Determination of de novo and pool emissions of terpenes in

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