Atmos. Chem. Phys., 25, 17205–17236, 2025
https://doi.org/10.5194/acp-25-17205-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

R
esearch

article

BVOC and speciated monoterpene concentrations and
fluxes at a Scandinavian boreal forest

Ross C. Petersen1, Thomas Holst1, Cheng Wu2,3,a, Radovan Krejci2,3, Jeremy K. Chan4,
Claudia Mohr2,3,b,c, and Janne Rinne1,5

1Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
2Department of Environmental Science, Stockholm University, Stockholm, Sweden

3Bolin Centre for Climate Research, Stockholm University, 11418, Stockholm, Sweden
4Center for Volatile Interactions (VOLT), Department of Biology, University of Copenhagen,

Universitetsparken 15, 2100 Copenhagen Ø, Denmark
5Bioeconomy and Environment, Natural Resources Institute Finland (Luke), Helsinki, Finland

anow at: Department of Chemistry and Molecular Biology, University of Gothenburg,
41296, Gothenburg, Sweden

bnow at: Department of Environmental Systems Science, ETH Zürich, 8006 Zürich, Switzerland
cnow at: PSI Center for Energy and Environmental Sciences, Paul Scherrer Institute, 5232 Villigen, Switzerland

Correspondence: Janne Rinne (janne.rinne@luke.fi)

Received: 3 November 2024 – Discussion started: 12 December 2024
Revised: 10 July 2025 – Accepted: 15 July 2025 – Published: 1 December 2025

Abstract. Boreal forests emit terpenoid biogenic volatile organic compounds (BVOCs) that significantly affect
atmospheric chemistry. Our understanding of the variation of BVOC species emitted from boreal ecosystems is
based on relatively few datasets, especially at the ecosystem-level. We conducted measurements to obtain BVOC
flux observations above the boreal forest at the ICOS (Integrated Carbon Observation System) station Norunda
in central Sweden. The goal was to study concentrations and fluxes of terpenoids, including isoprene, speci-
ated monoterpenes (MTs), and sesquiterpenes (SQTs), during a Scandinavian summer. Measurements (10 Hz
sampling) from a Vocus proton-transfer-reaction time-of-flight mass spectrometer (Vocus PTR-ToF-MS) were
used to quantify a wide range of BVOC fluxes, including total MT (386 (± 5) ng m−2 s−1; β = 0.1 °C−1), using
the eddy-covariance (EC) method. Surface-layer gradient (SLG) flux measurements were performed on selected
daytime sampling periods, using thermal-desorption adsorbent tube sampling, to establish speciated MT fluxes.
The effect of chemical degradation on measured terpenoid fluxes relative to surface exchange rates (F/E) was
also investigated using stochastic Lagrangian transport modeling in forest-canopy. While the effect on isoprene
was within EC-flux uncertainty (FISO/EISO < 5 %), the effect on SQT and nighttime MT was significant, with
average F/E ratios for nighttime FMT/EMT = ca. 0.9 (0.87–0.93), nighttime FSQT/ESQT = 0.35 (0.31–0.41),
and daytime FSQT/ESQT = 0.41 (0.37–0.47). The main compounds contributing to MT flux were α-pinene and
13-carene. Summer shifts in speciated MT emissions for 13-carene were detected, featuring a decrease in its
relative fraction among observed MT compounds from June to August sampling periods, indicating that closer
attention to seasonality of individual MT species in BVOC emission and climate models is warranted.

Published by Copernicus Publications on behalf of the European Geosciences Union.



17206 R. C. Petersen et al.: BVOC and speciated monoterpene concentrations

1 Introduction

Biogenic volatile organic compounds (BVOCs) play a cen-
tral role in tropospheric chemistry, influencing both regional
air quality and global climate (Fall, 1999). Many BVOCs par-
ticipate in new particle formation (NPF) and growth (Bianchi
et al., 2016; Boucher et al., 2013; Kirkby et al., 2016; Mohr
et al., 2019; Riipinen et al., 2012; Tunved et al., 2006; Went,
1960). As BVOCs react with ozone, OH, and NO3 (in the
case of nighttime chemistry), the subsequent reaction prod-
ucts often have lower volatility that in suitable conditions can
condense into new secondary organic aerosol (SOA) parti-
cles or contribute to the growth of existing aerosol particles
(Bonn et al., 2009; Hallquist et al., 2009; Hodzic et al., 2016;
Kulmala et al., 2004). BVOCs also affect the production and
lifetime of tropospheric ozone through their photo-oxidation
in the presence of NOx , as well as through their interactions
with OH and other radicals (Atkinson and Arey, 2003). As re-
active BVOCs compete with methane for reacting with ambi-
ent OH, they may also have an influence on the atmospheric
lifetime of this greenhouse gas (e.g., Kaplan et al., 2006).
Oxygenated VOCs, such as acetone – one of the most abun-
dant in the atmosphere (e.g., Singh et al., 1994), can also
modify OH concentrations in the upper troposphere (Fehsen-
feld et al., 1992; McKeen et al., 1997; Monks, 2005) and/or
contribute to the formation of PAN that can act as a reservoir
for NOx (Read et al., 2012; Roberts et al., 2002).

Globally, BVOC emissions are several times greater than
anthropogenic emissions, accounting for up to ca. 90 %
of total VOC emissions worldwide (Guenther et al., 1995;
Müller, 1992). In densely populated European regions, an-
thropogenic VOC emissions are estimated to exceed bio-
genic emissions, but in most cases, particularly those areas
which are sparsely populated, biogenic emissions are dom-
inant (Lindfors and Laurila, 2000; Simpson et al., 1999).
These include sparsely populated countries in the European
boreal zone (Simpson et al., 1999). The boreal vegetation
zone, one of the Earth’s largest terrestrial biomes and form-
ing a near-continuous band around the Northern Hemisphere,
is one of the major sources of BVOCs to the atmosphere at
the global scale (Guenther et al., 1995; Sindelarova et al.,
2014). Across all plant functional types (e.g., Guenther et
al., 2012), boreal monoterpene (MT) emissions make up as
much as ca. 26.3 % of the global summertime MT emission
inventory (June: 28.3 %, July: 29.7 %, and August: 20.7 %),
and up to ca. 37.1 % of it (June: 39.2 %, July: 41 %, and Au-
gust: 30.5 %) for the Northern Hemisphere (Sindelarova et
al., 2022).

Terpenoid compounds are an important fraction of BVOCs
emitted globally (Guenther et al., 1995) and also from bo-
real forests (Rinne et al., 2009), and include isoprene (C5H8),
the MTs (C10H16), the sesquiterpenes (SQTs; C15H24), and
so on to diterpenes and larger compounds. Typically, for
European boreal forests such as those largely composed of
Scots pine and Norway Spruce (Rinne et al., 2009), isoprene

emissions are relatively low while MT emissions predomi-
nate during typical ambient conditions (Hakola et al., 2006;
Hakola et al., 2017), with isoprene emission about 10 %–
15 % of MT emissions by mass (Rinne et al., 2009). When
under significant stresses such as insect herbivory or drought,
boreal forests are also known to be significant SQT emitters
(Rinne et al., 2009; Niinemets, 2010). Isoprene is mainly in-
volved in influencing production and lifespan of tropospheric
ozone (Atkinson and Arey, 2003) but is relatively ineffective
at enhancing tropospheric SOA yields compared to MT. In
the lower troposphere, isoprene can even inhibit SOA NPF
and early-stage particle growth, such as when present as an
isoprene + MT mixture relative to a pure MT mixture (e.g.,
Heinritzi et al., 2020; Kiendler-Scharr et al., 2009; McFig-
gans et al., 2019). However, MTs as such emitted by the bo-
real forest biome are mainly involved in SOA particle forma-
tion and growth (Spracklen et al., 2008).

Globally, α-Pinene, β-pinene, and limonene typically have
the highest net atmospheric abundance for MTs. Many MT
isomers, however, are present and vary widely in structure
(Geron et al., 2000; Guenther et al., 2012). In boreal forests,
the emission of MT from predominant tree species, par-
ticularly Scots pine and Norway spruce, can vary as dis-
tinct chemotypes (referred to as α-pinene and 13-carene
types) (Hiltunen and Laakso, 1995; Holzke et al., 2006; Jan-
son, 1993; Komenda and Koppmann, 2002; Manninen et al.,
2002; Tarvainen et al., 2005). The chemical speciation of MT
emissions can also vary significantly within the same tree
species population (e.g., Persson et al., 2016). This has direct
effects on the resulting atmospheric chemistry, as MT reac-
tivity and SOA formation potential varies by isomeric struc-
ture (Friedman and Farmer, 2018; Griffin et al., 1999; Lee et
al., 2006a; Zhao et al., 2015). For example, Lee et al. (2006b)
reported SOA yields from ozonolysis of MTs ranging from
17 % for β-pinene to 54 % for13-carene (41 % for α-pinene)
and in photolysis experiments (Lee et al., 2006a) found that
13-carene had an SOA mass yield that is ca. 16 %–30 % and
15 %–22 % greater than that of β-pinene and α-pinene, re-
spectively. Accurately characterizing the speciation of MT
fluxes from boreal forests is therefore of significant impor-
tance to parameterizing chemical species-specific emission
factors in current and future climate and air quality models.

Observations of BVOC concentration above forest canopy
are important for interpreting atmospheric chemistry pro-
cesses, particularly in the lower troposphere and surface
boundary layer directly above the forest, which are depen-
dent on the concentration of precursor BVOC compounds
(Atkinson, 2000; Pryor et al., 2014; Tunved et al., 2006).
Observed concentrations are affected by both BVOC emis-
sion rate and micrometeorological conditions in the sur-
face boundary layer (e.g., Karl et al., 2004; Petersen et al.,
2023), as well as the chemical lifetime of relatively short-
lived BVOC compounds (Atkinson and Arey, 2003). Very
brief chemical lifetimes, such as for SQTs (τSQT – seconds),
can affect their observed above-canopy fluxes as well (e.g.,

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R. C. Petersen et al.: BVOC and speciated monoterpene concentrations 17207

Rinne et al., 2012). Evaluating the ecosystem–atmosphere
exchange of reactive BVOCs in the absence of chemical
degradation, and hence isolating the roles of surface emis-
sion, deposition, and physical transport from its effects, rep-
resents an important goal for separating the relative influ-
ences on BVOC ecosystem-scale surface exchange and phys-
ical transport processes from atmospheric chemistry. Mean-
while, BVOC flux observations are important for understand-
ing BVOC exchange between forest ecosystems and the at-
mosphere. Quantifying fluxes is also important for accu-
rately parameterizing the functional dependencies of BVOC
emissions on environmental parameters, such as temperature
and solar radiation, as well as non-constitutive influences on
ecosystem-scale emissions such as drought and disturbance
stress, for regional and global atmospheric chemistry models
(e.g., Rinne et al., 2007; Taipale et al., 2011).

There are multiple methods used to measure BVOC con-
centrations and fluxes (Rantala et al., 2014; Rinne et al.,
2021). Proton-transfer-reaction mass spectrometry (PTR-
MS) has been widely used to study BVOCs in the atmo-
sphere (e.g., Yuan et al., 2017; Lindinger et al., 1998). In
this technique, proton-transfer reactions with H3O+ ions are
used to ionize atmospheric VOCs for detection of the prod-
uct ions by mass spectrometry. In recent years, it has be-
come possible to perform eddy-covariance (EC) measure-
ments of BVOC fluxes using fast-response (τ1 s) PTR time-
of-flight MS (PTR-ToF-MS) over a wide range of BVOC
molar masses (Müller et al., 2010). The EC flux approach,
based on the covariance between fast (10–20 Hz) observa-
tions of the fluctuations in chemical concentration and verti-
cal wind speed (see Sect. 2.4), has the advantage of being the
most direct and accurate approach for measuring ecosystem-
level BVOC fluxes, and thus is an important component for
biosphere BVOC emission research. Fluxes measured by mi-
crometeorological methods can be used to study the effects
of environmental parameters on BVOC emissions. They can
also be applied in up-scaling studies, where canopy-scale
measurements provide an important intermediate step be-
tween leaf-level and regional-scale for use in model verifi-
cation (Guenther, 2012; Peñuelas and Staudt, 2010; Rinne et
al., 2009). A high sampling rate is essential to resolve fluc-
tuations from small, short-lived eddies (0.1–5 Hz) that drive
turbulent transport, as lower rates can lead to significant at-
tenuation of measured fluxes due to unresolved turbulence.
The high sampling rate capability of PTR-ToF-MS (> 10 Hz)
makes it well-suited for measuring BVOC fluxes using the
EC method. Additionally, EC-based methods utilizing PTR-
ToF-MS can be implemented for various mobile platforms,
including aircraft, for spatially resolved landscape-scale flux
assessments over wide areas (Pfannerstill et al., 2023). When
combined with the high sensitivity and accuracy of modern
instrumentation (e.g., Krechmer et al., 2018), PTR-ToF-MS
stands as one of the most effective tools currently available
for measuring ecosystem-scale BVOC fluxes. A limitation of
PTR-MS analysis is, however, that it identifies compounds

collectively by their mass-to-charge ratio, and cannot differ-
entiate between compounds with the same molar mass and
molecular composition (i.e., isomeric compounds). For ex-
ample, MTs (C10H16), which can be emitted in a boreal forest
as any one of many structurally unique compounds, all have
a protonated nominal mass-to-charge (m/z) ratio of 137.130.

