Physicochemical Properties of Forest Wood Biomass for Bioenergy Application: A Review

Abstract

Forest wood biomass is a key renewable resource for advancing energy transition and mitigating climate change. This review analyzes the physicochemical properties of forest biomass from major European tree species to assess their suitability for bioenergy applications. This study encompasses key parameters, such as moisture content, ash content, volatile matter, fixed carbon, elemental composition, bulk density, and energy content (HHV and LHV). This review analyzed data from 43 publications and extracted 140 records concerning the physicochemical properties of the most common European forest species used for bioenergy. The most commonly represented species were Quercus robur, Eucalyptus spp., and Fagus sylvatica. Moisture content, referring to fresh matter, ranged from 5% to 65%; ash content, referring to a dry basis, ranged from 0.2% to 3.5%; and higher heating value (HHV), referring to dry matter, ranged from 17 to 21 MJ kg⁻¹. This study highlights variability among species and underscores the importance of standardizing biomass characterization methods and the scarcity of data on bulk density and other key logistical parameters. These findings emphasize the need for consistent methodologies and species-specific selection strategies to optimize sustainability and efficiency in forest biomass utilization for bioenergy.

1. Introduction and Scope

Biomass is a renewable energy resource of growing interest for addressing the challenges related to energy transition and climate change mitigation due to its renewable nature [1]. Among its many forms, biomass stands out for its abundance and versatility, making it a key raw material for the production of biofuels and other energy carriers [2]. However, recent policy developments and scientific debate have raised concerns about the sustainability of using certain types of forest biomass for energy, leading to regulatory restrictions in some contexts.

In this context, the heterogeneity of biomass, stemming from the diversity of species, growth conditions, and treatments, necessitates a detailed characterization of its chemical, physical, and thermal properties to optimize its use in conversion processes [3]. A detailed analysis is required to understand the interactions between biomass components and their characteristics, given their structural complexity and compositional variability. This knowledge enhances pretreatment efficiency and optimizes subsequent chemical transformations. Recent studies have shown that a thorough understanding of the chemical and physical properties of biomass can further the development of better effective technologies for producing advanced biofuels and other renewable chemical products [4].

Indeed, understanding the chemical characteristics and physical properties of biomass is essential for optimizing its utilization in energy and industrial valorization processes. The chemical composition, including elements such as carbon, oxygen, hydrogen, nitrogen, and inorganic compounds, directly impacts energy performance and emissions during conversion processes. Similarly, physical properties, such as density, moisture content, particle size, and cellular structure, influence the logistics, treatment, and transformation efficiency of biomass [5].

Lignocellulosic biomass is a crucial renewable resource for producing bio-based materials and chemicals. Its biodegradability, sustainability, and unique structural properties make it a promising alternative to fossil-based materials and energy sources. Forests, along with forestry management practices, are among the primary suppliers of lignocellulosic biomass, playing a significant role in the global bioeconomy [6].

Forest biomass is generally defined as the biodegradable fraction of products, waste, and residues of biological origin derived from forestry and related industries. This definition has been adopted internationally, including in European Directive 2009/28/EC, which describes it as “the biodegradable fraction of products, waste, and residues of biological origin from agriculture (including vegetable and animal substances), forestry, and related industries, including fisheries and aquaculture, as well as the biodegradable fraction of industrial and municipal waste” [7].

Forest biomass represents the main source of bioenergy production and, when harvested and managed sustainably, is considered a viable solution for climate change mitigation. This view is supported by the Renewable Energy Directive II and reaffirmed in Directive III, recognizing the carbon neutrality of sustainably sourced biomass [8,9,10].

Proximate and ultimate analyses are commonly used to evaluate the characteristics of forest biomass for bioenergy production. Proximate analysis measures the moisture content, volatile matter, fixed carbon, and ash content of the biomass. These parameters directly influence the material’s combustion behavior and energy yield. Ultimate analysis, on the other hand, determines the elemental composition, including carbon, hydrogen, nitrogen, sulfur, and oxygen percentages [11]. This information is crucial for predicting emissions and optimizing energy conversion processes. For instance, a study published in Forests analyzed the proximate and ultimate properties of various tree species to determine their energy potential, highlighting the importance of these analyses in selecting appropriate biomass feedstocks for energy applications [11]. The study concluded that selecting the right tree species and biomass fractions is essential for optimizing fuel quality and combustion efficiency [12,13]. For example, while maritime pine offers higher energy content, its elevated nitrogen and sulfur contents necessitate careful management to minimize atmospheric emissions [11,14].

Proximate analysis provides essential information about biomass properties relevant to combustion. The moisture content affects the energy efficiency of combustion, as higher moisture levels require more energy to evaporate water during burning. Volatile matter refers to the compounds released as gases when biomass is heated in the absence of air; a higher volatile matter content typically indicates better flammability and reactivity during combustion. Fixed carbon represents the solid combustible material remaining after volatile compounds are released, contributing to the char combustion phase and influencing the overall energy yield. Ash content denotes the inorganic residue left after complete combustion; high ash content can lead to operational challenges such as slagging and fouling in combustion systems [15]. Understanding these parameters is crucial for optimizing biomass selection and processing in bioenergy applications.

Numerous studies have emphasized the unique characteristics of forest biomass in comparison to other lignocellulosic sources [16,17,18]. Ultimate analysis or detailed chemical analyses reveal that forest biomass is rich in carbon and oxygen, with varying concentrations of inorganic compounds such as calcium, potassium, and silicon, which are strongly influenced by tree species, growth conditions, and harvesting methods [19]. For instance, significant chemical differences have been observed between wood and bark: bark typically contains higher levels of ash and silicon, while wood has a higher carbon content and is less contaminated by inorganic materials [20,21]. Furthermore, the higher heating value (HHV) of a fuel represents the total energy released during complete combustion, including the latent heat of vaporization of water in the combustion products. In contrast, the lower heating value (LHV) excludes this latent heat, assuming that water remains in the vapor phase. This distinction is determinative for accurately assessing the energy content of fuels and their performance in various applications [22,23].

