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The measurements of aerosol particles with a filter inlet for gases and aerosols
(FIGAERO) together with a chemical ionisation mass spectrometer (CIMS) yield the overall chemical composition of the particle phase. In addition, the
thermal desorption profiles obtained for each detected ion composition
contain information about the volatility of the...
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... acquisition of the data set investigated in this study was described in detail in Buchholz et al. (2019) and in the Supplement. The schematic overview of the set-up is shown in Fig. 1. Briefly, three types of SOA were formed via combined ozonolysis and photo-oxidation of α-pinene in an oxidative flow reactor (OFR). They are characterised as low, medium, and high O : C based on their elemental composition (oxygen-to-carbon (O : C) ratio of 0.53, 0.69, and 0.96, respectively, as derived from the aerosol mass ...
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... of signal noise. The detailed calculation for this type of error is given in the Supplement. The resulting error values (Poisson-like -"PLerror") will trace the shape of the thermogram signal with higher absolute values for those parts of the thermogram that have higher intensity (i.e. the "peak") and give less weight to this region ( Fig. S1 in the Supplement). This is the correct approach for the analysis of long time series data in which rapid changes are most likely caused by instrument noise or data outliers. For FIGAERO-CIMS thermograms, the main information lies in the rapidly increasing and decreasing part of the data (i.e. the "peak"; data points 10-50 in Fig. 2a) ...
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... calculated in the same way as for the PLerror (see the Supplement for details). Note that by omitting the first term in Eq. (7), Eq. (8) does not correspond to the true measurement error of the FIGAERO-CIMS data. Rather, it is the simplest way of weighting the PMF runs to put more emphasis on each thermogram peak and less on the fronts and tails (Fig. S1 shows an example of the values for the two error schemes for one exemplary ion). The signal-to-noise values are up to 3 orders of magnitude higher in the peak region for the CNerror case, which clearly gives them a stronger weight in the optimisation. As a direct consequence of the modified error value, the value for Q/Q exp is not ...
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... shown by Yan et al. (2016) for gas-phase CIMS data, a solution with a low overall Q/Q exp value may still have large variations in the scaled residual with time or with different ions. We carefully investigated the time series (Q j /Q exp ) of individual ions (e.g. C 5 H 5 O − 6 in Fig. A1b and c) in particu- lar and present the details of this case study in Appendix A. There were a few specific ions for each SOA type that were not captured well in the data set until a certain number of factors was chosen (e.g. 7 in the high-O : C case) -even if the overall fraction of explained variance for the solutions was already larger ...
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... schemes create different results, we investigated the behaviour of the PMF solutions for individual ions. We selected two ions with similar signal strength and different error scheme responses. Both error schemes provided comparable PMF results for the ion [C 7 H 8 O 6 +I] − (Fig. A2) while the thermogram behaviour of the ion C 5 H 5 O − 6 ( Fig. A1) was not captured well with the PLerror scheme. Note that the latter represents the group that mostly contained ions that were affected by aqueous-phase chemistry. For the 6-factor solution (red line in Fig. A1b and d), the residual time series for this ion has similar values for thermogram scans III and IV in both error schemes, but ...
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... provided comparable PMF results for the ion [C 7 H 8 O 6 +I] − (Fig. A2) while the thermogram behaviour of the ion C 5 H 5 O − 6 ( Fig. A1) was not captured well with the PLerror scheme. Note that the latter represents the group that mostly contained ions that were affected by aqueous-phase chemistry. For the 6-factor solution (red line in Fig. A1b and d), the residual time series for this ion has similar values for thermogram scans III and IV in both error schemes, but increasing the number of factors by 1 seems to only have a noticeable effect in the case of the CNerror. This is because, in this case, the Q ion ...
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... values. Together, they account for 15 % of the overall Q/Q exp value in the 6-factor case. So, adding an additional factor to describe that portion of the data set will strongly decrease Q ion and also Q/Q exp , which indicates a better fit. In the case of the PLerror, the Q ion values exhibit very similar profiles for all four thermogram scans ( Fig. A1d and e). Thus, changing any parameter for C 5 H 5 O − 6 will have little effect on the Q ion values and, therefore, on the overall Q/Q exp . This example clearly shows how the selection of the error values guides the focus of PMF, i.e. which part of the data set still needs improvement when the number of factors is increased. In Fig. A3, the ...