While BVOC flux studies have made great strides due
the development of PTR instrumentation, improvements to
the study of speciated MT fluxes are still lacking. In addi-
tion to total fluxes (such as total MT flux), speciated flux
information can be used to further improve model verifica-
tion for compound-specific emission potentials used in emis-
sion models (e.g., MEGAN) (Guenther et al., 2012). Meth-
ods used to measure speciated BVOC fluxes include variants
of the gradient method (see Sect. 2.6), which assumes that a
compound’s ecosystem–atmosphere turbulent flux is propor-
tional to its vertical concentration gradient above the forest
canopy (where the proportionality constant is the turbulent
exchange coefficient) (Fuentes et al., 1996; Goldstein et al.,
1995; Guenther et al., 1996; Rinne et al., 2000a; Schween
et al., 1997). By using automated thermal desorption (ATD)
gas chromatograph–mass spectrometry (GC-MS) for adsor-
bent tube sampling of the vertical BVOC gradient, in con-
junction with PTR-ToF-MS measurements of the EC-derived
BVOC fluxes, the information from these two approaches to
BVOC flux observations can provide additional insight into
the ecosystem–atmosphere exchange of BVOCs between bo-
real forests and the troposphere.

In this work, concentrations and fluxes of BVOCs, par-
ticularly the MTs, at a Swedish site in the European boreal
zone are presented. Six weeks of Vocus PTR-ToF-MS mea-
surements were performed from 21 July to 26 August 2020.
Thermal desorption (TD) tube samples of the BVOC con-
centration were collected during the daytime (typically be-
tween 09:00 am and 05:00 pm) at 37 m and 60 m a.g.l. on the
Norunda flux tower over 3 d periods, during 8–10 June (prior
to Vocus deployment), 22–24 July, and 16–18 August.

2 Methods

2.1 Measurement site

This study was conducted at the Norunda research station,
located at 60°05′ N, 17°29′ E, ca. 30 km north of Uppsala, in
central Sweden. The station is part of the Integrated Carbon
Observation System (ICOS) research infrastructure (https:
//www.icos-cp.eu, last access: 14 October 2025; (Heiska-
nen et al., 2022)), a network for monitoring greenhouse
gases and short-lived climate forcers (more recently, part of
Aerosol, Clouds, and Trace Gases Research Infrastructure
(ACTRIS) in Sweden (https://www.actris.eu, last access: 14
October 2025) as well). The station was surrounded by a
mixed-conifer forest of Scots pine (Pinus sylvestris) and Nor-
way spruce (Picea abies). This forest was between 80 and
120 years old (Lagergren et al., 2005) and the forest canopy

https://doi.org/10.5194/acp-25-17205-2025 Atmos. Chem. Phys., 25, 17205–17236, 2025

https://www.icos-cp.eu
https://www.icos-cp.eu
https://www.actris.eu


17208 R. C. Petersen et al.: BVOC and speciated monoterpene concentrations

Figure 1. Forest map, station location, and BVOC inlet setup for ICOS Norunda. (a) A map of tree heights surrounding the station flux tower
(out to 1500 m radially from tower base) for the Norunda forest. (b) Location and coordinates of ICOS station Norunda in Sweden. (c) BVOC
inlet, infrastructure, and instrumentation setup for Vocus PTR-ToF-MS measurements on the Norunda tower (BVOC inlet at 35 m). Shown
are the heights of the on-site collection of 3D sonic anemometers (blue diamonds) and BVOC inlet (red cross) at the station flux tower. The
canopy top height was at approximately 28 m. Sonic-profile anemometers were located at 1.8, 4.4, 14.8, 20.8, 26.6, 29.6, 32.8, 35, 37.9,
44.8, 59.5, 74,88.5, and 101.8 m on the Norunda tower. The instrument shed contained the Vocus PTR-ToF-MS and zero-air generator for
the Vocus. A blower was used to pull air through the tower inlet.

height was ca. 28 m (Wang et al., 2017). The forest (subse-
quently clearcut in 2022 for lumber) has been managed for
economic purposes for approximately the last 200 years. The
research station has been in operation since 1994 as a CO2
and trace gas flux station and is equipped with a 102 m tower
(Lindroth et al., 1998; Lundin et al., 1999). A station map
and its location in Sweden are presented in Fig. 1.

Together, Norway spruce (53.4 %) and Scots pine (39.8 %)
make up 93.2 % of mean tree number per hectare. There was
also a small number of deciduous trees, consisting primar-
ily of black alder (Alnus glutinosa (L.) Gaertn; 2.5 %) and
downy birch (Betula pubescens Ehrh.; 3.9 %). The dominant
ground vegetation at the station was bilberry (Vaccinium myr-
tillus) and lingonberry (Vaccinium vitis-idaea), in addition
to several species of dwarf shrubs, ferns, and grasses. The
bottom layer vegetation predominantly consisted of a thick
layer of feather moss (Pleurozium schreberi and Hylocomium
splendens). During the 25 years prior to 2020, the mean
monthly temperature varied between −5 and 25 °C, and
the mean annual precipitation was approximately 540 mm.
The growing season, with daily air temperatures above 5 °C,
ranges typically from May to September. During the same
calendar period as the collected 2020 Norunda campaign

measurements (8 June–August 28), the local 25-year clima-
tological average daily temperature was 16.4± 2.7 °C, with
an average nighttime minimum of 10.7± 3 °C and daytime
maximum of 21.2± 3.6 °C. New needle growth typically be-
gins in April. Foliation of existing deciduous trees and plants
usually occurs in May and senescence usually in October
(± 15 d). From 2009 to 2014, the leaf area index (LAI) of
the Norunda forest in proximity of the tower was determined
to be approximately 3.6 (± 0.4) using a LAI 2000 (Li Cor
Inc., Lincoln Nebraska, USA).

2.2 Instrumentation and sampling setup

The canopy temperature of the Norunda forest was measured
using a precision SI-111 infrared radiometer (Campbell Sci-
entific Inc., Logan UT, USA) mounted at 55 m on the station
flux tower. The wind velocity components were measured us-
ing a three-dimensional sonic anemometer (USA-1, Metek
GmbH, Germany). BVOC volume mixing ratios were mea-
sured using a Vocus PTR-ToF-MS (Vocus-2R, TOFWERK,
Thun, Switzerland) (Krechmer et al., 2018). The Vocus has
several advantages over previous PTR-ToF-MS designs, such
as in detecting low-volatility BVOC compounds (Krechmer

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R. C. Petersen et al.: BVOC and speciated monoterpene concentrations 17209

et al., 2018). The Metek sonic anemometer and BVOC sam-
pling inlet were co-located at 35 m on the Norunda flux
tower. Ambient air at 35 m was transported from the BVOC
inlet head through a heated and insulated PFA Teflon tube
mounted on the station flux tower (Fig. 1) to the instrument
shed housing the Vocus PTR-ToF-MS using a side-channel
blower. The tower Teflon tubing was ca. 49 m in length with
an outer diameter of 3/8 in (inner diameter 1/4 in). The
flow rate through the BVOC inlet tubing to the Vocus was
20 L min−1 to minimize BVOC losses from prolonged resi-
dence time within sample tubing. Sample air residence time
in tower Teflon tubing before reaching instrumentation was
ca. 4.7± 1 s.

Inside the instrumentation shed, 6 L min−1 of the flow
coming from the main inlet tubing were directed to the Vocus
inlet, and 100 mL min−1 were sampled into the Vocus, and
the remainder was directed to the inlet exhaust. The Vocus
ion source was set at 2 mbar. From field measurements, the
mass-resolving power (m/1m) of the Vocus, where 1m is
the full-width at half-maximum (fwhm) of a spectrum peak,
was found to be ca. 9900 Th/Th fwhm for the MT parent
ion C10H16H+ (nominalm/z= 137). During the campaign,
data from the Vocus were recorded at a frequency of 10 Hz.
Reference measurements to determine the instrumental back-
ground of the Vocus PTR-MS were periodically performed
(for 1 min each hour) using zero air from a heated catalytic
converter (Zero Air Generator, Parker Balston, Haverhill,
MA, USA). The mass flow controller for the Vocus zero-
air was set to 500 mL min−1. The Vocus calibration was per-
formed using a gravimetrically prepared calibration standard
(Ionimed Analytik, Innsbruck, Austria). The calibration stan-
dard gas was diluted before sampling by the Vocus using a
gas calibration unit (GCU-b, Ionicon, Innsbruck, Austria).

2.3 Vocus data processing

For compounds present in the calibration gas standard bot-
tle, the corresponding calibration factor was applied directly
to the processed Vocus trace data. For the remaining com-
pounds, calibration factors were calculated using the analysis
approach implemented by the PTR data toolkit as described
by Jensen et al. (2023) (See Table C2 of calibration coeffi-
cients and calculated terpenoid sensitivities). The fractiona-
tion rate estimate for SQTs (0.20± 0.1) is based on PTR Li-
brary reference of proton-transfer reactions in trace gas sam-
pling (Pagonis et al., 2019).

The processing of the raw Vocus data was performed using
Julia-based analysis scripts from the software package TOF-
Tracer2 (Breitenlechner et al., 2017; Fischer et al., 2021;
Stolzenburg et al., 2018), modified for use with the Vo-
cus PTR-ToF-MS dataset. For each campaign day, all Vocus
spectra data acquired within 24 h were mass-scale calibrated
every 6 min and averaged for peak shape analysis. The pro-
gram PeakFit (Fischer et al., 2021) was used for peak fitting
and identification. PeakFit was used to create a mass peak

list of more than 2000 identified compounds for our Vocus
dataset. Based on this mass peak list and using the modified
TOF-Tracer2 scripts, the 10 Hz time traces were obtained by
integrating the spectra for intervals around each mass, then
applying deconvolution to reduce cross-talk caused by sig-
nal contributions from neighboring masses and isotopes in
each spectrum (Müller et al., 2010). The script runtime on
a 10-core processor system with 1Tb hard drive and 96 GB
of RAM is approximately 2.5 h for 24 h of 10 Hz data. The
recorded amount of raw Vocus data collected each day in the
native HDF5-file format was approximately 20 GB.

2.4 Eddy-covariance fluxes

The most direct method for measuring a chemical flux above
a forest canopy is the EC method. It requires simultane-
ous, fast (10–20 Hz) measurements of the compound con-
centration (c) and vertical wind velocity (w). The covariance
between the time-dependent fluctuations of these variables
gives the flux (Fc), with

Fc = w′c′ =
1

t2− t1

t2∫
t1

w′ (t)c′ (t)dt, (1)

where the overbar denotes time averaging from time t1 to
time t2, c′ = c− c, and w′ = w−w. Further information re-
garding the EC approach can be found in the literature (e.g.,
Aubinet et al., 2012a). As a significant proportion of a tur-
bulent flux in the atmospheric surface layer is carried by rel-
atively small eddies, for the EC-flux method to accurately
quantify the vertical exchange, the BVOC concentration
and vertical wind speed must be measured by fast-response
instrumentation. Typically, many analyzers for greenhouse
gas fluxes have response times of around 0.1 s. While the
PTR analyzer’s characteristic response time τ (i.e., time for
100× 1/e % ≈ 63.2% of instrument signal to fully transition
between two levels of concentration) is around 1 s, due to
transit time of the BVOC product ions in the Vocus drift tube
reaction chamber (Krechmer et al., 2018), the effect of this
is relatively minimal for evaluating BVOC EC flux (high-
frequency attenuation of total flux signal is about 1 %), and
can be accounted for by a transfer function correction factor
(Striednig et al., 2020). This is sufficient for fluxes measured
above tall vegetation, such as forest canopies (Rantala et al.,
2014).

