# Characterization of gaseous and particulate atmospheric emissions, including the condensable particulate fraction, from small biomass boilers (150 kW-1.65 MW) in France

Sergio Harb, Benjamin Cea, Nicolas Karoski, Adrien Dermigny, Vincent Fuvel, Benjamin Cuniasse, Florence Paulus, Isaline Fraboulet

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Sergio Harb, Benjamin Cea, Nicolas Karoski, Adrien Dermigny, Vincent Fuvel, et al.. Characterization of gaseous and particulate atmospheric emissions, including the condensable particulate fraction, from small biomass boilers (150 kW-1.65 MW) in France. Atmospheric environment: X, 2025, pp.100391. 10.1016/j.aeaoa.2025.100391. ineris-05366217

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# Characterization of gaseous and particulate atmospheric emissions, including the condensable particulate fraction, from small biomass boilers (150 kW-1.65 MW) in France

Sergio Harb<sup>a,*</sup> ID, Benjamin Cea<sup>a,b</sup>, Nicolas Karoski<sup>a</sup>, Adrien Dermigny<sup>a</sup>, Vincent Fuvel<sup>a</sup>, Benjamin Cuniasse<sup>c</sup>, Florence Paulus<sup>d</sup>, Isaline Fraboulet<sup>a,**</sup>

## ARTICLE INFO

### Keywords:

ACIBIOQA project

Small biomass boilers (< 1 MW)

Emission factors

French emission inventory

Solid particles

Condensable fraction of particles

Combustion gases

PAHs

Heavy metals

## ABSTRACT

Biomass combustion is the primary source of renewable energy in France. However, it also significantly contributes to outdoor air pollution. This energy sector is undergoing major changes, including evolving regulations, improved fuel types, and enhanced emission control technologies. While the performance of larger installations (>1 MW) is well documented, less is known about biomass boilers with lower capacities. It has become crucial to better understand the operation of smaller biomass boilers, assess their contribution to atmospheric pollution, and identify methods to reduce their emissions.

The ACIBIOQA project investigated emissions from six biomass boilers with nominal power outputs ranging from 150 kW to 1.65 MW across France. Measurements included combustion gas-phase characterization (O₂, CO₂, CO, NOx, and organic gaseous compounds: OGCs) and particulate-phase characterization, covering total particulate matter (TPM), including both solid (SP) and condensable fractions, PM₁₀, PM₂.₅, and PM₁ by mass, 15 heavy metals, 8 polycyclic aromatic hydrocarbons (PAHs).

Measured concentrations (corrected to 6 vol% O₂) varied widely: CO ranged from 153 to 8841 mg Nm⁻³, NOx from 172 to 395 mg eq. NO₂ Nm⁻³, and organic gaseous compounds from <1 to 634 mg eq. C Nm⁻³. TPM ranged from <1.5 to 475 mg Nm⁻³, with SP between <0.3 and 475 mg Nm⁻³. The condensable fraction contributed 4–26 % to TPM. PM₁ dominated the mass size distribution, accounting for 60–100 %. PAHs ranged from 0.1 to 340 µg Nm⁻³, and heavy metals from 0.03 to 6.7 mg Nm⁻³.

Emission levels were influenced by boiler nominal power output, combustion load, operating regime, and filtration technology. Particularly, CO, OGCs, and PAHs were highest under unstable and low-load conditions. SP emissions increased with the number of combustion cycles and decreased operating load, while bag filters appeared to reduce SP levels. Higher condensable fractions were observed in boilers with complete on/off cycles and lower nominal outputs.

This study provides new insights into emissions from small biomass boilers and represents a pioneering effort to characterize the condensable particulate fraction at the national level. Emission factors were calculated and compared with literature and national inventory values.

## 1. Introduction

Biomass is considered as the most dominant source of renewable energy fuel worldwide, and its combustion remains the most common

and widespread method for heating buildings (Vicente and Alves, 2018). In recent years, the biomass boiler market in France has experienced significant growth, largely driven by the energy crisis and rising prices, particularly for electricity and gas, as well as by the strengthening of

<footnote><sup>a</sup> Institut National de l'Environnement industriel et des Risques (INERIS), 60550 Verneuil en Halatte, France</footnote>

<footnote><sup>b</sup> SPR/UICPE, Direction Régionale de l'Environnement, de l'Aménagement et du Logement, Provence-Alpes-Côte d'Azur, France</footnote>

<footnote><sup>c</sup> Centre interprofessionnel technique d'étude de la pollution atmosphérique (Citepa), 75010, Paris, France</footnote>

<footnote><sup>d</sup> Cabinet Conseil et Ingénierie en Développement Durable (INDDIGO), 73024, Chambéry, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>** Corresponding author.
E-mail addresses: sergio.harb@ineris.fr (S. Harb), isaline.fraboulet@ineris.fr (I. Fraboulet).</footnote>
regulations aimed at reducing the share of fossil fuels and promoting the use of renewable energy sources.

Over the last decade, emissions of various pollutants from biomass combustion appliances have decreased significantly due to technological and regulatory improvements. These include: (i) optimized combustion control, such as automated air supply and staged combustion, which ensures more complete fuel burnout and reduces particulate and gaseous pollutants; (ii) the implementation of high-efficiency filtration technologies, such as electrostatic precipitators and advanced filtration systems; (iii) improvements in fuel quality, including pelletization and moisture reduction; and (iv) the reinforcement of regulatory frameworks, such as the EU Ecodesign Directive, which imposes strict emission limits (Valente et al., 2020; Ozgen, 2024; Mileva et al., 2025; Tomlin, 2021). Despite these advances, biomass combustion remains a significant source of atmospheric pollutants, which can have harmful health effects (Bruns et al., 2017; Fourtziou et al., 2017; Vicente et al., 2024). Exposure to emissions from wood burning has been linked to increased risks of premature mortality and morbidity (Tomlin, 2021; Paisi et al., 2024; Sigsgaard et al., 2015). Sigsgaard et al. (2015) (Sigsgaard et al., 2015) estimated that biomass smoke contributes to at least 40000 premature deaths per year in Europe. Similarly, Paisi et al. (2024) (Paisi et al., 2024) reported that PM<sub>2.5</sub> from residential combustion (mainly wood) leads to approximately 72000 excess deaths annually across the continent. Numerous studies have demonstrated associations between biomass smoke exposure and increased risk of respiratory illness, including higher incidence and severity of acute bronchitis and respiratory infections, as well as impaired pulmonary immune defenses (e.g. (Sällsten et al., 2006; Zelikoff et al., 2002; Boman et al., 2003; Naeher et al., 2007; Zhang et al., 2023)). For example, a recent study (Zhang et al., 2023) found that residents exposed to biomass smoke had 2.6 times higher odds of developing chronic obstructive pulmonary disease (COPD). Some components of biomass smoke, such as polycyclic aromatic hydrocarbons (PAHs), are of particular concern due to their mutagenic and carcinogenic properties (Agudelo-Castañeda and Teixeira, 2014; IARC Working Group on the Evaluation of Carcinogenic Risks to Humans, 2010); PAHs are known to cause DNA adducts (Godschalk et al., 2000; Squadrito et al., 2001). Additionally, transition metals present in particulate matter may induce DNA strand breakage through the generation of reactive oxygen species (Chapman et al., 1997).

Numerous studies have highlighted the major role of residential wood combustion in ambient air pollution, particularly during winter in France. For instance, multi-site source apportionment studies have shown that it can significantly contribute to PM<sub>10</sub> and PM<sub>2.5</sub> levels (e.g. (Weber et al., 2019; Bressi et al., 2014; Favez et al., 2009)), with wintertime contributions to PM<sub>2.5</sub> reaching 20–24 % in the Paris region ((Bressi et al., 2014; Favez et al., 2009)). Other studies have further shown that wood burning accounts for 35–50 % of organic aerosol (OA) during winter at different sites in France (e.g. (Chebaicheb et al., 2023; Petit et al., 2014)). In addition, residential wood burning has been identified as the main wintertime source of volatile organic compounds (VOCs) in urban areas, contributing up to 50 % of total VOCs in the Paris region, for example (Languille et al., 2020; Baudic et al., 2016). It is also a major source of PAHs in France, as shown in several studies (e.g. (Chimjarn et al., 2021; Tomaz et al., 2016)), with concentrations found to be 5 to 7 times higher in winter than in summer in cities like Grenoble, primarily due to increased biomass burning for residential heating. These results are consistent with broader European studies showing that biomass burning remains a major source of air pollutants during winter (e.g. (Navarro-Barboza et al., 2025; Denier van der Gon et al., 2015; Lim et al., 2022)).

