Introduction
Boreal forests, covering approximately 12.2 × 106 km2 of
the earth's surface, which is 30 % of the world's forest area (Keenan et
al., 2015), emit volatile organic compounds (VOCs), which have an important
impact on the chemistry and composition of the atmosphere. These emitted
compounds can react with O3, NO3 or OH and form a multitude of new
VOCs, which can contribute to the formation and growth of aerosol particles
and thereby affect climate change (Tunved et al., 2006; Spracklen et al.,
2008). An understanding of these emissions and their quantity is necessary
for numerical assessments of future climate and air quality (Guenther et
al., 2012; Makkonen et al., 2012). The direct measurement of VOC fluxes
above the canopy is one key element in this process. Eddy covariance (EC) has
become a reference method for measuring canopy exchange (Baldocchi, 2014).
The ecosystem-scale VOC fluxes result from emissions caused by biological
activity and chemistry which take place in soil, forest floor and
understory vegetation, trees and within the canopy airspace. The prevalent
ability to synthesize and release VOCs to the atmosphere varies between
species and often changes with seasons and during the life cycle of a plant
(Karl et al., 2003; Hakola et al., 2006; Holzke et al., 2006). The more
heterogeneous a habitat, the more potential sources of variation as regards
VOC fluxes exist. Although managed boreal forests are commonly considered to
be relatively homogeneous habitats, they are still relatively heterogeneous
when compared to grasslands or other agricultural areas (Newbold et al.,
2015). In addition to the array of tree species, the understory and forest
floor vegetation vary significantly in terms of species selection,
luxuriance and coverage as a result of soil properties, orientation, canopy
coverage and water availability. Even a single species, as exemplified by
Scots pine, may express considerable intra-species variation in terms of
emission composition (Bäck et al., 2012) and capacity (Aalto et al.,
2014, 2015). Soils and forest floors are very complex but as yet poorly
understood sources of VOCs (Aaltonen et al., 2011, 2013); however, the
diverse forms of microbial activity seem to play a central role in VOC
emissions from the soil (Bäck et al., 2010). In this context, above-canopy VOC flux measurements represent a vast array of signals from biogenic
activity, which largely complicates drawing conclusions on the effects of
biological and ecophysiological phenomena on VOC emissions from forests.
However, the above-canopy VOC flux method is a crucial tool in studying VOC
emission in a continuum from the subcellular scale up to the effects of
VOCs on atmospheric composition and processes on local, regional and global
scales.
VOC concentrations and emissions have been measured during campaigns since
1998 at SMEAR II (Station for Measuring Ecosystem–Atmosphere Relations).
The VOC flux measurements at the station have consisted of different
instruments and techniques: disjunct eddy covariance (DEC; e.g., Rinne et
al., 2007), the surface layer profile (SLP) method (e.g., Rantala et al.,
2014) using a proton transfer reaction quadrupole (PTR-Quad) mass
spectrometer and a gas chromatograph mass spectrometer using the gradient
method (e.g., Rinne et al., 2000). In recent years, proton transfer
reaction time-of-flight (PTR-TOF) mass spectrometers have been used to
measure EC fluxes (Ruuskanen et al., 2011; Kaser et al., 2013a;
Park et al., 2013; Brilli et al., 2016; Schallhart et al., 2016), but the
number of these studies is still low. With this new measurement setup, it is
possible to identify the elemental composition of the compounds with
detectable fluxes. Furthermore, the preselection of the measured compounds
is no longer necessary, as the PTR-TOF measures full mass spectra. Its
ability to measure all VOCs with 10 Hz time resolution leads to noise
reduction compared to the DEC method using PTR-Quad, which uses a lower
measurement frequency (typically 0.05 to 1 Hz; e.g., Rinne and Amman, 2012).
This study used a PTR-TOF to measure VOC fluxes during the snowmelt (9 days), start of the growing season (9 days) and summer (21 days) 2013 and
these are the first results from EC measurements of VOCs above a boreal
forest. The main objective was to investigate how the set of compounds with
detectable fluxes and their flux magnitude change during the transition from
winter to summer. A second objective was to compare these PTR-TOF EC
measurements with the long-term PTR-Quad measurements of SLP fluxes.
Finally, the results from the 21 days of flux measurements in June were
compared with other VOC flux studies using EC and PTR-TOF.
Methods
Site description
The measurements were carried out from April until the end of June 2013 at
SMEAR II in
Hyytiälä, southern Finland. The station is 180 m above sea level and
located in the middle of a Pinus sylvestris (Scots pine) dominated stand, while Picea abies (Norway
spruce) covers 15 % of the forest. In addition to the dominating Scots
pine and Norway spruce, other tree species are present at the study site,
e.g., Betula pendula and Betula pubescens (silver and downy birch), Populus tremula (trembling aspen),
Sorbus aucuparia (rowan) and Salix caprea (goat
willow). The forest was planted in 1962, and since then the trees have grown to 18 m height and the stand density is approximately 1300 ha-1. Depending
on the season, the leaf area index varies between 2 and 2.5 (Rautiainen et
al., 2012). More information about the surroundings of the station can be
found in Hari and Kulmala (2005) and Ilvesniemi et al. (2010). The mean
annual temperature was 3.5 ∘C and the mean annual precipitation
was 711 mm during the climatological normal period 1981–2010 (Pirinen et
al., 2012). The measurements were conducted on a scaffold tower
(61.847407∘ N, 24.295150∘ E) and the inlet height was 23 m. The temperature varied between -2 and 27 ∘C during
the measurement periods and the main wind direction was south-southwest.
Flux measurement setup
The measurements were conducted with a PTR-TOF 8000 (Ionicon Analytic GmbH;
Jordan et al., 2009; Graus et al., 2010). It was operated with a drift tube
voltage of 600 V and a drift tube pressure of 2.3 mbar. Together with the
drift tube temperature of 60 ∘C, the EPTR/NPTR
(EPTR being the electrical field strength and NPTR the gas number
density) was calculated to be 130 Td. The instrument was placed in an
air-conditioned cottage next to the measurement tower. Sample air was pumped
through a 30 m long (8 mm inner diameter, i.d.) PTFE inlet, with a flow of
20 L min-1. To prevent condensation on the inlet walls, the tube was
heated with an 8 W m-1 passive heating wire. A subsample of 0.5 L min-1
was collected via a 10 cm PTFE tubing (1.6 mm i.d.), which led over a
three-way valve (type 6606 with ETFE, Bürkert GmbH & Co.KG) and 20 cm
of PEEK tubing (1 mm i.d.) to the PTR-TOF. The PTR-TOF data were analyzed
with the tofTools, which are described in more detail in Junninen et al. (2010). A 3-D ultrasonic anemometer (HS-1199, Gill instruments) was placed
10 cm above the inlet. Both VOC and wind measurements were recorded at 10 Hz
resolution. In addition, EC fluxes of carbon dioxide (CO2)
are routinely measured at the site using a closed-path infrared gas analyzer
(Licor 6262, USA; Mammarella et al., 2009). CO2 fluxes were calculated
by using EddyUH software (Mammarella et al., 2016).