The EC flux calculations were performed using the
MATLAB-code innFLUX (Striednig et al., 2020). The anal-
ysis routines implemented in this code include a wind sector-
dependent tilt correction, lag time determination, and calcu-
lation of several quality tests. In particular, a tilt-correction
was performed on the Metek sonic data to align the instru-
ment’s coordinate system with the mean wind streamlines
(e.g., Wilczak et al., 2001). The time delay between the
Metek sonic and Vocus BVOC signals was determined by

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17210 R. C. Petersen et al.: BVOC and speciated monoterpene concentrations

maximizing the correlation coefficient of the Vocus signals
with the vertical wind component. For the final processing
of the data points for the Vocus EC flux time series, 30 min
ensemble averages were selected.

2.5 Adsorption sampling and ATD-GC-MS analysis

For a gradient-flux approach targeting the speciated MT
fluxes, ambient air BVOC samples were collected over three-
day periods, approximately once a month, from June to Au-
gust, from two heights. These air samples were simultane-
ously collected at 37 and 60 m on the Norunda flux tower for
30 min periods, once an hour, starting at 09:30 am and con-
cluded with the collection of the last sample-pair at 05:00 pm
from the tower.

Air samples were collected using stainless steel TD tubes
(10 cm in length and 1/4 inch in diameter) and filled with
Tenax GR and Carbograph 5TD (Markes International Inc.,
USA). Ambient air was pumped through these TD tubes
at 200 mL min−1 for 30 min (6 L total per sample) using
flow-controlled sampling pumps (Pocket Pump Pro, SKC
Ltd., Dorset, UK). Two MnO2-coated copper mesh filters
(50 mm grid, type ETO341FC003, Ansyco, Karlsruhe, Ger-
many) were placed inside a Teflon inlet head affixed in front
of each sampling tube, to remove ozone from the ambient
air. In previous studies it was found that these MnO2-coated
mesh filters destroy about 80 % of the ozone but leave α-
pinene, β-pinene, limonene,13-carene, and other terpenoids
intact (e.g., Bäck et al., 2012; Calogirou et al., 1996). TD
samples were refrigerated and then analyzed within 30 d of
collection. Calibration tubes were also prepared and analyzed
to calibrate the subsequent GC-MS analysis (e.g., Bai et al.,
2016).

TD tubes were thermally desorbed and cryo-focused us-
ing a Perkin Elmer TurboMatrix 650 ATD. This ATD was
operated in splitless mode. Samples were then injected into
a gas chromatograph–mass spectrometry system (GC-MS,
Shimadzu QP2010 Plus, Shimadzu Corporation, Japan). For
the gas chromatography portion of the ATD-GC-MS anal-
ysis, the BVOC gas samples were separated using a BPX5
capillary column (50 m, I.D. 0.32 mm, film thickness 1.0 µm,
Trajan Scientific, Australia). The carrier gas was helium.
The GC oven temperature program was as follows: start
temperature, 40 °C; hold time, 1 min; ramp 1, 6 °C min−1

to 210 °C; ramp 2, 10 °C min−1 to 25 °C; final hold time,
5 min. The GC-MS was run simultaneously in both SIM and
SCAN modes (e.g., Duhl et al., 2013). This choice was to
enable detection of both common and unanticipated com-
pounds. For sample calibration, a pure standard solution con-
taining isoprene, α-pinene, β-pinene, p-cymene, eucalyptol,
limonene, 3-carene, linalool, α-humulene, β-caryophyllene,
longifolene, and myrcene (Merck KGaA, Darmstadt, Ger-
many) was prepared in methanol. This standard solution was
injected onto conditioned adsorbent sampling tubes in a he-
lium stream, with cartridges receiving different concentra-

tions of the standard solution. These prepared TD tubes were
then analyzed with the samples to provide a calibration curve
(e.g., Noe et al., 2012).

2.6 Gradient-method fluxes

In the surface-layer gradient (SLG) method we obtain the tur-
bulent flux by using the vertical gradient of the measured vol-
ume mixing ratios (i.e., concentrations c) and a turbulent ex-
change coefficient K in the manner analogous to molecular
diffusion, with Fc =−K

δc
δz

. The turbulent exchange coeffi-
cient K can be obtained in several approaches, such as us-
ing results from another scalar quantity through the modified
Bowen ratio approach or by application of Monin–Obukhov
similarity theory (e.g., Rannik, 1998; Rantala et al., 2014). In
the case of BVOC gradient sampling using TD tubes at two
heights, we use the Monin–Obukhov similarity approach as
in e.g., Fuentes et al. (1996) and Rinne et al. (2000b),

Fc =
−ku∗ [c (z1)− c(z2)]

ln
(
z2−d
z1−d

)
+ψh

(
z1−d
L

)
−ψh

(
z2−d
L

) , (2)

where u∗ is the friction velocity, d is the displacement height,
L is the Obukhov length, ψh is the Monin–Obukhov stability
function for heat, k is the von Kármán constant, and c (z1)
and c(z2) are the BVOC concentrations at heights z1 and
z2, respectively. The values of the integrated stability func-
tions ψh were calculated using the Businger–Dyer equations
(Dyer, 1974), with

ψh = 2 ln
(

1+Y
2

)
, (for ζ < 0) ,

ψh =−βhζ, (for ζ > 0) ,
(3)

where ζ = (z− d)/L, and coefficients Y = (1− 12ζ )1/2 and
βh = 7.8 were selected based on those used in previous
investigations (Businger et al., 1971; Dyer, 1974; Rannik,
1998; Rantala et al., 2014; Rinne et al., 2000a).

In the atmosphere directly above rough surfaces, such as
forest canopy, flux-gradient laws tend to break down (Cel-
lier and Brunet, 1992; Fazu and Schwerdtfeger, 1989; Gar-
ratt, 1980; Högström et al., 1989; Mölder et al., 1999; Simp-
son et al., 1998). The layer in which these laws are not di-
rectly applicable is called the roughness sublayer (RSL). In
the RSL, the eddy diffusivities are increased, and conse-
quently, vertical gradients are decreased compared to their
non-dimensionalized gradient form, which for a scalar con-
centration C is expressed by 8C(ζ )= k(z−d)

c∗

∂C
∂z

, where c∗
is the scalar flux concentration (e.g., Rannik 1998). The de-
crease in gradients due to the influence of the RSL layer can
be quantified and corrected for by using the non-dimensional
factor γ , expressed as

γ =
8S

8
, (4)

where 8S is the dimensionless gradient of a scalar accord-
ing to the Monin–Obukhov similarity theory (i.e., Eq. 4) and

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R. C. Petersen et al.: BVOC and speciated monoterpene concentrations 17211

8 is the dimensionless gradient according to the measure-
ments. The observed γ -coefficients above a forest canopy
at the heights our TD samples were collected typically vary
from unity to 3 depending on the measurement height and
type of the forest (e.g., Simpson et al., 1998).

From γ , it is possible to calculate a mean enhancement
factor 0 by integrating the γ -coefficient from the measure-
ment height z1 to z2 (e.g., Rinne et al., 2000b), such that in
general we have

0 (z1, z2)=
1

z2− z1

 z2∫
z1

γ (z)dz

 . (5)

Details of the γ -coefficient profile analysis for the RSL
above the Norunda canopy, as well as determination of dis-
placement height d (24 m), are included in the Appendix.
Based on this analysis, we have found a 0-coefficient value
of 1.36± 0.09 for TD gradient-flux calculation.

2.7 Gradient-method error analysis

For the two-point SLG gradient method, as alluded to by the
form of Fc =−K

δc
δz

and Eq. (2), the sources of uncertainty
can be divided into those from the gradient (i.e., measured
concentration difference c (z1)− c (z2)) and those from to the
turbulent exchange coefficient K . A detailed evaluation of
SLG-related uncertainties for BVOC flux measurements is
presented in Rinne et al. (2000b).

The adsorption sampling and analysis of the BVOCs rep-
resents the largest single source of uncertainty in the flux
calculation. This is due to the relatively small difference in
concentrations between sampling heights as compared to the
uncertainty of the concentration measurements themselves.
The two error sources which can be evaluated for the chem-
ical gradient measurements are the sampling uncertainty and
the analysis uncertainty from the ATD-GC-MS (e.g., Kajos
et al., 2015). Measurement results from the ATD-GC-MS in-
clude values for peak area mean and standard deviation, as
well as signal-to-noise ratio, which were used in the uncer-
tainty analysis. Sampling uncertainty during field measure-
ments includes the sampling pump flow rate (typically± 5 %
of set flow rate), whereas sources of uncertainty in the anal-
ysis include the preconditioned tube background, as well as
the ATD-GC-MS instrumental uncertainty and standard cal-
ibration uncertainty. From the combined (in quadrature) un-
certainties of TD sampling and laboratory analysis, a total es-
timated uncertainty of ± 15 % is assumed for each MT com-
pound. The total uncertainty of the measured concentration
difference, c (z1)− c (z2), is then determined by summing the
uncertainties of c (z1) and c(z2) in quadrature.

In addition to these concentration-related uncertainties, the
random and systematic uncertainties associated with the tur-
bulent exchange were also considered. Following the ap-
proach of Rinne et al. (2000), the principal uncertainties
of the turbulent exchange coefficient can be further divided

into those originating from the Norunda tower flux mea-
surements used in calculating K and those arising from the
parametrization of K . For the former, uncertainties in K are
dominated by measurement noise and sampling error in the
EC-derived friction velocity and buoyancy flux, contributing
an estimated random error of ± 20 %. For the latter, sys-
tematic uncertainties in K primarily arise from the use of
universal flux-gradient relationships. While consensus exists
on their functional form, a range of values for the empirical
constants used to parameterize these relationships has been
reported in the literature (e.g., Businger et al., 1971; Dyer,
1974; Wieringa, 1980; Högström, 1988; Oncley et al., 1996).
In practice, alongside the von Kármán constant, the constants
used in parameterizing the Businger–Dyer relationships are
not determined independently from each other and hence, in
principle, should be treated as a single parameter set. Vari-
ability among reported parameter sets produces up to 25 %
systematic uncertainty in calculated estimates of K. Evalu-
ated directly from Norunda station data (e.g., Rantala et al.
2014), the zero-plane displacement height d was treated as an
independent parameter and contributed an estimated system-
atic error of ± 10 %. The final uncertainty of the SLG flux is
then assessed by applying the standard propagation of error
method for summing up these four key uncertainties for each
SLG flux estimate.

2.8 Correction for chemical degradation

One of the common assumptions of any surface-layer flux
measurement technique is the constancy of the vertical flux
between the surface and the measurement level. In the case
of reactive trace gases, such as VOCs, this assumption is not
strictly valid, and the invalidity can cause systematic errors
if interpreting the measured fluxes as surface exchange rates
(SERs). This systematic error can be quite substantial for
compounds with high Damköhler number (Da), i.e., the ratio
of time scale of the turbulent mixing to that of the chemical
reactions, such as certain SQTs (Rinne et al., 2012). The ra-
tio of flux to the surface exchange (R = F/E) depends on
the chemical lifetime (τc) of the compound in question and
its turbulent mixing time scale (τt). The chemical lifetime of
BVOCs typically depends on O3, OH, and NO3 mixing ra-
tios and reactivities of the VOC in question against these re-
actants. The turbulent mixing time scale depends on friction
velocity (u∗) and measurement height (z). Thus, we can esti-
mate the SER of a compound if we can estimate the reactant
levels and friction velocity by

E =
F

R (Da (τt/τc) ,z/h)
, (6)

and using the look-up tables by Rinne et al. (2012) to esti-
mate the R. Furthermore, R varies considerably depending
on the emission height, i.e., between canopy and soil surface
emission. For example, for isoprene,Da ranged from 0.0003
to 0.014. For MT (based on the reactivities of α-pinene), Da

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17212 R. C. Petersen et al.: BVOC and speciated monoterpene concentrations

ranged from 0.005 to 0.037, and for SQT (based on the reac-
tivities β−caryophyllene) between 0.3 to 0.9.

