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Discussion papers
https://doi.org/10.5194/acp-2018-784
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2018-784
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 30 Aug 2018

Research article | 30 Aug 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

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

David C. S. Beddows1 and Roy M. Harrison1,a David C. S. Beddows and Roy M. Harrison
  • 1National Centre for Atmospheric Science, School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
  • aalso at: Department of Environmental Sciences/Center of Excellence in Environmental Studies, King Abdulaziz University, P.O. Box 80203, Jeddah, 21589, Saudi Arabia

Abstract. 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.

David C. S. Beddows and Roy M. Harrison
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Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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David C. S. Beddows and Roy M. Harrison
David C. S. Beddows and Roy M. Harrison
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Latest update: 20 Nov 2018
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Short summary
Airborne particles are a cause of illness and premature death. Cost-effective control of particles in the atmosphere depends upon a reliable knowledge of their sources. This paper proposes and tests a new method for attributing particles quantitatively to the sources responsible for their emission or atmospheric formation.
Airborne particles are a cause of illness and premature death. Cost-effective control of...
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