Journal of Aerosol Science ] (]]]]) ]]]–]]]
Contents lists available at SciVerse ScienceDirect
Journal of Aerosol Science
journal homepage: www.elsevier.com/locate/jaerosci
Characteristics, sources and water-solubility of ambient submicron
organic aerosol in springtime in Helsinki, Finland
Hilkka Timonen a,b,n, Samara Carbone a, Minna Aurela a, Karri Saarnio a, Sanna Saarikoski a,
Nga L. Ng c, Manjula R. Canagaratna d, Markku Kulmala e, Veli-Matti Kerminen e,
Douglas R. Worsnop a,d,e, Risto Hillamo a
a
Air Quality Research, Finnish Meteorological Institute, P.O. Box 503, FI-00101 Helsinki, Finland
Department of Science and Technology, University of Washington, Bothell, US
c
School of Chemical and Biomolecular Engineering and School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
d
Aerodyne Research, Inc. 45 Manning Road, Billerica, MA 01821-3976, USA
e
Department of Physics, University of Helsinki, P.O. Box 64, FI-00014 University of Helsinki, Finland
b
a r t i c l e i n f o
Keywords:
Urban aerosol
Aerosol mass spectrometer
PMF
Organic matter
Water-solubility
abstract
In this study the characteristics, sources and water-solubility of submicron organic
aerosol (OA) were investigated in Helsinki, Finland. An Aerodyne high-resolution timeof-flight aerosol mass spectrometer (HR-ToF-AMS) was used to determine the submicron non-refractory aerosol components nitrate, sulfate, ammonium, chloride and
organics between April 9 and May 8, 2009. The concentrations of the major watersoluble ions and water-soluble organic carbon (WSOC) were measured by a particleinto-liquid sampler (PILS) combined with a total organic carbon (TOC) analyzer and two
ion chromatographs (IC) between April 25 and May 28, 2009. Parallel measurements
of the submicron particulate matter (PM1), organic carbon (OC), black carbon (BC),
meteorological quantities and trace gases were used to complement and validate the
AMS and PILS-TOC-IC data.
Sources or atmospheric processes affecting the organic aerosol were investigated by
applying the Positive Matrix Factorization (PMF) analysis to the high-resolution mass
spectra of the HR-ToF-AMS organics. All together seven factors were needed to describe
the variation in the obtained dataset. The factors consisted of two different types of
low-volatility oxygenated OA (LV-OOA), local and long-range-transported (LRT) biomass burning OA (BBOA), semi-volatile OA (SV-OOA), hydrocarbon-like OA (HOA), and
one local source (coffee roastery). These factors were interpretable and could be
connected to specific sources or chemical characteristics (biomass burning, traffic,
biogenic emissions, oxidized long-range-transported aerosols, marine-processed aerosols and nearby industrial activity) of ambient aerosols. In order to study the organic
fraction and PMF factors further, the elemental ratios OM:OC, O:C, H:C and N:C were
calculated. The value of the OM:OC ratio varied between 1.4 and 2.1. A high OM:OC
ratio (1.5–2.1) was observed for the highly-oxidized and water-soluble fraction,
whereas this ratio was clearly lower (1.2–1.4) for local and fresh sources such as
traffic. Two different factors representing local and long-range-transported biomass
burning were observed. Local biomass burning emissions had a lower OM:OC ratio,
indicating that this factor was less aged and had a different source area compared with
the LRT BBOA. The water-solubilities of the OA factors were studied by investigating the
correlation between these factors and WSOC and by reconstructing the concentration of
n
Corresponding author at: Finnish Meteorological Institute, P.O. Box 503, FI-00101 Helsinki, Finland. Tel.: þ 358 9 1929 5503;
fax: þ 358 9 1929 5403.
E-mail address: hilkka.timonen@fmi.fi (H. Timonen).
0021-8502/$ - see front matter & 2012 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.jaerosci.2012.06.005
Please cite this article as: Timonen, H., et al. Characteristics, sources and water-solubility of ambient submicron organic
aerosol in springtime in Helsinki, Finland. Journal of Aerosol Science (2012), http://dx.doi.org/10.1016/
j.jaerosci.2012.06.005
2
H. Timonen et al. / Journal of Aerosol Science ] (]]]]) ]]]–]]]
water-soluble particulate organic matter (WSPOM) from the OA factors. The reconstructed WSPOM had a good correlation with the measured concentration of WSPOM.
& 2012 Elsevier Ltd. All rights reserved.
1. Introduction
Organic aerosol (OA) comprises a large fraction of the ambient submicron aerosol mass and has therefore a significant
impact on climate, visibility and human health (e.g. Kanakidou et al., 2005; Jimenez et al., 2009; Heald et al., 2011). Due to
its complex nature, the knowledge about the sources, behavior and chemical composition of the organic fraction is limited.
The compounds in the organic fraction are typically divided into primary and secondary organic aerosol (POA and SOA,
respectively) according to their origin. POA refers to organic compounds that are directly emitted in a particulate form or
vapors that condense onto particles without undergoing gas-phase chemistry, whereas SOA is formed in the atmosphere
by gas-to-particle conversion. The fact that the OA consist of thousands of different compounds that continually change
both chemically and physically depending on atmospheric conditions (Robinson et al., 2007; Hallquist et al., 2009; Jimenez
et al., 2009) makes the characterization of OA challenging.
In order to understand the atmospheric behavior of OA and to determine its health and climate effects, the chemical
composition of OA must be known. During the last decade, significant progress has been made in characterizing the
organic compounds of atmospheric aerosols on a molecular level. Individual compounds have been shown to be tracers of
specific sources, so measuring such tracer compounds makes it possible to get detailed insights into aerosol precursors and
aerosol formation processes (Hallquist et al., 2009). The major tracers for biomass burning, biogenic SOA or marine
aerosols have been chemically characterized (e.g. Simoneit et al.; 1999; Phinney et al., 2006; Hallquist et al., 2009 and
references therein; Yasmeen et al., 2011; Zhang et al., 2011 and references therein). However, a full and extensive chemical
characterization of organic fraction based on offline sampling and chemical analyses is usually not feasible. Therefore,
other approaches have been applied to gather information on the organic fraction of the atmospheric aerosol. A large
number of high-time-resolution, online devices, such as the Aerodyne aerosol mass spectrometer (AMS), semi-continuous
EC/OC carbon aerosol analyzer and particle-into-liquid sampler (PILS) coupled with either ion chromatographs (IC) or total
organic carbon (TOC) analyzer, have been developed and used intensively during the last decade. Compared with offline
sampling and subsequent chemical analyses, online methods typically offer artifact-free data with high time resolution. In
addition, time-resolved data enable the chemical characterization of OA during fast processes, including different
combustion processes (Liu et al., 2011) and evolution and aging of aerosols (Zhang et al., 2007; Jimenez et al., 2009;
Heald et al., 2010; Morgan et al., 2010a,b; Ng et al., 2010; Hennigan et al., 2011; Ng et al., 2011a).
The atmospheric organic aerosol can be divided into a water-soluble and water-insoluble fraction. The water-soluble
fraction represents the highly-oxidized and typically long-range transported aged fraction of OA. The water-solubility
affects the chemical and physical properties of aerosols, such as its acidity and radiative properties as well as its ability to
act as cloud condensation nuclei (e.g. Jacobson et al., 2000; Saxena & Hildemann, 1996). The main sources of water-soluble
OA are secondary organic aerosol formation and biomass burning (Saxena & Hildemann, 1996; Decesari et al., 2006;
Hennigan et al., 2009; Sun et al., 2011). The water-insoluble fraction often represents local or regional fresh emissions, like
traffic.
