Real-time, in situ molecular composition measurements of the organic
fraction of fine particulate matter (PM2.5) remain challenging,
hindering a full understanding of the climate impacts and health effects of
PM2.5. In particular, the thermal decomposition and ionization-induced
fragmentation affecting current techniques has limited a detailed
investigation of secondary organic aerosol (SOA), which typically dominates
OA. Here we deploy a novel extractive electrospray ionization time-of-flight
mass spectrometer (EESI-TOF-MS) during winter 2017 in downtown Zurich,
Switzerland, which overcomes these limitations, together with an Aerodyne
high-resolution time-of-flight aerosol mass spectrometer (HR-TOF-AMS) and
supporting instrumentation. Positive matrix factorization (PMF) implemented
within the Multilinear Engine (ME-2) program was applied to the EESI-TOF-MS data to
quantify the primary and secondary contributions to OA. An 11-factor
solution was selected as the best representation of the data, including five primary and six secondary factors. Primary factors showed influence from
cooking, cigarette smoke, biomass burning (two factors) and a special local
unknown event occurred only during two nights. Secondary factors were
affected by biomass burning (three factors, distinguished by temperature and/or
wind direction), organonitrates, monoterpene oxidation, and undetermined
regional processing, in particular the contributions of wood combustion.
While the AMS attributed slightly over half the OA mass to SOA but did not
identify its source, the EESI-TOF-MS showed that most (>70 %) of
the SOA was derived from biomass burning. Together with significant
contributions from less aged biomass burning factors identified by both AMS
and EESI-TOF-MS, this firmly establishes biomass burning as the single most
important contributor to OA mass at this site during winter. High
correlation was obtained between EESI-TOF-MS and AMS PMF factors where specific
analogues existed, as well as between total signal and POA–SOA
apportionment. This suggests the EESI-TOF-MS apportionment in the current study
can be approximately taken at face value, despite ion-by-ion differences in
relative sensitivity. The apportionment of specific ions measured by the
EESI-TOF-MS (e.g., levoglucosan, nitrocatechol, and selected organic acids) and
utilization of a cluster analysis-based approach to identify key marker ions
for the EESI-TOF-MS factors are investigated. The interpretability of the
EESI-TOF-MS results and improved source separation relative to the AMS within
this pilot campaign validate the EESI-TOF-MS as a promising approach to source
apportionment and atmospheric composition research.
Introduction
Organic aerosol (OA) is relevant due to its roles in several atmospheric
processes including radiative forcing, visibility, heterogeneous reactions,
and uncertain effects on human health (Nel, 2005; Docherty et al., 2008;
Stocker et al., 2013). OA sources are typically classified as either directly
emitted primary organic aerosol (POA) or secondary organic aerosol (SOA)
formed from gas-to-particle conversion after chemical reactions. SOA is
estimated to comprise approximately 20 % to 90 % of OA, depending on
location and time of year (Jimenez et al., 2009; Hallquist et al., 2009).
Many studies have successfully linked POA to specific sources, but the level
of chemical characterization achieved by conventional online instrumentation
has been in most cases proven insufficient for quantitative resolution of
SOA source contributions and/or formation pathways. Therefore, the effects
of individual SOA sources on health and climate remain poorly constrained,
hampering the design of efficient emissions control policies.
A range of methods to measure molecular composition of aerosol particles
have so far mostly been conducted offline, using filter samples (Wang et
al., 2009, 2017; Daellenbach et al., 2017). Compared to online
methods, offline methods have low time resolution typically integrating
aerosol over hours and introducing sampling/storage artifacts
(Timkovsky et al., 2015). Moreover, offline measurement
techniques like gas chromatography–mass spectrometry (GC–MS) or liquid
chromatography–mass spectrometry (LC–MS), are chemically highly specific,
but often struggle with the fraction of mass that can be characterized
(typically <20 % of the total OA), which hinders our
understanding of the SOA.
Currently available online speciation techniques to measure aerosol particle
composition often rely on some type of thermal desorption and/or hard
ionization leading to thermal decomposition and/or ionization-induced
fragmentation of the original molecules. For example, the Aerodyne aerosol
mass spectrometer (AMS) vaporizes molecules at 600 ∘C followed by
electron ionization at 70 eV, facilitating quantification but yielding
extensive decomposition and fragmentation (Jayne et al., 2000; Sasaki et
al., 2001; Samy et al., 2011; Hayes et al., 2013). The chemical analysis of
an aerosol online proton-transfer-reaction mass spectrometer (CHARON-PTR-MS)
has no significant thermal decomposition but the ionization scheme fragments
typical SOA molecules (Eichler et al., 2015; Muller et al., 2017).
Several semicontinuous methods have also been developed, including
thermal desorption aerosol GC (TAG-MS, GC family; Williams et
al., 2006) and a gas and aerosol chemical-ionization time-of-flight mass
spectrometer (FIGAERO-CIMS; Lopez-Hilfiker et al.,
2014). However, these systems remain subject to some degree of thermal
decomposition, as well as potential reaction on the collection substrate,
and significantly lower time resolution. Above all, an online instrument
able to detect the original OA and resolve its chemical composition at the
molecular level with higher time resolution is needed. The Paul Scherrer
Institute (PSI) has developed such an instrument, i.e., the extractive
electrospray ionization time-of-flight mass spectrometer (EESI-TOF-MS),
measuring particles at the molecule level with a time resolution of seconds
while overcoming the usual limitations, e.g., thermal decomposition,
ionization-induced fragmentation, semicontinuous operation (Lopez-Hilfiker
et al., 2019).
Due to the lacking ability to apportion SOA to specific sources, a
terminology based on properties rather than sources was previously
introduced, such as the AMS-based discrimination into semi-volatile and
low-volatility oxygenated organic aerosol (SV-OOA and LV-OOA, respectively).
The current state-of-the-art SOA source apportionment is to be improved
based on large laboratory experiments which generate a “library” of
species of the SOA products (H. Zhang et al., 2015; Bianchi et al., 2017;
Nakao et al., 2011; Nah et al., 2016; X. Zhang et al., 2017). An isoprene OA
source was identified based on fragments in AMS and ACSM (aerosol chemical
speciation monitor) mass spectra that are consistent with those of
laboratory-generated isoprene SOA (via reactive uptake of epoxydiols,
IEPOX; Xu et al., 2015; Y. Zhang et al., 2017). Offline analysis
identified winter OOA and summer OOA, which to some extent appear to be
linked to sources (Daellenbach et al., 2017, 2016;
Bozzetti et al., 2017), even though the corresponding sources cannot be
retrieved. Zhang et al. (2018) combined the offline GC–MS method and
online FIGAERO-CIMS measurements to better characterize summertime
monoterpene SOA.
Domestic wood combustion has been identified as a major source of OA in
central Europe (Lanz et al., 2010; Herich et al., 2014), as well as in
Asia (Sun et al., 2013; Quan et al., 2014). Recent studies have been
devoted to the chemical characterization of the gas and particle-phase
emissions from biomass burning in the laboratory, to provide information for
a better source apportionment of primary and secondary biomass burning OA
(Iinuma et al., 2010; Nakao et al., 2011; Ofner et al., 2011; Chan et
al., 2005; Bruns et al., 2017; Bertrand et al., 2018). Various tracer
compounds for biomass burning were reported, including levoglucosan, which
is a sugar anhydride compound produced from the pyrolysis of cellulose and
hemicellulose (Fine et al., 2001), or methoxyphenols (e.g., guaiacol and syringol), derived from the pyrolysis of lignin
(Coeur-Tourneur et al., 2009; Veres et al., 2010; He et al., 2018), and
methyl-nitrocatechols, nitrated aromatic compounds from biomass burning
(Iimuma et al., 2010). Furthermore, biomass burning has been shown to
produce significant SOA in laboratory measurements (Bruns et al., 2016;
Nakao et al., 2011; Yee et al., 2013; Stefenelli et al., 2019a), but this
component has not yet been resolved in the field with the partial exception
of winter OOA.
Here, we report on a study in Zurich, a midsize city in central Europe,
utilizing the EESI-TOF-MS, complemented with AMS source apportionment results
for a winter case. Summer measurement and source apportionment are presented
in the companion paper (Stefenelli et al., 2019b). In both cases,
due to the enhanced chemical resolution of the EESI-TOF-MS we are able to
resolve more POA and SOA sources than in previous studies at the same site.
