Communal biofuel burning for district heating: Emissions and immissions from medium-sized (0.4 and 1.5 MW) facilities

Accepted Manuscript Communal biofuel burning for district heating: Emissions and immissions from medium-sized (0.4 and 1.5�MW) facilities Friederike Fachinger, Frank Drewnick, Reto Gieré, Stephan Borrmann PII:

S1352-2310(18)30152-3

DOI:

10.1016/j.atmosenv.2018.03.014

Reference:

AEA 15884

To appear in:

Atmospheric Environment

Received Date: 9 August 2017 Revised Date:

4 March 2018

Accepted Date: 6 March 2018

Please cite this article as: Fachinger, F., Drewnick, F., Gieré, R., Borrmann, S., Communal biofuel burning for district heating: Emissions and immissions from medium-sized (0.4 and 1.5�MW) facilities, Atmospheric Environment (2018), doi: 10.1016/j.atmosenv.2018.03.014. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Communal biofuel burning for district heating: Emissions and immissions from mediumsized (0.4 and 1.5 MW) facilities

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Friederike Fachinger1*, Frank Drewnick1*, Reto Gieré2, and Stephan Borrmann1,3

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5 1) Particle Chemistry Department, Max Planck Institute for Chemistry, 55128 Mainz, Germany

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2) Department of Earth and Environmental Science, University of Pennsylvania, Philadelphia, USA

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3) Institute for Atmospheric Physics, Johannes Gutenberg University, 55128 Mainz, Germany

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*Corresponding authors: [email protected]; [email protected]

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Keywords: biomass burning facility; district heating; emission factor; immissions; particle chemical composition

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Abstract

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Particulate and gaseous emissions of two medium-sized district heating facilities (400 kW, fueled with miscanthus, and 1.5 MW, fueled with wood chips) were characterized for different operational conditions, and compared to previously obtained results for household wood and pellet stoves. SO2 and NOx emission factors (reported in mg MJFuel-1) were found to not only depend on fuel sulfur/nitrogen content, but also on combustion appliance type and efficiency. Emission factors of SO2, NOx, and PM (particulate matter) increased with increasing load. Particle chemical composition did not primarily depend on operational conditions, but varied mostly with combustion appliances, fuel types, and flue gas cleaning technologies. Black carbon content was decreasing with increasing combustion efficiency; chloride content was strongly enhanced when burning miscanthus. Flue gas cleaning using an electrostatic precipitator caused strong reduction not only in total PM, but also in the fraction of refractory and semi-refractory material within emitted PM1. For the impact of facilities on their surroundings (immissions) not only their total emissions are decisive, but also their stack heights. In immission measurements downwind of the two facilities, a plume could only be observed for the 400 kW facility with low (11 m) stack height (1.5 MW facility: 30 m), and measured immissions agreed reasonably well with predicted ones. The impact of these immissions is nonnegligible: at a distance of 50 m from the facility, apart from CO2, also plume contributions of NOx, ultrafine particles, PM1, PM10, poly-aromatic hydrocarbons, and sulfate were detected, with enhancements above background values of 2-130 %.

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1. Introduction

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In recent years, the use of biomass fuels for residential heating has increased, due to both economic and ecological reasons (EEA, 2016; WHO, 2015). Since residential biomass combustion is an 1

ACCEPTED MANUSCRIPT important source of particulate matter (PM) (EEA, 2016), the characterization especially of the related particulate emissions is an important topic of research, especially due to their influence on human and public health (Arif et al., 2017; Dornhof et al., 2017; Naeher et al., 2007; Sarigiannis et al., 2015). According to the World Health Organization, each year 61,000 premature deaths in Europe and 10,000 in North America are attributable to ambient air pollution caused by residential heating with coal and wood (WHO, 2015). Since increased usage of biomass is nonetheless desirable from a climate change perspective, a possible option to address both climate change and air quality concerns is the use of biomass-based heating options with higher energy efficiencies (WHO, 2015), e.g. larger district heating facilities with more efficient flue gas cleaning rather than individual household stoves.

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Typical household-scale installations for wood combustion include wood stoves and pellet stoves. Of these, due to the automatically controlled fuel and oxygen supply, pellet stoves lead to more efficient combustion and therefore lower emissions (Bäfver et al., 2011; Boman et al., 2011; Pettersson et al., 2011). Even under ideal burning conditions, wood stoves cannot compete with this efficiency, and their emissions can increase further due to the user’s influence on fuel load and air supply (Fachinger et al., 2017; Pettersson et al., 2011). Larger district heating installations, on the other hand, do not only have higher combustion efficiency, but are usually also equipped with flue gas cleaning devices such as multicyclones, baghouse filters, or electrostatic precipitators, all leading to reduced particulate emissions (Ghafghazi et al., 2011; Williams et al., 2012). Of the different filtering techniques, cyclones are least efficient and remove only the very coarse particle fraction (>10 µm), whereas electrostatic precipitators and baghouse filters are capable to efficiently remove smaller particles down to below 100 nm (Ghafghazi et al., 2011).

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While many studies have investigated gaseous and particulate emissions from small scale pellet and wood stoves (up to ~20 kW) (e.g. Boman et al., 2011; Kaivosoja et al., 2013; Tissari et al., 2008) and large district heating facilities with typically >>1 MW (Ghafghazi et al., 2011, and references therein; Kaivosoja et al., 2013), few comprehensive studies investigating both particulate and gaseous emissions from medium-sized district heating facilities are available (Chandrasekaran et al., 2011). Most studies on the latter have been focusing on gaseous emissions only (Díaz-Ramírez et al., 2014; Lundgren et al., 2004); however, due to the potentially strong contribution of such facilities to local PM immissions, specifically the characterization of the amount and the physical and chemical properties of the emitted particles is of importance. From the measured emission factors, the influence of these facilities on the immission concentrations in their surroundings can be predicted, an approach that can be validated by dedicated immission measurements. So far, field studies on immissions from residential biomass burning have been mostly concerned with individual household solid fuel combustion (Brandt et al., 2011; Glasius et al., 2006; Hellén et al., 2008), which however exhibits emission characteristics that differ from those of larger district heating facilities.

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In this study, we investigate the emissions and associated immissions of two district heating facilities located in Alsace (France) and the Black Forest (Germany), both fired with locally produced biomass and providing heat and hot water for a large fraction of the respective villages’ residents. In a first step, we characterize emissions of these facilities under different operational conditions measuring a variety of gaseous pollutants (SO2, NOx, CO, CO2) and variables characterizing particulate emissions (particle number concentration, ultrafine particles, PM1, PM10, particle chemical composition). These results are discussed in comparison to results from previous laboratory measurements of emissions from a regular household wood stove and a pellet stove. Furthermore, we measure immission 2

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concentrations downwind of the two facilities with the same instrumental setup, and compare them to the immission concentrations predicted from a simple dispersion model. Using these results, we discuss the potential impact of biomass combustion on local pollutant concentrations using individual versus district heating.

