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Recent ozone trends in the Chinese free troposphere: role of the local emission reductions and meteorology.

Authors :
Dufour, Gaëlle
Hauglustaine, Didier
Zhang, Yunjiang
Eremenko, Maxim
Cohen, Yann
Gaudel, Audrey
Siour, Guillaume
Lachatre, Mathieu
Bense, Axel
Bessagnet, Bertrand
Cuesta, Juan
Ziemke, Jerry
Thouret, Valérie
Zheng, Bo
Source :
Atmospheric Chemistry & Physics Discussions; 7/5/2021, p1-35, 35p
Publication Year :
2021

Abstract

Free tropospheric ozone (O<subscript>3</subscript>) trends in the Central East China (CEC) and export regions are investigated for 2008-2017 using the IASI O<subscript>3</subscript> observations and the LMDZ-OR-INCA model simulations, including the most recent Chinese emission inventory. The observed and modeled trends in the CEC region are -0.07 ± 0.02 DU/yr and -0.08 ± 0.02 DU/yr respectively for the lower free troposphere (3-6 km column), and -0.05 ± 0.02 DU/yr and -0.06 ± 0.02 DU/yr respectively for the upper free troposphere (6-9 km column). The statistical p-value is smaller to 0.01 for all the derived trends. A good agreement between the observations and the model is also observed in the region including Korea and Japan and corresponding to the region of pollution export from China. Based on sensitivity studies conducted with the model, we evaluate at 60 % and 52 % the contribution of the Chinese anthropogenic emissions to the trend in the lower and upper free troposphere, respectively. The second main contribution to the trend is the meteorological variability (34 % and 50 % respectively). These results suggest that the reduction of NO<subscript>x</subscript> anthropogenic emissions that occurred since 2013 in China lead to a decrease in ozone in the Chinese free troposphere, contrary to the increase in ozone at the surface. We designed some tests to compare the trends derived by the IASI observations and the model to independent measurements such as IAGOS or other satellite measurements (OMI/MLS). These comparisons do not confirm the O<subscript>3</subscript> decrease and stress the difficulty to analyze short-term trends using multiple datasets with various sampling and the risk to overinterpret the results. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
16807367
Database :
Complementary Index
Journal :
Atmospheric Chemistry & Physics Discussions
Publication Type :
Academic Journal
Accession number :
151260054
Full Text :
https://doi.org/10.5194/acp-2021-476