Total hydroxyl radical (OH) reactivity measurements were conducted at the second Station for Measuring
Ecosystem–Atmosphere Relations (SMEAR II), a boreal forest site located in Hyytiälä, Finland, from April
to July 2016.
The measured values were compared with OH reactivity calculated from a combination of data from the
routine trace gas measurements (station mast) as well as online and offline analysis with a gas chromatographer
coupled to a mass spectrometer (GC–MS) and offline liquid chromatography.
Up to 104 compounds, mostly volatile organic compounds (VOCs) and oxidized VOCs, but also inorganic compounds,
were included in the analysis, even though the data availability for each compound varied with time.
The monthly averaged experimental total OH reactivity was found to be higher in April and May (ca. 20 s-1)
than in June and July (7.6 and 15.4 s-1, respectively). The measured values varied much more in spring with
high reactivity peaks in late afternoon, with values higher than in the summer, in particular when the soil was
thawing.
Total OH reactivity values generally followed the pattern of mixing ratios due to change of the boundary layer
height.
The missing reactivity fraction (defined as the difference between measured and calculated OH reactivity) was
found to be high. Several reasons that can explain the missing reactivity are discussed in detail such
as (1) missing measurements due to technical issues, (2) not measuring oxidation compounds of detected
biogenic VOCs, and (3) missing important reactive compounds or classes of compounds with the available measurements.
In order to test the second hypothesis, a one-dimensional chemical transport model (SOSAA) has been used to
estimate the amount of unmeasured oxidation products and their expected contribution to the reactivity
for three different short periods in April, May, and July. However, only a small fraction
(<4.5 %) of the missing reactivity can be explained by modelled secondary compounds (mostly oxidized VOCs).
These findings indicate that compounds measured but not included in the model as well as unmeasured primary
emissions contribute the missing reactivity. In the future, non-hydrocarbon compounds from sources other than
vegetation (e.g. soil) should be included in OH reactivity studies.
Introduction
Terrestrial vegetation is responsible for about 90 % of the emissions of biogenic volatile organic
compounds (BVOCs) into the atmosphere . Isoprene and monoterpenes are the
most abundant BVOCs globally with 44 % and 17 % of total biogenic emissions, respectively . These compounds
are very reactive and their lifetimes range from minutes to hours, thus influencing tropospheric
chemistry.
Total hydroxyl radical (OH) reactivity measurements can be used as a method to assess our understanding
of tropospheric chemistry . Many observations of total OH
reactivity have been performed in the past few decades and compared to calculated OH reactivity derived
from known chemical composition of the atmosphere. While for urban environments the unexplained (or
missing) reactivity fraction remains low, it is often more than 50 % in forested environments
see the review by. Based on these observations,
modelled the global OH reactivity, as well as hypothetical missing chemical sink, which was found to be
mostly localized above forested areas and in a few areas with large anthropogenic emissions.
Large fractions of missing reactivity were first observed in a forest in northern Michigan
and later also observed in other forested environments
e.g..
Also, in the tropical forest of Borneo up to 70 % of the measured total OH reactivity remained
unexplained .
In addition, identified a large difference of missing OH reactivity
between the dry and wet seasons in the Amazon rainforest, with 79 % on average and between
5 % and 15 %, respectively. They then identified the forest floor as an important but poorly
characterized source of OH reactivity, and recently identified strong
sesquiterpene emissions from soil microorganisms at the same site.
Also in the boreal forest, which represents approximately one-third of the Earth's forested surface
, a large discrepancy was observed between the total measured OH reactivity
and the reactivity calculated from individual compounds present in the forest air
. Up to 89 % of the measured total OH reactivity could
not be explained for periods in which the forest experienced stress conditions (elevated temperature).
The two main assumptions for the missing reactivity are (1) missing primary emissions and (2) missing
oxidation products from the emissions. Several studies have been conducted to investigate these
hypotheses. , for instance, found an increasing missing fraction of
Norway spruce (Picea abies) emissions from about 15 % to 27 % in spring and early summer and up to
70 %–84 % in late summer and autumn. In contrast, found no significant
unknown primary BVOC contributing to OH reactivity (for red oak, white pine, beech, and red maple) during
their study period in July 2009 in a forest in Michigan. They also found that the missing reactivity from
ambient measurement at this site could be explained by oxidation products from isoprene.
found in an isoprene-dominated forest in Alabama that emissions and their
modelled oxidation products reduced the unexplained reactivity to 5 %–20 % during the day and 20 %–32 %
at night, and they attribute the missing reactivity to unmeasured primary emissions.
also demonstrated that including modelled oxidation products in OH reactivity calculations reduces the
difference with measurements significantly.
and conducted their studies at the second Station
for Measuring Ecosystem–Atmosphere Relations SMEAR II; in Hyytiälä, Finland,
for about 3 weeks in
August 2008 and for about 3.5 weeks in July–August 2010, respectively, with the
comparative reactivity method CRM,.
modelled the full year of OH reactivity at SMEAR II for 2008, based on
modelled emissions, known chemistry, and environmental conditions. A comparison with results from
showed that compounds other than monoterpenes, isoprene, and methane contribute to
only about 8 % of the measured OH reactivity. Taking all compounds into account, about 61 % of the
OH reactivity remained unexplained on average during that period.
also compared modelled reactivity at SMEAR II with OH reactivity
measurements from , using measured trace gases as input, but found on
average about 65 % of unexplained reactivity, similarly to the previous study.
In order to investigate OH reactivity at SMEAR II in more detail, in particular its missing fraction and
the seasonal variations which are often neglected for summer intensive campaigns, a new implementation of
the CRM was developed at the Finnish Meteorological Institute . It was installed
at SMEAR II along with instrumentation to measure VOCs in spring and summer 2016.
MethodsMeasurement site
Measurements were conducted at the boreal forest site SMEAR II
in Hyytiälä, Finland (61∘51′ N, 24∘17′ E, 181 m a.s.l.).
The site is located in a ca. 60-year-old managed
conifer forest with modest height variation in the terrain.
The stand is dominated by Scots pine (Pinus sylvestris L.) homogeneously for about 200 m in all
directions, extending to the north for about 1.2 km. Tampere is the largest city near the station
about 60 km to the S–SW.
The instruments were located inside a container in an opening about 115 m from the site mast,
from which meteorological data as well as ozone (O3), nitrogen oxides (NOx), methane (CH4),
carbon monoxide (CO) and sulfur dioxide (SO2) concentrations were retrieved to be included in the analysis.
Proton-transfer-reaction mass spectrometer (PTR-MS) measurements of VOCs usually operated at the station mast
were not operational during the measurement period and could not be used in this study.
In situ measurements of the total OH reactivity (Sect. ) and of VOC concentrations
(Sect. ) were performed at the container, sampling outside air at a height of about 1.5 m
(Fig. ).
Station data (from the mast, measurement towers, and soil) are open data under the Creative Commons 4.0 Attribution
licence (CC BY 4.0) and were retrieved from the online SmartSMEAR interface
(https://avaa.tdata.fi/web/smart/smear, last access: 30 July 2019, ).
Temperature and relative humidity (RH) are taken at 4.2 m above ground on the mast, soil properties are an
average of five locations throughout the site, and radiation and precipitation data are collected at 18 m height
on a nearby tower.
Orthophotograph of the SMEAR II station in Hyytiälä and its surroundings with the marked location
of the station mast and the container where the measurements were performed. (Source: Land Survey of Finland
Topographic Database 09/2018).
