Direct Sampling and Analysis of Atmospheric Particulate Organic

Subscriber access provided by UNIV OF CAMBRIDGE

Article

Direct Sampling and Analysis of Atmospheric Particulate Organic Matter by Proton-Transfer-Reaction Mass Spectrometry Markus Muller, Philipp Eichler, Barbara D'Anna, Wen Tan, and Armin Wisthaler Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b02582 • Publication Date (Web): 15 Sep 2017 Downloaded from http://pubs.acs.org on September 15, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Analytical Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

Direct Sampling and Analysis of Atmospheric Particulate Organic Matter by Proton-Transfer-Reaction Mass Spectrometry Markus Müller,†,‡ Philipp Eichler,†,# Barbara D’Anna,§, ∥ Wen Tan,⊥ and Armin Wisthaler*,†,⊥ †

Institut für Ionenphysik und Angewandte Physik, Universität Innsbruck, Technikerstraße 25, Innsbruck, Austria Ionicon Analytik GmbH, Eduard-Bodem-Gasse 3, Innsbruck, Austria § CNRS, UMR5256, IRCELYON, Institut de recherches sur la catalyse et l’environnement de Lyon, 2 Avenue Albert Einstein, Villeurbanne, Université de Lyon, Lyon, 69626, France ‡

⊥

Department of Chemistry, University of Oslo, Postboks 1033, Blindern, Oslo, Norway

KEYWORDS: CHARON PTR-ToF-MS, ambient organic aerosol, source apportionment

ABSTRACT: We report on a new method for analyzing atmospheric submicrometer particulate organic matter which combines direct particle sampling and volatilization with on-line chemical ionization mass spectrometric analysis. Technically, the method relies on the combined use of a CHARON (“Chemical Analysis of Aerosol On-line”) particle inlet and a proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS). Laboratory studies on target analytes showed that the ionization conditions in the PTR-ToF-MS lead to extensive fragmentation of levoglucosan and cis-pinonic acid, while protonated oleic acid and 5α-cholestane molecules remain intact. Potential problems and biases in quantitative and qualitative analyses are discussed. Side-by-side atmospheric comparison measurements of total particulate organic mass and levoglucosan with an aerosol mass spectrometer (AMS) were in good agreement. Complex and clearly distinct organic mass spectra were obtained from atmospheric measurements in three European cities (Lyon, Valencia, Innsbruck). Data visualization in reduced-parameter frameworks (e.g. oxidation state of carbon vs. carbon number) revealed that the CHARON-PTR-ToF-MS technique adds significant analytical capabilities for characterizing particulate organic carbon in the Earth’s atmosphere. Positive matrix factorization (PMF) was used for apportioning sources of atmospheric particles in late fall in Innsbruck. The m/z signatures of known source marker compounds (levoglucosan and resin acids, polycyclic aromatic hydrocarbons, nicotine) in the mass spectra were used to assign PMF factors to biomass burning, traffic and smoking emission sources.

A significant fraction (20-90%) of the submicrometer particulate mass in the Earth’s atmosphere is organic in nature.1,2 Sources of submicrometer particulate organic matter (sub-µm POM) include direct emissions from fossil fuel and biomass combustion as well as photochemical formation in the atmosphere from trace gases of biogenic and anthropogenic origin.3 Sub-µm POM acts as a radiative forcing agent and thus affects the Earth’s climate.4,5 More importantly, sub-µm POM also contains reactive and toxic species and its inhalation has been associated with adverse human health effects.6–8 The chemical analysis of sub-µm POM in the Earth’s atmosphere is difficult. It consists of a plethora of compounds, ranging from pure hydrocarbons to highly functionalized organic molecules.9,10 Analytes include nonpolar and polar species, many of them being reactive or labile. The sampling of sub-µm POM becomes difficult if the target analytes are chemically reactive or partition between the gas and the particle phases.11,12 Particle collection onto a substrate (e.g. filter sampling) may generate measurement artefacts via reactive transformations on the filter, perturbation of the gasparticle equilibrium during sampling and thermal breakdown of analytes during desorption from the substrate. Subsequent off-line chromatographic analyses are work-intensive, slow and usually limited to selected classes of target compounds.13

The drawbacks of offline methods are partly overcome in the recently developed filter inlet for gases and aerosols chemical ionization mass spectrometer (FIGAERO-CIMS)14 but collection and desorption artefacts remain an issue.15 Direct sampling via an aerodynamic lens, as implemented in single particle laser ionization mass spectrometers16,17 and the aerosol mass spectrometer (AMS)18,19, largely eliminates sampling artefacts and generates chemical-analytical information on inorganic and organic particle constituents in real time. UV-photon and 70 eV electron ionization do, however, fragment most organic analytes, which complicates the interpretation of the obtained particle mass spectra. Atmospheric analytical chemists are thus putting intense efforts in combining direct sampling methods with soft ionization techniques for organic molecules. Ambient pressure ionization techniques such as extractive electrospray ionization (EESI) and aerosol flowing atmospheric-pressure afterglow mass spectrometry (AeroFAPA-MS) are two recently developed methods with promising results.20,21 Atmospheric pressure ionization methods typically produce molecular analyte ions in high abundances. Instrumental response factors to different classes of analytes do, however, usually span many orders of magnitude. Nonpolar or low-polar analytes are not detectable or exhibit a poor instrumental response. External standards are required for analyte quantification and are often not available for atmospherically relevant species.

