2.1.1 The CLOUD chamber experiment

We conducted experiments in the CLOUD chamber at CERN to study pure biogenic new particle formation (NPF) without the presence of nitrogen oxides (NO_x). The CLOUD chamber is a stainless-steel cylinder with a volume of 26.1 m³ and has been extensively described by Kirkby et al. (2011) and Duplissy et al. (2016). To create the particles, NPF was induced by continuously adding α-pinene and ozone into the chamber. The monoterpene concentration was regulated by an evaporation source, in which dry nitrogen (N₂) passes through an evaporator containing liquid α-pinene at a precisely controlled temperature. Ozone was produced by exposing cryogenic O₂ to UV light and was introduced directly into the chamber via a separate line. The relative humidity was adjusted with a temperature-controlled Nafion humidifier using ultrapure Millipore water. All the precursor gases were homogenously mixed in the chamber by two magnetically driven Teflon fans placed at the top and at the bottom of the chamber. The temperature was kept constant by an insulated thermal housing, which surrounds the chamber. The α-pinene mixing ratio was measured by a proton transfer reaction time-of-flight (PTR-TOF) mass spectrometer (Graus et al., 2010; Breitenlechner et al., 2017), whereas ozone was measured by a TEI 49C ozone analyzer (Thermo Environmental Instruments). The experiments relevant for this work were performed at −50, −30, and −10 ^∘C. The α-pinene mixing ratio ranged between 1 and 8 ppbv, and ozone was approximately 100 ppbv.

Table 1 presents the most important features for the instruments used in this work. We categorize the techniques according to certain criteria: continuous- or discontinuous-operation mode, evaporation method, phase measured, ionization technique, reagent ion, target substances, occurrence of significant thermal decomposition, and whether the technique allows size-resolved analysis of aerosol particles to be performed.

Table 1Instruments for measuring particle-phase chemical composition used in the CLOUD chamber experiments; n/a: not applicable.

^a TD–DMA can take both size-resolved and non-size-resolved measurements. For this work, non-size-resolved measurements were chosen in order to maximize the mass collected and to intercompare with the particle-phase instruments. ^b In this work, only the particle-phase measurements are reported. ^c Gas and particle phases can be measured by using the dual EESI-TOF configuration.

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2.1.2 Thermal desorption–differential mobility analyzer (TD–DMA) coupled to a NO${}_{\mathrm{3}}^{-}$ chemical ionization–atmospheric-pressure-interface–time-of-flight (CI–APi–TOF) mass spectrometer

The TD–DMA coupled to a NO${}_{\mathrm{3}}^{-}$ CI–APi–TOF analyzes the chemical composition of nanoparticles in a semicontinuous mode of operation. The design and characterization have been reported by Wagner et al. (2018). This method allows gas- and particle-phase measurements using the same ionization technique. Individual results of gas- and particle-phase comparison of the same chemical system as in this study were reported in Caudillo et al. (2021). While the gas-phase measurement is taking place with the CI–APi–TOF mass spectrometer, the TD–DMA samples particles from the chamber. The TD–DMA can perform size-resolved and non-size-resolved measurements. In any case, the particles are first charged by an X-Ray source and then transferred to the differential mobility analyzer (DMA) unit. When a size-resolved measurement is desired, a specific voltage is applied to the central electrode inside the DMA unit. Subsequently, a sheath flow will carry only particles with specific electrical mobility and will conduct them through the DMA. In contrast, during a non-size-resolved measurement, no voltage to the central electrode and no sheath flow are applied, and a fraction of the particles charged by the X-ray source will pass through the DMA unit. For the experiments reported in the present study, we performed non-size-resolved measurements in order to maximize the mass collected and to be comparable to the other methods that do not perform size selection.

The particle collection takes place by electrostatic precipitation on a platinum–rhodium (90:10) filament placed inside the central electrode. After a certain collection time (∼ 3 h for the experiment at −30 ^∘C and ∼ 5 h for the experiment at −50 ^∘C), an electric current is applied to the filament, which causes its direct heating. We estimate based on the filament resistance that the temperature gradually increased up to approximately 600 ^∘C in a period of ∼ 1 min (details of the heating curve are discussed in Sect. 3.2).