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2.1 Investigated facilities

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Two district heating facilities were probed in this study. One is located in Ammertzwiller (at 47.68873°N, 7.16936°E, 300 m a.s.l.), a village of ~400 inhabitants in Alsace, France. Here, a rotation combustion boiler (Köb Pyrot, Viessmann Group) of 400 kW nominal power is installed, which provides heat and hot water for ca. 60-70 households and for communal facilities such as the school and the town hall. The boiler is fueled with locally grown miscanthus, doped with 2 wt% Ca(OH)2 to prevent slagging. Flue gas is cleaned using a multicyclone. The facility is located in the center of the village, in close proximity to several buildings. The stack has a height of ca. 11 m.

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The second facility is located in St. Peter (at 48.0211°N, 8.0356°E, 730 m a.s.l.), a village of ~2500 inhabitants in the Black Forest, Germany. It is located in the outskirts of the village in a small industrial area. A moving grate boiler of 1.5 MW nominal power is installed (K11-1500, HKI GmbH), which provides heat and hot water for ca. 220 households and some larger facilities, like the local abbey and the public swimming pool. The boiler is fueled with wood chips, predominantly softwood from the local forests. The stack height is 30 m, and flue gas is cleaned using an electrostatic precipitator (ESP; 250V/1F-3x2-6, BETH Filtration GmbH).

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2.2 Measurement instrumentation and setup

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All measurements were performed using the mobile laboratory MoLa, described in (Drewnick et al., 2012). Here, only a brief overview of the instruments deployed in MoLa is given.

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CO, SO2, and NOx were measured using an AirPointer (recordum Messtechnik GmbH); CO2 was measured using an LI-840 (Licor Environmental GmbH). Three different instruments were used to determine particle number size distributions in the particle diameter range 6 nm to 32 µm (FMPS model 3091 and APS model 3321, TSI Inc.; OPC model 1.109, Grimm Aerosol Technik GmbH & Co. KG). The chemical composition of sub-micrometer non-refractory particles was analyzed with a highresolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS, Aerodyne Inc.; DeCarlo et al., 2006). Two further instruments (MAAP model 5012, Thermo Scientific; PAS 2000, Ansyco GmbH) were deployed to measure sub-micrometer mass concentrations of black carbon (BC) and of polyaromatic hydrocarbons on or near the surface of particles (sPAH), respectively. Additionally, meteorological variables, including wind direction and wind speed, were measured (WXT520, Vaisala; and CMP3 pyranometer, Kipp & Zonen). Measurement uncertainties for all instruments are ≤30 % (Fachinger et al., 2017). Time resolution of all measurements was 1–60 s depending on the instrument.

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ACCEPTED MANUSCRIPT For the emission measurements, samples were taken directly from the stack downstream of the flue gas cleaning devices. After hot dilution (at 120 °C and 85 °C at the 1.5 MW and 400 kW facility, respectively) in an ejector diluter (dilution factor 1:10, KHG 10, Palas GmbH), the aerosol was led through 1” stainless steel tubing to the roof inlet of MoLa, from where it was distributed to the various instruments. AirPointer was sampling diluted air in parallel through a ¼” Teflon® tube. The CO2 content in the particle-free, compressed air used to operate the dilution stage was continuously measured using an LI-840A (Licor Environmental GmbH) in order to correct for dilution air CO2 fluctuations.

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Furthermore, immission measurements, sampling ambient air downwind of each facility, were performed using MoLa. The measurement site downwind of the 1.5 MW facility was to its WestNorthwest at 210 m distance, with a wooded area, but no buildings in between. The measurement location was slightly (by 20 m) elevated compared to that of the facility. The measurement site downwind of the 400 kW facility was within the center of the village at a distance of 50 m to the North-East of the facility, with a small park in between. Sampling height (through the roof inlet of MoLa) was 6 m a.g.l. in both cases. Meteorological parameters were measured at the same height (Table 1).

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Table 1: Meteorological conditions (average and standard deviation of 15 s data; wind direction: median and 25th to 75th percentile) during the immission measurements downwind of the 1.5 MW and 400 kW facilities. During both measurements no precipitation occurred. Local date and time

1.5 MW

12 Feb 2013, 9:40 - 14:05 19 Feb 2014, 10:16 - 13:42

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Temperature / °C

Pressure / hPa

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-3.3 ± 0.6 8.7 ± 1.1

919.7 ± 0.7 981.0 ± 0.3

Relative humidity /% 76 ± 4 68 ± 4

Wind speed / m s-1 0.8 ± 0.5 2.1 ± 0.7

Wind direction /° 137 (101 – 266) 209 (198 – 220)

Solar radiation / W m-2 166 ± 46 292 ± 166

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2.3 Data analysis

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Time series of particulate non-refractory organics, nitrate, sulfate, ammonium, and chloride concentrations were extracted from AMS data (Canagaratna et al., 2007) using the software tools SQUIRREL v.1.55 and PIKA v.1.14 (SQUIRREL/PIKA, 2016). For emission measurements, an AMS collection efficiency of 100 % was used following Heringa et al. (2012), who reported this collection efficiency (which mostly depends on particle chemical composition and mixing state) for particles from pellet stoves (see also Fachinger et al., 2017). Since we found (Sect. 3.1) that AMS-measured composition of (non-refractory) particulate emissions from pellet stoves is similar to that of mediumscale facilities, we adopted this collection efficiency value also for the emission measurements of the latter. For the immission measurements, a collection efficiency of 50 % was applied (Canagaratna et al., 2007). Ionization efficiency and sulfate and ammonium relative ionization efficiencies were determined in calibrations prior to each measurement campaign. All AMS measurements were corrected for instrument background effects using measurements of particle-free air; AMS data from

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ACCEPTED MANUSCRIPT emission measurements additionally were corrected for varying CO2 content in the measured flue gas.

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The sum of non-refractory (AMS-measured) PM1 and BC in PM1 is given as AMS/BCPM1, which does not include any semi-refractory or refractory species other than BC (e.g. ashes). Additionally, from the measured size distributions, total PM1, PM2.5, and PM10 as well as the ultrafine particle fraction UFPPM (PM of particles with a diameter ≤100 nm) were calculated following the procedure described in (Fachinger et al., 2017). A density of 2.25 g cm-3 was assumed for the emission measurements (Fachinger et al., 2017; Sippula et al., 2007). For the ambient measurements, since the coarse particle fraction (PM10 - PM1) was negligible, we used the average particle density calculated from the measured AMS/BCPM1 composition following Salcedo et al. (2006) (neglecting soil). The uncertainty of the calculated PM is approximated as 30 % (Fachinger et al., 2017). Furthermore, from the FMPS measurement, particle number concentration (PNC) was calculated.

