Environ. Sci. Technol. 2008, 42, 214–220
Source Attribution of Submicron Organic Aerosols during Wintertime Inversions by Advanced Factor Analysis of Aerosol Mass Spectra VALENTIN A. LANZ,† M. RAMI ALFARRA,‡ URS BALTENSPERGER,‡ BRIGITTE BUCHMANN,† C H R I S T O P H H U E G L I N , * ,† S Ö N K E S Z I D A T , ‡,§ M I R I A M N . W E H R L I , § L U K A S W A C K E R , | S I L K E W E I M E R , ‡,⊥ A L E X A N D R E C A S E I R O , #,∇ HANS PUXBAUM,# AND ANDRE S. H. PREVOT‡ Laboratory for Air Pollution and Environmental Technology, Empa, Swiss Federal Laboratories for Materials Testing and Research, Dübendorf, Switzerland, Laboratory of Atmospheric Chemistry, Paul Scherrer Institut, 5332 Villigen PSI, Switzerland, Department of Chemistry and Biochemistry, Bern University, 3012 Bern, Switzerland, Institute for Particle Physics, ETH Hönggerberg, 8093 Zurich, Switzerland, Laboratory for Internal Combustion Engines, Empa, Swiss Federal Laboratories for Materials Testing and Research, Dübendorf, Switzerland, Institute of Chemical Technologies and Analytics, Vienna University of Technology, 1060 Vienna, Austria, and CESAM and Department of Environment and Planning, University of Aveiro, 3810-193 Aveiro, Portugal
Received March 23, 2007. Revised manuscript received August 27, 2007. Accepted October 4, 2007.
Real-time measurements of submicrometer aerosol were performed using an Aerodyne aerosol mass spectrometer (AMS) during three weeks at an urban background site in Zurich (Switzerland) in January 2006. A hybrid receptor model which incorporates a priori known source composition was applied to the AMS highly time-resolved organic aerosol mass spectra. Three sources and components of submicrometer organic aerosols were identified: the major component was oxygenated organic aerosol (OOA), mostly representing secondary organic aerosol and accounting on average for 52–57% of the particulate organic mass. Radiocarbon (14C) measurements of organic carbon (OC) indicated that ∼31 and ∼69% of OOA originated from fossil and nonfossil sources, respectively. OOA estimates were strongly correlated with measured particulate ammonium. Particles from wood combustion (35–40%) and 3–13% traffic-related hydrocarbon-like organic aerosol (HOA) accounted for the other half of measured organic matter (OM). Emission ratios of modeled HOA to measured nitrogen
* Corresponding author phone: +41-44-823 46 54; fax: +41-44823 62 44; e-mail:
[email protected]. † Laboratory for Air Pollution and Environmental Technology, Empa. ‡ Paul Scherrer Institut. § Bern University. | Institute for Particle Physics. ⊥ Laboratory for Internal Combustion Engine, Empa. # Vienna University of Technology. ∇ University of Aveiro. 214
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oxides (NOx) and OM from wood burning to levoglucosan from filter analyses were found to be consistent with literature values.