The primary goal of this study was to explore the sources and characteristics of the submicron organic aerosol at an
urban background station in Helsinki, Finland. The OA was measured with an Aerodyne high-resolution time-of-flight
aerosol mass spectrometer (HR-ToF-AMS), and the measured data were analyzed using Positive Matrix Factorization
(PMF). The results obtained from the HR-ToF-AMS measurements were compared with simultaneous high-resolution
measurements of water-soluble organic carbon (WSOC), trace gas measurements and meteorological quantities. To our
knowledge, this is the first study in Europe where highly time-resolved WSOC and inorganic ion data have been available
concurrently with the HR-ToF-AMS data, making it possible to investigate the solubility of different fractions of OA. An
additional aim of this study was to investigate whether it is possible to reconstruct the water-soluble organic fraction of
the aerosol based on AMS data.
2. Experimental
2.1. Measurement site
The measurements were conducted at an urban background station, SMEAR III (601 120 , 241 570 , 30 m a.s.l., Järvi et al.,
2009). The SMEAR III station has been established for conducting long-term measurements of chemical and physical
properties of atmospheric aerosols, trace gas concentrations, meteorological quantities and turbulent fluxes. The SMEAR III
station has an air-conditioned container for scientific instruments and a 31-m-high measurement tower for flux and
meteorology measurements at different heights. The site is situated at the University of Helsinki campus area about 5 km
northeast from the city center of Helsinki. Next to the station there are the buildings of the Finnish Meteorological Institute
Please cite this article as: Timonen, H., et al. Characteristics, sources and water-solubility of ambient submicron organic
aerosol in springtime in Helsinki, Finland. Journal of Aerosol Science (2012), http://dx.doi.org/10.1016/
j.jaerosci.2012.06.005
H. Timonen et al. / Journal of Aerosol Science ] (]]]]) ]]]–]]]
3
(FMI) and University of Helsinki. About 200 m east from the station, there is a major road with heavy traffic (60,000 cars/day).
A small forest and some buildings are also located west from the station.
2.2. Aerodyne high-resolution time-of-flight aerosol mass spectrometer
An Aerodyne high-resolution time-of-flight aerosol mass spectrometer (AMS; Aerodyne Research Inc, USA) was used to
determine the concentrations of major species of the submicron particulate matter (PM1): organics, sulfate, nitrate,
chloride and ammonium. The measurement method has been described in detail by DeCarlo et al. (2006). Briefly, in the
HR-ToF-AMS an aerodynamic lens is used to form a narrow beam of particles that is transmitted into the detection
chamber, in which the non-refractory fraction of the aerosol is flash-vaporized upon impact on a hot surface (600 1C) under
high vacuum (Jayne et al., 2000). Crustal material, sea salt and black carbon are not vaporized at this temperature and
therefore cannot be detected using this technique. The vaporized compounds are ionized using electron ionization (70 eV)
and the formed ions are guided to the time-of-flight chamber. The time-of-flight chamber has two ion optical modes:
V-mode and W-mode. The V-mode is a single-reflection configuration, in which ions follow a V-shaped path from the
extraction region to a reflector and then back to the detector. The W-mode is a two-reflection configuration, in which ions
follow a W-shaped bath from the extraction region to a reflector, back to the hard mirror, a second time to the reflector,
and finally to the detector. The W-mode has a higher resolving power (up to 4000 at m/z 200) than the V-mode (up to
2000 at m/z 200), but a lower sensitivity. A multi-channel plate is used as a detector. In this study, the time resolution of
the AMS measurements was five minutes and the instrument was alternating between the V-mode (2.5 min) and W-mode
(2.5 min). The high resolution of W-mode facilitates the separation and identification of isobaric ions, which are ions with
same nominal mass but different exact mass (Gross 2004). The source apportionment accomplished in the W-mode
dataset presented similar results to the V-mode, including the numbers of factors chosen in the solution and their mass
spectra. However, due to the hard mirror malfunction there were several interruptions in the W-mode dataset and
therefore the V-mode was chosen for being more continuous. The one-minute detection limits for the submicron aerosol
are o0.04 mg m 3 for all the compounds in the V-mode (DeCarlo et al., 2006).
The collection efficiency (CE) represents the fraction of sampled particle mass that is detected with the AMS. The value
of CE is required for the estimation of aerosol mass concentration measured by the AMS. The CE of an aerosol mass
spectrometer is affected, for example, by the incomplete collection of particles in the vaporizer and by properties (e.g.
phase, composition and morphology) of vaporized particles (Huffman et al., 2005; Matthew et al., 2008; Cross et al., 2009).
Based on previous studies (Canagaratna et al., 2007) and comparisons with the other instruments in this study (PILS, semicontinuous EC/OC carbon aerosol analyzer, and tapered element oscillating micro-balance), the value of 0.5 for the CE was
used in this paper.
2.2.1. Positive matrix factorization analysis
Positive Matrix Factorization (PMF; Paatero & Tapper, 1994; Paatero, 1997) is a multivariate factor analysis tool that
decomposes a sample data matrix into two or more matrices with varying factor contributions (in this case the
concentration at given time) and constant factor profiles (in this case the mass spectrum representing certain source or
similar chemical composition). The IGOR 6.11 (Wavemetrics, Lake Oswego, OR), Squirrel 1.47 (Sueper, 2011) and a PMF
Evaluation Tool (PET; described by Ulbrich et al., 2009) were used to analyze the AMS data. The APES (Analytic Procedure
for Elemental Separation; Aiken et al., 2007, 2008) was used for the elemental analysis of the AMS data.
2.3. PILS-TOC-IC
A particle-into-liquid sampler combined with a total organic carbon analyzer (TOC-VCPH, Shimadzu, Japan) and two ion
þ
chromatographs (Dionex, Sunnyvale, USA) were used to measure the concentrations of major ions (Na þ , NH4 , K þ , Mg2 þ ,
2
2þ
Ca , Cl , NO3 , SO4 , oxalate) and WSOC online. The method is described in detail by Timonen et al. (2010). Shortly, one
TOC-VCPH and two IC instruments were connected to a PILS to enable simultaneous measurements of water-soluble
organic carbon and major ions. A PM1 cyclone (sharp cut cyclone, SCC 1.829, BGI Inc. US) and two denuders (a parallel plate
carbon filter denuder from Sunset Laboratory Inc., Portland, OR and an annular denuder URG-2000, 30 242 mm, Chapel
Hill, NC coated with H3PO4) were used to cut off supermicron particles, gaseous organic compounds and ammonia prior to
the PILS. One half of the sample liquid produced by the PILS was filtered by a polytetrafluoroethylene (PTFE) filter in order
to remove the water-insoluble carbonaceous particles from the sample that was then fed to the TOC-VCPH analyzer for a
subsequent online WSOC analysis. The other half of the collected PILS sample was further split into two equal fractions by
a simple t-shape splitter. The split liquid flows were fed to the sample loops of the two IC systems (Dionex ICS-2000;
2
þ
Dionex, Sunnyvale, USA) in order to measure anions (Cl–, NO3 , SO4 , oxalate) and cations (Na þ , NH4 , K þ , Mg2 þ , Ca2 þ ).