The vapors that evaporate from the heated particles are carried by an N₂ flow to the nitrate CI–APi–TOF for chemical composition analysis. For chemical ionization of the vapors, nitrate reagent ions (HNO₃)_n NO${}_{\mathrm{3}}^{-}$ with n=0–2 are created by a corona discharge (Kürten et al., 2011; Kürten et al., 2014). Some of the vapor molecules are ionized and subsequently detected by the APi-TOF mass spectrometer. A second heating cycle of the particle-collecting filament is performed afterwards (without particle collection) in order to estimate the instrumental background due to the heating of the inlet line; this enables a more accurate determination of particle constituents. The particle constituents are estimated by subtracting the second heating from the first heating. Besides the particle and background estimations, a second heating up to ∼ 600 ^∘C ensures that the filament is clean and avoids memory effects for the next measurement. With the nitrate ionization technique, sulfuric acid (Jokinen et al., 2012), iodic acid (He et al., 2021), methane sulfonic acid (Shen et al., 2022), and highly oxygenated molecules (Kirkby et al., 2016; Simon et al., 2020) can be detected.

2.1.3 Filter inlet for gases and aerosols (FIGAERO) coupled to an I⁻ high-resolution time-of-flight chemical ionization mass spectrometer (HRToF-CIMS)

FIGAERO coupled to an I⁻ HRToF-CIMS was first described by Lopez-Hilfiker et al. (2014) and optimized for CLOUD operation conditions by Wang et al. (2020). FIGAERO uses a multi-port to measure in alternation both the gas and particle phases following the same general procedure as the TD–DMA–CI–APi–TOF. While the gas phase is analyzed, particle collection takes place on a polytetrafluoroethylene (PTFE) filter, and after a certain collection time (in this study 15 min), the filter is automatically moved into the ion–molecule reactor and exposed to a pure N₂ gas flow. The N₂ flow is gradually heated to evaporate the particles by thermal desorption using a temperature-programmed heating curve. For the measurements reported in this study, the temperature was slowly ramped from room temperature up to 150 ^∘C in approximately 15 min (an example of the heating curve is discussed in Sect. 3.2). When the heating cycle ends a new collection starts, and the process repeats. Thus, the 15 min collection period is followed by a 15 min desorption period, which implies two collections per hour (resulting in a resolution of 30 min). The detection technique is based on iodide–molecule adduct ionization. Iodine ions I⁻, I${}_{\mathrm{3}}^{-}$, and (H₂O)I⁻ are generated from a solution of methyl iodide (CH₃I) and a Po-210 ion source (Lee et al., 2014). With this soft-ionization technique, the FIGAERO–HRToF-CIMS can detect intermediately oxygenated organic molecules (Wang et al., 2020; Stolzenburg et al., 2018; Lopez-Hilfiker et al., 2016), organosulfates, and inorganic acids such as sulfuric acid and nitric acid.

2.1.4 Extractive electrospray Na⁺ ionization time-of-flight mass spectrometer (EESI-TOF)

The extractive electrospray ionization time-of-flight mass spectrometer (Lopez-Hilfiker et al., 2019) is a technique used for online particle-phase measurements without batch collection. This technique aims to provide the chemical composition of organic particles in real time (1 s). It is also possible to measure the gas phase by using the dual configuration (Lee et al., 2022). In the beginning of the sampling process, the aerosol sample passes through the inlet line, where a carbon denuder is located to remove the gas-phase molecules. The particles then collide with electrospray droplets, and the soluble components are extracted and ionized. Then, solvent evaporation occurs in a heated stainless-steel capillary, leading to Coulomb fissioning and/or direct ion desorption. Finally, ions enter a time-of-flight mass spectrometer. The EESI-TOF here uses an electrospray solution of pure water doped with 100 ppm NaI and is running in the positive-ion mode. This enables the measurements of SOA species as adducts with Na⁺. With this ionization method, most organic compounds that are relevant for atmospheric SOA particles can be analyzed, such as intermediately oxygenated organic molecules with the exception of species that are not oxygenated and organosulfates (Lopez-Hilfiker et al., 2019).

