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.
Highlights
Atmospheric particulate matter especially fine particulate matter smaller than 2.5 μm (PM2.5), can reduce visibility, degrade air quality, boost health expenditures, and increase respiratory diseases and mortality (Xing et al, 2021; Huang et al, 2014; Lelieveld et al, 2015)
These results indicate that the quantitative decoupling analysis (QDA) method can provide researchers and policy makers with valuable information for understanding of key factors to heavy pollution, and help the modelers to find out the sources of uncertainties among numerical models
To investigate the variation in the contributions of emissions, meteorology and chemistry at the different stages of this 170 haze event, we divided the whole episode into four stages based on the temporal characteristics of the PM2.5 concentration in Beijing (Fig. 3): (1) the pre-contamination stage (February 17/08:00–19/14:00 LST) when the PM2.5 concentration was low and its variation was limited, representing a relatively clean period; (2) the accumulation stage (February 19/15:00–23/08:00 LST) when the PM2.5 concentration increased the most rapidly; (3) the pollution maintenance stage (February 23/09:00– 26/18:00 LST) when the PM2.5 concentration remained high with small fluctuations; and (4) the pollution removal stage 175 (February 26/19:00–27/08:00 LST) when the PM2.5 concentration rapidly dropped
Summary
Atmospheric particulate matter especially fine particulate matter smaller than 2.5 μm (PM2.5), can reduce visibility, degrade air quality, boost health expenditures, and increase respiratory diseases and mortality (Xing et al, 2021; Huang et al, 2014; Lelieveld et al, 2015). The ambient PM2.5 concentration is controlled by complex atmospheric processes, including emission, meteorology and chemical reactions (Gelencsér et al, 2007; Jia et al, 2015; Wang et al, 2015; He et al, 2016; Sun et al, 2016). IPR methods have been applied to study the formation process and mechanism of 45 ozone and particulate matter in many cities (Liu et al, 2010; Li et al, 2014; Fan et al, 2014; Huang et al, 2016; Chen et al, 2019a; Chen et al, 2019c; Fu et al, 2020). The scenario analysis approach (SAA) has been employed 50 to assess the response of PM2.5 by changing emissions under certain meteorological fields, For example the studies of Zheng et al (2015b) found that the heavy pollution occurring in winter 2013 was mainly caused by the stable weather conditions in most parts of Northeast China rather than driven by a sudden increase in anthropogenic emissions. Due to the nonrepeatability of individual pollution cases, sensitivity experiments 55 considering meteorological conditions or emission changes cannot fully reproduce the individual cases
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