Shuiyuan Cheng 1, Fang Wang1,2, Jianbing Li 3, Dongsheng Chen1, Mingjun Li1, Ying Zhou1, Zhenhai Ren4

  • 1 College of Environmental & Energy Engineering, Beijing University of Technology, Beijing 100124, China
  • 2 Institute of Environmental Engineering, Beijing General Research Institute of Mining & Metallurgy, Beijing 100070, China
  • 3 Environmental Engineering Program, University of Northern British Columbia, Prince George, British Columbia V2N 4Z9, Canada
  • 4 Chinese Research Academy of Environmental Sciences, Beijing 100012, China

Received: June 20, 2012
Revised: August 31, 2012
Accepted: August 31, 2012
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Cite this article:
Cheng, S., Wang, F., Li, J., Chen, D., Li, M., Zhou, Y. and Ren, Z. (2013). Application of Trajectory Clustering and Source Apportionment Methods for Investigating Trans-Boundary Atmospheric PM10 Pollution. Aerosol Air Qual. Res. 13: 333-342.



A modeling framework was proposed to investigate the impact of trans-boundary air pollutant transport on regional air quality. This was based on a combination of the HYSPLIT trajectory model, the CAMx air quality model, and the MM5 meteorological model. The examination of atmospheric PM10 pollution in Guangzhou within the Pearl River Delta (PRD) region of southern China was used as a case study. The HYSPLIT and MM5 models were used to qualitatively identify the dominant PM10 pollutant transport pathways that led to PM10 pollution events in Guangzhou, with five clusters of air mass trajectories being examined. The emission source contribution through each transport pathway to Guangzhou’s PM10 concentration was then quantified using a MM5-CAMx modeling system. The results illustrated that the trans-boundary PM10 transport played a critical role in the formation of PM10 pollution events in Guangzhou, with a mean contribution ratio of nearly 49%. In particular, two air mass trajectory clusters that originated from Guangzhou’s surrounding regions were found to be the main pollutant transport pathways, and three surrounding cities (Foshan, Dongguan and Huizhou) had a total emission contribution of nearly 30% to Guangzhou’s PM10 concentration through these two pathways. The emissions from these three cities also accounted for 70 to 94% of the total trans-boundary contributions from Guangzhou’s nine surrounding cities through the five transport pathways. As a result, in order to improve Guangzhou’s air quality, coordinated effort is required to reduce emissions in both Guangzhou itself and its three surrounding cities. It is expected that the presented modeling approach can be applied to air quality studies in many other regions.

Keywords: Air pollution; Cluster analysis; CAMx/PSAT model; Emission source contribution; HYSPLIT model

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