In situ measurements of volatile organic compounds
VOCs were measured with two in situ gas chromatographer–mass spectrometers (GC–MSs). The first GC–MS was used for the measurements of mono- and
sesquiterpenes, isoprene, 2-methyl-3-butenol (MBO), and C5-10 aldehydes. With this GC–MS air was
drawn at the flow rate of 1 L min-1 through a metre-long fluorinated ethylene propylene (FEP)
inlet (i.d. 1/8 in.) and for O3 removal through a metre-long heated
(120 ∘C) stainless-steel tube (o.d. 1/8 in.).
VOCs were collected from a 40 mL min-1 subsample flow in the cold trap (Carbopack B/Tenax TA)
of the thermal desorption unit (TurboMatrix, 650, PerkinElmer) connected to a gas chromatograph
(Clarus 680, PerkinElmer) coupled to a mass spectrometer (Clarus SQ 8 T, PerkinElmer). A HP-5 column
(60 m, i.d. 0.25 mm, film thickness 1 µm) was used for separation.
The second GC–MS was used for the measurements of C4-8 alcohols and C2-7 volatile organic acids
(VOAs). Samples were taken every other hour. The sampling time was 60 min. Samples were analysed in situ with
a thermal desorption unit (Unity 2 + Air Server 2, Markes International Ltd, Llantrisant, UK) connected to a
gas chromatograph (Agilent 7890A, Agilent Technologies, Santa Clara, CA, USA) and a mass spectrometer
(Agilent 5975C, Agilent Technologies, Santa Clara, CA, USA). A polyethylene glycol column DB-WAXetr (30 m,
i.d. 0.25 mm, a film thickness 0.25 µm) was used for the separation. These methods and measurements
have been described in more detail by .
Offline measurements of volatile organic compounds
Additional sampling took place between 27 April and 3 May in canisters and through adsorption cartridges
(24 h time resolution) to be analysed by a GC–flame ionization detector (FID) (C2-6 hydrocarbons) and liquid chromatography with an ultraviolet absorption detector (LC–UV) (carbonyls), respectively.
During this period, Tenax tube samples were also taken (4 h time resolution) and
analysed later in the laboratory with GC–MS. These results were used as backup to fill in data during
interruptions of the online GC–MS measurements.
Between 20 and 29 July, additional sampling through adsorption cartridges for offline analysis with LC–UV
was performed.
Mixing layer height measurements
The mixing layer height (MLH) was estimated from measurements with a 1.5 µm pulsed Doppler lidar
Halo Photonics Stream Line; similar to .
MLH was determined from a combination of turbulent kinetic energy dissipation rate profiles and conical
scanning at a 30 ∘ elevation angle according to the method described in . With this method MLH could be determined from 60 m a.g.l. (above ground level) to more than 2000 m a.g.l. at SMEAR II. Periods when MLH was <60 m a.g.l. could be identified, although the actual MLH was not determined due to minimum range limitations. MLH was not determined for rainy periods. For more detailed specifications of the lidar system and the applied MLH determination method; see .
Total OH reactivity measurements: the comparative reactivity method (CRM)
The OH reactivity, ROH, is defined as the sum of the concentration of individual compounds
Xi multiplied by their respective reaction rate coefficient with respect to OH (kOH+Xi).
This can be summarized by the following equation:
ROH=∑i[Xi]kOH+Xi.
The OH reactivity of a compound is the inverse of the OH chemical lifetime due to its reaction with that compound.
High OH reactivity values correspond to short lifetimes, and long-lived species (such as methane) have a low
reactivity.
Our analysis includes up to over 100 individual species from two GC–MS, GC–FID and LC–UV measurements
(see Sect. and ).
However, not all compounds have been measured at all times (see Fig. c). In addition
NOx, O3, SO2, and CO concentrations were retrieved from the mast
of the SMEAR II station, about 115 m away from the sampling position of total OH reactivity and VOCs.
Measurements of total OH reactivity (Rexp) have been conducted using the comparative
reactivity method CRM; . Our particular
implementation of the method is described in .
The CRM is based on the monitoring of pyrrole (C4H5N) mixed in a 100 mL reactor with zero air and
ambient air, alternatively. The total flow through the reactor is about 465 mL min-1 and the residence
time in the reactor is estimated at about 12–15 s.
Pyrrole detection is performed with a gas chromatograph (GC) equipped with a photon ionization
detector (PID) every 2 min (Synthec Spectras GC955, Synspec BV, Groningen, the Netherlands).
The sensitivity of this detector is independent of the RH of the sample (Fig. ),
but decreased from 1797 ppbv-1 (data from April to June) to 1290 ppbv-1 (July data).
In both cases, the uncertainty of the sensitivity (Us) is 2.5 %. In addition we consider the uncertainty
of the pyrrole levels (Up) based on the uncertainty of the pyrrole standard (Upyr,std = 10 %)
and of the uncertainty of the dilution (Udil=8 %), so Up=Upyr,std2+Udil2=12.8 % .
Based on earlier tests, the sensitivity of the GC–PID does not depend on RH in the reactor (Fig. ,
right). The sensitivity differs by only 2.8 % between humid and dry conditions, which is roughly the uncertainty of
the sensitivity.
(a) Sensitivity of the GC–PID for pyrrole used in the present study.
(b) Same day (30 June 2015) sensitivity test for sensitivity of GC–PID for pyrrole in humid and dry
conditions.
OH is produced by the photolysis of water (H2O) in a nitrogen flow (99.9999 % N2) using ultraviolet
(UV) radiation and introduced into the CRM instrument reactor.
Note that hydroperoxyl radicals (HO2) are concurrently produced from the reaction
of hydrogen (H) with molecular oxygen (O2).
In the zero air mixture, all OH radicals are consumed by pyrrole (C2 level), while ambient air contains other
reactive compounds that compete for OH, leading to a higher pyrrole concentration (C3 level). The instrument
switches between measurement of zero air and ambient air every 8 min. Stabilization of the conditions takes
a couple of minutes and the first data point after each switch is discarded. From the difference between C2
and C3 pyrrole levels and taking into account the amount of pyrrole in the reactor in the absence of OH
(C1, typically between 26 and 43 ppbv), the total OH reactivity Reqn can be derived from the following equation:
Reqn=C3-C2C1-C3×kp×C1,
with kp the reaction rate of pyrrole with OH
1.2×10-10 cm3 s-1; . C1 is measured by introducing a
large concentration of 0.6 % propane (C3H8) in nitrogen (N2) to act as an OH scavenger
. Therefore, C1 takes into account the photolysis of pyrrole due
to the UV radiation entering the reactor (8 %–13 %), which decreases the pyrrole concentration from the
total amount of pyrrole injected in the reactor (C0 level).
Equation assumes that OH levels are identical during C2 and C3 measurements. Therefore,
variations in RH within the reactor but also the presence of O3 need to be taken into account. Therefore
C3 in Eq. results from the following:
C3=C3,exp+ΔC3,H2O+ΔC3,O3,
with C3,exp the measured level of pyrrole in C3 mode, ΔC3,H2O the
correction due to different RH in C2 and C3 (the difference in RH is 4 % or less 99 % of the time,
which corresponds to a change of no more than 5 % for Reqn), and ΔC3,O3 the
correction due to the presence in the reactor of additional O3 from sampled air. This last correction is
discussed in detail in Sect. .
In addition, because of the dilution of the sampled air with humid nitrogen, the experimental total OH reactivity
(Rexp) is derived from the following equation:
Rexp=D×Rmeasured,
with D the dilution factor (ratio of sampling flow over total flow through the reactor) and
Rmeasured the reactivity inside the reactor after applying corrections
to Reqn (see Sect. ).