In terms of physical properties, the density of forest biomass has been identified as a critical parameter for optimizing logistics operations and enhancing performance in conversion processes [24]. Experiments show that species such as pine and fir exhibit greater density than fast-growing woods such as poplar, thus affecting energy yield and transportation costs [25]. In addition, the particle size derived from shredding processes has been correlated with combustion and gasification efficiency, highlighting the need to optimize pretreatment steps [26,27].

Although numerous research papers and reviews have been published on this topic over the past few decades, the introduction of new standardized analyses and detailed insights into the main features makes it valuable to reassess the current state of knowledge. This review aims to provide a detailed analysis of the chemical characteristics and physical properties of forest biomass, focusing on the most representative lignocellulosic species in terms of the volume of genus found in Europe. This review will provide a comprehensive examination of the chemical characteristics and physical properties documented in the literature. The analysis aims to offer a consolidated knowledge base to support the more efficient and sustainable use of forest biomass in the energy and industrial sectors.

2. Method

This review focused on the physicochemical characteristics of primary wood biomass, excluding residues, bark, leaves, and other non-wood components, as well as mixed samples. The data considered refer to unprocessed primary forest woody biomass in terms of density increase. Thus, in the context of this review, the term “forest biomass” refers specifically to stemwood and major wood components directly derived from tree trunks and large branches. This focus allows for a more consistent comparison of physicochemical parameters relevant to bioenergy applications.

Relevant publications were retrieved from the search databases Scopus and Web of Science using the following keywords: “forest biomass”, “wood biomass”, “biomass physicochemical properties”, “biomass physicochemical characteristics”, and “forest biomass characterization for energy”. Boolean operators (AND, OR) were employed to refine the search queries and were used to link the keywords. In addition, the search was performed specifically for the major tree species and genus in Europe (

Table 1. Europe’s growing stock of major tree species and genera in 2020 [28].

Species	Wood Volume (%)
Pinus spp. (Pines)	29.6
Picea spp. (Spruce)	23.0
Fagus sylvatica L. (Beech)	11.9
Quercus spp. (Oaks)	10.0
Betula spp. (Birch)	6.6
Abies alba Mill. (Fir)	3.2
Other broadleaf	12.8
Other conifers	2.9

) by including the scientific or common name of the relevant keywords (e.g., “beech physicochemical properties”). The species taken into consideration are the most representative of Europe’s forest growing stock (m³) according to the “State of Europe’s Forests” report [28]. As the report states, “Six genera of tree species represent 83.8% of growing stock”; the species under consideration belong to these genera, as well as others mentioned in the cited report [28]. The parameters taken into consideration for the characterization of wood biomass were the content of moisture, ash, volatile matter, fixed carbon, energy and elemental composition, and bulk density.

Exclusion criteria included studies that did not focus on the species of interest, that addressed only residues, or that did not specify the type of biomass considered. This selection criterion significantly reduced the number of articles eligible for analysis. This limitation is acknowledged and underscores the need for greater standardization in biomass characterization practices. Additionally, studies lacking standardized analytical methodologies or employing methods divergent from established technical standards were excluded to ensure that data remained comparable. Articles with names that did not allow for clear identification of the species were also excluded, as well as papers that did not present data for at least one of the parameters considered. During the review process, several challenges emerged, mainly inconsistencies in species nomenclature and variations in characterization methodologies, highlighting the importance of standardizing procedures in future studies.

Only peer-reviewed articles were considered, but in addition, gray literature (e.g., regulations, technical standards, and reports) was considered. Publications were selected against the exclusion criteria through title and abstract screening. Once suitable articles were identified, full-text analysis was carried out, allowing for data collection with the aim of creating a dataset with at least one record for each species for at least one variable. Basic descriptive statistics were performed for the data, pointing out the need for consistency in detection methods to ensure comparability. Based on the review process, 140 records related to the physicochemical characterization of forest or woody biomass were selected from 43 eligible publications and compiled into a dedicated dataset. All values are expressed on a dry matter basis (% DM) unless otherwise specified.

3. Physicochemical Properties of Forest Biomass

3.1. Moisture Content

The water content of biomass significantly impacts its conversion into biofuels or bio-oil through either biochemical or thermochemical processes. Moisture content is defined as the amount of water present in biomass, quantified as a percentage of the total weight of the material. This factor affects several stages, from the early steps of harvesting and milling to the later stages of transportation, storage, and conversion [29]. Efficient management of moisture is critical, as premature harvesting can lead to high drying costs, increasing the overall expense. Moisture in biomass is categorized as either external or inherent. External moisture, which resides outside the cell walls, exceeds the equilibrium moisture content in the biomass. In contrast, inherent moisture is embedded within the cell walls. The energy required to evaporate this moisture during conversion processes is substantial and cannot be recovered, thus reducing the net energy yield from the biomass. Proper moisture management is vital for optimizing the efficiency of biomass conversion and enhancing the quality of the final products [17]. In the literature, different moisture values can be found, depending on the measurement system used (as received, air-dried, or oven-dried). Typically, wood biomass moisture content ranges from 4% to 63%, with an average of 20% [16].

