Emissions and source allocation of carbonaceous air pollutants from wood stoves in developed countries: A review

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Review Article

Emissions and source allocation of carbonaceous air pollutants from wood stoves in developed countries: A review Yulia Olsena, Jacob Klenø Nøjgaardb,∗, Helge Rørdam Olesenb, Jørgen Brandtb, Torben Sigsgaardf, Sara C. Pryorc, Travis Anceletd, María del Mar Vianae, Xavier Querole, Ole Hertelb a

Department of Public Health – Institute of Environmental and Occupational Medicine, Aarhus University, Aarhus, 8000, Denmark Department of Environmental Science, Aarhus University, Roskilde, 4000, Denmark c Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, 14853, USA d GNS Science, Lower Hutt, 5040, New Zealand e Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona, 08034, Spain f Department of Public Health – Section of Environment, Occupation & Health, Aarhus University, Aarhus, 8000, Denmark b

ARTICLE INFO

ABSTRACT

Keywords: Residential wood combustion Wood stoves Ambient particulate pollution Carbonaceous aerosol Source apportionment

In recent years, residential wood combustion (RWC) has become a major source of ambient particulate matter (PM) in many developed countries, and in some of these countries even the largest source of primary particle emissions. While other sources of PM have been regulated intensively during the past decades, RWC has been subject to only minor regulation despite of its impact on climate and health. This review covers recent research publications on RWC contributions to ambient PM in different regions of Europe, North America and Australasia, and on key species associated with RWC. Furthermore, factors governing emissions from wood stoves (as the typical appliance used in residential heating) are evaluated. State-of-the-art methods for estimating RWC as a source of ambient PM are discussed. We conclude by highlighting important areas for future research and policies.

1. Introduction The World Health Organization (WHO) reports that in 2012 one in eight of total global deaths (around 7 million people) resulted from exposure to air pollution. Most of these air pollution related deaths are believed to be associated with exposure to particulate matter (PM). WHO summarizes finding from the past 2-3 decades by stating that PM is associated with increased morbidity, total mortality, and mortality related to cardiovascular and respiratory diseases, diabetes, and lung cancer (WHO, 2013c). These findings on PM related health effects indicate that there is no PM threshold below which no damage to health is observed (WHO, 2013b, c). Current WHO guidelines on ambient PM levels differentiate PM only on the basis of size, although there are reasons to believe that chemical composition plays an important role. Thus, there is growing evidence that combustion-derived carbonaceous PM, i.e black carbon (BC) and organic carbon (OC) are more strongly linked to health effects than other fractions of the particle mass (Janssen et al., 2011; WHO, 2013c). Combustion of wood leads to the

release of gaseous pollutants such as carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOCs), and a number of particulate organic compounds. Many of these organic compounds are known to cause adverse health effects, i.e. benzo[a]pyrene (BaP), other polycyclic aromatic hydrocarbons (PAH), benzene, formaldehyde, 1,3butadiene, phenols and cresols (WHO, 2013a, 2016). In Europe, Residential Wood Combustion (RWC) is the main source of BaP (EEA, 2014; EEA, 2017). Furthermore, the current way of assessing RWC impact is based only on primary emissions, i.e. sampled at the source, therefore, not accounting for the volatile and semivolatile compounds that are formed during chemical transformation over time (Bruns et al., 2016). However, photooxidation of RWC emissions produce Secondary Organic Aerosols (SOA) similar to or exceeding the Primary Organic Aerosol (POA) up to a factor of 3–7 (Bruns et al., 2016; Corbin et al., 2015a; Bertrand et al., 2017). There are different estimates on RWC effect on annual premature deaths: 29,000 annual premature deaths in Europe and North America (Chafe et al., 2015), or above 40,000 solely in Europe, corresponding to

Peer review under responsibility of Turkish National Committee for Air Pollution Research and Control. ∗ Corresponding author. Frederiksborgvej 399, 4000, Roskilde, Denmark. E-mail address: [email protected] (J.K. Nøjgaard). https://doi.org/10.1016/j.apr.2019.10.007 Received 24 May 2019; Received in revised form 10 October 2019; Accepted 12 October 2019 1309-1042/ © 2019 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.

Please cite this article as: Yulia Olsen, et al., Atmospheric Pollution Research, https://doi.org/10.1016/j.apr.2019.10.007

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at least 10% of all health effects related to air pollution (Brandt et al., 2013a, 2013b; Sigsgaard et al., 2015). Air pollution associated with RWC may cause specific health reactions that can be distinguished from reactions related to other combustion sources (Morandi and Ward, 2010; Naeher et al., 2007; Sigsgaard et al., 2015; WHO, 2013c; Rokoff et al., 2017). BC, which is a key carbonaceous component related to RWC, is strongly associated with health effects (Brook et al., 2010; Heal et al., 2012) and has an impact on climate (Bond et al., 2013; Savolahti et al., 2019). BC and brown carbon, BrC, (Andreae and Gelencser, 2006; Kirchstetter and Thatcher, 2012; Mohr et al., 2013; Kumar et al., 2018), both of which arise from wood combustion, absorb radiation, and warm the atmosphere (as does methane), furthermore, these compounds deposit on snow and ice, affecting the albedo of these surfaces and thereby amplifying polar warming (Bond et al., 2013). In contrast to fossil fuels, wood burning is often referred as a “clean” or “green” sustainable and natural energy source (Richter et al., 2009; Dale et al., 2017) due to climate policies promoting use of energy from renewable sources (Directive 2009/28/EC). Along with rising energy costs, the use of wood burning for domestic heating has been increasing in developed countries over the last couple of decades (Mitchell et al., 2017; Aguilar, 2015; Richter et al., 2009). Thus, from 2007 to 2017 only in Europe the quantity of renewable every increased by 64% with wood and solid fuels accounting for 42% of primary production (Eurostat, 2019). As a result, RWC is one of the largest sources of airborne PM in Europe (Brandt et al., 2013a; Denier van der Gon et al., 2014; Viana et al., 2016), dominating the emissions of primary PM10 and PM2.5 (particular matter with aerodynamic diameter ≤ 10 μm and ≤2.5 μm, respectively), followed by industry and transport (EEA, 2014; EEA, 2017), andaccounting for 53% of PAH and 72% of BaP emissions (EEA, 2017). Therefore, concerns are rising about overlooked effects of RWC, as an important source of air pollution (Viana et al., 2016; Savolahti et al., 2019; Font and Fuller, 2017; Chafe et al., 2015; Robinson, 2015; Burki, 2018; Rokoff et al., 2017). Hence, RWC is currently at the top of the environmental policy agenda in many western countries (Bjørner et al., 2019; Bailey et al., 2019; Lopez-Aparicio et al., 2018; Ward et al., 2017; Burki, 2018; Lefebvre et al., 2016). The purposes of this review are: (1) to summarize the factors affecting relative abundance of different types of carbonaceous emissions (2), to characterize the particulate emissions from experiments with wood burning stoves, (3) to provide state-of-the-art knowledge on the wood stoves contribution to ambient carbonaceous PM in different geographical areas in developed countries, additionally, critically review methods used for evaluating the contribution from wood stoves to ambient PM (described in the Supplementary Material), and (4) to conclude with a number of recommendations for future work in this field and policies for emissions reduction. To achieve the above described purposes, the on-line search using the words “wood stove burning”, “residential wood combustion”, “wood smoke/residential combustion contribution” was performed on www.googlescholar.com from the year of 2000 onward, including search in the publications’ reference lists with focus only on the reports from developed countries. Additionally, we consulted international experts, working in the field.

efficiency and higher PM mass emissions due to more incomplete combustion (Johansson et al., 2004; Jordan and Seen, 2005; Seljeskog et al., 2013; Fachinger et al., 2017). The supply of oxygen, necessary for combustion reactions, is provided in wood stoves by either forced or natural draft, manually as in older versions or automatically as in advanced modern stoves (Mack et al., 2017). A conventional old technique for wood combustion is natural updraft combustion, where the primary air is drawn into the combustion chamber at the bottom, with its further passage through the chamber to the chimney, which is the engine of the wood burning stove. A proper chimney draught, which is correlated with chimney height, is crucial for efficient operation of a wood stove, enabling its stable operation (Illerup et al., 2015; Schleicher and Boje, 2007). A European survey on real life operation of biomass room heating appliances reported that 66% of wood stoves have chimney height between five and 10 m (Wöhler et al., 2016), which corresponds to the draught of 20–30 Pa (Reichert et al., 2017). The comparative combustion tests under 12/24/48 Pa conditions showed that different appliances exhibited different relationship with increase in draught conditions (Reichert et al., 2017). The impact of different chimney draught on gaseous emissions was reported highly stove dependent (Mack et al., 2017), while its impact on PM emissions – limited (Reichert et al., 2017; Mack et al., 2017). Generally, emissions from automated stoves are more homogeneous than those from manually operated stoves due to more efficient combustion (Schmidl et al., 2011; Mack et al., 2017; Carvalho et al., 2016). Pellet stoves that burn compressed wood or biomass are self-igniting and operated under automatic air and thermostatic control, have substantially lower PM mass emissions than common log wood stoves (Bäfver et al., 2011; Heringa et al., 2011; Nussbaumer et al., 2008; Schmidl et al., 2011; Vicente et al., 2015c; Fachinger et al., 2017; Ozgen et al., 2017; Corsini et al., 2017). The combustion of pellets has the advantage of homogeneous fuel and continuous supply/feeding under optimal mixing of air: in most models, a fan provides the necessary oxygen to the combustion chamber and pulls the hot gases out of the combustion chamber and chimney. This leads to a more complete combustion, compared to the manually operated wood stoves, and reduces the products of incomplete combustion, i.e. organic material (Corbin et al., 2015a; Fachinger et al., 2017; Corsini et al., 2017), with higher shares of inorganic content (Schmidl et al., 2011; Vicente et al., 2015c; Corsini et al., 2017). Thus, in experiment with a modern welloperated wood stove and a modern pellet stove, highly oxygenated primary OM was reported from the wood stove, and its contribution increased when a new added batch failed to ignite (Corbin et al., 2015a). In contrast, the modern pellet stove produced negligible amounts of OM relative to its total PM emissions (Corbin et al., 2015a). Manually-operated conventional wood stoves can emit almost 10 times more primary PM under typical conditions relative to automatically controlled pellet stoves (Nussbaumer et al., 2008; Schmidl et al., 2011). This was illustrated in a series of replicate experiments, where the abundance of emissions varied by 30% for manually operated wood stoves, whereas less variation was observed for automatically controlled appliances (Schmidl et al., 2011). 2.2. Wood combustion cycle and user practices