The instrumental background of the PTR-TOF was measured by guiding ambient
air through a catalytic converter, which removed the VOCs. This VOC-free air
was measured three times a day, starting at 00:02, 08:02 and 17:02, and each
measurement session lasted for 25 min. This led to a reduced amount of flux
data during these hours. Switching between ambient air and the VOC-free air
was done with a three-way valve (type: 6606 with ETFE, Bürkert GmbH & Co.
KG) controlled by the PTR-Manager (Ionicon Analytic GmbH).
For calibration this VOC-free air was mixed with a calibration gas (Apel
Riemer Environmental Inc.) containing 16 different compounds. For
uncalibrated compounds the sensitivities were categorized into three groups
CxHy (calculated from isoprene, benzene, toluene, o-xylene,
trimethylbenzene, naphthalene, α-pinene combined with C6H9
fragment), CxHyOz (based on acetaldehyde, acrolein, acetone,
2-butanone) and CxHyNz (set to acetonitrile) similar to the
setup Schallhart et al. (2016). The average sensitivities for the different
compound groups were 11.4 ± 2.5 ncps ppb-1 for CxHy,
18.6 ± 3.1 ncps ppb-1 for CxHyOz and 17.7 ± 2.0 ncps ppb-1
for CxHyNz. Overall the sensitivities are comparable with
Schallhart et al. (2016); only the standard deviation increased due to the
longer time period of the measurements. The setup for background and
calibration measurements is described in more detail in Schallhart et al. (2016).
Flux and lag time calculations
VOC fluxes were derived using the EC method (e.g., Aubinet
et al., 2012). The EC flux is calculated using the covariance
w′c′‾λ=1n∑i=1nw′(i-λ/Δt)c′(i),
where w′ and c′ are high-frequency fluctuations of vertical wind and
concentration, respectively, i is the number of the measurement, n the sum of
all measurements during the flux averaging time (30 min in this study; n= 18 000), Δt the sampling interval (0.1 s) and λ the lag time caused
by the sampling system. The cross-covariance functions (CCFs) were calculated
by varying λ from -200 to 200 s. In this study, vertical wind and VOC
concentrations were both recorded at 10 Hz frequency. The flux calculation
procedure is called the automated method and is similar to that in Park et al. (2013) and Schallhart et al. (2016); only the lag time was determined
differently.
(a) Normalized cross-covariance functions (CCFs) for monoterpene measurements in June, without
time shift correction. The shift between the two computer clocks is clearly
visible. (b) CCFs after correcting for lag time shifts. (c) The CCFs of
monoterpenes after the final lag time correction. In the final (third) step,
the smoothed maximum of the CCF function (see Fig. 2) was sought for each
compound and 30 min data individually, in a ±10 s window of the
previous lag time (step 1 and 2).
The VOC and the wind data were recorded on two different computers and their
clocks shifted considerably (continuous shift of 2 to 5 s day-1; Fig. 1a). Finding the correct lag time is especially challenging when the flux is
close to the detection limit. To estimate the proper lag times, three
corrections were made.
First, the artificial clock shifting was removed using linear regressions.
Therefore, the regressions from the monoterpene CCFs were used to correct
the CCFs from all compounds (Fig. 1b).
In the second step, an average, absolute CCF was calculated (Fig. 2) for
each compound. For this the absolute value of each 30 min CCF between 10:00
and 16:00 was taken and then all the absolute CCFs for the time period of
interest were averaged. To reduce the influence of noise, especially when
small fluxes are measured, a running mean (±24-step averaging) was
used. Then the position of the maximum was searched for a ±10 s time
window and used as the lag time. This step corrects possible differences in
the average lag time between the compound of interest and the monoterpenes.
This lag time was calculated for each month and each compound separately.
The third and last step was used to correct for smaller shifts, in case the
first correction, with the linear regression, was not precise enough.
Therefore, each individual 30 min CCF was smoothed by a running mean
(±24-step averaging) and the location of the maximum in a ±10 s time window around the previously calculated lag time was recorded
(Taipale et al., 2010), as shown for the monoterpenes in Fig. 1c.
To classify how many of the hundreds of measured compounds show an exchange,
a method described in Park et al. (2013) and Schallhart et al. (2016) was
used. This method compares the maximum of the averaged, absolute CCF with a
certain noise threshold. To reduce the impact of noise, the averaged,
absolute CCF was smoothed (±12-step averaging) and the location of
the maximum in a ±10 s time window detected. This position was used
in the average, absolute CCF (not smoothed) and compared with the σnoise (standard deviation of the noise). The σnoise was
calculated for 60 s at the borders of the average absolute CCF. If the ratio
between the calculated maximum and the σnoise was higher than
three, the compound was classified to have detectable flux (Fig. 2). This
method was applied to flux determinations for each month separately.
The flux underestimation caused by high-frequency attenuation was estimated
using a parametrization described by Horst (1997). The method uses a system
response time and information about stability and horizontal wind speed for
estimating the attenuation. The system response time was determined to be
around 1.2 s for monoterpenes and the same response time was also used for
all the other compounds. One should note that the determined response time
describes the flux attenuation of the whole measurement setup, including the
tubing, a horizontal separation between the inlet and the anemometer and the
instrument itself. In this case, the average attenuation factor was 18 %.
On average, the correction factor was smaller during the day (16 %; 09:00
to 17:00) and larger at night (23 %; 20:00 to 04:00).
No additional corrections were applied to the measured fluxes (e.g., storage
correction).
Flux quality criteria
The measured fluxes were filtered by three quality criteria, to reduce the
systematic uncertainty and ensure their representativeness.
