Measurements were conducted at SMEAR II in Hyytiälä, Finland, 61∘51′ N, 24∘17′ E, 181 m above sea level; see, about 60 km northeast of the city of Tampere. The station is located in a ca. 60-year-old managed mixed conifer forest dominated by Scots pine (Pinus sylvestris) homogeneously for about 200 m in all directions from its mast, which carries instrumentation for various observations. These data and additional data acquired at the site are available via the Smart-SMEAR portal https://avaa.tdata.fi/web/smart/smear/search, last access: 7 February 2020;.

For this study, the measurements were done at a container located next to an opening about 115 m south of the mast. The instrumentation to measure VOC emissions (Sect. ) and TOHRE (Sect. ) was located inside the container. The seedlings used in this study (Sect. ) were located just outside of the container and received direct sunlight for most of the day. Branch enclosures (Sect. ) were used to investigate their emissions.

Seedlings for each of the studied tree species – Scots pine (Pinus sylvestris), Norway spruce (Picea abies), and downy birch (Betula pubescens) – were brought from a commercial nursery (Harviala Oy, Harviala, Finland) to the site. The seedlings were 100–150 cm tall, and they were planted in 10 L plastic pots in a mixture of sand and peat and were watered regularly. The use of seedlings in pots was mostly practical as it was easier to bring them close to the instruments that characterise the emissions; moving the instruments' container closer to the trees of interest is not possible. Additionally, extremely long sampling lines and wall losses could be avoided. Emissions from the seedlings might not be representative per se. Nevertheless, put in perspective with results from other studies, they provide valuable information for any potential upscaling effort.

For each tree, the enclosure was moved to a different branch twice during the campaign. Each time this occurred and at the end of the last measurement period, the branch from which the emissions were measured last was cut in order to determine the dry weight of the needles' or leaves' biomass for three periods for each tree. To do so, the needles or leaves from the cut branches were dried at 80 ∘C overnight and subsequently weighed. Dry weights of the needles or leaves of the different branches can be found in Table  in Appendix B.

No correction for the growth of the biomass was applied during the growth period (May–June) as the cutting of the branches happened in general right after the measurement period, so it can be assumed that the changes in biomass remain small compared to other uncertainties of total OH reactivity measurements.

showed that branch-to-branch variability (for sun-exposed branches) is of a similar magnitude as tree-to-tree variability (for the evergreen oak). However, a large difference (190 %) was observed between sun-exposed branches and shade-adapted branches. In our study, the branches are exposed to both sunlight and shade, depending on the time of the day. We assume that the variability from branch to branch to also be similar to that from tree to tree in the present study.

describe the method used in detail. Briefly, the enclosure consists of a ca. 6 L cylinder made of transparent Teflon, which is attached to the branch on one side and to a Teflon frame equipped with inlet and outlet ports on the other side. VOC-free air provided by a generator (HPZA-7000, Parker Balston, Lancaster, NY, USA) flows through the enclosure at about 4 L min-1 (flow f). The relative humidity (RH) and the temperature in the enclosure were recorded with a thermistor (Philips KTY 80/110, Royal Philips Electronics, Amsterdam, Netherlands), and the photosynthetically active radiation (PAR) was measured with a quantum sensor (LI-190SZ, LI-COR, Biosciences, Lincoln, USA) placed on top of the enclosure frame.

In this study, three branch enclosures were used so that they could be set up 1 or 2 weeks before the measurements of the emissions in order to reduce the stress (and the association emissions) caused by handling the branches to a minimum. During that time, the enclosure was left open, and it was only when the measurement started that the enclosure was carefully closed with transparent Teflon film, which could nevertheless result in a low level of stress.

The temperature difference between ambient conditions and those inside the enclosure is presented in Appendix C (Fig. ). For a large majority of the data (74 %), the difference lies within 3 ∘C. For another 22 % of the data, the difference is between 3 and 10 ∘C. The maximum temperature difference is 27.5 ∘C. Large temperature differences happened when prolonged direct sunlight heated up the enclosure.