1. Introduction Atmospheric aerosols have several adverse effects on human health (1) and atmospheric visibility (2), but partly compensate climate forcing (3). These effects depend on particle diameter and chemical composition, as well as on mass concentration which exhibits large temporal and spatial variability. Measurements, classification, and trend estimation are needed for successful abatement strategies. Elevated concentrations of PM10 and PM2.5 (particulate mass with aerodynamic diameters d < 10 and 2.5 µm, respectively) are typical for most parts of Switzerland during wintertime (4). This phenomenon has also been observed at many other sites, e.g., for PM2.5 in the San Joaquin Valley (5) or for organic aerosols in particular, e.g., in Chinese cities (6) and in Spain (7). Takegawa et al. (8) found organics to be the dominant component (40–60% of the total nonrefractory aerosol mass) in submicrometer particles (PM1) in Japan irrespective of the season. Low temperatures and thermal inversions are typical for Swiss Plateau winters (9). They are often accompanied by persistent stratus clouds and low photochemical activity. During high pressure conditions, inversions typically last several days and can lead to smog formation. In such episodes, aerosol mass concentrations are strongly influenced by meteorology (dilution with clean air or precipitation) rather than by variation of source activities (4). Unlike in summer (10), primary pollutants from different sources, therefore, can show similar temporal variabilities at the background site of Zurich-Kaserne in winter, limiting the applicability of bilinear mixing models. The multilinear engine (ME-2) is a numerical method designed to solve widely different multilinear problems (11): In atmospheric science, ME-2 has been used to incorporate independent variables such as meteorological data (12) or particle size information (13). In this study, a hybrid receptormodel is solved by ME-2. The used approach can be regarded as a hybrid of chemical mass balance (CMB) (14) and bilinear models which can be solved, e.g., by positive matrix factorization (PMF) (15). In standard CMB, the source profiles have to be known in advance, but the latest developments in CMB modelling allow for variable source compositions (16, 17). On the other hand, bilinear models give estimates for an assumed number of source profiles without any a priori knowledge about emission sources. Approaches to utilize available prior information in bilinear models have been reported (e.g., refs18–20). Hybrid models are promising when bilinear unmixing techniques fail and partial a priori information about emission sources is available. The ME-2 program can solve hybrid models in a very flexible way regardless of the number of constraint factors and elements, and degree of constriction; a property shared with iterated confirmatory factor analysis (ICFA; refs 21 and 22). With ME2, the hybrid model can be adapted to multilinear problems and enhanced with parallel equations; it uses a robust algorithm with convergence times from seconds to minutes even for large data matrices (e.g., highly time-resolved spectral data), and its basic idea is widespread in atmospheric science through the use of PMF2 and PMF3 (which can solve special cases of multilinear problems). In this study, nonrefractory submicrometer aerosols were measured by an Aerodyne aerosol mass spectrometer (AMS) 10.1021/es0707207 CCC: $40.75
2008 American Chemical Society
Published on Web 11/21/2007
FIGURE 1. Concentrations of AMS-organics (assuming a collection efficiency of 0.5), PM10, temperature, and ozone at Zurich-Kaserne during the measurement campaign in January 2006. Due to data acquisition problems, there is a four-day gap in the AMS data set (14-18 January). during wintertime inversions. A factor-analytical approach that allows for incorporating a priori knowledge is presented for organic AMS spectra and the sensitivity of source contributions to a priori constraints is examined. The results from the factor-analytical model were verified by comparing them with several tracers such as radiocarbon (14C) content of the organic carbon (OC) fraction, levoglucosan, inorganic AMS species, trace gases, as well as with published AMS reference spectra.
