The impact of domestic combustion of biomass pellets on the environment and human health: Example from Poland

doi:10.21203/rs.3.rs-5223246/v1

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The impact of domestic combustion of biomass pellets on the environment and human health: Example from Poland

https://doi.org/10.21203/rs.3.rs-5223246/v1

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In the context of the European Union's intensified efforts to curb greenhouse gas emissions and meet climate targets, wood pellets have emerged as a pivotal element in the renewable energy strategy. Yet, biomass pellet combustion has been linked to a range of pollutants impacting air quality and public health. As biomass utilization gains popularity as a fuel for residential heating, it is important to determine this impact and enhance sustainable practices throughout the entire biomass energy production cycle.

This study investigates the intricate dynamics of biomass pellet properties on their combustion emissions, with a specific focus on the differences observed between pellets of woody and non-woody origins. The data reveal a variation in pellet characteristics, especially regarding their ash and fines contents, mechanical durability, and impurity levels, and significant differences in the type and amount of utilization emissions. The results highlight potential health risks posed by the combustion of biomass fuels, particularly non-woody (agro) pellets, due to elevated concentrations of emitted particulate matter (PM), carbon monoxide (CO), nitrogen dioxide (NO₂), hydrogen sulfide (H₂S), ammonia (NH₃), chlorine (Cl₂), sulfur dioxide (SO₂), and formaldehyde (HCHO), all surpassing recommended limits.

Moreover, the study reveals that emissions from pellet combustion could be partially predicted by analyzing pellet characteristics. Statistical analysis identified several key variables—including bark content, fines content, mechanical durability, bulk density, heating value, net calorific value, sulfur, and nitrogen content—that impact emissions of CO, NO₂, H₂S, SO₂, HCHO, and respiratory tract irritants. These findings underscore the need for proactive measures, including the implementation of stricter standards for fuel quality and emissions, alongside public education initiatives promoting the cleanest and safest fuels possible.

biomass fuel

wood pellets

combustion

emissions

health impact

The production of wood pellets has increased globally from 19.5 to 48.8 million tons in the past 10 years following European political commitments to reduce greenhouse gas emissions and raise the share of renewable energy use (Bioenergy Europe, 2018, 2024; European Council, 2023). Yet, inherent challenges are presented by sustainable sourcing of biomass materials, and subsequently utilizing pellets promoted as a carbon-neutral fuel. As the demand for feedstock rises, ensuring responsible sourcing practices becomes paramount to mitigate deforestation and habitat destruction. Moreover, biomass combustion is a significant source of gaseous pollutants and particulate matter emissions that can harm human health and our environment. Consequently, adopting sustainable pellet production practices and thoughtful policy frameworks that minimize pollution and enhance combustion efficiency is crucial for reducing these concerns (Abdoli et al., 2018; Chandrasekaran et al., 2013; Chen et al., 2017, 2023; Ebadian et al., 2021; Jelonek et al., 2021; Křůmal et al., 2023; Leoni et al., 2024; Mawusi et al., 2023; Mitchell et al., 2016; Orasche et al., 2012; Perez-Jimenez, 2015; Rabaçal, 2015; Rabbat et al., 2023; Rybarczyk, 2023; Shumway, 2023; Sterman et al., 2018, 2022; Szyszlak-Barglowicz et al., 2020; Venturini et al., 2018; Zhang et al., 2020).

Examining the relationships between fuel quality, combustion emissions, and air pollution is important for understanding the associated human health risks of biomass utilization. Despite the growing production and consumption of biomass pellets, the complexity of these relationships remains inadequately understood, and a holistic and interdisciplinary approach is needed to unravel the intricacies. This is especially critical as research has shown that some biomass fuels on the market display low quality, which can lead to elevated emissions and affect combustion efficiency (Drobniak et al., 2022, 2023a, 2024; Georgaki et al., 2024; Jelonek et al., 2020, 2021; Jiang et al., 2023; Leoni et al., 2024; Mack et al., 2022, 2023; Moreno et al., 2016; Risholm-Sundman and Vestin, 2005; Szczurek et al., 2021; Wöhler et al., 2017).