Volatile organic compounds (VOCs) were measured with two in situ gas chromatographs coupled with mass spectrometers (GC–MSs), which have been previously described in more detail by . One GC–MS measured the concentrations of mono- and sesquiterpenes, isoprene, 2-methyl-3-butenol (MBO), and C5–10 aldehydes in the emissions. These compounds were collected for 30 min from a 40 mL min-1 subsample flow of the CRM instrument sampling flow in the cold trap (Carbopack B/Tenax TA) of the thermal desorption unit (TurboMatrix, 650, Perkin-Elmer) connected to the GC (Clarus 680, Perkin-Elmer) coupled with the MS (Clarus SQ 8 T, Perkin-Elmer). A HP-5 column (60 m, i.d. 0.25 mm, film thickness 1 µm) was used for separation. The instrument was calibrated for MBO, aldehydes, and mono- and sesquiterpenes using liquid standards in methanol solutions. Isoprene was calibrated using a gaseous standard (National Physical Laboratory, 32 VOC mix at the 4 ppbv level). Limits of detections for mono- and sesquiterpenes are between 0.5 and 4.7 pptv, and the uncertainty of the measurements lies at 17 %–20 % .

The other GC–MS measured the concentrations of alcohols and volatile organic acids (VOAs). Every other hour, a sample was taken for 60 min and analysed with a thermal desorption unit (Unity 2 + Air Server 2, Markes International Ltd, Llantrisant, UK) connected to the GC (Agilent 7890A, Agilent Technologies, Santa Clara, CA, USA) and the MS (Agilent 5975C, Agilent Technologies, Santa Clara, CA, USA). A polyethylene glycol column DB-WAXetr (30 m, i.d. 0.25 mm, film thickness 0.25 µm) was used for the separation. These compounds were calibrated as well with standards in methanol solutions. The detection limits are in the 1–130 pptv range, and the uncertainty is 32 %–76 % .

For both instruments, measured compounds that had no standard available were quantified using calibrations of similar compounds. The uncertainty and the detection limits were estimated the same way.

OH reactivity, ROH, can be calculated from the sum of the concentration of individually emitted compounds Xi, [Xi], multiplied by their respective reaction rate coefficient with OH (kOH+Xi): 1ROH=∑i[Xi]kOH+Xi.

The experimental total OH reactivity, Rexp, can be measured with the comparative reactivity method CRM; . The specific instrument used for this study is described in , and the measurement principle is briefly explained in the following section together with the application of the method to measure the OH reactivity of emissions (OHRE).

We used a typical model for VOC emissions to test the light and temperature dependence of TOHRE. The temperature-only dependence is the same dependence as for monoterpene emissions and is expressed with the following equation: 10TOHRE=TOHRES⋅exp[β(T-TS)].

TOHRES is the TOHRE at standard temperature TS (303 K), and T is the leaf surface temperature. In the present study, we assume that the leaf surface temperature, which was not recorded, is roughly the same as the temperature inside the enclosure. mention that for a similar system as the one used in the present study, the leaf temperature is at most 2 K higher than in the enclosure. β describes the temperature dependence (so-called β-factor) and is estimated to be 0.09 K-1 for monoterpenes.

A hybrid algorithm based on both temperature and light can be used to model emissions that also follow in illumination . The dependence on light and temperature for TOHRE is then formulated as follows: 11TOHRE=TOHRE0,pool⋅exp[β(T-TS)]+TOHRE0,synth⋅cL⋅cT, with TOHRE0,pool and TOHRE0,synth as the standard TOHRE pool emission potential (stored compounds, temperature dependent) and TOHRE synthesis emission potential (newly synthesised compounds, light- and temperature-dependent), respectively. Additionally, cL and cT are light and temperature activity coefficients, respectively, defined as 12cL=αcL1Q1+α2Q2,13cT=expcT1(T-TS)RTST1+expcT2(T-TM)RTST. T and TS are the same as above, and Q is the PAR measured just above the enclosure. The empirical coefficients are α (0.0027), cL1 (1.066), cT1 (95 000 mol J-1), cT2 (230 000 mol J-1), and TM (314 K). Finally, R is the gas constant (8.314 J K-1 mol-1).