2. Factors affecting emissions

The emissions of PM and individual chemical species may vary markedly between combustion phases. The importance of the combustion phases has been demonstrated by studying mass spectra of organic aerosol particles emitted from the burning of different wood types as a function of burning conditions. The differences in mass spectra between flaming and smoldering phases for a given wood type is larger than the differences between wood types within the same phase (Weimer et al., 2008). Multiple experiments have reported highest emissions during the ignition phase, which is critical as it is associated with high emissions of unburned compounds (Corbin et al., 2015b; Heringa et al., 2011;

2.1. Appliance type Wood stoves around the world show great diversity in terms of design, fuel type, principle of operation (e.g. log wood stoves, pellet stoves, masonry heaters, sauna stoves, iron stoves, and tiled stoves) and age, in addition to chimney configuration (Carvalho et al., 2016; Wöhler et al., 2016). The abundance and composition of emissions vary between appliances (Gonçalves et al., 2011; McDonald et al., 2000; Nussbaumer et al., 2008; Trojanowski and Fthenakis, 2019; Carvalho et al., 2016). Typically, older models are characterized by low 2

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Lamberg et al., 2011; Miljevic et al., 2010; Nussbaumer et al., 2008; Schmidl et al., 2008, 2011; Tissari et al., 2009; Vicente et al., 2015a; Wöhler et al., 2017) that consists of organic compounds (Orasche et al., 2012) resulting from volatilization followed by rapid condensation and oxidation of VOCs and SVOCs present in the fuel (Gonçalves et al., 2011; Vicente et al., 2015b). The kindling technique can influence emissions during the start-up phase. In a small survey in Austria, around 80% of participants answered that they light the fire at the bottom of the ignition batch (Reichert et al., 2016). Bottom-up ignition causes simultaneous firing of the whole batch of fuel, which leads to higher combustion rates and oxygen depletion, resulting in incomplete combustion; whereas top-down ignition develops combustion in a gradual manner through the fuel batch, resulting in more efficient combustion. It has been shown that ignition from the top can reduce total PM emissions from 50% to 80% compared to traditional ignition from the bottom (Nussbaumer et al., 2008; Vicente et al., 2015a). However, using undiluted flue gas measurements, Reichert et al. (2017) did not observe statistical difference between the emissions using topdown and bottom-up techniques. The temperature in the chamber during ignition also affects the magnitude of the emissions. Cold startup phase typically causes higher emissions. Orasche et al. (2012) performed a series of experiments comparing measured emissions over the whole batch of beech and spruce with the cold-start inflaming (the top ignition), to the levels measured from a sequence of batches. The experiments demonstrated that emissions from the initial batches ignited in cold stoves were substantially higher with high amounts of SVOC and VOC than emissions from sequenced batches (Orasche et al., 2012). Similar results were obtained in experiments with wood stoves and fireplaces typical for Portugal. Emissions of PM2.5, alkanes, PAHs, ketones, alkanols, resin acids, and alkyl esters of acids were independent of the appliance when a cold start was used (Gonçalves et al., 2011).This was related to a lower degree of conversion (oxidation) of the biomass (solid and pyrolysis products) resulting in a higher emission of unburned chemical compounds (Gonçalves et al., 2011). The time-series of several consequent batches had repetitive peaks for organic matter emissions (comparable to the start-up emissions) during addition of fuel (Corbin et al., 2015a; Pettersson et al., 2011; Weimer et al., 2008). After the ignition phase, providing steady-state combustion conditions (e.g. constant oxygen supply, sufficient mixing, optimal combustion temperature and heat output, regular fuel feeding), the PM mass emissions typically decrease and reach lower, nearly constant levels (Schmidl et al., 2008), while number distribution peak shifts to smaller particle sizes (Pagels et al., 2013). However, the ideal combustion conditions are not achievable in real-life settings due to varying user practices that are accompanied by large variation in emission patterns (Fachinger et al., 2017; Ozgen et al., 2017). Using poor quality wood (e.g. wood with high moisture content), overloading the firebox or insufficient air supply, unavoidable fluctuations in the firebox temperature, promote higher PM emissions than combustion under more optimal conditions (Illerup et al., 2015; Kocbach Bølling et al., 2009; Nyström et al., 2017; Fachinger et al., 2017; Carvalho et al., 2016). Thus, in a study with automatically- and manually-fired appliances, Schmidl et al. (2011) found that the amount of fuel and the airflow setting, which depend on user practices, could increase PM10 emissions up to a factor of 6. This, in turn, also influenced the particle composition, as decreased air supply favors higher organic emissions (Schmidl et al., 2011). In the experiments by Orasche et al. (2012, 2013) with the same wood types (i.e. spruce and beech), PM emission factors increased by more than a factor of two under poor combustion conditions. Pettersson et al. (2011) showed large variations in performance and emissions in a single natural draft wood stove operated under different modes. Corbin et al., 2015b reported a shift from the dominating OM signal in the start-up phase to little OM and significant BC amounts during the flaming phase, as a consequence of more efficient combustion during this phase.

Under depletion of fuel, the combustion cycle enters a smoldering (burning-out) phase, which is characterized by decreasing temperature, higher oxygen and carbon monoxide (CO) concentrations (Lamberg et al., 2011; Pettersson et al., 2011; Tissari et al., 2009; Vicente et al., 2015a). Consistent with the above, higher emissions of EC were observed in experiments with “hot start” wood stoves with higher OC/EC ratio during the “cold start” tests (Gonçalves et al., 2011). Also, Pagels et al. (2013) found higher OC/EC ratio during low-temperature combustion (addition of fuel, start-up, and choked combustion). However, comparison of OC/EC ratios between studies is challenged by the application of different types of instruments, e.g. AMS and various thermal/ optical instruments using different temperature protocols (see S1.1), which are known to impact the split between OC and EC (Cavalli et al., 2010). 2.3. Loading, logs size, moisture content, and wood type The amount of fuel used for one batch affects the emissions from wood stoves, as overloaded firing boxes prevent good mixing and obstruct relevant oxygen supply, leading to poor combustion. Vicente et al., 2015b found a positive association between higher loading and OM mass fractions of PM10, but a negative association with levoglucosan concentrations in experiments with both soft and hardwood logs. They reported a 38.5% OC content in PM10 for medium load and 51.5% for high load, concluding that high loads led to higher release of VOCs and to oxygen deficiency, promoting incomplete combustion conditions. Orasche et al. (2012) demonstrated that PAH concentrations were doubled by use of log loads more than twice of those recommended by the stove manufacture. In a study with a Finnish masonry heater and sauna stoves, when doubling the batch size, CO, VOCs, and PM1 mass emissions were increased by factor 2, 3, and 2 respectively, while PM1 number concentration decreased by factor 2, due to enhanced gasification rate and insufficient air supply (Tissari et al., 2009). However, this and other studies have shown an even larger increase in emissions of PM by firing bigger logs, which is likely due to an extended duration of start-up phase (Vicente et al., 2015a). Further, in an experiment using prevalent Australian wood species, PM2.5 mass emission for leaf and branch samples were much higher than those for the wood samples (Wardoyo et al., 2006). Higher moisture content in the wood promotes higher emissions of PM and VOC as a result of increasing organic content (Fernandes et al., 2011; McDonald et al., 2000; Shen et al., 2013; Price-Allison et al., 2019), e.g. phenolic compounds, anhydrous sugars (Orasche et al., 2012, 2013), PAHs (Shen et al., 2013). Moreover, emissions differ depending on the nature of wood and, whereas presence of certain chemical compounds in emissions can be explained by the type (soft/hard) of the wood, the emissions factors are still very species dependent. Several studies with log combustion in wood stoves have reported that softwoods, due to their lower densities, burned at higher rates than hardwoods, by this possibly reducing the duration of their start-up phase, which resulted in lower PM emissions overall (Fernandes et al., 2011; Gonçalves et al., 2010, 2011; McDonald et al., 2000; Orasche et al., 2012; Schmidl et al., 2008). In the experiments by Corsini et al. (2017) conifer wood logs were reported to generate more PAH emissions in the ultrafine mode (< 100 nm) PM compared with beech wood logs. Fernandes et al. (2011) found lower OC/EC ratio values for softwood (0.85) than for hardwood (3.14–4.39). In a study with combustion of four common Portugal woods in cast iron stove, OC/EC ratios for the hardwoods ratio varied between 1 and 4.4, whereas the ratio for the soft wood (Maritime Pine) was close to 1 (Gonçalves et al., 2010). However, experimental studies with woodstoves do not consistently indicate that OC/EC ratios differ between soft and hard wood burning, indicating the prevailing influence of the wood nature (Alves et al., 2011; Fine et al., 2004b; Schmidl et al., 2011; Vicente et al., 2015b). 3

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A number of experiments have been reported for pellet fuel (Arranz et al., 2015; Sippula et al., 2007; Vicente et al., 2015c; Bäfver et al., 2011; Klauser et al., 2018; Fachinger et al., 2017). Sippula et al. (2007) looked at variation in emission factors amongst six types of pellets made from stem versus bark of different wood species, and reported the highest PM1 emissions for bark fuels ≈11.7 g kg−1, and the lowest for stem fuels ≈0.8 g kg−1 (Sippula et al., 2007). Pellet stove UFP mass concentrations were higher for the fir pellets (34 mg m−3) compared with beech pellets (29 mg m−3) in experiment by Ozgen et al. (2017). Vicente et al., 2015c reported emission factors for PM10 from a pellet stove of 0.5–1.8 g kg−1 for four different commercial pellets available in Southern Europe. The pellets with the certification ENplus had the lowest emission factor of 0.5 g kg−1, whereas the three remaining types of pellets produced higher emission factors in the range of 1.4–1.8 g kg−1 and also had high contents of As, Cu, Cr, Pb, and Zn (Vicente et al., 2015c).

to be reliable in terms of repeatability and reproducibility (Klauser et al., 2018). On the contrary to the official type test (i.e. EN 13240), which evaluates emissions and efficiency of roomheaters under optimal conditions (at nominal load not considering the ignition of the first batch, the heating up and cooling down), the beReal method includes emissions during the whole batch (a heating cycle with eight consecutive batches) and also for partial load operation (Reichert et al., 2018a). Therefore, the emissions obtained by using official type tests versus beReal test were significantly different (Reichert and Schmidl, 2018; Reichert et al., 2018a). Thus, in experiment with 13 different log wood stoves and four pellet stoves, all measured emissions were more than by a factor 3 higher during the beReal testing for the log wood stoves and at least by factor 2 higher (PM emissions) for the pellet stoves, while the efficiency was 11% lower, compared with the official tests (EN 13240 for wood log stove and EN 14785 for pellet stoves) at nominal load (Rönnbäck et al., 2016). However, the real life emissions from RWC can reach higher values, as the beReal test protocol does not consider possible maloperation situations which can occur during the real-life usage of an appliance, e.g. using of inappropriate fuel or ignition technique, fuel-overloading, different draft conditions (Reichert et al., 2018a).

