An algorithm for automatic fitting and formula assignment in atmospheric mass spectra

Section S1.Instruments, particle collection approach, and discussion of potential sampling artifacts in this work S1.1 Instruments SOA concentration was online detected by a high-resolution time-of-flight Aerosol Mass Spectrometer (HR-AMS, Aerodyne Inc.).The HR-AMS employs electron impact (EI) ionization, a hard ionization method that induces extensive fragmentation of organic molecules, being able to provide quantitative information on the bulk mass of organic aerosol but lacks molecular-level information.In addition, the particle-phase chemical composition of SOA was detected by a chemical ionization mass spectrometer (CIMS) coupled with a filter inlet for gas and aerosols (FIGAERO) using iodide (I -) as reagent ions with 1 Hz time resolution (Lopez-Hilfiker et al., 2014;Lee et al., 2014).FIGAERO-iodide-CIMS uses a soft ionization technique that is highly sensitive to polar and oxygenated organic compounds.Consequently, the organic aerosol components detected by CIMS represent a subset of the total organic aerosol, specifically those that are oxygenated organic compounds (Lee et al., 2014).The chemical composition measured by the HR-AMS and FIGAERO-iodide-CIMS is expected to be consistent, as the nature of SOA is oxygenated.However, the sensitivity of FIGAERO-iodide-CIMS towards different organic compounds varies significantly, inhibiting a quantification of individual molecules or the sum of the compounds detected by FIGAERO-iodide-CIMS without extensive calibration.Data analysis was done using Tofware 3.2.2(Aerodyne).Note that the reagent ion I -(m/z = 126.904)was subtracted from the molecular mass weights presented in this work.The mass resolution of FIGAERO-CIMS was 4000.Formulas were assigned with a mass accuracy of better than 20 ppm based on the mass resolution.We also note that the sensitivity of FIGAEROiodide-CIMS is highly dependent on the functionalities of the organic compounds and can vary by orders of magnitudes (Lopez-Hilfiker et al., 2016a;Lee et al., 2014;Riva et al., 2019).Therefore, the results shown in this work are based on signal intensities but not mass concentrations. S1.2 Particle collection approachThe particle data presented in this work stems from offline analysis.Particles were collected on Polytetrafluorethylene (PTFE, 1 m, SKC Inc.) filters with a sampling flow of 6.4 L/min for 5 minutes, when particle concentrations stabilized during the experiments.The number of samples obtained at each temperature was as follows: one at 213 K, three at 243 K, and two each at 273 K, 298 K, and 313 K.In addition, one filter was sampled after warming was completed.The filter samples were analyzed using a FIGAERO-iodide-CIMS. Thermal desorption was performed under a flow of ultra-high-purity nitrogen as the carrier gas.The temperature was first ramped from 296 K to 473 K over 15 min and then held at 473 K for an additional 20 min, resulting in a total desorption time of 35 min (Lopez-Hilfiker et al., 2016a;Lee et al., 2014;Riva et al., 2019). S1.3 Discussion of potential sampling artifacts in this workThe filter sampling setup and all instruments were installed in the laboratory outside the chamber at room temperature (2952 K).To minimize the effect of temperature differences between the chamber temperatures (213 K, 243 K, 273 K, 298 K, 313 K) and room temperature on the gas-phase chemical composition, the sampling line to the CIMS was partially insulated and the residence time in the Teflon sampling line was only 2 s.The residence time in the stainless-steel line for filter sampling was about 1 s.The sampling time of Telflon filters at all experiments were typically 5 minutes plus a few minutes of handling time before the sample

