Dynamic Changes of the Aerosol Composition and Concentration

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Dynamic Changes of the Aerosol Composition and Concentration during different Burning Phases of Wood Combustion Michael Elsasser, Christian Busch, Jürgen Orasche, Claudia Schön, Hans Hartmann, Jürgen Schnelle-Kreis, and Ralf Zimmermann Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/ef400684f • Publication Date (Web): 17 Jul 2013 Downloaded from http://pubs.acs.org on July 20, 2013

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Dynamic Changes of the Aerosol Composition and Concentration during different Burning Phases of Wood Combustion Michael Elsasser†,‡, Christian Busch‡, Jürgen Orasche†,‡,§, Claudia Schön∥, Hans Hartmann∥, Jürgen Schnelle-Kreis†, and Ralf Zimmermann†,‡ †

Joint Mass Spectrometry Centre, Cooperation Group Comprehensive Molecular Analytics, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany ‡

Joint Mass Spectrometry Centre, Universität Rostock, Institut für Chemie, Lehrstuhl für Analytische Chemie, Dr.-Lorenz-Weg 1, 18059 Rostock, Germany

§

Department of Sedimentology & Environmental Geology, Georg-August-University Göttingen, Goldschmidtstr. 3, 37077 Göttingen, Germany

∥

Technology and Support Centre in the Centre of Excellence for Renewable Resources (TFZ), Department of Solid Biofuels, Schulgasse 18, 94315 Straubing, Germany

KEYWORDS wood combustion, emission, PMF, burning phases, AMS, PAH

1 ACS Paragon Plus Environment

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ABSTRACT

The presented wood combustion emission study employing a logwood stove showed that during a combustion batch four burning phases of different aerosol composition and different amounts of emitted particulate matter (PM) can be observed. As a novel approach of this study the burning phases were defined by chemical changes in the aerosol gas phase during the combustion instead of being linked to pre-defined time periods or the amount of PM emission. This deeper view into the aerosol chemistry at the different burning phases was possible by employing online mass spectrometer techniques with high time resolution. A special soft ionization technique enabled a selective detection of polyaromatic hydrocarbons (PAH) in the gas phase whereas changes in the particle phase were observed by an aerosol mass spectrometer (AMS).

The employment of the AMS allowed the description of changes in the particle phase and the amount of emitted PM during the burning phases as well as verification of the observed burning phases. Finally, it was shown that the organic fraction and the amount of particles emitted during the ignition phase were main contributors to the emitted PM. The definition of these four burning phases could be supported by a more detailed view into the chemistry of the organic matter by using a Van-Krevelen description (H:C ratios in average of 1.32 - 1.64 and O:C ratios of 0.25 0.44, with single values up to 1.4) and by our novel approach to employ positive matrix factorization (PMF) as a source apportionment tool for the separation of one emission source into different combustion dominated processes. In addition, the high dynamic and complexity of wood combustion emission was revealed by these analysis methods. It was shown that different combustion conditions exhibit a strong impact on the amount of emitted PM. For instance, an


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experiment with an overloaded stove emitted a roughly four-fold higher amount of PM mass compared to a stove run at manufacturer recommended (normal) combustion conditions. This experiment showed a much higher amount of PAH, which are strongly harmful for the human health.

1. INTRODUCTION Ambient aerosol, especially the organic fraction, exhibit severe effects on human health and climate.1-3 The emission source type has a strong impact on the toxicity4 and the composition5 of the aerosol. Wood combustion is known as an important emission source of ambient aerosol, especially in winter time.6,7 Several studies on aerosol emissions from wood combustion pointed out that the fuel type, the combustion conditions and the type of furnace have a strong impact on the aerosol concentration and composition.8-11 Another study pointed out the increase of toxicity by formation of polycyclic aromatic hydrocarbon (PAH) and oxygenated compounds at different combustion stages in the flame and the impact of the air flow rates on the combustion and their products.12 However, in most cases wood combustion is considered as a source of constant emission by using averaged values for a batch8,13,14 and not as a dynamically varying source with varying impact on the composition and concentration of the emissions especially at chimney stove operation. Hence, the present work is primary focused on the analysis of the different burning phases of wood combustion in a logwood stove and their impact on the emitted aerosol composition and concentration. The key to achieve this target was the employment of high time-resolved online measurement techniques during a campaign at the Technology and Support Centre Straubing, Germany in 2011. These measurement techniques enable the classification of the whole batch in