The time resolution of the PILS-TOC-IC measurements depended on the sample volume needed for the subsequent analysis
and sensitivity of the analytical instrument. Therefore, a six-minute time resolution was used for the WSOC and 15-min
time resolution was used for the inorganic ions. Blank values were measured for the PILS-TOC-IC system by directing the
air flow through a PTFE filter prior to the PILS. The limit of quantification for the WSOC in the TOC-VCPH was 4 mg l 1,
which is equal to the concentration of 0.15 mg m 3 in air. Based on the analyzed test samples, the uncertainty of the IC
Please cite this article as: Timonen, H., et al. Characteristics, sources and water-solubility of ambient submicron organic
aerosol in springtime in Helsinki, Finland. Journal of Aerosol Science (2012), http://dx.doi.org/10.1016/
j.jaerosci.2012.06.005
4
H. Timonen et al. / Journal of Aerosol Science ] (]]]]) ]]]–]]]
analysis was of the order of 10–15% for all analyzed ions. The quantification limit for the major ions was 2.5 ng ml 1,
which is equal to the concentration of 0.1 mg m 3 in air.
The measured WSOC concentrations were multiplied by the estimated average molecular weight per carbon weight in
aerosols in order to estimate the amount of particulate organic matter (WSPOM). For WSOC, a constant organic matter to
organic carbon (OM:OC) ratio of 1.6 was used based on Turpin & Lim (2001) and Saarnio et al. (2010).
2.4. Semi-continuous EC/OC carbon aerosol analyzer
A semi-continuous EC/OC carbon aerosol analyzer (Sunset Laboratory Inc, Oregon) was used to measure the
concentrations of elemental (EC) and organic carbon (OC) (Turpin et al., 1990). The method has been described in detail
by Saarikoski et al. (2008). Shortly, during a measurement cycle the instrument collects a sample for 164 min. After the
sampling period, particles deposited on a collection filter are heated in a quartz oven, in which elemental and organic
carbon concentrations are individually quantified. During the first phase of the analysis, the sample is purged with helium
and the temperature is raised in steps up to 650 1C in order to vaporize organic carbon. In the second phase, the sample is
purged with a helium–oxygen mixture and the temperature is again raised in steps from 650 to 850 1C to oxidize all
elemental carbon. In the first measurement phase, pyrolysis converts a part of organic carbon to a light-absorbing
substance that resembles EC (Johnson & Huntzicker, 1979; Viidanoja et al., 2002). This part of OC is not measured until it
has been oxidized in the second phase. The value of laser transmission is used to separate the pyrolyzed OC from EC.
Vaporized carbon compounds formed in the oven are purged to MnO2 catalyst, in which they are further oxidized to
carbon dioxide and quantified with a non-dispersive infrared detector. Since elemental carbon concentrations were rather
low in Helsinki, ‘‘optical EC’’ concentrations instead of ‘‘thermal EC’’ ones were used in this study. ‘‘Optical EC’’ is
determined by optical methods (light transmission) and is therefore referred to as black carbon (BC) hereafter.
2.5. Tapered element oscillating microbalance
A tapered element oscillating microbalance (TEOM1400a, Rupprecht & Patashnick Co. Inc, USA) was used to measure
PM1.3 mass. In the TEOM, the sample is collected to a filter that is placed on the top of an oscillating tapered cone. The mass
concentration is calculated from changes in the oscillation frequency. When comparing the TEOM measurements with
those of other instruments, 30-min average concentrations were used. A virtual impactor was used to cut off particles
larger than 1.3 mm of aerodynamic diameter prior to the TEOM. The TEOM has been observed to underestimate ambient
PM concentrations due to the evaporation of semi-volatile material from the TEOM collection filter (Grover et al., 2006;
Wilson et al., 2006). The error caused by this evaporation was not corrected in this study, since the required correction
factor was uncertain (probably less than 20% of the concentrations, Saarikoski et al., 2007) and should have no influence on
our main conclusions.
2.6. Meteorological and gas data
Local meteorological data were obtained from the Finnish Meteorological Institute Kumpula weather station (Vaisala,
Milos 500) situated next to the SMEAR III station. The temperature was measured using a Pt100 (Pentronic Ab) sensor, the
relative humidity was measured with a HMP45D (Vaisala Oyj) sensor, and the global radiation was measured with a CM11
(Kipp & Zonen) sensor.
Carbon monoxide (CO) concentrations were measured at the SMEAR III station using a Horiba APMA 370 Analyzer
(Horiba, Kyoto, Japan). To establish the potential source areas of the measured aerosol particles, 120-h air mass back
trajectories were calculated for the sampling periods using FLEXTRA (Stohl & Wotawa, 1995).
3. Results and discussion
3.1. Description of the measurement period
The online instruments, their time resolutions, measurement periods and measured components are summarized in
Table 1. The HR-ToF-AMS measurements (major inorganic ions and organics) were conducted from April 9 to May 8, 2009.
Table 1
Instruments, analyzed components and their time resolutions during the spring 2009 intensive campaign.
Component/property
Instrument
Cutoff size (mm)
Time resolution
Measurement period
Ions, OC, mass
Ions, WSOC
HR-ToF-AMS
PILS-TOC-IC
1
1
April 9–May 8, 2009
April 29–May 29, 2009
OC, BC
PM
semi-continuous EC/OC analyzer
TEOM
2.5
1.3
2 min
WSOC: 6 min
Ions: 15 min
3h
1h
April 9–May 1, 2009
Continuous measurements
Please cite this article as: Timonen, H., et al. Characteristics, sources and water-solubility of ambient submicron organic
aerosol in springtime in Helsinki, Finland. Journal of Aerosol Science (2012), http://dx.doi.org/10.1016/
j.jaerosci.2012.06.005
360
300
240
180
120
60
0
5
15
10
5
0
-5
BC
OC
WSOC
8
Concentration (µg m-3)
20
Temperature
Wind direction
Temperature (°C)
WD (°)
H. Timonen et al. / Journal of Aerosol Science ] (]]]]) ]]]–]]]
6
4
2
0
30
Organics
Sulfate
Total mass
20
Nitrate
Ammonium
10
0
4/11/2009
4/15/2009
4/19/2009
4/23/2009
4/27/2009
5/1/2009
5/5/2009
5/9/2009
Fig. 1. Wind direction (a), the concentrations of WSOC, black carbon and OC measured by the PILS-IC-TOC and semi-continuous EC/OC analyzer (b), and
the concentrations of major ions and organics measured by the AMS (c) between April 9 and May 8, 2009.
The PILS-TOC-IC was used to measure the concentrations of major ions and WSOC from April 25 to May 29, 2011. Due to
technical problems, the PILS-TOC-IC system was not working until April 25, and therefore the overlapping period of
the AMS and PILS-TOC-IC measurements was only from April 25 to May 8, 2009. The concentrations of BC and OC were
measured from April 9 to May 1, 2009, with the semi-continuous EC/OC carbon analyzer. The total mass of PM1.3 was
continuously measured using the TEOM. Fig. 1 represents the time series of the concentrations of inorganic ions, BC,
WSOC, OC and PM1.3 for the measurement period from April 9 to May 8, 2009. On average, the PM1.3 concentration was
6.6 mg m 3 during the measurement period. The major inorganic ions, black carbon and organic matter were responsible
for 46%, 9% and 45%, respectively, of the analyzed particulate mass. During the PILS-TOC-IC measurements, 51% of the
submicron particulate organic matter was water-soluble, on average. An excellent correlation was observed between the
concentrations of all the high-time-resolution online measurement devices (Timonen et al., 2010).
The temperature and wind direction during the intensive measurement period is presented in Fig. 1. The temperature
was in the range 5 to 20 1C with a clear day-to-night variation representing typical springtime conditions in Finland. The
prevailing wind direction was west/southwest (Fig. 1). Two short biomass burning episodes were observed during the
measurement period. The backward trajectories showed that the air masses were long-range transported to Helsinki from
forest fire areas in Russia from April 14 to 15 and again from April 26 to 29, 2009. During these periods, the measured
PM1.3 concentrations (10–20 mg m 3) and concentrations of secondary ions (sulfate and nitrate), BC and OC were elevated.
