Photochemical production of aerosols from real plant emissions

Abstract

Emission of biogenic volatile organic compounds (VOC) which on oxidation form secondary organic aerosols (SOA) can couple the vegetation with the atmosphere and climate. Particle formation from tree emissions was investigated in a new setup: a plant chamber coupled to a reaction chamber for oxidizing the plant emissions and for forming SOA. Emissions from the boreal tree species birch, pine, and spruce were studied. In addition, α-pinene was used as reference compound. Under the employed experimental conditions, OH radicals were essential for inducing new particle formation, although O3 (≤80 ppb) was always present and a fraction of the monoterpenes and the sesquiterpenes reacted with ozone before OH was generated. Formation rates of 3 nm particles were linearly related to the VOC carbon mixing ratios, as were the maximum observed volume and the condensational growth rates. For all trees, the threshold of new particle formation was lower than for α-pinene. It was lowest for birch which emitted the largest fraction of oxygenated VOC (OVOC), suggesting that OVOC may play a role in the nucleation process. Incremental mass yields were ≈5% for pine, spruce and α-pinene, and ≈10% for birch. α-Pinene was a good model compound to describe the yield and the growth of SOA particles from coniferous emissions. The mass fractional yields agreed well with observations for boreal forests. Despite the somewhat enhanced VOC and OH concentrations our results may be up-scaled to eco-system level. Using the mass fractional yields observed for the tree emissions and weighting them with the abundance of the respective trees in boreal forests SOA mass concentration calculations agree within 6% with field observations. For a future VOC increase of 50% we predict a particle mass increase due to SOA of 19% assuming today's mass contribution of pre-existing aerosol and oxidant levels.

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Atmos. Chem. Phys., 9, 4387–4406, 2009
www.atmos-chem-phys.net/9/4387/2009/
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmospheric
Chemistry
and Physics
Photochemical production of aerosols from real plant emissions
Th. F. Mentel1, J. Wildt2, A. Kiendler-Scharr1, E. Kleist2, R. Tillmann1, M. Dal Maso1, R. Fisseha1, Th. Hohaus1,
H. Spahn1, R. Uerlings2, R. Wegener1, P. T. Griffiths3, E. Dinar4, Y. Rudich4, and A. Wahner1
1Inst. for Chemistry and Dynamics of the Geosphere, Inst. 2: Troposphere, Research Centre J¨ ulich, 52425 J¨ ulich, Germany
2Inst. for Chemistry and Dynamics of the Geosphere, Inst. 3: Phytosphere, Research Centre J¨ ulich, 52425 J¨ ulich, Germany
3Centre for Atmospheric Science, Dept. of Chemistry, Lensfield Road, Univ. of Cambridge, Cambridge, CB2 1EW, UK
4Dept. of Environmental Sciences, Weizmann Institute, Rehovot 76100, Israel
Received: 17 November 2008 – Published in Atmos. Chem. Phys. Discuss.: 29 January 2009
Revised: 15 May 2009 – Accepted: 2 June 2009 – Published: 7 July 2009
Abstract. Emission of biogenic volatile organic compounds
(VOC) which on oxidation form secondary organic aerosols
(SOA) can couple the vegetation with the atmosphere and
climate. Particle formation from tree emissions was investi-
gated in a new setup: a plant chamber coupled to a reaction
chamber for oxidizing the plant emissions and for forming
SOA. Emissions from the boreal tree species birch, pine, and
spruce were studied. In addition, α-pinene was used as ref-
erence compound. Under the employed experimental condi-
tions, OH radicals were essential for inducing new particle
formation, although O3(≤80ppb) was always present and a
fraction of the monoterpenes and the sesquiterpenes reacted
with ozone before OH was generated. Formation rates of
3nm particles were linearly related to the VOC carbon mix-
ing ratios, as were the maximum observed volume and the
condensational growth rates. For all trees, the threshold of
new particle formation was lower than for α-pinene. It was
lowest for birch which emitted the largest fraction of oxy-
genated VOC (OVOC), suggesting that OVOC may play a
role in the nucleation process. Incremental mass yields were
≈5% for pine, spruce and α-pinene, and ≈10% for birch. α-
Pinenewasagoodmodelcompoundtodescribetheyieldand
the growth of SOA particles from coniferous emissions. The
mass fractional yields agreed well with observations for bo-
real forests. Despite the somewhat enhanced VOC and OH
concentrations our results may be up-scaled to eco-system
level. Using the mass fractional yields observed for the tree
Correspondence to: Th. F. Mentel
(t.mentel@fz-juelich.de)
emissions and weighting them with the abundance of the re-
spective trees in boreal forests SOA mass concentration cal-
culations agree within 6% with field observations. For a fu-
tureVOCincreaseof50%wepredictaparticlemassincrease
due to SOA of 19% assuming today’s mass contribution of
pre-existing aerosol and oxidant levels.
1Introduction
Vegetation and climate are possibly coupled through emis-
sions of reactive volatile organic compounds (VOC) from
plants(Barthetal., 2005; Kulmalaetal., 2004a). Theemitted
VOC react in the atmosphere and their oxidation products as-
sist in the growth of freshly nucleated particles (usually from
sulfuric acid, water, and ammonia) or condense onto existing
particles, thus contributing to particle mass and size (Kul-
mala, 2003; Kulmala et al., 2004b) often forming blue haze
sometimes observed over forested areas (Went, 1960). The
interaction between vegetation and the climate is not unidi-
rectional. For example, the formed aerosols may cool the
climate by nucleating clouds and intercepting solar radiation
(Kerminen et al., 2005; Kulmala, 2003). Conversely, un-
der changing climatic conditions, the biological sources of
biogenic aerosols may be subjected to environmental stress
(heat, drought, pollution etc.), thereby potentially altering
the emission strength and character in a yet unknown way.
This important biological component of atmospheric aerosol
and the coupling between vegetation and climate is among
the least studied and understood (compare Kulmala et al.,
2004a).
Published by Copernicus Publications on behalf of the European Geosciences Union.

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4388Th. F. Mentel et al.: Aerosols from real plant emissions
The boreal forest occupies substantial parts of the Earth’s
northern latitudes, where climate is also expected to change
(Denman et al., 2007). Long term observations over the bo-
real forest in Finland suggest that monoterpenes (MT) and
possibly sesquiterpenes (SQT) contribute to formation and
growth of new particles (Bonn et al., 2008, Laaksonen et al.,
2008) besides sulfuric acid (Kulmala et al., 2006). The po-
tential of MT and SQT as precursors for secondary organic
aerosol (SOA) formation has been intensively studied in lab-
oratory and simulation chamber studies with OH radicals and
ozone as oxidants (e.g. see a recent review by Kroll and Se-
infeld, 2008 and references therein). However, most of these
studies were performed with elevated concentrations of sin-
gleprecursorcompounds(mostlyα-pinene), comparedtothe
mixing ratios of MT and SQT measured near the canopy
(Haapanala et al., 2007; Kuhn et al., 2007; Spirig et al.,
2005). Oxidant levels were also often higher than in the nat-
ural atmosphere
Besides MT and SQT other biogenic VOC are also emitted
from vegetation, among these are short chained oxygenated
VOC such as methanol or acetone (Folkers et al., 2008; Seco
et al., 2007). Such short chained VOC are believed to be
of minor importance for particle formation (Carrasco et al.,
2007), because their reaction products have too high vapour
pressures, unless they are able to form oligomeric structures
similar to those formed by glyoxal or methylglyoxal (Fu et
al., 2008; Healy et al., 2008; Volkamer et al., 2006). In
addition, alcohols and aldehydes containing 6 carbon atoms
(wound-induced VOC or products from lipoxygenase activ-
ity, e.g. Croft et al., 1993; Heiden et al., 2003), long chained
saturated aldehydes (e.g. Ciccioli et al., 1993; Wildt et al.,
2003) or aromatic compounds like methyl salicylate (Hei-
den et al., 1999) are also emitted from plants. However,
most of these emissions are stress-induced and strongly de-
pend on the physiological state of the plants. Depending on
the plant species and on the physiological state, the stress-
induced emission strengths can exceed those of basal mono-
and sesquiterpenes (e.g. Heiden et al., 2003). However, there
is limited information about the role of such compounds in
aerosol formation.
Atmospheric oxidation of such biogenic compounds con-
tributes to formation of SOA. But, the high diversity of these
VOC does not allow for the investigation of their individ-
ual impact on aerosol formation.
understand and quantify possible feedback effects between
climate, air chemistry, and the biosphere, the impact of all
compounds on particle formation should be known. To ob-
tain more realistic emission scenarios in laboratory environ-
ments it is advantageous to directly use plants as the source
for aerosol precursors. Such experiments better reflect im-
portant aspects of the large diversity of compounds in the
highly mixed atmosphere than experiments with individual
compounds.
Direct plant emissions were already used to investigate
particle formation. In order to elucidate the new particle
Nevertheless, to better
formation in coastal areas McFiggans et al. (2004) gener-
ated particles by oxidizing emissions from macro-algae at
elevated ozone levels. Joutsensaari and coworkers studied
particle formation using VOC emissions from white cabbage
exposed to methyl jasmonate and directly adding elevated
ozone levels (Joutsensaari et al., 2005; Pinto et al., 2007).