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four burning phases based on the chemical profiles of the emissions. Earlier studies stated predefined burning phases with fixed time periods or classified only two phases of flaming and smoldering due to the CO:CO2 ratio.15-17 Another study of our group18 that used a combustion reactor classified these two phases due to the variation of organic marker compounds in the gas phase. Based on this study, that primarily used a logwood stove, the novel approach determining organic compounds in gas and particulate phase with a high time resolution allowed the description of the chemical profiles in four distinct burning phases and thus to classify the complete batch. Additionally, positive matrix factorization (PMF), usually employed to separate ambient emission into different source related factors for source apportionment, was employed to separate the wood combustion emission into different combustion factors (processes). Thereby, the classification of the particular burning phase could be validated. The main instruments in this study, beside the test bench for wood combustion experiment including a full stream dilution tunnel, were an Aerodyne aerosol mass spectrometer (AMS) for size-resolved particle composition analysis and a home-made laser mass spectrometer (REMPIToF-MS) for detection of aromatic hydrocarbons, especially polycyclic aromatic hydrocarbons (PAH) in the gas phase.19 For the burning phase classification results of these REMPI-ToF-MS together with measurements of carbon monoxide (CO) were applied. Additionally, the investigation of the impact of different fuel (beech or spruce wood) and furnace types (logwood boiler or stove) on the emissions was another important subject of the study. Special focus of interest was set on the combustion behavior under non-optimal conditions, as domestic heating very often is not performed as recommended by the manufacturer of the stove. For this reason, experiments were carried out with wood of different moisture content (2% and 19%), with an overloaded stove or under oxygen deficiency. These experiments provided a comparison of different particulate matter (PM) emissions and Van-Krevelen


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descriptions (H:C and O:C ratios) with a discussion of the chemistry of the organic matter, especially of the PAH emission. A detailed discussion of different PAH chemistry with the focus on the REMPI-ToF-MS results will be presented in another publication. 2. MATERIALS AND METHODS 2.1. Test bench and other instruments The wood combustion experiments were performed at the test facility at the Technology and Support Centre in Straubing, Germany. The test bench (Fig. 1) included a full stream dilution tunnel and was operated with a logwood stove and a logwood boiler. The logwood stove, typically burner for domestic heating, had a nominal heat output of 8 kW and was equipped with primary air supply through the grate and window rinsing air (details by Orasche et al.4). The 30 kW logwood boiler, typically used for building heating, was operated with a two-stage air system and employed the so-called downdraught combustion principle. The logs slid down to the combustion zone by gravity. The primary air supply for the combustion in the fuel bed allowed sucking down the accrued gases and flames into a post combustion chamber, where the secondary air allowed a more complete combustion. After the combustion chambers the combustion exhaust passed a water filled heat-exchanger for the building heating system before being ventilated to the chimney. The boiler was equipped with an automatic control system for combustion via a λ probe for controlling the primary and secondary air supply (details by Orasche et al.4). The undiluted hot flue gas passed the online mass spectrometer REMPI-ToF-MS (refer to 2.3.) for gas phase analysis, and a Fourier transform-infrared spectrometer (FT-IR) (Gasmet CX 4000 FTIR, Ansyco, Karlsruhe, Germany) as well as several gas monitors for general gas phase analysis (e.g. CO, CO2 and organic gaseous carbon (OGC)) before it reached the dilution tunnel. For the REMPI-ToF-MS the wood combustion emissions were sampled undiluted directly


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through a glass fibre filter with a flow rate of 5 l/min (gas sampler GS 312, Desaga Gmbh, Wiesloch, Germany). The REMPI-ToF-MS drew a small fraction of 1.5 ml/min from this sampled air through a capillary (deactivated fused silica, inner diameter: 200 µm) into the ion source of the mass spectrometer. The complete sampling line was heated to 280°C to prevent condensation of low-volatile compounds and a subsequent clogging. Before and within the dilution tunnel CO2 monitors (BINOS 1004, Fisher-Rosemount, Hasselroth, Germany) were located for the determination of the dilution ratio. The dilution ratio in the dilution tunnel typically amounted to about 1:4. In the dilution tunnel the wood combustion exhaust passed two to three ejector diluters (Palas, Karlsruhe, Germany), depending on the experimental conditions, with a dilution factor of 1:10 each. These additional dilution steps were required for the online particle analysis instruments HR-ToF-AMS (refer to 2.2.) and a scanning mobility particle sizer (SMPS) (TSI, model 3080, 3022A, Shoreview, MN, USA). The SMPS was run continuously and in parallel with the AMS and provided particle number size distributions in the size range from 15 to 638 nm. All data presented in this study are non-normalized to oxygen content or temperature. 2.2. Aerosol mass spectrometer The Aerodyne AMS (Aerodyne Research Inc., Billerica, MA, USA) was used to measure the submicron non-refractory particulate aerosol components, such as organics, sulphates, nitrates, and ammonium20, in real-time and particle size-resolved. A detailed description of the AMS system and data analysis can be found in several publications21,22. For the measurements a highresolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) was used, that allows to determine the elemental composition of the molecular fragments21 and elemental ratios, like the H:C or O:C ratio23.


