1
1
Supporting Information S1
2
Fig. S1 Isoprene and monoterpene (mono-TPS) synthase activities in (A), Norway spruce
3
(B) Scots pine, and (C) Holm oak. Enzyme (ISPS and mono-TPS) activities in conifer
4
needles were analyzed with the respective buffer system optimized in Fischbach et al.
5
(2002). Analysis of Holm oak enzyme activities was performed according to Schnitzler et al.
6
(2004a). In European larch and Silver birch extracts no enzyme activities could be detected.
7
1) α-thujene (
8
and 6) isoprene (
), 2) α-pinene (
), 3) camphene (
), 4) sabinene (
), 5) ß-pinene (
) (n = 3 ± SE, for Norway spruce n = 5; n.d. = not detectable).
9
A
2.0
-1
mono-TPS activity [kat kg protein ]
0.12
1.5
0.08
1.0
0.04
0.5
0.6
B
0.6
0.4
0.4
0.2
0.2
1.0
0.8
0.6
0.4
0.2
C
1.0
0.8
0.6
0.4
0.2
1 2 3 4 5 6
no. of terpene
10
11
12
1
-1
ISP activity [kat kg protein ]
0.16
),
2
1
Supporting Information S2
2
3
Modeling the ecosystem scale emission
4
The monoterpene emission can be calculated using a hybrid model described by the
5
equation
6
E  E synth  E pool
7
Here Esynth represents the monoterpene emission originating directly from biosynthesis and
8
Epool that originating from specialized storage structures. Here non-specialized storage in the
9
lipid phase, from which the evaporation into the atmosphere happens, is assumed to be very
.
(1)
10
small, and its effect on emission dynamics is thus ignored (Grote & Niinemets 2008). The
11
diurnal cycle of monoterpene emission originating directly from de novo synthesis is light and
12
temperature dependent and can be described using the algorithm developed by Guenther et
13
al. (1991) for isoprene emission,
14
E synth  E0, synthCT C L
15
Here E0,synth is normalized synthesis emission potential, CT the temperature activity factor
16
and CL light activity factor. The former describes the functional dependence of enzyme
17
activity on temperature and the latter the dependence of electron transport rate on light. The
18
diurnal cycle of monoterpene emission from large pools in specialized storage structures can
19
be calculated by using the algorithm developed by Guenther et al. (1991),
20
E pool  E 0, pool
21
where E0,pool is normalized pool emission potential, γ is the temperature activity factor, which
22
describes the dependence of monoterpene saturation vapor pressure on temperature. One
23
must note that the functional form of CT and γ is different, with the former having an optimum
24
around 40C after which it declines, while the latter takes exponential form (Guenther et al.
25
1991).
26
To test the applicability of the hybrid model we formulated the following algorithm:
.
,
27
(2)
(3)
(4)
28
where fdenovo is the fraction of the emission originating directly from synthesis. We set this
29
parameter to 0.58, i.e. the fractions for pool and de novo emissions to 42 and 58%, as
30
indicated for the Scots pine by the labeling experiment. The functional forms of CT, CL and ,
31
as well as the values for their parameters, were taken from Guenther (1997). Thus, we fitted
32
the model against the data using only one parameter, E0, in order to match the model to the
33
emission level. In order to keep the model as simple as possible we utilized a big-leaf
34
approach, in which the shadowing effects of the canopy are neglected.
35
2
3
1
Ecosystem scale monoterpene flux measurements
2
To test applicability of the hybrid model we used ecosystem scale monoterpene emission
3
data measured at a Scots pine forest. The emission measurements were conducted using
4
the disjunct eddy covariance method (Rinne et al. 2001; Karl et al. 2002) with PTR-MS as
5
analyzer. The details of the measurement setup and PTR-MS calibrations are described in
6
detail by Rinne et al. (2007) and Taipale et al. (2008). The measurement site was the
7
SMEAR II research station in Hyytiälä, Finland (Hari & Kulmala, 2005). The forest at the site
8
is dominated by Scots pine sown in early 1960’s. The forest has relatively open canopy with
9
needle biomass density of 540 g m-2 (Rinne et al. 2007). We used data from six warm days
10
in July 2006 when daytime maximum temperatures ranged from 21 to 29C and night time
11
minima from 11 to 16C.
12
13
References
14
Hari P. & Kulmala M. (2005). Station for measuring ecosystem-atmosphere relations
15
16
(SMEAR II). Boreal Environment Research 10, 315-322.
Grote R. & Niinemets Ü. (2008) Modeling volatile isoprenoid emissions – a story with split
17
18
ends. Plant Biology 10, 8-28.
Guenther, A. (1997) Seasonal and spatial variations in natural volatile organic compound
19
emissions. Ecological Applications 7, 34-45.
20
Guenther A.B., Monson R.K. & Fall R. (1991). Isoprene and monoterpene emission rate
21
variability: Observations with eucalyptus and emission rate algorithm development.
22
Journal of Geophysical Research 96, 10799-10808.
23
Karl, T.G., Spirig, C., Rinne, J., Stroud, C., Prevost, P., Greenberg, J., Fall, R., Guenther, A.,
24
(2002) Virtual disjunct eddy covariance measurements of organic trace compound fluxes
25
from a subalpine forest using proton transfer reaction mass spectrometry. Atmospheric
26
Chemistry and Physics 2, 279-291.
27
Rinne, H.J.I., Guenther, A.B., Warneke, C., de Gouw, J.A., Luxembourg, S.L., (2001)
28
Disjunct eddy covariance technique for trace gas flux measurements. Geophysical
29
Research Letters 28, 3139-3142.
30
Rinne J., Taipale R., Markkanen T., Ruuskanen T.M., Hellén H., Kajos M.K., Vesala T. &
31
Kulmala M. (2007) Hydrocarbon fluxes above a Scots pine forest canopy: measurements
32
and modeling. Atmospheric Chemistry and Physics 7, 3361-3372.
33
Taipale R., Ruuskanen T.M., Rinne J., Kajos M.K., Hakola H., Pohja T. & Kulmala M. (2008)
34
Technical Note: Quantitative long-term measurements of VOC concentrations by PTR-
35
MS measurement, calibration, and volume mixing ratio calculation methods. Atmospheric
36
Chemistry and Physics 8, 6681-6698.
3