Multi-criteria ranking and source apportionment of fine particulate matter in Brisbane, Australia
Adrian J. Friend A and Godwin A. Ayoko A BA International Laboratory for Air Quality and Health, School of Physical and Chemical Sciences, Queensland University of Technology, 2 George Street, GPO Box 2434, Qld 4001, Australia.
B Corresponding author. Email: g.ayoko@qut.edu.au
Environmental Chemistry 6(5) 398-406 https://doi.org/10.1071/EN09035
Submitted: 19 March 2009 Accepted: 26 August 2009 Published: 22 October 2009
Environmental context. There are serious global concerns about the environmental and health effects of atmospheric air pollutants. However, estimates of pollutants from measurements made in the proximity of a source do not always represent the ultimate atmospheric concentrations. Therefore alternative methods of attributing pollutants to sources, and estimating their contributions to atmospheric concentrations, as demonstrated in the current work, will become an increasingly important area of environmental research.
Abstract. This paper reports the application of multicriteria decision making techniques, Preference Ranking Organisation Methods for Enrichment Evaluation (PROMETHEE) and Graphical Analysis for Interactive Assistance (GAIA), and receptor models, principal component analysis/absolute principal component scores (PCA/APCS) and positive matrix factorisation (PMF), to data from an air monitoring site located on the campus of Queensland University of Technology in Brisbane, Australia and operated by Queensland Environmental Protection Agency (QEPA). The data consisted of the concentrations of 21 chemical species and meteorological data collected between 1995 and 2003. PROMETHEE/GAIA separated the samples into those collected when leaded and unleaded petrol were used to power vehicles in the region. The number and source profiles of the factors obtained from PCA/APCS and PMF analyses were compared. There are noticeable differences in the outcomes possibly because of the non-negative constraints imposed on the PMF analysis. Whereas PCA/APCS identified 6 sources, PMF reduced the data to 9 factors. Each factor had distinctive compositions that suggested that motor vehicle emissions, controlled burning of forests, secondary sulfate, sea salt and soil were the most important sources of fine particulate matter at the site. The most plausible locations of the sources were identified by combining the results obtained from the receptor models with meteorological data. The study demonstrated the potential benefits of combining results from multi-criteria decision making analysis with those from receptor models in order to gain insights into information that could enhance the development of air pollution control measures.
Additional keywords: air quality, PMF, receptor models, source identification.
Acknowledgements
The authors acknowledge the Queensland Environmental Protection Agency for providing the data used for this study and the reviewer for useful comments.
[1]
[2]
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.
|
CAS |
PubMed |
[Verified 7 October 2009]
[7]
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.
| Crossref | GoogleScholarGoogle Scholar |
CAS |
PubMed |
[Verified 7 October 2009]
[36]
N. N. Maykut ,
J. Lewtas ,
E. Kim ,
T. V. Larson ,
Source apportionment of PM2.5 at an urban IMPROVE site in Seattle, Washington.
Environ. Sci. Technol. 2003
, 37, 5135.
| Crossref | GoogleScholarGoogle Scholar |
CAS |
PubMed |
[37]
Z. Li ,
P. K. Hopke ,
L. Husain ,
S. Qureshi ,
V. A. Dutkiewicz ,
J. J. Schwab ,
F. Drewnick ,
K. L. Demerjian ,
Sources of fine particle composition in New York city.
Atmos. Environ. 2004
, 38, 6521.
| Crossref | GoogleScholarGoogle Scholar |
CAS |
[38]
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.
|
CAS |
| Crossref |
[39]
Y. L. Xie ,
P. K. Hopke ,
P. Paatero ,
L. A. Barrie ,
S. M. Li ,
Identification of source nature and seasonal variations of arctic aerosol by positive matrix factorization.
J. Atmos. Sci. 1999
, 56, 249.
| Crossref | GoogleScholarGoogle Scholar |
[40]
S. L. Miller ,
M. J. Anderson ,
E. P. Daly ,
J. B. Milford ,
Source apportionment of exposures to volatile organic compounds. I. Evaluation of receptor models using simulated exposure data.
Atmos. Environ. 2002
, 36, 3629.
| Crossref | GoogleScholarGoogle Scholar |
CAS |
[41]
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. Instr. Meth. Phys. Res. B 2004
, 219–220, 145.
| Crossref | GoogleScholarGoogle Scholar |