To estimate the moisture content of a biomass, it should be dried in an oven at 105 °C until it reaches a constant mass and plotted using the following equation (Equation (1)):

$$U\left(\%\right)={\displaystyle \frac{Wi-Wd}{Wi}}\times 100$$

(1)

where the following apply:

Wi is the initial weight of the biomass (wet weight), measured immediately after collection, and Wd is the dry weight of the biomass, obtained after oven-drying at 105 °C until a constant weight is achieved [30].

3.2. Ash Content

Ash content in biomass indicates the solid residue left after complete combustion. It mainly consists of oxides from elements such as silica, aluminum, and calcium, among others. Understanding the chemical and physical properties of biomass ash is critical for assessing its effects on equipment, such as boilers, and on the characteristics of char and gas produced during pyrolysis and gasification [17].

Ash yield, determined at temperatures between 550 and 600 °C, typically ranges from 0.1% to 12%, but it decreases significantly at higher temperatures (1000–1300 °C) due to more intense element volatilization. This highlights that ash yield data should be considered alongside the composition and source of the biomass for accurate interpretation [16].

Extreme values in ash content (i.e., above 2.5–3%) were carefully considered during data screening. While some studies reported significantly higher values, these were found to be associated with biomass potentially contaminated by soil, sand, or other inorganic matter—typically resulting from the direct collection of logging residues from the ground using machinery such as chippers. For clean woody biomass, ash content usually ranges between 0.2% and 1.0%, depending on species.

Compared to some fossil fuels, biomass generally has a lower ash content, with exceptions as in sewage sludge or treated woods that contain higher levels of impurities. For example, in woodland waste biomass, foliage often has a higher ash content than wood due to nutrient absorption during growth.

Despite challenges in precisely determining the inorganic components of biomass, routinely measuring ash yield is essential for estimating the inorganic content, understanding element affinity, and identifying potential biomass contamination.

After being dried in the oven, the sample is weighed, and the following equation (Equation (2)) is applied:

$$A\left(\%\right)={\displaystyle \frac{Wa}{Wi}}\times 100$$

(2)

where the following apply:

Wa—the mass of residual ash obtained after burning the sample; Wi—the initial mass of the dried biomass sample [31].

3.3. Volatile Matter Content

In biomass used for energy, volatile matter refers to the fraction that, when heated in the absence of oxygen, is released as gases or vapors, excluding moisture and residual ash. This fraction typically includes hydrocarbons, CO, CO₂, CH₄, and other organic compounds [32]. The high volatile matter content found in biomass, compared to fossil fuels, makes it particularly suitable for thermochemical processes, namely, combustion, pyrolysis, and gasification, where it directly impacts efficiency, reactivity, and environmental emission [16,33,34]. This component is a fundamental parameter in the characterization of biomass for energy production because it significantly affects the behavior of biomass in thermochemical conversion processes, such as combustion, gasification, and pyrolysis [35].

Biomass with a high volatile matter content, such as eucalyptus wood, ignites and combusts more rapidly, releasing energy efficiently at lower temperatures [32,36,37]. This characteristic enhances the fuel performance in energy systems but also presents challenges such as increased nitrogen oxide emissions and ash deposition, which require specific system management [16]. For example, eucalyptus wood, known for its high reactivity, outperforms other biomasses, such as pine or acacia, in terms of ignition and energy release [32]. However, these benefits come with the need for optimized conditions to control emissions and ensure efficient operation [17]. The volatile matter content is typically measured through proximate analysis, involving the heating of biomass samples to around 900 °C in an oxygen-free environment. The gases released during this process are quantified, and the results are expressed as a percentage of the sample’s initial weight. This measurement is a critical step in understanding the combustion behavior and energy potential of biomass. Standardized procedures, such as those outlined by ASTM and EN, ensure reliable and consistent results [35]. The influence of volatile matter extends beyond simple combustion, as it plays a vital role in determining the design and optimization of energy systems. High reactivity and energy release require precise control of combustion conditions to limit pollutant emissions while maximizing efficiency. Research has shown that volatile matter correlates with other biomass properties, such as heating value and elemental composition, making it a key factor in predicting the performance and yield of thermochemical conversion processes [34]. The high content of volatile matter in lignocellulosic biomasses underscores their suitability for energy applications while highlighting the necessity of optimized conversion processes to address environmental and operational challenges. The ongoing development of analytical techniques and process models is enhancing the understanding and utilization of volatile matter in biomass energy systems [16,33,34,35]. The proposed equation for calculating VM is (Equation (3)):

$$VM\left(\%\right)={\displaystyle \frac{Wi-Wa-Wm-Wfc}{Wi}}\times 100$$

(3)

where the following apply:

Wi is the initial weight of the biomass sample (wet basis), measured before heating; Wa is the residual ash weight, obtained after complete combustion of the sample; Wm is the moisture content of the sample, determined before heating; Wfc is the fixed carbon content, representing the solid residue after volatile matter has been released [38].

3.4. Fixed Carbon Content

Fixed carbon is the residual fraction of solid fuel that remains after all volatile matter and moisture have been removed during heating under anaerobic conditions, excluding ash. It represents the portion of carbon that contributes to prolonged combustion and heat generation in biomass energy systems [16]. This component is essential for evaluating the energy yield of a fuel, as it indicates the amount of energy stored in solid form that can be released during combustion [17]. The fixed carbon content in biomass varies depending on the composition and type of material. While generally lower than that found in fossil fuels such as coal, it is sufficient to sustain combustion [13]. In thermochemical processes, fixed carbon is primarily responsible for the slow, long-term reactions occurring in the solid bed of the combustor, making it crucial for applications, as seen in gasification and biochar production [33]. The determination of fixed carbon content is conducted indirectly by subtracting the volatile matter, moisture, and ash content from the total weight of the sample [35]. This parameter is closely related to the energy density of biomass, influencing fuel characteristics such as combustion duration and heat stability [34]. Fixed carbon, along with ash and volatile matter content, is fundamental for the design and optimization of energy conversion systems, as it defines the overall behavior of the fuel under high-temperature processes, namely, the ability to prolong combustion and ensure stable and sustained energy output [37]. There is no specific technical standard for fixed carbon determinations, but those for moisture, ash, and volatile matter determinations are applied, and the following equation is used (Equation (4)) [30,31,38].

$$FC=1-M-A-VM$$

(4)

where FC = fixed carbon, M = moisture, A = ash, and VM = volatile matter [30,31,38].

