Introduction
The boreal forest is the largest terrestrial biome, forming an almost
continuous belt around the Northern Hemisphere. The boreal forest zone is
characterized by a short growing season and a limited number of tree
species. The most common tree species are Scots pine, Norway spruce, and
silver and downy birch and they produce and emit vast amounts of biogenic
volatile organic compounds (VOCs) (Bourtsoukidis et al., 2014a, b; Bäck
et al., 2012; Cojocariu et al., 2004; Grabmer et al., 2006; Hakola et al.,
2001, 2006; Tarvainen et al., 2005; Yassaa et al., 2012). The compounds are
mainly isoprene, monoterpenes (MTs), sesquiterpenes (SQTs), and oxygenated
volatile organic compounds (OVOCs) (Tarvainen et al., 2007). There is a
variety of factors controlling these emissions – both biotic (Pinto-Zevallos
et al., 2013; Joutsensaari et al., 2015) and abiotic stress (Vickers et al.,
2009; Bourtsoukidis et al., 2012, 2014c) factors can
initiate or alter VOC emissions. Abiotic stress factors have been reviewed
by Loreto and Schnizler (2010). Terpenes, for example, relieve oxidative and
thermal stresses of trees. Many stress factors can also interact and cause
additive effects (Niinemets, 2010; Holopainen and Gershenzon, 2010). Biotic
stresses such as acarid species infestation have been shown to initiate
farnesene and linalool emissions in spruce seedlings (Kännaste et al.,
2008). Emission potential and composition varies a lot between different
tree species (Guenther et al., 2012). However, there is also a lot of
variation in the emissions of different individuals of the same tree
species. Bäck et al. (2012) showed that Scots pine trees of the same
age, growing in the same environment, emit very different monoterpene
selections. These so-called different chemotypes cause uncertainties in
emission modelling.
In the atmosphere VOCs are oxidized, which affects the tropospheric ozone
formation (Chameides et al., 1992) and contributes to the lifetime of methane
by consuming hydroxyl radicals. In addition, reaction products of VOCs also
participate in the formation and growth of new particles (Tunved et al.,
2006). In smog chamber studies secondary organic aerosol (SOA) yields for
different hydrocarbons and even for different MTs have been found to vary
considerably (Griffin et al., 1999). Jaoui et al. (2013) studied SOA
formation from SQT and found that the high reactivity of SQT produced
generally high conversion into SOA products. Furthermore, they found that
the yields were dependent on the oxidant used and were highest for nitrate
radical (NO3) reactions. Of the SQT acidic products, only β-caryophyllinic acid has been observed in ambient samples (Jaoui et al.,
2013; Vestenius et al., 2014). Due to their high reactivity, SQT are not
usually found in ambient air. Hakola et al. (2012) detected longifolene and
isolongifolene in boreal forest air during late summer. Hence, the best way
to evaluate the atmospheric impact of SQTs is to measure them from
emissions.
In addition to isoprene and MTs and SQTs, plants also emit large amounts of
oxygenated compounds – i.e. alcohols, carbonyl compounds, and organic acids
(Koppmann and Wildt, 2007). OVOCs containing six carbon atoms (C6) are
emitted directly by plants often as a result of physical damage (Fall, 1999; Hakola et al., 2001). Saturated aldehydes (hexanal, heptanal,
octanal, nonanal, and decanal) have also been found in direct emissions of
plants (Wildt et al., 2003) as well as methanol, acetone, and acetaldehyde
(Bourtsoukidis et al., 2014b).
In the present study we conducted online gas-chromatographic measurements
of emissions of MTs and SQTs as well as C4–C10 saturated aliphatic
carbonyls from Norway spruce (Picea abies L. Karst) branches. Although Norway spruce is
one of the main forest tree species in central and northern Europe, there
are relatively limited amount of data on its emissions (Hakola et al., 2003;
Grabmer et al., 2006; Bourtsoukidis et al., 2014a, b; Yassaa et al., 2012). Rinne et al. (2009) identified knowledge gaps concerning VOC
emissions from the boreal environment and concluded that there is a lack of
knowledge in non-terpenoid emissions from most of the boreal tree species.
They also pointed out that chemotypic variations are not well enough
understood to be taken into account in emission modelling. To fill this
knowledge gap we conducted biogenic volatile organic compound (BVOC) emission measurements from Norway spruce.
The online gas chromatograph mass spectrometer (GC-MS) was chosen because in
addition to detection of individual MTs it allows sensitive detection of
SQTs, which is often difficult to perform under field conditions. The
online measurements were considered essential for evaluating the factors
affecting emission rates, for example their temperature and light
dependence. Our campaigns cover periods of years 2011, 2014, and 2015 during
spring and summer, altogether about 14 weeks. In 2015 also carbonyl
compounds were added to the measurement scheme, since there are no earlier
data of their emissions.
Methods
VOC measurements
The measurements were conducted at the SMEAR II station (Station for
Measuring Forest Ecosystem–Atmosphere Relations; 61∘51′ N,
24∘ 18′ E; 181 a.s.l.) in Hyytiälä, southern Finland (Hari
and Kulmala, 2005) in 2011, 2014, and 2015. The measurements took place in
spring/early summer 2011 (2 weeks in April, 5 days in May and 3 days in June), spring/summer 2014 (1 week in May, 2 weeks in June and
1 week in July), and summer 2015 (1 week in June and 2 weeks in
August) and they were conducted using an in situ gas chromatograph.
The emission measurement setup.