2. Materials and Methods 2.1. Measurements. An Aerodyne quadrupole aerosol mass spectrometer (Q-AMS) was deployed during 19 days in winter 2006 (6-25 January) at Zurich-Kaserne, an urban background site in Switzerland (10). The first period of AMS measurements (6-14 January) coincided with the mid/end-phase of an extended wintertime inversion, with temperatures of -4 to 0 °C and daily radiation maxima between 100 and 200 W m-2 (Figure 1). The second sampling period (18-25 January) can be described by a rapid temperature decrease from about 2-7 °C (18-22 January) to below zero (-2 to -7 °C from 22-25 January). This time span represents the beginning of another inversion event. More than 4000 aerosol mass spectra (MS) were acquired (5 min averaging time). The organic MS is defined by 270 m/z’s or mass-to-charge ratios (23, 24) and is used for this study. A concise description of the AMS measurement principles, its operation modes and data analysis were provided elsewhere (23, 25–28) A collection efficiency (CE) value is required for the estimation of aerosol mass concentration measured by the AMS (29). A value of 0.5 is used here for reporting absolute concentrations. Continuous measurements (10 min averaging time) of meteorological parameters, trace gases (e.g., NOx, CO, and ozone), and PM10 were measured with conventional instruments (SI-1, Supporting Information). Seven PM1 samples were collected on preheated quartzfiber filters with a high-volume sampler during 10-25 January 2006. These filters were analyzed for levoglucosan measured with a Dionex HPLC system with a high pH anion exchange
chromatography column followed by pulsed amperometry detection (for further details see SI-2 in the Supporting Information). In two of these samples (12 January 00:00-17: 20; 23 January 17:30–25 January 09:25), OC was isolated by two-step combustion and 14C was measured in this fraction using accelerator mass spectrometry (29). Fossil and nonfossil sources of OC were apportioned as described by Szidat et al. (30). 2.2. Hybrid Receptor Modeling. Linear two-way receptor problems can be represented by the following notation: p ˜
(ORG)ij )
∑ G˜
˜
ikFkj ) (ORG)ij + Eij
ˆ
(1)
k)1
where (ORG) represents the matrix of n columns (measured aerosol fragments) and m rows (samples in time). Reduced ˜. factors or aerosol sources are denoted by p ˜ (m × p ˜ In bilinear unmixing (eq 1), both scores, G ˜ (p -matrix) and loadings, F ˜ × n-matrix), have to be estimated. Their matrix product approximates measured organics, (ORG), up to an error matrix, E (m × n-matrices). The model given by eq 1 can be stated including q known source profiles: ˜ p ) p + q
(2)
where p equals the number of free factors and q is the number of constraint or a priori factors. The model according to eqs 1 and 2 can therefore be both a CMB approach if p ) 0 (i.e., all factors are known) as well as a bilinear model if q ) 0 (i.e. all factors are unknown), and all hybrid stages in between. Equation 1 in this general form can be solved by ME-2, where it is also possible to define ranges in which a given a ˜ -matrix can priori source profile, f new (1 × n vector), of the F evolve. Symmetric upper and lower limits for each measured aerosol fragments in f new can be defined by a scalar value, a, ranging between 0 and 1: f
new
) (ms) ( a(ms)
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where (ms) is a n × 1-vector a priori profile, e.g., a measured aerosol mass spectrum from literature. The parameter a is called the “a-value” or degree of variability of the a priori profile: e.g., setting the a-value to 0 means that it is fixed and cannot be changed during iterative fitting. As another example, a priori profiles can evolve from 0 to 200% of the original signal intensities when the a-value is set to 1. The ˜ are estimated by minimizing the rowmatrices ˜ G and F and column-wise sum of squared uncertainty weighted residuals, Q: m
Q ) min min G
F
n
∑ ∑ [E(scaled) ]
ij 2
(4)
i)1 j)1
The ME-2 program uses a modified form of a preconditioned conjugate direction algorithm to minimize Q in eq 4, where E(scaled)ij )
˜ F ˜ Xij - G ik kj Sij
(5)
and S is the measurement uncertainty matrix. Uncertainty for each data point in S was estimated using the AMS error calculation routine (27) and by assuming a modeling error of 5% for each signal. The sensitivity of the results towards the assumed modelling error is discussed in SI-3 of the Supporting Information. The default settings for the ME-2 run and modifications of the script code are quoted in SI-4 of the Supporting Information. The mathematics of the hybrid receptor model are given in SI-5 of the Supporting Information at full-length. Implications for Environmental Modeling. Choosing the number of factors is subjective within all techniques of dimensionality reduction. The hybrid model (eqs 1–3) is descriptive and its solutions should be accepted if they yield ˜. meaningful source profiles ˜ F and source contributions G Therefore, mathematical model diagnostics (see the Supporting Information), a discussion of physical meaningfulness (Section 2.3), and the verification of calculated factors (Section 3) are necessary. 2.3. Measure of Spectral Similarity. To compare real and ˜ ) were calculated factors, the computed profiles (rows of F first normalized with respect to the sum of all n ) 270 modeled organic fragments. These spectra were then correlated with reference AMS spectra from literature––normalized in the same way––and the coefficient of determination, R2, was calculated as a measure of spectral similarity. This measure is sensitive, widely applicable, and has been used in previous publications (10, 24). To see if there is a strong influence of a few high intensity masses (typically with m/z < 45) in regression analysis, R2 was checked for masses with m/z > 44 separately.