2.1.5 Offline analysis of filters (FILTER) using ultra-high-performance liquid chromatography (UHPLC) and heated electrospray ionization (HESI) coupled to an Orbitrap high-resolution mass spectrometer (HRMS)

This procedure was optimized and described in detail by Ungeheuer et al. (2021). The method enhances the separation of organic compounds with high resolution and enables the determination of the accurate mass. The analysis consists mainly of four steps: sampling, extraction, separation, and detection.

First, the particles were collected from a flow of 5 L min⁻¹ on a 47 mm diameter Emfab™ filter (Pall Life Science, USA) during approximately 17 h for the experiment at −10 ^∘C (see Table 2 and Fig. S1). After sampling, the filter was stored at −18 ^∘C to avoid possible losses by evaporation. The filter was cut into small pieces (approximately 3×3 mm) and extracted two times in 0.2 mL solution (mixture of 90 % water and 10 % methanol) for 20 min. After each extraction step, the extract was filtered through a syringe filter (PTFE with a pore size of 0.2 µm). For chromatographic separation a gradient of ultrapure water (eluent A, Milli-Q Reference A+, Merck Millipore) and methanol (eluent B, Optima LC/MS grade, Fisher Scientific) was applied. Both eluents were mixed with 0.1 % formic acid ($v/v$) for improved chromatographic performance. The injection volume was 5 µL, the flow rate was 400 µL min⁻¹, and the temperature was 40 ^∘C. The gradient started with 1 % eluent B (0–0.5 min), increased linearly to 99 % eluent B (0.5–14 min), stayed at 99 % eluent B (14–16 min), was backflushed in 1 min, and equilibrated in 3 min, resulting in a total run time of 20 min. Negative ionization mode was used for the detection, in which the molecular ions [M-H]⁻ are produced by deprotonation. The ion source settings used for this purpose were −3.5 kV spray voltage, 40 psi sheath gas, 8 arbitrary units auxiliary gas, and 262.5 ^∘C capillary temperature. The scan range in full MS was 50–750 $m/z$, with a mass-resolving power of about 70 k at $m/z$ 200. For data-dependent MS² (ddMS²) the resolution was 17.5 k. Fragments were produced in a higher-energy collisional dissociation (HCD) cell with stepped collision energies of 15, 30, and 45 eV.

Table 2Sample size conditions during the experiments performed at −30, −10, and −50 ^∘C. For the purposes of this intercomparison study, the sampling times were aligned base on the TD–DMA and FILTER collection times. CPC_2.5: particle number concentration in number per cubic centimeter measured by the counter particle condensation (CPC) with a cut-off diameter of 2.5 nm

^a Average during the sample collection period. ^b Maximum value during the sample collection period.

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2.2.1 Data processing

TD–DMA, FIGAERO, and EESI-TOF data were processed using IGOR Pro 7 (WaveMetrics, Inc., USA) and Tofware (Version 3.1.2, Aerodyne Inc., USA). The data from the offline method were processed with Compound Discoverer 3.2 (Thermo Fisher Scientific). The postprocessing was done using MATLAB R2022a (MathWorks, Inc., USA).

TD–DMA data were corrected by the mass-dependent transmission efficiency in the mass classifier (Heinritzi et al., 2016) and normalized by the nitrate reagent ions. FIGAERO data were averaged to 1 min and normalized by the reagent ions. EESI-TOF signals were averaged to 10 s and normalized by the most abundant electrospray ion (NaINa⁺). In order to align the sampling times for the different techniques and perform a more direct particle-phase comparison, we selected as a reference the TD–DMA and FILTER collection times. Thus, for the comparison at −30 and −50 ^∘C, EESI-TOF particle signals were averaged, and FIGAERO particle signals were integrated during the period in which the TD–DMA collected particles (∼ 3 and 5 h, respectively), while for the comparison at −10 ^∘C, the EESI-average and FIGAERO-integration period corresponded to the time when the particles were collected with the FILTER for the offline analysis (∼ 17 h). Figure 1 provides an overview of a representative experiment at −30 ^∘C. The overview of the experiments at −10 and −50 ^∘C are shown in Fig. S1 in the Supplement. Table 2 summarizes the sampling conditions during the experiments reported in this study (for the purpose of the intercomparison), including the particle number concentration, mass concentration, and median mass diameter (MMD) calculated from the scanning mobility particle sizer (SMPS). The MMD indicates the particle size measured by the SMPS for which 50 % of the aerosol mass is contained in smaller particles, and 50 % is contained in larger particles. From Table 2 it can be seen that, for the experiments at −30 and −50 ^∘C, MMD_av < 100 nm, while for the experiment at −10 ^∘C, MMD_av ∼ 106 nm.