MethodologyMeasurement campaign
Measurements were performed from 25 January to 5 February 2017 at the Swiss
National Air Pollution Monitoring Network (NABEL) station at Zurich Kaserne,
Switzerland (Richard et al., 2011). The
station is located in the center of the metropolitan area of Zurich (1.3 million inhabitants). It is characterized as an urban background site,
although several restaurants are nearby (Lanz et
al., 2007). Long-term measurements at the site include ambient
meteorological data such as temperature, relative humidity (RH), solar
radiation, wind speed and direction, trace gas measurements comprising
nitrogen oxides (NOx, Thermo Environmental Instruments 42i, Thermo
Electro Corp., Waltham, MA) and ozone (O3, Thermo Environmental
Instruments 49C, Thermo Electro Corp., Waltham, MA), and particle
measurements which include size distributions (scanning mobility particle
sizer, SMPS, TSI) and number concentration (condensation particle counter,
CPC). Although the measurement period is relatively short (12 d), the
similarity of the AMS results obtained in the current study compared to
previous AMS and ACSM measurements at the same site (Lanz et al., 2007;
Canonaco et al., 2013; Richard et al., 2011; Daellenbach et al., 2016) gives
us high confidence that the sampled aerosol is representative of typical
wintertime conditions. Exceptions to this are resolved by the source
apportionment into unique event-driven factors, as discussed in the Results
section.
For the intensive campaign, an EESI-TOF-MS, an HR-TOF-AMS (Aerodyne Research
Inc.) and an SMPS were additionally deployed. The sampling was performed in
a mobile trailer installed outside the NABEL station. Ambient air was
sampled through a PM2.5 cyclone to remove coarse particles
(∼75cm above the trailer roof and ∼5m above
ground). The air passed through a stainless steel (∼6mm)
tube into the AMS, EESI-TOF-MS, and SMPS, installed on the same line and in
close proximity.
InstrumentationExtractive electrospray ionization time-of-flight mass spectrometer
(EESI-TOF-MS)
The extractive electrospray ionization time-of-flight mass spectrometer
(EESI-TOF-MS) is a novel instrument for real-time measurement of organic
aerosol without thermal decomposition or ionization-induced fragmentation.
The instrument is discussed in detail elsewhere (Lopez-Hilfiker et al.,
2019) and a brief overview is presented here. Ambient aerosol is
continuously sampled at 900 cm3min-1, either directly or through
a particle filter to yield a background measurement. In this study, 10 min
ambient air sampling was alternated with 2 min through the filter with
spectra recorded with 40 s time resolution. The flow then passes through a 5 cm long 6 mm outer diameter (OD) multichannel extruded carbon denuder
housed in a stainless steel tube, which removes most trace gas-phase
species. The denuder eliminates artifacts from semi-volatile species
desorbing from the filter, and also improves detection limits by reducing
the gas-phase background. The particle-laden flow then intersects a spray of
charged droplets generated by a conventional electrospray capillary.
Particles collide with the electrospray droplets and the soluble components
are extracted, ionized by Coulomb explosion of the charged droplets, and
detected by TOF-MS (resolution ∼4000 at mass-to-charge ratio
(m/z) 185). The electrospray droplets are generated by a commercially available
360 µm OD untreated fused silica capillary with an inner diameter of 50 µm (BGB Analytik). The sample flow remains unheated until after
extraction of aerosol material into the electrospray droplets, minimizing
volatilization of labile particle-phase components and thermal
decomposition. The droplets then enter the mass spectrometer through a
capillary heated to 250 ∘C; however, the very short residence
time in this capillary means that the effective temperature experienced by
the analyzer is much lower and no thermal decomposition is observed. The
electrospray working solution is a 50:50water/acetonitrile (>99.9 %, Sigma-Aldrich) mixture, which has less background signal compared
to the water/methanol mixture, with 100 ppm of sodium iodide (NaI) as a
charge carrier. Spectra are recorded in positive ion mode, in nearly all
cases as adducts with Na+. Depending on voltage settings in the ion
transfer optics (i.e., collision energy), clusters with acetonitrile can
potentially be detected; however, these clusters were observed to be
negligible with our settings. The recorded signals are linear with mass and
free of detectable matrix effects, in part due to the suppression of
ionization pathways other than Na+ adduct formation (Lopez-Hilfiker et
al., 2019). Here we report the signal measured by the EESI-TOF-MS in terms of
the mass flux of ions to the microchannel plate detector (ag s-1, neglecting Na+), calculated as shown in Eq. (1).
Mx=Ix⋅(MWx-MWcc)
Here Mx is the mass flux of ions united in attograms per second; x represents the
measured molecular composition. Ix is the recorded signal measured by
EESI-TOF-MS. MWx and MWcc represent the molecular weight of the ion
and the charge carrier (e.g., H+, Na+), respectively. Note that
this measured mass flux can be related to ambient concentration by the
instrument flow rate, EESI extraction/ionization efficiency, declustering
probability, and ion transmission, where several of these parameters are
ion-dependent (Lopez-Hilfiker et al., 2019). A comparison of the EESI-TOF-MS
mass flux to the AMS signal in terms of total signal or mass, bulk
properties, and source apportionment results is presented in Sect. 3.5.
With the EESI-TOF-MS, we almost continuously collected data from 25 January to
5 February 2017 (84.6 %), missing a few data points due to instrumental
calibration and issues such as cleaning the electrospray capillary due to
lost or unstable signal. EESI-TOF-MS stability and linearity with mass were
confirmed by periodic measurement of nebulized levoglucosan aerosol with
quantification of the mass concentration with an SMPS.
Data processing was executed using Tofware version 2.5.7 (Tofwerk AG, Thun,
Switzerland). The total number of 1125 fitted ions (including 882 Na+
adducts, one H+ adduct, and 242 unknown ions) between m/z 135 and 400 were
identified. Negligible signal was detected below m/z 135 due to the selected
mass spectrometer transmission window. Data were pre-averaged to 1 min time
resolution, and high-resolution peak fitting was performed. Individual 1 min
spectra were classified as ambient measurements, background sampling
(through the particle filter), or transitional measurements immediately
after switching between ambient and background sampling. Transitional
measurements were excluded from further analysis. Background spectra were
averaged across each 2 min filter period, and these filter periods were
interpolated to estimate the background spectrum during each ambient
sampling period. The estimated backgrounds were subtracted from individual
ambient spectra to yield the final ion time series of difference spectra.
Ions with a mean signal-to-noise ratio (SNR) below 2 were removed from
further analysis. No corrections for the relative sensitivity of individual
ions or drift in instrument sensitivity were applied. For the Multilinear
Engine (ME-2) source apportionment analysis (Sect. 2.3), data were
re-averaged to 2 min. The corresponding error matrix σij,
which has the same dimensions as the data matrix, follows the model of Allan
et al. (2003), whose calculation includes the uncertainty deriving from
electronic noise, ion-to-ion variability at the detector, and ion counting
statistics. The error estimates in this case incorporate the uncertainties
related to both the ambient measurements (δi) (direct ambient
sampling periods) and the background (βij) (filter blank
measuring periods; both are processed with Tofware), which are combined in
quadrature according to Eq. (2):
σij=δij2+βij2.
The final data matrix and error matrix have the size of 10 165 (time series) × 892 (variables).
Aerosol mass spectrometer (AMS)
An HR-TOF-AMS was deployed for online measurements of non-refractory (NR)
PM2.5 (with an inline PM2.5 cyclone). A detailed description of
the instrument can be found elsewhere (Jayne et al., 2000; DeCarlo et
al., 2006). The AMS recorded data with 1 min time resolution, of which 30 s
was spent recording the ensemble mass spectrum (mass spectrum (MS) mode) and
30 s recording size-resolved mass spectra (“particle time-of-flight (ePToF)
mode”). A Nafion dryer was used to dry the sampled air stream, which kept
the relative humidity (RH) of air below 30 % within the AMS. Particles
are continuously sampled (∼0.8Lmin-1) through a 100 µm critical orifice and are focused by a recently developed PM2.5
aerodynamic lens (Williams
et al., 2013). The
particles impact on a heated tungsten surface (heated to 600 ∘C) at
∼10-7 Torr (∼10-5Pa) and the NR components are flash vaporized.
The resulting gases are ionized by electron ionization (EI, ∼70eV) and the m/z values of the resulting fragments are determined by the TOF
mass spectrometer. The AMS was calibrated for inlet flow and ionization
efficiency (IE) at the beginning, middle, and end of the campaign following
standard protocols.
AMS data were analyzed in Igor Pro 6.36 using the Squirrel (version 1.57)
and Pika (1.16) analysis software (Donna Sueper, ToF-AMS high-resolution
analysis software). The collection efficiency (CE) was estimated using a
composition-dependent collection efficiency (CDCE) algorithm
(Middlebrook et al., 2012) implemented in Squirrel. A
CE=0.5 was assumed except in the case of strongly acidic aerosols, and
high ammonium nitrate content where the approach by Middlebrook et al. (2012) was applied.
