Abstract
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.
Highlights
Airborne particulate matter (PM) is recognised as a major public health concern across the EU, with costs estimated at EUR 600 billion in 2005 (Official Journal of the European Union, 2008)
The results for PM mass complement recent work on PM2.5 mass which compared the implementation of a chemical mass balance (CMB) model using organic and inorganic markers with source attribution by application of Positive matrix factorisation (PMF) to continuous measurements of non-refractory chemical components of particulate matter using an aerosol mass spectrometer (AMS) (Yin et al, 2015) and the AMS PMF carried out by Young et al (2015)
It must be remembered that the AMS is limited to sampling non-refractory aerosol and PM0.8, which will be different to the composition of PM10 considered in this study
Summary
Airborne particulate matter (PM) is recognised as a major public health concern across the EU, with costs estimated at EUR 600 billion in 2005 (Official Journal of the European Union, 2008). Most have provided some positive and often statistically significant associations with given source factors, chemical components or size fractions (Thurston et al, 2005; Mostofsky et al, 2012; Ostro et al, 2011), but to date there is no coherence between the results of different studies and there is no generally agreed ranking in the toxicity of particles from different sources (WHO, 2013) In this context, source apportionment methodology is tending to run ahead of epidemiology and is providing the tools for source apportionment which, far, epidemiological research has yet to utilise fully. Chemical composition and particle NSD data sets separately followed by analysis of the combined data set to test whether this provides advantages in terms of greater capacity to distinguish between source categories
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