ABSTRACT
DE LA CRUZ, FLORENTINO BANAAG. Fate and Reactivity of Lignin in Municipal Solid Waste (MSW) Landfill. (Under the direction of Dr. Morton A. Barlaz).
More than 50% of the 149 million metric tons of municipal solid waste (MSW)
landfilled in the U.S. annually are derived from lignocellulosic materials (e.g. food and yard
waste, wood, and pulp and paper products). As lignin is approximately 15% of residential
solid waste, understanding its behavior during anaerobic decomposition in landfills is
important for a complete description of carbon decomposition and storage in landfills. The
overall objective of this dissertation was to use lignin in characterizing the decomposition of
lignocellulose during anaerobic decomposition. This document is divided in three major
sections to address (1) work to explore CuO oxidation as an alternative analytical method to
improve quantification of lignin; (2) use of CuO lignin monomers as molecular markers to
quantitatively estimate the amount of lignocellulose in mixed samples and; (3) to evaluate the
chemical transformations of lignin during mesophilic and thermophilic anaerobic
decomposition.
To evaluate the feasibility of CuO oxidation as an alternate method to quantify lignin,
experiments were conducted to evaluate potential for some common components of MSW to
interfere with the CuO oxidation. Results showed the analysis of lignin phenols can be
simplified by omission of the liquid – liquid extraction step traditionally employed as a
purification step for subsequent GC/MS analysis. The study also showed that plastics, metals
and lipophilic extractives do not affect CuO lignin in mixed MSW. It was also demonstrated
the CuO lignin of ball-milled samples were not significantly different when compared to the
CuO lignin of samples ground to pass a 60 mesh screen in a Wiley mill. Application of CuO
oxidation of lignin on excavated landfill samples showed that similar conclusions can be
obtained by using the traditional indicator of decomposition (Cellulose [Cel] +
Hemicellulose[H])/Klason Lignin [KL]) and a new ratio based on sum of eight phenolic
monomers (Λ8) from CuO oxidation (Cel+H)/Λ8. (Cel+H)/Λ8 in theory is more robust
parameter in samples that may contain interferences as it is based on lignin specific measure.
However, further study is necessary to explore the sensitivity and efficacy of (Cel+H)/Λ8 as a
metric for the extent of MSW decomposition.
To evaluate the applicability of CuO oxidation to estimate lignocellulose composition, end member mixing model was developed and calibrated using data from 93 different species of hardwood (HW), softwood (SW), leaves and grasses (LG) and needles (GN). Relatively accurate predictions were demonstrated in pure tissues. Poor predictions of GN in synthetic mixtures could be attributed to the deviation of CuO lignin of the selected end-members used formulating the synthetic mixtures from the mean value which was used in model development. Application of the model to estimate composition of landfill samples showed that majority of samples are comprised of HW and SW or pulp derived from these materials.
To look for evidence of chemical transformations of lignin under mesophilic and thermophilic anaerobic conditions, Hardwood (HW), softwood (SW), grass and old newsprint (ONP) were decomposed in the laboratory and were analyzed both by chemical degradation and spectroscopic techniques. Significant carbon losses were observed in all materials with highest in grass (40% and 47% for mesophilic and thermophilic decomposition, respectively). For HW, grass and ONP, the carbon losses could be attributed
primarily to cellulose and hemicellulose and some fraction of lignin. In SW, the carbon loss
could be attributed to the uncharacterized carbon fractions (e.g. extractives etc.). 2D nuclear
magnetic resonance (NMR) spectra showed slight reduction of β-O-4 linkage in HW, grass
and ONP. Klason and acid soluble lignin as well as CuO lignin data indicate no substantial
de-polymerization suggesting that reactivity of lignin were limited to side chain with no destruction of the aromatic structure.
© Copyright 2014 Florentino B. De la Cruz
All Rights Reserved
Fate and Reactivity of Lignin in Municipal Solid Waste (MSW) Landfill
by Florentino B. De la Cruz
A dissertation submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the Degree of Doctor of Philosophy
Civil Engineering
Raleigh, North Carolina
2014
APPROVED BY:
Morton A. Barlaz Detlef Knappe Chair of Advisory Committee Advisory Committee
Christopher Osburn Dimitris Agyropoulos Advisory Committee Advisory Committee
DEDICATION
Para sa aking mga magulang Eriberto De la Cruz at Gaudencia Banaag-De la Cruz salamat sa pamanang edukasyon sa kabila ng kahirapan.
(To my parents Eriberto De la Cruz and Gaudencia Banaag-De la Cruz thank you for the gift of education.)
ii
BIOGRAPHY
Florentino De la Cruz received his Bachelor of Science degree in Chemical
Engineering from the University of the Philippines Los Baños in 2001. In 2002, he worked as
a laboratory analyst at United Laboratories Inc. In 2003, he was employed as an instructor at
Department of Engineering Science at the University of the Philippines Los Baños where he
taught courses such as advance engineering graphics, differential equations and fluid
mechanics. He received his Master of Science degree in Chemical Engineering with minor in
Environmental Science from the same university in 2007. In 2008, he joined Dr. Morton
Barlaz at North Carolina State University and started working on his doctoral degree in
Environmental Engineering. As a PhD student, he has been involved in several solid waste
projects including the development of a model to estimate carbon footprint of a landfill,
hydrogen sulfide attenuation and municipal solid waste decomposition. His dissertation
research is about the chemical characterization and understanding of the chemical changes
during anaerobic decomposition of lignocellulose in anaerobic environment such as landfills.
iii
ACKNOWLEDGEMENTS
I am glad to admit that I was quite inadequate in so many aspects when I started this work because in that way I was compelled to reach out for help and in the process meet quite
a number of wonderful people sharing not only lessons in professional field but more
importantly on how to become a better person. I apologize if for whatever reason, I missed to
mention anyone to whom I am indebted.
To my adviser Dr. Barlaz for the guidance and patience. It has been a good learning
years being under your supervision. I have learned about so many things with every project
that I handled. Thank you for everything.
To my committee (Drs. Knappe, Osburn and Agyropoulos) for your suggestions and for your considerations. Discussions with you have been important input for the improvement of this work.
To my family in the Philippines who have been my source of strength when I felt like giving up.
To Gigi for the support and inspiration.
To my UPLB ES Department family, thank you for believing in me. I am hoping to re- join the department soon.
To David Black for assistance in the lab and for helping students beyond what is called for by your job.
iv
To Yinglong Guan and Michael Mondou for the conscientious work on CuO analysis.
Without your help I would not have been able to finish this work.
To Dr. Gracz of the Department of Structural Biochemistry thank you for trusting in me, for your support and for the inspiration to finish this work. I have learned so much about
NMR spectroscopy in just a short time.
To Dr. Pezslen of Department of Forest Biomaterials for the help in wood identification and for sharing your wood collection for this work. I could finally answer yes whenever you ask me if I graduated already.
To Drs. Dittmar and Niggeman of Max Planck Institute in Germany thank you for our exchanges of ideas and for letting me visit your lab to learn more about CuO analysis.
To Joe Cox College Forest Manager NCSU Department of Forestry and Environmental
Resources for facilitating in sample collection at Schenk Forest.
To Dr. Richard Braham of Department of Forestry as well as Dr. Alexander Krings and
Dr. Mark Weathington (life is too short for boring plants) of JC Raulston Arboretum for the help in the collection and identification of different plant tissues
To Jason Patskotsky for help with Matlab programming.
To my Filipino friends at NC State Jason So, Remil, Tony, Jules Cacho and Dr. De los
Reyes for the company. You guys made living away from family more bearable.
To Saquing Family, thank you for making me feel like a part of your family. Hope to
get together with you soon.
v
To Drs. Osborne and Maity for the advice on statistics.
To NC State Turf People Bill Beardall and April Bauder for providing grass samples.
The golf cart ride was fun!
To Dr. Yelle for generously sharing knowledge about NMR spectroscopy especially
HSQC contour assignments and for letting me visit your lab for research collaboration.
To the Solid Waste Research Group (aka Hot room residents), Jenny Padgett (my first research lab mate), Xiaoming, Jim, Johnsie, Milla, Joe, Wenji and Mei for the friendship.
To the undergrads who have helped me in different projects: Australia (Josh, John);
Plastics Biodegradation (Mark); Wood decomposition (Niel); Sulfide (Analee, Caroline,
Mike, Matt, Chris, Trevon, Rachel, Mark).
To my officemates at Mann 223 and to Environmental lab people thanks for the conversations whenever we had time.
To Birgit Anderson from Textiles department for help within FT-IR spectroscopy.
To Dr. Ralph for answering my questions and his inputs on NMR spectroscopy and plant cell wall dissolution.
To my roommates Brian and Thomas thank you for the listening to my stories. I know you didn’t really understand some of the technical stuff I was telling but still you listen.
To Raleigh Catholic community and Sacred Heart Cathedral Saturday Folk Choir where I met wonderful people and at the same time learn more about music.
vi
To Carolina canoe club for the kayaking instructions. One of the things I do when not working to maintain sanity.
To the Filipino community in the Raleigh area: Bangi, Cariño, Fernandez family thank you for inviting us during holidays when we can’t be with our family. See you again in the next Paquiao match.
vii
TABLE OF CONTENTS
LIST OF TABLES………………………………………………………………………...... v LIST OF FIGURES………………………………………...... vi
1. Lignin: Biomarker of Composition and Decomposition of Lignocellulose ...... 1 1.1 Introduction and General Research Objectives ...... 1 1.2 The Chemistry of Plant Cell Wall ...... 4 1.2.1 Ultrastructure of wood ...... 4 1.2.2 Cellulose ...... 7 1.2.3 Hemicellulose ...... 8 1.2.4 Lignin ...... 8 1.3 Lignin Biosynthesis ...... 9 1.4 Isolation of Lignin ...... 13 1.4.1 Ball Milling Principle ...... 14 1.4.2 Milled Wood Lignin (MWL) ...... 14 1.4.3 Klason Lignin ...... 15 1.5 Analysis of Lignin by Chemical Degradation Techniques ...... 18 1.5.1 Acetyl Bromide Method ...... 18 1.5.2 Thioglycolic Acid Method ...... 19 1.5.3 Kappa Number ...... 20 1.5.4 Roe Number ...... 20 1.5.5 Nitrobenzene Oxidation ...... 20 1.5.6 Metal Oxide Oxidation ...... 21 1.5.7 CuO Oxidation ...... 21 1.6 Lignin Decomposition and Preservation ...... 29 1.6.1 Aerobic Environment ...... 29 1.6.2 Anaerobic Environment ...... 30
2. Adoption of Copper Oxide Oxidation to Improve Quantification of Lignin in Municipal Solid Waste ...... 40 2.1 Abstract ...... 40 2.2 Introduction ...... 42 2.3 Materials and Methods ...... 44 2.3.1 Experimental Design ...... 44 2.3.2 Effect of Ethyl Acetate Extraction ...... 45 2.3.3 Sample Collection and Preparation ...... 48 2.3.4 Analytical Methods ...... 49
viii
2.3.5 Data Analysis ...... 52 2.4 Results and Discussion ...... 52 2.4.1 Klason lignin of different plastics ...... 53 2.4.2 Effect of Ethyl Acetate Extraction ...... 54 2.4.3 Effect of MSW Interferences ...... 56 2.4.4 Effect of Lipophilic Extractives ...... 58 2.4.5 Effect of Ball-milling ...... 60 2.4.6 Relationship between Klason lignin and CuO lignin ...... 61 2.4.7 Application to Excavated Samples from KY Landfill ...... 63 2.5 Summary and Conclusions ...... 68 2.6 Acknowledgements ...... 69 2.7 Supporting Information ...... 71
3. Determination of Sources of Organic Matter in Landfill by Analysis of Copper Oxide Oxidation Products of Lignin ...... 75 3.1 Abstract ...... 75 3.2 Introduction ...... 76 3.3 Materials and Method ...... 78 3.3.1 Experimental Design ...... 78 3.3.2 Sample collection and preparation ...... 78 3.3.3 Analytical Methods ...... 79 3.3.4 Preparation of Decomposed Material ...... 82 3.3.5 Estimate of MSW Composition ...... 83 3.4 Results and Discussion ...... 86 3.4.1 Compositional Variation of Different Taxonomic Classes of Plants and Pulp and Paper Products ...... 86 3.4.2 Estimate of MSW Composition ...... 95 3.4.3 Application to Excavated Samples from Outerloop, KY Landfill ...... 98 3.5 Summary and Conclusions ...... 100 3.6 Acknowledgements ...... 100 3.7 Supporting Information ...... 101
4. Chemical Structure Changes during Anaerobic Decomposition of Lignocellulose under Mesophilic and Thermophilic Conditions ...... 111 4.1 Abstract ...... 111 4.2 Introduction ...... 113 4.3 Materials and Methods ...... 116 4.3.1 Experimental Design ...... 116
ix
4.3.2 Sample collection and preparation ...... 117 4.3.3 Preparation of Anaerobically Decomposed Materials ...... 118 4.3.4 Analytical Methods ...... 119 4.4 Results and Discussion ...... 127 4.4.1 Carbon Loss ...... 127 4.4.2 Structural Carbohydrates ...... 129 4.4.3 Lignin CuO Oxidation Products ...... 133 4.4.4 Lignin Methoxyl ...... 136 4.4.5 Klason and Acid Soluble Lignin ...... 138 4.4.6 Lignin Sub-structures ...... 141 4.5 Summary and Conclusions ...... 152 4.6 Acknowledgements ...... 152 4.7 Supporting Information ...... 154
5. Conclusions ...... 155
6. Recommendations ...... 157
7. References ...... 159
x
LIST OF TABLES
Table 1.1. Proportions of different types of linkages connecting the phenylpropane units of lignin (12) a ...... 11
Table 1.2. Klason lignin of lignocellulosic materials (25) ...... 16
Table 1.3. Suites of lignin phenols liberated during CuO oxidation (35)...... 23
Table 1.4. Reactions of lignin during pulping process (87) ...... 33
Table 2.1. Synthetic waste composition of different MSW components based on the 2008 EPA Waste Characterization (105)...... 46
Table S2.1. Effect of different MSW components on CuO oxidation products of ONP...... 71
Table S2.2. Correlation matrix of individual lignin phenols and Klason lignin (KL)a ...... 73
Table 3.1. Proportions of end members in binary and quaternary synthetic mixtures formulated from simplex lattice to validate the mixture model...... 85
Table S3.1. Compositional variation in CuO phenolic monomers in different lignocellulosic materialsa...... 102
Table S3.2. Decomposed materials included in the model...... 103
Table S3.3. Chemical characteristics of different groups of lignocellulosic materialsa...... 104
Table S3.4. Chemical characteristics of different MSW and pulp and paper productsa...... 108
Table 4.1. Structural characteristics of initial and anaerobically decomposed lignocellulose calculated from volume integration of the 1H– 13C correlation signals in the HSQC spectrum normalized to lignin C9 unit...... 151
Table S4.1. Chemical characteristics of initial materials testeda...... 154
xi
LIST OF FIGURES
Figure 1.1. Structural representation of softwood fiber (10)...... 5
Figure 1.2. Metabolic pathways leading to the biosynthesis of plant cell wall components (12) ...... 10
Figure 1.3. Structure of the monolignol precursors for the biosynthesis of lignin (5) ...... 10
Figure 1.4. Dimer structure of lignin C9 units...... 12
Figure 1.5. C-C and ether bonds linking monomeric units in a proposed structure of lignin from beech [Nims, 1974 as cited by Lin, 1992(7)] ...... 13
Figure 1.6. Proposed mechanism for the CuO oxide oxidation of 4-hydroxymethylguaiacol in alkali (5)...... 24
Figure 1.7. Identification of tissue sources based on CuO oxidation products (35)...... 28
Figure 1.8. General Scheme for the anaerobic decomposition natural biomolecules (67) ..... 31
Figure 1.9. Benzoyl CoA pathway showing the common precursor leading to the anaerobic metabolism of aromatic molecules such as lignin-derived compounds (99)...... 39
Figure 2.1. Klason lignin of plastics and rubber typically found in MSW...... 54
Figure 2.2. Effect of ethyl acetate extraction step on lignin phenol yields from different tissue samples. Error bar represents ±sd, n=3)...... 55
Figure 2.3. Effect of different contaminants in MSW tested for interference with CuO oxidation of ONP at 2× the level in the 2008 US EPA Waste Characterization report (9). Acronyms are defined in the text. Error bar represent ±sd. The number of replicates is found in Table S2.1...... 57
Figure 2.4. Effect of extractives on the CuO oxidation of different classes of lignocellulose. Error bars represent ±sd. (n=3)...... 59
Figure 2.5. Effect of ball-milling on the CuO oxidation of different types of samples of plant origin. (a) HW- Quercus rubra; (b) SW-Pinus taeda (c) Grass- Cynodon dactylon; (d) ONP. Error bars represent ±sd (n=3)...... 61
Figure 2.6. Relationship between KL and Λ8...... 63
xii
Figure 2.7. Compositional variation in the CuO lignin phenols of the excavated MSW samples from KY landfill (n=30 samples). (a) Individual lignin phenols; (b) S/V vs. C/V plot; (c) (Ad/Al)V. Index represent one sample replicate...... 65
Figure 2.8. Relationship between (Cel+H)/KL, (Cel+H)/Λ8 and waste age...... 67
Figure 2.9. Relationship between (Cel+H)/KL and (Cel+H)/Λ8...... 68
Figure S2.1. Scatterplot matrix of individual lignin phenols and Klason lignin (KL)...... 72
Figure S2.2. Relationship between KL (%) and Λ8...... 74
Figure 3.1. Compositional variations of different lignin phenols liberated during CuO oxidation of lignin from different types of samples. SW-softwood; HW- hardwood; LG-leaves and grass; GN-needles; ONP-Old newsprint; OCC-Old corrugated cardboard; OMG-Old magazine; MSW-Municipal solid waste...... 88
Figure 3.2. (Ad/Al)V of different taxonomic groups of plants, lignified pulp and paper products and excavated MSW samples...... 89
Figure 3.3. Variation in KL (mg 100 mg OC-1) of different tissues...... 91
Figure 3.4. S/V as a function of C/V ratio in the tissue samples end member. A-HW; G-SW; a-leaves and grasses; g-needles. G is difficult to see but is congregated in the lower left corner of the figure (S/V and C/V ~ 0)...... 93
Figure 3.5. S/V as a function of C/V ratio in pulp and paper and MSW...... 94
Figure 3.6. Estimate of composition for (a) calibration and (b) validation data set. In part a, individual pure tissue samples are presented as a sample number on the x-axis. All samples were analyzed in duplicates that are presented in sequential order. The y-axis represents the predicted composition of the pure tissue. Therefore, in a perfect model, all data points would be at 1.0. In part b, three samples of each of the four pure tissue types were analyzed in duplicate and are presented sequentially for six samples...... 96
Figure 3.7. Predicted composition for synthetic mixtures defined in Table 3.1...... 98
Figure 3.8. Estimate of composition of excavated samples from KY landfill...... 99
Figure 4.1 In preparation for high resolution NMR spectroscopy, the samples are dissolved in a solvent system of DMSO/NMI and then acetylated in-situ...... 124
xiii
Figure 4.2. Carbon loss during anaerobic decomposition of different lignocellulosic materials under mesophilic and thermophilic conditions. HW-Hardwood; SW-Softwood; ONP-Old newsprint. Error bar represents ±sd...... 128
Figure 4.3. Sugars composition during decomposition under mesophilic and thermophilic conditions normalized to the initial mass of the sample. Ara – Arabinose; Xyl – Xylose; Man – Mannose; Gal – Galactose; Glu – Glucose...... 130
Figure 4.4. Cellulose and hemicelluloses (i.e, holocellulose) composition during anaerobic decomposition under mesophilic and thermophilic conditions. Error bar represents ±sd...... 132
Figure 4.5. Changes in CuO lignin composition during anaerobic decomposition under mesophilic and thermophilic conditions normalized to the initial mass of the sample. Error bar represents ±sd...... 135
Figure 4.6. Changes in lignin methoxyl composition during decomposition under mesophilic and thermophilic conditions normalized to the initial mass of the sample. Error bar represents ±sd...... 137
Figure 4.7. Changes in Klason and acid-soluble lignin composition during decomposition under mesophilic and thermophilic conditions normalized to the initial mass of the sample. Error bar represents ±sd...... 139
Figure 4.8. Linkages and substructures in lignin and lignocellulose. L-lignin polymer...... 146
Figure 4.9 HSQC spectra of HW (Quercus rubra) (a) Aliphatic Region; (b) Aromatic Region...... 147
Figure 4.10. HSQC spectra of SW (Pinus taeda) (a) Aliphatic Region; (b) Aromatic Region...... 148
Figure 4.11. HSQC spectra of Grass (Cynodon dactylon) (a) Aliphatic Region; (b) Aromatic Region...... 149
Figure 4.12. HSQC spectra of ONP (a) Aliphatic Region; (b) Aromatic Region...... 150
xiv
1. Lignin: Biomarker of Composition and Decomposition of Lignocellulose
1.1 Introduction and General Research Objectives
Landfilling of lignocellulosic materials has contrasting implications on global climate change. On one side, the decomposition of lignocellulose produces CH4, a greenhouse gas 25
times the global warming potential of CO2. On the other side, landfilling of lignocellosic
materials results in carbon storage as not all carbon in lignocellulosic materials decomposes.
It is estimated that more than 50% of the 149 million metric tons of municipal solid waste
(MSW) landfilled in the U.S. annually are derived from lignocellulosic materials (e.g. food
and yard waste, wood, and pulp and paper products). As lignin is approximately 15% of
residential solid waste, understanding its behavior during anaerobic decomposition is
important for a complete description of carbon decomposition and storage in landfills. The
overall objective of this dissertation was to use lignin in characterizing the decomposition of
lignocellulose in anaerobic environment.
Lignin can be defined as an amorphous, polyphenolic material that is generated from
the oxidative coupling of three phenylpropenoid precursors: coniferyl, sinapyl and p-
coumaryl alcohols (5-7). There are three primary motivations in the study lignin. First, lignin
monomers can be used as biomarkers to make inference on the sources of lignocellulose, a
major component of organic matter both in natural and engineered environment. Lignin in
different taxonomic groups of plants is chemically different and these differences can be used
to characterize the sources of organic matter. As applied to a landfill, molecular markers of
lignin monomers can be used to obtain information on waste composition.
1
Second, lignin monomers are an important biomarker for decomposition (i.e. acid to aldehyde ratio (Ad/Al)V of lignin phenols from CuO oxidation products). Lignin remnants from the decomposition of plant tissues have been an important tool in reconstructing the changes vegetation of the past and the environmental forces such as erosion, warming, desertification that has driven these changes. Along with other metrics, lignin has been a valuable tool in studying the past environmental conditions.
Third, because of its recalcitrance, lignin is an important part of carbon stock in the environment and the reactivity of lignin affects carbon turnover in the environment. In addition, lignin limits the bioavailability of carbohydrates in lignocellulose that contributes to its preservation. In fact, it is estimated that landfilling of food and yard waste accounts for
1% the total carbon stock due to land use, land use change and forestry in 2008 (8). This value does not include the carbon storage associated with landfilling of wood and paper which account for about 30% of the discarded waste in 2007 (9).
Chapter 2 tackles the analytical issue with lignin measurement in municipal solid waste (MSW). While cellulose and hemicellulose are accurately quantified, the most common method to measure lignin is subject to interferences when applied to MSW. Klason lignin is measured as the volatile fraction after acid hydrolysis with 72% H2SO4. In fresh tissues, Klason lignin is widely accepted as the most precise measure of the total lignin.
However, some synthetic materials that are typical components of MSW such as plastics, rubber and textiles are being measured as lignin resulting in artificially high measurements.
2
In Chapter 3, the use of CuO oxidation products to characterize the sources of lignin was evaluated. The major components of the organic fraction of MSW are lignocellulosic materials. These materials are composed of three basic biopolymers: cellulose, hemicellulose and lignin. The chemical difference in lignin was used to qualitatively and quantitatively estimate the sources of organic matter according to the following tissue types: woody angiosperm (HW-hardwood), non-woody angiosperm (leaves and grasses), woody gymnosperm (SW-softwood), and non-woody gymnosperm (needles). A total of 93 species consisting of HW, SW, leaves, grasses, needles and pulp and paper products both fresh and decomposed materials were analyzed to formulate a mixing model.
Chapter 4 attempted to answer the question – does lignin degrade in anaerobic environment? The use of lignin as a parameter to monitor decomposition in landfills relies on the premise that lignin is recalcitrant to anaerobic decomposition. In this chapter, laboratory experiments were conducted to study the decomposition of lignin and its apparent effect on the extent of methane conversion from cellulose and hemicellulose within temperature ranges that are representative of landfill conditions. Evidence of decomposition was evaluated using both chemical degradation and spectroscopic methods. To our knowledge this is the first application two-dimensional high resolution nuclear magnetic resonance spectroscopy
(NMR) to look at the transformations of lignocellulose components in its native form.
In Chapters 5 and 6, the role and importance of lignin in characterizing the decomposition behavior of lignocellulose in landfills are summarized along with some recommendations for future work. The remainder of chapter 1 provides some background information as well review of literature on lignin sources, biosynthesis and decomposition.
3
1.2 The Chemistry of Plant Cell Wall
1.2.1 Ultrastructure of wood
The plant cell wall is basically a naturally-occurring composite of lignin, hemicellulose
and cellulose microfibrils. The ultrastructure of wood is presented in Figure 1.1 showing the
different layers which include middle lamella (ML), primary wall (P), secondary wall (S) and
the hydrophobic warty layer (W). Each layer differs in terms of its microstructure and
chemical composition. The middle lamella bonds the wood fiber with the adjacent fiber and is composed primarily of lignin. The primary wall which is composed primarily of pectin is also heavily lignified and a more or less random network of microfibrils.
4
Figure 1.1. Structural representation of softwood fiber (10).
The thickest layer is the secondary or S layer. The secondary layer is subdivided into
three layers (S1, S2 and S3) characterized by the changes in the angle at which fiber would in
a helical pattern around the cell wall. Cellulose and hemicellulose constitute S1. S2 is the
thickest layer and is made up of cellulose fibers (microfibrils). About 80-95% of cell wall material can be found in the S2 layer which is the reason why 70% of the total lignin content of the cell wall can be attributed to the secondary wall (11). Both S1 and S3 are on the order
of four to six clusters of cellulose microfibrils while the S2 layer can vary from 40-60 in thin- walled early wood cells and up to about 150 or more in latewood cells (10). It has been shown that microfibril angle of the secondary layer affects both the strength and shrinkage
5
behavior of the wood as well as the pulp and paper products properties including paper
strength, stretch modulus of elasticity and stiffness and moisture-induced expansion. While
the lignin concentration in the secondary layer is lower than the other cell wall layers, a
majority of the lignin in wood was derived from the secondary layer because the secondary layer is much thicker than the other layers. Finally, the warty layer is the thin membrane in the inner surface of the cell wall. A new development in understanding the supramolecular structure of wood has been recently reported (2) proposing that lignin monomers
(monolignols) condensation occurs mostly between adjacent growing ends of the oligolignols within tubular aggregates.
There are several types of cells that are present in both softwood and hardwood performing different functions in the plant. Cells such as tracheid fibers in softwood and hardwood provide strength and rigidity (mechanical functions) in a tree. Both longitudinal and radial conduction of fluid and nutrients are accomplished by different types of cells. Ray cells function in the conduction of fluid and nutrients along the radial direction while longitudinal cells such as fiber tracheid transport fluid and nutrients along the trunk. A special type of cells found only in hardwood called vessels is also used for transport.
Secretion of new cells is accomplished by the epithelial cells. Radial growth of the tree occurs as a result of the production of new cells within the cambial layer which is a few cell layers below the bark. As a tree grows, some cells remain alive while some die. The part of the trunk that is alive and actively dividing is called the sapwood while the center of the trunk where the cell has already died is called heartwood. Heartwood is more resistant to decomposition and insect attack, perhaps a result of the accumulation of lignin and
6
extractives. In terms of appearance, heartwood has a darker color than sapwood. Heartwood
imparts structural strength and rigidity of a tree.
Reaction wood, as the name implies, is produced in response to the environmental
stress on wood such as gravity and wind during growth. Hardwood and softwood exhibit
different reaction wood. One manifestation of reaction wood is the formation of eccentric
growth rings. Compression wood is produced in softwood while tension wood is produced by
hardwood. Compression wood is characterized by a thicker S1 layer and higher degree of
lignification compared to normal wood making it less desirable for paper making. On the
other hand, tension wood is produced in hardwoods. Tension wood is characterized by higher
cellulose and lower lignin content as well as by the formation of an extra layer of crystalline
cellulose known as the G layer (10).
1.2.2 Cellulose
Cellulose is a homopolymer of glucose and is one of the most abundant biopolymers
on earth (12). In wood, cellulose has an average degree of polymerization (DP) of about
3500. Cellulose is linear and unbranched, enabling cellulose chains to be packed in an ordered crystalline manner. In between the crystalline cellulose region is the amorphous region where cellulose is not very ordered. Native cellulose exists in two polymorphs, cellulose I and cellulose II which differs according to their chain orientation. The chain orientation of cellulose I is parallel while cellulose II is anti-parallel. Transformation from cellulose I to cellulose II is achieved by mercerization using 17-20% NaOH. The chain orientation of cellulose can be confirmed by selectively staining the end groups. Since the
7
cellulose has distinct end groups, the reducing end can be selectively stained to confirm the
chain orientation (e.g. to confirm that the chain orientation of native cellulose I is parallel). It has been shown that the crystallinity of cellulose affects reactivity during anaerobic decomposition (13).
1.2.3 Hemicellulose
Hemicellulose is a heteropolymer of five different sugars glucose, mannose, galactose, xylose and arabinose. The ratio of different sugars depends on the type of tissue. For example, xylan is dominant in hardwood while glucomannan is dominant in softwood.
Unlike cellulose, hemicelluloses are branched and could be acetylated.
1.2.4 Lignin
Lignin is one of the most abundant organic materials on earth accounting for about
3×1011 metric tons (14). Lignin can be defined as an amorphous, polyphenolic material arising from an enzyme-mediated dehydrogenative polymerization of three phenylpropenoid monomers coniferyl, sinapyl and p-coumaryl alcohols (5-7). The study of lignocellulose decomposition requires understanding of lignin and its association with cellulose and hemicellulose. Lignification is associated with the growth of plants as well the environmental stresses that it undergoes (10).
Lignin exists in most vascular plant at varying proportions and monomer composition.
Lignin content varies from 24 - 33% in softwood, 19 -28% in hardwood and 11 - 27 % in
8
non-woody tissues which would include leaves, grass and even food waste. Lignin also varies with the location within a tree. A juvenile wood has a higher lignin content compared with latewood (10).
1.3 Lignin Biosynthesis
Lignin formation is composed of two distinct metabolic pathways as illustrated in
Figure 1.2. The shikimic acid is the primary pathway for the formation of aromatic molecules. In the shikimate pathway, a series of biochemical reactions converts glucose to tyrosine and phenylalanine. The secondary pathway (the cinnamate pathway) involves the conversion of these phenolic intermediates to monolignol precursors p-hydroxyphenyl, coniferyl, and syringyl alcohols (Figure 1.3) (12).
9
Figure 1.2. Metabolic pathways leading to the biosynthesis of plant cell wall components (12)
Figure 1.3. Structure of the monolignol precursors for the biosynthesis of lignin (5)
10
Monolignols are not abundant in their free form. The formation of monolignol glucosides allows for more favorable properties such as solubility for transport of monolignols into the cell wall where lignin is synthesized. Moreover, by formation of glucosides the toxicity of monolignols is also reduced.
The monolignols then undergo polymerization to form lignin. It has been widely accepted that lignin is produced from the oxidative coupling of monolignols via the so called
“combinatorial” coupling of phenoxy radicals according to the original work of Freudenberg
(6).