Finally, the missing fraction of the total OH reactivity is obtained by comparing Rexp with
ROH:
Rmissing,faction=Rexp-ROHRexp
Ozone correction factor
As discussed in and by
for the CRM system of the Max Planck Institute, the pyrrole signal obtained during analysis of ambient air
must be corrected for the presence of O3. Even though O3 is present in the reactor due to the UV lamp used
to produce OH (about 170 ppbv in the instrument used in the present study), additional O3 sampled
from ambient air (up to a 30 % increase) affects the OH concentration in the reactor, most probably by being
photolysed and producing O(1D), which reacts further with H2O, yielding two OH radicals.
used a correction (ΔC3,O3) independent of pyr / OH as
the experimental pyr / OH for the measurements was in a narrow range close to 2. However as pyr / OH
varied from 1.0 to 5.3 in this study, a pyr / OH-dependent correction has been derived.
The corrections ΔC3,O3 were derived experimentally for various pyr / OH values by injecting
a known amount of O3 in the CRM's reactor (Fig. a) and
then the slope of the linear fit (through the origin) for each pyr / OH (FO3) was plotted against
pyr / OH (Fig. b). Based on these data, a linear fit has been derived to
calculate FO3 according to pyr / OH, and the uncertainty of this correction (UFO3)
is 30.8 %. When correcting ambient data in this study, the correction for a pyr / OH of 3 (FO3 =
0.079) has been applied when pyr / OH was higher than 3 due to the lack of experimental data at higher pyr / OH
values.
Note that a couple of experiments (with pyr / OH 1.27 and 1.05) were performed with
additional injection of propane (C3H8) as the pyrrole signal would have decreased to zero otherwise and no
ΔC3,O3 could have been determined.
(a) Correction of C3 (ΔC3,O3) as a function of
ozone in the reactor (O3,reactor). (b)O3 correction factor (FO3) as a function
of pyr / OH.
The correction ΔC3,O3 is then derived from the following equation:
ΔC3,O3=FO3[O3]=(0.022×(pyr/OH)+0.013)×[O3].
As observed in , inhomogeneity of the air composition at the sampling site can affect
the comparison between experimental total OH reactivity and calculated reactivity from known composition.
It can for instance be directly affected by meteorology or changes in concentrations between the various
sampling locations due to local emissions during low mixing periods (see ).
As VOCs in this study were sampled at the same location as the total OH reactivity, the effect of
inhomogeneity of the air composition is minimized. However, the ozone mixing ratio used to derive the
ozone correction (described in Sect. ) is retrieved from the station mast
(115 m away) and at a height of 4.2 m. It is very likely that emissions from soil and understorey
vegetation (or from standing water close to the OH reactivity sampling location) would further deplete
the ozone close to the ground, leading to an overestimation of the correction.
Under some circumstances, such as when there is a strong O3 gradient below canopy
, the correction might be overestimated.
For instance, on 29 and 30 April total OH reactivity around 125 to 150 s-1 in the afternoon is
followed by O3 concentration drops below canopy (Fig. ; see also
). While the high reactivity peaks themselves are likely not affected by an
overestimation of the correction, the period following them (night-time) might be slightly overestimated
due to the sampling of O3 further away and higher above ground.
This effect is difficult to take into account in retrospect. The concentration of O3 should have been
measured immediately next to the CRM system. Similar conditions were observed during nights between 11 and
16 May and to some extent in July (without reaching such high total OH reactivity values as in spring).
This effect on the inhomogeneity of the forest air composition might affect total OH reactivity measurements
and in turn partly explain some of the missing fraction.
1st-order correction factor
used a two-equation model to correct for the deviation from
pseudo-1st-order kinetics ([Pyr]≫[OH]). used more detailed modelling
taking into account OH recycling reactions, but could not match the model results with their experimental
data.
For this reason, favoured the experimental approach to correct the reactivity
data. Nevertheless, the experimental approach also has drawbacks. For instance, impurities from standards and
changes over time (ageing) might alter its reactivity. Also it is based on calibrations using one compound at
a time, which do not represent complex ambient mixtures of reactive gases.
Nevertheless, reactivity calibrations were performed for the present study with a 10 ppmv C3H8
standard as well as with an in-house gas mixture containing α-pinene with small impurities from
aromatic compounds.
The concentrations of the C3H8 and in-house α-pinene standards were checked periodically by taking
adsorbent tube samples and analysing them by GC–MS. At the same time impurities (4.7 %–17 % of the reactivity)
could be measured and taken into account.
The comparison between OH reactivity expected from the standard (Rstd)
and the measured OH reactivity (Rmeasured) is presented in Fig. . Note that the
data have been corrected for deviation from pseudo-1st-order kinetics similarly to the work of
by using numerical simulations and fitting the relationship between
Reqn and Rtrue for various pyr / OH ratios with equations of the form
Rtrue=F1×ReqnF2+F3, so that F1, F2, and F3 are
pyr / OH-dependent coefficients (see Appendix for additional details).
The 1 h averages of total measured OH reactivity, Rexp, and ozone mixing ratios at
4.2 and 125.0 m above ground. Mixing layer height (MLH) is shown as a grey shadow.
Note that the detection limit for MLH is 60 m and values below this limit are displayed at 30 m (and zeros
denote gaps in the data).
The calibration for C3H8 is consistent with the one from . Due to the high
reactivity of α-pinene, the calibration consistently underestimates the expected reactivity and because
monoterpenes constitute the most important class of compounds in the boreal forest, this needs to be taken into
account by applying the overall correction for α-pinene to the ambient data in this study, which has an
uncertainty (UF) of 6.3 % based on the uncertainty of the fit. The reactivity measured in the reactor
Rmeasured is then derived from the following equation:
Rmeasured=(Reqn+0.449)/0.497.
Based on these reactivity calibrations the precision of the measurements (Uprec) is derived. Reqn
and its standard deviation (σReqn) are calculated for every C3 value measured.
Dividing σReqn by the mean of Reqn (Reqn‾) for stable conditions
yields Uprec, which varies with Reqn values and is described by the following function:
Uprec=0.15+5.35×105×e-5.53×Reqn‾.
This derivation is shown in Figure of the Appendix.
Uncertainty of the measured total OH reactivity
The total uncertainty for the measured total OH reactivity is derived from the following equations:
9URexp=Up2+Us2+Uσ+UΔC32+UkOH,pyr2+UD2+UF2+Uprec2,10UΔC3=Ufit,O32+U[O3]2,
with UkOH,Pyr as 15 % and UD as 2.8 %
(UD2=Utotalflow2+Usamplingflow2,
with Utotalflow=Usamplingflow = 2 %),
and the other uncertainties are as mentioned previously.
The model to Simulate the concentrations of Organic vapours, Sulfuric Acid and Aerosols (SOSAA)
In this study we applied the model to Simulate the concentrations of Organic vapours, Sulfuric Acid and Aerosols
(SOSAA) to simulate the OH reactivity at the SMEAR II station for selected days in April, May, and July 2016.
SOSAA is a one-dimensional chemical transport model comprised of boundary layer meteorology, biogenic emission
of VOCs, gas-phase chemistry, aerosol dynamics, and gas dry deposition
e.g. , and it has been previously used to simulate OH reactivity at this site .
The boundary layer meteorology was derived from scalar distribution SCADIS;, as
described in . The biogenic emission module was deactivated because in situ measurements
were used to provide input concentrations. Biogenic compounds were set to the measured values up to 18 m
(canopy height), while aromatic compounds were set to the measured values at all heights. Measured inorganic
gas concentrations at SMEAR II were used as input.
The gas-phase chemistry was created using the Kinetic PreProcessor (KPP; ).
The chemical reaction equations used in this study were selected from the Master Chemical Mechanism v3.3.1
MCMv3.3.1.