Figure 1Experimental overview of a representative biogenic new-particle-formation experiment (α-pinene ozonolysis at −30 ^∘C and 20 % RH). (a) Mixing ratio in parts per billion by volume for the precursor gases, α-pinene and ozone. (b) Particle size distribution measured by the SMPS; the color scale represents the log 10 of the normalized particle concentration per cubic centimeter (cm⁻³). The median mass diameter (MMD) is shown with a dashed black line. (c) Particle number concentration in number per cubic centimeter measured by the CPC with a cut-off diameter of 2.5 nm and mass concentration in micrograms per cubic meter (obtained by integrating the normalized mass concentration from the SMPS). (d) Particle-phase signal measured continuously by the EESI-TOF; the gray shaded area refers to the period where the EESI-TOF was averaged for the intercomparison with TD–DMA and FIGAERO. (e) Particle phase measured by FIGAERO; the gray shaded areas refer to the particle collection period and the yellow shaded areas to the desorption period. FIGAERO measured in a semicontinuous mode, namely 15 min particle collections followed by 15 min desorption periods. In order to intercompare with EESI-TOF and TD–DMA, FIGAERO signals were integrated during the period where the TD–DMA collected particles (∼ 3 h); seven FIGAERO particle samples were integrated during the 3 h comparison period. (f) Particle phase measured by the TD–DMA; the TD–DMA collection period was approximately 3 h, while the desorption period lasted around 1 min, followed by a second heating for estimating the background.

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2.2.2 PMF analysis

One of the main questions we want to answer in this work is whether the thermal desorption methods (TD–DMA and FIGAERO) experience significant decomposition during the desorption process. To answer this question, we utilized PMF analysis. This method was originally described by Paatero and Tapper (1994) for analyzing time series of mass spectra from ambient observations, and it was implemented by Buchholz et al. (2020) with thermal desorption data for identifying different processes during particle evaporation. We therefore applied the same procedure as Buchholz et al. (2020) to the TD–DMA and FIGAERO thermal desorption profiles (for the α-pinene oxidation experiment at −30 ^∘C and 20 % RH only). For the analysis, we separately processed 1 s TD–DMA and 1 s FIGAERO thermograms. Since the FIGAERO measures in a semicontinuous mode, we chose a representative thermogram. Both TD–DMA and FIGAERO data sets were background subtracted. For the TD–DMA background, we used the second heating cycle that is performed immediately after the first heating (described in Sect. 2.1.2). For the FIGAERO background, we used a period in which no significant particle load was present in the chamber (at the beginning of the experiment). We considered only the organic compounds and excluded the reagent ions for this analysis. We ran the PMF software using the CNerror scheme (based on the noise of each ion) and up to 10 different solutions. Four-factor TD–DMA and six-factor FIGAERO solutions (discussed later in Sect. 3.2.1) were chosen as the most interpretable results by (a) comparing the residuals and by looking at which solution captured the total signal and certain species the best (e.g., C₈H₁₂O₄) and (b) by finding an equilibrium between good reconstructed signal and physically interpretable results. This means that for the solutions presented here, a higher number of factors likely improves the residuals; nevertheless, we chose the solution with the smallest number of factors that can still provide realistic information.

Volatility classes

We estimated the volatilities of the detected particle-phase compounds and associated them with defined volatility classes. We used the parametrization introduced in Donahue et al. (2011) and modified by Stolzenburg et al. (2018). This approach has been also discussed in Simon et al. (2020) and Wang et al. (2020). The volatility was approximated from the number of carbon and oxygen atoms in the specific molecules, and it was first defined at 300 K. By using the Clausius–Clapeyron equation, the volatility was then shifted according to the corresponding experimental temperature. The evaporation enthalpy was approximated according to Donahue et al. (2011) and Epstein et al. (2009). Thereafter the volatility was associated with any of the following classes: ULVOCs, ELVOCs, LVOCs, SVOCs, and IVOCs.