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A new methodology for performing long-term source apportionment (SA) using positive matrix factorization (PMF) is presented. The method is implemented within the SoFi Pro software package and uses the multilinear engine (ME-2) as a PMF solver. The technique is applied to a one-year aerosol chemical speciation monitor (ACSM) dataset from downtown Zu...
Citations
... To reduce the complexity of data analysis, dimension reduction techniques are necessary, which compress various variables in a dataset into a few to a dozen of factors/clusters based on the underlying correlation/similarity of different variables, e.g., in terms of their sources or physicochemical properties, while retaining the major chemical and kinetic information of investigated systems and thus making the data analysis easier and more effective (Äijälä et al., 2017;Buchholz et al., 2020;Koss et al., 2020;Yan et al., 2016;. ...
Oxidation of volatile organic compounds (VOCs) can lead to the formation of secondary organic aerosol (SOA), a significant component of atmospheric fine particles, which can affect air quality, human health, and climate change. However, the current understanding of the formation mechanism of SOA is still incomplete, which is not only due to the complexity of the chemistry but also relates to analytical challenges in SOA precursor detection and quantification. Recent instrumental advances, especially the development of high-resolution time-of-flight chemical ionization mass spectrometry (CIMS), greatly improved both the detection and quantification of low- and extremely low-volatility organic molecules (LVOCs/ELVOCs), which largely facilitated the investigation of SOA formation pathways. However, analyzing and interpreting complex mass spectrometric data remain a challenging task. This necessitates the use of dimension reduction techniques to simplify mass spectrometric data with the purpose of extracting chemical and kinetic information of the investigated system. Here we present an approach to apply fuzzy c-means clustering (FCM) to analyze CIMS data from a chamber experiment, aiming to investigate the gas phase chemistry of the nitrate-radical-initiated oxidation of isoprene. The performance of FCM was evaluated and validated. By applying FCM to measurements, various oxidation products were classified into different groups, based on their chemical and kinetic properties, and the common patterns of their time series were identified, which provided insight into the chemistry of the investigated system. The chemical properties of the clusters are described by elemental ratios and the average carbon oxidation state, and the kinetic behaviors are parameterized with a generation number and effective rate coefficient (describing the average reactivity of a species) using the gamma kinetic parameterization model. In addition, the fuzziness of FCM algorithm provides a possibility for the separation of isomers or different chemical processes that species are involved in, which could be useful for mechanism development. Overall, FCM is a technique that can be applied well to simplify complex mass spectrometric data, and the chemical and kinetic properties derived from clustering can be utilized to understand the reaction system of interest.
... Note that thermal decomposition of larger organic compounds during particle desorption with the FIGAERO could contribute, in particular, to the IVOC fraction (Huang et al., 2019b;Lopez-Hilfiker et al., 2016). Resolving and subtracting the thermal decomposition compounds using multipeak fitting methods or with the help of positive matrix factorization (Buchholz et al., 2020) may, however, introduce uncertainties due to some ambiguity during the implementation and interpretation (Graham et al., 2023;Voliotis et al., 2021). Using the thermal decomposition estimation approach by Wu et al. (2021), the decomposition fraction is estimated to be 5.8 %-35.9 % (see more discussions below), which is close to the thermal fragmentation contributions (1 %-27 %) for nitrate SOA reported in a recent chamber study (Graham et al., 2023). ...
... Thermal decomposition of larger oligomeric molecules can bias the C sat and, to a lesser extent, the sum thermogram shape and sumT max towards higher volatilities due to the dominance of monomer species (i.e., ending up with a longer tail). There have been attempts to separate the thermal decomposition contribution for individual thermograms Buchholz et al., 2020;Wu et al., 2021), but this poses the threat of introducing new uncertainties due to the difficulty in, e.g., differentiating isomers from thermal decomposition products and monomers from dimers in ambient samples with complex VOC precursors (Graham et al., 2023;Voliotis et al., 2021). Overall, however, for a limited number of our datasets from the exact same instrument, the lower apparent volatility (i.e., higher sumT max ) agrees qualitatively with the lower log 10 C sat values, corroborating potential relationships and interconnections between volatility and chemical composition across different environments and systems despite the large uncertainties and artifacts of both methods. ...