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The AMS was operated with time resolutions of 10 to 20 seconds in the V-mode (single stage reflectron) during this study. The V-mode alternates between the mass spectrum (MS) mode for the chemical composition of the total non-refractory PM1 particle mass (2.5- and 5-second intervals for chopper opened and closed every 10 and 20 seconds of measuring time, respectively) and the particle time-of-flight (PToF) mode for the particle-size distribution (5- and 10-second intervals every 10 and 20 seconds of measuring time, respectively). The heater was set to 600°C. Special calibration of the AMS, such as servo position check, lens alignment, flow and size calibrations were performed at the beginning of the campaign. Routine calibrations of baseline, m/z and single ion were carried out every day during the experiment, whereas the ionisation efficiency (IE) was calibrated every week. For the AMS data analysis the software package Igor Pro 6.12A (Wavemetrics, Lake Oswego, OR, USA) was used together with the standard AMS data analysis tools (SQUIRREL 1.51H and PIKA 1.10H)24. The fragmentation table20 was modified according to the fragmentation table suggested by Aiken et al.23 and the gas phase composition, which was measured by using a high efficiency particulate air (HEPA) filter in front of the sampling inlet. For CO2 correction a dynamic factor according to the CO2-monitor data was additionally included in the fragmentation table. According to instrument comparisons a collection efficiency (CE) of 0.5 was applied for the AMS data, according to Bahreini et al.25 this value may vary by around 20%. 2.2.1. Positive matrix factorization The positive matrix factorization (PMF) is a bilinear unmixing model and provides the opportunity to describe the measured organic fraction by a linear combination of factors. A factor contains a constant mass spectrum (factor profile) exhibiting a variable contribution with time (strength of the factor). These factors are normally dominated by specific sources, e.g. primary organic aerosol from combustion, and are linked to them with uncertainties according to this kind


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of mathematical apportionment. The obtained factor values of the strength / time series and profile are constrained to be positive, thereby representing positive concentrations and contributions to the organic matter. A detailed description of the PMF model and analysis can be found in several publications26-29. The PMF analysis followed the procedure described by Ulbrich et al.26 and the results, containing an error consideration, are provided in detail in section SI-2 of the Supplement. The AMS PMF toolkit version 2.03 developed by Ulbrich et al.26 was employed for this analysis together with the Igor Pro 6.12A software. The PMF analysis was performed for the following data presented by 446 time points and 214 mass-to-charge ratios (m/z) from the unit-mass-resolution signals of m/z 12 to 250. The PMF results are based on the FPEAK -0.4 solution. A detailed PMF discussion can be found in section SI-2 of the Supplement. 2.3. REMPI-ToF-MS The resonance-enhanced multi photon ionization time-of-flight mass spectrometer REMPIToF-MS was employed for selective detection of aromatic hydrocarbons, e.g. polycyclic aromatic hydrocarbons (PAH), in the gas phase. The instrument uses the REMPI process, a two-step ionization process, which allows a soft ionization of aromatic compounds. A detailed description of this instrument and about the REMPI process can be found in Heger et al.30. In this study we used a Nd:YAG laser (BIG SKY ULTRA, Quantel, Les Ulis, France) for the generation of ultraviolet (UV) photons (266 nm ≙ 4.66 eV, repetition rate: 10 Hz, pulse width: 10 ns, power density: ~7 × 106 W/cm2). Data processing and calibration was accomplished by a LabView (National Instruments Corporation, Austin, TX, USA) based custom-made software. Origin 8.1 (OriginLab, Northampton, MA, USA) and Igor Pro 6.12A (Wavemetrics, Lake Oswego, OR, USA) were used for further statistical evaluations.