ACS Paragon Plus Environment


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Importantly, at atmospheric pressure analyte ions may react with other sample molecules (e.g. via dimerization or complexation) in the ion source or during transfer into the mass spectrometer. The products of these ion-molecule reactions may be falsely interpreted as neutral constituents of the atmospheric particles.22 Proton-transfer-reaction mass spectrometry (PTR-MS) is a well-established method for measuring organic trace gases in the Earth’s atmosphere.23–26 In PTR-MS, organic analytes are ionized in a low-pressure drift tube via proton transfer reactions from H3O+ ions. This has several advantages over other ionization techniques, which makes PTR-MS particularly attractive for use in organic particle analysis: • Ionization is universal since H3O+ ions react with virtually all non-polar and polar organic analytes in sub-µm POM. • Ionization is quantitative and linear, since proton transfer reactions occur at the collisional limit under pseudofirst-order kinetic conditions. The instrumental response factor can be calculated solely based on the analyte’s molecular weight, isotropic polarizability and dipole moment. Instrumental response factors vary only by up to a factor of three and are not (or very weakly) humidity-dependent. An external calibration is not needed. • Ionization is simple as it generates only protonated analyte molecules and fragments of those. Secondary ion chemistry and ion hydration is efficiently suppressed in the drift tube. One research group has routinely used PTR-MS for offline analysis of atmospheric sub-µm POM.27–31 They collect particles onto a stainless-steel surface for several minutes, then heat the surface and analyze the emanating gases in a PTR-MS instrument. The drawback of this method is the loss of the real-time capability and the potential occurrence of measurement artefacts during sample collection and heating. We have recently developed the CHARON (“Chemical Analysis of Aerosol On-line”) inlet, which directly introduces sub-µm POM into proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS) instruments.32 A commercialization of this particle sampler is imminent and we have already reported one successful application in which we characterized sub-µm POM in the exhaust of a ship diesel engine.33 Over the past two years, we have carried out a series of exploratory ambient air measurements in three European cities (Lyon, Valencia, Innsbruck) under various pollution conditions. This work presents a selection of the obtained data and shows the potential of the CHARON-PTRToF-MS technique for measuring atmospheric sub-µm POM.

MATERIALS AND METHODS CHARON-PTR-ToF-MS. A detailed description of the CHARON inlet has been provided elsewhere.32 Ambient air is drawn through an activated carbon monolith denuder which removes gaseous organics without affecting particle mass (≤6% mass loss for ammonium nitrate particles). An aerodynamic lens is used for enriching the particle concentration in the PTR-MS subsampling flow by a factor of ∼40. The subsampling flow is then heated to ∼150 °C for completely evaporating sub-µm POM of low, semi and intermediate volatility. For the studies presented herein, the CHARON inlet was coupled to a commercial PTR-TOF 8000 analyzer (Ionicon Analytik GmbH, Innsbruck, Austria).

Page 2 of 14

This instrument and the methods of data analysis are described elsewhere.24,34,35 Operational details and routines are given in the Supporting Information. Elemental composition determination was based on accurate m/z information (∼10 ppm) and isotopic pattern analyses. Molecular formulas including only C, H and O atoms were used in cases where the elemental composition analysis returned multiple candidates. Typically, 85 to 95% of the signals below m/z 300 were elementally resolved in ambient air mass spectra. Above m/z 300, a molecular sum formula was only assigned to selected m/z signals. It has been firmly established that in PTR-MS ionization of organic analytes (with the exception of a few small hydrocarbons) occurs at the collisional rate, which can be accurately predicted by ion-molecule collision theories.36 We used the Langevin-Gioumousis-Stevenson theory37,38 for calculating instrumental response factors to pure hydrocarbons. The Su and Chesnavich parameterized capture rate theory39 was used for calculating instrumental sensitivities of heteroatomcontaining hydrocarbons. This means in practice that instrumental response factor can be calculated from the molecular weight, isotropic molecular polarizability and dipole moment of an analyte molecule. We used the observed m/z (-1 to account for the added proton) as a proxy for the molecular weight, assuming that analyte molecules do not fragment upon protonation. The bias or error introduced by this assumption is discussed later in this work. Isotropic molecular polarizabilities were determined from the analyte ions’ elemental composition using the parametrization proposed by Bosque and Sales.40 Dipole moments cannot be predicted solely from the molecular sum formula and a constant value of 2.75 D was used for all heteroatom-containing analyte ions. This value represents an average of typical dipole moments of oxygenated hydrocarbons (1-4.5 D). This introduces a maximum quantification uncertainty of ±40%. Signals with unknown elemental composition were quantified using the acetone sensitivity as a proxy, the maximum uncertainty in the response factor being -30/+60%. The total mass concentration of sub-µm POM was calculated by summing the mass concentrations associated with all detected m/z peaks. The average oxidation state of carbon atoms in an analyte ion 41 തതതതത (ܱ All formulae ௌ஼ ) was calculated according to Kroll et al. used in our analyses are given in the Supporting Information.

Laboratory Experiments. For pure compound analysis and calibration, we nebulized dissolved oleic acid (>99%, Sigma), 5α-cholestane (>97%, Sigma), cis-pinonic acid (98%, Aldrich) and levoglucosan (99%, Aldrich), respectively, in zero air at a flow rate of 3 l min-1. The nebulizer outflow was branched, diluted with dry zero air, introduced into an activated charcoal denuder for solvent removal, and subsequently injected into an electrostatic classifier (TSI 3071A, Shoreview, MN, USA). The transmitted 300 nm particles were introduced into the CHARON-PTR-ToF-MS analyzer. Ambient Measurements. In 2015, we carried out exploratory CHARON-PTR-ToF-MS measurements of ambient air in Lyon (France), Valencia (Spain) and Innsbruck (Austria) during the periods March 22-24, June 19-22 and October 24-29, respectively. Sampling periods were relatively short (2.5-5 days) as atmospheric measurements only occurred during breaks in concurrent laboratory studies. A more detailed description of the sampling sites and additional instrumentation is given in the Supporting Information. In Lyon, the CHARON-PTR-ToF-MS analyzer was operated


Page 3 of 14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


side-by-side to a C-ToF-AMS instrument (Aerodyne Research Inc., Billerica, MA, USA)42 whose data were analyzed using standard methods.43,44 The PMF Evaluation Tool (PET)45 was used for carrying out a positive matrix factorization (PMF) analysis on the CHARON PTR-ToF-MS data obtained in Innsbruck.

RESULTS AND DISCUSSION Laboratory study on analyte ion fragmentation. PTR-MS is usually perceived as a soft ionization technique that only produces protonated molecules, [M+H]+, from organic analytes. This holds for most small gas-phase analytes that are routinely monitored by PTR-MS in the Earth’s atmosphere.23 In the particle phase, analyte molecules are larger and often heavily functionalized, which makes them more susceptible to fragmentation. Figure 1 shows exemplary PTR-MS mass spectra obtained when particles composed of pure levoglucosan, cis-pinonic acid, oleic acid and 5αcholestane, respectively, were sampled using the CHARON inlet. These compounds are important constituents (or proxies) of atmospheric sub-µm POM.