The chemistry scheme included more than 15000 reactions and a total of 3525 chemical species representing the
complete reaction paths for isoprene, α-pinene, β-pinene, limonene, β-caryophyllene, methane,
2-methyl-3-buten-2-ol (MBO), benzene, toluene, styrene, ethylbenzene, 1,2-dimethylbenzene, 1,3-dimethylbenzene,
1,4-dimethylbenzene, 1,2,3-trimethylbenzene, 1,2,4-trimethylbenzene, 1,3,5-trimethylbenzene,
1-ethyl-2-methylbenzene, 1-ethyl-3-methylbenzene, 1-ethyl-4-methylbenzene, heptane, octane, nonane, butanal,
pentanal, methacrolein, and relevant inorganic reactions. The 1st-order reactions between OH, O3, and NO3
with the following monoterpenes were also included in the chemistry: Δ3-carene, myrcene, camphene, and
1,8-cineole. Likewise, 1st-order reactions between OH, O3, NO3, and β-farnesene were included. The
photochemistry has been improved by calculating the photodissociation constants more precisely using data from
, as described in .
The OH reactivity has been calculated as in .
The condensation sinks for sulfuric acid and nitric acid, based on differential mobility particle sizer (DMPS)
and aerodynamic particle sizer (APS) data from SMEAR II, were included . Since sulfuric
acid and nitric acid make up most of the condensation sinks, sinks of VOCs into the particle phase are not taken into
account, thereby the aerosol module is turned off.
Comparison between measured OH reactivity for C3H8 and α-pinene standards with the expected
OH reactivity.
The model runs in the present study include the dry deposition module implemented in SOSAA by
and extended in . The latter describes the explicit simulation of the loss of every compound in the
model by dry deposition inside the canopy for all height levels and provides a detailed comparison of measured and modelled fluxes of certain selected VOCs including some secondary organic species at SMEAR II.
Results and discussionOverview
An overview of the measured total OH reactivity together with the calculated OH reactivity from up to
104 compounds, depending on data availability, as well as selected ancillary data, such as environmental
conditions (air and surface soil temperatures as well as surface soil water content), and contributions
from different compounds and groups of compounds are presented in Fig. .
The following sections discuss in detail various aspects of the results such as (a) seasonality,
(b) diurnal variations, and (c) missing reactivity. Nevertheless, from this overview, the following
observations can be made.
The range of measured total OH reactivity values is similar to that of previous studies at the same site
in August 2008 and July–August 2010 ,
with notably higher values in the spring.
These high total OH reactivity peaks in the spring
(with values higher than at the end of July) seem to be associated with changes in the soil water content
resulting from soil thawing.
The calculated OH reactivity from measured compounds is in general lower than the measured
total OH reactivity (also for periods with a large number of compounds included in the analysis), leading to
a large fraction of missing reactivity (see Sect. ).
Inorganic compounds (CH4, CO, O3, and NO2) form an important fraction of the calculated
OH reactivity.
(a) Experimental total OH reactivity Rexp
(1 h average) and calculated OH reactivity ROH, (b) environmental conditions
(air and surface soil temperatures, as well as surface soil water content), (c) Pyr / OH
in the CRM reactor, (d) data availability from the different
instrumentation/sources, (e) fraction of experimental total OH reactivity,
and (f) fraction of calculated OH reactivity. The periods shaded in grey
in (a) to (d) represent the periods investigated with SOSAA
(see Sect. ).
Total OH reactivity
Monthly means and standard deviations (SD) of experimental total OH reactivity
(Rexp), the missing OH reactivity fraction
(Rmissing,fraction),
monoterpene and sesquiterpene mixing ratios ([MT] and [SQT], respectively), photosynthetically
active radiation (PAR), precipitation (Precip), relative humidity (RH), air temperature (T),
surface soil temperature (Tsoil,humus), surface soil water content (wsoil,humus),
and mixing layer height (MLH). Coefficients a and b from linear regressions between the weekly means
of these variables and weekly averaged Rexp and the corresponding coefficients of determination (r2).
ndays indicates the number of days with measurements. n denotes the number of Rexp
observations. Note that all other means (except MLH) have been derived for the same measurement period as
Rexp. nMLH indicates the number of observations with overlapping Rexp and
MLH measurements.
Keeping in mind that the experimental data have not always been acquired continuously, the total experimental
OH reactivity (Rexp) monthly mean was high in April and May (about 20 s-1) compared to June (7.6 s-1) and July (15.4 s-1), due to few very high values at night-time (Table ).
Consequently, no strong correlation could be found between Rexp and
other variables looking at weekly means. The highest coefficient of determination (r2) was obtained for
the correlation with wsoil,humus (r2 = 0.35), which indicates that soil moisture might be an important
driver for the high reactivity values measured in spring.
The highest reactivity peaks happened when the
surface soil water content was the highest as the surface soil temperature started to increase above
1.5 ∘C, indicating thawing of the soil, a possible source of OH reactive compounds. Forest floor
emissions of monoterpenes are known to be high in spring after snow has melted
, and VOC emission bursts have been
observed after wetting events e.g..
There has also been some indication that thawing snow/soil could be a source of volatile organic amines
. In the present study, the soil was snow-free already on 8 April, but a short
snowfall episode happened later with 5 cm of snow measured on the morning of 25 April (which was gone on
the next day). This episode happens just before the first OH reaction peak (at about 56 s-1), but this single occurrence is too little information to reach conclusions about the role
of snow in the large OH reactivity values observed, and it might well be due to a combination of factors
(including snowfall and immediate melting).
These results deviate however from the conclusions of , which suggested that
a wet (and cold) soil in the Amazon rainforest acts as a sink for reactive compounds.
The data for July cover days that were cloudier and more humid (both air and soil) but warmer than the
period covered by the data in June leading to higher total OH reactivity.
Monthly means of ambient concentrations of locally emitted terpenoids had a weak correlation (r2=0.43)
with temperature (see also ), which is not reflected in the correlation of total
measured OH reactivity with temperature, as observed earlier
e.g.. However, these studies were performed during summer,
which highlights the different regimes governing OH reactivity in various seasons and how most likely other
(unknown) compounds in addition to terpenes contribute to OH reactivity during spring. In other words, while
conditions that favour high OH reactivity values seem to favour BVOC (terpene) emissions in the summer as well,
OH reactivity is driven by other parameters in spring.
It should be noted, though, that the use of a correction factor based on α-pinene throughout the
measurement period even though the air composition varied might lead to an overestimation of the measured
total OH reactivity. However, average mixing ratios of monoterpenes were similar in April and June
(94.0 and 83.6 pptv, respectively), so that relative differences in measured
total OH reactivity cannot be explained this way. This further indicates that non-terpene compounds that were
not measured in the spring might have contributed to the total OH reactivity.
Diurnal variations
The calculated OH reactivity of various groups of compounds shows different diurnal patterns, which vary
with the season as well. Their normalized values are depicted in Fig. (second to fourth
row), separated by month (April to July in columns), together with the normalized diurnal patterns of
Rexp and its missing fraction and temperature difference between measurements at 4.2 m and
125.0 m above ground as a proxy for mixing layer height (top row). Compounds that had a 24 h sampling time
were removed from this analysis.
did not measure a clear OH reactivity diurnal pattern during their 2-week
measurement period and the modelling of the OH reactivity also showed no diurnal pattern
. However, modelled a weak diurnal pattern
with a maximum at night, mostly due to improvements in the meteorological scheme. The observations in the present
study, even though at higher OH reactivity levels, show this pattern from May to July.
, for measurements roughly at the same time of the year, identified a similar
diurnal pattern with a maximum at night during the identified stress period. For normal boreal forest conditions,
they measured large variations in the afternoon reactivity, sometimes leading to a maximum, which they associated
with long-range transport.