The effect of chemical degradation on isoprene, MT, and
SQT SERs was explored using the modeling work of Rinne
et al. (2012), which made use of a stochastic Lagrangian
transport chemistry model, and by parameterizing the diurnal
cycle of the reaction rates for ozone, OH, and NO3, to esti-
mate the SERs for the 2020 Norunda campaign’s terpenoid
EC flux dataset. The OH, O3, and NO3 reaction rate constants
of isoprene, MT, and SQT for the chemical degradation anal-
ysis are from those reported by Atkinson (1997) and Shu and
Atkinson (1995). In Sect. 3.3.1, the reaction rate coefficients
of α-pinene and β-caryophyllene were implemented to as-
sess SERs from the measured fluxes of total MT and total
SQT, respectively, obtained using Vocus PTR-ToF-MS. Both
compounds are common and frequently dominant examples
of their terpenoid classes in the emissions of Norunda and
similar boreal forests (e.g., Hakola et al., 2006; Hellén et
al., 2018; Rinne et al., 2009; Rinne et al., 2012; Wang et
al., 2018).” It is important to note, however, that the domi-
nance of a particular compound in total emissions, such as
β-caryophyllene among SQTs, might not always be the case,
particularly for non-constitutive (stressed) emissions, such as
from insect herbivory (e.g., Wang et al., 2017). Care must be
taken when inferring total SERs that underlying assumptions
regarding the relative mixture of emitted compounds are cor-
rect. A full description of the SER calculations, as well as
the influence of relative speciation on total exchange rate es-
timate uncertainties, can be found in the Appendix.

2.9 Emission algorithm fitting

To estimate the mixed contribution of de novo biosynthesis
and storage pool emission to terpene ecosystem-scale emis-
sions, a hybrid emission algorithm (Taipale et al., 2011; Ghi-
rardo et al., 2010) was fitted to emission measurements. This
hybrid algorithm was developed under the hypothesis that the
two origins of emission can combined as E = Esynth+Epool,
where Esynth represents emission originating directly from
biosynthesis and Epool represents emission from specialized
storage structures, such as resin ducts . This hybrid algorithm
formulation takes the form

E = Eo
[
fdenovoCTCL+ (1− fdenovo)γ

]
, (7)

where Eo = Eo,denovo+Eo,pool is the total emission poten-
tial, fdenovo = Eo,denovo/Eo is the ratio of the de novo emis-
sion potential to the total emission potential, CT and CL are
the synthesis activity factors for temperature and light, re-
spectively (Guenther 1997), and γ is the temperature activity
factor (exp[β (T − 30°C)]) for the traditional pool emission
algorithm, where β is an empirical constant (°C−1) and T is
the canopy temperature (°C).

To determineEo, β, and fdenovo, the fitting of Eq. (7) to the
campaign data was performed using nonlinear regression. As
isoprene is widely understood to have no storage source for

emission (Guenther et al., 1993, 1995), fitting for isoprene
emission was investigated by setting fdenovo to unity (i.e.,
pure de novo synthesis emission). Meanwhile, pool emission
for MT and SQT was investigated by setting fdenovo to 0. In
the case of investigating the fraction of MT and SQT emis-
sions deriving as a mix of de novo synthesis and pool emis-
sion, fdenovo was allowed to vary as a fitting parameter.

3 Results

3.1 Meteorological and other conditions during
campaign

Temperatures during the campaign varied between 13 and
22 °C. The typical prevailing wind direction was between
south and northwest. Light precipitation occurred on man-
ual sampling day 9 June. The meteorological conditions
during the campaign EC measurements are displayed in
Fig. 2. Typical peak daytime photosynthetic photon flux den-
sity (PPFD) varied from 700 to 1500 µmol m−2 s−1. Con-
ditions during days when TD sampling was performed
(8–10 June, 22–24 July, and 16–18 August) had consis-
tent temperature (mean 17.4 ± 3.7 °C) and PPFD (mean
901± 319 µmol m−2 s−1) conditions. Ozone monitoring was
available throughout the campaign from the nearby Norunda-
Stenen station, via a Model E400 Teledyne ozone analyzer,
located 1.4 km east of the Norunda tower. One interruption to
Vocus sampling occurred on 12–13 August due to an electri-
cal failure in the instrument shelter. A summary of the cam-
paign time series is presented in Fig. 2, in which the cam-
paign observations of the station water vapor, vapor pressure
deficit (VPD), and dew point temperature are also included.

3.2 Gradient sampling conditions during campaign

3.2.1 Sampling footprint comparison

To accurately interpret BVOC fluxes derived from con-
centration gradient measurements, it is important to as-
sess the gradient-flux method footprint for the two TD
sampling levels (37 and 60 m). A flux footprint analysis
at their geometric-mean height (47.1 m), as suggested by
Horst (1999) for two-height gradient-profile flux estimates,
was conducted for each daily period corresponding to TD
tube BVOC gradient sampling on the Norunda flux tower.
Each footprint was calculated using the flux footprint model
developed by Kljun et al. (2015). The Flux Footprint Predic-
tion (FFP) model is a two-dimensional parameterization for
the flux footprint based on a scaling approach to its crosswind
distribution (e.g., Kljun et al., 2004, 2015). It was found that
the footprints, particularly for ca. 85th percentile and below
footprint contours (depicted in Fig. 3), in general compared
well with each other in terms of the forest area and compo-
sition covered. Since the geometric-mean height for the SLG
estimates is above the Vocus inlet height (35 m), the total

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R. C. Petersen et al.: BVOC and speciated monoterpene concentrations 17213

Figure 2. The 30 min BVOC concentrations (ppbv) and fluxes (nmol m−2 s−1) sampled at the 35 m BVOC inlet at ICOS Norunda, as well
as related meteorological measurements. Shaded areas depict manual TD BVOC sampling periods at 37 and 60 m on the Norunda flux tower.
The pie charts indicate the relative speciation of MT compound (top row) concentrations at 37 m and (bottom row) fluxes (via SLG flux
method) as determined from the TD BVOC samples (percentages shown are > 5%). (a) MT, isoprene, and SQT concentrations (ppbv) and
(b) EC-derived fluxes (nmol m−2 s−1) from the Vocus PTR-ToF-MS. The equivalent mass-flux (ng m−2 s−1) for isoprene, MT, and SQT is
depicted along the right-hand y-axis. (c) Ozone concentration (ppbv) and water vapor (mmol mol−1). (d) PPFD (µmol m−2 s−1; at height
55 m) and air temperature (°C; at 37 m). (e) wind speed (m s−1), wind direction (°), and precipitation (mm). Set of displayed measurements
span from 21 July to 27 August 2020.

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17214 R. C. Petersen et al.: BVOC and speciated monoterpene concentrations

Figure 3. Daytime (from 09:00 to 17:00 CEST) average footprint estimates for SLG-derived fluxes using the two TD BVOC sampling
heights (37 and 60 m) on the Norunda flux tower. Footprint contour lines (green) are shown in 10 % increments from 10 % to 90 %. Displayed
footprints assessed at geometric-mean height (47.1 m) of TD sampling levels, following Horst (1999) for footprint estimation of SLG-method
surface fluxes under unstable atmospheric stratification above-canopy (see Fig. 5f). The panels show these footprints for (a–c) 8, 9 and
10 June, (d–f) 22, 23, and 24 July and (g–f) 16, 17, and 18 August respectively.

extent of the estimated SLG footprints tended to be slightly
larger than the EC flux footprint.

During the campaign TD measurements approximately
90 % of the flux measured by the Vocus tower inlet at the
35 m level originated within 350 m of the tower itself. For
comparison, at 47.1 m, approximately 90 % of the observa-
tions originated from within 420 m of the tower.

3.2.2 Vertical micrometeorological conditions

An important prerequisite for the gradient-flux method is that
vertical fluxes remain constant within the observation layer.
To validate this constant-flux assumption, we compared the

friction velocities, heat fluxes, and roughness lengths mea-
sured at both the 37 and 60 m measurement levels.

Measured sensible heat and momentum fluxes were sim-
ilar at both measurement heights (see Fig. 4), with the
mean linear fit of friction velocity and sensible heat flux be-
tween the 37 and 60 m heights being u∗, 37 m = 0.93u∗, 37 m+

0.056ms−1 and Hf,37 m = 1.04Hf,60 m+ 6.9Wm−2, respec-
tively. Table C1 (see Appendix C) lists a summary of these
friction velocity and sensible heat flux ratios under different
stability conditions and wind directions. Atmospheric insta-
bility above canopy (i.e., Obukhov length L−1 <−1000; see
Fig. 5f) prevailed during the times of daytime TD sampling.
Stable atmospheric conditions were generally observed at
night, while near-neutral conditions were typically observed

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R. C. Petersen et al.: BVOC and speciated monoterpene concentrations 17215

Figure 4. Comparison between friction velocity u∗ (m s−1) and sensible heat flux Hf (W m−2) measured at two heights above the forest
canopy. (a, c) A best-fit comparison between 37 and 60 m for (a) friction velocity and (c) sensible heat flux during the 3 d sampling periods in
June, July and August. The dashed line indicates a 1 : 1 relation, while the solid red line indicates the best fit linear regression. (b, d) Diurnal
mean contour profiles for the 2020 Norunda campaign of (b) friction velocity u∗ and (d) Hf with respect to the normalized height z/h
(canopy height h= 28 m) in and above the Norunda forest. The relative heights of the 37 and 60 m sampling levels on the normalized height
scale are indicated on the y-axis. In (b) and (d) panels, mean daily sunrise (solid vertical line) and sunset (dotted vertical line) are indicated.
Shaded region indicates the range of sunrise and sunset times during the 9 June to 22 August campaign period.

during the transition in stability following sunrise and pre-
ceding sunset. It was found that, while near-neutral and stable
conditions could lead to relatively large deviations in these
ratios, for unstable atmospheric conditions the ratios were
close to unity.

3.3 Concentrations and fluxes of terpenes and other
VOCs

During the campaign, the mean daytime isoprene concentra-
tion measured by the Vocus PTR-ToF-MS was 250 pptv. As
shown in Fig. 2, the maximum concentration values occurred
during daylight hours, at a time when temperature and PPFD
were high (> 20 °C and > 1000 µmol m−2 s−1) as well, with
concentrations falling to daily lows towards the evening. Lit-
tle to no isoprene flux was observed at night. Peak total
MT concentrations (1–1.4 ppbv) were typically significantly
higher than isoprene concentrations. Unlike isoprene, peak
total MT concentration typically occurred at night during sta-

ble atmospheric conditions in the canopy similarly to the ob-
servations by Petersen et al. (2023).

Such diurnal cycles in concentration were regularly ob-
served throughout the campaign. Figure 5 shows the diurnal
variation in isoprene, total MT, and total SQT from the Vo-
cus PTR-ToF-MS measurements collected (21 July to 27 Au-
gust) during the 2020 Norunda field campaign.

