Synergistic effects of synoptic weather patterns and topography on air quality: a case of the Sichuan Basin of China

  • Guicai Ning
  • Steve Hung Lam YimEmail author
  • Shigong WangEmail author
  • Bolong Duan
  • Canqi Nie
  • Xu Yang
  • Jinyan Wang
  • Kezheng Shang


Heavy air pollution is strongly influenced by weather conditions and is thus sensitive to climate change. Especially, for the areas with complex topography such as the Sichuan Basin (SB), one of the most polluted areas of China, the synergistic effects of synoptic weather patterns and topography on air quality are unclear and warrant investigation. This study examined the typical synoptic patterns of SB in winter days of 2013–2017 and revealed their synergistic effects with topography on air quality. Three categories of synoptic patterns including dry low-trough, high-pressure, and wet low-vortex patterns accompanying heavy, medium, and slight air pollution, respectively, were identified. In particular, the dry low-trough patterns occur most frequently, accounting for around 62% of the total days. In the case of this pattern, westerly wind prevails over the SB and the aloft atmosphere is warmer than the Tibetan Plateau (TP) at the same height, which induces the cold air over TP moving eastward to the SB. Under the synergistic effects of the cold air eastward movement and TP, a strong descending motion (known as foehn) is observed on the leeward slope of the towering TP. This foehn warming causes a stable layer above the planetary boundary layer (PBL), which suppresses secondary circulation and PBL. These features restrict atmospheric pollutant dispersion, resulting in poor air quality. In contrast, for the high-pressure and wet low-vortex patterns, cold air masses from the north invade southward and cover the northwest SB. This invasion remarkably decreases the atmospheric stability of the lower troposphere, deepens the PBL, and enhances the height of secondary circulation, thereby facilitating air pollutant dispersion. Moreover, the wet low-vortex pattern is accompanied by frequent precipitation events (with 80% rainy days), further bringing down air pollution levels. These findings provide an insight for improving air pollution forecast in the complex terrain areas under global warming.


Synoptic weather patterns Complex topography Synergistic effects Air pollution Sichuan Basin Foehn warming 



This work was supported by the National Natural Science Foundation of China (91644226), the Vice-Chancellor’s Discretionary Fund of The Chinese University of Hong Kong (Grant No. 4930744), the National Key Research Project of China-Strategy on Black Carbon Reduction and Evaluation of the Health Effects of Climate Change (2016YFA0602004), and the Fundamental Research Funds for the Central Universities (lzujbky-2017-68). We would like to thank the following departments for the provided data, the Ministry of Ecology and Environment of the People’s Republic of China, the China Meteorological Administration, the University of Wyoming, and the European Centre for Medium-Range Weather Forecasts.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Guicai Ning
    • 1
    • 2
  • Steve Hung Lam Yim
    • 1
    • 4
    • 5
    Email author
  • Shigong Wang
    • 2
    • 3
    Email author
  • Bolong Duan
    • 6
  • Canqi Nie
    • 7
  • Xu Yang
    • 8
  • Jinyan Wang
    • 2
  • Kezheng Shang
    • 2
  1. 1.Institute of Environment, Energy and SustainabilityThe Chinese University of Hong KongShatinChina
  2. 2.The Gansu Key Laboratory of Arid Climate Change and Reducing Disaster, College of Atmospheric SciencesLanzhou UniversityLanzhouChina
  3. 3.Sichuan Key Laboratory for Plateau Atmosphere and Environment, School of Atmospheric SciencesChengdu University of Information TechnologyChengduChina
  4. 4.Department of Geography and Resource ManagementThe Chinese University of Hong KongShatinChina
  5. 5.Stanley Ho Big Data Decision Analytics Research CentreThe Chinese University of Hong KongShatinChina
  6. 6.Lanzhou Central Meteorology ObservatoryLanzhouChina
  7. 7.Kaizhou Meteorological BureauChongqingChina
  8. 8.Tianjin Environmental Meteorological CenterTianjinChina

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