3. Results and Discussion Best interpretability was found for the 3-factorial model with one a priori profile (Section 3.2.): less than 1% of the data points within this model exceeded the outlier limits [-4,4] for the scaled residuals (eq 5) and were down-weighted. The accurate estimation of the aerosol measurements by this model is also reflected by the fit of regressed scores versus measured organic time series, yielding a slope of 1.00 (p < 2 10–16) and R2 > 0.99, and by the approximate normaldistribution of the scaled residuals. A justification of this solution in terms of mathematical diagnostics is given in SI-6-8 of the Supporting Information. 3.1. Model Specifications. In this section, the actual ˜ ) p + q, is discussed choice of the total number of factors, p first. Then, the degree of variability for the prescribed factor q will be examined in Section 3.3. This discussion is based on spectral similarity as defined in Section 2.3. 216
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Using a Priori Profiles. For this winter data set, no physically meaningful source profiles could be retrieved by applications of bilinear models (e.g., PMF). On the other hand, prior knowledge of winter aerosol is limited for this site, the CMB approach, therefore, leaves significant mass fractions unexplained. Therefore, we used the hybrid model (eqs 1–3), starting with a single a priori profile (q ) 1). In general, only profiles from sources that are likely to have an impact on organic aerosol (OA) measured at the receptor site were considered as prescribed sources. At an urban site, this can be assumed for hydrocarbon-like organic aerosols (HOA) that were found to be traffic-related (10). We furthermore hypothesize that HOA does not exhibit as much seasonal and regional variability as wood burning or secondary organic aerosols (SOA). Additionally, the variability of published HOA profiles (31) is smaller than for the profiles of other source types, including SOA from biogenic and anthropogenic precursor compounds (32, 33), food cooking (34), and wood burning (35, 36) (Supporting Information SI-9). The MS from diesel bus emissions derived from chasing experiments (31) was used here as the first-guess a priori profile since it represents ambient conditions, in contrast to other available HOA spectra. In laboratory or chamber studies, emission samples are often taken at relatively high concentrations and standard temperatures that may not mimic atmospheric conditions, as some semivolatile components evaporate on dilution (37), whereas others will condense on cooling. The HOA profile as calculated for summer at ZürichKaserne (10) was tested as a starting profile too (Supporting Information SI-10). While the constraints of this latter spectrum are relaxed, its spectral similarity to the measured diesel spectrum (31) increases and, for a-values of 0.3 and higher, is even greater than the similarity compared to its initial values (Figure S6 in the Supporting Information). In addition, a European diesel car spectrum (35) was used as a priori HOA profile as well. However, we found that the choice of the a priori HOA profile has a minor influence on the results. Three-Dimensional Solution Space. An appropriate number of factors ˜ p was explored by varying the number of free factors (p ) 1-10) while keeping the HOA profile (q ) 1) fixed. Solution spaces for q * 1 were explored as well and were not found to increase interpretability of the model results. The 3-factorial solution (p ) 2, q ) 1) is capable to retrieve freely fitted profiles of wood burning and oxygenated organic aerosol (OOA) (Figure 2). The resulting free source profiles were labeled according to their high correlation to MS from literature (Section 3.3), indications from fragmental AMS tracers (m/z 60, 73, 137 for wood burning; m/z 57, 71 for diesel emissions) and because their source activities could be nicely related to independently measured tracer compounds, such as levoglucosan (Section 3.4). On the other hand, the 2-factorial solution (p ) 1, q ) 1) yields a freely fitted spectrum that is approximately a linear combination of OOA (24) and ambient wood burning (36), resulting in R2 ) 0.98 (R2 ) 0.96 for m/z’s > 44). This suggests that these two different types of sources are lumped together in this case and more factors should be used. Four- and higher-factorial solutions (p ) 3, 4, . . ., q ) 1) do not produce factors similar to any of the reference spectra available from the AMS Spectral Database (http://cires.colorado.edu/jimenez-group/AMSsd/index.html-; status: 4 December 2006) or given elsewhere (34, 35). There is evidence that the wood burning profile retrieved from the 3-factorial solution is split into two artificial spectra when four factors are assumed: one contains small masses with high intensities, whereas the other shows relatively prominent large masses (Figure 2). The sum of the split profiles is similar to the wood