The monomeric units in lignin are linked together by C-C and ether bonds. In both hardwood and softwood, β-O-4 linkages account for the majority of the C9 interunitary linkages as shown in Table 1.1 and Figure 1.4 (12). Figure 1.5 is a proposed structure of lignin from Beech (7) showing the various types of linkages.
Table 1.1. Proportions of different types of linkages connecting the phenylpropane units of lignin (12) a
Percent of Total linkages Linkage Type Dimer Structure Softwood Hardwood β-O-4 Arylglycerol-β-aryl ether 50 60 α-O-4 Noncyclic benzyl aryl ether 2-8 7 β-5 Phenylcoumaran 9-12 6 5-5 Biphenyl 10-11 5 4-O-5 Diaryl ether 4 7 β-1 1, 2-Diaryl propane 7 7 β-β Linked through side chains 2 3 a. The dimeric linkages is are depicted in Figure 1.4
11
OH
OH OH OH
O OH
HO O O
HO OH α-O-4 β-O-4
OH β-5 OH OH OH HO HO
OH O OH OH OH OH 5-5 4-O-5 β-1 OH OH
OH OH β-β
Figure 1.4. Dimer structure of lignin C9 units.
12
Figure 1.5. C-C and ether bonds linking monomeric units in a proposed structure of lignin from beech [Nims, 1974 as cited by Lin, 1992(7)]
1.4 Isolation of Lignin
Perhaps due to the complexity of lignin, the major challenge in the study of lignin is the selection of an appropriate analytical method. Approaches to study lignin include
isolation and dissolution with a suitable solvent followed by further structural analysis such
as chemical degradation techniques. The fundamental question then is whether the isolated
lignin is representative of the lignin in wood often times referred to as protolignin.
13
Analytical methods for lignin are application- specific. For example, the pulp and paper
industry uses KMnO4 instead of Klason lignin because lignin content of pulp is highly modified as a result of pulping process.
1.4.1 Ball Milling Principle
Most lignin isolation and preparation requires particle size reduction to render it amenable to dissolution with dioxane such as in milled wood lignin (MWL) and access by enzymes such as in cellulolytic enzyme lignin (CEL) preparations. Thus, ball milling of plant cell wall has been an important part of lignin isolation method. Bjorkman(15) recommended ball-milling in a non-swelling liquid such as toluene which is further supported by Ikeda et al. (16). Toluene is believed to act as cooling agent and as a radical scavenger as well to prevent the condensation reactions during the ball-milling. Several studies have evaluated the extent of chemical modifications brought about by ball milling for lignin isolation (17).
1.4.2 Milled Wood Lignin (MWL)
Milled wood lignin (MWL) is considered as the most representative of the native lignin isolated from plant tissues (5, 18). MWL retains the chemical bonds that are found in native lignin. Milled wood lignin is isolated from ball milled plant tissue by extraction with 9:1 dioxane-water (15). The extracted lignin is purified by a series of precipitation processes.
About 30-50% of Klason lignin was the highest recorded yield of MWL which contains from a few percent to as little as 0.05% carbohydrate contaminations (19). Modifications of the
MWL lignin have been published with the goal of increasing lignin yield and reducing
14
carbohydrate contamination (20). One major concern over the use MWL is the possibility
that some bonds are actually being formed during the ball-milling, raising doubts whether the
bonds observed MWL are artifacts of ball-milling (21). For example, aqueous ball milling
resulted in an increased hydroxyl substituent (18).
1.4.3 Klason Lignin
Klason lignin involves removal of lipophilic extractives followed by a two-stage acid
digestion to hydrolyze the carbohydrates. The residue is then combusted and the volatile
fraction is reported as lignin. Standard method for Klason lignin is the Official test method
T222 om-83 of the Technical Association of Pulp and Paper Industry (TAPPI). There are
several variations to Klason lignin. Whiting (22) and Effland (23) modified the standard procedure to accommodate a smaller sample size. Yoshihara et al. (24) shortened the analysis by autoclaving at 121 °C for 30 min. Table 1.2 shows the reported acid-insoluble Klason lignin from different plant tissues as summarized in (25).
15
Table 1.2. Klason lignin of lignocellulosic materials (25)
Material Lignin (%) Softwood Norway spruce normal wood 28.6 Norway spruce compression wood 38.8 Larch sapwood 26.0 Larch heartwood 28.6 Pine early wood 28.8 Pine latewood 27.4 Hardwood White birch normal wood 22.0 White birch tension wood 16.1 Birch Fibers 12.6 Birch ray cells 26.7 Non-wood fiber Bagasse 19.6 Bamboo 22.2 Wheat straw 17.0 Kenaf 10.9 Sorghum 7.9 Pulp Pine Kraft 4.8 Birch Kraft 5.0 Spruce Kraft 2.8 Birch acid sulfite 3.2 Spruce bisulfate (Mg base) 9.9 Birch bisulfate (Mg base) 4.0 Scots pine Kraft (>50 mesh) 5.2 Scots pine Kraft (<300 mesh) 23.4 Norway Spruce TMP (>150 mesh) 23.5 Norway Spruce TMP (<150 mesh) 35.8
As shown in Table 1.2, variation exists according to source (wood vs. annual plants), anatomical features (normal vs. reaction wood, early wood vs. late wood and sapwood vs. heartwood). In pulps, differences in Klason lignin are observed among different pulping
16
process (acid sufite, bisulfite, Kraft) as well among different particle size (fines compared to fiber fractions) (25).
In 1957, Kratzl and Billek (26) identified a fundamental consideration that restricts the applicability of the Klason lignin method. Klason lignin is highly modified and it is likely that reactions (i.e. pulping and bleaching) result in the loss of lignin yet still capable of yielding residue when treated with 72% H2SO4 in the Klason procedure.
Brauns et al.(27) discussed the factors that affect the Klason lignin analysis. They found that the yield and composition of Klason lignin is affected by the concentration of
H2SO4, hydrolysis time and temperature. At H2SO4 concentrations of less than 65%, hydrolysis of carbohydrates is not complete in a reasonable length of time. At H2SO4 concentration greater than 80%, formation of acid insoluble products as a result of humification occurs (19). Hydrolysis time and temperature also affects Klason lignin.
Incomplete carbohydrate hydrolysis occurs when both hydrolysis time and temperature are too low. On the other hand, time and temperature that are too high favors the co-condensing of carbohydrates with lignin.
Brauns et al. (27) also analyzed the effect of extraction step. They showed that pre- extraction procedure with hot water and ethanol can reduce the amount of lignin depending on the sample type. They showed that the hot water extraction may be omitted in case of chemical pulps and ethanol-benzene extraction may be disregarded for pulps produced under alkaline conditions.
17
In some samples, significant solubilization of lignin was also recorded and thus there are times when the Klason lignin must be corrected for acid-soluble lignin (25). Acid soluble lignin accounts for about 0.2-0.5% SW and Kraft pulps, 3-5% in HW and non-wood fibers
(5) while as much as half or more of the total lignin in semi-bleached pulps. Study showed that syringyl linkages are readily hydrolyzed in 72% H2SO4 which contributes to the
solubilization of some lignin in angiosperm tissues (28).
1.5 Analysis of Lignin by Chemical Degradation Techniques
Chemical degradation methods involve characterization of the lignin based on the
degradation products. Chemical degradation methods have also been used for structure
elucidation. For example, CuO and nitrobenzene oxidation products have been used to
characterize the linkages present in lignin (5). Chemical oxidation of lignin can result in: (1)
the formation of aromatic aldehydes, ketones and carboxylic acids; (2) destruction of the
aromatic structures; or (3) can be limited to specific groups in the polymer (5).
1.5.1 Acetyl Bromide Method
Indirect determination of lignin involves dissolution of lignin in an appropriate solvent
and analyzing the resulting solution spectrophotometrically. The two most common indirect
methods are acetyl bromide and thioglycolic acid method. Acetyl bromide method (29, 30)
involves digestion of wood samples in a reaction bottle containing acetyl bromide in acetic
and perchloric acid at 70°C for 20-120 minutes. The digestate is then diluted and the
18
absorbance is measured at 280 nm. This method gained wide acceptance and was shown to be comparable with Klason lignin for both fresh tissues and pulps (30).
1.5.2 Thioglycolic Acid Method
The procedure for the thioglycolic acid method is similar to acetyl bromide. The sample is digested in solution of 2 M HCl and thioglycolic acid for 4h at 95°C. The lignothioglycolate is extracted with NaOH. Lignin quantification can be done by constructing a calibration curve using DHP-thioglycolate.
It was noted that the spectral absorption of lignin may have been altered to such a degree as to make measurement of absorbance at 280 nm meaningless (25), especially when it has undergone extensive chemical reaction such as for example, oxidation during bleaching. Thus, spectrophotometric methods may not be a viable analytical approach in some cases (25). The main problem with the spectrophotometric method is that the absorptivities are very specific to the type of samples being analyzed (25, 31). Correction to the percent lignin based on absorbance must be done for different types of samples. For example, lignin must be corrected by subtracting 1.7 for Kraft pulps and 1.38 for sulfite pulps
(29). In addition, hydroxymethylfurfural which is among the products of acid hydrolysis of sugars exhibits high absorbance at 280nm and interferes with lignin detection.
19
1.5.3 Kappa Number
Kappa number is based on the modification by of Tasman and Berzine (32). The analysis is based on the KMnO4 oxidant consumption and is applicable to semi-chemical, unbleached pulp and semi-bleached pulp of less than 60% and pulp yields of up to 70%. The major problem with the method is that apparent oxidant consumption can occur without accompanying loss of lignin which can occur during co-condensation of carbohydrates and from the reaction of hexenuronic acid which is produced during pulping.
1.5.4 Roe Number
Roe or chlorine number involves measurement of uptake of Cl2 of a known amount of sample pulp (TAPPI standard method T202 ts-66). This is done by reacting the pulp with acidified (to promote production of Cl2) sodium or calcium hypochlorite under rigidly controlled conditions and the Cl2 consumption is measured titrimetrically. Methods based on oxidant demand such as Kappa and Roe number are used exclusively for unbleached pulps and on the assumption that lignin consumes oxidants faster than carbohydrates (7).
1.5.5 Nitrobenzene Oxidation
Nitrobenzene lignin oxidation involves 2e- transfer converting 20-75% of lignin to aromatic fragments. Briefly, the method involves digestion of lignin-containing materials in nitrobenzene and 2M NaOH at 170-180 °C for 2-5h. Quantification of lignin fragments is commonly done by using HPLC or GC/MS. One problem associated with nitrobenzene oxidation is the formation of secondary oxidation products such as azoxybenzene,
20
azobenzene and p-hydroxyazobenzene (33). Nitrobenzene oxidation is one of the most well- understood lignin degradation methods (5).
1.5.6 Metal Oxide Oxidation
Metal oxide oxidation involves oxidative degradation of lignin without the destruction of the aromatic nuclei. The most common reagents are cupric, mercuric, silver and cobalt oxides. Silver oxide has the highest oxidation potential therefore gives higher phenolic acid yields. The mechanism of metal oxide oxidation is not as well understood as nitrobenzene oxidation. However it is proposed that the reaction involves one electron transfer leading to the formation of phenoxy radical which in turn forms resonance-stabilized quinone methide
(QM) intermediate (5).
1.5.7 CuO Oxidation
CuO is preferred over other metal oxide reagents for the chemical degradation of lignin
because it is reactive enough to break down lignin bonds but mild enough to not further
oxidize the phenolic oxidation products. Similar to nitrobenzene, CuO oxidation of lignin
yields a suite of phenolic aldehydes, ketones and carboxylic acids (Table 1.3). Pepper et al.,
(34) noted that CuO is more preferable over nitrobenzene for lignin structural study because of its milder and more selective degrading action. For example, acetovanillone and
acetosyringone are stable under CuO oxidation conditions but are oxidized in nitrobenzene
oxidation conditions. In addition, isolation and identification of oxidation products are much
21
simpler due to the absence of interfering organic by-products generated from nitrobenzene itself.
22
Table 1.3. Suites of lignin phenols liberated during CuO oxidation (35).
Lignin Phenol Structure Possible sources O p-hydroxybenzaldehyde 1
OH H3C O Gymnosperm wood Non-woody gymnosperms p-hydroxyacetophenone 2 Angiosperm wood 4’-Hydroxyacetophenone Non-woody angiosperms OH HO O
3 p-hydroxybenzoic acid
OH O Vanillin 4 4-Hydroxy-3-methoxybenzaldehyde OCH 3 OH Gymnosperm wood H3C O Non-woody gymnosperms Acetovanillone 5 Angiosperm wood 4’-Hydroxy-3’-methoxyacetophenone OCH 3 Non-woody angiosperms OH HO O Vanillic Acid 6 4-Hydroxy-3-methoxybenzoic acid OCH 3 OH O Syringaldehyde 7 4-Hydroxy-3,5-dimethoxybenzaldehyde H3CO OCH 3 OH H3C O Acetosyringone Angiosperm wood 8 4’-Hydroxy-3’,5’-dimethoxyacetophenone Non-woody angiosperms H3CO OCH 3 OH HO O Syringic Acid 9 4-Hydroxy-3,5-dimethoxy-benzoic acid H3CO OCH 3 OH HO O
p-coumaric acid 10 trans-4-Hydroxycinnamic acid Non-woody gymnosperms OH HO O Non-woody angiosperms
Ferulic acid 11 trans-4-Hydroxy-3-methoxycinnamic acid
H3CO OH
23
The use of CuO oxidation for lignin degradation was first reported by Pearl (36). As
with other metal oxides, CuO oxidation involves 1e- transfer. According to Kratzl et al., (37)
it seems likely that the oxidation of lignin by CuO involves the formation of resonance
stabilized free phenoxy radicals involving the formation of quinone methide intermediate.
Oxidative coupling is a characteristic reaction involving 1e- transfer. However, it is
interesting to note that that no significant oxidative coupling products during CuO oxidation
of lignin was reported (5).
- - HO HO HO HO O
-e-
OCH 3 OCH 3 OCH 3 O O OH QM
Figure 1.6. Proposed mechanism for the CuO oxide oxidation of 4-hydroxymethylguaiacol in alkali (5).
It is likely that the rate of CuO oxidation of lignin is a competitive reaction between
oxidative coupling sequence and two consecutive one electron transfer reactions (38). The
following steps outline the reactions: (1) formation of phenoxy radicals; (2) successive one electron transfers; (3) radical coupling. Since step 1 is much slower than steps 2 and 3, the
24
rate limiting step in the overall reaction is the formation of phenoxy radicals (5). Thus, under
excess CuO concentration, the radical coupling reaction is unlikely to occur. This hypothesis
explains the observation by (36) where highest vanillin yield when the ratio of lignin building
unit to CuO was raised to 13.5 and temperature was increased to 170-190 °C. Low vanillin
was observed when oxidation was carried out at reduced CuO: lignin ratio and low temperature. Under this condition it is likely that coupling reactions occurred to some degree
(26).
1.5.7.1 CuO oxidation products as biomarker of decomposition
While lignin lacks the ordered and repeating units found in other biopolymers such as cellulose and hemicellulose, the information on the monomeric units can be used as a valuable tool in making inferences on lignin sources. The alkaline CuO oxidation products of different representative vascular plants have been studied extensively (35, 39-43). The
signature phenolic dimers and monomers CuO oxidation products have been used to
characterize the sources and flux of vascular plants in ocean and river sediments (44-47).
This methodology has been has been substantiated with δ13C isotope studies (48).
During alkaline CuO oxidation the ether linkages in lignin are preferentially
hydrolyzed (43). Not only does the analysis of CuO oxidation products gives an idea of the
tissue sources of lignin, but it also provides insight on the relative abundance of the different
types of bonds present in lignin molecules. Phenolic monomers obtained from CuO oxidation
has been initially applied to identify the plant tissue sources of lignin from a given sediment
sample (39, 49-54).
25
Aside from phenolic monomers, the analysis of phenolic dimers from the CuO
oxidation can also provide a means to characterize lignin sources. Goni et al.(43) identified a
total of thirty lignin derived phenolic dimers and fourteen additional monomers from
different plant tissue sources. These dimers and monomers can be broadly categorized
according to the parent monolignol and degree of oxidations.
In gymnosperm wood, the CuO dimers obtained are characterized by the presence of
C-C linkages. Gymnosperm wood has the highest yield of dimer of about 3mg/100 mg
organic carbon (OC) and a relatively high monomer yield (16 mg/100 mg OC). The majority
of the dimer recovered was 55’VV dimer (vanillyl phenols linked at 5-5’ position) which is derived from guaiacyl units in lignin. The formation of 55’VV can be attributed to the
availability of C5 position coniferyl monolignols to undergo oxidative coupling. Meanwhile,
about 92% of the monomer yields are simple vanillyl phenols.
Less than 50% lignin was recovered from non-woody gymnosperm tissues but the
same trend was observed as with their wood counterpart. It is interesting to note the
distinctively high dehydrovanillinvanillic acid dimer which accounts for about 30% of the
total 55’VV yield compared with less than 18% for other tissue types. There were also high
yields of cinnamyl phenols in needles and barks.
In angiosperm woods, it was observed that the yields of dimers are very low. High
monomer yield of as much as 25mg/100mg OC was recorded. Dimers recovered from the
CuO oxidation of angiosperm wood is characterized by low abundance of 55’vv. In all
angiosperm tissues about 40-80% of the monomers are syringyl phenols.
26
1.5.7.2 CuO lignin parameters
Several studies have shown that the action of microorganisms can result to the modification of lignin structure resulting in the changes in the CuO oxidation products.
Evidence has been shown that elevated dimer/monomer ratio and carboxyvanillyl/vanillyl
monomer ratio are indicative of pronounced fungal and bacterial degradation of lignin (39,
40). These differences in the yields of phenolic monomers and dimers are important biomarkers of the source of the vascular plant in the bulk organic matter. Figure 1.7
illustrates the utility of lignin monomers as biomarker for making inferences about the
sources of lignin. Several parameters have been developed as: (1) indicator of sources of
organic matter; (2) indicator of lignin decomposition. Studies showed that there are
remarkable differences in the type of lignin found in woody tissues, leaves and grasses (35,
43, 52, 55). Syringyl (S) phenols are found primarily in angiosperm tissues while vanillyl (V) phenols are found both in angiosperms and gymnosperm tissues. Cinnamyl (C) phenols are dominant in non-woody tissues (Table 1.3). Thus, S/V ratio can be used to discriminate between angiosperm and gymnosperm while C/V can be used to discriminate between woody and non-woody tissue as shown in Figure 1.7 (35). While there are significant differences in
the type lignin present in different of organic matter sources, lignin regardless of source is
composed of basic units (monomers) of varying proportions which can be used to
characterize tissue sources (35). The sum of S, V and C is an indicator of the contribution of
plant tissue in the bulk organic matter in soil and sediments.
27
Figure 1.7. Identification of tissue sources based on CuO oxidation products (35).
Lignin parameters have also been developed as indicator of the chemical changes in lignin. Since S and C are relatively more labile compared to V phenols, the changes in S/V,
C/V ratio can also be used as indicator of degradation of S and C lignin monomer units (39,
56, 57). However, C/V and S/V ratios are seldom used as indicator of decomposition because
these parameters vary also with tissue sources as shown in Figure 1.7.
28
The Ad/Al ratio increase because of the cleavage of Cα-Cβ of phenylpropanoid unit of
lignin during decomposition (40, 56-59). It is estimated that the for a fresh angiosperm wood
the range of (Ad/Al)V is 0.1-0.2 while wider range of 0.2-1.6 for non woody tissues (60).
1.6 Lignin Decomposition and Preservation
1.6.1 Aerobic Environment
The most common and well-studied group of organisms that can degrade lignin are fungi. Fungi are capable of producing enzymes known as “ligninases” (61) that can degrade lignin rendering the cellulose fiber and hemicellulose component of the wood free for fungal attack. Wood-degrading fungi are usually aerobic and are classified according to their mode of attack on the wood [decay, soft rot, and brown rot, stain fungi and molds (10)].
In aerobic environment, both bacteria and fungi are capable of degrading lignin resulting in significant mass loss and extensive chemical modification of lignin component
(39, 62). Aerobic catabolic pathways are usually initiated by mono or dioxygenases which requires molecular oxygen as the terminal electron acceptor in an oxidative ring cleavage
(18, 62).
Bacterial decomposition of wood is not as pronounced as that of the fungi. However, several studies have been reported that tried to understand the role of bacteria in natural wood degradation. Bacteria particularly of the group Pseudomonas and Actinomycetes are the most commonly reported that can degrade the β-1 and β-O-4 lignin-related dimeric compounds in both aerobic and anaerobic systems (10, 61, 63).
29
Greaves (64) classified bacteria according to their role in wood degradation as follows:
(1) Those that affect permeability but cause no strength loss; (2) Those that attack wood
structure; (3) Those that work synergistically to break-down wood; (4) Passive colonizers that may be antagonist to other bacterial populations.
Bacteria can inhabit extreme conditions such as that in chemically-treated wood.
Bacteria therefore are the key to wood degradation. It was hypothesized that bacteria have already colonize the wood even before fungi do making the conditions more favorable for succeeding populations (65).
1.6.2 Anaerobic Environment
The general scheme for the decomposition of lignocellulose in anaerobic environments is illustrated in Figure 1.8 where complex biopolymers are hydrolyzed to oligomers and monomers, which are then fermented to produce short chain fatty acids. These acid intermediates are then utilized via a synthrophic methanogenic pathway and then finally converted to gaseous end products methane and carbon dioxide. Anaerobic metabolism
- 2- involves non-oxygen electron acceptors such as NO3 and SO4 . In methanogenesis, no
external electron acceptor is required as part of the parent molecule is oxidized and part
reduced (66).
30
Figure 1.8. General Scheme for the anaerobic decomposition natural biomolecules (67)
The anaerobic metabolism of cellulose and hemicellulose in both mesophilic and thermophilic environments is well-documented (68-72). The evidence of lignin degradation on the other hand is fragmentary. Studies on different lignin-derived monomers, oligomers and lignin isolates have provided the foundation of our understanding of the anaerobic decomposition behavior of lignin and lignin-derived compounds. Lignin-derived phenolic monomers have been completely mineralized anaerobically via nitrate reduction (73-75), methanogenesis (76-80), as well as by photosynthetic bacteria (81, 82). Several studies have
31
demonstrated the methanogenic fermentation of benzoate by anaerobic consortia (62, 73, 76,
80, 81, 83, 84). In addition, lignin monomer substituent, -OCH3, can serve as a C1 substrate
for acetate-producing acetogenic bacteria (85).
As demonstrated by Owens (13), the presence of a high percentage of lignin tends to
inhibit decomposition. Lignin acts as a matrix that bond cell walls together making cellulose
and hemicellulose nonbioavailable. The nature of these bonds in lignin is important in
studying action of bacteria on lignin and its ability to cleave a particular type of bond. In
1992, Cespedes et al. (63) isolated and characterized a microbial consortium from a decaying
wood. The bacterial isolates were enriched in a β-O-4, lignin related dimeric compound under anaerobic conditions. They found out that these cultures prefer to utilize β-O-4 related dimeric compounds.
The biodegradation of Kraft lignin was reported by Raj et al. in 2007 (86). The
objective of their study was to isolate and characterize Kraft lignin-degrading bacterium from sludge of pulp and paper mill. Kraft Lignin (KL) is a waste by-product from the alkaline sulfide treatment of lignocellulose in the pulp and paper industry and is the main contributor to the color and toxicity of the plant effluent. Bacteria were isolated from samples of sludge from a pulp and paper mill effluent. The bacterial isolates were characterized using 16S
rDNA sequencing and biochemical identification. They also conducted decolorization and
lignin degradation studies. Results showed that the bacteria isolated are 99% homology with
A. aneurinilyticus DSM-5562T (Accession No AB112724). Lignin decomposition studies
showed that the major by-products are guaiacol, acetoguaiacone, fallic acid and ferulic acid.
Degradation by the isolated bacteria resulted in 58% color reduction and 43% reduction of
32
lignin. They concluded that the bacterium can oxidize of the sinapylic and coniferylic units
of lignin. It should be noted however, that Kraft lignin has undergone substantial chemical
modification/depolymerization during pulping process. As shown in Table 1.4, a number of
reactions occur during pulping process chemically modifying lignin.
Table 1.4. Reactions of lignin during pulping process (87)
Kraft Process Acid Sulfite process 1. Cleavage of the α-aryl ether bonds 1.Introduction of sulfonic acid groups into the α-positions on the side chains 2. Cleavage of phenolic β-Aryl ether bonds 2. Opening of the pinoresinol structures with extensive depolymerization 3. Limited demethylation of methoxyl 3. Aryl-alkyl ether cleavages groups forming catechol structures 4. Shortening of some side chains 4. Various condensation reactions particularly at the α-positions of the side chains 5. Various ill-defined condensation 5. Introduction of quinoid structures reactions 6. Introduction of the quinoid structures
In-vitro anaerobic degradation of lignocellulosic wastes by rumen microorganisms
under anaerobic conditions was reported (88). The study examined lignin degradation in waste straw by Atomic Force Microscopy (AFM). Surface topography of de-waxed sample waste straw after 5-, 9- and 13-day anaerobic incubation period was investigated.
Comparison of the surface topographies in different treatments showed apparent surface attack by rumen microorganisms on the rice straw. Based on the morphological features, the
33
authors concluded that the mode of attack of the rumen microorganisms on the waste straw is
by tunneling where there has been cellulose that was exposed as a result of degradation.
Perhaps because of its complexity, only fragmentary evidence shows that lignin is
significantly degrading in anaerobic environment. Thus, lignin is generally regarded as
recalcitrant to anaerobic degradation (71, 89). Several studies on the degradation of lignin in anaerobic environment have been published. Laboratory studies using rumen inoculum and grass as substrate showed that there is no significant lignin loss due to anaerobic fungi and syringyl and vanillyl structures are preserved. They also demonstrated that loss of lignin is due primarily to bacterial attack (90-92).
Using anaerobic sludge as inoculum no significant dearomatization and demethoxylation was observed after 60 days of incubation with [14C-labelled lignin]
lignocellulose of slash pine (Pinus elliotti) as substrate (69). About 2% and 4%
mineralization was observed for slash pine and dehydrogenative polymer (DHP) as
substrates, respectively. The authors noted that increasing the temperature can increase the
extent of lignin degradation (69).
Model compounds such as benzoate were used to elucidate the catabolism of aromatic
molecules. Biodegradation of aromatic compounds was pioneered by Buswell (93) who
demonstrated that benzoate, cinnamic acid, phenol and syringic acid were fermented to
methane by a mix sewage sludge digester cultures. Quantitative radiotracer techniques using
(Carboxy 14C) benzoate, (ring 14C, 1) benzoate and (ring 14C, 4) benzoate as substrate
demonstrated that when benzoate was used as sole carbon source for a culture of
34
microorganisms obtained from sewage digester sludge or rumen liquor, (carboxy 14C)
14 14 benzoate behave like exogenous CO2 and not reduced to CH4 (77-79). With (ring C, 1)
14 14 benzoate, CH4 was the main radio-labelled gaseous product while CO2 was the obtained
from (ring 14C, 4) benzoate. They noted the presence of other volatile fatty acids such as
propionate, acetate and formate in the fermentation liquor. In these studies they also found
out that the propionate was labelled in the carboxyl C when (ring 14C, 4) benzoate was used
but not when the (carboxy 14C) benzoate or (ring 14C, 1) benzoate was used (77-79, 84).
Keith as cited by Evans (62) proposed that the benzoate degradation involves stepwise
reduction cyclohexane carboxylate followed by a reductive cleavage of the C, 1-C, 2 bonds
to give heptanoate. This is in turn followed by the production of volatile fatty acids such as
valerate, propionate and acetate, via β-oxidation. This observation was confirmed by Balba
and Evans (84) who detected cyclohexane carboxylate, cyclohex-1-ene carboxylate,
propionate, and acetate from benzoate in both rumen and sewage sludge methanogenic
culture. They noted the operation of microbial food chain (80). In summary, the benzene
nucleus was first reduced and then cleaved to aliphatic acids. The acids are then converted to
suitable substrates for methanogenesis. Electrons are excreted as H2 which is used for the
reduction of CO2 to CH4.
In a similar study, Healy and Young (94) tested the anaerobic biodegradability of 11
phenolic lignin derivatives (vanillin, vanillic acid, ferulic acid, cinnamic acid, benzoic acid,
catechol, protocatechuic acid, p-hydroxybenzoic acid, phenol, p-hydroxybenzoic acid,
syringic acid and syringaldehyde). Cultures were acclimated with the mentioned aromatic
substrates. The inoculum was obtained from a laboratory digester fed with heat-treated
35
newsprint or sludge. Results showed that 63-102% of the aromatic substrates were converted
to methane and carbon dioxide. For some enrichment, conversion is close to that predicted
from stoichiometry. One interesting result obtained in this study is that some enrichment
cultures acclimated to one substrate is capable of degrading different aromatic substrate at
significantly reduced or no lag time at all.
The catabolism of aromatic compounds proceeds via the benzoyl coA pathway, where
the aromatic molecule undergoes simplification and then destruction of the aromatic structure
by reduction followed by subsequent ring opening hydrolytically (62, 76). To determine the effect of the degree of polymerization of lignin on the extent of decomposition, Zeikus (89)
conducted decomposition studies using 10 mL lake sediment + lignin model compounds and
alkali degraded lignin incubated at 30°C. Synthetic lignins labelled at side chain, methoxyl or
ring moities were not mineralized to CO2 or CH4 in 30 days which confirms results in (95).
14 Addition of sulfate increases the decomposition of phenols but reduced the CH4/CO2 ratio
from approximately 1 to 0.5 which indicates that sulfate alters the electron flow in the system
(96).
Lignin related dimer containing β-O-4 bonds, Kraft industrial lignin and low molecular
weight (Mr) aromatic degradation products of synthetic lignin are decomposed by anaerobic
microbial process which suggests that the presence of an inter-unit ether linkage per se does
not limit anaerobic degradation. Results imply that lignin is a recalcitrant, natural compound
in anoxic, neutral environments because of its high Mr value and the inability of in-situ
anaerobic microbes to significantly depolymerize the substrate (89). At least 15%
mineralization of low molecular weight fraction (Mr<350) was recorded while no significant
36
mineralization was observed in high molecular weight fraction of lignin (Mr>850). The large
molecular size, poor solubility and complex cross-linking of lignin make it quite inaccessible to microorganisms and enzymes (89).
Stinson and Ham (72) used the biochemical methane potential test to study the biodegradation of lignin fraction of lignocellulose and its inhibitory and toxic effect under anaerobic conditions. They used filter paper and newspaper as test substrates. The objective was to determine the effect of delignification with acid chlorite as well as the possible toxicity effect of MWL on the decomposition of lignocellulose. They found out that lignin does not affect degradation by chemical toxicity and MWL does not inhibit cellulose degradation. Ball milling increases cellulose degradation of newsprint from 50% to 100%.
The rate of cellulose decomposition in the delignified NP is the same as that of the filter paper suggesting that the lignin inhibition in cellulose degradation is due primarily to the non-bioavailability of cellulose when in association with lignin.
Colberg and Young (97) studied the methanogenic decomposition of oligomers of
[14C-lignin] lignocellulose. They separated alkali treated lignocellulose and 3 fractions-
1000-1400; 400-1000; and less than 400 with corresponding TOC content of 27, 60 and 13% soluble lignin substrate. Mineralization was recorded at 6.1%, 25.8%, and 33.6% for fractions 1, 2 and 3 respectively. The result of the study demonstrated that a major constraint
to the decomposition of lignin polymer is its high molecular weight. The researchers also identified that solubility could also be a factor they suggest that mineralization of smaller
MW lignin fractions.