Particle formation rates similar to rates observed during at-
mospheric nucleation events where observed, but with higher
growth rates. Joutsensaari et al. (2005) concluded that the
condensing species do not significantly contribute to nucle-
ation in the atmosphere and that stress-induced plant emis-
sions may be important for new particle formation. How-
ever, because of the high ozone levels (which can also induce
stress and cause enhanced VOC emissions) there was a cer-
tain difficulty in interpreting the direct stress-induced effect
due to the impact of the added methyl jasmonate on particle
formation.
Van Reken et al. (2006) investigated particle formation
from ozonolysis of holm oak and loblolly pine emissions.
To circumvent possible interferences, they separated VOC
emissions and the oxidation chamber by housing the plant in
a “biogenic emission enclosure” and transferring the emit-
ted VOC into a second, dark reactor, where ozonolysis and
subsequent particle formation took place. The VOC con-
centrations were several ppb and ozone levels were around
50ppb. Particle formation from holm oak and loblolly pine
emissions was compared to that obtained in a control exper-
iment with α-pinene (6ppb). Van Reken et al. (2006) found
that holm oak emissions were a less efficient particle source,
whereas emissions from loblolly pine were more efficient
than α-pinene. However, simple relations between emission
pattern and stress were not evident.
Besides the VOC emissions and sulfuric acid (Hoppel
et al., 2001), the physico-chemical conditions of the atmo-
sphere (RH and T) impact particle formation (Bonn and
Moortgat, 2002; Cocker et al., 2001; Pathak et al., 2007b;
Saathoff et al., 2008; Tillmann et al., 2009). To understand
both impacts, the influence of the physico-chemical condi-
tions and the plant emissions must be studied independent
of each other. This requires separation between plant cham-
ber and reaction chamber. Here we used a new experimental
setup that fulfills this requirement and balances between the
environmental complexity and controlled conditions. Similar
to the Van Reken et al. (2006) experiments, we coupled two
chambers. Air from a chamber containing single or several
plants (“plant chamber”) is transferred to an empty reaction
chamber. The plants are kept under well defined tempera-
ture, photosynthetic photon flux density (PPFD), and relative
humidity conditions, which could be varied independently of
each other allowing for variation of strengths and patterns
of VOC emissions without changing other physico-chemical
properties of particle formation that takes place in a separate
reaction chamber. Variations of the conditions in the reaction
chamber such as temperature or humidity allows studying the
physico-chemical characteristics of particle formation from a
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Th. F. Mentel et al.: Aerosols from real plant emissions4389
given VOC mixture. Holding the conditions in the reaction
chamber constant while varying the conditions in the plant
chamber allows investigation of the impact of the mixture
composition on particle formation without interference with
particle formation itself.
Here we focus on the impact of VOC mixtures emitted
from trees on particle formation. Maintaining constant con-
ditions in the reaction chamber is prerequisite for compari-
son of aerosols formed from different mixtures. As a first
case study, we investigated particle formation from emis-
sions from pine, birch and spruce which are common species
in the boreal forests. As a benchmark, similar experiments
were conducted by directly injecting α-pinene, the best stud-
ied standard for such experiments, from a diffusion source.
The goal of this study was to relate emission strength and
pattern to particle formation and particle yield and to com-
pare aerosol formation from complex plant emission with
that from α-pinene.
Although MT react rapidly with ozone, their major loss
pathway in the troposphere is by OH oxidation. We therefore
used OH radicals in addition to O3allowing a better sim-
ulation of the atmosphere where nucleation events are nor-
mally coupled to photo-chemical activity (Boy et al., 2005;
Kulmala et al., 2004b). To the best of our knowledge this
is the first simulation of photo-chemical particle formation
from complex plant emissions under such controlled condi-
tions.
2 Experimental
2.1 General description of the experimental setup
Experiments were carried out in the plant chamber facility at
Forschungszentrum J¨ ulich, Germany (J¨ ulich Plant Aerosol
Atmosphere Chamber, JPAC). A simplified schematic pre-
sentation of the system setup is shown in Fig. 1. The facility
consists of three Borosilicate glass chambers (164L, 1150L,
and 1450L) with Teflon floors. Each chamber is mounted
in a separate climate-controlled housing (10 to 50◦C with
a stability of ±0.5◦C). The chambers operate as continu-
ously stirred tank reactors (CSTR) with Teflon fans provid-
ing homogeneous mixing. Typical mixing times in the CSTR
were about two minutes in all chambers. Either one of the
two smaller chambers was used as plant chamber (164L or
1150L, depending on the size of the investigated plant) fol-
lowed by the large chamber which served as reaction cham-
ber (1450L). Residence timesof theair inthe plantchambers
were 5 to 20min and 65min in the reaction chamber.
Air was purified by an adsorption dryer (KEA 70; Zander
Aufbereitungstechnik GmbH & Co. KG, Essen, Germany).
In addition VOC at the inlet of the plant chamber were de-
stroyed by a palladium catalyst at 450◦C to below the detec-
tion limit of the GC-MS instruments (less than 1ppt). Ozone,
NO, and NO2were removed after passing the purification

44
1
2
3
Figure 1. Plant chamber setup: the chamber housing the plant is coupled to the reaction
pled to the reaction chamber, where the plant emitted VOC were
oxidized by OH and O3and particles were formed. Analytical in-
struments monitored the flow in and out of the chambers. Aerosol
instrumentssampletheairattheoutletofthereactionchamber. Two
supply streams were used to maintain stable RH and O3mixing ra-
tio in the reaction chamber.
4
chamber, where the plant emitted VOC were oxidized by OH and O3 and particles were 5
formed. Analytical instruments monitored the flow in and out of the chambers. Aerosol 6
instruments sample the air at the outlet of the reaction chamber. Two supply streams were 7
used to maintain stable RH and O3 mixing ratio in the reaction chamber.
8
Fig. 1. Plant chamber setup: the chamber housing the plant is cou-
system. Concentrations of CO2and water vapour were also
reduced by the adsorption dryer. The CO2concentration in
the plant chamber was kept at levels of about 350ppm by
adding CO2at the inlet. The dew point in the plant chamber
was restricted to a maximum of 15◦C to avoid condensation
in the transfer line.
Discharge lamps (HQI 400 W/D; Osram, Munich, Ger-
many) simulated the solar light spectrum. At full illumina-
tion and at typical mid-canopy heights, photosynthetic pho-
ton flux density (PPFD) was 480µmolm−2s−1in the 1150L
chamber and 800µmolm−2s−1in the 164L chamber. In-
frared radiation (between 750 and 1050nm) was blocked by
filters (type IR3; Prinz Optics GmbH, Stromberg, Germany).
Due to absorption of UV light by the glass walls, the shortest
wavelength in the plant chambers was about 350nm. More
details of the setup and the performance of individual cham-
bers are described in previous publications (Schimang et al.,
2006; Schuh et al., 1997; Wildt et al., 1997). Here we focus
on the properties and the potential of this special setup with
coupled chambers.
A portion of the air leaving the plant chamber
(≈16Lmin−1) was fed into the reaction chamber also op-
erating as a CSTR. The maximum visible light flux in this
chamber (PPFD ≈360µmolm−2s−1) led to a J(NO2) in the
rangeof10−3s−1. Asmallportionoftheairleavingtheplant
chamber was directed to the analytic instruments allowing
VOC measurements.
Two additional air streams were introduced to the reac-
tion chamber, one for ozone and the second for controlling
the relative humidity (RH). The air flow used for humid-
ification (≈4.6Lmin−1) and the air flow used for O3 ad-
dition (≈1.6Lmin−1) were controlled by mass flow con-
trollers. By regulating the water vapor, the PPFD-dependent
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4390Th. F. Mentel et al.: Aerosols from real plant emissions
transpiration of the plants was compensated and the RH was
kept constant at levels around 65%. RH was 50% in one case
(see below). The ozone inlet was placed opposite to the inlet
of air from the plant chamber to prevent immediate nucle-
ation under uncontrolled conditions.
The total air flow through the reaction chamber was regu-
larly determined by measuring the residence time of the air
in the reaction chamber using ozone as a tracer. Exponential
fit to the changing ozone concentration following switching
ozone on/off in the inlet stream yielded the residence time.
OH radicals were generated by ozone photolysis and sub-
sequent reaction of O1D with water. The reaction chamber
was equipped with an internal UV lamp (Philips, TUV 40W,
λmax=254nm). A part of this UV lamp was shielded by glass
tubes which absorb the UV radiation. Changing the size of
the gaps between these glass tubes allowed changing the rate
of ozone photolysis (J(O1D)) and thus changing the rate of
OH production.