A recent study examined the variation in fuel characteristics and the quality of biomass pellets used for residential heating in the Upper Silesia region of south-central Poland (Drobniak et al. 2024). Many tested pellets, even those with DINplus and/or ENplus certifications, were not meeting recommended quality thresholds. Moreover, among 30 tested samples, only one passed the quality thresholds set by the US-PL BIO, a newly established certification in Poland that combines quality assessments following DINplus with an optical microscopy analysis (PL-US BIO 2023). The primary issues causing decreased pellet quality included elevated ash and fines content, compromised mechanical durability, too low ash melting temperature, and the inclusion of undesirable components like bark, inorganic matter, fossil fuels (like coal), and petroleum products (mainly glues and plastics). These concerns may impact various aspects of bioenergy, encompassing energy production, environmental sustainability, public health, and the overall long-term viability of biomass as a renewable and clean energy source.

In pursuit of enhanced environmental sustainability and the optimized utilization of resources, our research aims to comprehensively evaluate the emissions produced by the combustion of biomass pellets in residential settings. The primary objective was to analyze the type and quantity of emissions generated, specifically focusing on understanding the interplay between the pellets' physical, chemical, and petrographic properties with emitted gases and particulate matter. By investigating the impact of biomass pellet combustion on the ecosystem, we hope to contribute insights toward informed decision-making and strategies for a more eco-conscious approach to fuel production and residential energy generation.

3.1 Biomass samples

A total of 30 biomass pellet sample bags, each weighing 15 kg, were purchased from retail outlets in the Upper Silesia region of south-central Poland. Among the acquired fuels, 26 were wood pellets and 4 were of non-wood origin (agro pellets), including sunflower husk, dry bean pods, and bran. Almost half of the samples were ENplus and/or DINplus certified (Table 1; Drobniak et al., 2024).

All samples underwent physicochemical analysis according to the DINplus certification, ensuring a thorough examination of key physical and chemical attributes. These parameters included pellet diameter and length, fines content, mechanical durability, and bulk density, as well as moisture, ash, sulfur, and nitrogen contents, ash melting temperature, and net calorific value (DINplus 2023).

Additionally, samples were submitted for petrographic analysis to evaluate their composition and detect possible contaminants. The process involved the preparation of microscopic plugs, a 1,000-point petrographic analysis using reflected light, and the identification and classification of pellet components, following the methodology outlined by Drobniak et al. (2022, 2023b). The identified components included woody and non-woody biomass, bark, charcoal, fossil and processed organic matter, inorganic matter, petroleum products, binders, and other additives. Based on the data, the total amount of impurities in each sample was calculated.

3.2 Description of combustion experiments

To measure the type and amount of released emissions gases and particulate matter, aliquots of pellet samples were combusted in an Invicta Bassano 5 pellet stove located on the ground floor of a one-story building and operating at the maximum power of 5 kW. The stove was cleared of ash after each sample was burned. The next sample was then incinerated for 30 minutes before emission measurements were initiated. Combustion data were collected at the chimney's exit on the building’s roof (Figure 1).

Table 1. Information about the pellet fuel provided by producers. NP – information not provided.

Pellet type	Source of biomass	Pellet certification
Wood pellets	NP	ENplus A1
	NP	ENplus A1
	NP	ENplus A1
	NP	ENplus A1
	NP	none
	NP	ENplus A1
	NP	none
	NP	ENplus A1 and DINplus A1
	coniferous wood	none
	NP	none
	NP	ENplus A1
	NP	ENplus A1
	spruce and pine	none
	NP	none
	NP	ENplus A1
	coniferous wood	DINplus
	NP	ENplus A1 and DINplus
	NP	ENplus A1 and DINplus
	coniferous wood	ENplus A1
	NP	none
	NP	DINplus
	NP	none
	NP	none
	coniferous wood	none
	pine and oak	none
	pine and hornbeam	none
Non-wood pellets	sunflower husks	none
	sunflower husks	none
	sunflower husks and bean pods	none
	bran	none

Wind speed and direction were also recorded by anemometer, and based on this information, the analyzers were positioned upwind before each measurement.

