Abstract
China is the largest CO2 emitting country on Earth. During the COVID-19 pandemic, China implemented strict government control measures on both outdoor activity and industrial production. These control measures, therefore, were expected to significantly reduce anthropogenic CO2 emissions. However, large discrepancies still exist in the estimated anthropogenic CO2 emission reduction rate caused by COVID-19 restrictions, with values ranging from 10% to 40% among different approaches. Here, we selected Nanchang city, located in eastern China, to examine the impact of COVID-19 on CO2 emissions. Continuous atmospheric CO2 and ground-level CO observations from January 1st to April 30th, 2019 to 2021 were used with the WRF-STILT atmospheric transport model and a priori emissions. And a multiplicative scaling factor and Bayesian inversion method were applied to constrain anthropogenic CO2 emissions before, during, and after the COVID-19 pandemic. We found a 37.1–40.2% emission reduction when compared to the COVID-19 pandemic in 2020 with the same period in 2019. Carbon dioxide emissions from the power industry and manufacturing industry decreased by 54.5% and 18.9% during the pandemic period. The power industry accounted for 73.9% of total CO2 reductions during COVID-19. Further, emissions in 2021 were 14.3–14.9% larger than in 2019, indicating that economic activity quickly recovered to pre-pandemic conditions.
Original language | English (US) |
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Article number | 119767 |
Journal | Environmental Pollution |
Volume | 309 |
DOIs | |
State | Published - Sep 15 2022 |
Bibliographical note
Funding Information:Cheng Hu is supported by the National Natural Science Foundation of China (grant no. 42105117 ), and the Natural Science Foundation of Jiangsu Province (grant no. BK20200802 ). Lingjun Xia is supported by the National Natural Science Foundation of China (grant no. 42105159 ). Wei Xiao is supported by the National Key R&D Program of China (grants 2020YFA0607501 & 2019YFA0607202 ).
Publisher Copyright:
© 2022 Elsevier Ltd
Keywords
- Bayesian inversion method
- City scale
- Top-down method
- WRF-STILT model