Adrian J. Friend

Multi-criteria ranking and receptor modelling of airborne fine particles at three sites in the Pearl River Delta region of China.

The multi-criteria decision making methods, Preference Ranking Organization METHods for Enrichment Evaluation (PROMETHEE) and Graphical Analysis for Interactive Assistance (GAIA), and the two-way Positive Matrix Factorization (PMF) receptor model were applied to airborne fine particle compositional data collected at three sites in Hong Kong during two monitoring campaigns held from November 2000 to October 2001 and November 2004 to October 2005. PROMETHEE/GAIA indicated that the three sites were worse during the later monitoring campaign, and that the order of the air quality at the sites during each campaign was: rural site>urban site>roadside site. The PMF analysis on the other hand, identified 6 common sources at all of the sites (diesel vehicle, fresh sea salt, secondary sulphate, soil, aged sea salt and oil combustion) which accounted for approximately 68.8±8.7% of the fine particle mass at the sites. In addition, road dust, gasoline vehicle, biomass burning, secondary nitrate, and metal processing were identified at some of the sites. Secondary sulphate was found to be the highest contributor to the fine particle mass at the rural and urban sites with vehicle emission as a high contributor to the roadside site. The PMF results are broadly similar to those obtained in a previous analysis by PCA/APCS. However, the PMF analysis resolved more factors at each site than the PCA/APCS. In addition, the study demonstrated that combined results from multi-criteria decision making analysis and receptor modelling can provide more detailed information that can be used to formulate the scientific basis for mitigating air pollution in the region.

Source apportionment of ultrafine and fine particle concentrations in Brisbane, Australia

PurposeTo investigate the significance of sources around measurement sites, assist the development of control strategies for the important sources and mitigate the adverse effects of air pollution due to particle size.MethodsIn this study, sampling was conducted at two sites located in urban/industrial and residential areas situated at roadsides along the Brisbane Urban Corridor. Ultrafine and fine particle measurements obtained at the two sites in June–July 2002 were analysed by positive matrix factorization.ResultsSix sources were present, including local traffic, two traffic sources, biomass burning and two currently unidentified sources. Secondary particles had a significant impact at site 1, while nitrates, peak traffic hours and main roads located close to the source also affected the results for both sites.ConclusionsThis significant traffic corridor exemplifies the type of sources present in heavily trafficked locations and future attempts to control pollution in this type of environment could focus on the sources that were identified.

Sources of ultrafine particles and chemical species along a traffic corridor: comparison of the results from two receptor models

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.

Source apportionment of PM2.5 at two receptor sites in Brisbane, Australia

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.

Source apportionment of fine particles at a suburban site in Queensland, Australia

Environmental context Airborne 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. Abstract Airborne 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.