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Environmental context Identifying the sources responsible for air pollution is crucial for reducing the effect of the pollutants on human health. The sources of the pollutants were found here by applying two mathematical models to data consisting of particle size distribution and chemical composition data. The identified sources could be used as the basis for controlling or reducing emissions of air pollution into the atmosphere. Abstract Particulate matter is common in our environment and has been linked to human health problems particularly in the ultrafine size range. In this investigation, the sources of particles measured at two sites in Brisbane, Australia, were identified by analysing particle number size distribution data, chemical species concentrations and meteorological data with two source apportionment models. The source apportionment results obtained by positive matrix factorisation (PMF) and principal component analysis–absolute principal component scores (PCA–APCS) were compared with information from the gaseous chemical composition analysis. Although PCA–APCS resolved more sources, the results of the PMF analysis appear to be more reliable. Six common sources were identified by both methods and these include: traffic 1, traffic 2, local traffic, biomass burning and two unassigned factors. Thus motor vehicle related activities had the greatest effect on the data with the average contribution from nearly all sources to the measured concentrations being higher during peak traffic hours and weekdays. Further analyses incorporated the meteorological measurements into the PMF results to determine the direction of the sources relative to the measurement sites, and this indicated that traffic on the nearby road and intersection was responsible for most of the factors. The described methodology that utilised a combination of three types of data related to particulate matter to determine the sources and combination of two receptor models could assist future development of particle emission control and reduction strategies.

Environmental contextAirborne 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. AbstractAirborne 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.

Environmental contextFine 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. AbstractIn 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.

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.

Environmental context. Atmospheric submicrometre particles have a significant impact on human health, visibility impairment, acid deposition and global climate. This study aims to understand the size distribution of submicrometre particles and new particle formation in eastern Australia and the results indicate that photochemical reactions of airborne pollutants are the main mechanism of new particle formation. The findings will contribute to a better understanding of the effects of aerosols on climate and the reduction of submicrometre particles in the atmosphere. Abstract. An intensive measurement campaign of particle concentrations, nitrogen oxides and meteorological parameters was conducted at a rural site in subtropical eastern Australia during September 2006. The aim of this work was to develop an understanding of the formation and growth processes of atmospheric aerosols, and the size distributions under various meteorological conditions. In order to achieve this, the origins of air arriving at the site were explored using back trajectories cluster analysis and the diurnal patterns of particle number concentration and size distribution for the classified air masses were investigated. The study showed that the photochemical formation of nucleation mode particles and their consequent growth was often observed. Furthermore, the nucleation mode usually dominated the size distribution and concentration of the photochemical event in the first 3–4 h with a geometric mean diameter of 26.9 nm and a geometric standard deviation of 1.28. The average particle growth rate was estimated to be 1.6 nm h–1, which is lower than that observed at urban sites, but comparable to the values reported in clean environments. The potential precursors of the photochemical events are also discussed.