2.4. Measuring emissions in experimental settings 2.4.1. Measurement protocol Reported emission factors for wood stoves show large variations between countries and studies (Kocbach Bølling et al., 2009; Nussbaumer et al., 2008; Reichert and Schmidl, 2018). Apart from the differences attributed to the appliances and fuels tested, which typically represent those that are currently in use in a specified region, this large variation is also caused by the different testing protocols (Reichert and Schmidl, 2018; Viana et al., 2016; Seljeskog et al., 2017). These include differences in requirements for sampling techniques (e.g. gravimetric filter sampling of PM in hot flue gas from chimney vs. diluted flue gas from dilution tunnel), fuel characteristics (amount of wood, content of moisture, size of logs, etc.), combustion conditions (air supply, combustion temperature), period of filter sampling, fuel load settings, and number of the repeated experiments (Reichert and Schmidl, 2018; Seljeskog et al., 2017). Thus, Norway reports the highest wood stove emission factor in Europe with 1297 mg MJ−1, which corresponds to an average emission factor of 24 g kg−1, whereas Germany reports an emission factor of 106 mg MJ−1. The difference is mainly caused by the standard test method these countries use. In Germany, the measurements are performed in chimneys in the hot undiluted flue gas (EN 13240), while in Norway the Norwegian standard (NS 3058) prescribes measurements of cooled gases in the dilution tunnel (SM Table S2) (Nussbaumer et al., 2008; Seljeskog et al., 2013). The Norwegian method includes the contribution from condensable semi-volatile organic compounds (SVOC), that are not included in the German method. Besides, the Norwegian standard stoves are tested under less favorable combustion conditions with reduced burn rates, which leads to much higher emissions (Seljeskog et al., 2013). In Sweden, a method with heated filters without dilution tunnel is applied (Nielsen et al., 2010), this is more in line with the German method, and, therefore, Swedish emission factors are substantially lowerer than those applied in Norway (CLRTAP, http://www.ceip.at/). Danish emissions factors are based on measurements in accordance with the Norwegian method. An overview of the existing test standards can be found in Reichert and Schmidl (2018).

2.5. Emission reduction techniques In addition to controlling for the factors affecting emissions (see sections 2.1-2.4), emission control systems, such as Electric Precipitators (ESP) and catalytic converters are available to further reduce flue gas emissions from RWC. However, a critical review of these techniques is beyond the scope of this article. Electrostatic Precipitators (ESP) applies a high voltage between a discharge electrode and a grounded electrode, which ionizes gasses and particles. Chargeable particles are thus removed from the gas stream, which is not the case for gasses such as CO and NOX and VOCs. The latter implies that SOA formation can still occur, since only primary particles are removed by the ESP. In small scale appliances, ESP is capable of removing larger particles and nanoparticles with efficiencies depending on resistivity and thus the chemical composition of particles, and the cleaning state of the ESP (Bologa et al., 2010, 2011; Migliavacca et al., 2014). Catalytic converters essentially lower the temperature, which is needed to oxidize a pollutant. Often Pt or Pd is coated on ceramic or metallic honeycomb structures (Wöhler et al., 2017). In addition to particulate matter, catalytic converters are capable of removing CO and VOCs, which are precursors for SOA, though their application is still challenged (Hukkanen et al., 2012; Pieber et al., 2018). For example, Pieber and coworkers demonstrated 50% conversion of NMVOC using a Pt-based honeycomb catalytic converter at realistic chimney temperatures (Pieber et al., 2018), while Reichert and coworkers demonstrated more than 95% reduction of CO, more than 60% reduction of oxygenated gaseous emissions and more than 30% reduction of PM (Reichert et al., 2018b). 2.6. Summary The abundance and composition of emissions from RWC varies between appliances, and with experimental design and methods. The lack of a harmonized standard measurement protocol for determining emission factors in combustion experiments which also reflects the reallife stove operation represents a significant barrier for evaluating the performance of the appliance in terms of emissions and, therefore, adding uncertainties to the quality of emission inventories. The highest emission factors are associated with inefficient combustion typical for older models of wood stoves operated manually, whereas automatically operated stoves, can provide and maintain the most effective combustion conditions by controlling adequate oxygen and fuel supply result in lower PM emissions. Fuel in the form of pellets offers an additional

2.4.2. Real-life operation The existing standard type testing methods have been shown to produce lower emissions than obtained during the real-life operation conditions (Rönnbäck et al., 2016; Reichert and Schmidl, 2018; Reichert et al., 2018a). Recently, an EU standard testing method for measurements of PM and gaseous organic emissions from log wood and pellet stoves has been developed in the joint European co-normative research project EN-PME-TEST and the EU-Project “Advanced testing methods for better real-life performance of biomass room heating appliances beReal” (Reichert and Schmidl, 2018; Reichert et al., 2018a; Oehler et al., 2016; Sturmlechner et al., 2016), which has been shown 4

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advantage of controlling for the fuel moisture content and chemical composition, reducing the variation in emissions caused by the differences in wood type inherent for different wood logs.

comparable to offline studies applying PM1, since the chemical composition differs among PM1, PM2.5 and PM10 (e.g.Claeys et al., 2010; Wagener et al., 2012). Furthermore, no separation of sampled compounds occurs in the AMS, since all particulate species are desorbed and measured at the same time. Rather than targeting single compounds, except for a few stable species such as PAH's, the AMS analyses molecular fragments of hundreds or thousands of species and group them into classes of compounds (DeCarlo et al., 2006). However, these molecular fragments provide information about contributing sources (see section S2.5).

3. Emissions from residential wood combustion 3.1. Physicochemical properties of particles Particles emitted from wood combustion can be divided in three classes: (1) BC, or elemental carbon (EC), associated with soot, i.e. carbon from incomplete combustion processes having a graphitic structure, (2) organic matter (OM) or organic carbon (OC), associated with organic compounds, and (3) inorganic species, i.e. ash particles. BC and EC do not target one well-defined chemical species, but are operationally defined by light absorption and thermal optical analysis, respectively. Most modern Carbon Analyzers apply a Thermal/Optical technique, where carbonaceous material deposited on a quartz filter is thermally desorbed in a quartz oven in two stages (Birch and Cary, 1996). Simpler versions exist, though these are more prone to artefacts. See Supplementary Material for further details on carbon analysis. OM comprises organic species including the atoms associated with carbon, e.g. hydrogen (H), oxygen (O), and nitrogen (N), whereas OC refers to the carbon-only of this organic matter. OM can be obtained from comprehensive analysis of filter samples, but is more commonly calculated by application of a conversion factor, e.g. OM = 1.6 × OC for urban aerosols or OM = 2.1 × OC for non-urban aerosols (Turpin and Lim, 2001). OM includes a vast number of compounds, such as the abundant sugar anhydride levoglucosan (C6H10O5) from pyrolysis of cellulose, methoxy phenols, and PAH. High OM emissions are typical for poor combustion conditions, which inevitably occur during the start-up phase of wood burning in the stove (Corbin et al., 2015a, Corbin et al., 2015b; Eriksson et al., 2014). Carbonaceous PM emissions can be reported as total carbon (TC), which equals the sum of EC and OC. TC emissions generally decrease during effective combustion conditions, while the proportion of particulate inorganic species that consist of non-combustible material, becomes more dominant (Corbin et al., 2015b; Orasche et al., 2012; Torvela et al., 2014). RWC appliances, such as wood stoves, are often operated under poor combustion conditions, and the fine particles emitted from them are mainly composed of OC followed by EC, as the second largest component. The present review reports OC and EC mass emissions from log wood stoves in the ranges of 16–78 and 0.82–42% of PM, respectively, and within 6–100% and 1–53% of PM, respectively for fireplaces (Table 1). In Table 1, different temperature protocols are used, and a rigid comparison of the yields in Table 1 is not meaningful. On examination of available profiles for RWC, Chow et al. (2011) [in Tables S–1] reported a narrower range of 22–58% and 4–33% of OC and EC, respectively. PM10 and PM2.5 OC/EC ratios less than unity have been reported in ambient air in road tunnels (Pio et al., 2011). Performances of light and Heavy Duty Vehicles during Dynamometer tests showed somewhat higher ratios of OC/EC, i.e. 0.2–2.9 (Pio et al., 2011). Studies with wood burning appliances typically show higher ratios well above unity, in some cases even above 20 (Table 1). Zhang et al. (2013) compared OC/EC ratios in field and laboratory studies for various types of wood. They found higher ratios in prescribed burns and controlled stove combustion for green foliage and branches (19.2 ± 4.2) compared with dry, wooden logs (7.3 ± 1.9). Aerosol Mass Spectrometry has become a popular online technique for analysis of ambient submicron aerosol, since its introduction two decades ago. Aerosol Mass Spectrometers (AMS) provide information about the molecular composition of submicron aerosol particles. In particular, AMS resolves the organic composition in addition to major inorganic species such as NH4+, NO3−, SO42− and organic Cl in submicron particles. Inlets for PM2.5 have only recently become available for Aerosol Mass Spectrometers (Xu et al., 2017). For this reason, the vast majority of studies that apply Aerosol Mass Spectrometry are only

3.2. Particle size Emissions of particle mass from RWC are dominated by fine fraction particle, however, the size distribution of PM emissions depends on the combustion conditions in the stove (Kocbach Bølling et al., 2009; Sigsgaard et al., 2015). PM emissions from RWC have been shown to be dominated by particles below 1000 nm (PM1) (Trojanowski and Fthenakis, 2019). Thus, Hedberg et al. (2002) concluded that most particle mass was present in PM0.9, with the largest fraction around 500 nm; Pettersson et al. (2011) reported 75–95% of the bulk PM1; Price-Allison et al. (2019) found the majority (93–99% of mass depending on the wood moisture content) of PM mass is less than 1000 nm in diameter; the contribution of particles larger than 1000 nm (PM1-PM10) was negligible in experiments by Fachinger et al. (2017) and Nyström et al. (2017). The size distribution of particle mass shifts towards larger particles during incomplete combustion as a result of condensation and agglomeration processes induced by the low temperatures and poor mixing (Pettersson et al., 2011; Tissari et al., 2008; Torvela et al., 2014; Vicente et al., 2015a). Such incomplete combustion takes place e.g. during the ignition phase (Bäfver et al., 2011; Pettersson et al., 2011; Wardoyo et al., 2006) and during addition of fuel (Pagels et al., 2013), under high burn rate when the stove is overloaded (Nyström et al., 2017). This was demonstrated by Eriksson et al. (2014), who reported larger vacuum aerodynamic diameter (Dva) of 600–700 nm for the particles emitted on addition of fuel compared with the particles from the intermediate combustion phase with Dva of 200 nm. Pagels et al. (2013) likewise found increasing (up to 260 nm) geometric mean diameter (GMD) for the vacuum aerodynamic (Dva) size distribution of the particles (measured by an aerosol time of flight mass spectrometer, ATOFMS, not to be confused with AMS) during addition of fuel, startup, and choked combustion phases, while the smallest GMDs were found for flaming effective combustion (150–175 nm). Fachinger et al. (2017) measured larger particles (mostly in accumulation mode) during warm start phase. Torvela et al. (2014) studied the morphology of freshly emitted fine particles and reported GMDs of 25, 65, and 160 nm for the efficient, intermediate, and smoldering conditions, respectively. The combustion conditions also altered the overall distribution of the inorganic species over the particle population: ash particles remained in the ultrafine fraction (< 100 nm) during efficient combustion, whereas during smoldering or intermediate conditions, both ash and soot were found in the accumulation particle size mode (> 100 nm) as an internal mixture; furthermore, ultrafine particles produced in efficient combustion were uniform (Torvela et al., 2014). Conversely, intermediate and smoldering conditions tend to result in bimodal particle size distributions (Boman et al., 2011; Lamberg et al., 2011; Torvela et al., 2014). 3.3. PAHs PAHs are formed as a result of incomplete combustion of organic materials (Abdel-Shafy and Mansour, 2016) and hence RWC contributes to measured ambient PAH levels (Jang et al., 2013; Mandalakis et al., 2005; Wåhlin et al., 2010; Piazzalunga et al., 2013; Tunno et al., 2019). Specifically, ambient concentrations of BaP were often used as a tracer 5

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Table 1 Relative OC and EC mass contributions to PM emissions in experiments with different RWC appliances (Soft wood – the wood from a conifer; hard wood – the wood from the broadleaved tree; wood pellets – compressed wood (or any biomass) material). Ref.