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Local contaminations e.g. from passing cars during immission measurements were removed from the time series of all variables. They were determined by examining the time series of various pollutants (especially PNC and CO2) for spikes during time intervals when contaminations could be expected due to the observed situation around MoLa. After removal of such contaminations and correction for sampling delays (i.e. the transport times from the main inlet to the various instruments) all data were averaged on a common 30 s time axis (15 s for immission measurements). All volume mixing ratios presented within this work are given at actually measured H2O content. For comparability with other studies, all emission concentrations were normalized to 25 °C and 1013 hPa, at measured H2O and O2 content. Concentrations from immission measurements are given for the respective ambient temperature and pressure (Table 1).

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All emission factors presented in this work give the emitted amount of pollutant per theoretically available total fuel energy (indicated as MJFuel-1), i.e. the thermal efficiencies of the facilities and heat losses in the distribution systems are not considered. Emission factors were calculated from the measured emission concentrations following Fachinger et al. (2017). Carbon contents and higher heating values of 50 wt% and 20.1 MJ kg-1 (softwood, 1.5 MW facility) and of 48.1 wt% and 19.2 MJ kg-1 (miscanthus, 400 kW boiler) were assumed (Fachinger et al., 2017; Phyllis, 2016).

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A simple Gaussian plume model for continuous sources as described e.g. in (Hanna et al., 1982) was used for dispersion calculations. For the dispersion coefficients, the parameterization according to Briggs (1973) for urban environments and the meteorological condition during the measurements (i.e. Pasquill stability class) was used (Hanna et al., 1982).

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3. Results and discussion

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3.1 Emission measurements

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Influence of operational conditions:

Figure 1: CO2 volume mixing ratio (absolute emissions, at actual flue gas water content) measured during the emission measurements at the 1.5 MW (left) and the 400 kW facility (right), with the time intervals of different operational conditions marked.

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At each site, emission concentrations were measured at various operational conditions of the facility (Fig. 1). At the 1.5 MW boiler, emission concentrations were sampled at different power outputs, marked as “low” (~400 kW), “operational” (~1600 kW, typical operational condition, close to the nominal load for this facility), and “elevated” (~1800 kW, the highest possible load for this facility) in Fig. 1. Average emission factors of several key pollutants for these time intervals, as well as for the transition intervals when the facility was e.g. powering up from “low” to “operational” power, are given in Table 2.

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The CO emission factor (which is closely related to combustion efficiency) was found to be strongly dependent on the load of the facility. Both CO and BC emission factors were lowest at operational load, which is close to the nominal load of 1500 kW. Highest CO emission factors, but also strongly increased PM-related emission factors, were found during the transition intervals. For PNC, UFP-PM, PM1 and PM10, but also for NOx and SO2, slightly lower emission factors were observed at low compared to higher load, similar to previous observations made for small residential boilers (Chandrasekaran et al., 2013). Chandrasekaran et al. (2013) additionally found differences in SO2 and NOx emission factors between different types of furnaces fueled with the same fuel. In agreement with our findings, this shows that, although fuel sulfur and nitrogen contents are generally the dominating factors determining SO2 and NOx emissions from biomass combustion (Williams et al., 2012), additional influences from e.g. combustion efficiency, combustion temperature, or air supply might be relevant. Knowledge of emission factors of the different pollutants for both low and operational load is important, since district heating facilities that also provide hot tap water can be expected to be running at operational load during winter, but often at lower load in the warmer season.

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At the 400 kW facility, after measuring at constant power for a few hours (“stable” in Fig. 1), the boiler was switched off and restarted after cooling down for an hour, such that “burnout” and “start”

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ACCEPTED MANUSCRIPT phases could be sampled as well (Fig. 1 and Table 2). “Burnout” was defined from the data point where the modified combustion efficiency MCE (MCE = [CO2]/([CO]+[CO2])) started to decline, until both [CO] and [CO2] volume mixing ratios reached their baseline (lowermost) value. “Start” was defined as lasting from the increase in CO2 until MCE reached ~1. In absence of more precise information, we assume that the facility was running at nominal power (400 kW) during both “stable” intervals.

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The first stable interval (named “stable 1 (slightly disturbed)”) showed higher emission factors compared to the second one for most variables; in the first interval, conditions seemed more unstable, as visible in the high temporal variability of CO2 (Fig. 1). Potentially, this is due to the facility running at a slightly lower than nominal load, as suggested by the slightly lower average CO2 volume mixing ratio than during the second stable interval (Fig. 1). Strongly increased emission factors were found for most variables in the burnout phase, but absolute emission concentrations were much lower. The starting interval showed strongly increased CO and PM10 emission factors (and emission concentrations), whereas PNC, BC and PM1 emission factors were not or only slightly elevated. Apparently, during the cold start, a large amount of unburnt material is emitted in the coarse particle fraction, possibly due to material being blown away before being completely burnt in the cold boiler. Exceptionally high emission factors were found during the first seven minutes of the startup, both for CO (300 ± 90 mg MJFuel-1) and PM10 (40 ± 10 mg MJFuel-1).

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243 Repeatability:

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For both facilities, repeatability of the measurements was investigated by comparing average emission factors obtained during two independent measurements at the same load, i.e. “elevated 1” and “elevated 2” for the 1.5 MW boiler, and “stable 1 (slightly disturbed)” and “stable 2” for the 400 kW facility (see exemplary emission factors in Table 2). In both cases, relative differences were found to be typically ≤30 %. This uncertainty associated with measurement repeatability is comparable to that found previously for a common household wood stove (Fachinger et al., 2017). Larger variability was found for CO (which is very sensitive to slight changes in combustion efficiency) and for some individual particle components especially for the 400 kW boiler, which is likely explained by a slightly different load in the two “stable” intervals (see above). Since typical instrumental uncertainties are similar or considerably smaller (~10-30 %, Fachinger et al., 2017), we always consider a total uncertainty of 30 % for further discussions.

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District heating versus residential biomass combustion:

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For a comparison of the typical emission characteristics, the reported emission factors in Table 2 for “operational” load for the 1.5 MW facility, and “stable 2” for the 400 kW boiler are the most suited: operational load (1600 kW) is close to the nominal power of 1500 kW, and CO emission factor is at its lowest, indicating most complete combustion. “Stable 2” shows much lower variability than “stable 1 (slightly disturbed)” for the 400 kW facility, and is assumed to represent its nominal load, as discussed above. For comparison, furthermore emission factors determined in previous work (Fachinger et al., 2017) for two household stoves, using the same instrumental setup, are given (Table 2): a pellet stove (8 kW, median emission factors from experiments with two different pellet 7

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types) and a wood stove (6 kW, median emission factors from log wood burning of twelve different wood species).

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268 Table 2: Average emission factors for several key pollutants (in mg MJFuel-1; sPAH in µg MJFuel-1, PNC in 1012 MJFuel-1) for the different measurement intervals (see Fig. 1) for the 1.5 MW and 400 kW facilities. NOx is reported as NO2. For comparison, emission factors for a common household wood stove and a pellet stove are also given (Fachinger et al., 2017). All values are reported with an uncertainty of 30 % (see text). n/a: not available; LOD: limit of detection. All values are reported at 25 °C and 1013 hPa, at measured oxygen and water content.

