Wintertime fine aerosol particles composition and its evolution in two megacities of southern and northern China.

The Science of the total environment(2024)

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摘要
Study on fine aerosols composition can help understand the particles formation and is crucial for improving the accuracy of model simulations. Based on field data measured by a Q-ACSM (Quadrupole-Aerosol Chemical Speciation Monitor), we have comprehensively compared the characteristics, evolution, and potential formation mechanisms of the components in NR-PM2.5 during wintertime at two megacities (Beijing and Guangzhou) of southern and northern China. We show that as PM pollution intensifies, the mass fraction of the primary aerosols (e.g., COA, HOA) in PM2.5 in Guangzhou increased, along with a slight decline in proportion of both the secondary organic (SOA) and inorganic (SIA) aerosols; In contrast, in Beijing, the proportion of the SIA ramped up from 28 % to 53 % with the pollution evolution; and the fraction of SOA in total OA also increased due to a substantial increment in the proportion of MO-OOA (from 29 % to 48 %), suggesting a significance of the secondary processes in worsening aerosols pollution in Beijing. Our further analysis demonstrates a leading role of aqueous pathway in the secondary formation of aerosols at the Beijing site, presenting an exponential rising of SIA and SOA with the relative humidity (RH) increase. Compared to Beijing, however, we find that the photochemical oxidation other than aqueous process in Guangzhou plays a more critical role in those secondary aerosols formation. Combined with the Hysplit trajectory model, we identify the high humid conditions in Guangzhou are typically affected by clean marine air masses, explaining the slower response of secondary components to the RH changes. Moreover, the particles in Guangzhou were observed less hygroscopic that is adverse to the aerosol aqueous chemistry. The results provide basis for the precise control of PM pollution in different regions across China and would be helpful in improving model simulations.
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