Browsing by Author "Simpson, R"

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    Apportionment of sources of fine and coarse particles in four major Australian cities by positive matrix factorisation
    (Elsevier, 2008-01) Chan, YC; Cohen, DD; Hawas, O; Stelcer, E; Simpson, R; Denison, L; Wong, N; Hodge, M; Comino, E; Carswell, S
    In this study, 437 days of 6-daily, 24-h samples Of PM2.5, PM2.5-10 and PM10 were collected over a 12-month period during 2003-2004 in Melbourne, Sydney, Brisbane and Adelaide. The elemental, ionic and polycyclic aromatic hydrocarbon composition of the particles were determined. Source apportionment was carried out by using the positive matrix factorisation software (PMF2). Eight factors were identified for the fine particle samples including 'motor vehicles', 'industry', 'other combustion sources', 'ammonium sulphates', 'nitrates', 'marine aerosols', 'chloride depleted marine aerosols' and 'crustal/soil dust'. On average combustion sources, secondary nitrates/sulphates and natural origin dust contributed about 46%, 25% and 26% of the mass of the fine particle samples, respectively. 'Crustal/soil dust', 'marine aerosols', 'nitrates' and 'road side dust' were the four factors identified for the coarse particle samples. On average natural origin dust contributed about 76% of the mass of the coarse particle samples. The contributions of the sources to the sample mass basically reflect the emission source characteristics of the sites. Secondary sulphates and nitrates were found to spread out evenly within each city. The average contribution of secondary nitrates to fine particles was found to be rather uniform in different seasons, rather than higher in winter as found in other studies. This could be due to the low humidity conditions in winter in most of the Australian cities which made the partitioning of the particle phase less favourable in the NH4NO3 equilibrium system. A linear relationship was found between the average contribution of marine aerosols and the distance of the site from the bay side. Wind erosion was found associated with higher contribution of crustal dust on average and episodes of elevated concentration of coarse particles in spring and summer. © 2007, Elsevier Ltd.
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    Source apportionment of ambient volatile organic compounds in major cities in Australia by positive matrix factorisation
    (Clean Air Society of Australia and New Zealand, 2008-05) Chan, AYC; Christensen, E; Golding, G; King, GF; Gore, W; Cohen, DD; Hawas, O; Stelcer, E; Simpson, R; Denison, L; Wong, N
    Source apportionment of the 6-daily, 24 h volatile organic compound (VOC) samples collected during 2003–2004 in Melbourne, Sydney and Brisbane was carried out using the Positive Matrix Factorisation software (PMF2). Fourteen C4-C10 VOCs were chosen for source apportionment. Biogenic emissions were not covered in this study because tracer VOCs such as isoprene were not measured. Five VOC source factors were identified, including the ‘evaporative / fuel distribution’ factor (contribute to 37% of the total mass of the 14 VOCs on average), the ‘vehicle exhaust / petrochemical industry’ factor (24%), the ‘biomass burning’ factor (13%), the ‘architectural surface coatings’ factor (5%) and the ‘other sources’ factor (14%). The relative contributions of the source factors to the ambient VOC concentration at the sampling sites were comparable to the relative emission loads of the local sources in Australian air emission inventories. The high contribution from evaporative emissions indicates that introduction of reduction measures for evaporative emissions could substantially reduce the VOC emissions in Australian cities. The total VOC mass and the contributions from vehicle related sources and biomass burning were higher in winter and autumn, while the contributions from surface coatings were higher in summer. © 2008, Clean Air Society of Australia and New Zealand
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    Using multiple type composition data and wind data in PMF analysis to apportion and locate sources of air pollutants.
    (Elsevier, 2011-01) Chan, YC; Hawas, O; Hawker, D; Vowles, P; Cohen, DD; Stelcer, E; Simpson, R; Golding, G; Christensen, E
    In this study a small but comprehensive data set from a 24-hourly sampling program carried out during June 2001 in an industrial area in Brisbane was chosen to investigate the effect of inclusion of multiple type composition data and wind data on source apportionment of air pollutants using the Positive Matrix Factorisation model, EPA PMF 3.0. The combined use of aerosol, VOC, main gaseous pollutants composition data and wind data resulted in better values of statistical indicators and diagnostic plots, and source factors which could be more easily related to known sources. The number of source factors resolved was similar to those reported in the literature where larger data sets were used. Three source factors were identified for the coarse particle samples, including ‘crustal matter’, ‘vehicle emissions’ and ‘sea spray’. Seven source factors were identified for the fine particle and VOC samples, including ‘secondary and biogenic’, ‘petroleum refining’, ‘vehicle emissions’, ‘petroleum product wholesaling’, ‘evaporative emissions’, ‘sea spray’ and ‘crustal matter’. The factor loadings of the 16 wind sectors and the calm wind sector from the PMF analysis were also used to quantify the directional contribution of the source factors. While the contributions were higher in the prevailing wind directions as expected, calm winds were also found to contribute up to 17% of the pollutant mass on average. The factor loadings, normalised by the overall abundance of the wind sectors, were also used to assess the directional dependences of the source factors. The results matched well with the location of known sources in the area. There was also a higher contribution potential from calm winds for local sources compared to that for distant sources. The results of directional effect using the PMF factor loading approach were similar to those by using the other approaches. This approach, however, also provides estimates of the mass contribution of source factors by wind sector and also the uncertainty of the results. © 2011, Elsevier Ltd.