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
Authors: Canonaco Francesco , Tobler Anna , Chen Gang , Sosedova Yulia , Slowik Jay Gates , Bozzetti Carlo , Daellenbach Kaspar Rudolf , El Haddad Imad , Crippa Monica , Huang Ru-Jin , Furger Markus , Baltensperger Urs , Prévôt André Stephan Henry 
Published: February 2021

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 1-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 d) 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. 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 (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 fractions of m/z 43 and 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 the 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 to 1.1 µg m−3 (3.4 %–15.9 %), from 0.6 to 2.2 µg m−3 (7.7 %–33.7 %) and
from 0.9 to 3.1 µg m−3 (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 ±34 %, ±27 %, ±30 %, ±11 %, ±25 % and ±12 %, respectively.