First, the data were flagged if the tilt angle, resulting from the
coordinate rotation of sonic anemometer wind velocity components (Kaimal and
Finnigan, 1994), was more than 5∘, which was the case for
11.9 % of the data. Second, all 30 min records with a friction velocity
less than 0.2 m s-1 were flagged. Following this, 11.2 % of the data
were flagged. Finally, the flux steady-state test was applied according to
Foken and Wichura (1996). All flagged flux values were removed from further
analysis. The rejection rate between April and June was 34.1, 35.1
and 30.5 % for acetone, butene + butanol, and the monoterpenes,
respectively. For the monoterpenes, the rejection rates for the measurement
periods in April, May and June were 19.1, 17.6 and 30 % (daytime)
and 25.6, 24.0 and 43.7 % (nighttime), respectively.
For each compound which had a maximum above 3σnoise, the deviation of the CCF maxima from zero was used as
the lag time correction.
Flux selection
The temporal behavior of the VOC exchange was investigated by measuring
periods in April, May and June 2013. These periods give insight to the VOC
exchange during snowmelt, the start of the growing season and summer. Because of
technical problems with the anemometer, which stopped recording data on
several occasions during the start of the growing season, only 423 × 30 min files (∼ 9 days of data) of VOC fluxes could
be calculated in the period from 4 to 24 May 2013. The standard
deviation of noise in the averaged, absolute CCFs (σnoise)
determines the exchange threshold and is directly dependent on the amount of
data. Therefore, the same amount of data (423 × 30 min files) was
selected to represent each period from snowmelt to summer and make a
comparison between those periods possible. For all three periods, the
absolute mean of the CCFs between 10:00 and 16:00 (UTC + 2) was used to find
compounds with statistically significant flux (Park et al., 2013; Schallhart
et al., 2016). For the snowmelt, the measurements were from 14 to
24 April 2013 and for summer the hottest period was selected, from 1 to 12 June 2013.
Results and discussion
The PTR-TOF measures the mass of the VOCs in the ambient sample, which can
be used to calculate the elemental composition. Therefore, no structural
information of the measured molecules is known and the identification of
compounds relies on literature and gas chromatograph measurements. Major
masses affected by fragmentation and compounds with high uncertainties are
discussed in the following.
The exchange of the different compounds in during the measurements
in April, May and June. All the presented emission (E) and deposition (D)
values are in percentages in relation to the total emission or deposition of
the month (stated in the last line). Emissions of butene + butanol are in
bold as they are anthropogenic (Sect. 3.4) and, therefore, not included in
the total exchange.
Mass
Elemental
Possible
Snowmelt
Start of growing
Summer
(Da)
composition
compound
season
E
D
E
D
E
D
33.0335
CH5O+
methanol
39.6
49.1*
42.1
16.5*
137.1325
C10H17+
monoterpenes
47.7
0
26.0
0
21.0
0
59.0491
C3H7O+
acetone
52.3
100*
18.9
0
14.3
< 1*
57.0699
C4H9+
butene + butanol
170.5
0
12.1
0
8.9
0
45.0335
C2H5O+
acetaldehyde
5.5
4.0*
69.0699
C5H9+
isoprene + MBO
2.4
< 1*
4.6
1.2*
61.0284
C2H5O2+
acetic acid
2.6
4.1*
43.0178
C2H3O+
fragment
2.4
7.7*
41.0386
C3H5+
fragment
2.9
3.7*
2.5
< 1*
31.0178
CH3O+
formaldehyde
< 1
41.4
60.0471
60.0471
unknown
3.8
6.8*
1.3
9.0*
93.0699
C7H9+
toluene + p-cymene
2.6
< 1*
< 1
1.8
69.0352
69.0352
unknown
< 1
< 1*
67.0542
C5H7+
cyclopentadiene
< 1
< 1*
70.0696
70.0696
unknown
< 1
2.6
< 1
1.7*
99.0201
99.0201
unknown
< 1
3.6*
84.9500
84.9500
unknown
< 1*
4.8
95.0491
C6H7O+
phenol
< 1
1.1*
135.1168
C10H15+
p-cymene
< 1*
< 1*
46.0287
CH4ON+
formamide
2.1
23.9
118.9456
118.9456
unknown
< 1*
9.0
71.0491
C4H7O+
MVK and MACR
< 1
7.9
Total emission and deposition (nmol m-2 s-1)
0.25
-0.01
1.24
-0.17
2.87
-0.12
* Values under the limit of detection (2σind). σind
was calculated using the propagation of error formula and the standard
deviation at the borders of the individual 30 min CCFs.
The mass 69.0699 Da with the elemental composition C5H9+ was
called isoprene + 2-methyl-3-buten-2-ol (MBO), as MBO fragmented to this
mass and had a substantial influence on the signal (e.g., Kaser et al.,
2013b). Similarly, the mass 93.0699 Da with the elemental composition
C7H9+ was called toluene + p-cymene, as p-cymene fragments
were suspected to affect the signal (Tani et al., 2003). Formaldehyde has
only a slightly higher proton affinity compared with the primary ion and
therefore back reactions, which are humidity dependent, from protonated
formaldehyde to water occur (de Gouw and Warneke, 2006; Inomata et al.,
2008; Vlasenko et al., 2010). This may have led to an artificial flux, which
was caused by water vapor fluctuations. Therefore, the formaldehyde fluxes
are very uncertain. The signal at mass 57.0699 Da with a protonated composition
of C4H9+ was called butene + butanol, as butanol was
suspected to have substantial influence on the signal (see Sect. 3.4).
Furthermore, as the butene + butanol fluxes were expected to be caused by
the aerosol instrumentation, they were disregarded in all reported net
fluxes and total emissions. The monoterpenes (C10H17+) were
measured at mass 137.1325 Da only. The monoterpene fragment at mass 81.0699 Da (C6H9+) was identified by its Pearson correlation of 0.99
(30 min integrated data) with the signal at 137.1325 Da and was disregarded
from further analysis.
Contribution of each compound to the 24 h average net flux of the
respective month. Bars with thick outlines and * above them correspond to
negative fluxes, where the absolute value was taken before plotting them in
the logarithmic scale. See Table 1 for corresponding compound names.
Emissions of C4H9+ are
highlighted with a red box as they are anthropogenic (Sect. 3.4) and, therefore,
are not included in the net flux.