3.5. Elemental Composition

Ultimate analysis is crucial for quantifying the elemental composition of biomass, specifically carbon (C), hydrogen (H), nitrogen (N), sulfur (S), and oxygen (O). This method involves incinerating a biomass sample in a controlled environment, transforming the elements into gases such as CO₂, H₂O, NO_x, and SO₂, which are analyzed to provide detailed insights into the chemical properties of the biomass. Such analysis is vital for evaluating the environmental impacts of biomass combustion, including potential emissions and combustion efficiency. The results are typically presented on a dry, ash-free (daf) basis to allow for meaningful comparisons across different biomass types and solid fossil fuels (SFFs) [17,39].

Key observations from ultimate analysis show that the carbon content in biomass generally ranges between 42% and 71%, which is lower compared to peat and coal, where it varies from 56% to 87%. High carbon values are often found in materials like wood barks and certain waste products. Oxygen content in biomass varies significantly as well, from 16% to 49%, which is higher than the 4% to 36% found in peat and coal, with materials including pepper residues and coffee husks, which typically have a high oxygen content.

In forest biomass, the hydrogen content generally falls within the range of 5% to 7% on a dry basis, depending on species, moisture history, and analytical methods. Significantly higher values are rare and typically associated with non-forest or highly processed biomass types [16].

Sulfur, S, is often an element considered, but in this case, the choice was to exclude it because it is often not reported, and in cases where it is detected, it occurs in trace amounts or below the threshold of quantification.

Understanding these elemental properties is key to optimizing biomass use as an energy source and managing its environmental impacts more effectively.

The technical standard used is UNI EN ISO 16948:2015 [40], and the only element that is calculated and not instrumentally determined is oxygen using the equation (Equation (5)):

$$\mathrm{O}\%=100-(C+H+N+S+A)$$

(5)

where the following apply:

C, H, N, and S are the percentages of carbon, hydrogen, nitrogen, and sulfur, and A indicates the percentage of ash.

3.6. Bulk Density

Bulk density is a critical physical property of wood-based biomass materials, as it affects storage, transport, and handling efficiency. It represents the mass of material per unit volume, including void spaces between particles [17,41]. This parameter varies depending on the species due to the growth rate and moisture content. Higher bulk density offers several advantages, including reduced storage volume requirements, lower transportation costs, and improved handling efficiency, especially in automated systems. The moisture content plays a crucial role: higher levels cause particle swelling, reducing bulk density, while drying enhances compaction and increases density [42]. The wood species also significantly influence bulk density: hardwoods such as oak and beech exhibit higher densities, typically ranging from 400 to 800 kg m⁻³, due to their denser cellular structure, while softwoods as pine and spruce generally range between 200 and 400 kg m⁻³ [43,44]. The bulk density is particularly important for biomass energy applications, as higher values improve logistical efficiency, enhance energy density, and optimize material use in bioenergy systems [45]. Hardwoods, with their higher bulk densities, are often preferred for applications requiring greater thermal output or compact storage solutions. Additionally, a higher bulk density and specific energy of biomass feedstock increase the break-even transportation distance, making the supply chain more energy efficient [45].

The bulk density is determined by measuring the mass of a biomass sample and the volume it occupies (Equation (6)) [46]:

$$BD={\displaystyle \frac{Wi}{Vi}}$$

(6)

where the following apply:

BD is the bulk density, expressed in kg m⁻³; Wi is the sample weight in kg; and Vi is the sample volume, expressed in m³.

3.7. Energy Content

This section describes the properties of the heating value in terms of higher and lower (HHV and LHV). The energy content of biomass, represented by its calorific value, is a critical factor in evaluating its suitability for energy applications [17]. This parameter is typically expressed as the higher heating value (HHV) and lower heating value (LHV) [16]. The HHV includes the total heat released during complete combustion, accounting for the latent heat of water formed in the process, while the LHV excludes this latent heat, focusing on the practical energy available for use [17,43,47,48].

The biomass energy content is primarily influenced by its chemical composition, especially the proportions of carbon, hydrogen, and oxygen. Higher carbon and hydrogen contents are associated with greater energy yield, while oxygen, often abundant in biomass, negatively impacts heating values due to its role in combustion reactions [48,49]. For instance, forest biomass such as softwoods and hardwoods typically exhibit HHV values in the range of 19–22 MJ kg⁻¹ depending on species, moisture, and structural characteristics [49,50]. The energy content also varies among specific biomass types and tree species. For example, studies show that coniferous species as Pinus radiata D. Don. possess relatively high HHV due to their chemical makeup and low ash content, making them attractive for energy valorization [49,50]. Similarly, hardwoods tend to have slightly higher energy content compared to softwoods due to their denser structure and lower moisture retention. Biomass energy content is a key determinant of its viability as a renewable energy source.

