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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/amt-2020-204
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-204
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  17 Jul 2020

17 Jul 2020

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This preprint is currently under review for the journal AMT.

A new method for long-term source apportionment with time-dependent factor profiles and uncertainty assessment using SoFi Pro: application to one year of organic aerosol data

Francesco Canonaco1,2, Anna Tobler2, Gang Chen2, Yulia Sosedova1, Jay Gates Slowik2, Carlo Bozzetti1,2, Kaspar Rudolf Daellenbach3, Imad ElHaddad2, Monica Crippa4, Ru-Jin Huang5, Markus Furger2, Urs Baltensperger2, and André Stefan Henri Prévôt2 Francesco Canonaco et al.
  • 1Datalystica Ltd., Park innovAARE, CH-5234 Villigen, Switzerland
  • 2Paul Scherrer Institute, Laboratory of Atmospheric Chemistry, CH-5232 Villigen PSI, Switzerland
  • 3Institute for Atmospheric and Earth System Research, Helsinki, Finland
  • 4European Commission, Joint Research Centre (JRC), Via Fermi, 2749, 21027 Ispra, Italy
  • 5State Key Laboratory of Loess and Quaternary Geology, Center for Excellence in Quaternary Science and Global Change, and Key Laboratory of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China

Abstract. A new methodology for performing long-term source apportionment (SA) using positive matrix factorization (PMF) is presented. The method is implemented within the SoFi Pro software package and uses the multilinear engine (ME-2) as a PMF solver. The technique is applied to a one-year aerosol chemical speciation monitor (ACSM) dataset from downtown Zurich, Switzerland.

The measured organic aerosol mass spectra were analyzed by PMF using a small (14 days) and rolling PMF window to account for the temporal evolution of the sources. The rotational ambiguity is explored and the uncertainty of the PMF solutions were estimated. Factor/tracer correlations for averaged seasonal results from the rolling window analysis are higher than those retrieved from conventional PMF analyses of individual seasons, highlighting the improved performance of the rolling window algorithm for long-term data.

In this study four to five-factors were tested for every PMF window. Factor profiles for primary organic aerosol from traffic (HOA), cooking (COA) and biomass burning (BBOA) were constrained. Secondary organic aerosol was represented by either the combination of semi-volatile and low-volatility organic aerosol (SV-OOA and LV-OOA, respectively), or by a single OOA when this separation was not robust. This scheme leads to roughly 40 000 PMF runs. Full visual inspection of all these PMF runs is unrealistic and is replaced by predefined user-selected criteria, which allow factor sorting and PMF run acceptance/rejection. The selected criteria for traffic (HOA) and biomass burning (BBOA) were the correlation with equivalent black carbon (eBCtr) and the explained variation of m/z 60, respectively. COA was assessed by the prominence of a lunchtime concentration peak within the diurnal cycle. SV-OOA and LV-OOA were evaluated based on the fraction of m/z 43 and m/z 44 in their respective factor profiles. Seasonal pre-tests revealed a non-continuous separation of OOA into SV-OOA and LV-OOA, in particular during the warm seasons. Therefore, a differentiation between four-factor solutions (HOA, COA, BBOA and OOA) and five-factor solutions (HOA, COA, BBOA, SV-OOA and LV-OOA) was also conducted based on the criterion for SV-OOA.

HOA and COA contribute between 0.4–0.7 μg m−3 (7.8–9.0 %) and 0.7–1.2 μg m−3 (12.2–15.7 %) on average throughout the year, respectively. BBOA shows a strong yearly cycle with the lowest mean concentrations in summer (0.6 μg m−3, 12.0 %), slightly higher mean concentrations during spring and fall (1.0 and 1.5 μg m−3, or 15.6 and 18.6 %, respectively), and highest mean concentrations during winter (1.9 μg m−3, 25.0 %). In summer, OOA is separated into SV-OOA and LV-OOA, with mean concentrations of 1.4 μg m−3 (26.5 %) and 2.2 μg m−3 (40.3 %), respectively. For the remaining seasons the seasonal concentrations of SV-OOA, LV-OOA and OOA range from 0.3–1.1 μg m−3 (3.4–15.9 %), 0.6–2.2 μg m−3 (7.7–33.7 %) and 0.9–3.1 μg m−3 (13.7–39.9 %), respectively. The relative PMF errors modelled for this study for HOA, COA, BBOA, LV-OOA, SV-OOA and OOA are on average ±34 %, ±27 %, ±30, ±11 %, ±25 % and ±12 %, respectively.

Francesco Canonaco et al.

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Francesco Canonaco et al.

Francesco Canonaco et al.

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Short summary
Long-term ambient aerosol mass spectrometric data was analyzed with a statistical model (PMF) to obtain source contributions & fingerprints. The new aspects of this manuscript involve time-dependent source fingerprints by a rolling technique and the replacement of the full visual inspection of each run, by a user-defined set of criteria to monitor the quality of each of these runs more efficiently. More reliable sources will finally provide better instruments for political mitigation strategies.
Long-term ambient aerosol mass spectrometric data was analyzed with a statistical model (PMF) to...
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