Free Standard AU & NZ Shipping For All Book Orders Over $80!
Register      Login
Environmental Chemistry Environmental Chemistry Society
Environmental problems - Chemical approaches
RESEARCH ARTICLE

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
+ Author Affiliations
- Author Affiliations

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 https://doi.org/10.1071/EN11056
Submitted: 11 July 2011  Accepted: 2 August 2011   Published: 17 November 2011

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.


References

[1]  Environmental and Workplace Health – Health Effects of Air Pollution 2006 (Health Canada). Available at http://www.hc-sc.gc.ca/ewh-semt/air/out-ext/effe/health_effects-effets_sante_e.html [Verified 23 December 2010].

[2]  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 |

[3]  P. Brimblecombe, The Effects of Air Pollution on the Built Environment 2003 (Imperial College Press: London).

[4]  S.-T. Kim, Y. Maeda, Y. Tsujino, Assessment of the effect of air pollution on material damages in northeast Asia. Atmos. Environ. 2004, 38, 37.
Assessment of the effect of air pollution on material damages in northeast Asia.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXptFGksbo%3D&md5=82ad71d0d7c5235ff54f4338984b4bdfCAS |

[5]  S. J. Dutton, S. Vedal, R. Piedrahita, J. B. Milford, S. L. Miller, M. P. Hannigan, Source apportionment using positive matrix factorization on daily measurements of inorganic and organic speciated PM2.5. Atmos. Environ. 2010, 44, 2731.
Source apportionment using positive matrix factorization on daily measurements of inorganic and organic speciated PM2.5.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXnslags7Y%3D&md5=ed9ebe94ec4d8a231eb91c9d58c97863CAS |

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

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

[8]  J. Freeman, W Webber (Eds), State of the Environment Queensland 2007 (National Environmental Protection Council: Brisbane).

[9]  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 |

[10]  S. Raja, K. F. Biswas, L. Husain, P. K. Hopke, Source apportionment of the atmospheric aerosol in Lahore, Pakistan. Water Air Soil Pollut. 2010, 208, 43.
Source apportionment of the atmospheric aerosol in Lahore, Pakistan.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXksVemurk%3D&md5=cff382450fa3155863e3901f689f9bd8CAS |

[11]  J.-M. Lim, J.-H. Lee, J.-H. Moon, Y.-S. Chung, K.-H. Kim, Source apportionment of PM10 at a small industrial area using positive matrix factorization. Atmos. Res. 2010, 95, 88.
Source apportionment of PM10 at a small industrial area using positive matrix factorization.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXhsV2mtbbN&md5=d10023a7d7a50e5028aa38abec0ae716CAS |

[12]  P. Lestari, Y. D. Mauliadi, Source apportionment of particulate matter at urban mixed site in Indonesia using PMF. Atmos. Environ. 2009, 43, 1760.
Source apportionment of particulate matter at urban mixed site in Indonesia using PMF.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXhvFantL4%3D&md5=8f68de74ccab79cdc0835911a76d1117CAS |

[13]  G. D. Thurston, J. D. Spengler, A quantitative assessment of source contributions to inhalable particulate matter pollution in metropolitan Boston. Atmos. Environ. 1985, 19, 9.
A quantitative assessment of source contributions to inhalable particulate matter pollution in metropolitan Boston.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaL2MXhslWrsb4%3D&md5=768b2ceb84cde41401fd4da3b1f474a2CAS |

[14]  R. C. Henry, Multivariate receptor modeling by N-dimensional edge detection. Chemom. Intell. Lab. Syst. 2003, 65, 179.
Multivariate receptor modeling by N-dimensional edge detection.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXptlykug%3D%3D&md5=1fab8f6f43d9e2158bab14e5fbdde787CAS |

[15]  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 |

[16]  R. Tauler, M. Viana, X. Querol, A. Alastuey, R. M. Flight, P. D. Wentzell, P. K. Hopke, Comparison of the results obtained by four receptor modelling methods in aerosol source apportionment studies. Atmos. Environ. 2009, 43, 3989.
Comparison of the results obtained by four receptor modelling methods in aerosol source apportionment studies.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXos1Orur0%3D&md5=95807b2f012ab1bd813a84ce0482a203CAS |

[17]  J. G. Watson, L. W. A. Chen, J. C. Chow, P. Doraiswamy, D. H. Lowenthal, Source apportionment: findings from the US Supersites Program. J. Air Waste Manage. Assoc. 2008, 58, 265.
Source apportionment: findings from the US Supersites Program.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXivVyit78%3D&md5=2a939fa252bfab7d6414888af06ef780CAS |