It is possible to determine the superior calorific value both experimentally and analytically. One common method involves using a bomb calorimeter, such as Mahler’s bomb. In this process, a known quantity of fuel is placed inside the calorimeter and combusted in a pure oxygen environment. The apparatus is surrounded by water, and the resulting temperature change is measured to calculate the heat released during combustion [51].

This is expanded further with the following equation (Equation (7)) [52]:

$$HHV=0.3491\times C+1.1783\times H-0.1034\times O+0.0151\times N+0.1005\times S-0.0211\times A$$

(7)

where the following apply:

C, H, O, N, S, and A: weight percentages of carbon, hydrogen, oxygen, nitrogen, sulfur, and ash (respectively) in the fuel, expressed as dry fractions (% on a dry basis). Fixed values indicate the energy contribution (in MJ kg⁻¹) of each chemical element or fuel component (e.g., carbon, hydrogen, oxygen, etc.) to the gross heating value. These values were determined by statistical regressions based on experimental fuel data. One of the equations proposed to determine LHV is as follows (Equation (8)) [53]:

$$LHV=HHV-10.55(M+9H)$$

(8)

where the following apply:

H: the weight percentage of hydrogen in the fuel, M the percentage of moisture in the fuel, 2442: the value of the latent heat from the evaporation of water, expressed in MJ kg⁻¹.

4. Discussion

This study analyzed the physicochemical properties of European forest biomass species, revealing significant variability across key parameters.

In the study conducted, 140 records of characterization values, forest biomass or woody biomass, were extracted out of 43 total publications eligible and examined for database construction.

The sample showed a significant imbalance between broadleaf trees and conifers, with broadleaf species comprising 63.6% of the total, compared to 36.4% for conifers. Among the species surveyed, Quercus robur L. was the most frequently encountered, followed closely by eucalyptus, beech, and maritime pine. In contrast, black locust and black pine were the least represented, with only three and two occurrences, respectively (Figure 1).

Moisture records revealed inconsistencies in the information provided across studies. In many cases, the reported moisture content refers not to the freshly collected sample but to one that has already undergone some degree of dehydration. Most recorded values fall between 40% and 60% moisture; however, a significant number of studies report values below 20%, with a notable concentration below 10%. The reported moisture content range (40–60%) is consistent with the findings of Álvarez et al. [11], who documented a range of 38–58% for comparable species. However, not all studies provide moisture content data, with 38.6% of values missing—a trend observed across all other variables as well. Among the recorded variables, the most frequently reported is the higher heating value (HHV), followed by ash content, elemental composition parameters, and, lastly, the lower heating value (LHV). Volatile matter (VM) and fixed carbon (FC) were reported in less than 50% of cases, while the bulk density (BD) was the least documented, appearing in only 18% of studies (Figure 2). The lack of bulk density data is particularly concerning, given its crucial role in biomass transportation efficiency. As noted by Obernberger and Thek [42], a higher bulk density is directly associated with lower transport costs and improved supply chain logistics. The limited availability of bulk density data in the reviewed literature may be explained by the form of biomass analyzed or the scope of the original studies. In many cases, the bulk density is not reported when the material is not used for transport, storage, or densification purposes. This highlights the importance of clearly identifying the physical form of biomass beyond the presence of size-reduction or density-increase pretreatments and the intended use in future characterizations.

This highlights a significant disparity in the type of characterization data reported. Establishing a standardized set of essential biomass properties for study and testing—ranging from proximate to ultimate analysis—would be highly beneficial.

Regarding research contributions, Portugal leads, with 23% of studies; followed by Spain, with 19%; and Poland, with 12%. The remaining contributions account for 6%, 4%, and 2%, respectively (Figure 3).

As for the values themselves, ash showed a strong difference among species. Many of them are less than 1% or slightly above, but poplars and oaks exceed 2% and reach 3%. Spruce exceeds 4%, going above the threshold values for all categories defined by ISO 17225-2 wood pellets, ISO 17225-3 wood briquettes and ISO 17225-5 firewood [54,55,56]. A high ash content, particularly in Populus nigra L. and Quercus cerris L., may lead to slagging and fouling issues in combustion systems, consistent with findings by Jenkins et al. [12]. As for volatile matter, percentages are between 75 and about 85 for all species except for Larix decidua Mill. and Pinus nigra J.F. Arnold, which were around 60 percent. In each case, we keep the values of this variable very uniform. Fixed carbon ranged between 16% and 23%, with the exceptions of Quercus ilex L. and Robinia pseudoacacia L. at 11 and 13%, respectively, and Pinus nigra J.F. Arnold at 32.8%. Again, the values of this variable remain fairly consistent across species.

There are no strong variations in carbon concentration between 46 and 54 percent. The highest values are taken by Pinus sylvestris L., Betula pendula Roth, and Picea abies (L.) H. Karst., and the lowest by Castanea sativa Mill., Quercus ilex L., and Robinia pseudoacacia L. For hydrogen, the lowest values were observed in Populus nigra L. and Pinus pinea J.F. Arnold, at 4% and 5%, and the highest in Quercus robur L. and Fagus sylvatica L., at 7 and 7.6%. For nitrogen, greater variability was observed. In general, lignocellulosic biomasses do not have excessive nitrogen volatiles in each case. The highest values, which were above 1%, were reached by Pinus pinaster Aiton, Quercus robur L., Castanea sativa Mill., and Quercus cerris L. The higher heating value (HHV) also remained fairly homogeneous, varying between 17.8 and 20.4 MJ kg⁻¹. The LHV showed wider variability since it depends directly on moisture and water formed in combustion; values below 10 MJ kg⁻¹ were observed in Castanea sativa Mill. and Quercus robur L., up to a maximum of 18.6 in Pinus pinea L. The bulk density remains the least present variable in the literature reviewed and shows expected values, where beech, larch, and oak showed high values, whereas fir and poplar values were lower (Table 2).

