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Discussion papers
https://doi.org/10.5194/acp-2019-279
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2019-279
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 08 May 2019

Research article | 08 May 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Fine particulate matter (PM2.5) trends in China, 2013–2018: contributions from meteorology

Shixian Zhai1,2, Daniel J. Jacob2, Xuan Wang2, Lu Shen2, Ke Li2, Yuzhong Zhang2, Ke Gui3, Tianliang Zhao1, and Hong Liao4 Shixian Zhai et al.
  • 1Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • 2John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA
  • 3Key Laboratory for Atmospheric Chemistry, Chinese Academy of Meteorological Sciences, CMA, Beijing 100081, China
  • 4Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China

Abstract. Fine particulate matter (PM2.5) is a severe air pollution problem in China. Observations of PM2.5 have been available since 2013 from a large network operated by the China National Environmental Monitoring Center (CNEMC). The data show a general 30–50 % decrease of annual mean PM2.5 across China over the 2013–2018 period, averaging 5.2 μg m−3 a−1. Trends in the five megacity cluster regions targeted by the government for air quality control are 9.3 ± 1.8 μg m−3 a−1 (±95 % confidence interval) for Beijing-Tianjin-Hebei, 6.1 ± 1.1 μg m−3 a−1 for Yangtze River Delta, 2.7 ± 0.8 μg m−3 a−1 for Pearl River Delta, 6.7 ± 1.3 μg m−3 a−1 for Sichuan Basin, and 6.5 ± 2.5 μg m−3 a−1 for Fenwei Plain (Xi'an). Concurrent 2013–2018 observations of sulfur dioxide (SO2) and CO show that the declines in PM2.5 are qualitatively consistent with drastic controls of emissions from coal combustion. However, there is also a large meteorologically driven interannual variability of PM2.5 that complicates trend attribution. We used a stepwise multiple linear regression (MLR) model to quantify this meteorological contribution to the PM2.5 trends across China. The MLR model correlates the 10-day PM2.5 anomalies to wind speed, precipitation, relative humidity, temperature, and 850 hPa meridional wind velocity (V850). We find that meteorology made a minor but significant contribution to the observed 2013–2018 PM2.5 trends across China and that removing this influence reduces the uncertainty on the emission-driven trends. The mean PM2.5 decrease across China is 4.6 μg m−3 a−1 in the meteorology-corrected data, 12 % weaker than in the original data. The residual trends in the five megacity clusters attributable to changes in anthropogenic emission are 8.0 ± 1.1 μg m−3 a−1 for Beijing-Tianjin-Hebei (14 % weaker than the observed trend), 6.3 ± 0.9 μg m−3 a−1 for Yangtze River Delta (3 % stronger), 2.2 ± 0.5 μg m−3 a−1 for Pearl River Delta (19 % weaker), 4.9 ± 0.9 μg m−3 a−1 for Sichuan Basin (27 % weaker), and 4.9 ± 1.9 μg m−3 a−1 for Fenwei Plain (Xi'an; 25 % weaker). 2015–2017 observations of flattening PM2.5 in the Pearl River Delta, and increase in the Fenwei Plain, can be attributed to meteorology rather than to relaxation of emission controls.

Shixian Zhai et al.
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Short summary
Observed annual mean PM2.5 decreased by 30–50 % in China from 2013–2018. Observed SO2 and CO show that PM2.5 declines are consistent with emissions controls. However, meteorologically PM2.5 variability complicates trend attribution. We used a stepwise multiple linear regression model to quantify meteorological impacts. We find that meteorology made a minor but significant contribution to the PM2.5 trends and that removing this influence reduces PM2.5 trend uncertainty.
Observed annual mean PM2.5 decreased by 30–50 % in China from 2013–2018. Observed SO2 and CO...
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