Pyrolysis of Foliage from 24 U.S. Plant Species with Recommendations for Physics-Based Wildland Fire Models

Abstract

Pyrolysis of 24 samples of foliage from three U.S. regions with frequent wildland fires (Southeastern U.S., northern Utah and Southern California) was studied in a fuel-rich flat-flame burner system at 765 °C (for Southeastern U.S. samples) and 725 °C (for northern Utah and Southern California species), with a heating rate of approximately 180 °C/s. These conditions were selected to mimic the conditions of wildland fires. Individual plant samples were introduced to the high temperature zone in a flat-flame burner and pyrolysis products were collected. Tar was extracted and later analyzed by GC/MS. Light gases were collected and analyzed by GC/TCD. The estimated range for the average yields of tar and light gases were 48 to 62 wt% and 18 to 31 wt%, respectively. Apart from Eastwood’s manzanita (Arctostaphylos glandulosa Eastw.), aromatics were the major constituents of tar. The variations in the concentrations of tar compounds likely resulted from differences in biomass composition and physical characteristics of the foliage. The four major components of light gases from pyrolysis (wt% basis) were CO, CO₂, CH₄ and H₂. Tar contributed more than 82% of the high heating value of volatiles. These data can be used to improve physical-based fire propagation models.

1. Introduction

Fire is a naturally occurring phenomenon which may have both positive and negative effects on ecosystems. However, in recent years, multiple factors, such as development in the wildland–urban interface, invasive species, climate change and fire suppression efforts have led to increasingly destructive fires [1,2].

The increase in number, size and intensity of wildland fires, coupled with finite resources available to forest services to manage wildland fires highlights the urgency for the accurate prediction of fire spread and its growth [3]. Fire behavior models enable the fire management services to predict the spread of fire and prioritize firefighting efforts more effectively [4]. An improved understanding of chemical reactions and physical mechanisms occurring during wildland fires is necessary to improve the models used to predict fire spread and growth [5].

Modeling the propagation of wildland fires is challenging, due to complexities of the chemical reactions and physical mechanisms responsible for the nature of the fire. The ignition and propagation of wildland fires involve intricate processes and sophisticated mechanisms across multiple scales, ranging from micrometer-level phenomena (e.g., pyrolysis on the surface of the fuel) to large-scale influences such as wind and terrain, which affect fire spread on the scale of several kilometers [6].

Depending on the phenomena, scale and modeling techniques accounting for modeling fire behavior, various classifications of wildfire propagation models have been proposed [7]. One of the most used methods to classify fire behavior models was proposed by Sullivan [8]: physics-based, empirical and quasi-physics (hybrid). The models were categorized based on a model’s dependence on physical principles versus empirical data.

Physics-based models, such as the Fire Dynamics Simulator (FDS), Wildland–Urban Interface Fire Dynamics Simulator (WFDS), FIRETEC and the Generalized Pyrolysis Model (GPyro), use physics-based equations, such as Navier–Stokes and heat transfer correlations, to describe the behavior of the fluid and temperature variations during complex reactions occurring in fires [9,10,11,12,13].

Empirical fire models were developed based on statistical or observational data collected during past fires. While these models can describe fire behavior well under similar conditions, their dependence on historical fire data may compromise their validity if fire conditions are novel or extrapolated beyond the data range [8,10,14,15]. Quasi-physical (hybrid) models combine empirical data with some physical principles to balance accuracy and computational efficiency. They offer a better predictive capability than empirical models, but are less complex than full physical models. They are simpler and computationally efficient, but limited to the conditions for which they were developed [8,9].

Pyrolysis of the materials prior to combustion is an important step during fires, as the compounds released during pyrolysis combust and propagate the fires. Information on the compounds released during pyrolysis, such as concentration, rate of generation and the heat produced, is valuable, as it affects fire. Equations governing the kinetics and chemical reaction mechanisms of pyrolysis may be utilized by physics-based models to:

1

Predict the compounds generated during fires and their effect on fire behavior;

2

Calculate the heat produced as the released compounds later react with oxygen and combust;

3

Provide a basis for the calculation of pollutants and smoke formation.

It has been shown that model predictions of the rate of spread of wildland fires are particularly sensitive to windspeed and the thermal decomposition temperature of the burning vegetative fuel [16]. Unfortunately, despite this sensitivity, there is no comprehensive dataset on the thermal decomposition of many common foliage fuels. Moreover, in many existing cases, the fuel properties are inconsistent [17,18,19,20].

Sporri et al. used FDS to study the charring properties of timber during pyrolysis [21]. Their study demonstrated that accurate simulation of timber pyrolysis using FDS required careful specification of both the physical properties of the fuel, such as density (which is essential to calculate the mass loss and heat transfer rate), moisture content and specific heat capacity. The importance of detailed pyrolysis parameters was also identified, including a reaction scheme (which defines the number of reactions) and the respective pyrolysis products (e.g., volatiles, char, ash). Leventon et al. [22] studied the thermal decomposition behavior of several U.S. plant samples, using the FDS model. Incorporating species-specific thermal properties into these wildfire simulations showed that predicted fire spread rates varied between 0.50 m/s and 1.09 m/s. This highlights the sensitivity of fire spread predictions to the thermal decomposition characteristics of different fuels. Using properties derived from ultimate and proximate analysis, coupled with a variety of chemical analyses of several Southeastern U.S. species, Dietenberger et al. proposed a thermal degradation model of vegetation for FDS [23].

WFDS was first developed to model the behavior of wildland fires approaching urban structures. WFDS utilizes pyrolysis data, including tar formation, compounds released during pyrolysis and the kinetics of pyrolysis equations. There are some examples of using pyrolysis data to improve predictions of WFDS in the current literature. Mell et al. [24] estimated the temperature of the fuel bed during fires by numerically solving a set of equations that accounted for pyrolysis on the surface of solid biomass. Shotorban et al. [25] focused on enhancing WFDS to better predict the behavior of firebrands (embers traveling from the fire). Since the firebrands go through pyrolysis, their mass loss due to thermal decomposition is calculated. By including solid-particle tracking capabilities into WFDS, Shotorban et al. described firebrand dynamics, including their generation, travel and descent more accurately, improving fire spread predictions [25].

GPyro simulates a material’s decomposition as it undergoes pyrolysis. This model describes the chemical and thermal behavior of the solid material, as the solid temperature increases due to exposure to fire [26]. The data provided by GPyro is sometimes used as input for CFD-based models, such as FDS or WFDS [27]. Detailed data on the composition of the volatiles released during pyrolysis, such as compounds and their concentration, can potentially improve the understanding of the reactions that happen during pyrolysis on a molecular level.

FIRETEC accounts for turbulence and coupled atmosphere–fire interactions by simulating how fire affects the local wind and weather and their subsequent influence on fire growth. There is some research on incorporating pyrolysis data into the model to improve it. For example, Colman et al. [28] studied pyrolysis and combustion as two distinct yet dynamically linked processes which influence each other. By modeling pyrolysis separately from combustion, FIRETEC can simulate delayed ignition, the formation of volatile-rich areas ahead of the flame front, non-uniform combustion intensity and dynamic behaviors such as spotting and flare ups, which are often influenced by the generation and release of volatiles [28].

These examples show efforts to incorporate detailed pyrolysis reactions and chemical processes, including tar production and volatile compounds, into various physics-based models to improve the present understanding of fires and predict the behavior of complex fires more precisely. The purpose of this paper is to study the pyrolysis of foliage samples from 24 important wildland fuels from three U.S. regions (Southeastern U.S., northern Utah and Southern California) to provide approximate ranges for the yields of pyrolysis products (tar and light gases) and their major compounds. These data may improve current physics-based models of wildland fires [29]. The heating rate (180 °C/s) and pyrolysis temperature were carefully selected to mimic those during wildland fires. Butler et al. reported temperatures between 800 °C and 1000 °C during the described wildland fires [30]. The pyrolysis data from northern Utah plant species presented here are previously unpublished. The data from the Southeastern U.S. and Southern California were published previously [31,32,33,34], but are included here with some additional analysis and discussion to provide general summaries to guide wildland fire modeling.

2. Materials and Methods

The brief description of the instruments and procedures used during these experiments are provided here, since they are explained extensively elsewhere [33,35].

