Chemical composition and speciation of particulate organic matter from modern residential small-scale wood combustion appliances.

Science of the Total Environment 612 (2018) 636–648

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Chemical composition and speciation of particulate organic matter from modern residential small-scale wood combustion appliances Hendryk Czech a, Toni Miersch a, Jürgen Orasche a,b,c, Gülcin Abbaszade b,c, Olli Sippula c,d, Jarkko Tissari d, Bernhard Michalke e, Jürgen Schnelle-Kreis b,c, Thorsten Streibel a,b,c,⁎, Jorma Jokiniemi c,d, Ralf Zimmermann a,b,c a

Joint Mass Spectrometry Centre, Chair of Analytical Chemistry, Institute of Chemistry, University of Rostock, 18059 Rostock, Germany Joint Mass Spectrometry Centre, Cooperation Group “Comprehensive Molecular Analytics” (CMA), Helmholtz Zentrum München – German Research Centre for Environmental Health, 85764 Neuherberg, Germany c Helmholtz Virtual Institute of Complex Molecular Systems in Environmental Health (HICE), Germany d Fine Particle and Aerosol Technology Laboratory, Department of Environmental and Biological Sciences, University of Eastern Finland, FIN-70211 Kuopio, Finland e Research Unit Analytical BioGeochemistry, Helmholtz Zentrum München – German Research Centre for Environmental Health, 85764 Neuherberg, Germany b

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• PM2.5 composition from two modern wood combustion appliances • Advanced targeted and non-targeted mass spectrometric techniques • Emission factors for N 100 organic particle constituents • Low abundance of established wood combustion markers • Slow ignition can shift emission pattern compared to regular combustion.

a r t i c l e

i n f o

Article history: Received 27 June 2017 Received in revised form 23 August 2017 Accepted 26 August 2017 Available online xxxx Editor: D. Barcelo Keywords: Diagnostic ratios Wood smoke PAH OPAH Phenolics Photoionisation mass spectrometry

a b s t r a c t Combustion technologies of small-scale wood combustion appliances are continuously developed decrease emissions of various pollutants and increase energy conversion. One strategy to reduce emissions is the implementation of air staging technology in secondary air supply, which became an established technique for modern wood combustion appliances. On that account, emissions from a modern masonry heater fuelled with three types of common logwood (beech, birch and spruce) and a modern pellet boiler fuelled with commercial softwood pellets were investigated, which refer to representative combustion appliances in northern Europe In particular, emphasis was put on the organic constituents of PM2.5, including polycyclic aromatic hydrocarbons (PAHs), oxygenated PAHs (OPAHs) and phenolic species, by targeted and non-targeted mass spectrometric analysis techniques. Compared to conventional wood stoves and pellet boilers, organic emissions from the modern appliances were reduced by at least one order of magnitude, but to a different extent for single species. Hence, characteristic ratios of emission constituents and emission profiles for wood combustion identification and speciation do not hold for this type of advanced combustion technology. Additionally, an overall substantial reduction of typical wood combustion markers, such as phenolic species and anhydrous sugars, were observed. Finally, it was found that slow ignition of log woods changes the distribution of characteristic resin acids and phytosterols as

⁎ Corresponding author at: Joint Mass Spectrometry Centre, Chair of Analytical Chemistry, University of Rostock, 18059 Rostock, Germany.

http://dx.doi.org/10.1016/j.scitotenv.2017.08.263 0048-9697/© 2017 Elsevier B.V. All rights reserved.

H. Czech et al. / Science of the Total Environment 612 (2018) 636–648

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well as their thermal alteration products, which are used as markers for specific wood types. Our results should be considered for wood combustion identification in positive matrix factorisation or chemical mass balance in northern Europe. © 2017 Elsevier B.V. All rights reserved.

1. Introduction In many countries of Europe as well as in North America, the residential combustion of biomass, in particular wood, as a renewable energy source is encouraged by legislation to decrease the dependence on fossil fuels and contribution to global warming. Conventional logwood stoves, pellet stoves and pellet boilers have been recognised to be substantial emitters of greenhouse gases, black or elemental carbon and brown carbon (Evtyugina et al., 2014; Martinsson et al., 2015; Orasche et al., 2012), which counteract the widely considered CO2-neutrality and thus affect the climate (Andreae and Ramanathan, 2013). Furthermore, residential wood burning causes elevated ambient concentrations of inhalable particulate matter (PM) with an aerodynamic diameter below 2.5 μm (PM2.5) (Fuller et al., 2013), which has been associated with toxicological responses, such as genotoxicity, cytotoxicity, oxidative stress, systemic inflammation and cardiovascular diseases (Croft et al., 2017; Miljevic et al., 2010; Naeher et al., 2007; Sehlstedt et al., 2010). Also, the emissions of volatile organic compounds (VOCs) from wood combustion cover a substantial potential for secondary organic aerosol (SOA) formation (Bruns et al., 2016), which may have different health effects than the primary emissions (Künzi et al., 2013; Nordin et al., 2015) and additionally contributes to ambient PM2.5 levels. In order to tackle these emissions, new and more complete combustion technologies were developed which may significantly reduce the toxicity potential of primary wood combustion aerosols (Jalava et al., 2012). Secondary air supply through air staging became an established emission abatement strategy, which substantial reduce the emissions of several organic and inorganic pollutants (Nuutinen et al., 2014). Air staging is characterised by splitting the total combustion air into under-stoichiometric air-to-fuel ratios for primary air (λ b 1) and consecutive addition of low excess secondary air (λ N 1.5), which is well-mixed with the pyrolysis gases through different feedings. Thus, high temperatures and more complete combustion are achieved (Nussbaumer, 2003). However, some pollutants correlate inversely with increasing secondary air flow rates, such as the trade-off between CO and NOx (Khodaei et al., 2017). Emissions from a masonry heater and a pellet boiler, equipped with air staging technology, were previously investigated regarding PM, CO, NOx (NO + NO2), total VOC (Lamberg et al., 2011a; Lamberg et al., 2011b; Nuutinen et al., 2014) and VOC composition (Czech et al., 2017; Czech et al., 2016; Reda et al., 2015), as well as their SOA formation potential (Kari et al., 2017; Tiitta et al., 2016). In this study, an additional detailed characterisation of the PM2.5 composition is presented with emphasis on the organic constituents, whose emission factors (EFs) are compared to conventional and other modern wood stoves. The implications for source apportionment studies of the results on chemical patterns of emissions are also discussed, especially the implications for selecting common diagnostic ratios to trace sources and quantify source contributions from biomass burning to ambient PM concentrations. Moreover, differences of EFs and emission profiles between wood types are related to effects observed in toxicological studies (Kasurinen et al., 2017). Although the masonry heater and the pellet boiler of this study are only representative domestic combustion appliances in northern Europe, it was pointed out that especially features of regional emission activity are important for emission inventories and source apportionment (Hellén et al., 2008; Pastorello et al., 2011). In the end, the EFs from the two combustion appliances can be also involved in simulations

of future emission scenarios to estimate benefits of emission abatement technologies on air quality (Fountoukis et al., 2014). 2. Material and methods 2.1. Experimental setup 2.1.1. Combustion appliances The emissions of two small-scale wood combustion appliances with advanced secondary air supply for residential heating were investigated. The modern masonry heater (Hiisi 4, Tulikivi Ltd., Finland) comprises of a massive soap stone of app. 1.3 tons for slow heat release, an upright enclosed firebox and a double glass window door. In the firebox, the secondary air is supplied through panels with small rifts in the upper combustion chamber (air staging) to generate a secondary combustion zone, which was shown to substantially reduce emissions of CO, VOCs and organic matter (Nuutinen et al., 2014). The modern masonry heater was operated at approximate nominal load. The second appliance is an automatically-fired top-feed pellet boiler (PZ25RL, Biotech Energietechnik GmbH, Austria), which was continuously operated at its nominal power of 25 kW. A more detailed description of the pellet boiler and the effect of air staging can be found in Lamberg et al. (2011b). In the following, the term “modern” refers to wood combustion appliances with air staging and “conventional” without. This study was part of experiments by Helmholtz Virtual Institute of Complex Molecular System in Environmental Health (HICE), which aims to explore biological effects of emissions on human lung epithelial cells (Kanashova et al., 2017). In particular, emissions from state-of-theart combustion appliances and emerging fuels are of interest as they may represent future scenarios. The two combustion appliances were chosen due to their known low emissions of bulk components from previous studies (Nuutinen et al., 2014; Lamberg et al., 2011b) and their representativeness for single houses in terms of energy output and market availability. 2.1.2. Fuels and combustion procedure The modern masonry heater was fuelled by beech (Fagus sylvatica) and spruce (Picea abies), which are common firewood in central Europe, as well as birch (Betula pubescens), which is a typical firewood in northern Europe. In Table 1, physico-chemical properties of the three types of firewood are summarised. In total 15 kg of logwood was burned in a single experiment, split up in six consecutive batches of 2.5 kg each. The ignition of the first batch in the cold stove was carried out from top down with 150 g of small wood sticks/chips. After 35 min, the next batch was put in the wood combustion residues for selfignition and burned for further 35 min. Subsequent to the sixth batch, remaining ember was stoked and the secondary air supply was blocked according to manufacture instructions for 30 min to cover a total experimental time of 4 h. In two (spruce* and birch*) of the total log wood combustion experiments, a slow ignition of the first batch was observed. Due to qualitative and quantitative differences in emissions of these experiments compared to the other ones, the effect of slow ignition on the total EFs is discussed more in detail in Section 3.5. The pellet boiler was operated with commercial softwood pellets (produced from pine and spruce stem wood, physico-chemical properties in Table 1) under stable conditions over 4 h and connected to a heat exchanger. Those boilers are usually connected to a reservoir of water for

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Table 1 Physico-chemical properties of the wood fuels. Parameter

Unit

Limit of quantification

Method

Beech logs

Birch logs

Spruce logs

Softwood pellets

Moisture Ash content C H N O S Cl Zn Si K Net heating value

% (w/w) % (w/w, dry) % (w/w, dry) % (w/w, dry) % (w/w, dry) % (w/w, dry) % (w/w, dry) % (w/w, dry) mg/kg (dry) mg/kg (dry) mg/kg (dry) kJ/kg

0.1 0.1 0.2 0.1 0.05

DIN EN 14774-2 DIN EN 14775 DIN EN 15104 DIN EN 15104 DIN EN 15104 Calculated DIN EN 15289 DIN EN 15289 DIN EN ISO 17294-2 DIN EN ISO 11885 DIN EN ISO 11885 DIN EN 14918