In France, biomass boilers with a power output between 1 MW and 50 MW are subject to ICPE (Classified Installations for Environmental Protection) regulations under section 2910-A (Combustion of Biomass). Boilers in this category must comply with specific emission thresholds for atmospheric pollutants (NOx, SO<sub>2</sub>, TSP, OGCs), as defined by the

ministerial decree of August 3, 2018. This decree transposes the European MCP (Medium Combustion Plants) Directive (2015/2193/EU), which sets emission limit values (ELVs) across EU member states based on plant size and fuel type. For instance, existing biomass boilers between 1 and 5 MW must meet ELVs by 2029, including 200 mg Nm<sup>-3</sup> for SO<sub>2</sub>, 650 mg Nm<sup>-3</sup> for NOx, and 50 mg Nm<sup>-3</sup> for dust (all at 6 vol% O<sub>2</sub>). New biomass boilers installed after December 2018 face stricter ELVs, including 20 mg Nm<sup>-3</sup> for dust (while SO<sub>2</sub> and NOx thresholds remain the same). These regulations also require periodic inspections for biomass combustion facilities down to 1 MW power output to ensure compliance with emission standards.

In contrast, biomass boilers with a power output of less than 1 MW are not covered by the MCP Directive and are generally not subject to ICPE regulations and regulatory controls. At the European level, these small-scale installations fall under the EU Ecodesign Directive (2009/125/EC), through specific implementing regulations aimed at improving the environmental performance of energy-related products. For instance, Commission Regulation (EU) 2015/1189 sets laboratory-based emission limits for particulate matter (PM), CO, OGCs, and NOx for biomass boilers with a power output <500 kW. However, these limits apply only under controlled test conditions and not to real-world operation. At the national level, two decrees (dated September 15, 2009, and October 2, 2009) provide target values for particulate emissions from biomass boilers <1 MW to be reached (80 mg Nm<sup>-3</sup> and 225 mg Nm<sup>-3</sup> at 6 vol% O<sub>2</sub> content, respectively). Since 2009, the Biomass Heat Fund (BHF), managed by ADEME (French Agency for Ecological Transition), has supported renewable heat production and the development of heat networks in France. The BHF imposes specific emission requirements for projects producing more than 1200 MWh/year (400–600 kW), particularly for boilers between 500 kW and 1 MW, where emissions are limited to 50 mg Nm<sup>-3</sup> for TSP, 500 mg Nm<sup>-3</sup> for NOx, and 250 mg Nm<sup>-3</sup> for CO (at 6 vol% O<sub>2</sub> content). This overview highlights the regulatory gap concerning atmospheric emissions from small-scale biomass boilers, especially those below 1 MW. When emission limits exist, they often target only a few pollutants. To reduce pollution and improve air quality, there is a clear rationale for moving toward stricter standards, with lower ELVs and comprehensive coverage for biomass boilers under 1 MW. This underscores the need for real-world emissions data for a wide range of pollutants from these small-scale combustion systems.

Among the pollutants that remain unregulated is the condensable fraction of particles, which refers to material that is gaseous at flue gas temperature but condenses into particles as the exhaust cools. With the rapid development of technologies and regulations reducing solid particulate emissions, the relative importance of the condensable fraction of particles has increased (Feng et al., 2018). Most studies on condensable particulate emissions have focused on coal-fired sources, whereas research on biomass combustion is comparatively limited and mainly concerns small heating appliances such as pellet and logwood stoves (Feng et al., 2018; Yang et al., 2018a; Wang et al., 2022). Results show that the contribution of the condensable fraction compared to the solid fraction of particles can range from a few percent to becoming the dominant fraction under certain conditions depending on several factors, including the operating mode of the appliance, excess air ratio, combustion temperature, and the measurement method applied (Wang et al., 2022; Trubetskaya et al., 2021; Seljeskog et al., 2017; Klauser et al., 2018; Nystrom et al., 2017).

Moreover, Denier van der Gon et al. (2015) demonstrated that most European emission inventories did not include the condensable fraction of particles from residential wood heating appliances, leading to discrepancies between observed and modeled PM<sub>2.5</sub> concentrations in ambient air (Denier van der Gon et al., 2015). Specifically, the concentrations of organic particles measured in the atmosphere are higher than expected from models based on reported emission factors (EFs). In 2020, an international expert group convened under the EMEP program recommended the inclusion of condensables in emission inventories (EMEP Technical Report MSC-W 4/2020) (Bergström, 2020). Similarly,
Wang et al. (2022), in their review "The state of the art of condensable particulate matter," highlighted the need to include the condensable fraction of particles in emission inventories (Wang et al., 2022).

Within this context, the ACIBIOQA project, coordinated by INDDIGO in collaboration with INERIS and Citepa and co-funded by ADEME, aimed to: (i) provide real-world data on emissions of various gaseous and particulate pollutants from small biomass boilers (<2 MW), with particular focus on those <1 MW, a category that is poorly studied in France and not subject to regulatory controls; (ii) include the measurement of the condensable fraction of particles, a class of pollutants that is often overlooked but has been shown to be important; (iii) assess the impact of different abatement technologies on emissions; and (iv) examine how combustion conditions influence emission levels.

To achieve these objectives, we conducted measurement campaign on six biomass boilers with power outputs ranging from 150 kW to 1.65 MW. The measurement campaigns covered TPM, including both solid and condensable fractions, as well as PM₁₀, PM₂.₅, and PM₁ by mass, 15 heavy metals, 8 PAHs, and combustion gases (O₂, CO₂, CO, NOx, and OGCs). The influence of various operating conditions and emission control technologies on pollutant levels was systematically assessed. This study provides critical insights into emissions from biomass boilers around and below 1 MW, and represents a pioneering effort to characterize the condensable fraction of particulate matter emitted by these sources in France.

## 2. Material & methods

### 2.1. Biomass boilers and measurement campaigns

The ACIBIOQA project aimed at characterizing and quantifying emissions from small-scale biomass boilers, particularly those with power outputs around and below 1 MW. Six biomass boilers with nominal power outputs ranging between 150 kW and 1.65 MW were selected for the present study. The selection criteria included their power output, the capability to be instrumented for the characterization of atmospheric emissions, and the filtration system installed at the outlet of the biomass boiler to reduce emissions. Their technical specifications and the periods of the measurement campaigns are detailed in Table 1.

Although one unit slightly exceeds the 1 MW threshold, it was included for several reasons: (i) the limited availability of suitable field sites, with campaigns carried out during the COVID-19 pandemic, which imposed significant logistical constraints on planning and site accessibility; (ii) the 1.65 MW unit represents a small-scale biomass boiler very close to the 1 MW. Its inclusion enabled us to compare two biomass boilers under comparable operating conditions (see Section 3.1), thereby improving the robustness of the dataset; (iii) this boiler is subject to the French ICPE regulatory framework, providing a valuable reference for comparison using the same measurement and sampling methodologies as for the smaller boilers.

For each boiler, two to three days of measurements were dedicated to characterizing emissions, covering various pollutants of interest in both aerosol and gas phases (Table 1). The gas phase composition was continuously monitored using two analyzers: i) a Horiba PG-250 for measuring O₂ (using a Galvanic cell), CO₂ (using a non-dispersive infrared absorption with a pyroelectric detector), CO (using a non-dispersive infrared absorption with an optical-pneumatic detector), and NOx (using a chemiluminescence detection method by cross-flow modulation), and ii) a JUM portable heated flame ionization (FID) analyzer to measure OGCs. Both instruments were calibrated daily using certified zero and span gases, with calibration performed each measurement day in the morning before starting measurements and verification carried out in the evening after measurements. Drift checks were conducted at the end of each day, with corrections applied for deviations between 2 and 5 %, and data invalidated if deviations exceeded 5 %. A calibration schedule was followed before and after each campaign, and annual maintenance and calibration of analyzers were carried out. All

analyzers were QAL1 certified according to EN 14181 and EN 15267-4, operated with a data acquisition interval of 10 s, and had a combined response time (including sampling line transfer) of less than 60 s. Detailed calibration gas concentrations, measurement ranges, and instrument limits of quantification are provided in Table S1 and Table S2.

The characterization of the aerosol phase included the measurement of TPM, including both solid and condensable fractions, as well as PM₁₀, PM₂.₅ and PM₁ by mass, 15 heavy metals (As, Cd, Co, Cr, Cu, Mn, Ni, Pb, Sb, Se, Te, Ti, Tl, V, and Zn), and PAHs (Fluoranthene, Benzo[a] anthracene, Benzo[b]fluoranthene, Benzo[k]fluoranthene, Dibenzo[a, h]anthracene, Benzo[g,h,i]perylene, and Indeno[1,2,3-c,d]pyrene). The sampling methods used in this study are presented in Fig. 1.

