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

Environ Sci Pollut Res Int. 2013 Apr;20(4):2398-409. doi: 10.1007/s11356-012-1129-0. Epub 2012 Aug 29.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Air Pollutants / analysis*
  • Air Pollution / analysis*
  • Air Pollution / statistics & numerical data
  • China
  • Chrysenes / analysis
  • Coal
  • Energy-Generating Resources
  • Factor Analysis, Statistical
  • Gas Chromatography-Mass Spectrometry
  • Glucose / analogs & derivatives
  • Glucose / analysis
  • Linear Models
  • Models, Theoretical
  • Particulate Matter / analysis
  • Particulate Matter / chemistry*
  • Polycyclic Aromatic Hydrocarbons / analysis*
  • Principal Component Analysis
  • Seasons
  • Spatio-Temporal Analysis
  • Triterpenes / analysis
  • Vehicle Emissions

Substances

  • Air Pollutants
  • Chrysenes
  • Coal
  • Particulate Matter
  • Polycyclic Aromatic Hydrocarbons
  • Triterpenes
  • Vehicle Emissions
  • hopane
  • 1,6-anhydro-beta-glucopyranose
  • picene
  • Glucose