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author:

Luo, Ming (Luo, Ming.) [1] | Chen, Chongcheng (Chen, Chongcheng.) [2]

Indexed by:

CPCI-S

Abstract:

This study investigates the potential sources and transport pathways of PM2.5 in Shanghai, China from January 2013 to December 2014 by using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) modeling, K-means clustering, and concentration-weighted trajectory (CWT) analysis methods. Six transportation pathways that contribute to PM2.5 in Shanghai were identified by trajectory clustering. The primary potential pathway originated from the nearby northwestern regions, including Hebei, Henan, Shandong, Jiangsu, and Anhui, which mainly affect PM2.5 in winter and spring. The second primary potential pathway originated from northern and north-western regions, such as Mongolia. The lowest PM2.5 concentrations were found in the southern pathway with clean marine air masses from the South China Sea and Pacific Ocean in summer. The CWT method also identified the primary potential sources on the basis of the trajectory clustering results.

Keyword:

Air pollution Backward trajectory Concentration-weighted trajectory analysis HYSPLIT K-means clustering PM2.5 Potential source

Community:

  • [ 1 ] [Luo, Ming]Chinese Univ Hong Kong, Inst Environm Energy & Sustainabil, Hong Kong, Hong Kong, Peoples R China
  • [ 2 ] [Chen, Chongcheng]Chinese Univ Hong Kong, Inst Environm Energy & Sustainabil, Hong Kong, Hong Kong, Peoples R China
  • [ 3 ] [Luo, Ming]Fuzhou Univ, Natl Engn Res Ctr Geospatial Informat Technol, Fuzhou, Peoples R China

Reprint 's Address:

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    [Luo, Ming]Chinese Univ Hong Kong, Inst Environm Energy & Sustainabil, Hong Kong, Hong Kong, Peoples R China

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Source :

PROCEEDINGS 2015 SECOND IEEE INTERNATIONAL CONFERENCE ON SPATIAL DATA MINING AND GEOGRAPHICAL KNOWLEDGE SERVICES (ICSDM 2015)

Year: 2015

Page: 104-107

Language: English

Cited Count:

WoS CC Cited Count: 3

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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