2.1. Description of the Flat-Flame Burner

A flat-flame burner apparatus was used during pyrolysis experiments. It consisted of a 20 cm × 27 cm perforated metal base, surrounded by ceramic glass windows. The ceramic (Neoceram) windows were 30.5 cm high. One of the glass windows featured a 5 cm diameter circular opening to insert the foliage samples. Samples were placed in an alligator clip connected to a metal rod to enter the high temperature zone. A balance weighed the metal rod apparatus and the weight vs. time was recorded. Flame conditions were set to be fuel rich (equivalence ratio of 1.13) to support an oxygen-free, high-temperature environment for the pyrolysis of samples. The fuel mixture feeding the pre-mixed flame was made up of hydrogen and methane. The gas temperature at the location of the sample was 725 °C during pyrolysis of Southern California and northern Utah species and 765 °C during the pyrolysis of Southeastern U.S. species.

Figure 1 shows the schematic of the flat-flame burner, cooling bath and sample collection system during pyrolysis experiments. Pyrolysis products were collected through a funnel positioned on the top of the high-temperature area. Figure 2 shows a photo of the flat-flame burner, foliage sample and funnel. The pyrolyzed foliage sample is glowing slightly red in this photo. The funnel was attached to a line that was heated to 300 °C (to prevent tar compounds from condensing in the tube), leading to a vacuum pump. Pyrolysis products went through the cooling system consisting of test tubes, sitting in an ice bath. Tar compounds and moisture condensed as their temperature decreased and became trapped in the glass wool inside the test tubes. The light gases generated during pyrolysis were collected in 1 L Tedlar^® bags (Manufactured by CEL Scientific, Cerritos, CA, U.S.)and were later analyzed.

2.2. Tar Extraction

As the pyrolysis products passed through the ice bath, their temperature decreased, leading to condensation of moisture and tar. The condensable products were captured in the test tubes. After the experiment, CH₂Cl₂ was added to the test tubes to dissolve tar. To remove moisture, approximately 2 g of Anhydrous CaSO₄ was added to the tar-CH₂Cl₂ mixture. After a few minutes, phase separation transpired inside the test tubes, with tar being in the liquid phase and moisture being adsorbed by the CaSO₄ and turning into a solid.

2.3. Tar Yield

Tar yield is defined as the weight percentage of condensable pyrolysis products, aside from moisture, released per mass of the initial sample. Tar yield is reported on a dry, ash-free basis. The difference between the mass of the test tubes before and after the experiments was used to estimate the moisture and tar generation during experiments.

Moisture in this experiment originated from three sources: (a) the inherent moisture in each sample; (b) the water produced by combustion of the FFB gas mixture (H₂, CH₄); and (c) the water produced by the pyrolysis of the foliage sample. The moisture content of the foliage samples was measured by a Computrac^® MAX 1000 moisture content analyzer (Manufactured by Arizona Instrument, L.L.C., Chandler, AZ, U.S., the use of trade or firm names in this publication is for reader information and does not imply endorsement by the U.S. Department of Agriculture of any product or service) twice daily, before and after the experiments, and the average of the two measured values was recorded as the moisture content of the samples each day. Moisture contribution from the flame was estimated through control experiments in the FFB with no foliage sample.

2.4. Char Yield

Char yield was calculated as the weight percentage of solid pyrolysis products relative to the mass of the initial sample, calculated on a dry, ash-free basis. Following each experiment, the mass of the residue on the alligator clip represented the combined char and ash for that experiment. Sudden changes in mass indicated detachment of portions of the sample, which occurred occasionally, and hence, those data were not used in the determination of char yield.

2.5. Gas Yield

Gas yield was defined as the weight percentage of non-condensable light gas products generated relative to the mass of the initial sample, on a dry, ash-free basis. Tar and char yields were measured separately, following pyrolysis experiments; the gas yield was calculated by difference using Equation (1).

Tar yield + Gas Yield + Char Yield = 1

(1)

2.6. Description of GC/MS

Tar was analyzed using a 1310 Thermo Fisher^® gas chromatography mass spectroscopy (GC/MS) system (Manufactured by Thermo Fisher Scientific, Waltham, MA, U.S.) equipped with a Rxi-1 ms capillary column (60 m × 0.25 mm × 1 μm, Manufactured by Restek, Bellefonte, PA, U.S.). After tar extraction, 1.0 μL of the CH₂Cl₂–tar mixture was injected into the GC/MS system. The oven temperature was adjusted, based on a pre-established program. The temperature of the injected mixture was held at 50 °C for 5 min, then was raised to 250 °C at a rate of 10 °C/min and subsequently was maintained at 250 °C for 5 min.

Ultra-high purity (UHP) helium was used as a carrier gas in the GC/MS system. Each test was performed three times to ensure reproducibility, and the average of the measured values was reported, along with the 95 percent confidence interval. Identification of tar compounds was achieved by comparing the mass spectra of the detected compounds with the NIST23 spectral database [36]. Analysis of tar samples revealed that many tar compounds were present in low concentrations, resulting in a low signal-to-noise ratio, below the detection limit. This complicated the identification of some of these tar compounds. Concentrations of identified tar compounds were reported as mole fractions. To calculate the mole fraction of a specific tar compound, the area under the GC peak associated with that specific compound was divided by the sum of areas under all detected and identified GC peaks.

2.7. Description of GC/TCD

Post-pyrolysis gases were collected in 1 L Tedlar^® gas bags and were immediately analyzed using a gas chromatograph (Agilent Technologies 7890A, Manufactured by Agilent Technologies Inc., Santa Clara, CA, U.S. equipped with a Supelco Carboxen 1004 packed column (2 m × 1.5 mm), Manufactured by Merck, Darmstadt, Germany) and a thermal conductivity detector (GC/TCD). Ultra-high purity (UHP) helium was used as a carrier gas. Analysis of the gases showed that the four major species present in light gases from pyrolysis are CO₂, CO, H₂ and CH₄, on a wt% basis. After each experiment, 1 mL of the gas sample from the gas bag was injected manually. The oven temperature was adjusted to start at 50 °C and was held at this value for 5 min, then increased to 225 °C at 20 °C/min. The temperature was then maintained at 225 °C for 6.5 min; the total run time was 21 min.

2.8. Plant Species Studied

The plant species pyrolyzed during these experiments were from three parts of U.S.: Southeastern U.S., northern Utah and Southern California.

Table 1. Plant species, their common and scientific names and regions where they commonly grow in the United States.

Region	Common Name	Scientific Name
Southeastern U.S.	Darrow’s blueberry	Vaccinium Darrowii Camp
Southeastern U.S.	Dwarf palmetto	Sabal minor (Jacq) Pers.
Southeastern U.S.	Fetterbush	Lyonia lucida (Lam) K.Koch
Southeastern U.S.	Inkberry	Ilex glabra (L.) A. Gray
Southeastern U.S.	Live oak	Quercus virginiana Mill.
Southeastern U.S.	Little bluestem	Schizachyrium scoparium (Michx) Nash
Southeastern U.S.	Saw palmetto	Serenoa repens (W. Bartram) Small
Southeastern U.S.	Sparkleberry	Vaccinium arboreum Marshall
Southeastern U.S.	Swamp bay	Persea palustris (Raf.) Sarg.
Southeastern U.S.	Water oak	Quercus nigra L.
Southeastern U.S.	Wax myrtle	Morella cerifera (L.) Small
Southeastern U.S.	Pineland threeawn	Aristida stricta Michx
Southeastern U.S.	Yaupon	Ilex vomitoria Aiton
Southeastern U.S.	Longleaf pine	Pinus palustris Mill
northern Utah	Big sagebrush	Artemisia tridentata Nutt.
northern Utah	Utah juniper	Juniperus osteosperma (Torr.) Little
northern Utah	Gambel oak	Quercus gambelii Nutt.
northern Utah	Bigtooth maple	Acer grandidentatum Nutt.
Southern California	Chamise	Adenostoma fasciculatum Hook. and Arn.
Southern California	Eastwood’s manzanita	Arctostaphylos glandulosa Eastw.
Southern California	Scrub oak	Quercus berberidifolia Liebm.
Southern California	Hoaryleaf ceanothus	Ceanothus crassifolius Torr.

contains the names and regions of the species studied here. These plant species are all involved in wildland fires in the United States. The Southeastern U.S. samples, studied by Safdari and Amini, were young nursery bedding plants, 4–8 inches high (see [37]). All other samples were from live mature plants. The Utah juniper samples were obtained on Highway 6, one half-mile northeast of Eureka, Utah (approximately 39°58′02″ N 112°05′34″ W). All other northern Utah samples were taken at the Rock Canyon Trailhead in Provo, Utah (approximately 40°15′52″ N 111°37′51″ W). The Southern California samples were taken from the North Mountain Experimental Area, adjacent to the San Bernardino National Forest (approximately 33°50′20″ N 116°55′36″ W).

