Time-dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling positive matrix factorisation (PMF) window
Authors: Chen Gang , Sosedova Yulia , Canonaco Francesco , Fröhlich Roman , Tobler Anna , Vlachou Athanasia , Daellenbach Kaspar R. , Bozzetti Carlo , Hueglin Christoph , Graf Peter , Baltensperger Urs , Slowik Jay G. , El Haddad Imad , Prévôt André S. H. 
Published: October 2021

Abstract. We collected 1 year of aerosol chemical speciation monitor (ACSM) data in
Magadino, a village located in the south of the Swiss Alpine region, one of
Switzerland's most polluted areas. We analysed the mass spectra of organic
aerosol (OA) by positive matrix factorisation (PMF) using Source Finder
Professional (SoFi Pro) to retrieve the origins of OA. Therein, we deployed
a rolling algorithm, which is closer to the measurement, to account for the temporal changes in the source
profiles. As the first-ever application
of rolling PMF with multilinear engine (ME-2) analysis on a yearlong dataset that was collected
from a rural site, we resolved two primary OA factors (traffic-related
hydrocarbon-like OA (HOA) and biomass burning OA (BBOA)), one mass-to-charge
ratio (m/z) 58-related OA (58-OA) factor, a less oxidised oxygenated OA
(LO-OOA) factor, and a more oxidised oxygenated OA (MO-OOA) factor. HOA
showed stable contributions to the total OA through the whole year ranging
from 8.1 % to 10.1 %, while the contribution of BBOA showed an apparent
seasonal variation with a range of 8.3 %–27.4 % (highest during winter,
lowest during summer) and a yearly average of 17.1 %. OOA (sum of LO-OOA
and MO-OOA) contributed 71.6 % of the OA mass, varying from 62.5 % (in
winter) to 78 % (in spring and summer). The 58-OA factor mainly contained
nitrogen-related variables which appeared to be pronounced only after
the filament switched. However, since the contribution of this factor was
insignificant (2.1 %), we did not attempt to interpolate its potential
source in this work. The uncertainties (σ) for the modelled OA
factors (i.e. rotational uncertainty and statistical variability in the
sources) varied from ±4 % (58-OA) to a maximum of ±40 %
(LO-OOA). Considering that BBOA and LO-OOA (showing influences of biomass
burning in winter) had significant contributions to the total OA mass, we
suggest reducing and controlling biomass-burning-related residential heating as a mitigation
strategy for better air quality and lower PM levels in this region or
similar locations. In Appendix A, we conduct a head-to-head comparison
between the conventional seasonal PMF analysis and the rolling mechanism. We
find similar or slightly improved results in terms of mass concentrations,
correlations with external tracers, and factor profiles of the constrained
POA factors. The rolling results show smaller scaled residuals and enhanced
correlations between OOA factors and corresponding inorganic salts compared to
those of the seasonal solutions, which was most likely because the rolling
PMF analysis can capture the temporal variations in the oxidation processes
for OOA components. Specifically, the time-dependent factor profiles of
MO-OOA and LO-OOA can well explain the temporal viabilities of two main ions
for OOA factors, m/z 44 (CO2+) and m/z 43 (mostly
C2H3O+). Therefore, this rolling PMF analysis provides a more
realistic source apportionment (SA) solution with time-dependent OA sources.
The rolling results also show good agreement with offline Aerodyne aerosol
mass spectrometer (AMS) SA results from filter samples, except for in winter.
The latter discrepancy is likely because the online measurement can capture
the fast oxidation processes of biomass burning sources, in contrast to the
24 h filter samples. This study demonstrates the strengths of the rolling
mechanism, provides a comprehensive criterion list for ACSM users to
obtain reproducible SA results, and is a role model for similar analyses of
such worldwide available data.