The SPC-IPA method, developed at INERIS (Cea et al., 2021), was employed to measure the concentration of TPM, including the solid and condensable fractions of particles. Briefly, this technique involves combining the sampling of the solid fraction (SP) of particles on a heated filter (75 mm diameter, quartz filter heated to 160 °C, with the temperature choice based on NF X 44–052 and EN 13284-1) with the collection of the condensable fraction using two washing bottles placed in series in a cooling unit to maintain a sampling temperature below 20 °C and filled with 150 mL of isopropanol (IPA) to collect the condensable fraction of particles. The SPC-IPA method is based on the US EPA 5H method, which determines particulate emissions from wood heaters at stack locations. In the original US EPA 5H method, condensables are collected in water and then extracted using dichloromethane before evaporation and weighing. In SPC-IPA, water is replaced by IPA, as described in CEN TS 15439, which provides guidance for sampling and analyzing tars and particles in biomass gasification product gases. The main advantage of using IPA is that it eliminates the need for dichloromethane (classified as a Group 2B carcinogen by IARC) and allows direct evaporation of IPA to dryness for weighing the collected condensables. Comparative tests were conducted between the SPC-IPA method and the US EPA 5H method under identical conditions (gas flow temperature maintained at 125 ± 10 °C, seven successive samples). The results showed similar trends, and statistical analysis confirmed that confidence intervals for both methods overlap, indicating no significant difference between them. This supports the reliability of SPC-IPA as an alternative to US EPA 5H (PEREN2BOIS; 20th ETH NPC).

Additionally, a Dekati PM₁₀ impactor (Dekati Ltd.), a three-stage cascade impactor, was installed in the stack and operated according to ISO 23210:2009 standards to determine the particle gravimetric mass size distributions (PM₁₀, PM₂.₅, and PM₁). For heavy metal measurements, the procedure followed was NF EN 14385. The sampling assembly included a heated glass tube and a heated filter holder (at 180 °C, with a 75 mm quartz filter), followed by three sintered glass absorbers filled with an absorption solution consisting of 1.5 % m/m H₂O₂ and 3.3 % m/m HNO₃, then a drying cartridge, a dry gas meter, and a pump operating at 2 L min⁻¹. At the end of sampling, filters and absorption solutions were collected in glass Petri dishes and polyethylene bottles, respectively, and both the probe and absorbers were rinsed with nitric acid solution. Dust quantity was determined by weighing filters before and after sampling and after drying. The rinse residues were evaporated to dryness in a beaker, and the dry extract was weighed after desiccation. The mass obtained was added to the filter mass increase to determine the weight index, and control filter and beaker mass differences were deducted from collected dust masses. Particulate heavy metals trapped on the filter were mineralized and measured separately. Dry extracts from probe rinses were mineralized and analyzed along with absorption solutions. Heavy metals were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) according to NF EN ISO 17294-2. For metals sampling, absorption efficiency was determined by performing a separate analysis of the last absorber for each metal and each sampling campaign. Typical absorption efficiencies were generally >90 %; data were not corrected. Sampling duration was at least 1 h, with a minimum sampled volume of 1.3 m³. For PAH measurements, the procedure followed is described in NF X 43–329. The eight compounds
<caption>Table 1</caption>

Characteristics of the six selected biomass boilers, the measured parameters, the periods of the measurement campaigns, and the sampling durations. “ND” (No Data) indicates that a measurement was not performed or that sampling failed. When multiple samplings were performed on the same day, the duration of each sampling is reported, and the different durations are separated by “/”. When different parameters are listed (combustion gases and particle mass size distribution), the reported duration applies to all since they were measured simultaneously.

<table><thead><tr><th></th><th>Biomass boiler 1</th><th>Biomass boiler 2</th><th>Biomass boiler 3</th><th>Biomass boiler 4</th><th>Biomass boiler 5</th><th>Biomass boiler 6</th></tr></thead><tbody><tr><td>Implementation year</td><td>2018</td><td>2018</td><td>2007</td><td>2010</td><td>2006</td><td>2017</td></tr><tr><td>Nominal power output</td><td>800 kW</td><td>1.65 MW</td><td>720 kW</td><td>150 kW</td><td>200 kW</td><td>500 kW</td></tr><tr><td>Filtration System</td><td>Electrostatic filter</td><td>Cyclone and bag filters</td><td>Cyclone</td><td>External multi-cyclone</td><td>Cyclone</td><td>2 ACS cyclones</td></tr><tr><td>Fuel</td><td>Wood chips</td><td>Wood chips and wood</td><td>Green waste and pallet shredding</td><td>Wood chips</td><td>Wood</td><td>Wood chips</td></tr><tr><td>Fuel moisture content</td><td>25-45 %</td><td>30-35 %</td><td>35 %</td><td>25 %</td><td>30 %</td><td>20-30 %</td></tr><tr><td>Boiler operational load</td><td>50 %</td><td>90 %</td><td>30 %</td><td>50 %</td><td>40 %</td><td>80 %</td></tr><tr><td></td><td>031020<br>Day 1<br>Duration<br>(min)</td><td>021120<br>Day 2<br>Duration<br>(min)</td><td>070720<br>Day 1<br>Duration<br>(min)</td><td>070820<br>Day 2<br>Duration<br>(min)</td><td>031320<br>Day 1<br>Duration<br>(min)</td><td>121520<br>Day 2<br>Duration<br>(min)</td><td>121620<br>Day 3<br>Duration<br>(min)</td><td>021621<br>Day 1<br>Duration<br>(min)</td><td>021721<br>Day 2<br>Duration<br>(min)</td><td>021821<br>Day 3<br>Duration<br>(min)</td><td>022321<br>Day 1<br>Duration<br>(min)</td><td>022421<br>Day 2<br>Duration<br>(min)</td><td>022521<br>Day 3<br>Duration<br>(min)</td><td>041321<br>Day 1<br>Duration<br>(min)</td><td>041421<br>Day 2<br>Duration<br>(min)</td></tr><tr><td>Combustion gases: O<sub>2</sub>, CO<sub>2</sub>, CO, NOx and OGCs</td><td>360</td><td>ND</td><td>300</td><td>240</td><td>ND</td><td>480</td><td>453</td><td>240</td><td>420</td><td>ND</td><td>360</td><td>240</td><td>180</td><td>120</td><td>383</td></tr><tr><td>Particle mass size distribution: PM<sub>10</sub>, PM<sub>2.5</sub>, and PM<sub>1</sub></td><td>15</td><td>ND</td><td>ND</td><td>300</td><td>10/5/3</td><td>ND</td><td>ND</td><td>ND</td><td>ND</td><td>10/10/10</td><td>25/39/20</td><td>ND</td><td>ND</td><td>30/15/<br>15/15</td><td>ND</td></tr><tr><td>Particle composition: 8 PAHs</td><td>117/124</td><td>ND</td><td>300</td><td>ND</td><td>ND</td><td>ND</td><td>120</td><td>ND</td><td>154</td><td>ND</td><td>60</td><td>ND</td><td>ND</td><td>ND</td><td>82</td></tr><tr><td>Particle composition: 15 heavy metals</td><td>70/60/60</td><td>ND</td><td>ND</td><td>305</td><td>ND</td><td>165</td><td>ND</td><td>ND</td><td>50</td><td>ND</td><td>ND</td><td>60</td><td>ND</td><td>ND</td><td>167</td></tr><tr><td>Solid Particles (SP) mass concentration</td><td>70/60/60</td><td>63/113/<br>70</td><td>305/164/<br>118</td><td>ND</td><td>ND</td><td>40/51/46/<br>60/60/60</td><td>ND</td><td>30/51/90</td><td>50/65/42</td><td>ND</td><td>80/110/<br>67</td><td>60/60/64</td><td>ND</td><td>ND</td><td>55/45/45</td></tr><tr><td>Total Particulate Matter (TPM<sup>a</sup>) mass concentration</td><td>70/60/60</td><td>63/113/<br>70</td><td>305/164/<br>118</td><td>ND</td><td>ND</td><td>40/51/46/<br>60/60/60</td><td>ND</td><td>30/51/90</td><td>50/65/42</td><td>ND</td><td>80/110</td><td>60/60/64</td><td>ND</td><td>ND</td><td>55/45/45</td></tr></tbody></table>

<footnote>a Solid and Condensable Particles.</footnote>
<figure><img src="image_3.png" /><figcaption>Fig. 1. Sampling methods deployed during the six measurement campaigns.</figcaption></figure>

listed in the standard, considered representative of the PAH family in terms of carcinogenic potential, were targeted. Sampling was performed using a complete glass assembly comprising a glass probe, a 75 mm quartz fiber filter maintained at the lowest possible temperature but above the gas dew point to avoid condensation (heated at 120–125 °C), and a cartridge filled with pre-conditioned XAD-2 resin, preceded by a condenser to keep the resin inlet below 20 °C, along with a condensate pot. Flue gases were drawn using a pump with adjustable flow rate, and the sampled volume was measured with a dry gas meter. Resin flow rate was maintained below 35 cm s⁻¹; resin volume was 2 cm⁻³. Trapping media (filter, condensates, resin) and rinsing solvents were processed in the laboratory. Solid samples were extracted with dichloromethane (DCM) using Soxhlet, and aqueous samples by liquid-liquid extraction with DCM. The 8 PAHs were analyzed by ultraperformance liquid chromatography (UPLC) with UV-Fluorescence detection (FLD).