2.9. Moisture Content Measurement

For each of the species studied, the moisture content was measured twice daily (at the beginning and the end of the day) by a Computrac MAX 1000 moisture analyzer. The average of these two values was reported as the moisture content for each of the samples used that day.

2.10. Ash Content for Species Studied

The chemical compositions of the plant samples primarily comprise carbon, oxygen, hydrogen and nitrogen, with trace amounts of elements such as heavy metals and alkali metals [38]. These metals contribute to the ash content of the sample. Ultimate and proximate analyses for species studied here are provided in the Supplementary Material (Table S1). To determine the ash content, the sample was placed in a crucible and was heated in an oven from 105 to 500 °C over the course of one hour. Then, its temperature increased to 750 °C over the next hour. The sample remained in the oven for almost 24 h. Eventually, the residual mass in the crucible was reported as the ash content of the original sample. The ash content for the plant species used in these experiments varied between 1.84 and 5% (See

Table 2. Ash content of the plant species studied.

Region	Plant Species	Wt% Ash (Moisture-Free Basis)
S.E. U.S.	Darrow’s blueberry	2.85%
S.E. U.S.	Dwarf palmetto	3.26%
S.E. U.S.	Fetterbush	2.24%
S.E. U.S.	Inkberry	1.88%
S.E. U.S.	Live oak	2.71%
S.E. U.S.	Little bluestem	4.12%
S.E. U.S.	Saw palmetto	3.19%
S.E. U.S.	Sparkleberry	3.10%
S.E. U.S.	Swamp bay	1.84%
S.E. U.S.	Water oak	4.18%
S.E. U.S.	Wax myrtle	2.41%
S.E. U.S.	Pineland threeawn	4.34%
S.E. U.S.	Yaupon	4.89%
S.E. U.S.	Longleaf pine foliage	2.02%
N. Utah	Big sagebrush	4.00%
N. Utah	Utah juniper	4.20%
N. Utah	Gambel oak	2.80%
N. Utah	Bigtooth maple	3.30%
S. CA	Chamise	3.10%
S. CA	Chamise twig	3.70%
S. CA	Eastwood’s manzanita	2.50%
S. CA	Eastwood’s manzanita twig	3.40%
S. CA	Scrub oak	4.80%
S. CA	Hoaryleaf ceanothus	2.70%

).

3. Results and Discussion

It is important to note that all organic compounds identified were, by definition, a product of plant metabolism or derived from the heating and/or oxidation of a plant metabolite. The chemistry associated with plants is complex and the majority of the estimated 450,000 species in existence today have probably not been subjected to detailed determination of their chemical composition [39]. In plants, primary metabolites are described as those essential to plant survival, and are thus found in the bulk of, if not all, plants. Secondary metabolites may be present only in certain plant species, specific plant organs (roots, leaves, stem, flower, etc.) or at a specific growth stage, when the plant is under stress. Many of the compounds identified in tar are considered secondary metabolites [40,41].Their presence or absence in the samples were due to the factors listed previously; concentrations that were potentially below detection levels also contributed to the measured composition.

3.1. Tar, Gas and Char Yields

Table 3. Pyrolysis product yields of the plant species studied. For Southeastern U.S. species and northern Utah species, averages, along with 95% confidence intervals, are provided. All data are provided on a dry, ash-free basis.

Region	Plant Species	Tar	Gas	Char	Total Yield of Volatiles
S.E. U.S.	Darrow’s blueberry	57 ± 2.5	22 ± 2.5	21 ± 4.3	79 ± 5.0
S.E. U.S.	Dwarf palmetto	62 ± 5.0	18 ± 5.0	20 ± 5.0	80 ± 10.0
S.E. U.S.	Fetterbush	54 ± 6.6	24.3 ± 1.4	21.7 ± 5.7	78.3 ± 8.0
S.E. U.S.	Inkberry	59 ± 5.0	22 ± 2.5	19 ± 4.3	81 ± 7.5
S.E. U.S.	Live oak	56 ± 5.0	23 ± 2.5	21 ± 6.6	79 ± 7.5
S.E. U.S.	Little bluestem	61 ± 2.5	20 ± 6.6	19 ± 7.5	81 ± 9.1
S.E. U.S.	Saw palmetto	53 ± 2.5	25 ± 5.0	22 ± 6.6	78 ± 7.5
S.E. U.S.	Sparkleberry	55 ± 2.5	23 ± 5.0	22 ± 6.6	78 ± 7.5
S.E. U.S.	Swamp bay	58 ± 5.0	22 ± 2.5	20 ± 7.5	80 ± 7.5
S.E. U.S.	Water oak	56.7 ± 3.8	23 ± 2.5	20 ± 3.3	79.7 ± 6.3
S.E. U.S.	Wax myrtle	55 ± 2.5	23 ± 2.5	22 ± 5.0	78 ± 5.0
S.E. U.S.	Pineland threeawn	59 ± 2.5	23 ± 2.5	18 ± 5.0	82 ± 5.0
S.E. U.S.	Yaupon	61 ± 2.5	22 ± 5.0	17 ± 6.6	83 ± 7.5
S.E. U.S.	Longleaf pine foliage	57 ± 5.0	23 ± 2.5	20 ± 6.6	80 ± 7.5
N. Utah	Big sagebrush	54 ± 6.6	28 ± 5.0	18 ± 2.5	82 ± 11.6
N. Utah	Utah juniper	55 ± 2.5	25 ± 2.5	20 ± 5.0	80 ± 5.0
N. Utah	Gambel oak	53 ± 5.0	29 ± 2.5	18 ± 6.6	82 ± 7.5
N. Utah	bigtooth maple	58 ± 6.6	23 ± 5.0	19 ± 10.8	82 ± 11.6
S. CA	Chamise	59	23	18	82
S. CA	Chamise twig	57	23	20	80
S. CA	Eastwood’s manzanita	50	28	22	78
S. CA	Eastwood’s manzanita twig	48	27	25	75
S. CA	Scrub oak	56	24	20	80
S. CA	Hoaryleaf ceanothus	51	31	18	82

and Figure 3, Figure 4 and Figure 5 show the yields of pyrolysis products (i.e., tar, light gases and char) from all 24 plant samples. The total volatile yield was defined as the combined tar and light gas yield. While several factors affect the generation of pyrolysis products, temperature and heating rate are the two main factors [31]. Safdari et al. investigated the effect of heating rate and temperature on the total yield of volatiles released from longleaf pine litter. The heating rate was maintained at 0.5 °C/s, while the temperature increased from 500 °C to 765 °C. This resulted in an 11 wt% average increase in the total yield of volatiles and a 9 wt% increase in the tar yield [31].

Operating conditions during pyrolysis of the Southeastern U.S. samples were 765 °C and heating rate of 180 °C/s. While the heating rate during the pyrolysis of the northern Utah and Southern California species was identical to the Southeastern U.S. samples, the temperature was slightly lower (725 °C). The averages for the total yield of volatiles (and confidence intervals) released from various samples are provided in Table 3 and Figure 3. The average of the total yield of volatiles for selected Southeastern U.S. samples ranged from 78 to 83 wt% (daf). The northern Utah plants yielded between 80 and 82 wt% (daf) of volatile compounds while the Southern California samples generated between 75 and 82 wt% (daf). Comparing the total yields of volatile compounds from the pyrolysis of the Southeastern U.S., northern Utah and Southern California samples showed only a slight variation in volatile yield with pyrolysis temperature, ranging from 75 to approximately 83 wt% (daf) of the initial foliage.

