Preprints
https://doi.org/10.5194/gmd-2021-259
https://doi.org/10.5194/gmd-2021-259

Submitted as: development and technical paper 01 Sep 2021

Submitted as: development and technical paper | 01 Sep 2021

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

A quantitative decoupling analysis (QDA v1.0) method for the assessment of meteorological, emission and chemical contributions to fine particulate pollution

Junhua Wang1,3, Baozhu Ge1,3, Xueshun Chen1,3, Jie Li1,3, Keding Lu2, Yayuan Dong1,3, Lei Kong1,3, Zifa Wang1,3, and Yuanhang Zhang2 Junhua Wang et al.
  • 1State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS), Beijing 100029, China
  • 2College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
  • 3College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China

Abstract. A comprehensive understanding of the effects of meteorology, emission and chemistry on severe haze is critical in the mitigation of air pollution. However, such understanding is largely hindered by the nonlinearity of atmospheric chemistry systems. Here, we developed a novel quantitative decoupling analysis (QDA) method to quantify the effects of emission, meteorology, chemical reaction, and their nonlinear interactions on the fine particulate matter (PM2.5) pollution based on the accompanying simulations for different atmospheric processes. Via embedding the QDA method into the Weather Research and Forecasting-Nested Air Quality Prediction Modeling System (WRF-NAQPMS) model, we first employed this method into a typical heavy haze episode in Beijing. Different from the previously sensitive simulation method, which usually linked to a certain period, the QDA achieves the fully decomposing analysis of PM2.5 concentration during any pollution event into seven different parts, including meteorological contribution (M), emission contribution (E), chemical contribution (C), and interactions among these drivers (i.e., ME, MC, EC and MCE). The results show that the meteorology contribution varied significantly at different stages of episode, from 0.21 µg·m−3·h−1 during accumulation period to −11.82 µg·m−3·h−1 during the removal period, dominating the hourly changes of PM2.5 concentrations. The chemical contributions were shown to increase with the level of haze, which become largest (0.37 µg·m−3·h−1) at the maintenance period, 25 % higher than that during the clean period. The contribution of primary emission is relatively stable in all stages due to the use of fixed emission during the simulation. Besides, the QDA method highlights that there exist nonnegligible coupling effects of meteorology, emission and chemistry on PM2.5 concentrations (−1.83 to 2.44 µg·m−3·h−1), which were commonly ignored in previous studies and the development of heavy-pollution control strategies. These results indicate that the QDA method can not only provide researchers and policy makers with valuable information for understanding of key factors to heavy pollution, but also help the modelers to find out the sources of uncertainties among numerical models.

Junhua Wang et al.

Status: open (until 27 Oct 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-259', Anonymous Referee #1, 22 Sep 2021 reply
  • RC2: 'Comment on gmd-2021-259', Anonymous Referee #2, 28 Sep 2021 reply

Junhua Wang et al.

Junhua Wang et al.

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Short summary
This paper developed a novel quantitative decoupling analysis (QDA) method to quantify the contributions of emission, meteorology, chemical reaction, and their nonlinear interactions on PM2.5 and applied it to a pollution episode in Beijing. This method can provides the researchers and policy makers with valuable information for understanding of key factors to heavy pollution, but also help the modelers to find out the sources of uncertainties among numerical models.