37
Benner 1984 (68) studied the anaerobic decomposition of 14C labeled lignin-
lignocellulose. After 294 d of anaerobic decomposition about 16.9% lignin and 30%
carbohydrates were converted for grass S. alterniflora. In HW, only 1.5% lignin and 4.1% carbohydrates cellulose and hemicellulose was converted to gaseous end products. A higher
extent of decomposition was recorded in synthetic lignin at 3.7% to gaseous product.
While proof of lignin-derived phenolic substances degradation have been shown, lignin
generally is considered preserved in the natural environment such as sulfate reducing
conditions in mangrove sediments (45), and anoxic zones of ocean sediments. This evidence
leads researchers to believe that the rate-limiting steps to the biological degradation of
natural aromatic polymers such as lignin occur at the stage when it is necessary to dismember
lignin into their small molecule aromatic components (62). This is artificially done by
treatment of lignocellulose with base at high temperature such as during pulping. Anaerobic
biodegradation of lignin is also favored in highly alkaline gastrointestinal tracts of higher
termites (98). Several studies suggested that the initial steps for the decomposition of lignin
polymer involve the removal/modification of one or more aromatic substituent groups (85,
99) to form benzoyl CoA (Figure 1.9) and eventually to reductive ring cleavage. Aside from
the complexity of lignin, the decomposition of cellulose and hemicellulose and other more
labile substrates will take precedence over lignin and other aromatic compounds. This is
another factor contributory to lignin preservation when more abundant labile molecules are
present.
38
Figure 1.9. Benzoyl CoA pathway showing the common precursor leading to the anaerobic metabolism of aromatic molecules such as lignin-derived compounds (99).
39
2. Adoption of Copper Oxide Oxidation to Improve Quantification of Lignin in
Municipal Solid Waste
(Formatted for submission to Environmental Science and Technology)
Florentino B. De la Cruz†, Thorsten Dittmar‡, Jutta Niggeman‡, Christopher L. Osburn☼
and Morton A. Barlaz†
†Department of Civil, Construction, and Environmental Engineering, Campus Box 7908,
North Carolina State University, Raleigh, North Carolina 27695-7908
‡Max Planck Research Group Marine Geochemistry, Carl von Ossietzky University
Institute for Chemistry and Biology of the Marine Environment (ICBM), Carl-von-
Ossietzky-Str. 9-11 D-26129 Oldenburg, Germany
☼Department of Marine, Earth and Atmospheric Sciences, North Carolina State University,
Raleigh, North Carolina 27695-8208
*Corresponding author phone: (919)513-4421; fax: (919)515-7908; email:
2.1 Abstract
The ability to quantify lignin is an important tool for characterizing the extent
decomposition of municipal solid waste (MSW). Traditionally, acid insoluble or Klason
40
lignin (KL) has been used to measure lignin. However, synthetic organic materials such as plastics and rubber that are routinely present in MSW interfere with the traditional KL method, resulting in artificially high measurements. Another method for lignin analysis is
CuO oxidation in which the lignin polymer is broken down to phenolic monomers that are then quantified by HPLC or GC/MS. The objectives of this study were to evaluate the applicability of CuO oxidation to MSW and to apply the CuO oxidation method to characterize the extent of MSW decomposition. Results showed that neither MSW components (e.g. plastics and metals) nor lipophilic extractives present in MSW affected the
CuO oxidation. The study demonstrated that the analysis of lignin monomers can be simplified by skipping the ethyl acetate extraction step that has been used traditionally and directly injecting an acidified aliquot of the digestate onto an HPLC column. The study also demonstrated that ball-milling is not necessary to optimize lignin phenol yields from CuO oxidation. To assess the extent of degradation, the ratio of cellulose plus hemicellulose to the sum of CuO lignin, (Cel+H)/Λ8, was compared to the traditional indicator, (Cel+H)/KL for
MSW samples. While (Cel+H)/Λ8 is a more robust indicator of lignin, similar trends were observed for both decomposition metrics and further study is necessary to explore the sensitivity and efficacy of (Cel+H)/Λ8 as a metric for the extent of MSW decomposition .
Keywords: lignin, CuO oxidation, HPLC, MSW
41
2.2 Introduction
Lignocellulosic materials such as wood, leaves, grass, needles and paper constitute the
bulk of organic matter in municipal solid waste (MSW) discarded in landfills (9). Cellulose
(Cel), hemicellulose (H) and lignin (i.e. Klason lignin – KL) are the three major biopolymers
that make up the organic fraction of MSW. Both cellulose and hemicellulose are
biodegradable while lignin is considered recalcitrant (71). Because lignin is what remains
after decomposition, lignin has been utilized as a conservative marker during decomposition
of organic matter in landfills. For example, lignin is used as a monitoring parameter in
(Cel+H)/KL during decomposition (100). A major advantage of this parameter is that it
eliminates the effect of dilution from soil cover in landfill samples.
KL is the most widely used method to measure lignin in both plant tissues (5, 7) and in
MSW (100). KL is operationally-defined and involves removal of lipophilic extractives
followed by a two-stage acid digestion to hydrolyze the carbohydrates. The residue is then
combusted and the volatile fraction is reported as KL. The standard method for KL is the
Official test method T222 om-83 of the Technical Association of Pulp and Paper Industry
(TAPPI).
Recently, it has been shown that some synthetic organic materials that are typically
present in MSW interfere with the KL method and these interferences result in artificially
high lignin measurements (101). This is because materials such as plastics and rubber are not
dissolved during the acid digestion and measured as KL. Thus, a more lignin-specific method is necessary to measure lignin in MSW.
42
There are several alternatives to measure lignin. Most of the methods were developed for the pulp and paper industry where the samples are relatively homogeneous such as fresh tissues or pulp, in contrast to MSW which is a highly heterogeneous mixture. Lignin can be measured by isolation using mechanical and chemical processes followed by dissolution in a suitable solvent such as in milled wood lignin (MWL) (5, 25). This method however, is very
laborious because of the ball-milling, solvent extraction and precipitation steps, making it
difficult to implement as a routine analysis for large number of samples. Spectrophotometric
methods to measure lignin include the acetyl bromide and thioglycolic acid methods, where
lignin is dissolved in suitable solvent and followed by measurement of absorption at 280 nm.
However, these methods may not be applicable to MSW because of interferences. The main
limitation with spectrophotometric methods is that the absorptivity of the sample should be
known and this parameter varies in MSW due to its heterogeneity.
CuO oxidation is a lignin-specific measure that involves breaking down of lignin via
alkaline CuO oxidation to a suite of phenolic aldehydes, ketones and carboxylic acids. In
addition to monomers, oligomers are released in this process (43). CuO oxidation involves
one electron transfers to form phenoxy-radicals followed by eventual formation of quinone
methide intermediates (5). The lignin monomers are traditionally quantified by capillary gas
chromatography GC/MS or by HPLC (7, 25). The CuO oxide oxidation products of different
classes of lignocellulose are well-documented (35, 42, 47, 49, 53, 102). Furthermore,
application of CuO oxidation of lignin has been demonstrated in the study of the fluxes and
sources of organic matter in natural environments (42, 47, 48, 51, 53, 58, 102, 103).
43
While CuO oxidation is lignin specific, making it less susceptible to interferences, the
monomers liberated during CuO oxidation are only a fraction of the total lignin, primarily the
uncondensed fraction of lignin, which accounts for up to 35% of Klason lignin (104). Thus, it
is necessary to correlate phenolic monomers from CuO oxidation products with Klason
lignin. Nevertheless, consistent degradation patterns have been reported in different classes
of terrestrial plants (35, 43, 52).
The objective of this study was to evaluate the adoption of the CuO oxidation method
to quantify lignin in MSW based on the presence of phenolic monomer building blocks that
are unique to lignin. Work was conducted to explore potential interferences not typically
present in the natural environment (e.g., plastics) and application of the method to excavated
landfill samples.
2.3 Materials and Methods
2.3.1 Experimental Design
The objective of this study was to explore the adoption of CuO oxidation as a measure
of lignin and to characterize decomposition in MSW. Work was conducted to (1) evaluate
elimination of the ethyl acetate (or diethyl ether) liquid-liquid extraction step prior to HPLC analysis, a step traditionally applied to purify lignin phenols prior to derivatization for subsequent GC/MS analysis; (2) explore potential interferences including plastics, metals and extractives; (3) evaluate the effect of ball milling samples prior to CuO digestion; (4) evaluate the relationship between KL and the sum of CuO lignin monomers (Λ8) for different
44
plant tissues and (5) apply CuO oxidation to measure lignin and characterize decomposition in excavated landfill samples.
2.3.2 Effect of Ethyl Acetate Extraction
Solvent extraction using diethyl ether (52) or ethyl acetate (59) has traditionally been a part of the CuO lignin analysis to purify lignin phenols for subsequent derivatization prior to
GC/MS analysis. With the use of an aqueous based HPLC method, the extraction step can be omitted by directly injecting an aliquot of the acidified digestate into the HPLC, resulting in significant reduction in resources and analysis time. The goal of this test was to determine if
CuO oxidation can be simplified by skipping the ethyl acetate extraction step. Different types of samples [HW (hardwood) Quercus rubra (11-176), SW (softwood) Picea sp. (07-151),
Raleigh grass mixture (09-386) and old newsprint (ONP 09-379)] were analyzed with and without ethyl acetate extraction. Aliquots of the acidified digestate were diluted as necessary and injected directly to the HPLC and lignin phenol concentrations were compared with samples analyzed with ethyl acetate extraction.
2.3.2.1 Effect of MSW Interferences
Tests were conducted to determine whether plastics and possible radical scavengers that are typical components of MSW interfere with CuO oxidation by releasing phenolic substances or by altering the reaction mechanism during CuO oxidation of lignin. Possible interferences were evaluated by their addition to old newsprint (ONP) which is biogenic and will not interfere with CuO oxidation. The interferences tests are listed in Table 2.1. In each
45
case, tests were conducted by adding potential interferences to ONP at double their estimated
concentration in MSW (Table 2.1). Differences in CuO oxidation products relative to a
newsprint control would indicate interference. In addition to plastics, the effect of potential
radical scavengers such as carbonates (as calcium carbonate), protein, metals and glass were
tested. For carbonate, a mixture was formulated to contain calcium carbonate (twice the typical MSW ash content) which is present in food wastes and as fillers in some papers.
Table 2.1. Synthetic waste composition of different MSW components based on the 2008 EPA Waste Characterization (105).
Double Component Source wt. % High Density HDPE Milk jug 5.73 Polyethylene Polystyrene PS Plastic fork 3.12 Polyvinyl chloride PVC Pipe 1.99 Polyethylene PET Soda Bottle 3.61 terephthalate Polypropylene PP Prescription drug bottle 4.94 Glass Clear bottle 11.20 Fe Metal powder (Fisher Scientific, USA) 12.46 Al Metal powder (Fisher Scientific, USA) 3.23 ACS grade chemical (Fisher Scientific, CaCO a 20 3 USA) Bovine Serum Albuminb Protein From Fisher Scientific, USA 8 a. Estimate of CaCO3 was based on ash content of MSW (100)
b. Protein estimate was based on Wu et al. (106)
46
2.3.2.2 Effect of Lipophilic Extractives
To determine the contribution of lipophilic extractives to lignin monomers during CuO oxidation, samples of HW (red oak Quercus rubra 11-176), SW (loblolly pine Pinus taeda
11-158), Grass (Bermuda grass Cynodon dactylon 11-436) were treated by Soxhlet extraction for 4 h in toluene: ethanol (2:1 by vol.) to remove the lipophilic extractives. The CuO lignin of initial and extracted material was compared.
2.3.2.3 Effect of Ball-milling
Four materials [HW (red oak Quercus rubra 11-176), SW (loblolly pine Pinus taeda
11-158), Grass (Bermuda grass Cynodon dactylon 11-436) and old newsprint (Washington
Post 11-449)] were ground to pass a 60 mesh screen (0.251 mm opening) in a Wiley mill.
Two grams of these materials were ball-milled in toluene using alumina fortified porcelain jars charged with zirconia grinding media (6.4 x 6.4 mm) (Cole-Parmer, USA) in a 0.4L jar mill rotating at 30 rpm under N2 (7, 16, 17). Optimal charging of 45-55% (v/v) was used per
manufacturer’s instructions. The ball-milled sample was recovered by centrifugation at 2850
rpm for 10 min. The toluene layer was aspirated and the residual toluene was removed by
evaporation at 50°C under vacuum. The ball-milling time was determined from a preliminary
work as the time required to completely dissolve a sample in a dimethylsulfoxide (DMSO)
and N-methylimidazole (NMI) solvent system (107) and ranged from 14 to 28 days depending on the sample. The CuO oxidation products of ball-milled samples were compared to samples that were ground in a Wiley mill.
47
2.3.2.4 Relationship between Klason Lignin and CuO Lignin
The relationship between KL and Λ8 was evaluated from the data compiled by
chemically characterizing a total of 221 samples including 93 different species of HW, SW,
leaves and grasses, and needles.
2.3.3 Sample Collection and Preparation
Wood samples were obtained from a local lumber yard and from the collection of Dr.
Ilona Peszlen of the Department of Forest Biomaterials at NC State University (NCSU).
Leaves and needles samples were collected from Raulston arboretum from July to September
2011 and August to September 2012. Grass samples were collected from the NCSU Turf
Farm in September 2011. Plant tissues samples were dried at 50 °C, ground to pass a 60-
mesh screen in a Wiley Mill and then re-dried at 50 °C to constant weight. Samples were
stored in mason jars at room temperature until analyzed. Similarly, paper samples were dried,
shredded, ground and stored in air tight mason jars.
Landfill samples used in this research were selected from an inventory of samples to include a range of ratios of cellulose + hemicellulose to lignin (Cel+H)/KL (0.4 to 3.5), which traditionally has been used as a metric of the extent of solids decomposition (100).
Excavated landfill samples were obtained from the Outerloop Kentucky (KY) landfill that receives a mixture of residential and commercial solid waste. Samples were obtained using a
0.91m bucket auger. After reaching the surface, samples from a specified depth interval were mixed by shovel and then grab samples were collected. These samples were obtained as part
48
of a program to monitor solids decomposition (KY) (108). The excavated refuse was
shredded, dried to constant weight, ground and stored as above.
2.3.4 Analytical Methods
2.3.4.1 Soxhlet Extraction
Removal of lipophilic extractives from the sample was done by Soxhlet extraction as
described previously (7). Sample weighing about 1g was refluxed for 24 cycles (about 4h) in toluene: ethanol (2:1 by vol.).
2.3.4.2 Alkaline CuO Digestion and HPLC Detection
A sample containing 2-5 mg C equivalent was placed in a 25 mL Teflon vial with 0.5 g
CuO powder, 0.1g Fe(NH4)2(SO4)2∙6H2O and 5 mL of 2M NaOH (prepared using deionized
water boiled and cooled under N2). The vial was then sealed and locked under a N2
headspace in an anaerobic hood. Digestion was conducted in a furnace at 150°C for 3h. After
digestion, the vial was partially immersed in an ice bath to stop the reaction and allowed to
cool to room temperature.
The vial was then sonicated for 15 min after which the contents were quantitatively
transferred to a 50 mL centrifuge tube for centrifugation at 3500 rpm for 10 minutes. The
extract was decanted into a glass centrifuge tube and the remaining solids were washed with
5 mL of 2M NaOH. The mixture was centrifuged at 3500 rpm for another 10 minutes and the
49
extract was combined with the initial extract. The extract was acidified (pH≤2) with 6 mL of
12M HCl and then transferred quantitatively to a 100-mL volumetric flask. An aliquot of this
solution was further diluted as necessary to obtain a concentration within the calibration
range (0.05-25 µM for each phenolic monomer).
Samples were analyzed using a Shimadzu LC-20AT high performance liquid
chromatograph (HPLC) equipped with a degasser (DGU-20A5) and a SPD 20A Photodiode
Array Detector (PDA). Chromatographic analyses were conducted using a gradient program
at 55°C using a Phenomenex Kinetex 2.6u C18 100A column as described previously (109).
Mobile phase A is H3PO4 (pH = 2.5), mobile phase B is methanol, and mobile phase C is acetonitrile. The HPLC system was reconditioned to the initial condition by flushing with acetonitrile and then with mobile phase A, after every sample. The system pressure ranged from 105 to 145 bar during analysis. After each injection, the sampler was purged with methanol/water 1:1 (v/v). All HPLC samples and standard solutions were filtered (0.2 µm,
Millipore Millex Teflon filter) prior to injection. Aqueous solutions were prepared using filtered (0.2 µm) and UV treated de-ionized water.
Lignin phenols were identified by using retention times and light absorption characteristics from external standards (0.05-25 µM). The calibration curve was linear within this concentration range. The spectra of each monomer were measured in the wavelength range of 230 to 370 nm, which covers the maximum absorption for all lignin phenols.
Integration of the peaks for each lignin phenol was done at its respective maximum wavelength using Shimadzu LC solutions software. The 11 lignin phenols analyzed are as follows: [4-hydroxybenzoic acid (PAD); 4-hydroxybenzaldehyde (PAL); 4-hydroxy-3-
50
methoxy-benzoic acid (vanillic acid, VAD); 4-hydroxyacetophenone (PON); 4-hydroxy-3- methoxy-benzaldehyde (vanillin, VAL); 3,5-dimethoxy-4-hydroxy-benzoic acid (syringic acid, SAD); 4-hydroxy-cinnamic acid (p-coumaric acid, CAD); 3,5-dimethoxy-4-hydroxy- benzaldehyde (syringaldehyde, SAL); 4-hydroxy-3-methoxy-acetophenone (acetovanillone,
VON); 4-hydroxy-3-methoxy cinnamic acid (ferulic acid, FAD); 3,5-dimethoxy-4-hydroxy- acetophenone (acetosyringone, SON)]. Standard lignin phenols were purchased from Sigma-
Aldrich, USA at the highest purity available.
2.3.4.3 Cellulose, Hemicellulose and Klason lignin
To analyze the solids for their cellulose, hemicellulose and Klason lignin concentrations, a samples was first extracted with toluene: ethanol (2:1 by vol.) to extract lipophilic material. Samples were then subjected to a two-stage acid hydrolysis. In the first stage, the sample was hydrolyzed in 3 mL of 72 % (w/v) sulfuric acid for 1 h at 30 °C. This was followed by a secondary hydrolysis after the addition of 83 mL of deionized water and a fucose internal standard. The secondary hydrolysis was done in an autoclave at 121 °C for 1 h. Sugars (arabinose, galactose, glucose, mannose, and xylose) were analyzed by HPLC using a pulsed electrochemical detector (110, 111). The mass of glucose was converted to the mass of cellulose while the mass of the other sugars was converted to the mass of hemicellulose by anhydro-correction. Klason lignin was measured from the solids remaining after acid hydrolysis as the weight loss on ignition at 550°C for 2 hr.
51
2.3.4.4 Total organic carbon (TOC)
TOC was measured with a CHN analyzer (Perkin-Elmer PE 2400 CHN Elemental
Analyzer, Perkin-Elmer Corp.). All TOC samples were acid washed (1 M HCl) to eliminate inorganic carbon prior to analysis (112).
2.3.5 Data Analysis
To test the effect of different factors (i.e. analysis method, interferences and ball- milling) on the 11 lignin phenol yields, Multivariate Analysis of Variance (MANOVA) was conducted. MANOVA is a multivariate technique to test the hypothesis that one or more independent variables or factors have an effect on a set of two or more dependent variables
(113). An omnibus test of hypothesis like MANOVA reduces the probability of making a
Type I error that is rejecting the null hypothesis when it is in fact true. Details of the analysis as well as the underlying assumptions of MANOVA have been described (113). Tests for significance were determined using the Wilks Lambda test. The MANOVA routine was implemented using R (114). When a significant difference was obtained, separate univariate
Analysis of Variance (ANOVA) tests were conducted in which the level of significance was divided by the number of tests conducted. All statistical comparisons were at done at α=0.05 level of significance.
2.4 Results and Discussion
The 11 lignin phenols liberated during CuO oxidation of lignin were identified and quantified in a 30-minute HPLC run which is significantly shorter than that previously
52
reported (109, 115) because of the use a shorter solid core column with smaller particle size and relatively low pressure. The effects of interferences and skipping the ethyl acetate
extraction are presented. This is followed by evaluation of the relationship between KL and
CuO lignin. Finally, the CuO method was applied to characterize lignin in excavated samples
from a landfill in Kentucky (KY), USA.
2.4.1 Klason lignin of different plastics
When the Klason lignin method was applied to plastics and rubber, these materials
were shown to behave as lignin (Figure 2.1). Specifically, the Klason lignin content of these
materials was measured as 89 % to 100 % (116). For a given set of samples, Klason lignin in
fresh refuse is about 16.0 ± 6.6 % (average ± standard deviation [sd]) (100) and the extent of
contribution of plastic to this lignin value is unknown. The significance of this interference
increases as the plastics content of MSW increases. Plastics discards have grown from 0.5%
of discards in 1960 to 17.8 % in 2011 (117) . For example, assuming 15% lignin based on the
lignocellulosic material content in MSW (100), the presence of 17.8% plastics being
quantified as lignin would result in double the measured Klason lignin content. In addition,
plastics composition in MSW increase as MSW decomposes.
53
100 90 80 70 60 50 40 30 Klason Lignin Lignin Klason (%) 20 10 0
Figure 2.1. Klason lignin of plastics and rubber typically found in MSW.
2.4.2 Effect of Ethyl Acetate Extraction
The objective of this test was to determine whether the phenol monomer analysis can be simplified by direct injection of aqueous acidified samples as opposed to ethyl acetate (or diethyl ether) extractions. Unlike the traditional GC/MS method that requires a derivatization
prior to sample analysis, HPLC analysis of lignin phenols was done in aqueous solutions.
The lignin phenol yields with and without ethyl acetate extraction for different sample types are presented in Figure 2.2.
54
HW SW 10.0 8.0 w/ extraction w/ extraction 7.0 8.0 w/o extraction w/o extraction 6.0 6.0 5.0 4.0 OC) 4.0 OC) 3.0 2.0 2.0 1.0 0.0 0.0 Lignin Phenol Yield (mg/100 mg (mg/100 mg Yield Phenol Lignin Lignin Phenol Yield (mg/100 mg (mg/100 mg Yield Phenol Lignin PAL PON PAD VAL VON VAD SAL SON SAD CAD FAD PAL PON PAD VAL VON VAD SAL SON SAD CAD FAD (a) (b) Grass ONP 1.0 4.5 w/ extraction w/ extraction 4.0 0.8 w/o extraction w/o extraction 3.5 3.0 0.6 2.5 OC) OC) 2.0 0.4 1.5 0.2 1.0 0.5 0.0 0.0 Lignin Phenol Yield (mg/100 mg (mg/100 mg Yield Phenol Lignin Lignin Phenol Yield (mg/100 mg (mg/100 mg Yield Phenol Lignin PAL PON PAD VAL VON VAD SAL SON SAD CAD FAD PAL PON PAD VAL VON VAD SAL SON SAD CAD FAD (c) (d)
Figure 2.2. Effect of ethyl acetate extraction step on lignin phenol yields from different tissue samples. Error bar represents ±sd, n=3).
As expected, the major lignin phenols in hardwood are syringyl (S) phenols SAL, SON
and SAD, typical of angiosperm tissues, while vanillyl (V) phenols VAL, VON and VAD are
dominant in SW, a gymnosperm. The presence of small amounts of S phenols in softwood
has been documented previously although S phenols are typically associated with HW, and
leaves and grasses (5, 118). The reported S phenols in SW are significantly different from zero (detection limit = ~50 ng g-1 for all lignin phenols). The low cinnamyl (C) phenols CAD,
FAD in HW and SW are characteristic of woody tissues. The major components of grass are
S and C phenols that are characteristic of non-woody angiosperm. The mean lignin phenols
55
reported in this study are generally within the range reported by Hedges (35) with differences
attributed to species variation as the tissues used in this study and (35) do not exactly match.
The ONP used in this test appears to have been derived from SW pulp as indicated by the
low S/V ratio (0.06).
One-factor MANOVA was used to compare the data across different sample types.
Based on MANOVA Test statistic (Wilks lambda), there is no significant difference (p>0.05)
in any of the lignin phenol yields with and without ethyl acetate extraction. As a result, all
subsequent work was conducted without ethyl acetate extraction.
2.4.3 Effect of MSW Interferences
The results of tests to evaluate whether certain MSW components could interfere with
CuO analysis are presented in Figure 2.3 and Table S2.1 (supporting information - SI).
Mixtures of ONP and different MSW components that represented potential interferences to
CuO lignin analysis were tested. The major lignin phenols of the ONP sample are SAL (1.56
± 0.24 mg 100 mg OC-1) and VAL (2.24 ± 0.21 mg 100 mg OC-1) with average coefficients
of variation (CV = sd/mean × 100%) of 9 % and 16 %, respectively across all of the interference tests. The low C/V (0.02) and high S/V (0.67) of the ONP sample suggest that pulp from which it was derived is HW or a mixture of HW and SW.
One-factor MANOVA was implemented to test the significance of different MSW components on 11 lignin phenols. Based on the MANOVA test statistic (Wilks lambda), there is no significant difference in lignin phenol yields (p > 0.05). This indicates that in
56
mixtures of a test compound with potential interferences at double their level in MSW, there were no interferences.
4.0 ONP Control 3.5 ONP+HDPE ONP+PVC OC) - 3.0 ONP+PET ONP+Glass 2.5 ONP+Al ONP+Fe 2.0 ONP+PS ONP+PP 1.5 ONP+CaCO3 ONP+Protein 1.0
0.5 Lignin Phenol Yield (mg/100 mg ONP
0.0 PAL PON PAD VAL VON VAD SAL SON SAD CAD FAD
Figure 2.3. Effect of different contaminants in MSW tested for interference with CuO oxidation of ONP at 2× the level in the 2008 US EPA Waste Characterization report (9). Acronyms are defined in the text. Error bar represent ±sd. The number of replicates is found in Table S2.1.
The results of the effect of metals (Al and Fe) are contrary to results of Hernes et. al.
(119) where it was shown that some fraction of lignin is irreversibly sorbed to mineral particles and cannot be measured in CuO analysis. However, there are two main differences
57
between the tests conducted by Hernes et. al. and this study. First, in the study by Hernes et.
al., CuO analysis was done on the lignin sorbed on mineral particles. Second, the starting
organic carbon in the Hernes study was a water-soluble fraction of the lignocellulose and not
the solid lignocellulose sample itself. Thus, the lignin used by Hernes et al. may not
representative of the bulk lignin content of the sample. For example, assuming 15 g of blue
oak leached in 1.35 L of water and then 1.25 L of this leachate was diluted to 5L to achieve a
final concentration of 340 mg L-1, this account for about 12% of the total organic carbon of the initial material. The leaching process results in fractionation of the organic matter and it
is likely that a small portion of the lignin is in the solution as lignin is insoluble in water (5).
It is possible that the CuO lignin measured in Hernes’ study may have come from extractives
or perhaps a small fraction (water-soluble fraction) of lignin. Another difference is that soil
minerals contain metals in oxidized form as opposed to elemental metals as used here.
2.4.4 Effect of Lipophilic Extractives
In addition to lignin, lignans, fatty acids, terpenes, waxes and other compounds
collectively called extractives are present in the plant cell wall. The effect of the removal of
extractives on CuO oxidation products of lignin is presented Figure 2.4. One-factor
MANOVA showed that there is no significant difference (p > 0.05) between the lignin
phenols of different samples with and without Soxhlet extractions. This confirms that a
majority of the lignin fractions in plant cell walls is present in the secondary layer (10) and
the presence of some lignin in the extractives did not affect CuO lignin. SAL in HW appears
to be an outlier because the extractives in SW is higher compared to HW thus the effect of
58
extractives on CuO oxidation should be more apparent in SW. In addition, looking at the other lignin phenols the difference is more likely due to measurement bias.
HW SW 10.0 5.0 Control Control 8.0 4.0 Soxhlet Extracted Soxhlet Extracted 6.0 3.0
OC) 4.0 OC) 2.0
2.0 1.0
0.0 0.0 Lignin Phenol Yield (mg/100 mg (mg/100 mg Yield Phenol Lignin (mg/100 mg Yield Phenol Lignin PAL PON PAD VAL VON VAD SAL SON SAD CAD FAD PAL PON PAD VAL VON VAD SAL SON SAD CAD FAD
(a) (b)
Grass 2.5 Control 2.0 Soxhlet Extracted 1.5
OC) 1.0
0.5
0.0 Lignin Phenol Yield (mg/100 mg (mg/100 mg Yield Phenol Lignin PAL PON PAD VAL VON VAD SAL SON SAD CAD FAD
(c)
Figure 2.4. Effect of extractives on the CuO oxidation of different classes of lignocellulose. Error bars represent ±sd. (n=3).
59
2.4.5 Effect of Ball-milling
The effect of ball-milling on the CuO oxidation products of lignin is presented in
Figure 2.5. Ball-milling is a part of standard procedure in isolating lignin from plant tissues
(7). Ball-milling is a physical process but the heat generation during impact could result in
cleaved and/or condensed lignin structures. While it has been previously shown that the
lignin ball-milling results in only minor cleaved structures (16, 17), its effect on CuO
oxidation products of lignin is unknown.
One factor MANOVA showed that the CuO oxidation product concentrations after
grinding to pass a 60 mesh screen are statistically the same as concentrations in ball-milled samples.
60
HW SW 10.0 5.0 HW Mesh 60 SW Mesh 60 8.0 4.0 HW Ball-milled SW Ball-milled 6.0 3.0 OC) OC) 4.0 2.0
2.0 1.0
0.0 0.0 Lignin Phenol Yield (mg/100 mg (mg/100 mg Yield Phenol Lignin Lignin Phenol Yield (mg/100 mg (mg/100 mg Yield Phenol Lignin PAL PON PAD VAL VON VAD SAL SON SAD CAD FAD PAL PON PAD VAL VON VAD SAL SON SAD CAD FAD (a) (b) Grass ONP 3.0 6.0 Grass Mesh 60 ONP Mesh 60 2.5 5.0 Grass Ball-milled ONP Ball-milled 2.0 4.0
1.5 3.0 OC) OC) 1.0 2.0
0.5 1.0
0.0 0.0 Lignin Phenol Yield (mg/100 mg (mg/100 mg Yield Phenol Lignin (mg/100 mg Yield Phenol Lignin PAL PON PAD VAL VON VAD SAL SON SAD CAD FAD PAL PON PAD VAL VON VAD SAL SON SAD CAD FAD (c) (d)
Figure 2.5. Effect of ball-milling on the CuO oxidation of different types of samples of plant origin. (a) HW- Quercus rubra; (b) SW-Pinus taeda (c) Grass- Cynodon dactylon; (d) ONP. Error bars represent ±sd (n=3).
2.4.6 Relationship between Klason lignin and CuO lignin
The relationship between KL and Λ8 (Λ8 = VAL + VON + VAD + SAL + SON + SAD
+ CAD + FAD) was evaluated by analyzing a total of 221 samples including 93 different species of HW, SW, leaves and grasses, and needles. The scatterplot matrix (Figure S2.1) and correlation matrix (Table S2.2) of individual lignin phenols are presented in the
Supporting Information (SI). The individual S phenols (SAL, SON, SAD), V phenols (VAL,
VON, VAD) and C phenols (CAD, FAD) are positively correlated with each other (r = 0.84 –
61
0.94, 0.84 – 0.96 and 0.89, respectively). The data also showed that KL is positively
correlated with V phenols while negatively correlated with S and C phenols. This can be
explained by the fact that lignin is generally higher, for example in SW lignin where the CuO
lignin (Λ8) is lower. Similarly, lignin is generally lower in HW where Λ8 is higher. This is
because lignin monomers measured during CuO oxidation are primarily the uncondensed
fraction. While SW generally contain more lignin, SW lignin is more condensed as a result of
the availability of the 5-position for branching in the guaiacyl units that dominates SW. This
higher degree of condensation compared to HW results in the smaller amount of monomers
released during CuO oxidation and hence the negative correlation.