The apparent volatility of atmospheric organic aerosol (OA) particles is determined by their chemical composition and environmental conditions (e.g., ambient temperature). A quantitative, experimental assessment of volatility and the respective importance of these two factors remains challenging, especially in ambient measurements. We present molecular composition and volatility of oxygenated OA (OOA) particles in different rural, urban, and mountain environments (including Chacaltaya, Bolivia; Alabama, US; Hyytiälä, Finland; Stuttgart and Karlsruhe, Germany; and Delhi, India) based on deployments of a filter inlet for gases and aerosols coupled to a high-resolution time-of-flight chemical ionization mass spectrometer (FIGAERO-CIMS). We find on average larger carbon numbers (nC ) and lower oxygen-to-carbon (O : C) ratios at the urban sites (nC: 9.8 ± 0.7; O : C: 0.76 ± 0.03; average ±1 standard deviation) compared to the rural (nC: 8.8 ± 0.6; O : C: 0.80 ± 0.05) and mountain stations (nC: 8.1 ± 0.8; O : C: 0.91 ± 0.07), indicative of different emission sources and chemistry. Compounds containing only carbon, hydrogen, and oxygen atoms (CHO) contribute the most to the total OOA mass at the rural sites (79.9 ± 5.2 %), in accordance with their proximity to forested areas (66.2 ± 5.5 % at the mountain sites and 72.6 ± 4.3 % at the urban sites). The largest contribution of nitrogen-containing compounds (CHON) is found at the urban stations (27.1 ± 4.3 %), consistent with their higher NOx levels. Moreover, we parametrize OOA volatility (saturation mass concentrations, Csat) using molecular composition information and compare it with the bulk apparent volatility derived from thermal desorption of the OOA particles within the FIGAERO. We find differences in Csat values of up to ∼ 3 orders of magnitude and variation in thermal desorption profiles (thermograms) across different locations and systems. From our study, we draw the general conclusion that environmental conditions (e.g., ambient temperature) do not directly affect OOA apparent volatility but rather indirectly by influencing the sources and chemistry of the environment and thus the chemical composition. The comprehensive dataset provides results that show the complex thermodynamics and chemistry of OOA and their changes during its lifetime in the atmosphere. We conclude that generally the chemical description of OOA suffices to predict its apparent volatility, at least qualitatively. Our study thus provides new insights that will help guide choices of, e.g., descriptions of OOA volatility in different model frameworks such as air quality models and cloud parcel models.
... 7,21−24 More recent development of a filter inlet for gases and aerosols 25 coupled to chemical ionization mass spectrometer utilized thermal desorption and detection of untargeted OA components from filter samples collected in laboratory and field studies. 26,27 That method allowed assessment of volatility classes and thermal stabilities of individual OA species inferred from the experimentally observed temperatures (T max ) corresponding to the maxima of the ions' intensity. 26 Consequently, estimations of saturation vapor pressure for the observed species were performed based on the comparison of experimental results with predictions by available semiempirical models. ...
... 26,27 That method allowed assessment of volatility classes and thermal stabilities of individual OA species inferred from the experimentally observed temperatures (T max ) corresponding to the maxima of the ions' intensity. 26 Consequently, estimations of saturation vapor pressure for the observed species were performed based on the comparison of experimental results with predictions by available semiempirical models. 28,29 While significant advances in the knowledge about gas−particle partitioning of OA have been provided by the existing measurement methods, further developments are much needed to establish new experimental techniques capable to probe systems approaching complexity of the real-world atmospheric mixtures. ...
