Structural Characterization of Freshwater Dissolved Organic Matter From

Structural Characterization of Freshwater Dissolved

Organic Matter from Arctic and Temperate Climates

Using Novel Analytical Approaches

Gwen C. Woods

A thesis submitted in conformity with the requirements

for the degree of Doctor of Philosophy

Graduate Department of Chemistry

University of Toronto

polar and temperate climates using novel analytical approaches

Doctor of Philosophy Degree, 2012

Gwen C. Woods

Department of Chemistry

University of Toronto

ABSTRACT

Dissolved organic matter (DOM) is comprised of a complex array of molecular constituents that are linked to many globally-relevant processes and yet this material is still largely molecularly uncharacterized. Research presented here attempted to probe the molecular complexity of this material from both Arctic and temperate climates via multifaceted and novel approaches. DOM collected from remote Arctic watersheds provided evidence to suggest that permafrost-disturbed systems contain more photochemically- and biologically-labile material than undisturbed systems. These results have large implications for predicted increasing temperatures where widespread permafrost melt would significantly impact stores of organic carbon in polar environments. In attempting to address the complexities and reactivity of DOM within global environments, more information at the molecular-level is necessary. Further research sought to unravel the molecularly uncharacterized fraction via use of nuclear magnetic resonance (NMR) spectroscopy in conjunction with hyphenated and varied analytical techniques. Directly hyphenated high performance size exclusion chromatography (HPSEC) with NMR was explored. This hyphenation was found to separate DOM into structurally distinct fractions but proved limited

ii at reducing DOM heterogeneity. Of the many high performance liquid chromatography

(HPLC) techniques tested, hydrophilic interaction chromatography (HILIC) was found the most effective at simplifying DOM. HILIC separations utilizing a sample from Florida resulted in fractions with highly resolved NMR signals and substantial reduction in heterogeneity. Further development with a 2D-HILIC/HILIC system to achieve additional fractionation was employed. This method produced fractions of DOM that were homogenous enough to produce excellent resolution and spectral dispersion, permitting 2D and 3D NMR experiments to be performed. Extensive NMR analyses of these fractions demonstrated strong evidence for the presence of highly oxidized sterols. All fractions, however, provided

2D NMR spectra consistent with oxidized polycyclic structures and support emerging data and hypotheses suggesting that cyclic structures, likely derived from terpenoids, are an abundant, refractory and major component of DOM. Research presented within this thesis demonstrates that HILIC and NMR are excellent co-techniques for the analysis of DOM as well as that oxidized sterols and other cyclic components with significant hydroxyl and carboxyl substituents are major constituents in DOM.

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ACKNOWLEDGEMENTS

There are a number of individuals for whom I would like to extend my gratitude. First and foremost I would like to thank my supervisor, Prof. André Simpson, who has made this dissertation possible. I would particularly like to thank André for his wonderful insight and assistance with NMR as well as for a continuously positive attitude. I would also like to thank Prof. Myrna Simpson for her help in analyzing data from the High Arctic, rides to

Kingston to visit colleagues at Queen’s University and general helpfulness in many aspects of the research presented. Further gratitude is extended to the chair of my committee, Prof.

Jonathan Abbatt as well as examination committee member Prof. Jennifer Murphy both of whom inspired and encouraged thoughts of my future career. I am additionally thankful to

Prof. Lihini Aluwihare for taking the time and effort to be the external examiner.

A special thank you to all the wonderful members of both Simpson groups, past and present. You have been a tremendous source of support and entertainment. I have had invaluable conversations with many of you with regards to scientific inquiries, future careers and life in general. Best of luck to all of you!

I would like to acknowledge funding sources for the projects contained within this thesis: the Natural Sciences and Engineering Research Council (NSERC) of Canada (AJS) and the Government of Canada International Polar Year program (MJS) for funding provided as well as the Polar Continental Shelf Project (Natural Resources Canada) for logistical support; the government of Ontario for an Early Researcher Award and the Natural Sciences and Engineering Research Council (NSERC) of Canada Strategic Grant (AJS); a University of Toronto Fellowship, a Teaching Reduction Fellowship, a UTF Conference Travel Grant and a Doctoral Thesis Completion Grant (GCW).

A special thank you is extended to all of the wonderful people I have met while living in Toronto for graduate studies. Many of you have become good friends over the years, kept me entertained and always been supportive; I am lucky to have you. I would especially like to thank the Chabane family for greeting me into their home, welcoming me on holidays and treating me as family.

This short acknowledgement cannot begin to convey the gratitude I feel toward my family. To my parents, you have given so much. I know that you are and always will be my biggest supporters. Thank you for everything. Galen, Alyn and Ellenore, you have taught me some of life’s most interesting and not always easiest lessons! I have missed you so much the last few years but look forward to seeing so much more of you!

Lastly I need to thank my personal chef, best friend and love of my life. Stefan, I could not have done this without you.