Appliance

Wood type

OC%

EC%

OC/EC

Vicente et al., 2015b

Cast iron wood log stove

Alves et al. (2011)

Cast iron wood stove

Gonçalves et al. (2011) Schmidl et al. (2011)

Wood stove Chimney wood stove 1

Soft wood Hard wood Soft wood Hard wood

32.3–51.6 34.1–42.9 49.2 45–53.6 30–50 35.1–51.6 26.8–38.8 15.8–42.8 22.2–35.6 39.3 45.1–55.2 26.1 19.7–42.8 43.6–77.8 51.2–59.4 15.8–77.8

8.4–30.8 12.2–35.2 3.9 1.9–7.7 0.8–9.3 16.8–40.3 24.2–31.6 7.3–41.7 29.8–37 12.4 2.91–6.46 37.1 11.3–24.3 7.6–21.9 3.3–22.8 0.82–41.7

1.1–6.1 1.07–3.4 12.6 5.9–28.2 4–51.6 0.87–1.24 1.1–1.2 0.37–5.86 0.74–0.96 4.5 12.9–18.7 0.85 1–4.4 2.95–10.2 2.6–16.8 0.37–51.6

Chimney wood stove 2 Fernandes et al. (2011)

Cast iron stove Log wood stove

Fine et al., 2004b

Wood stove

Range Wood Stoves Vicente et al., 2015c Schmidl et al. (2011) Sippula et al. (2007)

Soft wood Hard wood Soft wood Hard wood Soft wood Hard wood Soft wood Hard wood Soft wood Hard wood

Pellet stove Pellet stove Pellet stove

4 types of wood pellets Wood pellets Commercial pellets Soft wood pellets Hard wood pellets

_ 4.7–22 _ _ _ _

_ 13.7–15.8 _ _ _ _

0.9–4.2 0.3–1.4 4.2 7.9 1.4–7 0.3–7.9

McDonald et al. (2000)

Fireplace

Fine et al., 2004b

Fireplace

Alves et al. (2011)

Fireplace

Gonçalves et al. (2011) Fernandes et al. (2011)

Fireplace Fireplace

Soft wood Hard wood Soft wood Hard wood Soft wood Hard wood _ Soft wood Hard wood

_ _ 79.7–96.5 73.9–103.4 49.2 6.2–15.1 20–40 41 39.9–50 6.2–100

_ _ 3.5–32.5 1.1–4.6 3.9 1.9–53.4 1.1–17 11.75 2.42–6.5 1.1–53.4

3.9 9 2.45–25.6 22.5–68.7 12.6 5.9–28.2 2.23–35.5 4.8 10.7–23.7 2.2–68.7

Range Pellet Stoves

Range Fire Places

monitoring program with a limit value of 1 ng m−3 (DIRECTIVE 2004/ 107/EC). PAH emissions tend to increase during poor combustion. Thus, in a study of time-resolved emissions of particulate PAHs through the combustion cycle in log wood stove and a pellet stove, the highest PAH emissions were found during fast burning under hot, air-starved combustion conditions in both stoves, reporting 40% contribution of PAHs to OA (while the increase in OA was moderate), likely due to thermal degradation of other condensable species with the distribution shifted toward larger molecules (Eriksson et al., 2014). Pagels et al. (2013) reported substantial enhancement of PAHs during start-up, associated with inefficient combustion, similarly, high concentrations of PAHs were found during initial inflaming of wood logs in the experiment by Orasche et al. (2012). Hytönen et al. (2009) found 7–14 times higher PAH emissions during smoldering combustion conditions, compared to more stable combustion, with the gas-particle distribution shifting towards the particle phase due to PAHs condensation increase (Hytönen et al., 2009). Lamberg et al. (2011) found that 38–58% of total PAH by mass were genotoxic under incomplete combustion conditions. They, furthermore, found that while appliances at various combustion conditions could have roughly similar PM1 emissions, their PAH emissions decrease 10fold when combustion conditions are improved (Lamberg et al., 2011). Orasche et al. (2012, 2013) calculated toxic equivalency (TEQ) values on the basis of PAH emissions from various combinations of wood fuel and combustion systems (Orasche et al., 2012) and for various combustion conditions (Orasche et al., 2013) (using the approach developed by The German Research Foundation). TEQ is calculated from the mass of the individual PAHs multiplied with a Toxic Equivalence Factor (TEF). The TEF of benzo[a]pyrene was set to 1, and the other PAHs

to monitor RWC emission trends in ambient air (Viana et al., 2016). In several wood combustion experiments PAH concentrations were found to be positively associated with combustion temperatures and burn rates, i.e. the amount of fuel burnt per unit of time (Fine et al., 2004b; Fitzpatrick et al., 2007; Gonçalves et al., 2011; Jordan and Seen, 2005; Lamberg et al., 2011; Orasche et al., 2012, 2013; Pettersson et al., 2011; Avagyan et al., 2016; Fachinger et al., 2017; Nyström et al., 2017). Phenanthrene, fluoranthene, pyrene, BaP, fluorene, and anthracene have been reported in high concentrations during wood combustion experiments (Table S1). In experiments involving burning of birch logs in a soapstone stove, fluorene, phenanthrene, anthracene, fluoranthene, and pyrene were reported to contribute more than 70% of the total PAH mass with 31 PAHs analyzed by gas chromatographymass spectrometry (GC-MS) by Hedberg et al. (2002). Hays et al. (2003) applying direct thermal desorption GC-MS found that 27 individual PAHs contributed between 0.01 and 0.07% of PM2.5 mass emissions from two types of wood, with BaP as a major component (Hays et al., 2003). Gullett et al. (2003) found the range of 0.12–0.38% (depending on the wood and facility type) mass contribution of 32 PAHs (GC-MS) to the PM mass. Weimer et al. (2008), using an Aerodyne quadrupole aerosol MS, reported 0.4–2.2% contribution of PAHs to the total OM signal, with the exception of 30% contribution during burning of bark. Although, the values of PAHs contribution to PM in the abovementioned experiments cannot be directly compared due to differences in the methodologies and sampling conditions, and due to the associated uncertainties and differences in the number of individual compounds analyzed, PAHs are typically shown to constitute a small fraction of the PM mass. However, several of the PAHs are known to be toxic and/or carcinogenic at very low concentrations. As an example, the carcinogenic BaP is monitored in the European air quality 6

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calculated relative to BaP. Higher TEQ values were found for log wood stoves compared with automatically operated pellet boilers. Importantly, the variations in TEQ with combustion conditions were even larger than the variations in total PM mass emissions; the ratio of the sum of PAHs from log wood stove to the sum of the same individual PAHs from a pellet boiler was 50 compared with 200 when using TEQ values (Orasche et al., 2012). This implies that the way of determining emissions from wood combustion by PM mass does not take into account PAH potential toxicity which leads to underestimating the emissions health impact (Hedberg et al., 2002; Orasche et al., 2012; Lamberg et al., 2011). PAHs have limited value as tracer compounds for RWC since they degrade during atmospheric transport (Schauer et al., 2003), they are also produced by other sources (Abdel-Shafy and Mansour, 2016; Srogi, 2007), and they are chemically unstable in PM captured for offline analysis. For example, the compound retene, which is notably present in the emissions from burning soft wood, is labile, restraining its use as a tracer compound for wood smoke in studies with integrated filter samples (Fine et al., 2004b; Gonçalves et al., 2011; Schmidl et al., 2008). PAH derivatives which are oxygenated (OPAH) and nitrated compounds (NPAH) are emitted either directly during the combustion process, but can also be formed during gas-phase oxidation by atmospheric oxidants or on the particle surface. PAH derivatives may account for a substantial fraction of SOA in urban locations (Chen et al., 2016).

Table 2 Levoglucosan/Mannosan ratios reported in wood combustion experiments (Sw–soft wood; Hw–hard wood).

3.4. Anhydrosugars Levoglucosan is the most abundant organic compound emitted from the thermal breakdown of wood constituents (Simoneit, 2002; Simoneit et al., 1999) and was introduced as a specific tracer for wood burning by Simoneit et al. (1999). Levoglucosan is formed from the pyrolysis of cellulose (the main constituent of wood) at temperatures higher than 300 °C. Other anhydrosugars, i.e. monosaccharide anhydrides (MAs), mannosan and galactosan (isomers of C6H10O5), are also formed during pyrolysis of hemicellulose. However, these are emitted in substantially lower amounts. All three MAs are present in the emissions of any type of wood combustion. Higher mannose levels in the hemicelluloses of soft wood lead to higher emission of mannosan and ratios of levoglucosan/mannosan have been proposed as an indicator of the type of wood combusted, with higher ratios typical for hardwoods and lower for softwoods (Bari et al., 2009; Fine et al., 2004a; Schmidl et al., 2008). The literature on studies for various wood types confirms consistently higher levoglucosan/mannosan ratio for hardwoods, although, the value of the ratio for the same wood type varies between experiments (Table 2). Levoglucosan concentrations change through the combustion cycle, decreasing due to elevated temperatures and improved combustion efficiency (Corbin et al., 2015b; Jordan and Seen, 2005; Schmidl et al., 2008; Weimer et al., 2008). Levoglucosan has been measured in filter samples by offline analytical techniques, typically by use of LC-MS (Yttri et al., 2007) or GC-MS (Fine et al., 2001), or online from its molecular fragments (mass to charge ratio (m/z) 60 and 73, corresponding to C2H4O2+ and C3H5O2+, respectively) by use of AMS (Alfarra et al., 2007). C2H4O2+ signal spiked during the start-up phase and addition of each batch, and then died away thereafter during the flaming phase, when most volatilized organics were destroyed in the flames (Corbin et al., 2015b). In a study comparing mass spectra of emissions from a log wood stove with similar emissions from an automatic burner, Weimer et al. (2008) reported less pronounced m/z 60 and 73 for the automatic system, owing this to further decomposition of levoglucosan during more complete combustion (Weimer et al., 2008). As mentioned earlier, effective combustion as in automatically operated combustion systems, characterized by decreased share of organic emissions in favor of inorganic particles (Corbin et al., 2015a; Weimer

Wood Type

Wood Class

Stove Description

Lev/Man

Ref.