The AMS tracers of biomass burning (m/z 60.0211 and m/z 73.029) were also elevated.
3.2. PMF analysis of organic aerosol
High-resolution AMS data combined with Positive Matrix Factorization has proven to be very useful in the
characterization of organic aerosol (e.g. Lanz et al., 2007; Aiken et al., 2009; Ulbrich et al., 2009; Allan et al., 2010). By
using the PMF method, the OA can be divided into factors representing the contribution of different sources like cooking,
traffic or biomass burning, or into factors that represent compounds with similar chemical characteristics, such as lowvolatility oxygenated organic aerosol (LV-OOA) and nitrogen containing OA (Aiken et al., 2009; Ulbrich et al., 2009).
In this study, the PMF was conducted on high-resolution, V-mode organic mass spectra measured with the HR-ToF-AMS.
The final number of factors in the PMF is defined by the user, which is typically the most subjective part of the PMF analysis
(Ulbrich et al., 2009; Zhang et al., 2011). If the number of factors is too low, the PMF will lump organic species from distinct
sources and processes into single factors. Using too many factors, on the other hand, can result in splitting of PMF factors
with unrealistic factor time trends and mass spectra (Ulbrich et al., 2009). In order to find an optimal number of factors for
this dataset, PMF solutions with a number of factors varying from one to ten were calculated. The known m/z tracers in mass
spectra, correlations with external data (e.g. inorganic ions, BC, gases and meteorological data) and statistical parameters
calculated by the program (e.g. Q-value, Q/Qexp), were subsequently used to determine the number of factors needed to
describe the variation in the dataset and to interpret the origin of the factors.
In this study, a solution with nine factors was needed to identify seven different probable sources, from the local to the
regional scale. A summary of diagnostics and results from the different factor solutions are shown in supplement (Table 1,
Fig. S7 contributions of factors in 7–10 factor solutions and Fig. S8 Q/Qexp). With the smaller number of factors ( o9) the
factor containing fragments of methane sulfonic acid (LV-OOAþMSA) could not be separated, and with the larger number
of factors ( 49) a further splitting of factors was observed. The nine-factor solution explains almost all variation in the
data, but unfortunately, in this solution LV-OOA splits into three factors with similar time series and mass spectra. The
three LV-OOA factors were summed up in the final solution. Fig. 2 represents the time series of the factors and Fig. 3
Please cite this article as: Timonen, H., et al. Characteristics, sources and water-solubility of ambient submicron organic
aerosol in springtime in Helsinki, Finland. Journal of Aerosol Science (2012), http://dx.doi.org/10.1016/
j.jaerosci.2012.06.005
6
H. Timonen et al. / Journal of Aerosol Science ] (]]]]) ]]]–]]]
fraction of signal
Fig. 2. The time series of the PMF factors during the spring 2009 campaign between April 9 and May 8, 2009.
Cx, HO
0.15
0.10
0.05
0.00
0.15
0.10
0.05
0.00
0.15
0.10
0.05
0.00
0.15
0.10
0.05
0.00
0.15
0.10
0.05
0.00
0.12
0.08
0.04
0.00
0.15
0.10
0.05
0.00
CH
CHN
CHO1
CHO1N
CS
CHOgt1
CHOgt1N
HOA
SV-OOA
Local BBOA
Coffee Roastery
LRT BBOA
LV-OOA + MSA
LV-OOA
10
20
30
40
50
60
70
80
90
100
110
120
130
m/z
Fig. 3. The mass spectra of the factors during the spring 2009 campaign between April 9 and May 8, 2009.
represents the mass spectra of these factors. In this paper, only the results of this recombined seven factor solution are
shown, and the corresponding results of the nine-factor solution are shown in the supplement (Supplement Fig. S1–S6).
3.2.1. PMF analysis
Seven distinct organic aerosol factors were identified from the AMS data using the PMF analysis (Figs. 2 and 3). The
identified factors included hydrocarbon-like OA (HOA) and three oxygenated OA (OOA) factors, of which two were lowvolatility OOA and one semi-volatile OOA (SV-OOA). Two factors were observed to represent emissions from biomass
burning, the first representing local emissions and the second one long-range-transported emissions. The seventh factor
Please cite this article as: Timonen, H., et al. Characteristics, sources and water-solubility of ambient submicron organic
aerosol in springtime in Helsinki, Finland. Journal of Aerosol Science (2012), http://dx.doi.org/10.1016/
j.jaerosci.2012.06.005
7
2.0
0.8
LV-OOA
1.5
0.6
HOA
0.4
1.0
SV-OOA
Local BBOA
0.2
0.5
LRT BBOA
LV-OOA + MSA
LV-OOA (µg m-3)
Coffee Roastery, LV-OOA + MSA, LRT BBOA,
Local BBOA, SV-OOA, HOA (µg m-3)
H. Timonen et al. / Journal of Aerosol Science ] (]]]]) ]]]–]]]
Coffee Roastery
0.0
0.0
4
8
12
Hour of day
16
20
24
Fig. 4. Diurnal cycles of the factors.
Table 2
The relative contribution of each factor to the total organic mass measured by the
HR-ToF-AMS during the spring 2009 campaign (April 9 to May 8, 2011).
Factor
Contribution to
OA mass (%)
LV-OOA
LV-OOA þ MSA
Local BBOA
Coffee roastery
LRT BBOA
SV-OOA
HOA
56
5
4
1
9
14
11
was identified to represent emissions from a nearby coffee roastery because the mass spectrum resembled that of caffeine
from the NIST Chemistry WebBook (http://webbook.nist.gov/chemistry). Diurnal cycles calculated for each factor are
shown in Fig. 4.
3.2.1.1. Characterization of factors. The oxygenated organic aerosol fraction is typically divided into two types by the PMF,
called the low-volatility oxygenated organic aerosol (LV-OOA) and semi-volatile oxygenated organic aerosol (SV-OOA). The
LV-OOA is expected to be aged, highly oxidized and processed organic fraction, originating mainly from secondary aerosol
þ
formation (Jimenez et al., 2009; Ng et al., 2010). The LV-OOA is dominated by the fragment CO2 . This signal is assumed
to originate mainly from acids or acid-derived compounds (Alfarra et al., 2004; Duplissy et al., 2011; Ng et al., 2011a,b)
that are known to be mostly water-soluble (Decesari et al., 2007). In chamber studies, it has been observed that the
þ
hygroscopicity of SOA is correlated strongly with the relative abundance of the fragment CO2 (Duplissy et al., 2011). The
þ
þ
SV-OOA has two main fragments, C2H3O and C3H7 , and it has been shown to be composed of more-volatile, lessoxygenated and less-chemically-processed secondary material (Jimenez et al., 2009; Ng et al., 2010). The fragment C2H3O þ
is predominantly due to non-acid oxygenates (Ng et al., 2011a). The hygroscopicity of SV-OOA has been demonstrated to
be very low (Raatikainen et al., 2010). The LV-OOA has been observed to correlate well with WSOC in previous studies
(Kondo et al., 2007; Sun et al., 2011 and Xiao et al., 2011), whereas the SV-OOA is clearly less correlated with WSOC.
In the combined seven-factor solution, the LV-OOA represented on average 56% of the total organic mass (Table 2). The
LV-OOA correlated well with inorganic ions, sulfate, nitrate and ammonium (r¼0.75, r¼0.61 and r¼0.84, respectively). In
the time series of LV-OOA, two likely long-range transport episodes, with elevated LV-OOA concentrations, were observed.