Figure 1. Relative mass spectra obtained when particles composed of pure levoglucosan, cis-pinonic acid, oleic acid and 5αcholestane were measured by CHARON-PTR-ToF-MS. Open squares designate [M+H-H2O]+ ions, the filled square designates the [M+H-2H2O]+ ion and open circles designate the main ions arising from the fragmentation of the carbon backbone in protonated levoglucosan and cis-pinonic acid.

The upper two panels of Figure 1 show the PTR-MS mass spectra of levoglucosan and cis-pinonic acid, respectively. For both molecules, the [M+H]+ peak is small and fragment ions dominate the two mass spectra (see figure caption for details). The lower two panels of Figure 1 show the PTR-MS mass spectra of oleic acid and 5α-cholestane, respectively. Protonation of oleic acid mostly forms [M+H]+ ions, while 5α-cholestane is predominantly detected in the form of [MH]+ ions. We show in the Supporting Information that the calibrated and calculated instrumental response to levoglucosan, cis-pinonic acid, oleic acid and 5α-cholestane agreed to within 2%. Our laboratory study did only include -OH groups as substituents that easily leave organic analyte molecules upon protonation. We note that sub-µm POM also contains -OOH, -ONO2 and -OSO3H functional groups (hydroperoxides, organonitrates and organosulfates) that may also be ejected upon protonation. While work is in progress

for such analytes, our unpublished results from previous work suggest that [M+H]+ ions may be formed in small abundances from such species and that protonation occurs at (or close to) the collisional rate. PTR-MS mass spectra generated from atmospheric subµm POM contain hundreds of mass peaks and it is usually not possible to distinguish fragment and parent ions. For deriving the total sub-µm POM mass concentration, we assume that the observed m/z (-1 to account for the added proton) reflects the molecular weight of individual analytes. The mass associated with fragment ions is obviously underestimated, since the mass of neutral fragments is not taken into account. If all analytes in atmospheric particles fragmented as strongly as cis-pinonic acid and levoglucosan, the total sub-µm POM mass concentration would be underestimated by 32.3% and 33.1%, respectively. Since less fragmenting species are also present in sub-µm POM, the error is expected to be smaller for atmospheric measurements. Fragmentation also needs to be considered when deriving instrumental response factors for specific analytes (e.g. levoglucosan). A quantitative external standard is not needed, but the pure compound needs to be sampled for determining the relative abundance of the [M+H]+ peak. Importantly, fragmentation also affects reduced parameters that are commonly used for visualization and interpretation of complex organic mass spectra. In such analyses, each elementally resolved mass peak is assigned a number of carbon atoms (nC), a number of oxygen atoms (nO), a hydrogen-to-carbon ratio (H:C), an oxygen-to-carbon ratio (O:C) തതതതത஼ ). Ion populations and an average carbon oxidation state (ܱܵ are then plotted in two-dimensional reduced-parameter തതതതത஼ vs. nC).46 Generally frameworks (H:C vs. O:C, nO vs. nC, ܱܵ speaking, fragmentation shifts ion populations towards lower H:C, O:C, nC, nO, and തതതതത ܱܵ஼ values, although some fragments may have higher H:C, O:C and തതതതത ܱܵ஼ values than the parent molecule (see Supporting Information). It is worth noting തതതതത that dehydration does not affect ܱ ௌ஼ which makes this parameter particularly useful for the analysis of PTR-MS data.

Intercomparison measurements with an AMS instrument. Figure 2 summarizes the results from side-byside measurements with a C-ToF-AMS analyzer. The upper two panels show exemplary mass spectra as obtained by PTR-MS and AMS on March 21, 2015 12:00 UTC, respectively. Both mass spectra were modified to only include organic analyte ions. The AMS mass spectrum is characterized by a steep decrease in signal intensities with increasing m/z. Particle vaporization at 600 °C and hard ionization of the emanating gases with 70 eV electrons lead to extensive fragmentation of higher molecular weight analytes. The PTR-MS mass spectrum remains populated up to m/z 550. The signal-weighted average m/z’s were 57.5 and 200.9 in the AMS and PTR-MS mass spectra, respectively. It is worth noting that the PTR-TOF 8000 instrument generated only a factor of 5 lower total signal count rate than the C-ToF-AMS analyzer, which is the most sensitive version of all AMS instruments. The lower left panel shows the time evolution of the total organic mass concentration as measured by the CToF-AMS and CHARON-PTR-ToF-MS instrument, respectively (green and black curves).



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 14

Figure 2. Exemplary mass spectra as obtained by PTR-MS (upper panel) and AMS (middle panel) from ambient particle measurements in Lyon on March 21, 2015 12:00 UTC. Both mass spectra were modified to only include organic analyte ions. The time evolutions of the total organic mass and levoglucosan as measured by AMS and PTR-MS are shown in the lower left panel. Shaded areas reflect the measurement uncertainties. No uncertainty has been reported for AMS levoglucosan data. The lower right panels are scatter plots of AMS vs. PTR-MS data. The plots include the 1:1 lines and the regression lines obtained from simple linear regressions. For total organics, the slope is 1.13, the offset is 0.72 µg m-³ and the regression coefficient R² is 0.76. For levoglucosan, the slope is 0.65, the offset is 0.14 µg m-³ and the regression coefficient R² is 0.90.

The shades indicate the measurement uncertainties which 47 are ±38 % for the AMS instrument and -10/+40 % for the PTR-MS analyzer. The two instruments show very similar concentration variations throughout the entire measurement period. Absolute concentrations agreed well during certain periods, while significant differences of up to a factor of two were observed during others. The measurement discrepancies may seem large to the strict analytical chemist, but the data are mostly within the combined measurement uncertainties and for an atmospheric study with such complex organic analytes this is a pleasing result. A scatter plot of AMS versus PTR-MS total organics is shown in the center-right panel of Figure 2. As discussed above, the PTR-MS data are biased low due to fragmentation of analyte ions but this cannot fully account for the observed differences. At high particle loadings, the AMS data are biased high due to interferences from inorganic species.48 The ammonium nitrate interference was characterized and a positive bias of up to 1.0 µg m-3 can be expected for the AMS organics data shown in Figure 2. Other inorganics and matrix effects may have caused an even higher positive bias. The discrepancy at low particle loadings indicates a background subtraction problem in either or both of the methods. The complete set of AMS and SMPS data is given in the Supporting Information. The lower left panel of Figure 2 also shows the time evolutions of the organic equivalent concentrations derived from the m/z 60 signal in the AMS mass spectrum (brown curve) and from the m/z 85, 127, 145 and 163 signals in the PTRMS mass spectrum (violet curve). These signals derive from C6-anhydrosugars, which are the main constituents of biomass-burning derived particles. For simplicity, we here only refer to the main isomer which is levoglucosan. Yet again, the measurement agreement was excellent during some periods and worse during others. Since we calibrated our CHARON-PTR-ToF-MS system for levoglucosan, we have high confidence in the accuracy (±10%) of the reported data.

