Receptor modelling of both particle composition and size distribution from a background site in London, UK - the two step approach.

Some air pollution datasets contain multiple variables with a range of measurement units, and combined analysis by Positive Matrix Factorization (PMF) is problematic, but can offer benefits from the greater information content. In this work, a novel method is devised and the source apportionment of a mixed unit data set (PM₁₀ mass and Number Size Distribution NSD) is achieved using a novel two-step approach to PMF. In the first step the PM₁₀ data is 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 apportioned NSD data associated with the 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. [ABSTRACT FROM AUTHOR]