Secondary formation of OA in polluted urban environments involves diverse precursors and complex pathways. When the severity and frequency of heatwaves are expected to rise, little is about the heatwave impacts on the OA formation in such polluted environments. Here we deployed a long time-of-flight aerosol mass spectrometer (LTOF-AMS) to measure the real-time chemical composition of submicron aerosols along with advanced time-of-flight chemical ionization mass spectrometers (TOF-CIMS) to detect gaseous and particulate oxidation products in Beijing in recent years. Six process-level secondary OA (SOA) factors are resolved and unique molecular tracers for each of the six processes are identified. The six SOA factors can be explained by intensified photochemical and heterogeneous reactions with higher volatile organic compounds emissions and oxidant level, increased aerosol surface, stronger aerosol acidity, and higher ammonia concentration etc. The six-factor SOA seperation provides a machanistic understanding of the net enhancement of SOA during heatwave events, which have been observed in many places worldwide in summer. We further applied machine learning methods to identify the key drivers of the SOA enhancement and used the simulated key parameters from the GEOS-Chem model to perturb the SOA formation during the heatwave episode under clean air actions and climate change scenarios. Our results suggest that the SOA enhancement due to heatwave will be increasingly important in the future. This study underscores the urgency of validating temperature responses of organic aerosol in chemical transport models to facilitate air quality management in a warming world.

New instruments are beginning to reveal the chemical complexity of atmospheric aerosol particles. Exploitation of the plethora of information being made accessible through aerosol particle spectrometry and other techniques requires new strategies for data interpretation. This paper demonstrates and evaluates several analysis methods used to exploit this single-particle high-time-resolution data. In the first part of this study, Standard Reference Material (SRM) particulate matter samples were analyzed by an Aerosol Time-of-Flight Mass Spectrometer (ATOFMS) in order to evaluate the use of a modified, logarithm based, method of clustering mass spectra using the Adaptive Resonance Theory (ART-2a) algorithm. In the second part of this study, data obtained from the ATOFMS during the four seasons of 2007 were interpreted using a variety of approaches so as to elucidate the nature and sources of particles influencing the great lakes region of North America. This dataset is believed to represent the longest time-span of single-particle data ever analyzed in a study of this nature. These mass spectra were clustered into 21 different particle types using the supervised log-transformed ART-2a algorithm. Both long-term seasonal trends and high-time-resolution temporal patterns of particle type concentrations were examined. Source identification was supported by comparison with known source samples. Potential source contribution functions were used to identify source regions. This paper describes and evaluates these approaches to data interpretation using examples from the ambient air study to illustrate the methodology and highlight the findings. Furthermore, these ambient examples demonstrate how the application of these strategies enhances the interpretation of single-particle ambient aerosol data. Copyright 2012 American Association for Aerosol Research

The source apportionment of organic aerosols (OA) in background locations is one of the important issues in contemporary air quality protection. A better understanding of the atmospheric aerosols origin and their source apportionment in different locations is essential for improving air quality.Measurements of non-refractory PM1 (NR-PM1) were simultaneously performed during winter (1st February – 10th March) and summer (1st July – 20th August)  2021 at three background sites, National Atmospheric Observatory Košetice (N 49°35′, E 15°05′; 534 m a.s.l.) and Frýdlant (N 50°94′, E 15°07′; 366 m a.s.l.) in Czech Republic, and Melpitz (N 51°32', E 12°56'; 86 m a.s.l.) in Germany. Each site was equipped with an Aerosol Mass Spectrometer: a ToF- Aerosol Chemical Speciation Monitor (ToF-ACSM) at Košetice, a Compact Time-of-Flight Aerosol Mass Spectrometer C-ToF-AMS) at Frýdlant, and a High-Resolution Time-of-Flight Aerosol Mass Spectrometer (HR-ToF-AMS) at Melpitz.A Positive Matrix Factorization (PMF) with multi-linear engine (ME-2) using Source Finder (SoFi) (Canonaco et al., 2013) was applied to OA data sets to separate OA into different factors in terms of their mass spectra and time series. Four sources of OA were identified for the winter season. Three of these sources, hydrocarbon-like organic aerosol (HOA), biomass burning organic aerosol (BBOA), and coal combustion organic aerosol (CCOA), belong to the primary sources. The last factor, designated oxidised organic aerosol (OOA), belongs to the secondary sources. Four sources of OA have also been identified for the summer season. Two of these sources were designated as primary sources (HOA and BBOA) and two as secondary sources, more oxidized organic aerosol (MO-OOA) and less oxidized organic aerosol (LO-OOA). Acknowledgements:This work was supported within a German-Czech cooperation in the TRACE project funded by the GACR under grant 20-08304J and by DFG under grant 431895563, also by the MEYS of the Czech Republic under grants ACTRIS-CZ LM2018122 and ACTRIS-CZ RI (CZ.02.1 .01 / 0.0 / 0.0 / 16_013 / 0001315), and European Union's Horizon 2020 research and innovation program ACTRIS IMP (871115). Reference:Canonaco, F., Crippa, M., Slowik, J. G., Baltensperger, U., Prévôt, A. S.: SoFi, an IGOR-based interface for the efficient use of the generalized multilinear engine (ME-2) for the source apportionment: ME-2 application to aerosol mass spectrometer data, Atmos. Measur. Tech., 6(12), 3649-3661, 2013.