VOC flux variation during the campaign
During the three 9-day measurement periods in April, May and June 22
compounds with a flux were found, of which 16 were identified by their
elemental compositions (Table 1). Five compounds, CH3O+
(formaldehyde), CH4ON+ (formamide), C4H7O+
(crotonaldehyde) and two unidentified peaks with the masses of 84.9500
and 118.9456 Da, had a negative net flux; each contributed around 1 % or
less to the total net flux (Fig. 3). As expected, the average net flux
increased from snowmelt (0.24 nmol m-2 s-1) to the start of the
growing season (1.07 nmol m-2 s-1) and summer (2.75 nmol m-2 s-1).
The compounds with detectable fluxes increased from 3 during
the snowmelt to 12 at the start of the growing season and 19 in summer. Over
75 % of the net flux comprised of methanol, acetone and monoterpenes. Of
those three main compounds, acetone and monoterpenes had similar emission
patterns (based on the total net flux) over the measurement period, while
methanol had no detectable flux in April. The development of the diurnal
cycle can also be seen in Fig. 4, as the measurements in April had a minor
flux variation between day and night, whereas the periods in May and June
showed a clear dependence on the temperature. The maximum emission was
detected between 14:00 and 16:00 (Fig. 4); this is in agreement with the
fact that VOC synthesis is driven by temperature and light (Ghirardo et al.,
2010; Taipale et al., 2011), while potential evaporation from storage pools
is primarily driven by temperature alone (Guenther et al., 1993). The
maximum temperatures were typically measured during mid-afternoon, when the
light availability has still not yet decreased to a great extent when
compared to the light conditions around noon. Figure 5 shows that the
highest emissions of monoterpenes and isoprene + MBO coincided with the highest
temperatures. Furthermore, the high monoterpene emissions during low photosynthetically active radiation (PAR)
(< 200 µmol m-2 s-1; gray data points in Fig. 5)
conditions can be explained by pool emissions, whereas the de novo isoprene
emissions during this time were low. Unlike the maximum emission time, which
was similar for all months, the minimum net flux was between 20:00 and 21:00
during the snowmelt, 03:00 and 04:00 at the start of the growing season and
01:00 and 02:00 in summer. Table 1 shows all the compounds with detectable
flux for the 3 months and their 24 h average emission and deposition.
Diurnal pattern of the nine compounds with the highest fluxes, the
remaining compounds being summed up as “other”. The panels show the fluxes
for snowmelt (a), start of growing season (b) and summer (c). The
number of data points per hour is dependent on the quality criteria
filtering and whether it is in an hour when the automatic background was
measured. * The butene + butanol exchange is anthropogenic and thereby
not emitted by the forest (Sect. 3.4).
Scatterplot of 30 min isoprene and monoterpene flux. The gray data
points are values where the PAR was smaller than 200 µmol m-2 s-1. Isoprene fluxes are very low and especially during low PAR
conditions they are heavily affected by noise and a mirroring effect
(Langford et al., 2015).
Low emissions during snowmelt
As expected the total emission (0.25 nmol m-2 s-1) was smallest
during the measurement period in April (compared to the start of the growing
season and summer). The snow melted during this period and the average
temperature and PAR were at their
lowest, 4.4 ∘C and 268 µmol m-2 s-1,
respectively. The total deposition (-0.01 nmol m-2 s-1) was also
weakest. Acetone contributed with over 50 % to the emissions and was the
only compound during the snowmelt for which a diurnal deposition was
detected. Between 22:00 and 23:00 the measured flux reached a minimum of
-0.12 nmol m-2 s-1, whereas between 13:00 and 14:00 the emission
peaked with 0.40 nmol m-2 s-1. The heaviest measured compounds
with detectable flux were the group of monoterpenes, which contributed
48 % to the total emission and had the highest emissions between 14:00 and
15:00 with 0.24 nmol m-2 s-1. The lowest emissions of 0.06 nmol m-2 s-1 were measured during morning between 06:00 and 07:00. The
anthropogenic emissions of butene + butanol dominated by a factor of 1.7
over the biogenic emissions. C4H9+ had the highest emissions
between 14:00 and 15:00 with 0.82 nmol m-2 s-1 and the lowest
between 21:00 and 22:00 with 0.08 nmol m-2 s-1.
Increase of emissions at start of growing season
The total emission during the start of the growing season in May was 1.24 nmol m-2 s-1
and the total deposition was more than 10 % of the
emission, -0.17 nmol m-2 s-1. The night temperatures during this
period were above zero and the sun warmed late afternoons to around
20 ∘C. This led to a mean temperature of 11.4 ∘C and the
mean PAR was 301 µmol m-2 s-1. Methanol dominated the
emissions with 40 % contribution to the total emission. The diurnal
maximum of methanol occurred in the late afternoon at 2.11 nmol m-2 s-1.
These results agree with other studies, which showed that methanol
is released in plant growth (e.g., Galbally and Kirstine, 2002). In contrast, methanol showed also the highest deposition, comprising almost 50 %
of the total deposition. The midday deposition of -0.47 nmol m-2 s-1
between 11:00 and 12:00 can be explained by rain during or right
before this time window, which happened twice during the measurements in the
May period. The water-soluble methanol was suspected to be dry deposited on
the wet surfaces in the forest (Laffineur et al., 2012; Wohlfahrt et al.,
2015; Schallhart et al., 2016).
The monoterpenes were the second most emitted compound group and contributed
26 % to the total emission. Their maximum emission was 0.76 nmol m-2 s-1 between 15:00 and 16:00 and the minimum emission
was 0.13 nmol m-2 s-1 between 03:00 and 04:00. Recently, Aalto et al. (2014, 2015)
have shown that Scots pine needles are a pronounced source of monoterpenes
in spring already before growth onset, and especially after bud break, when
the formation of new biomass releases large amounts of terpenoids and other
VOCs. The results of this study are in general consistent with those
findings in terms of detected mean fluxes and diurnal patterns. Acetone
contributed with 19 % to the total emission and was the third most emitted
compound. It had the maximum emission of 0.61 nmol m-2 s-1 between
10:00 and 11:00 and the minimum between 03:00 and 04:00 with 0.04 nmol m-2 s-1. Formamide passed the 3σnoise criteria only
in the May period, where it explained 2 % of the total emission and 24 %
of the total deposition. The emissions were highest between 19:00 and 20:00
with 0.13 nmol m-2 s-1 and the deposition peaked between 12:00 and
13:00 with -0.20 nmol m-2 s-1.