For ME-2 analysis, the input matrices consisted of the time series of fitted
ions from high-resolution mass spectral analysis, together with their
corresponding uncertainties (Allan et al., 2003). According to the
recommendations of Ulbrich et al. (2009), a minimum error value was added to
the error matrix and ions were assessed and treated according to their
signal-to-noise ratio (SNR) as follows: ions with an SNR less than 0.2 were
excluded from ME-2 analysis, while those with an SNR between 0.2 and 2 were
down-weighted by increasing their uncertainties by a factor of 2. Further,
ions that were not independently fit but rather calculated from
CO2+ were removed to avoid overweighting CO2+. Likewise,
isotopes were not included in the matrices to avoid overweighting the parent
ions. The source apportionment input matrices consisted of 257 ions between
m/z 12 and 120.
Source apportionment techniques
Source apportionment was performed on the organic AMS and EESI-TOF-MS data
separately using PMF as implemented by the Multilinear Engine (ME-2)
(Paatero, 1997) and with model configuration and analysis
executed via the SoFi (Source Finder, version 6.39) interface
(Canonaco et al., 2013), coded in Igor Pro
(WaveMetrics 6.37). PMF represents the input data matrix as a linear
combination of characteristic factor profiles and their time-dependent
contributions, which can be expressed in matrix notation as
X=G×F+E.
The measured X is an m×n matrix, representing m
measurements of nm/z. G and F are m×p and p×n matrices,
respectively, where p is the number of factors contained in a given model
solution and is selected by the user.
Equation (3) is solved using a least-squares algorithm that iteratively
minimizes the quantity Q (Eq. 4), defined as the sum of the squared residuals
weighted by their respective uncertainties, where the uncertainty may
contain the measurements and model uncertainty:
Q=∑i∑jeijσij2.
Here, eij represents the residuals (elements of E), with i
and j denoting respectively the time and m/z indices, and σij is the
corresponding measurement uncertainty. Rotations are explored by using the
a value approach, here implemented by constraining one or more output factor
profiles to resemble a selected source, improving source separation
(Crippa et al., 2014; Canonaco et al., 2013). The a value (ranging from 0
to 1) determines how much the constrained factor (fj, solution) is
allowed to vary from its anchor (fj), as defined in Eq. (5).
fj,solution=fj±a⋅fj
Execution of PMF analysis on separated AMS and EESI-TOF-MS datasets minimizes
the complexity of the analysis, while maximizing the factor resolution
ability of the EESI-TOF-MS. The factor related to traffic was constrained for the
AMS analysis, while a factor related to cigarette smoke was constrained for
the EESI-TOF-MS. Details are presented in Sect. 3.1 and 3.2. Different factors
were constrained in the two datasets due to the fundamental differences
between the AMS and EESI-TOF-MS measurements. Specifically, the absence of
fragmentation in the EESI-TOF-MS allowed clear separation of cooking without
the need for constraints, while separation of a cigarette smoke factor was
only achieved for the EESI-TOF-MS. Constraining an AMS cigarette smoke factor
was attempted but failed.
Wind regression analysis
Wind regression analysis has been developed as a means of using
meteorological and pollutant data to estimate the percent of a given
pollutant originating from a specific wind sector. This study utilizes the
sustained wind incidence method (SWIM), a quantitative model that estimates
the weighted pollutant concentrations and uncertainties from a given wind
direction and speed (Henry et al., 2009; Olson et
al., 2012). The expected concentration (E) of a pollutant for each wind
direction–wind speed pair (θ, u) is calculated as a weighted average
of the concentration data in a window around (θ, u) represented by
smoothing parameters σ and h using a weighting function K(θ,u,σ,h)=K1(θ,σ)K2(u,h), given by Eq. (6):
6θE(C|u)=∑i=1NK1θ-Wiσ⋅K2u-Uih⋅Ci∑i=1NK1θ-Wiσ⋅K2u-Uih,7K1(x)=12π⋅e-0.5⋅x2,-∝<x<∝,8K2x=0.75⋅1-x2,-1<x<1,9Wi=CiUimax(CiUi)⋅(σθi)¯σθi,
where Ci, Ui, and
Wi are the observed concentrations of a
particular pollutant, resultant wind speed, and directional standard
deviation, respectively; N is the total number of
observations; K1 (Eq. 7) and
K2 (Eq. 8) are smoothing Gaussian kernel
and the Epanechnikov kernel; and σ and h are smoothing parameters for
wind direction and wind speed, respectively. The conditional probability of
a pollutant concentration (Eq. 6) is then weighted by the frequency of the
wind using a joint probability of wind speed and wind direction, resulting
in the following expression for the mean value of the pollutant
concentration associated with winds from the sector defined by the intervals
U (U=[u1,u2]) and Θ (Θ=[θ1,θ2]).
SΘ,U=∫u1u2∫θ1θ2f(θ,u)E(C|θ,u)dθdu
The joint probability of wind speed and wind direction (f) is calculated by
using a kernel density, estimated as
fθ,u=1Nσh∑i=1NK1θ-WiσK2(u-Ui)h.
Calculations have been performed on Igor Pro with the ZeFir package
(Petit et al., 2017).
Identification of source-specific ions
To determine ions characteristic of individual factors (or groups of related
factors), agglomerative hierarchical clustering was conducted on the
EESI-TOF-MS matrix of PMF profiles and standardizing data along the ions,
clustering first along the columns (producing row-clustered groups of
factors), and then along the rows (producing the clustered ions for each
group) in the matrix data. In hierarchical cluster analysis, a dendrogram,
used to show relationships between members of a group, i.e., a family tree
with the oldest common ancestor at the base and branches for various
divisions of lineage, was generated with the following steps (MATLAB R2017b).
(1) Calculate the distance by using Euclidean distance to find the
similarity or dissimilarity between every ion and every pair of factors in
our dataset. (2) Link pairs of ions and factors that are in close proximity
using the average linkage function. (3) Use the cluster function to prune
branches off the bottom of hierarchical tree, and assign all the objects
below each cut to a single cluster. Here, the clustergram function
transforms the standardized values so that the mean is 0.
Results and discussion
Results of AMS and EESI-TOF-MS PMF analyses are presented in Sect. 3.1 and
3.2, respectively. Section 3.3 focuses on the EESI-TOF-MS. PMF results are then
exploited to assess the apportionment of specific ions related to key marker
compounds (Sect. 3.3) and to identify groups of molecules uniquely
characteristic of the retrieved factors (Sect. 3.4). However, quantitative
interpretation of the EESI-TOF-MS PMF results is complicated by differences in
the relative sensitivity of the EESI-TOF-MS to different molecules. Therefore
Sect. 3.5 presents a comparison of the EESI-TOF-MS and AMS results in terms
of total signal, bulk atomic composition, and relative apportionment to the
different factors.
AMS source apportionment
The AMS PMF analysis yielded seven OA factors: hydrocarbon-like OA
(HOAAMS), cooking-related OA (COAAMS), biomass burning OA
(BBOAAMS), two oxygenated OA factors (less oxygenated, LO-OOAAMS,
and more oxygenated, MO-OOAAMS), nitrogen-containing OA (NOAAMS),
and a factor due to an isolated local event (EVENTAMS). The factor mass
spectra are shown in Fig. 1, while Fig. S1 in the Supplement shows the factor time series,
together with selected external tracers, and diurnal cycles, which may be
less convincing due to the short period of the measurement. Salient
characteristics of these factors are discussed below; HOAAMS,
COAAMS, BBOAAMS, LO-OOAAMS, and MO-OOAAMS are similar to
factors frequently observed in other studies (Crippa et al., 2013a; Zhang
et al., 2011; Young et al., 2016).
Factor profiles for the seven-factor AMS PMF solution. HOAAMS is
constrained by an a value of 0.1. The total signal of each factor is normalized to
unity (HOAAMS: hydrocarbon OA; COAAMS: cooking-related OA;
BBOAAMS: biomass burning OA; OOAAMS: oxygenated OA; NOAAMS:
nitrogen-containing OA; EVENTAMS, an isolated local event).
HOAAMS was constrained using a factor mass spectrum from Paris
(Crippa et al., 2013b) and an a value of 0.1
(the a value was selected according to the correlations between the time
series of HOA with the traffic species NOx), yielding a factor with a low
O:C ratio (0.04) and high H:C ratio (1.8), consistent with a dominant
contribution from aliphatic hydrocarbons. Strong signals from
CxHy+ ions are evident, especially C3H5+,
C3H7+, C4H7+, and C4H9+ ions.