The prevailing wind direction was from southeast to southwest (125–2251) during the episodes (Fig. 5). Long-range
transported emissions from southern Europe are typically observed in Finland when the wind direction is from the southern
sector (Niemi et al., 2004; Saarikoski et al., 2008). The LV-OOA correlated clearly with WSOC (r¼ 0.90), whereas there was no
correlation between the SV-OOA and WSOC (Fig. 6). On average, the SV-OOA represented 14% of the total organic fraction
during the measurement period. For SV-OOA, the largest concentrations were observed when the wind direction was from
the north with wide forested areas (Fig. 5). The observed SV-OOA mass spectrum for this study is similar to that of the
oxidation products of a-pinene observed in chamber experiments (Bahreini et al., 2005; Kiendler-Scharr et al., 2009). The
fragments of oxidation products of secondary VOC’s typically observed in ambient measurements of biogenically-influenced
þ
aerosol (e.g. fragments of methyl furan (C5H6O þ (m/z 82) and C4H5 (m/z 53), Robinson et al., 2011), are also clearly seen in
Please cite this article as: Timonen, H., et al. Characteristics, sources and water-solubility of ambient submicron organic
aerosol in springtime in Helsinki, Finland. Journal of Aerosol Science (2012), http://dx.doi.org/10.1016/
j.jaerosci.2012.06.005
8
H. Timonen et al. / Journal of Aerosol Science ] (]]]]) ]]]–]]]
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8
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2 4 6 8
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4
3
2
1
0
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90
1 2 3 4
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135
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0
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1.5
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180
315
0
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1.5
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90
0.511.522.53
225
90
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0.20.40.60.81
180
225
315
45
225
180
315
0
1
0.8
0.6
0.4
0.2
0
180
45
5/1/2009
2 4 6 8
90
4/21/2009
4/11/2009
135
225
180
Fig. 5. Concentrations (mg m 3) of the individual factors as a function of the wind direction. The traces are colored by time. (a) LV-OOA, (b) LVOOA þ MSA, (c) LRT BBOA, (d) Coffee Roastery, (e) Local BBOA, (f) SV-OOA and (g) HOA. (For interpretation of the references to color in this figure legend,
the reader is referred to the web version of this article.)
the SV-OOA spectrum. A factor that was associated with the biogenic emissions from boreal forests was also observed in a
recent study made in Hyytiälä, Finland (Finessi et al., 2012).
Previous studies have shown that boreal forests emit large amounts of biogenic volatile organic compounds (BVOCs)
when the biogenic activity is high (Hakola et al., 2003; Kulmala et al., 2004). Tunved et al. (2006) showed that a substantial
gas-to-particle formation of BVOC to SOA takes place over the boreal forests in northern Europe. The composition and
water-solubility of SOA formed from BVOCs have been intensively studied during the last decade, and the major tracers of
BVOCs have been chemically characterized (Yasmeen et al., 2011; Gómez-González et al., 2012). Some oxidation products
of BVOC’s typically observed in ambient aerosols in boreal areas, e.g. pinic and pinonic acid (Cavalli et al., 2006; Laaksonen
et al., 2008; Gómez-González et al., 2012), are known to be water-soluble. Cavalli et al. (2006) found that during nucleation
events when the contribution of water-soluble biogenic oxidation products was high, the amount of water-insoluble
compounds was also increased, indicating that the oxidation products are to a large extent water-insoluble, at least at the
beginning of their atmospheric lifespan.
þ
In the spectra of LV-OOAþMSA, the fragments of MSA (fragments CHS þ and CH3SO2 , Phinney et al., 2006) can clearly
be seen (Fig. 3). On average, the LV-OOAþMSA factor represents 4% of total OA. When the contribution of LV-OOA þMSA
factor was high, the wind direction was typically from the Baltic Sea (SW, Fig. 5), indicating that this factor represents
Please cite this article as: Timonen, H., et al. Characteristics, sources and water-solubility of ambient submicron organic
aerosol in springtime in Helsinki, Finland. Journal of Aerosol Science (2012), http://dx.doi.org/10.1016/
j.jaerosci.2012.06.005
H. Timonen et al. / Journal of Aerosol Science ] (]]]]) ]]]–]]]
9
8
LV-OOA, SV-OOA (µg m-3)
LV-OOA (slope =0.77, Intercept 0.48, R=0.91)
SV-OOA (slope =0.01, Intercept 0.43, R=0.07)
6
4
2
0
0
2
4
WSPOM (µg m-3)
6
8
Fig. 6. The correlation of water-soluble particulate organic matter (WSPOM) with low-volatility OA (LV-OOA, r ¼0.91) and semi-volatile OA (SV-OOA,
r ¼0.07).
emissions from sea areas. The LV-OOAþ MSA factor did not correlate with WSOC although MSA itself is water-soluble.
Bubble bursting, especially when the biogenic activity is high, has been proposed to have an important role in the transfer
of organic matter from the sea surface to the atmosphere (Facchini et al., 2008 and references therein; Sciare et al., 2009;
Ovadnevaite et al., 2011). The submicron organic aerosol generated by bubble bursting in marine areas has been found to
be almost completely water-insoluble (O’Dowd et al., 2004; Facchini et al., 2008; Sciare et al., 2009). The lack of correlation
between LV-OOAþMSA and WSOC was not surprising, since the WSOC represents highly-oxidized, aged and long-range
transported fraction of aerosols, whereas a big fraction of MSA is likely associated with primary marine aerosols produced
by bubble bursting.
One PMF factor was observed to represent the emissions originating from a local coffee roastery. When the wind came
from the direction of the roastery (southwest 2101), the smell of roasted coffee could clearly be noticed around the
measurement station. On average, the contribution of the coffee roastery factor was only 1% of OA, but during the plumes
the concentration of this factor was up to 3 mg m 3. Typically factors with less than 5% contribution to the mass cannot be
retrieved by PMF (Ulbrich et al., 2009). Although this factor had a very low contribution in terms of mass, it could be
retrieved likely due to its distinct mass spectrum. The mass spectra of coffee roastery factor resembled closely that of
þ
þ
caffeine, having all the main peaks formed in the ionization of caffeine (m/z ’s C2H4N þ 42.0344, C4H7 55.0548, C3H3N2
þ
þ
þ
67.0296, C6H9 81.0704, C6H10 82.0783, C7H9O 109.065: NIST Chemistry WebBook, http://webbook.nist.gov/chemistry),
including the molecular ion C8H10N4O2 at m/z 194.0804. High concentrations of the coffee roastery factor were observed
mostly in the mornings between about 09:00 and 12:00 (Fig. 3) when the wind direction was from the coffee roastery
located 2 km from the SMEAR III station (Fig. 5). Most likely the reasons for the higher concentration in the mornings are
due to the diurnal industrial processes. In addition, the detection of this source depends on the wind direction, but there
are only limited number of episodes for a more detailed analysis. Emissions from this atypical source have also been
indentified during the winter of 2009 at the same site (Carbone et al., in preparation). In terms of the aerosol mass size
distribution, the organic matter emitted by the coffee roastery was found to be mainly below 200 nm of vacuum
aerodynamic diameter (Carbone et al., in preparation).