Prior to the sampling, the quartz filters were carbonized at 500 °C for 6 h to eliminate any possible contamination. Additionally, blank filters for each measured component were collected and analyzed to check for possible contamination. Measurements were compared with site blanks for each parameter and each campaign. If the measurement was below the site blank, the site blank value was used; if the measurement was above the site blank, the measured value was retained.

It is important to note that, in this study, non-isokinetic sampling was employed for particulate-phase measurements primarily because the flue gas velocities emitted by biomass boilers are often quite low, making accurate measurement challenging. Samples were collected in the direction of the gas flow. The impact of non-isokinetic sampling on particle size distribution is expected to be minimal in our case. PM₂.₅ generally represents between 63 % and 91 % of the total suspended particulates emitted by biomass boilers (using the SP method) (Evaluation des performances énergétiques et environnementales de chaufferies biomasse, 2022). Moreover, the NF EN ISO 23210 standard, which describes methods for determining PM₂.₅ and PM₁₀ using cascade impactors, indicates that non-isokinetic conditions have very limited influence when aerosols are mainly composed of PM₂.₅; collection efficiency remains above 95 % even with a velocity ratio of 2. This supports the validity of our measurements despite the non-isokinetic conditions.

The sampling time and the number of tests were adjusted according

to the pollutants and the gas matrix characterized to obtain the appropriate limits of quantification (LQ = analytical LQ/volume of gas sampled). The calculated LQ for each component from manual sampling and for each test campaign is presented in Table 2. It is important to note that all emission concentrations hereafter are reported at dry standard state conditions of temperature and pressure (283.34 K and 101.325 kPa) and corrected to 6 vol% O₂ content.

The rules for handling LQ values, based on NF X 43–551, were applied as follows. For automatic methods, if the measured concentration (C) is greater than the LQ, the result equals the measured value. If LQ/2 < C < LQ, the compound is considered detected, and the result is set to LQ/2. If C < LQ/2, the compound is considered not detected and the result is set to zero. For manual methods, if C > LQ, the result equals the measured value; if LQ/3 < C < LQ, the compound is considered detected and the result is set to LQ/2; if C < LQ/3, the compound is considered not detected and the result is set to zero. When concentrations result from sums of different phases (e.g., particulate and gaseous for heavy metals), or compartments (e.g., filter and probe rinsing solution), these rules are applied to each component before summing. These rules also apply to measurements, site blanks, and bubbler absorption efficiencies.

<caption>Table 2</caption>

Limit of Quantification (LQ) calculated for each pollutant and each biomass boiler. “ND” (No Data) indicates that a measurement was not performed or that sampling failed.

<table><thead><tr><th>Biomass Boilers</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th></tr></thead><tbody><tr><td>Power Output</td><td>800<br>kW</td><td>1.65<br>MW</td><td>720<br>kW</td><td>150<br>kW</td><td>200<br>kW</td><td>500<br>kW</td></tr><tr><td></td><td colspan="6" style="text-align: center;">Limit of Quantification</td></tr><tr><td>PM₁ PM₂.₅ and<br>PM₁₀ (mg Nm⁻³)</td><td>1.6</td><td>0.2</td><td>4.5</td><td>5.3</td><td>1.9</td><td>2.7</td></tr><tr><td>SP (mg Nm⁻³)</td><td>2.3</td><td>0.3</td><td>9.5</td><td>11.6</td><td>3.3</td><td>3.01</td></tr><tr><td>TPM (mg Nm⁻³)</td><td>5.7</td><td>1.5</td><td>5.1</td><td>8.5</td><td>3.5</td><td>ND</td></tr><tr><td>8 PAHs (µg Nm⁻³)</td><td>0.006</td><td>0.003</td><td>0.188</td><td>0.387</td><td>0.111</td><td>0.058</td></tr><tr><td>15 Heavy Metals<br>(mg Nm⁻³)</td><td>0.017</td><td>0.027</td><td>0.023</td><td>0.044</td><td>0.003</td><td>0.370</td></tr></tbody></table>
## 2.2. Emission factors

In order to compare the results of this study with those from the literature, as well as national and international emission inventories, it is essential to convert the measured pollutant concentrations into emission factors. This conversion is achieved using the following expression (Stewart, 2012):

$$EF = [\text{Conc.}] \times (\text{SDFGV}) \div 1000 \quad (\text{Eq. 1})$$

Where EF represents the emission factor of the pollutant considered, expressed in g GJ⁻¹; [Conc.] is the pollutant concentration measured, in mg Nm⁻³ at a defined oxygen concentration; and SDFGV is the specific dry flue gas volume per GJ net heat input, expressed in Nm³ GJ⁻¹, for dry flue gas at 0 °C, 101.3 kPa, and at the same defined oxygen concentration as the pollutant concentration. The factor of 1000 is used to convert milligrams to grams. The SDFGV coefficient varies based on the type and moisture content of the fuels. In the absence of precise data regarding biomass consumption during the tests and the calorific values of the fuels used, the SDFGV values presented in Table 3, derived from literature (Stewart, 2012; Graham et al., 2012; EMEP/EEA air pollutant emission inventory), were used to convert measured concentrations into EFs.

## 2.3. Statistical analysis

Statistical tests were performed to examine the significance of differences in gas-phase emission pollutants (CO, OGCs and NOx). A permutation-based ANOVA (PERMANOVA) was applied. This method does not rely on distributional assumptions and is well-suited for small, non-normal, and potentially autocorrelated datasets. For each emission parameter (CO, OGCs, and NOx), the observed F-statistic was calculated based on daily mean values, and a null distribution was generated by randomly permuting group labels 100,000 times. The p-value was estimated as the proportion of permutations yielding an F-statistic equal to or greater than the observed one. Results indicated statistically significant differences between boilers (CO: F = 78.6, p ≈ 0.00017; OGCs: F = 11.34, p ≈ 0.0052; NOx: F = 10.11, p ≈ 0.0113).

Additionally, a multivariate correlation analysis was conducted to explore relationships between emissions and operational parameters,

<table><caption>**Table 3**
SDFGV Coefficients by fuel type: At 10 % O₂ and at measured O₂ levels, used for converting gas pollutant concentrations into EF.</caption><thead><tr><th>Biomass Boilers # (Nominal power output)</th><th>Fuels</th><th>Moisture (%)</th><th>O₂ measured (vol%)</th><th>SDFGV at 10 vol % O₂ (Nm³ GJ⁻¹)</th><th>SDFGV calculated at measured O₂ (Nm³ GJ⁻¹)</th></tr></thead><tbody><tr><td>Biomass boiler 1 (800 kW)</td><td>Wood chips</td><td>25–45</td><td>12.9</td><td>475.5</td><td>645.7</td></tr><tr><td>Biomass boiler 2 (1.65 MW)</td><td>Wood chips and Wood</td><td>30–35</td><td>8.5</td><td>475.5</td><td>418.4</td></tr><tr><td>Biomass boiler 3 (720 kW)</td><td>Green waste and pallet shredding</td><td>35</td><td>12.7</td><td>518.6</td><td>687.3</td></tr><tr><td>Biomass boiler 4 (150 kW)</td><td>Wood chips</td><td>25</td><td>16.2</td><td>475.5</td><td>1089.7</td></tr><tr><td>Biomass boiler 5 (200 kW)</td><td>Wood</td><td>30</td><td>14.3</td><td>475.5</td><td>780.7</td></tr><tr><td>Biomass boiler 6 (500 kW)</td><td>Wood chips</td><td>20–30</td><td>9.6</td><td>475.5</td><td>458.8</td></tr></tbody></table>

including operating regime, load, number of cycles, combustion gas composition (O₂, CO₂, CO, OGCs and NOx), gas moisture and temperature, particle size distributions, and concentrations of TPM, condensables, SP, PAHs, and heavy metals. The results of this analysis (Fig. S1) are presented and discussed in the following section.

However, we acknowledge that the number of replicates per boiler was limited (n = 1–3) and the number of studied boilers was small, which reduces the precision of all statistical tests and calls for cautious interpretation of the results.

