Responses to reviewers’ comments for manuscript Measurement report: Molecular composition and volatility of gaseous organic compounds in a boreal forest: from volatile organic compounds to highly oxygenated organic molecules

The molecular composition and volatility of gaseous organic compounds were investigated during April–July 2019 at the Station for Measuring Ecosystem – Atmosphere Relations (SMEAR) II situated in a boreal 15 forest in Hyytiälä, southern Finland. In order to obtain a complete picture and full understanding of the molecular composition and volatility of ambient gaseous organic compounds (from volatile organic compounds, VOCs, to highly oxygenated organic molecules, HOMs), two different instruments were used. A Vocus proton-transferreaction time-of-flight mass spectrometer (Vocus PTR-ToF; hereafter Vocus) was deployed to measure volatile organic compounds (VOCs) and less oxygenated VOCs (i.e., OVOCs). In addition, a multi-scheme chemical 20 ionization inlet coupled to an atmospheric pressure interface time-of-flight mass spectrometer (MION APiAPIToF) was used to detect less oxygenated VOCs (using Br as the reagent ion; hereafter MION-Br) and more oxygenated VOCs (including highly oxygenated organic molecules, HOMs; using NO3 as the reagent ion; hereafter MION-NO3). The comparison among different measurement techniques revealed that the highest elemental oxygen-to-carbon ratios (O:C) of organic compounds were observed by the MION-NO3 (0.9 ± 0.1, 25 average ± 1 standard deviation), followed by the MION-Br (0.8 ± 0.1); and lowest by Vocus (0.2 ± 0.1). Diurnal patterns of the measured organic compounds were found to vary among different measurement techniques, even for compounds with the same molecular formula, suggesting contributions of different isomers detected by the different techniques and/or fragmentation from different parent compounds inside the instruments. Based on the complementary molecular information obtained from Vocus, MION-Br, and MION-NO3, a more complete picture 30 of the bulk volatility of all measured organic compounds in this boreal forest was obtained. As expected, the VOC class was the most abundant (about 49.4 53.2%), followed by intermediate-volatility organic compounds (IVOC, about 48.945.9 %). Although condensable organic compounds (low-volatility organic compounds, LVOC; extremely low-volatility organic compounds, ELVOC; and ultralow-volatility organic compounds, ULVOC) only

6 isomers is the rule, not the exception, at that different isomers may have significantly different instrument sensitivities, the authors should keep this in mind as they interpret their results. Its not really clear to me why diurnals tend to be the metrics by which isomer composition is being compared -why not point-by-point correlations, which should be high if they are truly the same isomers? One suggestion is, while isoprene is lower than monoterpenes, you may see the C5-methyltetrols (C5H12O4). This specific species is helpful because there are not a lot of likely ways to draw that formula since it is saturated and a dominant isoprene product (though there are a few peroxide options), so if multiple instruments see it, it might give some benchmark as to how correlated ions might be when they are very likely the same set of isomers.
We agree with the reviewer that different instruments may have very different sensitivities towards isomers. As also pointed out by the reviewer in the "Specific comments" 8, even with the same diurnal patterns and high point-by-point correlations, it's still possible to be different isomers (e.g., different isomers of monoterpenes could have similar diurnal patterns). Therefore, we have abandoned the scaling approach through comparing the diurnal patterns of organic compounds observed in common by  Instead, after comparing the ambient sulphuric acid concentrations measured by , we scaled the sulphuric acid calibration factor of MION-Br to that of . The reason why we scaled the sulphuric acid calibration factor of MION-Br to that of MION-NO3 is because Br mode has been found to be more sensitive to RH (Hyttinen et al., 2018) and the high RH in the calibration kit (Kürten et al., 2012) could cause some uncertainties in its calibration factor. This scaling approach is more reasonable since the calibrations were done for sulphuric acid (compound representing the kinetic limit sensitivity; Viggiano et al., 1997;Berresheim et al., 2000) for . The scaling factor of sulphuric acid was determined to be 0.53 (median value; see Figure R2b). We have therefore deleted the sentence in Line 133-137 and added the following sentence in Line 130 (Section 2.2.1, 1 st paragraph) of the manuscript: "By comparing the ambient H2SO4 concentrations measured by the median value (0.53) was used to scale down the H2SO4 concentration measured by MION-Br, due to that the high RH in the calibration kit could cause some uncertainties in its calibration factor (Hyttinen et al., 2018;Kürten et al., 2012)." The corrected organic concentrations for MION-Br were also updated in Figure 1, 2, 7, S5, S10, and S11.
