Aerosol Mass Spectrometric Features of Biogenic SOA: Observations

Secondary organic aerosol (SOA) is known to form from a variety of anthropogenic and biogenic precursors. Current estimates of global SOA production v...
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Environ. Sci. Technol. 2009, 43, 8166–8172

Aerosol Mass Spectrometric Features of Biogenic SOA: Observations from a Plant Chamber and in Rural Atmospheric Environments A S T R I D K I E N D L E R - S C H A R R , * ,† QI ZHANG,‡ THORSTEN HOHAUS,† EINHARD KLEIST,† AMEWU MENSAH,† THOMAS F. MENTEL,† CHRISTIAN SPINDLER,† RICARDA UERLINGS,† RALF TILLMANN,† ¨ RGEN WILDT† AND JU Institut fu ¨ r Chemie und Dynamik der Geospha¨re, Forschungszentrum Ju ¨ lich, D-52425 Ju ¨ lich, Germany, and Department of Environmental Toxicology, University of California, One Shields Ave., Davis, California 95616

Received May 13, 2009. Revised manuscript received September 5, 2009. Accepted September 15, 2009.

Secondary organic aerosol (SOA) is known to form from a variety of anthropogenic and biogenic precursors. Current estimates of global SOA production vary over 2 orders of magnitude. Since no direct measurement technique for SOA exists, quantifying SOA remains a challenge for atmospheric studies. The identification of biogenic SOA (BSOA) based on mass spectral signatures offers the possibility to derive source information of organic aerosol (OA) with high time resolution. Here we present data from simulation experiments. The BSOA from tree emissions was characterized with an Aerodyne quadrupole aerosol mass spectrometer (Q-AMS). Collection efficiencies were close to 1, and effective densities of the BSOA were found to be 1.3 ( 0.1 g/cm3. The mass spectra of SOA from different trees were found to be highly similar. The average BSOA mass spectrum from tree emissions is compared to a BSOA component spectrum extracted from field data. It is shown that overall the spectra agree well and that the mass spectral features of BSOA are distinctively different from those of OA components related to fresh fossil fuel and biomass combustions. The simulation chamber mass spectrum may potentially be useful for the identification and interpretation of biogenic SOA components in ambient data sets.

1. Introduction Aerosols markedly affect the radiative balance in Earth’s atmosphere and play a central role in cloud formation and climate (1). There is considerable uncertainty about the secondary organic aerosol (SOA) formed when the atmospheric oxidation products of volatile organic compounds (VOCs) undergo gas-particle transfer. It is estimated that 10 000 to 100 000 different organic compounds may exist in the atmosphere (2). Although clear progress has been made in recent years in identifying key biogenic and anthropogenic * Corresponding author e-mail: [email protected]. † Institut fu ¨ r Chemie und Dynamik der Geospha¨re. ‡ University of California. 8166