FIGURE 2. (a) Computed HOA, wood burning, and OOA profiles (3-factorial solution, a-value of 0.6). (b) The 2-factorial solution showing a coerced profile: the wood burning profile, and the OOA component remain unresolved. (c) The split of the wood burning factor into two unrealistic, calculated MS (4-factorial solution). burning profile as estimated by the 3-factorial model, yielding R2 ) 0.91 (0.94). The sum of their average mass contribution to OM (35%) is similar to 38% attributed to the 3-factorial wood burning MS. A more detailed discussion is provided in SI-8 of the Supporting Information. 3.2. Calculated Source Profiles and Specification of the a-Value. The number of factors (p ) 2 and q ) 1) was chosen as described in Section 3.1. In this section, the optimum constraint for the HOA profile is discussed based on spectral similarities. The first free factor obtained from this 3-factorial model was labeled “wood burning”-factor. For an a-value of 0.4, it is most similar (R2 ) 0.94) to the ambient wood burning spectrum from Roveredo (36), which was shown to have 0.86) up to a ) 0.6 and then decrease rapidly. Thus, an a-value of 0.6 represents a good choice regarding the similiarities to different wood burning reference profiles (ambient, stove emission, and model compound). Moreover, the a-value of 0.6 is associated with high correlations between the calculated HOA and the measured reference spectra. Irrespective of the a-values, the OOA profiles remain very similar (R2 > 0.8) to reference MS, such as aged aerosol in
FIGURE 3. (a) Spectral similarity (R 2) of the calculated HOA factor to reference profiles and evolution of the HOA factor as a function of the a-value. Similarity (R2) of the wood burning (b) and OOA factor (c) to MS from the literature as a function of a, the degree of variability for constraining the HOA factor. Vancouver (28), fulvic acid model aerosol (32) or secondary particles extracted from the data of Pittsburgh (24). Source strengths will therefore be discussed for a ) 0.6. Nevertheless, the source apportionment results were analyzed with respect to their sensitivity towards the selection of the a-value (Figure 4). By specifying different degrees of variability for the prescribed HOA profile, a range of mass contributions to total OM can be determined for each factor, providing a measure for the stability of the derived solution. 3.3. Analysis of Estimated Source Contributions. Hydrocarbon-Like Organic Aerosol (HOA). Hourly medians were calculated for estimated source strengths (scores regressed on the measured organics) of HOA, wood burning, and OOA (Figure 4). On average, the relative contribution of HOA to OM was highest in the morning due to traffic emissions during rush-hours. The daily pattern of absolute HOA concentrations shows two maxima (morning and evening), as it is the case for traffic-related pollutants. The time series of the calculated HOA source strength shows the highest correlation with NOx (R2 ) 0.70) which is formed, e.g., at high temperatures in internal combustion engines through the oxidation of ambient nitrogen (Figure 4 and Supporting Information SI-11). A linear regression of HOA against NOx yields a slope of 19.8 ( 0.2 µg m-3 ppmv-1, which is in the range of 16 µg m-3 ppmv-1 representing the emission ratio calculated for summer conditions (10) and reported for diesel trucks (38). Note that these ratios calculated from ambient data are uncertain due to NO2 reactivity and to the uncertainty of the collection efficiency. The relative HOA contributions account on average for 7% of the total organics (a ) 0.6) in winter as well as in summer (10). The mass fraction attributed to the HOA factor VOL. 42, NO. 1, 2008 