Besides, based on the reviewer 1's and reviewer 2's suggestion we have also calculated the correlation coefficients for several dominant CHO and CHON species (including C7H10O4,C8H12O4, discussed in the manuscript, for a simplified examination of isomer content for individual compound (see Table R1). The corresponding information was added to Line 293 (Section 3.3, 2 nd paragraph) of the manuscript: " […] The inconsistent trends in time series and the varying correlations of these above-mentioned dominant CHO and CHON species indicate different isomer contributions detected by different measurement techniques (Figure S8 and Table S3).
Similar behaviors were also evident for […]". The original Figure (Li et al., 2020).  (Li et al., 2020). Therefore, their summed-up concentration may also show a flat diurnal pattern. As we state at several occasions in the manuscript (e.g.,, the fragmentation may partly explain why Vocus is not preferred for detecting dimers. With the fragmentation present for Vocus, it may influence our understanding of the elemental composition, absolute signal, and volatility to some extent. The carbon backbone and signals measured by Vocus may be biased to be shorter and lower, while the volatility may be biased to be higher. However, in order to fully understand the fragmentation pattern, it would require comprehensive laboratory experiments to study on this, which however lies outside the scope of this paper. To clarify more on the role of fragmentation and the potential reasons for the flat diurnals in Figure 4, the following information was added to the manuscript: Line 154 (Section 2.2.1, 2 nd paragraph): "Signals were pre-averaged over 30 min before the analysis. We stress here that the fragmentation of organic compounds inside the instrument (Heinritzi et al., 2016)

may bias the signals of parent ions towards lower values and the signals of fragment ions towards higher values."
Line 179 (Section 2.2.2, 3 rd paragraph): "Besides, the fragmentation of organic compounds inside the instruments (e.g., Vocus) may also bias the Csat results towards higher volatilities (Heinritzi et al., 2016)." Line 231 (Section 3.2, 1 st paragraph): " […] respectively. We stress here that the fragmentation of organic compounds inside the Vocus may bias the chemical composition towards shorter carbon backbone." Line 278-279 (Section 3.3, 1 st paragraph): " […] CHO compounds measured by Vocus […] have also been reported to follow more the CH trends (Li et al., 2020b). Their relatively flat diurnal pattern could be resulted from the smearing effect after summing up the much less oxygenated CHO molecules (mostly peak at night) and comparatively more oxygenated CHO molecules (mostly peak during daytime) (Li et al., 2020b). " Line 284-286 (Section 3.3, 1 st paragraph): "The potential reason could be partly due to its lower sensitivity towards larger organonitrates (see Fig. S5) caused by their losses in the sampling lines and on the walls of the inlet (Riva et al., 2019) and/or their fragmentation inside the instrument (Heinritzi et al., 2016). Another potential reason could be resulted from the smearing effect after summing up the much less oxygenated CHON molecules (mostly peak at night or early morning) and comparatively more oxygenated CHON molecules (mostly peak during daytime) (Li et al., 2020b)." Line 316 (Section 3.4, 1 st paragraph): "We stress here that the fragmentation of organic compounds inside the Vocus may bias the Csat results towards higher volatilities." Specific comments: 1. "VOC" is usually pluralized as VOCs when used in a plural sense.
Changed as suggested throughout the manuscript.
Sentence rephrased as following: "However, Vocus PTR-ToF is not preferred for detecting HOMs or dimers (Li et al., 2020b;Riva et al., 2019). The potential reason for the latter case could be resulted from the fragmentation inside the instrument (Heinritzi et al., 2016) and/or losses in the sampling lines and on the walls of the inlet (Riva et al., 2019)." 3. Line 73-76. Run-on sentence, somewhat confusing. Changed to "API-ToF" throughout the manuscript.
5. Line 75. It's not clear to me: is MION just a switching reagent ionization approach, which has been shown previously using a PTR, but as applied to an API-ToF? Because it is discussed in the same "breath" as the the Vocus, my initial reading is that it is a functionality of the Vocus, but I gather that the MION instrument is a physically distinct ToF-CIMS. This confusion makes it a bit hard to understand or parse the rest of this paragraph. I think this paragraph just needs some editing and further clarification and detail.