The findings of this review are consistent with those reported by Stolarski et al. (2022), who analyzed forest dendromass from 21 genera and found that the higher heating value (HHV) of wood ranged from 19.4 MJ kg⁻¹ in deciduous species to 19.9 MJ kg⁻¹ in conifers (dry matter basis). Ash content was generally below 1% DM for wood samples, while hydrogen and carbon contents ranged from 6.0% to 6.7% and 48.3% to 51.7%, respectively. Their results also confirmed that significant differences exist not only among genera but also between different tree organs (e.g., stemwood vs. bark or foliage), reinforcing the need for clear identification of biomass fraction when reporting physicochemical data [57,58].

A key limitation observed during the literature review is the lack of consistent information regarding the intended application of the biomass analyzed. Information is often provided as to whether the reported moisture content is of the sample “as received” or whether it has undergone some type of pretreatment process, but hardly about the end use. Parameters such as moisture content, which are critical for combustion efficiency and logistics, are strongly influenced by whether the biomass is intended for direct combustion or further processing. Future studies should distinguish biomass types according to their processing pathway to improve data harmonization and relevance to specific bioenergy applications.

Thus, it appears from the information gathered that the greatest variations are at the expense of nitrogen, ash, and LHV. This implies an impact on processes, from the formation of solid residues to the possibility of forming NO_x or changes in energy yield [17]. We started by considering multiple species, which, however, were discarded because not enough values or variables were found to be presented or because of unclear nomenclature. As a result, there is uncertainty in the type of species used. Thus, the amount of records at the onset of the study was reduced according to the criteria of inclusion and exclusion. It was observed that bulk density, despite being a crucial parameter for supply chain efficiency, is one of the most frequently overlooked metrics.

Additionally, there is a significant inconsistency in the way species names, and biomass characterization variables are reported despite the existence of technical standards.

Table 2. Species and characterization variables analyzed. Data refer to unprocessed primary forest woody biomass. With the exception of moisture, which is assessed by referring to fresh matter, all other values are expressed on a dry matter basis (%) unless otherwise specified.

Specie	SI	M %	A %	VM %	FC %	C %	O %	H %	N %	HHV MJ kg−1	LHV MJ kg−1	BD kg m−3	Ref
Betula pendula Roth	M	34.8	0.6	80.4	18.9	53.1	40.3	6.3	0.3	19.7	11.2	nd	[59,60]
	SD	17.7	0.3	0.8	0.6	2.4	2.5	0.1	0.1	0.1	3.6	nd
Castanea sativa Mill.	M	42.8	1.2	77.8	18.9	46.5	46.0	6.0	1.3	17.8	7.6	597.5	[11,48,61,62,63,64]
	SD	19.4	0.9	2.5	2.0	1.1	2.2	0.6	1.0	0.6	3.2	9.6
Eucalyptus globulus Labill.	M	32.0	1.2	80.2	16.2	48.2	44.2	6.7	0.8	18.6	11.5	627.0	[14,48,61,62,64,65,66,67,68,69,70]
	SD	23.5	0.9	6.4	1.8	2.7	3.4	1.9	0.9	1.6	5.4	4.2
Fagus sylvatica L.	M	33.6	1.2	81.4	17.8	48.9	41.6	7.6	1.0	19.3	12.4	750.0	[61,63,64,71,72,73,74,75,76,77]
	SD	23.5	0.9	6.4	1.8	2.7	3.4	1.9	0.9	1.6	5.4	4.2
Populus nigra L.	M	52.0	2.9	77.5	19.6	48.0	46.2	4.3	0.5	18.5	17.2	285.1	[48,61,70,78,79]
	SD	62.9	nd	nd	nd	2.9	5.4	2.7	0.3	0.2	1.5	nd
Populus tremula L.	M	40.8	1.7	79.4	18.6	50.6	43.1	6.1	0.3	19.1	10.4	nd	[59,73,74]
	SD	21.1	1.0	0.7	0.4	1.3	1.2	0.1	0.1	0.6	4.1	nd
Quercus cerris L.	M	33.6	3.0	77.8	17.0	47.9	43.8	6.2	1.4	18.9	nd	500.0	[5,76,80]
	SD	15.5	1.9	4.9	1.3	1.2	3.2	0.4	1.3	1.1	nd	nd
Quercus ilex L.	M	8.9	2.0	80.8	11.3	46.5	44.9	5.8	0.4	18.4	17.1	890.0	[65,81,82]
	SD	nd	1.9	2.7	nd	2.4	5.1	0.1	0.2	0.9	0.1	nd
Quercus robur L.	M	40.9	2.0	78.7	16.3	47.2	44.2	7.1	1.2	18.3	9.0	621.0	[48,59,61,62,63,64,73,83,84,85]
	SD	19.1	2.7	1.8	7.6	3.7	3.8	2.1	0.9	0.6	4.4	27.0
Robinia pseudoacacia L.	M	42.6	0.8	86.2	12.8	46.3	46.2	6.1	0.1	18.4	14.9	285.5	[74,79,86]
	SD	nd	0.7	nd	nd	nd	nd	nd	nd	0.9	2.6	nd
Abies alba Mill.	M	8.6	4.1	74.0	18.2	49.2	44.0	5.7	0.4	18.6	17.4	510.0	[49,87,88,89]
	SD	5.8	5.0	8.9	10.8	1.8	2.2	1.0	0.2	1.1	nd	nd
Larix decidua Mill.	M	33.6	0.9	61.9	23.0	50.9	42.6	5.8	0.3	19.5	10.2	732.0	[49,59,83,90]
	SD	22.9	0.5	24.6	1.3	4.5	5.5	1.2	0.1	1.5	3.7	73.5
Picea abies L.	M	26.6	0.9	74.6	23.0	52.7	38.3	6.1	0.4	20.1	13.4	nd	[49,59,73,75,76,91,92]
	SD	20.6	0.7	7.5	4.3	3.9	4.0	0.2	0.3	0.7	4.5	nd
Pinus nigra Arnold.	M	9	0.35	58.1	32.8	50.5	43.3	5.72	0.38	19.58	17.77	nd	[49,76]
	SD	nd	0.4	nd	nd	0.64	nd	0.8	0.2	1.2	nd	nd
Pinus pinaster Aiton	M	36.9	0.6	78.4	18.6	49.3	43.1	6.2	1.0	18.5	12.2	498.7	[11,14,49,61,62,64,65,70,76,93,94]
	SD	26.4	0.3	10.4	7.9	3.0	3.3	0.5	1.0	5.2	6.1	31.5
Pinus pinea L.	M	8.9	0.4	76.2	22.0	48.1	46.4	5.1	0.2	19.1	18.6	474	[48,49,65,70]
	SD	nd	0.1	9.6	5.0	5.6	6.1	0.9	0.1	0.6	0.7	nd
Pinus sylvestris L.	M	28.5	0.8	76.2	20.7	54.5	37.4	6.3	0.6	20.4	13.9	nd	[49,59,71,73,95,96]
	SD	22.4	0.7	7.0	5.6	3.3	3.5	0.3	0.8	1.9	5.9	nd