In the present study, afternoon reactivity maxima were dominating April's diurnal pattern.
When the total measured OH reactivity hourly average is at a minimum during the day and a maximum at night (May
to July), it follows the pattern of BVOC concentrations (and calculated OH reactivity) due to the low mixing
layer height and despite slightly lower emissions due to the lower temperatures at night
.
The hourly average of missing reactivity fraction remained consistently high (between 71 % and 92 %), similar
to values from and despite the inclusion of more compounds in our analysis
(see Sect. for a detailed discussion).
Normalized monthly averaged diurnal variations in experimental OH reactivity Rexp and
the missing fraction as well as temperature gradient between 4.2 and 125.0 m above ground as a proxy for
mixing layer height (top row), and calculated OH reactivity separated by group of compounds (second to
fourth rows).
While the OH reactivity daily patterns from monoterpenoids and MBO had a minimum during the day for all
months, other groups of compounds showed this reactivity pattern only for some periods. Isoprene showed
this pattern except in July, where the light-induced emissions during the day dominated.
Sesquiterpenes, other carbonyls, and NO2 showed a similar pattern with a daytime minima from May to July,
while C7-10 alkenes, aromatics, C3-10 aldehydes, and methacrolein showed a pattern
with daytime minimum only in May and June.
Alcohols exhibit an OH reactivity pattern with a maximum in the morning (09:00–11:00). The absolute OH
reactivity of alcohols is low and dominated by 1-butanol, which is used in aerosol measuring devices at the
site. It is not clear what causes the diurnal pattern, but SO2 reactivity had a similar pattern in April
and May, and NOx had such a pattern in April, when the photochemistry is not yet very strong.
Measured total OH reactivity (Rexp), calculated OH reactivity from measured compounds
(ROH), calculated OH reactivity including measured and modelled
compounds (ROH,model) (1 h averages, a, b, c), and normalized contributions to Rexp for various compounds and groups of compounds (d, e, f) for the three periods investigated with SOSAA (see main text
for details).
Overall, from May to July the total OH reactivity exhibits a minimum during the day and a maximum at night,
following the OH reactivity pattern for biogenic compounds (except for isoprene in July, which is present in
low concentrations in this pine forest, and has a maximum in the afternoon then). In April, the total OH
reactivity has a maximum in the afternoon, and sesquiterpenes, even though present
in low concentrations, show a similar reactivity pattern. found high levels of
sesquiterpenes from soil emissions at the same site in spring. This is an additional indication that unknown
primary emissions (in particular from soil) could drive the reactivity during that time of the year.
Missing OH reactivity
The comparison between the calculated and measured OH reactivity is challenging as the calculated values
are derived from a number of compounds that varies because of the availability of the measurements
(Fig. d). Some periods include only a few inorganic compounds from the station mast while
other periods include a large number of (O)VOCs analysed by the GC–MSs. The contribution to the known reactivity
is shown in Fig. f.
It is also good to keep in mind that part of the missing reactivity can be explained by measurement uncertainties
and potential overestimation due to applied correction factors.
As the data in this study have been uniformly corrected based on α-pinene calibrations, it is
likely that the obtained values are an upper limit for the reactivity considering that α-pinene
(and monoterpenes in general) do not dominate the air composition and reactivity at the site for the whole
measurement period.
It should also be remembered that because of technical problems, PTR-MS data (VOC data) from the station mast
are unavailable for our measurement period. Some compounds such as acetaldehyde were measured during two short
periods with offline 24 h sampling methods. In late July, acetaldehyde contributed on average 0.13 s-1
to the OH reactivity, which can be a small but significant fraction for low OH reactivity values. This is likely
the case for other compounds that were not measured at all in the present study such as formaldehyde,
acetonitrile, or methanol, to name a few. In addition, this also makes the comparison with previous studies
difficult. Despite the higher number of compounds included in the present work, the impossibility to include
aforementioned compounds in the analysis partly explains why missing OH reactivity fractions remain high.
Therefore, even with the maximum number of compounds used to calculate OH reactivity
(late April–early May) a large fraction of the measured total OH reactivity remains unexplained
(missing reactivity, Fig. e).
However, this period also coincided with high reactivity peaks observed likely due
to soil thawing as mentioned previously. Only sesquiterpenes peaked at the same time as the total OH reactivity,
but their concentrations are still low, which is why we mentioned amines and non-terpene BVOCs as potential
classes of compounds contributing to the observed total OH reactivity.
identified various non-hydrocarbon classes of compounds associated with biomass burning
that potentially contribute to OH reactivity. However, even if long-range-transported biomass burning emissions
are observed occasionally at the measurement site of this study , no significant
increase in CO concentrations above 250 ppbv was observed during the measurement period as in
. Only between 23 and 26 July were concentrations of 150 ppbv (slightly
above the average background levels of 100 ppbv) detected. Nevertheless, these classes of
compounds could potentially be emitted by local sources of a different kind.
As has been shown for forests dominated by isoprene emitters
, oxidation products from BVOCs might contribute
significantly to the missing OH reactivity. As oxidation products of monoterpenes and sesquiterpenes
are neither measured routinely nor were they monitored for this study, the SOSAA model was used
(see Sect. ) using measured trace gases and meteorological conditions as inputs in order
to calculate the potential contribution of terpene oxidation products to missing OH reactivity.
Three periods of 2 to 3 d for the months of April, May, and July were simulated. The results for
the inclusion of modelled oxidation compounds in the analysis are presented in Fig. .
These compounds labelled modelled OVOCs are mostly peroxides, alcohols, and carbonyl compounds due to the
generally low NOx levels at the site. Modelled inorganics, whose contributions are negligible, regroup
molecular hydrogen (H2), hydrogen peroxide (H2O2), nitrous acid (HONO), peroxynitric acid (HO2NO2),
nitric acid (HNO3), and the nitrate radical (NO3). To check the model performance for the concentrations of
the secondary organic species, we compared nopinone measured by GC–MS with the model output. The plots for all three
selected periods (Fig. in the Appendix) show that the trend of the model concentrations for nopinone
are comparable to the measured values and the absolute values are mostly inside estimated 50 % uncertainties of the
measurements.
The only exceptions are the difference during daytime in May when the measurements show a very strong decrease in
the morning, but the model follows this behaviour much more weakly.
No other specific secondary species is available for such comparison due to the lack of measurements.
Contributions of various compounds and groups of compounds to the
measured total OH reactivity (Rexp). For clarity, labels for
fractions smaller than 2.0 % have been omitted.
While the trend of ROH,model qualitatively follows the general trend of Rexp,
ROH,model underestimates Rexp, especially at night.
Total OH reactivity values are in general lower during the day and they are closer to
ROH,model values.
In late April and early May, the high peaks in the late afternoon indicate missing
primary emissions, which also contribute (or their oxidation products) to the missing
reactivity in the following nights.
Retrieving the additional reactivity from these modelled compounds that were not included in
ROH reduced the missing reactivity by only a small fraction (about 4.5 % for the
studied period in July and less for the other periods) as seen in Fig. .
A detailed breakdown of the individual compounds contributing to the reactivity and their
mixing ratios can be found in Appendix .
Most of the missing reactivity could then be due to oxidation products that are
not included in the model from measured precursors such as Δ3-carene, myrcene,
camphene, 1,8-cineol, β-farnesene, or unidentified sesquiterpenes (in contrast with the
well-studied isoprene chemistry scheme), but the
contribution to the OH reactivity from these precursors is small due to their low
atmospheric concentrations, so that the contribution from their oxidation products is also
expected to be small .