Two different trees were measured; tree 1 in 2011 and tree 2 in 2014 and
2015. The selected trees were growing in a managed mixed conifer forest
(average tree age ca. 50 years), and located about 5 m from the
measurement container. The height of tree 1 in 2011 was about 10 m
(age about 40 years). The measured branch was a fully sunlit, healthy lower
canopy branch pointing towards a small opening at about 2 m height. In
2014 and 2015 a younger tree (tree 2, ca. 1 m tall, age ca. 15 years) about 5 m away from the tree used in 2011 was selected for the study. The
branches were placed in a Teflon enclosure and the emission rates were
measured using a dynamic flow-through technique. The setup is shown in Fig. 1. The volume of the cylindrical transparent Teflon enclosure was
approximately 8 L and it was equipped with inlet and outlet ports and a
thermistor (Philips KTY 80/110, Royal Philips Electronics, Amsterdam,
Netherlands) covered with Teflon tubing inside the enclosure. The
photosynthetically active photon flux density (PPFD) was measured just above
the enclosure by quantum sensor (LI-190SZ, LI-COR Biosciences, Lincoln, USA).
The flow through the enclosure was kept at about 3–5 L min-1.
Ozone was removed from the incoming air using manganese oxide (MnO2)-coated copper nets. The emission rates were measured using the
online GC-MS. From the enclosure outlet port air was directed through the
6 m long fluorinated ethylene propylene (FEP) inlet line (i.d. 1/8 in.) to
the GC-MS with a flow of ∼ 0.8 L min-1. Subsamples were taken
from this main flow with a flow of 40–60 mL min-1 directly into the cold
trap of a thermal desorption unit (Perkin Elmer ATD-400) packed with Tenax
TA in 2011 and Tenax TA/Carbopack-B in 2014 and 2015. The trap material was
changed since isoprene was found not to be retained fully in the cold trap
in 2011. The trap was kept at 20 ∘C during sampling to prevent
water vapour present in the air from accumulating in the trap. The thermal
desorption instrument was connected to a gas chromatograph (HP 5890) with
DB-1 column (60 m, i.d. 0.25 mm, f.t. 0.25 µm) and a mass selective
detector (HP 5972). One 20 min sample was collected every other hour.
The system was calibrated using liquid standards in methanol injected on
Tenax TA-Carbopack B adsorbent tubes. The detection limit was below 1 pptv
for every MT and SQT.
The following compounds were included in the calibration solutions:
2-methyl-3-buten-2-ol (MBO) (Fluka), camphene (Aldrich), 3-carene (Aldrich),
p-cymene (Sigma-Aldrich), 1,8-cineol (Aldrich), limonene (Fluka), linalool
(Aldrich), myrcene (Aldrich), α-pinene (Sigma-Aldrich), β-pinene (Fluka), terpinolene (Fluka), bornylacetate (Aldrich), longicyclene
(Aldrich), isolongifolene (Aldrich), β-caryophyllene (Sigma),
aromadendrene (Sigma-Aldrich), α-humulene (Aldrich), β-farnesene (Chroma Dex). Isoprene was calibrated using gaseous standard
from the National Physical Laboratory (NPL). We had no standard for sabinene and
therefore it was quantified using the calibration curve of β-pinene,
because both species elute close to each other and their mass spectra are
similar. Therefore the results for sabinene are only semi-quantitative, but
it enables the observations of diurnal and seasonal changes. Compared to
offline adsorbent methods this in situ GC-MS had clearly lower background
for carbonyl compounds and in 2015 we were able to measure also
acetone/propanal and C4–C10 aldehyde emission rates. The
aldehydes included in the calibration solutions were: butanal (Fluka),
pentanal (Fluka), hexanal (Aldrich), heptanal (Aldrich), octanal (Aldrich),
nonanal (Aldrich), and decanal (Fluka). Unfortunately, acetone co-eluted with
propanal and the calibration was not linear due to high acetone background
in adsorbent tubes used for calibrations.
Calculation of emission rates
The emission rate is determined as the mass of compound per needle dry
weight and per time according to
E=C2-C1Fm.
Here C2 is the concentration in the outgoing air, C1 is the
concentration in the incoming air, and F is the flow rate into the enclosure.
The dry weight of the biomass (m) was determined by drying the needles and
shoot from the enclosure at 75 ∘C for 24 h after the
last sampling date. We also measured needle leaf areas, and the specific leaf
area (SLA) is 136 m2 g-1.
Emission potentials
A strong dependence of biogenic VOC emissions on temperature has been seen
in all emission studies of isoprene, MTs, and SQTs (e.g. Kesselmeier and
Staudt, 1999; Ciccioli et al., 1999; Hansen and Seufert 2003; Tarvainen et al., 2005; Hakola et al., 2006). The temperature-dependent pool emission rate is
usually parameterized using a log–linear formulation:
ET=ESexp(βT-TS),
where E(T) is the emission rate (µg g-1 h-1) at leaf
temperature T and β is the slope dlnEdT
(Guenther et al., 1993). ES is the emission rate at standard temperature TS (usually
set at 30 ∘C). The emission rate at standard temperature is also
called the emission potential of the plant species, and while it is
sometimes held to be a constant it may show variability related to, for
example,
season or the plant developmental stage (e.g. Hakola et al., 1998, 2001,
2003, 2006; Tarvainen et al., 2005; Aalto et al., 2014).
As well as the temperature-dependent nature of the biogenic emissions, light
dependence was also discovered in early studies of plant emissions
(see, e.g., the review of biogenic isoprene emission by Sanadze, 2004 and
Ghirardo et al., 2010). The effect of light on emission potentials is
based on the assumption that emissions follow a similar pattern of
saturating light response to that which is observed for photosynthesis, and the
formulation of the temperature effect is adopted from simulations of the
temperature response of enzymatic activity. The algorithm formulation is
given, for example, in Guenther et al. (1993) and Guenther (1997).