The emissions of butene + butanol, which is discussed in Sect. 3.4, were
not related to the start of the growing season. The flux of
C4H9+ decreased by almost two-thirds compared to the snowmelt
period and would increase the total emission by 11 %. The maximum flux of
0.29 nmol m-2 s-1 was between 15:00 and 16:00 and the minimum of
0.02 nmol m-2 s-1 between 22:00 and 23:00. However, during the
start of the growing season, most of the emissions were biogenic.
Maximum emissions during summer
During the first weeks of June the highest average temperature and PAR were
measured, with 17.2 ∘C and 466 µmol m-2 s-1,
respectively. As temperature and light are the drivers of biogenic emissions
(Guenther et al., 2012), the highest 24 h total emission of the campaign,
2.87 nmol m-2 s-1, was recorded during this time (Table 1). In
Fig. 4 the maximum diurnal 1 h net flux is shown at 14:30 with 9.10 nmol m-2 s-1. Similar as in the period in May, methanol, the group of
monoterpenes and acetone were the most emitted compounds and the emissions
of the 10 most emitted compounds in summer all increased when compared to
the measurement period in May (Table 1).
In the summer period methanol was the most emitted compound, comprising
42 % of the total emission and 17 % of the deposition. The methanol flux
was highest between 15:00 and 16:00 with 4.56 nmol m-2 s-1, while
between 03:00 and 04:00 it was deposited (-0.26 nmol m-2 s-1).
Growing leaf biomass is expected to release methanol due to cell wall
demethylation (Galbally and Kirstine, 2002; Hüve et al., 2007; Aalto et
al., 2014). The increase of the 24 h methanol emissions from undetectable
during the snowmelt to about 0.5 nmol m-2 s-1 during the start of
the growing season and finally well above 1 nmol m-2 s-1 in summer
coincides with the typical coniferous needle biomass growth onset at the
beginning of May and maximum needle elongation rate around mid-summer (Aalto
et al., 2014).
Comparison between different studies using a PTR-TOF with the EC
method. The listed compounds are limited to the ones measured in
Hyytiälä. Numbers in parentheses describe deposition and emission,
respectively. All values are 24 h averages, except values marked withc. Emissions of butene + butanol are in bold as they are
anthropogenic (Sect. 3.4) and, therefore, not included in the total
exchange.
Net flux (nmol m-2 s-1)
Mass
Elem.
This study (21
Schallhart et
Park et
Brilli et
This study (21
Kaser et
(Da)
comp.
days June)
al. (2016)
al. (2013)b
al. (2016)
days June)c
al. (2013a)c
33.0335
CH5O+
0.965a
1.168
1.655
0.884
2.09c
3.53c
(-0.044/1.010)
(-0.365/1.533)
(-0.102/1.757)
41.0386
C3H5+
0.050a
0.085
0.10c
(-0.001/0.051)
(-0.005/0.089)
42.0338
C2H4N+
0.003
0.046
< 0.01c
(-0.008/0.011)
(-0.005/0.051)
43.0178
C2H3O+
0.027a
0.075
0.07c
(-0.011/0.038)
(-0.001/0.076)
45.0335
C2H5O+
0.099a
0.228
0.133
0.004
0.17c
1.05c
(-0.004/0.103)
(-0.001/0.229)
(-0.016/0.148)
57.0699
C4H9+
0.199
0.016
0.30c
(0/0.199)
(-0.011/0.027)
59.0491
C3H7O+
0.297a
0.335
0.281
0.035
0.48c
0.13c
(-0.001/0.297)
(-0.01/0.345)
(-0.004/0.286)
60.0471
unknown
0.022a
0.03c
(-0.005/0.026)
61.0284
C2H5O2+
0.044a
0.214
0.413
0.09c
1.64c
(-0.003/0.048)
(-0.096/0.311)
(-0.005/0.418)
67.0542
C5H7+
0.006a
0.012
0.01c
(-0.001/0.007)
(-0.004/0.017)
69.0352
unknown
0.013a
0.03c
(-0.001/0.013)
69.0699
C5H9+
0.083a
6.466
0.025
1.009
0.17c
5.87c
(-0.003/0.086)
(0/6.466)
(-0.001/0.025)
70.0696
unknown
0.007a
0.01c
(-0.001/0.008)
89.0386
C7H5+
0.001
> -0.01c
(-0.004/0.005)
93.0699
C7H9+
0.020a
0.058
0.04c
(-0.001/0.021)
(0/0.058)
99.0769
unknown
0.001
0.01c
(-0.004/0.004)
137.1325
C10H17+
0.430
0.219
0.290
0.005
0.63c
0.71c
(0/0.430)
(-0.001/0.219)
(0/0.290)2
Net flux in study
2.07
9.78
4.43
1.99
3.93c
15.07c
Length of data set (days)
21
21
33
129
21
31
Highest emitted compound
methanol
isoprene
methanol
isoprene
methanol
MBO and
isoprene
No. of compounds with flux
17
29
494
13
17
15d
a Values for downward flux are under the respective limit of detection (2σind). σind
was calculated using the propagation of
error formula and the standard deviation at the borders of the individual 30 min CCFs. b Park et al. (2013) published the 24 h values of the
identified masses, therefore no comparison of the unidentified compounds was
possible. c 8 h average daytime values (10:00–16:00). d 8 of
the 15 compounds were only recorded after a hail storm event.
Other biogenic emissions include the group of monoterpenes contributing
21 % and acetone contributing 14 % to the total emission. Similar to
methanol, the highest monoterpenes and acetone emissions were observed in
the late afternoon, with 1.46 and 1.08 nmol m-2 s-1, respectively. Formaldehyde contributed over 40 % to the total
deposition in June, resulting in a flux minima of -0.23 nmol m-2 s-1. However, formaldehyde flux measurements are uncertain, as discussed
in Sect. 3. The anthropogenic flux of butene + butanol had a flux between
0.70 and 0.02 nmol m-2 s-1.
Diurnal variation of the nine most emitted compounds during the 21
days of measurement in June. The remaining compounds are summed up as
“other”. The high variation in the flux seen in Fig. 4 (diurnal plot of 9
days of measurements in June) is reduced, as meteorological events (e.g.,
rain) have less impact on the result. * The butene + butanol exchange
is anthropogenic and thereby not emitted by the forest (Sect. 3.4).