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2.4. Experiments In general, each experiment was performed under the same conditions and run procedure. The different experiments varied only in one distinct parameter, e.g. amount of fuel or oxygen supply (Table 1). All experiments included five batches of approximately 1.6 kg fuel (standard logs with 15% moisture content and 25 cm length with varying radius) and small additional batches between the measured batches with 0.5 to 1.0 kg fuel depending on the experiment course due to technical requirements. Beside the batches in between, these conditions were recommended by the stove manufacturer and are indicated as “normal conditions” in the following. For each batch the measurement started right after loading and the ignition when the door was closed. The air supply was set at partial load. The measurement was terminated when only 4 wt.% of the original mass of the loaded fuel was reached. Therefore the stove was placed on a scale for continuous weight determination. One set of experiments consisted of five batches: an initial batch, three batches under nominal load and a final batch. The final batch was terminated when the fuel was completely burned and the scale measuring a constant weight. In this study only the three repeat batches and the final batch were considered. The experiments with the logwood stove included two experiments under normal combustion conditions with different fuel types (beech and spruce wood) and four experiments under nonoptimal combustion conditions with beech wood. Later experiments laid the focus either on the wood properties (wet and dry wood with a moisture content of 19% and 2%, respectively) or on the furnace conditions (overloaded stove with around 4.3 kg fuel used instead of 1.6 kg fuel and experiment under oxygen deficiency). The two experiments at different moisture content used standard beech wood logs with a dimension of 17 cm x 7 cm x 7 cm and 1 cm space between the logs. The two experiments with the logwood boiler were performed under manufacturer recommended combustion conditions either with beech or spruce wood. However, the amount of


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fuel and loading steps of the logwood boiler experiments differed compared to the logwood stove procedure.

3. RESULTS AND DISCUSSION 3.1. Definition of Burning Phases One batch of a wood combustion experiment consists of four different burning phases. These burning phases exhibited different durations that varied for each batch. Thus, separation into these phases was carried out for each experiment due to chemical changes in the gaseous phase of the flue gas indicating different processes during the combustion.16,31-33 Figure 2 shows an example for the phase separation of a batch of beech wood in the logwood stove under normal combustion conditions. Phase 1 of the batch is defined as ignition phase and is linked to the guaiacol signal (m/z 124) of the REMPI-ToF-MS measurements (Fig. 2). Guaiacol derives from the pyrolysis of lignin, an important biopolymer in the wall of wood cells, during combustion. By using the guaiacol signal, it is possible to describe the pyrolysis processes. Ledesma et al.34 described the PAH formation under pyrolysis conditions. Additionally, the high carbon monoxide (CO) concentration and the low exhaust temperature indicate more a pyrolysis than an optimal combustion period.35 At the end of the guaiacol signal phase 2 followed. This harsh combustion phase is characterized by a slow decrease of the naphthalene signal (m/z 128) of the REMPI-ToF-MS measurement. This link describes decaying pyrolysis processes besides the dominating combustion processes. During harsh combustion the increase of the exhaust temperature and the anti-proportionally decrease of the CO concentration as well indicate the upcoming combustion processes and the decrease of naphthalene during this phase. Phase 3 starts with the end of the naphthalene decrease and represents a phase of stable combustion conditions. In this stable combustion phase different processes coincide, char being continuously formed


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besides the combustion processes. The exhaust temperature and the CO concentration are stable during this phase. Additionally, the high exhaust temperature represents the exothermic processes of a complete combustion.35 Phase 4, the burnout phase after flame extinction, is dominated by the oxidation process of char and reduced combustion processes,32 thus the burnout phase starts with a strong increase of CO concentration15 measured by the FT-IR (Fig. 2). Additionally, the exhaust temperature that peaks at the end of phase 3 decreases during phase 4. The following sections 3.3 and 3.4 will show and confirm the validation of this separation into different burning phases. 3.2. General results Figure 3 shows the variation with time of the different particle components and PM mass in the flue gas of beech wood combustion under normal combustion conditions (top) and with an overloaded stove (bottom) at the different burning phases measured by the AMS in the diluted flue gas. The organic matter was the main contributor to the total mass (TM) of particles measured by the AMS in each burning phase (approximately 93%, refer to Figure SI-1.1 of the Supplement). Fine et al.14 and Schmidl et al.10 found similar results for the aerosol composition emitted by a logwood stove. The largest organic contribution was yield in the wet wood experiment with approximately 99%. The logwood boiler experiments using beech or spruce wood yielded less organic matter, similar to Heringa et al.36, and the relative contribution of the inorganic compounds increased compared to phase 1. In case of the logwood stove, organic matter contributed the largest part of the aerosol measured by the AMS, especially during the ignition phase up to 99% (e.g. for the beech wood combustion under normal condition). During the following phases, the contribution of the organic matter decreased down to 80% during at the burnout phase. The contribution of inorganic compounds showed a different behavior during the burning phases. Particulate nitrate increased strongly in the burnout phase up to 10%, whereas


