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 (
Atmospheric particulate matter (PM) affects human health and climate. In
particular, it influences the radiative balance
(IPCC, 2014; von Schneidemesser et al., 2015),
reduces visibility (Chow et al., 2002;
Horvath, 1993), and negatively affects human health by triggering
respiratory and cardiovascular diseases and allergies
(Daellenbach et
al., 2020; Dockery and Pope, 1994; Mauderly and Chow, 2008; Monn, 2001; Pope
and Dockery, 2006; von Schneidemesser et al., 2015). Fine PM exposure
strongly correlates with the global mortality rate.
Lelieveld et al. (2015) estimated that outdoor air
pollution, mostly PM
Organic aerosol (OA) constitutes 20 %–90 % of fine PM (Jimenez et al., 2009; Murphy et al., 2006; Zhang et al., 2007) and contain millions of chemical compounds. Since OA is the subject of an extremely complex mixture of chemical constituents, with highly dynamic spatial and temporal (seasonal, diurnal, etc.) variability in directly emitted particles and gas-phase precursors and complex chemical processing in the atmosphere, elucidation of the chemical composition and physical properties of OA remains challenging. Identification and quantification of OA sources with a sophisticated interpolation of spatial and temporal variabilities are essential for developing effective mitigation strategies for air pollution and a better assessment of the aerosol effect on both health and climate.
OA source apportionment (SA) and PM composition have been studied
extensively using an Aerodyne aerosol mass spectrometer (AMS)
(Canagaratna et al., 2007).
However, due to the complexity of the AMS measurements and their high
operational expenses, AMS campaigns are often limited to short periods of a
few weeks to months. The aerosol chemical speciation monitor (ACSM) allows
for unattended long-term observation (
Positive matrix factorisation (PMF; see Sect. S3.1 in the Supplement) has
been used in various studies for SA of OA
(Lanz et al., 2007; Aiken et al., 2009; Hildebrandt et al., 2011; Zhang et
al., 2011; Mohr et al., 2012; Schurman et al., 2015). The multilinear engine
(ME-2) implementation of PMF (Paatero, 1999) improves model
performance by allowing the use of a priori information (constraints on source
profiles and/or time series) to direct the model towards environmentally
meaningful solutions
(Canonaco
et al., 2013; Crippa et al., 2014; Fröhlich et al., 2015; Lanz et al.,
2008; Ripoll et al., 2015). For long-term data (1 year or more) with a high
time resolution, the composition of a given source could change considerably
due to meteorological and seasonal variabilities. However, a major
limitation of PMF is the assumption of static factor profiles, such that it
fails to respond to these temporal changes. Therefore, long-term chemically
speciated data have been evaluated monthly or seasonally
(Petit et al., 2014; Canonaco et al., 2015; Minguillón et al., 2015;
Ripoll et al., 2015; Bressi et al., 2016; Reyes-Villegas et al., 2016) to take at
least the seasonal variations into account. To improve the analysis of
long-term ACSM datasets, a novel approach that utilises PMF analysis over a
shorter rolling time window was first proposed by
Parworth et al. (2015) and further refined using ME-2 by Canonaco et al. (2021). The short
length of the rolling PMF window allows the PMF model to take the temporal
variations in the source profiles into account (e.g. biogenic versus
domestic burning influences on oxygenated organic aerosol (OOA)), which
normally provides better separation between OA factors. In addition, using
this technique together with bootstrap resampling and a random
In this work, we conducted a 1-year ACSM measurement campaign from September 2013 to October 2014 in Magadino, located in an alpine valley in southern Switzerland. We present a comprehensive analysis of the ACSM dataset measured in Magadino using a novel PMF technique, the “rolling PMF”. In addition, we also compare the results of the rolling PMF with the SA of offline AMS filter samples (Vlachou et al., 2018) and conventional seasonal PMF analysis.
Magadino, where the sampling site is located, is in a Swiss alpine valley (
This study measured chemical composition and mass loadings of non-refractory
constituents of ambient submicron aerosol particles (NR-PM
The quantification of ACSM data requires an estimation of the fraction of
NR-PM
Meteorological data, including temperature, precipitation, wind speed, wind
direction, and solar radiation, are monitored at the NABEL station. In
addition, concentrations of trace gases (SO
In this study, we used acsm_local_1610
software (Aerodyne Research Inc.) to prepare the PMF input matrix. In total,
this dataset includes 19 708 time points and 67 ions. Of these,
CO
In this study, we conducted a series of steps (Sect. S3.2 and S3.3 in the Supplement) to obtain the results we present in this paper. In summary, we first tested potential sources for each season with seasonal PMF pre-tests. Secondly, we obtained stable seasonal solutions from bootstrap seasonal analysis. Then, we conducted rolling PMF with certain settings (constraints, number of repeats, length of the window size, and step of rolling window). Lastly, we were able to retrieve robust results using specific criteria to define environmentally reasonable solutions. Please refer to Sect. S3.2 and S3.3 in the Supplement for more detailed description of each step. This section focuses on the general introduction of rolling PMF with ME-2, the differences between our method vs. the method developed by Canonaco et al. (2021), and the general settings of the rolling PMF analysis in this study.
