Register      Login
Environmental Chemistry Environmental Chemistry Society
Environmental problems - Chemical approaches
RESEARCH ARTICLE

Source apportionment of fine particles at a suburban site in Queensland, Australia

Adrian J. Friend A , Godwin A. Ayoko A B and Sohair G. Elbagir A
+ Author Affiliations
- Author Affiliations

A Discipline of Chemistry, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia.

B Corresponding author. Email: g.ayoko@qut.edu.au

Environmental Chemistry 8(2) 163-173 https://doi.org/10.1071/EN10112
Submitted: 15 October 2010  Accepted: 10 January 2011   Published: 2 May 2011

Environmental context. Airborne fine particles affect local, regional and global air quality and deteriorate the environment. Therefore comprehensive information on the locations and strengths of particle sources is critical for the development of strategies for mitigating the adverse effects of aerosols. The multivariate data analysis techniques used in this paper allowed the benefits of a previous control measure to be assessed and provided vital information for the application of further pollution reduction strategies to this and other areas of the world.

Abstract. Airborne fine particles were collected at a suburban site in Queensland, Australia between 1995 and 2003. The samples were analysed for 21 elements and Positive Matrix Factorisation (PMF), Preference Ranking Organisation Methods for Enrichment Evaluation (PROMETHEE) and Graphical Analysis for Interactive Assistance (GAIA) were applied to the data. PROMETHEE provided information on the ranking of pollutant levels from the sampling years whereas PMF provided insights into the sources of the pollutants, their chemical composition, most likely locations and relative contribution to the levels of particulate pollution at the site. PROMETHEE and GAIA found that the removal of lead from fuel in the area had a significant effect on the pollution patterns whereas PMF identified six pollution sources, including railways (5.5%), biomass burning (43.3%), soil (9.2%), sea salt (15.6%), aged sea salt (24.4%) and motor vehicles (2.0%). Thus the results gave information that can assist in the formulation of mitigation measures for air pollution.

Additional keywords: leaded petrol, PMF, PM2.5, PROMETHEE.


References

[1]  B. J. Finlayson-Pitts, J. N. Pitts, Chemistry of the Upper and Lower Atmosphere 2000 (Academic Press: San Diego, CA).

[2]  S. Lee, W. Liu, Y. Wang, A. G. Russell, E. S. Edgerton, Source apportionment of PM2.5: Comparing PMF and CMB results for four ambient monitoring sites in the south-eastern United States. Atmos. Environ. 2008, 42, 4126.
Source apportionment of PM2.5: Comparing PMF and CMB results for four ambient monitoring sites in the south-eastern United States.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXmt1eht7o%3D&md5=c99ba5372dc46d3bb385d20cfd3d328aCAS |

[3]  P. Khare, B. P. Baruah, Elemental characterization and source identification of PM2.5 using multivariate analysis at the suburban site of north-east India. Atmos. Res. 2010, 98, 148.
Elemental characterization and source identification of PM2.5 using multivariate analysis at the suburban site of north-east India.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhtFKksbzK&md5=fca3883d07a93f8f2de221f9cfaf945aCAS |

[4]  H. Shaka’, N. A. Saliba, Concentration measurements and chemical composition of PM10–2.5 and PM2.5 at a coastal site in Beirut, Lebanon. Atmos. Environ. 2004, 38, 523.
Concentration measurements and chemical composition of PM10–2.5 and PM2.5 at a coastal site in Beirut, Lebanon.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXpsFemtb0%3D&md5=b86f2a026b8c7867895c5be9bd07a5bcCAS |

[5]  A. Petroeschevsky, R. W. Simpson, L. Thalib, S. Rutherford, Associations between outdoor air pollution and hospital admissions in Brisbane, Australia. Arch. Environ. Health 2001, 56, 37.
Associations between outdoor air pollution and hospital admissions in Brisbane, Australia.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3MXitlSisrw%3D&md5=5cac00623c8ece492623f505c46eca0dCAS | 11256855PubMed |

[6]  D. D. Cohen, J. Crawford, E. Stelcer, V. T. Bac, Characterisation and source apportionment of fine particulate sources at Hanoi from 2001 to 2008. Atmos. Environ. 2010, 44, 320.
Characterisation and source apportionment of fine particulate sources at Hanoi from 2001 to 2008.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXjtVWrsA%3D%3D&md5=757c7abe4664ef074ee4604224a1086eCAS |

[7]  National Environmental Protection (Ambient Air Quality) Measure 1998 (National Environmental Protection Council: Adelaide).