Figures 4 and 5 show the contribution of each volatility class to the total particle signal for the corresponding experiment and for each technique. The results at −50 ^∘C are reported in Fig. S6 in the Supplement. For the experiment at −30 ^∘C (Fig. 4), LVOCs constitute the higher fraction for the TD–DMA, FIGAERO, and EESI-TOF. EESI-TOF detects the highest fraction of ULVOCs (∼ 12 % compared to 6 % and 2 % measured by TD–DMA and FIGAERO, respectively). An IVOC fraction (∼ 8 %) is only detected by the EESI-TOF. At −10 ^∘C (Fig. 5), semi-volatile organic compounds (SVOCs) contribute the most to the total particle signal for the FILTER, FIGAERO, and EESI-TOF. Very small fractions of ULVOCs are also detected by all the techniques. The EESI-TOF detects a higher fraction of IVOCs (20 %) than FILTER and FIGAERO (2 % and 4 %), respectively. Taking into account the particle load (∼ 1–3 µg m⁻³) and size of the particles (diameter < 100 nm), it is possible that a significant fraction of IVOCs measured by the EESI-TOF results from measurement artifacts, as seen in previous studies using EESI (Surdu et al., 2021). Several reasons (or a combination of them) can explain this feature: (a) the Na⁺ ionization technique may be more sensitive to less oxygenated organic compounds than the I⁻ or NO${}_{\mathrm{3}}^{-}$ techniques, (b) some amount of the gas phase likely broke through the charcoal denuder (although its efficiency is >99 %) and reached the detector (Lee et al., 2021, reported that the EESI-TOF is more sensitive toward gas-phase analytes as compared to their particle-phase counterparts), and (c) some ion-induced fragmentation can possibly occur.

Figure 4Distribution of volatility classes for α-pinene oxidation products in the particle phase at −30 ^∘C and 20 % RH measured by three different techniques: thermal desorption–differential mobility analyzer (TD–DMA) coupled to a NO${}_{\mathrm{3}}^{-}$ chemical ionization–atmospheric-pressure-interface–time-of-flight mass spectrometer, filter inlet for gases and aerosols (FIGAERO) coupled to an I⁻ high-resolution time-of-flight chemical ionization mass spectrometer, and extractive electrospray Na⁺ ionization time-of-flight mass spectrometer (EESI-TOF). The level of α-pinene was between 1 and 6 ppbv, while the ozone level was ∼ 100 ppbv. The volatility classes (ULVOC, ELVOC, LVOC, SVOC, IVOC) were defined as in Donahue et al. (2012) and in Schervish and Donahue (2020).

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Figure 5Distribution of volatility classes for α-pinene oxidation products in the particle phase at −10 ^∘C and 80 % RH measured by three different techniques: offline analysis of filters (FILTER) using ultra-high-performance liquid chromatography and heated electrospray ionization coupled to an Orbitrap high-resolution mass spectrometer, filter inlet for gases and aerosols (FIGAERO) coupled to an I⁻ high-resolution time-of-flight chemical ionization mass spectrometer, and extractive electrospray Na⁺ ionization time-of-flight mass spectrometer (EESI-TOF). The level of α-pinene was between 1 and 3 ppbv, while the ozone level was ∼ 100 ppbv. The volatility classes (ULVOC, ELVOC, LVOC, SVOC, IVOC) were defined as in Donahue et al. (2012) and in Schervish and Donahue (2020).

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Overall, we observed that the contribution of the lowest-volatility classes (ULVOC, ELVOC, and LVOC) increases as the temperature decrease. This observation reflects two opposing temperature effects, as discussed in Ye et al. (2019) based on FIGAERO results: autoxidation and thus the extent of oxidation are reduced at low temperatures, but any given compound is much less volatile at low temperatures because of the strong dependency between saturation concentration and temperature.