An overview of monthly averages for TOHRE and missing TOHRE (absolute and fraction) can be found in Table . The highest TOHRE monthly averages were found for birch in May and June (1.6–2.6 10-3 m3 s-2 gdw-1), which is mostly unaccounted for (missing OHRE fraction 96 %–99 %). The monthly TOHRE averages from spruce were high in July and August (1.1–1.5×10-3 m3 s-2 gdw-1), while the highest monthly average for TOHRE from pine was in July (6.1×10-4 m3 s-2 gdw-1). A few compounds per class of biogenic VOCs were identified as the main drivers of the reactivity, which will be discussed in the following subsections for each tree individually.

In general, the missing OHRE fraction was higher in spring and decreased as the seasons proceeded (see Table ). The missing OHRE fraction from birch remained high from May to July (99 %–84 %), making it the least understood reactivity. Pine and spruce had similar fractions of missing OHRE (59 %–78 % and 56 %–82 %, respectively), partly due to uncertainties on both the measured TOHRE and COHRE. For TOHRE, the correction for deviation from pseudo first-order kinetics applied to CRM data is based on calibration with α-pinene as a surrogate for biogenic emissions, but monoterpenes do not always represent the largest fraction of the emissions, which result in some uncertainty in TOHRE (roughly a factor of 1.5 at most). On the other hand, unidentified sesquiterpenes have been found in emissions from all three tree species (see Appendix ), their quantification was performed using surrogates, and their reaction rates were assumed to be average based on the reaction rates for other sesquiterpenes (10-10 cm3 s-1). COHRE is derived from up to 67 compounds. Considering the uncertainty from the GC–MS measurements and from the reaction rates used to derive COHRE for its contributing compounds (never all 67 compounds simultaneously), the estimated uncertainty of COHRE stays mostly around 25 %–50 %.

This also introduces some uncertainty. Notwithstanding these uncertainties, it will appear in the following discussion that the averages of high missing OHRE values are driven by low reactivity values and measurement scatter. When TOHRE is clearly above the background values, the missing fraction is reduced, which indicates a generally good understanding of emissions with the exception of periods dominated by green leaf volatiles (GLVs). GLVs form a family of C6 compounds, including aldehydes, alcohols, and esters, which are emitted rapidly and in large amounts during stress periods e.g.. Stress can have various abiotic and biotic causes (e.g. drought, attack by pathogens or herbivores). During these periods, TOHRE values were high, but the missing fraction also remained high, and this cannot be explained only by measurement and calculation uncertainties.

The results also illustrate how reactive biogenic emissions are influenced by time of the year and the tree species found in the forested areas. In addition, high measured TOHRE is related to a change in the emission profiles with a larger fraction of GLVs.

The data for pine are shown in Fig. , divided into periods labelled with “P” (for “pine”), a number (for each different branch measured), and possibly a letter to indicate various measurement periods of the same branch. Branches were cut on 15 June, 16 August, and 11 October. TOHRE, its 1 h average, and COHRE are displayed in the top row, the relative contribution of measured compounds to COHRE is shown in the middle row, and the missing OHRE fraction is shown in the bottom row.

The highest TOHRE values from pine were measured in early July and early October. These two periods, the end of period P2.a (3–5 July), and the beginning of period P3.b (4–11 October) are marked with a fraction of GLVs up to roughly 35 % (mostly due to cis-3-hexenol). At the same time, emissions from monoterpenes and terpinolene increase as well. Between 3 and 5 July, TOHRE increased and was high even at night, while it is usually close to zero at that time. Interestingly, 3 July marks the end of a warm and sunny period, with a maximum temperature in the branch enclosure of 30–40 ∘C for 5 d in a row and the beginning of a cooler and cloudier period with some precipitation. It is not clear, though, whether stress emissions are related to the change in environmental conditions or if they are a result of stress experienced during the previous days.

