Wenxin Wang1, Xiaohua Wang2, Xue Li1, Chao Chen1, Yu Wang1, Zhiwen Xing1, Kun Li1, Min Wei1,3, Xiao Sui This email address is being protected from spambots. You need JavaScript enabled to view it.1,3, Houfeng Liu This email address is being protected from spambots. You need JavaScript enabled to view it.1,3 

1 College of Geography and Environment, Shandong Normal University, Jinan 250014, China
2 Rizhao Polytechnic, Rizhao 276826, China
3 Center for Environmental Technology and Policy Research, Shandong Normal University, Ji’nan 250014, China

Received: June 1, 2023
Revised: October 21, 2023
Accepted: November 30, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.230127  

Cite this article:

Wang, W., Wang, X., Li, X., Chen, C., Wang, Y., Xing, Z., Li, K., Wei, M., Sui, X., Liu, H. (2024). Seasonal Particle Size Distribution and its Influencing Factors in a Typical Polluted City in North China. Aerosol Air Qual. Res. https://doi.org/10.4209/aaqr.230127


  • Generalized assistive model (GAM) was employed to investigate particle size distribution.
  • PM2.5 was positively correlated with CO and RH, while negatively correlated with wind PM>10 and O3.
  • PM2.5 and PM2.5-10 were mainly transported from the SE and NE regions in winter and spring.


In this study, the particle size distribution and its influencing factors were conducted using a multi-channel particle size sensor coupled with Pearson and generalized additive model (GAM) during winter 2021 to autumn 2022 in Jinan, North China. The results revealed that heavy pollution episodes were mainly caused by fine particles (PM<1 and PM1-2.5) in winter and coarse particles (PM2.5-10 and PM>10) in spring. Pearson and generalized additive model (GAM) analysis indicated PM2.5 was positively correlated with relative humidity (RH), CO, NO2, SO2 and PM2.5-10 concentrations, negatively correlated with wind speed, O3 and coarse particles (PM>10) concentrations. Moreover, there was also a strong correlation between PM2.5 concentration and meteorological-air pollutant factors interactions. PM2.5-10 was found to be positively correlated with gaseous pollutants such as NO2, SO2, and CO, as well as RH and air pressure. Besides, PM>10 was positively associated with CO, SO2, and RH, but negatively correlated with NO2 and wind speed. The particle size distribution was also effected by regional transport, particular in winter and spring. In detail, PM2.5 and PM2.5-10 were mainly transported from the east and north, PM>10 mainly from the north and southwest in winter. In spring, particle matters were mainly transported from the northeast and southeast, and PM2.5 was more influenced by northeast short-range transport. Local particulate generation was mainly raised by mobile sources from vehicles and industries such as oil refineries, chemical plant and steel plants. Therefore, the emission controls on VOCs, NO2, SO2 and regional joint pollution prevention are preferred to reduce urban air pollution in future.

Keywords: Air pollution, Size distribution, Generalized additive model, Long-range transport

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