9.0 1.3 50.3 5.8 0.36 42.3 0.037 b0.005 5 6.3 14.5 17,790

7.2 0.69 51.0 6.0 0.40 41.9 0.006 b0.005 33 17.5 9.3 18,140

7.4 0.58 52.0 5.9 0.36 41.1 0.009 0.005 25 5.8 14.5 18,640

7.3 0.36 51.7 6.0 0.29 41.6 0.006 0.006 14 14.5 12.1 18,380

0.005 0.005 1 1 1 200

warm water supply and therefore have to constantly produce heat in contrast to pellet stoves, which refers to room heating appliances. A more detailed description of the combustion experiments can be found in Reda et al. (2015). 2.2. Instrumentation 2.2.1. Gas measurements Flue gases were led from the firebox to the upper combustion chamber and then downwards through side ducts into the stack. The stack was placed below a hood, and draught was regulated with two dampers. The target value for the pressure in the stack was (12.0 ± 0.5) Pa below ambient pressure. CO, CO2, NOx and O2 were continuously measured by a gas analyser system (ABB, Limas 11 UV, Switzerland). Organic gaseous carbon (OGC), a quantity for total organic vapours, were quantified by a flame ionisation detector (ABB, Multi-FID 14, Switzerland), which was calibrated against propane. All gaseous emissions were measured directly from undiluted stack gas through an insulated and externally heated sampling line at 180 °C. From the analysis of O2 in the flue gas, emission factors (EFs) were calculated based on the instruction of the Finnish Standard Association method SFS 5624 as described in Reda et al. (2015). 2.2.2. Filter sampling of PM2.5 The emitted aerosol of the wood combustion appliances was isokinetically sampled from the stack, diluted at a dilution ratio of 40 by using a porous-tube/ejector dilutor (Venacontra, Finland) and cooled to room temperature to enable condensation of semi-volatile species. Subsequently, particles were segregated to an aerodynamic diameter smaller than 2.5 μm and isokinetically sampled on quartz fibre filters (T293, Munktell, Sweden) over the total experiment duration of 4 h. Finally, the filter samples were stored at −20 °C until analysis. An overview about the total experimental setup can be found in Weggler et al. (2016). Please note that gas-particle partitioning, which affects the emission factors, are dependent on the applied dilution technique and dilution ratio (Nuutinen et al., 2014). The applied dilution ratio around 40 has been widely used for sampling PM emissions, but it was demonstrated that towards higher dilutions the sampled PM mass decreases substantially (Lipsky and Robinson, 2006). Thus, the presented EFs can be regarded as an upper limit. 2.2.3. Organic PM2.5 constituents The analysis of the organic fraction of the PM2.5 emissions was carried out by targeted and untargeted mass spectrometric techniques. Thermal/optical carbon analysis (TOCA) coupled to resonanceenhanced multi-photon ionisation time-of-flight mass spectrometry (REMPI-TOFMS) refers to an untargeted technique and enables to quantify organic (OC) and elemental carbon (EC), but also to investigate the molecular composition of the thermal sub-fractions related to OC (Diab et al., 2015). A filter punch of 0.5 cm2 was placed into thermal-optical carbon analyser (DRI model 2001a) and analysed following the

ImproveA protocol (Chow et al., 2007). During the four thermal subfractions of OC (OC1 to OC4) with upper temperature limits of 140 °C, 280 °C, 480 °C and 580 °C, approximately 8% of the total flow enters the TOFMS through a deactivated transfer capillary (inner diameter of 320 μm), which is connected to the carbon analyser oven by a modified quartz cross stepwise heated from 230 °C to 245 °C to prevent condensation (Grabowsky et al., 2011). In the ion source, UV radiation of 266 nm which is provided by the fourth harmonic generation of an Nd:YAG laser (Spitlight400, Innolas, Germany; 20 Hz repetition rate, 1064 nm fundamental radiation, energy of 1 mJ at 266 nm) ionises selectively desorbed aromatic constituents of the particles in the REMPI process. In addition to the optical selectivity, REMPI denotes a soft ionisation technique leading to predominantly molecular ions and low yields of fragments (Boesl, 2000). The generated ions were subsequently analysed by a TOFMS (compact reflectron time-of-flight spectrometer II, Stefan Kaesdorf Geräte für Forschung und Industrie, Germany) with a mass resolution of 1000 at m/z 78 and 1 s time resolution. Finally, the obtained mass spectra were summed according to their occurrence in the four fractions of OC. For targeted analysis, in-situ derivatisation direct thermal desorption gas chromatography time-of-flight mass spectrometry (IDTD-GCTOFMS) with electron ionisation (Orasche et al., 2011) was applied to quantify in total 105 particle-bound organic compounds covering the substance classes of polycyclic aromatic hydrocarbons (PAHs), oxygenated PAHs (OPAHs), hydroxy-PAH (OH-PAHs), polycyclic aromatic sulphur-containing hydrocarbons (PASH), anhydrous sugars, phenolic species, resin acids and phytosterols. The derivatisation is based on silylation with N-Methyl-N-trimethylsilyltrifluoroacetamide (MSTFA) during the step of thermal desorption from quartz fibre filter. Detected compounds were identified by library match of electron ionisation spectra as well as retention index and quantified by isotope-labelled internal standards of the same substance or chemically similar substances. 2.2.4. Inorganic PM2.5 constituents Samples of particulate matter were reconditioned prior to elemental analysis by inductively-coupled plasma optical emission spectrometry (ICP-OES, Spectro Ciros Vision, SPECTRO Analytical Instruments GmbH & Co., Germany) as described in Sections 1.1 to 1.3 of the Supplementary data. In total 19 elements, including Al, B, Ba, Bi, Ca, Cd, Cr, Cu, Fe, K, Li, Mn, Mo, Na, Pb, S, Ti, W and Zn were quantified and presented if at least 50% of the samples of one wood type contained concentrations higher than the limit of quantification. 2.2.5. Data analysis and statistics For all statistical analysis, including cluster analysis and analysis of variance (ANOVA), Matlab® basic functions and Statistic Toolbox (R2014b, The MathWorks Inc., Massachusetts, USA) was used. In case of significance at a level of 0.05, ANOVA was followed by Bonferroni correction method to avoid accumulation of type I error at multiple testing. If not otherwise indicated, reported p-values in the following refer to results of multiple comparison tests. A result is called significant if p-


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values appear below the significance level α of 0.05. A summary of fold changes and p-values for all comparisons between the four wood types is depicted in volcano diagrams (Figs. S1–S6). Compounds with concentrations below the limit of quantification in each single experiment for one wood fuel were not considered in the visualisation. 3. Results & discussion 3.1. Gaseous emissions Regarding the main gaseous constituents in the flue gas of the masonry heater, only marginal differences between the mean EFs of the different wood types could be observed, which is also reflected by the similar modified combustion efficiencies (MCE = ΔCO2 / (ΔCO2 + ΔCO; Δ denotes background correction) in the range of 0.983 (spruce) to 0.988 (birch). In contrast to that, the more controlled and continuous combustion in the pellet boiler resulted in about one order of magnitude lower emissions of CO and OGC, despite lower excess flue gas O2 concentration. Generally, emissions of NOx predominantly result from fuel nitrogen and appear in the range of 83 to 109 mg MJ−1, which is comparable to other wood stoves and pellet boilers of older types (Orasche et al., 2012). A comparison of the gaseous and VOC emissions from these two wood combustion appliances with literature data from logwood stoves, pellet stoves and boiler of older types was already described by Czech et al. (2016) and Czech et al. (2017). 3.2. Inorganic PM2.5 constituents Inorganic emissions from the wood combustion appliances are affected by the content of the respective element in the fuel, the fuel bed temperature, the volatility of the element and concentrations of possible binding partners. K, S, Na and Zn denote the most abundant inorganic particle constituents. In particular, the highest emissions of alkali, alkaline earth and transient metals were detected in emissions from birch wood and pellet combustion. In all multiple comparisons after significant ANOVA results, significant elevated EFs for Zn, Cd and W were found for birch wood with a fold change above 1.5. The pellet boiler operated on softwood pellets revealed significantly increased EFs for Na, K and S (Figs. S3, S5 and S6). Interestingly, Bi, Li and Mo were even only detected in particles from pellet combustion. On the contrary, Cd was only detected in the particles from logwood combustion although the Cd content of the woods was below the limit of quantification of 0.2 mg/kg for all investigated wood fuels. Of all analysed metals, only Zn showed a steady increase and significant linear correlation (r = 0.907, p = 1.2 · 10−6) between particlebound Zn emissions and increasing content in the fuel, independently from the combustion appliance (Fig. 1). Fractions of 19%, 37%, 22% and 18% of the fuel-Zn are released in particle emissions for beech, birch, spruce and pellets, respectively, which agrees well with earlier observations on Zn release from various wood-fired combustion appliances (Lamberg et al., 2011b; Sippula et al., 2017). Particulate Zn, solely present as ZnO in wood smoke particles, is of great interest because it has been found to be an important driver of cytotoxicity (Uski et al., 2015). For all possible comparisons, birch wood led always to significantly enhanced Zn emissions with a minimum fold change of 4. On that account, Zn emission can be lowered by preferring Zn-poor firewood or detaching the bark in which Zn is enriched (Wiinikka et al., 2013). K in PM2.5 emissions, which used as one indicator for biomass combustion in source apportionment studies (Watson et al., 2001), did not correlate with fuel-K. The release of K is generally affected by the availability and ratio of Si to K and S to Cl. These factors have been found to influence the partitioning of K between coarse-sized ashes (bottom ash and coarse fly ash) and fine fly ashes, which forms part of the fine particulate emission (Sippula et al., 2017; Sommersacher et al., 2012). A higher content of Cl in the wood fuel leads to the formation of more

Fig. 1. Scatter plot of the Zn (upper panel) and K (lower panel) content of the wood fuels beech (circles), birch (diamonds), spruce (triangles) and pellets (pentagrams) versus EF of Zn and K. Numbers in the lower panel refers to sulphur-to-chlorine ratio of the wood fuels.