In this work we have carried out nonlinear regression analysis with two fitted parameters, arriving at individual standard emission rates and slope values for the modelled MT, SQT and carbonyl compounds during each model period. The
compounds analysed with the temperature-dependent pool emission rate were
the most copiously emitted MTs and SQTs, other MTs, other SQTs, acetone, and
sum of aldehydes. The light- and temperature-controlled instant emission
rates were obtained for isoprene. An alternative modelling approach was
tested using a hybrid emission algorithm, which has both the
temperature-dependent pool emission and the light and temperature-controlled
instant emission terms.
Chemotype measurements
In order to estimate the between-tree variability of the emissions, we
conducted a study in 2014, where we made qualitative monoterpene analysis
from six different spruces (trees 3–8) growing in the same area not farther
than about 10 m from each other. All the trees were about 1 m high and
naturally regenerated from local seeds. A branch was enclosed in a Teflon
bag and after waiting for 5 min we collected a 5 min sample on a
Tenax TA/Carbopack-B tube for analysis later in a laboratory using a
Perkin-Elmer thermodesorption instrument (Turbomatrix 650) connected to a
Perkin-Elmer gas chromatograph (Clarus 600) mass spectrometer (Clarus 600T)
with DB-5 column. The samples were taken during one afternoon on 24 June 2014.
OH and O3 reaction rate coefficients used in reactivity
calculations.
Species
kOH (cm3 s-1)
Reference
kO3 (cm3 s-1)
Reference
Isoprene
2.7 × 10-11 ⋅ e390/T
Atkinson et al. (2006)a
1.03 × 10-14 e-1995/T
Atkinson et al. (2006)a
2-Methyl-3-buten-2-ol
6.3 × 10-11
Atkinson et al. (2006)a
1.0 × 10-17
Atkinson et al. (2006)a
α-Pinene
1.2 × 10-11 ⋅ e440/T
Atkinson et al. (2006)a
8.05 × 10-16 ⋅ e-640/T
IUPACb
Camphene
5.33 × 10-11
Atkinson et al. (1990a)
6.8 × 10-19
IUPACb
Sabinene
1.17 × 10-10
Atkinson et al. (1990a)
8.2 × 10-17
IUPACb
β-Pinene
1.55 × 10-11 ⋅ e467/T
Atkinson and Arey (2003)
1.35 × 10-15 ⋅ e-1270/T
IUPACb
Myrcene
9.19 × 10-12 ⋅ e1071/T
Hites and Turner (2009)
2.65 × 10-15 ⋅ e-520/T
IUPACb
3-Carene
8.8 × 10-11
Atkinson and Arey (2003)
4.8 × 10-17
IUPACb
p-Cymene
1.51 × 10-11
Corchnoy and Atkinson (1990)
<5.0 × 10-20
Atkinson et al. (1990b)
Limonene
4.2 × 10-11 ⋅ e401/T
Gill and Hites (2002)
2.8 × 10-15 ⋅ e-770/T
IUPACb
1,8-Cineol
1.11 × 10-11
Corchnoy and Atkinson (1990)
<1.5 × 10-19
Atkinson et al. (1990b)
Linalool
1.59 × 10-10
Atkinson et al. (1995)
≥3.15⋅10-16
Grosjean and
Grosjean (1998)
Terpinolene
2.25 × 10-10
Corchnoy and Atkinson (1990)a
1.6 × 10-15
IUPACb
Bornylacetate
1.39 × 10-11
Coeur et al. (1998)
–
Longicyclene
9.35 × 10-12
AopWin™ v1.92
–
Isolongifolene
9.62 × 10-11
AopWin™ v1.92
1.0 × 10-17
IUPACb
β-Caryophyllene
2.0 × 10-10
Shu and Atkinson (1995)a
1.2 × 10-14
IUPACb
β-Farnesene
1.71 × 10-10
Kourtchev et al. (2012)
1.5 × 10-12 ⋅ e-2350/T
IUPACb
α-Humulene
2.9 × 10-10
Shu and Atkinson (1995)a
1.2 × 10-14
IUPACb
Alloaromadendrene
6.25 × 10-11
AopWin™ v1.92
1.20 × 10-17
AopWin™ v1.91
Zingiberene
2.87 × 10-10
AopWin™ v1.92
1.43 × 10-15
AopWin™ v1.91
Acetone
8.8 × 10-12 ⋅ e-1320/T
Atkinson et al. (2006)a
–
+ 1.7 × 10-14 ⋅ e423/T
Butanal
6.0 × 10-12 ⋅ e410/T
Atkinson et al. (2006)a
–
Pentanal
9.9 × 10-12 ⋅ e306/T
Thévenet et al. (2000)
–
Hexanal
4.2 × 10-12 ⋅ e565/T
Jiménez et al. (2007)
–
Heptanal
2.96 × 10-11
Albaladejo et al. (2002)
–
Octanal
3.2 × 10-11
AopWin™ v1.92
–
Nonanal
3.6 × 10-11
Bowman et al. (2003)
–
Decanal
3.5 × 10-11
AopWin™ v1.92
–
aIUPAC recommendation. bIUPAC Task Group on Atmospheric Chemical Kinetic Data Evaluation
(http://iupac.pole-ether.fr).
Calculating the reactivity of the emissions
We calculated the total reactivity of the emissions (TCREx) by combining
the emission rates (Ei) with reaction rate coefficients (ki,x):
TCREx=∑Eiki,x.