We also emphasize the significantly better precision (less noise) in the PTR-MS data despite the lower signal integration time (PTR-MS: 1 min, AMS: ~5 min). We thus feel confident to state that the CHARON-PTR-ToF-MS instrument can be used for real-time monitoring of levoglucosan (and isomers) in atmospheric particles at ng m-3 levels.

PTR-MS mass spectra of atmospheric particles. The left panels of Figure 3 show the median mass spectra as derived from 2, 3 and 5 days of CHARON-PTR-ToF-MS measurements in Lyon, Valencia and Innsbruck, respectively. Signal count rates were converted into mass concentrations as described in the Material and Methods section. Mass concentrations associated with individual m/z signals ranged from 0.4 ng m-3 to ~100 ng m-3.

Figure 3. Median mass spectra (left panels) as obtained during 2, 3 and 5 days of CHARON-PTR-ToF-MS measurements in March, June and October of 2015 in Lyon, Valencia and Innsbruck, respectively. Mass distributions (right panels) associated with CxHy+ and CxHyOz+ analyte ions, resolved by nc and nO.


Page 5 of 14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


The total median sub-µm POM mass concentrations measured were 3.13 µg m-3, 0.95 µg m-3 and 0.81 µg m-3 in Lyon, Valencia and Innsbruck, respectively. The highest m/z peaks are listed in the Supplementary Information. We note that PTR-MS analysis generated clearly distinctive organic mass spectral signatures from different types of urban particle pollution, even at total organic mass loadings below 1 µg m-3. The most apparent distinctive feature in the three mass spectra is the different signal population in the high m/z region. In Lyon, the median mass spectrum was densely populated up to m/z 550, while the Valencia and Innsbruck mass spectra show almost no signals above m/z 230 and 330, respectively. The absence of high m/z signals in the Valencia mass spectrum warrants a short discussion. In Valencia, orange trees are widely present in the local and regional vegetation and concurrent gas-phase PTR-MS measurements showed the abundant presence of monoterpenes. We have observed similar low-m/z dominated spectra in a chamber study during the simulated atmospheric degradation of tree emissions.49 Terpene oxidation is known to form condensable low molecular weight products.50 We were able to elementally resolve 77, 86 and 90% of the total sub-µm POM mass detected in Lyon, Valencia and Innsbruck, respectively. 56, 70 and 67% of the total detected mass was associated with oxygenated hydrocarbon ions (CxHyOz+). 2, 3 and 6% of the mass was composed of pure hydrocarbon ions (CxHy+). 19, 13 and 17% of the mass was associated with organic ions that contained a nitrogen or sulfur atom. We note that in the Lyon mass spectrum most of the peaks above m/z 300 remained unassigned due to insufficient mass resolution and accuracy of the PTR-TOF 8000 instrument. This is a common problem of field-deployable time-of-flight mass spectrometers, which typically have a mass resolution of less than 10000. It is thus yet another advantage of proton transfer chemical ionization that atmospheric sub-µm POM mostly forms analyte ions with m/z < 300. Chemical ionization methods based on adduct ion formation would shift the mass spectrum (e.g. plus m/z 126.9 if an I- adduct is formed) into regions where an elemental composition assignment is even more challenging. The right panels of Figure 3 show the mass distributions associated with CxHy+ and CxHyOz+ analyte ions, resolved by nC and nO. As discussed above, we expect a bias towards lower nC and nO values due to analyte ion fragmentation. The Lyon and Innsbruck data were corrected for fragmentation of protonated levoglucosan. The known fragment ions were added to the C6H11O5+ signal which clearly dominates the nC and nO distributions in these cities. In Valencia, we mainly detected analyte ions in the C2-to-C10 range. We note that the low nC species are most likely fragments from C10-terpenoid derivatives which are particularly prone to fragmentation in the PTR-MS instrument.

Distribution of measured ions in reduced parameter frameworks. Complex mass spectra of organic matter can be visualized in two-dimensional reduced-parameter frameworks such as H:C vs. O:C, nO vs. nC or തതതതത ܱௌ஼ vs. nC. Figure 4 shows the CHARON-PTR-ToF-MS data obtained in Lyon, Valencia and Innsbruck in these reduced-parameter frameworks. Ellipses contain 90% of the measured mass. The reader is cautioned that fragmentation results in a shift

തതതതത஼ of ion populations towards lower H:C, O:C, nC, nO, and ܱܵ values. 46

Isaacman-VanWertz et al. have recently introduced this suite of plots for characterizing the analytical capabilities of advanced mass spectrometers used for chemical analysis of atmospheric organic carbon. The CHARON-PTR-ToF-MS technique covers large regions within the (H:C, O:C), (nO, തതതതത nC) and (nC, ܱ ௌ஼ ) spaces that are not covered to the same extent by other techniques with more selective ionization 46 methods (see Figure 1 in Isaacman-VanWertz et al. ). CHARON-PTR-ToF-MS thus adds significant analytical capabilities for fully characterizing atmospheric organic carbon, especially if combined with gas-phase PTR-ToF-MS measurements. Figure 4 also shows a main weakness of the CHARON-PTR-ToF-MS analyzer which is its inability to തതതതത detect high nO (and thus also high ܱ ௌ஼ ) compounds that are typically referred to as extremely low volatility organic compounds (ELVOCs) or highly oxidized molecules (HOMs). These are lost to instrumental surfaces before ionization due to their extremely low vapor pressures.