Tar obtained from the Southeastern U.S. samples varied from 53 wt% (saw palmetto) to 62 wt% (dwarf palmetto), while the yield of light gases changed, respectively (see Figure 3). The tar yield ranged from 53 wt% (Gambel oak) to 58 wt% (bigtooth maple) for the northern Utah samples, while Southern California samples exposed to similar pyrolysis conditions yielded between 48 wt% (Eastwood’s manzanita twigs) and 59 wt% (chamise twigs with leaves) tar. The slight disparities in the tar yield of plants from different U.S. regions (Southeastern U.S., northern Utah and Southern California) is likely due to inherent chemical and physical differences between various plant species. Tar from pyrolysis of plants during wildfires ranges from 48 wt% to 62 wt% of the plant.

Light gases released from the Southeastern U.S. samples ranged from 18 wt% (dwarf palmetto) to 25 wt% (saw palmetto), while the range of light gases from northern Utah species varied from 23 wt% (bigtooth maple) to 29 wt% (Gambel oak). The Southern California samples generated approximately 23 wt% (chamise twigs) to 31 wt% (hoaryleaf ceanothus). The overall yield of light gases from pyrolysis of plants in these experiments ranged from 18 wt% to 31 wt% of the dry ash-free plant (see Figure 5).

3.2. Compounds Present in Tar

Table S2 contains the complete list of compounds and their concentrations in tar from pyrolysis of the various plant species. Compounds were classified as aromatic or non-aromatic to help us to understand possible mechanisms involved in the thermal degradation of samples. Aromatic compounds were divided into subgroups of benzenoid aromatics, heterocyclic aromatics and polyaromatic hydrocarbons (PAHs). In this paper, benzenoid aromatics were defined as aromatic compounds with only one benzene ring (containing no hetero atoms) in their chemical structure, while aromatics with more than one benzene ring were classified as PAHs. Compounds such as 2-methyl pyrimidine and 2-furan methanol, which are aromatic and contain at least one hetero atom (N or O) in their ring structure were classified as heterocyclic aromatics. Phenol, 2-methyl phenol, 3-methyl phenol, dimethyl phenol and ethyl phenol were all classified as benzenoid aromatics. However, these compounds are sometimes referred to as “phenolic compounds” to help compare the data to the existing literature.

Non-aromatic compounds were divided into cycloalkanes, cycloalkenes, cyclo esters, cyclic ketones, ketones, acids, amines, alcohols and heterocyclics. Heterocyclic compounds are cyclic non-aromatic compounds with at least one hetero atom (nitrogen, sulfur, oxygen, etc.) in their ring structure.

Table 4. Tar aromatic content for the plant species studied (mol%).

Region	Plant Species	Aromatic	Non-Aromatic
S.E. U.S.	Darrow’s blueberry	99.9	0.1
S.E. U.S.	Dwarf palmetto	97.8	2.2
S.E. U.S.	Fetterbush	99.9	0.1
S.E. U.S.	Inkberry	100	0.0
S.E. U.S.	Live oak	100	0.0
S.E. U.S.	Little bluestem	100	0.0
S.E. U.S.	Saw palmetto	100	0.0
S.E. U.S.	Sparkleberry	100	0.0
S.E. U.S.	Swamp bay	100	0.0
S.E. U.S.	Water oak	100	0.0
S.E. U.S.	Wax myrtle	100	0.0
S.E. U.S.	Pineland threeawn	100	0.0
S.E. U.S.	Yaupon	98.9	1.1
S.E. U.S.	Longleaf pine foliage	100	0.0
N. Utah	Big Sagebrush	87.6	12.4
N. Utah	Utah juniper	78.8	21.2
N. Utah	Gambel oak	90.5	9.5
N. Utah	Bigtooth maple	87.0	13.0
S. CA	Chamise	92.1	7.9
S. CA	Chamise twig	83.6	16.4
S. CA	Eastwood’s manzanita	37.4	62.6
S. CA	Eastwood’s manzanita twig	50.5	49.5
S. CA	Scrub oak	86.7	13.3
S. CA	Hoaryleaf ceanothus	84.9	15.1

Note: Error bars for the Southern California species were not available, due to the limited supply of fresh samples.

contains the distribution of aromatic versus non-aromatic compounds in tar from the various plant species. Aromatics are the major constituents of chemicals released as tar. The Southeastern U.S. plant species generated tar with the highest overall concentration of aromatics, compared to the northern Utah and Southern California samples. The overall concentration of aromatics in tar from Southeastern U.S. plant species varied from 97.8 mol% (dwarf palmetto) to 100 mol% (inkberry, live oak, little bluestem, saw palmetto, sparkleberry, swamp bay, water oak, wax myrtle and pineland threeawn). The total concentration of aromatics in tar from the northern Utah plant species ranged from 78.8 mol% (Utah juniper) to 90.5 mol% (Gambel oak). Two of the samples from the Southern California plant species produced significantly lower amounts of aromatics compared to other examined plant species; Eastwood’s manzanita tar contained 37.4 mol% aromatics, whereas tar derived from its twigs (no foliage attached) had 50.5 mol%. The rest of the tar samples from Southern California contained higher contents of aromatics, ranging from 83.7 mol% (chamise twigs) to 92.1 mol% (chamise). The aromatic content of tar from sparkleberry is reported to be 100 mol% by Safdari [33], whereas tar from the same plant contained 90.9 mol% when examined later by Alizadeh et al. [34], using cuttings obtained from Fort Jackson, South Carolina. The difference may have been due to the difference in the maturity of the samples studied. The disparities in the concentration of aromatics released into tar may also be due to slight differences in operating conditions; the pyrolysis temperature during experiments conducted by Safdari reached 765 °C, while the temperature during experiments performed by Alizadeh was maintained slightly lower at 725 °C. If the pyrolysis temperatures are low, primary tar reactions will be more prevalent compared to secondary reactions. As the temperatures continue to rise, long chain aliphatic compounds, cycloalkenes and single-ring aromatics polymerize and form benzenoid compounds with higher molecular weight substitutes and PAHs.

Aromatics in tar from the Southeastern U.S., northern Utah and Southern California belong to one of the two major categories: benzenoids (aromatic compounds with one benzene ring) and PAHs (aromatic compounds with more than one benzene ring). The concentration of benzenoids in tar from the Southeastern U.S. samples varied from 11.2 mol% (little bluestem) to 60.7 mol% (Darrow’s blueberry). Northern Utah species released from 24.2 mol% (big sagebrush) to 55.7 mol% (Utah juniper), whereas Southern California plants released from 16.8 mol% (Eastwood’s manzanita) to 74.6 mol% (chamise) into tar. This comparison shows that the overall concentration of benzenoids in tar varies significantly between different species from different regions of the U.S. (Southeastern U.S., northern Utah and Southern California). This is likely due to differences in the biochemical structure of the foliage. Since lignin degradation is reported to be the main contributing factor to the concentration of phenolic compounds in tar, variations in the relative ratio of lignin to other major biopolymers in lignocellulosic mass (cellulose and hemicellulose) may impact the concentration of phenolic chemicals in tar [42]. Matt et al. analyzed the chemical composition of 12 plants from the Southeastern U.S. and reported differences in the levels of proteins, sugar and phenolic compounds present in live foliage [43]. The variations in concentrations of tar compounds likely stem from differences in biomass composition and characteristics. The overall concentration of benzenoids in tar can be estimated to vary from 11 mol% to 75 mol%, which is a huge range. The overall concentration of PAHs in tar from the Southeastern U.S. samples varied from 39.2 mol% (Darrow’s blueberry) to 88.8 mol% (little bluestem). Tar from northern Utah species contained from 19.6 mol% (Utah juniper) to 63.3 mol% (big sagebrush) PAHs, whereas Southern California species released from 13.4 mol% (scrub oak) to 20.7 mol% (chamise twigs). Like benzenoids, the overall concentration of PAHs varies significantly, ranging from 13 mol% to 89 mol%.

As the distribution of tar compounds from various samples was examined, it was observed that as the concentration of benzenoids decreased, the concentration of PAHs increased. There were only two samples that do not follow this pattern: Eastwood’s manzanita foliage and its twigs. These two samples had a significantly higher concentration of cycloalkenes in their tar compared to others.