Two PMF factors were related to biomass burning, the first one for local burning and the other one for long-rangetransported (LRT) material from biomass burning. Both factors had signals at m/z values previously associated with
þ
þ
biomass burning (C2H4O2 at m/z 60.0211 and C3H5O2 at m/z 73.029; Aiken et al., 2009). Biomass burning associated with
domestic heating and operation of saunas are quite typical in Finland, especially in winter and spring (Frey et al., 2009, and
references therein). The measurement site used in this study was located close to the residential area, so local biomass
burning emissions were expected to be seen. LRT biomass burning emission episodes are typically observed in Finland in
springtime (Niemi et al., 2004; Saarikoski et al., 2008). Based on satellite observations deploying MODIS sensor on board of
NASA EOS Terra satellite and on the backward trajectories of air masses, two long-range-transported biomass burning
episodes were observed during the measurement campaign (April 14–15 and April 26–29, 2009). Fig. 5 represents the
polar graphs in which the concentrations of local and LRT BBOA are shown as a function of the wind direction. High LRT
BBOA emissions were observed when the wind direction was between south and east. Emissions of LRT BBOA seemed to
come in distinct plumes when the wind direction was from south, and between the plumes the LRT BBOA concentration
was close to zero. Local biomass burning emissions originated from all the directions, and the concentration of the local
BBOA seemed to be less variable with only some changes due to wind direction. The local and LRT BBOA concentrations
correlated only slightly with each other (r ¼0.47), indicating that they did not represent same emissions. Both the local and
LRT BBOA were strongly correlated with the WSOC (Fig. 7, local r ¼0.80, LRT r ¼0.80). This is expected as biomass burning
is a major source of WSOC in northern Europe (Saarikoski et al., 2008; Yttri et al., 2009). Both local and LRT BBOA had
an intercept in Fig. 7, suggesting that there was always some WSOC that was not related to BBOA. Compared with the
Please cite this article as: Timonen, H., et al. Characteristics, sources and water-solubility of ambient submicron organic
aerosol in springtime in Helsinki, Finland. Journal of Aerosol Science (2012), http://dx.doi.org/10.1016/
j.jaerosci.2012.06.005
10
H. Timonen et al. / Journal of Aerosol Science ] (]]]]) ]]]–]]]
WSPOM (µg m-3)
8
6
4
2
LRT-BBOA (slope=5.8, intercept=1.5, R=0.76)
Local BBOA (slope=3.8, intercept=0.5, R=0.80)
0
0.0
0.5
1.0
1.5
-3
LRT-BBOA, Local BBOA (µg m )
2.0
2.5
Fig. 7. The correlation of water-soluble particulate organic matter with the local (r ¼0.80) and long-range transported BBOA (r ¼0.76).
Slope=0.42, Intercept -0.24
R=0.94
5/7/2009
3.0
5/3/2009
4/29/2009
LRT BBOA (µg m-3)
2.5
4/25/2009
2.0
4/21/2009
4/17/2009
1.5
Slope=0.11, Intercept=-0.01
R=0.69
4/13/2009
1.0
0.5
0.0
0
1
2
3
4
5
6
7
LV-OOA (µg m-3)
Fig. 8. The correlation of the long-range-transported BBOA with the low-volatile OA (LV-OOA). The two biomass burning episodes (April 14–15 and April 26–29,
2009) are clearly separated.
LRT BBOA, the high-resolution mass spectra of the local BBOA showed a stronger fragmentation pattern of aliphatic
hydrocarbons indicative of fresher and less oxidized fraction of OA. Fig. 8 shows the correlation between the LRT BBOA and
LV-OOA. The two biomass burning episodes (April 14–15 and April 26–29, 2009) had clearly different relations between
these two OA types. The wind direction was slightly different during the episodes, and also the backward trajectories
indicated that the plumes had slightly different source areas. The first plume was long-range transported from the middle
parts of Russia to the east of the station, whereas the second plume originated from southern Russia. In a previous study by
Saarnio et al. (2010), it was observed that the emissions from forest fires were clearly affected by the burning type (flaming
versus smoldering), burning material (wood type, hay etc.), and the distance from which the emissions were transported
before arriving in the measurement site. With the limited data available for this study, the reasons for the observed
differences between the two plumes cannot be identified in detail. In general, the sum of BBOAs (local and LRT) had
slightly lower contribution in this study (13%) than the annual-average in Helsinki in 2006–2007 (19%; Saarikoski et al.,
2008). However, the contribution was similar to that obtained in Helsinki in spring 2006 (12%).
Hydrocarbon-like OA factor represents typically primary emissions associated strongly with vehicle exhaust emissions.
þ
þ
þ
HOA has a characteristic hydrocarbon pattern (main fragments C2H3O þ , C4H7 and C4H9 ) with little or no signal for CO2
(Canagaratna et al., 2004; Zhang et al., 2005; Ng et al., 2011b). In this study, HOA represented 11% of total organic fraction
and it had a clear diurnal cycle with the strongest peak during the morning rush hour (Fig. 4). The diurnal cycle and the
correlation with BC (further discussed in Chapter 3.3.2) indicated that the traffic was the main source of HOA in this study.
3.2.1.2. Oxidation state of PMF factors. In order to further characterize the PMF factors, a triangle plot (f44 against f43, where
f44 ¼m/z 44/org and f43 ¼m/z 43/org) was constructed (Ng et al., 2010). Ng et al. (2010) has shown that the OOA
components typically cluster within a well-defined triangular region in the f44 vs. f43 space. Since photochemical aging
leads to an increase in the fraction of m/z 44, the f44 axis can be considered as an indicator of atmospheric aging (Ng et al.,
2010). Differences in f43 arise from different sources and chemical formation pathways of OOA components.
In the f44 against f43 plot, the factors were clearly separated from each other (Fig. 9). The most oxygenated factors, LRT
BBOA, LV-OOA and LV-OOAþMSA, were situated at the top of the triangle. The HOA, SV-OOA and coffee roastery factors
Please cite this article as: Timonen, H., et al. Characteristics, sources and water-solubility of ambient submicron organic
aerosol in springtime in Helsinki, Finland. Journal of Aerosol Science (2012), http://dx.doi.org/10.1016/
j.jaerosci.2012.06.005
H. Timonen et al. / Journal of Aerosol Science ] (]]]]) ]]]–]]]
11
0.30
0.25
5/6/2009
5/1/2009
0.20
4/26/2009
f44_HR
LV-OOA
0.15
4/21/2009
4/16/2009
LV-OOA + MSA
4/11/2009
LRT BBOA
0.10
Local BBOA
0.05
Coffee Roastery
HOA
0.00
0.00
0.05
0.10
SV-OOA
0.15
0.20
f43_HR
Fig. 9. The f44 against f43 plot derived from the spring 2009 campaign data
2.5
H:C-Ratio
N:C -Ratio
O:C -Ratio
OM:OC -Ratio
2.0
Ratio
1.5
1.0
0.5
0.0
4/11/2009
4/16/2009
4/21/2009
4/26/2009
5/1/2009
5/6/2009
Fig. 10. The time series of the ratios OM:OC, O:C, H:C and N:C calculated from the HR-ToF-AMS data.
were located at the bottom of the triangle, indicating that they represented less oxidized fraction of the OA. The local BBOA
was situated in the middle of the triangle, indicating that it was more aged than e.g. HOA but not as processed as the longrange transported fractions. The LRT BBOA had a clearly higher f44 value than local BBOA, which shows that this factor was
more aged fraction of biomass burning emissions. The SV-OOA had a very low value of f44 and quite high value of f43,
suggesting that it originates from relatively fresh local emissions. Similar to our SV-OOA that we speculate to originate
from biogenic emissions, there is some evidence that the compounds with a biogenic influence are often located on the
lower right corner of the triangle (Ng et al., 2010).