Isoprene had its highest concentrations above forest
canopy during the day, with peaks typically in the morn-
ing between 07:00–11:00 CEST and more strongly between
16:00 CEST and sunset, whereas MT and SQT concentra-
tions typically peaked at night. Isoprene and terpene fluxes,
meanwhile, generally all peaked around noon. This diurnal
concentration behavior by isoprene and total SQT, as was
observed for total MT, was due to interplay of emission dy-
namics and the changes in atmospheric stability in the sur-
face boundary layer. For isoprene, this was due to the light-
dependent nature of emissions (i.e., de novo synthesis emis-
sions (e.g., Guenther et al., 1995, 1993)), with emissions ef-

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17216 R. C. Petersen et al.: BVOC and speciated monoterpene concentrations

Figure 5. Diurnal range of fluxes and concentrations for (a, b) isoprene, (d, e) total MT, and (g, h) total SQT, as measured from the tower
BVOC inlet using Vocus PTR-ToF-MS during the Norunda 2020 BVOC field campaign. Diurnal fluxes shown represent 30 min EC ensemble
averages and diurnal concentrations represent 1 min averaged time series data. Shaded regions in panels indicate the 5th-to-95th and the 25th-
to-75th quantile range, as well as the mean (solid line). (c) Net radiation (red) and canopy temperature (blue). Shaded regions for net radiation
and temperature indicate one standard deviation. (f) Plot of the inverse of the Obukhov length (L−1), indicating atmospheric stability above
the canopy (measured at 36 m). (i) local diurnal ozone concentration (ppbv). In all the panels, mean sunrise and sunset (solid and dotted
vertical lines, respectively) for the period of Vocus deployment (21 July 21 to 27 August) are indicated. Vertical bars (gray) indicate the
range of sunrise and sunset times during this period.

fectively shut down with the cessation of photosynthesis ac-
tivity at night. On the other hand, the MT and SQT emissions
of evergreen boreal tree species like Scots pine and Norway
Spruce still have considerable nighttime emission from resin
ducts and other tissue structures that are temperature-only-
dependent (i.e., storage emission, rather than wholly de novo
emission (e.g., Ghirardo et al., 2010; Guenther et al., 1995,
1993; Tingey et al., 1980)). It should also be noted that many
SQTs have a short (∼ 0.1–10 s) chemical lifetime relative to
MT, and that this is reflected in the observed concentrations
measured by the Vocus, as a substantial fraction is expected
to react with atmospheric radicals or other compounds before
reaching the measurement height (e.g., Atkinson and Arey,
2003; Rinne et al., 2012). This diurnal behavior highlights
the interplay between constitutive (non-stress induced) emis-
sions, driven by environmental conditions (principally, pho-
tosynthetic light and temperature), and the atmospheric sta-
bility within the forest canopy. Despite lower emission rates
of terpenes at night, as observed from their EC fluxes (see
Fig. 5d and g), the stable nighttime atmosphere, as indicated
by the Obukhov length (see Fig. 5f), causes a buildup in their

concentrations at night. It was observed that terpene concen-
trations are typically greatest at or just following sunrise.
Similar diurnal behavior was observed in other VOC com-
pounds measured by the Vocus, such as acetone, acetalde-
hyde, and toluene, among others, and is also consistent with
previous BVOC PTR-MS field campaigns at ICOS Norunda
(Petersen et al., 2023). This diurnal terpene behavior is also
consistent with observations at other boreal sites (e.g., Bors-
dorf et al., 2023; Hakola et al., 2012; Hellén et al., 2018).

3.3.1 Surface exchange rates

To quantify the actual SER of reactive VOCs, the effect
of chemical degradation on the SER of isoprene, MT, and
SQT was estimated. For ecosystem–atmosphere exchange,
whereas above-canopy fluxes quantify the cumulative effect
of within-canopy processes, including chemical degradation,
the SER characterizes the net emission and deposition oc-
curring at the ecosystem’s soil and vegetation surfaces in the
absence of this chemical sink. The effect of chemical degra-
dation on the ratio of measured flux to SER, R = F/E, is
shown in Fig. 6. Following the modeling work of Rinne et

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R. C. Petersen et al.: BVOC and speciated monoterpene concentrations 17217

Figure 6. Boxplot of mean diurnal F/E ratios for chemical degradation estimates of isoprene (cyan), MT (purple), and SQT (orange) for
the 2020 Norunda BVOC campaign. Black dash of whisker plot indicates the median value.

al. (2012) for a similar boreal forest, the F/E ratios for MT
are based on the reaction rate constants of α-pinene and for
SQT on the reaction rate constants of β-caryophyllene. The
effect of the chemical degradation rate of isoprene and MT,
relative to the observed fluxes, was found to be minimal.
The influence of chemical degradation on isoprene (F/E =
0.981± 0.013 at night and 0.957± 0.013 during day) was
within the EC flux measurement uncertainty. The main influ-
ence of chemical degradation for total MT was found to be
at night and, in terms of absolute emission increases, during
peak flux periods around noon. For total MT, a daytime av-
erage of F/E = 0.957± 0.013 due to chemical degradation
was calculated for daytime (within the uncertainty of EC flux
measurements), while the nighttime loss was estimated at an
average F/E = 0.90± 0.03.

In contrast, the measured SQT flux was significantly af-
fected by chemical degradation (see Fig. 7). For the full cam-
paign, the estimated SQT nighttime F/E ratio is typically
ca. 0.35 (varying from 0.31 to 0.41), while the daytime ra-
tio is ca. 0.41 (varying from 0.37 to 0.47). The mean diur-
nal measured fluxes and inferred SERs of isoprene, MT, and
SQT, for their respective OH, O3, and NO3 reaction rate con-
stants (Table C3 in Appendix C), are shown in Fig. 7. As a
consequence, for the diurnal average over the full campaign,
peak SQT nighttime emission rates are typically ca. 240 %
to 310 % (mean ca. 290 %) times greater, and SQT day-
time emissions ca. 240 % to 290 % (mean ca. 260 %) times
greater than would otherwise be inferred solely from EC flux
measurements if the effect of chemical degradation on SQT

exchange and subsequent SQT flux observations were ne-
glected.

The effect of chemical degradation on the F/E ratio be-
tween the TD sampling heights was also investigated. This
was done to gauge an underpinning assumption of the SLG
method: that there is no significant chemical sink between the
sampling heights used to determine the above-canopy gradi-
ent. This was performed by evaluating, instead of R = F/E,
the value of R2/R1 = F2/F1, where R1 and R2 are the flux-
to-emission ratios for the sampling heights z1 = 37 m and
z2 = 60 m, respectively. This provides a measure for the
percent of flux lost between the lower and upper heights
for the gradient measurement. For MT, there was typically
only 1.7 (± 0.5) % flux loss between sampling heights, with
R2,MT/R1,MT = 98.3 (varying from 97.8 to 98.8). This is
less than the F/E ratio previously found for the total MT
EC flux measurements. In contrast, for SQT, there was typi-
cally between 33 % and 43 % loss in SQT flux between these
heights, with R2,SQT/R1,SQT = 61.8 (varying from 57.1 to
66.6). As expected, this demonstrates significant SQT chem-
ical loss between the two intervening heights, and that the
SLG method would be inapplicable to SQT fluxes without
significant modification to account for chemical degradation.

3.3.2 Comparing measured terpenoid emissions with
emission algorithms

The presented emission algorithm regression results (specif-
ically, for the terpenes) are presented in Fig. 8. As the esti-
mated isoprene SERs were within the range of uncertainty of

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17218 R. C. Petersen et al.: BVOC and speciated monoterpene concentrations

Figure 7. Mean diurnal SERs for isoprene, MT, and SQT during the 2020 Norunda campaign. Shaded region indicates the range of uncer-
tainty for the modeled ozone, OH, and NO3 reaction rate coefficients. Dashed black line indicates the corresponding measured flux.

the measured fluxes, only the measured isoprene fluxes were
used for the regression analysis. For isoprene, we used the
fixed β = 0.09 °C−1 of Guenther et al. (1993), and the emis-
sion algorithm was set to pure de novo synthesis emission by
setting fdenovo to unity. The fitted isoprene Eo was found to
be 85.0 (± 1.5) ng m−2 s−1.

For MT emissions based on the temperature-dependent
pool emission algorithm (Guenther et al., 1993, 2012),
by setting fdenovo to 0, the fitted MT standardized emis-
sion Eo was found to be 386 (± 5) ng m−2 s−1 for β =
0.1 °C−1 (Guenther et al., 2012). Allowing β to vary as a
regression coefficient for the pool algorithm as well yields
370 (± 9) ng m−2 s−1 and β = 0.094 (± 0.003) °C−1. Apply-
ing the full hybrid MT emission algorithm, combining de
novo synthesis and pool storage emission (e.g., Taipale
et al., 2011), with the previously fitted β = 0.094 °C−1,
yields Eo = 374 (± 7) ng m−2 s−1, and the fraction of MT
emissions originating from de novo synthesis as fdenovo =

26 (± 4) %. When using the MT SERs instead of the ob-
served EC fluxes at 37 m, fitting the hybrid algorithm then
yields Eo = 393 (± 8) ng m−2 s−1 and fdenovo = 24 (± 4) %.

As the SQT emission rate is significantly under-
represented by the measured flux due to chemical degra-
dation, we fitted the hybrid emission algorithm to the esti-
mated SQT SERs for the campaign. For fitting from SQT
SERs, based on the temperature-dependent pool emission al-
gorithm of Guenther et al. (1993, 2012), the fitted standard-
ized SQT emission Eo was found to be 171 (± 3) ng m−2 s−1

for β = 0.17 °C−1 (Guenther et al., 2012). Allowing β to
vary as a regression coefficient for the pool algorithm as well
yields 160 (± 5) ng m−2 s−1 and β = 0.156 (± 0.004) °C−1.
Applying the hybrid emission algorithm, combining de
novo synthesis and pool storage emission (e.g., Taipale

et al., 2011), with the previously fitted β = 0.156 °C−1,
yields Eo = 156 (± 3) ng m−2 s−1, and the fraction of SQT
emissions originating from de novo synthesis as fdenovo =

45 (± 8) %. Fitting the hybrid algorithm using the estimated
SERs for SQT yielded better fits than simply applying the
measured SQT flux data without the corresponding correc-
tion for chemical degradation. From the SERs, the hybrid
algorithm estimates for fdenovo for both MT and SQT were
found to be quite similar (38 (± 8) % and 31 (± 7) %, respec-
tively).

3.3.3 Other VOCs

Acetaldehyde (m/z+= 45.034) exhibited a mean daily con-
centration of 0.7 ppbv. Concentrations of toluene (m/z+=
93.07) were generally low during daytime (∼ 12 pptv) and
increased during nighttime (∼ 30 pptv), This behavior by
toluene is consistent with the buildup of anthropogenic
background emissions during night in the shallow noc-
turnal boundary layer (Karl et al., 2004). Similar behav-
ior was found for the mass peak atm/z+= 95.049 (i.e.,
phenol), which had a concentration minimum during day-
time (∼ 9 pptv) and maximum during nighttime (∼ 40 pptv).
Acetic acid (m/z+= 61.028) was typically lowest after
sunrise (∼ 10 pptv), gradually increasing throughout the
day and peaking before sunset (∼ 33 pptv), then declin-
ing overnight. The exception to this trend occurred when
high nighttime canopy concentrations coincided with sim-
ilar peaks in acetone and acetaldehyde. The diurnal signal
form/z+= 41.039 andm/z+= 103.112, representing the
PTR-protonated hexanol fragment and the hexanol parent
ion, respectively, followed a similar pattern to acetone. The
minimum in hexanol concentration (∼ 50 pptv) typically oc-
curred in the morning following sunrise and peaked after

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R. C. Petersen et al.: BVOC and speciated monoterpene concentrations 17219

Figure 8. Hybrid emission algorithm comparison to terpene measurements. (a, b) Comparison of measured vs. modeled emission (using the
fitted coefficients) for (a) MT and (b) SQT SERs. The red line indicates the linear regression best-fit line for the data points, and dashed gray
line indicates the one-to-one line. (c, d) Plots of the canopy temperature vs. (c) MT emission and (d) SQT emission. Blue line shows the
best-fit regression line.

sunset (∼ 130 pptv). Methyl vinyl ketone and methacrolein
(MVK+MACR,m/z+= 71.049), two important intermedi-
ate products from the photochemical oxidation of isoprene,
averaged 7 pptv daily.