Emission data were recorded every second for 15 minutes using a Testo 440 and two Nanosens AtmonFL S.M.O.K pollution analyzers, which measured concentrations of CO, CO₂, NO₂, NH₃, Cl₂, PM_2.5, PM₁₀, H₂S, SO₂, HCHO, and respiratory tract irritants (NO₂ + O₃ + Cl₂ + HC), as well as outside temperature, pressure, and humidity. Information on the instruments’ precision for particulate matter and gases measurements is available on the manufacturers’ websites (AeroMind 2024; Testo 2024). Furthermore, the air quality surrounding the building underwent daily analysis before initiating combustions. The emission data presented in this paper have had background values subtracted, focusing solely on emission levels resulting from fuel combustion.

3.3 Statistical analysis

Statistica 13 software by StatSoft (Poland) was used to explore relationships between physical properties, chemical composition, petrographic components of pellets, and their emission parameters. Each combustion variable in the database underwent testing for its probability distribution. Due to a significant number of peak values, the variables generally did not follow a normal distribution (Shapiro-Wilk test, p < 0.05; Schinazi, 2022). Therefore, the median value for analyzed emissions was used, along with the 25th and 75th percentiles of data distribution, as well as maximum and minimum values. Furthermore, to prevent an ill-conditioned matrix, parameters exhibiting a Pearson correlation coefficient (r²) above 0.9999 were removed. Given the close correlation between biomass and impurities contents derived from petrographic analysis (their combined sum equals 100 vol. %) and hence, their redundancy, only the impurities values were integrated into the model.

A progressive stepwise regression analysis was utilized to assess whether the combustion behavior of pellets can be predicted using various predictor variables. This method iteratively selects independent variables for the model based on their statistical significance, intending to enhance predictive accuracy while minimizing complexity. The progressive stepwise regression aimed to construct equations that predict the expected value of the dependent variable based on the values of several independent variables. The general form of the progressive stepwise regression equation can be expressed as:

Y = β₀ + β₁X₁ + β₂X₂+... + β_pX_p + ε

where Y represents the predicted or expected value of the dependent variable, while X₁ through Xₚ denote p distinct independent or predictor variables, ε is a random error, and the coefficients β₀ through βₚ represent the estimated regression coefficients.

Subsequently, F-tests were conducted to evaluate the overall significance of the regression models, and each coefficient was assessed using statistical tests, such as t-tests or p-values, to determine individual contributions of predictor variables. This process ensured that only statistically significant variables were retained in the final regression models, thus enhancing the validity and interpretability of results. Furthermore, coefficients of determination (R²) were calculated to evaluate the extent to which independent parameters included in the regression models account for the variance in the dependent variable. This analysis offered insights into the overall "Goodness-of-Fit" of the regression models, enabling the assessment of the effectiveness of the selected predictor variables in explaining the variability in pellet combustion behavior.