SI: statistical index (M: mean; SD standard deviation); M: moisture; A: ash; VM: volatile matter; FC: fixed carbon; C: carbon; O: oxygen; H: hydrogen; N: nitrogen; HHV: higher heating value; LHV: lower heating value; BD: bulk density; nd: no data.

Table 2. Species and characterization variables analyzed. Data refer to unprocessed primary forest woody biomass. With the exception of moisture, which is assessed by referring to fresh matter, all other values are expressed on a dry matter basis (%) unless otherwise specified.

Specie	SI	M %	A %	VM %	FC %	C %	O %	H %	N %	HHV MJ kg−1	LHV MJ kg−1	BD kg m−3	Ref
Betula pendula Roth	M	34.8	0.6	80.4	18.9	53.1	40.3	6.3	0.3	19.7	11.2	nd	[59,60]
	SD	17.7	0.3	0.8	0.6	2.4	2.5	0.1	0.1	0.1	3.6	nd
Castanea sativa Mill.	M	42.8	1.2	77.8	18.9	46.5	46.0	6.0	1.3	17.8	7.6	597.5	[11,48,61,62,63,64]
	SD	19.4	0.9	2.5	2.0	1.1	2.2	0.6	1.0	0.6	3.2	9.6
Eucalyptus globulus Labill.	M	32.0	1.2	80.2	16.2	48.2	44.2	6.7	0.8	18.6	11.5	627.0	[14,48,61,62,64,65,66,67,68,69,70]
	SD	23.5	0.9	6.4	1.8	2.7	3.4	1.9	0.9	1.6	5.4	4.2
Fagus sylvatica L.	M	33.6	1.2	81.4	17.8	48.9	41.6	7.6	1.0	19.3	12.4	750.0	[61,63,64,71,72,73,74,75,76,77]
	SD	23.5	0.9	6.4	1.8	2.7	3.4	1.9	0.9	1.6	5.4	4.2
Populus nigra L.	M	52.0	2.9	77.5	19.6	48.0	46.2	4.3	0.5	18.5	17.2	285.1	[48,61,70,78,79]
	SD	62.9	nd	nd	nd	2.9	5.4	2.7	0.3	0.2	1.5	nd
Populus tremula L.	M	40.8	1.7	79.4	18.6	50.6	43.1	6.1	0.3	19.1	10.4	nd	[59,73,74]
	SD	21.1	1.0	0.7	0.4	1.3	1.2	0.1	0.1	0.6	4.1	nd
Quercus cerris L.	M	33.6	3.0	77.8	17.0	47.9	43.8	6.2	1.4	18.9	nd	500.0	[5,76,80]
	SD	15.5	1.9	4.9	1.3	1.2	3.2	0.4	1.3	1.1	nd	nd
Quercus ilex L.	M	8.9	2.0	80.8	11.3	46.5	44.9	5.8	0.4	18.4	17.1	890.0	[65,81,82]
	SD	nd	1.9	2.7	nd	2.4	5.1	0.1	0.2	0.9	0.1	nd
Quercus robur L.	M	40.9	2.0	78.7	16.3	47.2	44.2	7.1	1.2	18.3	9.0	621.0	[48,59,61,62,63,64,73,83,84,85]
	SD	19.1	2.7	1.8	7.6	3.7	3.8	2.1	0.9	0.6	4.4	27.0
Robinia pseudoacacia L.	M	42.6	0.8	86.2	12.8	46.3	46.2	6.1	0.1	18.4	14.9	285.5	[74,79,86]
	SD	nd	0.7	nd	nd	nd	nd	nd	nd	0.9	2.6	nd
Abies alba Mill.	M	8.6	4.1	74.0	18.2	49.2	44.0	5.7	0.4	18.6	17.4	510.0	[49,87,88,89]
	SD	5.8	5.0	8.9	10.8	1.8	2.2	1.0	0.2	1.1	nd	nd
Larix decidua Mill.	M	33.6	0.9	61.9	23.0	50.9	42.6	5.8	0.3	19.5	10.2	732.0	[49,59,83,90]
	SD	22.9	0.5	24.6	1.3	4.5	5.5	1.2	0.1	1.5	3.7	73.5
Picea abies L.	M	26.6	0.9	74.6	23.0	52.7	38.3	6.1	0.4	20.1	13.4	nd	[49,59,73,75,76,91,92]
	SD	20.6	0.7	7.5	4.3	3.9	4.0	0.2	0.3	0.7	4.5	nd
Pinus nigra Arnold.	M	9	0.35	58.1	32.8	50.5	43.3	5.72	0.38	19.58	17.77	nd	[49,76]
	SD	nd	0.4	nd	nd	0.64	nd	0.8	0.2	1.2	nd	nd
Pinus pinaster Aiton	M	36.9	0.6	78.4	18.6	49.3	43.1	6.2	1.0	18.5	12.2	498.7	[11,14,49,61,62,64,65,70,76,93,94]
	SD	26.4	0.3	10.4	7.9	3.0	3.3	0.5	1.0	5.2	6.1	31.5
Pinus pinea L.	M	8.9	0.4	76.2	22.0	48.1	46.4	5.1	0.2	19.1	18.6	474	[48,49,65,70]
	SD	nd	0.1	9.6	5.0	5.6	6.1	0.9	0.1	0.6	0.7	nd
Pinus sylvestris L.	M	28.5	0.8	76.2	20.7	54.5	37.4	6.3	0.6	20.4	13.9	nd	[49,59,71,73,95,96]
	SD	22.4	0.7	7.0	5.6	3.3	3.5	0.3	0.8	1.9	5.9	nd