The mixing ratios of VOC in the outlet air of the plant
chamber were measured by two GC-MS systems. One sys-
tem was optimized to measure VOC from C5 to C20 in-
cluding isoprene, mono- and sesquiterpenes as well as com-
poundsfromlipoxygenaseactivity(LOXproducts)ormethyl
salicylate (Heiden et al., 1999). The second GC-MS system
quantified the concentrations of short chained oxygenated
VOC from methanol up to C10VOC (Folkers, 2002). Both
systems were used for VOC identification and quantification.
Calibration of both systems was conducted as described in
Heiden et al. (2003).
A Proton Transfer Reaction Mass Spectrometer (PTR-MS,
Ionicon) was used to determine the concentrations of VOC
(and oxidation products) every 10min. The PTR-MS was
switched continuously between the outlet of the plant cham-
ber and the outlet of the reaction chamber. The actual mixing
ratios of VOC emissions in the plant chamber were measured
at the outlet of the plant chamber. The reactant concentra-
tions in the reaction chamber were deduced by applying the
dilution factor by the ozone and water vapor containing air
flows to the VOC at the outlet of the plant chamber. The
actual concentrations of oxidation products and non-reacted
precursors in the reaction chamber were measured at the out-
let of the reaction chamber. The differences in the concen-
tration of the incoming and the remaining VOC reflects the
progress of the reactions and can be described by a numerical
model for ideal CSTR (see Sect. 2.3.1). The measurements
at the outlet of the plant chamber were also used as in-situ
inter-calibration with the GC-MS systems.
An Ultrafine Condensation Particle Counter (UCPC,
TSI3025A) with a nominal activation diameter of 3nm was
directly connected to the reaction chamber (6mm straight
stainless steel tube, 0.5m). This UCPC was used to count
the total number of particles formed in the chamber. The
reaction chamber was also equipped with a 12mm stain-
less steel tube connecting other aerosol instruments to the
reaction chamber. This line was pumped with a total flow
of 12–13Lmin−1. A SMPS (TSI3081+TSI3786) measured
the number size distribution between 10 and 500nm. The
obtained size distributions were used to determine particle
growth rates and were converted into volume distributions
to determine particle volume yields. A Quadrupole Aerosol
Mass Spectrometer (Q-AMS) was operated to measure the
chemical composition of the aerosols (Aerodyne Research
Inc. Jayne et al., 2000). Here only the particle time of flight
data of the Q-AMS were used to determine the mean effec-
tive SOA density. The analytical equipment used to charac-
terize the gas-phase composition and aerosol number is listed
in Table 1.
2.2 Material and methods
Experiments were conducted in the following way: three to
four years old plants that had been stored outdoors in pots
(cylinders with 30cm height and 30cm diameter) were in-
troduced into the plant chamber. Plants used for the exper-
iments were pine (Pinus sylvestris L., 8 plants together in
the 1150L chamber), birch (Betula pendula L., 1 plant in the
164L chamber), and spruce (Picea abies L., 1 plant in the
164L chamber). Measurements started after an adaptation
period of one day.
A diurnal cycle was simulated by switching on and off the
lamps in the plant chamber (9–10h darkness, 1h twilight, 12
or 13h illumination 1h twilight). During periods of illumi-
nation the chamber temperature increased by 3 to 4K com-
paredtotheperiodsofdarkness. Bothphotosyntheticactivity
during the illumination period and the increased temperature
enhanced the VOC emissions.
The reaction chamber was permanently illuminated by the
visible light to prevent transient variations of the reactor tem-
perature. Temperature in the reaction chamber was kept con-
stant at 17◦C and RH was stabilized around 65% RH during
periods of new particle formation. For technical reasons dur-
ing the experiments with spruce the RH had to be adjusted at
a lower level of about 50% at 17◦C. Possible consequences
will be discussed in detail later. The birch faced some wound
stress on being placed inside the plant chamber. Taking into
account the changed VOC patterns as observed with the GC-
MS systems, the data of the first three days were not consid-
ered in the following analysis. Thereafter the birch relaxed
but some stress remained as indicated by the emission pattern
(probably by biting insects). Additionally the first event day
of the spruce experiment may be influenced by stress. Nev-
ertheless we included these data to enhance the data base for
this first exploratory study, since these situations described
here cover situations and variability often found in nature.
2.3 Procedures
We investigated a total of 17 new particle formation events
from tree emissions, 6 for birch, 5 for pine and 6 for
spruce. In addition 10 experiments with α-pinene diffusion
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Th. F. Mentel et al.: Aerosols from real plant emissions4391
Table 1. Instrumentation at the J¨ ulich Plant Aerosol Atmosphere Chamber (JPAC). .
QuantityInstrumentSample portComments
VOC (MT, SQT) GC-MS Agilent (HP 5890 mit 5972A MSD,
Gerstel TDS-G u. KAS3-Injektor)
PC* outlet GC systems occasionally
switched to RC outlet
OVOC GC-MS Agilent (HP 6890 with 5973 MSD,
Gerstel TDS-G u. KAS4-Injector)
PC outletFolkers (2003)
VOC, oxygenated productsPTR-MS, IONICONPC outlet,
RC** outlet
Switched every 6min
Particle numberUCPC, TSI 3025ARC outlet Cut point Dp>5nm,
Particle number distributionSMPS TSI 3080/TSI 3786RC outlet Cut point Dp>18nm
Size resolved particle compositionQ-AMS, Aerodyne ResearchRC outlet
Particle compositionTeflon FilterRC outlet Offline analysis
Hygroscopic growth HTDMA, self builtRC outlet Only efflorescence curves
Droplet activation CCN counter, DMTRC outlet Total activated fract.
at 0.2–1.2% ss
O3
Thermo Environmental Instruments, TE49RC outlet
NO ECO Physics CLD 770 AL pptRC inlet+outlet
CO2
Emmerson Process Manag., Binos 100 4pPC inlet+outlet
PPFD LI-COR LI-189PC
TemperaturePT 100 PC , RC
Temperature Vaisala PT 100RC
Dew point Walz, Dew point mirror MTS-MK-1PC inlet+outlet
RH Vaisala RC inlet+outlet
RC inlet+outlet
∗PC: plant chamber
∗∗RC: reaction chamber
source were conducted. The experimental and evaluation
procedures are described using a typical set of experiment
data obtained for pine (Figs. 2–6). The ozone concentration
in the reaction chamber was ≈80ppb when the UV lamp was
off. Several VOC emitted by the plants were continuously
consumed by reaction with ozone and provided a steady state
level of “background” vapors. Switching on the UV light in-
duced OH formation and a drop of the O3concentration from
≈80ppb to ≈40–50ppb. Several minutes after UV illumina-
tion, particle formation bursts were observed and monitored
by the UCPC and SMPS. The VOC emission strength was
varied from experiment to experiment by changing the tem-
perature in the plant chamber.
2.3.1 Gas phase observations and modeling
Plant emissions of the MT (m/z=137) and SQT (m/z=205)
precursors were determined by the PTR-MS at the outlet of
the plant chamber. Non-reacted precursors were measured at
the outlet of the reaction chamber. Figure 2 shows the di-
urnal variation of MT concentrations emitted from pine as
measured with the PTR-MS. During darkness in the plant
chamber (19:00h–05:00h) the sum of MT mixing ratios in
the plant chamber was around 0.8ppb. The dilution by the
addition of humid air and ozone to the reaction chamber re-
duced the concentrations in the reaction chamber by 26%
compared to those observed in the plant chamber in this ex-
periment. Additionally, due to ozonolysis, the MT mixing ra-
tios in the reaction chamber were lower and ranged between
0.3 and 0.4ppb. Together with the onset of twilight the emis-
sions from the plants increased due to their dependence on
PPFD and temperature. The emissions stabilized after sev-
eral hours and remained almost constant at about 2.5ppb in
the plant chamber while illuminated. The MT concentra-
tions in the reaction chamber increased in parallel to con-
centrations in the plant chamber but they reached only about
1.2ppb. This lower increase was due to MT consumption by
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4392 Th. F. Mentel et al.: Aerosols from real plant emissions
3
2
1
0
Mixing Ratio [ppb]
00:0006:0012:00
Time
18:0000:00
vis on
UV on
Fig. 2. Diurnal cycle of monoterpenes (MT) emitted from pine
trees. The two traces show the total MT mixing ratios in the plant
chamber (blue) and in the reaction chamber (red) as measured by
PTR-MS. The MT mixing ratio rose in both chambers as soon as
the visible light was switched on in the plant chamber (yellow hor-
izontal bar) and reached a stable value around 09:00h. The MT
mixing ratio in the reaction chamber dropped close to zero as soon
as UV light was switched on at 11:30h (violet horizontal bar). At
this point the particle formation event was initiated. Before 11:45h
the MT in the reaction chamber were lower than in the plant cham-
ber because of reactions with O3and dilution by the supply flows
stabilizing the RH and providing O3.
O3and dilution. With the onset of UV illumination (11:45h–
19:40h, violet bar in Fig. 2), the MT concentrations in the
reaction chamber dropped to 0.1–0.2ppb and remained low
as long as the UV light was on. This additional drop was
attributed to reactions of MT with OH produced by ozone
photolysis.