# 3. Results & discussions

## 3.1. Operating conditions

The six biomass boilers selected for the ACIBIOQA project are all dedicated to heating and providing hot water for buildings or offices. It is important to mention that some of these biomass boilers were studied during the COVID-19 pandemic and the containment periods of 2020 (biomass boilers 3, 4, and 5). Therefore, the demand for these three biomass boilers during measurements may not necessarily reflect normal operating conditions. Measurements of O₂ and CO₂ were conducted on the emissions from the biomass boilers, and the results are presented in Fig. 2. Evaluation of the variations in concentrations of these two gases provides information on the operating regime. Near to stable concentrations of CO₂ and O₂ indicate quite a stable combustion i. e. a stable regime of the boiler, while cycling concentrations of CO₂ and O₂ refer to non-constant combustion conditions and an operation of the boiler made of cycles. A constant operating regime was observed for biomass boilers 2 and 6 only. Depending on the studied biomass boiler, a varying number of operating cycles are observed. A cycle is characterized by a start-up phase (from a maximum value of O₂), a stable phase (reaching a minimum value of O₂), and a shutdown phase (returning to a maximum value of O₂). Biomass boilers 4 (150 kW) and 5 (200 kW) have the lowest power outputs and operate with periodic "On/Off" cycles, where each biomass boiler is completely turned off (21 vol% O₂ content) before restarting (10 vol% O₂ content). Moreover, biomass boiler 4 has the highest number of cycles during the sampling period, with approximately 20 cycles recorded over 7 h of measurements, while biomass boiler 5 exhibits fewer cycles (about 4 cycles in 6 h). Biomass boilers 1 and 3 (800 kW and 720 kW, respectively) cycle around O₂ values between 10 vol% and 16 vol%, but unlike biomass boilers 4 and 5, the combustion is not completely turned-off between cycles. It is important to mention that for biomass boiler 3, the cycle duration is longer, meaning the boiler remains in a low-power mode for an extended period, with O₂ levels varying between 12 vol% and 16 vol%. Finally, biomass boilers 2 and 6 (1.65 MW and 500 kW, respectively) exhibited more stable operation modes with few or no cycles, and O₂ levels around 8 vol% and 10 vol%, respectively. During the measurement campaign, the combustion conditions depended directly on the demand of the users and were identified as follows: biomass boiler 1 operated at 50 % of its nominal output power, biomass boiler 2 at 90 %, biomass boiler 3 at 30 %, biomass boiler 4 at 50 %, biomass boiler 5 at 40 %, and biomass boiler 6 at 80 %.

## 3.2. Emissions characterization

The emission flue of each biomass boiler was characterized by measuring the gas flow rate, gas moisture, flue gas temperature (Table 4), combustion gases including CO, CO₂, O₂, NOx, and OGCs (Table 5), and aerosol phase including SP, TPM, PM₁₀, PM₂.₅, PM₁, 8 PAHs, and 15 heavy metals (Table 6 and Table S3). Data for combustion gases (Table 4) represent averages of daily mean values. Daily mean values were calculated over the entire combustion cycle during each testing day. Measurements were replicated over two to three days for all biomass boilers except biomass boiler 1, which was monitored for one day only. For the aerosol phase (Table 6), mass concentration,
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** O₂ concentrations (blue lines) and CO₂ concentrations (red lines) of the six biomass boilers. The blue squares in the corners represent the combustion conditions of the biomass boilers according to the users' demands (equivalent to the % of nominal power output). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

<caption>**Table 4**</caption>

Flue gas characteristics measured from the emissions of the six selected biomass boilers. For measurements repeated over two or three days, values are reported as mean ± inter-day SD.

<table><thead><tr><th>Biomass Boilers</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th></tr><tr><th>Nominal power output</th><td>800 kW</td><td>1.65 MW</td><td>720 kW</td><td>150 kW</td><td>200 kW</td><td>500 kW</td></tr><tr><th>Combustion conditions</th><td>50 %</td><td>90 %</td><td>30 %</td><td>50 %</td><td>40 %</td><td>80 %</td></tr><tr><th>Gas characteristics</th><th colspan="6">Mean ± inter-day SD</th></tr></thead><tbody><tr><th>Gas flow rate (Nm³ h⁻¹)</th><td>1229</td><td>1619</td><td>719</td><td>327</td><td>449</td><td>1405</td></tr><tr><th>Gas moisture (%)</th><td>10 ± 3</td><td>15</td><td>9</td><td>8</td><td>21</td><td>13</td></tr><tr><th>Gas temperature (°C)</th><td>81</td><td>112 ± 20</td><td>69</td><td>97 ± 13</td><td>64 ± 2</td><td>108 ± 15</td></tr></tbody></table>

gravimetric size distribution, and aerosol composition are based on the average of intermittent measurements taken over the study period.

### 3.2.1. Combustion gases

Combustion gases (CO, OGCs, and NOx) were measured for the six

selected biomass boilers. The corresponding results are presented below.

**CO.** The presence of CO in flue gases indicates an incomplete combustion process, which can occur mainly due to low local temperatures or local oxygen deficiency (Marynowski et al., 2014; Krûmal et al., 2023). CO levels were found to be influenced by the facility's operating mode and load conditions (Fig. 3). The highest levels were observed for boilers operating under low load and intermittent regimes (specifically biomass boilers 3, 4, and 5 and to a lesser extent, boiler 1), with biomass boiler 3, operating at the lowest load (30 %) and remaining in a low-power mode for extended periods during each cycle, exhibiting the highest CO emissions, reaching up to 8841 mg Nm⁻³.

This observation aligns with previous findings, where CO emissions increased significantly at reduced loads due to excess air and lower combustion temperatures. Kindbom and Mawdsley (2018) observed a substantial rise in CO emissions during tests on various appliances, including modern and old log boilers as well as a pellet boiler, when operating at 30 % of their nominal load. Similarly, Hrdlicka et al. (2016) reported higher CO emissions, along with increased NOx and SO₂ levels and reduced energy efficiency, at 40 % of nominal power.

In contrast, biomass boilers 2 and 6, operating at the highest loads (90 % and 80 %, respectively), close to their nominal power output and under stable operation modes, showed the lowest CO levels (153 mg Nm⁻³ and 495 mg Nm⁻³, respectively).

<caption>**Table 5**</caption>

Combustion gas concentrations (reported as average of the daily mean values) measured from the emissions of the six selected biomass boilers. All concentrations are corrected to 6 % O₂. For measurements repeated over two or three days, values are reported as mean ± inter-day SD.

<table><thead><tr><th>Biomass Boilers</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th></tr><tr><th>Nominal power output</th><td>800 kW</td><td>1.65 MW</td><td>720 kW</td><td>150 kW</td><td>200 kW</td><td>500 kW</td></tr><tr><th>Combustion conditions</th><td>50 %</td><td>90 %</td><td>30 %</td><td>50 %</td><td>40 %</td><td>80 %</td></tr><tr><th>Operating regime</th><td>Incomplete on/off cycles</td><td>Constant, no cycles</td><td>Incomplete on/off cycles</td><td>Complete on/off cycles</td><td>Complete on/off cycles</td><td>Constant, no cycles</td></tr><tr><th>Number of cycles (over 4 h)</th><td>8</td><td>0</td><td>3</td><td>12</td><td>2</td><td>0</td></tr><tr><th>Combustion gases</th><th colspan="6">Mean ± inter-day SD</th></tr></thead><tbody><tr><th>O₂ (%)</th><td>12.9</td><td>8.5 ± 0.2</td><td>12.7 ± 0.1</td><td>16.2 ± 0.5</td><td>14.3 ± 0.6</td><td>9.6 ± 1</td></tr><tr><th>CO₂ (%)</th><td>7.9</td><td>12.2 ± 0.2</td><td>7.6 ± 0.2</td><td>3.9 ± 0.2</td><td>6.6 ± 0.6</td><td>11.2 ± 1</td></tr><tr><th>CO (mg Nm⁻³)</th><td>925</td><td>153 ± 136</td><td>8841 ± 908</td><td>3043 ± 741</td><td>2508 ± 265</td><td>495 ± 81</td></tr><tr><th>NOx (mg NO₂ Nm⁻³)</th><td>172</td><td>210 ± 20</td><td>205 ± 9</td><td>395 ± 79</td><td>358 ± 4</td><td>235 ± 53</td></tr><tr><th>OGCs (mg C Nm⁻³)</th><td>14</td><td>&lt;1</td><td>634 ± 231</td><td>180 ± 34</td><td>130 ± 49</td><td>27 ± 3</td></tr></tbody></table>
<caption>**Table 6**</caption>

Particulate phase characterization from the emissions of the six selected biomass boilers. All concentrations are corrected to 6 % O₂. The limits of quantification for the different measured parameters are also presented. "< LQ" stands for data below the limit of quantification, and "ND" (no data) indicates that a measurement was not performed or that sampling failed.