volatile KCl, whereas higher fuel-sulphur favours the formation of less volatile K2(SO4) (Sippula et al., 2007; Sippula et al., 2008). In agreement with that, a significant moderate correlation (r = 0.55, p = 0.02) was found for the ratio of Cl to S and K. However, for some fuels only upper limits for the ratio of Cl to S could be used because of Cl concentrations below the limit of quantification (Table 1). Furthermore, the Si content of the fuel can decrease the release of K through formation of low-volatile potassium silicates, which remain in the bottom ash (Sommersacher et al., 2012). However, a correlation between the ratio of Si to K of the fuel and K in the emissions could not be found. 3.3. Targeted analysis of carbonaceous PM2.5 constituents 3.3.1. Organic and elemental carbon The combustion of all types of logwood in the masonry heater led to more than one order of magnitude higher emissions of both OC and EC compared to the pellet boiler (OC: 0.15 mg MJ−1; EC: 0.27 mg MJ−1). However, some significant differences were recognised between the logwood types. The combustion of the hardwoods beech and birch emitted more total carbon (TC) than spruce, but birch produced 47 mg MJ−1 EC compared to 28 mg MJ−1 EC for beech and only 13 mg MJ−1 EC for spruce. Regarding OC, a three-fold higher EF was found for beech (20 mg MJ−1) compare to the equal EFs for birch and spruce (6.1 mg MJ−1). Generally, carbonaceous particulate emissions were substantially lower compared to wood combustion appliances without air-staging (Calvo et al., 2015; Orasche et al., 2012) and comparable to other advanced stoves with primary, secondary and postcombustion air supply (Tschamber et al., 2016). The ratio of OC to EC (OC/EC) has been widely applied in atmospheric sciences for the apportionment of emission sources, such as to biomass burning, combustion of fossil fuel or SOA. In particular, the minimum OC/EC plays an important role to estimate the amount of SOA on ambient particles while OC/EC b 1 was associated with fossil fuel combustion (Pio et al., 2011). Although it has been demonstrated that the air-staging of the investigated modern masonry heater has a

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only small effect on the emission of total carbon in PM2.5 (Lamberg et al., 2011a; Nuutinen et al., 2014), the data reveals consistently OC/ EC b 1 for each single combustion experiment, which might lead to misinterpretation of ambient air studies in areas where this type of wood stoves are used. Moreover, the combustion of birch logs generated particles with the lowest OC/EC of 0.13 ± 0.02, which is significantly different compared to beech logs (0.71 ± 0.14) and pellets (0.65 ± 0.23). Compared to spruce (0.48 ± 0.13), the difference of OC/EC from birch logs was not significant, but substantial (p = 0.058). In this context it must be mentioned that the amount of carbonates (inorganic carbon, IC), which were not analysed in this study, may bias the OC/EC results. In particular for the pellet boiler IC can cover the same range as OC or EC (Lamberg et al., 2011a; Lamberg et al., 2011b). However, in TOCA the contribution of IC either to OC or EC depends on the decomposition temperature and therefore on the cation of the carbonate. 3.3.2. Anhydrous sugars Among all anhydrous sugars, levoglucosan denotes the most relevant one because of its established application as marker compound for biomass combustion (Simoneit, 2002). It originates from the thermal decomposition of cellulose (Shafizadeh, 1968) and generally refers to one of the most abundant organic compounds in wood combustion particles. Despite the large differences in mean EFs of levoglucosan between the combustion experiments, ranging from 113 μg MJ− 1 (beech) to 10 μg MJ− 1 (pellet), no significance was identified by ANOVA. The highest concentrations of the levoglucosan are emitted during the ignition of the wood (Elsasser et al., 2013), which is concurrently the part of the combustion with the highest variation and might give an explanation of this finding. It was observed that logs of birch ignited faster than the other two logwoods and showed the lowest amount of residues in the chamber before the introduction of the consecutive batch, which supports the explanation. However, the percentage of levoglucosan to the total particle mass in the presented experiments was generally very low. Although total PM2.5 mass was not determined, it can be estimated by assuming that TC represents approximately 50% of PM2.5. Hence, levoglucosan accounts for b 0.3% of PM2.5 for all particle emissions from the masonry heater, which is very low compared to the literature data (Orasche et al., 2012; Schmidl et al., 2008; Vicente et al., 2015a) and might be a feature of the applied combustion technology and associated higher temperatures in the firebox. In this context, only the pellet boiler led to comparable results to previous studies with 2.7% levoglucosan in PM (Orasche et al., 2012; Vicente et al., 2015b). However, please note that Vicente et al. (2015a), Vicente et al. (2015b) and Schmidl et al. (2008) sampled PM10 instead of PM2.5 as in this study which might affect the results as single analytes can vary over size fractions of combustion particles (Kleeman et al., 2008). Although wood combustion PM2.5 from the two appliances of this study even appear solely in the fraction of PM1, it is not known for stoves of previous studies, so a direct comparison was omitted. Nevertheless, the obtained ratios of levoglucosan to OC, quantified from PM2.5, of 9% for beech logwood (Calvo et al., 2015) and 19% and 10% for beech and spruce logwood (Orasche et al., 2012) from conventional stoves were substantially higher than for the modern masonry heater (beech: 0.58%; birch: 0.88%; spruce: 1.18%), which supports our findings. The EFs of the further quantified anhydrous sugars, mannosan and galactosan, were one order of magnitude lower than levoglucosan in all experiments. Mannosan is released from the decomposition of hemicellulose, thus the ratio of levoglucosan to mannosan (LGS/MNS) has been proposed to differentiate between hardwood and softwood combustion. Hard- and softwoods contain different amounts of cellulose and hemicellulose, which is also reflected in the emission. LGS/MNS of 15.0 ± 7.1 for beech, 19.3 ± 13.5 for birch and 8.5 ± 2.4 for spruce reveal the same trend as previously reported, but with higher standard deviations (Schmidl et al., 2008). In addition to that, this concept does

not work for automatically-fired combustion appliances even though levoglucosan and mannosan could be detected (Schmidl et al., 2011). For the pellet boiler, LGS/MNS of 24.8 ± 8.1 exceeded the higher LGS/ MNS for hardwood although the pellets were solely comprised of softwood. Galactosan was the lowest abundant anhydrous sugar without significant differences between the three logwoods. Regarding the pellet boiler particles, galactosan concentrations in each single combustion experiment were found below the limit of quantification. 3.3.3. Phenolic species The EFs of 23 quantified phenolic species cover a range from below limit of quantification to 36 μg MJ−1 with molecular patterns according to the current knowledge of lignin monomers in different wood species. Syringyl and sinapyl alcohols and their derivatives on particulate emissions are associated with hardwoods, whereas softwood burning emissions contain higher relative amounts of coniferyl-derived compounds. Guaiacol as another abundant building block of the wood is emitted by both wood families in equal amounts (Simoneit, 2002). These three basic rules also hold for the performed experiments. Concerning total particle-bound phenolic species, beech wood showed the highest EF (72 μg MJ− 1), followed by birch (20 μg MJ− 1), spruce (8.4 μg MJ−1) and pellets (0.59 μg MJ−1). In the pellet emissions only 5 of the 23 presented phenolic species appeared above the limit of quantification, which complicates the identification of pellet boiler emissions in atmospheric studies. Furthermore, the question of possibly enhanced toxicity of the released particles is raised because phenolic species are known as antioxidants and scavengers of reactive oxygen species. Thus phenolic species could hypothetically mitigate the overall toxicity of harmful substances (Kjällstrand and Petersson, 2001). For atmospheric studies and a comparison between the investigated stoves with stoves of older design, the relative amount of phenolic species to other particle constituents is more important than the absolute quantity. Therefore, the total phenolic content of the particles was normalised to OC. Beech, birch and pellet combustion featured similar, but significantly higher contents of phenolic species per OC (3.7 mg/gOC, 3.4 mg/gOC and 3.9 mg/gOC) than spruce (1.7 mg/gOC). In comparison with combustion appliances without air-staging (Table 3), absolute emissions of particle-bound phenolic species and phenolic species per OC from both masonry heater and pellet boiler were one order of magnitude lower. Although different combustion scenarios are considered, the comparison can be regarded as conservative because only the hot ignition of three consecutive batches, known for lower emissions of phenolic species than cold starts (Orasche et al., 2012), was taken into account, whereas the combustion in this study included six consecutive batches and a char-burning phase, but a cold start as well. Moreover, the analysed 23 phenolic species were the most abundant ones in this study. Therefore, the emissions of these two stoves with air-staging secondary air supply represent a different type of emission pattern, which becomes even more relevant since combustion technologies in smallscale appliances are currently developing and old stoves are constantly replaced by more efficient modern stoves. 3.3.4. Resin acids and phytosterols The detected resin acids comprise derivatives and thermal degradation products of abietic acid and its isomers pimaric and isopimaric acid. These resin compounds are known to be mainly released during the start phase of the combustion of coniferous wood (Orasche et al., 2012). Of all resin compounds, dehydroabietic acid was the most abundant compound with a mean EF of 42.12 μg MJ−1 for spruce wood, but partially oxidised derivatives and products from elimination reactions of abietic acid were present as well. Small amounts of dehydroabietic acid were also found on particles from birch wood combustion (8.56 μg MJ−1), which raise suspicion that these EFs were caused by carryover effects. However, this EF results from two of four birch wood combustion experiments which were not carried out directly after spruce


wood. Furthermore, a porous-tube dilutor, which was cleaned every day, was used in the sampling instead of a dilution tunnel which was proposed to minimise carry-over effects (Orasche et al., 2012). Therefore, we regard the detected quantities as truly abundant in the birch particulate emissions. In addition to spruce, one characteristic substance was found for each of the investigated wood fuels. β-Sitosterol was only abundant in the particulate emissions of the pellet boiler (10.4 μg MJ−1) in two of three experiments. According to the manufacturer, the main material of the pellets was spruce and pine wood, but concentrations of βsitosterol occurred below the limit of quantification in every single combustion experiment of spruce logwood. Substantial quantities of this triterpenoid were measured on particles from the combustion of different pine species (Oros and Simoneit, 2001a), thus the emission of βsitosterol is likely associated with content of pine wood of the pellets. Lupa-2,22(29)-dien-28-ol refers to an thermal alteration product of betulin, which is one of the major components in essential oil of birch wood, especially in its bark (Laszczyk et al., 2006). Its EF accounts for 29.0 μg MJ− 1 for birch wood, but some smaller quantities could be also detected for spruce wood. For beech, substantial amounts of stigmasta-3,5-dien-7-one, an alteration product of stigmastan, was found with an EF of 173.90 μg MJ−1. Minor amounts (5.41 μg MJ−1) were also detected for birch, which belongs to deciduous trees as well as beech, that this stigmastan and derivatives were previously proposed as marker for this type of woods (Gonçalves et al., 2011). It must be added that the analysed resin acids and triterpenoids only represent a minor part of the total alteration products of abietic acid, betulin and stigmastan (Oros and Simoneit, 2001a; Oros and Simoneit, 2001b). On that account, marker substances for specific wood species or classes cannot be derived. 3.3.5. Parent and alkylated PAH 3.3.5.1. Emission profiles and factors. In all combustion experiments, phenanthrene, pyrene and fluoranthene were the most abundant PAHs and accounted from 48.1% (spruce) up to 73.0% (pellets) of the total analysed PAHs. However, the gas-particle partitioning of especially these PAHs is sensitive to even small changes in sampling temperature, which could have affected the presented EFs for particle-bound PAHs. Although significant differences could be found between the logwoods, the combustion of spruce logs slightly shifted the emission pattern towards PAHs of higher molecular weight, which might be caused by pyrolysis of resin components. Additionally, a higher degree of alkylation can be observed for spruce wood which is relevant for the development of diagnostic ratios for source apportionment (Tobiszewski and Namieśnik, 2012). A more detailed discussion on aromatic profiles can be found in Section 3.4. The EF of total PAHs from the pellet boiler (0.89 μg MJ−1) was more than one order of magnitude lower than from the masonry heater fuelled with any logwood (beech: 35.31 μg MJ− 1; birch: 32.31 μg MJ−1; spruce: 22.16 μg MJ−1), which emphasises the efficiency of this combustion appliance. Generally, the EFs of particle-bound PAH were one to two orders of magnitude lower compared to conventional wood stoves and pellet boilers (Orasche et al., 2012), but similar to other modern masonry heaters (Tschamber et al., 2016), which also agrees with the trend of volatile aromatic emissions investigated in previous studies (Czech et al., 2017; Czech et al., 2016). 3.3.5.2. PAH toxicity equivalents (PAH-TEQ). PAHs adsorbed on particulate matter are well-known for their carcinogenic and mutagenic activity (Lewtas, 2007). On that account, the approach of PAH toxicity equivalent (PAH-TEQ) was developed to assess the health risk potential at workplace exposures by multiplication of single PAH concentrations or EFs with its corresponding toxicity equivalent factor (TEF). TEFs are related to benzo[a]pyrene which accounts for 1 and taken from the