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1.5 MW facility CO Low 103 ± 31 Low → 344 ± 103 operational Operational 33 ± 10 Operational → 229 ± 69 elevated Elevated 1 94 ± 28 Elevated 2 163 ± 49 400 kW facility CO Stable 1 (slightly 21 ± 6 disturbed) Burnout 4425 ± 1328 Start 58 ± 17 Stable 2 7±2 Residential CO a Wood stove 2159 ± 648 Pellet stove a 415 ± 124 a (Fachinger et al., 2017).

NOx 78 ± 23

PNC / 1012 MJFuel-1 0.28 ± 0.08

UFP-PM 0.016 ± 0.005

PM1 0.11 ± 0.03

PM1 0.4 ± 0.1

PM10 0.4 ± 0.1

BC 0.0015 ± 0.0005

sPAH / µg MJFuel-1 n/a

1.7 ± 0.5

83 ± 25

n/a

n/a

1.3 ± 0.4

n/a

n/a

0.010 ± 0.003

n/a

1.9 ± 0.6

84 ± 25

n/a

n/a

0.6 ± 0.2

n/a

n/a

< LOD

n/a

1.8 ± 0.6

82 ± 25

n/a

n/a

1.1 ± 0.3

n/a

n/a

0.0005 ± 0.0001

n/a

1.9 ± 0.6 2.0 ± 0.6 SO2

83 ± 25 82 ± 25 NOx

n/a 2.8 ± 0.8 PNC / 1012 MJFuel-1

n/a 0.24 ± 0.07 UFP-PM

1.0 ± 0.3 1.1 ± 0.3 AMS/BC PM1

n/a 1.9 ± 0.6 PM1

n/a 1.9 ± 0.6 PM10

< LOD 0.0030 ± 0.0009 BC

n/a n/a sPAH / µg MJFuel-1

2.4 ± 0.7

103 ± 31

37 ± 11

3±1

1.8 ± 0.5

19 ± 6

20 ± 6

0.5 ± 0.2

14 ± 4

25 ± 8 2.0 ± 0.6 2.7 ± 0.8 SO2 5±2 2.6 ± 0.8

79 ± 24 102 ± 31 99 ± 30 NOx 47 ± 14 65 ± 19

216 ± 65 41 ± 12 33 ± 10 PNC / 1012 MJFuel-1 59 ± 18 88 ± 26

13 ± 4 4±1 3.2 ± 0.9 UFP-PM 4±1 11 ± 3

5±2 1.4 ± 0.4 1.5 ± 0.4 AMS/BC PM1 3±1 2.0 ± 0.6

33 ± 10 18 ± 5 18 ± 5 PM1 15 ± 5 14 ± 4

96 ± 29 26 ± 8 19 ± 6 PM10 15 ± 5 14 ± 4

1.3 ± 0.4 0.22 ± 0.07 0.19 ± 0.06 BC 2.8 ± 0.8 1.5 ± 0.4

13 ± 4 7±2 9±3 sPAH / µg MJFuel-1 128 ± 38 161 ± 48

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ACCEPTED MANUSCRIPT Gaseous species: CO, as an indicator for combustion efficiency, is significantly lower (factor 10 to 300) in the district heating facilities, compared to the household stoves. The SO2 emission factor is roughly doubled for the wood stove compared to the other appliances. Since fuel sulfur content in log wood is not expected to be higher than in wood chips, this result also suggests that the SO2 emission factor is not only related to fuel sulfur content but also to the type of facility. This is in agreement with the findings described above regarding different operational conditions as well as with previous studies, which found dependence of SO2 emission factors on fuel sulfur content and facility type (i.e. combustion conditions) (Chandrasekaran et al., 2013).

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Similarly, both facility type and fuel nitrogen content influence NOx emission factors. The NOx emission factors are clearly enhanced for the district heating facilities compared to the household stoves (Table 2), probably due to different combustion temperatures and/or air supply (Díaz-Ramírez et al., 2014). The highest NOx emission factor was found for the boiler burning miscanthus, in agreement with the fact that miscanthus has a higher nitrogen content than wood (Phyllis, 2016).

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Figure 2: Average particle composition (BC and non-refractory components in PM1) measured at the two facilities under normal operation conditions (“operational” and “stable 2”, left). Shown for comparison (right) are median particle compositions measured at a residential pellet stove and a wood stove (Fachinger et al., 2017). AMS/BCPM1 is the sum of measured BC and non-refractory species shown in the pie charts. The difference between PM1 and AMS/BCPM1 represents the unassigned mass (probably mostly semi-refractory and refractory species). For the 1.5 MW facility, AMS/BCPM1 and PM1 for “elevated 2” are also given (in parentheses). n/a: not available (missing data); LOD: limit of detection.

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Particle composition: While no strong differences in measured particle composition were found between the different operational conditions at each location, clear differences were identified between the two facilities. Figure 2 shows average particle composition at “operational” load for the 1.5 MW boiler, “stable 2” measurement at the 400 kW facility, and median particle compositions of emissions from a household wood stove and a pellet stove (Fachinger et al., 2017).

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Both combustion efficiency and flue gas cleaning can have an influence on the measured particle chemical composition. We first discuss the differences between PM1 chemical composition of the 10

ACCEPTED MANUSCRIPT 400 kW facility and the household stoves: Since particles below 10 µm diameter (and therefore also PM1 chemical composition) are almost not affected by flue gas cleaning using a multicyclone as it is applied in the 400 kW boiler (Ghafghazi et al., 2011), observed differences between them can be attributed predominantly to different combustion efficiencies. Both absolute and relative BC content (wood stove > pellet stove > 400 kW boiler) are found to be inversely related to combustion efficiency (as indicated by CO emission factor, Table 2). Furthermore, the ratio of organic to nonrefractory inorganic content is strongly enhanced for the wood stove compared to the other appliances, likely due to more organic material escaping unburnt. This is consistent with previous observations of much larger emission factors of organic gaseous compounds for wood stoves compared to pellet stoves (Bäfver et al., 2011). In all cases, sulfate was found to be the most important non-refractory inorganic component; furthermore, for the 400 kW boiler, the chloride emission factor was almost as large as that of sulfate. This is due to higher fuel chloride content of miscanthus compared to wood: also when burning miscanthus in the wood stove, enhanced chloride emission factors were observed (Fachinger et al., 2017). In all cases, ammonium emission factors were very low, consistent with the dominance of metal salts (e.g. potassium salts like K2SO4) in the emissions, as found in previous work (e.g. Frey et al., 2009).