Consistent with previous studies, the HOA mass spectrum is similar to
vehicle emission studies (Zhang et al., 2005; Sun et al., 2012; Young et
al., 2016).
The COAAMS mass spectrum is similar to primary cooking emissions
(Crippa et al., 2013b) and exhibits a
unique diurnal pattern peaking during lunch- and dinnertime. The COAAMS
spectrum is characterized by a C4H7+:C4H9+
ratio and a high fraction of C3H3O+ and C4H7+,
consistent with COAAMS factors previously identified at urban locations
(Crippa et al., 2013a; Ge et al., 2012; Mohr et al., 2012).
BBOAAMS has been identified as a significant source of aerosol in
previous wintertime source apportionment studies in Switzerland and central
Europe (Lanz et al., 2008; Daellenbach et al., 2017). Similar to previous
studies, BBOAAMS shows a high fraction C2H4O2+ at
m/z 60 and C3H5O2+ at m/z 73 and explains most of the
variation in these ions (77 %, 65 %, respectively). A strong diurnal
trend is evident, with concentrations peaking overnight and decreasing
during the day.
LO-OOAAMS and MO-OOAAMS mass spectra are characterized by dominant
peaks at m/z 28 (CO+) and 44 (CO2+), similar to OOAAMS factors
observed at other sites (Sun et al., 2011; Ng et al., 2010). The main
difference between the LO-OOAAMS and MO-OOAAMS mass spectra is the
relative contribution of C2H3O+ compared to CO2+,
with C2H3O+ enhanced in LO-OOAAMS. Also enhanced in
LO-OOA are ions at m/z 39 (C3H3+), 41 (C3H5+), and 55
(C4H7+). Further insight into the OOAAMS factors is
obtained through the EESI analysis (Sect. 3.2).
NOAAMS exhibits a significantly higher N:C ratio (0.04) than the other
factors and explains most of the organic nitrogen signal. This factor
includes a strong signal from C5H10N+ signal (m/z 84), which is
consistent with N-methyl-pyrrolidine, which has previously been identified in
AMS spectra as a tracer for cigarette smoke (Struckmeier et al.,
2016). This ion is also observed in the EI mass spectra of nicotine (NIST,
https://webbook.nist.gov/cgi/cbook.cgi?ID=C54115&Mask=200\#Mass-Spec, last access: May 2018).
However, other spectral features (e.g., the high CO2+ signal) are
not typical of primary cigarette smoke and suggest a contribution from
secondary formation processes. This interpretation is consistent with
correlations of NOAAMS with EESI-TOF-MS factors, suggesting NOAAMS to
be a mixed factor, as discussed in Sect. 3.2 and 3.4.
The EVENTAMS factor is a special case in our study as the mass spectrum
is dominated by m/z 15 (CH3+), 27 (C2H3+), 31
(CH3O+), and 43 (C2H3O+). The time series only
contributes during two nights (28 and 29 January) from 00:00 to
07:00 UTC+2 with the concentrations peaking at 3.8 µgm-3 but being
below 0.2 µgm-3 for the rest of the study. No associations with
any markers are evident.
EESI-TOF-MS source apportionment
An 11-factor solution was selected as the best representation of the
EESI-TOF-MS data, with five factors attributed mostly to POA and six to SOA. The POA
factors include cooking-related OA (COAEESI), two less aged biomass
burning factors (LABB1EESI and LABB2EESI), which are mostly
dominated by primary organic aerosol compounds, cigarette-smoke-influenced
OA (CS-OAEESI), and a factor related to an isolated special event
(EVENTEESI). The SOA factors consist of three more aged biomass burning
factors dominated by secondary organic aerosol compounds and distinguished
by mean daily temperature (MABB_LOWEESI,
MABB_HIGHEESI, and MABB_TRANSEESI,
corresponding to low temperature, high temperature, and transition periods,
respectively), two additional SOA factors lacking a clear attribution to
biomass burning (SOA1EESI and SOA2EESI), and nitrogen-containing
SOA (NSOAEESI). This solution was obtained by constraining the
CS-OAEESI factor with an a value of 0.1, and all other factors
unconstrained. This constraining approach and the solution selection
criteria are discussed in Sect. 3.2.1, while the POA and SOA factors are
discussed in Sect. 3.2.2 and 3.2.3, respectively. A detailed investigation
of the factor mass spectra is presented in Sect. 3.4.
Selection of PMF solution
In selecting the PMF solution that best represents the EESI-TOF-MS dataset, we
considered both mathematical diagnostics (e.g., Q/Qexp and residuals) as
a function of the number of factors, as well as the interpretability of the
retrieved factors. Interpretability was judged according to
correlation of the time series and diurnal patterns between the AMS and
EESI factors
comparison of factor profiles with mass spectra retrieved from less and more
aged biomass burning exhaust from simulation chamber experiments at PSI
(Bertrand et al., 2018)
similarities to EESI-TOF-MS factor mass spectra retrieved from summer
measurements at the same site in Zurich (Stefenelli et al., 2019b)
identification of key ions in the factor profiles, including ions
contributing a major fraction of the total factor signal, ions apportioned
predominantly to a certain factor or related to a set of factors, and ions
established in the literature as known tracers for specific
sources/processes
interpretation of the temporal behavior in terms of meteorological data,
including temperature, solar radiation, and wind speed/direction.
For the EESI-TOF-MS source apportionment, we considered unconstrained solutions
from 7 to 20 factors (see Fig. S2a). Of these solutions, a 10-factor
solution was found to best explain the data at a preliminary stage. This was
preferred to lower-order solutions because all factors were interpretable
according to the above criteria. Solutions with more factors lead to
additional factors related to more aged biomass burning without obvious
additional information. In addition, the investigation of Q/Qexp as a
function of the number of factors (Fig. S2b) did not show any significant
change with the increase in the a value from seven factors. Figures S3 and S4
show the mass spectra and time series of the 8- to 11-factor solutions.
Nonetheless, the unconstrained 10-factor solution revealed evidence of
factor mixing, as the cooking-related (COAEESI) factor mass spectrum
had a strong contribution from m/z 163 (C10H15N2, nicotine),
which should rather be associated with cigarette smoke (Fig. S5). This
suggests that at least one more factor remains to be resolved. The
difficulty in separating these factors, despite their expected chemical
differences, is likely due to strong temporal correlation between cooking
and cigarette-smoking emissions due to the proximity of local restaurants
(Fig. S6, the diurnal patterns of nicotine and COAEESI factors), where
people gather outside to smoke during mealtimes. We therefore attempted to
obtain a clean cigarette smoking signature from the dataset to serve as an
anchor profile with which to constrain this source. For solutions with
15 to 20 factors, a factor was retrieved with a mass spectrum
dominated by nicotine and to which >90 % of nicotine was
apportioned. We therefore constructed a profile (average from 15 to 20 factors)
for this nicotine-containing factor (apportioned to cigarette smoke, i.e., CS-OAEESI). This profile was then constrained in an 11-factor solution
(based on the selection of a 10-factor unconstrained solution, as discussed
above) using an a value approach (from 0 to 1 with steps of 0.1, 0.1 was
chosen finally). The main criterion of the constraint was the fraction of
nicotine apportioned to the constrained factor. Also in our case, the R
(Pearson) for the correlations between the time series of the solutions was
constructed with the final 11-factor solution. Based on these
considerations, we concluded that the source apportionment solution with
11 factors was the optimal solution.
Figure 2a shows the time series of the five EESI-TOF-MS factors attributed to
primary organic aerosol: COAEESI, LABB1EESI, LABB2EESI,
CS-OAEESI, and EVENTEESI. Also shown are relevant ancillary
measurements, including AMS PMF factors and meteorological parameters.
Figure 2b shows the corresponding factor mass spectra, colored by the number
of nitrogen atoms. A discussion of each factor follows. Figure 3a shows the
diurnal patterns of the LABBEESI factors, as well as COAEESI and
COAAMS.
Time series of the POA factors retrieved from EESI-TOF-MS PMF analysis,
along with ancillary data (a), and corresponding factor profiles (b). For
all y axes, EESI-TOF-MS data are shown in mass flux (ags-1), AMS data are
shown in micrograms per cubic meter, and other units are given. Factor profiles are
molecular weighted and are normalized such that the sum of each profile is 1.
(a) Diurnal cycles of the EESI-TOF-MS less aged biomass burning and
cooking factors, together with AMS cooking. (b) Van Krevelen plot (atomic H:C
vs. O:C of each neutral compound) for the COAEESI factor mass spectrum,
with points sized by the fraction of each neutral compound apportioned to
COAEESI and colored by number of carbon atoms.