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German Research Foundation as described in Orasche et al. (2012). Finally, the TEQs of single PAHs were summed up to the total PAH-TEQ for a single combustion experiment. PAH-TEQ EFs of logwood combustion range from 0.57 μg MJ−1 to 1.4 μg MJ−1 without significant differences between the investigated wood types. Similar to the PAH-EFs, PAH-TEQ EFs of the pellet boiler were almost two orders of magnitude lower than the logwoods of this study with values from 0.019 μg MJ−1 to 0.034 μg MJ−1. These findings are in line with other small-scale wood stoves with advanced combustion technology and more than one order of magnitude lower than conventional stoves (Fig. 2) and agree with the general trend of decreasing TEQ-EFs for automatically-fired wood combustion appliances. However, the concept of PAH-TEQ only considers the abundance of PAHs and does not include other harmful compounds or even easing synergetic effects, such as the influence of antioxidants. 3.3.6. PAH-derivatives In this section, aromatic hydrocarbons with heteroatoms within or at the periphery of the core structure are discussed. The most abundant hydroxyl- and oxy-PAHs (OH-PAHs and OPAHs) were denoted by 1,8naphthalic anhydride, 9H-fluoren-9-one, 1-hydroxynaphthalene and 9,10-anthracenedione. All OH\\ and OPAHs were significantly lower in the pellet boiler particle emissions (1.90 μg MJ−1 in total) or appeared below the limit of quantification, whereas differences between the logwood were less pronounced. Only for spruce significantly elevated EFs were observed for 7H-benzo[c]fluoren-7-one, 11H-benzo[b]fluoren11-one and 7H-benzo[de]anthracen-7-one compared to birch. Nevertheless, the highest EFs for total OH\\ and OPAH were obtained for beech (63.4 μg MJ−1) with almost equal EFs for birch (40.9 μg MJ−1) and spruce (40.4 μg MJ−1). Dibenzothiophene was the only sulphurcontaining polycyclic aromatic compound (PASH) found on the particles with substantial increased EFs for birch wood. Compared to conventional stoves and pellet boilers, the total emission of OH\\ and OPAH was decreased by more than one order of

Fig. 2. EFs of PAH-TEQ values for different wood combustion appliances. The dashed line separates results of this study (left) from literature data (aTschamber et al., 2016; b Orasche et al., 2012) (right). WABI and XP54-IN denote wood stoves with modern combustion technology, while pellet and LS (logwood stove) are conventional wood combustion appliances at cold start (CS) or nominal load (NL). Error bars represent minimum and maximum PAH-TEQ of repeated experiments. The results demonstrate that modern technology for batchwise wood combustion not only reduces EFs by one order of magnitude, but also the toxicological potential of the emissions related to the same heating values of the wood fuels. PAH-TEQ EFs for the pellet boiler are even reduced by an additional order of magnitude.

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magnitude. Additionally, their distribution was shifted towards compounds of lower molecular weight for the same type of fuel wood (Orasche et al., 2012). Therefore, the differences in OH\\and OPAH patterns might be explained by the level of combustion technology. 3.4. Untargeted analysis of particle-bound aromatic compounds by TOCAREMPI-TOFMS An approach for the rapid characterisation of aromatic compounds in thermal fractions of particulate matter offers the hyphenation of resonance-enhanced multi-photon ionisation time-of-flight mass spectrometry (REMPI-TOFMS) to thermal/optical carbon analysis (TOCA). The first two OC-fractions defined by the ImproveA protocol for carbon analysis were combined because it is assumed that organic particle constituents predominantly evaporate without decomposition. In OC3 and OC4, pyrolysis occurs which shifts the mass spectrum towards lower m/ z (Diab et al., 2015). In Fig. 4, mean REMPI mass spectra for each wood fuel are illustrated with labels of the most important homologue alkylation series of phenanthrene (m/z 178), pyrene (m/z 202), benzo[a]anthraces/ chrysene (m/z 228) and dibenzofuran (m/z 168) with m/z increments of + 14. Although response factor (photoionisation cross sections) can vary substantially (Boesl, 2000), peak intensities reflected the relations between the wood fuels described by the quantitative results from IDTD-GCMS. Highest peak intensities were observed for phenanthrenes and pyrenes with its respective maximum at the parent PAH and a steady decay towards higher degrees of alkylation, which was also found for particles from other wood stoves (Bente et al., 2008; Diab et al., 2015) and volatile PAH emissions as well (Elsasser et al., 2013). However, a slight increase from methyl- to C2-phenanthrene (sum of all isomers of dimethyl- and ethyl-phenanthrene) can be observed in beech-, birch- and pellet-derived particles, which is possibly an issue of mass interference with another isobaric compound of m/z 206, e.g. phenanthrene-9-carboxaldehyd. Furthermore, the thermal degradation of abietic acid from spruce wood leads to the formation of retene (m/z 234), but to lower alkylated phenanthrenes as well (e.g. pimanthrene (Simoneit, 2002)), so the findings from other PAH homologue series cannot be transferred to phenanthrenes. In contrast to distinct abundancies of phenolic species in PM2.5 of conventional wood stoves (Diab et al., 2015), the absence of such m/z supports the results from IDTD-GC/MS measurements and the general low emissions of the modern masonry heater. In the m/z range of the spectra, which can be regarded as fingerprint region for the wood types, peaks up to m/z 400 were still detected. Although reliable assignments to molecular structures were not possible, the number of potential formula can be reduced by the constraints that the detected compound must be an aromatic constituent of wood smoke due to the ionisation selectivity. Hence, it is hypothesised that the peaks in the m/z range from 300 to 400 (small panels on Fig. 3) belong to lignans, i.e. dimers of monomers derived from lignin, or alteration products of steroids and triterpenoids, which may become aromatised under thermal stress and thus become accessible for REMPI. The latter proposed compound class seems to be more likely because substantially more peaks were detected for birch wood combustion. In contrast to the other wood fuels, birch contains high amounts of triterpenoids, such as betulin or betulinic acid, in its bark (Laszczyk et al., 2006), which leads to a variety of possible thermal alteration products (Oros and Simoneit, 2001b) and may justify the higher number of peaks in the upper m/z range. In the pyrolysis fraction OC3 (Fig. S7), a shift towards lower m/z, including the appearance of volatile aromatic compounds such as naphthalene or phenol, could be observed as expected because of the decomposition of larger molecular structures. Apart from retene as an indicator for the combustion of coniferous wood, no specific peaks or aromatic patterns were found. The obtained mass spectra of OC4 were

poor in peak number and intensity and therefore were not considered further. In the preceding sections, the discussion of the two experiments with slow ignition, spruce* and birch*, were omitted because of differences in aromatic patterns. With all combustion experiments, a hierarchical cluster analysis was performed based on unweighted pair group method with arithmetic means (Sokal and Rohlf, 1962) with coefficient of congruence as distance metric (Abdi, 2010). Three clusters were obtained and assigned to spruce, pellets and the hardwoods birch and beech (Fig. 4). Despite the correct assignment of spruce*, its distance was remarkably larger than between the other spruce combustion experiments. Moreover, birch* appeared isolated from the other birch combustion experiments closer related to the pellets. Both spruce* and birch* emitted higher amounts of OGC during the first of the six batches, which prompts an inappropriate or slow ignition. In a previous study, the molecular composition of the VOCs during the first batch of spruce* revealed higher quantities of typical primary decomposition products from lignin or carbohydrates, e.g. coniferyl alcohol and levoglucosenone, as it can be observed when combustion efficiency decreases (Czech et al., 2016). Due to the fact that these combustion events of a relatively short period remarkably affect the emissions of the entire 4 h experiment, spruce* and birch are separately discussed in the following section. 3.5. Effect of slow ignition 3.5.1. Spruce wood The REMPI spectrum of spruce* with slow ignition showed a higher number of peaks, a broader m/z range and a different pattern than the spruce wood experiments with proper ignition (Fig. 5, top). Parent PAHs were less dominating the homologue series so that the general degree of alkylation is higher than in the regular combustion experiments. Moreover, larger PAHs, such as the homologue series of benzopyrenes and benzoperylenes, appeared with higher abundances. Many peaks were located between the signals of PAH homologue series and likely belong to monomers of the lignin decomposition, which are increasingly released at lower combustion temperature, i.e. during smouldering (Kjällstrand and Petersson, 2001). Additionally, abietic acid (m/z 302) undergoes aromatisation of its saturated rings through thermal alteration and become accessible for REMPI, for example as dihydroretene or simonellite (Oros and Simoneit, 2001a). Peaks of m/z N 302 may be assigned to lignans or larger PAHs as discussed in a previous section. Generally, the observed higher intensities for spruce* compared to the regular experiments were confirmed by the higher EFs for several organic compounds from IDTD-GCMS analysis. With the EFs of Table 2, a Dean-Dixon outlier test (at α = 0.05) was performed to identify significantly increased or decreased substances for slow ignition. Dean-Dixon test was chosen because it does not assume normal distribution of the data and is generally recommended for small sample sizes (Dean and Dixon, 1951). In addition to previously mentioned enhancement of OGC, a variety of PAHs, OH\\ and OPAHs, among others benzo[a]anthracene, benzo[a]pyrene and 7H-benzo[de]anthracen-7-one, were identified as outliers with increased EFs. Therefore, it was not surprising that the PAH-TEQ EF were changed by a factor of six (8.82 μg MJ− 1) compared to the regular spruce combustion (1.44 μg MJ− 1 ). Furthermore, primary decomposition products from lignin, such as vanillic acid, coniferyl aldehyde and guaiacylacetone, were more prominent in spruce*, in agreement with the REMPI mass spectra, as well as the anhydrous sugars levoglucosan and mannosan. Regarding inorganic emissions, spruce* was the only spruce combustion experiment in which chromium was found above the limit of quantification. However, on the basis of the presented data this phenomenon could not be explained, but since chromium does not belongs to volatile metals in wood combustion, contamination is assumed. Further inorganic particle constituents were not affected by the slow ignition.