PMF results

The results of the PMF analysis of the TD–DMA data are shown in Fig. 7, which contains the factor mass spectra (Fig. 7a–d), the factor thermograms (Fig. 7e), and the contribution of each factor to the total signal (Fig. 7f). We found that four factors are the best choice to reconstruct the TD–DMA data and to provide the most interpretable results (the residuals are shown in Fig. S7 in the Supplement). We numbered the factors according to their peak desorption temperatures (Fig. 7e). F1_TD–DMA, which desorbs at the very first stages of the heating cycle, includes organic compounds with molecular masses between 150 and 250 Da (M_av=211.6 Da), with a carbon, hydrogen, and oxygen average content (CHO_ac) of 9.2, 14.6, and 5.4, respectively. F1_TD–DMA contains mainly compounds in the monomer region (see Fig. S8 in the Supplement). F2_TD–DMA desorbs right after F1_TD–DMA. The mass average (M_av) is 230.7 Da, and the CHO_ac is 10.0, 16.1, and 5.9. Compounds in the monomer region also contribute to this factor (see Fig. S8 in the Supplement). F3_TD–DMA shows a clear contribution of both monomers and dimers for the time when the time series shows a broadened peak. CHO_ac is 10.8, 16.7, and 5.5, and M_av=234.5 Da. Lastly, F4_TD–DMA is dominated (∼ 60 % of the signal intensity) by a high signal with molecular mass of 172.18 Da, which corresponds to C₈H₁₂O₄. This is reflected by lower values of CHO_ac and M_av compared to the other factors (CHO_ac=8.6, 12.8, and 4.5; M_av=188.2 Da). By looking closer into F4_TD–DMA (Fig. 7d), we observe that some compounds with mass < 200 Da also contribute to this factor. By integrating each factor thermogram (Fig. 7e), we calculated that F1_TD–DMA and F2_TD–DMA contribute to ∼ 70 % of the total signal, while F3_TD–DMA and F4_TD–DMA make up ∼ 30 % of the total signal, as shown in Fig. 7f.

Figure 7PMF-suggested solution of the particle phase detected by the TD–DMA in the α-pinene ozonolysis experiment at −30 ^∘C and 20 % RH. (a–d) Factor mass spectra, (e) factor thermograms, and (f) factor fraction. Each factor mass spectrum is normalized and colored according to the order of appearance in the thermogram: F1_TD–DMA (yellow), F2_TD–DMA (red), F3_TD–DMA (green), and F4_TD–DMA (blue). The thermogram (e) is expressed as a function of the temperature, which is an estimation based on the filament resistance. The particle-phase signal has been background-corrected.

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For the purpose of comparison, we present in Fig. 8 the results from applying PMF to the FIGAERO thermal desorption data for a solution with six factors. Figure 8a–f contain the factor mass spectra, Fig. 8g contains the factor thermograms, and Fig. 8h shows the contribution of each factor to the total particle signal (the residuals and the factors expressed in terms of their oxygen content and mass are shown in Figs. S9 and S10 in the Supplement, respectively). F1_FIGAERO, F2_FIGAERO, and F3_FIGAERO (Fig. 8a–c) show a distinct contribution from monomers and similar mass spectra but display different thermal profiles (in Fig. 8g). Specifically, F2_FIGAERO and F3_FIGAERO exhibit well-defined thermal profiles (∼ 15 ^∘C difference in T_max). However, F1_FIGAERO shows a broader profile with no distinct maximum. We suspect that F1_FIGAERO can be related to some of the following causes to some extent: (a) limited resolution of the chosen PMF solution at T< 50 ^∘C, likely related to the presence of two neighboring factors that were not resolved completely; (b) interference from volatile material already evaporating at the beginning of the thermogram; and (c) adsorption of gaseous compounds. Reason (b) could be related to the procedure initiating the desorption, where the filter is flushed with N₂ at ambient temperature before starting the heating ramp, which would likely affect the most volatile material. F4_FIGAERO and F5_FIGAERO (Fig. 8d and e) show a contribution from both monomers and dimers, with a very similar CHO_ac and M_av. However, they show different thermogram behavior (∼ 20 ^∘C difference for T_max; Fig. 8g). F6_FIGAERO shows mainly contributions from compounds with low mass (< 200 Da) and desorbs mainly at the very end of the heating curve. The contribution of each factor to the total signal is shown in Fig. 8h.

