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Environmental Science and Pollution Research
Article . 2012 . Peer-reviewed
License: Springer TDM
Data sources: Crossref
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Tracer-based source apportionment of polycyclic aromatic hydrocarbons in PM2.5 in Guangzhou, southern China, using positive matrix factorization (PMF)

Authors: Zhou Zhang; Xinming Wang; Hai Guo; Zhen Hao Ling; Bo Gao; Bo Gao; Xiu Ying Zhao; +1 Authors

Tracer-based source apportionment of polycyclic aromatic hydrocarbons in PM2.5 in Guangzhou, southern China, using positive matrix factorization (PMF)

Abstract

From 28 November to 23 December 2009, 24-h PM2.5 samples were collected simultaneously at six sites in Guangzhou. Concentrations of 18 polycyclic aromatic hydrocarbons (PAHs) together with certain molecular tracers for vehicular emissions (i.e., hopanes and elemental carbon), coal combustion (i.e., picene), and biomass burning (i.e., levoglucosan) were determined. Positive matrix factorization (PMF) receptor model combined with tracer data was applied to explore the source contributions to PAHs. Three sources were identified by both inspecting the dominant tracer(s) in each factor and comparing source profiles derived from PMF with determined profiles in Guangzhou or in the Pearl River Delta region. The three sources identified were vehicular emissions (VE), biomass burning (BB), and coal combustion (CC), accounting for 11 ± 2%, 31 ± 4%, and 58 ± 4% of the total PAHs, respectively. CC replaced VE to become the most important source of PAHs in Guangzhou, reflecting the effective control of VE in recent years. The three sources had different contributions to PAHs with different ring sizes, with higher BB contributions (75 ± 3%) to four-ring PAHs such as pyrene and higher CC contributions (57 ± 4%) to six-ring PAHs such as benzo[ghi]perylene. Temporal variations of VE and CC contributions were probably caused by the change of weather conditions, while temporal variations of BB contributions were additionally influenced by the fluctuation of BB emissions. Source contributions also showed some spatial variations, probably due to the source emission variations near the sampling sites.

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Keywords

China, Energy-Generating Resources, Chrysenes, Gas Chromatography-Mass Spectrometry, Spatio-Temporal Analysis, Air Pollution, Polycyclic Aromatic Hydrocarbons, Vehicle Emissions, Air Pollutants, Principal Component Analysis, Models, Theoretical, Triterpenes, Coal, Glucose, Linear Models, Particulate Matter, Seasons, Factor Analysis, Statistical

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
42
Top 10%
Top 10%
Top 10%