Preprints
https://doi.org/10.5194/acp-2021-28
https://doi.org/10.5194/acp-2021-28

  10 Mar 2021

10 Mar 2021

Review status: this preprint is currently under review for the journal ACP.

Separating emission and meteorological contribution to PM2.5 trends over East China during 2000–2018

Qingyang Xiao1,, Yixuan Zheng2,, Guannan Geng1,3, Cuihong Chen4,5, Xiaomeng Huang4, Huizheng Che6, Xiaoye Zhang6, Kebin He1,3, and Qiang Zhang4 Qingyang Xiao et al.
  • 1State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
  • 2Atmospheric Environment Institute, Chinese Academy of Environmental Planning, Beijing 100012, China
  • 3State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
  • 4Ministry of Education Key Laboratory for Earth System Modelling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
  • 5Satellite Environment Center, Ministry of Ecology and Environment of the People’s Republic of China, Beijing 100094, China
  • 6State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
  • These authors contributed equally to this work.

Abstract. The contribution of meteorology and emissions to long-term PM2.5 trends is critical for air quality management but has not yet been fully analyzed. Here, we used a combination of machine learning model, statistical model and chemical transport model to quantify the meteorological impacts on PM2.5 pollution during 2000–2018. Specifically, we first developed a two-stage machine learning PM2.5 prediction model with a synthetic minority oversampling technique to improve the satellite-based PM2.5 estimates over highly polluted days, thus allowing us to better characterize the meteorological effects on haze events. Then we used two methods, a generalized additive model (GAM) driven by the satellite-based full-coverage daily PM2.5 retrievals as well as the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modelling system, to examine the meteorological contribution to PM2.5. We found good agreements between the GAM model estimations and the CMAQ model estimations of meteorological contribution to PM2.5 on monthly scale (correlation coefficient between 0.53–0.72). Both methods revealed the dominant role of emission changes in the long-term trend of PM2.5 concentration in China during 2000–2018, with notable influence from the meteorological condition. The interannual trends in meteorology-associate PM2.5 were dominated by the fall and winter meteorological conditions, when regional stagnant and stable conditions were more likely to happen and haze events frequently occurred. From 2000 to 2018, the meteorological contribution became more unfavorable to PM2.5 pollution control across the North China Plain and central China, but were more beneficial to pollution control across the southern part, e.g., the Yangtze River Delta. The meteorology-adjusted PM2.5 over East China peaked at 2006 and 2011, mainly driven by the emission peaks in PM2.5 and PM2.5 precursors in these years. Although emissions dominated the long-term PM2.5 trends, the meteorology-driven anomalies also contributed −3.9 % to 2.8 % of the annual mean PM2.5 concentrations in East China estimated from the GAM model. The meteorological contributions were even higher regionally, e.g., −6.3 % to 4.9 % of the annual mean PM2.5 concentrations in the Beijing-Tianjin-Hebei region, −5.1 % to 4.3 % in the Fen-wei Plain, −4.8 % to 4.3 % in the Yangtze River Delta and −25.6 % to 12.3 % in the Pearl River Delta. Considering the remarkable meteorological effects on PM2.5 and the worsening meteorological conditions in the northern part of China where air pollution was severe and population was clustered, stricter clean air actions are needed to avoid haze events in the future.

Qingyang Xiao et al.

Status: open (until 05 May 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-28', Anonymous Referee #2, 01 Apr 2021 reply
  • RC2: 'Comment on acp-2021-28', Anonymous Referee #1, 04 Apr 2021 reply
  • RC3: 'Comment on acp-2021-28', Anonymous Referee #3, 05 Apr 2021 reply

Qingyang Xiao et al.

Qingyang Xiao et al.

Viewed

Total article views: 577 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
412 160 5 577 0 9
  • HTML: 412
  • PDF: 160
  • XML: 5
  • Total: 577
  • BibTeX: 0
  • EndNote: 9
Views and downloads (calculated since 10 Mar 2021)
Cumulative views and downloads (calculated since 10 Mar 2021)

Viewed (geographical distribution)

Total article views: 574 (including HTML, PDF, and XML) Thereof 574 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 15 Apr 2021
Download
Short summary
We used both statistical methods and the chemical transport model to assess the contribution of meteorology and emissions to PM2.5 during 2000–2018. Both methods revealed that emissions dominated the long-term PM2.5 trend with notable meteorological effects ranged up to 37.9 % of regional annual average PM2.5. The meteorological contribution became more beneficial to PM2.5 control in south China but more unfavorable in north China, thus strict clean air actions are needed to avoid haze events.
Altmetrics