(a, b) Van Krevelen plot (atomic H:C vs. O:C ratio) of the
LABB1EESI and LABB2EESI factor mass spectra. Points are sized by
the fraction of each neutral compound apportioned to LABB1EESI and
LABB2EESI and colored by number of carbon atoms.
Less aged biomass burning
(LABB1EESI and
LABB2EESI)
The LABB factors are both enhanced at night, consistent with domestic
heating activities. Considering the full campaign time series (Fig. 2a),
this repeating pattern, opposed to solar radiation, is evident for
LABB1EESI, while the time series of LABB2EESI is driven by intense
events (∼6.5 times higher than LABB1EESI) during two
nights: from 18:00 on 27 January to 08:00 on 28 January, and from 18:00 on
28 January to 08:00 on 29 January. As shown in Fig. 2b, both factor profiles
are dominated by C6H10O5 and C8H12O6.
C6H10O5 is attributed primarily to levoglucosan, which is a
well-established tracer for biomass burning. The mass spectrum features of
both factors are very similar to less aged biomass burning emissions
measured directly from a domestic biomass combustion appliance in the PSI
smog chamber (Bertrand et al., 2018). Figure 4a and b show Van
Krevelen plots (i.e., atomic ratios H:C as a function of O:C) for
LABB1EESI and LABB2EESI, respectively, with points colored by the
number of carbon atoms and sized by the fraction of each neutral compound
apportioned to the respective factor. Both LABB1EESI and LABB2EESI
are dominated by compounds with low H:C (1.04) and low O:C (0.35, excluding
the sugars C6H10O5 and C8H12O6, which
exhibit high variability; Table S1 in the Supplement), suggesting a strong contribution from
primary or slightly aged aromatics. The wind regression of these two factors
is shown in Fig. S7. LABB1EESI does not correspond to a specific wind
direction, in contrast, LABB2EESI originates predominantly from a
single wind direction, excluding the smaller sources to the SE on the third
day. Figure S8 compares the BBOAAMS factor (Fig. 2a) with
LABB1EESI, LABB2EESI, and the sum of LABB1EESI+LABB2EESI, with R values of 0.59, 0.79, and 0.82, respectively. The
correlation is generally good except during the first part of the campaign
(25 to 27 January), which as discussed later, relates to the
complexity of wood burning classification between the EESI-TOF-MS and AMS. The
correlation of BBOAAMS with either LABB2EESI or
LABB1EESI+LABB2EESI is rather high at night (R=0.59 to 0.82),
while the concentrations of the LABBEESI factors are consistently lower
than the one of BBOAAMS during the day. We assign the high correlation
of LABB2EESI with BBOAAMS to the high abundance of levoglucosan,
which drives the variation in f60 in the AMS. Some specific features of
BBOAAMS do not appear in any LABB factor because less aged and more
aged biomass burning OA are not unambiguously separated in the AMS.
Cooking-related OA (COAEESI)
The COAEESI and COAAMS factor time series are strongly correlated
(R=0.88), as shown in Fig. 2a. The diurnal variation in the COAEESI is
also similar to COAAMS, with strong peaks at lunch- and dinnertime
(Fig. 3a). In addition to this diurnal pattern, both COAEESI and
COAAMS are significantly elevated during two periods: from 18:00 on 27 January to 01:00 on 28 January (Friday night), and from 18:00 on 28 January
to 01:00 on 29 January (Saturday night). These periods occur on the same
evening as the unknown special event giving rise to the EVENTAMS and
EVENTEESI factors, but are slightly offset in time, with the COA
factors peaking approximately 4 h earlier. The distinct contribution
from the COAEESI factor is due in part to the location of several
restaurants within a 100 m radius, including one adjacent to the site.
As shown in Fig. 2b, the COAEESI mass spectrum is unique in having most
of the mass at ions with higher m/z. Several of the dominant ions can be
attributed to fatty acids and alcohols, which are associated with cooking
emissions and oils. For example, C13H22O4 (dibutyl
itaconate), C16H30O3 (2-oxo-tetradecanoic acid), and
C18H34O3 (ricinoleic acid) are prominent, and contribute
0.89 %, 1.7 %, and 2.0 %, respectively, of the total mass spectrum.
Figure 3b shows a Van Krevelen plot of the COAEESI factor mass
spectrum, with points sized by the fraction of each neutral compound
apportioned to COAEESI and colored by the number of carbon atoms. The
dominant contribution of ions with higher carbon number (C13–C25) and high
H:C ratio (greater than 1.5) but low O:C ratio (below 0.2) indicates that
these compounds are consistent with fatty acids or alcohols rather than
aromatic-derived compounds.
Special event (EventEESI)
The time series of EVENTEESI is highly correlated with EVENTAMS
(R=0.99, Fig. 2a). Both factors are near zero except for two intense events
beginning at approximately midnight and lasting till the early morning on 28
and 29 January, supporting the hypothesis of a unique event as opposed to
variation in BBOA. The Zurich game festival was taking place on the weekend
(the event is apparently held in a building on the SW side of the
courtyard), though no human activities in the immediate vicinity of the
sampling inlet were evident by inspection of the on-site camera. The
EESI-TOF-MS factor mass spectrum is dominated by an ion at m/z 174.08, tentatively
assigned to C8H11N2O. However, the EESI-TOF-MS does not provide
structural information and to our knowledge no compound with this formula
has been reported as a major constituent of an atmospheric emission source,
preventing its use as a diagnostic tracer. Other significant ions are
C8H12O4 and C8H18O5. The
C8H12O4 ion likely represents 1,2-cyclohexane dicarboxylic
acid diisononyl ester, a plasticizer for the manufacture of food packaging,
belonging to the group of aliphatic esters from a chemical point of view.
This indicates that the source may be from food plastic burning in a nearby
restaurant.
Cigarette-smoke-influenced OA
(CS-OAEESI)
Cigarette-smoke-influenced OA (CS-OAEESI) is a constrained factor,
based on a reference profile retrieved from higher-order PMF solutions as
described in Sect. 3.2.1. The mass spectrum of CS-OAEESI is dominated
by the C10H14N2H+ ion (Fig. 2b). This ion is the only
ion (out of 892 ions) that does not appear as an adduct with Na+.
Instead, the observed molecular formula corresponds to that of nicotine with
an extra hydrogen. As a reduced nitrogen compound, nicotine likely forms a
stable ion by abstracting a hydrogen from water, leading to the observed
cation. However, the time series and the mass flux of this ion should be
interpreted with caution: because it is formed by a different ionization
pathway than the majority of the spectrum, its relative sensitivity may be
significantly different from that of the other ions. Additionally, we have
not characterized such non-Na adducts in terms of ion suppression or matrix
effects and cannot rule out a nonlinear response to mass. However, the
comparison of the CS-OAEESI factor with AMS PMF results and individual
ions discussed below suggests that such nonlinear effects are not
significant.
(a) Van Krevelen plot (atomic H:C vs. O:C ratio) of the cigarette
smoking (CS-OAEESI) factor mass spectrum. Points are sized by the
fraction of each neutral compound apportioned to CS-OAEESI. Colors
denote CxHyOz, CxHyN1Oz, and CxHyN2Oz groups. (b) Comparison of
CS-OAEESI and C5H10NAMS+, colored by time.
Oxidized organic nitrogen species such as CxHyN1Oz (34.9 %) and CxHyN2Oz (6.8 %) are also significant in
the CS-OAEESI factor, as shown in Figs. 2b and 5a. CS-OAEESI
is only slightly oxygenated (O:C=0.31) and has an H:C ratio of
approximately 1.51 (Table S1). The CS-OAEESI time series exhibits two
large evening peaks (27 and 28 January). These peaks are likely associated
with cigarette smoking outside the nearby restaurants. A high correlation is
observed between the time series of CS-OAEESI and the AMS
C5H10N+ ion (R=0.91, Fig. 5b), which has been proposed as a
tracer for nicotine (Struckmeier et al., 2016).