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In contrast, PM1 composition measured at the 1.5 MW boiler is influenced by both combustion efficiency and flue gas cleaning, which, using an ESP, also efficiently removes sub-micrometer particles. Consequently, PM1 and PNC emission factors are much smaller (about ten times) than for the 400 kW facility or the household stoves. Additionally, the ratio of AMS/BCPM1 to PM1 is much higher for the 1.5 MW facility (shown for elevated load in Fig. 2 due to missing PM1 data for operational load) compared to the 400 kW boiler and the household stoves. This lower fraction of “unassigned mass” within PM1 points to a much lower fraction of semi-refractory and refractory material in PM1 (e.g. ashes) compared to the other facilities. Also BC content is strongly depleted (<1.5 % of AMS/BC PM1, independent of load). These findings are consistent with the assumption that within the sub-micrometer aerosol, refractory material (inorganics and BC) is completely in the particle phase at the point of flue gas cleaning, and therefore efficiently removed in the ESP. Behind the ESP, semivolatile gaseous species condense upon further cooling of the flue gas, forming new particles or condensing onto existing particles. Therefore, after flue gas cleaning with the ESP, a much larger fraction of the sub-micrometer particles is non-refractory in nature. The remainder can be hypothesized to be made up of mostly semi-refractory material, which was however not measured quantitatively. This explains why the ratio of chloride to sulfate is also higher in the 1.5 MW facility compared to the pellet stove: Most likely, this is not caused by the fuel properties, but due to the fact that sulfate forms particles more efficiently (e.g. condensed alkali sulfates are more stable at higher temperatures than alkali chlorides, (Jöller et al., 2005)), and therefore is more efficiently removed with the ESP. Further work is desirable in order to investigate this influence of flue gas cleaning on chemical composition of the emitted particles, and related implications for their health effects.

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To meet the heating energy demand of a residential area with biomass, both the installation of central facilities and of individual stoves are feasible options. In order to assess the consequence of 11

ACCEPTED MANUSCRIPT the selection of these options on local immissions, we estimate the overall emissions and distanceresolved immission as a function of the fractional contribution of both types of heating installations.

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Overall emissions: To determine the expected overall emissions we use the emission factors related to fuel input (Table 2) as a first approximation. This approach neglects different efficiencies of the appliances and thermal losses during heat transport. Overall thermal efficiencies of wood and pellet stoves typically are in the order of 70–85 %, with wood stoves on the lower and pellet stoves on the upper end of this range (Orasche et al., 2012; Sippula et al., 2007). Efficiencies of medium-sized facilities can be expected to be higher (~90 % at nominal load, Chandrasekaran et al., 2011), which however is largely compensated by additional thermal losses of ~10 % during the distribution of the heating energy to the individual households (NVE, 2015).

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From the emission factors reported in Table 2 (for nominal load, i.e. “operational” and “stable 2” for the two boilers), the benefit of using central facilities instead of individual stoves becomes directly apparent. For gaseous pollutants (except NOx), much smaller overall emissions are to be expected from the larger facilities for the same amount of energy distributed. Particulate emissions depend very much on the flue gas cleaning device used: with the use of an ESP, emissions are strongly reduced, whereas a multicyclone does not reduce total PM1 emissions. However, as shown in the previous section, the more complete combustion in larger facilities decreases the soot content of PM1, and also the emission of sPAH is strongly reduced (Table 2). Additionally, potential aerosol mass (from volatile organic compounds, not measured in this study) can be expected to be lower, especially compared to wood stoves (Bäfver et al., 2011).

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Immissions: In order to compare the impact of communal facilities and of individual household stoves on locations in their immediate surroundings, not only overall emissions, but also the differences in spatial distribution of the immission have to be taken into account. A larger facility is a distinct single point source within a residential area, whereas household stoves are rather homogeneously distributed. This aspect of our study was investigated by simple transport modeling for two situations, resembling those around the 400 kW and the 1.5 MW boiler, respectively (Table 3).

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Table 3: Parameters for the case studies I and II.

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Case I Heat demand 400 kW Emission factor according to Table 2: - household wood stove - central facility 400 kW boiler, “stable 2” Stack height / m - household 7 - central facility 11 Radius village / m 350 Pasquill turbulence type b A/C/D/E -1 Wind speed / m s 2/4/6 a ”elevated 2” for BC and PM1. b see (Hanna et al., 1982).

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Case II 1500 kW wood stove 1500 kW boiler, “operational” a 7 30 550 A/C/D/E 2/4/6

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Figure 3: Contour plots showing the relative changes in immission (contours, in percent), as a function of the distance from the facility, for different shares (i.e. relative contribution) of central facility (400 kW, left, and 1500 kW, right) and individual household wood stove combustion (base case: only household combustion). The lowermost panels show the radial dependence of the impact of pollutants per emission rate (from the center of the village) from a central facility and from homogeneously distributed household stoves, which multiplied with the total emission rate of all sources gives the respective immission concentration enhancement.

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ACCEPTED MANUSCRIPT In this case study, the two villages are represented by circular areas of the same size as the actual village areas, with the facility in the center. Using a Gaussian dispersion model (e.g. Hanna et al., 1982), we calculated the average pollutant concentrations near ground (0-5 m height) for different radial distances from the source (i.e. distance-resolved immission), assuming an even distribution of wind directions and flat terrain. The emission rates were calculated from the emission factors (Table 2) and the respective heat demands (Table 3). For the calculation of the expected immission from household stoves, we calculated the average radial (from the center of the village) concentration distribution resulting from a large number of small sources homogeneously distributed over the entire circular area, which together represent the total emission strength. For simplification, wind speed is assumed to be uniform with height in the relevant altitude range.

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Figure 3 summarizes the relative changes in immission concentrations (for BC, PM1, and NOx) as a function of the distance from the central facility (x axis), for different shares of heating energy production in the central facility and in individual households (y axis). As reference, we use the case in which all heating energy is provided by individual household stoves (0 % share of the facility); contours with negative/positive numbers indicate relative reductions/enhancements of immission concentrations compared to the reference case. Shown are results for “neutral” stability conditions (Pasquill type D) and 2 m s-1 wind speed. Using different wind speeds does not alter the results in this simple model (wind speed is assumed to be uniform with height). Different stability conditions result in different numbers for immission reduction (smallest/strongest influence by the central facility at stable/unstable atmospheric conditions), but the overall trends remain similar. In Fig. 3g,h, the radial impact of pollutants per emission rate from evenly distributed households and from a central facility is shown. Note that the values for the household stoves differ in both cases due to the different assumed village radii (larger radius meaning a stronger average dilution, since the average distance of individual sources is larger). However, when accounting for the different heat demands (i.e. different emission rates), these lower impacts per emission rate in Fig. 3h than in Fig. 3g still result in higher immission concentrations in the case of the larger village.

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In Figure 3, two main influences on the impact of the emissions from a central facility on its surrounding can be observed: the facility’s overall emissions, and its stack height. The 400 kW facility has a much lower stack than the 1500 kW facility (Table 3), causing the plume to reach ground much closer to the source, where pollutants are not as strongly diluted yet (Fig. 3g,h). In the case of NOx, this causes a much higher relative concentration enhancement in the critical distance (the distance of maximum concentration) of the 400 kW facility compared to the 1500 kW facility than would be expected from the respective emission factors alone (Fig. 3e,f; Table 2). In absolute terms, the maximum NOx immission concentration enhancement due to a complete switch from household stoves to a central facility in these examples would correspond to ~0.3 and ~2 µg m-3 in the critical distance of the large (~150 m) and the small facility (~50 m), respectively, still considerably below the EU yearly limit (40 µg m-3). Increasing the stack height would improve the situation further; e.g. a stack of 30 m for the smaller facility would reduce immissions in all distances from the facility at the given conditions.

