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Article << Previous     |     Next >>   Contents Vol 8(6)

Source apportionment of PM2.5 at two receptor sites in Brisbane, Australia

Adrian J. Friend A, Godwin A. Ayoko A C, Eduard Stelcer B and David Cohen B

A International Laboratory for Air Quality and Health, Discipline of Chemistry, Queensland University of Technology, QLD 4001, Australia.
B Institute for Environmental Research, Australian Nuclear Science and Technology Organisation, Locked Bag 2001, Kirrawee DC, NSW 2232, Australia.
C Corresponding author. Email: g.ayoko@qut.edu.au

Environmental Chemistry 8(6) 569-580 http://dx.doi.org/10.1071/EN11056
Submitted: 11 July 2011  Accepted: 2 August 2011   Published: 17 November 2011

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Environmental context. Fine particles affect air quality locally, regionally and globally. Determining the sources of fine particle is therefore critical for developing strategies to reduce their adverse effects. Advanced data analysis techniques were used to determine the sources of fine particles at two sites, providing information for future pollution reduction strategies not only at the study sites but in other areas of the world as well.

Abstract. In this study, samples of particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5) collected at two sites in the south-east Queensland region, a suburban (Rocklea) and a roadside site (South Brisbane), were analysed for H, Na, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Br, Pb and black carbon (BC). Samples were collected during 2007–10 at the Rocklea site and 2009–10 at the South Brisbane site. The receptor model Positive Matrix Factorisation was used to analyse the samples. The sources identified included secondary sulfate, motor vehicles, soil, sea salt and biomass burning. Conditional probability function analysis was used to determine the most likely directions of the sources. Future air quality control strategies may focus on the particular sources identified in the analysis.

Additional keywords: fine particles, Positive Matrix Factorisation, receptor modelling.


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