Douglas Fir Loblolly Pine White Oak Red Maple Sugar Maple Spruce

Sw Sw Hw Hw Hw Sw

Catalyst-equipped wood stove

3.5–5.8 5.5 22.7–26 19.3 16.3 3.6

Fine et al., 2004b

Larch Beech Oak Birch

Sw Hw Hw Hw

Spruce Briquettes Beech Oak Maritime Pine Eucalyptus Cork Oak Golden Wattle Maritime Pine Golden Wattle Holm Oak Eucalypt Olive Cork oak Portuguese oak Briquettes Spruce Spruce briquettes Beech Spruce Beech Conifer Beech

Sw Sw Hw Hw Sw Hw Hw Hw Sw Hw Hw Hw Hw Hw Hw Hw Sw Sw Hw Sw Hw Sw Hw

Tiled stove typical for Austria

Finnish sauna stove Masonry heater Common in midEurope “Chimney type” log wood stove Chimney type logwood stove Portugal wood stove

Log Wood Stove Log Wood Stove Log Wood Stove

3.9 14.8 14.4 32 16.4 3.5 2.5 14 17 3.0 34.9 24.8 10.4 1.2–1.4 1.3–8.9 1.6–16.5 7 3.3 2.2–5.5 1.6 1.4–30 2.3 3.3–7.4 16.1–52.6 5.2–10 27.3–31 3 14.5

Schmidl et al. (2008)

Saarnio et al. (2012) Schmidl et al. (2011) Gonçalves et al. (2010) Gonçalves et al. (2011)

Orasche et al. (2013) Orasche et al. (2012) Corsini et al. (2017)

et al., 2008; Fachinger et al., 2017; Corsini et al., 2017; Vicente et al., 2015c; Ozgen et al., 2017). After detecting anhydrous sugars only during the start-up phase for automatically fired appliances, Schmidl et al. (2011) concluded that levoglucosan is a more suitable tracer for manually fired systems and less suitable for the automated fired wood combustion techniques, for which potassium (K+) could serve as a better tracer compound. Similarly, Harrison et al. (2012) suggested that it may be possible to differentiate the wood smoke coming from wood stoves and fireplaces by a high content of carbonaceous species, including levoglucosan, and one from modern appliances such as pellet burners with a high burn-out efficiency, best represented by K. A further complication to this issue is the atmospheric degradation of organic tracers such as levoglucosan by atmospheric oxidants, e.g. the hydroxyl radical (Lai et al., 2014), while elements such as potassium are inert and only prone to loss by deposition. 3.5. Other organic compounds In wood combustion experiments, VOC emissions are dominated by methane that constitutes on average 41–54% of total VOC mass (Boman et al., 2011; Pettersson et al., 2011; Tissari et al., 2007). Methane is followed by high emissions of non-methane VOCs: ethane, acetylene, ethane, benzene, toluene (Boman et al., 2011; McDonald et al., 2000; Pettersson et al., 2011; Tissari et al., 2007). Formaldehyde and acetaldehyde are also very common for the emissions from RWC appliances (McDonald et al., 2000; Tissari et al., 2007). However, the presence of these compounds in the emissions is typical for a combustion process. Due to the differences in natural lignin structure of soft and 7

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hardwoods, hardwoods release higher amounts of syringyl compounds, whereas softwoods release higher amounts of guaiacyl and vanillyl compounds (Table S1), making these compounds representative of RWC and potentially useful for differentiation between the wood types. Resin acids are emitted during combustion of coniferous wood. Isopimaric, dehydroabietic (DHA), abietic, and pimaric acids were detected as significant components of softwood emissions (Fine et al., 2004b; Gonçalves et al., 2010, 2011). These compounds were reported to occur mainly during the inflaming phase, with higher amounts during cold start-up ignition, and decay during more effective combustion. Therefore, emissions of resin acids were lower for pellet burning systems compared with log burning stoves (Gonçalves et al., 2011; Orasche et al., 2012), and can be used mainly for characterizing manually-operated wood stoves. Terpenes, i.e. 3-carene, pinene, limonene, were emitted in higher amounts during the combustion of soft wood (pine) compared with hard woods (McDonald et al., 2000; Pettersson et al., 2011). High amounts of 4-methylsyringole was shown to be emitted during the combustion of beech wood and may thus serve as a specific tracer for hardwoods (Orasche et al., 2012). Friedelin was proposed as a specific tracer for white oak (Fine et al., 2004b). Stigmasterol was found in the smoke of only hardwood species and might serve as a potential tracer for the smoke from deciduous trees (Fine et al., 2004b; Gonçalves et al., 2010, 2011). In a study with emissions from different types of biomass burning, combustion of leaves from a broadleaf shrub was characterized by significant emissions of inositols and arabitols, which indicates their potential use as tracers for green foliage combustion (Zhang et al., 2013). Gonçalves et al. (2010) underlined that methosyphenols, phytosterols, resin acids have not been found in emissions from gasoline and diesel powered vehicles, neither were they pointed out as tracers for meat cooking, and plastic burning, and therefore they may serve as tracers for biomass burning source in source apportionment studies. Another approach is to use ultrahigh resolution mass spectrometry to identify classes of compounds associated to RWC based on field and laboratory studies. In this way, variations in filter content of CXHYOZ, CXHYOZNW, CXHYOZSP, CXHYOZNWSP during atmospheric aging, and different seasons were studied using laboratory generated wood burning particles as reference (Daellenbach et al., 2019). BrC is light absorbing organic aerosol and is composed by a wide range of species with different absorption characteristics, which are largely unknown. However, biomass combustion is believed to be a major source of BrC (Kumar et al., 2018). BrC absorbs light more strongly at shorter wavelengths in the UV/VIS spectrum (Andreae and Gelencser, 2006) and contributes to a positive radiative forcing (Jo et al., 2016). BrC represents a substantial part of SOA formed during atmospheric oxidation of biomass burning (Kumar et al., 2018).

Obviously, large differences in modelled particulate matter concentrations from residential wood combustion will result depending on which emission data is fed into the models. Still, the current way of addressing RWC only accounts for primary particulate matter. That is, particulate matter which is mainly in the condensed phase at ambient conditions (dilution, temperature and pressure) at the time it was emitted. An emerging issue is to account for the Volatile (VOC) and SemiVolatile Organic Compounds (SVOC) that are emitted during RWC and chemically transformed to particulate matter over time. Factors that influence SOA formation include precursor mixture, organic aerosol concentration, oxidant type as well as concentrations and duration of aging, multiphase chemistry, temperature, relative humidity, and radical branching (Hallquist et al., 2009). During aging, oxidation of biomass burning emissions produce Secondary Organic Aerosols (SOA) similar to or exceeding the Primary Organic Aerosol (POA) mass by up to a factor of 3–7 (Bruns et al., 2016; Corbin et al., 2015a; Bertrand et al., 2017). Experiments have demonstrated that SOA accounts for a substantial part of the total Organic Aerosol (OA). In a study on formation of Brown Carbon (BrC) in POA and SOA particle fractions, Kumar et al. (2018) measured a SOA/POA ratio of about 5 after aging, and concluded that formation of SOA in biomass burning plumes is a relevant source of BrC. However, field and laboratory studies indicate that the SOA mass produced during biomass burning is highly variable. For example, Tkacik et al. (2017) reported an average enhancement of OA by 1.78 in experiments, where biomass burning emissions from the same fire were filled into identical Teflon bags, one of them being perturbed. Perturbations were exposure to UV-light, UV-light and nitrous acid, or exposure to ozone in the absence of light (Tkacik et al., 2017). By comparison of the mass concentrations in the two Teflon bags, final yields spanned a rage from 30% loss to 440% enhancement (Tkacik et al., 2017). Under ambient conditions, these enhancements will probably be lower due to dilution of the smoke and following evaporation of semi-volatile compounds from the particle phase. Tkacik et al. (2017) concluded that the variability between repetitions due to the uncontrolled nature of biomass burning was larger than the effect of the particular perturbation. Still, ozone produced on average the largest OA enhancements. A large fraction of emitted VOCs remains unidentified. Recently, more effort has been allocated to characterize emitted VOCs, since SOA formation cannot be explained by known precursors alone(Hatch et al., 2015; Yokelson et al., 2013; Warneke et al., 2011). For example, Hatch et al. (2015) identified 708 compounds including abundant isomers of aliphatic and aromatic hydrocarbons, phenol derivatives, monoterpenes, and sesquiterpenes. They calculated that aromatic compounds contributed mostly by 31–78% of SOA from the selected fuels. However, they also demonstrated a large potential for furans using published SOA yields. It is speculated that part of the unidentified VOCs from biomass combustion are high molecular weight compounds, which are rapidly transformed to SOA (Bhattu et al., 2019; Tkacik et al., 2017). The poor understanding of SOA has implications for modelling of atmospheric aerosols and greatly contributes to uncertainties in their climate and healtheffects estimates.

3.6. Secondary Organic Aerosols Biomass burning is the largest source of fine primary particles worldwide, and the second largest source of Non-Methane Volatile Organic Compounds (NMVOC) (Akagi et al., 2011; Bond et al., 2004; Crutzen and Andreae, 1990), which are the precursors for Secondary Organic Compounds (SOA). In fact, emitted NMVOCs from biomass combustion exceed fine particulate matter by far. Yokelson and coworkers found an average NMVOC/PM2.5 mass ratio of around 3 from a comprehensive set of measurements of biomass burning emissions (Yokelson et al., 2013). Absolute emissions strongly depend on appliance type with automatic devices emitting methane and NMVOCs 1-3 orders of magnitude lower than batch operated devises (Bhattu et al., 2019). Emission factors vary greatly amongst studies and countries due to differences in appliances, fuel and combustion conditions within the test protocols, but also depending on whether particulate matter is sampled from a hot flue gas or a diluted one (section 2.4.1). In this way emissions factors can vary between 106 mg MJ−1 in the Germany test protocol and 1297 mg MJ−1 in the Norwegian protocol (section 2.4.1).