THEORY

Although the combustion of biomass leads to emissions of gaseous pollutants, volatile organic compounds (VOC) including polycyclic aromatic hydrocarbons (PAHs), and particulate matter, the human health risks associated with the emissions in residential settings remain inadequately investigated. Though there has been significant improvement in our comprehension of the dynamic interplay between fuel properties, combustion conditions, emissions, and their impact on human health in recent years, further research is needed to fully grasp the complexities of these relationships (Badyda et al., 2020; Chandrasekaran et al., 2013, 2016; Chen et al., 2017, 2023; Drobniak et al., 2023a, 2024; Freiberg et al., 2018; Gordon et al., 2014; Harun and Afzal, 2016; Jelonek et al., 2020, 2021; Jenkins et al., 1998; Karanasiou et al., 2021; Kasurinen et al., 2016; Kurmi et al., 2012; Naeher et al., 2007; Najser et al., 2019; Orasche et al., 2012; Ozgen et al., 2021; Perez-Jimenez, 2015; Petrocelli and Lezzi, 2014; Price-Allison et al., 2023; Rabaçal, 2015; Rabaçal et al., 2013; Rabbat et al., 2023; Shojaeiarani et al., 2019; Sigsgaard et al., 2015; Sterman et al., 2022; Su et al., 2023; Tran et al., 2023; Venturini et al., 2018; Vicente et al., 2018; Williams et al., 2012; Yang et al., 2019; Zeng et al., 2016; Zhang et al., 2020).

Research into a prediction of combustion emissions and their effects on human health is, however, a difficult task considering the intricate interaction of various factors like the variability of the biomass feedstock, complexity of the combustion process, atmospheric chemistry, meteorological factors, and variability of human exposure. Additionally, the challenge involves setting up a combustion experiment, an analytical approach, and subsequently appropriate statistical methods to draw conclusions.

Each type of biomass carries unique compositional characteristics and combustion behavior. Emissions from combustion can vary depending on the technical specifications of stoves, boilers, or grills, and on the chosen temperature and ventilation settings. Once released from chimneys, the emitted pollutants can either disperse via wind or pose a health hazard when concentrated in smoggy conditions. Equally difficult is estimating the human health impact of biomass utilization, particularly concerning long-term and cumulative effects. Previous studies, often combined with air quality monitoring, have been carried out to assess exposure levels and associated health risks (such as respiratory and cardiovascular diseases), and toxicological evaluations provided insight into the mechanisms by which biomass emissions affect human health and impact climate (Tomlin 2021; Vicente et al. 2021; Lazaro and Baba 2023; Jiang et al. 2024). Nevertheless, integrating data from various sources and incorporating additional factors like proximity to emission sources, weather conditions, seasons, human outdoor time-activity patterns, and individual susceptibility is challenging.

However, the results of such research are invaluable, as they shed light on challenges associated with biomass utilization and provide data allowing the enhancement of sustainable practices throughout the entire biomass energy production and utilization cycle. This, in turn, allows us not only to capitalize on the advantages of biomass as a renewable energy source but also to effectively address associated environmental and health challenges in pursuit of sustainable energy solutions.

5.1 Pellet properties and emissions

The analyzed pellets displayed variations in their physical and chemical properties as well as in petrographic composition, especially when comparing wood and non-wood pellets (Fig. 2). In numerous instances, even certified samples failed to meet the predefined DINplus quality standards (DINplus, 2021, 2023), particularly concerning mechanical durability (values up to 36 % below the threshold, ash (5 to 139 % above the threshold, and fines content (4 to 155 % above the threshold.

Moreover, among the 30 tested samples, 21 revealed impurity levels surpassing the designated limit of 3 vol. % (PL-US BIO 2023) by margins ranging from 3 % to a staggerig 500 %. Among these ndesirable inclusions, bark in quantities from 0.5 to 17.8 vol. % emerged as the most frequent component, followed by inorganic matter (0.1 to 1.3 vol. %), traces of petroleum products most commonly in the form of glues and plastic (0.1 to 0.8 vol. %), and fossil fuels (coal and coke of up to 0.8 vol. %). The content of binders and additives was estimated from 0 to 2.2 vol. %, with one sample containing binder in an amount above the certification threshold (Drobniak et al., 2024; PL-US BIO, 2023).