TABLE OF CONTENTS

ABSTRACT……………………………………………………………………….…….… ii ACKNOWLEDGEMENTS……………………………………………………….…….… iv TABLE OF CONTENTS………………………………………………………….…….... vi LIST OF TABLES..……………………………………………………………….…….… xi LIST OF FIGURES...……………………………………………………………….….…. xii LIST OF ABBREVIATIONS……….………………………………………………..…… xvii PREFACE…………………………….……………………………………………….…... xix

CHAPTER 1: Introduction: Structural characterization of dissolved….… 1 organic matter, global implications and analytical approaches

1.1 Introduction…………………………………………………………………………. 2 1.1.1 Global significance and motivation for DOM research…………………..…. 3 1.1.2 Global significance and motivation for Arctic DOM research…………..….. 4

1.2 Analytical approaches to DOM……………………………………………….… 6 1.2.1 The application of high performance liquid chromatography with DOM………... 8 1.2.2 The application of nuclear magnetic resonance spectroscopy with DOM………… 9 1.2.3 The application of multidimensional NMR spectroscopy with DOM………….…. 12 1.2.3.1 Two dimensional correlation spectroscopy………………………... 14 1.2.3.2 Two total dimensional correlation spectroscopy…………………... 14 1.2.3.3 Heteronuclear single quantum correlation………………………… 15 1.2.3.4 Heteronuclear multiple bond connectivity…………………………. 16 1.2.4 Analysis of lignin-derived phenol biomarkers in DOM…………………….. 16 1.2.5 Analysis of DOM with 3D EEMs and parallel factor analysis……………… 17

1.3 Study objectives…………………………………………………………………… 20

1.4 Thesis summary…………………………………………………………………… 21

1.5 References………………………………………………………………………….. 24

CHAPTER 2: Evidence for the enhanced lability of dissolved organic…... 39 matter following permafrost slope disturbance in the Canadian High Arctic

2.1 Abstract……………………………………………………………………………… 40

2.2 Introduction………………………………………………………………………… 42

2.3 Experimental………………………………………………………………………. 45 2.3.1 Study area and sampling……………………………………………………. 45 2.3.2 Solution-state 1H NMR analysis…………………………………………… 49 2.3.3 Organic carbon content and lignin-derived phenol extraction…………...... 50 2.3.4 EEMs and PARAFAC analyses……………………………………………. 51 2.3.5 Photolytic degradation experiments………………………………………... 53

2.4 Results and discussion…………………………………………………………… 55 2.4.1 Solution-state 1H NMR…………………………………………………….. 55 2.4.2 Characteristics of lignin-derived phenols in Arctic DOM………………..... 58 2.4.3 PARAFAC components derived from fluorescence EEMs………………… 66 2.4.4 Effects of simulated solar radiation on Arctic samples…………………….. 71

2.5 DOM biogeochemistry in paired Arctic watersheds……………………….. 74

2.6 Acknowledgements……………………………………………………………….. 76

2.7 References………………………………………………………………………….. 78

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CHAPTER 3: Online high-performance size exclusion chromatography-… 89 nuclear magnetic resonance for the characterization of dissolved organic matter

3.1 Abstract……………………………………………………………………………… 90

3.2 Introduction………………………………………………………………………… 91

3.3 Materials and methods…………………………………………………………… 93 3.3.1 Sample collection and preparation………………………………………….. 93 3.3.2 HPSEC separation…………………………………………………………… 94 3.3.3 Fraction collection…………………………………………………………… 95 3.3.4 Solution 1H NMR……………………………………………………………. 95

3.4 Results and discussion…………………………………………………………… 96 3.4.1 Online techniques…………………………………………………………… 96 3.4.2 Effects of concentration……………………………………………………... 102 3.4.3 Structural information……………………………………………………….. 104 3.4.4 Variability of environmental samples……………………………………….. 106

3.5 Acknowledgements……………………………………………………………….. 109

3.6 References…………………………………………………………………………... 110

CHAPTER 4: HILIC-NMR: toward the identification of individual…….... 114 molecular components in dissolved organic matter

4.1 Abstract……………………………………………………………………………… 115

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4.2 Introduction………………………………………………………………………… 116

4.3 Experimental …………………………………………………………………….. 119 4.3.1 HILIC separation…………………………………………………………..... 119 4.3.2 NMR analysis……………………………………………………………….. 120 4.3.3 Fluorescence analysis……………………………………………………….. 121 4.3.4 Statistical Analyses…………………………………………………………. 121

4.4 Results and discussion……………………………………………………………. 122 4.4.1 HILIC separation and NMR analyses………………………………………. 122 4.4.2 Fluorescence analysis……………………………………………………….. 129 4.4.3 Statistical analysis of NMR and PARAFAC……………………………...... 130

4.5 Acknowledgements……………………………………………………………….. 133

4.6 References…………………………………………………………………………... 134

CHAPTER 5: Oxidized sterols as a significant component of dissolved….. 138 organic matter: evidence from 2D HPLC in combination with 2D and 3D NMR spectroscopy