This determines approximately the relative role of the compounds or compound
classes in local OH and O3 chemistry. The reaction rate coefficients
are listed in Table 1. When available, temperature-dependent rate
coefficients have been used. When experimental data were not available, the
reaction coefficients were estimated with the AopWin™ module of
the EPI™ software suite
(https://www.epa.gov/tsca-screening-tools/epi-suitetm-estimation-program-interface,
EPA, USA).
Results and discussion
Weather patterns during the measurements
According to the statistics of the Finnish Meteorological Institute, the
weather conditions in Finland were close to normal during the growing season
in the years the measurements were carried out. The main features of the
weather patterns are characterized here briefly to provide an average
estimate of the conditions in the measurement years compared with the
long-term average (the 30-year climatological normal period) conditions in Finland.
In 2011, the spring was early and warm. Thermal spring (mean daily
temperature above 0 ∘C) started in the whole country during the
first few days of April. The average temperatures in central Finland were
2–3∘C higher than the normal long-term average temperatures. The
precipitation in April was about 70 % of the long-term average, and even
a little less in central Finland.
The same pattern continued in May, with slightly higher temperatures than
the normal long-term average. Towards the end of the month the weather
turned more unstable, with more rains and cooler night temperatures. The
average temperature in June was a little over 2 ∘C higher than the
normal long-term average, and there were some intense thunderstorms.
In 2014, the weather conditions in May were quite typical, with the average
temperatures close to the long-term average values in all parts of the
country. The month started with temperatures cooler than the long-term
average, and the cool period continued for about 3 weeks. After the cool
period the weather became warmer with a southeastern air flow, and hot
(over 25 ∘C) air temperatures were observed in southern and
central parts of the country. Towards the end of May, cooler air spread over
the country from the northeast, and the temperature drops could be high in
eastern Finland. May was also characterized with precipitation, especially
in eastern Finland. June started with a warm spell, but towards the end the
weather was much cooler, with the average temperatures 1 to 2∘C lower
than the long-term average. The precipitation was regionally quite variable
in June, the amount could be double the long-term average in some areas,
while the amounts were only half of it in many places in central Finland.
July was much warmer than long-term average temperature, especially in
western Finland and in Lapland. July also had very little rain.
In 2015, the June average temperatures were 1 to 2∘C below the
long-term averages, especially in the western parts of central Finland, and
southern Lapland. There were also more rain showers than normally. In July
the cold spell and rainy days continued, with average temperatures below
the long-term averages, especially in the eastern parts of the country.
Highest precipitation rates were measured in the southern and western
coastal regions, and in the eastern parts of the country. In August the
warmth returned after two cooler months, with average temperatures 1 to 2∘C above the long-term average values. August also had very little
rain, except for some parts in eastern Finland and in Lapland.
The observed mean temperature and precipitation amounts at the Juupajoki
weather station in Hyytiälä during each measurement month in 2011,
2014, and 2015 are shown in Table 2.
Mean temperatures (∘C) and rain amounts (mm)
during each measurement month in Hyytiälä.
2011
2014
2015
temp
rain
temp
rain
temp
rain
April
4.5
17.4
May
9.3
44.3
9.4
57.4
June
15.8
65.3
11.8
94.8
11.9
81.5
July
18.6
44.1
14.6
86.7
August
15.2
12.6
Season mean box and whisker plots of isoprene, MT, SQT,
acetone, C4–C10 aldehydes (butanal, pentanal, hexanal, heptanal, octanal,
nonanal, and decanal) and linalool. Boxes represent second and third
quartiles and vertical lines in the boxes median values. Whiskers show the
highest and the lowest observations.
Seasonal mean emission rates of isoprene, 2-methylbutenol
(MBO), MT, SQT, acetone, and
C4–C10 carbonyls in ng g(dw)-1 h-1. “na” means that
the compounds were not included in the analysis. Spring is April–May, early
summer 1.6–15.7, and late summer 16.7–31.8. bdl: below detection
limit. Values are averages and standard deviations for the three measurement
years (2011, 2014, 2015). Other SQT: sum of all other SQTs in
emissions. The number of the measurements each season is in parentheses.
Spring (337)
Early summer (534)
Late summer (159)
average
SD
average
SD
average
SD
Isoprene
1.3
3.7
6.0
12
MBO
2.1
4.2
2.4
3.8
Camphene
1.1
1.8
2.9
4.4
3.8
4.1
3-Carene
0.3
0.7
1.1
1.7
0.9
0.6
p-Cymene
0.3
0.6
0.9
1.8
0.5
0.5
Limonene
2.7
3.4
6.1
12.2
7.7
9.5
Myrcene
0.2
0.4
1.7
3.7
3.9
5.1
α-Pinene
2.1
3.4
5.8
11.1
9.6
11
β-Pinene
1.0
2.2
1.8
6.2
0.9
1.1
Sabinene
0
0.1
0.5
1.5
0.9
1.6
Terpinolene
0
0.2
0.1
0.4
0.3
0.9
Bornylacetate
0
0.2
0.5
2.0
1.1
2.1
1,8-Cineol
0.7
0.7
2.1
3.9
1.8
2.2
Linalool
na
1.4
2.2
7.9
12
β-Caryophyllene
0
0
0.4
2.1
7.2
5.9
β-Farnesene
0
0
1.1
4.3
42
29
Other SQT
0.1
0.4
1.4
4.7
35
30
Acetone
na
17
11
17
9.0
Butanal
na
2.0
0.7
0.3
0.3
Pentanal
na
4.1
1.1
2.4
0.9
Hexanal
na
5.0
3.0
4.9
2.1
Heptanal
na
5.2
1.2
7.5
2.4
Octanal
na
0.3
0.1
0.4
1.1
Nonanal
na
6.3
2.3
9.9
4.5
Decanal
na
5.6
2.3
7.4
3.8
Variability of the VOC emissions
Seasonal mean emission rates of isoprene, 2-methyl-3-buten-2-ol (MBO), MTs,
and SQTs are presented in Table 3 and Fig. 2. Typical diurnal variations of
the most abundant compounds for each season are shown in Fig. 3. Since most
of the emission rates of the measured compounds were higher in late summer
than in early summer, we calculated the spring (April and May), early summer
(June to mid-July) and late summer (late July and August) mean emissions
separately. This describes that the emission rate changes better than monthly
means.