Similar to MION inlet, Vocus is also possible to run multi-ion operation (Breitenlechner et al., 2017;Krechmer et al., 2018 The reviewer is right. Sulphuric acid has been reported to represent the kinetic limit sensitivity (Viggiano et al., 1997;Berresheim et al., 2000) and therefore has been used as a floor for organic compounds (e.g., Ehn et al., 2014;Berndt et al., 2015). With the maximum sensitivity applied, the organic compound concentrations therefore represent  Viggiano et al., 1997;Berresheim et al., 2000), were determined to be […] With the maximum sensitivity applied, the concentrations therefore represent a lower limit. The uncertainties in […]".
8. Line 134-137. Similar diurnal patterns is a poor approach to determining isomer content. Take, for example, monoterpenes, for which there are usually around a dozen isomers, but all are expected to have a similar diurnal. Point-to-point correlations (R2) might be a better metric than diurnals, but it will still suffer from this example issue (just perhaps less so). Since you are comparing across two different ionizations, this approach is perhaps a bit more reasonable (if an ionization scheme sees one group of isomers, the other one probably does too), but it is still has serious issues. Isomers can vary in their senstivity by an order of magnitude within an ionization scheme (e.g., iodide, Lee et al. CITE), so one ionization scheme could see one set of isomers with high sensitivity, and the other could see a different set with high sensitivity, but these could still have similar diurnals. All-in-all, I'm sympathetic to the need to do something about potential overlap and the uncertainties in bulk calibration of CIMS, but scaling one instrument to another based on diurnals is built on fairly shaky assumptions that need a more robust examination. Are there trends in correlations between ionizations as a funciton of ion elemental ratios that might allow you to tease out when they are seeing the same isomers and when they are not? Or any other features within the data?
Simply put, similar diurnals is insufficient evidence for "likely to be the same species", and more caution is warranted in acting on this conclusion.
Here we refer to our response to the "General comments" 3.  Table S3 for a simplified examination of isomer content for individual compound (see also Table R1).
9. Line 155-157. This is a better/more conservative approach to handling overlap.
We agree on this. Different measurement techniques may have different sensitivities towards the same molecular formula. Therefore, this approach is preferred when combining different measurement techniques (Stolzenburg et al., 2018).
10. Line 182. A Pt100 should be defined/described.  Table S1 and the time series of total organic compound concentrations including them are shown in Figure S4)." 12. Line 233. Is this average composition of organic gases, or does it include ACSM measured organic particles? 13. Line 238. "followed-by groups" is not something I've seen before in written English.
We changed it to "The second most abundant group".
14. Lines 226-243. While PTR is fairly soft, it is known to have non-negligible fragmentation (Yuan et al., 2017, e.g., Figure 5 therein). How might this impact both the quantification of the total measurement by this instrument, and/or understanding of the elemental compositions? This issue of course does not involaidate the Vocus, or these measurements, but the effects of fragmentation and its impacts on the potential interpretation and conclusions in this work should be considered and discussed.
We have added more discussions about the effect of fragmentation inside Vocus on our results and interpretation including the quantification, the elemental composition, and the volatility. Please see our response for the "General comments" 4.
15. Line 268. It's not clear to me the C20 is necessarily diterpenes. While the SI does show some diterpenes (which is very exciting and interesting, and sadly buried in the 14 SI), monoterpenes are known to dimerize and for C20 compounds. I note that the bar on C20 looks like it is mostly O>=12, so highly oxygenated. Is this not just monoterpene dimers? It might provide some insight into the influence of monoterpene-dimers vs.
diterpene-monomers to looks at distributions of oxygen number.
The C20HO(N) compounds can be diterpene monomers or monoterpene dimers, depending on the oxidation extent. The C20 bar in the bottom panel of Figure 3 is not obvious due to the small contributions of C20 compounds. C20 compounds with the number of oxygen atoms bigger than 12 were found to contribute only ~41% to the total C20 compounds measured by MION-NO3. Besides, from the distribution of CHO and CHON compounds as a function of number of oxygen atoms vs. number of carbon atoms (Figure S5), we also clearly see substantial contributions of less oxygenated C20 compounds, which are likely to be diterpene monomers. To clarify this, the following sentence was added to Line 269 (Section 3.2, 3 rd paragraph) of the manuscript: " […] We emphasize here that using the number of carbon atoms as a basis to relate the CHOX to their precursor VOCs is a simplified assumption, as negative or positive artifacts can arise from fragmentation or accretion reactions (Lee et al., 2016)." 16. Line 284-286. Why would this smearing occur for Vocus data, but not the other data? Is it related to the tendency for nitrates to fragment in PTR?
