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Detection of fossil-fuel CO(2) plummet in China due to COVID-19 by observation at Hateruma

The COVID-19 pandemic caused drastic reductions in carbon dioxide (CO(2)) emissions, but due to its large atmospheric reservoir and long lifetime, no detectable signal has been observed in the atmospheric CO(2) growth rate. Using the variabilities in CO(2) (ΔCO(2)) and methane (ΔCH(4)) observed at H...

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Detalles Bibliográficos
Autores principales: Tohjima, Yasunori, Patra, Prabir K., Niwa, Yosuke, Mukai, Hitoshi, Sasakawa, Motoki, Machida, Toshinobu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7596474/
https://www.ncbi.nlm.nih.gov/pubmed/33122844
http://dx.doi.org/10.1038/s41598-020-75763-6
Descripción
Sumario:The COVID-19 pandemic caused drastic reductions in carbon dioxide (CO(2)) emissions, but due to its large atmospheric reservoir and long lifetime, no detectable signal has been observed in the atmospheric CO(2) growth rate. Using the variabilities in CO(2) (ΔCO(2)) and methane (ΔCH(4)) observed at Hateruma Island, Japan during 1997–2020, we show a traceable CO(2) emission reduction in China during February–March 2020. The monitoring station at Hateruma Island observes the outflow of Chinese emissions during winter and spring. A systematic increase in the ΔCO(2)/ΔCH(4) ratio, governed by synoptic wind variability, well corroborated the increase in China’s fossil-fuel CO(2) (FFCO(2)) emissions during 1997–2019. However, the ΔCO(2)/ΔCH(4) ratios showed significant decreases of 29 ± 11 and 16 ± 11 mol mol(−1) in February and March 2020, respectively, relative to the 2011–2019 average of 131 ± 11 mol mol(−1). By projecting these observed ΔCO(2)/ΔCH(4) ratios on transport model simulations, we estimated reductions of 32 ± 12% and 19 ± 15% in the FFCO(2) emissions in China for February and March 2020, respectively, compared to the expected emissions. Our data are consistent with the abrupt decrease in the economic activity in February, a slight recovery in March, and return to normal in April, which was calculated based on the COVID-19 lockdowns and mobility restriction datasets.