Atmospheric organic aerosols (OA) have profound effects on air quality, visibility, and radiative forcing of climate. Quantitative assessment of gas-particle equilibrium of OA components is critical to understand formation, growth, distribution, and evolution of OA in the atmosphere. This study presents a novel ambient pressure measurement approach developed and tested for untargeted screening of individual components in complex OA mixtures, followed by targeted chemical speciation of identified species and assessment of their physicochemical properties such as saturation vapor pressure and enthalpies of sublimation/evaporation. The method employs temperature-programmed desorption (TPD) experiments coupled to "direct analysis in real time" (DART) ionization source and high resolution mass spectrometry (HRMS) detection. Progression of the mass spectra is acquired in the TPD experiments over a T = 25-350 °C temperature range, and extracted ion chromatograms (EIC) of individual species are used to infer their apparent enthalpies of sublimation/evaporation (ΔHsub*) and saturation vapor pressure (pT*, Pa, or CT*, μg m-3) as a function of T. We validate application of this method for analysis of selected organic compounds with known ΔHsub and CT values, which showed excellent agreement between our results and the existing data. We then extend these experiments to interrogate individual components in complex OA samples generated in the laboratory-controlled ozonolysis of α-pinene, limonene, and β-ocimene monoterpenes. The abundant OA species of interest are distinguished based on their accurate mass measurements, followed by quantitation of their apparent ΔHsub* and CT* values from the corresponding EIC records. Comparison of C298K* values derived from our experiments for the individual OA components with the corresponding estimates based on their elemental composition using a "molecular corridors" (MC) parametrization suggests that the MC calculations tend to overestimate the saturation vapor pressures of OA components. Presented results indicate very promising applicability of the TPD-DART-HRMS method for the untargeted analysis of organic molecules in OA and other environmental mixtures, enabling rapid detection and quantification of organic pollutants in the real-world condensed-phase samples at atmospheric pressure and without sample preparation.
... Recently, there have been multiple studies applying various dimension reduction techniques on oxidation chamber data sets illustrating that the selection of the technique can have a large impact on the interpretation of the results. 62,[83][84][85] For this type of study (aging of the emission by oxidation), an ideal factorization would include the oxidation products from the same generation in one factor. However, more detailed investigation of the most suitable dimension reduction technique is beyond the scope of this work. ...
Small-scale wood combustion is a significant source of particulate emissions. Atmospheric transformation of wood combustion emissions is a complex process involving multiple compounds interacting simultaneously. Thus, an advanced methodology is needed to study the process in order to gain a deeper understanding of the emissions. In this study, we are introducing a methodology for simplifying this complex process by detecting dependencies of observed compounds based on a measured dataset. A statistical model was fitted to describe the evolution of combustion emissions with a system of differential equations derived from the measured data. The performance of the model was evaluated using simulated and measured data showing the transformation process of small-scale wood combustion emissions. The model was able to reproduce the temporal evolution of the variables in reasonable agreement with both simulated and measured data. However, as measured emission data are complex due to multiple simultaneous interacting processes, it was not possible to conclude if all detected relationships between the variables were causal or if the variables were merely co-variant. This study provides a step toward a comprehensive, but simple, model describing the evolution of the total emissions during atmospheric aging in both gas and particle phases.
... When a temperature ramp is applied, the species that are adsorbed on the surface gradually desorb (as represented on a thermogram). In order to evaluate whether the thermal desorption methods lead to significant decomposition during evaporation, we applied a method called positive matrix factorization (PMF) (Paatero and Tapper, 1994;Buchholz et al., 2020), in which a dataset matrix is expressed in terms of the sum of factors matrices and a residual matrix. Lastly, we present an overview on the advantages and 125 disadvantages for the different methods. ...
... This method was originally described by Paatero and Tapper (1994) for analyzing time series of variable (e.g. mass spectra data) from ambient observations, and it was implemented by Buchholz et al. (2020) to 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 235 oxidation experiment at -30 ºC and 20 % RH). ...
... mass spectra data) from ambient observations, and it was implemented by Buchholz et al. (2020) to 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 235 oxidation experiment at -30 ºC and 20 % RH). For the analysis, we processed separately 1-second TD-DMA and 1-second FIGAERO thermograms. ...
