Multi-criteria ranking and source apportionment of fine particulate matter in Brisbane, Australia


Autoria(s): Friend, Adrian; Ayoko, Godwin
Data(s)

2009

Resumo

This paper reports the application of multicriteria decision making techniques, PROMETHEE and GAIA, and receptor models, PCA/APCS and 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. While 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 sulphate, sea salt and road dust/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.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/29232/

Publicador

CSIRO Publishing

Relação

http://eprints.qut.edu.au/29232/1/c29232.pdf

http://www.publish.csiro.au/nid/188/issue/5056.htm

Friend, Adrian & Ayoko, Godwin (2009) Multi-criteria ranking and source apportionment of fine particulate matter in Brisbane, Australia. Environmental Chemistry, 6(5), pp. 398-406.

Direitos

Copyright 2009 CSIRO

Fonte

Faculty of Science and Technology; Institute of Health and Biomedical Innovation; School of Physical & Chemical Sciences

Palavras-Chave #020203 Particle Physics #040101 Atmospheric Aerosols #Air Quality #Source Identification #Receptor Models #PMF
Tipo

Journal Article