A chemical ionization mass spectrometer (CIMS) and a flowing afterglow apparatus were used to study reactions of benzene cations (C6H6+ and (C6H6)2+) with a series of volatile organic compounds (VOCs). Both cations react at the collision rate with compounds of lower ionization potential than benzene, such as isoprene (C5H8), other conjugated dienes, and aromatics. These ions are generally unreactive with substances of higher ionization potential such as alkanes, simple alcohols, simple carbonyls, etc. The results demonstrate that C6H6+ and (C6H6)2+ are excellent reagent ions for the sensitive detection of isoprene in air with a CIMS. However, 2‐methyl‐3‐buten‐2‐ol (MBO) and C5H8 conjugated dienes were identified as potential interferences to this technique. This indicates that the selectivity of the CIMS isoprene measurement must be tested by intercomparison with well‐established methods, e.g. gas chromatography techniques.

Characteristics and sources of water-soluble organic aerosol in a heavily polluted environment in Northern China

Abstract. Organic aerosol (OA) represents a significant and often major fraction of the non-refractory PM1 (particulate matter with an aerodynamic diameter da < 1 μm) mass. Secondary organic aerosol (SOA) is an important contributor to the OA and can be formed from biogenic and anthropogenic precursors. Here we present results from the characterization of SOA produced from the emissions of three different anthropogenic sources. SOA from a log wood burner, a Euro 2 diesel car and a two-stroke Euro 2 scooter were characterized with an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-TOF-AMS) and compared to SOA from α-pinene. The emissions were sampled from the chimney/tailpipe by a heated inlet system and filtered before injection into a smog chamber. The gas phase emissions were irradiated by xenon arc lamps to initiate photo-chemistry which led to nucleation and subsequent particle growth by SOA production. Duplicate experiments were performed for each SOA type, with the averaged organic mass spectra showing Pearson's r values >0.94 for the correlations between the four different SOA types after five hours of aging. High-resolution mass spectra (HR-MS) showed that the dominant peaks in the MS, m/z 43 and 44, are dominated by the oxygenated ions C2H3O+ and CO2+, respectively, similarly to the relatively fresh semi-volatile oxygenated OA (SV-OOA) observed in the ambient aerosol. The atomic O:C ratios were found to be in the range of 0.25–0.55 with no major increase during the first five hours of aging. On average, the diesel SOA showed the lowest O:C ratio followed by SOA from wood burning, α-pinene and the scooter emissions. Grouping the fragment ions revealed that the SOA source with the highest O:C ratio had the largest fraction of small ions. The HR data of the four sources could be clustered and separated using principal component analysis (PCA). The model showed a significant separation of the four SOA types and clustering of the duplicate experiments on the first two principal components (PCs), which explained 79% of the total variance. Projection of ambient SV-OOA spectra resolved by positive matrix factorization (PMF) showed that this approach could be useful to identify large contributions of the tested SOA sources to SV-OOA. The first results from this study indicate that the SV-OOA in Barcelona is strongly influenced by diesel emissions in winter while in summer at SIRTA at the southwestern edge of Paris SV-OOA is more similar to alpha-pinene SOA. However, contributions to the ambient SV-OOA from SOA sources that are not covered by the model can cause major interference and therefore future expansions of the PCA model with additional SOA sources is recommended.