The higher concentration of heavy molecular weight aromatics such as PAHs may be attributed to the prevalence of secondary pyrolysis reactions during the thermal degradation of these species. Disparities in the temperature profile inside the biomass, which occur due to their different physical characteristics, also affect the chemical reactions occurring during biomass degradation. If the pyrolysis temperatures are low, primary tar reactions will be more prevalent compared to secondary reactions. As the temperatures continue to rise, single-ring aromatics, such as phenolic compounds, polymerize and form benzenoid compounds with higher molecular weight substitutes and PAHs.

Comparisons between some of the compounds detected in tar from Southeastern U.S., northern Utah and Southern California species revealed similarities in the functional groups and main ring structures of some of these compounds, suggesting they may have originated from the same intermediates. For example, tar collected from Southeastern U.S. and northern Utah species contained anthracene, while tar from Southern California species contained 11,12-diacetyl-9,10-Ethanoanthracene.

Indolizine, 1H-Indenol and indole are all compounds with similar structures (See Figure 6) that are detected in tar from northern Utah, Southeastern U.S. and Southern California species.

Tar from most Southeastern U.S. species contained indole and, when detected, its concentration in tar varied between 0.45 mol% and 4.11 mol%. Among the northern Utah samples, only bigtooth maple released indole during pyrolysis. The concentration of indole in bigtooth maple tar was 1.4 mol%. This compound was not detected in tar from Southern California species.

Additionally, 1H-indenol was present in tar from all northern Utah samples, with its concentration varying between 1 mol% in bigtooth maple and 7.5 mol% in Gambel oak. This compound was not detected in tar from Southern California or Southeastern U.S species. The similarities in the chemical structure of these three compounds (indole, 1H-indenol and indolizine) suggest that at some point during pyrolysis reactions, these compounds may have stemmed from the same intermediate.

Catechols and their derivatives are products of the thermal degradation of lignin [42] that favor the production of biphenyl, naphthalene and phenanthrene. Further analysis of tar from northern Utah species showed that no guaiacols or catechols were detected in these tar samples. This was probably due to the short lifetime of these intermediate compounds and the prevalence of secondary reactions that transformed guaiacols into catechols and, eventually, to biphenyls and naphthalenes. The concentration of 3-hydroxy biphenyl in tar from the northern Utah samples varied from 1.6 mol% (in Utah juniper) to 13 mol% (Gambel oak). Tar from bigtooth maple did not contain 2-phenyl naphthalene, but its concentration in the other northern Utah samples varied between 3.5 and 12.5 mol% (daf). Among Southern California species, only chamise contained low concentrations (1.8 mol%) of 4-ethyl guaiacol. No catechol, biphenyl or naphthalene (or any of their derivatives) were detected in the Southern California tar. Analysis of tar from various Southeastern U.S. species shows that the overall concentration of catechols (3-methyl catechol, 4-methyl catechol, 4-ethyl catechol, 3-methoxy catechol) ranged from 1.5 mol% to 28.2 mol%. The overall concentration of guaiacols (guaiacol and 4-ethyl guaiacol) in Southeastern U.S. tar varied from 0 to 1.9 mol%. The low concentration of guaiacols in the Southeastern U.S. tar followed the same pattern as the Southern California and northern Utah samples and showed that guaiacols most likely transformed into catechol and, eventually, naphthalenes and other PAHs.

Most non-aromatic tar compounds were classified as cycloalkanes, cycloalkenes, amines, ketones, esters and acids. Tar from Southeastern U.S. species contained few non-aromatic compounds. However, this was not the case for tar from northern Utah and Southern California species. Acidic compounds were the major contributors to the non-aromatic composition of tar from northern Utah species, with their concentration varying from approximately 6.2 mol% (bigtooth maple) to 18.5 mol% (Utah juniper). The main portion of acidic compounds in the tar had a straight chain structure, except for benzoic acid. Contrary to the northern Utah samples, tar from the Southern California and Southeastern U.S. samples contained no acidic compounds.

No cycloalkenes were detected in tar from the Southeastern U.S. samples. Among the northern Utah samples, bigtooth maple was the only one containing small concentrations of cycloalkenes (1.1 mol%) in its tar. However, cycloalkenes were the major constituents of non-aromatics in tar from Southern California species. Tar collected from Eastwood’s manzanita foliage and its twigs had the highest overall concentration of cycloalkenes (48.1 mol% and 35.7 mol%, respectively).

The only compound detected in tar from the Southeastern U.S. and Southern California samples that could be classified as a furan was 2,3-dihyro benzofuran. Among the Southeastern U.S. samples containing 2,3-dihydro benzofuran, its concentration varied from 0.9 mol% (yaupon) to 3.1 mol% (Darrow’s blueberry), while tar from northern Utah species (except for big sagebrush) contained between 5.2 and 6.1 mol% of furans (2-furan methanol, 2,3-dihyro benzofuran and 2,2′-bifuran). No furans were detected in tar from big sagebrush. The Southern California samples generated tar yielding from 3 mol% (Eastwood’s manzanita) to 17.4 mol% (scrub oak) 2,3-dihyrobenzofuran.

Additionally, 2H-1-benzopyran-2-one is 1 of over 300 compounds found in plants known collectively as coumarins. It was detected in tar from northern Utah species, except for juniper. Its concentration varied from 4.9 mol% in Gambel oak to 16.5 mol% in bigtooth maple. The presence of 2H-1-benzopyran-2-one in tar from these species may have resulted from their condensation after evaporating and releasing it from the complex structure of the plant as they were exposed to high temperatures. Lack of 2H-1-benzopyran-2-one in tar from pyrolysis of Southeastern U.S. species and Southern California plants may be due to several factors. The chemical makeup of Southeastern U.S. and Southern California species may contain very small concentrations or no amounts of coumarins. This will result in either a very small concentration of these compounds, which will be below the detection limit of the GC/MS system used, or no detectable compounds at all.

Analysis of tar from all northern Utah samples showed that quinoline was present in tar. Tar from Utah juniper contained the lowest concentration of quinoline (0.6 mol%), while bigtooth maple generated the highest (5.9 mol%). Southern California species, except for Eastwood’s manzanita, contained quinoline, with its concentration ranging from 2.5 mol% (chamise branches with foliage) to 6.2 mol% (hoaryleaf ceanothus).

In summary, the differences in the biochemical composition of various plant species and the differences in their physical characteristics may contribute to the variations in the yields of pyrolysis products and the composition of chemicals detected in tar. While the yields of tar and light gases from different plants vary, the total volatile and char yields show only minor variations. Tar from all species (except for Eastwood’s manzanita) primarily comprises aromatic components. Single-ring aromatics (i.e., benzenoids such as phenol, 2-methyl phenol and 3-methyl phenol), followed by PAHs (such as fluoranthene, pyrene, anthracene, benzofluorene and phenanthrene), are the most prevalent aromatics in tar from all species, except for Eastwood’s manzanita. The higher ratio of PAHs compared to benzenoids in tar may be due to the prevalence of secondary pyrolysis reactions during their thermal degradation. Despite similarities in the functional groups of some of the tar compounds, the differences in the chemical composition of biomass may lead to the presence of different compounds in tar. For instance, while phenol is a compound that is detected in tar from all species, anthracene, 9,10-ethanoanthracene, 9,10-dihydro-11,12-diacetyl- and benz(a)anthracene are only detected in tar from some of the plant species. Considering the structural similarities of these compounds, it is highly probable that they shared common intermediates at some point during their formation. This shows that despite the similarities in tar from various species, it is necessary to collect and analyze tar from any specific biomass to have a better understanding of its chemical composition.

3.3. Average Molecular Weight for Tar

Table 5. Average MW and elemental composition for tar from various species.