3.3.4 Speciated MT concentrations and fluxes

During the campaign, α-pinene, 13-carene, β-pinene, cam-
phene, myrcene, and limonene were detected in the ATD-
GC–MS analysis. The most abundant MT species through-
out the campaign was α-pinene, followed by 13-carene (see
Figs. 2 and 9). Limonene concentrations contributed approxi-
mately 5 %–10 % of total MT concentrations. An overview of
the monthly sampling-period mean concentrations and SLG
method-derived fluxes of the speciated MT compounds, ob-
served via thermal desorption sampling at 35 m above the
forest canopy, during the 3 d monthly sampling periods in
June, July, and August is shown in Fig. 9. A daily mean

evaluation can be found in the Appendix. During all sam-
pling periods, α-pinene was the most prevalent MT com-
pound present, fairly constant and typically representing be-
tween ca. 28 % to 34 % of the total MT concentration. The
second most prevalent MT compound was 13-carene. From
June to August, fraction of 13-carene among the MT com-
pounds decreases by ca. 8 %, from monthly sampling-period
averages of ca. 30 % in June, to ca. 24 % in July and ca.
22 % in August. During the June measurements, the typi-
cal concentration abundance at 37 m above the forest canopy
among the MT species were 32 (± 4) % α-pinene, 30 (± 4) %
13-carene, 7.6 (± 0.8) % myrcene, 5 (± 0.6) % limonene,
and 10 (± 1.1) % β-pinene, 7.2 (± 0.6) % camphene, and
5.6 (± 0.5) % cymene. In July, the typical abundances
were 31 (± 3) % α-pinene, 24 (± 6) %13-carene, 12 (± 2) %
myrcene, 7(± 2) % limonene, and 10.8 (± 0.9) % β-pinene,
4.5 (± 0.7) % camphene, and 4.5 (± 0.7) % cymene. In Au-
gust, the typical abundances were 32 (± 3) % α-pinene,

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17220 R. C. Petersen et al.: BVOC and speciated monoterpene concentrations

Figure 9. Monthly sampling-period mean concentrations of MT compound species at 37 (red) and 60 m (blue) on the station flux tower at
the ICOS Norunda boreal forest, as well as sampling period mean speciated MT fluxes (green), during 8–10 June (a), 22–24 July (b) and
16–18 August (c). Vertical error bars indicate the standard mean error. The daily mean concentrations and fluxes are shown in Fig. C1 in
Appendix C.

22 (± 2) % 13-carene, 5.9(± 0.4) % myrcene, 6.9 (± 0.6) %
limonene, 13 (± 2) % β-pinene, 10.3 (± 0.8) % camphene,
and 7 (± 0.5) % cymene.

3.3.5 Vocus and ATD-GC-MS concentration comparison

We compared the sum of speciated MTs measured by GC–
MS with the total MT concentration measured by Vocus
PTR-ToF-MS. The comparison is displayed in Fig. 10. Based
on comparison with 2022 precut TD measurements, it is ex-
pected that the sum of MT compounds identified by TD
sample analysis represent 70 %–80 % of total atmospheric
MT concentration. During 22–24 July the ATD-GC–MS
obtained a concentration of 610 (± 30) ng m−3 while the
Vocus measured 851 (± 27) ng m−3 for MTs. During 16–
18 August, the ATD-GC–MS obtained a daytime concen-
tration of 1201 (± 73) ng m−3 while the Vocus measured
1790 (± 73) ng m−3 for MTs.

3.4 Vocus EC flux results

During daytime sampling, the values of the Vocus MT flux
typically ranged from 100 to 150 ng m−2 s−1 (see Figs. 2
and 5). Nighttime flux measurements should be treated with
care, as stable atmospheric conditions during the evening
lead to large uncertainties (e.g., Aubinet et al., 2012b). A
combined estimate of the mean flux from the three-day sam-
pling periods in June, July (start of Vocus campaign), and
August (end of combined campaign) was performed for the
SLG fluxes. The mean flux for each of these three-day sam-

pling periods was calculated to reduce the large uncertainty
in the SLG flux estimates, by reducing the influence of ran-
dom errors in the calculation. For the period of 8–10 June
gradient samples, the estimated mean summed MT flux was
108 (± 14) ng m−2 s−1. For the period of 22–24 July TD gra-
dient samples, the estimated mean of the summed MT flux
was 70 (± 20) ng m−2 s−1. For the period of 16–18 August
TD gradient samples, the estimated mean summed MT flux
was 90 (± 20) ng m−2 s−1. For comparison, during the 2020
campaign period when the Vocus was also operating, for
the daytime sampling periods when TD sampling was per-
formed the mean total MT flux from Vocus EC measure-
ments for 22–24 July was 105 (± 3) ng m−2 s−1 and during
16–18 August was 155 (± 7) ng m−2 s−1. For these same pe-
riods, the estimated isoprene flux was 20 (± 9) ng m−2 s−1

and 23 (± 10) ng m−2 s−1, respectively.
To determine whether there were any significant changes

in the relative mixture of speciated MT compounds emit-
ted over the course of the 2020 campaign, a two-way anal-
ysis of variance statistical analysis was performed using the
gradient-derived speciated MT fluxes. First, to eliminate the
temperature-dependence of the MT emissions, the speciated
fluxes were used to calculate temperature-normalized (to °C)
emissions E0 using the equation E = E0exp[β (T − 30°C)]
(Guenther et al., 1993), where E is the total MT emissions,
β is an empirically fitted coefficient (found for the Norunda
2020 campaign to be β = 0.094), and T is the temperature in
°C. Next, for the two-way analysis of variance analysis, the
flux data was sorted according to the monthly period (i.e.,

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R. C. Petersen et al.: BVOC and speciated monoterpene concentrations 17221

Figure 10. Comparison of MT concentration measurements from the Vocus PTR-ToF-MS (red) and the sum of speciated MT concentrations
(black) from the TD sample GC-MS analysis. Summed TD MT displayed were measured at the 37 m TD sampling height. Error bars indicate
(red) standard deviation of 30 min Vocus MT concentration and (black) uncertainty of summed TD MT concentrations based on the error
analysis for TD sampling and analysis presented in Sect. 2.7. Shown are half-hourly time series of the MT concentration measurements from
both Vocus and manual TD sampling for the (a–c) 22–24 July 2020 and (d–f) 16–18 August 2020 TD sample periods.

June, July, or August), and day of each sampling period (i.e.,
day 1, 2, or 3) when each gradient sample was collected. This
allowed for the comparison of MT fluxes between sampling
days and between sampling periods. The p-values from the
analysis indicated that there was no significant (p < 0.05)
trend in the measured flux between the monthly sampling
periods for the speciated MT compounds observed, with the
exception of a weak negative trend in the emission of 13-
carene when comparing the three monthly sampling periods,
in particular, between June and August (p < 0.0309). This
indicates that at an ecosystem-scale level of emissions from
the Norunda forest canopy, following from the gradient sam-
ples, at least during the summer months of June to August,
no statistically significant variation was noted in the relative
mixture of MT compounds emitted with respect to the time
of season.

4 Discussion

We conducted a measurement campaign in a boreal forest
in order to estimate speciated MT concentrations and fluxes
during Scandinavian summer. The flux of MT compounds at
Norunda was observed to primarily consist of α-pinene, fol-
lowed by13-carene (at approximately 60 %–70 % the rate of
α-pinene). This is similar to the preponderance of α-pinene,
followed by 13-carene, observed by Hakola et al. (2012)
above the boreal forest at the Hyytiälä research station in Fin-
land, a predominantly Scots pine forest with nearby Norway
Spruce, as well. Speciated MT emissions showed no signifi-
cant differences during summer-seasonal cycle. Only a slight
weakening of the temperature-normalized emission rate for

13-carene was observed over the course of the 2020 mea-
surements (June–August). It should be noted that observa-
tions did not extend to the spring and autumn, hence further
attention to the seasonal behavior of individual MT species
in BVOC emission and climate models may be warranted in
future investigations.

These gradient-method flux measurements allow the spe-
ciated flux to be evaluated at an ecosystem-level scale, which
sidesteps a potential issue for understanding ecosystem–
atmosphere MT exchange, the large variability in speciated
MT emissions that can exist among pine or spruce groups.
Such variability, as observed in chamber measurement stud-
ies, can occur even among members of the same tree species
and population (Bäck et al., 2012; Hakola et al., 2017).

The observed MT emissions were significantly
temperature-dependent (fitting well to the storage-based
pool emission algorithm of Guenther et al., 1993) and most
of the forest MT emission originates from plant storage
structures rather than from de novo synthesis as is the case
for certain boreal tree species (e.g., Ghirardo et al., 2010).
This is consistent with the forest tree species composition
and the fact that MT mixing ratios were higher at night,
with the lower boundary-layer height and stable atmosphere,
despite that nighttime MT emission rates are lower (e.g.,
Rinne et al., 2009).

The influence of chemical degradation on the surface ex-
change rates of isoprene, MT, and SQT was also investi-
gated. While the effect of chemical degradation on isoprene
exchange rates was negligible (< 5%; less that EC flux un-
certainties), a significant influence on the nighttime MT ex-
change rate was observed, with the exchange rate being on

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17222 R. C. Petersen et al.: BVOC and speciated monoterpene concentrations

average ca. 10.8 % (varying from 6.8 % to 14.1 %) greater
than measured MT flux. This was far more evident with the
overall SQT exchange rates, which diurnally were on aver-
age ca. 160 % (varying 130 % to 180 %) greater during day
and ca. 190 % (varying from 140 % to 230 %) greater during
night than the measured SQT flux.

The effect of MT and SQT chemical speciation on inferred
SER rates should also be noted. Since the various individual
MT and SQT compounds react at different rates with OH,
O3, and NO3, these differences in turn affect the total MT and
SQT fluxes that are ultimately observed above canopy using
Vocus PTR-ToF-MS. Conversely, the estimation of SER rates
from total MT and total SQT flux observations is therefore
dependent on the relative mixture of emitted compounds con-
sidered. For example, for 13-carene (the second most com-
monly observed MT compound during the 2020 Norunda
campaign, following α-pinene) the reaction rates for the
radicals OH, O3, and NO3 are 8.8× 10−11, 3.7× 10−17,
and 9.1× 10−12 cm3 molec.−1 s−1, respectively (Atkinson,
1997). When these values are implemented in our quantifi-
cation of chemical degradation effects on the MT SER rates,
in lieu of the reaction rate constants for α-pinene, it yields
nighttime SER values that are ca. 12.6 % (varying 7.7 % to
16.9 %) greater than measured flux. This is an increase from
the nighttime SER estimate based on the reaction rates of
α-pinene of ca. 10.8 % (varying 6.8 % to 14.1 %) for this
SER-to-flux comparison. Also present are MT compounds
that are far more reactive than either α-pinene or 13-carene.
For example, relative to individual MT compounds, it was
observed that the combined concentration of b-myrcene and
d-limonene was exceeded only by α-pinene and 13-carene.
The OH, O3, and NO3 rate constants for β-myrcene are
2.2×10−10, 4.7×10−16, and 1.1×10−11 cm3 molec.−1 s−1,
respectively, while for d-limonene they are 1.7× 10−10,
2× 10−16, and 1.22× 10−11 cm3 molec.−1 s−1, respectively
(Atkinson, 1997). When we substitute these reaction rate
constants into our SER evaluation procedure, for our SER-
to-flux comparison, we find for b-myrcene during nighttime
ca. 24.6 % (varying from 16.7 % to 33.3 %) and during day-
time ca. 17.5 % (varying from 13.1 % to 21.8 %). Mean-
while, for d-limonene, we find during nighttime ca. 19.3 %
(varying from 12.7 % to 26.4 %) and during daytime ca.
10.8 % (varying from 7.9 % to 13.4 %). These values are
ca. 2.4 and 1.8 times greater during nighttime, and ca. 3.8
and 2.3 times greater during daytime, for b-myrcene and
d-limonene, respectively, than the corresponding increases
found for α-pinene. For the average concentration apportion-
ment, over the course of the 2020 Norunda campaign, of the
identified MT compounds α-pinene, 13-carene, b-myrcene,
d-limonene, β-pinene, and camphene, the mean values of
their reactivities for OH, O3, and NO3 are 8.9× 10−11,
9.79× 10−17, and 6.83× 10−12 cm3 molec.−1 s−1, respec-
tively. When using these reaction rate constants instead of
those of α-pinene for the MT SER evaluation, this yields a di-
urnal SER estimate for total MT that is very close (∼11 %)

to the estimate formed using the OH, O3, and NO3 reactiv-
ities of α-pinene, with an nighttime increase in SER from
the measured MT fluxes of 12 % (varying from 7.7 % to
15.7 %), and a daytime increase of 6.1 % (varying from 4.3 %
to 7.8 %). This indicates that α-pinene likely acts as a good
proxy for the mixture of MT emissions from this and similar
boreal forests when attempting to infer the effect of chemical
degradation on observed fluxes as collected using total MT
measurement instruments such as PTR-ToF-MS.