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chloride and sulphate showed their highest concentrations in the harsh and stable burning phase with an average of around 6 to 7% and 4 to 5%, respectively. The latter anions, especially chloride, are encountered with potassium as counter-ion. A higher amount of potassium was found e.g. by Echalar et al.37 and Lee et al.17 in the flaming phase of their batch. Together with the flaming character of the harsh and stable combustion phase, the results of chloride and sulphate found here match with the observations of these studies. The other batches of this beech wood combustion experiment exhibited similar particle compositions. However, a variation was visible. Chloride exhibited the highest variation of the contributions here (refer to Figure SI-1.1 of the Supplement). In studies of wood combustion emission by mass spectrometry methods, especially in ambient studies, the signal of m/z 60 is of special concern7. Elsasser et al.7 pointed out the correlation of m/z 60 signal with the combustion marker levoglucosan38,39, but mentioned the complexity of this m/z. It is not possible to refer m/z 60 solely to levoglucosan. Several other compounds contribute to the signal of m/z 60, e.g. additional wood combustion products like carboxylic acids, additional anhydrous sugars, and cellulose. Due to this ambiguity m/z 60 is only discussed briefly in the following. The strength of the m/z 60 signal relative to the total organic mass (Fig. 3) had its maximum in the first two phases and decreased to an almost stable and low contribution to the total organic matter in phases 3 and 4. This observation, together with the overall decrease of the organic matter, suggests that most of the levoglucosan precursor cellulose was already converted in the earlier phases (1 - 2).16,31 The experiment with wet wood showed lower exhaust temperatures of up to 184°C compared to 229°C in the normal or dry wood experiments. This reduction by higher moisture content yields a higher relative signal of m/z 60 with a similar variation with time suggesting an increased lifetime of instable compounds like levoglucosan and cellulose during the combustion.


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The total mass of particles strongly varied during the different burning phases. However, the emitted PM mass during Phase 1 usually provided the largest contribution to TM, which was due to the high PM concentration during this phase, but was in contrast to the low duration of this phase. In contrast, the experiment with dry wood showed the largest contribution to TM during phase 2. The reason could be the faster start of the combustion processes due to reduced evaporation processes in phase 1. These results showed again that wood combustion observations are complex and may vary due to the dynamic of the combustion (refer Figure SI-1.2 of the Supplement). In general, the experiments under non-optimal combustion condition (e.g. overloaded stove) provided a similar composition of the particle, excepting the above mentioned differences. However, a detailed view on the organic matter in the following sections will reveal differences in the chemical properties of the organic matter. One important difference was the amount of emitted PM mass, as it can be seen in Figure 3. Standardized to the experiment time and amount of used fuel (4.6 kg instead of 1.6 kg fuel) the overloaded stove experiments with beech wood showed an about 4-fold higher PM mass emission compared to the normal load. Additionally, the experiment under oxygen deficiency yielded a 4.5-fold higher PM mass emission. An overview of the results of different experiments of beech wood combustion under normal combustion conditions, with an overloaded stove and under oxygen deficiency is provided in the Supplemental Information (Fig. SI-1.3). A high difference in the emitted PM could be as well observed in the wet wood experiment (around 5.5-fold higher). 3.3. Chemical properties of organic aerosol The Van-Krevelen diagram was established to characterize the quality of fossil fuel and the coalification of biomass.40 Nowadays it is often used to reveal and discuss characteristics of the organic particulate matter. According to the high amount of oxygenated species12 in wood