Figure 8PMF-suggested solution of the particle phase detected by FIGAERO in α-pinene ozonolysis experiment at −30 ^∘C and 20 % RH. (a–e) Factor mass spectra, (g) factor thermograms, and (h) factor fraction. Each factor mass spectrum is normalized and colored according to the order of appearance in the thermogram: F1_FIGAERO (yellow), F2_FIGAERO (red), F3_FIGAERO (green), F4_FIGAERO (blue), F5_FIGAERO (purple), and F6_FIGAERO (black). The thermogram is expressed as a function of the temperature which causes the desorption. The particle-phase signal has been background-corrected.

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As mentioned previously, we observed some factors with similar CHO_ac and M_av but different thermal behavior, possibly due to the presence of isomers. Molecules with the same composition but different structure and functional groups may exhibit different volatilities. In fact, functionality is one of the driving factors that determines volatility (Pankow and Asher, 2008; Wang et al., 2020). The mass spectrometry techniques reported here are not able to determine the molecular structure. We further note that the factors F4_TD–DMA and F6_FIGAERO desorb mainly at the very last stage of the heating curves, although they both have a clear contribution of compounds with mass < 200 Da and one of the lowest oxygen contents of all factors (i.e., the lowest degree of oxidation). We suspect that F4_TD–DMA (Fig. 7d) and F6_FIGAERO (Fig. 8g) are comprised primarily of products of thermal decomposition; the heat applied to desorb the particles instead cleaves certain chemical bonds in (larger) molecules before these could desorb. Small compounds are generally expected to desorb before the transmitted thermal energy (i.e., the desorption temperature) is high enough to cause such decomposition. However, F4_TD–DMA and F6_FIGAERO thermal profiles also exhibit a small peak at lower temperatures (observed more clearly in Fig. 8g at ∼ 40–50 ^∘C), which is likely direct desorption. This may suggest that the low-temperature peak stems from monomers desorbing directly, while the broad high-temperature peak represents decomposing dimers/oligomers, which are then detected at the composition of the corresponding monomers. Previous studies applying PMF analysis to FIGAERO thermal desorption data also observed the presence of one or more factors dominated by thermal decomposition products for α-pinene- and sesquiterpene-derived SOA (Buchholz et al., 2020; Li et al., 2021). Those studies concluded that thermal decomposition was the main volatilization process at desorption temperatures above 100 ^∘C, with differences between the observed SOA types.

By applying PMF analysis to thermal desorption data we observed that often, several factors are needed to explain the behavior of a single ion. One example is shown in the thermal profile of C₈H₁₂O₄ for both FIGAERO and TD–DMA (in the Supplement Fig. S11). Particularly, F4 _TD–DMA and F6_FIGAERO explain the C₈H₁₂O₄ signal at higher temperatures. This is consistent with previous observations. Lopez-Hilfiker et al. (2015) reported a significant contribution of thermal decomposition to the detection of C₈H₁₂O₄ in the α-pinene ozonolysis system and stated that small acids present in higher-than-expected concentrations in SOA are likely entirely due to thermal decomposition. In our previous work (Caudillo et al., 2021), we presented individual results of the gas and particle phases of the same chemical system as in this study using the same ionization and detention scheme. We found that C₈H₁₂O₄ contributed ∼ 10 times more to the particle than to the gas phase.

Furthermore, in the present study, the presence of the other PMF factors suggests that either there are at least three isomers with distinguishable volatility, or there are different thermal decomposition processes occurring at different desorption temperatures which all form C₈H₁₂O₄ as a stable product. There are some studies that suggest possible decomposition pathways. For example, Hyttinen et al. (2022) investigated the two possible thermal decomposition reactions (dehydration and decarboxylation) proposed by Yang et al. (2021) and explored which reactants provide C₈H₁₂O₄ as a product. They reported two isomers of C₈H₁₂O₄ formed from C₉H₁₂O₆ decarboxylation and one isomer formed from C₈H₁₄O₅ dehydration. Certainly, the mechanisms that explain our observation remain uncertain and need to be further investigated.