Mean diurnal variations of different compound groups in
each season. Spring refers to April and May; early summer, June–mid-July;
late summer, mid-July–August. Aldehydes are the sum of all
C4–C10 aldehydes (butanal,
pentanal, hexanal, heptanal, octanal, nonanal, and decanal).
Isoprene emission rates were low in spring and early summer, but increased
in August. In spring emission rates were below detection limit most of the
time and early and late summer means were 1.3 ± 3.7 and 6.0 ± 12 ng g(dry weight)-1 h-1, respectively. The highest daily maxima
isoprene emissions were about 70–80 ng g(dw)-1 h-1, but usually
they remained below 20 ng g(dw)-1 h-1. Our measured values (Table 3) match very well with the measurements by Bourtsoukidis et al. (2014b) who
report season medians varying from 1.6 ng g(dry weight)-1 h-1 in
autumn to 3.7 ng g(dw)-1 h-1 in spring. However, while
the highest emission rates were measured in late summer in the present
study, Bourtsoukidis et al. (2014b) found highest emission rates in spring.
MT emission rates were below 50 ng g(dw)-1 h-1 most of the time in
April and May, and still in the beginning of June for every measurement year,
below 50 ng g(dw)-1 h-1 most of the time. At the end of June the
MT emission rates started to increase (about 30 %) to the level where
they remained until the end of August, the daily maxima or their sum
remaining below 300 ng g(dw)-1 h-1. In comparison with the study
by Bourtsoukidis et al. (2014b), MT emission rates in Finland are four to
ten times lower than those measured in Germany and their seasonal cycles are
different. As with isoprene, they measured the highest MT emission rates
during spring, whereas our highest emissions take place late summer. Median
seasonal values reported by them are 203.1, 136.5, and 80.8 ng g(dw)-1 h-1 for spring, summer, and autumn, respectively. Our averages are 8, 21,
and 28 ng g(dw)-1 h-1 for spring, early summer, and late summer,
respectively (Table 3).
A substantial change in the emission patterns took place at the end of July,
when SQT emission rates increased up to 3–4 times higher than the MT
emission rates at the same time (Table 3). Such a change in emissions was
not observed in the study by Bourtsoukidis et al. (2014b). Instead of
late summer increase, they again observed highest emissions during
the spring (118.6 and 64.9 ng g(dw)-1 h-1 in spring and summer,
respectively) after which emissions significantly declined. Moreover, they
report that MTs dominated the Norway spruce emissions through the entire
measuring period (April–November), SQT emission rates being equal to MT
emission rates during spring, but only about half of MT emission rates
during summer and about 20 % during autumn. One potential explanation for
such a different seasonality and emission strengths may lie in the
differences between site-specific factors such as soil moisture conditions,
local climate (winter in Germany is much milder and the trees do not face as
dramatic change as in Finland when winter turns to spring), stand age, or
stress factors. The tree measured in Germany was much older (about 80 years). In a boreal forest, late summer normally is the warmest and most
humid season favouring high emissions, as was also the case in our study
periods. In contrast, in central Germany July was relatively cold and
wet, and according to the authors, reduced emissions were therefore not
surprising (Boutsourkidis et al., 2014b).
Another interesting feature can be seen in the specified emission rates of
different compounds. In the present study the main SQT in spruce emissions
was β-farnesene. About 50 % of the SQT emission consisted of β-farnesene and its maximum emission rate (155 ng g(dw)-1 h-1) was measured on the afternoon of 31 July 2015. Two other
identified SQTs were β-caryophyllene and α-humulene. There
were two more SQTs, which also contributed significantly to the total SQT
emission rates, but since no calibration standards were available for these, their quantification is only tentative. Linalool emissions increased
simultaneously with SQT emissions (Fig. 2) reaching maximum concentrations
during late summer in August, in the same way as was previously observed in
the measurements of Scots pine emissions in the same forest in southern
Finland (Hakola et al., 2006), where emissions were found to increase late
summer concomitant with the maximum concentration of the airborne pathogen
spores, and Hakola et al. (2006) suggested a potential defensive role of the
conifer linalool and SQT emissions. Several other reports point to similar
correlations between SQT (in particular β-farnesene) and oxygenated
MTs such as linalool emissions and biotic stresses in controlled
experiments. For example, increases in farnesene, methyl salicylate (MeSA),
and linalool emissions were reported to be an induced response by Norway
spruce seedlings to feeding damage by mite species (Kännaste et al., 2009), indicating that their biosynthesis might prevent the trees from being
damaged. Interestingly, the release of β-farnesene seemed to be mite
specific and attractive to pine weevils, whereas linalool and MeSA were
deterrents. Blande et al. (2009) discovered pine weevil feeding to clearly
induce the emission of MTs and SQTs, particularly linalool and (E)-β-farnesene, from branch tips of Norway spruce seedlings, Also, in a
licentiate thesis of Petterson (2007) linalool and β-farnesene were
shown to be emitted due to stress. The emissions from Norway spruce
increased significantly after trees were treated with methyl jasmonate
(MeJA). Martin et al. (2003) discovered that MeJA triggered increases in the
rate of linalool emission more than 100-fold and that of SQTs more than
30-fold. Emissions followed a pronounced diurnal rhythm with the maximum
amount released during the light period, suggesting that they are induced de
novo after treatment. Our study shows that such major changes in emission
patterns can also occur in trees in field conditions, and without any clear
visible infestations or feeding, indicating that they probably are systemic
defence mechanisms rather than direct ones (Eyles et al., 2010).