[8]  Variation to the National Environment Protection (Ambient Air Quality) Measure for Particles as PM 2003 (National Environment Protection Council: Adelaide).

[9]  M. Behzadian, R. B. Kazemzadeh, A. Albadvi, M. Aghdasi, PROMETHEE: A comprehensive literature review on methodologies and applications. Eur. J. Oper. Res. 2010, 200, 198.
PROMETHEE: A comprehensive literature review on methodologies and applications.Crossref | GoogleScholarGoogle Scholar |

[10]  G. A. Ayoko, L. Morawska, S. Kokot, D. Gilbert, Application of multicriteria decision making methods to air quality in the microenvironments of residential houses in Brisbane, Australia. Environ. Sci. Technol. 2004, 38, 2609.
Application of multicriteria decision making methods to air quality in the microenvironments of residential houses in Brisbane, Australia.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2cXislCmtrs%3D&md5=5c967060050dae389428369371944f7fCAS | 15180057PubMed |

[11]  M. C. H. Lim, G. A. Ayoko, L. Morawska, Z. D. Ristovski, E. R. Jayaratne, Effect of fuel composition and engine operating conditions on polycyclic aromatic hydrocarbon emissions from a fleet of heavy-duty diesel buses. Atmos. Environ. 2005, 39, 7836..

[12]  A. J. Friend, G. A. Ayoko, Multi-criteria ranking and source apportionment of fine particulate matter in Brisbane, Australia. Environ. Chem. 2009, 6, 398.
Multi-criteria ranking and source apportionment of fine particulate matter in Brisbane, Australia.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXhsFSkurnO&md5=9182c892ab04899e8a8585ce559f8354CAS |

[13]  G. Ayoko, S. El-tahir, Multivariate statistical evaluation of the chemical composition of fine particles: a case study, in 14th International IUAPPA World Congress, Brisbane, Australia, 9–13 September 2007 2007 (CD-ROM) (Clean Air Clean Air Society of Australia and New Zealand (CASANZ)).

[14]  I. Hwang, P. K. Hopke, Estimation of source apportionment and potential source locations of PM2.5 at a west coastal IMPROVE site. Atmos. Environ. 2007, 41, 506.
Estimation of source apportionment and potential source locations of PM2.5 at a west coastal IMPROVE site.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28Xhtlajsr7L&md5=60c112f9c2f3b53b18a7aa5a4d292a8fCAS |

[15]  P. K. H. Lee, J. R. Brook, E. Dabek-Zlotorzynska, S. A. Mabury, Identification of the major sources contributing to PM2.5 observed in Toronto. Environ. Sci. Technol. 2003, 37, 4831.
Identification of the major sources contributing to PM2.5 observed in Toronto.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXnsl2msrk%3D&md5=9e74ff3461eaa396245b80c7189d4e59CAS | 14620807PubMed |

[16]  Y. Song, S. D. Xie, Y. H. Zhang, L. M. Zeng, L. G. Salmon, M. Zheng, Source apportionment of PM2.5 in Beijing using principal component analysis/absolute principal component scores and UNMIX. Sci. Total Environ. 2006, 372, 278.
Source apportionment of PM2.5 in Beijing using principal component analysis/absolute principal component scores and UNMIX.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28Xht1agsb%2FF&md5=29a3382664d72ceeaf0ffdbb7a88e885CAS | 17097135PubMed |

[17]  X. H. Song, A. V. Polissar, P. K. Hopke, Sources of fine particle composition in the northeastern US. Atmos. Environ. 2001, 35, 5277.
Sources of fine particle composition in the northeastern US.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3MXmvVGgtbY%3D&md5=f3ee3917b4f9bce213ce7b4c784bf85eCAS |