<table><thead><tr><th></th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th></tr></thead><tbody><tr><td>Biomass Boilers</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Nominal power output</td><td>800 kW</td><td>1.65 MW</td><td>720 kW</td><td>150 kW</td><td>200 kW</td><td>500 kW</td></tr><tr><td>Combustion conditions</td><td>50 %</td><td>90 %</td><td>30 %</td><td>50 %</td><td>40 %</td><td>80 %</td></tr><tr><td>Operating regime</td><td>Incomplete on/off cycles</td><td>Constant, no cycles</td><td>Incomplete on/off cycles</td><td>Complete on/off cycles</td><td>Complete on/off cycles</td><td>Constant, no cycles</td></tr><tr><td>Number of cycles (over 4 h)</td><td>8</td><td>0</td><td>3</td><td>12</td><td>2</td><td>0</td></tr><tr><td>Filtration system</td><td>Electrostatic filter</td><td>Cyclone & bag filters</td><td>Cyclone</td><td>External multicyclone</td><td>Cyclone</td><td>2 ACS cyclones</td></tr><tr><td></td><td></td><td></td><td><u>Particulate matter</u></td><td></td><td></td><td></td></tr><tr><td>SP (mg Nm<sup>-3</sup>)</td><td>84</td><td>&lt; LQ</td><td>341</td><td>435</td><td>79</td><td>120</td></tr><tr><td>LQ SP (mg Nm<sup>-3</sup>)</td><td>2.3</td><td>0.3</td><td>9.5</td><td>11.6</td><td>3.3</td><td>3.0</td></tr><tr><td>TPM (mg Nm<sup>-3</sup>)</td><td>104</td><td>&lt; LQ</td><td>407</td><td>475</td><td>109</td><td>ND</td></tr><tr><td>LQ TPM (mg Nm<sup>-3</sup>)</td><td>5.7</td><td>1.5</td><td>5.1</td><td>8.5</td><td>3.5</td><td>ND</td></tr><tr><td>Contributions of condensables (%)</td><td>4-21</td><td>&lt; LQ</td><td>4-7</td><td>11-26</td><td>21-24</td><td>ND</td></tr><tr><td></td><td></td><td></td><td><u>Particle size concentration</u></td><td></td><td></td><td></td></tr><tr><td>PM<sub>1</sub> (mg Nm<sup>-3</sup>)</td><td>37</td><td>0.2</td><td>357</td><td>267</td><td>70</td><td>106</td></tr><tr><td>PM<sub>2.5</sub> (mg Nm<sup>-3</sup>)</td><td>43</td><td>0.2</td><td>388</td><td>275</td><td>73</td><td>120</td></tr><tr><td>PM<sub>10</sub> (mg Nm<sup>-3</sup>)</td><td>56</td><td>0.2</td><td>452</td><td>276</td><td>74</td><td>131</td></tr><tr><td>&gt; PM<sub>10</sub> (mg Nm<sup>-3</sup>)</td><td>65</td><td>0.2</td><td>493</td><td>277</td><td>75</td><td>140</td></tr><tr><td>LQ (mg Nm<sup>-3</sup>)</td><td>1.6</td><td>0.2</td><td>4.5</td><td>5.3</td><td>1.9</td><td>2.7</td></tr><tr><td></td><td></td><td></td><td><u>Particle size contributions</u></td><td></td><td></td><td></td></tr><tr><td>D &lt; PM<sub>1</sub> (%)</td><td>57</td><td>100</td><td>71</td><td>97</td><td>93</td><td>72</td></tr><tr><td>PM<sub>1</sub> &lt; D &lt; PM<sub>2.5</sub> (%)</td><td>10</td><td>0</td><td>9</td><td>3</td><td>5</td><td>11</td></tr><tr><td>PM<sub>2.5</sub> &lt; D &lt; PM<sub>10</sub> (%)</td><td>20</td><td>0</td><td>11</td><td>0</td><td>1</td><td>8</td></tr><tr><td>D &gt; PM<sub>10</sub> (%)</td><td>13</td><td>0</td><td>9</td><td>0</td><td>1</td><td>9</td></tr><tr><td><u>8 PAHs (µg Nm<sup>-3</sup>)</u></td><td>1.6</td><td>0.1</td><td>340</td><td>176</td><td>55</td><td>16</td></tr><tr><td>LQ 8 PAHs (µg Nm<sup>-3</sup>)</td><td>0.006</td><td>0.003</td><td>0.188</td><td>0.387</td><td>0.111</td><td>0.058</td></tr><tr><td><u>15 Heavy Metals (mg Nm<sup>-3</sup>)</u></td><td>2.3</td><td>0.03</td><td>3.3</td><td>6.7</td><td>1.1</td><td>3.6</td></tr><tr><td>LQ 15 Heavy Metals (mg Nm<sup>-3</sup>)</td><td>0.017</td><td>0.027</td><td>0.023</td><td>0.044</td><td>0.003</td><td>0.370</td></tr></tbody></table>

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** CO concentrations reported as daily means and average of daily means for the six biomass boilers. Measurements were replicated over two to three days for all biomass boilers except biomass boiler 1, which was monitored for one day only.</figcaption></figure>
To further explore these relationships, a multivariate correlation analysis was performed. CO showed a strong negative correlation with operational load ($r = -0.70$), confirming that lower loads are associated with higher CO emissions. Additionally, CO was moderately negatively correlated with the “Constant, no cycles” operating regime ($r = -0.55$), and moderately positively correlated with the “Incomplete On/Off cycles” operating regime ($r = +0.46$), suggesting that unstable combustion conditions contribute to incomplete combustion and elevated CO levels.

**OGCs.** OGCs are another indicator of combustion conditions, often associated with incomplete combustion and the presence of unburned hydrocarbons (Krümal et al., 2023; Johansson et al., 2003). Similar to CO, OGC emissions (Fig. 4) were higher for biomass boilers 3, 4, and 5 operating at low load, reaching several hundred mg eq. $C \text{ Nm}^{-3}$, with biomass boiler 3, operating at the lowest load, reaching up to 634 mg eq. $C \text{ Nm}^{-3}$. On the other hand, concentrations below 27 mg eq. $C \text{ Nm}^{-3}$ were observed for biomass boilers 2 and 6, which had the highest operating loads. Despite running at a relatively low output (50 %), biomass boiler 1 exhibited low OGC emissions. Comparing it to biomass boiler 3, both operated under unstable conditions, with combustion not completely turned off between cycles. However, biomass boiler 3 remained in low-power mode for a significantly longer period, i.e., under poor combustion conditions, which clearly explains the high levels of OGCs and CO.

To further investigate these relationships, a multivariate correlation analysis was performed. OGCs showed a very strong positive correlation with CO ($r = +1.00$), highlighting their common link to incomplete combustion. Additionally, OGCs were strongly negatively correlated with operational load ($r = -0.70$), and moderately negatively correlated with the “Constant, no cycles” operating regime ($r = -0.42$), reinforcing the link between unstable, low-load combustion and elevated OGC emissions.

**NOx.** The nitrogen content of the fuel is a major influencing factor for NOx emissions in solid fuel combustion (Laskin et al., 2009; Chen et al., 2010; Ozgen et al., 2014; Yang et al., 2018b). Unfortunately, in this study, no analyses were performed to determine the chemical composition of the biomass fuels used. Higher NOx levels were found in the emissions of biomass boilers 4 and 5 ($>350 \text{ mg eq. } \text{NO}_2 \text{ Nm}^{-3}$), which are the smallest biomass boilers (150 kW and 200 kW, respectively), by burning biomass chips and biomass. The other biomass boilers exhibited NOx levels around 200 mg eq. $\text{NO}_2 \text{ Nm}^{-3}$ by consuming biomass chips, green waste, and shredded pellets (Fig. 5).

To further investigate the influence of operational parameters, a multivariate correlation analysis was performed. NOx levels showed a

strong negative correlation with nominal power output ($r = -0.69$), indicating that smaller boilers tended to emit higher NOx concentrations. Additionally, NOx was very strongly positively correlated with the “Complete On/Off cycles” operating regime ($r = +0.96$). These findings suggest that frequent complete On/Off cycling may contribute to elevated NOx emissions in our study.

### 3.2.2. Particulate phase characterization

Physical and chemical characterizations of the particulate phase from the six biomass boilers were performed, and the results are presented below.