As expected, from the properties of the pellets, the combustion of samples resulted in contrasting emissions, which were considerably higher for non-wood pellets (Fig. 3). Emissions data (after subtracting background values) indicated elevated concentrations of particulate matter, CO, NO₂, NH₃, Cl₂, SO₂, and HCHO compared to recommended emission limits, which except CO₂ and H₂S exceeded defined thresholds, especially for the non-wood pellets (CDC, 2024; WHO, 2021, 1984, 2021).

The predominant type of emitted particulate matter from combusted samples was the PM_2.5 fraction (Fig. 3AB) with a median value of 107 µm/m³ for wood pellets and 987 µm/m³ for non-wood pellets. Comparatively, the World Health Organization's (WHO) daily mean exposure limit is set at 25 µm/m³, which shows that the combustion of agricultural biomass can pose significant environmental and public health concerns (WHO, 1984, 2021). This is especially true during winter months with numerous, closely spaced emission sources in residential neighborhoods and limited air turnover, which can significantly impact air quality (Rabaçal 2015; Rabbat et al. 2023; Chen et al. 2023).

Elevated emissions of CO were measured especially from the combustion of non-woody biomass. While wood pellets showed a median CO emission of 57 ppm, agricultural-based fuels yielded a median value of 835 ppm, with a blend sample of sunflower husk and dry bean pods emitting a maximum measured value of 3,720 ppm and vastly exceeding the threshold of 1,200 ppm that is considered immediately hazardous to health and life (Fig. 3C; CDC, 2024).

CO₂ emissions and the natural carbon cycle of biomass pellets have been widely discussed and researched as the combustion of biomass fuels is inevitably associated with CO₂ emissions (Berndes et al., 2020; Brack, 2017; Proskurina and Mendoza-Martinez, 2023; Shumway, 2023; Sterman et al., 2018, 2022). The combustion of samples in our study yielded a significant amount of CO₂, with the median value at 0.7 vol. % for non-woody pellets (7,000 ppm), and 0.5 vol. % for wood pellets (5,000 ppm) (Fig. 3D).

In contrast to wood pellets, the combustion of non-woody biomass resulted in notably elevated emissions of NO₂, Cl₂, H₂S, and SO₂ related to higher concentrations of nitrogen, sulfur, and chlorine in pellets produced from agricultural residue (Figs. 2–3, Kaczyński et al., 2019). Bran pellets, in particular, exhibited the highest emissions among the tested materials, with levels reaching 11.5 ppm for NO₂, 22 ppm for Cl₂, 6.7 ppm for H₂S, and 222.3 ppm for SO₂ (Figs. 3E-H). These findings raise significant concerns regarding the suitability of bran as a fuel source, especially given that the emitted levels of Cl₂ and SO₂ exceeded thresholds considered immediately hazardous to health and life, as outlined by the CDC (2024).

The emission analysis revealed that pellets derived from a blend of sunflower husk and dry bean pods exhibited the highest emission levels of ammonia (NH₃), reaching a concentration of 46 ppm (Fig. 3I). Conversely, the highest emissions of formaldehyde (HCHO) and respiratory tract irritants (RESP) were recorded from samples sourced from a sunflower product available in popular home and garden stores. This finding emphasizes the critical importance of monitoring and regulating emissions from biomass combustion, especially those associated with non-woody pellets. Such elevated concentrations of hazards can pose health risks and warrant attention from policymakers, environmental agencies, and the public alike.

5.2 Correlating pellet characteristics with emission levels

The complex relationships between combustion emissions and the physio-chemical and petrographical properties of pellets are crucial for understanding the environmental impact of biomass combustion. As biomass pellets become increasingly popular as a residential heating fuel, the task of evaluating potential health effects becomes ever more critical. Therefore, one of the objectives of this project was to identify measured variables that can serve as predictors for potentially hazardous emissions (Table 2, Equations 1–6).

Emission data from our experiments alongside measured pellet properties suggest that the combustion behavior of pellets can be partially predicted based on certain pellet characteristics. Only data from wood pellets were used for the statistical analysis since our data set is more representative and statistically valid for wood pellets (26 samples) than for non-woody pellets (4 samples; Table 2).