3.7. Summary The reviewed emission studies have large differences in experimental design and methods, nevertheless their results point to the quality of combustion as the main determinant driving physico-chemical characteristics of the emissions. Effective combustion conditions lead to reductions in PM, BC, CO, PAH mass emissions, therefore in emissions of carbonaceous particles, and to lower toxicity of PAH emissions. The levoglucosan/manosan ratio can serve as an indicator of the type of wood combusted, with higher ratios typical for hardwoods and lower for softwoods. Although levoglucosan is a highly specific wood burning tracer, its yield can vary according to the combustion efficiency. In addition to levoglucosan and potassium, a number of organic 8

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compounds exhibit marked differences in relative abundance of emissions from different types of wood and therefore might be used as specific indicators for wood burning in source apportionment studies. None of the standard methods at the moment is capable to account for secondary PM formed during gas-phase oxidation of VOCs released from biomass combustion. This creates an additional obstacle for improving the quality of existing emission inventories and precision of modeling RWC impact on climate and on human health, through accessing the scale of toxic compounds emissions.

no major roads or other emission sources, but on average every house had 2.3 furnaces, indicated short-time peaks (up to 1000 μg m−3 minute averages), which were assumed to be explained by local wood combustion (Hellen et al., 2008). Molnar and Sallsten (2013) found an increment of 0.9 μg m−3 in the Swedish village of Tanumshede (with mean winter PM2.5 concentration 5.6 μg m−3) for the days when the daily mean temperature was below 0 °C. In addition to PM concentrations, concentrations of PAHs (Glasius et al., 2008; Hellen et al., 2008; Wåhlin et al., 2010; Hellen et al., 2017), VOCs (Hellen et al., 2008), BC (Molnar and Sallsten, 2013), soot, and wood combustion tracers (Rad et al., 2018) were also elevated at the residential sites. RWC contributed on average 10% of measured PM10 during two months of winter and summer seasons (Nov.–Dec. 2011; Jun.–Jul. 2012) at both rural and urban Danish sites (Massling et al., 2011; Nøjgaard et al., 2015). The share of wood burning in PM2.5 reached ≈28% (average of 7 weeks measurements in Dec.2003–Feb.2004) in areas close to the source (Glasius et al., 2006; Olesen et al., 2012). RWC represented the second largest source of EC (36%) in Denmark and contributed 22% to OC according to the averages of two campaigns in winter 2011 and summer 2012 (Nov.–Dec. 2011; Jun.–Jul. 2012) at both rural and urban sites (Nøjgaard et al., 2015). Contributions of ≈25% to ambient PM2.5 were reported at urban sites in Norway and Finland, where winters are longer and colder, and RWC has been estimated to contribute an even greater fraction of PM2.5 at suburban sites in Helsinki during cold seasons of 2008–2009. In Finland, sauna stoves are traditionally used twice a week and are operated for a short time and at a high combustion rate (Tissari et al., 2009). Thus, RWC shares of 66% for PM2.5 were reported for Oct. to Dec. 2008 in suburban area of Helsinki (Saarnio et al., 2012). In Sweden during winter (Jan.–Mar. 2002) the contribution from RWC to PM2.5 in the residential area of Lycksele, a typical northern inland city, was found to be 18% (Denby et al., 2010). Krecl et al. (2008) reported the contributions from RWC to be 44–57% to 25–606 nm particles, 36–82% to PM10, and 31–83% to PM1 in Lucksele during winter 2005/2006. Measurements were conducted in the same city, highlighting enhanced contributions during weekends. Increased RWC shares of OC and EC were found at a rural site compared to an urban site in Gothenburg, Sweden (Szidat et al., 2009), whereas in Oslo, Norway, RWC accounted for almost all particulate OC at the suburban site in winter (Yttri et al., 2009). RWC in the Nordic countries has been reported as the largest source of total BC emissions (ACAP, 2014; Nøjgaard et al., 2015).

4. RWC source contribution to ambient particulate concentrations at different locations 4.1. Europe Multiple European studies have assessed the influence of the wood smoke/or RWC on the ambient PM concentrations (Tables S3-5). 4.1.1. Nordic countries In Nordic countries, where winter temperatures are often sub-zero, wood stoves are commonly used in private houses as an additional and cheaper source of heating and as a part of interior design. Wood stoves with a closed metal chamber and adjustable air supply are widespread. Masonry heaters and sauna stoves are less common, with the exception of Finland, where they are the main wood burning appliances (ACAP, 2014). In Finland, 23% of detached houses have a wood heater as primary heating source, and in nearly 90% of new detached houses are equipped with one or several wood stoves (Savolahti et al., 2019). Residential heating installations (open fireplaces and ovens) in Norway are estimated to reach over 2.5 million (Lopez-Aparicio et al., 2018; Grythe et al., 2019). In Denmark, according to the survey in 2015, 842,000 wood burning appliances are estimated to be in use, with 57% of those produced before 2005 (23% before 1990 and 35% between 1990 and 2005) (EA Energianalyse, 2016). Thus, RWC is an important PM source in Nordic countries during colder seasons (Im et al., 2019; Kindbom et al., 2019) (Table S3), e.g. for year 2016 emissions in Denmark, RWC was modelled to contribute to 66% of PM2.5, 53% of BC, 68% of PAHs emissions and 59% of dioxin and furans (Nielsen et al., 2018). Kukkonen et al. (2019) modelled the influence of RWC on ambient air quality in four Nordic countries: in the metropolitan areas of Copenhagen (Denmark), Oslo (Norway), Helsinki (Finland), and Umeå (Sweden). Wood as fuel was used mainly for heating in larger blocks and flats in Oslo, in Copenhagen and Helsinki for heating outside of the city center and suburban areas, while in Umeå both in the city center and its surroundings (Kukkonen et al., 2019). RWC contributed 0–15%, 0–20%, 8–30%, 0–60% to annual average PM2.5 concentrations (ranging spatially within the urban regions) in Helsinki, Copenhagen, Umeå and Oslo, respectively (Kukkonen et al., 2019). A number of Nordic studies (Table S3) have evaluated the impact of local wood burning stoves on the air quality in small, rural, residential areas. These studies used a paired sampling approach in areas with a high density of houses actively using wood-combustion for heating and at background sites situated upstream of the residential areas. The increment in PM, soot, wood smoke tracers, and other chemical species concentrations (e.g. PAH, VOC) between the sites were quantified. Among three Danish campaigns, two were implemented in villages where 50% of the houses were equipped with wood stoves. Increments of PM2.5 during a winter period equal to 4 μg m−3 were found in Gundsømagle (where the average winter PM2.5 concentration of 16 μg m−3 was comparable to the average ambient PM2.5 at a busy street in Copenhagen) (Glasius et al., 2006), and equal to 2 μg m−3 during an unusually warm winter in Slagslunde (Olesen et al., 2012; Wåhlin et al., 2010). An increment of 1.2 μg m−3 was reported in the third campaign, carried out in a village with a moderate use of wood stoves (Glasius et al., 2008). Continuous measurements of PM2.5 concentrations in a residential area of Kurkimäki (Finland), where there are

4.1.2. Europe A two-year study of carbonaceous aerosol, conducted within the framework of the CARBOSOL (A Study of the Present and Retrospective State of the Organic Versus Inorganic Aerosol over Europe) project (Legrand and Puxbaum, 2007), revealed a surprisingly high winter contribution of wood smoke to organic matter in the air of east-west transect across Europe of six rural and background sites representing oceanic, rural, and continental environments (Gelencsér et al., 2007; Lukács et al., 2007; Puxbaum et al., 2007), supporting earlier publications focusing on the importance of RWC (Kupiainen and Klimont, 2007; Sillanpää et al., 2005) (Table S4). Using CARBOSOL data, Puxbaum et al. (2007) assessed the impact of biomass combustion on the European PM2.5 aerosol background by the determination of levoglucosan concentrations and application of wood-type-specific conversion factors. The sites were classified in low-level and high-level sampling sites. High relative concentrations of biomass smoke in OM of measured PM2.5 were observed at all sites during winter, ranging from 18% at the maritime site to 68% at the other two low-level sites, giving typical mid and west European levels of the biomass smoke contributions of around 10–30% on an annual basis and around 20–50% during the cold season (Puxbaum et al., 2007). These findings were confirmed by source apportionment of radiocarbon measurements with ranges of 9%–64% for wood smoke shares in primary organic PM2.5, and an exceptionally high share in winter SOA (55–80%) with increased primary 9

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Table 3 Biomass Burning Organic Aerosols (BBOA) source contribution to OA (PM1) calculated by multivariate models applied to aerosol mass spectrometry data. Site

Type

Season

Type of AMS

Method

BBOA %

Ref.

Switzerland Zurich Switzerland Zurich Switzerland Massongex France Grenoble Spain Barcelona Spain Montseny Spain Montsec Germany Augsburg

Urban background

Jul.–Aug. 2005

Aerodyne AMS

PMF

10

Lanz et al. (2007)

Urban background

Jan. 2006

Aerodyne AMS

ME-2

35–40

Lanz et al. (2008)

Rural/industrial

Nov.–Dec. 2006

Aerodyne AMS

ME-2

49

Perron et al. (2010)

Urban background

Jan. 2009

c-TOF AMS

PMF

38

Favez et al. (2010)

Urban background

Feb.–Mar. 2009

HR-ToF-AMS

PMF

11

Mohr et al. (2012)

Regional background

Winter 2012–2013

ACSM

ME-2

28

Minguillón et al. (2015)

Regional background

Winter 2011–2012

ACSM

ME-2

24

Ripoll et al. (2015)