Anumericalmodel(basedonFACSIMILE,AEATechnol-
ogy) considering an ideal CSTR was developed to study the
gas-phase processes in the reaction chamber. The measured
mixing ratios of MT and SQT were smoothed and imposed
asinputparameters. Themodelconsidereddilutionbytheair
flows containing ozone and water vapor and calculated con-
centrations of reactants and products in the reaction chamber,
which were compared to PTR-MS measurements at the exit
of the reaction chamber. As a first approximation we ap-
plied the rate coefficients kα−pinene+OHand kα−pinene+O3and
kβ−caryophyllene+OHand kβ−caryophyllene+O3to model the oxi-
dation of MT and SQT (Atkinson and Arey, 2003; Calogirou
et al., 1999).
The OH steady state concentrations during UV illumina-
tion were calculated from the MT consumption and resulted
in (OH)=(3±2)×107cm−3. These OH steady state levels are
consistent with the OH production rate, which was estimated
from O3 consumption caused by UV photolysis assuming
that the reaction of O1D with water vapour is the main addi-
tional loss process for O3when the UV light is on. Detailed
chemical interpretation will be subject of another paper.
vis on
UV on
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Mixing Ratio [ppb]
00:0006:0012:00
Time
18:0000:00
A
vis on
UV on
0.4
0.3
0.2
0.1
0.0
Mixing Ratio [ppb]
00:0006:0012:00
Time
18:0000:00
B
Fig. 3. The reaction chamber as continuous stirred tank reactor.
Shown are the MT (A, squares) and SQT (B, triangles) emitted by
pines as measured by PTR-MS in the plant chamber (blue) and in
the reaction chamber (red). The observations were modeled by as-
suming the reactivity of α-pinene with respect to OH and ozone
as average reactivity for MT. As average reactivity for SQT the re-
activity of β-caryophyllene with OH and 1/15 of the reactivity of
β-caryophyllene with ozone were applied. Model input (continu-
ous blue lines) were the smoothed MT and SQT mixing ratios in
the plant chamber. Model output (continuous red lines) were the
MT and SQT mixing ratios in the reaction chamber. In this exper-
iment OH concentration was estimated to 3×107cm−1. The black
lines describe the total throughput of MT and SQT, while the dotted
turquoise and magenta lines give the contributions of OH and O3
reactions, respectively.
Figure 3 presents results from the model calculations for
pine-emitted MT (Fig.3A) and SQT (Fig. 3B). The experi-
mental data and the model results show excellent agreement
for the MT oxidation supporting the applicability of a CSTR
model. Even small features such as the slight rise of the MT
concentrations at the outlet of the reaction chamber when the
UV light was turned off (19:40h) were quantitatively cap-
tured by the model.
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Th. F. Mentel et al.: Aerosols from real plant emissions4393
6x104
4x104
2x104
0x104
Particle Number [cm-3]
06:0009:0012:00 15:00 18:00 21:00
Time
vis on
UV on
lifetime 48.4 min.
formation rate
93 cm-3 s-1
Fig. 4. Number density of particles as measured by the UCPC for
pine reacted VOC. A few minutes after UV light was switched on
(violet bar) in the reaction chamber a particle formation burst with
a peak value of about 50000 particles was observed. The nearly
linear rising edge was used to determine the particle formation rate
j3nm. The quasi exponential decay thereafter was used to estimate
the lifetime of particles in the reaction chamber. Please note, that
there was no new particle formation observed before the UV light
was switched on. At 15:00h a second weaker particle formation
event occurred which lasted until UV light was switched off.
The reaction of MT with OH consumed the residual MT
instantaneously, generated a pulse of oxidation products and
initiated particle formation (shown in Fig. 4 and Fig. 5). The
separation of the overall MT consumption (black line) into
an O3(magenta) and OH contribution (turquoise) shows that
up to 90% of MT react with the OH radicals at the conditions
in the reaction chamber (Fig. 3A).
Figure 3B shows the analogous plot with SQT data.
Sesquiterpene emissions from pine were lower and thus more
noisy than those of MT. SQT emissions increased after on-
set of twilight in a similar manner as the MT emissions and
the SQT mixing ratios at the inlet of the reaction chamber
reached a maximum of 400ppt. No SQT were detectable
at the outlet of the reaction chamber even when the UV
light was off. This is attributed to the high reactivity of
the SQT with ozone. In the first model calculations an av-
erage reactivity of the SQT towards OH and O3such as β-
caryophellene was applied (Atkinson and Arey, 2003; Calo-
girou et al., 1999), however, as shown in Fig. 3B, a 15-
fold reduced average rate coefficient of the SQT with O3
(7.7×10−16molec.cm−3s−1) is sufficient to reproduce the
experimental observations. Although virtually all SQT were
consumed in the reactions with O3no particle formation was
observedwithoutUVlight. WhentheUVlampwasswitched
on, at OH concentrations of ≈3×107cm−3, about 75% of
the SQT reacted with OH (assuming a 15 times slower re-
activity towards O3 than for β-caryophyllene). Neverthe-
2
3
4
5
6
7
8
9
100
2
3
Particle Diameter [nm]
10:00 12:0014:0016:00
Time
18:00 20:0022:00
60
40
20
Mode Position [nm]
12:00 13:00 14:0015:00
UV on
Growth Rate:
14 nm/h
Fig. 5. Size distributions of particles formed by the oxidation of
pine VOC’s, shown as color plot. The colors give particle num-
ber concentration. When UV light was switched on (violet hor-
izontal bar) a particle formation event took place. The particles
were flushed out from the reaction chamber which was operated as
CSTR. At 15:00h a second particle formation event occurred. Here
we focus on the first main event. From the mode position as func-
tion of time the growth rate was derived which was nearly constant
over three hours (insert).
less, at the conditions in our chamber ([O3]≈50ppb and
[OH]≈3×107cm−3) the fraction of SQT reacting with O3
was still quitehigh (≈25%) compared tothat of MT (<10%).
TheinitialVOCconcentrationinthereactionchamberwas
determined by GC-which were averaged for three hours be-
fore the particle formation event was initiated. The precursor
concentrations as well as the speciation used for the analysis
below are based on this 3h average.
2.3.2Particulate phase observations
At the conditions in the reaction chamber the products of
MT and SQT ozonolysis did not induce new particle forma-
tion with detectable yields. However, the UV illumination
initiated consumption of the residual VOC by OH (Fig. 2,
11:45h) and particle formation (see Fig. 4 and Fig. 5). A
maximum particle number Nmaxof more than 4×104cm−3
was reached about 15min after the UV light was switched on
(Fig. 4). Thereafter the particle number density decreased for
two reasons. First, most of the VOC in the reaction chamber
were oxidized by OH and the delivery of VOC from the plant
chamber was not sufficient to keep the particle formation at
a level as high as directly after OH production. Second, the
increase of particle surface area provided a sufficient large
condensational sink to suppress nucleation/activation.
The decrease of the particle number density with time fol-
lowed an exponential curve with an e-fold lifetime of about
50min which is somewhat shorter than the residence time
of the air in the reaction chamber (≈65min). The differ-
ence between residence time and decay time implied some
contribution by coagulation and wall losses to the decay of
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4394Th. F. Mentel et al.: Aerosols from real plant emissions
4x108
3x108
2x108
1x108
0x108
Total Particle Volume [nm3 cm-3]
06:0009:0012:0015:00 18:0021:00
Time
vis on
UV on
Fig. 6. Particle volume as a function of time. The total particle vol-
ume was calculated from the measured size distributions (Fig. 5).
Periods of vis light in the plant chamber and UV light in the reac-
tion chamber are indicated by yellow and violet bars. The maxi-
mum Vmax, here at 13:30h, as distinguished point was used for the
further analysis (see text).
particle number density. However, the dilution by flush out
was the dominant loss process.
Approximate particle formation rates were deduced from
the fast rising edge of the particle number density before
reaching Nmax, here 93cm−3s−1. Some hours later, a sec-
ond weaker event occurred presumably because flush-out of
aerosols reduced the condensational sink. An increase of
the particle loss rate was observed when the UV lamp was
switched off, indicating a small but persistent particle source
in the reaction chamber after the second event.
The dynamics of the aerosol size distribution in our reac-
tion chamber was characterized by particle formation, con-
densational growth, coagulation and flush-out. In particular
the flush-out limited the count median diameter (CMD) to
typically less than 100nm (Fig. 5). Growth rates were de-
rived from the increase of the CMD in time and were typi-
cally linear over several hours (here 14nmh−1, see insert in
Fig. 5). The total particle volume was calculated from inte-
grated size distribution measurements. Figure 6 shows the
total particle volume during the pine experiment. The maxi-
mum particle volume Vmaxappeared typically about 90min
after the maximum in number density Nmax. During this pe-
riod, the particles grew fast enough to overcompensate the
loss in particle number by flush out. A second, smaller par-
ticle formation event is recognizable by the small, relative
maximum at 17:00h. For the analysis below we chose the
observed Vmaxas distinguished reference points.
