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© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 07 Nov 2018

Research article | 07 Nov 2018

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

Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia

Athanasia Vlachou1, Anna Tobler1, Houssni Lamkaddam1, Francesco Canonaco1, Kaspar R. Daellenbach1,a, Jean-Luc Jaffrezo2, María Cruz Minguillón3, Marek Maasikmets4, Erik Teinemaa4, Urs Baltensperger1, Imad El Haddad1, and André S. H. Prévôt1 Athanasia Vlachou et al.
  • 1Department of General Energy Research, Paul Scherrer Institute, Villigen PSI, CH-5232, Switzerland
  • 2Université Grenoble Alpes, CNRS, IRD, G-INP, IGE, 38000 Grenoble, France
  • 3Institute of Environmental Assessment and Water Research (IDAEA), CSIC, 08034 Barcelona, Spain
  • 4Estonian Environmental Research Centre, 10617, Tallinn, Estonia
  • anow at: Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, P.O. Box 64, 00014, Helsinki, Finland

Abstract. Bootstrap analysis is commonly used to capture the uncertainties of a bilinear receptor model such as the positive matrix factorisation (PMF) model. This approach can estimate the factor related uncertainties and partially assess the rotational ambiguity of the model. The selection of the environmentally plausible solutions though can be challenging and a systematic approach to identify and sort the factors is needed. For this, comparison of the factors between each bootstrap run and the initial PMF output, as well as with externally determined markers, is crucial. As a result, certain solutions that exhibit sub-optimal factor separation should be discarded. The retained solutions would then be used to test the robustness of the PMF output. Meanwhile, analysis of filter sample with the Aerodyne aerosol mass spectrometer and the application of PMF and bootstrap analysis on the bulk water soluble organic aerosol mass spectra has provided insight into the source identification and their uncertainties. Here, we investigated a full yearly cycle of the sources of organic aerosol (OA) at three sites in Estonia, Tallinn (urban), Tartu (suburban) and Kohtla–Järve (KJ, industrial). We identified six OA sources and an inorganic dust factor. The primary OA types included biomass burning, dominant in winter in Tartu accounting for 73 % ± 21 % of the total OA, primary biological OA which was abundant in Tartu and Tallinn in spring (21 % ± 8 % and 11 % ± 5 %, respectively) and two other primary OA types lower in mass. A sulphur containing OA was related to road dust and tire abrasion which exhibited a rather stable yearly cycle and an oil OA was connected to the oil shale industries in KJ prevailing at this site comprising 36 % ± 14 % of the total OA in spring. The secondary OA sources were separated based on their seasonal behaviour: a winter oxygenated OA dominated in winter (36 % ± 14 % for KJ, 25 % ± 9 % for Tallinn and 13 % ± 5 % for Tartu) and was correlated with benzoic and phthalic acid implying an anthropogenic origin. A summer oxygenated OA was the main source of OA in summer at all sites (26 % ± 5 % in KJ, 41 % ± 7 % in Tallinn and 35 % ± 7 % in Tartu) and exhibited high correlations with oxidation products of α-pinene like pinic acid and 3-methyl-1, 2, 3-butanetricarboxylic acid (MBTCA) suggesting a biogenic origin.

Athanasia Vlachou et al.
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Status: final response (author comments only)
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Athanasia Vlachou et al.
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Short summary
The resolution of rotational ambiguity in positive matrix factorization (PMF) models is a major challenge. Here, we developed a method based on bootstrapping and correlations to extract environmentally meaningful solutions from PMF analysis based on offline aerosol mass spectrometry data. The method has been tested on a dataset that covers one full year of filter samples collected at three different sites in Estonia.
The resolution of rotational ambiguity in positive matrix factorization (PMF) models is a major...