Currently, the complete chemical characterization of nanoparticles (<100 nm) represents an analytical challenge, since these particles are abundant in number but have negligible mass. Several methods for particle-phase characterization have been recently developed to better detect and infer more accurately the sources and fates of ultra-fine particles, but a detailed comparison of different approaches is missing. Here we report on the chemical composition of secondary organic aerosol (SOA) nanoparticles from experimental studies of α-pinene ozonolysis at -50 ºC, -30 ºC, and -10 ºC, and inter-compare the results measured by different techniques. The experiments were performed at the Cosmics Leaving OUtdoor Droplets (CLOUD) chamber at the European Organization for Nuclear Research (CERN). The chemical composition was measured simultaneously by four different techniques: 1) Thermal Desorption-Differential Mobility Analyzer (TD-DMA) coupled to a NO3- chemical ionization-atmospheric-pressure-interface-time-of-flight (CI-APi-TOF) mass spectrometer, 2) Filter Inlet for Gases and AEROsols (FIGAERO) coupled to an I- high-resolution time-of-flight chemical-ionization mass spectrometer (HRToF-CIMS), 3) Extractive Electrospray Na+ Ionization time-of-flight mass spectrometer (EESI-TOF), and 4) 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). Intercomparison was performed by contrasting the observed chemical composition as a function of oxidation state and carbon number, by calculating the volatility and comparing the fraction of volatility classes, and by comparing the thermal desorption behavior (for the thermal desorption techniques: TD-DMA and FIGAERO) and performing positive matrix factorization (PMF) analysis for the thermograms. We found that the methods generally agree on the most important compounds that are found in the nanoparticles. However, they do see different parts of the organic spectrum. We suggest potential explanations for these differences: thermal decomposition, aging, sampling artifacts, etc. We applied PMF analysis and found insights of thermal decomposition in the TD-DMA and the FIGAERO.
... Since it was introduced by Paatero and Tapper (1994), PMF has been widely used to identify the contribution of different sources of trace compounds in ambient measurements (Ulbrich et al., 2009;Zhang et al., 2011;Yan et al., 2016). More recently, PMF has been adapted to analyze laboratory experiments for understanding chemical or physical aspects of systems of interest (Craven et al., 2012;Buchholz et al., 2020). Regarding a FIGAERO-CIMS data set, PMF can separate sample signals from filter background and contamination. ...
... But more than that, this method can also identify multiple factors which represent not only isomeric compounds with different volatilities but also thermally decomposed products for each ion. Following the procedure outlined in Buchholz et al. (2020), constant error values (CNerror) which were derived from the noise at the end of thermogram scans were applied to all ions without further downweighting. The PMF results were calculated using the PMF Evaluation Tool (PET 3.05) with 1 to 12 factors and five Fpeak rotations from −1 to +1. ...
... Factors which showed contributions in particle samples but not in filter blank samples were assumed to describe the collected particle sample and thus defined as type F ("sample") factors. In Buchholz et al. (2020), these sample factors were distinguished into ones dominated by direct desorption of compounds (type V) and those dominated by products of thermal decomposition (type D). The careful analysis of the sample factors in this study showed that we could not make such a strict distinction. ...
Efforts have been spent on investigating the isothermal evaporation of α-pinene secondary organic aerosol (SOA) particles at ranges of conditions and decoupling the impacts of viscosity and volatility on evaporation. However, little is known about the evaporation behavior of SOA particles from biogenic organic compounds other than α-pinene. In this study, we investigated the isothermal evaporation behavior of the α-pinene and sesquiterpene mixture (SQTmix) SOA particles under a series of relative humidity (RH) conditions. With a set of in situ instruments, we monitored the evolution of particle size, volatility, and composition during evaporation. Our finding demonstrates that the SQTmix SOA particles evaporated slower than the α-pinene ones at any set of RH (expressed with the volume fraction remaining, VFR), which is primarily due to their lower volatility and possibly aided by higher viscosity under dry conditions. We further applied positive matrix factorization (PMF) to the thermal desorption data containing volatility and composition information. Analyzing the net change ratios (NCRs) of each PMF-resolved factor, we can quantitatively compare how each sample factor evolves with increasing evaporation time or RH. When sufficient particulate water content was present in either SOA system, the most volatile sample factor was primarily lost via evaporation, and changes in the other sample factors were mainly governed by aqueous-phase processes. The evolution of each sample factor of the SQTmix SOA particles was controlled by a single type of process, whereas for the α-pinene SOA particles it was regulated by multiple processes. As indicated by the coevolution of VFR and NCR, the effect of aqueous-phase processes could vary from one to another according to particle type, sample factors, and evaporation timescale.