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Fig. 3. Mean REMPI mass spectra of summed intensities of OC1 + OC2 for the four investigated wood types. Symbols denote the most abundant series of parent PAHs and alkylated homologues, namely dibenzofuran (green triangles), phenanthrene (red squares), pyrene (cyan stars) and chrysene/benzo[a]anthracene (blue circles). Panels in the upper right corner belong to fingerprint regions of the wood types, which contain alteration products of phytosterols and lignans and enable wood type identification despite similar alkylation patterns. Figures with logarithmic y-scale can be found in the Supplementary data (Fig. S8). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Out of nine quantified components or alteration products of the spruce wood resin, four (isopimaric acid, abietic acid, dehydroabietic acid and dehydroabietic acid methyl ester) were found as outliers with higher abundance than in the regular spruce wood combustion and one (7-oxo-abietic acid) with lower abundance. Isopimaric acid and abietic acid were even not detected in each of the regular spruce combustion experiments, but with EFs of 73.27 μg MJ−1 and 435.58 μg MJ−1 in spruce*, suggesting an enhanced distillation-like release during the start phase of the first batch (Orasche et al., 2012). In contrast, the absence of 7-oxo-abietic acid in spruce* implies its formation during higher temperatures sufficient to partially oxidise abietic acid. Due to the fact that pimaric acid, a primary resin constituent as well, were only found in the regular spruce combustion experiments, a possible heat-induced isomerisation between pimaric, isopimaric and abietic acid might occur. All together, these results emphasise the necessary detection of many possible resin acids and their alteration products if quantitative conclusion are drawn in source apportionment studies since the abundance of a single markers strongly depends on the combustion condition. Fig. 4. Dendrogram from cluster analysis of REMPI mass spectra from OC1 + OC2 using the unweighted pair group method with arithmetic means and coefficient of congruence as distance metric. Experiments spruce* and birch* were regarded as outliers and separately discussed in the Section 3.5.

3.5.2. Birch wood Although birch* was classified as slow ignition experiment as well, it exhibited a different emission pattern than spruce* (Fig. 5, bottom). In this case, the slow ignition caused a distinct shift towards higher m/z

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Fig. 5. Mean REMPI mass spectra of summed intensities of OC1 + OC2 for combustion experiment with slow ignition. Symbols denote the homologue series of dibenzofuran (green uppointing triangles), phenanthrene (red squares), pyrene (cyan stars), chrysene/benzo[a]anthracene (blue circles), benzpyrene (magenta diamonds) and benzo[ghi]perylene (black downpointing triangles). Panels in the upper right corner belong to fingerprint regions of the wood fuels, which contain lignans and alteration products of phytosterols. Although both combustion experiments reveal a shift towards larger PAH emissions, the spectrum of spruce* contains numerous peaks, possibly from thermal resin alteration, while for birch* emissions parent PAHs are more pronounced in the alkylation series. Figures with logarithmic y-scale can be found in the Supplementary data (Fig. S9). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

and an elevated contribution of parent PAHs to their homologue series. Moreover, pyrene (m/z 202) replaced phenanthrene (m/z 178) as the most abundant PAH. However, apart from PAH homologue series the number of peaks and peak intensities were low, indicating that primary decomposition products from lignin were not or less elevated in this combustion scenario. Therefore, birch* was located closer to the pellet boiler particles in the cluster analysis (Fig. 4). Also in this case the findings from the REMPI mass spectra agree with the results obtained from IDTD-GCMS analysis and Dean-Dixon test. 25 PAHs, among others the carcinogenic benzo[a]pyrene, dibenz[ah]anthracene and anthanthrene, and seven alkylated PAHs were found to be statistical outliers compared to the regular birch combustion experiments, which accompany the 14-fold increased PAH-TEQ (birch*: 11.3 μg MJ−1; mean regular birch: 0.82 μg MJ−1). Additionally, 12 OPAHs with two to five rings were classified as outliers as well with fold changes between 5 and 34, which is more pronounced than for spruce*. The majority of the 12 OPAHs and 25 PAHs were found to correlate well and significantly with the formation of reactive oxygen species (Sklorz et al., 2007) and therefore OPAHs are of toxicological interest although they are not considered in the TEQ concept. With the Dean-Dixon test primary decomposition products were not identified as outliers except syringol, so birch* deals with a different influence of the slow ignition than spruce*. However, it could not be concluded whether the different emission patterns belong to features of the wood species or combustion conditions because of the number of experiments. 4. Conclusion In this comprehensive study of wood combustion PM2.5 from two modern small-scale combustion appliances (masonry heater and pellet boiler) equipped with air staging, several emission factors for organic and inorganic particle constituents were presented and compared to other state-of-the-art and conventional wood stoves. Generally, masonry heater emissions appeared in the same order of magnitude as for a woods stoves equipped with primary, secondary and post-combustion air (Tschamber et al., 2016), but more than one order of magnitude lower than the conventional wood stove (Orasche et al., 2012). However, the pellet boiler even reduces carbonaceous emissions by an additional order of magnitude. Pellet boiler PM2.5 is mainly comprised of

inorganic constituents, which differ qualitatively and quantitatively from the investigated masonry heater emissions. For both modern masonry heater and pellet boiler, wood-specific combustion products from the decomposition of lignin and carbohydrates were detected, but substantially lower compared to conventional wood stoves and pellet boilers. Although the major single component of organics in the PM2.5 was levoglucosan, its contribution to OC was found at the lower limit described in the literature, which also holds for phenolic species. Conversely, the higher relative amount of general products of incomplete combustion, such as parent PAHs and OPAHs, complicates the assignment of this type of emissions to wood combustion in atmospheric source apportionment studies. This holds especially for CMB modelling and simpler approaches, such as the estimation of wood smoke mass from ambient levoglucosan concentrations (Schmidl et al., 2008), but also for PMF factor identification as tracers for wood combustion are substantially reduced (Bari et al., 2009). This might become a more important issue in the future because of the rather long period of use for wood stoves and boiler. Especially the pellet boiler lacks wood combustion markers, but β-sitosterol was found to be only component which was more abundant in the pellet boiler emissions than in the modern masonry heater emissions. However, β-sitosterol cannot be regarded as marker for pellet emissions because of its high dependency on the wood type. Nevertheless, our results provide comprehensive emission profiles of modern wood combustion appliances which can be already used in chemical mass balances for areas of northern Europe. With the hyphenation of thermal/optical carbon analysis to resonance-enhanced multi-photon ionisation time-of-flight mass spectrometry (TOCA-REMPI-TOFMS), differences in aromatic fingerprints and PAH alkylation series of the different PM2.5 samples were discussed. Moreover, it was shown that slow ignition of the first batch in batchwise logwood combustion increased EFs of single species in different manners and also shifted the aromatic pattern to larger structures. Slow ignition could be linked to elevated PAH-TEQ values as well as different quantities of phytosterols and the distribution of their thermal alteration products, which are used as wood-specific markers. Finally, emission source apportionment should be connected to more sophisticated toxicological approaches instead of simple concepts of PAH-TEQ. Due to the presence of substantial amounts of antioxidants in the wood smoke which may counteract harmful effects on human health by single particle constituents, further studies under realistic


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Table 2 Mean emission factors (EFs) and concentrations of gaseous analytes, particulate carbons, inorganic elements, PAHs, PAH-derivatives, phenolic species, resin constituents and sterols as well as toxicity equivalents (TEQ) and modified combustion efficiency (MCE) from regular combustion experiments (modern masonry heater: beech, birch and spruce; pellet boiler: softwood pellets) and combustion experiments with slow ignition (modern masonry heater: spruce* and birch*; see Section 3.5). Minimum and maximum EFs are listed in Table S2. EFs can be related to the amount of burned fuel by using respective net heating values from Table 1. Entries of b.l.q. refer to EFs below the limit of quantification. Unit Number of experiments n

Beech

Birch

Spruce

Pellet

Spruce*

Birch*

4

4

3

3

1

1

Gaseous analytes O2 CO2 CO OGC NOx MCE

% % mg MJ−1 mg MJ−1 mg MJ−1 []

16.8 3.91 1320 24.0 110 0.987

16.0 4.55 1430 15.4 104 0.988

15.9 4.38 1640 20.1 83.0 0.983

12.0 8.55 42.4 1.25 89.0 0.999

16.1 4.48 1290a 45.0a 83.1 0.986a

15.5 4.56 1230 18.6 99.9 0.984a

Particulate carbon OC EC TC

mg MJ−1 mg MJ−1 mg MJ−1

20 27 47.0

6.1 47 53.1

6.1 13 19.1

0.15 0.27 0.42

11 17 28.0

5.7 34 39.7

Inorganic particle constituents Al B Ba Bi Ca Cd Cr Cu Fe K Li Mn Mo Na Pb S Ti W Zn

μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1

45.1 3.64 1.63 b.l.q. 83.7 0.97 2.42 15.5 20.5 1630 b.l.q. 4.75 b.l.q. 51.2 9.80 393 0.90 b.l.q. 53.5

82.5 5.45 1.67 b.l.q. 89.1 2.49 3.78 6.36 38.2 2050 b.l.q. 7.51 b.l.q. 52.6 8.89 370 1.62 13.0 683

39.1 4.35 1.94 b.l.q. 101.7 0.22 b.l.q. 4.18 29.0 1600 b.l.q. 7.41 b.l.q. 53.7 b.l.q. 337 1.16 3.76 302

22.3 5.31 1.16 15.74 24.2 b.l.q. 1.68 7.65 12.1 3010 4.95 9.31 0.84 924 1.21 754 0.47 2.80 139

38.5 b.l.q. 1.40 b.l.q. 51.3 b.l.q. 8.38a 6.52 21.8 1120 b.l.q. 4.80 b.l.q. b.l.q. b.l.q. 301 1.03 7.44 269