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The same effect is also visible in the distribution of PM1 immission reduction (Fig. 3c,d). Due to the strongly reduced emissions from the 1500 kW facility with efficient flue gas cleaning (Table 2), immission is reduced in this case at all distances from the source, and only a slightly lower (≤10 %) immission reduction is observed at the critical distance. In contrast, for the smaller facility, where the PM1 emission factor is similar to that of individual household fuel combustion, an immission 14

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ACCEPTED MANUSCRIPT concentration enhancement rather than a decrease is observed at the critical distance, though smaller than that for NOx.

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Finally, for BC, an immission reduction is observed at all distances for both boilers (Fig. 3a,b) due to strongly reduced emission factors as a consequence of more efficient combustion and, in the case of the larger facility, flue gas cleaning. In the latter case, where almost no BC is emitted from the facility, an immission reduction proportional to the share of the facility is observed independent of the distance from the source.

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These examples demonstrate how differently residents in the surrounding of central heating facilities are affected by their emissions, whereas emissions from individual combustion are more homogeneously distributed, though with lower influence at the outskirts of the village (Fig. 3g,h). Note that in this simple model we assume an even wind direction distribution, whereas in the real world some locations might be more strongly affected than others due to locally predominant wind directions. In order to minimize the impact of central facilities on their close surrounding, their stacks need to be sufficiently high, otherwise at a certain critical distance from the point source, immission might be even higher than with individual household combustion only. Additionally, flue gas cleaning can strongly reduce the impact of PM1 on the surrounding of the facility, leading to an improved situation for all residents compared to individual household combustion.

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3.3 Stationary immission measurements

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In this section, the immission predicted from emission measurements and a simple Gaussian dispersion model are compared to the real-world immission measured downwind of the two facilities.

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During the stationary immission measurement downwind of the 1.5 MW boiler, no emission plume from the facility could be detected. This is in agreement with results from the Gaussian dispersion model, which for the meteorological conditions at the time of the measurement predicts immission concentrations that are too low to be distinguishable from the observed background variability. Additionally, measurement conditions were not the most favorable: Wind direction was rather variable, with comparatively little coverage of the center of the plume, but due to the limitations of the terrain the measurement location could not be adapted accordingly. In order to detect a plume as weak as predicted, much more favorable measurement conditions and/or longer measurement times would be needed with the given measurement setup.

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In contrast, during the measurement downwind of the 400 kW facility, a distinct emission plume could be detected. Within this measurement, two time intervals with different, almost constant wind speeds and CO2 background values were encountered (Fig. 4a); these are considered separately in the following discussion. Average wind speed and CO2 background value (± standard deviation of the average) were 1.6 (± 0.03) m s-1 and 401.7 (± 0.4) ppm in interval 1, and 2.7 (± 0.04) m s-1 and 395.5 (± 0.2) ppm in interval 2, respectively. Both intervals lasted ~70 min.

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Figure 4: a) Time series of CO2 volume mixing ratio and wind speed measured ~50 m downwind of the 400 kW facility on 19 Feb 2014, with two time intervals with different prevailing wind speeds (averages indicated as dashed lines) marked. For these intervals, Panels b) and c) show measured, averaged (5° bin width), and predicted CO2 volume mixing ratios, as well as the ratio of predicted to measured CO2 enhancements (with respect to the background value reported within the figures), plotted versus the angular distance from the plume center.

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In Fig. 4b,c, measured (15 s time resolution) CO2 volume mixing ratios are plotted versus the angular distance from the emission plume center (calculated from the measured wind direction) for the two intervals. Additionally, CO2 volume mixing ratios averaged for 5° bins of angular distance are shown. During both intervals, a clear plume is visible; in interval 1, the whole plume could be detected, whereas for interval 2, only few data points are available for the right side of the plume (distance from plume center >0°), but more background values (< -45°) are available here.

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Using the Gaussian dispersion model taking into account the given meteorological conditions during the two different intervals and the respective CO2 background values, and assuming the calculated CO2 emission rate of the facility at nominal load (“stable 2” emission factor and 400 kW load), we calculated the expected plume (“predicted” in Fig. 4b,c) and compared it to the measured values. In both cases, the shape of the observed plume is well represented by the Gaussian model, but the predicted values are about a factor of 1.5 higher than the measured ones. This is within the uncertainty of the prediction, caused by the uncertainty of the emission factors and the load of the facility, and by potentially limited validity of the model for short distance (50 m), especially due to turbulences caused by surrounding buildings. In addition, measurement uncertainties cannot be neglected, especially for interval 2, where the measured data points are limited in some sections of the plume.

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From Fig. 4, the dependency of immission concentrations on wind speed becomes directly apparent: measured CO2 plume enhancements are smaller at higher wind speed (interval 2). When averaging 16

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ACCEPTED MANUSCRIPT the CO2 plume enhancements for a section of the plume which is similarly well covered during both measurement intervals (-30° to -10° distance from plume center), we obtain a ratio (interval 1/interval 2) of average CO2 enhancements of 5.9 ppm/2.7 ppm = 2.2, which is close to the expected ratio of 1.7 calculated from the average wind speeds during both intervals.

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In Table 4, the ratio of enhancement (plume minus background) of various variables measured in the plume relative to the enhancement of CO2 are presented together with expected enhancement ratios calculated from the measured emission factors. As background value, we use the respective average value measured for ≤ -35° angular distance from the plume center; for the concentration in the plume, we average all values from -30° to +30° distance from the plume center. Only those variables are reported in Table 4 for which the plume enhancement can be expected (from the measured emission factors) to significantly exceed the measured background value (i.e. to be larger than the standard deviation of the average of the background value). Possibly detectable pollutants are NOx, sPAH, PNC, UFP-PM, PM1, PM10, and sulfate. For other pollutants possibly associated with the plume (e.g. BC, organics, SO2), and for one of the two intervals in each case for NOx, PM1 and PM10, the variation of the background or the measurement uncertainty was too high to detect the expected small enhancement on top of the background.

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Table 4: Expected and observed enhancement ratios for several pollutants during the two observational intervals (Fig. 4) downwind of the 400 kW facility. All ratios are given for ambient conditions (Table 1). (ΔCO2 in ppm.) Also given are the enhancements in the plume above background, both absolute and relative compared to the background value.