[18]  B. A. Begum, E. Kim, S. K. Biswas, P. K. Hopke, Investigation of sources of atmospheric aerosol at urban and semi-urban areas in Bangladesh. Atmos. Environ. 2004, 38, 3025.
Investigation of sources of atmospheric aerosol at urban and semi-urban areas in Bangladesh.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2cXjslenurc%3D&md5=3eeca56b2cccbf4762337cc20efcda4aCAS |

[19]  Y. C. Chan, R. W. Simpson, G. H. Mctainsh, P. D. Vowles, D. D. Cohen, G. M. Bailey, Source apportionment of PM2.5 and PM10 aerosols in Brisbane (Australia) by receptor modelling. Atmos. Environ. 1999, 33, 3251.
Source apportionment of PM2.5 and PM10 aerosols in Brisbane (Australia) by receptor modelling.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK1MXjt1WitLo%3D&md5=4bceb350ab1474ba31ebfaf15d99d5abCAS |

[20]  Y.-C. Chan, O. Hawas, D. Hawker, P. Vowles, D. D. Cohen, E. Stelcer, R. Simpson, G. Golding, E. Christensen, Using multiple type composition data and wind data in PMF analysis to apportion and locate sources of air pollutants. Atmos. Environ. 2011, 45, 439.
Using multiple type composition data and wind data in PMF analysis to apportion and locate sources of air pollutants.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhsFWisLvO&md5=2e702d8f3b27d4a316be1f7d79e6c9d8CAS |

[21]  L. Zhou, P. K. Hopke, W. Zhao, Source apportionment of airborne particulate matter for the speciation trends network site in Cleveland, OH. J. Air Waste Manage. Assoc. 2009, 59, 321.
Source apportionment of airborne particulate matter for the speciation trends network site in Cleveland, OH.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXktVert7s%3D&md5=f7413ed641be278c83ff0d8b7b49a9eaCAS |

[22]  E. Kim, P. K. Hopke, P. Paatero, E. S. Edgerton, Incorporation of parametric factors into multilinear receptor model studies of Atlanta aerosol. Atmos. Environ. 2003, 37, 5009.
Incorporation of parametric factors into multilinear receptor model studies of Atlanta aerosol.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXot1Sksrk%3D&md5=d254cab27225dccdd311d098343251ffCAS |

[23]  J. B. Heo, P. K. Hopke, S. M. Yi, Source apportionment of PM2.5 in Seoul, Korea. Atmos. Chem. Phys. 2009, 9, 4957.
Source apportionment of PM2.5 in Seoul, Korea.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXhsFGlsLnI&md5=62d78c2baf4d32754fdbaac22144c253CAS |

[24]  E. Kim, P. K. Hopke, Source identifications of airborne fine particles using positive matrix factorization and US environmental protection agency positive matrix factorization. J. Air Waste Manage. Assoc. 2007, 57, 811.
Source identifications of airborne fine particles using positive matrix factorization and US environmental protection agency positive matrix factorization.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXosV2qu70%3D&md5=a5d03b5b518d6df4657ced3cec5e9cd3CAS |

[25]  M. Kim, S. R. Deshpande, K. C. Crist, Source apportionment of fine particulate matter (PM2.5) at a rural Ohio River Valley site. Atmos. Environ. 2007, 41, 9231.
Source apportionment of fine particulate matter (PM2.5) at a rural Ohio River Valley site.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXhtlOisr3J&md5=03ebb802d4b6cc6d7c6e6321108cb0d0CAS |

[26]  S. Khan, R. Simpson, Mesoscale trajectory modeling for the Brisbane airshed. Environ. Model. Assess. 1997, 2, 201.
Mesoscale trajectory modeling for the Brisbane airshed.Crossref | GoogleScholarGoogle Scholar |

[27]  D. D. Cohen, E. Stelcer, O. Hawas, D. Garton, IBA methods for characterization of fine particulate atmospheric pollution: a local, regional and global research problem. Nucl. Instrum. Methods Phys. Res. B 2004, 219–220, 145.
IBA methods for characterization of fine particulate atmospheric pollution: a local, regional and global research problem.Crossref | GoogleScholarGoogle Scholar |