... Further, note that only the particle-phase data collected during the temperature ramp were considered here as the integration of the data requires a linear increase in the desorption temperature (e.g. Buchholz et al., 2020). ...
... There are several reasons that may lead to this tailing effect, such as the existence of multiple isomers and/or fragments from the thermal decomposition of oligomers within each HR peak that have lower volatilities and/or a change in the evaporation behaviour due to the changing composition and thereby the activity coefficient of the sample (Schobesberger et al., 2018). There have been attempts to resolve any potential isomers/decomposition products using multi-peak fitting methods or explain the whole thermograms using positive matrix factorization (Buchholz et al., 2020); however, there is some ambiguity in the implementation and interpretation of such approaches. ...
Secondary organic aerosol (SOA) formation from mixtures of volatile precursors may be influenced by the molecular interactions of the components of the mixture. Here, we report measurements of the volatility distribution of SOA formed from the photo-oxidation of o-cresol, α-pinene, and their mixtures, representative anthropogenic and biogenic precursors, in an atmospheric simulation chamber. The combination of two independent thermal techniques (thermal denuder, TD, and the Filter Inlet for Gases and Aerosols coupled to a high-resolution time-of-flight chemical ionization mass spectrometer, FIGAERO-CIMS) to measure the particle volatility, along with detailed gas- and particle-phase composition measurements, provides links between the chemical composition of the mixture and the resultant SOA particle volatility. The SOA particle volatility obtained by the two independent techniques showed substantial discrepancies. The particle volatility obtained by the TD was wider, spanning across the LVOC and SVOC range, while the respective FIGAERO-CIMS derived using two different methods (i.e. calibrated Tmax and partitioning calculations) was substantially higher (mainly in the SVOC and IVOC, respectively) and narrow. Although the quantification of the SOA particle volatility was challenging, both techniques and methods showed similar trends, with the volatility of the SOA formed from the photo-oxidation of α-pinene being higher than that measured in the o-cresol system, while the volatility of the SOA particles of the mixture was between those measured at the single-precursor systems. This behaviour could be explained by two opposite effects, the scavenging of the larger molecules with lower volatility produced in the single-precursor experiments that led to an increase in the average volatility and the formation of unique-to-the-mixture products that had higher O:C, MW, OSc‾ and, consequently, lower volatility compared to those derived from the individual precursors. We further discuss the potential limitations of FIGAERO-CIMS to report quantitative volatilities and their implications for the reported results, and we show that the particle volatility changes can be qualitatively assessed, while caution should be taken when linking the chemical composition to the particle volatility. These results present the first detailed observations of SOA particle volatility and composition in mixed anthropogenic and biogenic systems and provide an analytical context that can be used to explore particle volatility in chamber experiments.
... 75 The temperature at which the maximum particle signal is observed (T max ) has also been used in the literature to infer volatility of particle-phase species. 40,[43][44][45]76,77 However, we find that T max does not appear to be a reliable indicator of volatility. Although the fraction of a compound in the particle phase (F p ) and its T max should be positively correlated, most organic species desorb around a narrow T max range (see Figure S4) making it difficult to see such a trend and to obtain volatility information from T max values. ...
... Additionally, the use of a GC column, which is selective towards a certain range of volatility and polarity of compounds, which limits the detection of compounds. Conversely, thermal desorption within TAG may fragment larger accretion products to form analytes not present in the original sample (Buchholz et al., 2020;Isaacman-VanWertz et al., 2016;Lopez-Hilfiker et al., 2016b;Stark et al., 2017) or may reverse particle-phase oligomerization reactions (Claflin and Ziemann, 2019). These fragments may not represent the ac- (Worton et al., 2017), southeastern USA by two-dimensional GC with derivatization (Zhang et al., 2018), and combined datasets from SOA formation measured by liquid chromatography (Bryant et al., 2019;Dixon, 2020). ...
