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Ozone in China: Spatial Distribution and Leading Meteorological Factors Controlling O3 in 16 Chinese Cities

Category: Urban Air Quality

Accepted Manuscripts
DOI: 10.4209/aaqr.2017.10.0368
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Xi Gong1,2, Song Hong 1,3, Daniel A. Jaffe 2,4

  • 1 School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
  • 2 School of Science, Technology, Engineering and Mathematics, University of Washington Bothell, Bothell, WA 98011, USA
  • 3 Shenzhen Research Institute, Wuhan University, Shenzhen 518000, China
  • 4 Department of Atmospheric Sciences, University of Washington Seattle, Seattle, WA 98195, USA

Highlights

We summarized 3 seasonal patterns of O3 for 16 cities including 7 megacities.
GAM is a useful tool to examine daily O3 with meteorological factors (R2 = 0.43–0.9).
Top 3 leading factors were identified in 16 cities using F statistic value of GAM.
Wind directions affect O3 for 3 coastal cities and Beijing categorized by HYSPLIT.
O3 in Beijing “Parade Blue” period is mainly controlled by meteorological factors.


Abstract

Tropospheric ozone (O3) is the one of the major air pollutants in China. The concentration of O3 is controlled by a series of complex chemical reactions but also different meteorological combinations. In this study, we examined the spatial-temporal distribution of O3 in 16 Chinese cities including 7 mega cities. Summer is the highest season in most polluted cities. Most of the meteorological factors have a non-linear relationship with O3. To better understand the meteorological effects on O3, we developed a statistical model (Generalized Additive Model or GAM) to estimate the maximum daily 8 h (MDA8) O3 as a function of different meteorological factors for 16 cities in China. The GAM can capture between 43–90% of the variations in daily MDA8. We also used the F statistic value from the model to identify the top 3 leading meteorological factors in each city and explore the effect of the individual meteorological factors on O3. We compared the influence of transport using HYSPLIT back trajectory data and found that transport direction is an important predictor for O3 in Beijing and other coastal cities. Finally, we used the Beijing “Parade Blue” in the summer of 2015, when primary emission were reduced, as a case study to look into the relationship between emission and meteorological factors on concentrations of NO2 and O3. While NO2 was reduced by 44.6%, MDA8 O3 was reduced by 15.7% and most of this change could be explained by meteorological variations.

Keywords

Ozone Spatial-temporal distribution Meteorology Generalized Additive Model China


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