3.2.2. Elemental analyses
Since the speciation of organic aerosol at the molecular level cannot be reached using the AMS measurement technique,
elemental ratios can provide insight into the composition and changes due to the aging of OA in the atmosphere (Heald
et al., 2010; Ng et al., 2011a). Fig. 10 represents the time series for the ratios OM:OC, O:C, H:C and N:C. The OM:OC ratio
varied between 1.4 and 2.1 during the measurement period. Similar values of this ratio have also been found in previous
studies (Turpin & Lim, 2001; Aiken et al., 2008; Saarnio et al., 2010). The atomic oxygen to carbon (O:C) ratio,
characterizing the oxidation state of the ambient OA, ranged from 0.03 to 0.7 in line with the previous studies (Aiken
et al., 2008). The H:C and N:C ratios were in the range 1.2–1.8 and 0.03–0.084, respectively.
The elemental ratios were also calculated for the PMF factors (Table 3). The highest values of the OM:OC ratio were
observed for the LV-OOA (2.00), LV-OOAþMSA (2.02) and LRT BBOA (1.97). The LV-OOA and LRT BBOA were aged and
Please cite this article as: Timonen, H., et al. Characteristics, sources and water-solubility of ambient submicron organic
aerosol in springtime in Helsinki, Finland. Journal of Aerosol Science (2012), http://dx.doi.org/10.1016/
j.jaerosci.2012.06.005
12
H. Timonen et al. / Journal of Aerosol Science ] (]]]]) ]]]–]]]
Table 3
The elemental ratios OM:OC, H:C and N:C for each factor.
0.55
Factor
OM:OC
O:C
H:C
N:C
LV-OOA
LV-OOA þ MSA
Local BBOA
Coffee Roastery
LRT BBOA
SV-OOA
HOA
2.03
2.02
1.55
1.44
1.97
1.39
1.20
0.68
0.63
0.32
0.15
0.62
0.20
0.03
1.20
1.47
1.34
1.60
1.33
1.52
1.81
0.018
0.012
0.012
0.084
0.024
0.002
0.004
1.42
0.50
1.75
OM:OC
O:C
1.38
1.36
1.65
21
20
N:C
0.30
1.70
H:C
0.35
22
1.40
0.45
0.40
23x10-3
1.44
1.80
1.34
1.60
19
1.32
18
1.30
0.25
4
8
12
16
Hour of day
20
24
Fig. 11. Diurnal cycles of the ratios O:C, N:C, H:C and OM:OC.
1.46
1.2
BC
HOA
H:C -ratio, weekdays
-3
1.44
0.8
1.42
0.6
1.40
0.4
1.38
0.2
1.36
H:C
BC, HOA (µg m )
1.0
1.34
0.0
4
8
12
16
20
24
Hour of the day
Fig. 12. Diurnal cycles of the black carbon and HOA concentrations and the H:C ratio.
highly-oxidized long-range transported fractions with good correlations with WSOC, so large values of OM:OC were
expected. The local BBOA had a significantly lower OM:OC ratio (1.55) than the long-range transported BBOA (1.97), as can
be expected for less-processed local emissions. The lowest values of OM:OC were observed for the HOA (1.2), coffee
roastery (1.39) and SV-OOA (1.39). The lowest N:C ratios were observed for HOA and SV-OOA, whereas the coffee roastery
factor had the highest N:C ratio. The caffeine (C8H10N4O2) molecule has four nitrogen atoms, which likely explained the
high value of N:C for the corresponding factor.
The OM:OC and O:C ratios had similar diurnal cycles, with a maximum in the afternoon between 15:00 and 19:00 and a
minimum in the morning between 05:00 and 08:00 (Fig. 11). In several earlier studies, the concentration of organic carbon
has been found to peak in the daytime due to the SOA formation (e.g. Plaza et al., 2006; Takegawa et al., 2006; Aiken et al.,
2008). The value of H:C had a maximum between 06:00 and 09:00 in the morning and a minimum in the afternoon. In
previous studies, a peak in black carbon concentrations, caused by a morning rush hour, has been typically observed
between about 06:00 and 09:00 (Järvi et al., 2008; Timonen et al., 2011). In order to evaluate the effect of traffic to the H:C
ratio, the diurnal cycle of the H:C ratio was calculated separately for weekdays, Saturdays and Sundays (Fig. S9). The peak
in value of H:C during the morning rush hour was seen only during weekdays (Fig. 12). In terms of black carbon
concentrations, the same morning peak was observed for the BC concentrations. Based on the diurnal cycles similar to that
Please cite this article as: Timonen, H., et al. Characteristics, sources and water-solubility of ambient submicron organic
aerosol in springtime in Helsinki, Finland. Journal of Aerosol Science (2012), http://dx.doi.org/10.1016/
j.jaerosci.2012.06.005
H. Timonen et al. / Journal of Aerosol Science ] (]]]]) ]]]–]]]
13
of BC and the fact that the morning peak was seen during the weekdays only, it seems likely that the peak in the value H:C
during the morning was caused by the emissions from traffic. The N:C ratio had a large peak in the morning between 09:00
and 11:00 due to the high contribution from the coffee roastery factor in the morning.
3.2.3. Van Krevelen diagram
The Van Krevelen diagram represents the hydrogen to carbon atomic ratio (H:C) as a function of the oxygen to carbon
atomic ratio (O:C). The diagram, developed by Van Krevelen (1950), was originally used to illustrate how the elemental
composition changes during coal formation. The Van Krevelen diagram has been found to be useful to illustrate the
changes due to aging in AMS data (Heald et al., 2010; Ng et al., 2011a). Heald et al. (2010) observed that the bulk
composition of the total OA occupies a narrow range in the space of a Van Krevelen diagram characterized by a slope of 1
(i.e. one hydrogen is lost due for each oxygen added due to oxidation). The atmospheric aging observed in the total OA
from ambient studies results from a range of processes including volatilization, oxidation, mixing of air masses and
condensation of further products.
In this study, the organic aerosol data points were scattered along an average Van Krevelen slope of 0.8 (Fig. S10) but
a steeper slope was observed for the values of O:C o0.2 and a shallower slope for the values of O:C 40.2. The observed
trend of a shallower slope at the higher O:C values is consistent with observations of Ng et al. (2011a) who extracted
an average Van Krevelen slope of around 0.5 from a compilation of OOA component observed in multiple ambient
environments.
The location of the PMF factors in the Van Krevelen diagram is shown in Fig. S4. HOA, representing mostly primary
emissions with the highest H:C and lowest O:C ratios, was located on the upper left corner of the Van Krevelen diagram.
The factors representing most likely the water-soluble fraction of OA (high O:C and low H:C ratios, LV-OOA and LRT BBOA)
were located on the lower right side of the Van Krevelen diagram. Again, the local and LRT BBOA had significantly different
elemental compositions indicating they did not represent the same emissions.
3.2.4. Reconstruction of WSOC
Only a few studies have been published in which parallel high-time-resolution WSOC and AMS measurements were
conducted. Kondo et al. (2007) measured submicron aerosol properties with a quadruple AMS and PILS-TOC during the
winter and summer of 2004. They found that the signal at m/z 44 and the derived OOA mass concentrations were highly
correlated with WSOC (r2 ¼ 0.78–0.91). The average OOA/WSOC ratio in their study was 3.2470.08 mg/mgC. Xiao et al.
(2011) measured the properties of submicron aerosols in the Pearl River Delta, China, in 2006 using a quadruple AMS and
PILS-TOC. They observed a good correlation between the WSOC and OOA (r2 ¼0.79) and estimated that approximately 86%
of the LV-OOA and 61% of the SV-OOA was water-soluble on the basis of carbon content comparison.