Region	Plant Species	Average MW for Tar (g/mol)	Average Elemental Composition of Tar
S.E. U.S.	Darrow’s blueberry	144.0	C9.85H8.4O1.04N0.02
S.E. U.S.	Dwarf palmetto	146.2	C10.41H8.51O0.7N0.05
S.E. U.S.	Fetterbush	159.6	C11.74H8.97O0.54N0.03
S.E. U.S.	Inkberry	167.6	C12.37H9.17O0.59N0.02
S.E. U.S.	Live oak	143.3	C10.03H8.46O0.78N0.07
S.E. U.S.	Little bluestem	180.2	C13.66H10.50O0.17
S.E. U.S.	Saw palmetto	151.5	C11H8.65O0.58N0.04
S.E. U.S.	Sparkleberry	154.2	C10.88H8.79O0.88N0.01
S.E. U.S.	Swamp bay	154.4	C11.07H8.82O0.72N0.03
S.E. U.S.	Water oak	147.5	C10.52H8.52O0.72N0.05
S.E. U.S.	Wax myrtle	146.4	C10.33H8.30O0.87N0.02
S.E. U.S.	Pineland threeawn	175.6	C13H10.16O0.46
S.E. U.S.	Yaupon	164.54	C12.1H9.33O0.55N0.03
S.E. U.S.	Longleaf pine foliage	172.2	C12.76H10.10O0.41
N. Utah	Big sage brush	175.5	C12.91H10.4O0.58N0.06
N. Utah	Utah juniper	140.0	C9.29H10.49O1.11N0.01
N. Utah	Gambel oak	166.6	C11.93H10.53O0.77N0.04
N. Utah	Bigtooth maple	151.4	C10.46H9.08O0.92N0.15
S. CA	Chamise	148.3	C9.61H9.95O1.37N0.09
S. CA	Chamise twig	176.4	C11.39H12.32O1.68N0.05
S. CA	Eastwood’s manzanita	310.2	C18.86H25.19O3.67N0.03
S. CA	Eastwood’s manzanita twig	269.0	C16.55H21.52O3.02N0.06
S. CA	Scrub oak	169.5	C11.06H13.02O1.43N0.07
S. CA	Hoaryleaf ceanothus	180.6	C12.11H15.04O1.22N0.06

lists the average molecular weight and elemental composition of tar collected from each plant. The tar’s molecular weight was calculated as the weighted average of mole percentage of each detected tar compound and the corresponding molecular weight. As seen in Table 5, among Southeastern U.S. plants, live oak released tar with lowest molecular weight (143.3 g/mol) while little bluestem yielded tar with the highest value (180.2 g/mol). For northern Utah species, tar from Utah juniper had the lowest molecular weight (140.0 g/mol) whereas tar from big sagebrush exhibited the highest value (175.5 g/mol). The average molecular weight for tar from Southern California species varied from 148.3 g/mol (chamise) to 310.2 g/mol (Eastwood’s manzanita).

3.4. Light Gases

CO, CO₂, CH₄ and H₂ were the major gas components observed for all plant species. Occasionally, peaks related to other gas species were observed in gas samples, but due to their low concentration, further detection and quantification of these gas components were not feasible here. Concentration of each of the major gas components was reported in the mole percentage of light gases. To obtain the mole percentage of each of the gas compounds, the area under the peak relating to that specific component was divided by the sum of the areas under all identified peaks. The mole percentage was then transformed into the respective weight percentage of the light gas sample, using the molecular weight. The resulting mass concentrations of all major components in the gas phase are presented in

Table 6. Concentration of major gas components generated from pyrolysis of the studied plant species (wt% of light gas).

Region	Species	CO	CO2	CH4	H2
S.E. U.S.	Darrow’s blueberry	62.1 ± 6.9	25.0 ± 3.4	10.9 ± 3.8	2.1 ± 0.3
S.E. U.S.	Dwarf palmetto	59.7 ± 2.5	31.2 ± 1.8	7.6 ± 1.3	1.5 ± 0.5
S.E. U.S.	Fetterbush	59.1 ± 7.5	30.9 ± 8.3	7.9 ± 1.3	2.1 ± 0.0
S.E. U.S.	Inkberry	59.8 ± 10.7	29.3 ± 7.8	9.0 ± 3.6	1.9 ± 0.1
S.E. U.S.	Live oak	60.5 ± 6.2	29.8 ± 5.2	8.1 ± 1.6	1.7 ± 0.1
S.E. U.S.	Little bluestem	62.1 ± 4.3	28.6 ± 6.8	8.0 ± 2.3	1.4 ± 0.4
S.E. U.S.	Saw palmetto	63.0 ± 3.3	29.1 ± 2.9	6.3 ± 0.9	1.6 ± 0.5
S.E. U.S.	Sparkleberry	61.7 ± 6.2	26.2 ± 3.8	10.3 ± 3.2	1.8 ± 0.5
S.E. U.S.	Swamp bay	53.4 ± 9.5	34.7 ± 5.1	9.8 ± 4.6	2.1 ± 0.2
S.E. U.S.	Water oak	59.2 ± 6.0	30.7 ± 4.3	8.4 ± 1.2	1.7 ± 0.6
S.E. U.S.	Wax myrtle	61.4 ± 7.2	26.7 ± 5.1	10.2 ± 2.2	1.8 ± 0.1
S.E. U.S.	Pineland threeawn	56.7 ± 11.2	32.9 ± 8.2	9.1 ± 2.8	1.3 ± 0.3
S.E. U.S.	Yaupon	57.9 ± 4.6	29.7 ± 3.0	10.6 ± 3.8	1.8 ± 0.2
S.E. U.S.	Longleaf pine foliage	60.6 ± 5.7	28.9 ± 4.7	9.2 ± 1.7	1.4 ± 0.1
N. Utah	Big sagebrush	56.8 ± 4.0	31.0 ± 4.62	11.0 ± 1.0	1.2 ± 0.3
N. Utah	Utah juniper	54.8 ± 1.5	34.0 ± 3.1	9.8 ± 2.5	1.4 ± 0.3
N. Utah	Gambel oak	59.1 ± 2.1	30.6 ± 3.9	8.7 ± 1.5	1.6 ± 0.4
N. Utah	Bigtooth maple	53.8 ± 3.7	34.5 ± 6.0	10.6 ± 2.1	1.1 ± 0.4
S. CA	Chamise	55.0 ± 1.4	34.8 ± 2.1	8.7 ± 2.1	1.5 ± 0.0
S. CA	Chamise twig	57.1 ± 2.3	33.4 ± 1.7	7.9 ± 1.3	1.6 ± 1.2
S. CA	Eastwood’s manzanita	58.3 ± 1.4	31.0 ± 1.5	9.4 ± 1.9	1.3 ± 0.1
S. CA	Eastwood’s manzanita twig	59.4 ± 1.9	32.1 ± 1.7	7.2 ± 1.9	1.4 ± 0.1
S. CA	Scrub oak	62.1 ± 2.0	29.4 ± 1.4	7.0 ± 1.2	1.6 ± 0.1
S. CA	Hoaryleaf ceanothus	56.8 ± 1.9	32.5 ± 1.5	9.2 ± 1.3	1.5 ± 0.1

, along with the 95% confidence intervals.

CO was the dominant light gas species in these experiments. Figure 7 represents the concentration of CO in light gases, from pyrolysis of various plant species. The Southeastern U.S. samples generated between 53.4 wt% (swamp bay) and 63.0 wt% (saw palmetto) carbon monoxide in their light gases. The concentration of CO in light gases from northern Utah species ranged between 53.8 wt% (bigtooth maple) and 59.1 wt% (Gambel oak), whereas the Southern California samples produced between 55.0 wt% (chamise) and 62.1 wt% (scrub oak). The average concentration of carbon monoxide in light gases from pyrolysis of foliage can be estimated to be between 53.4 wt% (daf) and 63.0 wt% (daf). Statistical analysis showed that changes in the concentration of CO due to the species type were statistically significant for Southeastern U.S. species (p = 0.025), northern Utah species (p = 0.003) and Southern California species (p = 2.37 ${\times 10}^{-6}$).

Figure 8 illustrates the concentration of CO₂ in light gases from pyrolysis of various plant species. The concentration of CO₂ generated during pyrolysis of Southeastern U.S. species ranged from 25.0 wt% (Darrow’s blueberry) to 34.7 wt% (swamp bay). Northern Utah species yielded between 30.6 wt% (Gambel oak) and 34.5 wt% (bigtooth maple), whereas light gases from the Southern California samples contained between 29.4 wt% (scrub oak) and 34.8 wt% (chamise). The average concentration of carbon dioxide in light gases from pyrolysis of foliage can be estimated to be approximately between 25 wt% (daf) and 35 wt% (daf) of the light gas.