ܱௌ஼ vs. nC plots as deFigure 4. H:C vs. O:C, nO vs. nC and തതതതത rived from all elementally resolved signals in the median mass spectra obtained in Lyon, Valencia and Innsbruck. Ellipses contain 90% of the measured mass. Since the AMS community works with bulk aerosol elemental ratios (ܱ: ‫ܥ‬, ‫ܪ‬: ‫)ܥ‬, we have calculated the concentration-weighted averages which are (0.38, 1.38), (0.42, 1.27) and (0.28, 1.32) for Lyon, Valencia and Innsbruck, respectively. The C-TOF-AMS-derived values51 for Lyon are (0.66, 1.54) and thus significantly higher than the values obtained from the PTR-ToF-MS mass spectra. As discussed above, ion fragmentation may lead to a negative bias in the PTRToF-MS data. We do, however, note that in the case of the low-resolution AMS the bulk elemental ratio is exclusively determined from the fractional abundance of the m/z 44 signal (f44). This cannot be more than a coarse approximation for a complex atmospheric particle mass spectrum as observed in Lyon. Electrospray ionization ultrahigh resolution mass spectrometry of filter samples with biomass burning and urban aerosols yielded lower (ܱ: ‫ܥ‬, ‫ܪ‬: ‫)ܥ‬-values which were similar to those obtained by CHARON-PTR-ToF52 MS.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 6 of 14

Figure 5. Factor mass spectra (including m/z’s associated with source marker compounds) (left panel), time evolution of the eight factors (middle panel) and the diurnal profiles of the factors (right panel) as determined on the basis of PMF analysis of a CHARON-PTR-ToFMS dataset acquired in fall 2016 in Innsbruck, Austria. Hours of the day are given in UTC, which corresponds to local time minus 1 hour. The first two days in the time series were weekend days, the third day was a public holiday, and the last two days were regular working days. Daytime temperatures varied between 15 and 18 °C. Nighttime temperatures were between 1 and 3 °C during the first two nights and increased to 5 and 8°C during the fourth and fifth night, respectively.

Source apportionment. Positive Matrix Factorization (PMF) is a well-established multivariate factor analysis 53,54 model for apportioning sources of air pollutants. In the past decade, PMF analysis has been routinely applied to 55 AMS data and mass spectra from other online particle 56 mass spectrometers . The multivariate analysis is based on the assumption that the measured bulk aerosol mass spectrum is a linear combination of constant mass spectra (“factors”). Ideally, each of the factors is specific to a particle emission source or particle formation process. The sources or source processes need to be identified from the chemical information in the factor mass spectra which is often a difficult task. The CHARON-PTR-ToF-MS technique greatly facilitates this undertaking as the molecular signatures of well-known source marker compounds (e.g. levoglucosan) are preserved in the factor mass spectra. This is exemplified on the CHARON-PTR-ToF-MS data collected in Innsbruck. PMF runs were carried out with 2 to 9 factors and a solution with 8 factor profiles was selected because this yielded the best interpretable results. Figure 5 summarizes the results from the PMF analysis. It includes the factor mass spectra and the m/z signals associated with source marker compounds (left panel), the time evolution of the eight factors (middle panel) and the diurnal profiles of the factors (right panel). The temporal characteristic of the factors has been included for further substantiating the interpretation of the factors. Factors 1, 3, 7 and 8 are related to biomass burning emissions as they all include the mass spectral signature of levoglucosan (m/z 85.028, 127.039, 145.050, 163.060). Factors 1 and 8 also include the m/z 301.217 (C20H29O2+) and m/z 303.232 (C20H31O2+) signals, which we assign to resin acids (e.g. dehydro abietic acid and abietic acid) typi57,58 cally emitted from pine wood combustion. The m/z + 187.060 (C8H11O5 ) and m/z 205.070 (C8H13O6+) signals in

factors 3 and 8 may stem from dimethoxyphenol (C8) derivatives formed during lignin pyrolysis. Interestingly, three of the four biomass burning related factors exhibit different temporal evolutions. Factor 1 gradually builds up overnight and slowly decays during the day. Factors 3 and 8 increase rapidly after sunset, peak at midnight (local time) and decay overnight. Factor 7 increases with sunset, but only for a few hours, remains relatively stable overnight and decays in the morning. Notably, factor 7 disappears during the last two nights, which were warmer and followed two regular working days. The different temporal characteristics may be indicative of different types or stages of biomass combustion or different atmospheric processing of the biomass burning aerosol. More work is needed to interpret these findings. The main message remains that biomass burning was the major source of subµm POM in Innsbruck in late autumn. This does not come as a surprise since wood is commonly used for domestic 59 heating in Tyrol. The mass spectrum of factor 4 includes the m/z 203.086 (C16H11+), 229.102 (C18H13+), 253.102 (C20H13+), 277.101 (C22H13+) signals which are characteristic for condensed polycyclic aromatic hydrocarbons (PAHs). The diurnal profile shows two maxima that coincide with the morning and evening rush hours. Concentrations were lower during the first three non-working days. We thus assign factor 4 to traffic emissions. Factor 5 shows a distinct diurnal maximum and the associated mass spectrum is mostly composed of oxygenated organic ions. These two observations suggest that this factor is associated with secondary organic aerosol (SOA). The mass spectrum of factor 6 has m/z 163.116 (C10H15N2+) as the base peak. We assign this peak to nicotine and assign this factor to cigarette smoking emissions. Mass concentrations were enhanced during the evening hours of the first three non-working days and during typical


Page 7 of 14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


morning working break hours. As a curiosity, we note the presence of the m/z 315.232 (C21H31O2+). A candidate molecule with this elemental composition is tetrahydrocannabinol (THC). We were not able to assign factor 2 to a specific source. This was the first ambient air study carried out by CHARON PTR-ToF-MS and obviously more needs to be learned for interpreting the results.

CONCLUSIONS We have demonstrated that real-time measurements by CHARON-PTR-ToF-MS can be used for quantitative and qualitative chemical analysis of urban sub-µm POM at the molecular level. Results from accompanying laboratory studies indicate that, although PTR-MS is known as a soft ionization technique, fragmentation of certain classes of organic analytes can be significant. The bias in the characterization of bulk organic aerosol mass and bulk chemical composition appears to be small but significant, and correction routines will need to be developed. The main analytical potential of CHARON-PTR-ToF-MS lies in its capability to monitor organic molecular source markers in atmospheric particulate matter in real time. More work is needed for characterizing m/z signals (or signal patterns) that are specific to known source markers (e.g. markers for biogenic and anthropogenic secondary organic particles). Finally, it would be of obvious analytical benefit if the CHARON inlet and drift tube ion source were coupled to an ultra-high resolution mass spectrometer.

ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: xx.xxxx/acs.analchem.xxxx. Operational details and routines of the CHARON-PTR-ToFMS instrument, mathematical formulae used for data analysis, information on sampling sites and additional instrumentation, calculated and calibrated instrumental response factors, impact of analyte ion fragmentation on reduced parameters, complete set of AMS and SMPS data for the time period shown in Figure 2, highest peaks in the ambient mass spectra.

AUTHOR INFORMATION Corresponding Author *

E-mail:

[email protected].

Phone:

+47-

22859139. ORCID Markus Müller: 0000-0003-4110-8950 Armin Wisthaler: 0000-0001-5050-3018

Present Addresses # now at: German Environment Agency, Wörlitzer Platz 1, 06844 Dessau-Roßlau, Germany ∥ now at: Aix-Marseille Université, CNRS, LCE FRE 3416, 13331, Marseille, France

Author Contributions M.M. carried out part of the measurements, analyzed all data and prepared the figures. P.E. designed, built and optimized the CHARON inlet, carried out part of the measurements and supported the data analysis. B.D’A. carried out the AMS measure-

ments and data analysis. W.T. supported the ambient air measurements. A.W. wrote the manuscript.

Notes The authors declare no competing financial interest.

ACKNOWLEDGMENT This work was funded through the PIMMS ITN supported by the European Commission’s 7th Framework Programme (Grant number 287382).

REFERENCES (1) Jimenez, J. L.; Canagaratna, M. R.; Donahue, N. M.; Prevot, A. S. H.; Zhang, Q.; Kroll, J. H.; DeCarlo, P. F.; Allan, J. D.; Coe, H.; Ng, N. L.; Aiken, A. C.; Docherty, K. S.; Ulbrich, I. M.; Grieshop, A. P.; Robinson, A. L.; Duplissy, J.; Smith, J. D.; Wilson, K. R.; Lanz, V. A.; Hueglin, C.; Sun, Y. L.; Tian, J.; Laaksonen, A.; Raatikainen, T.; Rautiainen, J.; Vaattovaara, P.; Ehn, M.; Kulmala, M.; Tomlinson, J. M.; Collins, D. R.; Cubison, M. J.; Dunlea, E. J.; Huffman, J. A.; Onasch, T. B.; Alfarra, M. R.; Williams, P. I.; Bower, K.; Kondo, Y.; Schneider, J.; Drewnick, F.; Borrmann, S.; Weimer, S.; Demerjian, K.; Salcedo, D.; Cottrell, L.; Griffin, R.; Takami, A.; Miyoshi, T.; Hatakeyama, S.; Shimono, A.; Sun, J. Y.; Zhang, Y. M.; Dzepina, K.; Kimmel, J. R.; Sueper, D.; Jayne, J. T.; Herndon, S. C.; Trimborn, A. M.; Williams, L. R.; Wood, E. C.; Middlebrook, A. M.; Kolb, C. E.; Baltensperger, U.; Worsnop, D. R. Science 2009, 326 (5959), 1525–1529. (2) Kanakidou, M.; Seinfeld, J. H.; Pandis, S. N.; Barnes, I.; Dentener, F. J.; Facchini, M. C.; Van Dingenen, R.; Ervens, B.; Nenes, A.; Nielsen, C. J.; Swietlicki, E.; Putaud, J. P.; Balkanski, Y.; Fuzzi, S.; Horth, J.; Moortgat, G. K.; Winterhalter, R.; Myhre, C. E. L.; Tsigaridis, K.; Vignati, E.; Stephanou, E. G.; Wilson, J. Atmos Chem Phys 2005, 5 (4), 1053–1123. (3) Hallquist, M.; Wenger, J. C.; Baltensperger, U.; Rudich, Y.; Simpson, D.; Claeys, M.; Dommen, J.; Donahue, N. M.; George, C.; Goldstein, A. H.; Hamilton, J. F.; Herrmann, H.; Hoffmann, T.; Iinuma, Y.; Jang, M.; Jenkin, M. E.; Jimenez, J. L.; Kiendler-Scharr, A.; Maenhaut, W.; McFiggans, G.; Mentel, T. F.; Monod, A.; Prévôt, A. S. H.; Seinfeld, J. H.; Surratt, J. D.; Szmigielski, R.; Wildt, J. Atmos Chem Phys 2009, 9 (14), 5155–5236. (4) In Climate Change 2013 - The Physical Science Basis; Intergovernmental Panel on Climate Change, Ed.; Cambridge University Press: Cambridge, 2014; pp 659–740. (5) Laskin, A.; Laskin, J.; Nizkorodov, S. A. Chem. Rev. 2015, 115 (10), 4335–4382. (6) Fuller, S. J.; Wragg, F. P. H.; Nutter, J.; Kalberer, M. 2014. (7) Künzi, L.; Krapf, M.; Daher, N.; Dommen, J.; Jeannet, N.; Schneider, S.; Platt, S.; Slowik, J. G.; Baumlin, N.; Salathe, M.; Prévôt, A. S. H.; Kalberer, M.; Strähl, C.; Dümbgen, L.; Sioutas, C.; Baltensperger, U.; Geiser, M. Sci. Rep. 2015, 5, 11801. (8) Nel, A. Science 2005, 308 (5723), 804–806. (9) Goldstein, A. H.; Galbally, I. E. Environ. Sci. Technol. 2007, 41 (5), 1514–1521. (10) Nozière, B.; Kalberer, M.; Claeys, M.; Allan, J.; D’Anna, B.; Decesari, S.; Finessi, E.; Glasius, M.; Grgić, I.; Hamilton, J. F.; Hoffmann, T.; Iinuma, Y.; Jaoui, M.; Kahnt, A.; Kampf, C. J.; Kourtchev, I.; Maenhaut, W.; Marsden, N.; Saarikoski, S.; SchnelleKreis, J.; Surratt, J. D.; Szidat, S.; Szmigielski, R.; Wisthaler, A. Chem. Rev. 2015, 115 (10), 3919–3983. (11) Donahue, N. M.; Epstein, S. A.; Pandis, S. N.; Robinson, A. L. Atmos Chem Phys 2011, 11 (7), 3303–3318. (12) Robinson, A. L.; Donahue, N. M.; Shrivastava, M. K.; Weitkamp, E. A.; Sage, A. M.; Grieshop, A. P.; Lane, T. E.; Pierce, J. R.; Pandis, S. N. Science 2007, 315, 1259. (13) Laskin, A.; Laskin, J.; Nizkorodov, S. A. Environ. Chem. 2012, 9 (3). (14) Lopez-Hilfiker, F. D.; Mohr, C.; Ehn, M.; Rubach, F.; Kleist, E.; Wildt, J.; Mentel, T. F.; Lutz, A.; Hallquist, M.;