Abstract. The Aerodyne aerosol mass spectrometer (AMS) is a frequently used instrument for on-line measurement of the ambient sub-micron aerosol composition. With the help of calibrations and a number of assumptions on the flash vaporization and electron impact ionization processes, this instrument provides robust quantitative information on various non-refractory ambient aerosol components. However, when measuring close to certain anthropogenic or marine sources of semi-refractory aerosols, several of these assumptions may not be met and measurement results might easily be incorrectly interpreted if not carefully analyzed for unique ions, isotope patterns, and potential slow vaporization associated with semi-refractory species. Here we discuss various aspects of the interaction of aerosol particles with the AMS tungsten vaporizer and the consequences for the measurement results: semi-refractory components – i.e., components that vaporize but do not flash-vaporize at the vaporizer and ionizer temperatures, like metal halides (e.g., chlorides, bromides or iodides of Al, Ba, Cd, Cu, Fe, Hg, K, Na, Pb, Sr, Zn) – can be measured semi-quantitatively despite their relatively slow vaporization from the vaporizer. Even though non-refractory components (e.g., NH4NO3 or (NH4)2SO4) vaporize quickly, under certain conditions their differences in vaporization kinetics can result in undesired biases in ion collection efficiency in thresholded measurements. Chemical reactions with oxygen from the aerosol flow can have an influence on the mass spectra for certain components (e.g., organic species). Finally, chemical reactions of the aerosol with the vaporizer surface can result in additional signals in the mass spectra (e.g., WO2Cl2-related signals from particulate Cl) and in conditioning or contamination of the vaporizer, with potential memory effects influencing the mass spectra of subsequent measurements. Laboratory experiments that investigate these particle–vaporizer interactions are presented and are discussed together with field results, showing that measurements of typical continental or urban aerosols are not significantly affected, while measurements of semi-refractory aerosol in the laboratory, close to anthropogenic sources or in marine environments, can be biased by these effects.

In August 1999 an intensive field campaign was conducted in Atlanta, Georgia, focusing on the characterization of urban particulate matter. During the study an aerosol time‐of‐flight mass spectrometer (ATOFMS) was used to measure continuously the aerodynamic size and chemical composition of individual particles in the fine fraction (0.2–2.5 μm) of the atmospheric aerosol. The inorganic and organic components of the particles were analyzed using laser desorption ionization time‐of‐flight mass spectrometry, generating positive and/or negative ion mass spectra. Here an overview of the ATOFMS results is presented with respect to the major species detected, including sodium, carbon (EC, OC, EC/OC), dust (Li, Na, Al, K, Ca, Fe), sulfate, nitrate, and ammonium. As described, many of the dust particle types have similar composition to those observed in ATOFMS coal source characterization studies. The ion signals, size distributions, and temporal evolution (30–60 min resolution) of the different particle types are described. The complexity of the Atlanta aerosol is shown here on an individual particle basis, demonstrating how single particle data obtained with ATOFMS can be used to gain unique insights into the mixing state of urban aerosols. The second paper in this two part series focuses on the scaling procedures used for converting the unscaled ATOFMS data presented in this paper into atmospherically representative number concentrations [Wenzel et al., 2002].

Essential oils analysis. I. Evaluation of essential oils composition using both GC and MS fingerprints.