Despite these observations, and with TOHRE being usually higher than COHRE, both display a similar time evolution. The Pearson's correlation coefficient between the overlapping periods of TOHRE and COHRE r is 0.89 (p value < 0.01).

The known OH reactivity for pine emissions is dominated by monoterpenes with a small fraction of sesquiterpenes, as expected from earlier studies . The profiles of known emissions from all three branches of this same seedling are similar. Here, COHRE is mostly driven by α-pinene, limonene, and Δ3-carene. Sesquiterpenes (mostly α- and β-farnesene) contribute up to 15 % of the known OH reactivity, and MBO represents an important fraction, especially in June and July. In September, the missing fraction is lowest due to the low TOHRE values measured, which are in the same range as the COHRE values (only with a much larger scatter).

found higher missing reactivity for ambient measurements for stress periods (elevated temperature) at SMEAR II, a boreal site dominated by Scots pine. In our study, these stress periods for pine, identified with GLV emissions, are not related to elevated temperature (see Sect. ). Missing OHRE was generally higher during these periods, but as terpenoids were monitored, they cannot explain the stress-related emissions of reactivity. Some oxidised volatile organic compounds were also measured, but not methanol, formaldehyde, and acetaldehyde, for instance, which could contribute – at least in part – to the missing OHRE. Paired sample t tests to compare monthly averages with each other show that the average missing OHRE values in June and July are not significantly different (p value 0.52), while it is significantly lower in August (p value < 0.05).

The data for spruce are shown in Fig. , and similarly to the previous section, they are divided into periods labelled with “S” (for “spruce”), a number (for each different branch measured), and possibly a letter to indicate various measurement periods of the same branch. Branches were cut on 21 June, 9 August, and 5 November. TOHRE, its 1 h average, and COHRE are displayed in the top row, the relative contribution of measured compounds to COHRE is shown in the middle row, and the missing OHRE fraction is shown in the bottom row.

For spruce, TOHRE follows the time evolution of COHRE, even though their absolute values do not match. The Pearson's correlation coefficient r for the TOHRE and COHRE overlapping periods is 0.78 (p value < 0.01). The highest TOHRE values are observed at the beginning of July (period S2), with one extremely high peak over 0.06 m3 s-2 gdw-1 on 9 July and another TOHRE peak the next day. However, almost all reactivity can be explained by monoterpenes and GLVs during that period (mostly cis-3-hexen-1-ol and cis-3-hexenylacetate, as well as limonene). found relatively high emissions of higher aldehydes, especially nonanal and decanal. In our study, these high emissions could not be observed, and their contribution to OHRE remained small.

It was dry and sunny on 9 and 10 July, with maximum temperatures in the branch enclosure close to 40 ∘C. After that, when the weather gets cooler and cloudier with some precipitation between 11 and 14 July, the GLV fraction decreases, and monoterpenes and sesquiterpenes account for most of the known reactivity. This is in stark contrast with the observed stress emissions from pine in this study, which increased during the colder period, after a warm spell.

The known reactivity of the emissions in May (periods S1.a and S1.b) is dominated by monoterpenes, which was expected from earlier studies . The main drivers are limonene, β-pinene, and β-phellandrene. Between 19 and 23 August (period S3.a), high TOHRE values (up to 0.01 m3 s-2 gdw-1) were measured (including at night), similarly to the stress period observed for pine. It can be seen that, during these periods with a larger fraction of GLVs, some needles were drying and falling (Appendix ), which confirms that the tree suffered stress (most probably drought). Other environmental conditions did not change much during that period, which was relatively cool and cloudy.

