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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 9 | Copyright
Atmos. Chem. Phys., 18, 6733-6748, 2018
https://doi.org/10.5194/acp-18-6733-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 14 May 2018

Research article | 14 May 2018

Synoptic meteorological modes of variability for fine particulate matter (PM2.5) air quality in major metropolitan regions of China

Danny M. Leung1, Amos P. K. Tai1,2, Loretta J. Mickley3,4, Jonathan M. Moch4, Aaron van Donkelaar5, Lu Shen3, and Randall V. Martin5,6 Danny M. Leung et al.
  • 1Earth System Science Programme and Graduate Division of Earth and Atmospheric Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
  • 2Institute of Environment, Energy and Sustainability, and Partner State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China
  • 3School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
  • 4Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts, USA
  • 5Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Canada
  • 6Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts, USA

Abstract. In his study, we use a combination of multivariate statistical methods to understand the relationships of PM2.5 with local meteorology and synoptic weather patterns in different regions of China across various timescales. Using June 2014 to May 2017 daily total PM2.5 observations from  ∼ 1500 monitors, all deseasonalized and detrended to focus on synoptic-scale variations, we find strong correlations of daily PM2.5 with all selected meteorological variables (e.g., positive correlation with temperature but negative correlation with sea-level pressure throughout China; positive and negative correlation with relative humidity in northern and southern China, respectively). The spatial patterns suggest that the apparent correlations with individual meteorological variables may arise from common association with synoptic systems. Based on a principal component analysis of 1998–2017 meteorological data to diagnose distinct meteorological modes that dominate synoptic weather in four major regions of China, we find strong correlations of PM2.5 with several synoptic modes that explain 10 to 40% of daily PM2.5 variability. These modes include monsoonal flows and cold frontal passages in northern and central China associated with the Siberian High, onshore flows in eastern China, and frontal rainstorms in southern China. Using the Beijing–Tianjin–Hebei (BTH) region as a case study, we further find strong interannual correlations of regionally averaged satellite-derived annual mean PM2.5 with annual mean relative humidity (RH; positive) and springtime fluctuation frequency of the Siberian High (negative). We apply the resulting PM2.5-to-climate sensitivities to the Intergovernmental Panel on Climate Change (IPCC) Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projections to predict future PM2.5 by the 2050s due to climate change, and find a modest decrease of  ∼ 0.5µgm−3 in annual mean PM2.5 in the BTH region due to more frequent cold frontal ventilation under the RCP8.5 future, representing a small climate benefit, but the RH-induced PM2.5 change is inconclusive due to the large inter-model differences in RH projections.

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This paper investigates how large-scale weather systems control fine particulate matter (PM2.5) air quality in China. We show that winter monsoons, onshore winds and frontal rains can drive daily PM2.5 variability in different regions of China. We further project future PM2.5 concentration change by 2050s due to climate change, and verify that climate change has little benefit on future PM2.5 in Beijing, implying cutting down emissions is necessary to mitigate pollutions in megacities of China.
This paper investigates how large-scale weather systems control fine particulate matter (PM2.5)...
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