100
90
80
70
60
50
40
30
20
10
0
Fraction of Total VOC Carbon
10.811.412.6 16.519.627.120.0 37.543.1 43.495.1

43.1 46.560.866.674.082.2
Total VOC Emission [ppbC]
100
90
80
70
60
50
40
30
20
10
0
MT1 MT2 MT3 Other MT SQT OVOC
Birch Pine Spruce
Fig. 7. Emission patterns of the boreal tree species classified in
monoterpenes (MT, green), sesquiterpenes (SQT, pink), and oxy-
genated VOC (OVOC, blue). For monoterpenes the fractions of the
three most abundant species of each tree MT1, MT2, and MT3 are
indicated (compare Table 2). The absolute VOC mixing ratios in
ppbC are shown on the category axis and are ordered with increas-
ing VOC for each tree.
2.3.3Retrieval of SOA volume and SOA mass under
CSTR conditions
The particle formation events themselves lasted only for a
few minutes, and the particle concentrations did not reach a
steady state in the CSTR. This complicated the interpretation
of the observed Vmax, because the same fraction of the num-
ber of particles in each size class is flushed out, but the vol-
ume is dominated by particles with large sizes. Number and
volume of the particles which are flushed out in the course
of an experiment were determined by applying a numerical,
sectional aerosol dynamic model considering condensation,
coagulation, and deposition within the CSTR. (This model
is based on the work of Korhonen et al., 2003 and will be
described elsewhere in detail.) The model calculations show
that for each event at the time of observation of Vmaxthe
particle volume already flushed out of the reaction chamber
is approximately the same as the observed particle volume
Vmaxitself. On average the flush out factor F was 2.0±0.03
and as a consequence we define the quantities Vmaxcwhich
are the observed Vmaxcorrected by F. The particle loss dur-
ingtheperiodofafewminutesbetweentheonsetoftheparti-
cle formation and the observed Nmaxis negligible and affects
the determined particle formation rates j3nmto less than 5%.
The effective densities of the SOA are calculated by com-
paring the modal positions of the mass size distribution
measured by the Q-AMS to those of the volume size dis-
tribution determined by the SMPS (DeCarlo et al., 2004;
Saathoff et al., 2008).The SOA density was on aver-
age 1.25±0.15gcm−3and compares well with densities of
monoterpene oxidation products determined by other groups
(Bahreini et al., 2005; Kostenidou et al., 2007; Ng et al.,
2006; Saathoff et al., 2008).For the experiment shown
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Th. F. Mentel et al.: Aerosols from real plant emissions4395
Table 2. Monoterpenes and sesquiterpenes on carbon basis.
VOC ClassBirch (% of all VOC) Pine (% of all VOC)Spruce (% of all VOC)
MT40%77% 90%
SQT18%8% 7%
OVOC*42% 15% 3%
Monoterpene** Birch (% of all MT)Pine (% of all MT)Spruce (% of all MT)
trans-ocimene45%
cis-ocimene 17%
limonene9% 49%
?3−carene
α−pinene
β−pinene
camphene
37%
32% 9%
9%
7%
Sesquiterpene***Birch (% of all SQT) Pine (% of all SQT)Spruce (% of all SQT)
α−farnesene
caryophyllene
15%23%
10%
cadinene19%
β−cubebene
longifolene
13%
39%
∗Due to stress impacts the plant emitted high amounts of OVOC such as (Z)-3-hexenol, (Z)-3-hexenylacetate, or methyl salicylate.
∗∗The fractions of the monoterpenes labeled with MT1, MT2, MT3 in order of their abundance are also given in Fig. 7.
∗∗∗The class sesquiterpenes contained several compounds not positively identified but assigned to this class due to the typical peak at
m/z=204. The contribution of these non attributed SQT to this class were <7% for birch, <15% for pine and <5% for spruce.
in Fig. 6 the observed maximum total volume Vmax was
0.3×109nm3cm−3, which by applying F and a density of
1.25gcm−3yields 0.75µgm−3organic mass at the maxi-
mum. The average density of 1.25gcm−3will be applied
in the following. Detailed Q-AMS results will be discussed
elsewhere.
3 Results
Figures 7 to 9 summarize 17 particle formation events ob-
served for birch, pine, and spruce trees as emission sources
as well as the 10 events done with α-pinene from a diffu-
sion source. The carbon mixing ratios at the abscissae in
Figs. 8 and 9 were obtained as follows. The tree emissions
observed during the 17 events were categorized into the three
classes: monoterpenes (MT), sesquiterpenes (SQT) and oxy-
genated VOC (OVOC) as shown in Fig. 7. Isoprene con-
centrations were negligibly small. This categorization was
chosen because MT and SQT comprise two well-defined iso-
prenoid classes and the class concentrations can be directly
measured by the PTR-MS. Almost all MT and SQT react fast
withOHradicals, oftenwithratecoefficientsclosetothegas-
kinetic limit (10−10cm3molec.−1s−1). However, rate coef-
ficients for reactions of some SQT with O3are quite high,
while many MT react slower with O3. Therefore for SQT
the ozonolysis constitutes a major pathway, whereas reaction
with OH is the dominant pathway for MT in the atmosphere
(and in our reaction chamber). The oxidation products of
MT and SQT contribute to atmospheric SOA mass and pre-
sumably play an important role in new particle formation.
In the reaction chamber, the bulk of MT and SQT were oxi-
dized, in particular when the UV lamp was switched on. OH
reactions alone consumed more than 90% of the MT (com-
pare Fig. 3A). As the rate constants for SQT+O3reactions
are quite high, an oxidative turnover of more than 98% is ex-
pected. This is confirmed by near-zero SQT concentrations
which were observed in the reaction chamber even before the
UV lamp was switched on (see Fig. 3B).
The OVOC category is composed of a diverse mixture
small molecules such as methanol (Folkers et al., 2008; H¨ uve
et al., 2007), LOX-products such as (Z)-3-hexenol (Heiden
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4396Th. F. Mentel et al.: Aerosols from real plant emissions
Table 3. Summary of observations and analysis.
QuantityBirchPine Spruce
α−Pinene
SOA Volume: Consideration of MT+SQT
Volume Efficiency [nm3cm−3ppbC−1]
Volume Formation threshold [ppbC]
47(±5)×10+06
4±1
0.986
23(±2)×10+06
5±4
0.988
18(±3)×10+06
42±10
0.951
23(±2)×10+06
67±11
0.920
R2of the linear regression
SOA Volume: Consideration of Various VOC Classes
Volume Efficiency MT only [nm3cm−3ppbC−1]
R2, linear regression MT only
44(±13)×10+06
0.804
24(±3)×10+06
0.968
15(±3)×10+06
0.889
23(±2)×10+06
0.920
Volume Efficiency MT+SQT+OVOC [nm3cm−3ppbC−1]
R2, linear regression MT+SQT+OVOC
38(±7)×10+06
0.910
21(±2)×10+06
0.981
19(±3)×10+06
0.910
New Particle Formation
Formation Rates j3nm[s−1cm−3]
Average Formation Rate [s−1cm−3]
Number Efficiency [s−1cm−3ppbC−1]
Number Formation Threshold [ppbC]
6–8918–1821–44 20–130
23±31
6.1±0.4
5±1
0.984
103±58
4.7±0.4
12±3
0.985
19±17
1.0±0.2
43±13
0.850
44±35
1.4±0.3
72±20
0.760
R2, linear regression (j3nm<140 !)
Particle Growth
Growth Efficiency [nm h−1ppbC−1]
Intercept Growth [nm h−1]
R2, linear regression
0.39±0.13
11±2
0.760
0.10±0.03
12±2
0.798
0.14±0.06
6±4
0.556
0.13±0.01
8±2
0.951
Fractional Mass Yields Consideration of MT only and MT+SQT
Fractional Mass Yield MT+SQT0.11±0.01
0.10±0.03
0.053±0.005
0.053±0.006
0.042±0.007
0.035±0.006Fractional Mass Yield MT only0.052±0.005
et al., 2003) or some long chain aldehydes such as nonanal
(Wildt et al., 2003). Most of the OVOC react slower with
ozone than the MT, so reaction with OH radicals is probably
their major loss pathway. The role of oxygenated compounds
as precursors for particle formation and growth is not quite
clear. In the reaction chamber the concentrations of some
OVOC were drastically reduced due to oxidation by OH.
Only for a few OVOC such as methanol and acetone, a strong
increase was observed when the UV lamp was switched on,
presumably due to oxidative degradation of other VOC.
ForeachclasstheVOCconcentrationswereweightedwith
the number of carbon atoms of the respective VOC leading
to carbon mixing ratios in ppbC. These were calculated from
the average of 3h of GC-MS measurements before new par-
ticle formation was initialized. The relative contributions of
MT, SQT and OVOC to the total amount of carbon averaged
over all events for each tree are given in Table 2. Birch had
the largest fraction of OVOC and SQT. Pine had a substan-
tial fraction of OVOC and spruce emissions were composed
mainly of MT and SQT. Figure 7 summarizes the emission
patterns for all events in terms of these classes as relative
concentrations. The absolute, total concentrations of VOC in
ppbC are given for each event at the category axis in Fig. 7.