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combustion emission and their impact on the O:C and H:C ratios the Van-Krevelen description is important, as it is in biomass characterization in the past decades. In Figure 4 the H:C and O:C ratio of the emitted particles are shown and colored according to the corresponding burning phases. Furthermore, Figure 4 provides the separation of the different O:C and H:C ratios due to the burning phases. A detailed view at the logwood stove experiments under normal combustion conditions with spruce and beech wood in Figure 4 b and 4 c, respectively, show that each burning phase exhibit a specific range of the O:C and H:C ratios. The Van-Krevelen plot additionally demonstrates a high variability of these ratios and of the wood combustion itself. In general, the H:C ratio in average was between 1.32 and 1.64 and the O:C ratio between 0.25 and 0.44 for all experiments. During the ignition and harsh combustion phase (phase 1 and 2), the H:C ratios were mainly above 1.3 and the O:C ratios between 0.1 and 0.4. Heringa et al.36 found similar ratios, especially their O:C ratios were in a good agreement and laid between 0.2 and 0.5 during their start-up phase. The burnout phase (phase 4) provided the highest O:C values, especially for the logwood boiler experiment. Some O:C ratios went up to 1.4, suggesting that most of the emitted carbon atoms in the organic matter were bounded to at least one oxygen. The AMS mass spectra of the experiments with high O:C ratios showed m/z 44 as major m/z signal, which derives mainly from the CO2-fragment. Weimer et al.16 and Heringa et al.36 also found high amounts of the m/z 44 signal in their smoldering phase. There are several explanations for a high m/z 44 signal. Typically, the CO2 derives from organic acids due to decarboxylation, but it could also derive from carbonate-containing inorganic minerals or directly from particle adsorbed CO2. Another reason could be the supposed increasing effect of a black carbon (BC) oxidation during the burnout phase. Latest studies show that it is possible to generate CO2 by oxidation of BC even at a temperature below the AMS heater temperature of 600°C. Matuschek et al.41 got a high signal


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of CO2 during thermal gravimetric analysis of BC. Bladt et al.42 studied the catalytic effect of minerals in soot during temperature-programmed oxidation measurements and the decrease of the temperature of maximum CO and CO2 emission with increasing iron-content. This would allow a “post-combustion” (oxidation) of BC on the surface of the heater of the AMS and may explain the high signal of m/z 44. Additionally, the combustion conditions influenced the O:C and H:C ratios. Figure 4a depict values with low H:C and O:C ratios marked by a light blue circle. All these data were derived under non-optimal combustion conditions with an overloaded stove during the harsh combustion phase (phase 2). Figure 5 compares the mass spectra of the REMPI-ToF-MS (gas phase) and AMS (particle phase) measurements during this phase and shows a shift to higher m/z signals compared to the other phases and combustion conditions. An AMS mass spectrum under normal combustion conditions yielded lower masses (refer to Fig. SI-1.4). Older studies of wood combustion with REMPI-ToF-MS measurements of the gas phase and particle phase showed similar mass spectra.43 These results and the low ratios suggest that much more PAH are produced under this non-optimal combustion condition, especially during the harsh combustion phase (phase 3). These findings are important, especially concerning human health. Insufficient oxygen supply during combustion favors the production and amount of higher PAH.44 In the above mentioned experiment, the oxygen supply was insufficient due to the high amount of fuel, but as well due to the missing space for post combustion in the stove under these conditions. Additionally, it could happen that zones of reduced oxygen concentrations occur during combustion supporting the formation of PAH.45 The wood moisture content affects the composition of the organic fraction in different ways. The wet wood experiment exhibited relative high O:C ratios with an average of 0.43. The dry wood experiment showed lower H:C ratios in phase 2 with specific signals for PAH in the MS due to a higher combustion temperature


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favouring the PAH production.12 These results also match the offline GC-MS observations by Orasche et al.13 during this campaign. 3.4. Phase separation according to PMF Positive matrix factorization (PMF) is a multivariate analysis tool that has been applied for source apportionment in ambient systems. PMF enables the separation of ambient aerosol into source related factors such as traffic related hydrocarbon-like organic aerosol (HOA) or nonsource related oxygenated organic aerosol (OOA).27 The description of the variation of the chemical profile during the emission of a single source is a novel approach. Similar to the chemical characterization of the phases by REMPI and FT-IR in section 3.1., PMF analysis yielded a separation of the batch into three different burning factors (Figure 6 and 7). These figures show the mass spectra (Fig. 6) and the variation in time of these factors (Fig. 7) obtained by PMF analysis of the batches of beech wood combustion under normal conditions. The PMF analysis of the other experiments revealed a three-factor solution as well. The first factor was almost exclusively contributing to the organic matter during the first burning phase (Fig. SI-2.2). Thus, this factor was named “ignition factor”, which exhibited a sharp maximum in the ignition phase and less contribution to the other phases. The second factor (“combustion factor”) dominated in the two combustion phases (phase 2 and 3) and reached its maximum during the harsh combustion phase (phase 2). The third factor increased continuously throughout the batches and accompanied the strong increase of CO during the burnout phase (phase 4). This behavior suggests a correlation to ember, the influence of which continuously increases during combustion. This “ember factor” was coincided with the combustion factor during the stable combustion phase (phase 3). The competition of two factors during the stable combustion phase reflects the complexity as well as the dynamic of wood combustion. Additionally, the PMF analysis approves the batch separation into different burning phases by linking the observed


