Receptor modelling of both particle composition and size distribution from a background site in London, UK
- 1National Centre for Atmospheric Science School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
- 2MRC PHE Centre for Environment and Health, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK
- 3Department of Environmental Sciences/Center of Excellence in Environmental Studies, King Abdulaziz University, P.O. Box 80203, Jeddah, 21589, Saudi Arabia
Abstract. Positive matrix factorisation (PMF) analysis was applied to PM10 chemical composition and particle number size distribution (NSD) data measured at an urban background site (North Kensington) in London, UK, for the whole of 2011 and 2012. The PMF analyses for these 2 years revealed six and four factors respectively which described seven sources or aerosol types. These included nucleation, traffic, urban background, secondary, fuel oil, marine and non-exhaust/crustal sources. Urban background, secondary and traffic sources were identified by both the chemical composition and particle NSD analysis, but a nucleation source was identified only from the particle NSD data set. Analysis of the PM10 chemical composition data set revealed fuel oil, marine, non-exhaust traffic/crustal sources which were not identified from the NSD data. The two methods appear to be complementary, as the analysis of the PM10 chemical composition data is able to distinguish components contributing largely to particle mass, whereas the number particle size distribution data set – although limited to detecting sources of particles below the diameter upper limit of the SMPS (604 nm) – is more effective for identifying components making an appreciable contribution to particle number. Analysis was also conducted on the combined chemical composition and NSD data set, revealing five factors representing urban background, nucleation, secondary, aged marine and traffic sources. However, the combined analysis appears not to offer any additional power to discriminate sources above that of the aggregate of the two separate PMF analyses. Day-of-the-week and month-of-the-year associations of the factors proved consistent with their assignment to source categories, and bivariate polar plots which examined the wind directional and wind speed association of the different factors also proved highly consistent with their inferred sources. Source attribution according to the air mass back trajectory showed, as expected, higher concentrations from a number of source types in air with continental origins. However, when these were weighted according to their frequency of occurrence, air with maritime origins made a greater contribution to annual mean concentrations.