In 2015 we measured also acetone/propanal and C4–C10 aldehyde
emission rates. The total amount of these measured carbonyl compounds was
comparable to the amount of MTs (Table 3) although with our method it was
not possible to measure emissions of the most volatile aldehydes,
formaldehyde and acetaldehyde, which are also emitted from trees in
significant quantities (Cojocariu et al., 2004; Koppmann and Wildt, 2007;
Bourtsoukidis et al., 2014b). In summer 2015 the carbonyl compounds
consisted mainly of acetone (30 %), and the shares for the other
compounds were as follows: nonanal (21 %), decanal (17 %), heptanal
(14 %), hexanal (10 %), and pentanal (5 %). The shares of butanal and
octanal were less than 2 % each. The seasonal mean values are shown in
Table 3. Aldehydes with shorter carbon backbones (butanal, pentanal,
hexanal) have higher emissions in early summer like most MTs, while
aldehydes with longer carbon backbones (heptanal, octanal, nonanal, decanal)
have higher emissions in late summer similarly to SQTs.
Diurnal variability of the emission rates of MT and SQT, acetone/propanal
and larger aldehydes are shown in Fig. 2. They all show similar temperature-dependent variability with maxima during the afternoon and minima in the
night. The SQT daily peak emissions were measured 2 h later than MT
and aldehyde peaks.
Average monthly abundances (%) of emitted MTs. T1
(tree 1) includes 2011 and T2 2014 and 2015 measurements. The number of the
measurements each month is in parentheses.
α-Pinene
Camphene
Sabinene
β-Pinene
Myrcene
Δ3-Carene
p-Cymene
Limonene
Terpinolene
April, T1 (160)
34
19
0
18
1
5
6
18
0
May, T1 (48)
59
9
1
7
1
1
9
10
3
June, T1 (34)
7
25
16
0
34
3
9
4
0
May, T2 (129)
16
11
0
10
5
5
2
51
0
June, T2 (396)
27
15
0
15
5
5
4
29
0
July, T2 (128)
32
15
2
5
7
5
2
27
1
Aug, T2 (134)
34
11
3
3
15
3
1
29
1
Tree-to-tree variability in emission pattern
When following the emission seasonality, we discovered that the MT emission
patterns were somewhat different between the two trees measured. The tree
measured in 2011 (tree 1) emitted mainly α-pinene in May, whereas
the tree measured in 2014 and 2015 (tree 2) emitted mainly limonene in May
(Table 4). As summer proceeded the contribution of limonene emission
decreased in both trees and the share of α-pinene increased in tree 2. The species-specific Norway spruce emissions have been measured earlier
at least by Hakola et al. (2003) and Bourtsoukidis et al. (2014a). The
measurements by Hakola et al. (2003) covered all seasons, but only a few daytime
samples for each season, whereas the measurements by Bourtsoukidis et al. (2014a)
covered 3 weeks in September–October in an Estonian forest. The main MTs
detected in the Estonian forest were α-pinene (59 %) and 3-carene
(26 %), but also camphene, limonene, β-pinene and β-phellandrene were detected. In the study by Hakola et al. (2003) the MT
emission composed mainly of α-pinene, β-pinene, camphene, and
limonene, but only very small amounts of 3-carene were observed, similarly
to the present study. This raises the question of whether spruces would have
different chemotypes in a similar way as Scots pine has (Bäck et al.,
2012).
In order to find out how much variability there was between the trees in
monoterpene emission pattern, we conducted a study in June in 2014, where we
made qualitative analysis from six different spruces growing in the same area
(labelled as trees 3–8). The results for MT emissions are shown in
Fig. 4. SQT emissions were not significant at that time (about 1 ng g(dw)-1 h-1). As expected, the MT emission pattern of the trees
was quite different; terpinolene was one of the main MT in the emission of
four trees whereas tree 3 emitted only 3 % terpinolene. Also limonene and
camphene contributions were varying from a few percent to about a third of the
total MT emission. All the measured trees emitted rather similar proportions
of α- and β-pinene. The shares of myrcene, β-pinene,
and 3-carene were low in every tree. Since different MTs react at different
rates in the atmosphere (Table 1), the species-specific measurements are
necessary when evaluating MTs influence on atmospheric chemistry. Currently,
air chemistry models very often use only single branch measurements and this
can lead to biased results when predicting product and new particle
formation. This study and the study of Scots pine emissions by Bäck et al. (2012)
show that species-specific measurements are necessary, but also
that flux measurements are more representative than branch-scale emission
measurements, and that averaging over larger spatial scale may be better suited
for air chemistry models.
Relative abundances of emitted monoterpenes in six
different spruce individuals on 24 June 2014.
Standard (30 ∘C) MT, SQT, acetone, and C4–C10
aldehyde emission potentials obtained in 2011, 2014, and 2015. For isoprene
the standard (1000 µmol photons m-2 s-1, 30 ∘C)
emission potentials are from the 2015 campaign. The standard emission
potential ES and the β coefficient are given with the standard
error of the estimate (SE, in parentheses). R squared and the number of
measurements (N, in parentheses). The fits were made for the spring (April–May), early summer (June–mid-July) and late summer (late July–August)
periods.
