Composition, mixing state, and size distribution of single submicron particles during pollution episodes in a coastal city in southeast China
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Size-resolved particle composition, size distribution, and mixing state were characterized at the single-particle level during two air pollution episodes during 12–25 January, 2016 in a coastal city in southeast China. The two pollution episodes occurred under distinct meteorological conditions (i.e., different wind speeds, relative humidity, and backward trajectories); thus, they were assigned to stagnation and transport episodes, respectively. Single-particle data, obtained from single-particle aerosol mass spectrometry (SPAMS), showed that carbonaceous particles were the predominant particles during the whole study period, accounting for more than 60% of the total particles. However, the number fractions of carbonaceous particles and nitrate-containing particles significantly increased in the stagnation episode, while the number fractions of sulfate- and ammonium-containing particles both increased in the transport episode compared to the levels over the whole study period. The potassium-rich (K-rich) particle class was more abundant and more strongly mixed with sulfate in the transport episode, which indicates the impact of biomass burning emissions and the subsequent aging process by acquiring sulfate during transport. The particle classes (e.g., carbonaceous and K-rich classes) had a broader size distribution during the pollution episodes than during the clean episode. The diameters of the size distribution peak for all particle classes (except for dust class) were observed to be larger in the transport episode than in the stagnation episode. This suggests that the particles underwent an extensive aging process through the addition of sulfate and ammonium during transport, leading to the growth of particles.
KeywordsSingle particle Mixing state Stagnation episode Transport episode Secondary inorganic aerosols
The authors gratefully acknowledge Mr. Chunjin Zhuo from Hexin Analytical Instrument Company for providing the SPAMS equipment, and Ms. Dandan Zhong for her assistance in using the data processing software.
This work was supported by the National Natural Science Foundation of China (NO. U1405235; 21507127; 41575146 and 201607148), and the National Key Research and Development Program (NO. 2016YFC0200501 and 2016YFE0112200), and the Chinese Academy of Sciences Interdisciplinary Innovation Team Project (2016).
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