When we compared the selected 9 days to the 21 days in June (Fig. 6 and
Table 2), we found that five compounds no longer fulfilled the 3σnoise criteria. Using the longer period lead to the rejection of
formaldehyde, phenol, p-cymene and two unidentified masses 84.9500 and
99.0201 Da (Tables 1 and 2). The rejection of formaldehyde was the
major reason for the change of deposition from -0.12
to -0.09 nmol m-2 s-1. Three new masses had a detectable flux,
acetonitrile (42.0338 Da) and two unidentified masses 89.0386 and 99.0769 Da. The total emission decreased from 2.87 to
2.16 nmol m-2 s-1. This difference can be explained by the lower
average temperature of 15 ∘C and the lower PAR of 406 µmol m-2 s-1 during the longer period.
Average net flux of the major carbon emitters (June). Compounds whose
elemental composition could not be identified were disregarded. See Table 1
for the corresponding compound names. The butene + butanol emissions were
disregarded in this figure.
Footprint of the SLP and EC method. The higher nominal
measurement height of the SLP fluxes increases the footprint drastically,
when compared to the EC measurements. This could be a possible cause for the
discrepancies in the results.
EC VOC fluxes above different ecosystems
Ecosystems and their phenomenology define which VOCs
are released. In this study 25 compounds were exchanged, with 17 of them
emitted during the 21 days in June. The measured emissions and the observed
amount depend on many environmental aspects as well as meteorology,
experimental setup (e.g., inlet length) and length and time of the
measurements. The 24 h net emission during 21 days in June were 2.07 nmol m-2 s-1, which is on the low side compared to other PTR-TOF fluxes
from other ecosystems (Table 2). In the Pinus sylvestris (Scots pine) forest in
Hyytiälä, the major emissions in June were from methanol,
monoterpenes and acetone. Measurement gaps were excluded when calculating
the length of the data sets. Most of the studies used a data set between 20
and 35 days, Brilli et al. (2016) being an exception with 129 days. All
measurements were carried out around summer, when the plant activities were
high. This study used data from June, Park et al. (2013) and Schallhart
et al. (2016) used data from June and July, Kaser et al. (2013a) used data from
August and September and Brilli et al. (2016) used data from June until the
end of October. Out of these studies, the lowest 24 h net flux was measured
at a 2-year-old Populus (poplar) plantation (Brilli et al., 2015) in Belgium, with
1.99 nmol m-2 s-1. Isoprene was emitted most, followed by
methanol, acetone and the group of green leaf volatiles (measured via a
fragment). The low emission can be partly explained by the long measurement
period that extended over the summer. Park et al. (2013) reported a net flux
of 4.43 nmol m-2 s-1 above an orange grove. The most emitted
compounds were methanol, acetic acid, monoterpenes and acetone. The study
was exceptional because it measured significant fluxes for several hundreds of
VOCs. Despite that, the highest net flux was measured by Schallhart et al. (2016) above a mixed Quercus (oak)
Carpinus betulus (hornbeam) forest with 9.78 nmol m-2 s-1. In their study the most emitted compounds were isoprene, methanol,
acetone and methyl vinyl ketone + methacrolein. The high emissions can be
explained by the ecosystem, as oaks are known to be strong isoprene emitters
(e.g., Potosnak et al., 2014). Kaser et al. (2013a) reported 8 h daytime
fluxes only, so a direct comparison with the 24 h net flux from the other
studies is not possible. However, the Pinus ponderosa (Ponderosa pine) flux was dominated
by MBO + isoprene fluxes, followed by methanol and acetic acid. The net flux
is almost a factor of 4 higher than in boreal forest in Hyytiälä.
However, it should be noted that the day length in summer is much longer in
Hyytiälä (62∘ N) when compared to the Ponderosa pine
forest in Colorado (39∘ N) and, therefore, elevated emissions last
longer than 8 h.
Diurnal mean and standard deviation of the eddy covariance (red
triangles) and the surface layer profile (black squares) flux of the major
compounds measured by EC and SLP. The data are from April to the end of June
2013.
The net carbon flux of the VOCs during the campaign in Hyytiälä was
7.25 nmol C m-2 s-1 (Fig. 7). The group of monoterpenes was the
highest emitter of carbon with 59 % of the net carbon exchange, followed
by methanol (13 %), acetone (12 %), isoprene + MBO (5 %) and acetaldehyde
(3 %). The C3H5+ fragment, toluene + p-cymene
contributed 2 %, while acetic acid and the sum of the
remaining compounds both contributed 1 %. Compared to the CO2 net
ecosystem exchange (NEE) of -4266 nmol C m-2 s-1, the carbon
released as VOC represents less than 0.2 % of the NEE of the corresponding
period. In Brilli et al. (2016) the VOCs, with 6.36 nmol C m-2
s-1, represent 0.8 % of the carbon exchange and in Schallhart et al. (2016) VOCs had a carbon flux of 41.8 nmol C m-2 s-1, which
corresponded to 1.7 % of the NEE. Juráň et al. (2017) measured EC
exchange of VOCs in a Picea abies (Norway spruce) forest in the Czech Republic. In
their study the ratio between the carbon released from VOCs and the NEE was
0.3 % during the 5 days of data in July. Other ratios from DEC studies
using PTR-Quad are 0.7 % above an oil palm plantation (E. guineensis × E. oleifera hybrid)
in Malaysia (Misztal et al., 2011) and 0.16 and 0.24 % above two P. halepensis (Aleppo
pine) forests in Israel (Seco et al., 2017).
There was an order of magnitude difference between the proportion of net
assimilated carbon released as VOCs between a Mediterranean oak–hornbeam
forest (Schallhart et al., 2016) and a boreal evergreen forest. Also, a
middle European poplar plantation (Brilli et al., 2016) clearly released a
higher proportion of the assimilated carbon as VOCs than a boreal site in
this study. The spruce forest in central Europe came closest to the results
in Hyytiälä. These findings strongly imply that there are
significant differences between the ecosystems in how they allocate carbon
to VOCs; however, the reasons for that are rather related to light and
thermal conditions, species selection and age structure and soil properties
than to the efficiency of an ecosystem to produce VOCs as an indefinable
concept. In boreal ecosystems with relatively northern locations, the
majority of carbon assimilation is concentrated within a couple of months
around mid-summer with very short nights. The structure of forest and the
tree species are effective in utilizing the high light availability during
the summer months, whereas in more southern locations the light availability
and thermal conditions allow more even carbon assimilation throughout a
considerably longer period. This partly explains the lower proportion of C
released as VOCs determined in this study, when compared to those measured
in more southern locations. Additionally, the constitutive emission
capacities of boreal evergreen species are known to be low when compared to
deciduous species more common in central and southern Europe (Rinne et al.,
2009; Ghirardo et al., 2010).