Running PMF over the long-term ACSM datasets assumes that the OA source
profiles are static within this time window. It can lead to large errors
since OA chemical fingerprints are expected to vary over time
(Paatero et al., 2014). For example,
Canonaco
et al. (2015) showed that summer and winter OOA variability cannot be
accurately represented by a single pair of OOA profiles. A common way to
reduce the model uncertainty arising from this source is to choose a proper
number of OA factors (Sug Park et al., 2000) and then
perform a PMF analysis on a subset of measurements to capture temporal
features of OA chemical fingerprints. Such characterisation of OA sources on
a seasonal basis has been demonstrated in several studies
(Lanz
et al., 2008; Crippa et al., 2014; Petit et al., 2014; Minguillón et
al., 2015; Ripoll et al., 2015; Zhang et al., 2019).
Parworth et al. (2015) introduced the rolling PMF by running PMF in a small window (14 d), which advanced with a step of 1 d. This novel technique enables the
source profiles to adapt to the temporal variabilities. Canonaco et al. (2021) combined the rolling PMF technique with ME-2 (Sect. S3.1 in the
Supplement) to deal with the rotational ambiguity of the PMF analysis. In
addition, it also used the bootstrap resampling strategy (Efron,
1979) and random
This study mostly followed the methods developed by Canonaco et al. (2021) but with some modifications. The settings of the rolling PMF window is explicitly explained in Sect. S3.2.3 of the Supplement). In addition, we also performed a test of the rolling window size (i.e. 1, 7, 14, and 28 d) using a similar approach (Sect. S4 in the Supplement). As Canonaco et al. (2021) did, we also used the criteria-based selection function developed by Canonaco et al. (2021) to evaluate our PMF runs. The settings of the criteria are provided in Sect. S3.2.4 of the Supplement.
However, instead of using published reference factor profiles like Canonaco
et al. (2021) have done, we retrieved the reference profiles of primary and
local factors from seasonal bootstrap analysis (Sect. S3.2 in the
Supplement). Specifically, the reference profiles of the hydrocarbon-like OA
(HOA) factor and biomass burning OA (BBOA) factor were retrieved from the winter
(December, January, and February; DJF) bootstrapped PMF solution as shown in
Fig. S4, and we obtained the
Considering that the major part of eBC is within PM
The daily average PM
Chemical composition of PM
Seasonally averaged diurnal cycles of NR-PM
Seasonal, diurnal cycles of the measured PM
The automated rolling PMF analysis requires the knowledge of the reference
profiles as well as the number of factors. This section presents how the
number of factors was determined based on seasonal PMF pre-tests (refer to Sect. S3.2.1 in the Supplement for methodology). Initially, unconstrained PMF (three to six factors) was performed separately for the different seasons by following the SA guidelines provided by Crippa
et al. (2014). Typically, the HOA profile is characterised by a high
contribution of alkyl fragments (e.g.
No evidence for the presence of a cooking-related OA (COA) factor was found
based on the seasonal pre-analysis of the key fragments (
For the factor(s) with a secondary origin, we performed PMF models with a
different number of factors (three–six) to assess if the oxygenated OA (OOA)
factor is separable without mixing with primary organic aerosol (POA)
factors (with a high contribution of
We analysed the winter data first by constraining an HOA factor profile
(Crippa
et al., 2013) with a tight
After the bootstrap seasonal PMF runs of the winter data (details in Sect. S3.2.2 of the Supplement), we extracted the HOA and BBOA profiles to use them
as the reference factor profiles (Fig. S4) for the pre-tests of other
seasons. For the spring, summer, and autumn seasons, three- to six-factor PMF
solutions were modelled separately for each season by constraining the HOA
(
Here we present the optimised time window size (14 d) (details of the
time window optimisation are given in Sect. S4 of the Supplement and in
Fig. S10). In total, we considered 53.4 % of the PMF runs (11 087
out of 20 750) with only 11 non-modelled data points. The results of the
full-year PMF analysis of the 30 min resolved ACSM data are summarised in
Fig. 3. The relative contributions of the OA
factors are in addition shown in Fig. 3b. The primary traffic-related HOA had tiny variation (seasonal
averages between 8.1 % and 10.1 %) throughout the year
(Fig. 4). In contrast, BBOA showed a
distinct yearly cycle (8.3 %–27.4 %) with a yearly averaged contribution of
17.1 %. They increased significantly (to 27.4 %) in winter which is
typical of Alpine valleys (Szidat et al., 2007). This means
that biomass burning was the most important primary OA source during the
cold season in Magadino. The eBC
Annual cycles of OA components:
OA pie charts for the whole year and for the different seasons.