30.4 3.92 1.17 b.l.q. 109 1.59 2.17 11.7 18.3 1440 b.l.q. 5.99 1.40a 49.1 6.44 266 1.87 9.49 513

Particle-bound PAH Phenanthrene Anthracene Fluoranthene Acephenanthrylene Pyrene Benzo[a]fluorene Benzo[b]fluorene Benzo[c]phenanthrene Benzo[ghi]fluoranthene Benz[a]anthracene Cyclopenta[cd]pyrene Chrysene 2,2′-Binaphthalene sum Benzo[b,j,k]fluoranthene Benz[e]pyrene Benz[a]pyrene Perylene Indeno[1,2,3-cd]fluoranthene Benzo[b]triphenylene Indeno[7,1,2,3-cdef]chrysene 1-Phenylpyrene Dibenz[ah]anthracene Indeno[1,2,3-cd]pyrene Benzo[b]chrysene Pentaphene Picene Benzo[ghi]perylene Anthanthrene Coronene Sum PAH PAH-TEQ

μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1

9.34 1.27 7.65 0.63 6.74 0.43 0.43 0.37 1.78 0.75 b.l.q. 1.38 b.l.q. 2.02 0.78 0.88 0.03 b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. 0.33 b.l.q. b.l.q. b.l.q. 0.50 b.l.q. b.l.q. 35.3 1.26

11.4 1.28 6.38 0.71 5.28 0.14 0.20 0.41 1.69 0.55 0.10 1.14 b.l.q. 1.31 0.48 0.55 0.02 0.24 b.l.q. b.l.q. b.l.q. b.l.q. 0.18 b.l.q. 0.04 b.l.q. 0.18 b.l.q. b.l.q. 32.4 0.818

2.62 0.42 4.47 0.33 3.56 0.46 0.55 0.34 1.38 0.83 b.l.q. 1.46 0.09 2.24 0.88 0.97 0.10 b.l.q. 0.01 0.04 0.01 0.04 0.48 0.01 0.01 b.l.q. 0.69 0.02 0.15 22.2 1.44

0.25 0.02 0.22 0.01 0.18 0.01 0.01 0.01 0.03 0.01 b.l.q. 0.03 b.l.q. 0.04 0.02 0.02 b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. 0.02 b.l.q. 0.01 0.9 0.028

1.30 b.l.q. 3.40 1.16 4.62 1.52 1.33 1.23a 4.31 4.73a 6.75a 5.00a b.l.q. 11.6a 3.86a 5.81a 1.01a 0.12a 0.10a 0.57a b.l.q. 0.23 1.75 b.l.q. b.l.q. 0.31a 3.23 0.50a 1.3a 65.8 8.82a

16.6 b.l.q. 20.6a 4.08a 20.1a 3.15a 1.69a 2.33a 10.1a 5.99a 2.71a 10.2a 0.48a 14.2a 5.68a 7.77a 1.07a 0.06 0.07a 0.33a 0.10a 0.46a 3.6a 0.08a 0.06 0.04a 5.51a 0.40a 1.9a 139.3a 11.3a

Particle-bound OH-PAH, OPAH & PASH 1-Hydroxynaphthalene 2-Hydroxynaphthalene 1,8-Dihydroxynaphthalene

μg MJ−1 μg MJ−1 μg MJ−1

7.5 0.38 b.l.q.

1.5 0.61 b.l.q.

1.7 0.44 b.l.q.

1.0 b.l.q. b.l.q.

b.l.q. b.l.q. 1.4a

2.5 2.8 b.l.q.

(continued on next page)

646


Table 2 (continued) Unit Number of experiments n

Beech

Birch

Spruce

Pellet

Spruce*

4

4

3

3

1

Birch* 1

1-Naphthalenecarboxaldehyde 2-Naphthalenecarboxaldehyde 1(2H)-Acenaphthylenone 9H-Fluoren-9-one 1H-Phenalen-1-one Xanthone 9,10-Anthracenedione Benzo[b]naphtho[1,2-d]furan Cyclopenta(def)phenanthrenone 1,8-Naphthalic anhydride Benzo[b]naphtho[2,1-d]furan 2,3-5,6-Dibenzoxalene Benzo[b]naphtho[2,3-d]furan Benzo[kl]xanthene 11H-Benzo[a]fluoren-11-one 7H-Benzo[c]fluorene-7-one 11H-Benzo[b]fluoren-11-one 7H-Benzo[de]anthracen-7-one Naphtho[2,1,8,7-klmn]xanthene Benz[a]anthracene-7,12-dione 5,12-Naphthacenedione Phenanthro[3,4-c]furan-1,3-dione 6H-Benzo[cd]pyren-6-on Dibenzothiophene

μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1

b.l.q. 0.08 0.07 6.9 3.3 3.5 3.9 1.8 3.5 27 1.2 0.35 0.37 0.33 0.20 0.09 0.45 0.83 0.46 b.l.q. b.l.q. 0.09 1.8 0.01

0.10 0.35 0.07 10 2.2 2.0 3.1 0.22 2.5 15 0.46 0.19 0.11 0.16 0.07 0.03 0.11 0.24 0.54 0.15 b.l.q. b.l.q. 0.73 0.06

b.l.q. b.l.q. 0.05 5.1 3.6 2.8 5.2 0.44 3.6 8.0 0.92 0.37 0.37 0.38 0.43 0.14 1.0 1.4 0.55 0.6 b.l.q. b.l.q. 3.3 0.01

b.l.q. b.l.q. b.l.q. 0.30 0.07 0.08 0.10 0.19 0.08 b.l.q. 0.02 0.01 0.01 b.l.q. 0.01 b.l.q. 0.01 0.01 0.01 b.l.q. b.l.q. b.l.q. 0.02 b.l.q.

b.l.q. b.l.q. 0.02 0.88 20a 3.9 6.6 b.l.q.a 3.4 50a b.l.q. b.l.q. b.l.q. b.l.q. 1.4 b.l.q. 2.8 5.0a b.l.q. 0.31 b.l.q. b.l.q. 3.0 b.l.q.

0.26 0.55 0.28 17 20a 6.0 17a 0.43a 17 49.7 4.6a 1.1 1.7 1.6a 1.4a 0.54a 3.8a 7.2a 1.8 1.1 0.21a b.l.q. 15a 0.13

Particle-bound alkylated PAH 9-Methylphenanthrene 3,6-Dimethylphenanthrene Retene Sum 2-/8-methylfluoranthene Sum 1-/3-/7-methylfluoranthene 4-Methylpyrene 2-Methylpyrene 1-Methylpyrene 4,5-Dimethylpyrene 2,3,6,7-Tetramethylanthracene 1-Methyl-benz[a]anthracene

μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1

b.l.q. 0.02 b.l.q. 0.45 0.62 0.22 0.59 0.46 0.06 b.l.q. b.l.q.

b.l.q. 0.22 0.20 0.19 0.28 0.07 0.31 0.23 b.l.q. b.l.q. b.l.q.

b.l.q. 0.51 6.5 0.48 0.64 0.28 0.69 0.52 b.l.q. 0.35 b.l.q.

b.l.q. b.l.q. 0.04 0.01 0.02 b.l.q. 0.02 0.02 b.l.q. b.l.q. b.l.q.

b.l.q. b.l.q. 11a 0.64 1.0 0.62 1.2 1.9a b.l.q. b.l.q. b.l.q.

0.19a 0.37 b.l.q. 2.0a 2.9a 2.0a 2.5a 2.4a b.l.q. b.l.q. 0.53a

Particle-bound anhydrous sugars Galactosan Mannosan Levoglucosan


2.3 7.9 110

2.2 3.8 53

2.9 9.2 72

b.l.q. 0.47 10

15 150a 1100a

5.9 6.7 56

Particle-bound phenolic species 4-Hydroxyphenylethanol 4-Hydroxybenzoic acid Vanillin Isoeugenol Acetovanillone Methylvanillate Vanillic acid 3-Guaiacylpropanol Coniferaldehyde Guaiacylacetone 4-Guaiacylbutanoic acid Syringol 4-Methylsyringol 4-Ethylsyringol Allylsyringol Syringaldehyde Syringylpropene Acetosyringone Syringic acid, methyl ester Syringic acid Homosyringic acid Sinapylaldehyde 3,5-Dimethoxy-4,4′-dihydroxystilbene Sum phenolic species

μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1

1.3 4.0 10.1 2.45 2.1 1.1 0.37 0.60 1.30 b.l.q. 0.57 1.74 1.11 1.19 0.08 36 0.71 4.4 0.91 0.91 0.12 0.62 0.95 72.1

b.l.q. b.l.q. 14.0 b.l.q. 0.5 b.l.q. 1.5 b.l.q. b.l.q. b.l.q. b.l.q. 0.04 b.l.q. b.l.q. b.l.q. 3.7 0.13 0.29 0.04 0.17 b.l.q. b.l.q. b.l.q. 20.4

b.l.q. b.l.q. 5.94 b.l.q. 0.8 0.27 b.l.q. b.l.q. 0.80 b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. 0.44 0.02 0.03 b.l.q. 0.10 b.l.q. b.l.q. b.l.q. 8.42

b.l.q. b.l.q. 0.37 b.l.q. b.l.q. 0.01 b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. 0.09 0.01 b.l.q. b.l.q. 0.11 b.l.q. b.l.q. b.l.q. 0.59

b.l.q. b.l.q. 13.7 b.l.q. b.l.q. b.l.q.a 16a b.l.q. 470a 0.63a b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. 0.50a b.l.q. b.l.q. b.l.q. 504a

b.l.q. b.l.q. 11.6 b.l.q. 0.61 b.l.q. 0.8 b.l.q. b.l.q. b.l.q. b.l.q. 0.46a b.l.q. b.l.q. b.l.q. 1.9 0.06 0.21 0.03 0.12 b.l.q. b.l.q. b.l.q. 15.8

Particle-bound resin constituents Pimaric acid Isopimaric acid Abietic acid 6-Dehydrodehydroabietic acid Dehydroabietic acid 7-Oxodehydroabietic acid

μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1 μg MJ−1

b.l.q. b.l.q. b.l.q. b.l.q. b.l.q. b.l.q.

b.l.q. b.l.q. b.l.q. b.l.q. 8.6 b.l.q.

0.06 b.l.q. b.l.q. 1.1 42 0.08


b.l.q. 73a 440a b.l.q. 710a b.l.q.a



647

Table 2 (continued) Unit Number of experiments n

Beech

Birch

Spruce

Pellet

Spruce*

4

4

3

3

1

Birch* 1

6-Dehydrodehydroabietic acid, methyl ester Dehydroabietic acid, methyl ester 7-Oxodehydroabietic acid, methyl ester


b.l.q. b.l.q. b.l.q.

b.l.q. 0.16 b.l.q.