The same trend was also observed when KL is expressed as % by weight (Figure S2.2)
where the relationship with Λ8 is weak (r = -0.3), with more variability in KL when Λ8 is
small (leaves, grasses and needles). To eliminate the effect of inorganic matter such as
minerals from cover soil in landfill, lignin is normalized to organic carbon (Figure 2.6).
Figure 2.6 suggests that KL is not correlated to Λ8. This lack of correlation is perhaps due to
the fact that the CuO oxidation monomers represent only a small fraction of the total lignin,
10% on the average with a range of 0.6 % (Cornus florida leaves) to 46% (Betula papyrifera
HW). Preliminary partial least square regression (PLSR) analysis was also conducted with the individual lignin phenols as well as S, V and C as independent variables and KL as response. The goal of PLSR is obtain orthogonal predictors from the linear combination of the original variables optimizing the correlation between the response and transformed variable. PLSR analysis showed that only a maximum 35% of the variation in KL could be explained by the CuO lignin. Thus, prediction of KL from CuO is not reliable.
62
Figure 2.6. Relationship between KL and Λ8.
2.4.7 Application to Excavated Samples from KY Landfill
The compositional variation in the lignin phenols of the excavated MSW samples from
the KY landfill is presented Figure 2.7a. The majority of the lignin phenols in the MSW
samples are VAL (0.91 ± 0.38 mg 100 mg OC-1) and SAL (0.72 ± 0.42 mg 100 mg OC-1).
63
The very low cinnamyl phenols suggest that the lignin from the MSW samples is
predominantly from woody tissues. Based on the S/V vs. C/V plot (Figure 2.7b), the S/V > 0 and C/V ~ 0 indicate that the lignin in the MSW samples are coming primarily from HW with SW content in many samples. The absence of non-woody tissue (i.e., grass, leaves, and needles) may be due to the separate collection of yard waste in Louisville and/or grass decomposition.
The (Ad/Al)V ratio has been used as an indicator of the extent of decomposition of
lignin in geochemical samples (40, 56-59). The (Ad/Al)V of 0.18 ± 0.04 for the MSW
samples (Figure 2.7c) is within the range of values previously reported of 0.1 - 0.2 for fresh
tissues (60). While some samples have (Ad/Al)V ratios greater than 0.3 (04-10, 03-262 and
05-178), the C/V ratios of these samples are not zero (i.e. non-woody tissue). Non-woody tissues have a wider range of (Ad/Al)V of 0.2 – 1.6 (60) which suggests that the higher
(Ad/Al)V ratio of these select samples are due to the confounding effect of both
decomposition and non-woody tissues being the source of lignin.
64
(a) (b)
(c)
Figure 2.7. Compositional variation in the CuO lignin phenols of the excavated MSW samples from KY landfill (n=30 samples). (a) Individual lignin phenols; (b) S/V vs. C/V plot; (c) (Ad/Al)V. Index represent one sample replicate.
The ratio of the sum of the degradable component of MSW - cellulose and hemicellulose - to lignin has been used an indicator of the extent of MSW decomposition
65
(Cel+H)/KL. This is because cellulose and hemicellulose are depleted relative to lignin
during MSW decomposition. Thus, enrichment in lignin as indicated by a low (Cel+H)/KL is
indicative of a high degree of decomposition based on the assumption that lignin is
conservative (71). The comparison of the historic indicator of decomposition, (Cel+H)/KL
ratio, and (Cel+H)/Λ8 is presented in Figure 2.8, where Λ8 is the sum of 8 phenolic monomers. These 8 lignin phenols are typically used as biomarkers. The p-hydroxy phenols
(PAL, PON and PAD) are excluded as they could have non-lignin sources. Traditionally,
(Cel+H)/KL has been used as an indicator of the extent of decomposition and it typically decreases with waste age. Of course, waste age is an imperfect surrogate for decomposition, given heterogeneity in both waste composition and moisture distribution in landfills. Figure
2.8 shows a similar trend (Cel+H)/KL ratio and with (Cel+H)/Λ8 with waste age, suggesting
that the effect of plastics as an interference with Klason lignin was not significant in these
samples. Parameters (Cel+H)/KL and (Cel+H)/Λ8 were positively correlated as indicated by high correlation coefficient (r = 0.8) (Figure 2.9). However, as can be seen the relationship between (Cel+H)/KL ratio and with (Cel+H)/Λ8 (and thus, KL and Λ8) is not perfectly linear.
While similar conclusions can be derived from using both parameters, (Cel+H)/Λ8 in theory,
is more robust as Λ8 is derived from compound specific method as opposed KL which is an
operationally-defined bulk parameter and may include interferences that artificially increase the denominator (KL). Further study however is necessary to explore the sensitivity and efficacy of (Cel+H)/Λ8 as a metric for the extent of decomposition of MSW.
66
Figure 2.8. Relationship between (Cel+H)/KL, (Cel+H)/Λ8 and waste age.
67
Figure 2.9. Relationship between (Cel+H)/KL and (Cel+H)/Λ8.
2.5 Summary and Conclusions
The study demonstrated that the analysis of lignin phenols can be simplified by omission of the ethyl acetate extraction step which is traditionally employed as a purification step for subsequent GC/MS analysis. The study also showed that plastics, metals and lipophilic extractives do not affect CuO oxidation products of lignin in mixed MSW, and that
68
ball milling samples has no effect on CuO products relative to grinding to pass a 60 mesh
screen.
Source-type identification based on the S/V and C/V ratio suggests that the source
lignin in MSW samples excavated from a KY landfill was primarily from HW with some
SW, with little contribution from tissues such as grass and leaves, perhaps a result of
aggressive diversion of organics such as yard waste from landfills. While the cellulose and
hemicellulose were depleted in a majority of the samples, the low (Ad/Al)V ~ 0.2 indicates
that the lignin in the samples has not been significantly decomposed. The use of an alternative parameter (Cel+H)/Λ8 provided similar trends to the traditional (Cel+H)/KL for
the sample set analyzed. The (Cel+H)/Λ8 in theory is more robust indicator of
decomposition in MSW samples that contain interferences. Further study however is
necessary to explore the sensitivity and efficacy of (Cel+H)/Λ8 as a metric for the extent of decomposition of MSW. There was not a relationship between KL and Λ8 which significantly
reduces the utility of CuO analysis for lignin in MSW. This study was motivated by the need
to identify an alternative to KL analysis due to interferences in MSW. An alternative strategy
may be to manually remove visible plastics rubbers and textiles from MSW samples prior to
shredding to increase the accuracy of the KL method.
2.6 Acknowledgements
The authors would like to thank David Black of Environmental Engineering laboratory,
NCSU and Jennifer Dickson Brown of Marine, Earth and Atmospheric Science, NCSU for the help with analytical chemistry; Dr. Ilona Pezslen of the Department of Forest
69
Biomaterials, NCSU for providing the wood samples; April Bauder of NCSU turf farm for providing the grass samples; and Dr. Alexander Krings, and Dr. Mark Weathington of
Raulston arboretum for assistance on plant tissues collection and identification. Funding for this research was provided by the National Science Foundation and Waste Management Inc.
70
2.7 Supporting Information
Table S2.1. Effect of different MSW components on CuO oxidation products of ONP.
a -1 Lignin Phenols (mg lignin phenols 100mg OC ) n PAL PON PAD VAL VON VAD SAL SON SAD CAD FAD
ONP Control 7 0.05 (0.01) 0 (0.01) 0.02 (0.01) 2.24 (0.21) 0.31 (0.07) 0.37 (0.14) 1.56 (0.24) 0.26 (0.06) 0.15 (0.08) 0 (0.01) 0.04 (0.05) ONP+HDPE 7 0.05 (0.01) 0 (0.01) 0.02 (0.01) 2.25 (0.11) 0.3 (0.05) 0.33 (0.12) 1.61 (0.18) 0.26 (0.04) 0.13 (0.06) 0.01 (0.01) 0.03 (0.04) ONP+PVC 4 0.05 (0) 0 (0) 0.01 (0.01) 2.25 (0.18) 0.3 (0.04) 0.33 (0.07) 1.67 (0.22) 0.25 (0.05) 0.12 (0.03) 0 (0) 0.02 (0.02) ONP+PET 5 0.05 (0) 0 (0) 0.02 (0.01) 2.19 (0.18) 0.26 (0.03) 0.27 (0.05) 1.53 (0.29) 0.22 (0.05) 0.1 (0.03) 0 (0) 0.01 (0.02) ONP+Glass 3 0.05 (0) 0 (0) 0.02 (0.01) 2.15 (0.19) 0.25 (0.03) 0.3 (0.07) 1.43 (0.28) 0.2 (0.04) 0.1 (0.04) 0 (0) 0 (0) ONP+Al 3 0.04 (0) 0 (0) 0.01 (0.01) 2.2 (0.19) 0.25 (0.04) 0.23 (0.04) 1.45 (0.24) 0.2 (0.04) 0.07 (0.02) 0 (0) 0 (0) ONP+Fe 5 0.05 (0.01) 0 (0) 0.01 (0.01) 2.42 (0.11) 0.33 (0.05) 0.29 (0.07) 1.82 (0.19) 0.28 (0.03) 0.12 (0.05) 0 (0) 0.04 (0.05) ONP+PS 6 0.06 (0.02) 0 (0) 0.02 (0.01) 2.47 (0.3) 0.33 (0.05) 0.39 (0.16) 1.91 (0.28) 0.29 (0.04) 0.14 (0.08) 0 (0) 0.01 (0.02) ONP+PP 6 0.05 (0.01) 0 (0) 0.02 (0.01) 2.37 (0.34) 0.33 (0.07) 0.32 (0.07) 1.71 (0.28) 0.27 (0.07) 0.13 (0.05) 0.01 (0.01) 0.04 (0.05) ONP+CaCO3 3 0.05 (0) 0 (0) 0.01 (0.01) 2.19 (0.2) 0.25 (0.05) 0.24 (0.05) 1.56 (0.36) 0.23 (0.07) 0.09 (0.03) 0 (0) 0.03 (0.02) ONP+Protein 6 0.06 (0.01) 0 (0) 0.03 (0.02) 2.27 (0.25) 0.3 (0.03) 0.37 (0.1) 1.81 (0.27) 0.3 (0.04) 0.15 (0.05) 0 (0) 0.04 (0.01)
a. sd- standard deviation is presented parenthetically.
71
0.0 1.4 0 8 0.0 0.0 5
VA 0
0.0 VO
VA 0.0
0 SA
SO 0.0
0.0 SA 3
CA 0
0.0 FA
KL 40 0 4 0.0 0.0 2.0 0 3 40
Figure S2.1. Scatterplot matrix of individual lignin phenols and Klason lignin (KL).
72
Table S2.2. Correlation matrix of individual lignin phenols and Klason lignin (KL)a
VAL VON VAD SAL SON SAD CAD FAD KL
VAL 1 0.94 0.84 0.18 0.16 0.11 -0.3 -0.22 0.22 VON 0.94 1 0.88 0 -0.01 -0.04 -0.27 -0.18 0.28 VAD 0.84 0.88 1 0.05 0.04 0.1 -0.29 -0.21 0.17 SAL 0.18 0 0.05 1 0.96 0.84 -0.24 -0.18 -0.3 SON 0.16 -0.01 0.04 0.96 1 0.86 -0.09 0 -0.35 SAD 0.11 -0.04 0.1 0.84 0.86 1 -0.16 -0.13 -0.34 CAD -0.3 -0.27 -0.29 -0.24 -0.09 -0.16 1 0.89 -0.24 FAD -0.22 -0.18 -0.21 -0.18 0 -0.13 0.89 1 -0.32 KL 0.22 0.28 0.17 -0.3 -0.35 -0.34 -0.24 -0.32 1 a. The acronym for lignin phenols are defined as follows: 4-hydroxy-3-methoxy-benzaldehyde (vanillin, VAL); 4-hydroxy-3- methoxy-acetophenone (acetovanillone, VON); 4-hydroxy-3-methoxy-benzoic acid (vanillic acid, VAD); 3,5-dimethoxy- 4-hydroxy-benzaldehyde (syringaldehyde, SAL); 3,5-dimethoxy-4-hydroxy-acetophenone (acetosyringone, SON); 3,5- dimethoxy-4-hydroxy-benzoic acid (syringic acid, SAD); 4-hydroxy-cinnamic acid (p-coumaric acid, CAD); 4-hydroxy-3- methoxy cinnamic acid (ferulic acid, FAD).
73
Figure S2.2. Relationship between KL (%) and Λ8.
74
3. Determination of Sources of Organic Matter in Landfill by Analysis of Copper
Oxide Oxidation Products of Lignin
(Formatted for submission to the Environmental Science and Technology)
Florentino B. De la Cruz1*, Jason Osborne2 and Morton A. Barlaz1
1Department of Civil, Construction, and Environmental Engineering, Campus Box 7908,
North Carolina State University, Raleigh, North Carolina 27695-7908
2Department of Statistics, 2311 Stinson Drive, Campus Box 8203, North Carolina State
University, Raleigh, North Carolina 27695-7622
*Corresponding author phone: (919)513-4421; fax: (919)515-7908; email:
3.1 Abstract
The decomposition behavior of lignocellulosic materials is related to the taxonomic
class of plant tissues. Thus, methods to characterize the composition of different taxonomic classes of plants in mixtures of lignocellulosic materials such as municipal solid waste
(MSW) are useful. One way to characterize tissue types is by the use of CuO oxidation products of lignin. This method has traditionally been implemented qualitatively or semi-
quantitatively to make inference on the flux of organic matter in the environment. The objective of this study was to determine if the chemical composition of the monomers present
75
after CuO oxidation can be used as molecular markers to infer information on the content of woody angiosperms (hardwood-HW), non-woody angiosperms (leaves and grasses – LG), woody gymnosperms (softwood – SW), and non-woody gymnosperms (needles – GN).
Results showed that the composition of HW and SW in synthetic mixture can be estimated from CuO oxidation products of lignin with an average error of 0.11 and 0.12, respectively
(about 15 – 60 % error from the expected value). The high uncertainty in the estimate of composition in synthetic mixtures can be attributed to the fact that the components selected for the synthetic mixtures had different CuO oxidation products compared to the average which was used in developing the mixing model. Application of the mixing model to estimate the composition of samples from a Kentucky (KY) landfill showed that the majority of the samples are from HW and SW with little to no non-woody tissues (leaves, grasses and needles).
Keywords: CuO, Lignin, angiosperms, gymnosperms, MSW, solid waste
3.2 Introduction
Lignocellulose in MSW, including food and yard waste, wood and paper products constitutes the bulk of organic matter and is composed of three major biopolymers; cellulose hemicelluloses and lignin. Cellulose (Cel) and hemicellulose (H) are about 50% and 12% of municipal solid waste (MSW) on a dry weight basis (100). These biopolymers represent the major substrates that are converted to CH4 and CO2 in landfills. Klason lignin (KL) is present
76
at about 15% of MSW dry weight and is recalcitrant to anaerobic decomposition (71).
Because of its recalcitrance, lignin is often used as conservative marker in making inferences about the composition of the biodegradable fraction of MSW.
Decomposition of wastes is central to many processes in landfills including geotechnical stability, methane production, leachate quality, and carbon storage. The decomposition behavior of MSW in landfills is directly related to the composition of the wastes originally buried. For example, Staley and Barlaz (120) demonstrated differences in the calculated methane yield (L0) as well as the carbon storage factors (CSF) for bulk waste calculated based on varying waste composition data. Thus, knowledge of waste composition is important to predict decomposition behavior. While data on decomposition rates and methane yield of individual MSW components have been reported, the amount of each biodegradable component in MSW is usually not available. MSW is a heterogeneous mixture and decomposition of the organic fraction of MSW renders some organic wastes indistinguishable, adding to the difficulty in characterizing waste composition.
The objective of this study was to evaluate the use CuO oxidation products as molecular markers to infer information on MSW composition according to the following plant taxonomic classes: woody angiosperms (hardwood-HW), non-woody angiosperms
(leaves and grasses-LG), woody gymnosperms (softwood-SW), and non-woody gymnosperms (needles-GN). A second objective was to estimate the composition of excavated landfill samples from a landfill in Kentucky (KY), USA.
77
3.3 Materials and Method
3.3.1 Experimental Design
A total of 221 samples including 93 different species of HW, SW, leaves and grasses, and needles were chemically characterized by CuO oxidation. These data were used to estimate the composition of mixtures. A summary of the characteristics of the plant tissues used for model development and validation is presented in Table S3.1 though Table S3.3 of the supporting information (SI). These tissues were selected to represent different classes of vascular plants. In addition, 30 samples of lignified pulp and paper products including old newsprint (ONP), old corrugated containers (OCC) and old magazine (OMG) were characterized. Both fresh samples and samples that were decomposed in the laboratory were included in the model formulation. To evaluate the performance of the model, validation was conducted using an independent set of end members (i.e., pure tissues) and synthetic mixtures. Finally, the model was applied to estimate the composition of excavated samples from a KY landfill.
3.3.2 Sample collection and preparation
Wood samples were obtained from a local lumber yard and from the collection of Dr.
Ilona Peszlen of the Department of Forest Biomaterials at NC State University (NCSU).
Leaves and needles samples were collected from the NCSU Raulston arboretum from July to
September 2011 and August to September 2012. Grass samples were collected from the
NCSU Turf Farm in September 2011. Plant tissues samples were dried at 50 °C, ground to
78
pass a 60-mesh screen in a Wiley Mill and then re-dried at 50 °C to constant weight. Samples were stored in mason jars at room temperature until analyzed. Similarly, paper samples were dried, shredded, ground and stored in air tight mason jars.
Landfill samples used in this research were selected from an inventory of samples to include a range of ratios of (Cel+H)/KL (0.4 to 3.5), which traditionally has been used as a metric of the extent of solids decomposition (100). Excavated landfill samples were obtained from the Outerloop KY landfill that receives a mixture of residential and commercial solid waste. Samples were obtained using a 0.91m bucket auger. After reaching the surface, samples from a specified depth interval were mixed by shovel and then grab samples were collected. These samples were obtained as part of a program to monitor solids decomposition
(KY) (121). The excavated refuse was shredded, dried to constant weight, ground and stored as above.
3.3.3 Analytical Methods
3.3.3.1 Alkaline CuO Digestion and HPLC Detection
A sample containing 2-5 mg C equivalent was placed in a 25 mL Teflon vial with 0.5 g
CuO powder, 0.1g Fe(NH4)2(SO4)2∙6H2O and 5 mL of 2M NaOH (prepared using deionized water boiled and cooled under N2). The vial was then sealed and locked under a N2 headspace in an anaerobic hood. Digestion was conducted in a furnace at 150°C for 3h. After digestion, the vial was partially immersed in an ice bath to stop the reaction and allowed to cool to room temperature.
79
The vial was then sonicated for 15 min after which the contents were quantitatively
transferred to a 50 mL centrifuge tube for centrifugation at 3500 rpm for 10 minutes. The
extract was decanted into a glass centrifuge tube and the remaining solids were washed with
5 mL of 2M NaOH. The mixture was centrifuged at 3500 rpm for another 10 minutes and the
extract was combined with the initial extract. The extract was acidified (pH ≤2) with 6 mL of
12M HCl and then transferred quantitatively to a 100-mL volumetric flask. An aliquot of this
solution was further diluted as necessary to obtain a concentration within the calibration
range (0.05-25 µM for each phenolic monomer).
Samples were analyzed using a Shimadzu LC-20AT high performance liquid
chromatograph (HPLC) equipped with a degasser (DGU-20A5) and a SPD 20A Photodiode
Array Detector (PDA). Chromatographic analyses were conducted using a gradient program
at 55°C using a Phenomenex Kinetex 2.6u C18 100A column as described previously (109).
Mobile phase A is H3PO4 (pH = 2.5), mobile phase B is methanol, and mobile phase C is acetonitrile. The HPLC system was reconditioned to the initial condition by flushing with
acetonitrile and then with mobile phase A, after every sample. The system pressure ranged
from 105 to 145 bar. After each injection, the sampler was purged with methanol/water 1:1
(v/v). All HPLC samples and standard solutions were filtered (0.2 µm, Millipore Millex
Teflon filter) prior to injection. Aqueous solutions were prepared using filtered (0.2 µm) and
UV treated de-ionized water.
Lignin phenols were identified by using retention times and light absorption
characteristics from external standards (0.05-25 µM). The calibration curve was linear within
this concentration range. The spectra of each monomer were measured in the wavelength
80
range of 230 to 370 nm, which covers the maximum absorption for all lignin phenols.
Integration of the peaks for each lignin phenol was done at its respective maximum
wavelength using Shimadzu LC solutions software. The 11 lignin phenols analyzed are as
follows: [4-hydroxybenzoic acid (PAD); 4-hydroxybenzaldehyde (PAL); 4-hydroxy-3-
methoxy-benzoic acid (vanillic acid, VAD); 4-hydroxyacetophenone (PON); 4-hydroxy-3-
methoxy-benzaldehyde (vanillin, VAL); 3,5-dimethoxy-4-hydroxy-benzoic acid (syringic
acid, SAD); 4-hydroxy-cinnamic acid (p-coumaric acid, CAD); 3,5-dimethoxy-4-hydroxy-
benzaldehyde (syringaldehyde, SAL); 4-hydroxy-3-methoxy-acetophenone (acetovanillone,
VON); 4-hydroxy-3-methoxy cinnamic acid (ferulic acid, FAD); 3,5-dimethoxy-4-hydroxy-
acetophenone (acetosyringone, SON)]. Standard lignin phenols were purchased from Sigma-
Aldrich, USA at the highest purity available.
3.3.3.2 Cellulose, Hemicellulose, Klason Lignin
To analyze the solids for their cellulose, hemicellulose and KL concentrations, a ~ 1g sample was extracted in 140 mL of 2:1 toluene methanol and then dried. A (~0.1-0.3 g) subsample was then subjected to a two-stage acid hydrolysis. In the first stage, the sample was hydrolyzed in 3 mL of 72 % (by wt.) sulfuric acid for 1 h at 30 °C. This was followed by a secondary hydrolysis after the addition of 83 mL of deionized water and a fucose
internal standard. The secondary hydrolysis was done in an autoclave at 121 °C for 1 h.
Sugars (arabinose, galactose, glucose, mannose, and xylose) were then analyzed by HPLC
using a pulsed electrochemical detector (110, 111). Anhydro-correction was used to convert
81
glucose to cellulose and the other sugars to hemicellulose. KL was measured from the solids
remaining after acid hydrolysis as the weight loss on ignition at 550 °C for 2 h.
3.3.3.3 Total Organic Carbon (TOC), H, N and Ash
Total organic carbon (TOC) was measured with a CHN analyzer (Perkin-Elmer PE
2400 CHN Elemental Analyzer, Perkin-Elmer Corp.). All samples were acid washed (1 M
HCl) to eliminate inorganic carbon prior to analysis (112). Ash was measured as the residue
after combustion of a known amount of sample at 550 °C for 2 h.
3.3.4 Preparation of Decomposed Material
The goal of the preparation of decomposed materials was to generate solids for
subsequent analysis with negligible background KL, cellulose and hemicelluloses attributable
to the inoculum. Anaerobically decomposed materials were prepared following the
biochemical methane potential (BMP) test with some modifications. The BMP represents an
upper limit on the methane potential of a sample as it is measured under optimal conditions
(70). Test material weighing 2 g was incubated in a 160 -mL serum bottle with 80 mL of
reduced media and 20 mL inoculum according as described (70). The bottle was capped with
a butyl rubber stopper and then sealed with an aluminum crimp. The head space was flushed
with a mixture of CO2/N2 (20/80). Experiments were conducted using mixed cultures. The
BMP inoculum described in Wang et al. (70) is rich in humic materials that could interfere with lignin analyses. Thus, it was necessary to prepare mesophilic inoculum with negligible background lignin.
82
The mesophilic inoculum has been maintained on ground (< 1 mm) residential MSW in
our laboratory for over 15 years at 37 °C and contained residual KL. Serial transfer (1:1 v/v)
of inoculum was done every two weeks into a fresh mixture of reduced media with copy
paper (< 1% KL) as the growth substrate until no significant KL was detected by CuO
analysis, a process that required about 14 transfers over 7 months. The copy paper was
initially tested for methane potential (~250 mL CH4/g).
A glass syringe lubricated with de-ionized water was used to measure overpressure.
Gas composition was analyzed using a gas chromatograph equipped with thermal
conductivity (TCD) and flame ionization detectors (FID) (SRI Instruments, Torrance, CA).
TCD measurement was used for methane concentrations greater than 5%, while the FID was
used to measure methane concentrations less than 5%. Gas volumes were corrected to dry
gas at standard temperature and pressure (STP).
3.3.5 Estimate of MSW Composition
An end-member mixture model was developed to estimate the content of HW, SW,
LG, and GN in MSW samples based on CuO oxidation products (VAL, VON, VAD, SAL,
SON, SAD, CAD, FAD). The p-hydroxy phenols (PAD, PAL, PON) were not used in the analysis as they could come from non-lignin sources. Using knowledge of the CuO
oxidation products of individual components (end members), their relative proportions in
unknown samples was estimated using an end-member mixing model (eq. (3.1)). The end-
member mixing model is an application of conservation of mass and is written in general
terms according to eq. (3.1),
83
g (3.1) Cj=∑π i Ce ij + j i=1
where Cj are the concentrations of lignin phenols in a given sample, ej is the error term, πi is
g th the proportions of i component or source, respectively where 0 ≤ πi ≤ 1 and ∑π i =1. Eq. i=1
(3.1) assumes that lignin CuO oxidation products are a conservative marker. A more detailed description of the end-member mixing model and some statistical challenges have been
presented (122-124). To illustrate the use of eq. 3.1, suppose there are i = 4 lignocellulose
containing components in refuse corresponding to HW, SW, LG and GN, which are identifiable by their CuO oxidation products (j = 8 dimensional variables corresponding to
VAL, VON, VAD, SAL, SON, SAD, CAD, FAD). The objective is to determine the proportions of each component in an unknown sample Cu.
Applying eq. (3.1) to write the mass conservation of VAL for example:
CVALU=+++ππ112 CCCC VAL VAL 23 π VAL 34 π VAL 4 (3.2)
-1 where CVALU is the concentration of VAL in the unknown sample (mg mg Λ8 ). Individual lignin phenols were normalized to the sum of 8 lignin phenols (Λ8 = VAL + VON + VAD +
SAL + SON + SAD + CAD + FAD) so that the concentrations is invariant to dilution due to
cover soil and enrichment due to cellulose and hemicellulose decomposition. Writing the
84
equations for all the other 7 lignin phenols similar to eq. (3.2) [eqs. (S3.1) to (S3.7) of the
SI], result in a system of linear equations with unknowns,π1 , π 2 , π 3 and π 4 . The unknowns
were estimated by inverse modelling, minimizing the sum of squares of residuals between the
measured values and expected value of each lignin phenol (mean lignin phenol
concentration). A numerical solution was determined by implementing box constrained
Broyden–Fletcher–Goldfarb–Shanno (L-BFGS-B) algorithm part of the optimx package in R
(114, 125). Model validation was done with 7 (binary and quaternary) synthetic mixtures of
end-members: HW (11-176 Quercus rubra), SW (11-162 Picea mariana), Grass (11-436
Cynodon dactylon), and needles (11-460 Pinus taeda), the proportions of which were
formulated according to a q=4 component simplex lattice (Table 3.1). After validation, the
model was used to characterize organic matter in samples from the Outer loop, KY landfill.
Table 3.1. Proportions of end members in binary and quaternary synthetic mixtures formulated from simplex lattice to validate the mixture model.
End member HW SW LG GN Synthetic NCSU ID 11-176 11-162 11-436 11-460 Mixture (Quercus rubra) (Picea mariana) (Cynodon dactylon) (Pinus taeda) SM1 11-492 0.5 0.5 0 0 SM2 11-493 0.5 0 0.5 0 SM3 11-494 0.5 0 0 0.5 SM4 11-495 0 0.5 0.5 0 SM5 11-496 0 0.5 0 0.5 SM6 11-497 0 0 0.5 0.5 SM7 11-498 0.25 0.25 0.25 0.25
85
3.4 Results and Discussion
A total of 93 vascular plant species forming a 221-point end member data set were used to develop and validate the waste composition and KL predictive model. This data set was comprised of different species of HW, SW, leaves and grasses and needles. A summary of the characteristics of the plant tissues used for model development and validation is presented in Table S3.1 though Table S3.3. In addition, 30 samples of different lignified pulp and paper products such as ONP, OCC and OMG were also characterized. Finally, the models were applied to estimate the composition of HW, SW, LG and GN as well as the KL in 30 excavated samples from KY landfill.
3.4.1 Compositional Variation of Different Taxonomic Classes of Plants and Pulp and Paper Products
The compositional variations of 8 lignin phenols from CuO oxidation of lignin used as
a biomarker to characterize taxonomic classes of plants are presented in Figure 3.1 and Table
S3.1 (SI). As expected, the major lignin phenols in hardwood are syringyl (S) phenols SAL,
SON and SAD, typical of angiosperm tissues while vanillyl (V) phenols VAL, VON and
VAD are dominant in SW, a gymnosperm. Both HW and SW have low concentrations of cinnamyl (C) phenols (CAD, FAD) which are characteristic of woody tissues. The major
components of LG are S and C phenols that are characteristic of non-woody angiosperm
while V and C are characteristic of GN which is a non-woody gymnosperm. The mean lignin
phenols reported in this study are generally lower but within the range reported by Hedges
86
(35). The difference in the reported lignin phenols are due to species variation as the tissues used in this study and (35) does not exactly match.
87
Figure 3.1. Compositional variations of different lignin phenols liberated during CuO oxidation of lignin from different types of samples. SW-softwood; HW- hardwood; LG-leaves and grass; GN-needles; ONP-Old newsprint; OCC-Old corrugated cardboard; OMG-Old magazine; MSW-Municipal solid waste.
88
The ratio of VAD to VAL, (Ad/Al)V which has been used as an indicator of the extent
of decomposition of lignin is presented in (Figure 3.2 and Table S3.1of SI). The values tend
to cluster around 0.1 - 0.3 with wider range observed in LG and GN. The average (Ad/Al)V
in HW and SW (Table S3.1) are within the range of 0.1-0.2 for fresh woody tissues (60).
Similarly, the average (Ad/Al)V of non-woody tissues are within the range 0.2-1.6 reported
by Thevenot (60) although a value of as high as 1.6 was not observed in 44 species of LG
and GN analyzed in this study. The (Ad/Al)V of anaerobically decomposed samples were not
significantly different from that of fresh samples suggesting that anaerobic decomposition of lignin was not significant.
Figure 3.2. (Ad/Al)V of different taxonomic groups of plants, lignified pulp and paper products and excavated MSW samples.
89
The variation in KL normalized to OC is presented in Figure 3.3. The KL of
angiosperms (HW and LG) is generally lower than that of gymnosperms (SW and GN) which
are consistent with previous reports (5, 7). While the KL of HW is lower than SW, the Λ8 of
HW is higher than SW (Table S3.1). This observation demonstrates the differences in the lignin composition of HW and SW and their reactivity during CuO oxidation. SW lignin is more complex because it is more condensed as a result of the predominance of guaiacyl units that contain 5 positions of the aromatic ring available for bonding (5). The KL of pulp and paper products ONP, OCC and OMG shows that they are within the range of values of the materials (HW and/or SW) from which they were derived. It is interesting to note that a KL of MSW as high as 194 mg 100 mg OC-1 (05-159) was observed in MSW which is well
above the range expected for the pure components. The cellulose and hemicellulose content
of this sample is relatively low (5.9% and 1.9%, respectively), suggesting that the sample was enriched in lignin as a result of decomposition and that the carbon content is primarily from lignin. The variability in the MSW samples tested is due to the fact that a range of samples, from relatively fresh to decomposed material was characterized. The variability in the OCC and OMG is higher than that in the ONP which is likely due to the presence of both chemical and mechanical pulps in these materials, while ONP is all mechanical pulp.