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burning factors to the identified burning phases; in particular the ignition phase and the ignition factor exhibit a pronounced coincidence.

4. CONCLUSIONS The study presented provides a detailed discussion on the dynamic changes of the aerosol composition and concentration during wood combustion batches. Hence, the complete batch was separated into four different burning phases with the novel approach to classify the burning phases according to the chemical processes during the whole combustion batch. This phase separation and characterization was possible by employing online measurement techniques with high time resolution, e.g. REMPI-ToF-MS for detection of naphthalene and guaiacol in the gas phase. Furthermore, the impact of different combustion conditions on the emitted PM mass and the chemical composition of the aerosol were investigated. Four different burning phases could be characterized, beginning with an ignition phase linked to the guaiacol signal, a harsh combustion phase linked to the naphthalene signal, a stable combustion phase and a burnout phase linked to the CO signal. These phases exhibit different impacts on the chemical composition and the amount of the emitted PM mass of the aerosol measured by the AMS. In general, the ignition phase provided the highest PM masses and the largest contribution to the total emitted mass. During this first phase as well as in the other phases the organic faction was the main contributor to the aerosol composition measured by AMS. However, the contribution of the organic matter to PM mass decreased in the subsequent burning phases. The inorganic compounds chloride and sulphate peaked during the two combustion phases. During the burnout phase the nitrate concentration increased, the exhaust temperature decreased and the highest O:C ratios were observed. These high O:C ratios are


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probably caused by a post-combustion of BC at the AMS heater besides a decarboxylation of carboxylic acids and carbonate. The observation of the burning phases by looking at the chemistry, e.g. Van-Krevelen plot and amount of emitted mass during these phases, reveals the high dynamic and complexity of the wood combustion emission. The Van-Krevelen description confirmed the separation of the batch into four different phases by yielding different H:C and O:C ratios for each phase. The novel approach of applying a statistical tool for source apportionment like PMF for the description of different processes during the emission of a single source proved the variation and dynamic of wood combustion emission. Additionally, it confirmed the phase separation of the batch and showed that the stable combustion phase can be described as a composition of different processdominated factors, i.e. combustion and ember factor. Similar to the chemical characterization of the phases, a separation of the batch into three different burning factors was found, especially the ignition phase and ignition factor were closely coincided to each other. It could be shown that different combustion conditions of the logwood stove exhibited variable impacts on the particle composition and PM mass emission, e.g. a high amount of PAH during the harsh combustion phase of an overloaded stove, which could be critical for human health. Like this experiment the second experiment under non-optimal operation (oxygen deficiency) showed an about four-fold higher PM mass emission. Additionally, the moisture content of the wood exhibits an important rule on the aerosol chemistry. Thus, the combustion of wet wood emitted more oxygenated and larger amounts of PM. The combustion of dry wood instead showed a non-negligible part of reduced species (PAH).


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FIGURES

Figure 1. Test bench including the furnaces (logwood stove (left) or logwood boiler (right)), dilution tunnel, the instruments for gas analysis: REMPI-ToF-MS and FT-IR in the hot undiluted flue gas and for particle analysis: HR-ToF-MS and SMPS after the ejector dilutors in the diluted flue gas.


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Figure 2. Time series of a logwood stove experiment with beech wood under normal combustion conditions; top: FT-IR results of carbon monoxide concentration (grey) and exhaust temperature (black); middle: REMPI-ToF-MS results of m/z signal 124 (linked to guaiacol, light blue) and m/z signal 128 (linked to naphthalene, dark green); bottom: AMS results of non-refractory aerosol component concentrations of organic matter (OM, green), nitrate (blue), sulphate (red), ammonium (orange) and chloride (magenta).