EESI-TOF-MS factors: secondary organic aerosols
Here we discuss the EESI-TOF-MS SOA factors in three groups: (1) more aged
wood-burning-related OA (MABB_LOWEESI,
MABB_TRANSEESI, and MABB_HIGHEESI);
(2) non-source-specific SOA (SOA1EESI and SOA2EESI); and (3) high-nitrogen-content SOA (NSOAEESI). Factor mass spectra for these factors
are shown in Fig. 6a, with the spectra colored by the number of N atoms and
normalized such that the sum of the peaks in each spectrum is 1. Figure 6b
shows a stacked time series of all six EESI-TOF-MS SOA factors, such that the sum
of the stacked plot represents the total EESI-TOF-MS mass flux attributed to
SOA. For comparison, the time series of the estimated AMS SOA is shown,
calculated as LO-OOAAMS+MO-OOAAMS. NOAAMS is excluded
from this calculation due to the contribution from primary cigarette smoke
as discussed above. The total EESI-TOF-MS SOA and AMS SOA estimates are in
general well-correlated (R=0.90), even though the EESI-TOF-MS mass flux is
proportionally lower during the first few days of the study.
Factor profiles (a) and stacked time series (b) of the six EESI-TOF-MS
SOA PMF factors, together with AMS OOA. Panel (b) also shows
meteorological data. All EESI-TOF-MS data are plotted in mass flux (ags-1), AMS data are in micrograms per cubic meter, and other units are included. Factor
profiles (b) are molecular weighted and are normalized such that the sum of
each profile is 1.
More aged biomass-burning-related factors
(MABB_LOWEESI,
MABB_TRANSEESI, and
MABB_HIGHEESI)
Three more aged biomass burning (MABB) factors are identified in this study:
MABB_LOWEESI, MABB_TRANSEESI, and
MABB_HIGHEESI. Each MABBEESI factor is enhanced
relative to the others during different parts of the campaign, which
correspond to changes in both the daily temperature cycle and wind
direction. As shown in Fig. 6b, the coldest part of the study, period 1,
occurs from 25 to 27 January (mean -5.4∘C, min -6.4∘C,
max -2.2∘C). During this period, MABB_LOWEESI
contributes 84 % of the total MABB (MABB_LOWEESI/(MABB_LOWEESI+MABB_TRANSEESI+MABB_HIGHEESI)). From 27 to 29 January, period 2,
temperature increases (mean 1.4 ∘C, min -2.2∘C, max
7.4 ∘C), and the MABB_TRANSEESI factor
constitutes the dominant MABBEESI fraction (65 %). Period 3, from 29 January to the campaign end on 4 February, corresponds to higher
temperatures (mean 5.7 ∘C, min 0.8 ∘C, max 8.7 ∘C), and the MABBEESI fraction is dominated by MABB_HIGHEESI (90 %) until a substantial precipitation event beginning on
31 January, after which relatively clean air is observed for the remainder
of the campaign. Figure 7 shows the source-specific wind sectors determined
by SWIM (see Sect. 2.4) for the three MABB factors. This analysis assigns
the three factors to distinct wind vectors: NNE for MABB_LOWEESI, NNW for MABB_TRANSEESI, and SE for
MABB_HIGHEESI. Because each factor is predominantly
observed during a single time period, it is difficult to assess the relative
importance of temperature vs. source region for these three factors.
Wind analysis results using the SWIM model on the concentrations of
MABB_LOWEESI, MABB_TRANSEESI, and
MABB_HIGHEESI. (a) Wind direction combined with
frequency; wind speed is in meters per second. (b) The wind speed and wind direction.
As shown in Fig. 6a, all three MABBEESI factor mass spectra are
qualitatively similar, with many of the same ions enhanced. These spectra
are also similar to the mass spectrum of aged biomass burning emissions
retrieved from a smog chamber experiment (Bertrand et al., 2018). For
both the MABBEESI and chamber spectra, the major ions,
C7H10O5, C9H14O4, and C8H12O6, are common. The main difference between the EESI-TOF-MS factors and the chamber
mass spectrum is that the chamber data show a higher fraction of signal at
lower m/z. This is likely due to the higher concentrations used during the
chamber experiments, causing increased partitioning of semi-volatile
compounds to the particle phase. MABB_LOWEESI also
exhibits somehow enhanced intensities at lower m/z compared to the other
MABBEESI factors. As MABB_LOWEESI is dominant
during the coldest period 1, the MABB_LOWEESI factor is
possibly separated from the other MABBEESI factors due to partitioning
of semi-volatile material to the particle phase due to colder temperatures.
Carbon oxidation state (OSc) as a function of number of carbon
atoms for the factors, more aged biomass burning_low
temperature, more aged biomass burning_transition, more aged
biomass burning_high temperature, secondary organic aerosol, and
less aged biomass burning. Points are colored by atomic H:C ratio and sized
by the fraction of each ion apportioned to the designated factor.
Further insight into the composition trends across the MABBEESI factors
is obtained through Fig. 8, which represents the three MABBEESI mass
spectra as the carbon oxidation state (OSc)
(Kroll et al., 2011) of each
ion as a function of the carbon number (nc). Data points are colored by
the H:C ratio and sized by the fraction of each ion apportioned to the
designated factor. The figure shows that MABB_LOWEESI is
enhanced in ions with low nc, consistent with condensation of
semi-volatile OA (C5H6O4, C8H6O4,
C5H8O7) at low temperature. Otherwise, all three
MABBEESI factors are rather similar. Figure 8 also shows the OSc
of non-MABBEESI (weighted average of SOA1EESI+SOA2EESI)
and LABBEESI (weighted average of LABB1EESI and LABB2EESI)
factors. Obviously, the non-MABBEESI and LABBEESI factors are less
oxidized than the MABB factors, with lower OSc.
Other SOA factors (SOA1EESI and SOA2EESI)
The mass spectra of SOA1EESI and SOA2EESI are qualitatively
similar to factors retrieved from PMF analysis of EESI-TOF-MS data from Zurich
during summer, when monoterpenes are the dominant SOA precursors (Stefenelli
et al., 2019b, Fig. S9). Major ions include C8H12O4,
C9H14O4, C10H16O4, C10H18O4,
C10H16O5, C10H16O2,
C10H16O3, and C10H18O4, separately. In contrast to
the MABBEESI factors, the SOAEESI factors have a negligible
contribution from levoglucosan (C6H10O5). Approximately 57 % of the total C10 ion signal is apportioned to the SOAEESI factors.
Figure 9a and b show the atomic ratio of H:C as a function of O:C for the
two SOAEESI factors. These H:C ratios are higher than typically
observed from the oxidation of aromatic emissions and are instead consistent
with monoterpene oxidation. The Van Krevelen plots show clear differences
between these two factors: SOA2EESI is less oxygenated than
SOA1EESI with a lower O:C ratio and lower H:C ratio. The time series of
SOA1EESI shows a higher contribution during the periods 1 and 2, while
SOA2EESI has a more regular cycle contribution during daytime (Fig. 6b). Since we have clear evidence that these EESI-retrieved factors are
related to secondary organic aerosol, we call them SOAEESI, in contrast
to the OOAAMS factors, where this evidence is less clear. A more
detailed comparison between the EESI-TOF_SOA factors and the
AMS_OOA factors is found in Sect. 3.5.
Van Krevelen plots (atomic H:C vs. O:C) for the SOA1EESI and
SOA2EESI factor mass spectra. The points are sized by the fraction of
each neutral compound apportioned to SOA1EESI and SOA2EESI and
colored by the number of carbon atoms.
Nitrogen-containing SOA factor
(NSOAEESI)
As mentioned in Sect. 3.1, the EESI-TOF-MS source apportionment also resolves a
nitrogen-containing SOA factor (NSOAEESI). NSOAEESI is dominated
by highly oxygenated organonitrate molecules, including
C8H13NO5, C10H15NO6, and
C10H19NO8. Ions like C6H10O5,
C10H16O2, and C8H12O6 are comprising another
fraction of the NSOAEESI signal, but are not unique to the
NSOAEESI factor and rather spread over many other factors. The
significant contribution of organonitrates results in an N:C ratio of 0.04
and suggests a secondary origin for this factor. Therefore, we call it
NSOAEESI, in contrast to NOAAMS for which the primary/secondary
origin is less certain. The time series of the factor is quite unique, showing
maximum mass flux at the end of this campaign with the highest peak in the
night of 3 to 4 February and a smaller peak during the night of 28 to 29 February.
Figure S10 shows a comparison of the NSOAEESI and CS-OAEESI time
series with the CHON ions from the EESI and CHN ions from the AMS,
respectively. The group of EESI_CHON ions shows the same
temporal variation as the NSOAEESI factor (Fig. S10) while the
AMS_CHN group is more correlated to the primary organic
group.