Es (SE) ng g(dw)-1 h-1
β K-1 (SE)
R2 (N)
Spring
α-Pinene
11.6 (0.7)
0.097 (0.006)
0.423 (331)
Camphene
2.5 (0.4)
0.045 (0.009)
0.071 (323)
β-Pinene
1.9 (0.2)
0.044 (0.007)
0.119 (324)
Myrcene
0.6 (0.1)
0.010 (0.011)
0.007 (157)
Limonene
5.0 (0.8)
0.032 (0.008)
0.049 (321)
Other MT
2.9 (0.2)
0.085 (0.005)
0.433 (329)
β-Caryophyllene
0.2 (0.1)
0.018 (0.059)
0.026 (6)
β-Farnesene
–
–
– (0)
Other SQT
0.7 (0.3)
0.046 (0.029)
0.029 (72)
Early summer
α-Pinene
14.1 (1.0)
0.058 (0.006)
0.145 (489)
Camphene
7.0 (0.3)
0.060 (0.004)
0.230 (492)
β-Pinene
5.2 (0.6)
0.062 (0.010)
0.076 (426)
Myrcene
5.8 (0.3)
0.078 (0.005)
0.326 (356)
Limonene
16.7 (0.9)
0.069 (0.005)
0.239 (497)
Other MT
7.0 (0.3)
0.074 (0.004)
0.385 (499)
β-Caryophyllene
4.8 (1.3)
0.018 (0.019)
0.023 (54)
β-Farnesene
6.9 (1.8)
0.012 (0.018)
0.007 (90)
Other SQT
6.2 (0.7)
0.055 (0.010)
0.087 (238)
Acetone
50.8 (7.2)
0.066 (0.010)
0.362 (71)
Aldehydes
59.1 (4.4)
0.043 (0.005)
0.503 (71)
Late summer
Isoprene
56.5 (4.2)
0.473 (70)
α-Pinene
39.3 (4.1)
0.153 (0.017)
0.359 (163)
Camphene
7.7 (1.2)
0.064 (0.016)
0.094 (161)
β-Pinene
2.5 (0.3)
0.075 (0.015)
0.160 (120)
Myrcene
21.1 (2.0)
0.191 (0.019)
0.476 (154)
Limonene
32.3 (3.6)
0.155 (0.018)
0.336 (163)
Other MT
9.9 (1.1)
0.133 (0.016)
0.298 (153)
β-Caryophyllene
11.0 (1.2)
0.020 (0.010)
0.032 (129)
β-Farnesene
76.9 (7.5)
0.060 (0.010)
0.183 (162)
Other SQT
67.3 (8.2)
0.059 (0.013)
0.132 (157)
Acetone
31.8 (2.2)
0.061 (0.007)
0.313 (163)
Aldehydes
36.8 (3.0)
0.008 (0.007)
0.009 (163)
Standard emission potentials
The standard emission potentials were obtained by fitting the measured
emission rates to the temperature-dependent pool emission algorithm
(Eq. 2) and the light- and temperature-dependent algorithm described in Sect. 2.3. For the temperature dependent algorithm, the
nonlinear regression was carried out with two fitted parameters, yielding
both the emission potentials and individual β coefficients for each
compound group. With the light- and temperature-dependent algorithm, only
emission potentials were obtained. The compounds' emissions fitted using the
temperature-dependent pool emission algorithm were the ones of the most abundant MT and SQT, and the carbonyls for each season, while the
analysis with the light- and temperature-dependent emission algorithm was
carried out for isoprene emissions. In the analysis, obvious outliers and
other suspicious data were not included. The excluded values typically were
the first values obtained right after starting a measurement period, which
might still show the effects of handling the sample branch. The isoprene
emissions obtained in 2011 were not taken into account in the analysis as
they were not properly collected on the cold trap. This was fixed in 2014
and 2015 by changing the adsorbent material. An approach with a hybrid
algorithm, where the emission rate is described as a function of two source
terms, de novo synthesis emissions and pool emissions, was also tested.
However, the results were not conclusive.
The standard emission potentials of isoprene, the selected MT and SQT,
acetone, and C4–C6 aldehyde sums are presented in Table 5.
Emission potentials are given as spring, early summer, and late summer
values. The coefficient of determination (R2) is also given, even
though it is an inadequate measure for the goodness of fit in nonlinear
models (e.g. Spiess and Neumeyer, 2010). A more reliable parameter for
estimating the goodness of fit is the standard error of the estimate, which
is also given.
The summertime emission potentials of MT and SQT reflect the typical
behaviour of the temperature variability in summer, with low emissions in
spring and high emissions in the higher temperatures of late summer. The
variability of the emission potential during the growing season and between
the individual compounds is large. In late summer limonene and α-pinene had the highest MT emission potentials. SQT exhibit a similar
behaviour as monoterpene emission potentials with very low springtime and
early summer emission potentials while the late summer emission potential is
high. In a review by Kesselmeier and Staudt (1999) the reported standard
emission potentials (30 ∘C, 1000 µmol m-2 s-1) of
Norway spruces for monoterpenes vary from 0.2 to 7.8 µg g(dry
weight)-1 h-1 and in a study by Bourtsoukidis et al. (2014b) mean
emission potential of Norway spruce was 0.89 µg g(dry weight)-1 h-1 for all data (spring, summer, fall). Our standardized MT emission
potentials are lower than earlier reported values, being 0.1 µg g(dry
weight)-1 h-1 during late summer, when they were at their highest.
