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Meteorological influence on persistent O3 pollution events in Wuxi in the Yangtze River Delta, China
Lijuan Shen1, Yiwei Diao1, Tianliang Zhao2
1School of Atmosphere and Remote Sensing, Wuxi University, Wuxi 214105, China.
Abstract:
The number of O3 pollution days indicates an overall increasing trend over 2014-2021 in Wuxi in the Yangtze River Delta, with the pollution concentrations of MDA8-O3 between 186 and 200 μg·m-3. Specifically, a total of 62 POPEs (persistent O3 pollution events), defined as episodes with 3 or more continuous O3 pollution days, were observed for the 8 years. Using a multi-linear regression model, we find that the meteorology can explain approximately 56.5 % of the O3 variations for the 8 years in Wuxi, with temperature being the most crucial meteorological factor, followed by relative humidity (RH) and wind speeds. High temperature, low RH, low wind speeds and downward airflows significantly correlate with POPE-O3 changes. Three types of synoptic circulations are further identified during the POPEs from 2014 to 2021 by the T-mode (T-PCA) classification method. The primary circulation patterns governing the interannual changes of POPEs are characterized by the largest positive anomalies of temperature and planetary boundary layer (PBL) height; moreover, a distinct vertical mixing process is observed with uplifting airflows in the convective PBL during the afternoon and sinking airflows in the stable PBL at night, which is incredibly conducive to the downward transport of O3 after its upward delivery during daytime and substantially contributes to midnight O3 at the surface. The other two circulation types are associated with uniform descending flows in the PBL; as a result, surface O3 accumulates only near the ground and decreases significantly at night due to the titration effect. This study systematically highlights the influence of critical meteorological factors regulated by different synoptic circulations on the POPE in Wuxi, which provides a scientific basis for pollution control and prediction.
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