Previous studies correlated the amount of emitted particulate matter versus the moisture content of combusted pellets (Price-Allison et al. 2023), versus the ash content and composition (Choiński et al. 2023; Rabbat et al. 2023), as well as versus the fines content of biomass pellets (Lyubov et al. 2022). Our analyses do not indicate any such statistically relevant correlations. While further analyses are required to understand the reason for different outcomes of our study, one potential explanation for the lack of correlation between pellet properties and particulate matter emissions could involve specifics of the combustion process. Under suitable conditions, such as the combustion temperature, the flow rate of flue gas, the available oxygen to sustain combustion, the humidity of the air, and the humidity of the flue gas, soot and creosote may accumulate on the interior surface of chimneys. This accumulation can decrease the amount of emitted particulate matter (Table 2).

Although no specific predictors were identified for the release of CO₂, Cl₂, and NH₃, our experimental findings indicate the potential for predicting CO emissions. Lyubov et al. (2022) reported that the fines content, bulk density, and net calorific value were statistically significant predictors of CO emissions (Table 2, Eq. 1). A higher fines content in wood pellets presumably leads to challenges during combustion, such as increased ash production, uneven burning, and reduced combustion efficiency, all of which can result in elevated CO emissions. However, our data reveal the opposite trend, namely a negative relationship between the percentage of fines in wood pellets and the concentration of carbon monoxide emitted during combustion (p = 0.0412, Table 2), suggesting that perhaps highly combustible fines facilitate more efficient combustion in the setup used in our study.

An even more statistically significant negative correlation was noted between bulk density and CO emissions (p = 0.0003), also indicating a trend contrary to prior research findings where these variables showed a positive correlation (Rabbat et al., 2023). These results indicate that higher bulk density, and therefore higher energy density of pellets (net calorific value), enhance combustion efficiency, mitigate incomplete combustion, and reduce the emission of CO.

Data on NO₂ emissions from combusted pellets correlate best with heating values (p = 0.0470) and the nitrogen content (p = 0.0005, Table 2 and Eq. 2), in agreement with earlier studies (Sommersacher et al. 2012; Rabaçal 2015). Statistical analysis also linked the emission of H₂S to the presence of bark (p = 0.0302) and the nitrogen content in pellets (p = 0.0037, Table 2 and Eq. 3). The main source of nitrogen in the tested pellets is likely attributed to bark which is known to contain two to three times more nitrogen than wood (Vasileios et al. 2018). Indeed, bark was identified as the primary impurity in wood pellets with concentrations ranging from 0.5 to 16.1 vol. %. Despite the sulfur and nitrogen content of pellets falling below specified quality thresholds (Fig. 2G-H), bark is known to typically contain higher levels of sulfur and nitrogen compared to wood (Liu Rong-Kun et al., 1992; Oglesby and Blosser, 1980) and thus could serve as a potential source of H₂S. We hypothesize that the elevated sulfur content in bark is derived from atmospheric trace SO₂ that becomes adsorbed into highly porous bark after oxidation to sulfate.