As we will show in the following, quantities characterizing
particle formation events such as SOA volume, particle for-
mation and growth rates are related to these carbon mixing
ratios in a relatively simple fashion. Table 2 specifies the
most abundant MT and SQT observed in the experiments
with the trees. For pine α-pinene and ?3-carene are the main
MT components together with some camphene. The main
MT component of the spruce was limonene, together with
similar contributions of α-pinene and β-pinene as secondary
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Th. F. Mentel et al.: Aerosols from real plant emissions4397
components. The birch emitted cis- and trans-ocimene, and
limonene. Together with the relative high SQT and OVOC
emissions, this indicates that the birch faced some stress.
Also the first day of spruce showed enhanced SQT fraction,
indicating delay in adaptation. We nevertheless included all
thesemoderatecasestoenhancethecoverageofpossiblesce-
narios.
In all cases, both UV-light and OH radicals were essen-
tial in inducing new particle formation at our experimen-
tal conditions and within our dynamic range of emissions
concentrations of up to 10ppbC. Herein, the OH reached
steady state concentrations of about 3×107cm−3, as de-
scribed above. Ozonolysis alone did not induce new particle
formation with detectable yields, although all SQT and a por-
tion of the MT were consumed by ozone (including a contri-
bution by dark OH arising from the ozonolysis of unsaturated
VOC, since we did not use OH-scavengers, see Atkinson and
Aschmann, 1993; Paulson et al., 1992, 1998).
In Fig. 8 we relate the total volume of SOA (Vmaxc) for
tree emissions and for α-pinene to carbon mixing ratios of
VOC. The Vmaxcare the observed Vmaxcorrected by F=2.0
for particle flush-out as discussed in Sect. 2.3.2. The car-
bon mixing ratios given on the x-axis in Fig. 8 are the 3h
average of the mixing ratios in the plant chamber corrected
for dilution, thus the hypothetical steady state levels of the
respective precursors in the reaction chamber if there were
no reactions. Figure 8 compares the importance of the emis-
sion classes in terms of MT and the sums of MT+SQT and
MT+SQT+OVOC. The data points in Fig. 8 consider the car-
bon mixing ratio of the sum of MT+SQT.
We found simple linear relationships between the mixing
ratios of MT + SQT and Vmaxcfor all tree species studied
and α-pinene. The linear regression curves have positive
x-intercepts. Slope, x-intercept, and correlation coefficient
(R2) of the linear regressions are shown in Table 3. A lin-
ear regression was also performed by taking into account the
MT portion only and by considering the total carbon com-
prising MT+SQT+OVOC. For comparison we added the re-
sulting best fit lines of these two cases in Fig. 8. The slope,
x-intercepts and R2of these fits are also listed in Table 3. For
each tree species the values of R2, i.e. the quality of the fit
did not depend much on the choice of the VOC basis.
The comparison of the best fit lines for the different ap-
proaches in Fig. 8 shows that neglecting the sesquiterpenes
in the sum of MT+SQT for pine and spruce does not have
much effect on the slopes and on the x-intercepts. This is
due to the relatively small contributions of SQT to the emis-
sions. Only the first data point for spruce comprises a relative
large fraction of SQT. In case of the birch where SQT on av-
erage contributed about 14% to the emissions, omission of
the SQT leads to a change of 7% in the slope and a decrease
of the x-intercept by 4ppbC to zero. The inclusion of OVOC
to MT+SQT has an effect in case of the birch because the
OVOC concentrations were high; the influence is weaker for
pine and negligible for spruce. Because of the uncertainty
Fig. 8. Linear relations between maximum particle volume Vmaxc
and the carbon mixing ratio in the reaction chamber for emissions
of birch (red circles), pine (blue diamonds), spruce (green squares),
and α-pinene (magenta triangles). Vmaxcwas corrected by F=2 for
flush out of particles from the reaction chamber operated as CSTR.
The symbols consider carbon mixing ratios based on MT+SQT,
solid lines are linear fits to the symbols. The dotted and dashed dot-
ted lines are linear regression lines based on carbon mixing ratios
of MT only and on all three classes MT+SQT+OVOC, respectively.
(details see text). The intercepts with the ppbC axis indicate vol-
ume formation thresholds, i.e. the amount of carbon which must be
consumed before particle volume can be formed.
about the fraction of OVOC that serve as potential SOA pre-
cursor, we will focus analysis and discussion on the sum of
MT+SQT.
The linear relations between MT+SQT carbon mixing ra-
tios and Vmaxcfor the trees and α-pinene have differences in
the slopes and significant differences in the intercepts. The
slopes of the linear regressions shown in Fig. 8 give a volume
efficiency of SOA formation for each tree species and for α-
pinene in terms of generated SOA volume per ppbC emission
concentration in the reactor. The volume efficiency obtained
for the emissions of pine and spruce was the same as that
for α-pinene within the errors. The average of these three
cases amounts to 21±3×106nm3cm−3ppbC−1. For birch
the SOA efficiency (47±5×106nm3cm−3ppbC−1) is about
a factor of two larger (Table 3). Note that a volume con-
centration of 1×106nm3cm−3corresponds to a mass con-
centration of 1ngm−3, if the unit density of 1gcm−3is ap-
plied. We calculated the x-intercepts at the ppbC–axis (see
Fig. 8 and Table 3), since these characterize a threshold for
the onset of new particle formation at the OH levels of a few
times 107cm−3in the reaction chamber. Independent of the
OVOC being included or not, all tree emissions had distinc-
tively lower ppbC-intercept, i.e. indicating potentially lower
particle formation thresholds than α-pinene.
We observed particle number formation rates j3nm be-
tween 1 and 182cm−3s−1for the tree emissions: Six events
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4398Th. F. Mentel et al.: Aerosols from real plant emissions
140
120
100
80
60
40
20
0
Particle Formation Rate j3nm [cm-3 s-1]
140120100 806040200
Mixing Ratio MT + SQT [ppbC]
Fig. 9. Particle formation rates j3nmas a function of the carbon
mixing ratio of MT+SQT for emissions of birch (red circles), pine
(blue diamonds), spruce(green squares), α-pinene (magenta trian-
gles). Only nucleation rates smaller than 140cm−3s−1were con-
sidered. The slope of the linear regression lines give particle for-
mation efficiency in terms of particles formed per volume and time
per ppbC consumed. Intercepts with the ppbC axis indicate parti-
cle formation thresholds, i.e. the amount of carbon which must be
consumed before new particles can be detected.
with j3nm≤10cm−3s−1were observed, 7 events with 10
<j3nm≤50cm−3s−1, 5 events with j3nm≥90cm−3s−1. The
average particle formation rate was 44cm−3s−1. In the α-
pinene reference experiments j3nmwas in the range of 20 to
130cm−3s−1, withthemajority(8outof10)between20and
40cm−3s−1. If we omit the largest particle formation rate of
182cm−3s−1for pine, the formation rates <140cm−3s−1
observed for the trees scale approximately linearly with the
concentration of MT+SQT in ppbC as defined above (Fig. 9).
This trend was also recognizable with α-pinene although the
data were more scattered (see R2, Table 3). The slopes of the
linear fits to j3nm=f(ppbC) define a number efficiency of the
formation of new particles in terms of particles formed per
cm3and s per ppbC for each tree and for α-pinene. Birch
(6 cm−3s−1ppbC−1) and pine (5cm−3s−1ppbC−1), were
more efficient in forming new particles than spruce and α-
pinene (both about 1cm−3s−1ppbC−1). As can be seen in
Fig. 9 the linear regression lines intercept the ppbC-axis at
about the same positive values within the errors as in the
case of Vmaxcin Fig. 8. This confirms our interpretation of
the positive intercepts at the ppbC-axis as particle formation
thresholds. We will discuss these thresholds later.
Growth was linear for each event over 2–3h as indicated
in the insert in Fig. 5. Thus we attribute specific growth
rates GR to each particle event. The GR for the tree emis-
sions were in the range 10–20nmh−1with an average of
14±3nmh−1for all events. In the dynamic range of emis-
sion concentrations investigated here, the GR depend lin-
early on the carbon mixing ratio of MT+SQT, like Vmaxcand
j3nm. The slopes of the linear regressions of the GR as a
function of the carbon mixing ratio define growth efficien-
cies (growth rate per ppbC). For the two conifers the growth
efficiencies are 0.10±0.03 and 0.14±0.01nmh−1ppbC−1,
respectively, which is the same as that for α-pinene of
0.13±0.01nmh−1ppbC−1within the uncertainties of the ex-
perimental results. For birch, a larger growth efficiency of
0.38±0.13nmh−1ppbC−1was observed (Table 3). The y-
intercepts are non-zero, in a range of 5–10nmh−1but the
same for all within the error limits.