Analysis of marker ions
Laboratory, as well as offline and semicontinuous field studies have
identified a number of tracer molecules that are useful for the
investigation of primary and secondary OA from various sources, including
biomass burning. The real-time and in situ measurement of these compounds is
a novel feature of the EESI-TOF-MS, and their apportionment gives further
insight into the nature of the factors described above. Here we investigate
the apportionment of eight ions associated with compounds of interest:
C6H10O5 (approximately assigned to levoglucosan),
C7H7NO4 (methyl-nitrocatechol), C9H10O5
(syringic acid), C8H8O4 (vanillic acid),
C8H6O4 (phthalic acid), C5H6O4 (glutaconic
acid), C7H8O4 (tetrahydroxy toluene), and
C7H10O5 (pentahydroxy toluene). Note that because the
EESI-TOF-MS can provide only a molecular formula, we cannot establish for
certain the identity of a compound or assess the relative isomeric
abundances. For example, C6H10O5 is likely to consist not
only of levoglucosan, but also other sugars such as mannosan and galactosan.
The named compounds are thus provided for reference, but their
identification should not be considered conclusive and the ions cannot be
assumed to be isomerically pure. Nevertheless, as these assignments are
based on molecular investigations of wood-burning-related emissions they are
likely to be qualitatively correct and provide a useful framework for
interpreting molecular aspects of the source apportionment results.
Apportionment of selected ions by EESI-TOF-MS PMF. (a) Time series of
the mass flux (ags-1) and (b) mean fraction apportioned to each
factor. Each ion is associated with a compound of interest having this
molecular formula; however, the relative isomeric abundance of this compound
cannot be confirmed by the EESI-TOF-MS.
Figure 10a shows a stacked time series of the mass flux of these compounds
representing the contribution of each EESI-TOF-MS PMF factor to the total mass
flux (assuming no significant conformational isomers). Levoglucosan, which
is derived from the pyrolysis of cellulose and hemicellulose, is commonly
used as an indicator for the presence of primary aerosols originating from
biomass combustion (Fine et al., 2001). Figure 10b shows that
levoglucosan appears in both POA (total contribution of 62 %, mostly from
LABB1EESI (22 %) and LABB2EESI (37 %), and minor
contributions by COAEESI, CS-OAEESI, and EVENTEESI) and SOA
(total contribution of 38 %, of which 36 % is related to the sum of
MABB_HIGHEESI, MABB_TRANSEESI, and
MABB_LOWEESI, plus minor contributions from
NSOAEESI). Due to the high biomass burning emission background and the
lifetime of levoglucosan, it is inevitable to find a contribution of
levoglucosan in the MABB factor, which is consistent with our aged biomass
burning discussion above. In contrast, nitrocatechol
(C7H7NO4) has been established as a secondary species
originating from the oxidation of biomass burning (Iinuma et al., 2010;
Finewax et al., 2018). Here 86 % of nitrocatechol is apportioned to the
less aged (49 %) and more aged (37 %) biomass burning factors.
Syringic acid and vanillic acid are phenolic acids derived from the
oxidation of lignin decomposition products (He et al., 2018),
which in turn are a major component of biomass combustion emissions, and are
apportioned primarily to the MABBEESI factors (52 % for syringic
acid and 66 % for vanillic acid).
Phthalic acid (C8H6O4) and glutaconic acid
(C5H6O4) are apportioned to the SOA factors (91 % and 94 % in total, respectively), with the main contributions from the MABBEESI
factors and in particular the MABB_LOWEESI factor (53 % and 59 %, respectively). These dicarboxylic acids are ubiquitous
water-soluble organic compounds which have been detected in a variety of
aerosol samples, and originate from the combustion of biomass burning and
fossil fuels, as well as from biogenic emission and photooxidation of
organic gases. For example, phthalic acid has been identified based on field
measurements, as a tracer of naphthalene oxidation
(Kleindienst et al., 2012) or oxidation products from
PAHs (Chan et al., 2009), and is also
consistently found in combustion products of lignin, which is likely to
explain the contribution in the MABB factors (Fu et al., 2010; Wang et
al., 2007).
Tetrahydroxy toluene (C7H8O4) and pentahydroxy toluene
(C7H8O5) are apportioned mainly to secondary factors (85 %
and 78 %, respectively). Tetrahydroxy toluene and pentahydroxy toluene
have been detected as dominant products in both the particle phase and gas
phase under low-NO oxidation of toluene (Nakao et al., 2012; Schwantes et
al., 2017). The o-cresol oxidation mechanism for tetrahydroxy toluene and
pentahydroxy toluene is found in MCM v3.3.1, based on Olariu et al. (2002).
This formation indicates that these two low-volatility ions are indeed
secondary organic compounds, consistent with our results shown in Fig. 10.
In addition, the temporal variation in the pentahydroxy toluene contribution
is consistent with the one of tetrahydroxy toluene except for the
EVENTEESI factor, which may indicate that during this night event an
isomer of pentahydroxy toluene was present.
EESI-TOF-MS cluster analysis
As evidenced from the previous section and Figs. 2 and 6, many of the
dominant ions in the EESI-TOF-MS PMF analysis are shared by multiple factors.
Here, we utilize a cluster analysis to identify ions unique or nearly unique
to a single factor or group of factors. As discussed in Sect. 2.5,
hierarchical agglomerative clustering is performed separately on the set of
all EESI-TOF-MS ions and all EESI-TOF-MS factor time series. Figure 11 shows the
resulting dendrogram of the ions and factors along the vertical and
horizontal axes, respectively; the ion dendrogram is colored subjectively to
guide the eye. Comparison of the ions to the factors yields a matrix, also
shown in Fig. 11, which is colored by the z score, with shades of dark red denoting high correlation. In this representation, an ion unique to a given
factor is brown for one and only one rectangle in the horizontal dimension.
Standardized matrix of individual EESI-TOF-MS ions vs. EESI-TOF-MS PMF
factors. Ions and factors are sorted according to the results of their
respective hierarchical clustering analysis; the resulting dendrograms are
shown on the respective axes. The color of the compounds' groups in the
dendrogram are chosen to make groupings convenient to read (color is
random).
The factor dendrogram identifies several groups of EESI-TOF-MS PMF factors
consistent with the interpretations provided above: (1) more aged biomass
burning factors (MABB_LOWEESI, MABB_TRANSEESI and MABB_HIGHEESI), (2) less aged biomass
burning factors (LABB1EESI and LABB2EESI), and (3) the
cooking-related OA and cigarette smoking OA factors. The more aged and less
aged biomass burning factor groups are themselves likewise grouped. This
clustering is consistent with our interpretation of these factors, as
discussed in the previous section. Ions are clustered to different groups
using the standardized values. In each factor, there are distinguished
molecules (lists of the specific ions (standardized value above 1.5) for
each factor are shown in Table S2). The other two resolved groups, one group
including the SOA1 and EVENT factors, one group containing the SOA2 and NSOA factors,
apparently do not retrieve the common ions, which make less sense for the
current study.
Mass defect filtering plot of factor-specific ions (identified from
the cluster analysis) for selected EESI-TOF-MS POA (a) and SOA (b) factors.
For several of the factors, the uniquely assigned ions exhibit systematic
patterns contributing to the identification or deconvolution of the factors.
Figure 12a shows the mass defect, defined as the exact m/z minus the nearest-integer m/z, as a function of m/z for the uniquely assigned ions for the five
POAEESI factors. Figure 12b shows the equivalent plot for the three
MABBEESI factors and SOA1EESI (SOA2EESI and NSOAEESI
have a high degree of scatter and are omitted to avoid masking trends in the
other secondary factors). The displayed factors exhibit linear correlations
or tight clusters of points; all factors are shown independently in Fig. S11. LABB1EESI and LABB2EESI have a lower mass defect and
shallower slope than COAEESI and CS-OAEESI, consistent with
increased aromaticity. The slopes are (4.9±0.4)×10-4,
(5.9±0.6)×10-4, (8±0.5)×10-4, and
(8±0.3)×10-4 for LABB1EESI, LABB2EESI,
COAEESI, and CS-OAEESI, respectively. The slopes of the two
LABB factors as well as those of COAEESI and CS-OAEESI are very
similar to each other and have a high possibility to be consistent with CH
addition for the former (i.e., C10+xH14+xO4-5, theoretical
slope 6×10-4), and CH2 addition for the latter (i.e., C10+xH20+2xO3-5 for COAEESI and
C10+yH15+2yNO3-5 for CS-OAEESI as nearly every
CS-OA-specific ion contains a single N atom, theoretical slope 1.1×10-3).
The MABB and LABB factors have similar slopes, despite different ion lists.