Table 2

Regression coefficients from forward stepwise multiple regression equations – results from the combustion process of woody pellets (N = 26). Note: only statistically significant regression coefficients with p < 0.05 (F-test) are shown.
		PELLET PARAMETERS BASED ON PHYSIO-CHEMICAL AND PETROGRAPHICAL TESTING								β₀	R²
		Bark content [vol. %]	Fines [wt %]	Mechanical durability [wt %]	Bulk density [g/l]	Heating value [kJ/kg]	Net calorific value [kJ/kg]	Sulfur [wt %]	Nitrogen [wt %]	β₀	R²
	PM_2.5 [µg/m³]	–	–	–	–	–	–	–	–	-1836.78	0.10
	PM₁₀ [µg/m³]	–	–	–	–	–	–	–	–	-1862.13	0.10
PELLET COMBUSTION EMISSIONS	CO [ppm]	–	-45.01 (p = 0.0412)	–	-0.85 (p = 0.0003)	–	0.057 (p = 0.0055)	–	–	-527.67	0.64
	CO₂ [vol. %]	–	–	–	–	–	–	–	–	-18243.10	0.12
	NO₂ [ppm]	–	–	–	–	-0.004 (p = 0.0470)	–	–	0.22 (p = 0.0005)	8.75	0.60
	Cl₂ [ppm]	–	–	–	–	–	–	–	–	-1.97	0.20
	H₂S [ppm]	0.16 (p = 0.0302)	–	–	–	–	–	–	1.04 (p = 0.0037)	11.71	0.64
	SO₂ [ppm]	–	–	–	-0.38 (p = 0.0129)	–	0.027 (p = 0.0267)	–	24.62 (p = 0.0001)	-210.45	0.70
	NH₃ [ppm]	–	–	–	–	–	–	–	–	-3.40	0.17
	HCHO [ppm]	–	–	–	–	–	–	-53.82 (p = 0.0010)	–	2.23	0.19
	RESP [ppm]	–	–	-0.99 (p = 0.0000)		0.19 (p = 0.0243)	–	–	–	3.27	0.26

β ₀ – intercept values, R² – coefficients of determination.

Equation 1:

CO [ppm] =-0.85*BULK.DEN + 0.057*NET.CAL + 1.088*MECH.DUR-45.01*FINES + 11.96*MOIST-5.77*NITR-527.67

Equation 2:

NO₂ [ppm] = 0.22*NITR − 9.51*SULF-0.00037*HEAT.VAL-0.10*MOIST + 0.009*MECH.DUR-002*BULK.DEN + 8.75283

Equation 3:

H₂S [ppm] = 1.04*NITR + 0.16*TOT.IMP + 0.005* NET.CAL-0.01*BULK.DEN-1.7*ASH-0.0048 HEAT.VAL

Equation 4:

SO₂ [ppm] = 24.62*NITR-0.38*BULK.DEN + 0.027*NET.CAL + 1.827 TOT.IMP-210.451

Equation 5:

HCHO [ppm] =-53.82*SULF + 0.17*NITR + 2.23

Equation 6:

RESP [ppm] =-0.99*MECH.DUR + 0.19*HEAT.VAL-0.10*FINES + 0.09*MOIST + 3.27

Equations attempting to predict combustion emissions of various gases with parameters of pellet properties. Where: BULK.DEN – bulk density (g/l), NET.CAL – net calorific value (kJ/kg), MECH.DUR – mechanical durability (%), FINES – fines content (wt. %), MOIST – moisture content (wt. %), NITR – nitrogen content (wt %), SULF – sulfur content (wt %), HEAT.VAL – heating value (kJ/kg), TOT.IMP – total impurities content (vol. %), ASH – ash content (wt. %).

Although emitted SO₂ is correlated with bulk density (p = 0.0129) and net calorific value (0.0267), the nitrogen content of pellets was the most significant predictor for SO₂ emissions (p = 0.0001; Table 2 and Eq. 4) for the same reason as stated for H₂S emissions. While the sulfur content could not be linked to emissions of SO₂, the sulfur was a significant predictor of HCHO emissions (p = 0.0019, Table 2 and Eq. 5). Although this relationship is not easy to explain(Cerqueira et al., 2013), the sulfur present in the pellets can, under specific combustion conditions, act as a catalyst in the formation of HCHO.

Finally, a strong relationship can be observed between the mechanical durability (p = 0) and the heating value of the pellets (p = 0.0243) with the emissions of respiratory tract irritants (RESP: NO₂ + O₃ + Cl₂ + HC, Table 2 and Eq. 6). The negative correlation observed between mechanical durability and RESP underscores the critical importance of proper manufacturing processes, which can effectively reduce emissions and support cleaner combustion.

