Quantitative Decoupling Analysis for Assessing the Meteorological, Emission, and Chemical Influences on Fine Particle Pollution

Abstract

A comprehensive understanding of meteorological, emission and chemical influences on severe haze is essential for air pollution mitigation. However, the nonlinearity of the atmospheric system greatly hinders this understanding. In this study, we developed the quantitative decoupling analysis (QDA) method by applying the Factor Separation (FS) method into the model processes to quantify the effects of emissions (E), meteorology (M), chemical reactions (C), and their nonlinear interactions and impact on fine particulate matter (PM_2.5) pollution. Taking a heavy-haze episode in Beijing as an example, we show that different from the integrated process rate (IPR) and the scenario analysis approach (SAA) in previous studies, the QDA method explicitly demonstrate the nonlinear effects by decomposing the variation of PM_2.5 concentration into individual contributions of E, M and C terms as well as the contributions from interactions among these processes. Results showed that M dominated the hourly fluctuation of the PM_2.5 concentration. The C terms increase with increasing the level of haze, reaching maximum (0.37 μg $·$ m⁻³ $·$ h⁻¹) at the maintenance stage. Moreover, our method reveals that there are non-negligible non-linear effects of meteorological, emission, and chemical processes during pollution stage, with the mean accounting for 50% of the increase in PM_2.5 concentrations, which is often ignored in the current air pollution control strategies. This study highlights that the QDA approach can be used to gain insight into the formation of heavy pollution, and to identify uncertainty in numerical models.