For the speciated MT fluxes it has been observed that, de-
spite the relatively large uncertainty involved with the spe-
ciated MT fluxes (compared to total MT EC flux measure-
ments), the fraction of MT flux from the most common MT
compound, α-pinene, is somewhat higher than the fraction
of their concentration relative to the other MT compounds
(see Figs. 2 and 10). This condition is also found in other
studies (e.g., Rinne et al., 2000a). A potential explanation
of this observation is the influence of chemical degrada-
tion, particularly with respect to more reactive compounds
such as myrcene and d-limonene, during turbulent transport
within and above canopy. Another potential influence on spe-
ciated MT flux vs. concentration observations is from trans-
port from outside the flux tower footprint. The observed con-
centrations used for gradient measurements are the result
of emissions, sinks, chemical transformation, and transport,
whereas the observed fluxes reflect the emissions and sinks
solely in the flux footprint of the tower.

5 Implications

A significant implication of this work is the role that seasonal
changes can have on the speciation of MT compounds and
subsequently their impact on air chemistry. BVOCs like MT
influence the tropospheric ozone budget (Archibald et al.,
2020). MTs also contribute to the formation and/or growth
of atmospheric SOA due to the gas/particle partitioning of
their reaction products in the troposphere. The structure of
MTs has a significant role in their reactivity. For example, en-
docyclic MTs (e.g., limonene, α-pinene, and 3-carene) have
a greater aerosol formation potential and tend to react faster
than compounds with exocyclic double bonds (e.g., β-pinene
and camphene). In addition, b-myrcene, an acyclic MT with
three double-bonds, has a significant effect on the overall MT
reactivity of MTs investigated by TD sampling, despite being
at lower concentrations than many of the observed MT com-
pounds. For example, in July, while less than 14 % of total
MT concentration, b-myrcene made up more than half (52 %)
of overall observed MT ozone reactivity. Fast-reacting MTs
can represent a significant fraction of total reactivity even
at low concentrations (e.g., Yee et al., 2018). Subsequently,
even small seasonal changes in their concentration can rep-
resent large changes in the reactivity of the overall MT pop-
ulation emitted.

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R. C. Petersen et al.: BVOC and speciated monoterpene concentrations 17223

From July to August, the average of the OH, ozone, and
NO3 reaction rate coefficients for the observed MT mixture
decreased. This was mainly driven by month-to-month trends
in the relative abundance of 3-carene and b-myrcene. While
overall reactivity of MT towards OH, ozone, and NO3 in-
creased from July to August, due to an increase in overall
MT concentration, that increase is less than would be ex-
pected if changes in speciation were neglected, particularly
for ozone (from July to August, for example, a 31± 18 %
overestimate of ozone’s MT reactivity). The relative average
decrease in total oxidative capacity between July and August
sampling periods amounts to 16± 10 %, when evaluated at
the average ozone and OH concentrations investigated dur-
ing the TD sampling periods. This is primarily due to the
impact of speciation changes on ozone’s oxidation capacity
(∼ 24± 11 % decrease for ozone, vs. ∼ 10± 3 % decrease
for OH). At night, when NO3 can form (modeled at 1 pptv),
and when average OH and ozone concentration are lower,
this drop in total oxidative capacity was 12± 3 % (decrease
for NO3’s capacity was 8± 3 %). While this is within the
uncertainty of modeled OH, ozone, and NO3 concentrations
utilized in our evaluation of chemical degradation, it is still
a significant difference that warrants further attention when
considering the effects of MTs on air chemistry at the ecosys-
tem scale for boreal forests in BVOC emission modeling.

While not the main focus of this investigation, the im-
pact on SQT speciation is also significant. For example, at
the 37 m height used for TD sampling, the measured con-
centration of longifolene greatly exceeds that of α-humulene
at a ratio of 80 %-to-20 %. However, the reactivity of α-
humulene towards ozone is more than 2.3×104 times greater
than that of longifolene. An approximate calculation us-
ing the same chemical degradation evaluation presented in
this investigation indicates the converse for the speciation of
these SQT emissions, indicating that α-humulene emission
dominates that of longifolene at a ratio of nearly 90 %-to-
10 %. As can be seen, this presents a useful potential tool for
investigating the ecosystem-scale speciation of SQT emis-
sions.

Another potential implication is the determination of SQT
SERs at the ecosystem scale using a combination of mea-
sured fluxes and modeled F/E ratio. The breakdown of SQT
has long been a limiting factor in evaluating surface emis-
sions based on observed fluxes (e.g., Duhl et al., 2008;
Helmig et al., 2006; Pollmann et al., 2005). The determina-
tion of the effect of chemical degradation on the surface ex-
change of reactive BVOC fluxes can be evaluated using mea-
surements of ozone and the radicals OH and NO3. While OH
measurements have classically been difficult to conduct (e.g.,
Heard, 2006; Heard and Pilling, 2003; Stone et al., 2012),
many stations regularly monitor ozone, often the leading con-
tributor (by an order of magnitude (see Fig. B1)) to the SQT
chemical degradation that attenuates observed SQT fluxes.
In parallel with GC-MS or more recent tools such as ultrafast
GC (e.g., Materiæ et al., 2015) for determining SQT speci-

ation at the PTR EC inlet height, combining PTR-ToF-MS
EC flux observations with chemical degradation analysis of-
fers a promising avenue for evaluating ecosystem-scale SERs
of SQTs and other highly reactive BVOC compounds.

Appendix A: Determination of displacement height d
and the Γ correction factor

The displacement height d was determined using the veloc-
ity u and friction velocity u∗ data from the vertical profile
of sonic anemometers on the Norunda flux tower, using the
approach described in Mölder et al. (1999), as well as with
the empirical relation d = 0.86 h found therein (where h is
canopy height). The u and u∗ data were filtered according
to the selection criteria: u (87m)− u (44m)> 0.2ms−1 and
u∗ > 0.1ms−1. Good agreement for the displacement height
d was found for d = 24 m.

To correct the SLG flux measurements of speciated MT
flux for the influence of the RSL above canopy, the 0 correc-
tion factor was determined by integrating the profile for γ (z)
above the canopy (and within the RSL) from the lower TD
sampling height (37 m) to upper sampling height (60 m).

The γ -coefficients above the canopy were calculated from
the sensible heat flux H , temperature T , and friction veloc-
ity u∗ from the sonic anemometer profile according to γ =
8MO
h (ζ )/8meas

h (z), where 8MO
h is the dimensionless gradi-

ent for sensible heat according to Monin–Obukhov similar-
ity theory (where ζ is the stability parameter (z− d)/L),
8meas
h (z) is the dimensionless gradient for sensible heat ac-

cording to measurements on the flux tower, k is the von Kár-
mán constant, ρ is the density of air, and cp is the specific
heat capacity of dry air. To minimize errors in the deriva-
tive of θ (z) when calculating 8meas

h (z), the gradient dθ/dz
was determined by fitting the profile sonic data for potential
temperature θ (z) using the equation θ = a+bln (z)+ cln2(z)
(e.g., Mölder et al., 1999).

Measured γ values during the TD sampling periods on
8–10 June, 22–24 July, and 16–18 August, between 09:30
to 17:00 CEST, were collected. This vertical γ -profile above
forest canopy was then used to fit the coefficients for a con-
tinuous profile for γ (z). We applied the following formula:

γ ∼= 1+Ae−B(z−d), (A1)

where A and B are undetermined coefficients. This fitting
formula derives from consideration of Garratt (1980) and
Harman and Finnigan (2007). Following nonlinear regres-
sion, the fitted coefficients were found to be A= 5.26 and
B = 0.12 (1A= 1.01,1B = 0.02). A plot of the vertical γ -
coefficient profile between the TD sampling levels at z1 = 37
and z2 = 60 m during the 2020 Norunda campaign is shown
in Fig. A1 (the fitted curve for Eq. (A1) appears in blue).

Using z1 = 37, z2 = 60, d = 24 m, and fitted coefficients
A=−5.26 and B = 0.12, the integral of equation A1 was
then used to determine a mean enhancement factor 0 =

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17224 R. C. Petersen et al.: BVOC and speciated monoterpene concentrations

Figure A1. Vertical profile of γ above the Norunda forest canopy
during daytime TD sampling. The x-axis shows the value of γ =
1/φh =8MO

h
(ζ )/8meas

h
, and the y axis shows the normalized

height z/h. Points indicate median γ (± standard error of median)
measured from 8MO

h
(ζ )/8meas

h
during for the TD sampling peri-

ods (09:30 to 16:30 CEST; 8–10 June, 22–24 July, and 16–18 Au-
gust 2024). Blue points (near or within RSL) indicate y used to fit
function for the RSL layer. Dark blue curve indicates regression fit
of γ = 1+Ae−B(z−d) to the measured γ -profile values (A= 5.26,
B = 0.12). Vertical dashed line indicates γ = 1. Horizontal lines
indicate (dashed black) displacement height d , (solid black) canopy
height h, and (green) the z1 = 37 m and z2 = 60 m TD sampling
heights.

1
z2−z1

∫ z2
z1
γ (z)dz. For the Norunda 2020 SLG flux measure-

ments, this yielded 0 = 1.36± 0.09.

0 (z1 = 37m, z2 = 60m)=
1

z2− z1

z2∫
z1

γ (z)dz

=
A

B

[
e−B(z2−d)

− e−B(z1−d)

z2− z1

]
+ 1∼= 1.36. (A2)

The uncertainty of this fitted estimate was determined using
the standard errors of coefficients A and B, and propagation

of uncertainty 10 =
√(

∂0
∂A1A

)2
+
(
∂0
∂B1B

)2 ∼= 0.09.

Appendix B: Effect of chemical degradation on
isoprene, MT, and SQT surface exchange rates

B1 Model calculations

The effect of chemical degradation on isoprene, MT, and
SQT SERs was explored using the modeling supplement
of Rinne et al. (2012), which made use of a stochastic La-
grangian transport chemistry model, and by parameterizing
the reaction rates for ozone, OH, and NO3, to estimate the
SERs for the 2020 Norunda campaign’s terpenoid EC flux
dataset.

The effect of chemical degradation on measured fluxes of
reactive compounds is closely related to the ratio the mixing
time scale τt to the compound’s chemical time scale τc, also
known as the Damköhler number Da (Damköhler 1940),
which can be written as

Da =
τt

τc
. (B1)

In the case of Da� 1, the flux at the measurement height
closely corresponds to the emission rate. For relatively large
Da values, however, the compound will be affected by chem-
ical degradation before reaching the measurement height,
and the observed flux will be lower than the primary emis-
sion.

To estimate the flux-to-emission ratio (F/E) from mea-
sured Da-number ratios, lookup tables were used from the
supplementary materials provided by Rinne et al. 2012.
These tables relate the canopy-top Damköhler number (Dah)
to the flux-to-emission ratio (F/E) for specific normalized
heights z/h above the forest canopy-top height h. In this
model, the LAI of the canopy, modeled according to the typi-
cal profile of a Scots pine forest, is 3.5. For comparison, LAI
at Norunda is 3.6 (± 0.4) m2 m−2 (Petersen et al., 2023). Us-
ing an approximation for the mixing time scale of τt = z/u∗,
the canopy-top Damköhler number (Dah) was calculated us-
ing the relation

Dah =
h

u∗τc
, (B2)

where the friction velocity u∗ was measured at 37 m on the
station flux tower (see example station BVOC inlet setup in
Fig. 1 of the main text), h is the canopy height (28 m), and
τc is the chemical lifetime of a given BVOC compound. The
chemical lifetime τc can be calculated as

τc =
(
kX,OH [OH]+ kX,O3 [O3]+ kX,NO3 [NO3]+ kX, photolysis

)−1
, (B3)

where [OH], [O3], and [NO3] are the concentrations of OH,
O3, and NO3, respectively, and where kX,OH, kX,O3 , and
kX,NO3 are the reaction rate constants between the compound
of interest X (i.e., isoprene, MT, SQT, etc.) and the radicals
OH, O3, and NO3, respectively. Finally, kX, photolysis is the
photolysis rate of compound X. As kX, photolysis for isoprene,
MT, and SQT is much less than the rate of reactions with rad-
icals OH, O3, and NO3, as in Rinne et al. (2012), it is dropped
from subsequent calculations.

The reaction rate constants for isoprene, α-pinene and β-
caryophyllene with respect to OH, O3, and NO3 used for the
Norunda 2020 campaign SER analysis are listed in Table C3.