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For PNC, UFP-PM, PM1 and PM10, expected and observed enhancement ratios agree within a factor of 0.5 to 2. All show typically large relative enhancements compared to the background value (14 to 80 %, Table 4). All other pollutants typically showed smaller (<30 %) relative enhancements (Table 4). For the pollutants with smaller relative enhancement the deviation from the expected enhancement ratio is typically larger (up to a factor of 9). Some of the deviations, of course, could also be due to real changes of pollutant concentrations within the emission plume during transport, e.g. due to 17

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gas/particle partitioning. NOx, on the other hand, should likely not be affected by processing on the very short transport timescale (Seinfeld and Pandis, 1998), and the observed deviation here probably is solely due to the associated uncertainties. More measurements would be needed in order to obtain statistically significant information on potential changes of the plume composition downwind of the source.

532 4. Summary and conclusion

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Emission measurements at a 400 kW district heating facility (burning miscanthus) and a 1.5 MW facility (burning wood chips) revealed a strong influence of the operational condition on the emission factors of various pollutants. We found increasing emission factors of NOx, SO2, and PM with increasing load, which is important since combustion at loads different from the nominal load can be expected to be encountered frequently (e.g. due to different heat demand in spring compared to winter). We found that SO2 and NOx emission factors are not only depending on fuel properties, but, in agreement with previous studies (Chandrasekaran et al., 2013), also on combustion appliance type and combustion efficiency.

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While no significant influence of operational condition on particle composition was observed, a strong influence of combustion appliance (mainly associated with combustion efficiency), fuel type, and flue gas cleaning technology was found. For both facilities, sulfate was the most abundant nonrefractory inorganic species within PM1, followed by chloride. The chloride content was found to be increased for the boiler burning miscanthus, probably due to higher fuel chloride content. BC content was inversely related to combustion efficiency (BC content wood stove > pellet stove > 400 kW facility), but also dependent on flue gas cleaning (strong reduction by ESP in 1.5 MW facility). Flue gas cleaning with an ESP strongly reduced total PM, but also changed the composition of the emitted particles: the fraction of refractory and semi-refractory material within PM1 was strongly reduced after flue gas cleaning with the ESP, probably due to semi-volatile material passing the ESP and condensing after the removal of the more refractory particles during further cooling of the flue gas. This finding needs further investigation, since differences in particle composition might have implications for potential health effects.

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Although pollutant emission factors of district heating facilities were mostly found to be lower in this work compared to those of household stoves, immission is not necessarily lower in an area where heat demand is covered with a central facility instead of individual household appliances. This is due to the fact that a central facility is a single, strong point source, whereas emissions from household stoves are more homogeneously distributed over the area. Here, stack height plays an important role: too low a stack can result in increased immission concentrations in some areas in the surrounding of a facility even when overall emissions are reduced. This is confirmed by immission measurements downwind of the two facilities: no emission plume was observable downwind of the 1.5 MW facility with a 30 m stack, whereas a distinct plume was observed from the 11 m stack of the 400 kW facility. The observed plume agreed reasonably well (both in shape and wind speed dependence of immission concentrations) with model predictions. Apart from CO2, also NOx, PNC, UFP-PM, PM1, PM10, sPAH, and non-refractory sulfate were significantly (by 2-130 %) enhanced in the plume.

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All in all, if a district heating facility is equipped with adequate flue gas cleaning devices and the facility’s stack is high enough, the installation of district heating facilities is to be preferred over individual biomass combustion with respect to local air quality.

571 572 Acknowledgments

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The authors thank Thomas Böttger for technical support for the measurements. The helpful support at the biomass burning facilities in Ammertzwiller and St. Peter, especially by Mathieu Ditner, Willi Schwaer and Markus Bohnert, is gratefully acknowledged. This work received financial support by the EU through its Interreg IV Program (Oberrhein, project C35 BIOCOMBUST; www.biocombust.eu).

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Arif, A. T., Maschowski, C., Garra, P., Garcia-Käufer, M., Petithory, T., Trouvé, G., Dieterlen, A., Mersch-Sundermann, V., Khanaqa, P., Nazarenko, I., Gminski, R., and Gieré, R.: Cytotoxic and genotoxic responses of human lung cells to combustion smoke particles of Miscanthus straw, softwood and beech wood chips, Atmospheric Environment, 163, 138-154, 2017. Bäfver, L. S., Leckner, B., Tullin, C., and Berntsen, M.: Particle emissions from pellets stoves and modern and old-type wood stoves, Biomass and Bioenergy, 35, 3648-3655, 2011. Boman, C., Pettersson, E., Westerholm, R., Boström, D., and Nordin, A.: Stove Performance and Emission Characteristics in Residential Wood Log and Pellet Combustion, Part 1: Pellet Stoves, Energy & Fuels, 25, 307-314, 2011. Brandt, C., Kunde, R., Dobmeier, B., Schnelle-Kreis, J., Orasche, J., Schmoeckel, G., Diemer, J., Zimmermann, R., and Gaderer, M.: Ambient PM10 concentrations from wood combustion Emission modeling and dispersion calculation for the city area of Augsburg, Germany, Atmospheric Environment, 45, 3466-3474, 2011. Briggs, G. A.: Diffusion estimation for small emissions. Air Resources Atmospheric Turbulence and Diffusion Laboratory, NOAA, 1973. Canagaratna, M. R., Jayne, J. T., Jimenez, J. L., Allan, J. D., Alfarra, M. R., Zhang, Q., Onasch, T. B., Drewnick, F., Coe, H., Middlebrook, A., Delia, A., Williams, L. R., Trimborn, A. M., Northway, M. J., DeCarlo, P. F., Kolb, C. E., Davidovits, P., and Worsnop, D. R.: Chemical and microphysical characterization of ambient aerosols with the Aerodyne aerosol mass spectrometer, Mass Spectrometry Reviews, 26, 185- 222, 2007. Chandrasekaran, S. R., Hopke, P. K., Newtown, M., and Hurlbut, A.: Residential-Scale Biomass Boiler Emissions and Efficiency Characterization for Several Fuels, Energy & Fuels, 27, 4840-4849, 2013. Chandrasekaran, S. R., Laing, J. R., Holsen, T. M., Raja, S., and Hopke, P. K.: Emission Characterization and Efficiency Measurements of High-Efficiency Wood Boilers, Energy & Fuels, 25, 50155021, 2011. DeCarlo, P. F., Kimmel, J. R., Trimborn, A., Northway, M. J., Jayne, J. T., Aiken, A. C., Gonin, M., Fuhrer, K., Horvath, T., Docherty, K. S., Worsnop, D. R., and Jimenez, J. L.: Field-Deployable,