The fragmentation for nitrates could be part of the reason. But the smearing effect could also happen after summing up the much less oxygenated CHON molecules (mostly peak at night or early morning) and comparatively more oxygenated CHON molecules (mostly peak during daytime) (Li et al., 2020b). Please see our response for "General comments" 4 for more details on the potential reason for the flat diurnal patterns.
17. Figure 1. Why use ppb for inorganics and cm-3 for organics? Organic gases are more commonly reported as ppb.
Both units are commonly used to report the concentrations of organics (e.g., Bianchi et al., 2019;Stolzenburg et al., 2018;Li et al., 2020). Different from the relatively more abundant levels of inorganics (e.g., ppbv), the abundance of many organic compounds are in trace levels and can vary across several orders of magnitude (see Figure 7). For example, the individual HOM concentration can vary between 10 4 to 10 8 cm -3 (roughly ppbv.
18. Figures 6 and 7. I recognize why the authors chose to plot these distributions on a log scale, but a bar chart on a log scale is inherently inaccurate/confusing, especially a stacked bar chart. Because there is no "zero", drawing a line to zero on a bar chart creates a wholly arbitrary scaling, which means the bar size is no longer in any way proportional to quantity. Consider Is it worth splitting these figures across two figures? It seems to me that 6a and 7a are showing basically the same data -couldn't you should had 6b-d to Figure 7. Relatedly, though they seem to be plotting the same data, I can't reconcile them quantitatively.
I wonder of this issue is related to the stacked log plot issue described above.
We understand the reviewer's concerns. The use of stacked bar chart in log scale is, however, not an exception in our manuscript. Stolzenburg et al. (2018) Figure 6a and 7a in their current version in the manuscript.
As for the Figure 6a and Figure 7a, they actually do not show the same data. Figure   6a shows the individual volatility distribution measured by each measurement techniques without taking into account the absolute quantity, since the goal is to show the distinct volatility distribution of different measurement techniques. However, the goal of Figure 7a is to obtain the complete picture of the volatility distribution as well as the bulk volatility of all measured organic compounds (from VOCs to HOMs) at our measurement site (see also Figure R1) (2016) approach (Daumit et al., 2013;Isaacman-VanWertz and Aumont, 2020 (Sanchez et al., 2016), as the calibration factor, − , was also calculated in a similar way. Following the approach by […]".
3. Line 120-125: As Br CIMS is kind of new reagent ion, can the authors provide some information about the types of compounds can be measured by Br CIMS. It would be if the advantages and also disadvantages for Br CIMS can be provided somewhere in the manuscript.
The Br-CIMS has been found to be capable of detecting hydroperoxyl radicals (Sanchez et al., 2016), peroxy radicals formed by autoxidation, and less-oxygenated organic molecules (Rissanen et al., 2019). Based on a computational study by Hyttinen et al. (2018), the instrumental sensitivity of Br − as the reagent ion is similar or even higher than that of iodide (I − ) towards OVOCs depending on humidity. We have also stated this at few occasions of the manuscript (e.g.,   Line 78 (Section 1, 2 nd paragraph): "Br-CIMS has been found to have similar or even higher sensitivities than that of iodide-CIMS towards OVOCs depending on humidity (Hyttinen et al., 2018). It has also been used for the detection of hydroperoxyl radicals (Sanchez et al., 2016) Table S3 for a simplified examination of isomer content for individual compound (see also Table R1).
5. Line 150: The quantification of PTR-TOF is also way too simple. It would be better to use the relationship between the kinetic reaction rate constants (H3O+ with VOCs) and calibrated sensitivity (Sekimoto et al., 2017 IJMS;Yuan et al., 2017 CR).
We thank the reviewer for this suggestion.  of the manuscript as following: "Quantification using the relationship between the kinetic reaction rate constants and calibrated sensitivity Yuan et al., 2017) did not show huge differences (slopes between 0.59-0.75; see Figure S2)  Yuan et al., 2017) Daumit et al. (2013) to reduce the uncertainties. Volatility parameterization has been tested quantitatively for terpene oxidation products (including organonitrates) by Wang et al. (2020) using FIGAERO thermal desorption measurements and also tested in particle growth rate closure studies in the CLOUD experiment by Stolzenburg et al. (2018). In both cases the parameterization has been shown to be accurate to within 1 order of magnitude (one decadal volatility bin).