[18]  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 |

[19]  Y.-C. Chan, D. D. Cohen, O. Hawas, E. Stelcer, R. Simpson, L. Denison, N. Wong, M. Hodge, E. Comino, S. Carswell, Apportionment of sources of fine and coarse particles in four major Australian cities by positive matrix factorisation. Atmos. Environ. 2008, 42, 374.
Apportionment of sources of fine and coarse particles in four major Australian cities by positive matrix factorisation.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXhsVGltb%2FK&md5=b74f5778cbfc049fbdd94e70c3366036CAS |

[20]  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 |

[21]  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 |

[22]  E. Lee, C. K. Chan, P. Paatero, Application of positive matrix factorization in source apportionment of particulate pollutants in Hong Kong. Atmos. Environ. 1999, 33, 3201.
Application of positive matrix factorization in source apportionment of particulate pollutants in Hong Kong.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK1MXjt1WitL4%3D&md5=c1f375fc8209b1bd6ae99d30edfefb25CAS |

[23]  A. J. Friend, G. A. Ayoko, S. G. Elbagir, Source apportionment of fine particles at a suburban site in Queensland, Australia. Environ. Chem. 2011, 8, 163.
Source apportionment of fine particles at a suburban site in Queensland, Australia.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXmt1yisr0%3D&md5=b533f264f4ca903f16fcd43d73c8d773CAS |

[24]  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 |

[25]  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 |

[26]  D. D. Cohen, E. Stelcer, O. Hawas, D. Garton, IBA methods for characterisation 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 characterisation of fine particulate atmospheric pollution: a local, regional and global research problem.Crossref | GoogleScholarGoogle Scholar |

[27]  G. Taha, G. P. Box, D. D. Cohen, E. Stelcer, Black carbon measurement using laser integrating plate method. Aerosol Sci. Technol. 2007, 41, 266.
Black carbon measurement using laser integrating plate method.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXjvVOjsr4%3D&md5=217125cc76dddeb28e4636257414cf7cCAS |

[28]  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.

[29]  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 |

[30]  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 |

[31]  S. Eberly, EPA PMF Version 1.1. 2005 (US Environmental Protection Agency: Durham, NC).

[32]  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 |

[33]  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 |

[34]  A. V. Polissar, P. K. Hopke, R. L. Poirot, Atmospheric aerosol over Vermont: chemical composition and sources. Environ. Sci. Technol. 2001, 35, 4604.
Atmospheric aerosol over Vermont: chemical composition and sources.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3MXnvFGmtL4%3D&md5=097f64ed5ddb413b2fbe7820bac99e6aCAS |

[35]  P. Paatero, P. K. Hopke, X.-H. Song, Z. Ramadan, Understanding and controlling rotations in factor analytic models. Chemom. Intell. Lab. Syst. 2002, 60, 253.
Understanding and controlling rotations in factor analytic models.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD38XksVyhuw%3D%3D&md5=86b6c8b25a26f55b1968d09b57bc03bfCAS |

[36]  D. Ogulei, P. K. Hopke, L. A. Wallace, Analysis of indoor particle size distributions in an occupied townhouse using positive matrix factorization. Indoor Air 2006, 16, 204.
Analysis of indoor particle size distributions in an occupied townhouse using positive matrix factorization.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28XmtFeksrw%3D&md5=976ce707a63fb63285f87ef701548805CAS |

[37]  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 |

[38]  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 |

[39]  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 |

[40]  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 |

[41]  State of the Environment Queensland 2003 (Queensland Environmental Protection Agency: Brisbane).

[42]  A. E. Gildemeister, P. K. Hopke, E. Kim, Sources of fine urban particulate matter in Detroit, MI. Chemosphere 2007, 69, 1064.
Sources of fine urban particulate matter in Detroit, MI.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXhtVOgsrbJ&md5=d073fb79909e7b0e9f4939bf32beefc6CAS |

[43]  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 |

[44]  H. Guo, A. J. Ding, K. L. So, G. A. Ayoko, Y. S. Li, W. T. Hung, Receptor modeling of source apportionment of Hong Kong aerosols and the implication of urban and regional contribution. Atmos. Environ. 2009, 43, 1159.
Receptor modeling of source apportionment of Hong Kong aerosols and the implication of urban and regional contribution.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXps1Sqsw%3D%3D&md5=a870f280f61c7e537f01aeb8c896f2d6CAS |

[45]  K. L. So, H. Guo, Y. S. Li, Long-term variation of PM2.5 levels and composition at rural, urban, and roadside sites in Hong Kong: increasing impact of regional air pollution. Atmos. Environ. 2007, 41, 9427.
Long-term variation of PM2.5 levels and composition at rural, urban, and roadside sites in Hong Kong: increasing impact of regional air pollution.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXhtlOisrnM&md5=10bb267641fae8ee2568ddccb192f879CAS |