Statistical evaluations of variations in the concentration of CO₂ in pyrolysis gases from the Southeastern U.S. samples showed statistically significant changes, due to the biomass type (p = 0.0007). However, the concentration of CO₂ in the northern Utah samples changed moderately due to the biomass type (p = 0.06). A statistically profound difference was observed in the concentration of CO₂ between Southern California species (p = 0.00008). Since operating conditions (pyrolysis temperature and heating rate) were the same during the Southern California and northern Utah experiments, this contradictory result was likely due to differences in the biomass composition.

The concentration of CH₄ produced from pyrolysis of various samples is shown in Figure 9. The concentration of CH₄ generated from the Southeastern U.S. samples varied from 6.3 wt% (saw palmetto) to 10.9 wt% (Darrow’s blueberry). The northern Utah samples yielded between 8.7 wt% (Gambel Oak) and 11.0 wt% (big sagebrush), whereas light gases from the Southern California samples contained between 7.0 wt% (scrub oak) and 9.4 wt% (Eastwood’s manzanita). The average concentration of methane in light gases from pyrolysis of foliage can be estimated to be between 6.3 wt% (daf) and 11 wt% (daf). Differences in the concentration of CH₄ in pyrolysis gases due to species type were statistically significant for the Southeastern U.S. (p = 0.0005) and Utah samples (p = 0.03). However, differences in wt% of CH₄ from Southern California species were only slightly significant (p = 0.08).

Figure 10 shows the concentration of H₂ (wt%) in light gases from pyrolysis of various plant samples. Pyrolysis light gases from the Southeastern U.S. samples contained between 1.3 wt% (pineland three awn) and 2.1 wt% (swamp bay) H₂. The average concentration of H₂ in light gases from northern Utah species varied between 1.1 wt% (bigtooth maple) and 1.6 wt% (Gambel oak), whereas the Southern California samples yielded between 1.3 wt% (Eastwood’s manzanita) and 1.6 wt% (scrub oak). The average concentration of hydrogen in light gases from pyrolysis of foliage can be estimated to be between 1.1 wt% (daf) and 2.1 wt% (daf). Variation in the concentration of H₂ released into light gases during pyrolysis of various types was statistically significant for all three regions (p = 0.003, 0.01 and 1.57${\times 10}^{-8}$ for Southeastern U.S., Utah and Southern California, respectively). However, the released H₂ represented only a small percentage of the mass of the original biomass.

3.5. High Heating Value

High heating value (HHV) is defined as the energy generated by a chemical compound (initially at 25 °C) once it is stoichiometrically combusted in O₂ and the products of combustion are cooled to 25 °C. HHV is reported as the unit of energy per unit mass or unit volume of the chemical compound. In an experimental setup, the HHV for a specific compound is measured by a bomb calorimeter. HHV can be used as the thermal efficiency of a fuel. Higher values of HHV for a fuel mean higher amounts of energy are generated during complete combustion of the fuel. Since the purpose of this paper is to study the volatiles released during pyrolysis of selected U.S. species to better understand their possible contribution to the propagation of wildland fires, high heating values of the volatiles generated during pyrolysis were estimated. If the volatiles generated during pyrolysis of biomass “A” have a higher HHV compared to those released from biomass “B”, it can be concluded that volatiles from biomass “A” may contribute more to the spread of a wildland fire than those of biomass “B”.

The HHVs for the overall volatiles released during pyrolysis were estimated according to the following balance equation:

HHV of Volatiles = (HHV of tar) × tar yield + (HHV of light gases) × gas yield

(2)

The HHV of tar was estimated as the weighted average of the HHVs of each individual tar compound and their corresponding mole percentage in tar. The HHVs of light gases were calculated in a similar manner, using the HHVs of each gas component and their associated mole percentages. HHVs for some of the tar components were not found in the literature. For these compounds, the HHVs were estimated using the Mott and Spooner correlation [45,46], as recommend by Richards et al. [47].

High heating values for tar and light gases generated during pyrolysis of the selected U.S. samples are reported in

Table 7. High heating values of tar, light gases and volatiles released from pyrolysis of selected U.S. plant species.

Region	Species	HHV of Tar (MJ/kg of Tar)	HHV of Gas (MJ/kg of Gas)	HHV of Volatiles (MJ/kg of Volatiles)
S.E. U.S.	Darrow’s Blueberry	34.13 ± 2.35	15.29 ± 2.16	22.82 ± 4.53
S.E. U.S.	Dwarf palmetto	35.53 ± 2.75	12.44 ± 1.10	24.27 ± 7.4
S.E. U.S.	Fetterbush	36.60 ± 2.89	13.40 ± 1.05	23.02 ± 4.03
S.E. U.S.	Inkberry	36.47 ± 2.89	13.83 ± 2.19	24.56 ± 5.55
S.E. U.S.	Live oak	35.37 ± 3.05	13.08 ± 1.14	22.82 ± 4.24
S.E. U.S.	Little bluestem	38.80 ± 3.06	12.73 ± 1.58	26.21 ± 9.50
S.E. U.S.	Saw palmetto	36.46 ± 2.62	12.20 ± 1.28	22.51 ± 5.20
S.E. U.S.	Sparkleberry	35.08 ± 2.86	14.58 ± 2.11	22.65 ± 6.26
S.E. U.S.	Swamp bay	35.87 ± 2.82	13.90 ± 2.64	23.86 ± 5.90
S.E. U.S.	Water oak	35.70 ± 2.91	13.08 ± 1.46	23.25 ± 4.36
S.E. U.S.	Wax myrtle	34.85 ± 2.90	14.43 ± 1.44	22.49 ± 3.93
S.E. U.S.	Pineland threeawn	37.50 ± 3.11	12.63 ± 2.11	25.03 ± 5.50
S.E. U.S.	Yaupon	36.76 ± 3.00	14.33 ± 1.95	25.58 ± 7.14
S.E. U.S.	Longleaf pine foliage	37.96 ± 1.33	13.21 ± 1.14	24.67 ± 2.76
N. Utah	Big sagebrush	36.81	13.60 ± 6.27	23.68
N. Utah	Utah juniper	35.59	13.03 ± 8.50	22.83
N. Utah	Gambel oak	36. 55	13.11 ± 7.29	23.17
N. Utah	Bigtooth maple	34.68	12.92 ± 9.75	23.09
S. CA	Chamise	33.54	12.62 ± 1.17	22.69
S. CA	Chamise twig	33.76	12.51 ± 1.02	22.12
S. CA	Eastwood’s manzanita	33.24	13.04 ± 1.14	15.27
S. CA	Eastwood’s manzanita twig	33.61	11.99 ± 1.22	19.37
S. CA	Scrub oak	35.20	12.49 ± 0.91	22.71
S. CA	Hoaryleaf ceanothus	37.19	13.04 ± 0.90	23.01

and Figure 11. The high heating value of tar from the Southeastern U.S. samples was estimated to be between 34.12 (MJ/kg of tar) for Darrow’s blueberry and 38.80 (MJ/kg of tar) for little bluestem. The HHV of tar from the northern Utah samples varied between 34.68 (MJ/kg of tar) for bigtooth maple and 36.81 (MJ/kg of tar) for big sagebrush. For the Southern California samples, the HHV of tar was estimated to be between 33.24 (MJ/kg of tar) for Eastwood’s manzanita and 37.19 (MJ/kg of tar) for hoaryleaf ceanothus. The high heating value for tar generated during pyrolysis of the plant samples examined here ranges between 33.24 (MJ/kg of tar) and 38.80 (MJ/kg of tar). These values are slightly higher than the reported HHVs for bio-oil in the literature, which are between 20 and 35 (MJ/kg of tar). There is a large amount of published research on the high heating value of bio-oil from various sources of biomass, due to its potential as fuel [48,49,50]. The composition of bio-oil varies due to several factors, such as operating conditions, reactor type and the composition of parent biomass. Variations in bio-oil composition eventually contribute to variations in the bio-oil HHV.

HHVs of light gases generated during pyrolysis of the Southeastern U.S. samples varied from 12.20 (MJ/kg of light gases) for saw palmetto to 15.29 (MJ/kg of light gases) for Darrow’s blueberry. HHVs of light gases from northern Utah species were estimated to be from 12.92 (MJ/kg of light gases) for bigtooth maple to 13.60 (MJ/kg of light gases) for big sagebrush. HHVs of Southern California species varied from 12.00 (MJ/kg of light gases) for Eastwood’s manzanita twig to 13.04 (MJ/kg of light gases) for hoaryleaf ceanothus.

