학술논문

Recent ozone trends in the Chinese free troposphere: role of the local emission reductions and meteorology
Document Type
article
Source
Atmospheric Chemistry and Physics, Vol 21, Pp 16001-16025 (2021)
Subject
Physics
QC1-999
Chemistry
QD1-999
Language
English
ISSN
6001-2021
1680-7316
1680-7324
Abstract
Free tropospheric ozone (O3) trends in the Central East China (CEC) and export regions are investigated for 2008–2017 using the IASI (Infrared Atmospheric Sounding Interferometer) O3 observations and the LMDZ-OR-INCA model simulations, including the most recent Chinese emission inventory. The observed and modelled trends in the CEC region are −0.07 ± 0.02 and −0.08 ± 0.02 DU yr−1, respectively, for the lower free troposphere (3–6 km column) and −0.05 ± 0.02 and −0.06 ± 0.02 DU yr−1, 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 the Korean Peninsula 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 in NOx anthropogenic emissions that has occurred since 2013 in China led 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 the In-service Aircraft for a Global Observing System (IAGOS) or other satellite measurements (Ozone Monitoring Instrument (OMI)/Microwave Limb Sounder (MLS)). These comparisons do not confirm the O3 decrease and stress the difficulty in analysing short-term trends using multiple data sets with various sampling and the risk of overinterpreting the results.