#### 3.2.2.1. Mass concentration of the solid and condensable particle fractions.
The highest SP emissions were observed for biomass boiler 4, reaching up to 435 mg $\text{Nm}^{-3}$ (Fig. 6). This boiler, the smallest in terms of nominal power output, operated with periodic On/Off cycles, including complete shutdowns, and had the highest number of cycles. However, biomass boiler 5, which also operated with periodic On/Off cycles but had fewer cycles, showed lower SP emissions (79 mg $\text{Nm}^{-3}$). These results suggest that On/Off cycles can affect SP emissions. This aligns with findings from Schmidl et al. (2016), who reported that particle emissions during ignition and shutdown phases were 4–5 times higher than those measured during stable operation at nominal or reduced load.

Chandrasekaran et al. (2011) investigated gas and particle emissions from different biomass boilers with power outputs between 150 kW and 500 kW using wood pellets and wood chips with moisture contents ranging from 4.6 % to 26 %. They compared the number of particles and their size distribution during different combustion phases. A shift in the average particle diameter (Geometric Mean Diameter, GMD) was observed during the start-up and shutdown phases compared to nominal operation. During the start-up phase, particles emitted were larger due to low combustion chamber temperatures, causing unburned material to be carried away by the incoming airflow (Chandrasekaran et al., 2011; Fachinger et al., 2018). Conversely, during the end-of-combustion phase, emissions of particles with a finer average diameter (GMD centered on 15 nm) than those observed during the stationary phase (GMD centered on 70 nm) were detected. These particles are likely formed by vapor release and the nucleation of small particles. Furthermore, emission levels varied across different combustion phases, with particle numbers being similar during start-up and steady-state but significantly higher during the shutdown phase. These results align well with observations of high SP emissions in biomass boilers with On/Off cycles.

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** OGC concentrations reported as daily means and average of daily means for the six biomass boilers. Measurements were replicated over two to three days for all biomass boilers except biomass boiler 1, which was monitored for one day only.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 5. NOx concentrations reported as daily medians and average of daily medians for the six biomass boilers. Measurements were replicated over two to three days for all biomass boilers except biomass boiler 1, which was monitored for one day only.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 6. Emissions of the solid and condensable fractions of particles for the six biomass boilers using the SPC-IPA method (SP collected on a heated quartz filter at 160 °C, and condensable fraction trapped in cooled isopropanol). “< LQ” stands for data below the limit of quantification, and “ND” (no data) indicates that a measurement was not performed or that sampling failed.</figcaption></figure>

Biomass boiler 2, operating with constant combustion conditions (without cycles), presents the lowest concentration levels of SP (<LQ, Fig. 6). In contrast, biomass boiler 6, despite having a constant combustion rate (>80 %), showed higher SP emissions (up to 120 mg Nm⁻³), though still lower than those observed for biomass boilers 3 and 4 (Fig. 6). The main difference between biomass boilers 2 and 6 is their filtration systems: bag filters in boiler 2 appear to be more efficient for removing particles compared to ACS cyclones in boiler 6. However, while maintaining a constant combustion condition (without cycles) is necessary, it is not sufficient on its own to limit SP emissions. The type of particulate filtration system used is also a crucial factor.

It is now evident that SP concentration levels depend on both the filtration system used and the combustion conditions. Unlike biomass boilers 4 and 5, biomass boilers 1 and 3 operated with periodic cycles without fully ceasing combustion. Biomass boiler 1 (800 kW) operated under partial load conditions (<50 %) with frequent cycles. Despite being equipped with an efficient particle filtration system (electrostatic filter), high SP concentrations were observed (Fig. 6). However, these levels were still lower than those produced by Biomass Boiler 3 (Fig. 6),

which is characterized by poor combustion conditions (<30 %) and longer phases of reduced load (O₂ at 16 vol%).

To further investigate the influence of operational parameters, a multivariate correlation analysis was performed. SP levels showed a strong positive correlation with the number of cycles (r = +0.62), indicating that frequent cycling contributes to increased particle emissions. SP also showed a strong negative correlation with nominal power output (r = -0.69), suggesting that smaller boilers from our study tend to emit more particles. Additionally, SP was moderately negatively correlated with the “Constant, no cycles” operating regime (r = -0.55) and moderately negatively correlated with operational load (r = -0.56), reinforcing the role of combustion stability and load in controlling particulate emissions.

The results in Fig. 6 show that the contribution of the condensable fraction of particle emissions from the studied biomass boilers varied between 4 % and 26 %, depending on the power of the biomass boilers and their operating regime. The contribution of the condensable fraction is higher (14 % and 22 %) for biomass boilers 4 and 5. In contrast, for biomass boiler 2, operating at steady state and nominal load, the
condensable fraction concentration was below the LQ. No measure-
ments were performed for the condensable fraction for biomass boiler 6
due to technical issues. These results align well with the fact that
biomass boilers operating at low output and undergoing frequent “On/
Off” cycles generally have higher organic compound emissions due to
more degraded combustion. The concentration levels of the condensable
fraction of particles emitted from the small biomass boilers studied
remain low compared to the concentration levels of SP. The contribution
of the condensable fraction to the total particulate matter from biomass
combustion reported in the literature is highly variable. Studies con-
ducted on wood stoves generally show higher contributions, sometimes
reaching up to 90 % in certain cases (Seljeskog et al., 2017; Klauser
et al., 2018; Nystrom et al., 2017; Nussbaumer et al., 2008), whereas for
biomass boilers, the contribution is typically below 30 % (Yang et al.,
2018a; Determination of condensable particulate matter released by the
combustion of solid fuels in low-power boilers, 2023; Rector et al.,
2017). It is important to note that several factors can influence the
emission of condensable particles and their contribution to total par-
ticulate matter. These include fuel type, operating conditions, flue gas
temperature, measurement methods, appliance type, and others
(Trubetskaya et al., 2021; Seljeskog et al., 2017; Yang et al., 2018b).

To further explore these relationships, a multivariate correlation
analysis was performed. The condensable fraction showed a very strong
negative correlation with the “Incomplete On/Off cycles” operating
regime (r = −0.90), and a very strong positive correlation with the
“Complete On/Off cycles” operating regime (r = +0.90), indicating that
full combustion shutdowns are associated with higher condensable
emissions in our study. Additionally, a strong positive correlation with
nominal power output (r = +0.84) was observed, suggesting that
smaller boilers in our study tend to emit a higher proportion of con-
densable particles.

3.2.2.2. Particle mass size distribution (PM₁, PM₂.₅ and PM₁₀). Regarding the gravimetric particle size distribution, the results show a predominance of particles with a diameter less than PM₁ (Fig. 7). This finding is consistent with data from the literature on small biomass combustion. Indeed, several studies have shown that small biomass combustion typically leads to particle emissions dominated by submicron particles (<1 µm), and no significant emissions of particles larger than 10 µm are normally found (e.g. (Johansson et al., 2003; Boman et al., 2005; Ohlstroem et al., 2005; Trojanowski and Fthenakis, 2019)).

3.2.2.3. Particle chemical composition. Heavy metals. Among the measured heavy metals, biomass boiler 2, equipped with highly efficient bag filters for particle removal, emits the least by far. Across all biomass boilers, zinc is the most emitted metal, followed by lead and manganese (Table 4).

PAHs. PAH emissions follow trends similar to those of CO and OGCs, with the highest emissions observed for biomass boiler 3 (Table 4), operating at the lowest load (30 %), reaching up to 339 µg Nm⁻³, and the lowest emissions for biomass boiler 2 (0.1 µg Nm⁻³), operating at the

highest load (90 %). These results confirm the general understanding
that PAHs are predominantly formed during the incomplete combustion
of organic material (Krůmal et al., 2019). Among the 8 PAHs, fluo-
ranthene appears to be the most dominant, contributing between 35 %
and 70 %, except for biomass boiler 3, where its contribution is only 2 %
(Krůmal et al., 2023; Ravindra et al., 2008).

To further investigate the influence of operational parameters, a multivariate correlation analysis was performed. PAH emissions showed a very strong positive correlation with CO (r = +1.00) and OGCs (r = +1.00), indicating that these pollutants are closely linked and likely originate from similar combustion inefficiencies. PAHs also showed a strong negative correlation with operational load (r = −0.68), suggesting that lower loads favor PAH formation. Additionally, PAHs were moderately negatively correlated with the “Constant, no cycles” operating regime (r = −0.51), and moderately positively correlated with the “Incomplete On/Off cycles” operating regime (r = +0.41), reinforcing the role of unstable combustion conditions in promoting PAH emissions.

