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

Abstract

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.