We acknowledge that biomass combustion emissions depend on the experimental setup and analysis conditions; therefore, our data may not be directly transferable to other settings. This may also be part of the reason why our relationships between pellet properties and emission characteristics may differ from those obtained in other studies. However, regardless of the analysis conditions and the equipment used, our results demonstrate that biomass combustion might negatively affect the environment and human health, exceeding the acceptable levels of various pollutants.

This study attempts to (i) identify properties of biomass pellets with predictive potential for certain types of emissions and (ii) tries to shed light on the relationship between material properties, combustion parameters, and emission characteristics. Our results highlight the complexity of biomass utilization and show that the combustion of biomass fuels, especially non-woody pellets, can pose serious health risks associated with emission exposure.

The results show that the combustion of biomass pellets can lead to emissions of particulate matter, CO, NO₂, NH₃, Cl₂, SO₂, and HCHO above the recommended emission thresholds, especially when combusting non-woody pellets. The combustion of pellets made from agricultural residue can pose health risks associated with direct emission exposures, and effective measures aiming at public awareness to promote cleaner and safer fuel alternatives are necessary.

The data indicate that the combustion behavior of pellets can, to some extent, be predicted by examining pellet characteristics which act as predictors of emissions. The statistical analysis of biofuels in our experiments identified bark, fines content, mechanical durability, bulk density, heating, and net calorific value, along with sulfur and nitrogen concentrations as variables with predictive potential for CO, NO₂, H₂S, SO₂, HCHO, and respiratory tract irritants’ emissions.

Data about emissions from various types of combusted biomass pellets underscore the need for proactive measures toward implementing stricter standards for fuel quality and emissions, and educating the public about fuel options. Additionally, further research is imperative to evaluate the variability of emissions across various biomass feedstocks, combustion technologies, and operational conditions.

Ethical approval:
Not applicable.

Consent to participate:
Not applicable.

Consent for publication:
Not applicable.

Competing interests:

The authors declare no competing interests.

Funding

The project is co-financed by the Polish National Agency for Academic Exchange within the Polish Returns Programme (BPN/PPO/2021/1/00005/DEC/1), the National Science Center, Poland (2022/01/1/ST10/00024), and by the funds granted under the Research Excellence Initiative of the University of Silesia in Katowice, Poland.

Authors Contributions:

Agnieszka Drobniak – Conceptualization, Data curation, Formal analysis, Funding acquisition; Project administration; Visualization, Writing - original draft, Writing - review & editing

Data availability

The data that support the findings of this study are openly available in the Zenodo repository at https://doi.org/10.5281/zenodo.10843482 (pellets properties) and https://doi.org/10.5281/zenodo.10843765 (combustion emissions).

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The impact of domestic combustion of biomass pellets on the environment and human health: Example from Poland

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Abstract

Figures

INTRODUCTION

MATERIAL AND METHODS

3.1 Biomass samples

3.3 Statistical analysis

THEORY

RESULTS AND DISCUSSION

5.1 Pellet properties and emissions

5.2 Correlating pellet characteristics with emission levels

CONCLUSIONS

Declarations

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Competing interests:

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Status:

Version 1

The impact of domestic combustion of biomass pellets on the environment and human health: Example from Poland

Status:

Version 1

Abstract

Figures

INTRODUCTION

MATERIAL AND METHODS

3.1 Biomass samples

3.3 Statistical analysis

THEORY

RESULTS AND DISCUSSION

5.1 Pellet properties and emissions

5.2 Correlating pellet characteristics with emission levels

CONCLUSIONS

Declarations

Ethical approval: Not applicable.

Consent to participate: Not applicable.

Consent for publication: Not applicable.

Competing interests:

Funding

Authors Contributions:

Data availability

References

Status:

Version 1

Ethical approval:
Not applicable.

Consent to participate:
Not applicable.

Consent for publication:
Not applicable.