Multi-criteria ranking and source apportionment of airborne particulate matter
Friend, Adrian J. (2012) Multi-criteria ranking and source apportionment of airborne particulate matter. PhD by Publication, Queensland University of Technology.
Particulate matter research is essential because of the well known significant adverse effects of aerosol particles on human health and the environment. In particular, identification of the origin or sources of particulate matter emissions is of paramount importance in assisting efforts to control and reduce air pollution in the atmosphere.
This thesis aims to: identify the sources of particulate matter; compare pollution conditions at urban, rural and roadside receptor sites; combine information about the sources with meteorological conditions at the sites to locate the emission sources; compare sources based on particle size or mass; and ultimately, provide the basis for control and reduction in particulate matter concentrations in the atmosphere.
To achieve these objectives, data was obtained from assorted local and international receptor sites over long sampling periods. The samples were analysed using Ion Beam Analysis and Scanning Mobility Particle Sizer methods to measure the particle mass with chemical composition and the particle size distribution, respectively.
Advanced data analysis techniques were employed to derive information from large, complex data sets. Multi-Criteria Decision Making (MCDM), a ranking method, drew on data variability to examine the overall trends, and provided the rank ordering of the sites and years that sampling was conducted. Coupled with the receptor model Positive Matrix Factorisation (PMF), the pollution emission sources were identified and meaningful information pertinent to the prioritisation of control and reduction strategies was obtained.
This thesis is presented in the thesis by publication format. It includes four refereed papers which together demonstrate a novel combination of data analysis techniques that enabled particulate matter sources to be identified and sampling site/year ranked.
The strength of this source identification process was corroborated when the analysis procedure was expanded to encompass multiple receptor sites. Initially applied to identify the contributing sources at roadside and suburban sites in Brisbane, the technique was subsequently applied to three receptor sites (roadside, urban and rural) located in Hong Kong. The comparable results from these international and national sites over several sampling periods indicated similarities in source contributions between receptor site-types, irrespective of global location and suggested the need to apply these methods to air pollution investigations worldwide. Furthermore, an investigation into particle size distribution data was conducted to deduce the sources of aerosol emissions based on particle size and elemental composition. Considering the adverse effects on human health caused by small-sized particles, knowledge of particle size distribution and their elemental composition provides a different perspective on the pollution problem.
This thesis clearly illustrates that the application of an innovative combination of advanced data interpretation methods to identify particulate matter sources and rank sampling sites/years provides the basis for the prioritisation of future air pollution control measures. Moreover, this study contributes significantly to knowledge based on chemical composition of airborne particulate matter in Brisbane, Australia and on the identity and plausible locations of the contributing sources. Such novel source apportionment and ranking procedures are ultimately applicable to environmental investigations worldwide.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||QUT Thesis (PhD by Publication)|
|Supervisor:||Ayoko, Godwin A. & Kokot, Serge|
|Keywords:||particulate matter, fine particles, receptor modelling, urban pollution, multivariate data analysis, positive matrix factorisation, PROMETHEE, PM2.5, motor vehicles, biomass burning, leaded petrol, particle number size distribution, ultrafine particles, urban, elements, roadside|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Institution:||Queensland University of Technology|
|Deposited On:||29 May 2013 04:36|
|Last Modified:||03 Sep 2015 00:58|
Repository Staff Only: item control page