7


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Worsnop, D.; Thornton, J. A. Atmos Meas Tech 2014, 7 (4), 983– 1001. (15) Stark, H.; Yatavelli, R. L. N.; Thompson, S. L.; Kang, H.; Krechmer, J. E.; Kimmel, J. R.; Palm, B. B.; Hu, W.; Hayes, P. L.; Day, D. A.; Campuzano-Jost, P.; Canagaratna, M. R.; Jayne, J. T.; Worsnop, D. R.; Jimenez, J. L. Environ. Sci. Technol. 2017, 51 (15), 8491–8500. (16) Gemayel, R.; Hellebust, S.; Temime-Roussel, B.; Hayeck, N.; Van Elteren, J. T.; Wortham, H.; Gligorovski, S. Atmos Meas Tech 2016, 9 (4), 1947–1959. (17) Allen, J. O.; Fergenson, D. P.; Gard, E. E.; Hughes, L. S.; Morrical, B. D.; Kleeman, M. J.; Gross, D. S.; Gälli, M. E.; Prather, K. A.; Cass, G. R. Environ. Sci. Technol. 2000, 34 (1), 211–217. (18) DeCarlo, P. F.; Kimmel, J. R.; Trimborn, A.; Northway, M. J.; Jayne, J. T.; Aiken, A. C.; Gonin, M.; Fuhrer, K.; Horvath, T.; Docherty, K. S.; Worsnop, D. R.; Jimenez, J. L. Anal. Chem. 2006, 78 (24), 8281–8289. (19) Jayne, J. T.; Leard, D. C.; Zhang, X.; Davidovits, P.; Smith, K. A.; Kolb, C. E.; Worsnop, D. R. Aerosol Sci. Technol. 2000, 33 (1–2), 49–70. (20) Brüggemann, M.; Karu, E.; Stelzer, T.; Hoffmann, T. Environ. Sci. Technol. 2015, 49 (9), 5571–5578. (21) Gallimore, P. J.; Kalberer, M. Environ. Sci. Technol. 2013, 47 (13), 7324–7331. (22) Müller, L.; Reinnig, M.-C.; Hayen, H.; Hoffmann, T. Rapid Commun. Mass Spectrom. RCM 2009, 23 (7), 971–979. (23) de Gouw, J.; Warneke, C. Mass Spectrom. Rev. 2007, 26 (2), 223–257. (24) Jordan, A.; Haidacher, S.; Hanel, G.; Hartungen, E.; Märk, L.; Seehauser, H.; Schottkowsky, R.; Sulzer, P.; Märk, T. D. Int. J. Mass Spectrom. 2009, 286, 122–128. (25) Lindinger, W.; Jordan, A. Chem. Soc. Rev. 1998, 27 (5), 347–375. (26) Sulzer, P.; Hartungen, E.; Hanel, G.; Feil, S.; Winkler, K.; Mutschlechner, P.; Haidacher, S.; Schottkowsky, R.; Gunsch, D.; Seehauser, H.; Striednig, M.; Jürschik, S.; Breiev, K.; Lanza, M.; Herbig, J.; Märk, L.; Märk, T. D.; Jordan, A. Int. J. Mass Spectrom. 2014, 368, 1–5. (27) Holzinger, R.; Kasper-Giebl, A.; Staudinger, M.; Schauer, G.; Röckmann, T. Atmos Chem Phys 2010, 10 (20), 10111–10128. (28) Holzinger, R.; Goldstein, A. H.; Hayes, P. L.; Jimenez, J. L.; Timkovsky, J. Atmos Chem Phys 2013, 13 (19), 10125–10141. (29) Salvador, C. M.; Ho, T.-T.; Chou, C. C.-K.; Chen, M.-J.; Huang, W.-R.; Huang, S.-H. Atmos. Environ. 2016, 140, 565–575. (30) Timkovsky, J.; Dusek, U.; Henzing, J. S.; Kuipers, T. L.; Röckmann, T.; Holzinger, R. J. Aerosol Sci. 2015, 79, 1–14. (31) Oyama, B. S.; Andrade, M. D. F.; Herckes, P.; Dusek, U.; Röckmann, T.; Holzinger, R. Atmos Chem Phys 2016, 16 (22), 14397–14408. (32) Eichler, P.; Müller, M.; D’Anna, B.; Wisthaler, A. Atmos Meas Tech 2015, 8 (3), 1353–1360. (33) Eichler, P.; Müller, M.; Rohmann, C.; Stengel, B.; Orasche, J.; Zimmermann, R.; Wisthaler, A. Environ. Sci. Technol. Lett. 2017, 4 (2), 54–58. (34) Graus, M.; Müller, M.; Hansel, A. J. Am. Soc. Mass Spectrom. 2010, 21 (6), 1037–1044. (35) Müller, M.; Mikoviny, T.; Jud, W.; D’Anna, B.; Wisthaler, A. Chemom. Intell. Lab. Syst. 2013, 127, 158–165. (36) Ellis, A. M.; Mayhew, C. A. In Proton Transfer Reaction Mass Spectrometry; John Wiley & Sons, Ltd, 2014; pp 25–48. (37) Gioumousis, G.; Stevenson, D. P. J. Chem. Phys. 1958, 29, 294–299. (38) Langevin, P. Ann Chim Phys 1903, 28, 433–530. (39) Su, T.; Chesnavich, W. J. J. Chem. Phys. 1982, 76, 5183– 5185. (40) Bosque, R.; Sales, J. J. Chem. Inf. Comput. Sci. 2002, 42 (5), 1154–1163.