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High-Resolution, Time-of-Flight Aerosol Mass Spectrometer, Analytical Chemistry, 78, 82818289, 2006. Díaz-Ramírez, M., Sebastián, F., Royo, J., and Rezeau, A.: Influencing factors on NOX emission level during grate conversion of three pelletized energy crops, Applied Energy, 115, 360-373, 2014. Dornhof, R., Maschowski, C., Osipova, A., Gieré, R., Seidl, M., Merfort, I., and Humar, M.: Stress fibers, autophagy and necrosis by persistent exposure to PM2.5 from biomass combustion, PLOS ONE, 12, e0180291, 2017. Drewnick, F., Böttger, T., von der Weiden-Reinmüller, S.-L., Zorn, S. R., Klimach, T., Schneider, J., and Borrmann, S.: Design of a mobile aerosol research laboratory and data processing tools for effective stationary and mobile field measurements, Atmos. Meas. Tech., 5, 1443-1457, 2012. EEA: Air quality in Europe - 2016 report. 2016. Fachinger, F., Drewnick, F., Gieré, R., and Borrmann, S.: How the user can influence particulate emissions from residential wood and pellet stoves: Emission factors for different fuels and burning conditions, Atmospheric Environment, 158, 216-226, 2017. Frey, A. K., Tissari, J., Saarnio, K. M., Timonen, H. J., Tolonen-Kivimaki, O., Aurela, M. A., Saarikoski, S. K., Makkonen, U., Hytonen, K., Jokiniemi, J., Salonen, R. O., and Hillamo, R. E. J.: Chemical composition and mass size distribution of fine particulate matter emitted by a small masonry heater, Boreal Environ. Res., 14, 255-271, 2009. Ghafghazi, S., Sowlati, T., Sokhansanj, S., Bi, X., and Melin, S.: Particulate matter emissions from combustion of wood in district heating applications, Renewable and Sustainable Energy Reviews, 15, 3019-3028, 2011. Glasius, M., Ketzel, M., Wåhlin, P., Jensen, B., Mønster, J., Berkowicz, R., and Palmgren, F.: Impact of wood combustion on particle levels in a residential area in Denmark, Atmospheric Environment, 40, 7115-7124, 2006. Hanna, S. R., Briggs, G. A., and Hosker, R. P.: Handbook on atmospheric diffusion. Technical Information Center, U.S. Department of Energy, 1982. Hellén, H., Hakola, H., Haaparanta, S., Pietarila, H., and Kauhaniemi, M.: Influence of residential wood combustion on local air quality, Science of The Total Environment, 393, 283-290, 2008. Heringa, M. F., DeCarlo, P. F., Chirico, R., Lauber, A., Doberer, A., Good, J., Nussbaumer, T., Keller, A., Burtscher, H., Richard, A., Miljevic, B., Prevot, A. S. H., and Baltensperger, U.: Time-Resolved Characterization of Primary Emissions from Residential Wood Combustion Appliances, Environ. Sci. Technol., 46, 11418-11425, 2012. Jöller, M., Brunner, T., and Obernberger, I.: Modeling of Aerosol Formation during Biomass Combustion in Grate Furnaces and Comparison with Measurements, Energy & Fuels, 19, 311323, 2005. Kaivosoja, T., Jalava, P. I., Lamberg, H., Virén, A., Tapanainen, M., Torvela, T., Tapper, U., Sippula, O., Tissari, J., Hillamo, R., Hirvonen, M. R., and Jokiniemi, J.: Comparison of emissions and toxicological properties of fine particles from wood and oil boilers in small (20–25 kW) and medium (5–10 MW) scale, Atmospheric Environment, 77, 193-201, 2013. Lundgren, J., Hermansson, R., and Dahl, J.: Experimental studies during heat load fluctuations in a 500 kW wood-chips fired boiler, Biomass and Bioenergy, 26, 255-267, 2004. Naeher, L. P., Brauer, M., Lipsett, M., Zelikoff, J. T., Simpson, C. D., Koenig, J. Q., and Smith, K. R.: Woodsmoke Health Effects: A Review, Inhalation Toxicology, 19, 67-106, 2007. NVE: Energy in Norway 2013 (2015 edition). Norges vassdrags- og energidirektorat (Norwegian Water Resources and Energy Directorate), 2015. 20

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Orasche, J., Seidel, T., Hartmann, H., Schnelle-Kreis, J., Chow, J. C., Ruppert, H., and Zimmermann, R.: Comparison of Emissions from Wood Combustion. Part 1: Emission Factors and Characteristics from Different Small-Scale Residential Heating Appliances Considering Particulate Matter and Polycyclic Aromatic Hydrocarbon (PAH)-Related Toxicological Potential of Particle-Bound Organic Species, Energy & Fuels, 26, 6695-6704, 2012. Pettersson, E., Boman, C., Westerholm, R., Boström, D., and Nordin, A.: Stove Performance and Emission Characteristics in Residential Wood Log and Pellet Combustion, Part 2: Wood Stove, Energy & Fuels, 25, 315-323, 2011. Phyllis: Phyllis2, database for biomass and waste, Energy research Centre of the Netherlands, https://www.ecn.nl/phyllis2, last access: 07.11.2016, 2016. Salcedo, D., Onasch, T. B., Dzepina, K., Canagaratna, M. R., Zhang, Q., Huffman, J. A., DeCarlo, P. F., Jayne, J. T., Mortimer, P., Worsnop, D. R., Kolb, C. E., Johnson, K. S., Zuberi, B., Marr, L. C., Volkamer, R., Molina, L. T., Molina, M. J., Cardenas, B., Bernabé, R. M., Márquez, C., Gaffney, J. S., Marley, N. A., Laskin, A., Shutthanandan, V., Xie, Y., Brune, W., Lesher, R., Shirley, T., and Jimenez, J. L.: Characterization of ambient aerosols in Mexico City during the MCMA-2003 campaign with Aerosol Mass Spectrometry: results from the CENICA Supersite, Atmos. Chem. Phys., 6, 925-946, 2006. Sarigiannis, D. Α., Karakitsios, S. P., and Kermenidou, M. V.: Health impact and monetary cost of exposure to particulate matter emitted from biomass burning in large cities, Science of The Total Environment, 524–525, 319-330, 2015. Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, John Wiley & Sons, Inc., New York, 1998. Sippula, O., Hytönen, K., Tissari, J., Raunemaa, T., and Jokiniemi, J.: Effect of Wood Fuel on the Emissions from a Top-Feed Pellet Stove, Energy & Fuels, 21, 1151-1160, 2007. SQUIRREL/PIKA: http://cires.colorado.edu/jimenezgroup/ToFAMSResources/ToFSoftware/index.html, last access: 14.07.2016, 2016. Tissari, J., Sippula, O., Kouki, J., Vuorio, K., and Jokiniemi, J.: Fine Particle and Gas Emissions from the Combustion of Agricultural Fuels Fired in a 20 kW Burner, Energy & Fuels, 22, 2033-2042, 2008. WHO: Residential heating with wood and coal: health impacts and policy options in Europe and North America. World Health Organization, Regional Office for Europe, Copenhagen, 2015. Williams, A., Jones, J. M., Ma, L., and Pourkashanian, M.: Pollutants from the combustion of solid biomass fuels, Progress in Energy and Combustion Science, 38, 113-137, 2012.

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Midsized biofuel heating plants’ emission factors depend on load and operational mode Flue gas cleaning with electrostatic precipitator changes composition of emitted PM

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Emission factors and stack height are decisive for immissions around facility

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Central biomass burning facilities with high stack preferable to household stoves