We have updated the Figures and Table (Figure 6, 7, S10, S11, and Table 2), and the corresponding numbers in the texts based on the modified parameterization method.
For the editor´s convenience, we show below the contribution of different compound groups (Table  E1 and Figure E1. Time series of total gaseous organics measured by Vocus before and after the correction.

Figure E2. Contribution of measured CHOX compounds with different number of oxygen atoms to total CHOX compounds as a function of the number of carbon atoms for Vocus before (a) and
after (b) the correction.

Figure E3. Contribution of organic compounds with different number of oxygen atoms to all organic compounds (including CHX compounds) as a function of the number of carbon atoms measured by Vocus before (a) and after (b) the correction.
parameterizations in transport and climate models.

Site description
The measurements were conducted between April 16-July 26, 2019 at the University of Helsinki Station for Measuring Ecosystem -Atmosphere Relations (SMEAR) II (Hari and Kulmala, 2005), which is located in a boreal 105 forest in Hyytiälä, southern Finland (61°51′N, 24°17′E, 181 m a.s.l.). This station is dominated by Scots pine (Pinus sylvestris), and monoterpenes are found to be the dominating emitted biogenic non-methane VOCs (Barreira et al., 2017;Hakola et al., 2012). The measurement station has been considered as a rural background site (Manninen et al., 2010;Williams et al., 2011), and the nearest big city is Tampere, with more than 200,000 inhabitants and located ~60 km in the SW of our measurement site. A sawmill which is located 6-7 km away to 110 the SE of our measurement site can contribute significantly to the OA loading in the case of SE winds, and the sawmill OA composition has been found to resemble biogenic OA a lot (Liao et al., 2011;Äijälä et al., 2017;Heikkinen et al., 2020).

Measurements, quantification, and volatility calculation of gaseous organic compounds
All mass spectrometers were set up in a temperature-controlled measurement container kept at ∼25 °C. Sampling

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inlets were located about 1.5 m a.g.l. All data are reported in Finnish winter time Eastern European Time (UTC+2).

Measurements and quantification of gaseous organic compounds
An APiAPI-ToF (Tofwerk Ltd.; equipped with a long ToF with a mass resolving power of ∼9000) equipped  (1) and (2), respectively: where [org] is the concentration (unit: cm -3 ) of the gaseous organic compound (obtained from high resolution 135 fitting of each nominal mass) to be quantified; the numerators on the right-hand side are its detected signal clustered with bromide or nitrate, and the denominators are the sum of the reagent ion signals; − and NO 3 − are the the H2SO4 concentration measured by MION-Br, due to that the high RH in the calibration kit could cause some uncertainties in its calibration factor (Hyttinen et al., 2018;Kürten et al., 2012). With the maximum sensitivity applied, the concentrations therefore represent a lower limit. The uncertainties in the measured organic compound concentrations using calibration factors for H2SO4 have been reported to be ±50 % (Ehn et al., 2014) or a factor of 2 (Berndt et al., 2015). However, the uncertainties could be higher with variations in e.g. temperature and relative 150 humidity (RH) in the field. Therefore, based on the concentrations of measured organic compounds in common for MION-Br and MION-NO3 (see Figure S1), 230 compounds out of 269 compounds were found to have similar diurnal behaviors and they were likely to be the same species ( instruments has been shown to relate to their elemental composition and functionality .
Some compounds were calibrated using authentic standards, including isoprene, monoterpenes, and some aromatic compounds. Compounds without authentic standards were divided into four different molecular groups, the CH

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(compounds with only carbon and hydrogen atoms), CHO (compounds with only carbon, hydrogen, and oxygen atoms), CHON (compounds with only carbon, hydrogen, oxygen, and nitrogen atoms), and others. Compounds with the formula of CH and CHO were quantified with the average sensitivities of the standards CH and CHO, respectively. For the groups of CHON and others, there was no standard available in the calibration mixture. We used the average sensitivity of all the CH and CHO standards to quantify CHON compounds and others.