In this study, we retrieved two OOA factors, LO-OOA and MO-OOA. Total OOA
(LO-OOA
In this work, we made head-to-head comparisons between the seasonal bootstrap solutions and the rolling PMF results (see Figs. A1–A3 and Table A1 in the Appendix) in terms of mass concentrations, factor profiles, scaled residuals, and correlations between time series for each factor and corresponding external tracers. We found consistent factor profiles and mass concentrations for the constrained factors (i.e. HOA, BBOA, and 58-OA), while OOA factors showed some noticeable differences in both mass concentrations and factor profiles. Rolling PMF provided slightly better correlations and smaller scaled residuals. Therefore, we consider rolling PMF results to be more environmentally reasonable than those of the seasonal PMF (more details in Appendix A).
The primary and secondary OA factors retrieved as an annual mean of all
optimised PMF solutions together with their diurnal cycles for all seasons
are shown in Fig. 5. Note that the primary
factors (HOA, BBOA, and 58-OA) were constrained: the 58-OA profile was
tightly constrained with an
Overview of the primary and secondary OA components in
Magadino in 2013–2014:
Due to extensive residential wood combustion combined with winter
inversions, the concentrations of BBOA and eBC
Figure 6 also presents the diurnal cycles of HOA, eBC
Diurnal cycles of HOA (grey symbols), black carbon
apportioned to traffic emissions eBC
While
The
As shown in Fig. 7a, the data points in September–October (in both 2013 and 2014) were located on the right side of the triangle first presented by Ng et al. (2010), while the November (2013) data points were located within the triangle. In addition, the spring and summer data points (Fig. 7c and d) were all located instead on the right side of the triangle, but the winter points lay within the triangle (Fig. 7b). We made a similar plot but with a monthly resolution and different colour codes in Fig. S9. The data points located within the triangle correspond to the time with a lower temperature than that of those that are closer to the right side of the triangle in Fig. S9. This could be explained by the increased biogenic OOA contributions when the temperature was higher, as biogenic OOA tends to be distributed along the right side of the triangle (Canonaco et al., 2015; Pfaffenberger et al., 2013). Also, when the temperature decreases, the increased biomass emissions make the OOA points lie vertically within the triangle (Canonaco et al., 2015; Heringa et al., 2011), which is the case for the winter data (Fig. 7b).
In July 2014, the rolling PMF LO-OOA moved towards the left side of the plot
due to increasing influences from
In winter, LO-OOA (Fig. 9b) was
highly affected by biomass burning emissions characterised by the presence
of
Absolute statistical uncertainties in PMF for HOA, BBOA,
58-OA, LO-OOA, MO-OOA, and total OOA (LO-OOA
Figure 7 also highlights the advantages of rolling PMF over
seasonal PMF due to its time-dependent source profiles. Both seasonal and
rolling results show that the linear combinations of OOA factors could
adequately explain most of the measured OOA points for all the seasons.
However, with the static OOA factors for seasonal PMF solutions, it remains
challenging to capture the variabilities in some measured data points. In
contrast, the rolling PMF OOA factors can move correspondingly with the
temporal changes in the clouds, which moves the factor profiles closer to
reality and potentially decreases the scaled residuals significantly
(Fig. A3). Figure S9 also shows the
movements of LO-OOA and MO-OOA factor profiles monthly, where LO-OOA moves
towards the right direction as the temperature increases, except for the two
light blue squares (June and July) in Fig. S9a. It is clear that
temperature plays an important role for the positions of LO-OOA and MO-OOA
in the
As suggested by Canonaco et al. (2021), combining the bootstrap resampling
and the random
The mass concentrations for HOA, BBOA, and total OOA were compared with
corresponding offline AMS results
(Vlachou
et al., 2018) (Fig. S11). Despite some disagreement during winter
(BBOA and total OOA), BBOA showed a high correlation – with the offline
results for both PM
In this study, we conducted the first rolling PMF analysis on a 13-month set of
ACSM data collected at a rural site in Switzerland. With the help of the
small rolling PMF time window and the random
This paper also provided a recommended criterion list (Table S1) and a novel way to define thresholds with minimum subjective judgements
(Student's
This paper assessed the statistical and rotational uncertainties in the PMF
solution by combining the bootstrap resampling technique and the random
We also presented a head-to-head comparison between seasonal PMF solutions
and the rolling PMF solution. The POA factors showed good agreement between
seasonal and rolling PMF solutions, while the OOA factors exhibited greater
differences. Overall, the rolling PMF provided slightly better agreements
with external tracers, especially between the OOA factors and corresponding
inorganic salts. In addition, the rolling PMF results provided a better
representation of the measurements by adapting the temporal variations in
OOA factors in the
Knowledge of diurnal, seasonal, and annual changes in OA sources is essential for interpreting the yearly cycles of OA and defining mitigation strategies for air quality. With the help of more accurate and realistic OA sources, together with an estimation of the statistical uncertainty in PMF, more constraints can be provided for both climate and air quality models. These improved results are therefore highly valuable for policymakers to solve aerosol-related environmental issues.