0.09 0.82 0.09

b.l.q. 0.01 b.l.q.

b.l.q. 7.33a b.l.q.


Particle-bound phytosterols β-Sitosterol Stigmasta-3,5-dien-7-one Lupa-2,22(29)-dien-28-ol


b.l.q. 170 b.l.q.

b.l.q. 5.4 29

b.l.q. b.l.q. 4.6

10 b.l.q. b.l.q.



a

Significant outlier from Dean-Dixon test at 0.05 significance level in comparison to regular combustion experiments.

Table 3 Emission factor of total phenolic species and their normalisation to OC from modern masonry heater (beech, birch and spruce logs) and pellet boiler (softwood pellets) compared to conventional logwood and pellet combustion from Orasche et al. (2012). Beech

This study Orasche et al., 2012a a

Birch

Spruce

Pellet

μg MJ−1

mg/gOC

μg MJ−1

mg/gOC

μg MJ−1

mg/gOC

μg MJ−1

mg/gOC

72 2220

3.7 150

20 –

3.4 –

8.4 690

1.4 62

0.59 18

3.4 26

“PB spruce”, “LS beech” and “LS spruce”.

exposure conditions, e.g. with lung cells in an air-liquid interface (Kanashova et al., 2017; Paur et al., 2011), may support a full assessment of improvements in stove construction as well as the influence of combustion condition and wood type. Acknowledgement The measurements were carried out at the University of Eastern Finland (UEF), Department of Environmental and Biological Science, in cooperation with the Helmholtz Virtual Institute of Complex Molecular Systems in Environmental Health (HICE), funded by The Helmholtz Impulse and Network Fund of the Helmholtz Association (Germany), the DACH-project WooShi (grant ZI 764/5-1), the Academy of Finland (grants 296645 and 304459) and the University of Eastern Finland for the project “sustainable bioenergy, climate change and health”. Appendix A. Supplementary data The supplementary data involves the ICP-OES method, minimum and maximum EF for experimental repetitions of the same conditions, volcano diagrams of ANOVA post-hoc tests, REMPI mass spectra of OC3 and REMPI spectra of OC1 + OC2 with logarithmic y-scale. Supplementary data associated with this article can be found in the online version, at http://dx.doi.org/10.1016/j.scitotenv.2017.08.263. References Abdi, H., 2010. Congruence: congruence coefficient, Rv-coefficient and Mantel coefficient. In: Salkind, N. (Ed.), Encyclopedia of Research Design. Sage Publications, Thousand Oaks, CA. Andreae, M.O., Ramanathan, V., 2013. Climate's dark forcings. Science 340, 280–281. Bari, M.A., Baumbach, G., Kuch, B., Scheffknecht, G., 2009. Wood smoke as a source of particle-phase organic compounds in residential areas. Atmos. Environ. 43, 4722–4732. Bente, M., Sklorz, M., Streibel, T., Zimmermann, R., 2008. Online laser desorptionmultiphoton postionization mass spectrometry of individual aerosol particles: molecular source indicators for particles emitted from different traffic-related and wood combustion sources. Anal. Chem. 80, 8991–9004. Boesl, U., 2000. Laser mass spectrometry for environmental and industrial chemical trace analysis. J. Mass Spectrom. 35, 289–304. Bruns, E.A., El Haddad, I., Slowik, J.G., Kilic, D., Klein, F., Baltensperger, U., et al., 2016. Identification of significant precursor gases of secondary organic aerosols from residential wood combustion. Sci Rep 6, 27881. Calvo, A.I., Martins, V., Nunes, T., Duarte, M., Hillamo, R., Teinilä, K., et al., 2015. Residential wood combustion in two domestic devices: relationship of different parameters throughout the combustion cycle. Atmos. Environ. 116, 72–82.

Chow, J.C., Watson, J.G., Chen, L.W.A., Chang, M.C.O., Robinson, N.F., Trimble, D., et al., 2007. The IMPROVE_A temperature protocol for thermal/optical carbon analysis: maintaining consistency with a long-term database. J. Air Waste Manage. Assoc. 57, 1014–1023. Croft, D.P., Cameron, S.J., Morrell, C.N., Lowenstein, C.J., Ling, F., Zareba, W., et al., 2017. Associations between ambient wood smoke and other particulate pollutants and biomarkers of systemic inflammation, coagulation and thrombosis in cardiac patients. Environ. Res. 154, 352–361. Czech, H., Sippula, O., Kortelainen, M., Tissari, J., Radischat, C., Passig, J., et al., 2016. On-line analysis of organic emissions from residential wood combustion with single-photon ionisation time-of-flight mass spectrometry (SPI-TOFMS). Fuel 177, 334–342. Czech, H., Pieber, S.M., Tiitta, P., Sippula, O., Kortelainen, M., Lamberg, H., et al., 2017. Time-resolved analysis of primary volatile emissions and secondary aerosol formation potential from a small-scale pellet boiler. Atmos. Environ. 158, 236–245. Dean, R.B., Dixon, W.J., 1951. Simplified statistics for small numbers of observations. Anal. Chem. 23, 636–638. Diab, J., Streibel, T., Cavalli, F., Lee, S.C., Saathoff, H., Mamakos, A., et al., 2015. Hyphenation of a EC/OC thermal-optical carbon analyzer to photo-ionization time-of-flight mass spectrometry: an off-line aerosol mass spectrometric approach for characterization of primary and secondary particulate matter. Atmos. Meas. Tech. 8, 3337–3353. Elsasser, M., Busch, C., Orasche, J., Schön, C., Hartmann, H., Schnelle-Kreis, J., et al., 2013. Dynamic changes of the aerosol composition and concentration during different burning phases of wood combustion. Energy Fuel 27, 4959–4968. Evtyugina, M., Alves, C., Calvo, A., Nunes, T., Tarelho, L., Duarte, M., et al., 2014. VOC emissions from residential combustion of Southern and mid-European woods. Atmos. Environ. 83, 90–98. Fountoukis, C., Butler, T., Lawrence, M.G., Denier van der Gon, H.A.C., Visschedijk, A.J.H., Charalampidis, P., et al., 2014. Impacts of controlling biomass burning emissions on wintertime carbonaceous aerosol in Europe. Atmos. Environ. 87, 175–182. Fuller, G.W., Sciare, J., Lutz, M., Moukhtar, S., Wagener, S., 2013. New directions: time to tackle urban wood burning? Atmos. Environ. 68, 295–296. Gonçalves, C., Alves, C., Fernandes, A.P., Monteiro, C., Tarelho, L., Evtyugina, M., et al., 2011. Organic compounds in PM2.5 emitted from fireplace and woodstove combustion of typical Portuguese wood species. Atmos. Environ. 45, 4533–4545. Grabowsky, J., Streibel, T., Sklorz, M., Chow, J.C., Watson, J.G., Mamakos, A., et al., 2011. Hyphenation of a carbon analyzer to photo-ionization mass spectrometry to unravel the organic composition of particulate matter on a molecular level. Anal. Bioanal. Chem. 401, 3153–3164. Hellén, H., Hakola, H., Haaparanta, S., Pietarila, H., Kauhaniemi, M., 2008. Influence of residential wood combustion on local air quality. Sci. Total Environ. 393, 283–290. Jalava, P.I., Happo, M.S., Kelz, J., Brunner, T., Hakulinen, P., Mäki-Paakkanen, J., et al., 2012. In vitro toxicological characterization of particulate emissions from residential biomass heating systems based on old and new technologies. Atmos. Environ. 50, 24–35. Kanashova T, Sippula O, Oeder S, Streibel T, Passig J, Czech H, et al. Emissions from a modern log wood masonry heater and wood pellet boiler: composition and biological impact on air-liquid interface exposed human lung cells. (manuscript in preparation) 2017. Kari, E., Hao, L., Yli-Pirilä, P., Leskinen, A., Kortelainen, M., Grigonyte, J., et al., 2017. Effect of pellet boiler exhaust on secondary organic aerosol formation from α-pinene. Environ. Sci. Technol. 51, 1423–1432. Kasurinen, S., Jalava, P.I., Happo, M.S., Sippula, O., Uski, O., Koponen, H., et al., 2017. Particulate emissions from the combustion of birch, beech, and spruce logs cause different cytotoxic responses in A549 cells. Environ. Toxicol. 32, 1487–1499. Khodaei, H., Guzzomi, F., Patiño, D., Rashidian, B., Yeoh, G.H., 2017. Air staging strategies in biomass combustion-gaseous and particulate emission reduction potentials. Fuel Process. Technol. 157, 29–41.