α-Pinene is a biogenic volatile organic compound (BVOC) that significantly contributes to secondary organic aerosols (SOA) in the atmosphere due to its high emission rate, reactivity, and SOA yield. However, the SOA yield measured in chamber studies from α-pinene photooxidation is limited in a purified air matrix. Assessing SOA formation from α-pinene photooxidation in real urban ambient air based on studies conducted in purified air matrices may be subject to uncertainties. In this study, α-pinene photooxidation and SOA yield were investigated in a smog chamber in the presence of NO and SO2 under purified air and ambient air matrices. With the accumulation of ozone (O3) during the photooxidation, an increasing part of α-pinene was consumed by O3 and finally nearly half of the α-pinene was oxidized by O3, facilitating the production of highly oxidized organic molecules and thereby SOA formation. Although the ambient air we introduced as matrix air was largely clean, with initial organic aerosol mass concentrations of ~1.5 μg m−3, the α-pinene SOA yield in the ambient air matrix was 42.3 ± 5.3%, still higher than that of 32.4 ± 0.4% in the purified air matrix. The chemical characterization of SOA by the high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) revealed that CxHy accounted for 53.7 ± 1.1% of the total signal in the ambient air matrix experiments, higher than 48.1 ± 0.3% in the purified air, while CxHyO and CxHyO>1 together constituted 45.0 ± 0.9% in the ambient air matrix, lower than 50.1 ± 1.0% in the purified air. The O:C ratio in the ambient air matrix experiments was 0.41 ± 0.01, lower than 0.46 ± 0.01 in the purified air. The higher SOA yield of α-pinene in the ambient air matrix compared to that in the purified air matrix was partly due to the presence of initial aerosols in the ambient air, which facilitated the low volatile organic compounds produced from photochemical oxidation to enter the aerosol phase through gas-particle partitioning. The in-situ aerosol acidity calculated by the ISORROPIA-II model in the ambient air matrix experiments was approximately six times higher than that in purified air, and the higher SOA yield in the ambient air matrix experiments might also be attributed to acid-catalyzed SOA formation.

Reactions of SiF 5− with atmospheric trace gases: ion chemistry for chemical ionization detection of HNO 3 in the troposphere

Abstract. Air pollution in urban environments has been shown to have a negative impact on air quality and human health, particularly in megacities. Over recent decades, Delhi, India, has suffered high atmospheric pollution, with significant particulate matter (PM) concentrations as a result of anthropogenic activities. Organic aerosols (OAs) are composed of thousands of different chemical species and are one of the main constituents of submicron particles. However, quantitative knowledge of OA composition, their sources and their processes in urban environments is still limited. This is important particularly in India, as Delhi is a massive, inhomogeneous conurbation, where we would expect the apportionment and concentrations to vary depending on where in Delhi the measurements/source apportionment is performed, indicating the need for multisite measurements. This study presents the first multisite analysis carried out in India over different seasons, with a focus on identifying OA sources. The measurements were taken during 2018 at two sites in Delhi, India. One site was located at the India Meteorological Department, New Delhi (ND). The other site was located at the Indira Gandhi Delhi Technical University for Women, Old Delhi (OD). Non-refractory submicron aerosol (NR-PM1) concentrations (ammonium, nitrate, sulfate, chloride and organic aerosols) of four aerosol mass spectrometers were analysed. Collocated measurements of volatile organic compounds, black carbon, NOx and CO were performed. Positive matrix factorisation (PMF) analysis was performed to separate the organic fraction, identifying a number of conventional factors: hydrocarbon-like OAs (HOAs) related to traffic emissions, biomass burning OAs (BBOAs), cooking OAs (COAs) and secondary OAs (SOAs). A composition-based estimate of PM1 is defined by combining black carbon (BC) and NR-PM1 (C-PM1= BC + NR-PM1). No significant difference was observed in C-PM1 concentrations between sites, OD (142 ± 117 µg m−3) compared to ND (123 ± 71 µg m3), from post-monsoon measurements. A wider variability was observed between seasons, where pre-monsoon and monsoon showed C-PM1 concentrations lower than 60 µg m−3. A seasonal variation in C-PM1 composition was observed; SO42- showed a high contribution over pre-monsoon and monsoon seasons, while NO3- and Cl− had a higher contribution in winter and post-monsoon. The main primary aerosol source was from traffic, which is consistent with the PMF analysis and Aethalometer model analysis. Thus, in order to reduce PM1 concentrations in Delhi through local emission controls, traffic emission control offers the greatest opportunity. PMF–aerosol mass spectrometer (AMS) mass spectra will help to improve future aerosol source apportionment studies. The information generated in this study increases our understanding of PM1 composition and OA sources in Delhi, India. Furthermore, the scientific findings provide significant information to strengthen legislation that aims to improve air quality in India.