Page 8 of 14

(41) Kroll, J. H.; Donahue, N. M.; Jimenez, J. L.; Kessler, S. H.; Canagaratna, M. R.; Wilson, K. R.; Altieri, K. E.; Mazzoleni, L. R.; Wozniak, A. S.; Bluhm, H.; Mysak, E. R.; Smith, J. D.; Kolb, C. E.; Worsnop, D. R. Nat. Chem. 2011, 3 (2), 133–139. (42) Drewnick, F.; Hings, S. S.; DeCarlo, P.; Jayne, J. T.; Gonin, M.; Fuhrer, K.; Weimer, S.; Jimenez, J. L.; Demerjian, K. L.; Borrmann, S.; Worsnop, D. R. Aerosol Sci. Technol. 2005, 39 (7), 637–658. (43) Canagaratna, M. R.; Jayne, J. T.; Jimenez, J. L.; Allan, J. D.; Alfarra, M. R.; Zhang, Q.; Onasch, T. B.; Drewnick, F.; Coe, H.; Middlebrook, A.; Delia, A.; Williams, L. R.; Trimborn, A. M.; Northway, M. J.; DeCarlo, P. F.; Kolb, C. E.; Davidovits, P.; Worsnop, D. R. Mass Spectrom. Rev. 2007, 26 (2), 185–222. (44) Lee, T.; Sullivan, A. P.; Mack, L.; Jimenez, J. L.; Kreidenweis, S. M.; Onasch, T. B.; Worsnop, D. R.; Malm, W.; Wold, C. E.; Hao, W. M.; Jr, J. L. C. Aerosol Sci. Technol. 2010, 44 (9), i–v. (45) Ulbrich, I. M.; Canagaratna, M. R.; Zhang, Q.; Worsnop, D. R.; Jimenez, J. L. Atmos Chem Phys 2009, 9 (9), 2891–2918. (46) Isaacman-VanWertz, G.; Massoli, P.; O’Brien, R. E.; Nowak, J. B.; Canagaratna, M. R.; Jayne, J. T.; Worsnop, D. R.; Su, L.; Knopf, D. A.; Misztal, P. K.; Arata, C.; Goldstein, A. H.; Kroll, J. H. Faraday Discuss. 2017. (47) Bahreini, R.; Ervens, B.; Middlebrook, A. M.; Warneke, C.; de Gouw, J. A.; DeCarlo, P. F.; Jimenez, J. L.; Brock, C. A.; Neuman, J. A.; Ryerson, T. B.; Stark, H.; Atlas, E.; Brioude, J.; Fried, A.; Holloway, J. S.; Peischl, J.; Richter, D.; Walega, J.; Weibring, P.; Wollny, A. G.; Fehsenfeld, F. C. J. Geophys. Res. Atmospheres 2009, 114 (D7), D00F16. (48) Pieber, S. M.; El Haddad, I.; Slowik, J. G.; Canagaratna, M. R.; Jayne, J. T.; Platt, S. M.; Bozzetti, C.; Daellenbach, K. R.; Fröhlich, R.; Vlachou, A.; Klein, F.; Dommen, J.; Miljevic, B.; Jiménez, J. L.; Worsnop, D. R.; Baltensperger, U.; Prévôt, A. S. H. Environ. Sci. Technol. 2016, 50 (19), 10494–10503. (49) Gkatzelis, G. I.; Tillmann, R.; Hohaus, T.; Müller, M.; Eichler, P.; Xu, K.-M.; Schlag, P.; Schmitt, S. H.; Wegener, R.; Kaminski, M.; Holzinger, R.; Wisthaler, A.; Kiendler-Scharr, A. Atmos Meas Tech Discuss 2017, 2017, 1–31. (50) Calogirou, A.; Larsen, B. R.; Kotzias, D. Atmos. Environ. 1999, 33 (9), 1423–1439. (51) Chen, Q.; Heald, C. L.; Jimenez, J. L.; Canagaratna, M. R.; Zhang, Q.; He, L.-Y.; Huang, X.-F.; Campuzano-Jost, P.; Palm, B. B.; Poulain, L.; Kuwata, M.; Martin, S. T.; Abbatt, J. P. D.; Lee, A. K. Y.; Liggio, J. Geophys. Res. Lett. 2015, 42 (10), 2015GL063693. (52) Willoughby, A. S.; Wozniak, A. S.; Hatcher, P. G. Atmosphere 2016, 7 (6), 79. (53) Paatero, P.; Tapper, U. Environmetrics 1994, 5 (2), 111– 126. (54) Paatero, P. Chemom. Intell. Lab. Syst. 1997, 38 (2), 223– 242. (55) Zhang, Q.; Jimenez, J. L.; Canagaratna, M. R.; Ulbrich, I. M.; Ng, N. L.; Worsnop, D. R.; Sun, Y. Anal. Bioanal. Chem. 2011, 401 (10), 3045–3067. (56) McGuire, M. L.; Jeong, C.-H.; Slowik, J. G.; Chang, R. Y.-W.; Corbin, J. C.; Lu, G.; Mihele, C.; Rehbein, P. J. G.; Sills, D. M. L.; Abbatt, J. P. D.; Brook, J. R.; Evans, G. J. Atmos Chem Phys 2011, 11 (15), 8133–8155. (57) Simoneit, B. R. T. Appl. Geochem. 2002, 17 (3), 129– 162. (58) Rogge, W. F.; Hildemann, L. M.; Mazurek, M. A.; Cass, G. R. Environ. Sci. Technol. 1998, 32 (1), 13–22. (59) Statistical Database of STATISTICS AUSTRIA http://statcube.at/statistik.at/ext/statcube/jsf/tableView/tableView.xh tml# (accessed Jun 7, 2017).


8

Page 9 of 14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Authors are required to submit a graphic entry for the Table of Contents (TOC) that, in conjunction with the manuscript title, should give the reader a representative idea of one of the following: A key structure, reaction, equation, concept, or theorem, etc., that is discussed in the manuscript. Consult the journal’s Instructions for Authors for TOC graphic specifications.


9


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Page 10 of 14

Page 11 of 14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36




1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34


Page 12 of 14

Page 13 of 14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33




1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41


Page 14 of 14

Direct Sampling and Analysis of Atmospheric Particulate Organic

Recommend Documents