The concentration of the WSOC cannot be directly derived from AMS measurements. Simultaneous measurements of
the WSOC with the PILS-TOC-IC and organic matter with the AMS provided a possibility to evaluate the water-solubility of
different organic factors obtained by the PMF. Based on the recent studies, water-soluble organic carbon consists of
secondary and highly-oxidized compounds (Hennigan et al., 2008; Salma & Lang, 2008). In this study, the LV-OOA and LRT
BBOA had a good correlation with WSOC. As shown before, both these PMF factors correspond to oxidized and aged
fractions of the OA, indicating that they probably represent a similar material as the WSOC. In the concentration time
series, the sum of the LV-OOA and LRT BBOA was very close to that of WSOC with a strong correlation (r ¼0.91) between
them. However, it seems likely that each factor contains both water-soluble and water-insoluble compounds. Therefore,
the Multiple Linear Regression (IGOR 6.11) was used to estimate the coefficients a, b, c, d, e, f and g in the following
equation:
Y ¼ a X1 þ b X2 þc X3 þ d X4 þ e X5þ f X6 þ g X7,
where Y¼ WSOC, X1¼LV OOA, X2 ¼(LV OOA þMSA), X3¼LRT BBOA, X4¼coffee roastery, X5¼BBOA, X6¼ SV OOA and
X7¼ HOA. In the first solution the multipliers for the factors LV OOA þMSA and Coffee Roastery were slightly negative,
but for the final solution these two multipliers were forced to zero. By forcing multipliers to zero did not change the
multipliers of the other factors significantly. Since the TOC-VCPH analyzer measured the concentration of water-soluble
carbon and the AMS measured the concentration of organic matter, the results of elemental analysis (number of atoms of
carbon present in the organic fraction) were used to convert the organic matter from the AMS to the organic carbon
concentration. The following multipliers were obtained for the factors to reconstruct the WSOC:
WSOC ¼ 0:88 LVOOA þ0 ðLVOOA þMSAÞ þ 1:63 LRT BBOA
þ0 Coffee roastery þ 0:96 localBBOAþ 0:14 SVOOA þ 0:11 HOA:
The time series of the reconstructed WSOC and WSOC measured by the PILS-TOC-IC had very similar temporal patterns
(Fig. 13). Also correlation between the reconstructed WSOC and the measured WSOC was good (r ¼0.93, Fig. S11).
Based on the strong correlation of WSOC with both LV-OOA and BBOA (both LRT BBOA and local BBOA), it seems likely
that the BBOA and LV-OOA were mainly water-soluble. Also the high contributions of the LV-OOA and BBOA to the
reconstructed water-soluble fraction from the AMS data indicate that the factors LV-OOA and BBOA were highly watersoluble. The most aged fractions, LV-OOA and LRT BBOA, had high values of O:C (0.68 and 0.62), whereas the O:C ratio
of the local BBOA was clearly lower (0.32). The values of O:C for the factors representing aged OA were close to that of
Please cite this article as: Timonen, H., et al. Characteristics, sources and water-solubility of ambient submicron organic
aerosol in springtime in Helsinki, Finland. Journal of Aerosol Science (2012), http://dx.doi.org/10.1016/
j.jaerosci.2012.06.005
14
H. Timonen et al. / Journal of Aerosol Science ] (]]]]) ]]]–]]]
6
Concentration ( µg m-3)
5
4
WSOC
reconstructed WSOC
LV-OOA
0.88 ± 0.021
LRT BBOA
1.63 ± 0.131
Local BBOA 0.96 ± 0.073
SV-OOA
0.14 ± 0.038
HOA
0.11 ± 0.029
3
2
1
0
4/25/09 4/26/09 4/27/09 4/28/09 4/29/09 4/30/09 5/1/09 5/2/09 5/3/09 5/4/09 5/5/09 5/6/09 5/7/09 5/8/09
Fig. 13. Measured time series of the WSOC concentration (PILS-TOC-IC) and the WSOC time series reconstructed from the PMF factors derived from the
AMS organics data.
water-soluble organic matter measured by Sun et al. (2011) (0.56). A similar approach to reconstruct the WSOC has been
made for the Pearl River Delta in the southern China (Xiao et al., 2011). Xiao et al. (2011) found that the WSOC
concentrations could be predicted with the following equation WSOC ¼0.42 LV OOA þ0.38 SV OOA þ0.29. They also
found a good correlation between the measured and predicted values of WSOC (r2 ¼ 0.79).
4. Conclusions
The data collected during the spring 2009 measurement campaign in Helsinki, Finland, represents a unique dataset
obtained using novel high-resolution measurement devices in an urban background area. The sources of organic aerosols
were studied using Positive Matrix Factorization. During the measurement period, a variety of different sources were
observed to contribute to the total aerosol loading. Altogether seven factors were needed to describe the variation in
the data set. The results of the PMF were validated by comparing them to parallel measurements of meteorological
parameters, trace gases and aerosol components. The factors found by the PMF described well the expected sources or
chemical characteristics of ambient aerosols in a boreal forest environment in springtime.
The factor LV-OOA was found to represent long-range-transported emissions from southern Europe. The LV-OOA
correlated with secondary ions and WSOC, which is typical for long-range-transported emissions. The LV-OOA þMSA had a
clear MSA signal and representing aerosols originating from marine areas. Although this factor was found to represent
highly-oxidized and aged aerosols, it did not correlate with the WSOC. Using conventional filter sampling, it is possible to
measure biomass burning emissions but it is almost impossible to separate local biomass burning emissions from longrange-transported biomass burning emissions. By using PMF, it was possible to separate local and long-range-transported
biomass burning. The local BBOA included local biomass burning associated with e.g. domestic heating, whereas the longrange-transported BBOA represented emissions from forest fires in Russia. Both factors had a clear biomass burning signal
þ
þ
at m/z 60 and m/z 73 (C2H4O2 and C3H5O2 ), but the elemental composition and oxidation state of the factors were
different as well as their origin. The SV-OOA was probably associated with biogenic emissions originating from the large
forest areas in northern Finland. The coffee roastery factor represented emissions from a small local source. The HOA
correlated with BC and had a diurnal pattern similar to the H:C ratio, indicating that it was related to local traffic emissions
from the Helsinki metropolitan area.
Simultaneous measurements of WSOC and organic matter from the AMS provided a possibility to evaluate the
solubility of organic fractions recognized by PMF. The LV-OOA and LRT BBOA, typical of highly-oxidized and aged fractions
of OA, correlated well with WSOC, thus indicating that they were mostly water-soluble. Also, it was found that it is
possible to reconstruct WSOC from the AMS data. The PMF combined with the highly-time-resolved measurements
provided an important tool for the source apportionment. By using PMF it was possible to distinguish emissions due to
small local sources from emissions that were aged, regional and long-range transported.
Acknowledgments
Financial support from the Graduate School in Physics, Chemistry, Biology and Meteorology of Atmospheric
Composition and Climate Change (University of Helsinki), European Union (EUCAARI, Contract no.: 036833), Helsinki
Energy and Ministry of Transport and Communications Finland (project number 20117) is gratefully acknowledged. The
research was also supported by the Academy of Finland Center of Excellence program (project number 1118615) and by
the Finnish Funding Agency for Technology and Innovation, Grant number 40209/08 (KASTU).
Please cite this article as: Timonen, H., et al. Characteristics, sources and water-solubility of ambient submicron organic
aerosol in springtime in Helsinki, Finland. Journal of Aerosol Science (2012), http://dx.doi.org/10.1016/
j.jaerosci.2012.06.005
H. Timonen et al. / Journal of Aerosol Science ] (]]]]) ]]]–]]]
15
Appendix A. Supplementary material
Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.jaerosci.
2012.06.005.
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