SI: statistical index (M: mean; SD standard deviation); M: moisture; A: ash; VM: volatile matter; FC: fixed carbon; C: carbon; O: oxygen; H: hydrogen; N: nitrogen; HHV: higher heating value; LHV: lower heating value; BD: bulk density; nd: no data.

The analysis of wood species for energy conversion, based on key variables such as moisture content (M%), ash (A%), volatile materials (VM%), fixed carbon (FC%), carbon (C%), oxygen (O%), hydrogen (H%), nitrogen (N%), higher heating value (HHV), lower heating value (LHV), and bulk density, showed significant differences among species. Among the most suitable ones are Eucalyptus globulus Labill. and Fagus sylvatica L., which are characterized by a low moisture and ash content, high carbon and fixed carbon values, high calorific values (HHV and LHV), and bulk density [61,65,71,73]. Species with higher bulk densities and lower moisture contents, such as Fagus sylvatica L., not only improve combustion efficiency but also reduce greenhouse gas emissions during transportation [45]. These parameters promote high energy efficiency and better logistics in harvesting and transportation. In contrast, species such as Populus nigra L. and Quercus cerris L. have high moisture and ash values, associated with lower carbon and fixed carbon contents, resulting in lower calorific powers and densities [5,49,80,96]. While Eucalyptus globulus Labill. and Fagus sylvatica L. exhibit favorable physicochemical profiles, their deployment should also consider ecological factors and regional availability. Conversely, despite the lower energy profiles of Populus nigra L., its fast growth and abundance may justify its use in certain contexts.

Of all the variables, the moisture content and lower heating value (LHV) were found to be the most critical factors, as they significantly affect energy efficiency and fuel quality. Choosing species with the best characteristics allows optimizing energy conversion processes, improving the sustainability and yield of the biomass used [12].

Further research should continue to investigate species and seasonal variations in biomass properties, assess the potential benefits of pretreatment methods such as torrefaction or mechanical pretreatments to improve fuel quality, and explore the influence of biomass provenance, as these aspects are already being explored in recent studies [17]. The continuation of this research is essential to refine biomass conversion processes and adapt them to varying feedstock characteristics throughout the year, taking into account even how geographic origin and environmental conditions affect physicochemical properties. Furthermore, while the review provides a focused evaluation of primary forest biomass, it is important to acknowledge that other types of biomasses, such as logging residues, bark, and byproducts (e.g., sawdust or wood chips), also play a significant role in the bioenergy sector. These materials were intentionally excluded from this analysis to improve data homogeneity and comparability across studies. However, this exclusion represents a limitation of the present review, which we recognize. Future studies may address these categories to offer a more comprehensive understanding of the full spectrum of forest biomass for energy applications.

5. Conclusions

Forest biomass remains a cornerstone in the development of sustainable energy systems, offering substantial potential for renewable energy generation and climate change mitigation. This review highlights significant variability in the physicochemical properties of European forest species, with key parameters such as moisture content, ash content, and nitrogen concentration exerting considerable influence on combustion efficiency, emission profiles, and overall energy yield. Eucalyptus globulus Labill. and Fagus sylvatica L. demonstrated the most favorable properties for bioenergy production, including a low moisture and ash content, high carbon and fixed carbon concentrations, and elevated calorific values. Conversely, species like Populus nigra L. and Quercus cerris L. exhibited less favorable profiles, mainly due to higher moisture and ash contents.

Despite the abundance of data, the analysis revealed significant gaps, particularly in bulk density, a key factor in supply chain logistics.

Moving forward, establishing a comprehensive and uniform dataset for biomass characterization will be crucial for optimizing feedstock selection, enhancing conversion processes, and improving the sustainability of biomass-based energy systems, prioritizing the development of standardized methodologies to ensure data comparability and facilitate practical applications in bioenergy supply chains. Each investigated variable plays a crucial role in positioning biomass as a renewable and strategic resource. A thorough understanding of this raw material is essential to ensure that forest resources are utilized in the most efficient and sustainable manner.