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SOA precursors, significant gaps still remain in our scientific knowledge on the formation mechanisms, composition and properties of SOA (3). On the global scale biogenic VOC (BVOC) emissions dominate anthropogenic emission by far. It is estimated that BVOC emissions account for roughly 90% of the global VOC emissions (4), which underlines the importance of BVOC oxidation as an important source of SOA. Based on aerosol composition measurements and scaling from the sulfate budget Hallquist et al. (3) recently revised estimates of global SOA formation to 115 TgC/yr, ranging between 25 and 210 TgC/yr. Case studies in the field systematically find larger observed SOA burdens than is predicted with state of the art models (5, 6). This discrepancy indicates that despite the considerable efforts and achievements of SOA chamber studies (e.g., 7-12), significant parts of the atmospheric formation process of SOA remain little understood. One reason for this might be so far unexplored SOA precursors. The direct use of plant emissions as SOA precursor can reduce this uncertainty as all BVOCs contribute to SOA formation independent of being measurable or not. In a few recent laboratory studies direct plant emissions were used to investigate particle formation (13-16). None of these studies presented aerosol chemical composition results. But, such information can help identifying major SOA precursors and thus help to reduce the current uncertainties. Aerosol mass spectrometry is now widely used as a sensitive technique for online measurements of aerosol chemical composition. The Aerodyne aerosol mass spectrometer (AMS) measures the inorganic and organic composition of nonrefractory submicrometer particles (NR-PM1) via thermal vaporization and electron impact ionization (17). A broad overview of organic aerosol (OA) chemistry in urban, rural, and remote atmospheres is evolving from deployment of AMS throughout the northern hemisphere (18). Although the ensemble average spectra that the AMS produces for OA are too complex to ensure the determination of individual species, mass spectral factor analysis provides a procedure for decomposing each ensemble spectrum as the linear combination of the mass spectra (MS) of several physically meaningful components weighted by their concentrations (18-21). The sources of ambient OA can be inferred based on the mass spectral features of OA factors obtained with multivariate statistical analysis techniques (18, 22, 23). To generalize and interpret the statistically derived factors from field measurements, their MS patterns are typically compared to reference spectra obtained in controlled environments, e.g., those of SOA produced in chamber studies (24, 25) and primary OA (POA) emitted from biomass combustion and vehicle combustion (26, 27). Here we present AMS mass spectral data from plant chamber measurements and compare them to field observations of a specific OA component which was strongly influenced by biogenic emissions during summer 2004 in Chebogue Point, Nova Scotia (28). We show that the average aerosol MS obtained in the plant chamber experiments highly resemble field observations. Specific mass spectral features of this biogenic SOA are discussed.

2. Experimental Section The plant chamber experiments were performed in the JPAC (Ju ¨ lich Plant Aerosol Chamber) facility. The experimental details of this set up are described elsewhere (16, 29). In brief 10.1021/es901420b CCC: $40.75

 2009 American Chemical Society

Published on Web 09/30/2009

TABLE 1. Summary of the Trees Investigated Together with Experimental Conditions and Major Resultsa

tree

VOC composition unreacted chamber VOC conc. sesquiVOC mAMS VSMPS (109 Geff CE temp. (°C) (ppbC) r-pinene monoterpene terpene othersb conc. (ppb)c (µg/m3) nm3/cm3) (g/cm3) (mAMS/mSMPS)

pine pine spruce birch + beech holm oak eucalyptus

20 35 20 20 20 20

97.8 39.8 145 101 404 512

11% 23% 7.6% 0.7% 15% 2.3%

R-pinene

20

25

100%

44% 45% 72% 99% 84% 11%

43% 8% 1% 44; SI Figure S4). Shown in Figure 3a is an average MS of all tree SOA spectra with the error bars indicating 1 standard error of variation. The correlation of each MS of Figure 2 with the average MS yields R2 ) 0.90-0.98 (Table 2). When comparing tree SOA to R-pinene SOA from our reference experiment (Figure 2g), we observe the highest mass spectral similarity (based on R2; Table 2) for the coniferous trees (pine and spruce), which VOL. 43, NO. 21, 2009 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 3. Mass spectra comparisons of average plant chamber BSOA (a) vs r-pinene SOA (b), a BSOA component derived from field data (c), diesel exhaust POA (d) (26), Pittsburgh OOA-2 (e) and OOA-1 (f) (21), and Zu¨rich BBOA (g) (20). also show the largest mass fraction of R-pinene in their VOC mixtures (Table 1 and pie chart inserts in Figure 2). However, when all tree species were considered, higher R-pinene content in the VOC mixtures does not always suggest higher similarity between the MS of plant chamber BSOA and that of R-pinene SOA. In addition, no strong correlation was observed between the fractional contributions of different