For the 2020 Norunda campaign SER analysis, model-
ing of the diurnal cycle of OH and NO3, as well as local
ozone data from the nearby Norunda-Stenen monitoring site,
were used to calculate the time series of τc for isoprene, MT,
and SQT, to then calculate Dah for applying the Rinne et
al. (2012) F/E lookup tables.

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R. C. Petersen et al.: BVOC and speciated monoterpene concentrations 17225

The radicals OH and NO3 were diurnally simulated for
the Dah calculations. The modeling choices for OH and
NO3 were informed by the HUMPPA-COPEC-2010 mea-
surement campaign in Hyytiälä, Finland, and the settings
implemented in Rinne et al. (2012). The OH concentra-
tion was assumed to vary diurnally between 0.3× 106 and
1.2× 106 molecules cm−3 over a Gaussian peak profile
(tmean = 12:00 CEST, σt = 3.5 h). The uncertainty1OH was
assumed to be 40 % of concentration.

NO3 was assumed to vary as a step function, with a
daytime concentration of zero and a nighttime concentra-
tion of 2.5× 107 molecules cm−3 (1 pptv), consistent with
the breakdown of NO3 under direct sunlight. The NO3 con-
centration was assumed to linearly ramp between daytime
and nighttime values during a 30 min widow before the daily
sunrise and after daily sunset. The uncertainty 1NO3 was
assumed to be 50 % of concentration.

The ozone data used for the τc-calculations came from the
Norunda-Stenen monitoring station (1.4 km east of Norunda
flux tower). To accurately reflect above-canopy nighttime
concentrations, as model ozone data was collected well be-
low canopy height, a nighttime minimum concentration was
set at 5× 1011 molec. cm−3 (ca. 20 s ppbv). This conforms
with previous campaign studies of the diurnal vertical ozone
profile within and above the Norunda forest summer canopy
(Petersen et al., 2023), where a significant nighttime sink was
observed below canopy height (28 m). The uncertainty 1O3
was assumed to be 25 % of concentration.

The diurnal mean reaction rates of ozone, OH, and NO3
with isoprene, as well as with α-pinene and β-caryophyllene
(used as examples for MT and SQT, respectively), are shown
in Fig. B1.

For the estimate of F/E fromDah, the lookup tables were
used from the supplementary materials provided by Rinne
et al. (2012). These tables relate the canopy-top Damköh-
ler number (Dah) to the flux-to-emission ratio (F/E) for
specific normalized heights z/h above the forest canopy-top
height h. In this model, the LAI of the canopy, modeled ac-
cording to the typical profile of a Scots pine forest, is 3.5.

The F/E modeling also includes the output for differ-
ent parameterizations, such as canopy-source emission vs.
ground-source emission, as well as the relative scaling of ox-
idation rates above vs. below-canopy. Several examples of
the estimated diurnal F/E ratios for the 2020 Norunda cam-
paign under these different parameterizations are shown be-
low (Fig. B2).

The ground source, for example, is likely appropriate for
certain types of forest floor SQT emission (e.g., Mäki et al.,
2019). In addition, daytime mixing may lead to equal or sim-
ilar oxidation levels above and below canopy, as the canopy
is well-mixed, while nighttime oxidation rates can become
very stratified within stable canopy air, as was frequently
observed for vertical profile of ozone within the Norunda
canopy at night (see Fig. 7 therein). For the SER estimates
presented in the main text, the source is modeled as being

in the canopy, and the relative oxidation rates above and be-
low canopy are modeled as being equal. For SQT (and for
predominantly a canopy source), this can be addressed by in-
terpolating between table lookup_C_o025.txt (i.e., nighttime
stratified ozone) and table lookup_C_o100.txt (i.e., daytime
well-mixed ozone), both found in the supplementary materi-
als of Rinne et al. (2012), using the Monin–Obukhov atmo-
sphere stability length L.

B2 Impact of chemical speciation on estimated surface
exchange rate uncertainty

Having described an approach for inferring SERs from mea-
sured fluxes, we now aim to investigate the conditions when
applying a proxy or group-average estimate for the R ratio
is justified when estimating emission rates from chemically
degraded flux measurements of total SQT and nighttime total
MT by PTR-MS instrumentation. Using the ratio R = F/E
(described in Sect. 2.8), we can write the total SQT flux mea-
sured by the Vocus PTR-ToF-MS as F =

∑
i

Fi =
∑
i

EiRi =

E
∑
i

χiRi , where χi is the fraction Ei/E that each compound

of the total mixture contributes to the total emission (with∑
i

χi = 1). We can then define an effective R ratio for de-

scribing the total mixture as Rtrue =
∑
i

χiRi , an emission-

weighted average of the compound-specific R values for all
compounds contributing to the total group emission rate. In
practice, even for the relatively well-behaved MT mixture,
our knowledge of χi is limited. For estimating the SER it can,
instead, be useful to use an estimate for R based on a com-
pound commonly observed to dominate total emissions of a
terpenoid group such as MT or SQT or, alternatively, use an
estimate for the approximate emission rates based on previ-
ous chamber emission measurements at the surrounding site.
The true and estimated total emission are Etrue = F/Rtrue
and Eest = F/Rest, where Rest is the assumed group aver-
age or compound-specific proxy used for estimating the total
emission rate. The relative error in the estimated total emis-
sion rate is ε = |Eest−Etrue|/Etrue =

∣∣∣ 1
Ravg
−

1
Rtrue

∣∣∣/( 1
Rtrue

)
,

which simplifies to

ε = |Rtrue−Rest|/Rest. (B4)

We proceed from here to estimate the impact that individual
compounds in the mixture might have on the estimated to-
tal emission rate. While Rtrue might be elusive in practice,
we can test the influence of individual compounds against
an idealized case. For a test case where we can specify the
emitted proportions, and in the case that the estimate for R is

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17226 R. C. Petersen et al.: BVOC and speciated monoterpene concentrations

Figure B1. Diurnal mean reaction rates of ozone (black), OH (green), and NO3 (blue) for isoprene, MT (α-pinene) and SQT (β-
caryophyllene). Lines indicate the mean (solid) reaction rates, as well as the minimum (dashed) and maximum (dotted) -estimated values of
the reaction rates based on the concentrations of OH, O3, and NO3 as well as the uncertainties 1OH, 1O3, and 1NO3.

based on a single compound, Eq. (B4) simplifies to

∣∣∣∣Rmix−Rk

Rk

∣∣∣∣=
∣∣∣∣∣∣
(
∑
i

χiRi)−Rk

Rk

∣∣∣∣∣∣
=

∣∣∣∣∣∣∣
(
∑
i 6=k

χiRi)− (1−χk)Rk

Rk

∣∣∣∣∣∣∣
=

∣∣∣∣∣∣∣
(
∑
i 6=k

χiRi)− (
∑
i 6=k

χi)Rk

Rk

∣∣∣∣∣∣∣=
∣∣∣∣∣∑
i 6=k

χi
Ri −Rk

Rk

∣∣∣∣∣ , (B5)

where χk and Rk = Rest are the emitted proportion and R ra-
tio, respectively, of the proxy compound used for estimating
the total SER, and Rmix is the weighted average of the R ra-
tio based on the specified emitted proportions of the test case
mixture. We can apply Eq. (B5) to a simple test based on
three compounds.

For this three-compound test, we assume that the SQT
mixture is similar to that observed in chamber emission
measurements at Norunda in July and August 2014 (e.g.,
Wang et al., 2018) and at a similar boreal forest by Hel-
lén et al. (2018). A summary of the compounds, pro-
portions, and calculated R ratios are listed in Table B1.
Based on observations, the proportion of longifolene is
likely much lower (< 1%). From Kim et al. (2011),
for an air temperature of 298 K and at sea-level pres-
sure, we calculate the reaction rate coefficients for β-
farnesene to be kOH = 2.9× 10−16 cm3 molec.−1 s−1 and
kozone = 6.9× 10−16 cm3 molec.−1 s−1. For the daytime OH
and ozone concentrations listed in Table C3, the chemical
lifetime of β-farnesene during the day is ca. 16 min.

Table B1. The proportions χi and calculated flux to surface ex-
change ratios Ri for the three compounds (β-caryophyllene, longi-
folene, and β-farnesene) used in the three-compound test case ex-
ample.

compound proportion χi calculated ratio Ri

β-caryophyllene 85 % 0.49
longifolene 5 % 0.992
β-farnesene 10 % 0.885

Applying this to Eq. (B5), we find that applying β-
caryophyllene as a proxy leads to an offset of approxi-
mately 13 %. In comparison, the influence of uncertainty
from modeled concentrations of daytime OH (± 40 %) and
ozone (± 25 %) on the calculated SERs from the chemical
degradation analysis 12 %) is approximately the same.

In a more general view, since Rtrue can be expressed
as a linear weighted average over the full set of com-
pounds making up the true emission, we can divide it
between a subset of known compounds and its comple-
ment subset. Let S be the subset of known compounds
making up a linear estimate for Rest and let its comple-
ment, Sc, be the remaining, missing group of unknown
compounds. We can then define χest =

∑
j∈S

χj and χmiss =∑
k∈Sc

χk = 1−χest where
∑
i

χi = χest+χmiss = 1. From this

step, we can separateRtrue into its known and unknown parts,
withRest =

1
χest

∑
j∈S

χjRj ,Rmiss =
1

χmiss

∑
k∈Sc

χkRk , andRtrue =∑
i

χiRi =
∑
j∈S

χjRj +
∑
k∈Sc

χkRk = χmissRmiss+χestRest. Ap-

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R. C. Petersen et al.: BVOC and speciated monoterpene concentrations 17227

Figure B2. Diurnal F/E ratios for isoprene, MT (α-pinene) and SQT (β-caryophyllene) for a range of different model parameterizations. (a,
b) parameterized for canopy source. (c, d) parameterized for ground source. (a, c) below-canopy oxidation rate equals that of above-canopy.
(b, d) below-canopy oxidation rate 0.25× that of above-canopy. The estimated F/E ratios for the minimum and maximum of the ozone,
OH, and NO3 reactions rates are shown as dashed red lines.

plying Eq. (B4) to these yields∣∣∣∣Rtrue−Rest

Rest

∣∣∣∣= ∣∣∣∣ (χmissRmiss+χestRest)−Rest

Rest

∣∣∣∣
= χmiss

∣∣∣∣Rmiss−Rest

Rest

∣∣∣∣< ε. (B6)

Hence, based on assumptions regarding Rest, we can esti-
mate roughly the average chemical lifetime that the unre-
solve components of total SQT emission must have in order
to fall outside the specified amount of acceptable uncertainty
ε, based on the inequality |Rmiss−Rest|<

ε
χmiss

Rest.
For example, if we are confident that at least 85 % of to-

tal SQT emissions consists of β-caryophyllene, use it as a
proxy for our total SQT estimate as in Sect. 2.8 and 3.3.1,
and are willing to accept an uncertainty bound ε = 15%,
then solving the above inequality for Rmiss places the con-
straint Rmiss < 2×Rest = 0.98. From this upper-bound on R,

we can estimate the corresponding value of Dah, and sub-
sequently τc,miss, by working back through the R-vs-Dah
lookup tables of the chemical degradation analysis outlined
earlier in the Appendix (in this case, yielding Dah < 0.005).
Using Eq. (B2), we can estimate the associated upper-
bound for the average chemical lifetime from τc <

h
u∗Dah

.
In this example (using Table C3 for daytime friction veloc-
ity u∗ = 0.65 m−2 s−1 and canopy height h= 28 m), to fall
within ε = 15%, the average chemical lifetime of the miss-
ing emission components is bounded by τc,miss < 2.4 h.

Appendix C: Additional tables and figures

For Table C1, the data was divided into three wind-direction
classes based on the two prevailing wind directions (between
245–310° and 310–80°) during the campaign TD sampling
on the Norunda flux tower on 8–10 June, 22–24 July, and 16–

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17228 R. C. Petersen et al.: BVOC and speciated monoterpene concentrations

18 August. The heterogeneity of the forest tree heights can be
observed from the lidar height map presented in Fig. 3.

Figure C1. Daily mean (09:30–17:00 CEST) concentrations of MT compound species at 37 (red) and 60 m (blue) on the station flux tower
at the ICOS Noru