Preprints
https://doi.org/10.5194/gmd-2023-22
https://doi.org/10.5194/gmd-2023-22
Submitted as: development and technical paper
 | 
02 May 2023
Submitted as: development and technical paper |  | 02 May 2023
Status: this preprint is currently under review for the journal GMD.

A quantitative decoupling analysis (QDA v1.0) method for assessing the contributions of meteorology, emissions, and chemistry to fine particulate pollution

Junhua Wang, Baozhu Ge, Xueshun Chen, Jie Li, Keding Lu, Yayuan Dong, Lei Kong, Zifa Wang, and Yuanhang Zhang

Abstract. A comprehensive understanding of the effects of meteorology, emissions, and chemistry on severe haze is critical in the mitigation of air pollution. However, such an understanding is greatly hindered by the nonlinearity of atmospheric systems. In this study, we developed the quantitative decoupling analysis (QDA) method to quantify the effects of emissions, meteorology, chemical reactions, and their nonlinear interactions on fine particulate matter (PM2.5) pollution by running built-in scenario simulations in each model step. Different from previous methods, the QDA method achieves a fully decomposed analysis of hourly changes in the PM2.5 concentration during pollution events into seven parts, including the pure meteorological contribution (M), the pure emissions contribution (E), the pure chemistry contribution (C), and the interactions among these processes (i.e., ME, MC, EC, and MCE). Via embedding the QDA method into the Weather Research and Forecasting–Nested Air Quality Prediction Modeling System, we employed this method and combined it with the Integrated Process Rate method to study a typical heavy haze episode in Beijing. We evaluate the model performance against in situ meteorological and air quality observations and describe the QDA analytical factors of this case. Results showed that M varied most significantly at different stages of the episode, from 0.21 µg⋅m−3⋅h−1 during the accumulation stage to −11.82 µg⋅m−3⋅h−1 during the removal stage, indicating that the pure meteorological contribution dominated the hourly fluctuation amplitude of the PM2.5 concentration. M acted as the most important cleaner for PM2.5 in non-polluting periods but stopped being effective at this and instead became a contributor in the accumulation stage such that PM2.5 tended to grow rapidly under the superimposed influence of emissions and chemical processes, which would probably mark the beginning of a heavy pollution event. The contribution of E ranged from 0.63 to 0.88 µg⋅m−3⋅h−1 owing to the diurnal variation of emissions. The pure chemical contribution was shown to increase with the level of haze, becoming the largest (0.37 µg⋅m−3⋅h−1) in the maintenance period, which was 25 % higher than during the pre-contamination period. And C+CE made a significant contribution in the accumulation and maintenance stages, indicating that chemical reactions are more important in the polluted period than in other periods. Nonnegligible nonlinear effects exist among the processes of meteorology, emissions, and chemistry on PM2.5 concentrations (−1.83 to 2.44 µg⋅m−3⋅h−1) – something that has generally been ignored in previous studies and during the development of heavy-pollution control strategies. The nonlinear effects are helpful in eliminating the interference of other processes and obtaining a more purified result of the target process and have important indicative significances. The ratio of CE to C is positively correlated with the chemical speed. For precursors like NH3, the smaller value of CE in the most polluted period indicated that NH3 was more deficient, and thus reducing emissions of it in that period would have had the most efficient controlling effect on the PM2.5. This study highlights that the QDA method can be used to realize an in-depth understanding of the effects of adverse meteorological conditions in haze and to judge whether the precursors are excessive or not. Not only can the QDA method provide researchers and policymakers with valuable information for understanding the key factors behind heavy pollution, but it can also help modelers to identify the sources of uncertainties in numerical models.

Junhua Wang et al.

Status: open (until 12 Jul 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-22', Anonymous Referee #1, 02 Jun 2023 reply

Junhua Wang et al.

Junhua Wang et al.

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Short summary
We developed a quantitative decoupling analysis (QDA) method to quantify the contributions of emissions, meteorology, chemical reactions, and their nonlinear interactions on PM2.5. We found the effects of adverse meteorological conditions and the importance of nonlinear interactions. This method can provide valuable information for understanding of key factors to heavy pollution, but also help the modelers to find out the sources of uncertainties in numerical models.