90
Figure 3.3. Variation in KL (mg 100 mg OC-1) of different tissues.
The CuO oxidation products of lignin can be used as a biomarker to infer the plant
tissue source of lignin in an unknown sample using the lignin-derived parameters S/V and
C/V. The S/V ratio has been used to discriminate between angiosperms (HW, leaves and grasses) and gymnosperms (SW and needles) while the C/V ratio has been used to
91
discriminate between woody (HW and SW) and non-woody tissues (leaves, grasses and needles) tissues (35). Based on Figure 3.4 and Figure 3.5, some of the ONP is primarily softwood fiber while some are a mixture of HW and SW. Similarly, the OCC and a majority of the MSW samples are a mixture of HW and SW. The MSW samples tend to cluster between the SW and HW region which suggests that these samples contain pulp and paper from a mixture of SW and HW sources (117).
92
Figure 3.4. S/V as a function of C/V ratio in the tissue samples end member. A-HW; G-SW; a-leaves and grasses; g-needles. G is difficult to see but is congregated in the lower left corner of the figure (S/V and C/V ~ 0).
93
Figure 3.5. S/V as a function of C/V ratio in pulp and paper and MSW.
94
3.4.2 Estimate of MSW Composition
The data set was divided into two groups, the calibration set and the validation set.
From the original data set, 3 each of HW, SW, LG and GN were used for validation. The
remainder of the data was used for calibrating and estimating the parameters of the end
member mixture model. For example, the overall average VAL of HW was 1.85 mg 100mg
OC-1 but the average used for calibration excluded 3 samples and was 1.71 mg 100mg OC-1.
Further validation was done using 7 synthetic mixtures of MSW.
The predicted composition of the calibration and validation data set is presented in
Figure 3.6. The average predicted composition (expected value =1) for the calibration data is
0.97, 0.96, 0.94 and 0.91 for HW, SW, LG and GN respectively. Using the validation set, the
average predicted compositions are 0.86, 0.96, 0.88 and 0.85 for HW, SW, LG and GN,
respectively. The error in prediction of GN is highest because the Λ8 of GN is relatively low and thus more sensitive to variability.
95
Figure 3.6. Estimate of composition for (a) calibration and (b) validation data set. In part a, individual pure tissue samples are presented as a sample number on the x-axis. All samples were analyzed in duplicates that are presented in sequential order. The y-axis represents the predicted composition of the pure tissue. Therefore, in a perfect model, all data points would be at 1.0. In part b, three samples of each of the four pure tissue types were analyzed in duplicate and are presented sequentially for six samples.
96
The result of the model validation using synthetic mixtures is presented in Figure 3.7.
Here too, estimates are weakest for GN (π4). For example, in SM3 which is a 50:50 binary mixture of HW and GN, the model predicted the composition as primarily a HW (0.67) and
LG (0.31) with some SW (0.02). In all synthetic mixtures the model overestimates HW but underestimates GN. The average errors of the composition estimate over the synthetic mixtures with corresponding % of the expected value in parenthesis are 0.11 (15% - 60%),
0.12 (-35% - 41%), 0.16 (-15% - 40%) and 0.25 (-69% - 100%) for HW (π1), SW (π2), LG
(π3), and GN (π4), respectively. The deviation of the predicted composition from that
expected is possibly because of the fact that the component species used in the analysis of
synthetic mixtures have different CuO oxidation monomer concentrations relative to the
mean for each different tissue type. For example, the VAL of the Quercus rubra used in the mixture is 2.14 mg 100 mg OC-1, is higher than the 1.85 mg 100 mg OC-1 average. Similarly,
the VAL content of GN used in formulating the synthetic mixture is about half of the average
value used in formulating the mixing model, resulting in an underestimate of GN. Because
of the constraint that the summation of composition should be equal to 1, the uncertainty in
one end member could be reflected in another.
97
1.0 0.9 0.8 )
π 0.7 0.6 GN 0.5 LG 0.4 SW
Composition ( Composition 0.3 0.2 HW 0.1 0.0 SM1 SM2 SM3 SM4 SM5 SM6 SM7 Synthetic Mixture
Figure 3.7. Predicted composition for synthetic mixtures defined in Table 3.1.
3.4.3 Application to Excavated Samples from Outerloop, KY Landfill
Estimates of the relative composition of landfill samples from the KY landfill are presented in Figure 3.8 showing that the samples are predominantly HW (π1 = 0.49 ± 0.15) and SW (π1 = 0.51 ± 0.15) with little to no LG and GN. This result supports the inference about the composition of this set of samples based on Figure 3.4 and Figure 3.5. The absence of non-woody tissues such as grass and needles in the KY landfill is perhaps a result of an aggressive diversion of yard waste. In addition to the presence of HW and SW lumber, it is likely that the dominant source of lignin in the landfill samples is from pulp and paper
98
products. The national average wood and paper content in discarded MSW is 7.3% and
23.8%, respectively (118).
Figure 3.8. Estimate of composition of excavated samples from KY landfill.
99
3.5 Summary and Conclusions
CuO oxidation products of lignin were used to estimate the composition of mixed samples. The composition of HW and SW can be estimated from CuO oxidation products of lignin with an average error of 0.11 and 0.12 respectively (about 15 – 60 % error from the expected value). The uncertainty in the estimate of synthetic mixtures can be attributed to the fact that the selected components used for the synthetic mixtures have different CuO oxidation products compared to the average which was used in developing the mixing model.
Application of the model to estimate the composition of excavated samples from the
KY landfill showed that the majority of the samples were comprised of HW and SW or pulp derived from these materials with little to no contribution from non-woody tissues (leaves, grasses and needles).
3.6 Acknowledgements
The authors would like to thank David Black of Environmental Engineering laboratory,
NCSU; Dr. Ilona Pezslen of the Department of Forest Biomaterials, NCSU for providing the wood samples; April Bauder of NCSU turf farm for providing the grass samples; and Dr.
Alexander Krings, and Dr. Mark Weathington of Raulston arboretum for assistance on plant tissues collection and identification. Funding for this research was provided by the National
Science Foundation and Waste Management Inc.
100
3.7 Supporting Information
Systems of linear equations used for end-member mixture modelling.
C=+++ππ CCCC π π (S3.1) VONU1 VON 12 VON 23 VON 34 VON 4
C=+++ππ CCCC π π (S3.2) VADU1 VAD 12 VAD 23 VAD 34 VAD 4
C=+++ππ CCCC π π (S3.3) SALU112 SAL SAL 23 SAL 34 SAL 4
C=+++ππ CCCC π π (S3.4) SONU1 SON 12 SON 23SON 34SON 4
C=+++ππ CCCC π π (S3.5) SADU1 SAD 12 SAD 23 SAD 34 SAD 4
CCADU=+++ππ1 CCCC CAD 12 CAD 23 π CAD 34 π CAD 4 (S3.6)
CFADU=+++ππ1 CCCC FAD 12 FAD 23 π FAD 34 π FAD 4 (S3.7)
ππππ1234+++=1 (S3.8)
101
Table S3.1. Compositional variation in CuO phenolic monomers in different lignocellulosic materialsa.
Unit HW (n=32)b SW (n=17)b LG (n=29) b GN (n=15) b ONP (n=15) b OCC (n=7) b OMG (n=8) b MSW (n=30) b
VAL mg/100mg OC 1.85 (0.53) 3.61 (1.1) 0.36 (0.27) 0.56 (0.28) 2.75 (0.92) 1.16 (0.31) 2.32 (0.77) 0.91 (0.38) VON mg/100mg OC 0.27 (0.1) 0.72 (0.23) 0.07 (0.07) 0.13 (0.07) 0.48 (0.25) 0.21 (0.1) 0.37 (0.17) 0.12 (0.08) VAD mg/100mg OC 0.39 (0.2) 0.91 (0.35) 0.09 (0.05) 0.14 (0.06) 0.45 (0.25) 0.24 (0.12) 0.36 (0.1) 0.15 (0.06) SAL mg/100mg OC 6.1 (1.95) 0.16 (0.09) 0.55 (0.45) 0.04 (0.05) 0.92 (0.84) 1.22 (0.72) 0.11 (0.1) 0.72 (0.42) SON mg/100mg OC 1.06 (0.36) 0.07 (0.05) 0.2 (0.19) 0.04 (0.06) 0.2 (0.13) 0.27 (0.16) 0.04 (0.08) 0.09 (0.11) SAD mg/100mg OC 0.69 (0.33) 0.02 (0.04) 0.13 (0.12) 0.05 (0.04) 0.09 (0.08) 0.11 (0.1) 0.02 (0.04) 0.03 (0.07) CAD mg/100mg OC 0.03 (0.07) 0.04 (0.03) 0.78 (0.99) 0.39 (0.17) 0.01 (0.03) 0.01 (0.01) 0.01 (0.02) 0 (0) FAD mg/100mg OC 0.11 (0.11) 0.15 (0.11) 0.58 (0.63) 0.17 (0.12) 0.12 (0.13) 0.07 (0.1) 0.11 (0.12) 0.02 (0.12) c S mg/100mg OC 7.85 (2.43) 0.24 (0.17) 0.88 (0.63) 0.13 (0.14) 1.21 (1.01) 1.6 (0.92) 0.16 (0.22) 0.84 (0.55) c V mg/100mg OC 2.51 (0.77) 5.23 (1.42) 0.52 (0.35) 0.82 (0.38) 3.67 (1.33) 1.61 (0.48) 3.05 (0.97) 1.19 (0.47) c C mg/100mg OC 0.14 (0.13) 0.19 (0.11) 1.36 (1.58) 0.56 (0.27) 0.13 (0.15) 0.08 (0.11) 0.12 (0.13) 0.02 (0.12) c Λ8 mg/100mg OC 10.5 (2.71) 5.67 (1.49) 2.75 (2.36) 1.51 (0.6) 5.01 (1.66) 3.28 (1.34) 3.33 (1.26) 2.04 (0.93) (Ad/Al)V - 0.21 (0.09) 0.26 (0.11) 0.31 (0.19) 0.29 (0.17) 0.16 (0.06) 0.21 (0.07) 0.16 (0.05) 0.18 (0.04) c S/V - 3.31 (1.05) 0.05 (0.04) 2 (1.43) 0.18 (0.21) 0.37 (0.33) 0.96 (0.51) 0.04 (0.04) 0.7 (0.37) c C/V - 0.06 (0.07) 0.04 (0.03) 2.31 (1.75) 0.79 (0.44) 0.03 (0.04) 0.04 (0.05) 0.04 (0.03) 0.02 (0.09) d KL mg/100mg OC 56.11 (9.61) 69.3 (10.21) 54.01 (12.87) 69.54 (8.73) 58.31 (7.8) 39.44 (13.55) 61.02 (20.21) 94.33 (38.07) a. Standard deviation (sd) is presented parenthetically. b. HW – Hardwood; SW – Softwood; LG – Leaves and Grass; GN – Needles; ONP – Old newsprint. c. Calculated individually then the average is reported. S – syringyl phenols; V – Vanillyl phenols; C – Cinnamyl phenols; Λ8 – Sum of S, V and C; (Ad/Al)V – Ratio of vanillin to vanillic acid. d. KL – Klason Lignin; ASL – Acid soluble lignin
102
Table S3.2. Decomposed materials included in the model.
Scientific Name NCSU ID Tissue Type Carbon Loss (%)a Cornus florida 11-141 HW 17.45 (0.4) Quercus rubra 11-176 HW 24.70 (1.60) Acer rubrum 11-184 HW 35.20 (0.34) Pinus taeda 11-158 SW 16.30 (0.30) Tsuga caroliniana 11-160 SW 12.11 (0.19) Picea mariana 11-162 SW 17.02 (0.2) Cynodon dactylon 11-436 LG 40.9 (1.90) Zoysia japonica 11-437 LG 54.54 (3.61) Eremochloa ophiuroides 11-438 LG 58.48 (1.65) Acer rubrum 11-432 LG 44.32 (3.48) Cornus florida 11-433 LG 31.42 (0.18) Quercus virginiana 11-461 LG 33.62 (0.02) Tsuga caroliniana 11-434 GN 38.76 (0.56) Picea abies 11-435 GN 28.77 (1.18) Pinus taeda 11-460 GN 27.12 (0.04) ONP New York Times 11-450 ONP 25.42 (0.36) ONP LA Times 11-459 ONP 40.57 (0.02) ONP News and Observer 11-466 ONP 42.3 (3.99) OMG 3rd class mail 11-454 OMG 52.85 (0.06) OMG ESPN magazine sept 2011 11-455 OMG 14.84 (2.11) OMG W magazine sept 2011 11-456 OMG 52.57 (0.1) OCC Bio rad laboratory chemicals box 11-457 OCC 54.98 (0.23) OCC Fisher scientific box 11-463 OCC 48.74 (3.35) OCC BD 60ml syringe box 11-462 OCC 45.32 (0.14) a. Expressed as % of the initial carbon, standard deviation is presented parenthetically.
103
Table S3.3. Chemical characteristics of different groups of lignocellulosic materialsa.
TOC Hemi- Klason Volatile Cellulose Extractives NCSU ID Scientific Name Common Name Typeb (mg cellulose lignin solids (%) (%) mg-1) (%) (%) (%) 11-389 Abies Firma Momi fir GN 0.52 19.17 11.67 69.22 96.2 15.45 11-400 Cupressocyparis leylandii Leyland cypress GN 0.51 13.80 11.17 66.55 94.7 16.18 11-407 Juniperus chinensis Chinese juniper GN 0.48 20.71 12.91 55.74 94.4 9.77 11-411 Metasequoia glyptostroboides Dawn redwood GN 0.48 11.61 6.83 72.92 94.3 9.95 11-416 Pinus strobus Torulosa GN 0.44 17.37 13.55 75.25 ND ND 11-419 Pseudolarix amabili Golden Larch GN 0.48 25.32 8.23 58.20 ND 6.35 11-422 Sequoia sempervirens California redwood GN 0.49 14.69 9.39 67.28 95.2 9.185 11-423 Taxodium distichum Cascade Falls GN 0.50 7.29 5.24 71.39 95.6 8.255 11-428 Thuja occidentalis Eastern Arborvitae GN 0.45 15.13 10.04 82.08 95.3 21.9 11-434 Tsuga caroliniana Carolina Hemlock GN 0.50 24.37 10.50 57.81 96.8 14.30 11-435 Picea abies Norway Spruce GN 0.50 20.97 11.17 57.60 94.9 16.99 11-460 Pinus taeda Loblolly pine GN 0.50 19.89 13.03 64.46 97.8 11.25 13-230 Pinus taeda (AD) 13-231(171) GN 0.33 13.21 8.83 80.70 ND ND 13-232 Picea abies (AD) 13-232(172) GN 0.32 10.23 7.24 74.82 ND ND 13-234 Tsuga caroliniana (AD) 13-234(174) GN 0.31 10.03 7.10 85.63 ND ND 07-151 Quercus sp. Jenny's Wood Samples HW 0.45 40.50 19.60 52.91 99.6 ND 11-137 Acer saccharum Sugar maple HW 0.42 36.19 15.50 57.76 99.2 1.08 11-141 Cornus florida Flowering dogwood HW 0.41 33.12 17.14 55.47 98.3 1.22 11-142 Ostrya virginiana Ironwood HW 0.41 30.16 18.22 60.72 98.9 2.03 11-143 Betula nigra Red birch HW 0.42 35.71 17.47 51.34 99.65 0.09 11-144 Fraxinus americana White ash HW 0.42 40.25 11.87 61.72 99.65 1.25 11-145 Carpinus caroliniana Blue beech HW 0.41 33.27 18.22 54.37 99.1 0.94
104 Table S3.3. Continued TOC Hemi- Klason Volatile Cellulose Extractives NCSU ID Scientific Name Common Name Typeb (mg cellulose lignin solids (%) (%) mg-1) (%) (%) (%) 11-146 Aesculus octandra Buckeye HW 0.42 30.49 10.87 55.52 99.6 0.55 11-147 Tilia americana Basswood HW 0.41 39.75 14.57 47.27 99.5 2.23 11-148 Ailanthus altissima Tree of Heaven HW 0.40 46.88 15.66 39.53 99.1 2.97 11-149 Salix nigra Black willow HW 0.41 45.99 12.92 48.77 99 3.20 11-150 Quercus nigra Water oak HW 0.42 37.73 14.33 51.01 98.8 3.12 11-151 Castanea dentata Chesnut HW 0.44 35.15 14.64 66.66 99.8 4.03 11-152 Prunus serotina Black cherry HW 0.42 36.79 18.09 55.89 99.8 3.68 11-153 Populus grandidentata Bigtooth aspen HW 0.41 44.32 15.22 45.20 99.65 1.82 11-157 Alnus rubra Red alder HW 0.42 40.22 13.76 58.42 99.55 0.51 11-159 Malus malus Apple HW 0.41 34.33 17.79 58.89 99.5 1.64 11-164 Quercus laevis Turkey oak HW 0.43 35.78 14.99 63.49 99 2.53 11-165 Quercus cinerea Blue jack oak HW 0.42 35.42 16.80 63.54 99 2.1 11-167 Populus trichocarpa Black cottonwood HW 0.43 44.22 13.86 53.37 100 1.29 11-168 Swietenia mahogani Mahogany HW 0.45 33.34 8.44 88.92 99.6 2.26 11-171 Pyrus communis Common pear HW 0.42 31.06 16.94 60.80 99.9 0.21 11-172 Quercus viginiana Live oak HW 0.43 33.68 12.83 65.65 99.5 1.95 11-173 Populus alba White Poplar HW 0.41 43.18 15.71 45.47 100.2 -0.11 11-174 Castanea dentata Chesnut HW 0.44 35.15 14.64 67.07 99.8 4.03 11-176 Quercus rubra red oak HW 0.42 37.77 17.36 52.13 99.8 3.11 11-179 Betula papyrifera Paper birch HW 0.46 38.95 21.72 37.26 99.7 2.47 11-183 Ulmus alata Winged elm HW 0.44 36.77 14.95 67.30 ND 2.12 11-184 Acer rubrum red maple HW 0.43 41.98 16.13 52.36 ND 2.27 13-216 Acer rubrum (AD) 13-216(156) HW 0.24 28.51 10.94 65.90 ND ND 13-218 Cornus florida (AD) 13-218(158) HW 0.30 25.91 10.96 55.75 ND ND 13-64 Quercus rubra (AD) M-HW reactor (13-65) HW 0.29 30.28 10.61 51.85 ND ND
105 Table S3.3. Continued TOC Hemi- Klason Volatile Cellulose Extractives NCSU ID Scientific Name Common Name Typeb (mg cellulose lignin solids (%) (%) mg-1) (%) (%) (%) 09-386 Fresh Raleigh Grass Eleazer LG 0.35 25.60 14.80 61.40 85 ND 11-309 Betula nigra Red birch LG 0.47 13.53 8.61 70.05 95.5 8.09 11-393 Camelia japonica white by the gate LG 0.44 13.05 7.72 58.95 93 6.62 11-395 Carpitus betulus European Hornbeam LG 0.46 16.02 9.30 48.58 94.7 3.76 11-397 Clethra alnifolia Sweet pepperbrush LG 0.46 10.53 8.38 84.54 92.9 3.63 11-401 Cyperus alternifolius Umbrella Palm LG 0.42 29.33 15.74 44.33 92.5 4.86 11-402 Dasylirion leiophyllum Desert candle LG 0.50 23.45 12.16 42.94 97.6 8.26 11-404 Ilex calina Carolina holly LG 0.49 17.11 8.59 55.38 94 9.21 11-412 Miscanthus sinensis Maiden grass LG 0.37 33.13 20.42 48.21 ND ND 11-414 Nyssa sylvatica Black tupelo LG 0.47 10.59 9.76 46.57 95.9 4.31 11-417 Poaceae muhlenbergia Pink Flamingos LG 0.43 34.76 22.81 39.88 94.2 4.44 11-418 Prunus incisa Fuji cherry LG 0.49 8.69 6.76 66.50 93.6 7.06 11-430 Yucca gloriosa Sapanish dagger LG 0.37 22.82 10.48 46.55 91 11.07 11-431 Iris unguicularis Dwarf winter iris LG 0.44 30.68 8.63 38.34 94 6.31 11-432 Acer rubrum red maple LG 0.48 12.33 5.23 49.74 91.9 16.52 11-433 Cornus florida Flowering dogwood LG 0.45 15.21 6.13 40.17 91.7 11.34 11-436 Cynodon dactylon Bermuda Grass LG 0.45 18.54 17.94 44.34 92.6 4.79 11-437 Zoysia japonica Zoysia grass LG 0.45 32.85 22.22 36.27 95.2 4.75 11-438 Eremochloa ophiuroides Centipede grass LG 0.45 28.57 20.84 37.56 92.1 5.46 11-439 Nolina nelsoni Blue nolina LG 0.43 25.53 11.30 49.52 ND 9.95 11-442 Yucca flaccida Weak leaf yucca LG 0.43 21.91 10.02 45.11 ND 14.48 11-443 Hemerocalis littoralis Beach spider lily LG 0.40 29.28 8.20 53.77 93.5 9.67 11-461 Quercus viginiana Live oak LG 0.47 15.88 9.80 67.56 95.3 6.54 13-224 Acer rubrum (AD) 13-224(164) LG 0.30 3.29 2.46 71.51 ND ND 13-226 Cornus florida (AD) 13-226(166) LG 0.29 6.18 4.26 70.30 ND ND
106 Table S3.3. Continued TOC Hemi- Klason Volatile Cellulose Extractives NCSU ID Scientific Name Common Name Typeb (mg cellulose lignin solids (%) (%) mg-1) (%) (%) (%) 13-228 Quercus virginiana (AD) 13-228(168) LG 0.30 8.57 6.24 90.24 ND ND 13-236 Zoysia japonica (AD) 13-236(176) LG 0.25 10.27 11.47 66.24 ND ND 13-238 Eremochloa ophiuroides (AD) 13-238(178) LG 0.23 9.20 6.81 63.42 ND ND 13-76 Cynodon dactylon(AD) M-Grass reactor (13-78) LG 0.26 9.36 11.49 64.81 ND ND 07-150 Picea sp. SW from Jenny's reactors SW 0.45 42.00 20.30 93.96 98.5 ND 11-136 Sequoia sempervirens Redwood SW 0.48 32.93 10.69 87.67 99.6 3.76 11-138 Tsuga heterophyla Western hemlock SW 0.44 35.63 11.70 78.89 99.15 0.65 11-139 Pinus monticola Western white pine SW 0.45 37.98 16.46 61.09 99.55 5.49 11-154 Pinus strobus Eastern white pine SW 0.45 36.79 15.91 69.28 99.7 2.92 11-156 Tsuga canadensis Eastern hemlock SW 0.44 38.47 15.20 70.44 99.65 1.46 11-158 Pinus taeda Loblolly pine SW 0.47 38.74 17.85 58.45 99.5 10.39 11-160 Tsuga caroliniana Carolina Hemlock SW 0.44 36.05 14.40 75.30 99.7 0.97 11-169 Pinus banksiana Jack Pine SW 0.45 40.78 17.23 61.72 100.25 3.12 11-170 Pinus serotina Pond pine SW 0.44 39.52 17.06 62.53 99.8 2.17 11-175 Pinus resinosa Red pine SW 0.45 37.88 19.15 59.55 99.5 5.46 11-178 Tsuga caroliniana Carolina Hemlock SW 0.45 32.21 13.70 76.86 99.2 2.45 13-220 Picea mariana (AD) 13-220(160) SW 0.29 23.76 11.66 76.79 ND ND 13-222 Tsuga caroliniana (AD) 13-222(162) SW 0.32 23.57 10.64 77.84 ND ND 13-70 Pinus taeda (AD) M-SW reactor SW 0.31 26.33 11.65 67.43 ND ND a. ND-no data; AD –anaerobically decomposed. b. HW- hardwood; SW-softwood; LG-leaves and grasses; GN- needles
107
Table S3.4. Chemical characteristics of different MSW and pulp and paper productsa.
NCSU TOC Hemi- Klason lignin Volatile Sample Description Typeb Cellulose (%) Extractives (%) ID (mg mg-1) cellulose (%) (%) solids (%) 03-175 Excavated KY landfill sample MSW 0.26 26.36 7.44 75.00 51.1 ND 03-200 Excavated KY landfill sample MSW 0.32 39.55 9.22 50.60 70.8 ND 03-201 Excavated KY landfill sample MSW 0.36 43.00 9.65 56.99 77.7 ND 03-209 Excavated KY landfill sample MSW 0.32 32.70 8.74 55.08 66.7 ND 03-262 Excavated KY landfill sample MSW 0.36 45.81 11.22 43.82 79.4 ND 03-267 Excavated KY landfill sample MSW 0.32 38.39 10.31 49.12 66.55 ND 04-10 Excavated KY landfill sample MSW 0.34 39.88 9.75 49.88 71.9 ND 04-13 Excavated KY landfill sample MSW 0.32 37.12 9.14 45.33 65.5 ND 04-15 Excavated KY landfill sample MSW 0.32 33.02 7.37 49.27 70.25 ND 04-16 Excavated KY landfill sample MSW 0.33 33.36 7.62 57.78 70.6 ND 04-18 Excavated KY landfill sample MSW 0.33 39.81 9.05 47.68 75.15 ND 05-147 Excavated KY landfill sample MSW 0.10 2.79 1.15 119.59 20.15 ND 05-148 Excavated KY landfill sample MSW 0.11 8.16 2.78 102.02 22.25 ND 05-151 Excavated KY landfill sample MSW 0.14 6.37 1.97 139.74 29.6 ND 05-157 Excavated KY landfill sample MSW 0.09 5.01 1.64 118.01 17.45 ND 05-162 Excavated KY landfill sample MSW 0.05 2.92 1.01 141.12 13.85 ND 05-165 Excavated KY landfill sample MSW 0.09 6.02 1.81 103.80 23.65 ND 05-166 Excavated KY landfill sample MSW 0.06 2.20 0.97 136.33 15.8 ND 05-168 Excavated KY landfill sample MSW 0.10 4.35 1.92 99.33 23.05 ND 05-169 Excavated KY landfill sample MSW 0.04 1.12 0.51 116.07 11 ND 05-170 Excavated KY landfill sample MSW 0.12 4.23 1.48 126.93 24.9 ND 05-171 Excavated KY landfill sample MSW 0.22 15.63 5.11 94.95 42.35 ND 05-172 Excavated KY landfill sample MSW 0.13 5.93 1.64 83.93 26.45 ND 05-177 Excavated KY landfill sample MSW 0.07 4.82 1.53 130.44 18.3 ND
108 Table S3.4. Continued NCSU TOC Hemi- Klason lignin Volatile Sample Description Typeb Cellulose (%) Extractives (%) ID (mg mg-1) cellulose (%) (%) solids (%) 05-178 Excavated KY landfill sample MSW 0.04 1.87 0.76 149.72 10.8 ND 05-179 Excavated KY landfill sample MSW 0.09 3.90 1.17 92.81 18.25 ND 05-185 Excavated KY landfill sample MSW 0.11 4.59 1.74 109.31 23.8 ND 05-188 Excavated KY landfill sample MSW 0.14 3.24 1.12 74.34 19.15 ND 05-195 Excavated KY landfill sample MSW 0.09 5.39 1.73 114.96 23.5 ND 05-202 Excavated KY landfill sample MSW 0.19 8.53 2.99 95.71 35.3 ND 09-391 Eleazer sample OCC 0.44 58.90 13.91 31.84 97.8 ND 11-457 OCC 1 OCC 0.44 63.27 13.82 23.15 96.3 2.11 11-462 OCC 3 OCC 0.46 60.62 15.13 30.83 98 1.4 11-463 OCC 2 OCC 0.44 57.57 14.25 31.22 96.2 1.97 13-240 Bio rad laboratory chemicals box (AD) OCC 0.23 25.88 5.24 53.66 ND ND 13-242 Fisher Scientific box (AD) OCC 0.26 26.22 5.07 60.07 ND ND 13-244 BD 60ml syringe box (AD) OCC 0.27 31.57 6.05 56.45 ND ND 08-82 Australia Paper OMG 0.34 34.49 11.86 57.57 71.4 ND 09-380 Eleazer sample OMG 0.43 57.30 9.90 48.37 98.2 ND 11-454 ESPN Magazine dated Sept 2011 OMG 0.32 36.26 11.34 60.48 75.9 2.72 11-455 W Magazine dated Sept 2011 OMG 0.33 39.26 9.73 44.17 72.2 3.77 11-456 CPU Magazine OMG 0.35 47.45 11.68 30.47 77 3.02 13-252 3rd class mail (AD) OMG 0.17 14.20 4.41 62.80 ND ND 13-254 ESPN magazine sept 2011 (AD) OMG 0.23 19.17 6.96 82.11 ND ND 13-256 W magazine sept 2011 (AD) OMG 0.16 9.92 4.06 89.52 ND ND 08-78 Australia Paper ONP 0.42 45.24 17.96 57.33 95.9 ND 08-79 Australia Paper ONP 0.42 43.25 17.91 60.84 95.6 ND 09-379 Eleazer sample ONP 0.42 48.50 9.00 57.41 98.5 ND 09-388 Eleazer sample ONP 0.43 48.50 9.00 55.58 98.5 ND 11-449 Washington Post dated Aug 2011 ONP 0.43 43.18 16.50 50.36 94.7 6.43
109 Table S3.4. Continued NCSU TOC Hemi- Klason lignin Volatile Sample Description Typeb Cellulose (%) Extractives (%) ID (mg mg-1) cellulose (%) (%) solids (%) 11-450 News and Observer dated Aug 2011 ONP 0.45 52.94 16.58 36.33 96.8 3.19 11-451 Houston Chronicle dated Sept 2011 ONP 0.42 42.39 17.38 57.26 96.3 8.40 11-458 Indianapolis star ONP 0.44 39.30 17.86 65.55 99.6 7.89 11-459 Los Angeles Times ONP 0.44 48.67 16.36 44.37 93.5 2.57 11-466 New York Times ONP 0.50 42.36 18.35 54.68 99.3 2.63 11-467 Chicago Tribune ONP 0.44 42.99 18.12 59.76 99.3 7.72 13-246 New York Times (AD) ONP 0.30 27.39 9.81 68.68 ND ND 13-248 LA Times (AD) ONP 0.26 22.88 8.16 71.23 ND ND 13-250 News and Observer (AD) ONP 0.27 22.85 6.68 63.08 ND ND 13-82 Washington Post dated (AD) ONP 0.28 16.19 7.30 69.83 ND ND
a. ND-no data; AD –anaerobically decomposed. b. HW- hardwood; SW-softwood; LG-leaves and grasses; GN- needles
110
4. Chemical Structure Changes during Anaerobic Decomposition of Lignocellulose
under Mesophilic and Thermophilic Conditions
(Formatted for submission to Organic Geochemistry)
Florentino B. De la Cruz*1, Daniel J. Yelle2, Hanna Gracz3 and Morton A. Barlaz1
1Department of Civil, Construction, and Environmental Engineering, Campus Box 7908,
North Carolina State University, Raleigh, North Carolina 27695-7908
2United States Forest Service, Forest Products Laboratory, 1 Gifford Pinchot Dr., Madison,
Wisconsin 53726
3Department of Molecular and Structural Biochemistry, 128 Polk Hall North Carolina State
University, Raleigh, North Carolina 27695-7622
*Corresponding author phone: (919)513-4421; fax: (919)515-7908; email:
4.1 Abstract
Anaerobic decomposition of plant biomass is an important aspect of global organic carbon cycling. While the anaerobic metabolism of cellulose and hemicellulose to methane and carbon dioxide are well-understood, evidence for the incipient stages in the decomposition of lignin in lignocellulosic materials is fragmentary. The objective of this
111
study was to look for evidence of chemical transformations of lignin in plant biomass under
both mesophilic and thermophilic conditions where lignin associations with structural
carbohydrates are retained in a more native state. Hardwood (HW- Quercus rubra), softwood
(SW- Pinus taeda), grass (Cynodon dactylon) and old newsprint (ONP) were anaerobically decomposed in the laboratory. The decomposed residue was analyzed both by chemical degradation and spectroscopic techniques. Results showed significant carbon loss in all materials with the highest in grass (40% and 47% for mesophilic and thermophilic decomposition, respectively). For HW, grass and ONP, the carbon losses could be attributed primarily to cellulose and hemicellulose and some fractions of lignin. On the other hand, the carbon loss in SW could be attributed to the uncharacterized carbon fractions (e.g. extractives etc.). The HSQC NMR spectra showed a slight reduction in the β-O-4 linkage of lignin in
HW and ONP. It was surprising to observe reduction of guaiacyl (G) units HSQC region in lignin in HW, grass and ONP. Nonetheless, the Klason and acid soluble lignin as well as
CuO lignin data indicate no substantial de-polymerization of lignin. It is possible that the loss of guaiacyl (G2) region is primarily a result of side chain reaction with no destruction of the
aromatic structure.