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Quantification using the relationship between the kinetic reaction rate constants and calibrated sensitivity Yuan et al., 2017) did not show huge differences (slopes between 0.59-0.75; see Figure   S2) for the concentrations of several CH species (e.g., sesquiterpenes and diterpenes) and several dominant CHO and CHON species (e.g., C7H10O4, C8H12O4, and C10H15NO6-7), compared to the above-mentioned quantification method we used. The Vocus data analysis was performed using the software package "Tofware" (provided by Tofwerk Ltd.) that runs in the Igor Pro environment (WaveMetrics Inc., USA). Signals were pre-averaged over 30 min before the analysis. We stress here that the fragmentation of organic compounds inside the instrument (Heinritzi et al., 2016) may bias the signals of parent ions towards lower values and the signals of fragment ions towards higher values.
When combining the organic compounds measured by the three different ionization techniques (i.e., MION-Br, MION-NO3, and Vocus), for organic compounds observed in all ionization techniques the highest concentration was used. Background subtraction was performed for all spectra and therefore a lower signal for the same compound detected by any of the ionization techniques suggests a lower ionisation efficiency of the corresponding method (Stolzenburg et al., 2018).

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Effective saturation mass concentrations (Csat), a measure for volatility of a compound, were parameterized for each organic compound using the approach by Li et al. (2016) as in equation (3): where nC, nO, nN, and nS are the number of carbon, oxygen, nitrogen, and sulfur atoms in the organic compound, respectively; C 0 is the reference carbon number; bC, bO, bN, and bS are the contribution of each atom to log10Csat, 190 respectively; bCO is the carbon-oxygen nonideality (Donahue et al., 2011). These "b" values depend on the composition of precursor gases, depending largely on whether the precursors are aliphatic (including terpenes) or aromatic. In addition to being derived from literature structure activity relations (i.e., SIMPOL; Pankow and Asher, 2008), the relations have been quantitatively confirmed for both aliphatic and aromatic systems using filter inlet for gases and aerosols (FIGAERO) thermal desorption CIMS measurements on carefully controlled precursor 195 oxidation experiments at the CLOUD (Cosmics Leaving Outdoor Droplets) facility at CERN (European Organization for Nuclear Research) (Ye et al., 2019;Wang et al., 2020). For the boreal forest conditions in this work we use the aliphatic (more volatile) parameterization and these "b" values can be found in Li et al. (2016).
Due to that the empirical approach by Li et al. (2016) was derived with very few organonitrates and could therefore lead to bias for the estimated vapor pressure (Isaacman-VanWertz and Aumont, 2020), we modified the Csat (298 200 K) of CHON compounds by replacing all NO3 groups as OH groups (Daumit et al., 2013).
which contains a quadrupole mass spectrometer, provided unit-mass resolution mass spectra every 30 min. This information was chemically speciated to organic, sulfate, nitrate, ammonium, and chloride concentrations by the ACSM analysis software. The mass concentrations of each species were calculated based on frequently conducted ionization efficiency calibrations. The data were corrected for collection efficiency, which was ca. 60 % during the measurement period. The sampling was conducted through a PM2.5 cyclone and a Nafion dryer (RH < 30 %) 230 with a stainless steel tube of ca. 3 m length and a flow rate of 3 L min −1 (only 1.4 cm 3 s −1 into the ACSM). The recorded data were analyzed using the ACSM local v. 1.6.0.3 toolkit (provided by Aerodyne Research Inc.) within the Igor Pro v. 6.37 (Wavemetrics Inc., USA). More details about ACSM operation and data processing can be found in Heikkinen et al. (2020). Vocus data presented in this study in order to focus on compounds actively involved in the fast photochemistry (all excluded compounds are listed in Table S1 and the time series of total organic compound concentrations including them are shown in Figure S4). As we can see from Figure 1a, most of the measurement days had strong photochemical activity with ambient temperature exhibiting clear diurnal patterns ranging between -3 and 32 °C.

Results and discussion
In general, the time series of the total organics (both gas phase and particle phase; see VOCs (such as O3) and/or anthropogenic pollutants (such as SO2 and NOx) also followed some of the elevated 250 concentrations of gaseous and/or particulate organics (e.g., April 19-May 3, May 17-24, and June 7-10; see Fig. 1c-d). The observations of the elevated organics could be resulted from higher VOC emissions (e.g., terpenes, the typically observed VOCs, Li et al., 2020a; Figure S3S4) influenced by meteorological conditions (i.e., temperature and/or light; Guenther et al., 1995;Kaser et al., 2013), different air mass origins (e.g., terpene pollutions from the sawmill in the case of SE winds; Liao et al., 2011;Äijälä et al., 2017;Heikkinen et al., 2020), as well as chemistry 255 initiated by/related with different trace gases (Yan et al., 2016;Massoli et al., 2018;Huang et al., 2019b;Heikkinen et al., 2020). The results suggest the important roles meteorological parameters, trace gases, and air masses play in the emission and oxidation reactions of organic compounds. Due to the soft ionization processes of organic molecules in the Vocus, MION-Br, and MION-NO3, molecular composition of organic compounds was obtained.