This is the first time we have applied fitting the traditional
temperature-based emission potential algorithms to measured carbonyl
emissions, and based on the spruce emission results, the approach appears to
be applicable also on these compounds. The best fit was obtained with the
temperature-dependent algorithm. The temporal variability of the emission
potential was similar to MT- and SQTs. Unfortunately, acetone/propanal and
C4–C10 aldehyde measurements were only carried out during the last
measurement campaign, but the emission pattern possibly indicates a
midsummer maximum, because emissions were clearly identified in June, and
already decreasing in late July–August. The isoprene emissions, fitted with
the light and temperature emission algorithm, also reflect the
light/temperature pattern of summer, with low emissions in spring and high
emissions in late summer.
In late summer when isoprene emissions were a bit higher the emission model
fits the data better and the emission potential for isoprene was 56.5 ng g(dry weight)-1 h-1. In a review by Kesselmeier and Staudt (1999)
the reported standard emission potentials (30 ∘C, 1000 µmol m-2 s-1) of isoprene vary from 0.34 to 1.8 µg g(dry
weight)-1 h-1. Our standardized late summer mean (56.5 ng g(dry
weight)-1 h-1) is much lower than these earlier reported values.
Relative reactivity of emissions
In summer in ambient air at this site most of the known OH reactivity (which
is ∼ 50 % of the total measured OH reactivity) is coming
from the VOCs (Sinha et al., 2010; Nölcher et al., 2012). Other trace
gases (NOx, CO, O3, CH4) have a lower contribution. Of these
VOCs, aromatic hydrocarbons have only minor contribution compared to the
terpenoids (Hakola et al., 2012). In these ambient air studies contribution
of SQTs has been much lower than MTs, but those results are misleading,
since lifetimes of most SQTs are so short that they cannot be detected in
ambient air and estimation of their contribution to the local reactivity is
possible only directly from the emissions. Here we studied the relative role
of different BVOCs to the reactivity of Norway spruce emissions.
The relative contribution from each class of compounds to the total
calculated OH and O3 reactivity of the emissions TCREOH and
TCREO3, respectively, is depicted in Fig. 5. Nitrate radicals are likely to
contribute also significantly to the reactivity, but since the reaction rate
coefficients were not available for the essential compounds like β-farnesene, the nitrate radical reactivity is not shown. SQT are very
reactive towards ozone and they clearly dominate the ozone reactivity.
Isoprene contribution is insignificant all the time towards ozone
reactivity, but it contributes 20–30 % of OH reactivity, although the
emission rates are quite low. SQT dominate also OH reactivity during late
summer due to their high emission rates, but early summer MT contribution is
equally important. Contribution of acetone to the TCREOH was very small
(∼ 0.05 % of total reactivity), but reactivity of
C4–C10 aldehydes was significant, averagely 15 % and sometimes
over 50 % of the TCREOH. Of the aldehydes decanal, nonanal, and heptanal
had the highest contributions. It is also possible to measure total OH
reactivity directly and experimental total OH reactivity measurements by
Nölscher et al. (2013) showed that the contribution of SQTs in Norway
spruce emissions in Hyytiälä was very small (∼ 1 %).
This is in contradiction to our measurements, where we found a very high share
of SQTs (75 % in late summer). Nölscher et al. (2013) also found a very
high fraction of missing reactivity (> 80 %) especially in late
summer. Their measurements covered spring, summer, and autumn. Emissions of
C4–C10 aldehydes, which were not studied by Nölscher et al. (2013), could explain part of the missing reactivity.
Relative O3 and OH reactivity of emissions for two periods in
early and late summer 2015. The compounds and reaction coefficients used for
reactivity calculations are presented in Table 1.
Conclusions
Norway spruce VOC emissions were measured in campaigns in 2011, 2014 and
2015. Measurements covered altogether 14 spring and summer weeks. The
measured compounds included isoprene, MT, and SQT and in 2015 also acetone
and C4–C10 aldehydes. MT and SQT emission rates were low during
spring and early summer. MT emission rates increased to their maximum at the
end of June and declined a little in August. A significant change in SQT
emissions took place at the end of July, when SQT emissions increased
substantially. The seasonality is different from that observed earlier in
Germany (Bourtsoukidis et al., 2014b). There Norway spruce emissions
(isoprene, MT, SQT) were highest in spring and declined thereafter. The
difference in seasonality can be due to different ages of the measured trees
(10–15 years in the current study, 80 years in Bourtsoukidis et al., 2014b),
different climate, or different stress factors. These same factors can also
cause the lower emission rates measured now in comparison with other studies.
The effect of age to the emission potentials should be studied.
In August SQT were the most abundant group in the emission, β-farnesene being the most dominant compound. SQT emissions increased
simultaneously with linalool emissions and these emissions were suggested to
be initiated due to stress effects. To our knowledge this is the first time
when β-farnesene and linalool emissions have been shown to increase
simultaneously in natural conditions, although they have been shown to
increase in the emissions together due to stress effects. Of the measured
compounds, SQTs had highest impact on local O3 and OH chemistry. This
clearly shows the importance of considering also SQTs in atmospheric studies
in boreal environment.
Acetone and C4–C10 aldehyde emissions were highest in July, when
they were approximately at the same level as MT emissions. C4–C10
aldehydes contributed as much as MT to the OH reactivity during late summer,
but early summer only about half of the MT share in early summer. This
demonstrates that also emissions of other BVOCs than the traditionally
measured terpenoids are important and should be included in atmospheric
studies.
The MT emission pattern varies a lot from tree to tree. During one afternoon
in June we measured the emission patterns of six different trees growing near
each other and especially the amounts of terpinolene, camphene, and limonene
were varying. Due to inconsistent emission patterns, species-specific
emission fluxes at canopy level should be conducted in addition to the leaf
level measurements for more representative measurements. However, only leaf-level measurements produce reliable SQT data.