Atmospheric oxidation products of volatile organic compounds consist of thousands of unique chemicals that have distinctly different physical and chemical properties depending on their detailed structures and functional groups. Measurement techniques that can achieve molecular characterizations with details down to functional groups (i.e., isomer-resolved resolution) are consequently necessary to provide understandings of differences of fate and transport within isomers produced in the oxidation process. We demonstrate a new instrument coupling the thermal desorption aerosol gas chromatograph (TAG), which enables the separation of isomers, with the high-resolution time-of-flight chemical ionization mass spectrometer (HR-ToF-CIMS), which has the capability of classifying unknown compounds by their molecular formulas, and the flame ionization detector (FID), which provides a near-universal response to organic compounds. The TAG-CIMS/FID is used to provide isomer-resolved measurements of samples from liquid standard injections and particle-phase organics generated in oxidation flow reactors. By coupling a TAG to a CIMS, the CIMS is enhanced with an additional dimension of information (resolution of individual molecules) at the cost of time resolution (i.e., one sample per hour instead of per minute). We found that isomers are prevalent in sample matrix with an average number of three to five isomers per formula depending on the precursors in the oxidation experiments. Additionally, a multi-reagent ionization mode is investigated in which both zero air and iodide are introduced as reagent ions, to examine the feasibility of extending the use of an individual CIMS to a broader range of analytes with still selective reagent ions. While this approach reduces iodide-adduct ions by a factor of 2, [M − H]− and [M + O2]− ions produced from lower-polarity compounds increase by a factor of 5 to 10, improving their detection by CIMS. The method expands the range of detected chemical species by using two chemical ionization reagents simultaneously, which is enabled by the pre-separation of analyte molecules before ionization.
... The detailed chemical analysis of the expected range of particle phase material would benefit greatly from the use of even higher resolution techniques and chromatographic methods since the compound separation and identification with the HR-ToF-CIMS employed here was challenging and, as demonstrated, substantial ambiguities have remained. In addition, future studies of ambient SOA using FIGAERO-CIMS (including the dataset obtained 505 here) will likely benefit from the currently on-going development of techniques to deconvolute and interpret thermogram data sets (Buchholz et al., 2020). Data availability. ...
Secondary organic aerosols (SOA) formed from biogenic volatile organic compounds (BVOCs) constitute a significant fraction of atmospheric particulate matter and have been recognized to affect significantly the climate and air quality. Many laboratory and field experiments have studied SOA particle formation and growth in the recent years. Most of them have focused on a few monoterpenes and isoprene. However, atmospheric SOA particulate mass yields and chemical composition result from a much more complex mixture of oxidation products originating from many BVOCs, including terpenes other than isoprene and monoterpenes. Thus, a large uncertainty still remains regarding the contribution of BVOCs to SOA. In particular, organic compounds formed from sesquiterpenes have not been thoroughly investigated, and their contribution to SOA remains poorly characterized. In this study, a Filter Inlet for Gases and Aerosols (FIGAERO) combined with a high-resolution time-of-flight chemical ionization mass spectrometer (CIMS), with iodide ionization, was used for the simultaneous measurement of gas and particle phase atmospheric SOA. The aim of the study was to evaluate the relative contribution of sesquiterpene oxidation products to SOA in a spring-time hemi-boreal forest environment. Our results revealed that monoterpene and sesquiterpene oxidation products were the main contributors to SOA particles. The chemical composition of SOA particles was compared for times when either monoterpene or sesquiterpene oxidation products were dominant and possible key oxidation products for SOA particle formation were identified. Surprisingly, sesquiterpene oxidation products were the predominant fraction in the particle phase at some periods, while their gas phase concentrations remained much lower than those of monoterpene products. This can be explained by quick and effective partitioning of sesquiterpene products into the particle phase or their efficient removal by dry deposition. The SOA particle volatility determined from measured thermograms increased when the concentration of sesquiterpene oxidation products in SOA particles was higher than that of monoterpenes. Overall, this study demonstrates the important role of sesquiterpenes in atmospheric chemistry and suggests that the contribution of their products to SOA particles is being underestimated in comparison to the most studied terpenes.