In the next section we will discuss the molecular composition of gaseous organic compounds measured by Vocus, During the measurement period, Vocus identified 72 CH compounds (Cx≥1Hy≥1) and 431 CHOX compounds (Cx≥1Hy≥1Oz≥1X0-n), with X being different atoms like N, S, or a combination thereof, while MION-Br and MION-NO3 detected 567 and 687 CHOX compounds, respectively. Substantial overlaps of organic compounds were 265 observed for these three ionization techniques while distinct organic compounds were also detected with individual method ( Figure S1). The average mass-weighted chemical compositions for organic compounds measured by  range of 425-600 Da, which are most likely to be more oxygenated HOM dimers (see Fig. 2b and Fig. S4S5).
We further investigated the contributions of the measured CHOX compounds with different number of oxygen atoms per molecule to total CHOX compounds as a function of the number of carbon atoms (Figure 3). Organic compounds which were detected with higher sensitivity by Vocus were those with the number of carbon atoms between 3 and 10 and the number of oxygen atoms between 1 and 3 (i.e., less oxygenated monomers); compounds and the latter particularly for compounds with the number of oxygen atoms larger than 9 (i.e., HOM monomers and dimers; Rissanen et al., 2019;Riva et al., 2019;Li et al., 2020b;see Fig. 3 and Fig. S4S5). In the MION-Br and 305 MION-NO3 data, CHOX compounds with the number of carbon atoms of 5, 10, 15, and even 20 exhibited relatively elevated contributions compared to their neighbours (Fig. 3), indicating contributions of their potential corresponding precursors, i.e., isoprene, monoterpenes, sesquiterpenes, and diterpenes (together accounting for 38.35.5 ± 12.51.3 % of total CH compounds; see Table S1S2, Fig. S3S4, and Fig. S5S6). We emphasize here that using the number of carbon atoms as a basis to relate the CHOX to their precursor VOCs is a simplified assumption, 310 as negative or positive artifacts can arise from fragmentation or accretion reactions (Lee et al., 2016). Similar pattern was also observed by Huang et al. (2019a) in a rural area in southwest Germany, based on filter inlet for gases and aerosols high-resolution time-of-flight chemical ionization mass spectrometer (FIGAERO-HR-ToF-CIMS) data. The consistency and complement of the results demonstrate the different capabilities of these instruments for measuring gaseous organic compounds with different oxidation extent (from VOCs to HOMs).

Diurnal characteristics of gaseous organic compounds
Median diurnal variations of total CH, total CHO, and total CHON compounds measured by Vocus, MION-Br, and MION-NO3 are shown in Figure 4. In general, the CH and CHO group measured by Vocus exhibited higher levels during the night (see Fig. 4a-b), mainly driven by the boundary layer height dynamics (Baumbach and Vogt, 2003;Zha et al., 2018). Besides, CHO compounds measured by Vocus were dominated by O1-2 compounds (see 320 Fig. 3 and Fig. S4S5) and have also been reported to follow more the CH trends (Li et al., 2020b). Their relatively flat diurnal pattern could be resulted from the smearing effect after summing up the much less oxygenated CHO molecules (mostly peak at night) and comparatively more oxygenated CHO molecules (mostly peak during daytime) (Li et al., 2020b). In contrast, the CHO and CHON group measured by MION-Br and MION-NO3 exhibited higher levels during the day (see Fig. 4b), due to strong photochemical oxidation caused by different 325 meteorological parameters (i.e., temperature and global radiation; see Fig. 1a and Fig. S6S7) and/or elevated trace gas levels (e.g., O3 and SO2; see Fig. 1c and Fig. S6S7; Yan et al., 2016;Massoli et al., 2018;Huang et al., 2019b;Bianchi et al., 2017). However, the CHON group measured by Vocus showed relatively stable signals throughout the day (see Fig. 4c). The potential reason could be that the immediate (local) formation effects were smeared out in time as a result of both the reaction of organic peroxy radicals (RO2) with NO as well as nighttime 330 NO3 radical chemistry (see Fig. S6; Yan et al., 2016). The potential reason could be partly due to its lower sensitivity towards larger organonitrates (see Fig. S5) caused by their losses in the sampling lines and on the walls of the inlet (Riva et al., 2019) and/or their fragmentation inside the instrument (Heinritzi et al., 2016). Another potential reason could be resulted from the smearing effect after summing up the much less oxygenated CHON molecules (mostly peak at night or early morning) and comparatively more oxygenated CHON molecules (mostly 335 peak during daytime) (Li et al., 2020b).