In contrast to stress periods in pine, monoterpene emissions from spruce were low when the GLV fraction increased. During this period, cis-3-hexen-1-ol, cis-3-hexenylacetate, and trans-2-hexenal mostly contribute to COHRE. In September, this branch had low TOHRE, and the known reactivity of the emissions was caused by monoterpenes and sesquiterpenes, similarly to the period between 16 and 19 August, before the large stress episode. The biggest contributor to the sesquiterpene reactivity fraction was α-farnesene (here and for other periods as well). The increase of the sesquiterpene fraction in the emissions is in agreement with observations from (up to 75 % of the emissions in late summer, mostly β-farnesene). In their study, they speculated on the possible defensive role of sesquiterpenes, but the lack of any visible infestations of feeding herbivores indicated a systemic defence mechanism rather than a direct one.

A direct comparison with the results for TOHRE and the missing OHRE of spruce from is difficult due to the many factors affecting the emissions. In addition, because of the different method used, report TOHRE with the units of m-3 s-2 gdw-1, while the present study reports TOHRE in m3 s-2 gdw-1. Nevertheless, knowing the volume of the enclosure used in 15 L;, it is possible to convert the values for quantitative comparison. reported TOHRE values for spruce ranging from 0.1639 (early autumn) to 0.8260 m-3 s-2 gdw-1 (later summer), corresponding to 3.69×10-5 and 1.86×10-4 m3 s-2 gdw-1, respectively. TOHRE measured in this study ranged from 2.5×10-4 to 1.5×10-3 m3 s-2 gdw-1, so clearly higher values. This reflects the variability of reactive emissions due to the age of the tree (seedlings in the present study) and the level of stress experienced by the tree. Moreover, they found that the missing OHRE fraction was lower in the spring and increased in the late summer and autumn to 70 %–84 %, while the present study suggests that the missing OHRE fraction decreases from May to August. Paired sample t tests show that the decrease is significant. All monthly averages are statistically different from each other (p values < 0.05). As discussed earlier, lots of high missing OHRE in the present study stems from low-reactivity periods with high scatter for TOHRE and values close to zero for COHRE. However, because assume a constant emission profile (measured in spring) throughout the year and otherwise rely on unspeciated data from a proton-transfer-reaction MS (PTR-MS), it is imaginable that the chemical compositions of the emissions changed with the season to more reactive monoterpenoids or sesquiterpenes, leading to an underestimation of the calculated OH reactivity.

The data for birch are shown in Fig. , and similarly to the previous sections, they are divided into periods labelled with “B” (for “birch”), a number (for each different branch measured), and possibly a letter to indicate various measurement periods of the same branch. Branches were cut on 21 June, 9 August, and 6 September. TOHRE, its 1 h average, and COHRE are displayed in the top row, the relative contribution of measured compounds to COHRE is shown in the middle row, and the missing OHRE fraction is shown in the bottom row.

The observed TOHRE shows relatively high values (due to the low dry weight mass) with almost no diurnal pattern. In late June (period B2.a), a weak pattern can be observed, and in mid-July (period B2.b), a few reactivity peaks can be observed. It is possible that the constant blank value subtracted from the measurements sometimes underestimates the actual background of the measurements, leading to high missing reactivity values because of overestimated TOHRE values. For this reason, the missing OHRE values reported during periods when TOHRE is close to the detection limit or does not display diurnal variation should be considered with caution.

Here, the Pearson's correlation coefficient r between TOHRE and COHRE for the periods when both are available is very low (0.02, p value 0.4), and the missing fraction of OHRE is consistently high with a statistically significant decrease from May to July (p value from paired-sample t tests < 0.05) . This is partly due to the generally low values of COHRE, which are dominated by sesquiterpenes for the first two branches (periods B1, B2.a, and B2.b), with a significant amount of monoterpenes (up to 40 %). Instances when the known reactivity is dominated by organic acids are the result of missing terpene measurements. In May (period B1), β-caryophyllene, α-humulene, another unidentified sesquiterpene, and sometimes cis-3-hexenylacetate contribute most to the reactivity of the emissions. In June and July (periods B2.a and B2.b), the emission profile is slightly different with β-caryophyllene, α-farnesene, linalool, and sometimes cis-3-hexenylacetate and cis-3-hexen-1-ol (co-emitted) contributing most.