As mentioned earlier, missing primary emissions also contribute to the missing reactivity, more so
in spring than in summer.
Amines released from soil, as mentioned previously, are a potential class of compounds that
could contribute to OH reactivity. identified various non-hydrocarbon classes of
compounds associated with biomass burning that potentially contribute to OH reactivity.
It is also good to keep in mind that part of the missing reactivity can be
explained by measurement uncertainties and potential overestimation due to applied correction factors.
As the data in this study have been uniformly corrected based on α-pinene calibrations, it is
likely that the obtained values are an upper limit for the reactivity considering that α-pinene
(and monoterpenes in general) do not dominate the air composition and reactivity at the site all the
time.
A previous study by modelled the OH reactivity at the SMEAR II site
for the year 2008, using modelled emissions, and estimated the OH reactivity to be about 2–3 s-1
between April and July. This is lower than the measured averages from the present and previous studies
and lower than the night-time modelled values in July from the present study.
report that secondary organics, β-caryophyllene, farnesene,
and MBO represent 8 % of the total OH reactivity, which represents the same magnitude as the results
from this study.
modelled the OH reactivity at the same site for July and August 2010
with the same methodology (including minor model improvements) and obtained values between 2.7 and
3.2 s-1. The higher modelled values in our study indicate that
modelled emissions lead to lower monoterpene concentrations than measured concentrations.
Our results are not entirely in line with other studies that showed reductions of the missing reactivity
by constraining VOC concentrations to model their oxidation products
e.g., as the reduction observed
remains small in this study.
This approach still leaves a large unexplained fraction of OH reactivity. This is a strong indication
that, on the one hand, non-terpenoid compounds
contribute to the total OH reactivity and that, on the other hand, more compounds have to be included
in the chemical model.
Finally, heterogeneous loss of OH to particles might be a contribution to missing OH
reactivity, but this process is poorly quantified .
Due to the low sampling flow and long FEP sampling line to the CRM instrument, it is unlikely
that particles will reach the reactor. Additionally, we could not find any correlation between
ambient particle numbers and either total measured OH reactivity or its missing fraction.
As a side note, total OH reactivity measurements were unfortunately not
available in the autumn, but , who measured
nitrate radical (NO3) reactivity at the same site,
found about 30 % of unexplained NO3 reactivity at night and about
60 % during daytime.
modelled NO3 reactivity at the site and
found a maximum in the early morning, while the measurements from
showed a maximum
at night. The modelled NO3 reactivity values were similar to the measured
ones without strong temperature inversion at night, while higher measured
values were recorded for nights with strong temperature inversion.
showed that the balance between the emissions
of VOCs and the production of oxidation compounds and the sinks varies with the
season, leading to different diurnal profiles for compounds such as isoprene,
C7-10 aldehydes, and nopinone. This can also be observed in terms of
OH reactivity in the present study (see Sect. ).
Conclusions
Total OH reactivity is not a simple function of a few variables. It includes
many complex processes involving sources and sinks that can change dramatically
depending on the environmental conditions and the time of the year.
Measurement uncertainties and data availability for comparison between measured
total OH reactivity values and calculated values also represent a challenge
when interpreting results.
In the present study total OH reactivity measurements were performed at a
Finnish boreal forest research site (SMEAR II). The monthly averaged
experimental total OH reactivity was high in April and May (about 20 s-1)
due to some very large afternoon reactivity peaks captured when the soil was
thawing. The low sampling height and the peaking of sesquiterpene
emissions, which are known to be emitted from soil, at the same time as OH reactivity in April
indicate that the forest floor is a potential important but overlooked source of reactive compounds.
The total OH reactivity diurnal pattern from May to July follows that of biogenic compounds with
high values during the night due to the low mixing height, even though emissions are lower at night.
A suite of online and offline (O)VOCs measurements was used to calculate the
known fraction of OH reactivity to compare it to the total OH reactivity measured.
The missing fraction of the OH reactivity remained high for the measurement period.
This can be due to various reasons.
As the data availability of (O)VOCs varies, the comparison between experimental
and calculated OH reactivity is difficult but three different explanations can
lead to high missing (unexplained) OH reactivity: (1) simply the lack of
measurements, (2) not measuring oxidation products (only their precursors),
and (3) not measuring the right class of compounds.
We showed that compounds not included (or only partially included) in the analysis
due to the unavailability of measurements (e.g. due to technical problems), such as acetaldehyde, might
contribute a small but significant fraction to the total OH reactivity, in particular for low reactivity
values.
Using a one-dimensional transport model to estimate oxidation product
concentrations from measured precursor concentrations for three short periods
of 2 to 3 d in various months (with most (O)VOC data availability) it
is demonstrated that only a small fraction (up to ca. 4.5 %) of the missing
reactivity can be explained by these oxidation products.
On the one hand, this is due to the absence in the model of a degradation scheme
for detected compounds in the ambient air (e.g. Δ3-carene,
β-farnesene), but on the other hand it is also possible that non-hydrocarbon
compounds contribute to the OH reactivity as well.
More measurements of oxidized compounds and identification of non-terpene reactive compounds
from emissions from sources other than vegetation (e.g. soil) are required to better understand the
reactivity and local atmospheric chemistry in the forest air in general, in particular during winter, spring,
and autumn, when the forest air chemistry is not dominated by emissions from the vegetation.
Data availability
All the data whose source is not explicitly specified in the text are available upon reasonable request to the corresponding author.
Pseudo 1st-order-kinetics correction
Numerical simulations of Rtrue as a function of
Reqn for various pyr / OH values and their corresponding fit curves of the form
Rtrue=F1×ReqnF2+F3(a). Fit coefficients
F1, F2, and F3 as a function of pyr / OH and corresponding exponential fit curves (b, c, d).
Precision of the measurements
Uncertainty of Reqn (UReqn) as a function of Reqn‾. The solid line
is the fit function for the precision of the measurements (Uprec).
Comparison of measured and modelled nopinone
Mixing ratio of measured (red line and shaded area for 50 % uncertainty) and modelled (blue line) nopinone for
the three modelled periods in April, May, and July (a–c).
Details for the modelled periods
Averages of individual compound mixing ratios (pptv) and calculated OH reactivity, ROH (s-1),
and group ROH for the three periods studied with SOSAA. “n.d.” means “not detected” and “n.m.” means “not measured”.