4 Discussion
4.1 Performance of the setup
The setup with separated plant and reaction chambers
demonstrates the ability to independently control the phys-
iological state of the plants (by managing their environment
−T, RH, CO2, PPFD) while keeping the conditions in the
reaction chamber constant. The size of the plant chamber al-
lows whole individual plants or groups of plants to be used
instead of branches or single leaves. By simulating a diur-
nal PPFD cycle and by applying different temperatures in
the plant chamber, the VOC emissions from the plants and
thustheinputintothereactionchamberwasvariedovermore
than an order of magnitude without exposing the plants to ex-
treme unnatural conditions. In the reaction chamber the con-
ditions were constant during particle formation events that
lasted for several hours. In the natural environment neither
temperature nor the intensity of visible light and UV light
are constant over such a long time. However, the constant
conditions enable comparison between the results obtained
for different plants and for α-pinene. Our setup thus allows
controlled experiments of particle formation, nevertheless re-
flecting the complexity and the highly mixed states of plant
emitted VOC.
The emission patterns of boreal trees in the plant chambers
reflect conditions observed in the boreal forest. Tarvainen
et al. (2007) identified α-pinene, β-pinene, ?3-carene, and
limonene as the major components in the south and middle
boreal forest from spring to autumn. The same compounds
were also the major MT components emitted by the plants in
our chamber (see Table 2). In addition, ocimenes are a major
late spring and summer emissions of birch, and a major com-
ponent of the MT emissions in the boreal forest during sum-
mer and autumn. The average SQT/MT ratio of 10% for all
the experiments with spruce and pine match the SQT/MT ra-
tio in the boreal forest during the growing season (Tarvainen
et al., 2007). The average mass SQT/MT ratio measured here
was higher (46%) only for experiments with birch. However,
such large SQT/MT ratios were reported for the late sum-
mer in the boreal forest (Tarvainen et al., 2007). We suggest
that the enhanced SQT/MT originate from plant stress. This
is supported by the high OVOC levels observed here, too.
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Th. F. Mentel et al.: Aerosols from real plant emissions4399
Possibly the birch trees in our experiments faced a similar
form of (unknown) stress as birch trees in the late summer.
Considering the natural plant-to-plant variability our experi-
ments simulate emission pattern in the Finnish boreal forest
quite well and provided thus a realistic source of precursors
for SOA formation in boreal forests.
The VOC concentrations during the plant chamber exper-
iments were well below 10ppb in most cases (<100ppbC,
see Fig. 7), which is on the low side compared to most other
laboratory and chamber studies. The MT concentrations are
still somewhat high compared to the sub-ppb levels of MT at
the canopy level in the boreal forest (Haapanala et al., 2007;
Hakola et al., 2003). It is noted though that the majority of
data points obtained in our experiments (12 out of 17) were
at MT levels below 4ppb.
The O3levels in the dark reaction chamber were ≈80ppb,
about 2 times higher than those typically observed in
Hyyti¨ al¨ a in spring and early summer during particle forma-
tion events (Lyubovtseva et al., 2005). However, when UV
light was turned on, the ozone level dropped to 30–40ppb,
comparable to those in Hyyti¨ al¨ a at day time. An obvious
difference between the environment and our reaction cham-
ber were the OH concentrations, which are estimated to be
1-5×107cm−3, roughly an order of magnitude higher than
values of 5×105– 5×106cm−3observed in spring and sum-
mer in Hyyti¨ al¨ a (Boy et al., 2005; Lyubovtseva et al., 2005).
Other properties that are necessary in order to classify the
conditions during SOA formation are NOx concentrations
and RH. The only NOxsource in our current setup is direct
NO emission from the plants (Wildt et al., 1997; Hari et al.,
2003). The NO concentrations at the exit of the plant cham-
ber were always below 300ppt, therefore maximum NOx
concentrations in the reaction chamber were always well be-
low 300ppt. The RH in the reaction chamber was regulated
to ∼65%, with one exception of ∼50% for the spruce exper-
iments. According to the classification of laboratory studies
of SOA formation, our experiments were carried out in the
low NOX, UV, and high RH regime (Pathak et al., 2007a).
The abscissae in Figs. 8 and 9 show the stabilized VOC
concentration in the plant chamber.
event, the steady state concentration of MT and SQT drops
to near zero, as they are almost quantitatively consumed by
OH radicals and ozone (Fig. 3A, Fig. 3B). Thus, the ini-
tial hypothetical steady state concentration of MT+SQT in
the reaction chamber is also a direct measure of the overall
MT+SQT consumption. The hypothetical steady state con-
centration cannot be observed in the reaction chamber be-
cause almost all SQT and about half of the MT are already
consumed by ozonolysis before the UV light in the reaction
chamber is turned on (compare Fig. 3A, Fig. 3 B). As a con-
sequence, a substantial portion of the carbon of the plant-
emitted VOC is already converted into ozonolysis products,
before the nucleation events were initiated. Nevertheless,
Vmaxc, and j3nmscale linearly with this hypothetical steady
state VOC concentration. We explain this by (i) that the oxi-
During a nucleation
dation by ozone still conserves the carbon mixing ratio in the
reaction chamber. This is inherent to a CSTR in steady state,
if there are no other substantial losses than the flush out. (ii)
The ozonolysis products are oxidized by OH when UV light
is on and their carbon contributes to Vmaxand j3nm. GC-
MS measurements in the reaction chamber during an event
confirmed that the first generation products (such as pinon-
aldehyde and nopinone) were indeed absent, and were likely
consumed by OH radicals.
Under the limitations discussed above, the experiments
were conducted as near as possible to those in the natural
environment. As argued in Rudich et al. (2007), enhanced
VOC concentrations and oxidant levels can cause difficulties
in the transferability of chamber results to atmospheric con-
ditions. The influence of the somewhat high VOC and OH
concentrations on particle formation in our system is esti-
mated based on the experiments with the α-pinene diffusion
source instead of plants. An incremental mass yield of 5.2%
can be derived from the slope for α-pinene in Fig. 8, applying
particle density of 1.25gcm−3. Although there are not many
low concentration data in the literature to which our results
can be compared, SOA mass fractions of 5% are found at
SOA mass loads around 2µgm−3in α-pinene experiments
summarized by Pathak et al. (2007a, b). The x-intercept in-
dicates an onset of particle formation at 7ppb (details see be-
low). These findings agree best with results at the low NOx,
UV, low RH regime compiled by Pathak et al. (2007a).
The mixing ratios of the VOC under investigation were
low (<10ppb on molecule basis) and the observed parti-
cle volumes Vmaxwere less than 0.8×109nm3cm−3for the
trees and in most α-pinene experiments. (Recall, that Vmaxc
in Fig. 8 are a factor of 2 larger than the actual observed
Vmaxbecause of the correction for flush out.) This corre-
sponds to actual mass loadings in the sub µg m−3range.
SOA mass concentrations of a few tenth of a µgm−3are
compatible with the organic mode in the range Dva=50–
100nm observed in Hyyti¨ al¨ a by Allan et al. (2006) 10–18h
after a nucleation event. Allan et al. (2006) attributed this
to the organic fraction in newly formed particles. Such low
mass loadings together with the linearity between consumed
ppbC and observed Vmaxc(Fig. 8) indicate that our measure-
ments were not strongly influenced by absorptive uptake of
volatile- and semi-volatile compounds. According to consid-
erations of Donahue et al. (2006), for a particle mass load
of 1µgm−3the vapor pressure of compounds which parti-
tion to equal fractions in gas phase and particulate phase is
also about 1µgm−3. For comparison: for pinonaldehyde
1µgm−3corresponds to a mixing ratio of 150ppt or a satu-
rated aqueous solution of glutaric acid has a vapor pressure
of about 1 ppb≈5µgm−3(Bilde et al., 2003; Koponen et al.,
2007). As a consequence, we believe that the enhanced OH
concentrations and the somewhat elevated VOC concentra-
tions merely compressed the temporal evolution of the entire
photo-chemical system and the time evolution of new parti-
cle formation.
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4400Th. F. Mentel et al.: Aerosols from real plant emissions
We thus conclude that our reaction system realistically
simulates many aspects of natural VOC oxidation and parti-
cle formation and that our results may thus be reliably scaled
down to lower VOC concentrations. This is corroborated by
the fact that both, our average GR and average particle for-
mation rate are scaling in similar fashion to those observed
in Hyyti¨ al¨ a. Our lowest GR of about 10nmh−1(average
14nm h−1) is 3–4 times larger than the average growth rate
observed in Hyyti¨ al¨ a (3nmh−1, Kulmala et al., 2001). At
the same time our lowest observed particle formation rates
j3nmare in the range 1–20cm−3s−1which is up to an order
of magnitude larger than particle formation rates in Hyyti¨ al¨ a
(1–2cm−3s−1, Kulmala et al., 2007).
4.2The role of ozonolysis vs. OH oxidation in new par-
ticles formation
New particle formation was not observed before OH-radicals
were generated, suggesting a minor direct role of MT and
SQT ozonolysis in new particle formation. In contrast to
MT, the SQT were quantitatively consumed by ozonolysis.
Therefore, we may conclude that, even at the somewhat en-
hanced VOC concentrations in our reaction chamber, 50–
65% RH, and 80ppb of O3, ozonolysis products of sesquiter-
penes do not initiate formation of new particles. We also
find that under these conditions, MT ozonolysis products
do not induce particle formation. This is in contrast to ob-
servations in static chambers and in contrast to the labora-
tory study with β-caryophyllene (Bonn and Moortgat, 2003).