For the last branch measured in August (period B3), a significant fraction (up to 50 %) of the known reactivity comes from GLVs (again, cis-3-hexenylacetate and cis-3-hexen-1-ol), but the fraction of sesquiterpenes (mostly α-farnesene) is smaller, while monoterpenes (carene, α-pinene, and α-terpineol) contribute more. Pictures in Appendix  show how some leaves turned brown, possibly indicating the end of the growing season and the senescence of the leaves.

found a large fraction of α-farnesene in mountain birch emissions in a given year, but they stressed that there was an important inter-annual variation in the emission profile, with almost no α-farnesene detected the following year for the same branch.

To also study the dependence of TOHRE on temperature, TOHRE has been plotted against the temperature in the enclosure, and exponential regressions using Eq. () have been performed (Fig.  and Table ). Excluding data when the temperature in the enclosure is higher than 30 ∘C leads to a change in β-factors within 15 % of the values reported here, except for the notable exception of the β-factor for spruce emissions in July, as discussed below. Similar figures for COHRE and missing OHRE can be found in Appendix , showing similar findings to TOHRE dependence on temperature.

Good correlations with temperature are found for the TOHRE of pine in June and August (R=0.70 and 0.61, respectively), in May and July for spruce (R=0.59 and 0.50), and in July for birch (R=0.71). Periods with no correlation were connected to either abiotic stress events (particularly July for pine) increasing TOHRE at night, or low TOHRE values during the daytime (as in September for pine and spruce and May–June for birch), possibly due to cooler and cloudier weather. Because of this, averaging the whole data set leads to low coefficients of correlation (R=0.23–0.37).

Considering values of β-factors from monthly regressions with R>0.5, they range from 0.0246 to 0.1853 K-1. recommended a value of 0.10 K-1 to model monoterpene emissions. For sesquiterpenes, average values of 0.14–0.22 have been reported e.g., even though values as low as 0.025, 0.05, and 0.056 were also found respectively. For pine, which is dominated by monoterpene emissions, β-factors are about 0.09–0.10 K-1 except for stress periods, when the β-factor is smaller than 0.003. For spruce, β-factors increase from 0.02 to 0.19 K-1 between May and July, demonstrating a clear regime change in the temperature dependence of the emissions, with an increasing contribution of less-volatile compounds (sesquiterpenes and GLVs). However, leaving out values when the temperature is higher than 30 ∘C, the β-factor for spruce in July is 0.093, indicating that the highest temperatures (that are not ambient temperatures in the boreal forest) might trigger emissions of more reactive species, which is not the case for usual warm summer temperatures in this type of forest. These changes in emission profiles are possibly driven by the volatility of the compounds emitted. For birch, when a good correlation with temperature was found (R=0.71) in July, the β-factor remains low, even though emissions are dominated by sesquiterpenes. This might be an indication of emissions of non-terpenoid volatile compounds.

The results of using Eq. () to include the effect of light on TOHRE (Hybrid algorithm, Table ) show that, in general, only small improvements (increases of R) are achieved. In a few cases, R was even slightly reduced. One notable exception is a large improvement of the coefficient of correlation R from 0.5 to 0.9 for spruce in July. The addition of a small TOHRE0,synth term seemed to be enough to capture the large peak that was reported as stress, indicating a radiation-induced stress in this case.

In general, β-factors are very similar to the results of the regression for the temperature-only dependence (when a good correlation was found in the first place). Note that in September (and to some extent in August), the temperature range remains small (about 10 K) and on the lower end, so that nothing conclusive can be inferred from these results. In summary, the effect of light on reactive remissions remains limited in the present study, but other factors such as abiotic stress can play a major role in the type and amount of reactive emissions.