[28]  J. F. Mejia, D. Wraith, K. Mengersen, L. Morawska, Trends in size classified particle number concentration in subtropical Brisbane, Australia, based on a 5 year study. Atmos. Environ. 2007, 41, 1064.
Trends in size classified particle number concentration in subtropical Brisbane, Australia, based on a 5 year study.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28XhtlClsbbM&md5=382c8444cb12b35ca8b2f0a9be35c4a4CAS |

[29]  L. Herngren, A. Goonetilleke, G. A. Ayoko, Analysis of heavy metals in road-deposited sediments. Anal. Chim. Acta 2006, 571, 270.
Analysis of heavy metals in road-deposited sediments.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28XmsVyis7g%3D&md5=84f24bb15c7251765727a4bc2100a213CAS | 17723448PubMed |

[30]  J. Figueira, S. Greco, M. Ehrgott, Multiple Criteria Decision Analysis: State of the Art Surveys 2005 (Springer: New York).

[31]  B. Espinasse, G. Picolet, E. Chouraqui, Negotiation support systems: a multi-criteria and multi-agent approach. Eur. J. Oper. Res. 1997, 103, 389.
Negotiation support systems: a multi-criteria and multi-agent approach.Crossref | GoogleScholarGoogle Scholar |

[32]  C.-H. Jeong, G. J. Evans, T. Dann, M. Graham, D. Herod, E. Dabek-Zlotorzynska, D. Mathieu, L. Ding, D. Wang, Influence of biomass burning on wintertime fine particulate matter: Source contribution at a valley site in rural British Columbia. Atmos. Environ. 2008, 42, 3684.
Influence of biomass burning on wintertime fine particulate matter: Source contribution at a valley site in rural British Columbia.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXls1KksbY%3D&md5=7dced917e5d56759f1d376c7ac3c2933CAS |

[33]  P. Paatero, U. Tapper, Positive matrix factorization – a nonnegative factor model with optimal utilization of error-estimates of data values. Environmetrics 1994, 5, 111.
Positive matrix factorization – a nonnegative factor model with optimal utilization of error-estimates of data values.Crossref | GoogleScholarGoogle Scholar |

[34]  P. Paatero, Least squares formulation of robust non-negative factor analysis. Chemom. Intell. Lab. Syst. 1997, 37, 23.
Least squares formulation of robust non-negative factor analysis.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK2sXivFKgtLc%3D&md5=9227e3b93282268ae1a213c3d03d34d3CAS |

[35]  E. Kim, P. K. Hopke, E. S. Edgerton, Source identification of Atlanta aerosol by positive matrix factorization. J. Air Waste Manage. Assoc. 2003, 53, 731..

[36]  R. C. Henry, Current factor analysis receptor models are ill-posed. Atmos. Environ. 1987, 21, 1815.
Current factor analysis receptor models are ill-posed.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaL2sXmtVChtLo%3D&md5=dca11c127554cbbd10431b0c17c75e20CAS |

[37]  S. Eberly, EPA PMF Version 1.1 2005 (US Environmental Protection Agency). Available at http://www.epa.gov/heasd/products/pmf/users_guide_old.pdf [Verified 5 April 2011].

[38]  A. Reff, S. I. Eberly, P. V. Bhave, Receptor modeling of ambient particulate matter data using positive matrix factorization: review of existing methods. J. Air Waste Manage. Assoc. 2007, 57, 146..