The bootstrapped seasonal PMF solutions were compared with the full-year
rolling PMF results as follows. The correlations with external data, the ion
intensities in the factor profiles, and the mass concentrations retrieved
from the two different source apportionment techniques were compared for
each factor. The correlations of the factor time series with external data
(i.e. NO
Correlation coefficients (
Figure A1 shows a good agreement for two
techniques, except for MO-OOA and LO-OOA. In general, the slope of 1.09 for
rolling total OOA vs. seasonal OOA suggests the seasonal PMF method tends to
apportion more OOA components, while the slope (
Comparison of the mass concentrations resulting from rolling PMF and from the seasonal analysis for each factor (colour-coded by date and time).
The LO-OOA and MO-OOA factors showed worse agreement than the POA factors for the whole dataset. They had good correlations in each meteorological season, however, with different slopes. For instance, seasonal PMF underestimated LO-OOA in spring and autumn 2014, but both seasons showed a high correlation with rather narrow scattering. The over-apportionment of MO-OOA compensated for the under-apportionment of LO-OOA by seasonal PMF for these two seasons. Therefore, the summed OOA still showed a high correlation between rolling and seasonal PMF results. This is expected, as the rolling PMF allows the source profiles to adapt to temporal variations, while seasonal PMF has only static source profiles.
The differences in the major variables of the OOA factors (i.e.
The profiles of the constrained factors (HOA, BBOA, 58-OA) from the rolling results show a very high correlation with the seasonal results (Fig. A2), which suggests that the primary factors and the tightly constrained factor (58-OA) were consistent with the static profiles from the seasonal PMF analysis.
Profile comparisons between rolling results and seasonal results for each factor (log scale).
Distribution of the scaled residuals over the whole year for the
seasonal solution
We compared the scaled residuals from both source apportionment techniques (Fig. A3). The rolling PMF solution had smaller scaled residuals (narrower histogram and the centre is closer to 0) than those of the seasonal PMF solution, which is expected because rolling PMF had more flexibility to adapt to the temporal variabilities in the OA sources.
Summarising, HOA and BBOA were consistent for rolling and seasonal PMF analysis in terms of the time series, correlations with external tracers, and factor profiles due to the consistency of their chemical factor profiles. In contrast, the MO-OOA and LO-OOA factors were more scattered in averaged factor profiles and mass concentration, suggesting that seasonal PMF analysis was insufficient for capturing these temporal variations in their oxidation processes. Also, rolling PMF showed smaller scaled residuals. Therefore, we conclude that the rolling PMF analysis provides more realistic results than the seasonal analysis.
Data related to this paper are available at
The supplement related to this article is available online at:
GC analysed the ACSM and eBC data, performed the rolling source apportionment, and wrote the manuscript. YS wrote the preliminary manuscript and analysed preliminary results. GC, YS, FC, AT, KRD, JGS, IEH, UB, and ASHP helped edit and review the manuscript. YS, RF, and PG helped to run the campaign. PG and CH provided external data to validate PMF solutions. FC provided technical support for SoFi Pro. FC, AT, KRD, AV, JGS, IEH, UB, and ASHP participated in discussions for this study.
Yulia Sosedova, Francesco Canonaco, Anna Tobler, Carlo Bozzetti are working for Datalystica Ltd., the company that developed the SoFi Pro software. All authors declare no competing interests in any form for this work.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The ACSM measurements were supported by the Swiss Federal Office for the Environment (FOEN). The leading role of the Laboratory for Air Pollution and Environmental Technology of the Swiss Federal Laboratories for Materials and Testing (Empa) in supporting the measurements is very much appreciated. Yulia Sosedova acknowledges support by the Wiedereinsteigerinnen-Programm at the Paul Scherrer Institute. This study was also supported by the COST Action of Chemical On-Line cOmpoSition and Source Apportionment of fine aerosoL (COLOSSAL, CA16109).
This research has been supported by the European Research Council, H2020 European Research Council (ERA-PLANET (grant no. 689443)) and a COST-related project of the Swiss National Science Foundation, the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (SAMSAM (grant no. IZCOZ0_177063)).
This paper was edited by Maria Cristina Facchini and reviewed by three anonymous referees.