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 4. Hourly boxplots of absolute (a) and relative (b) calculated source contributions. Time series of absolute and relative source strengths for HOA (gray) with nitrogen oxides (NOx), wood burning (blue) with tracer species carbon monoxide (CO; brown) and AMS potassium, and OOA (pink) with AMS-ammonium (c). Relative source contributions to total AMS-organics as a function of time (d) and averages for different a-values (e). For all given concentrations, a collection efficiency of 0.5 was assumed. Horizontal black bars in panels a and b represent the group medians and vertical hinges represent data points from the lower to the upper quartile. Any data observation which lies more than 1.5 times the interquartile range, IQR (1.5× IQR values represented by the T symbol), lower than the first quartile or 1.5× IQR higher than the third quartile is considered an outlier and represented by a circle. (3%) is smallest when it is kept fixed (a ) 0.0). On the other hand, an upper limit of the possible HOA contribution can be derived from combination of the AMS and 14C analysis: for a ) 0.8 and higher, the HOA contribution (13%) is overestimated. In this case, modeled HOA concentrations exceed the total estimated fossil OM concentration during one filter period, the resulting secondary fossil OM concentration would thus be below 0%, which is not meaningful. In addition, a-values close to 1 lead to unrealistic factors that are similar to the ones from the data analysis using PMF. 218
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Wood Burning-Like Aerosol. The calculated wood burning source strength correlated with CO (R2 ) 0.78), as well as with AMS-potassium (R2 ) 0.67), which was calculated as Im/z 39 + 0.0722 Im/z 39. We assume that AMS-potassium is only proportional to the real potassium concentration because of incomplete volatilization of KCl and K2SO4, and the production of K ions by surface ionization. In addition, we assume that possible interferences due to C3H3+ are negligible or mostly due to wood burning. Like organic wood burning aerosols, CO is a product of incomplete combustion. Khalil and Rasmussen (39) found three times higher OC emissions
factors at low temperature compared to wood combustion at hot temperatures. Particulate potassium is emitted from wood burning sources as well. Both CO and potassium have also other sources than wood burning (e.g., CO emission by traffic and K by fireworks), and therefore, no perfect correlation can be expected. The source strength of wood burning activities was highest during the evening and night, which is probably due to domestic heating (Figure 4). Like the primary HOA factor, wood burning aerosol concentrations peak in the morning at around 9 a.m. and then decrease again due to increased vertical mixing. For wood burning particles, a more dominant evening peak is observed at about 8 p.m., whereas the HOA evening peak height is similar to the HOA morning peak. This pronounced enhancement of the wood burning contribution in the evening was also found in an Alpine valley with wood burning as the dominant organic aerosol source (30, 36). The computed emission ratio of wood burning OM versus levoglucosan equals 0.08 ( 0.03 µg m-3/µg m-3. This result is in good agreement with values from literature summarized in Szidat et al. (40), where a ratio of 0.077 ( 0.045 µg m-3/µg m-3 was deduced by assuming OM/OC ) 2. On average, wood burning particles account for 35–40% of the OM (Figure 4). During the cold episodes from 10–14 and 20–21 January, when temperature decreased down to -7 °C (Figures 1), aerosols from wood combustion are the most abundant component (>50% of OM). This is probably due to increased domestic heating. Note that primary wood burning particles were found in summertime as well accounting on average for 10% of OM and were associated with outdoor leisure activities (10). No impact of such activities was observed in winter. Oxygenated Organic Aerosol (OOA). The OOA factor (oxygenated organic aerosol) showed the highest correlations with secondary inorganics, such as particulate ammonium (R2 ) 0.72) and the sum of nitrate and sulfate (R2 ) 0.72), whereas the correlations with nitrate (R2 ) 0.61) or sulfate (R2 ) 0.53) alone were slightly lower. OOA is, therefore, interpreted as aged