Statistics of the major compounds of SLP and the EC flux
measurements. The EC fluxes are in bold, whereas the SLP fluxes are written
in normal text. The fitting parameters describe the slope (upper value) and
the intercept (lower value) of the linear model between the EC and the SLP
fluxes. N is the number of the data points for each compound. The numbers in
parenthesis are lower and upper quartiles. The unit for the mean, median,
intercept and quantile values is nmol m-2 s-1.
Nominal mass
R2
Mean
Median
5 and 95 % quant.
Fitting
N
33 (methanol)a
0.374
0.447
0.204 (-0.177, 1.161)
-1.602, 2.920
0.810 ± 0.220
92
0.553
0.730 (-0.931, 1.910)
-2.107, 3.296
0.171 ± 0.306
42 (acetonitrile)
0.003
-0.009
-0.003 (-0.026, 0.005)
-0.052, 0.026
0.296 ± 1.545
55
-0.017
-0.050 (-0.103, 0.072)
-0.209, 0.228
-0.015 ± 0.040
45 (acetaldehyde)
0.033
0.019
0.024 (-0.106, 0.131)
-0.382, 0.481
-0.195 ± 0.310
49
0.063
0.114 (-0.116, 0.245)
-0.406, 0.507
0.067 ± 0.082
59 (acetone)
0.051
0.176
0.090 (0.004, 0.262)
-0.156, 0.696
0.242 ± 0.191
119
0.173
0.210 (-0.046, 0.373)
-0.469, 0.677
0.131 ± 0.074
61 (acetic acid)a,b
0.064
0.336
0.244 (0.059, 0.504)
0.003, 1.052
0.116 ± 0.130
49
0.025
0.058 (-0.029, 0.093)
-0.115, 0.195
-0.014 ± 0.061
69 (isoprene + MBO)
0.127
0.082
0.033 (0.005, 0.091)
-0.009, 0.383
0.274 ± 0.160
82
0.035
0.035 (-0.013, 0.052)
-0.054, 0.104
0.013 ± 0.025
93 (toluene + p-cymene)
0.089
0.138
0.081 (0.024, 0.209)
-0.063, 0.519
0.088 ± 0.061
88
0.027
0.037 (-0.013, 0.052)
-0.054, 0.104
0.015 ± 0.014
137 (monoterpenes)
0.364
0.282
0.208 (0.084, 0.365)
0.023, 0.754
0.503 ± 0.123
116
0.261
0.225 (0.135, 0.349)
0.019, 0.583
0.118 ± 0.051
a Sensitivity was derived from the instrumental transmission curve for the
PTR-Quad. b The acetic acid sensitivity was estimated.
Comparison between PTR-TOF and PTR-Quad measurements
Generally, the EC method detected more masses than the SLP measurements,
which can be explained by the preselection of the masses to be measured by
the PTR-Quad and its low duty cycle. The comparison between PTR-TOF using
the EC method and the PTR-Quad using the SLP method (see Rantala et al., 2015) between April and June revealed 12 more compounds
with exchange using the EC method (see Tables 1, 2 and 3). The PTR-TOF
measures all VOCs in a certain mass range, while the PTR-Quad needs a
preselection of masses, limiting the number of recorded compounds. However,
the classification if a compound has a measurable flux is different between
the two methods (Sect. 2.3 and Rantala et al., 2015). Even though the EC
method found almost twice the number of compounds with exchange, the total
exchange was on the same order of magnitude for both methods. The
discrepancy between the results of the two measurements is mainly due to
instrument and method differences. The horizontal distance between the
inlets of these two instruments was just 25 m and the PTR-Quad was measuring
from 13 m higher (calculated height 36 m) than the PTR-TOF, leading to a
larger footprint area for the SLP method. This can be seen in Fig. 8, where
the daytime (07:00 to 19:00) footprint calculated according to Kormann and
Meixner (2002) for the SLP and EC method are presented. Even though the
nominal measurement height of the SLP was 36 m, the shown footprint is
calculated for 33 m, as this was the closest height with horizontal wind
measurements.
The eddy covariance fluxes against the surface layer gradient
fluxes (methanol and monoterpenes, April–June 2013). In addition to the
actual scatter plots, the figures include linear fits (black solid lines)
with confidence intervals (black dashed lines) and R2 parameters.
On the left side the average C4H9+ flux is shown
(data of the whole campaign) in white. The orange circles illustrate the
locations of the aerosol instruments, which use butanol. The average fluxes
from north to south-southeast are under 0.05 nmol m-2 s-1. The map
was taken from Google Maps (Image ©2016 Google; map
data ©2016 Google). The wind rose of the C4H9+ fluxes
is shown on the right.
The main compounds (methanol, acetone and monoterpenes) were detected by
both methods. C4H9+ (57.0699 Da) could not be measured by the
PTR-Quad, as the unit mass resolution of the instrument is unable to
separate the signal from the water cluster isotope
H7O218O+ (57.0432 Da). In contrast, the SLP
measurements observed a flux of ethanol + formic acid which was not detected
by the PTR-TOF. Interestingly, formic acid fluxes have also been observed at
SMEAR II using the EC method with an iodide-adduct high-resolution
time-of-flight chemical ionization mass spectrometer in 2014 (Schobesberger
et al., 2016). Other VOC fluxes, which were not detected by the PTR-TOF were
methyl ethyl ketone (73 Da), a fragment of the green leaf volatiles (83 Da)
and MBO (87 Da). The formaldehyde fluxes were not included in the
comparison, as the PTR-TOF, detected them just during the first 8 days in
June, during which the PTR-Quad had technical problems resulting in less
than 10 overlapping data points from the two instruments.
Overall, the flux values used for the comparison were small, as the compared
data included values from April and May, and unfortunately the SLP
measurements were not working during the warm, i.e., high flux, period in the
beginning of June. Thus, the comparison was done using flux values that were
mostly close to the detection limits of the EC and the SLP setups. The
uncertainties of the turbulence measurements and noise of the VOC
measurements, together with the different measurement setups, methods and
footprints was seen in the scatter of the compared data, which affected
correlation, fitting parameters and their uncertainties (Table 3).
However, the magnitude of the studied fluxes as well as their diurnal
patterns were comparable for methanol, acetone, isoprene and monoterpenes
(Table 3 and Fig. 9). The monoterpene and methanol fluxes measured by the
two methods showed the highest correlation, which was barely above 0.6 (Fig. 10). The fitted slopes of the scatter plots for acetone, isoprene and the
monoterpenes were far from unity, as best R2 values were calculated
when using high intercepts.
