzene emission reduction.

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