3.3. Emission factors

The EFs for CO, OGCs, TPM, the eight PAHs and NOx were calculated according to Eq. (1) at the actual O₂ rate. To improve France's national emission inventories for small combustion appliances (<20 MW) and to align with the European goal of producing more accurate emission inventories, the following sections compare the EFs of the studied biomass boilers with those reported in the literature review (Emissions atmosphériques des chaufferies bois de puissance inferieure à 1 MW, 2023) (which complies emission factors from national and international studies considering the relevant power range) and with the EFs currently used in the French national emission inventory (Méthodologie de l'incidentaire - rapport Ominea, 2025). It is important to note that some biomass boilers supported by the BHF, which imposes specific emission requirements, have demonstrated better emission performance and may therefore have lower EFs. In the upcoming figure (Fig. 8), the green line represents the EFs used in the French national inventory for biomass boilers (1–20 kW) supported by the BHF, while the violet line corresponds to EFs from biomass boilers in the same power range not supported by the BHF. This comparison is provided for indicative purposes only, to assess whether the six studied boilers exhibit similar behavior in terms of emissions of certain pollutants compared to the literature and the emission factors currently used in the French national inventory. It is not intended to generalize the observed behavior to all boilers below 1 MW, especially given that the number of boilers tested in this study is limited to six. This comparison offers an initial overview and a starting point for further investigation.

CO. CO EFs from the six studied biomass boilers are highly variable (Fig. 8-A), ranging from 54 g GJ⁻¹ (Biomass boiler 2) to 3361 g GJ⁻¹ (Biomass boiler 3), depending on the biomass boiler and combustion conditions. These results were compared with the data from the literature review and the emission factor used in the national inventory (Fig. 8-A). However, while the CO emission factors used in the French emission inventory for the 1–20 MW power range is set at 250 g GJ⁻¹,

<figure><img src="image_9.png" /><figcaption>Fig. 7. Particle mass size distributions of the PM: D < PM₁, PM₁ < D < PM₂.₅, PM₂.₅ < D < PM₁₀ and D > PM₁₀ (%) according to the biomass boilers.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Fig. 8.** EFs for A) CO, B) OGCs, C) TPM, D) PAHs and E) NOx from the studied biomass boilers (red dots) compared to EFs from the literature review by power output range (blue horizontal lines) and EFs used in the French national inventory (blue vertical line: 1–20 MW BHF, green vertical line: 1–20 MW Non. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

the smaller biomass boilers from our measurement campaign (biomass boilers 3 and 4) appear to be more emissive, as well as biomass boiler 5 operating at the lowest load under poor combustion conditions.

**OGCs.** OGCs emission factors from the six selected biomass boilers ranged from 5 g GJ⁻¹ (Biomass boiler 1) to 242 g GJ⁻¹ (Biomass boiler 3), with the exception of biomass boiler 2, which exhibited significantly lower emissions at 0.1 g GJ⁻¹ (Fig. 8-B). These EFs were compared with those reported in the literature review and the EFs from the national French inventory. It is crucial to interpret these comparisons with caution, as the EFs discussed here represent non-methane organic gaseous compounds (NMOGCs) rather than total OGCs. Additionally, the units of measurement may vary (e.g., carbon equivalent, CH₄ equivalent, or total NMOGCs), further complicating direct comparisons.

If the EFs from our measurement campaigns fall outside the expected range for biomass boilers with a power output of less than 1 MW, this discrepancy likely stems from the limited number of data points available in the literature (n = 3). While the EFs of NMOGCs currently used in national French inventory for the 1–20 MW power range are set at 2 g GJ⁻¹ and 5 g GJ⁻¹, the biomass boilers studied here with power outputs below <1 MW may exhibit higher emissions. However, deriving reliable EFs from such a small dataset is challenging.

**TPM.** The EFs of TPM for the six studied biomass boilers varied between 32 g GJ⁻¹ (Biomass boiler 1) and 169 g GJ⁻¹ (Biomass boiler 4) (Fig. 8-C), except for biomass boiler 2, which had an EF of 0.7 g GJ⁻¹. This lower value is likely due to specific combustion conditions, including a constant operating regime, and the use of a bag filters combined with a cyclone.

**PAHs.** The EFs for the 8 PAHs from the six studied biomass boilers vary between 0.03 mg GJ⁻¹ (Biomass boiler 2) and 129 mg GJ⁻¹ (Biomass boiler 3) (Fig. 8-D). The emission factor used in the French national emission inventory for appliances with a power output of less than 20 MW is 8 mg GJ⁻¹. If some results from the measurement campaigns fall below the EF range observed for biomass boilers with a power output of less than 1 MW, this is likely due to the limited number of measurement points identified in the literature review. Only a few studies (less than 5) covering the 8 PAHs for the specific power range

below 1 MW were identified. Therefore, it is challenging to derive representative EF from such limited data.

**NOx.** The NOx emission levels from the six studied biomass boilers are distributed over a narrow range, between 60 g GJ⁻¹ (Biomass boiler 1) and 135 g GJ⁻¹ (Biomass boiler 4) (Fig. 8-E). These results were compared with data from the literature review and with the emission factors used in the French inventory. For biomass boilers with a power output between 1 and 20 MW, the national inventory proposes an EF of 132 g GJ⁻¹. This value is based on a substantial number of emission reports (more than 200 reports from over 100 biomass boiler plants) and can therefore be considered representative. For biomass boilers with a power output of less than 1 MW, the national inventory uses an EF of 200 g GJ⁻¹. However, emission factors from the studied biomass boilers for this power range are generally lower.

## 4. Conclusion

This study investigated emissions from six biomass boilers with power outputs below 2 MW in France, with a particular focus on units under 1 MW, a category that remains poorly studied nationally. It provided real-time data on various gas-phase and particulate-phase components. The study represents a pioneering effort to measure the condensable fraction of particles, which is often overlooked in emission assessments. The selected biomass boilers operated under different combustion loads and regimes and were equipped with various filtration systems, allowing for the evaluation of how these parameters influence emission profiles. The results highlight that CO, OGCs, and PAHs emissions strongly depend on the operating conditions of the facility. Biomass boilers operating under stable conditions and at high loads (e. g., biomass boilers 2 and 6), close to their nominal power output, exhibited the lowest emissions. In contrast, biomass boilers functioning at low loads with frequent cycling (e.g., biomass boilers 3, 4, and 5, and to a lesser extent boiler 1) showed significantly higher emissions due to degraded combustion conditions. SP concentration levels were influenced by both the filtration system and combustion conditions. The highest SP emissions were observed for biomass boiler 4, which had the
highest number of "On/Off" cycles. The presence of efficient filtration systems, such as bag filters, also played a crucial role in reducing particulate emissions. For example, biomass boilers 2 and 6, which operated close to their nominal power output under stable conditions, differed mainly in their filtration systems. Biomass boiler 2, equipped with a bag filter, showed lower particulate emissions compared to biomass boiler 6 using an ACS cyclone. The condensable fraction of particles varied between 4 % and 26 %, depending on the biomass boiler's power output and operating regime. Biomass boilers running at low output and undergoing frequent cycling had higher condensable emissions. Regarding the gravimetric particle size distribution, PM₁ was the dominant fraction.

EFs were also calculated and compared with values reported in the literature and those currently used in the French national emission inventory. While EFs of NOx were consistent with inventory values, CO, OGCs, and TPM emissions from the smaller biomass boilers studied (<1 MW) were found to be higher than current estimates.

In light of the limitations identified in this study - namely the restricted number of biomass boilers <1 MW, limited replicates for certain measurements, and the absence of fuel composition data - future research should aim to broaden the scope of investigated installations, incorporate more replicates, and include detailed fuel characterization. These improvements will enable the application of robust statistical analyses, thereby strengthening the reliability of emission assessments and supporting more informed regulatory and technological decisions in the biomass combustion sector.

### CRediT authorship contribution statement

**Sergio Harb:** Writing – review & editing, Writing – original draft, Investigation, Formal analysis, Data curation. **Benjamin Cea:** Writing – original draft, Validation, Methodology, Investigation, Conceptualization. **Nicolas Karoski:** Methodology, Investigation, Data curation, Conceptualization. **Adrien Dermigny:** Methodology, Investigation, Data curation, Conceptualization. **Vincent Fuvel:** Investigation, Data curation. **Benjamin Cuniasse:** Writing – original draft, Methodology, Investigation. **Florence Paulus:** Project administration, Conceptualization. **Isaline Fraboulet:** Visualization, Validation, Methodology, Investigation, Conceptualization. **Isaline Fraboulet:** Review & editing.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgement

The ACIBIOQA project (Amélioration de la Connaissance en matière d'Impact des chaufferies BIOmasse sur la Qualité de l'Air) was supported by ADEME (The French Environment and Energy Management Agency) under the 2018 CORTEA call (agreement number 19-03-C0042) and the French Ministry of Environment. The authors gratefully thank everyone from Citepa, ADEME, INERIS and INDDIGO who contributed to this work.

### Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.aeaoa.2025.100391.

### Data availability

Data will be made available on request.

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