Keywords: Anaerobic decomposition, CuO oxidation, lignin, HSQC, NMR.
112
4.2 Introduction
Woody tissues makes up about 75% of terrestrial plant biomass which in turn is
estimated to represent 0.95×1018g, or 29% of the active global organic carbon reservoir
(126). As plant tissues are comprised primarily of lignocellulosic material, the study of lignocellulose decomposition is essential to understanding carbon turnover in the environment. Plant biomass is made up primarily of three biopolymers: cellulose, hemicelluloses and lignin. While both cellulose and hemicellulose are readily converted to methane and carbon dioxide during anaerobic decomposition, lignin is generally considered preserved (71). Information on the chemical changes in lignocellulose during anaerobic decomposition is important towards understanding the fate and reactivity of lignocellulose- derived decomposition products in anaerobic environments such as landfills, which are
estimated to receive about 149 million metric tons of municipal solid waste (MSW) annually
in the U.S. (117). Lignocellulose in MSW takes the form of paper products, wood, food and
yard waste and the storage of carbon in landfills due to the recalcitrance of these materials is well-documented (101, 127, 128).
The decomposition of lignocellulose in anaerobic environments follows the general scheme by which complex biopolymers are hydrolyzed to oligomers and monomers, which are then fermented to produce short chain fatty acids. These intermediates are then utilized via a synthrophic methanogenic pathway, ultimately mineralizing the substrate to methane and carbon dioxide. Anaerobic metabolism involves non-oxygen electron acceptors such as
113
- 2- NO3 and SO4 . In methanogenesis, no external electron acceptor is required as part of the
parent molecule is oxidized while part of it is reduced (66).
The anaerobic metabolism of cellulose and hemicellulose in both mesophilic and
thermophilic environments are well-documented (68-72). However, because of its
complexity, the anaerobic metabolism of the lignin polymer is not as well-understood.
Studies on different lignin-derived monomers, oligomers and lignin isolates have provided the foundation of our understanding of the anaerobic decomposition behavior of lignin and lignin-derived compounds. Lignin-derived phenolic monomers have been completely mineralized anaerobically via nitrate reduction (73-75), methanogenesis (76-80), as well as by photosynthetic bacteria (81, 82). Several studies have demonstrated the methanogenic fermentation of benzoate by anaerobic consortia (62, 73, 76, 80, 81, 83, 84). In addition, lignin monomer substituent, -OCH3, can serve as a C1 substrate for acetate-producing
acetogenic bacteria (85).
Different lignin preparations vary in terms of their extent of decomposition (18, 85,
95). Colberg and Young studied different molecular weight fractions of lignin and
demonstrated that mineralization is inversely proportional to molecular weight (97). In other
studies, anaerobic decomposition of both naturally-derived milled wood lignin (MWL) and
synthetic 14C-labeled lignin resulted in minute mineralization (68, 72). These studies
provided evidence that the limiting factor to the decomposition of lignin is the initial
sequence of steps where the lignin polymer is cleaved to more degradable fractions. The
structural features of lignin impose unusual constraints on biodegradation and systems
114
responsible for initial attack on lignin must be extracellular, non-specific, and non-hydrolytic
(129). According to Heirder (99), one of the initial steps in anaerobic decomposition of lignin-like compounds is the demethylation of aromatic substituents, cleaving of β–O–4 linkage and eventually to the destruction of aromaticity by reductive ring cleavage.
Several approaches have been employed to study lignin degradation including lignin isolation and isotopic labeling (18, 68, 69, 130-132). The widely accepted lignin preparation representative of the native lignin is MWL (7, 19). MWL preparation involves removal of extractives by Soxhlet extraction, ball-milling, dioxane/water (96:4 by volume) extraction followed by precipitation in water as discussed in detail elsewhere (7, 15, 19). However, this
lignin preparation only accounts for up to 30% of total lignin (133) and still suffers from bias
from fractionation during solvent extraction with dioxane/water (7). Some modifications of
the procedure included addition of an enzyme treatment step to remove the carbohydrates
and to improve lignin yield (134). This preparation however, is not entirely free of
carbohydrates, thus, complicating the traditional stoichiometric approach to anaerobic
biodegradation study. Moreover, results from decomposition studies using lignin isolates do
not represent the actual behavior of lignin in its native form during decomposition of
lignocellulose. On the other hand, lignin isotopic labeling by growing plants fed with 14C –
labeled precursors such as phenylalanine to form [14C-lignin] lignocellulose has been limited
to soft/non-woody tissues and subject to 14C-protein contamination (69).
Advances in high resolution nuclear magnetic resonance spectroscopy (NMR) and the
development of a method to completely solubilize the entire plant cell wall (107, 135-137)
115 make it possible to observe structural transformations of lignocellulose components. For example, this approach has been previously employed to look for evidence of lignin decomposition by brown rot fungi (138, 139) and in the structural characterization of thermochemically treated plant biomass (140).
The objective of this study was to look for evidence of chemical changes in lignin during anaerobic decomposition of different plant materials under mesophilic and thermophilic conditions. To our knowledge, this is the first time that high resolution 2D
NMR of completely dissolved cell walls has been used to look for evidence of chemical transformations during anaerobic decomposition of different lignocellulosic materials in their native state, that is, when the natural complex associations between cellulose, hemicellulose and lignin are retained.
4.3 Materials and Methods
4.3.1 Experimental Design
Anaerobic decomposition experiments were conducted with lignocellulosic materials
(HW-hardwood Quercus rubra; SW-softwood Pinus taeda; grass Cynodon dactylon; and
ONP-old newsprint Washington Post) at mesophilic (37 °C) and thermophilic (55 °C) temperatures. Plant materials were selected based on their taxonomic classes which also represent the type of lignin that they contain. These classes included woody angiosperm
(HW), non-woody angiosperm (grass) and woody gymnosperm (SW). The characteristics of
116
the test materials are presented in Figure S4.1 of the supporting information (SI). ONP was
selected to represent a pulp and paper product in which lignocellulose is partially delignified.
Copy paper was selected as a positive control because it contains primarily cellulose and very
little lignin.
Each material was incubated under conditions that are optimal for anaerobic
decomposition including the presence of an inoculum and biological growth medium.
Decomposition was allowed to proceed until no significant methane production was
measured so to allow the bioavailable substrates to be consumed. In order of preference,
lignin degradation will not proceed unless readily degradable substrates such as cellulose and
hemicelluloses have been depleted. The incubation period for different materials ranged from
467 days to 585 days. At the completion of incubation, the solid residue was recovered by
drying the entire contents of the bottle at 50°C. The solid residue was subjected to the wet
chemical and spectroscopic analyses as described below.
4.3.2 Sample collection and preparation
Wood samples were obtained from the collection of Dr. Ilona Peszlen of the
Department of Forest Biomaterials at NC State University. Grass was collected from the NC
State University Turf farm in September 2011. The copy paper used in the experiments is a
commercially available white paper. The ONP was represented by the Washington Post and
was collected in August 2011. Samples were dried at 50°C, then ground to pass a 60-mesh
screen (0.251 mm opening) in a Wiley Mill, and then re-dried at 50°C to constant weight.
117
This temperature is well below the predicted glass transition temperature of lignin (141).
Samples were stored in mason jars at room temperature until they were processed for NMR
analysis.
4.3.3 Preparation of Anaerobically Decomposed Materials
The goal of the preparation of decomposed materials was to generate solids for
subsequent analysis with negligible background lignin, cellulose and hemicelluloses.
Anaerobically decomposed materials were prepared following the biochemical methane
potential (BMP) test with some modifications. The BMP represents an upper limit on the
methane potential of a sample as it is measured under optimal conditions (70). Test material
weighing 2 g was incubated in a 160 -mL serum bottle with 80 mL of reduced media and 20
mL inoculum according to Wang et al. (70). The bottle was capped with a butyl rubber
stopper and then sealed with an aluminum crimp. The head space was flushed with a mixture
of CO2/N2 (20/80). Experiments were conducted using mixed cultures. The BMP inoculum
described in Wang et al. (70) is rich in humic materials that could interfere with lignin
analyses. Thus, it was necessary to prepare mesophilic and thermophilic inocula with
negligible background lignin.
The mesophilic inoculum has been maintained on ground (< 1 mm) residential MSW in
our laboratory for over 15 years at 37 °C. The thermophilic inoculum was obtained from a
thermophilic anaerobic sludge digester (Carrboro, NC) operated at 55 °C and was collected in March 2011. Serial transfer (1:1 v/v) of the inoculum was done every two weeks into a
118
fresh mixture of reduced media with copy paper as the growth substrate until no significant
lignin was detected by CuO analysis, a process that required about 14 transfers over 7
months. The copy paper was initially tested for methane potential (~250 mL CH4/g) to ensure
that the paper does not contain inhibitory substances. Copy paper used in the experiments
contained less than 1% Klason lignin.
A glass syringe lubricated with de-ionized water was used to measure the overhead gas
volume. Gas composition was analyzed using a gas chromatograph equipped with thermal
conductivity (TCD) and flame ionization detectors (FID) (SRI Instruments, Torrance, CA).
TCD measurement was used for methane concentrations greater than 5%, while the FID was
used to measure methane concentration less than 5%. Gas volumes were corrected to dry gas at standard temperature and pressure (STP).
4.3.4 Analytical Methods
4.3.4.1 Alkaline CuO Digestion and HPLC Detection
A sample containing 2-5 mg C equivalent was placed in a 25 mL Teflon vial with 0.5 g
CuO powder, 0.1g Fe(NH4)2(SO4)2∙6H2O and 5 mL of 2M NaOH (prepared using deionized
water boiled and cooled under N2). The vial was then sealed and locked under a N2
headspace in an anaerobic hood. Digestion was conducted in a furnace at 150 °C for 3 h.
After digestion, the vial was partially immersed in an ice bath to stop the reaction and allowed to cool to room temperature.
119
The vial was then sonicated for 15 minutes after which the contents were quantitatively
transferred to a 50 mL centrifuge tube for centrifugation at 3500 rpm for 10 minutes. The
extract was decanted into a glass centrifuge tube and the remaining solids were washed with
5 mL of 2M NaOH. The mixture was centrifuged at 3500 rpm for another 10 minutes and the
extract was combined with the initial extract. The extract was neutralized and acidified (pH ≤
2) with 6 mL of 12 M HCl and then transferred quantitatively to a 100 -mL volumetric flask.
An aliquot of this solution was further diluted as necessary to obtain a concentration within
the calibration range (0.05-25 µM for each phenolic monomer) and was analyzed using a
Shimadzu LC-20AT high performance liquid chromatograph (HPLC) equipped with a
degasser (DGU-20A5) and a SPD 20A Photodiode Array Detector (PDA). Chromatographic
analyses were conducted using a gradient program at 55 °C using a Phenomenex Kinetex
2.6u C18 100A column as described previously (109). Mobile phase A is H3PO4 (pH = 2.5),
mobile phase B is methanol, and mobile phase C is acetonitrile. The HPLC system was
reconditioned to the initial condition by flushing with acetonitrile and then with mobile phase
A after every sample. The system pressure ranged from 105 to 145 bar during analysis. After each injection, the sampler was purged with a methanol/water solution 1:1 (v/v).
Lignin-based phenolic compounds were identified by using retention times and light absorption characteristics from external standard runs (0.05-25 µM). The calibration curve was linear within this concentration range. The spectra of each monomer were measured in the wavelength range of 230 to 370 nm, which covers the maximum absorption for all lignin phenols. Integration of the peaks for each lignin phenol was done at its respective maximum
120
wavelength using Shimadzu LC solutions software. The 11 lignin phenols liberated during
CuO oxidation that were analyzed are as follows with the corresponding acronyms: [4- hydroxybenzoic acid (PAD); 4-hydroxybenzaldehyde (PAL); 4-hydroxy-3-methoxy-benzoic acid (vanillic acid, VAD); 4-hydroxyacetophenone (PON); 4-hydroxy-3-methoxy- benzaldehyde (vanillin, VAL); 3,5-dimethoxy-4-hydroxy-benzoic acid (syringic acid, SAD);
4-hydroxy-cinnamic acid (p-coumaric acid, CAD); 3,5-dimethoxy-4-hydroxy-benzaldehyde
(syringaldehyde, SAL); 4-hydroxy-3-methoxy-acetophenone (acetovanillone, VON); 4- hydroxy-3-methoxy cinnamic acid (ferulic acid, FAD); 3,5-dimethoxy-4-hydroxy- acetophenone (acetosyringone, SON)]. All HPLC samples and standard solutions were filtered (0.2 µm, Millipore Millex Teflon filter) prior to injection. Aqueous solutions were prepared using filtered (0.2 µm) and UV-treated de-ionized water. Standard lignin phenols were purchased from Sigma-Aldrich, USA at the highest purity available.
4.3.4.2 Cellulose, Hemicelluloses, Klason Lignin and Acid-Soluble Lignin
To analyze the solids for their cellulose, hemicelluloses and Klason lignin concentrations, a ~ 1g sample was extracted in 140 mL of 2:1 toluene methanol and then dried. A known weight (~0.1-0.3 g) of ground sample was subjected to a two-stage acid hydrolysis. In the first stage, the sample was hydrolyzed in 3 mL of 72% (by wt.) sulfuric acid for 1 h at 30 °C. This was followed by a secondary hydrolysis after the addition of 83 mL of deionized water and a fucose internal standard. The secondary hydrolysis was done in an autoclave at 121 °C for 1 h. Sugars (arabinose, galactose, glucose, mannose, and xylose)
121
were then analyzed by HPLC using a pulsed electrochemical detector (110, 111). Anhydro-
correction was done to convert glucose to cellulose and the other sugars to hemicelluloses.
Klason lignin was measured from the solids remaining after acid hydrolysis as the weight
loss on ignition at 550 °C for 2 h. Acid-soluble lignin was determined from UV absorbance
at 205 nm of the filtered (0.45 µm) acid hydrolysate from the second stage digestion. The
maximum absorbance was recorded at 280 nm. However, due to formation hydroxymethylfurfural from sugars during acid hydrolysis, 205 nm was used (7, 142). The
Extinction coefficient of 110 at 205 nm was used to calculate the acid-soluble lignin from the
measured UV absorbance (142).
4.3.4.3 Total Organic Carbon (TOC), H, N and Ash
Total organic carbon (TOC) was measured with a CHN analyzer (Perkin-Elmer PE
2400 CHN Elemental Analyzer, Perkin-Elmer Corp.). All samples were acid washed (1 M
HCl) to eliminate inorganic carbon prior to analysis (112). Ash was measured as the residue
after combustion of a known amount of sample at 550 °C for 2 h.
4.3.4.4 Methoxyl Determination
The methoxyl content of the samples were determined by gas chromatography as
developed previously with some modifications (143-145). Briefly, a 10 mg sample was
digested with 0.5 mL of 57 % HI in a 22 -mL Teflon septa-sealed headspace vial (Tekmar,
Teledyne, USA) at 130 °C for 35 min. The digestate was allowed to cool to room
122 temperature and neutralized by addition of 0.5 mL of 6M NaOH. The headspace was analyzed for methyl iodide using an Agilent 7890A GC equipped with headspace auto sampler (Teledyne 7000, Teledyne, USA), capillary column (30 m × 0.25 mm × 0.1µm) and flame ionization detector (FID) at 250 °C. The carrier gas was 3.8 mL He /min. Headspace gas concentration was determined from an external calibration curve constructed by the full evaporation technique injecting 0.1 µL to 10 µL pure methyl iodide as previously described
(143).
4.3.4.5 Cell Wall Dissolution
Complete dissolution of plant cell walls for subsequent high resolution solution state nuclear magnetic resonance (NMR) spectroscopy has been described previously (107, 135-
137). A ground sample (< 60 mesh) weighing 2 g was ball-milled in toluene using alumina fortified porcelain jars charged with zirconia grinding media (6.4 × 6.4 mm) in a 0.4L jar mill rotating at 30 rpm under a N2 headspace (7, 16, 17). Optimal charging of 45-55% (v/v) was used as per manufacturer’s instructions (Cole-Parmer, USA). A ball-milling experiment showed that complete dissolution is possible after 14-28 days depending on the sample.
Studies have shown that ball-milling results in only a minor increase in detectable cleaved lignin structures and ball-milling in toluene is recommended as it serves to regulate temperature and acts as a radical scavenger (16, 17). After ball-milling, the sample suspended in toluene was centrifuged at 2850 rpm for 15 min. The toluene layer was aspirated and the remaining solvent was removed by evaporation at 50 °C under vacuum.
123
A ball-milled sample weighing 600 mg was dissolved in 10 mL of dimethylsulfoxide
(DMSO) and 5 mL N-methylimidazole (NMI). A clear amber color solution was produced
after stirring for at least 4 h. The solution was acetylated in-situ by addition of 3mL acetic
anhydride and stirred for 1.5 h after which a dark brown solution was formed (Figure 4.1).
The acetylated cell wall solution was precipitated in 2 L of de-ionized water, stirred
overnight, recovered by vacuum filtration (0.2 µm nylon filter), washed with 250 mL of DI
water and then dried under vacuum (50 °C). The yield of acetylated cell wall was about
136% of the initial weight of material which is typical for sample acetylation (107). After
drying, 90 mg of acetylated cell wall was dissolved in 0.75 mL DMSO-d6 (Cambridge
Isotope laboratories, MA) and then transferred to a 5 mm NMR tube for analysis.
(a) Ball-milled sample (b) Dissolution in DMSO/NMI (c) Acetylated Cell wall
Figure 4.1 In preparation for high resolution NMR spectroscopy, the samples are dissolved in a solvent system of DMSO/NMI and then acetylated in-situ.
124
4.3.4.6 Nuclear Magnetic resonance (NMR) Spectroscopy
One-bond 1H-13C Heteronuclear Single Quantum Coherence (HSQC) NMR spectra
were obtained at Bio-NMR facility of the Department of Molecular and Structural
Biochemistry, NC State University, using a Bruker Avance III 700 MHz spectrometer
equipped with QNP cryoprobe implementing the Bruker’s hsqcetgpsisp2.2 pulse sequence
program at 30 °C. HSQC is a 2D NMR experiment which creates cross-peaks that correlates
the hydrogen in a molecule with its directly attached carbon (4). The F2 acquisition time for
these NMR experiments were about 8h while about 1 h for F1 acquisition time. The DMSO-
d6 peak (δH/δC = 2.49 ppm/39.5 ppm) was used for internal calibration reference. Data processing and integration was done using Topspin 3.2. No attempts were made to do NMR experiments using non-acetylated samples. Acetylation of whole cell wall does result in the loss of information on the natural acetates found on mannan and xylan.
4.3.4.7 NMR Quantification
Peak assignment was performed using the lignin NMR database (146) and comparison with previously published lignin spectra for different lignin preparations (136, 147-157).
Calculations to obtain absolute quantification of different substructures were done as follows using the methoxyl content obtained from wet chemical analyses per reference (138).
= 2 × 3 푚푚표푙 퐶−푂퐶퐻 2� � 푙푖푛푘푎푔푒 푚푚표푙 푙푖푛푘푎푔푒 푔 − 푠푎푚푝푙푒 퐶 � � 퐷 3 푔 − 푠푎푚푝푙푒 퐷−푂퐶퐻
125
Where:
= concentration of a given linkage or substructure 푚푚표푙 퐶푙푖푛푘푎푔푒 �푔−푠푎푚푝푙푒� 2 = HSQC volume integral of a given linkage or substructure.
퐷푙푖푛푘푎푔푒 = concentration of methoxyl group in the sample as determined by wet 푚푚표푙 3 퐶−푂퐶퐻 �푔−푠푎푚푝푙푒� chemistry
2 = HSQC volume integral of methoxyl
−푂퐶퐻3 퐷
Since the intensity of the HSQC regions are dependent on a coupling constant,
comparisons of the different substructures were done on the basis of their Hα-Cα correlations
having a similar chemical environment. The resinol volume was divided by two as two correlations are involved in each linkage unit. To quantify the C9 units of lignin, the volume
integral for H2/6-C2/6 correlations of the HSQC spectra was used for H and S units while H2-
C2 was used for G units. The volume integral of H2-C2 correlation of G units was logically
doubled because it involves a single correlation (135). The different lignin substructures were
-1 -1 normalized to C9 units assuming a molecular weight of 220 mg mmol , 198 mg mmol , and
216 mg mmol-1 for HW, SW, and grass, respectively. The molecular weight ONP was
assumed to be equal to that of SW (see Results and Discussion).
126
4.4 Results and Discussion
4.4.1 Carbon Loss
The carbon loss from anaerobic decomposition of different lignocellulosic materials representative of MSW components is presented in Figure 4.2. The high carbon losses in the
positive control demonstrate the viability and the suitability of the anaerobic inocula to
anaerobically convert substrate to methane.
As expected, the carbon loss is highest in the non-woody grass sample while comparable carbon loss was observed in HW and ONP. The lowest carbon loss was recorded
in SW at about 16 ± 0.3% (average ± sd) and 13 ± 3% for mesophilic and thermophilic
decomposition, respectively. The carbon losses presented in Figure 4.2 are higher than that
previously reported using laboratory-scale decomposition experiments (158, 159). The
differences in yield are perhaps a result of the smaller particle size and lower solids to liquid
ratio in BMP tests conducted here relative to reactor studies designed to measure ultimate
methane yields under simulated landfill conditions in which materials were shredded (3 x 4
cm) and had a higher solids to liquid ratio.
127
100 Mesophilic 90 88.31 Thermophilic ) 80 75.25
70
60
50 47.4 40.1 40 32.8 29.2 29.8 30 C loss (% initial C mass C initial (% loss C 24.7 20 16.3 13.2 10
0 Copy Paper HW (Q. rubra) SW (P. taeda) Grass (C. ONP dactylon)
Figure 4.2. Carbon loss during anaerobic decomposition of different lignocellulosic materials under mesophilic and thermophilic conditions. HW-Hardwood; SW-Softwood; ONP-Old newsprint. Error bar represents ±sd.
The inoculum used in this study was enriched on copy paper which does not contain
significant amounts of lignin. This may have promoted shifts in the relative abundance of different microbial communities in the culture. However, comparison of the carbon losses
measured in this study for lignocellulosic substrates are comparable to values reported
previously in tests with an inoculum enriched on MSW that would contain lignocellulosic
substrates (70, 159). Thus, the use of a lignin-free inoculum did not influence the extent of
128
decomposition and the measured C conversions suggest that all the readily degradable
substrates have been depleted or that the accessibility to the readily degradable substrates is
limited by the lignin to which the polysaccharides are encased in, putting pressure on microorganisms to utilize more recalcitrant substrates such as lignin.
4.4.2 Structural Carbohydrates
The plant cell wall is composed of the structural carbohydrates cellulose and hemicelluloses, which account for the majority of its dry mass. Cellulose is a homopolymer of glucose linked together by β (1→4) glycosidic linkages. The linear structure of cellulose makes up its crystalline tertiary structure, which are the building blocks of cellulose microfibril. Hemicelluloses are heteropolymers of different sugar residues, the composition of which varies according to tissue type. Evidence has shown that hemicelluloses link cellulose microfibril and lignin together, making up the cell wall lignocellulose composite
(5). The plant cell wall’s rigidity and chemical reactivity is attributed to these intricate and complex associations between lignin and structural carbohydrates.
The changes in sugar residues after anaerobic decomposition of different lignocellulosic materials could provide insights on the reactivity and the preferential attack of anaerobic microorganisms to the different cell wall carbohydrates (Figure 4.3). For HW, the
major sugar residues are glucose from cellulose and xylose from xylan, with xylan being a major component in angiosperms. About 12% and 36% of the initial glucose and about 36%
129 and 30% initial xylose were lost after mesophilic and thermophilic anaerobic decomposition, respectively.
HW (Q. rubra) SW (P. taeda) 50 50 Initial Initial 45 45 Mesophilic Mesophilic 40 40 Thermophilic Thermophilic 35 35 30 30 25 25 20 20 15 15 10 10 5 5
Sugars Composition (% (% of Composition mass) initial Sugars 0
Sugars Composition (% (% of Composition mass) initial Sugars 0 Ara Gal Glu Xyl Man Ara Gal Glu Xyl Man Grass (C. dactylon) ONP 50 50 Initial Initial 45 45 Mesophilic Mesophilic 40 40 Thermophilic Thermophilic 35 35 30 30 25 25 20 20 15 15 10 10 5 5 Sugars Composition (% (% of Composition mass) initial Sugars Sugars Composition (% (% of Composition mass) initial Sugars 0 0 Ara Gal Glu Xyl Man Ara Gal Glu Xyl Man
Figure 4.3. Sugars composition during decomposition under mesophilic and thermophilic conditions normalized to the initial mass of the sample. Ara – Arabinose; Xyl – Xylose; Man – Mannose; Gal – Galactose; Glu – Glucose.
130
In grass, another angiosperm, the glucose loss shown in Figure 4.3 is equivalent to
53% and 84% while xylose loss is about 45% and 48% for mesophilic and thermophilic
decomposition, respectively. In ONP, a SW mechanical pulp (determined from CuO
oxidation), the glucose loss was 57% and 46%, while mannose loss was about 34% and 30%
for mesophilic and thermophilic decomposition respectively. Mannose sugar residue comes
from glucomannan which is a major component of SW hemicelluloses. The change in
cellulose and hemicelluloses composition after mesophilic and thermophilic decomposition is
presented in Figure 4.4. As expected, the highest losses of cellulose (52% and 81%) and
hemicelluloses (42% and 74%) were observed in grass, for mesophilic and thermophilic decomposition, respectively. Significant cellulose and hemicelluloses loss was also recorded in HW and ONP.
131
50 Cellulose 45 Hemicellulose 40 35 30 25 20 15 10 5 Holocellulose(% of Holocellulose(% of initial mass) 0 Initial Initial Initial Initial Mesophilic Mesophilic Mesophilic Mesophilic Thermophilic Thermophilic Thermophilic Thermophilic HW (Q. rubra) SW (P. taeda) Grass (C. dactylon) ONP
Figure 4.4. Cellulose and hemicelluloses (i.e, holocellulose) composition during anaerobic decomposition under mesophilic and thermophilic conditions. Error bar represents ±sd.
There was no statistically significant (p>0.05) change in cellulose and hemicelluloses in SW for both mesophilic and thermophilic decomposition). This result is surprising because we observed relatively small significant C losses in SW for both mesophilic and thermophilic decomposition (Figure 4.2). The result of the cellulose and hemicelluloses analysis suggests that the methane and carbon dioxide mineralization in softwood did not come from cellulose and hemicelluloses.
132
While ONP was derived from SW pulp (determined from CuO oxidation products as discussed below); its decomposition behavior is significantly different from the SW that we
tested. Significant carbon loss from cellulose and hemicellulose was recorded in ONP. This
demonstrates the effect of mechanical pulping as well as the role of lignin on the
accessibility/bioavailability of cellulose and hemicelluloses in SW.
4.4.3 Lignin CuO Oxidation Products
The CuO oxidation products of initial and anaerobically decomposed samples are
presented in Figure 4.5. For HW, the sum of 8 lignin phenols (Λ8) after mesophilic and
thermophilic decomposition is not significantly different when compared with the initial
-1 material (14.0 ± 0.6 mg 100mg OC). The Λ8 is a measure of total releasable CuO lignin
monomers normalized to OC and could indicate degradation/re-polymerization of lignin during decomposition. Similarly, for HW, the acid to aldehyde ratio of vanillyl phenols
(Ad/Al)V of the initial material (0.15), and after mesophilic (0.12) and thermophilic (0.17) decomposition are not significantly different (p>0.05) from each other. The (Ad/Al)V is a
measure of the extent of degradation of lignin. The result suggests that HW lignin was not
significantly degraded after anaerobic decomposition.
-1 For SW, the Λ8 of mesophilic (5.3 ± 0.3 mg 100mg OC) is not significantly different
-1 when compared with the initial material (4.9 ± 0.2 mg 100mg OC). The Λ8 increase in
thermophilic (7.5 ± 0.2 mg 100mg-1 OC) is anomalous and there is no indication that it is a result of decomposition. Meanwhile, the (Ad/Al)V of SW initial material (0.40), and the SW
133 after mesophilic (0.40) and thermophilic (0.40) decomposition are not significantly different
(p>0.05) from each other which suggests no significant lignin degradation.
Significant losses of cinnamyl phenols were recorded in Grass. The decreases shown in
Figure 4.5 corresponds to about 32% and 56% CAD loss (mesophilic and thermophilic conditions, respectively) and about 34 and 54% FAD loss (mesophilic and thermophilic conditions, respectively). Non-woody lignin such as grass is composed of the cinnamates p- coumarate and ferulate that have been demonstrated to be reactive under anaerobic decompositions (99, 160, 161). Meanwhile, the (Ad/Al)V of grass did not significantly change after either mesophilic or thermophilic decomposition when compared with the initial material. This suggests that the cinnamates were metabolized without significant de- polymerization of lignin, which is further supported by the Klason lignin result presented below.
The CuO oxidation products of lignin could also be used as a biomarker to fingerprint the plant tissue source of lignin in an unknown sample. The ratio of syringyl (sum of SAL,
SON and SAD) to vanillyl (sum of VAL, VON and VAD) (S/V) has been used to discriminate between angiosperms (HW, leaves and grasses) and gymnosperms (SW and needles) tissues while the ratio of cinnamyl (sum of CAD and FAD) to vanillyl lignin phenols (C/V) has been used to discriminate between woody (HW and SW) and non-woody tissues (leaves, grasses and needles) tissues (35). The low C/V and low S/V ratios indicate that the source of lignin in ONP is softwood.
134
HW (Q. rubra) SW (P. taeda) 10.0 10.0 9.0 9.0 Initial Initial 8.0 8.0 Mesophilic Mesophilic 7.0 7.0 Thermophilic Thermophilic 6.0 6.0 5.0 5.0 4.0 4.0 initial mass) initial initial mass) initial 3.0 3.0 2.0 2.0 1.0 1.0 0.0 0.0 Lignin Phenol Yield (mg/100 mg (mg/100 mg of OC Yield Phenol Lignin Lignin Phenol Yield (mg/100 mg (mg/100 mg of OC Yield Phenol Lignin VAL VON VAD SAL SON SAD CAD FAD VAL VON VAD SAL SON SAD CAD FAD Lignin Phenols Lignin Phenols
Grass (C. dactylon) ONP 5.0 7.0 4.5 Initial Initial 6.0 4.0 Mesophilic Mesophilic 3.5 5.0 Thermophilic Thermophilic 3.0 4.0 2.5 2.0 3.0 initial mass) initial initial mass) initial 1.5 2.0 1.0 1.0 0.5 0.0 0.0 Lignin Phenol Yield (mg/100 mg (mg/100 mg of OC Yield Phenol Lignin Lignin Phenol Yield (mg/100 mg (mg/100 mg of OC Yield Phenol Lignin VAL VON VAD SAL SON SAD CAD FAD VAL VON VAD SAL SON SAD CAD FAD Lignin Phenols Lignin Phenols
Figure 4.5. Changes in CuO lignin composition during anaerobic decomposition under mesophilic and thermophilic conditions normalized to the initial mass of the sample. Error bar represents ±sd.