VOC classes and normalized MS peaks (to the total organic intensity). Thus no clear fingerprint of the individual VOC or VOC classes was found in the SOA mass spectrum. Best correlation was found between the m/z 43 signal in the BSOA MS from an individual tree species and the fraction of R-pinene in its VOCs (R2 ) 0.53; SI Figure S5a). But the correlation between m/z 43 and the fraction of monoterpene was poor (R2 ) 0.043; SI Figure S5b). It is important to note that overall the MS of different tree SOA exhibit similar patterns and characteristic features as those of oxygenated organic particulates. In addition to R-pinene SOA (R2 ) 0.86 - 0.96), they also show relatively good correlation with the MS of two oxygenated OA (OOA) components in Pittsburgh (OOA-1: R2 ) 0.83-0.94 and OOA2: R2 ) 0.60-0.68), as well as oxidized diesel exhaust OA (R2 ) 0.76-0.85; Figure 3 and Table 2). However, all tree BSOA MS are considerably different than the spectra of ambient hydrocarbon-like OA (HOA) and POA from combustion sources (R2 ) 0.16-0.51; Figure 3 and Table 2). 3.3. Comparison to Field Observations and Implication for Biogenic SOA Source Apportionment. The average SOA spectrum of trees is very similar to that of a biogenic SOA component determined based on factor analysis of a Q-AMS data set acquired from Chebouge Pt, Nova Scotia in summer 2004 (Figure 3c). For the field study, the OA components were extracted using a hybrid of the multiple component analysis (MCA) method (18, 22) and the positive matrix factorization (PMF) method (23). Briefly, for this analysis, a reference MS of urban HOA and two component MS determined by the PMF were introduced to initialize the MCA analysis. The MCA then solves for the bilinear problem and outputs the mass spectra and time series of the components using an iterative algorithm based on evaluation of the residuals (Zhang et al., in preparation). Three OA components were determined and interpreted based on their correlations to tracer compounds. They include an HOA representative for diluted POA transported from urban locations, a highly oxidized OOA-1 associated with transport of urban emissions from the northeast of the U.S., and a less oxidized OOA-2 representative for biogenic SOA transported from forested Northern Canada (Zhang et al., in preparation). A comparison between the Chebouge Pt. BSOA component and the average plant chamber SOA is shown in SI Figure S6. The agreement between the patterns of these two MS is good (R2 ) 0.96). Larger discrepancies are seen at m/z 58, 30, 48, and 64. The m/z 58 is one of the peaks that show the largest variability among tree species (Figure 2). The m/z’s 30, 48, and 64 signals in the Chebouge Pt. BSOA spectrum may be biased low since most of these signals in ambient MS are by default assigned to inorganic nitrate and sulfate (31). In addition, the apparent discrepancies among larger m/z’s (>200) are due to the low signal-to-noise ratios. The BSOA spectra were analyzed for main ion series using the so-called delta analysis (24, 36). In this approach the regular distance between mass peaks of an ion series is

TABLE 2. Correlation Coefficients R2 of All Individual Mass Spectra Obtained for Different Trees as BVOC Precursor versus Different Reference Mass Spectra R2

