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dc.contributor.authorChan, Andrew Yiu-chungen_US
dc.contributor.authorHawas, Olgaen_US
dc.contributor.authorHawker, Darrylen_US
dc.contributor.authorVowles, Peteren_US
dc.contributor.authorD. Cohen, Daviden_US
dc.contributor.authorStelcer, Eduarden_US
dc.contributor.authorSimpson, Roden_US
dc.contributor.authorGolding, Garyen_US
dc.contributor.authorChristensen, Elizabethen_US
dc.date.accessioned2017-05-03T11:24:36Z
dc.date.available2017-05-03T11:24:36Z
dc.date.issued2011en_US
dc.date.modified2011-08-19T06:45:08Z
dc.identifier.issn13522310en_US
dc.identifier.doi10.1016/j.atmosenv.2010.09.060en_AU
dc.identifier.urihttp://hdl.handle.net/10072/40156
dc.description.abstractIn 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.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherPergamon Pressen_US
dc.publisher.placeUnited Kingdomen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofpagefrom439en_US
dc.relation.ispartofpageto449en_US
dc.relation.ispartofissue2en_US
dc.relation.ispartofjournalAtmospheric Environmenten_US
dc.relation.ispartofvolume45en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchEnvironmental Chemistry (incl. Atmospheric Chemistry)en_US
dc.subject.fieldofresearchcode039901en_US
dc.titleUsing multiple type composition data and wind data in PMF analysis to apportion and locate sources of air pollutantsen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.date.issued2011
gro.hasfulltextNo Full Text


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