[39]  P. Paatero, P. K. Hopke, Discarding or downweighting high-noise variables in factor analytic models. Anal. Chim. Acta 2003, 490, 277.
Discarding or downweighting high-noise variables in factor analytic models.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXmsVOlt7o%3D&md5=240d8b904068752f105b9e5944bf8cbcCAS |

[40]  P. K. Hopke, Recent developments in receptor modeling. J. Chemometr. 2003, 17, 255.
Recent developments in receptor modeling.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXlsVKqtLY%3D&md5=2c89d1b97658f8febcceb1b52404b5fbCAS |

[41]  K. J. Moon, J. S. Han, Y. S. Ghim, Y. J. Kim, Source apportionment of fine carbonaceous particles by positive matrix factorization at Gosan background site in East Asia. Environ. Int. 2008, 34, 654.
Source apportionment of fine carbonaceous particles by positive matrix factorization at Gosan background site in East Asia.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXnslanu7c%3D&md5=5685f849660046f2ee330081fa5765a7CAS | 18255146PubMed |

[42]  D. Ogulei, P. K. Hopke, L. Zhou, J. Patrick Pancras, N. Nair, J. M. Ondov, Source apportionment of Baltimore aerosol from combined size distribution and chemical composition data. Atmos. Environ. 2006, 40, 396.
Source apportionment of Baltimore aerosol from combined size distribution and chemical composition data.Crossref | GoogleScholarGoogle Scholar |

[43]  J. H. Lee, P. K. Hopke, Apportioning sources of PM2.5 in St. Louis, MO using speciation trends network data. Atmos. Environ. 2006, 40, 360.
Apportioning sources of PM2.5 in St. Louis, MO using speciation trends network data.Crossref | GoogleScholarGoogle Scholar |

[44]  E. Kim, P. K. Hopke, Comparison between conditional probability function and nonparametric regression for fine particle source directions. Atmos. Environ. 2004, 38, 4667.
Comparison between conditional probability function and nonparametric regression for fine particle source directions.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2cXlvFalt7w%3D&md5=17434495be1efd3ac4eced5616785671CAS |

[45]  E. Kim, P. K. Hopke, E. S. Edgerton, Improving source identification of Atlanta aerosol using temperature resolved carbon fractions in positive matrix factorization. Atmos. Environ. 2004, 38, 3349.
Improving source identification of Atlanta aerosol using temperature resolved carbon fractions in positive matrix factorization.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2cXjvFKqtLY%3D&md5=6f872d829e201a888939451cb5ec2464CAS |

[46]  R. Lorenzo, R. Kaegi, R. Gehrig, B. Grobety, Particle emissions of a railway line determined by detailed single particle analysis. Atmos. Environ. 2006, 40, 7831.
Particle emissions of a railway line determined by detailed single particle analysis.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28Xht1WmtbnP&md5=f59247a78811a4c7bce8f207ab536479CAS |

[47]  D. E. Henderson, J. B. Milford, S. L. Miller, Prescribed burns and wildfires in Colorado: impacts of mitigation measures on indoor air particulate matter. J. Air Waste Manage. Assoc. 2005, 55, 1516..

[48]  State of the Environment Queensland 2007 (Queensland Environmental Protection Agency: Brisbane).

[49]  F. Amato, M. Pandolfi, A. Escrig, X. Querol, A. Alastuey, J. Pey, N. Perez, P. K. Hopke, Quantifying road dust resuspension in urban environment by Multilinear Engine: A comparison with PMF2. Atmos. Environ. 2009, 43, 2770.
Quantifying road dust resuspension in urban environment by Multilinear Engine: A comparison with PMF2.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXltlSju7g%3D&md5=c58488ce64743ba8672a02392910367aCAS |

[50]  S. M. Almeida, C. A. Pio, M. C. Freitas, M. A. Reis, M. A. Trancoso, Source apportionment of atmospheric urban aerosol based on weekdays/weekend variability: evaluation of road re-suspended dust contribution. Atmos. Environ. 2006, 40, 2058.
Source apportionment of atmospheric urban aerosol based on weekdays/weekend variability: evaluation of road re-suspended dust contribution.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28XhsFGltb8%3D&md5=f7155d7a02d80f3b7a48e2a2f5c38c15CAS |

[51]  E. Kim, K. Turkiewicz, S. A. Zulawnick, K. L. Magliano, Sources of fine particles in the South Coast area, California. Atmos. Environ. 2010, 44, 3095.
Sources of fine particles in the South Coast area, California.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXovV2murY%3D&md5=47810551d1799e3cc1de6055f58f744fCAS |