A new method for long-term source apportionment with time-dependent factor profiles and uncertainty assessment using SoFi Pro: application to 1 year of organic aerosol data

• Main Authors: F. Canonaco, A. Tobler, G. Chen, Y. Sosedova, J. G. Slowik, C. Bozzetti, K. R. Daellenbach, I. El Haddad, M. Crippa, R.-J. Huang, M. Furger, U. Baltensperger, A. S. H. Prévôt
• Format: Article
• Language: English
• Published: Copernicus Publications 2021-02-01
• Series: Atmospheric Measurement Techniques
• Online Access: https://amt.copernicus.org/articles/14/923/2021/amt-14-923-2021.pdf

<p>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 1-year aerosol chemical speciation monitor (ACSM) dataset from downtown Zurich, Switzerland.</p> <p>The measured organic aerosol mass spectra were analyzed by PMF using a small (14 <span class="inline-formula">d</span>) and rolling PMF window to account for the temporal evolution of the sources. The rotational ambiguity is explored and the uncertainties 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.</p> <p>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 led 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 BBOA were the correlation with equivalent black carbon from traffic (eBC<span class="inline-formula">_tr</span>) and the explained variation of <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M3" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi>m</mi><mo>/</mo><mi>z</mi></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="23pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="d797e7418bb082ad5eec13189d6e5a75"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="amt-14-923-2021-ie00001.svg" width="23pt" height="14pt" src="amt-14-923-2021-ie00001.png"/></svg:svg></span></span> 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 fractions of <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M4" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi>m</mi><mo>/</mo><mi>z</mi></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="23pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="55e7511b997f0b1e1b80fcef0834494e"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="amt-14-923-2021-ie00002.svg" width="23pt" height="14pt" src="amt-14-923-2021-ie00002.png"/></svg:svg></span></span> 43 and 44 in their respective factor profiles. Seasonal <i>pre</i>-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.</p> <p>HOA and COA contribute between 0.4–0.7 <span class="inline-formula">µg m⁻³</span> (7.8 %–9.0 %) and 0.7–1.2 <span class="inline-formula">µg m⁻³</span> (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 <span class="inline-formula">µg m⁻³</span>, 12.0 %), slightly higher mean concentrations during spring and fall (1.0 and 1.5 <span class="inline-formula">µg m⁻³</span>, or 15.6 %<span id="page924"/> and 18.6 %, respectively), and the highest mean concentrations during winter (1.9 <span class="inline-formula">µg m⁻³</span>, 25.0 %). In summer, OOA is separated into SV-OOA and LV-OOA, with mean concentrations of 1.4 <span class="inline-formula">µg m⁻³</span> (26.5 %) and 2.2 <span class="inline-formula">µg m⁻³</span> (40.3 %), respectively. For the remaining seasons the seasonal concentrations of SV-OOA, LV-OOA and OOA range from 0.3 to 1.1 <span class="inline-formula">µg m⁻³</span> (3.4 %–15.9 %), from 0.6 to 2.2 <span class="inline-formula">µg m⁻³</span> (7.7 %–33.7 %) and from 0.9 to 3.1 <span class="inline-formula">µg m⁻³</span> (13.7 %–39.9 %), respectively. The relative PMF errors modeled for this study for HOA, COA, BBOA, LV-OOA, SV-OOA and OOA are on average <span class="inline-formula">±34 <i>%</i></span>, <span class="inline-formula">±27 <i>%</i></span>, <span class="inline-formula">±30 <i>%</i></span>, <span class="inline-formula">±11 <i>%</i></span>, <span class="inline-formula">±25 <i>%</i></span> and <span class="inline-formula">±12 <i>%</i></span>, respectively.</p>