HHV for total volatiles from selected Southeastern U.S. samples fluctuated between 22.49 (MJ/kg of volatiles) for wax myrtle and 26.21 (MJ/kg of volatiles) for little bluestem. The total HHV of total volatiles from the northern Utah samples ranged between 22.83 and 23.68 (MJ/kg of volatiles), which were in the same range as the values for the Southern California samples reported by Alizadeh et al. [34]. The HHV of total volatiles for the selected Southern California species varied between 19.37 and 23.01 (MJ/kg of volatiles).

Based on these results, the HHV of total volatiles generated during pyrolysis of foliage can be estimated to be between 19.37 and 26.21 (MJ/kg of volatiles), using Equation (2). The contribution of tar and light gases to the HHV of the total HHV from volatiles is illustrated in Figure 11. The contribution of tar to the HHV of volatiles varied from 82 to 92%, showing the importance of tar in the combustion of foliage.

It is interesting to compare the high heating values determined here with the results published by Susott [51], who used both evolved gas analysis (EGA) at a low heating rate of 20 °C to 500 °C and conventional bomb calorimetry on forest fuels. Many of the fuels studied by Susott were from trees, but he did study chamise, manzanita, big sagebrush and Utah juniper. It was not clear from the publication if the reported values were the high heating value (HHV) or the low heating value (LHV). The yields of volatiles reported by Susott for the four species in common ranged from 66.6 to 69.4 wt% daf, compared to 78 to 82 wt% daf for the same samples reported here from experiments at higher heating rates and temperatures. The heating values for total volatiles reported by Susott for these four common species ranged from 11.8 to 15.8 MJ/kg (daf), which are lower than the HHV range of 15.3 to 23.7 MJ/kg (daf) in these experiments. Manzanita volatiles have the lowest heating value in both studies. The total fuel heating values for these four species reported by Susott (which includes the combustion of volatiles and char) ranged from 21.7 to 23.28 MJ/kg, which are close to the HHVs for total volatiles for most of the species reported here. The higher yield of volatiles at higher heating rates seems to transfer energy from the char to the volatiles, resulting in higher HHVs of the volatiles. This finding emphasizes the need to perform experiments at realistic temperatures and heating rates to provide meaningful information to direct simulations.

4. Summary and Conclusions

In this paper, pyrolysis products’ yields and compounds, released from 24 foliage samples originating from three U.S. regions, were studied. The operating conditions for pyrolysis of samples during experiments were selected to mitigate the heating rate and temperature of wildland fires. Ranges of yields of tar and gas products were compared. Major components of tar and light gases were identified, and differences in their concentrations were investigated. The average molecular weights of the tar were estimated for each plant species. The high heating values (HHVs) of the tar, light gases and total volatiles (tar and light gases combined) were estimated and compared. The average values of yields and heating values from the 24 plant species are presented in

Table 8. Average yields and heating values for the 24 plant species studied.

Tar Yield (wt% daf)	56.0
Light Gas Yield (wt% daf)	24.0
Total Volatiles Yield (wt% daf)	80.0
Light Gas Composition (wt% dry)
CO	58.8
CO2	32.5
CH4	9.2
H2	1.5
HHV of Total Volatiles (MJ/kg dry)	23.19

for convenience.

The primary conclusions of this research are as follows.

The total yields of volatiles were similar for all plant species in the temperature and heating rate studies, ranging from 75 wt% to 83 wt%.

Tar was the principal pyrolysis product, ranging from 48 wt% to 62 wt% of the dry, ash-free plant samples and 57 to 70% of the volatiles released.

Tar from all species, except for Eastwood’s manzanita, was mainly composed of aromatics. The Southeastern U.S. samples produced the tar with the highest overall concentration of aromatics compared to the northern Utah and Southern California samples. The concentration of aromatics in tar from Southeastern U.S. species ranged from 97.8 mol% to 100 mol%. The concentration of aromatics in Eastwood’s manzanita tar was 37.4 mol%, while tar from other Southern California species contained between 83.7 mol% and 92.1 mol% aromatics. Northern Utah species generated tar with 78.8 to 90.5 mol% aromatics. These disparities in the concentration of aromatics in tar from various species show that biomass type affects the overall concentration of aromatics released into tar. Excluding Eastwood’s manzanita as an outlier, the overall concentration of aromatics in tar varied from 78 to 100 mol%.

Comparisons among tar samples from the Southeastern U.S., northern Utah and Southern California revealed some similarities and differences in the key components and their concentration. The primary compounds of tar from all Southeastern U.S. samples, excluding little bluestem, were benzenoids: phenol, 1,2-benzenediol, 1,4-benzenediol and 2,6-dimethoxy phenol. Tar from little bluestem was abundant in PAHs, such as fluorene and pyrene. Tar from Southern California species, excluding Eastwood’s manzanita, were mainly composed of phenol and phenol, 3-methyl. Tar from northern Utah species had a different composition from Southeastern U.S. and Southern California ones. Big sagebrush and Gambel oak yielded tar that was abundant in PAHs, such as fluoranthene, 2-phenyl naphthalene and anthracene. Tar from Utah juniper was primarily made up of phenol and phenol 3-methyl. Bigtooth maple, on the other hand, generated tar that was abundant in coumarin, anthracene and fluoranthene. This shows that while in most cases, tar from pyrolysis of biomass is mainly made up of aromatics, the detailed composition of tar depends on the biomass type, and therefore, a detailed analysis of tar is needed to predict what compounds are released during wildland fires.

The concentration of PAHs in tar from the Southeastern U.S. samples ranged from 39.2 mol% to 89 mol%. Tar from the Southern California samples contained much lower concentrations of PAHs, ranging from 13.4 mol% to 20.7 mol%, compared to one obtained from Southeastern U.S. samples. The northern Utah samples generated tar that contained between 19.6 mol% and 63.3 mol% PAHs. The differences in the concentration of PAHs in tar could be due to variations in the biochemical composition of the plants.

Light gases generated during pyrolysis of all species were collected and analyzed by GC/TCD. CO, CO₂, CH₄ and H₂ were the major constituents of pyrolysis gases on a wt% basis. CO was the main component of the light gases, succeeded by CO₂, CH₄ and H₂ on a wt% basis. The CO concentration ranged from 53.4 wt% to 63 wt%. CO₂ ranged between 25 wt% and 35 wt% of light gases, while CH₄ comprised 6.3 wt% to 11 wt% of the light gases. The concentration of H₂ was between 1.1 wt% and 2.1 wt%.

Concentration differences in CO and H₂ due to species type were statistically significant, although the concentration of H₂ was always quite low. The concentration of CO₂ in light gases from pyrolysis of the northern Utah samples changed moderately due to the plant species type. However, its concentration in light gases from the Southern California and Southeastern U.S. samples depended on the species type. Concentration of CH₄ in light gases from the Southern California samples changed moderately due to species type; however, its concentration in light gases from the northern Utah and Southeastern U.S. samples changed significantly, due to species type.

The high heating values (HHV) of volatiles generated during pyrolysis of foliage were estimated to be between 19.37 and 26.21 (MJ/kg of volatiles), with the lowest value being from Eastwood’s manzanita. The HHV of tar ranged from 33.24 to 38.80 MJ/kg of tar, representing between 82% and 91% of the heating value of the volatiles. The large contribution of tar to the heating value of the volatiles implies that tar cannot be overlooked in combustion simulations.

This work was limited to the plant species studied at the heating rates and temperatures pertaining to active fires. Different temperatures and heating rates would apply to smoldering. Intact foliage samples were used in all cases, which may be part of any differences with data from TGA studies, where small sections of foliage samples are used. The type and amount of large molecular weight tar species should be studied further, using GC columns at higher temperatures. Tar HHVs were only calculations; actual measurement of tar HHVs would be helpful.

These data on yields, gas and tar species and heating values obtained at relevant heating rates and temperatures should provide a basis for improving pyrolysis modeling and subsequent combustion modeling for wildland fire simulations.