29–30 April 22–25 May 24–26 July Mixing ratio (pptv) ROH (s-1) Mixing ratio (pptv) ROH (s-1) Mixing ratio (pptv) ROH (s-1) mean(SD)mean(SD)mean(SD)mean(SD)mean(SD)mean(SD)Alkanes0.065(0.013)3.5e-4(2.4e-4)3.5e-4(2.2e-4)Ethane2775(78)0.0115(0.0003)n.m.(–)–(–)n.m.(–)–(–)Propane576(79)0.014(0.002)n.m.(–)–(–)n.m.(–)–(–)n-Butane139(48)0.0078(0.0026)n.m.(–)–(–)n.m.(–)–(–)2-Methylpropane88(31)0.0045(0.0016)n.m.(–)–(–)n.m.(–)–(–)n-Pentane61(13)0.0055(0.0012)n.m.(–)–(–)n.m.(–)–(–)2-Methylbutane112(16)0.011(0.002)n.m.(–)–(–)n.m.(–)–(–)n-Hexane23(7)0.0028(0.0009)n.m.(–)–(–)n.m.(–)–(–)2-Methylpentane25(7)0.0034(0.0010)n.m.(–)–(–)n.m.(–)–(–)n-Heptane5.6(1.9)0.0020(0.0007)0.54(0.35)5.8e-5(10.9e-5)0.25(0.21)1.1e-5(3.8e-5)n-Octane7.3(2.5)0.0015(0.0005)1.0(0.4)1.9e-4(0.9e-4)1.9(0.4)3.2e-4(1.6e-4)n-Nonane3.3(1.2)8.1e-4(3.0e-4)0.43(0.14)1.0e-4(0.4e-4)0.13(0.07)2.4e-5(1.9e-5)n-Decane2.3(1.1)6.4e-4(3.1e-4)n.d.(–)–(–)n.d.(–)–(–)Alkenes0.38(0.04)–(–)–(–)Ethene354(26)0.077(0.006)n.m.(–)–(–)n.m.(–)–(–)Propene135(6)0.11(0.01)n.m.(–)–(–)n.m.(–)–(–)1-Butene47(4)0.042(0.004)n.m.(–)–(–)n.m.(–)–(–)trans-2-Butene46(8)0.086(0.016)n.m.(–)–(–)n.m.(–)–(–)cis-2-Butene27(4)0.043(0.007)n.m.(–)–(–)n.m.(–)–(–)1,3-Butadienen.d.(–)–(–)n.m.(–)–(–)n.m.(–)–(–)1-Pentene35(7)0.025(0.005)n.m.(–)–(–)n.m.(–)–(–)trans-2-Pentenen.d.(–)–(–)n.m.(–)–(–)n.m.(–)–(–)Ethyne260(21)0.0051(0.0004)n.m.(–)–(–)n.m.(–)–(–)Aromatics0.038(0.014)0.012(0.009)0.021(0.017)Benzene93(16)0.0028(0.0005)12(3)3.5e-4(1.0e-4)15(4)3.6e-4(2.0e-4)Toluene37(9)0.0058(0.0014)32(10)0.0046(0.0017)22(6)0.0026(0.0014)Ethylbenzene10(2)0.0018(0.0004)2.7(0.8)4.7e-4(1.5e-4)6.5(1.7)9.3e-4(5.0e-4)p/m-Xylene14(7)0.0067(0.0033)3.1(2.2)0.0014(0.0011)11(2)0.0041(0.0020)o-Xylene5.8(1.9)0.0020(0.0007)1.0(0.9)1.7e-4(2.9e-4)2.3(1.0)6.3e-4(4.1e-4)Styrene7.6(3.2)0.011(0.005)1.8(1.4)0.0025(0.0020)8.8(8.2)0.010(0.011)2-Ethyltoluene1.2(0.3)3.6e-4(1.0e-4)0.42(0.23)5.3e-5(7.7e-5)0.61(0.23)1.5e-4(0.9e-4)3-Ethyltoluenen.d.(–)–(–)0.80(1.53)3.1e-4(6.5e-4)0.28(0.16)1.1e-4(0.8e-4)4-Ethyltoluene0.12(0.05)3.6e-5(1.6e-4)0.27(0.12)3.2e-5(4.5e-5)0.35(0.29)3.2e-5(6.7e-5)1,2,3-Trimethylbenzene2.2(0.8)0.0019(0.0007)2.4(2.4)0.0019(0.0020)1.4(0.5)9.4e-4(5.7e-4)1,2,4-Trimethylbenzene3.3(1.0)0.0028(0.0008)0.56(0.52)2.8e-4(4.0e-4)0.44(0.21)2.9e-4(2.1e-4)1,3,5-Trimethylbenzene1.3(0.7)0.0021(0.0011)0.37(0.21)1.8e-4(3.1e-4)0.24(0.10)2.4e-4(1.9e-4)Isoprene3.9(3.0)0.010(0.009)8.0(6.4)0.020(0.017)29(14)0.060(0.041)Monoterpenoids0.53(0.39)0.47(0.53)1.7(1.4)α-Pinene221(143)0.33(0.22)120(134)0.16(0.19)635(318)0.70(0.51)β-Pinene27(26)0.059(0.058)24(27)0.047(0.053)105(74)0.17(0.15)Camphene22(16)0.030(0.022)29(29)0.037(0.038)52(33)0.057(0.047)Δ3-Carene44(35)0.10(0.08)72(82)0.16(0.18)224(141)0.40(0.33)p-Cymene5.5(2.3)0.0021(0.0009)23(24)0.0080(0.0087)11(5)0.0033(0.0022)Limonene1.8(1.4)0.0038(0.0061)12(14)0.050(0.058)97(65)0.33(0.28)Terpinolenen.d.(–)–(–)0.53(0.37)6.8e-4(16.0e-4)11(7)0.050(0.041)Myrcene0.25(0.26)1.6e-12(1.9e-12)4.2(3.0)1.8e-11(2.2e-11)14(8)8.0e-11(6.6e-11)1,8-Cineol2.6(2.4)7.6e-4(6.9e-4)12(9)0.0033(0.0024)22(9)0.0050(0.0032)Bornylacetate0.31(0.20)1.1e-4(0.7e-4)1.7(0.9)3.3e-4(3.8e-4)2.7(1.3)7.5e-4(5.2e-4)
Continued.
29–30 April 22–25 May 24–26 July Mixing ratio (pptv) ROH (s-1) Mixing ratio (pptv) ROH (s-1) Mixing ratio (pptv) ROH (s-1) mean(SD)mean(SD)mean(SD)mean(SD)mean(SD)mean(SD)Sesquiterpenes0.0015(0.0030)0.022(0.024)0.17(0.14)Longicyclene0.32(0.27)7.8e-5(6.6e-5)0.81(0.27)8.9e-5(10.4e-5)0.78(0.37)1.5e-4(1.0e-4)Iso-longifolene0.0600(0.0003)4.2e-5(6.8e-5)0.28(0.13)3.6e-5(16.8e-5)n.d.(–)–(–)β-Farnesenen.d.(–)–(–)n.d.(–)–(–)4.0(1.4)0.014(0.008)β-Caryophyllene0.94(0.60)0.0013(0.0027)7.3(3.7)0.020(0.023)28(16)0.11(0.09)α-Humulene0.0514(0.0001)7.1e-5(14.9e-5)0.21(0.15)0.0014(0.0011)n.d.(–)–(–)SQT1∗n.d.(–)–(–)n.d.(–)–(–)2.7(1.5)0.0055(0.0042)SQT2∗n.d.(–)–(–)n.d.(–)–(–)5.4(3.3)0.011(0.009)SQT3∗n.d.(–)–(–)n.d.(–)–(–)4.5(2.5)0.0072(0.0072)SQT4∗n.d.(–)–(–)n.d.(–)–(–)12(6)0.017(0.018)GLVs–(–)0.0021(0.0020)0.013(0.019)1-Hexanoln.d.(–)–(–)n.d.(–)–(–)7.8(4.2)0.0010(0.0017)cis-2-Hexen-1-oln.d.(–)–(–)n.d.(–)–(–)n.d.(–)–(–)trans-2-Hexen-1-oln.d.(–)–(–)n.d.(–)–(–)n.d.(–)–(–)cis-3-Hexen-1-oln.d.(–)–(–)n.d.(–)–(–)4.3(2.4)0.0026(0.0056)trans-3-Hexen-1-oln.d.(–)–(–)n.d.(–)–(–)6.7(1.5)0.0063(0.0096)trans-2-Hexenaln.d.(–)–(–)2.4(1.8)0.0021(0.0020)3.3(2.1)0.0027(0.0025)Hexylacetaten.d.(–)–(–)n.d.(–)–(–)n.d.(–)–(–)cis-3-Hexenylacetaten.d.(–)–(–)n.d.(–)–(–)n.d.(–)–(–)trans-2-Hexenyl-acetaten.d.(–)–(–)n.d.(–)–(–)n.d.(–)–(–)Aldehydes0.10(0.07)0.075(0.051)0.35(0.12)Formaldehyde122(111)0.028(0.025)n.m.(–)–(–)620(90)0.13(0.02)Acetaldehyde16.5(0.1)0.0018(0.0030)n.m.(–)–(–)342(62)0.13(0.02)Propanal86(32)0.046(0.017)93(49)0.040(0.027)112(36)0.017(0.027)Butanaln.d.