While the lignin CuO oxidation products of ONP are similar to that of SW, their decomposition behaviors are different. Significant loss of VAL phenols was recorded in ONP for both mesophilic and thermophilic decomposition. The (Ad/Al)V of ONP initial material
(0.13) is not significantly different from mesophilic (0.15) and thermophilic (0.14) decomposition (p>0.05). This indicates that the lignin polymer has not been significantly
135
depolymerized during decomposition. The loss of VAL without a corresponding increase in
(Ad/Al)V is an indication that no significant lignin cleavage occurred during anaerobic
decomposition of ONP. This result suggests that reaction of VAL is limited to side chain
oxidation without destruction of aromatic structure.
4.4.4 Lignin Methoxyl
The methoxyl content of initial and anaerobically-decomposed samples is presented in
Figure 4.6. The C9 normalized methoxyl contents of initial materials were 1.74, 1.00 and 0.54
-1 mmol –OCH3 mmol C9 unit for HW, SW, grass and ONP, respectively. As SW is
dominated by guaiacyl (G) units with minute amounts of syringyl units (S), it contains one methoxyl per C9 unit. Similarly, since HW is a combination of both S (that contains two
methoxyl per C9 unit) and G units, the methoxyl content per C9 unit is between one and two.
The methoxyl content of ONP confirms the SW character of this material having one
methoxyl per C9 unit. Meanwhile, the low methoxyl content of grass is because in addition S
and G units, an increased amount of p-hydroxyphenyl groups (H) which does not contain
lignin is also present in grass. For comparison, Miscanthus sinensis, a grass with comparable
lignin content as the grass used in this study (Cynodon dactylon) contains 0.45 mmol –OCH3
-1 mmol C9 unit (5).
After anaerobic decomposition, methoxyl loss was 23% and 13% for HW, 27% and
38% in Grass, and 35% and 20% for ONP for mesophilic and thermophilic decomposition,
respectively. The loss in the methoxyl content of SW for both mesophilic and thermophilic
136
decomposition was not statistically significant (p>0.05). The loss of methoxyl content in
grass and ONP are statistically similar between mesophilic and thermophilic conditions. In contrast, the methoxyl loss in HW was significantly greater under mesophilic conditions
(p<0.05).
2.0 Initial 1.8 Mesophilic 1.6 Thermophilic 1.4
1.2
1.0 OCH3/g mass) initial OCH3/g - 0.8
0.6
0.4 OCH3 (mmolOCH3 - 0.2
0.0 HW (Q. rubra) SW (P. taeda) Grass (C. dactylon) ONP
Figure 4.6. Changes in lignin methoxyl composition during decomposition under mesophilic and thermophilic conditions normalized to the initial mass of the sample. Error bar represents ±sd.
137
Evidence of the demethoxylation of lignin-derived molecules under anaerobic
14 conditions has been reported previously where C –labelled aromatic –OCH3 is metabolized to CO2 and acetates (85). The results of this study support the notion that one of the first steps in the metabolism of aromatic molecules is removal of aromatic substituents such as methoxyl groups (99) which in turn can be metabolized by acetogens (85). Perhaps because of the complexity of SW lignin polymer, these initial steps in lignin decomposition are more difficult compared to other lignocellulosic substrates.
4.4.5 Klason and Acid Soluble Lignin
The Klason and acid-soluble lignin (ASL) contents of initial and anaerobically decomposed samples are presented in Figure 4.7. While Klason lignin is operationally- defined as the volatile fraction of the acid-insoluble material remaining after acid hydrolysis, it is widely used to measure the total lignin of a sample (5, 7).
Results showed that the loss of HW Klason lignin under mesophilic conditions (13%) was significant (p<0.05) while no significant difference was observed under thermophilic conditions. In contrast, losses of HW ASL were only significant under thermophilic conditions. In some tissues such as in non-woody angiosperms, the amount of ASL can be significant. Moreover, the amount of acid-soluble lignin could also be an indicator of the extent of decomposition as some lignin can be broken down to lower molecular weight fractions and then to soluble fractions rendering them more amenable to anaerobic decomposition.
138
35 Acid-soluble lignin 30 Klason Lignin 25
20
15
10 Lignin (% (% Lignin of mass) initial 5
0 Initial Initial Initial Initial Mesophilic Mesophilic Mesophilic Mesophilic Thermophilic Thermophilic Thermophilic Thermophilic HW (Q. rubra) SW (P. taeda) Grass (C. ONP dactylon)
Figure 4.7. Changes in Klason and acid-soluble lignin composition during decomposition under mesophilic and thermophilic conditions normalized to the initial mass of the sample. Error bar represents ±sd.
There were no significant decreases either Klason lignin or ASL for SW. As noted above, anaerobic decomposition of SW resulted in small but significant carbon loss. It is interesting to note, however, that based on the result of the CuO oxidation products of lignin, structural carbohydrates, as well as Klason and acid-soluble lignin, the carbon loss recorded in SW was not due to the cellulose, hemicellulose or lignin but from other components. To evaluate whether this observation is plausible, the amount of carbon that can be attributed to
139
these major lignocellulosic components was estimated assuming empirical formulae for
cellulose, hemicellulose and lignin as (C6H10O5)n, (C5H8O4)n, C(9)H(7.92)O(2.40)(OCH3)(0.92),
respectively. Based on the empirical formulae and their corresponding compositions, about
85% of the carbon in SW can be attributed to cellulose and hemicellulose and lignin. Thus, the uncharacterized cell components of SW (e.g. proteins, tannins, extractives etc.) amounting to about 15% of the total carbon (estimated by difference), in theory could be a source of CH4 and CO2 which could explain the observed carbon loss. Furthermore, this
notion is supported by the result of extractives in the softwood sample which was determined
to be 17 ± 0.73% of the initial mass.
It is well-documented that SW lignin is dominated by guaiacyl units. This lignin unit in
is more complex because of more branching as a result of the availability of the 5-position in
lignin phenolic units to crosslink, making the SW lignin polymer more difficult to
depolymerize (5, 7). Moreover, the complex structure of SW lignin hinders microbial
enzyme access to structural carbohydrates, resulting in minor mineralization of cellulose,
hemicelluloses and lignin.
There was not a significant change in Klason lignin in grass while the ASL decreased
by 37% and 38% for mesophilic and thermophilic decomposition, respectively. This result
suggests that the observed losses of CAD and FAD from CuO oxidation are primarily due to
the loss of acid soluble lignin fractions. It should be noted, however, that the CuO lignin is a
measure of the releasable monomers mostly from the uncondensed fractions of lignin which
140
could only account for as much as 30% of Klason lignin (7) and can change depending on the
extent of decomposition.
4.4.6 Lignin Sub-structures
Decomposition of lignocellulose components may not necessarily result in mass loss due to mineralization to CH4 and CO2. The ability of lignin to de-polymerize and re-
polymerize makes the study of the transformation of lignin complicated (3). In this section,
evidence of transformations on different lignocellulose component is examined using HSQC
NMR spectra. The different lignin and carbohydrates sub-structures are illustrated in Figure
4.8. Figure 4.9 to Figure 4.12 shows the aliphatic and the aromatic regions of the HSQC spectra of different test materials. Partial assignment of the HSQC spectra was done by using the lignin NMR database (146) and by comparison with published spectra of similar tissue types (136, 147-157). The spectra for initial and decomposed materials are quite similar and the only difference was the intensities of different contours that is why only the spectra of the initial material are presented. The difference between the initial and decomposed material is reflected in the volume integrations of different contours corresponding to different lignin substructures as summarized in Table 4.1. Moreover, the HSQC spectra were color
referenced for easy comparisons.
Quantitative comparisons of the absolute amount of different sub-structures are
presented in Table 4.1 for different materials (initial material, and after mesophilic and thermophilic anaerobic decomposition). Absolute quantification of different sub-structures
141
was based on the methoxyl content determined from the wet chemical analysis. The different
lignin substructures were normalized to C9 units assuming molecular weights of 220 mg
mmol-1, 198 mg mmol-1, and 216 mg mmol-1 for HW, SW, and grass, respectively. The
molecular weight of ONP was assumed to be equal to that of SW because based on CuO
oxidation products, the ONP sample used in the study came from SW pulp.
The relative abundance of different lignin substructures have been reported for
different type of tissues. The majority of inter – C9 unit linkages in lignin are β-O-4, followed by significantly smaller and varying amount of resinol, phenylcoumaran, spirodienone, dibenzodioxocin structures (Figure 4.8). For example, in Birch (Betula pendula roth), the relative abundance of lignin inter – C9 unit linkages is about 69% β-O-4, 17% resinol, 3% phenylcoumaran and 4% spirodienone estimated from 2D HSQC spectra (157). Grasses, in addition to these structures, contains a high degree of acetylation and significant amounts cinnamyl end groups (155, 156, 162).
The HSQC spectra of HW are presented in Figure 4.9 (aliphatic region and aromatic region). The aliphatic region in Figure 4.9a, illustrates the well-resolved peak corresponding to the Hα-Cα (δH/δC = 5.91 ppm/74.3 ppm) correlation in the β-O-4 linkage with distinct
separation between the threo and erythro diastereomers. Figure 4.9a also shows the intense methoxyl peak (δH/δC = 3.60 ppm/55.3 ppm). The changes in lignin substructures as
calculated from the 2D HSQC spectra are presented in Table 4.1 for initial material,
mesophilic and thermophilic anaerobic decomposition. Three of the major inter C9 unit
linkages in lignin were quantified as presented in Table 4.1. While other lignin substructures
142
have been identified, their quantities were not calculated because the relative amount is small
and are subject to a lot of uncertainty. In addition to lignin substructures, Figures 4.9-4.12
show the different structures found in cellulose and hemicelluloses. The black regions in the aliphatic regions of HSQC spectra are unidentified overlapping peaks primarily due to carbohydrates.
In the aromatic regions (Figure 4.9b), the intense H2/6-C2/6 (red δH/δC = 6.65 ppm/102.7
ppm) contour region is due to syringyl units (S), which are major lignin phenols in
angiosperms such as HW. In a majority of the HSQC spectra, we detected NMI
contamination due to incomplete washing. This did not affect the NMR quantification as
NMI peaks did not overlap with regions that were used for quantification. The H2-C2
correlation in guaiacyl (G) units is also well-resolved in HW aromatic region of the HSQC
spectra. The low proportion of p-hydroxyphenyl groups (H) is a characteristic of woody
tissues.
As expected, the major lignin inter-C9 unit linkage is β-O-4. We observed a slight
-1 reduction in the amount of this linkage in HW from 0.136 mmol mmol C9 unit for the initial
-1 -1 material to 0.124 mmol mmol C9 unit and 0.112 mmol mmol C9 unit for mesophilic and thermophilic decomposition, respectively (Table 4.1). Anaerobic decomposition resulted in a
decrease in the amount of β-O-4 linkage brought about by α-oxidation. Anaerobic
decomposition of HW slightly increased the amount of α-carbonyl (S2/6 α-ketone) from
-1 -1 0.0179 mmol mmol C9 unit for the initial material to 0.0191 mmol mmol C9 unit and
-1 0.0214 mmol mmol C9 unit for mesophilic and thermophilic decomposition respectively.
143
The HSQC spectra of SW are presented in Figure 4.10. There is no evidence of lignin
degradation observed using degradative methods as described previously so the NMR spectra
of decomposed SW were not acquired. The spectrum of the initial material is presented here
for completeness.
The HSQC spectra of grass are presented in Figure 4.11. Unlike lignin from woody
tissue, non-woody lignin such as grass lignin is characterized by high degree of acetylation
and the presence of cinnamates (pCA and FA). Like in HW, the major inter-C9 unit linkage
in grass is β-O-4 which did not seem to have undergone significant change after
decomposition (Table 4.1). The absolute amount of β-O-4 in grass is significantly lower than
that of HW and SW which can be attributed to the composition of monomeric units (H, S, G and the presence of cinnamates) (19). The difference in the amounts of β-O-4 linkages in different tissues can also be demonstrated by the CuO oxidation products of these tissues.
During CuO oxidation, β-O-4 is the primary linkage that is cleaved, thus the yield of CuO oxidation (i.e. Λ8) can be an indicator of the absolute amount of β-O-4 in a sample. While
the lignin content of the grass sample used in this study is comparable with that of HW, the
difference in monomeric compositions results in a smaller amount β-O-4 which in turn resulted in smaller CuO monomer yields.
-1 The amount of G units in grass decreased from 0.108 mmol mmol C9 unit for the
-1 -1 initial material to 0.030 mmol mmol C9 unit and 0.060 mmol mmol C9 unit for mesophilic and thermophilic decomposition, respectively.
144
The HSQC spectra of ONP are presented in Figure 4.12. The aliphatic region is quite similar to that of SW with the well-resolved H2-C2 correlation of G aromatic peak (δH/δC =
7.01 ppm/110.8 ppm). Similar to a woody tissue, the majority of the lignin inter-C9 unit
linkages are β-O-4. We observed a slight reduction in the amount of β-O-4 of anaerobically
-1 decomposed ONP with 0.085 mmol mmol C9 unit for the initial material while 0.059 mmol
-1 -1 mmol C9 unit and 0.077 mmol mmol C9 unit for mesophilic and thermophilic
decomposition, respectively (Table 4.1). There seem to be no significant change in the both resinol and phenylcoumaran content of both mesophilic and thermophilic anaerobic decomposition of ONP.
In the aromatic region, the absence of S2/6 peak confirms that the ONP sample was
made from SW pulp. The very low proportion of p-hydroxyphenyl group is a characteristic
of woody tissues as observed in HW and SW. It is interesting to note that similar to HW, the
amount of G decreased when compared with the initial material.
145
L O OCH 3 OH γ H3CO OCH γ OH O 3 β O (H3CO) α β HO HO β α O γ' O β O α O HO β α' OH γ α β' OCH3 γ α OAr O H3CO
H CO 3 (OCH 3)
O A B C D E β-aryl ether (β-O-4) Resinol (β- β’) Phenylcoumaran Dibenzodioxocin Spirodienone (β-1’) (β-5) (5-5’/ β-O-4, α-O-4) O O L OH L1 OH L1 OH 1 L1 α OH α α 7 8
2 2 1 6 6 6 2 5 3 5 1 OCH3 H3CO OCH3
O O O L O L2 2 L2 L 2 H G S X pCA Cinnamyl p-coumarate Alcohol end group O O OH L1 OH O 4 6 5 O 7 8 O HO O 2 1 1 OH HO 3 OH n OCH3 O L2 FA F Ferulate Cellulose
Figure 4.8. Linkages and substructures in lignin and lignocellulose. L-lignin polymer.
146
(a) Aliphatic
(b) Aromatic
Figure 4.9 HSQC spectra of HW (Quercus rubra) (a) Aliphatic Region; (b) Aromatic Region.
147
(a) Aliphatic
(b) Aromatic
Figure 4.10. HSQC spectra of SW (Pinus taeda) (a) Aliphatic Region; (b) Aromatic Region.
148
(a) Aliphatic
(b) Aromatic
Figure 4.11. HSQC spectra of Grass (Cynodon dactylon) (a) Aliphatic Region; (b) Aromatic Region.
149
(a) Aliphatic
(b) Aromatic
Figure 4.12. HSQC spectra of ONP (a) Aliphatic Region; (b) Aromatic Region.
150
Table 4.1. Structural characteristics of initial and anaerobically decomposed lignocellulose calculated from volume integration of 1 13 the H– C correlation signals in the HSQC spectrum normalized to lignin C9 unit.
Linkage lignin Linkage amount (mmol mmol C unit-1) substructures 9 H-C HW (Q. rubra) SW (P. taeda) Grass (C. dactylon) ONP Correlation Ia Ma Ta Ia Ma Ta Ia Ma Ta Ia Ma Ta
β-O-4 structures (A) Hα-Cα 0.136 0.124 0.112 0.080 ND ND 0.010 0.011 0.007 0.085 0.059 0.077
Resinol (B) Hα-Cα 0.015 0.012 0.012 0.003 ND ND 0.006 0.000 0.000 0.006 0.005 0.007
Phenylcoumaran (C) Hα-Cα 0.010 0.004 0.006 0.014 ND ND 0.008 0.000 0.004 0.018 0.014 0.019 Aromatic Methoxylb H-C 1.744 1.480 1.490 1.010 1.008 0.906 0.539 0.434 0.383 1.097 0.658 0.883
H H2/6-C2/6 0.002 0.001 0.001 0.000 ND ND 0.001 0.000 0.001 0.002 0.002 0.001
G H2-C2 0.167 0.103 0.099 0.348 ND ND 0.108 0.030 0.060 0.311 0.195 0.133
S H2/6-C2/6 0.249 0.236 0.220 0.007 ND ND 0.035 0.031 0.038 0.002 0.002 0.001 S/G ratio 1.5 2.3 2.4 0.01 ND ND 0.3 1.0 0.9 0.04 0.02 0.02 a. I-initial material; M – mesophilic decomposition; T – Thermophilic decomposition. ND-no data. b. Determined by wet chemistry.
151
4.5 Summary and Conclusions
The anaerobic decomposition study of lignocellulosic materials is highly correlated
with the type of lignin. For example, the HW and SW lignin are less reactive than the soft tissue lignin such as grass. While the ONP tested came from softwood, higher carbon
conversions were recorded in ONP compared to SW. This demonstrates the capacity of lignin to limit the reactivity of cellulose and hemicellulose under anaerobic decomposition.
Variation in reactivity of lignin methoxyl was demonstrated. Except SW, significant
methoxyl loss was recorded in the materials tested in both mesophilic and thermophilic
decomposition.
While generally higher carbon losses were recorded under thermophilic compared to
mesophilic conditions, the HSQC spectra showed only slight reduction in β-O-4 linkage
suggesting no significant lignin depolymerization. The small losses of H2-C2 of G units in
HW, Grass and ONP is surprising as it is well-documented that the S units are more reactive
than G. Nonetheless, the Klason and acid soluble lignin data as well as CuO lignin data
indicate no substantial de-polymerization of lignin. It is possible that the loss of G2 region is
primarily a result of side chain reactions with no destruction of the aromatic structure.
4.6 Acknowledgements
The authors would like to thank David Black for the help in wet analytical chemistry work; Dr. Ilona Pezslen of the Department of Forest Biomaterials, NCSU for providing the
152 wood samples; and April Bauder of NCSU turf farm for providing the grass samples.
Funding for this research was provided by the U.S. National Science Foundation and Waste
Management Inc.
153
4.7 Supporting Information
Table S4.1. Chemical characteristics of initial materials testeda.
Elemental Materialb Carbohydratesc Total Lignine CuO Ligninf Analysisd Ara Gal Glu Xyl Man Celg HCh TOC Ash KL ASL S V C Λ (Ad/Al) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) (%) 8 V HW 0.78 1.7 42.62 16.48 0.74 38.36 17.34 44.94 0.40 18.94 4.27 10.97 2.79 0.22 13.98 0.15 (11-176) (0.02) (0.02) (0.14) (0.12) (0.01) (0.13) (0.08) (0.7) (0.00) (0.45) (0.16) (0.24) (0.05) (0.01) (0.28) (0.02) SW 0.88 2.47 37.28 4.98 9.24 33.55 15.46 46.57 0.50 27.22 0.62 0.19 4.56 0.15 4.90 0.40 (11-158) (0.02) (0.06) (0.09) (0.01) (0.01) (0.08) (0.09) (0.01) (0.00) (0.03) (0.09) (0.02) (0.12) (0.02) (0.12) (0.01) Grass 4.52 2.4 22 15.28 0.57 19.8 20.04 44.65 7.00 17.56 4.90 1.25 0.66 3.83 5.75 0.21 (11-436) (0.06) (0.08) (0.33) (0.24) (0.02) (0.3) (0.18) (0.00) (0.00) (0.16) (0.16) (0.43) (0.13) (0.12) (0.58) (0.05) ONP 40.56 15.5 0.94 1.4 45.06 7.11 8.16 43.01 5.30 19.11 1.10 0.95 6.72 0.44 8.11 0.13 (11-449) (0.12) (0.14) (0) (0.01) (0.13) (0.05) (0.1) (0.00) (0.00) (0.5) (0.06) (0.03) (0.34) (0.00) (0.38) (0.00) a. Standard deviation (sd) is presented parenthetically. b. HW – Quercus rubra; SW – Pinus taeda; Grass – Cynodon dactylon; ONP – Old newsprint. c. Ara – Arabinose; Gal – Galactose; Glu – Glucose; Xyl – Xylose; Man – Mannose. d. TOC – Total organic carbon. e. KL – Klason Lignin; ASL – Acid soluble lignin. -1 f. CuO oxidation products (mg 100 mg OC ); S – syringyl phenols; V – Vanillyl phenols; C – Cinnamyl phenols; Λ8 – Sum of S, V and C; (Ad/Al)V – Ratio of vanillin to vanillic acid. g. Cel – Cellulose; calculated from anhydro-correction of glucose. h. HC – Hemicellulose; calculated from anhydro-correction of other sugars in Table S4.1 except glucose.
154
5. Conclusions
The use of the CuO method to quantify the presence of lignin monomers was
demonstrated. The method was shown to be unaffected by potential interferences in MSW
including plastics (HDPE, PS, PVC, PET, PP), glass, iron, aluminum, calcium carbonate and
protein. It was demonstrated that the analysis of lignin phenols can be simplified by
omission of the ethyl acetate extraction step which is traditionally employed as a purification
step for subsequent GC/MS analysis. In addition, ball milling samples has no effect on CuO
products relative to grinding to pass a 60 mesh screen.
Source-type identification based on the S/V and C/V ratio suggests that the source lignin in MSW samples from a KY landfill was primarily HW with some SW, and little
contribution from tissues such as grass and leaves. While the cellulose and hemicellulose
were depleted in a majority of the samples, the low (Ad/Al)V ~ 0.2 indicates that the lignin in
the samples had not been significantly decomposed. The use of an alternate parameter,
(Cel+H)/Λ8, provided similar trends to the traditional (Cel+H)/KL for the sample set
analyzed. The (Cel+H)/Λ8 is in theory a more robust indicator of decomposition in MSW
samples that contain interferences.
Data on CuO oxidation products of 93 species of HW, SW, GL and GN were used to
develop an end member mixing model. The model was used to predict the composition of
seven synthetic mixtures with errors of 15-60% of the expected value for each component.
155
The anaerobic decomposition of lignocellulosic materials was highly correlated with the type of lignin. The HW and SW lignin were less reactive than the soft tissue lignin such as grass. While the ONP tested came from SW, higher carbon conversions were recorded in
ONP compared to SW. This demonstrates the capacity of lignin to limit the reactivity of cellulose and hemicellulose during anaerobic decomposition. Variation in reactivity of lignin methoxyl was demonstrated. Except SW, significant methoxyl loss was recorded in the materials tested under both mesophilic and thermophilic conditions.
While generally higher carbon losses were recorded under thermophilic compared to mesophilic conditions, the HSQC spectra showed only slight reduction in the β-O-4 linkage of lignin, suggesting no significant lignin de-polymerization. The small losses of H2-C2 of G units in HW, Grass and ONP are surprising as it is well-documented that the S units are more reactive than G. Nonetheless, the Klason and acid soluble lignin data as well as CuO lignin data indicate no substantial de-polymerization of lignin.
156
6. Recommendations
Further work on interferences in CuO lignin. The CuO of pure samples of
polyethylene terephthalate should be tested to determine of this results in interferences.
Reassessment of the mixing model. The synthetic mixtures used to evaluate the end member mixing model should be selected using components that are close to the mean value for each component (hardwood, soft wood, leaves and grass, needles).
Mechanism of CuO oxidation of different types of lignin. While studies have been
published about the mechanism of CuO oxidation of dimeric and monomeric lignin phenolic
compounds, the understanding of CuO oxidation of different types of lignin is limited. Using
chemical degradation and NMR spectroscopy, structural changes during CuO oxidation of
different types of lignin can be elucidated.
NMR experiments on non-acetylated cell walls. The work described here involves
NMR analysis of acetylated samples. However, because of acetylation, the information on
the natural acetylation of lignocellulose was lost. NMR experiments on non-acetylated
samples would allow us to see the structure of the cell wall in a more native state and with less processing.
Design experiment to determine the effect of digestion time and temperature on lignin phenols. CuO oxidation has been optimized with digestion time and temperature (59).
However, the uncertainties associated with these two variables are not adequately
157 characterized. Using a designed experiment, the main effects as well as the interactions of these two variables should be evaluated.
Use of Mahalanobis distance of the log ratio lignin parameter in simplex space.
One limitation of the estimate of composition in Chapter 3 is that the analysis is based on the mean and does not consider the covariance of the different lignin parameters. A possible improvement of the estimate of composition is to use the Mahalanobis distance (statistical distance) (163, 164) and clustering technique to further classify LG and GN that has been shown to be more sensitive to uncertainty.
158
7. References
1. Vanholme, R.; Demedts, B.; Morreel, K.; Ralph, J.; Boerjan, W. Lignin Biosynthesis and Structure. Plant Physiology 2010, 153 (3), 895-905; 10.1104/pp.110.155119.
2. Noritsugu, T.; Masato, Y.; Hafran, J.; Kazuhiko, F.; Ulla, W. Proposed supramolecular structure of lignin in softwood tracheid compound middle lamella regions. 2012, 66, 907.
3. Li, J.; Henriksson, G.; Gellerstedt, G. Lignin depolymerization/repolymerization and its critical role for delignification of aspen wood by steam explosion. Bioresour. Technol. 2007, 98 (16), 3061-3068; http://dx.doi.org/10.1016/j.biortech.2006.10.018.
4. Claridge, T.D. High-resolution NMR Techniques in Organic Chemistry. Elsevier Ltd.: Amsterdam, The Netherlands, 2009; Vol. 27, pp. 383.
5. Sarkanen, K.V. and Ludwig, C.H. Lignins: occurrence, formation, structure and reactions. Wiley-Interscience: New York, 1971; pp. 916.
6. Ralph, J.; Brunow, G.; Harris, P.; Dixon, R.; Schatz, P.; Boerian, W. Lignification: Are Lignins Biosynthesized Via Simple Combinatorial Chemistry Or Via Proteinaceous Control and Template Replication? In Recent Advances in Polyphenols Research, Daayf, F., El Hadrami, A., Adam, L. and Ballance, G.M., Eds.; Oxford, United Kingdom: Wiley- Blackwell Publishing: 2007; pp. 36-66.
7. Lin, S.Y. and Dence, C.W. Methods in lignin chemistry. Springer-Verlag: Berlin ; New York, 1992; pp. 578.
8. U.S. Environmental Protection Agency Inventory of U.S. Greenhouse gas emissions and sinks: 1990 – 2008. 2010, EPA 430-R-10-006.
9. U.S. Environmental Protection Agency Municipal solid waste in the United States 2007 Facts and Figures. 2008, EPA530-R-08-010.
10. Bowyer, J.L.; Schmulsky, R.; Haygreen, J.G. Forest Products and Wood Science 5th Edition. Iowa: Blackwell Publishing: 2007.
11. Argyropoulos, D.S. and Menachem, S.B. Lignin, In Biotechnology in the pulp and paper industry, 1st ed.; Eriksson, K., Ed.; Springer: Berlin ; New York, 1997; Vol.57 pp. 128-151.
12. Sjèostrèom, E. Wood chemistry : Fundamentals and applications. Academic Press: San Diego, 1993; pp. 293.
159
13. Owens, J.M. and Chynoweth, D.P. Biochemical methane potential of municipal solid waste (MSW) components. Water science and technology 1993, 27 (2), 1-14.
14. Lieth, H. Primary productivity of the biosphere. Springer-Verlag: New York, 1975.
15. Bjorkman, A. Lignin and Lignin-Carbohydrate Complexes. Industrial & Engineering Chemistry 1957, 49 (9), 1395-1398; 10.1021/ie50573a040.
16. Ikeda, T.; Holtman, K.; Kadla, J.F.; Chang, H.; Jameel, H. Studies on the Effect of Ball Milling on Lignin Structure Using a Modified DFRC Method. J. Agric. Food Chem. 2002, 50 (1), 129-135; 10.1021/jf010870f.
17. Holtman, K.M.; Chang, H.; Jameel, H.; Kadla, J.F. Quantitative 13C NMR Characterization of Milled Wood Lignins Isolated by Different Milling Techniques. J. Wood Chem. Technol. 2006, 26 (1), 21-34; 10.1080/02773810600582152.
18. Crawford, R.L. Lignin biodegradation and transformation. Wiley: New York, 1981; pp. 154.
19. Lai, Y.Z. and Sarkanen, K.V. Isolation and structural studies, In Lignins: occurrence, formation, structure and reactions, 1st ed.; Sarkanen, K.V. and Ludwig, C.H., Eds.; Wiley- Interscience: New York, 1971; Vol.1 pp. 165-240.
20. Guerra, A.; Filpponen, I.; Lucia, L.A.; Saquing, C.; Baumberger, S.; Argyropoulos, D.S. Toward a Better Understanding of the Lignin Isolation Process from Wood. J. Agric. Food Chem. 2006, 54 (16), 5939-5947; 10.1021/jf060722v.
21. Pew, J.C. Properties of powdered wood and isolation of lignin by cellulytic enzymes. TAPPI 1957, 40 (7), 553-558.
22. Whiting, P.; Favis, B.D.; St-Germain, F.G.T.; Goring, D.A.I. Fractional separation of middle lamella and secondary wall tissue from spruce wood. J. Wood Chem. Technol. 1981, 1, 29-42.
23. Effland, M.J. Modified procedure to determine acid insoluble lignin in wood and pulp. TAPPI 1977, 60, 143-144.
24. Yoshihara, K.; Kobayashi, T.; Fujii, T.; Akamatsu, I. A novel modification of Klason lignin quantitative method. J. Japan TAPPI 1984, 38, 466-475.
25. Dence, C.W. The determination of lignin, In Methods in lignin chemistry, Lin, S.Y. and Dence, C.W., Eds.; Springer-Verlag: Berlin, 1992; pp. 37.
160
26. Kratzl, K. and Billek, G. Synthesis and testing of lignin precursors. TAPPI 1957, 40, 269- 285.
27. Brauns, F.E.B. The chemistry of lignin. New York, Academic Press, 1952: 1952; .
28. Leary, G.J.; Newman, R.H.; Morgan, K.R. A Carbon-13 Nuclear Magnetic Resonance Study of Chemical Processes Involved in the Isolation of Klason Lignin. Holzforschung 1986, 40 (5), 267-272; 10.1515/hfsg.1986.40.5.267.
29. Marton, J. Determination of lignin in small pulp and paper samples using the acetylbromide method. TAPPI 1967, 50, 335-337.
30. Iiyama, K. and Wallis, A.F.A. An improved acetyl bromide procedure for determining lignin in woods and wood pulps. Wood Sci. Technol. 1988, 22 (3), 271-280.
31. Dean, J.F. Lignin Analysis, In Methods in plant biochemistry and molecular biology, 1st ed.; Dashek, W.V., Ed.; Springer-Verlag: Boca Raton, Florida, 1997; pp. 200-211.
32. Tasman, J.E. and Berzins, B. The permanganate consumption of pulp materials: The relationship of the kappa number to the lignin content of pulp materials. TAPPI 1957, 40 (9), 691-694.
33. Kavanagh, K.R. and Pepper, J.M. The alkaline nitrobenzene oxidation of Aspen wood and lignin model substances. Can. J. Chem. 1955, 33 (24), 24-30.