Urban background

Jan.–Mar. 2010

Aerodyne AMS

PMF

23

Elsasser et al. (2012)

emissions at surface sites and secondary production at mountain sites (Gelencsér et al., 2007). Lukács et al. (2007) showed that water-soluble brown carbon concentrations exhibit pronounced seasonal patterns at all continental sites, and RWC was identified as the major source for brown carbon in the late fall-winter period. OC/dehydroabietic acid ratios from Fine et al. (2004b) were used to produce a rough estimate of wood burning contribution in winter of 85% (OC) at the project lowland site (Oliveira et al., 2007). Although, the precise contribution shows considerable variation in time and space, the results of the studies demonstrate a large influence of RWC on ambient OC, thus, during colder seasons RWC comprised more than 40% OC in PM10 (Bari et al., 2009; Maenhaut et al., 2012; Piazzalunga et al., 2013) and in PM2.5 (Favez et al., 2009; Gelencsér et al., 2007; Saarikoski et al., 2008; Golly et al., 2019). In the OC fraction, submicron Biomass Burning Organic Aerosols (BBOA) (see section S2.5) are typically in the order of 10–40% in European rural and urban background (Table 3). Data on RWC contributions to EC is scarce and mainly estimated for various winter seasons at sites in the Alpine Region (Table S5), e.g. with as high RWC contribution as 56% to EC in PM10 at the rural site of Magadino in winter 2008–2009 (Gianini et al., 2012; Herich et al., 2014), and as 49% to EC in PM2.5 at the rural site of Ispra in winter 2007 (Gilardoni et al., 2011; Herich et al., 2014). In the southern parts of Europe, RWC source is less important than traffic in terms of its influence on PM ambient levels. However, wood burning has been found to contribute by 30% to ambient BC in the Athens Metropolitan Area (Kalogridis et al., 2018). Biomass burning shares of 64% of OC within TC for winter seasons of 2002–2004 were reported for the rural site in Portugal (Gelencsér et al., 2007) and RWC contribution to OC in measured PM1 was around 20% in Barcelona for March 2009 (Alves et al., 2012; Minguillón et al., 2011; Reche et al., 2012). In Barcelona, where 98% of domestic heating is based on natural gas, January, 2011 RWC monthly average contribution to PM2.5 was found to be around 8% (Viana et al., 2013), whereas on an annual (2009) basis Reche et al. (2012) found contribution of around 3% to PM2.5 in Barcelona. Alves et al. (2012) performed short-term (from 6 days to 1 month, depending on the site) measurements of organic compounds across 7 European sites, reporting RWC as the dominant emission source at the Swiss urban location (Zurich) with shares of 12–38% to OC in PM1. In an apportionment study on the framework of the AIRUSE-LIFE + project with 5 Southern European cities throughout 2013 (Athens, Barcelona, Florence, Milano, and Porto) with the use of a harmonized PM speciation protocols, PMF5, and levoglucosan concentration analysis, biomass burning contributed 14–24% to annually averaged PM10 and within18–21% to annually averaged PM2.5 in the Porto traffic site, Milano and Florence urban background sites, whereas contributions of 7% and 11% to annually averaged PM10 and PM2.5

respectively were found for Athens suburban site, and below 2% contributions to annual PM10 and PM2.5 at Barcelona urban background (Amato et al., 2015). Using radiocarbon analysis (see section S2.2) of atmospheric PAHs sampled at two background sites, i.e. in Croatia (spring of 2003) and on the island of Crete, Greece (summer of 2003) , biomass burning was estimated to contribute around 10% to the total PAH burden (Mandalakis et al., 2005). A recent Greek study demonstrated association between increased PM2.5 and wood burning tracers’ concentrations, relating this to the expanding use of wood in domestic heating in the city of Thessaloniki due to the crisis of 2013 (Saffari et al., 2013). Cordell et al. (2016) have examined the effect of biomass burning on air quality at five sites in Northwestern Europe, including Netherlands, Belgium, northern France, and the East Midlands of England. They found a distinct biomass burning period stretching from November to March, however, at the northern France site, for which the wood burning contribution to PM10 was highest (11.6%), biomass burning was also registered in spring (Cordell et al., 2016). The contribution of RWC in Flanders, Belgium, has been recently re-estimated, and found to be the most important source for all primary PM emissions with contribution of 37% (Lefebvre et al., 2016). 4.1.3. Alpine Region (Austria, Switzerland, northern Italy) The presence of deep large valleys, which increases local concentrations and decreases spatial variability, local meteorological conditions (e.g. inversion layers during winter), and wide use of wood stoves, cause Alpine regions to be especially affected by RWC (Table 3; Table S5) (Herich et al., 2014; Herich, 2013). Comparison of data from 1998/1999 and 2008/2009 from various sites in Switzerland indicates that wood combustion emissions have not decreased and make an approximately equal contribution to primary PM10 as road traffic emissions (Gianini et al., 2012). For example, during winter months of 2007, wood burning composed 64% ( ± 15%) of the TC concentrations of PM2.5 in the Po Valley, Northern Italy (Gilardoni et al., 2011). Wood burning was reported to contribute 24–33% to total BC concentration (0.8–3.03 μg m−3) as determined from seven-wavelength aethalometer data for three sites in Switzerland (Herich et al., 2011). The non-refractory PM1 mass (see sections S1.2, S2.5) concentrations measured by AMS instruments in 13 short-term campaigns across the greater Alpine region through 2002–2009 were reported to range from 10 to 30 μg m−3 (and could reach up to ≈60 μg m−3 during winter persistent thermal inversions events), consisting from 36% to 81% of the Organic Aerosols (OA) that were strongly influenced by wood burning emissions (26–49% OA) in winter (Lanz et al., 2010). In the Arve Valley, where the 24hrs concentration of 50 μg m−3 (PM10 European Union limit value) is frequently exceeded (45 days in 10

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2012, 58 in 2013, and 46 in 2014), contribution of wood smoke to BC (PM10) reached almost 50% (Chevrier et al., 2016). Under the research of DECOMBIO program (2013–2018) the impact of a large-scale renewal of non-efficient wood-burning appliances on PM10 concentrations in the Arve Valley was evaluated (Allard, 2018). A gradual decrease of PM10(resulted from wood burning) concentrations during winters at the 3 sites was reported for certain weather conditions, providing a tool for wood burning emissions monitoring in the area (Allard, 2018).

(Table S6). Generally, RWC is an important source of ambient particulate pollution in rural communities, where wood heating appliances are more common, and in colder climates where the frequency of burning wood is higher. Thus, a high RWC contribution to PM2.5 mass during winter were shown for Alaska (Ward et al., 2012), Montana (Ward and Lange, 2010; Ward et al., 2006), Port Angeles (Gaston et al., 2016). In Canada RWC was reported to contribute 70–84% of PM2.5 in Whitehorse, Yukon, during winter 2009 (Jones et al., 2014). Applying combination of fixed and mobile monitoring around Vancouver (LUR model) during two weeks in winter 2004–2005. Larson et al. (2007) identified the location of elevated, persistent night-time levels of fine fraction particles that were not captured by a relatively dense regulatory ambient monitoring network, with the correlation between model predictions of night-time light scattering and levoglucosan, which is consistent with the presence of wood smoke (Larson et al., 2007). Few studies have assessed the contribution of RWC source to the EC, OC fractions in North America. A Californian study apportioned 11% of submicron organic aerosols to be BBOA (Williams et al., 2010). Sheesley et al. (2007) reported 30–50% annual contribution from biomass smoke to fine particulate OC in North Carolina, US. During the winter months wood burning contributed 17–47% to OC fraction of PM0.1 in northern California (Xue et al., 2019) and 41% to OC (PM2.5) in central California (Gorin et al., 2006). RWC was a more important source than diesel for OC in wintertime in an urban valley of the Western US (Chen et al., 2012).

4.1.4. UK According to UK National Atmospheric Emission Inventory, due to recent increase in wood burning in the UK (Mitchell et al., 2017), residential combustion becomes the most significant source of primary PM10 and PM2.5 emissions (NAEI, 2018) accounting for 26% of PM10 and 41% of PM2.5 in 2017 (Richmond et al., 2019) (Table S4). In London RWC contribution of 9% to ambient monthly averaged PM10 (Jan.–Feb., 2010) was calculated on the basis of levoglucosan concentrations (Fuller et al., 2014), whereas another paper reported RWC contributions below 1% to annually averaged PM2.5 (Harrison et al., 2012). The project Clean Air for London (ClearfLo), based on January-February 2012 sampling campaign, reported wood smoke contribution of 15% and 28% to OC, and 4% and 7% to PM2.5, in London and Harwell respectively (Yin et al., 2015). Furthermore, the wood smoke mass in the range of 0.78–1.0 μg m−3 was found for London background urban site and two rural sites, with the peaks in levoglucosan and K+ concentrations during low ambient temperatures (Crilley et al., 2015). Using the long-term (2009–2016) data, Font and Fuller (2017) estimated PM from wood burning across 16 urban and four rural locations in the UK. Wood burning was measured almost exclusively in winters, in the evening, and greater in weekends, and since it was poorly correlated with daily temperature, it was concluded that wood burning in the UK is in larger part serves as decorative and not heating purposes (Font and Fuller, 2017). Jang et al. (2013) performed source apportionment with a dataset of 29 individual PAH collected from 14 UK urban sites between 2002 and 2006 and reported RWC source to be responsible for 13.1% of PAH concentrations (Jang et al., 2013), while for 2017 residential stationary combustion sources were calculated to contribute to 86% of PAHs emissions in the UK (Richmond et al., 2019).

4.3. New Zealand and Australia Outside the winter season, PM10 and PM2.5 concentrations across New Zealand are typically below 15 μg m−3 (Trompetter et al., 2010). During winter, however, PM10 concentrations regularly exceed the National Environmental Standard of 50 μg m−3 (24-h average) across the country (Ministry for the Environment and Statistics New Zealand, 2014). Numerous studies have shown that RWC is responsible for the large wintertime increases in PM10 and PM2.5 concentrations in New Zealand (Table S7) (Ancelet et al., 2012, 2014a; Davy et al., 2012; Tunno et al., 2019). During winter, RWC combustion generally contributes between 60 and 90% to PM10 concentrations in residential areas. The significant RWC contributions result in bimodal diurnal PM10 concentration profiles that are similar across New Zealand, featuring a large peak in concentrations between 22:00 and 0:00, and a smaller peak between 8:00 and 11:00, showing that residents typically re-light their fire in the morning (Ancelet et al., 2012, 2014b, 2014c; Trompetter et al., 2010). RWC contributions are not correlated with population, with some of New Zealand's smallest towns having the highest PM concentrations in the country, mainly caused by RWC emissions. Consistent with the dominance of RWC during winter, a recent study in Masterton, New Zealand found that OC contributed 31% of PM10 during winter (Ancelet et al., 2013). An emerging concern in New Zealand is the association of As with RWC, which is caused by Cu–Cr arsenate (CCA)-treated timber burned in wood stoves by residents (Ancelet et al., 2012, 2014b; Davy et al., 2012). It is very likely that wintertime As emissions cause annual average As concentrations to exceed the ambient air quality guideline value of 5.5 ng m−3 in a number of locations across the country (Ancelet et al., 2015). A study of extreme air pollution events causes in six cities from three distinct eco-climatic regions in Australia, showed that for the mainland cities, bushfires were the most frequent cause of these events, which mainly occurred during summer, while on the southern island of Tasmania (which experiences the coolest conditions in Australia), elevated PM occurred mainly during winter (Table S8) and most probably came from woodstoves (Johnston et al., 2011). This study offered partial confirmation of earlier studies in Launceston, the only inland city in Tasmania (Jordan et al., 2006), where a wood heater replacement program (2001–2004) led to a reduction in the prevalence of