(–)–(–)4.7(1.5)3.9e-4(10.4e-4)17(26)0.0085(0.0148)Pentanal19(6)0.015(0.005)24(20)0.011(0.015)41(16)0.028(0.011)Hexanal8.03(0.04)0.0017(0.0029)7.3(3.3)0.0052(0.0025)17(8)0.012(0.005)Heptanaln.d.(–)–(–)5.9(1.5)0.0043(0.0013)0.10(0.08)1.7e-6(14.3e-6)Octanaln.d.(–)–(–)4.2(1.0)0.0032(0.0009)6.1(1.7)0.0040(0.0021)Nonanaln.d.(–)–(–)2.8(1.0)0.0024(0.0009)9.4(4.1)0.0069(0.0045)Decanaln.d.(–)–(–)3.1(0.8)0.0026(0.0008)9.9(3.0)0.0070(0.0039)Methacrolein8.0(1.4)0.0030(0.0033)8.0(3.3)0.0058(0.0025)7.1(6.5)0.0043(0.0045)Crotonaldehyde1.6(0.1)7.9e-4(7.1e-4)n.m.(–)–(–)n.d.(–)–(–)Benzaldehyde26(2)0.0016(0.0035)n.m.(–)–(–)n.d.(–)–(–)Tolualdehyde75(7)0.0060(0.0129)n.m.(–)–(–)n.d.(–)–(–)Alcohols0.086(0.080)0.21(0.56)0.050(0.123)Isopropanol26(6)0.0035(0.0008)37(29)0.0041(0.0039)171(95)0.0069(0.0121)1-Butanol366(349)0.083(0.079)1122(2704)0.21(0.55)614(745)0.041(0.105)1-Pentanoln.d.(–)–(–)3.7(1.4)6.5e-5(28.5e-5)8.6(3.3)7.6e-4(129e-4)1-Penten-3-oln.d.(–)–(–)1.9(0.7)4.1e-4(12.0e-4)3.6(2.3)0.0015(0.0034)3-Methyl-2-buten-1-oln.d.(–)–(–)n.d.(–)–(–)n.d.(–)–(–)1-Octen-3-oln.d.(–)–(–)n.d.(–)–(–)1.5(0.4)2.6e-4(5.9e-4)2-Methyl-3-buten-2-ol (MBO)5.4(4.6)0.021(0.018)15(16)0.054(0.058)47(28)0.14(0.11)Other carbonyls0.014(0.018)0.0012(0.0019)0.050(0.022)Acetone (and acrolein)3060(4141)0.012(0.017)n.m.(–)–(–)9161(1632)0.038(0.007)6-Methyl-2-hepten-3-onen.d.(–)–(–)n.d.(–)–(–)1.5(0.6)1.5e-4(6.3e-4)Methyl ethyl ketone (MEK)n.d.(–)–(–)n.d.(–)–(–)9.0(0.3)2.3e-4(0.5e-4)Butylacetate2.9(1.3)5.1e-5(14.3e-5)n.d.(–)–(–)n.d.(–)–(–)4-Acetyl-1-methylcyclohexenen.d.(–)–(–)1.3(0.6)2.2e-4(10.5e-4)4.5(4.0)0.0082(0.0120)Nopinone4.8(3.2)0.0018(0.0012)2.9(2.5)0.0010(0.0009)10(4)0.0030(0.0019)
Continued.
29–30 April 22–25 May 24–26 July Mixing ratio (pptv) ROH (s-1) Mixing ratio (pptv) ROH (s-1) Mixing ratio (pptv) ROH (s-1) mean(SD)mean(SD)mean(SD)mean(SD)mean(SD)mean(SD)Organic acids0.071(0.013)0.024(0.018)0.029(0.044)Acetic acid2800(446)0.060(0.008)1507(430)0.020(0.014)3007(283)0.017(0.026)Propanoic acid142(25)0.0044(0.0008)84(26)0.0018(0.0013)160(52)0.0015(0.0024)Butanoic acid98(37)0.0046(0.0017)58(33)0.0019(0.0017)152(26)0.0021(0.0032)Isobutanoic acidn.d.(–)–(–)n.d.(–)–(–)32(14)2.8e-4(6.9e-4)Pentanoic acid20(13)0.0016(0.0015)21(11)5.5e-4(10.9e-4)176(36)0.0057(0.0086)Isopentanoic acid1.0(0.2)1.3e-6(12.2e-6)2.0(0.5)2.5e-6(23.4e-6)8.5(5.2)2.2e-4(4.6e-4)Hexanoic acid5.9(2.1)5.2e-5(21.6e-5)9.7(2.8)5.5e-5(27.5e-5)35(11)0.0015(0.0024)4-Methylpentanoic acidn.d.(–)–(–)n.d.(–)–(–)n.d.(–)–(–)Heptanoic acidn.d.(–)–(–)n.d.(–)–(–)13(3)5.0e-4(9.5e-4)Inorganics1.2(0.2)1.0(0.2)1.1(0.2)NO77(41)0.012(0.012)89(52)0.013(0.013)69(43)0.010(0.010)NO2449(374)0.14(0.12)418(295)0.11(0.08)149(94)0.033(0.027)O34.3e4(9e3)0.066(0.015)4.2e4(7.4e3)0.69(0.014)2.7e4(8.9e3)0.046(0.016)SO253(60)9.0e-4(13.1e-4)37(24)5.8e-4(5.4e-4)74(114)0.0012(0.0021)CO1.29e5(9e3)0.72(0.06)1.10e5(6e3)0.58(0.03)1.4e5(2e4)0.74(0.11)CH41.938e6(2e3)0.23(0.01)1.923e6(5e3)0.26(0.02)1.9e6(2.2e-16)0.28(0.01)Model OVOCs0.19(0.06)0.20(0.09)0.63(0.29)Model inorganics0.057(0.008)0.084(0.026)0.075(0.012)Missing61(40)13(8)16(8)Total64(41)15(10)20(10)
∗ Quantified with β-caryophyllene calibration and an estimated reaction coefficient (1e-10 cm3 s-1).
Author contributions
APP conducted total OH reactivity measurements, offline sampling, and LC–UV analysis; performed
data analysis; and led the writing of the paper. HH designed the measurement campaign,
conducted GC–MS measurements and data analysis, and commented on the paper. TT assisted with GC–MS
measurements and data analysis and commented on the paper. VV provided mixing layer height
data and their description in the method part and commented on the paper. DC, MB, and PZ
performed model runs with the help of DT and all commented on the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors thank Hannele Hakola for the continuous support. They also thank the staff at the
SMEAR II station for their help, Petri Keronen for providing the data that we retrieved from SmartSMEAR,
Jari Waldén for lending calibration standards and gas analysers, and Anne-Mari Mäkelä
for the analysis of the canister samples. The authors also wish to acknowledge the CSC IT Center for Science,
Finland, for computational resources.
Financial support
This research has been supported by the Academy of Finland (grant no. 275608), the Academy of Finland, Biotieteiden ja Ympäristön Tutkimuksen Toimikunta (grant no. 307957), and the Academy of Finland (grant no. 272041).
Review statement
This paper was edited by Yugo Kanaya and reviewed by two anonymous referees.
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