spruce pine (20 °c) pine (35 °c) holm oak eucalyptus birch + beech

8170

9

Average Tree BSOA

Chebouge BSOA

a-pinene SOA

Diesel Exhaust OM

Zurich Summer BBOA

Pittsburgh HOA

Pittsburgh OOA-1

Pittsburgh OOA-2

Oxidized Diesel OA

0.92 0.98 0.97 0.94 0.95 0.90

0.88 0.92 0.94 0.95 0.96 0.81

0.96 0.91 0.92 0.88 0.88 0.86

0.47 0.45 0.51 0.49 0.44 0.44

0.47 0.45 0.51 0.49 0.44 0.44

0.34 0.25 0.39 0.41 0.37 0.16

0.89 0.94 0.86 0.83 0.88 0.92

0.61 0.67 0.68 0.68 0.66 0.60

0.85 0.84 0.81 0.76 0.75 0.83

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FIGURE 4. Average ∆ values for ion groups of different sizes: m/z 12-61 (i.e., n ) 1-4), m/z 62-89 (i.e., n ) 5-6), m/z 90-145 (i.e., n ) 7-10), and m/z 146-285 (i.e., n ) 11-20) in the mass spectra of average tree BSOA (this study), BSOA component from Chebogue (Zhang et al., in preparation), chamber r-pinene SOA (this study), diesel exhaust POA (26), aerosolized lubricating oil (26), Pittsburgh OOA-1 and OOA-2 (21), oxidized diesel exhaust aerosol (37), fulvic acid and humic acid (22), and Zurich BBOA (20). Note that the pattern for the average tree SOA from our chamber experiments is very similar to the Chebogue BSOA ∆ pattern and clearly differs from the other patterns shown here. classified according to their delta values (∆) defined as ∆ ) ion mass - 14 × n + 1 (-7 e ∆ e 6). Note that m/z 28 was not included in the delta analysis. The ∆-pattern analysis of the average tree SOA shows a 20% contribution of ∆ ) +2 ions (i.e., ∆+2), accounting for largest fraction of the mass signatures. Typically ∆+2 ions are associated with alkyl (CnH2n+1+) or aldehyde and ketone (CnH2n-1O+) functionalities. The presence of m/z 15 in the ∆+2 ion series is indicative of alkyls and does not appear in the spectra from aldehydes or ketones. With roughly 14% of the total signal, the ∆+3 (radical cation) and ∆0 (alkenyl or cycloalkyl, CnH2n-1+) series are the second most abundant peak families, followed by ∆+5 (radical cation) and ∆-2 (aromatics, CnHen+). The average ∆ values for four ion groups in the average MS of tree SOA are shown in Figure 4. The ion groups correspond to m/z 12 - 61 (i.e., n ) 1-4), m/z 62-89 (i.e., n ) 5-6), m/z 90-145 (i.e., n ) 7-10), and m/z 146-285 (i.e., n ) 11-20). These four different nominal ranges are considered since the ∆-patterns shift from fragments of the n1-n4 category to those of the n5-n10 category. The n11-n20 category is considered separately to account for the low signal-to-noise at large m/z’s. The average ∆ values for the n1-n4, n5-n6, n7-n10, and n11-n20 ions are +1.54, -1.82, -1.45, and -1.04, respectively in the average tree BSOA spectrum. As also found by Bahreini et al. (24) larger fragments of the BSOA have dominantly negative ∆ values. The R-pinene SOA from our plant chamber investigations shows a very similar delta pattern to that reported in Bahreini et al. (24). Also shown in Figure 4 are the ∆ patterns for several reference MS acquired from ambient and laboratory studies, including diesel exhaust POA (26), aerosolized lubricating oil (26), Pittsburgh OOA-1 and OOA-2 (21), oxidized diesel exhaust aerosol (37), fulvic acid and humic acid (22), and Zu ¨ rich BBOA (20). The reference spectra were downloaded from the AMS Spectral Database compiled by Ulbrich, I.M., Lechner, M., and Jimenez, J.L. (URL: http://cires.colorado.edu/jimenez-group/AMSsd/). Clearly different sources result in distinctively different delta patterns for the organic aerosol. We show that the OOA-2 MS obtained in statistical analysis of data from Chebouge Pt. is representative of biogenic SOA and similar to the SOA observed in plant chamber simulation experiments. Furthermore, the average delta pattern is very similar between the plant chamber SOA and the biogenic SOA field component. This pattern analysis should be used in combination with other techniques to identify and interpret ambient BSOA components. The characteristic features of the tree SOA MS are useful to constrain sources of ambient

SOA. We suggest the use of our plant chamber SOA MS as a reference for BSOA.

Acknowledgments We gratefully acknowledge support by the European Commission (IP-EUCAARI, Contract No. 036833-2). Q.Z. was supported by the U.S. Department of Energy’s Atmospheric Science Program (Office of Science, BER), Grant No. DEFG02-08ER64627. We thank James Allan (U. Manchester) for the AMS data analysis software and anonymous reviewers for their constructive comments.

Supporting Information Available Figures S1-S7. This material is available free of charge via the Internet at http://pubs.acs.org.

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