4.2. US and Canada Although in recent decades natural gas, electricity, and petroleum products have become the main sources of heating energy for US households, the rising price of non-wood energy has had a positive effect on wood consumption and offset the downward trend effect in the last decade in the US (Song et al., 2012). Approximately 11.6 million homes in the US use wood burning as a heating source, of those 7.8 million have older, inefficient devices, and 2.8 million use wood as a primary heating fuel (EPA, 2013; Rogalsky et al., 2014). However, the geographical distribution of RWC as well as frequency of use is poorly documented. There are around 3.6 million wood burning appliances used in Canada (Environment and Climate Change Canada, 2017). It has been estimated that 104 kt of PM2.5 were emitted to the atmosphere in Canada from RWC in 2010, with higher emissions in Quebec, Ontario, and British Columbia (CCME, 2012). RWC in Canada is the second largest source of BC (28%) after diesel transport (McGuire, 2018). For the year 2012, according to the Woodstove Inventory Final Report Ministry of Environment in British Columbia the average age of wood stoves, that constitute 40% of RWC appliances in the region, was around 17 years and of fireplaces, that constitute 60% of RWC appliances, around 30 years. Several North American studies have aimed for quantifying the wood burning contribution to atmospheric particle concentrations 11

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wood stoves as the main source of heating from 66% to 30% of all households (Johnston et al., 2013). Reisen et al. (2013) found wood heaters to be the largest source of PM2.5, with a contribution of 77% to the ambient PM2.5 load compared to an 11% contribution from prescribed burns in the Southern Tasmania, highlighting that wood burning is a persistent night-time issue in this area, in contrast to short prescribed burning events (Reisen et al., 2013). PMF source apportionment of 14 VOCs in the mainland cities of Melbourne, Sydney, and Brisbane resulted in a biomass burning share of 13%, with higher contributions during winter and autumn (Chan et al., 2008), which is indicative of RWC impact. Measurements from a portable Radiance Research M903 nephelometer (Robinson et al., 2007) measured a fourfold increase in pollution levels (mean scattering coefficient) within 41 m in a small town of Armidale, New South Wales, where annual exposure to PM2.5 from wood smoke was more than double that from all sources in Sydney, emphasizing the importance of the emissions on a local scale and its effect on the population. The estimated population exposure from this study suggested that wood heaters increased mortality in this town by 7% (Robinson et al., 2007). A long-term study of fine particle pollution in the Sydney Basin demonstrated that wood heaters in the Liverpool area represented approximately 40% of PM2.5 during winter (Cohen et al., 2011). In Queensland, the contribution of biomass burning varied between 11% and 60% of PM, however, forest fires are frequent in this area, and these contributions cannot be assigned solely to RWC (Friend et al., 2011a, 2011b, 2012, 2013).

PM levels and the carbonaceous component of PM pollution, preventing exceedances of regulatory PM standards, which will result in the improved air quality (EPA, 2013), associated health costs benefits (Sigsgaard et al., 2015; Bjørner et al., 2019; Bailey et al., 2019), and in the reduction of RWC impact on climate. In several countries, the quality of new wood stoves entering the market is controlled by compulsory certification in compliance with stipulated national standards. Thus, US-EPA certifies wood stoves according to a weighted maximum average emission rate of 4.5 g h−1 PM. Regulatory standards for wood heaters in Australia and New Zealand are based on PM emission factors and energy efficiency, which are 2.5 g kg−1 and 55% for Australia and 1.5 g kg−1 and 65% for New Zealand. The Canadian standard, with a PM emission limit of 4.5 g h−1, is voluntary except where regulated provincially or municipally. Most EU member states currently do not regulate wood stove emissions (with the exception of e.g. Denmark, Norway, Sweden, Austria, and Germany). However, the newly adopted European Union Directive on Eco-design for wood stoves, coming into force in 2022, will require compliance with maximum PM emissions of 5 g per kg of fuel (dry matter) using dilution tunnel or 40 mg m−3 using the heated filter method, organic gaseous compounds (OGC), i.e. VOC of 120 mg C m−3, CO of 1500 mg m−3 at 13% O2, and NOx of 200 mg m−3 (OJ, 2015). In a number of European countries, there is a growing market demand for RWC appliances labeled by such voluntary ecological schemes as e.g. the Nordic Ecolabel, the P-mark quality label, The German Din+, the German Der Blau Engel, the French Flamme Verte, and the Austrian Umweltzeichen ecolabel, that have different standard procedures and requirements for emissions. Thus, regulation of RWC emissions by tightening of emission standards for the new technologies is currently in place and in development. However, the current absence of a common standard method for measuring the PM emissions from the RWC appliances limits the comparability and precision of emission factors obtained during different tests, performed under different national standards. Furthermore, the existing standards for certifying new wood stoves are based on mass PM emissions factors. Apart from PM, EC and OC emission factors can be considered as more accurate metrics in respect to PM impact on human health and climate. Moreover, an establishment of a well-defined, common standard test protocol that reflects RWC appliances' emissions under real life operating conditions (with high reproducibility) will further increase the reliability of emission data for the end user and for the emission inventories. This will promote technological development of low emission appliances with possible implementation of the protocol for a market surveillance concept, providing equal possibilities for the appliances’ producers (Reichert and Schmidl, 2018). There is a growing burden of evidence that automatically operated stoves and pellet stoves have substantially lower PM and organic gaseous compounds mass emissions compared to conventional wood stoves due to maintaining effective combustion conditions (Carvalho et al., 2016; Rönnbäck et al., 2016; Fachinger et al., 2017; Klauser et al., 2018; Reichert and Schmidl, 2018). In the recent years the market for wood pellets as a fuel for residential heating in the developed countries has been expanding (Dale et al., 2017; Thrän et al., 2019) and suitability of wood pellet heating for residential households has been recently reviewed (Thomson and Liddell, 2015). In order to further minimize pellet stove PM emissions, pellets composition calls for more attention (Arranz et al., 2015; Chandrasekaran et al., 2012). The replacement of older stoves by new certified ones can substantially decrease the RWC contribution to the ambient PM (Sigsgaard et al., 2015; Bailey et al., 2019; Allard, 2018) including EC and OC concentrations (Schleicher et al., 2011). Simulations show that replacement of current residential wood combustion technologies with pellets stoves can decrease the total outdoor PM2.5 mass by 15–40% on average during the winter in continental Europe (Fountoukis et al., 2014). However, the scale of these reductions (Toscano et al., 2014; Seljeskog et al., 2017) and their toxicological relevance (Orecchio et al.,

4.4. Summary RWC is a significant source of ambient PM in developed countries and naturally makes larger contributions in colder seasons, i.e. in November–March in Europe, US, Canada, and June–August in Australasia. During those months RWC may dominate the ambient PM10 and PM2.5 concentrations and increase average PM concentrations in areas with obstructed dispersion and frequent inversions. RWC is a major seasonal contributor to the ambient OC concentrations reaching more than 40% in PM10 and PM2.5 as reported for Alpine regions (Austria, Switzerland, Northern Italy), Central and Southern Europe, and Scandinavia. The influence of this source on ambient EC concentrations, which are highly correlated with BC concentrations, is an emerging concern (Bond et al., 2013; Janssen et al., 2011). Large RWC contributions to ambient EC and BC fractions, reported for the Alpine Region and New Zealand, clearly indicate, that RWC is associated with the atmospheric EC and BC concentrations. Several apportionment studies reported shares of wood burning, and not strictly RWC. The wood burning sources in developed countries are limited to forest fires, planned burning of agricultural fields, and RWC. Therefore, the study mainly investigated the shares of wood burning in colder seasons when the probability of forest fires and planned agricultural fields fires is low, and the contribution of wood burning can be roughly assigned to RWC. Further, adding to the complexity of the emissions power plants and industrial biomass boilers for heating nowadays can burn biomass in the form of e.g. wood chips or straw, and the scale of this source contribution is unknown. However, the European Union member states are obligated to meet target rations of renewable energy in their National Energy Budget by 2020 as stated in EU Directive 2009/28/EC. 5. Concluding remarks and recommendations Emissions from wood stoves make up a substantial fraction of ambient carbonaceous PM during heating seasons in the developed countries, where wood burning is often used as means of aesthetic enjoyment rather than primary heating source, with wood stoves installed in centrally heated households (Font and Fuller, 2017; Burki, 2018). Thus, targeting this source with more stringent policies and appropriate abatement technologies can substantially reduce ambient 12

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2016; Miljevic et al., 2010; Jalava et al., 2012; Corsini et al., 2017; Lamberg et al., 2011) need further evaluation. Apart from reducing overall total PM mass, more effective combustion has been shown to lead to the PM size reduction, resulting in higher number particle emissions (Trojanowski and Fthenakis, 2019), mainly composed of ash. These particles exhibit a large surface area per mass, which have been associated with more pronounced proinflammatory response than larger particles of the same material (Kocbach Bølling et al., 2009). Few studies attempted to assess the differences in toxic potential of emissions from different types of modern wood heating devices, and, therefore, the reduction in toxicity of particles emitted from modern/ automatically/pellet operated stoves needs to be supported with more evidence. Although, the shares of newer RWC installations in the developed world are growing (Grythe et al., 2019; Wöhler et al., 2016), the old stoves, produced decades ago, are still in use. An optimization of stove operation by developing and disseminating user friendly education programs, (that aim to increase the awareness among the wood stove users on their role in the scale of emissions, as well as about the impact of the wood burning emissions on health), may have a potential for the overall emissions reduction from the older stoves and the devices that are currently in use (Reichert et al., 2016; Wöhler et al., 2016; Carvalho et al., 2016). However, this would probably have a limited effect. The more stringent policies, directed towards regulating the old stoves, such as a ban on the old stoves and a tax scheme according to the year of stove production, have been recently assessed for the implementation in Denmark (Bjørner et al., 2019). Imposing a differentiated tax and a general ban on stoves not approved by Nordic Ecolabel were found to result in the largest net welfare gain, with most of the gains derived from the regulation of stoves installed in densely populated areas (Bjørner et al., 2019). Both regulations were found to facilitate spread of new technologies, while simultaneously phasing out old wood stoves. Despite being highly unpopular a total ban on all types of wood stoves in densely populated areas can be even a more effective measure for improving air quality in the areas where people spend most of their time. The effectiveness and plausibility of more stringent policies can be evaluated by studying the stove owner response to different regulatory instruments and associated welfare gains in different countries (Bjørner et al., 2019).

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Declaration of competing interest We declare no conflict of interest. Acknowledgments We acknowledge Aarhus University for supporting Research Network Air and Health. DCE – National Centre for Environment and Energy is acknowledged for the projects: WOODMAD and Health impacts and external costs from air pollution in Denmark over 25 years. NordForsk under the Nordic Program on Health and Welfare funded project #75007: Understanding the link between air pollution and distribution of related health impacts and welfare in the Nordic countries (NordicWelfAir). This study was supported by BERTHA – the Danish Big Data Centre for Environment and Health funded by the Novo Nordisk Foundation Challenge Programme (grant NNF17OC0027864). Finally, we acknowledge funding support from NSF (#1517365). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.apr.2019.10.007. References Abdel-Shafy, H.I., Mansour, M.S.M., 2016. A review on polycyclic aromatic

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