648


Kjällstrand, J., Petersson, G., 2001. Phenolic antioxidants in wood smoke. Sci. Total Environ. 277, 69–75. Kleeman, M.J., Robert, M.A., Riddle, S.G., Fine, P.M., Hays, M.D., Schauer, J.J., et al., 2008. Size distribution of trace organic species emitted from biomass combustion and meat charbroiling. Atmos. Environ. 42, 3059–3075. Künzi, L., Mertes, P., Schneider, S., Jeannet, N., Menzi, C., Dommen, J., et al., 2013. Responses of lung cells to realistic exposure of primary and aged carbonaceous aerosols. Atmos. Environ. 68, 143–150. Lamberg, H., Nuutinen, K., Tissari, J., Ruusunen, J., Yli-Pirilä, P., Sippula, O., et al., 2011a. Physicochemical characterization of fine particles from small-scale wood combustion. Atmos. Environ. 45, 7635–7643. Lamberg, H., Sippula, O., Tissari, J., Jokiniemi, J., 2011b. Effects of air staging and load on fine-particle and gaseous emissions from a small-scale pellet boiler. Energy Fuel 25, 4952–4960. Laszczyk, M., Jäger, S., Simon-Haarhaus, B., Scheffler, A., Schempp, C.M., 2006. Physical, chemical and pharmacological characterization of a new oleogel-forming triterpene extract from the outer bark of birch (Betulae cortex). Planta Med. 72, 1389–1395. Lewtas, J., 2007. Air pollution combustion emissions: characterization of causative agents and mechanisms associated with cancer, reproductive, and cardiovascular effects. Mutat. Res. Rev. Mutat. Res. 636, 95–133. Lipsky, E.M., Robinson, A.L., 2006. Effects of dilution on fine particle mass and partitioning of semivolatile organics in diesel exhaust and wood smoke. Environ. Sci. Technol. 40 (1), 155–162. Martinsson, J., Eriksson, A.C., Nielsen, I.E., Malmborg, V.B., Ahlberg, E., Andersen, C., et al., 2015. Impacts of combustion conditions and photochemical processing on the light absorption of biomass combustion aerosol. Environ. Sci. Technol. 49, 14663–14671. Miljevic, B., Heringa, M.F., Keller, A., Meyer, N.K., Good, J., Lauber, A., et al., 2010. Oxidative potential of logwood and pellet burning particles assessed by a novel profluorescent nitroxide probe. Environ. Sci. Technol. 44, 6601–6607. Naeher, L.P., Brauer, M., Lipsett, M., Zelikoff, J.T., Simpson, C.D., Koenig, J.Q., et al., 2007. Woodsmoke health effects: a review. Inhal. Toxicol. 19, 67–106. Nordin, E.Z., Uski, O., Nyström, R., Jalava, P., Eriksson, A.C., Genberg, J., et al., 2015. Influence of ozone initiated processing on the toxicity of aerosol particles from small scale wood combustion. Atmos. Environ. 102, 282–289. Nussbaumer, T., 2003. Combustion and co-combustion of biomass: fundamentals, technologies, and primary measures for emission reduction. Energy Fuel 17, 1510–1521. Nuutinen, K., Jokiniemi, J., Sippula, O., Lamberg, H., Sutinen, J., Horttanainen, P., et al., 2014. Effect of air staging on fine particle, dust and gaseous emissions from masonry heaters. Biomass Bioenergy 67, 167–178. Orasche, J., Schnelle-Kreis, J., Abbaszade, G., Zimmermann, R., 2011. Technical note: in-situ derivatization thermal desorption GC-TOFMS for direct analysis of particle-bound non-polar and polar organic species. Atmos. Chem. Phys. 11, 8977–8993. Orasche, J., Seidel, T., Hartmann, H., Schnelle-Kreis, J., Chow, J.C., Ruppert, H., et al., 2012. Comparison of emissions from wood combustion. Part 1: emission factors and characteristics from different small-scale residential heating appliances considering particulate matter and polycyclic aromatic hydrocarbon (PAH)-related toxicological potential of particle-bound organic species. Energy Fuel 26, 6695–6704. Oros, D.R., Simoneit, B.R.T., 2001a. Identification and emission factors of molecular tracers in organic aerosols from biomass burning part 1. Temperate climate conifers. Appl. Geochem. 16, 1513–1544. Oros, D.R., Simoneit, B.R.T., 2001b. Identification and emission factors of molecular tracers in organic aerosols from biomass burning part 2. Deciduous trees. Appl. Geochem. 16, 1545–1565. Pastorello, C., Caserini, S., Galante, S., Dilara, P., Galletti, F., 2011. Importance of activity data for improving the residential wood combustion emission inventory at regional level. Atmos. Environ. 45, 2869–2876. Paur, H.R., Cassee, F.R., Teeguarden, J., Fissan, H., Diabate, S., Aufderheide, M., et al., 2011. In-vitro cell exposure studies for the assessment of nanoparticle toxicity in the lung-a dialog between aerosol science and biology. J. Aerosol Sci. 42, 668–692. Pio, C., Cerqueira, M., Harrison, R.M., Nunes, T., Mirante, F., Alves, C., et al., 2011. OC/EC ratio observations in Europe: re-thinking the approach for apportionment between primary and secondary organic carbon. Atmos. Environ. 45, 6121–6132.

Reda, A.A., Czech, H., Schnelle-Kreis, J., Sippula, O., Orasche, J., Weggler, B., et al., 2015. Analysis of gas phase carbonyl compounds in emissions from modern wood combustion appliances: influence of wood type and combustion appliance. Energy Fuel 29, 3897–3907. Schmidl, C., Marr, I.L., Caseiro, A., Kotianová, P., Berner, A., Bauer, H., et al., 2008. Chemical characterisation of fine particle emissions from wood stove combustion of common woods growing in mid-European Alpine regions. Atmos. Environ. 42, 126–141. Schmidl, C., Luisser, M., Padouvas, E., Lasselsberger, L., Rzaca, M., Ramirez-Santa Cruz, C., et al., 2011. Particulate and gaseous emissions from manually and automatically fired small scale combustion systems. Atmos. Environ. 45, 7443–7454. Sehlstedt, M., Dove, R., Boman, C., Pagels, J., Swietlicki, E., Löndahl, J., et al., 2010. Antioxidant airway responses following experimental exposure to wood smoke in man. Part. Fibre Toxicol. 7. Shafizadeh, F., 1968. In: Tipson, MLWaRS (Ed.), Pyrolysis and Combustion of Cellulosic Materials. Advances in Carbohydrate Chemistry 23, pp. 419–474. Simoneit, B.R.T., 2002. Biomass burning - a review of organic tracers for smoke from incomplete combustion. Appl. Geochem. 17, 129–162. Sippula, O., Hytönen, K., Tissari, J., Raunemaa, T., Jokiniemi, J., 2007. Effect of wood fuel on the emissions from a top-feed pellet stove. Energy Fuel 21, 1151–1160. Sippula, O., Lind, T., Jokiniemi, J., 2008. Effects of chlorine and sulphur on particle formation in wood combustion performed in a laboratory scale reactor. Fuel 87, 2425–2436. Sippula, O., Lamberg, H., Leskinen, J., Tissari, J., Jokiniemi, J., 2017. Emissions and ash behavior in a 500 kW pellet boiler operated with various blends of woody biomass and peat. Fuel 202, 144–153. Sklorz, M., Briedé, J.J., Schnelle-Kreis, J., Liu, Y., Cyrys, J., De Kok, T.M., et al., 2007. Concentration of oxygenated polycyclic aromatic hydrocarbons and oxygen free radical formation from urban particulate matter. J. Toxicol. Environ. Health A Curr. Issues 70, 1866–1869. Sokal, R.R., Rohlf, F.J., 1962. The comparison of dendrograms by objective methods. Taxon 11, 33–40. Sommersacher, P., Brunner, T., Obernberger, I., 2012. Fuel indexes: a novel method for the evaluation of relevant combustion properties of new biomass fuels. Energy Fuel 26, 380–390. Tiitta, P., Leskinen, A., Hao, L., Yli-Pirilä, P., Kortelainen, M., Grigonyte, J., et al., 2016. Transformation of logwood combustion emissions in a smog chamber: formation of secondary organic aerosol and changes in the primary organic aerosol upon daytime and nighttime aging. Atmos. Chem. Phys. 16, 13251–13269. Tobiszewski, M., Namieśnik, J., 2012. PAH diagnostic ratios for the identification of pollution emission sources. Environ. Pollut. 162, 110–119. Tschamber, V., Trouvé, G., Leyssens, G., Le-Dreff-Lorimier, C., Jaffrezo, J.L., Genevray, P., et al., 2016. Domestic wood heating appliances with environmental high performance: chemical composition of emission and correlations between emission factors and operating conditions. Energy Fuel 30, 7241–7255. Uski, O., Jalava, P.I., Happo, M.S., Torvela, T., Leskinen, J., Mäki-Paakkanen, J., et al., 2015. Effect of fuel zinc content on toxicological responses of particulate matter from pellet combustion in vitro. Sci. Total Environ. 511, 331–340. Vicente, E.D., Duarte, M.A., Calvo, A.I., Nunes, T.F., Tarelho, L.A.C., Custódio, D., et al., 2015a. Influence of operating conditions on chemical composition of particulate matter emissions from residential combustion. Atmos. Res. 166, 92–100. Vicente, E.D., Duarte, M.A., Tarelho, L.A.C., Nunes, T.F., Amato, F., Querol, X., et al., 2015b. Particulate and gaseous emissions from the combustion of different biofuels in a pellet stove. Atmos. Environ. 120, 15–27. Watson, J.G., Chow, J.C., Houck, J.E., 2001. PM2.5 chemical source profiles for vehicle exhaust, vegetative burning, geological material, and coal burning in Northwestern Colorado during 1995. Chemosphere 43, 1141–1151. Weggler, B.A., Ly-Verdu, S., Jennerwein, M., Sippula, O., Reda, A.A., Orasche, J., et al., 2016. Untargeted identification of wood type-specific markers in particulate matter from wood combustion. Environ. Sci. Technol. 50, 10073–10081. Wiinikka, H., Grönberg, C., Boman, C., 2013. Emissions of heavy metals during fixed-bed combustion of six biomass fuels. Energy Fuel 27, 1073–1080.

Identification of significant precursor gases of secondary organic aerosols from residential wood combustion.

Size-segregated urban particulate matter: mass closure, chemical composition, and primary and secondary matter content.

Particulate PAH emissions from residential biomass combustion: time-resolved analysis with aerosol mass spectrometry.

Role of microbial and chemical composition in toxicological properties of indoor and outdoor air particulate matter.

Effectiveness of residential wood-burning regulation on decreasing particulate matter levels and hospitalizations in the San Joaquin Valley Air Basin.

Effect of fuel zinc content on toxicological responses of particulate matter from pellet combustion in vitro.

Indoor particulate matter in rural, wood stove heated homes.

Implications of ammonia emissions from post-combustion carbon capture for airborne particulate matter.

Comparative study for hardwood and softwood forest biomass: chemical characterization, combustion phases and gas and particulate matter emissions.

Atmospheric particulate matter levels, chemical composition and optical absorbing properties in Camagüey, Cuba.

Global chemical composition of ambient fine particulate matter for exposure assessment.

Organic speciation of ambient quasi-ultrafine particulate matter (PM0.36) in Augsburg, Germany: Seasonal variability and source apportionment.

Risk of preeclampsia from exposure to particulate matter (PM₂.₅) speciation chemicals during pregnancy.

Residential exposure to 60-Hz magnetic fields from appliances.

Chemical composition and physical properties of filter fly ashes from eight grate-fired biomass combustion plants.

Long term effect of land reclamation from lake on chemical composition of soil organic matter and its mineralization.

How soil organic matter composition controls hexachlorobenzene-soil-interactions: adsorption isotherms and quantum chemical modeling.

Optical and chemical characterization of aerosols emitted from coal, heavy and light fuel oil, and small-scale wood combustion.

Temperature Dependence of Photodegradation of Dissolved Organic Matter to Dissolved Inorganic Carbon and Particulate Organic Carbon.

Dust episodes in Beirut and their effect on the chemical composition of coarse and fine particulate matter.

Chemical and biological characterization of air particulate matter 2.5, collected from five cities in China.

Chemical Composition of Fine Particulate Matter and Life Expectancy: In 95 US Counties Between 2002 and 2007.

Total exposure to airborne particulate matter in cities: the effect of biomass combustion.

Spatial and temporal variation in fine particulate matter mass and chemical composition: the Middle East Consortium for Aerosol Research Study.