The slopes of two of the MABB factors (0.9×10-3), as shown in
Fig. 12b, are consistent with the addition of CHO functionality (theoretical
slope =0.1×10-2). Due to the high variability of the slopes
of the MABB factors, they may also contain other functionalities. Both mass
defect and slope are higher for MABB_LOWEESI than for
MABB_HIGHEESI, which is consistent with our discussion
in Sect. 3.2.3, assuming that the organics of the MABB_LOWEESI factor are more oxidized than those of the
MABB_HIGHEESI factors. In addition, the MABB intercepts
are more positive than those of LABB, consistent with the higher oxidation
state shown above.
Comparison of EESI-TOF-MS and AMS. Total EESI-TOF-MS mass flux (ags-1) as a function of AMS OA, points are colored by date (a) and the
fraction of levoglucosan (b). The EESI-TOF-MS and AMS comparison in terms of
H:C(c) and O:C(d); points are colored by date.
Comparison of AMS and EESI-TOF-MS
Figure 13a shows the sum of the mass flux of the ions measured by the
EESI-TOF-MS as a function of the OA concentration measured by the AMS, with the
points colored by date and time. We apply no ion-dependent sensitivity
corrections for the EESI-TOF-MS, although ion-by-ion differences are known to
exist (Lopez-Hilfiker et al., 2019). Note that the AMS signal includes the
minor OA source, HOAAMS, which is mostly insoluble in the electrospray
droplets and thus expected to be basically undetectable by the EESI-TOF-MS.
Nevertheless, the two instruments are well-correlated (R=0.94). The strong
correlation in Fig. 13a suggests that the overall EESI-TOF-MS sensitivity to OA
does not vary significantly throughout the study, and therefore it is
unlikely that the major individual EESI-TOF-MS PMF factors (which describe the
compositional variability) have dramatically different response factors. We
therefore interpret the EESI-TOF-MS PMF results without correction of the data
for factor-specific sensitivities. Several features are evident from the
dependence of the sensitivity on the mass flux of levoglucosan (Fig. 13b),
which may explain the discrepancy in the first part of the campaign (period 1) vs. the rest of the campaign. An SOA-dominated period with low
levoglucosan concentration (red line) toward the beginning of the campaign
exhibits a lower sensitivity than during a period with higher levoglucosan
concentrations (black line), which includes the events on 28 and
29 January 2017 characteristic of EVENTEESI (Lopez-Hilfiker et al., 2019).
Figure 13c and d show the O:C and H:C atomic ratios for the EESI-TOF-MS,
respectively, as a function of those for the AMS. Here again no ion-dependent
sensitivity corrections are applied. The EESI-TOF-MS and AMS O:C ratios are
correlated (R=0.62); however, the O:C ratios estimated by the EESI-TOF-MS are
systematically higher than those measured by the AMS. For the H:C ratios, we
do not observe a correlation. The EESI-TOF-MS values are scattered around
approximately 1.56, independent of the AMS H:C ratios, which vary between
1.11 and 1.44. The cause for this discrepancy is not yet understood but may
be related to differences in ion relative sensitivity (Bertrand et al.,
2018).
Comparison between EESI factors and AMS factors: time series of the
mass flux of each EESI PMF factor (a) and time series of concentrations of
each AMS PMF factor (b). Pie charts of source apportionment results from
the EESI (left) and AMS (right) (c). The thick block frame denotes the sum
of the primary OA for both datasets.
Figure 14 shows the stacked time series of the EESI-TOF-MS PMF factors (together
with total AMS OA concentration) and of the AMS PMF factors. Also shown are
pie charts denoting the mean OA PMF composition over the entire campaign
from the EESI-TOF-MS and AMS data. Despite uncertainties in the definition and
resolution of primary vs. more aged biomass burning, the AMS and EESI-TOF-MS
are in relatively good agreement with respect to the total POA and SOA
fractions. The SOA factors comprise 58.8 % of the mass flux for the
EESI-TOF-MS and 69.4 % of the mass for the AMS. The agreement may in fact be
better than these values indicate: as noted above, the NOAAMS factor,
comprising 17.9 % of the mass and fully associated with SOA in our
solution, is likely composed of both POA (derived from cigarette smoke, as
resolved in CS-OAEESI) and SOA (from organonitrate-containing SOA, as
resolved in NSOAEESI), resulting in a low total POA fraction in the AMS
solution. Since both CS-OAEESI and NSOAEESI are enriched with the
nitrogen-containing ions, we compare in Fig. 15 the O:C and N:C ratios for
these two factors, where the size of the colored stars and circles
corresponds to the H:C ratio. A distinct separation between CS-OAEESI
and NSOAEESI is evident due to a significantly higher O:C ratio for a
given N:C ratio, i.e., higher degree of oxygenation for the NSOAEESI
factor and a higher abundance of organic nitrate molecules in the
NSOAEESI factor. Moreover, this separation was not possible for AMS
PMF.
The atomic O:C vs. N:C plot of the CS-OAEESI and NSOAEESI
factors' mass spectra. Points are sized by the H:C value of each distinguished
ion of CS-OAEESI and NSOAEESI.
Both AMS and EESI-TOF-MS factor stacked time series (Fig. 14) show clearly
that biomass burning is dominated by secondary fractions early in the
campaign, mixed fractions in the middle of the campaign, and a primary
fraction late in the campaign. As discussed in Sect. 3.2.2, BBOAAMS is a
mixture of primary and secondary ions, and OOAAMS is a mixture of
biomass burning fragments and background SOA fragments from photochemistry
production ions. Although the fraction of OOA comprises more than 50 %
percent of total OA (Fig. 14), it is hard to define how much of AMS OOA is related to
biomass burning as a function of time. The EESI-TOF-MS separates the
biomass burning factors into LABBEESI and MABBEESI and splits the
background SOA factors into separate factors, which provides evidence that
biomass burning is the single most important contributor to the organic
aerosol at the measurement site during winter.
Conclusions
Real-time, near-molecular-level measurements of OA composition were
performed during winter in Zurich using a novel extractive electrospray
ionization time-of-flight mass spectrometer (EESI-TOF-MS). The lack of thermal
decomposition or ionization-induced fragmentation in the EESI-TOF-MS provides
an improved description of SOA in particular, facilitating SOA source
identification by PMF. We retrieve 11 factors, of which five are dominated
by POA and six by SOA. The POA factors include cooking-influenced OA
(COAEESI, which strongly correlates with an equivalent AMS factor),
cigarette-smoke-influenced OA (CS-OAEESI, characterized by a strong
contribution from nicotine), and a special event also captured by the AMS.
Two less aged biomass burning factors are also resolved. Of the six SOA
factors, three are clearly related to biomass burning and are distinguished
by temperature and possibly wind direction. We also observe two SOA factors
with no clear biomass burning signatures, one of which closely resembles
monoterpene oxidation. Finally, we observe a minor factor with a high
organonitrate fraction.
We performed cluster analysis of the EESI-TOF-MS ions followed by correlation
with the resolved factors, which identifies groups of ions characteristic of
each factor. These characteristic ions represent potential tracers for
future studies; they indicate strong aromatic influence in both less aged
and more aged biomass burning, and support the primary/secondary
assignment of biomass-burning-influenced factors.
The increased chemical specificity of the EESI-TOF-MS allows for additional,
meaningful factors to be resolved relative to the AMS. Comparisons of bulk
measurements, as well as of individual factors or groups of factors between
the EESI-TOF-MS and AMS, indicate good agreement, but with differences in
elemental ratios. This suggests that, despite significant uncertainties in
the relative response factors of individual ions measured by the EESI-TOF-MS,
responses at the level of the PMF factors are relatively similar, with the
main differences resulting from the high sensitivity to levoglucosan in the
EESI. Furthermore, source apportionment of EESI-TOF-MS provides more
classification of SOA factors, separating EESI biomass burning factors as
more or less aged instead of primary or secondary, and identifying organic-nitrogen-containing factors as a primary-dominated nitrogen factor or
organonitrate-containing secondary factor, which are not possible for AMS
PMF. As a result, the EESI-TOF-MS represents a promising new approach for
source apportionment and atmospheric composition studies.
Data availability
The data are available from the corresponding author upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-8037-2019-supplement.
Author contributions
LQ was the main author. LQ, GS, VP, YT, and CH conducted the field campaign.
MC, XG, JGS, ASHP, and UB were the supervisors. All contributed to the
corrections of the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We acknowledge the support by
the Federal Office for the Environment. Mao Xiao is acknowledged for useful
discussions. The authors gratefully acknowledge technical and logistical
support from Rene Richter (PSI).
Financial support
This research has been supported by the Swiss National Science Foundation (grant no. BSSGI0_155846) and the National Natural Science Foundation of China (grant nos. 91543115, 21577065).
Review statement
This paper was edited by Sergey A. Nizkorodov and reviewed by three anonymous referees.
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