The fluxes of acetonitrile, acetaldehyde, acetic acid and toluene + p-cymene did not agree similarly well. Therefore, the correlations between
the methods were poor for rest of the compounds. The toluene + p-cymene
flux discrepancy was likely caused by a different detection of the toluene + p-cymene signal in the two instrument and not due to the different flux
methods. The toluene + p-cymene measurements at mass 93 with PTR-Quad and
93.0454 with PTR-TOF resulted in different concentration values and should
be handled with care. Different fragmentation from p-cymene, influence from
two other mass peaks seen at nominal mass 93 (92.5 to 93.5 Da) and/or
unsuccessful calibrations may probably explain the observed differences.
Kajos et al. (2015) reported similar discrepancies in toluene + p-cymene
concentration measurements with the PTR-Quad at the site. Also, acetic acid
fragments when measured with the PTR method (Baasandorj et al., 2015).
Higher fragmentation in the PTR-TOF (61.0284 Da), when compared to the
PTR-Quad (60.5 to 61.5 Da), could account for a part of the lower acetic
acid fluxes with the EC method. However, even when the acetic acid main
fragment C2H3O+ (Baasandorj et al., 2015) is taken into
account, in addition to the signal from the parent mass, the EC fluxes are
still lower than the SLP results. Another uncertainty comes from the lack of
acetic acid in the calibration standard. For the uncalibrated compounds the
sensitivity can be calculated (Sect. 2.2; Rantala et al., 2015), but they
are more uncertain and can lead to systematic discrepancies. A
recent study compared PTR-TOF measurements with gas chromatography mass
spectrometer measurements, as in this study as well, the PTR-TOF underestimated
the acetic acid concentration (Helén et al., 2017). The authors
suggested that a possible memory effect in the inlet or instrument could
lead to this underestimation, which was also reported by de Gouw et al. (2003). This effect could lead to an additional attenuation of the acetic
acid signal and thus decrease the measured flux.
In addition to the differences between the PTR-Quad used in SLP and the
PTR-TOF used in EC, the flux calculation methods could also lead to
discrepancies, such as the SLP fluxes having larger footprints, which were seen as
different flux values, or the SLP or the EC method working improperly
for these compounds. One should also note that the comparison was based on a
small data set only (Table 3) and, thus, random variations also affect the
results. The net flux for all compounds in Table 3 was 1.120 nmol m-2 s-1 for the PTR-TOF and 1.471 nmol m-2 s-1 for the PTR-Quad.
If the net flux is calculated for all the compounds, which were measured by
the individual instrument, it is 1.252 nmol m-2 s-1 for the
PTR-TOF, not including butene + butanol, and 1.802 nmol m-2 s-1
for the PTR-Quad.
Anthropogenic flux of C4H9+
The identification of the compound with the elemental composition of
C4H9+ is problematic as it could be protonated butene, which
can be emitted by forests (Goldstein et al., 1996; Hakola et al., 1998) and
from anthropogenic sources (Harley et al., 1992; Na et al., 2004). Another
possible contribution to the C4H9+ signal comes from the
fragmentation of butanol, which as many other alcohols, can lose an OH
during ionization (Spanel and Smith, 1997). Denzer et al. (2014) reported
that the most abundant signal of butanol, when measured with PTR-Quad, is
for the C4H9+ mass. Fragmentation tests using the PTR-TOF
confirmed this. In Fig. 11 the average C4H9+ flux from
different wind sectors and the wind rose for the individual 30 min
C4H9+ fluxes are shown. The sources of the
C4H9+ clearly lay in the western part of the forest as there
are just low emissions and depositions in the east side. The highest average
flux of C4H9+ came from south-southwest (195∘)
with 0.57 nmol m-2 s-1 while another maximum lay in
north-northwest (345∘) with 0.42 nmol m-2 s-1. The
cottages where the butanol using aerosol measurements, condensation particle
counters (CPCs), of the station are located (Fig. 11, orange circles), lie
approximately in these directions. An additional CPC was mounted on a mast
located west of the VOC flux measurements. Therefore we conclude that the
C4H9+ signal is mainly from butanol used by the aerosol
instruments and thus the flux is anthropogenic. During 2013, approximately
100 L of butanol were evaporated in the CPCs at the station. During our
measurements the contribution of the butanol fragment to the total emission
was 63 % during the measurement period in April and 11 % in the May period
and 8 % during the measurements in June.
Conclusions
Overall, the exchange of 25 compounds was observed over a boreal Scots pine
forest. During the transition from early spring to mid-summer the net flux
increased by a factor of 5 and the number of compounds changed from 3
to 19. The highest emissions occurred in late afternoon, while deposition
was observed mainly at night. The majority of the net VOC flux was comprised
of methanol, monoterpenes, acetone and butene + butanol. The measured
butene + butanol flux was most likely a fragment of butanol and created
by evaporation in the particle counters used at SMEAR II. Twelve
compounds were measured either only in May or June, which implies a strong
seasonal cycle and a high diversity of VOC emissions from the boreal forest
in Hyytiälä.
Compared to EC fluxes from other ecosystems measured with the PTR-TOF, the
VOC emission in the boreal forest was small, 2.16 nmol m-2 s-1,
even though the measurements in June had the longest day length, up to 19.5 h. In relation to the CO2 exchange, the VOCs are only less than
0.2 % compared to the net ecosystem carbon exchange.
The EC fluxes measured with PTR-TOF and the SLP fluxes measured with
PTR-Quad had similar results for the main flux compounds – methanol,
monoterpenes and acetone – thus confirming the feasibility of the indirect
SLP method at the site. For small fluxes, like acetonitrile, isoprene and
acetaldehyde the results were affected by noise. Toluene + p-cymene and
acetic acid show significant differences, which could hint at differences in
the fragmentation patterns of the instruments. Further research is still
needed to close the gap between the fluxes measured by the two instruments.
Therefore, long-time measurements with the PTR-Quad or other instruments,
which create less data and do not need such work intensive data post-processing as the PTR-TOF, are essential. If a research network of sites
with VOC flux measurements is established in the future, cheaper and easier-to-use instruments are needed (Rinne et al., 2016). Still, intensive
campaigns with more selective instruments are important assets to
understand biosphere–atmosphere exchanges and air chemistry in different
ecosystems.