Different diurnal patterns among different measurement techniques can also be found for individual organic compounds with the same molecular formula, such as several dominant CHO and CHON species, C7H10O4 (molecular formula corresponding to 3,6-oxoheptanoic acid identified in the laboratory as limonene oxidation product by Faxon et al., 2018;Hammes et al., 2019), C8H12O4 (molecular formula corresponding to terpenylic acid identified in monoterpene oxidation product by Zhang et al., 2015;Hammes et al., 2019), and C10H15NO6-7 (identified in the laboratory as monoterpene oxidation products by Boyd et al., 2015;Faxon et al., 2018; see Figure   5). The inconsistent trends in time series and the varying correlations of these above-mentioned dominant CHO and CHON species indicate different isomer contributions detected by different measurement techniques ( Figure   S8 and Table S3). Similar behaviors were also evident for OVOCs with varying oxidation extent, like the terpene-345 related CxHO and CxHON compounds (x = 5, 10, 15, and 20; see Figure. S7S9), which in total accounted for up to 27 % and 39 % of their corresponding CHO and CHON groups (see Table S1S2). Most of the terpene-related CxHO(N) groups (x = 5, 10, 15, and 20) with different oxidation extent behaved similar among different measurement techniques, but some were also found to vary (see Fig. S7S9). This can be likely resulted from contributions of compounds with same number of carbon and oxygen atoms but different hydrogen atoms (i.e., 350 different saturation level), different isomers detected by the different techniques, and/or fragmentation products from different parent compounds inside the instruments (e.g., Heinritzi et al., 2016;Zhang et al., 2017).
The results indicate that organic compounds may behave differently among different measurement techniques during different time period. In the next section, we will investigate the volatility of these gaseous organic compounds, which can influence their lifetime and roles in the atmosphere. dominating Csat bin measured by Vocus was organic compounds with Csat of 10 6 µg m −3 (see Fig. 6a). Furthermore,
With the complementary molecular information of organic compounds from Vocus, MION-Br, and MION-NO3, a combined VBS volatility distribution was plotted to obtain the bulk volatility of all measured organic 385 compounds (with the approach described in section 2.2.1) at our measurement site (Figure 7). The combined volatility distribution covers very well from VOCs to HOMs, with varying O:C ratios and volatility ranges ( Figure   7a). It therefore provides a more complete picture of the volatility distribution of gaseous organic compounds in this boreal forest. The average mass-weighted log10Csat value representing the bulk of all measured gaseous organic compounds in this boreal forest was ~6.1 µg m −3 . In general, MION-NO3 measured >96 91 % of the ULVOC while 390 MION-Br measured >51 70 % of the ELVOC, and Vocus >84 98 % of the SVOC, IVOC, and VOC ( Figure S8S10).
As we can see from Fig. 7b, organic compounds with Csat of 10 6 μg m −3 accounted for the biggest contributions.
The VOC class was found to be the most abundant (about 49.4 53.2%), followed by the IVOC (about 48.945.9 %), indicating that the bulk gaseous organic compounds observed in this boreal forest were relatively fresh, which is also consistent with the bulk molecular composition's relatively low oxidation extent. Differences of the bulk 395 volatility of organic compounds between daytime (between 10:00 and 17:00) and nighttime (between 22:00 and 05:00) were not significant ( Figure S9S11). Given the location of the measurement station that is inside a borealforested area, the gaseous organic compounds were expected to be dominated by VOC and IVOC. The abundance of the CH compounds such as terpenes (see Table 1, Table S1S2, Fig. S3S4, and Fig. S5S6) as well as less oxygenated VOC (see Fig. 3 and Fig. S4S5) (Daumit 785 et al., 2013;Isaacman-VanWertz and Aumont, 2020