Bonn and Moortgat (2003) concluded that the reaction of
the sesquiterpenes with ozone should most likely be the ori-
gin of the observed atmospheric biogenic secondary organic
aerosol (SOA) formation.
Indeed, new particle formation occurs only during day
time in the boreal forest, in accordance with our observations
(Kulmala et al., 2004b). Lyubovtseva et al. (2005) find a cor-
relation between high O3levels and new particle formation
in springtime and wintertime, but this seems to be more re-
lated to O3dependent OH production than to direct ozonol-
ysis reactions. However, it is possible that oxidation prod-
ucts of the ozonolysis of SQT or MT, once they react further
with OH radicals, play a pivotal role in new particle forma-
tion and contribute to condensational growth and SOA mass.
We observe the consumption of ozonolysis products as soon
as the UV-light is switched on and OH radicals are gener-
ated. These findings are consistent with observations multi-
step oxidation in SOA formation (Ng et al., 2006), with the
suggestion of oxygenated precursors as a persistent source of
organic aerosols in the free troposphere (Heald et al., 2005),
and in the boreal forests (Laaksonen et al., 2008).
4.3 SOA formation potentials of tree emissions
One goal of these experiments is to attribute SOA formation
potentials to species and tree stands (i.e. several trees emit-
ting). A key to that are the simple linear relationships be-
tween the observed particle volume Vmaxcor the new parti-
cle formation rate j3nmand the carbon mixing ratio of the tree
emissions (Fig. 8 and Fig. 9). The slopes of these linear re-
gression lines enable to attribute SOA formation potentials to
each tree species with respect to production of new particles,
which we term the number efficiency, and to estimate pro-
duction of SOA volume, which we term volume efficiency.
Positive intercepts on the ppbC axis for both, Vmaxcand
j3nm(Fig. 8 and Fig. 9, Table 3) were found. These give
the minimum amount of VOC that must be oxidized under
the given conditions to generate sufficient low vapor pres-
sure products and thus new particles, in other words thresh-
olds for new particle formation. Remarkably, we obtain the
same ppbC intercepts in both, j3nmand Vmaxcfor each tree
and α-pinene within the statistical errors. The thresholds
for number and volume formation in systems without pre-
existing seed particles should indeed be the same, because
volume can only be formed by condensation after seeds had
nucleated. Thus, this agreement points out the consistency
of our observations. If we average the results from Vmaxc
and j3nmto a single particle formation threshold, we end up
with 5±1, 9±6, 43±16, 70±22ppbC for birch, pine, spruce
and α-pinene, respectively. It should be noted that the spruce
experiments were performed at somewhat lower RH, than all
others, 50% compared to 65%, and that there are indications
that j3nmdepends on humidity. This is currently under closer
investigation, and therefore all comparisons of spruce with
the other systems require some care. Taken together, these
data indicate that the particle formation thresholds of birch
and pine at 65% RH are identical within the errors, but lower
than that of spruce at 50% RH and α-pinene.
Considering number efficiencies (slopes in Fig. 9, Ta-
ble 3), again birch and pine are most effective, while spruce
(at 50% RH) and α-pinene are less efficient. Spruce and α-
pinene have about the same number efficiency of 1cm−3s−1,
however spruce reveals a lower particle formation threshold.
Overall, the number efficiencies tend to increase with de-
creasing thresholds, i.e. the lower the threshold to form new
particles the more are formed within the same time. Let’s in
addition include the growth efficiencies (Table 3). Recall that
the GR which entered the growth efficiencies are a measure
of the surplus of condensable vapors relative to their equilib-
rium vapor pressure. The growth efficiency for pine is about
thesameasforspruceandα-pinene, averagingto0.12±0.04.
The growth efficiency obtained for birch emissions is a fac-
tor of 3 larger. This suggests that upon oxidation the birch
emission form very low vapor pressure compounds that pro-
mote new particle formation in an early stage and condensa-
tion later on. Pine emissions, however, seem to provide only
small amounts of very low vapor pressure compounds that
support new particle formation, and which are less involved
in the condensation process at later stages. Spruce emissions
(at 50% RH) and α-pinene produce condensing vapors sim-
ilar to pine emissions, but significantly less of the very low
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Th. F. Mentel et al.: Aerosols from real plant emissions4401
pressure vapors that promote new particle formation. The
overall enhanced particle formation potential of the stressed
birch supports suggestions by Joutsensaari et al. (2005) that
plant stress may be an important factor in the processes of
natural new particle formation.
If we refer to the relative emission patterns in Fig. 7, low
particle formation thresholds go along with large fractions
of OVOC and SQT. Accordingly the number efficiencies fol-
low the same trend. However, considering the absolute con-
centrations, birch and pine emitted a larger OVOC concen-
tration than spruce, whereas absolute SQT concentrations
where about the same for all trees. We take this as an in-
dication that plant-emitted OVOC may play a pivotal role in
the process of new particle formation in agreement with the
importance of oxygenated organics in nucleation events over
boreal forests as recently pointed out by (Laaksonen et al.,
2008).
At the moment there is not enough information to suffi-
ciently understand the particle formation process in the re-
action chamber on the microphysical level. It could be self-
nucleation of VOC oxidation products or activation of pre-
existing thermodynamically stable clusters (e.g. Kulmala,
2006). We can not exclude beyond doubts that plant-emitted
sulfur compounds and thus sulfuric acid may be involved,
but their concentration should be below 10ppt. The pres-
ence of SO2would favor large nucleation rates as observed in
SOA formation in α-pinene ozonolysis (Hoppel et al., 2001).
Therefore at the moment the absolute values obtained for the
thresholds have only a meaning for the conditions regarding
VOC, OH, O3RH, and T of our reaction chamber. However,
our results suggest that VOC emissions from birch (stressed),
pine, and spruce (at 50% RH) very probably aid the early
stages of particle formation better than α-pinene. The poten-
tial to form new particles results in the series birch (stressed)
>pine>spruce (at 50%RH) >α-pinene. Studies with pure α-
pinene therefore will not reflect new particle formation pro-
cesses in the atmosphere very well.
4.4 Incremental SOA yields
As discussed above, the concentrations of MT and SQT in
the flow from the plant chamber to the reaction chamber pro-
vide a measure of the carbon available for particle formation
(hypothetical steady state concentrations of MT+SQT). They
further provide a direct measure of the carbon consumed in
the oxidation reactions, because the observed steady state
concentration of MT+SQT in the reaction chamber drops to
near zero as soon as the particle formation events are ini-
tiated by the OH radicals. Insofar the slopes of the linear
regressions to the Vmaxcvs. ppbC relations in Fig. 8 repre-
sent incremental SOA yields in terms of observed volume
increase per ppbC consumed. Since we observed particle
formation thresholds, the SOA yields are by definition lower
than the incremental yields, because one must also consider
the threshold amounts of MT+SQT, which were consumed to
Fig. 10. Aerosol mass concentration as a function of the terpene
mass concentration for emissions of birch (red circles), pine (blue
diamonds), spruce (green squares), and α-pinene (magenta trian-
gles). Filled symbols and solid lines consider mass concentrations
of MT+SQT as precursors; open symbols and dotted lines con-
sider mass concentrations of MT only as precursor. The slopes of
the lines are the fractional mass yields. The plant chamber data
were compared with results of trajectory analysis over boreal for-
est by Tunved et al. (2008) for the Hyyti¨ al¨ a station (slope=0.047,
intercept=0.92µgm−3, double dashed double dotted line). For the
conifers and α-pinene the mass fractional yields in the plant cham-
ber study agree within 10% with the analysis of field observations.
provide sufficient supersaturation of vapors to generate and
to grow the particles. Nevertheless, the incremental yields
are still generally meaningful because (i) they do not depend
on the magnitude of VOC consumption as long as the rela-
tions remain approximately linear. (ii) Given the linearity
over a wide range of the carbon mixing ratio, incremental
yields formally give the upper bound of the SOA yields, be-
cause with increasing VOC consumption the threshold car-
bon mixing ratio becomes negligible with respect to the con-
sumed carbon mixing ratio. (iii) To a large extent, incremen-
tal yields are independent of the history of the particle en-
semble, e.g. if particles were freshly formed or if the vapors
condensed on pre-existing seed particles.
For better comparison with the literature, we converted the
MT+SQT mixing ratios and the particle volume concentra-
tions to mass concentrations [µgm−3], by using the average
density of 1.25gcm−3as determined above. The data sets
on mass basis are shown in Fig. 10. From the slopes of the
linear regression to the data sets on mass basis it is possi-
ble to obtain dimensionless incremental mass yields, which
are given in Table 3. For α-pinene, we obtained a slope of
0.052±0.005, that corresponds to an incremental mass yield
of 5.2±0.5% (see Sect. 4.1). The corresponding incremental
mass yields for SOA formation from pine and spruce emis-
sions were 5.3±0.5% and 4.2±0.7%, respectively (Table 3).
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