and mostly secondary organics (Figure 4 and Supporting Information SI-11). In contrast to summer when OOA contributed 66% to the total OM (10), OOA did not split into a more volatile fraction with high correlation with nitrate and a less volatile fraction correlated with sulfate. We hypothesize that the temperature range in winter (-7 to 7 °C) was not high enough to resolve a third OOA component with even higher volatility than nitrate. OOA was found to be the dominant organic aerosol component on average (52–57%) and rather insensitive to the a-value assigned to the HOA a priori profile (Figure 4). The OOA fraction was especially dominant in the beginning of the campaign (>60% of OM). A possible explanation is that anticyclonic, fair weather accompanied by relatively high temperatures (Section 2.1) and high daily O3 maxima above 20 ppb (average of 9.8 ppb ( 8.3 ppb for January 2006) prevailed before the start of the measurement campaign. Generally, OOA is most abundant in the afternoon due to photochemistry and secondary processes. The absolute OOA concentration increases rapidly after 1 p.m. reaching its maximum at 3 p.m. (Figure 4). The enhanced concentrations persist until around 10 p.m. SOA is formed in the atmosphere when the oxidation of reactive organic gases (ROGs) leads to the formation of lowvolatility reaction products, which partition into the particle phase. Oxidation rates typically increase with solar radiation and temperature. Nevertheless, the hypothesis that SOA formation is of minor importance in winter due to low photochemical activity has already been revised by Strader et al. (41): stagnant air, the accumulation of precursors, and
cold air favor the formation of SOA as well. Thus, volatile compounds are expected to condense to a greater extent in winter due to low temperatures and the high concentrations of OA, explaining the high OOA fraction found here. 14C measurements of total OC further enable the distinction between fossil and nonfossil sources of OOA after subtraction of contributions from HOA and wood burning aerosol. For this, it is assumed that HOA is exclusively fossil (OM ) 1.2 OC), wood burning aerosol is a pure nonfossil source (OM ) 2 OC), and OOA is also characterized by OM ) 2 OC. 14C analysis revealed for the two samples (12 January 00:00–17:20; 23 January 17:30–25 January 09:25) a fossil fraction of 21 and 33% for total OC, which results in 20 and 42% fossil contributions to OOA, respectively. With this, ∼31% fossil and ∼69% nonfossil sources are apportioned for OOA on average. The large portion of nonfossil OOA may originate from the oxidation of ROGs emitted by wood burning (42) or by plants. Several ROGs can be emitted from both sources (e.g., terpenes), making a further source separation difficult. 3.4. Consequences for Receptor Modeling of AMS Data. Using hybrid receptor modeling, organic aerosol sources could be identified and quantified for a winter data set, whereas multivariate methods without assuming a priori profiles failed. Not only in Alpine valleys, but also in a city like Zurich particulate wood burning emissions are more important than traffic emissions. Several studies have provided evidence that the dominant OOA component mostly can be assigned to secondary organics (10, 24). It was, however, not yet known that also in winter at such low temperatures in Central Europe secondary organic aerosols are a major fraction. The combination with 14C analyses reveals that most of these secondary organic aerosols are from nonfossil sources: the tool presented here is an important step forward also for future organic aerosol source apportionment studies.
Acknowledgments The AMS measurements were supported by the Swiss Federal Office for the Environment (FOEN). The measurement trailer was provided by the Kanton Zürich. We thank U. Lohmann and P. Paatero for valuable comments and are grateful to M. Ruff for supporting the 14C measurements. We also acknowledge the Network of Excellence ACCENT for support.
Supporting Information Available The levoglucosan measurement technique; collocated, continuous measurements; ME-2 script code modifications; mathematical model diagnostics; sensitivity analyses. This material is available free of charge via the Internet at http:// pubs.acs.org.
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