Abstract
Abstract. Some air pollution datasets contain multiple variables with a range of measurement units, and combined analysis using positive matrix factorization (PMF) can be problematic but can offer benefits through the greater information content. In this work, a novel method is devised and the source apportionment of a mixed unit dataset (PM10 mass and number size distribution, NSD) is achieved using a novel two-step approach to PMF. In the first step the PM10 data are PMF-analysed using a source apportionment approach in order to provide a solution which best describes the environment and conditions considered. The time series G values (and errors) of the PM10 solution are then taken forward into the second step, where they are combined with the NSD data and analysed in a second PMF analysis. This results in NSD data associated with the apportioned PM10 factors. We exemplify this approach using data reported in the study of Beddows et al. (2015), producing one solution which unifies the two separate solutions for PM10 and NSD data datasets together. We also show how regression of the NSD size bins and the G time series can be used to elaborate the solution by identifying NSD factors (such as nucleation) not influencing the PM10 mass.
Highlights
It is unquestionable that worldwide, the scientific vista of air quality is expanding, whether it is the increasing number of observatories or the refinement of information mined from the increasing sophistication of measurements often incorporated in campaign work
We exemplify this using PM10 and number size distribution (NSD) data collected from the North Kensington receptor site in London and start with the premise that we are completely satisfied with the PM10 analysis and are using a rotation which gives quantified factors which best represent the urban atmosphere sampled, i.e. the output from Beddows et al (2015)
Because of the nature of any factor analysis, we have to make the assumption that each source chemical profile and size distribution remains unchanged between source and receptor but that they remain constant throughout the measurement campaign
Summary
It is unquestionable that worldwide, the scientific vista of air quality is expanding, whether it is the increasing number of observatories or the refinement of information mined from the increasing sophistication of measurements often incorporated in campaign work. Studies using positive matrix factorization (PMF) as a tool for source apportionment of particle mass using multicomponent chemical analysis data are published frequently using datasets from around the world. They do not always provide consistent outcomes (Pant and Harrison, 2012), and one means by which source resolution and identification can be improved is by inclusion of auxiliary data, such as gaseous pollutants (Thimmaiah et al, 2009), particle number count (Masiol et al, 2017) or particle size distribution (Beddows et al, 2015; Ogulei et al, 2006; Leoni et al, 2018). The study used particle size distribution data collected at the Marylebone Road supersite in London in the autumn of 2007 and put forward a 10factor solution comprised of roadside and background particle source factors. Sowlat et al (2016) carried out a similar analysis on number size distribution (13 nm–10 μm) data combined with several auxiliary variables collected in Los
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