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Figure 3. AMS variation with time of non-refractory aerosol components from a logwood stove experiment with beech: (top) wood under normal operation conditions and (below) a overloaded stove experiment; concentrations of organic matter (green), nitrate (blue), sulphate (red), ammonium (orange), chloride (magenta) and relative strength of m/z 60 signal (black).


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Figure 4. Van-Krevelen plot: O:C and H:C ratios coloured according to the different burning phases of a) all experiments, b) spruce wood combustion under normal conditions in the logwood stove and c) beech wood combustion under normal conditions in the logwood stove. The light blue circle in Figure a covers all data points of the experiment with an overloaded stove.


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Figure 5. Mass spectra of the overloaded logwood stove experiment during the harsh combustion phase: (top) REMPI mass spectrum (light blue) and (below) AMS mass spectrum of the nitrate equivalent concentration.


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Figure 6. Factor profiles according to the PMF analysis of the organic fraction of the beech wood combustion experiment under normal conditions. The stable mass spectra show the profiles of the ignition factor (blue), combustion factor (red) and ember factor (black) yielded by threefactor PMF analysis.


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Figure 7. Observed time series of the PMF factors and their relative contribution to the organic fraction of the beech wood combustion experiment under normal burning conditions. The three factors of the PMF analysis include an ignition factor (blue), combustion factor (red) and ember factor (black). The relative contribution of the factor is provided for four batches of this experiment. According to limitation of the PMF analysis the results showed a good reproducibility. The dotted lines represent the beginning and the end of a burning phase according to the phase separation in section 3.1. The ignition factor showed the highest contribution to the total organic fraction (44%), followed by the ember factor (30%) and the combustion factor (26%).


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TABLES Table 1. Experiment characteristics and fuel properties furnace

wood type

combustion conditions

load weight [kg]

C [g kg-1]

H

O [g kg-1]

N [g kg-1]

S

K

[g kg-1]

[g kg-1]

logwood stove

beech

regular

1.6

482c

62c

447c

2.2c

0.20c

logwood stove

spruce

regular

1.6

494c

64c

435c

1.3c

logwood stove

beech

over-loaded

2.6a / 4.3

482c

62c

447c

logwood stove

beech

oxygen deficiency

1.6

482c

62c

logwood stove

beech

dry wood

2.0a / 1.6

498c

logwood stove

beech

wet wood

2.4a / 2.0

logwood boiler

beech

regular

logwood boiler

spruce

regular

a

ash [g kg-1]

moisture content [%]

1.12c

4.7c

15

0.19c

0.58c

6.1c

15

2.2c

0.20c

1.12c

4.7c

15

447c

2.2c

0.20c

1.12c

4.7c

15

64c

437c

1.3c

n.d.

1.53c

6.5c

2

494c

64c

432c

1.4c

n.d.

1.53c

6.5c

19

12b

482c

62c

447c

2.2c

0.20c

1.12c

4.7c

15

12b

506c

63c

434c

1.3c

0.19c

0.58c

6.1c

17

[g kg-1]

load weight for initial batch; b wood consumption [kg h-1], c refer to dry matter.

ASSOCIATED CONTENT Supporting Information. It contains a full description of PMF analysis and results. Additionally, the Supporting Information provides the time series of the three main experiments shown and the aerosol and mass contribution during the different burning phases. This material is available free of charge via the internet at http://pubs.acs.org.


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AUTHOR INFORMATION Corresponding Author *J. Schnelle-Kreis ([email protected]) ACKNOWLEDGMENT The authors like to thank the Technology and Support Centre, Straubing, Germany for support of this work.

ABBREVIATIONS AMS: aerosol mass spectrometer; BC: black carbon; CE: collection efficiency; CO2: carbon dioxide; CO: carbon monoxide; FT-IR: Fourier transform-infrared spectrometer; HEPA: highefficiency particulate air; HOA: hydrocarbon-like organic aerosol; HR-ToF-AMS: highresolution time-of-flight aerosol mass spectrometer; IE: ionization efficiency; MS: mass spectrum, mass spectrometry; m/z: mass-to-charge ratio; OGC: organic gaseous carbon; OM: organic matter; OOA: oxygenated organic aerosol, PToF: particle time-of-flight; PM: particulate matter; PAH: polyaromatic hydrocarbons; PMF: positive matrix factorization; REMPI-ToF-MS: resonance enhanced multi photon ionization time-of-flight mass spectrometer; SMPS: scanning mobility particle sizer; TM: total mass.


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Dynamic Changes of the Aerosol Composition and Concentration

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