Abstract. The largest global source of secondary organic aerosol (SOA) in the atmosphere is derived from the oxidation of biogenic emissions. Plant stressors associated with a changing environment can alter both the quantity and composition of the compounds that are emitted. Alterations to the biogenic volatile organic compound (BVOC) profile could impact the characteristics of the SOA formed from those emissions. This study investigated the impacts of one global change stressor, increased herbivory, on the composition of SOA derived from real plant emissions. Herbivory was simulated via application of methyl jasmonate (MeJA), a proxy compound. Experiments were repeated under pre- and post-treatment conditions for six different coniferous plant types. Volatile organic compounds (VOCs) emitted from the plants were oxidized to form SOA via dark ozone-initiated chemistry. The SOA chemical composition was measured using a Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-AMS). The aerosol mass spectra of pre-treatment biogenic SOA from all plant types tended to be similar with correlations usually greater than or equal to 0.90. The presence of a stressor produced characteristic differences in the SOA mass spectra. Specifically, the following m/z were identified as a possible biogenic stress AMS marker with the corresponding HR ion(s) shown in parentheses: m/z 31 (CH3O+), m/z 58 (C2H2O2+, C3H6O+), m/z 29 (C2H5+), m/z 57 (C3H5O+), m/z 59 (C2H3O2+, C3H7O+), m/z 71 (C3H3O2+, C4H7O+), and m/z 83 (C5H7O+). The first aerosol mass spectrum of SOA generated from the oxidation of the plant stress hormone, MeJA, is also presented. Elemental analysis results demonstrated an O : C range of baseline biogenic SOA between 0.3 and 0.47. The O : C of standard MeJA SOA was 0.52. Results presented here could be used to help identify a biogenic plant stress marker in ambient data sets collected in forest environments.

Abstract. Cooking emissions can be a significant source of fine particulate matter in urban areas. In this study the aerosol- and gas-phase emissions from meat charbroiling were characterized. Greek souvlakia with pork were cooked using a commercial charbroiler and a fraction of the emissions were introduced into a smog chamber where after a characterization phase they were exposed to UV illumination and oxidants. The particulate and gas phases were characterized by a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and a proton-transfer-reaction mass spectrometer (PTR-MS) correspondingly. More than 99 % of the aerosol emitted was composed of organic compounds, while black carbon (BC) contributed 0.3 % and the inorganic species less than 0.5 % of the total aerosol mass. The initial O : C ratio was approximately 0.09 and increased up to 0.30 after a few hours of chemical aging (exposures of 1010 molecules cm−3 s for OH and 100 ppb h for ozone). The initial and aged AMS spectra differed considerably (θ = 27°). Ambient measurements were also conducted during Fat Thursday in Patras, Greece, when traditionally meat is charbroiled everywhere in the city. Positive matrix factorization (PMF) revealed that cooking organic aerosol (COA) reached up to 85 % of the total OA from 10:00 to 12:00 LST that day. The ambient COA factor in two major Greek cities had a mass spectrum during spring and summer similar to the aged meat charbroiling emissions. In contrast, the ambient COA factor during winter resembled strongly the fresh laboratory meat charbroiling emissions.