34. Pepper, J.M.; Casselman, B.W.; Karapally, J.C. Lignin oxidation. Preferential use of cupric oxide. Can. J. Chem. 1967, 45, 3009-3012.
35. Hedges, J.I. and Mann, D.C. The characterization of plant tissues by their lignin oxidation products. Geochim. Cosmochim. Acta 1979, 43 (11), 1803-1807; DOI: 10.1016/0016-7037(79)90028-0.
36. Pearl, I.A. Vanillin from Lignin Materials. J. Am. Chem. Soc. 1942, 64 (6), 1429-1431; 10.1021/ja01258a051.
37. Karl, K.; Josef, G.; Peter, C. Formation and Degradation of Biphenyl Structures During Alkaline Oxidation of Phenols with Oxygen, In Lignin Structure and Reactions; American Chemical Society: 1966; Vol.59 pp. 157-176.
38. Chang, H.M. and Allan, G.G. Oxidation, In Lignins: occurrence, formation, structure and reactions, 1st ed.; Sarkanen, K.V. and Ludwig, C.H., Eds.; Wiley-Interscience: New York, 1971; Vol.1 pp. 443-485.
161
39. Goñi, M.A.; Nelson, B.; Blanchette, R.A.; Hedges, J.I. Fungal degradation of wood lignins - geochemical perspectives from CuO-derived phenolic dimers and monomers. Geochim. Cosmochim. Ac. 1993, 57 (16), 3985-4002.
40. Hedges, J.I.; Blanchette, R.A.; Weliky, K.; Devol, A.H. Effects of fungal degradation on the CuO oxidation products of lignin: A controlled laboratory study. Geochim. Cosmochim. Acta 1988, 52 (11), 2717-2726; DOI: 10.1016/0016-7037(88)90040-3.
41. Goñi, M.A. and Eglinton, T.I. Stable carbon isotopic analyses of lignin-derived CuO oxidation products by isotope ratio monitoring gas chromatography mass spectrometry (irm- GC-MS). Org. Geochem. 1996, 24 (6-7), 601-615.
42. Goñi, M.A. and Hedges, J.I. Sources and reactivities of marine-derived organic-matter in coastal sediments as determined by alkaline Cuo oxidation. Geochim. Cosmochim. Ac. 1995, 59 (14), 2965-2981.
43. Goñi, M.A. and Hedges, J.I. Lignin dimers - structures, distribution, and potential geochemical applications. Geochim. Cosmoshim. Ac. 1992, 56 (11), 4025-4043.
44. Kuzyk, Z.Z.A.; Goñi, M.A.; Stern, G.A.; Macdonald, R.W. Sources, pathways and sinks of particulate organic matter in Hudson Bay: Evidence from lignin distributions. Mar. Chem. 2008, 112 (3-4), 215-229; DOI: 10.1016/j.marchem.2008.08.001.
45. Dittmar, T. and Lara, R.J. Molecular evidence for lignin degradation in sulfate-reducing mangrove sediments (Amazonia, Brazil). Geochim. Cosmochim. Ac. 2001, 65 (9), 1417- 1428.
46. Louchouarn, P.; Lucottea, M.; Canuela, R.; Gagnéb, J.; Richarda, L. Sources and early diagenesis of lignin and bulk organic matter in the sediments of the lower St. Lawrence Estuary and the Saguenay Fjord. Mar. Chem. 1997, 58 (1-2), 3-26.
47. Hedges, J.I.; Keil, R.G.; Benner, R. What happens to terrestrial organic matter in the ocean? Org. Geochem. 1997, 27 (5-6), 195-212; DOI: 10.1016/S0146-6380(97)00066-1.
48. Hedges, J.I. and Parker, P.L. Land-derived organic-matter in surface sediments from gulf of mexico. Geochim. Cosmochim. Ac. 1976, 40 (9), 1019-1029.
49. Keil, R.G.; Tsamakis, E.; Giddings, J.C.; Hedges, J.I. Biochemical distributions (amino acids, neutral sugars, and lignin phenols) among size-classes of modern marine sediments from the Washington coast. Geochim. Cosmochim. Acta 1998, 62 (8), 1347-1364; DOI: 10.1016/S0016-7037(98)00080-5.
162
50. Cowie, G.L. and Hedges, J.I. Biochemical indicators of diagenetic alteration in natural organic matter mixtures. Nature 1994, 369, 304.
51. Hedges, J.I. and Oades, J.M. Comparative organic geochemistries of soils and marine sediments. Org. Geochem. 1997, 27 (7-8), 319-361; DOI: 10.1016/S0146-6380(97)00056-9.
52. Hedges, J.I. and Ertel, J.R. Characterization of lignin by gas capillary chromatography of cupric oxide oxidation-products. Anal. Chem. 1982, 54 (2), 174-178.
53. Baldock, J.A.; Masiello, C.A.; Gélinas, Y.; Hedges, J.I. Cycling and composition of organic matter in terrestrial and marine ecosystems. Mar. Chem. 2004, 92 (1-4), 39-64; DOI: 10.1016/j.marchem.2004.06.016.
54. Hedges, J.I. and Weliky, K. Diagenesis of conifer needles in a coastal marine- environment. Geochimica et cosmochimica acta 1989, 53, 2659.
55. Goñi, M.A. and Hedges, J.I. Molecular-level characterization of marine-derived sedimentary organic matter by alkaline CuO oxidation: sources and reactivities of organic matter from Skan Bay (Alaska) sediments. Chem. Geol. 1993, 107 (3-4), 483-485; DOI: 10.1016/0009-2541(93)90236-C.
56. Opsahl, S. and Benner, R. Early diagenesis of vascular plant tissues: Lignin and cutin decomposition and biogeochemical implications. Geochim. Cosmochim. Acta 1995, 59 (23), 4889-4904; DOI: 10.1016/0016-7037(95)00348-7.
57. Otto, A. and Simpson, M. Evaluation of CuO oxidation parameters for determining the source and stage of lignin degradation in soil. Biogeochemistry 2006, 80 (2), 121-142.
58. Ertel, J.R. and Hedges, J.I. The lignin component of humic substances - distribution among soil and sedimentary humic, fulvic, and base-insoluble fractions. Geochim. Cosmochim. Acta 1984, 48 (10), 2065-2074; doi:10.1016/0016-7037(84)90387-9.
59. Goñi, M.A. and Montgomery, S. Alkaline CuO oxidation with a microwave digestion system: Lignin analyses of geochemical samples. Anal. Chem. 2000, 72 (14), 3116-3121.
60. Thevenot, M.; Dignac, M.; Rumpel, C. Fate of lignins in soils: A review. Soil Biol. Biochem. 2010, 42 (8), 1200-1211; DOI: 10.1016/j.soilbio.2010.03.017.
61. Zimmermann, W. Degradation of lignin by bacteria. Journal of biotechnology 1990, 13 (2-3), 119-130.
163
62. Evans, W.C. Biochemistry of the bacterial catabolism of aromatic compounds in anaerobic environments. Nature 1977, 270 (5632), 17-22.
63. Céspedes, R.; Salas, L.; Calderón, I.; Gonzáles, B.; Vicuña, R. Microbial and biochemical-characterization of a bacterial consortium isolated from decaying wood by growth on a beta-o-4 lignin-related dimeric compound. Archives of microbiology 1992, 158 (3), 162-170.
64. Greaves, H. Micromorphology of bacterial attack of wood. Wood Sci. Technol. 1969, 3 (2), 150-166.
65. Clausen, C.A. Bacterial Associations with Decaying Wood: a Review. International Biodeterioration and Biodegradation: 1996; pp. 101-107.
66. Mardigan, M.T. Brock biology of microorganisms. Benjamin Cummings: San Francisco, 2012.
67. Angelidaki, I.; Ellegaard, L.; Ahring, B.K. Applications of Anaerobic Digestion Process, In B, Anonymous ; Springer-Verlag: Ahring, Biomethanation II. Berlin, 2003.
68. Benner, R.; Maccubbin, A.E.; Hodson, R.E. Anaerobic Biodegradation of the Lignin and Polysaccharide Components of Lignocellulose and Synthetic Lignin by Sediment Microflora. Applied and Environmental Microbiology} 1984, 47 (5), 998-1004.
69. Benner, R. and Hodson, R.E. Thermophilic anaerobic biodegradation of [C-14] lignin, [C-14] cellulose, and [C-14] lignocellulose preparations. Appl. Environ. Microbiol. 1985, 50 (4), 971-976.
70. Wang, Y.S.; Byrd, C.S.; Barlaz, M. Anaerobic biodegradability of cellulose and hemicellulose in excavated refuse samples using a biochemical methane potential assay. J. Ind. Microbiol. 1994, 13 (3), 147-153.
71. Colberg, P. Anaerobic microbial degradation of cellulose, lignin, oligolignols and monoaromatic lignin derivatives, In Biology of Anaerobic Microorganisms, Zehnder, A.J.B., Ed.; Wiley-Interscience: New York, 1988; Vol.28 pp. 886-889.
72. Stinson, J.A. and Ham, R.K. Effect of Lignin on the Anaerobic Decomposition of Cellulose as Determined Through the Use of a Biochemical Methane Potential Method. Environ. Sci. Technol. 1995, 29 (9), 2305-2310; 10.1021/es00009a023.
73. Taylor, B.F.; Campbell, W.L.; Chinoy, I. Anaerobic Degradation of the Benzene Nucleus by a Facultatively Anaerobic Microorganism. J. Bacteriol. 1970, 102 (2), 430-437.
164
74. Williams, R.J. and Evans, W.C. The Metabolism of Benzoate by Moraxella Species Through Anaerobic Nitrate Respiration. Biochem. J. 1975, 148, 1-10.
75. Oshima, T. On the anaerobic metabolism of aromatic compounds in the presence of nitrate by soil microorganisms. Z. Allg. Mikrobiol. 1965, 5 (5), 386-394; 10.1002/jobm.19650050506.
76. Harwood, C.S.; Burchhardt, G.; Herrmann, H.; Fuchs, G. Anaerobic metabolism of aromatic compounds via the benzoyl-CoA pathway. FEMS Microbiology Reviews 1998, 22 (5), 439-458.
77. Fina, L.R.; Bridges, R.L.; Coblentz, T.H.; Roberts, F.F. The anaerobic decomposition of benzoic acid during methane fermentation. Arch. Microbiol. 1978, 118 (2), 169-172.
78. Fina, L.R. and Fiskin, A.M. The anaerobic decomposition of benzoic acid during methane fermentation. II. Fate of carbons one and seven, Arch. Biochem. Biophys. 1960, 91 (2), 163-165; DOI: 10.1016/0003-9861(60)90483-5.
79. Clark, F.M. and Fina, L.R. The anaerobic decomposition of benzoic acid during methane fermentation. Arch. Biochem. Biophys. 1952, 36 (1), 26-32; DOI: 10.1016/0003- 9861(52)90374-3.
80. Ferry, J.G. and Wolfe, R.S. Anaerobic degradation of benzoate to methane by a microbial consortium. Arch. Microbiol. 1976, 107 (1), 33-40.
81. Dutton, P.L. and Evans, W.C. The metabolism of aromatic compounds by Rhodopseudomonas palustris. A new, reductive, method of aromatic ring metabolism. Biochem. J. 1969, 113, 525-536.
82. Proctor, J.H. and Scher, R. Decomposition of benzoate by a photosynthetic bacterium. Biochem. J. 1960, 76, 33.
83. Grbic-Galic, D. and Young, L.Y. Methane Fermentation of Ferulate and Benzoate: Anaerobic Degradation Pathways. Appl. Environ. Microbiol. 1985, 50 (2), 292-297.
84. Balba, M.T. and Evans, W.C. The methanogenic fermentation of aromatic substrates. Biochem. Soc. Trans. 1977, 5, 302-304.
85. Young, L.Y. and Frazer, A.C. The fate of lignin and lignin-derived compounds in anaerobic environments. Geomicrobiology journal 1987, 5, 261.
165
86. Raj, A.; Reddy, M.M.K.; Chandra, R.; Purohit, H.J.; Kapley, A. Biodegradation of kraft- lignin by Bacillus sp isolated from sludge of pulp and paper mill. Biodegradation 2007, 18 (6), 783-792.
87. Gierer, J. The reactions of lignin during pulping- a description and comparison of conventional pulping processes. Sven. Papperstidn 1970, 73, 571-596.
88. Hu, Z.; Liu, S.; Yue, Z.; Yan, L.; Yang, M.; Yu, H. Microscale analysis of in vitro anaerobic degradation of lignocellulosic wastes by rumen microorganisms. Environ. Sci. Technol. 2008, 42 (1), 276-281.
89. Zeikus, J.; Wellstein, A.; Kirk, T. Molecular basis for the biodegradative recalcitrance of lignin in anaerobic environments. FEMS Microbiol. Lett. 1982, 15 (3), 193-197; 10.1111/j.1574-6968.1982.tb00066.x.
90. Akin, D.E. and Benner, R. Degradation of polysaccharides and lignin by ruminal bacteria and fungi. Appl. Environ. Microbiol. 1988, 54 (5), 1117-1125.
91. Akin, D.E. and Borneman, W.S. Role of rumen fungi in fiber degradation. Journal of Dairy Science: 1990; pp. 3023-3032.
92. Windham, W.R. and Akin, D.E. Rumen Fungi and Forage Fiber Degradation. Appl. Environ. Microbiol. 1984, 48 (3), 473-476.
93. Boruff, C.S. and Buswell, A.M. The Anaerobic Fermentation of Lignin. J. Am. Chem. Soc. 1934, 56 (4), 886-888; 10.1021/ja01319a037.
94. Healy, J.B. and Young, L.Y. Anaerobic biodegradation of eleven aromatic-compounds to methane. Appl. Environ. Microbiol. 1979, 38 (1), 84-89.
95. Kirk, T.K.; Higuchi, T.; Chang, H.; United States-Japan Cooperative Science Program Lignin biodegradation : microbiology, chemistry, and potential applications. CRC Press: Boca Raton, Fla., 1980.
96. Winfrey, M.R. and Zeikus, J.G. Effect of sulfate on carbon and electron flow during microbial methanogenesis in freshwater sediments. Appl. Environ. Microbiol. 1977, 33 (2), 275-281.
97. Colberg, P. and Young, L. Anaerobic degradation of soluble fractions of [C-14-lignin] lignocellulose. Appl. Environ. Microbiol. 1985, 49 (2), 345.
166
98. Breznak, J.A. and Brune, A. Role of Microorganisms in the Digestion of Lignocellulose by Termites. Annu. Rev. Entomol 1994, 39, 453-487.
99. Heider, J. and Fuchs, G. Anaerobic Metabolism of Aromatic Compounds. European Journal of Biochemistry 1997, 243 (3), 577-596; 10.1111/j.1432-1033.1997.00577.x.
100. Barlaz, M.A. Forest products decomposition in municipal solid waste landfills. Waste Manage. 2006, 26 (4), 321-333.
101. De la Cruz, F.B.; Chanton, J.P.; Barlaz, M.A. Measurement of carbon storage in landfills from the biogenic carbon content of excavated waste samples. Waste Manage. 2013, 33 (10), 2001-2005; http://dx.doi.org.prox.lib.ncsu.edu/10.1016/j.wasman.2012.12.012.
102. Hedges, J.; Hu, F.; Devol, A.; Hartnett, H.; Tsamakis, E.; Keil, R. Sedimentary organic matter preservation: A test for selective degradation under oxic conditions. The American journal of science 1999, 299 (7-9), 529-555.
103. Lee, C.; Wakeham, S., G.; Hedges, J.I. Compositions and fluxes of particulate organic material in the Amazon river. Limnol. Oceanogr. 1986, 31 (4), 717-738.
104. Chen, C.L. Nitrobenzene and Cupric oxide oxidation, In Methods in lignin chemistry, Lin, S.Y. and Dence, C.W., Eds.; Springer-Verlag: Berlin, 1992; pp. 37.
105. U.S. Environmental Protection Agency Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures for 2008. 2009, EPA-530-F-009-021, .
106. Wu, B.; Taylor, C.M.; Knappe, D.R.U.; Nanny, M.A.; Barlaz, M.A. Factors Controlling Alkylbenzene Sorption to Municipal Solid Waste. Environ. Sci. Technol. 2001, 35 (22), 4569-4576; 10.1021/es010893a.
107. Lu, F. and Ralph, J. Non-degradative dissolution and acetylation of ball-milled plant cell walls: high-resolution solution-state NMR. The Plant Journal 2003, 35 (4), 535-544; 10.1046/j.1365-313X.2003.01817.x.
108. U.S. Environmental Protection Agency Landfill Bioreactor Performance, Second Interim Report. 2006, EPA 600-R-07-060, .
109. Lobbes, J.; Fitznar, H.; Kattner, G. High-performance liquid chromatography of lignin- derived phenols in environmental samples with diode array detection. Anal. Chem. 1999, 71 (15), 3008-3012.
167
110. Petterson, R.C. and Schwandt, V.H. Wood Sugar Analysis by Anion Chromatography. Journal of Wood Chemistry and Technology 1991, 11 (4), 495-501.
111. Davis, M.W. A Rapid Modified Method for Compositional Carbohydrate Analysis of Lignocellulosics by High pH Anion-Exchange Chromatography with Pulsed Amperometric Detection (HPAEC/PAD). Journal of Wood Chemistry and Technology 1998, 18 (2), 235- 252.
112. Ryba, S.A. and Burgess, R.M. Effects of sample preparation on the measurement of organic carbon, hydrogen, nitrogen, sulfur, and oxygen concentrations in marine sediments. Chemosphere 2002, 48 (1), 139-147; 10.1016/S0045-6535(02)00027-9.
113. Everitt, B. A handbook of statistical analyses using R. CRC Press: Boca Raton, 2010; .
114. Team, R.C. R: A Language and Environment for Statistical Computing. 2013, .
115. Steinberg, S.; Venkatesan, M.I.; Kaplan, I.R. Analysis of the products of the oxidation of lignin by CuO in biological and geological samples by reversed-phase high-performance liquid chromatography. Journal of Chromatography A 1984, 298, 427-434; DOI: 10.1016/S0021-9673(01)92740-2.
116. De la Cruz, Florentino B. and Barlaz, M.A. Estimation of Waste Component-Specific Landfill Decay Rates Using Laboratory-Scale Decomposition Data. Environ. Sci. Technol. 2010, 44 (12), 4722-4728; 10.1021/es100240r.
117. U.S. Environmental Protection Agency Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures for 2011. 2013, EPA530-R-13-001.
118. Bland, D.E.; Ho, G.; Cohen, W.E. Aromatic Aldehydes from the Oxidation of Some Australian Woods and their Chromatographic Separation. Aust. J. Chem. 1950, 3 (4), 642- 648.
119. Hernes, P.J.; Kaiser, K.; Dyda, R.Y.; Cerli, C. Molecular Trickery in Soil Organic Matter: Hidden Lignin. Environ. Sci. Technol. 2013, 47 (16), 9077-9085; 10.1021/es401019n.
120. Staley, B.F. and Barlaz, M.A. Composition of Municipal solid waste in the US and implications for carbon sequestration and methane yield. J. Environ. Eng. 2009, 135, 901- 909.
168
121. Abichou, T.; Barlaz, M.A.; Green, R.; Hater, G. The Outer Loop bioreactor: A case study of settlement monitoring and solids decomposition. Waste Manage. 2013, 33 (10), 2035-2047; http://dx.doi.org.prox.lib.ncsu.edu/10.1016/j.wasman.2013.02.005.
122. Weltje, G. End-member modeling of compositional data: Numerical-statistical algorithms for solving the explicit mixing problem. Math. Geol. 1997, 29 (4), 503-549; 10.1007/BF02775085.
123. Christophersen, N. and Hooper, R.P. Multivariate analysis of stream water chemical data: The use of principal components analysis for the end-member mixing problem. Water Resour. Res. 1992, 28 (1), 99-107; 10.1029/91WR02518.
124. ERTEL, SOURCES OF SEDIMENTARY HUMIC SUBSTANCES - VASCULAR PLANT DEBRIS. Geochimica et cosmochimica acta 1985, 49, 2097.
125. Nash, J.C. and Varadhan, R. Unifying Optimization Algorithms to Aid Software System Users: optimx for R. Journal of Statistical Software 2011, 43 (9), 1-14.
126. Hedges, J.I. Global biogeochemical cycles: progress and problems. Mar. Chem. 1992, 39 (1-3), 67-93; DOI: 10.1016/0304-4203(92)90096-S.
127. Bogner, J.; Spokas, K.; Burton, E.; Sweeney, R.; Corona, V. Landfills as atmospheric methane sources and sinks. Chemosphere 1995, 31 (9), 4119-4130; 10.1016/0045- 6535(95)80012-A.
128. Barlaz, M.A. Carbon storage during biodegradation of municipal solid waste components in laboratory-scale landfills. Global Biogeochem. Cycles 1998, 12 (2), 373-380.
129. Kirk, T.K. and Farrell, R.L. Enzymatic "Combustion": The Microbial Degradation of Lignin. Annu. Rev. Microbiol. 1987, 41 (1), 465-501; 10.1146/annurev.mi.41.100187.002341.
130. Crawford, D.L. and Crawford, R.L. Microbial degradation of lignin. Enzyme Microb. Technol. 1980, 2 (1), 11-22; DOI: 10.1016/0141-0229(80)90003-4.
131. Crawford, D.L.; Crawford, R.L.; Pometto, A.L. Preparation of specifically labeled C- 14-(lignin)-lignocelluloses and C-14-(cellulose)-lignocelluloses and their decomposition by microflora of soil. Appl. Environ. Microbiol. 1977, 33 (6), 1247.
132. Benner, R.; Mccubbin, A.E.; Hodson, R.E. Preparation, characterization, and microbial- degradation of specifically radiolabeled [c-14] lignocelluloses from marine and fresh-water macrophytes. Appl. Environ. Microbiol. 1984, 47 (2), 381.
169
133. Santos, R.B.; Capanema, E.A.; Balakshin, M.Y.; Hou-Min Chang; Jameel, H. Effect of Hardwoods Characteristics on Kraft Pulping Process: Emphasis on Lignin Structure. BioResources 2011, 6 (4), 3623-3637.
134. Chang Hou-min; Cowling, E.B.; Wynford, B. Comparative Studies on Cellulolytic Enzyme Lignin and Milled Wood Lignin of Sweetgum and Spruce. Holzforschung - International Journal of the Biology, Chemistry, Physics and Technology of Wood 2009, 29, 153.
135. Mansfield, S.D.; Kim, H.; Lu, F.; Ralph, J. Whole plant cell wall characterization using solution-state 2D NMR. Nat. Protocols 2012, 7, 1579-1589.
136. Yelle, D.J.; Ralph, J.; Frihart, C.R. Characterization of nonderivatized plant cell walls using high-resolution solution-state NMR spectroscopy. Magn. Reson. Chem. 2008, 46 (6), 508-517; 10.1002/mrc.2201.
137. Kim, H.; Ralph, J.; Akiyama, T. Solution-state 2D NMR of Ball-milled Plant Cell Wall Gels in DMSO-d 6. BioEnergy Research 2008, 1 (1), 56-66; 10.1007/s12155-008-9004-z.
138. Yelle, D.J.; Ralph, J.; Lu, F.; Hammel, K.E. Evidence for cleavage of lignin by a brown rot basidiomycete. Environ. Microbiol. 2008, 10 (7), 1844-1849; 10.1111/j.1462- 2920.2008.01605.x.
139. Yelle, D.J.; Wei, D.; Ralph, J.; Hammel, K.E. Multidimensional NMR analysis reveals truncated lignin structures in wood decayed by the brown rot basidiomycete Postia placenta. Environ. Microbiol. 2011, 13 (4), 1091-1100; 10.1111/j.1462-2920.2010.02417.x.
140. - Chundawat, S.P.S.; - Donohoe, B.S.; - da Costa Sousa, L.; - Elder, T.; - Agarwal, U.P.; - Lu, F.; - Ralph, J.; - Himmel, M.E.; - Balan, V.; - Dale, B.E. - Multi-scale visualization and characterization of lignocellulosic plant cell wall deconstruction during thermochemical pretreatment. - Energy Environ. Sci. - 973.
141. Glasser, W.G.; Sarkanen, S.; American Chemical Society. Cellulose, Paper, and Textile Division; American Chemical Society. Meeting Lignin : properties and materials. American Chemical Society: Washington, DC, 1989; Vol. 397, pp. 545.
142. Maekawa, E.; Ichizawa, T.; Koshijima, T. An Evaluation of the Acid-Soluble Lignin Determination in Analyses of Lignin by the Sulfuric Acid Method. J. Wood Chem. Technol. 1989, 9 (4), 549-567; 10.1080/02773818908050315.
170
143. Li, H.; Chai, X.; Liu, M.; Deng, Y. Novel Method for the Determination of the Methoxyl Content in Lignin by Headspace Gas Chromatography. J. Agric. Food Chem. 2012, 60 (21), 5307-5310; 10.1021/jf300455g.
144. Goto, H.; Koda, K.; Tong, G.; Matsumoto, Y.; Meshitsuka, G. Formation of methyl iodide from methoxyl-free compounds by hydriodic acid treatment. Journal of Wood Science 2005, 51 (3), 312-314; 10.1007/s10086-005-0709-8.
145. Baker, S.M. Rapid methoxyl analysis of lignins using gas chromatography. Holzforschung - International Journal of the Biology, Chemistry, Physics and Technology of Wood 1996, 50, 573-574.
146. Ralph, S.A.; Ralph, J.; Landucci, L.L. NMR Database of Lignin and Cell Wall Model Compounds. Available at URL http://ars. usda. gov/Services/docs. htm?docid=10491 2004, .
147. Ralph, J.; Marita, J.M.; Ralph, S.A.; Hatfield, R.D.; Lu, F.; Ede, R.M.; Peng, J.; Quideau, S.; Helm, R.F.; Grabber, J.H.; Kim, H.; Jimenez- Monteon, G.; Zhang, Y.; Jung, H.G.; Landucci, L.L.; MacKay, J.J.; Sederoff, R.R.; Chapple, B.; Clint, A.M. Solution-State NMR of Lignins, In Advances in Lignocellulosics characterization, Argyropoulos, D.S. and Williams, P.A., Eds.; TAPPI Press: Atlanta, Georgia, 1999; pp. 55-108.
148. Ralph, J.; Lundquist, K.; Brunow, G.; Lu, F.; Kim, H.; Schatz, P.; Marita, J.; Hatfield, R.; Ralph, S.; Christensen, J.; Boerjan, W. Lignins: Natural polymers from oxidative coupling of 4-hydroxyphenyl- propanoids. Phytochemistry Reviews 2004, 3 (1-2), 29-60; 10.1023/B:PHYT.0000047809.65444.a4.
149. Liitia, T.M.; Maunu, S.L.; Hortling, B.; Toikka, M.; Kilpelainen, I. Analysis of Technical Lignins by Two- and Three-Dimensional NMR Spectroscopy. J. Agric. Food Chem. 2003, 51 (8), 2136-2143; 10.1021/jf0204349.
150. Capanema, E.A.; Balakshin, M.Y.; Kadla, J.F. A Comprehensive Approach for Quantitative Lignin Characterization by NMR Spectroscopy. J. Agric. Food Chem. 2004, 52 (7), 1850-1860; 10.1021/jf035282b.
151. Capanema, E.A.; Balakshin, M.Y.; Kadla, J.F. Quantitative Characterization of a Hardwood Milled Wood Lignin by Nuclear Magnetic Resonance Spectroscopy. J. Agric. Food Chem. 2005, 53 (25), 9639-9649; 10.1021/jf0515330.
152. Ibarra, D.; Chavez, M.I.; Rencoret, J.; Del RÃao, J.C.; Gutierrez, A.; Romero, J.; Camarero, S.; Martinez, M.J.; Jimenez-Barbero, J.; Martinez, A.T. Lignin Modification during Eucalyptus globulus Kraft Pulping Followed by Totally Chlorine-Free Bleaching: A Two-Dimensional Nuclear Magnetic Resonance, Fourier Transform Infrared, and
171
Pyrolysis−Gas Chromatography/Mass Spectrometry Study. J. Agric. Food Chem. 2007, 55 (9), 3477-3490; 10.1021/jf063728t.
153. David, I.; Chavez, M.I.; Jorge, R.; del Rao, J.C.; Gutierrez, A.; Susana, C.; Martinez, M.J.; Jimenez-Barbero, J.; Martinez, A.T. Structural modification of eucalypt pulp lignin in a totally chlorine-free bleaching sequence including a laccase-mediator stage. Holzforschung 2007, 61, 634.
154. Rencoret, J.; Gutierrez, A.; Nieto, L.; Jimenez-Barbero, J.; Faulds, C.B.; Kim, H.; Ralph, J.; Martinez, AT.; del Rao, J.C. Lignin Composition and Structure in Young versus Adult Eucalyptus globulus Plants. Plant Physiology 2011, 155 (2), 667-682; 10.1104/pp.110.167254.
155. Martínez, ÁT.; Rencoret, J.; Marques, G.; Gutiérrez, A.; Ibarra, D.; Jiménez-Barbero, J.; del Río, J.C. Monolignol acylation and lignin structure in some nonwoody plants: A 2D NMR study. Phytochemistry 2008, 69 (16), 2831-2843; 10.1016/j.phytochem.2008.09.005.
156. del Rio, J.C.; Rencoret, J.; Marques, G.; Gutierrez, A.; Ibarra, D.; Santos, J.I.; Jimenez- Barbero, J.; Zhang, L.; MartiÌ nez, A.T. Highly Acylated (Acetylated and/or p- Coumaroylated) Native Lignins from Diverse Herbaceous Plants. J. Agric. Food Chem. 2008, 56 (20), 9525-9534; 10.1021/jf800806h.
157. Rencoret, J.; Marques, G.; Gutiérrez, A.; Nieto, L.; Santos, I.J.; Jiménez-Barbero, J.; Martínez, ÁT.; del Río, J.C. HSQC-NMR analysis of lignin in woody (Eucalyptus globulus and Picea abies) and non-woody (Agave sisalana) ball-milled plant materials at the gel state. Holzforschung - International Journal of the Biology, Chemistry, Physics and Technology of Wood 2009, 63, 691-698.
158. Wang, X.; Padgett, J.M.; De la Cruz, Florentino B.; Barlaz, M.A. Wood Biodegradation in Laboratory-Scale Landfills. Environ. Sci. Technol. 2011, ; 10.1021/es201241g.
159. Eleazer, W.E.; Odle, W.S.; Wang, Y.S.; Barlaz, M.A. Biodegradability of municipal solid waste components in laboratory-scale landfills. Environ. Sci. Technol. 1997, 31 (3), 911-917.
160. Grbic-Galic, D. and Young, L.Y. Methane Fermentation of Ferulate and Benzoate - Anaerobic Degradation Pathways. Applied and environmental microbiology 1985, 50 (2), 292-297.
161. Frazer, A.C. and Young, L.Y. A Gram-Negative Anaerobic Bacterium That Utilizes O- Methyl Substituents of Aromatic Acids. Appl. Environ. Microbiol. 1985, 49 (5), 1345-1347.
172
162. del Rao, J.C.; Rencoret, J.; Prinsen, P.; Martinez, AT.; Ralph, J.; Gutierrez, A. Structural Characterization of Wheat Straw Lignin as Revealed by Analytical Pyrolysis, 2D-NMR, and Reductive Cleavage Methods. J. Agric. Food Chem. 2012, 60 (23), 5922-5935; 10.1021/jf301002n.
163. Aitchison, J. The Statistical Analysis of Compositional Data. Chapman \& Hall, Ltd: London, UK, UK, 1986; .
164. Aitchison, J. The Statistical Analysis of Compositional Data. Journal of the Royal Statistical Society.Series B (Methodological) 1982, 44 (2), 139-177.
173