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Volume 5, No. 1, June 2005, Pages 48-64 PDF(211 KB)  
doi: null   

Source Apportionment of Airborne Particles During Winter in Contrasting, Coastal Cities

Inoka Senaratne, Francis M. Kellihe, Christopher Triggs

1 School of Geography and Environmental Science, The University of Auckland, Private Bag 92019, Auckland, New Zealand.
2 Manaaki Whenua - Landcare Research, P.O.Box 69, Lincoln, New Zealand.
3 Department of Statistics, The University of Auckland, Private Bag 92019, Auckland, New Zealand.

 

Abstract

 

The coarse (2.5-10 μm) and fine (< 2.5 μm) fractions of airborne particles were sampled during winter 2000 and 2001 in Auckland and Christchurch, New Zealand. Sampling was carried out using a versatile air pollution sampler (VAPS) and particle analyses for elemental concentrations utilized proton induced x-ray emission (PIXE) and light reflection/transmission techniques. Emission source identification was by principal component factor analysis (PCFA) with Varimax rotation while the source apportionment used the absolute principal component analysis (APCA) receptor modeling method. The major emission sources identified were classified as sea spray (SS), suspended soil and road dust (SO & RD), domestic emissions (DE), and vehicle emissions (VE, diesel and petrol). In Auckland, source apportionment to SS, SO & RD, DE, and VE emissions averaged 22, 42, 14 and 22% respectively. The corresponding averages for Christchurch were 23, 29, 25, and 23%. The colder Christchurch climate corresponded with nearly twice the DE contribution of Auckland. Containing a significant contribution from vehicles, SO & RD made the highest contribution in both cities. In Auckland, as expected, road transport was considered the dominant source of PM10. Contrary to strong local perception, this is the first report to suggest that Christchurch’s PM10 has a significant contribution from road transport during winter.

 

 

Keywords: Versatile air pollution sampler (VAPS); Proton induced x-ray emission (PIXE); Principal component factor analysis (PCFA); Absolute principal component analysis (APCA).

 

 

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