5.1 Abstract……………………………………………………………………………… 139

5.2 Introduction………………………………………………………………………… 140

5.3 Experimental ……………………………………………………………………... 142 5.3.1 2D-HILIC/HILIC separation………………………………………………… 142 5.3.2 NMR analysis...……………………………………………………………… 148

5.4 Results and discussion………………………………………………………….... 151

5.4.1 One dimensional 1H NMR………………………………………………….. 151 5.4.2 Identification of small acids………………………………………………… 154 5.4.3 Evidence of sterols from DIPSI, DOSY and selective TOCSY experiments……………………………………………………………….. 154 5.4.4 Evidence of sterols with three dimensional NMR…………………….…..... 162 5.4.5 NMR spectral predictions for sterols………………………………….…….. 163 5.4.6 Variability in structures……………………………………………….…….. 169 5.4.7 Further considerations………………………………………………….….... 171

5.5 Acknowledgements………………………………………………………….……. 173

5.6 References………………………………………………………………………….. 174

CHAPTER 6: Conclusions and future directions……………………….……… 185

6.1 Conclusions and future directions………………………………….…….…….. 186

6.2 Future Arctic research……………………………………………………….…… 187

6.3 Theoretical approaches…………………………………………………………... 188

6.4 Sterol oxidation to identify possible end-members………………………… 189

6.5 Targeted approaches………………………………………………….…………… 190

6.6 References………………………………………………………………….……….. 192

APPENDIX……………………………………………………………………….……….. 194

LIST OF TABLES

Table 2.1. Summary of major structural groups, given as the percentage of 57 total NMR signal.

Table 2.2. Summary of lignin-derived phenol concentrations (µg/g DOC) 60 and molar ratios.

Table 2.3. Comparison of lignin-derived phenols from Cape Bounty, Melville 61 Island to other high latitude freshwater [Lobbes et al., 2000; Spencer et al., 2008] as well as samples from typical freshwater environments [Perdue and Ritchie, 2003].

Table 2.4. Parallel factor analysis (PARAFAC) components in Arctic DOM. 70 Components are presented as percentage of total modeled excitation-emission matrices (EEMs).

Table 2.5. Summary of percent loss of absorbance at 280 and 320 nm over 73 48 hours of simulated solar exposure.

Table 3.1. Parameters and apex retention volume for HPSEC-NMR analyses. 98

Table 5.1. Mobile phase composition for HILIC separations, 1st dimension; 145 flow rate of 0.3 ml/min.

Table 5.2. Structural assignments in 2D-HILIC/HILIC fractions provided 153 by COSY and confirmed with other multidimensional NMR experiments; references indicate previous studies with evidence for these compounds in humic substances.

LIST OF FIGURES

Figure 2.1. Map of study area at Cape Bounty, Melville Island (above) and 46 regional location map (below). Catchments are outlined by heavy lines and sampler site locations are indicated by circles.

Figure 2.2. A) Example NMR spectra of DOM obtained at Cape Bounty; 55 arom, aromatics; carb, carbohydrates (*note: methoxyl groups from lignin also contribute to this region); CRAM, carboxyl-rich alicyclic molecules; MDLT, material previously described as originating from linear terpenoids. B) Statistical data from integration of all NMR spectra. (*A) indicates that all river samples are variant from lake samples; (*W) West river varies from West lake; (*E) East river varies from East lake. Comparisons are at a level of statistical significance (2-tailed, unpaired t-test, unequal variance, p < 0.05). Error bars represent standard error.

Figure 2.3. Lignin-derived phenol ratios from the time series station in the 62 West river (WR), June 15th-July 5th, 2008. Samplers were deployed for 5 day intervals and collected on the date indicated. C/V, cinnamyl to vanillyl phenols; S/V, syringyl to vanillyl phenols; (Ad/Al)V, vanillyl acid to vanillyl aldehyde; (Ad/Al)S, syringyl acid to syringyl aldehyde.

Figure 2.4. Total lignin-derived phenol content (µg/g DOC) for riverine sites 65 in the West and East. Sites are displayed in order of decreasing elevation within respective catchments. (*Pt), recent slope disturbance; (*LG), historic disturbance.

Figure 2.5. C/V vs. S/V plot of Cape Bounty samples. Letters indicate sample 65 means with ranges for data obtained by Hedges and Mann [1979] (adapted from a figure in Ertel and Hedges [1984]). (A), woody angiosperm; (a), non-woody angiosperm; (G), woody gymnosperm; (g), non-woody gymnosperm.

Figure 2.6. PARAFAC loadings of Cape Bounty samples. C1-C6 are 67 detailed in the text; TRP, similar to fluorescence from free tryptophan; TYR, similar to fluorescence from free tyrosine; (*), similar to components previously linked to terrestrial material; (†), similar to components previously linked to microbial material; R.U., Raman units.

Figure 2.7. PARAFAC scores of Cape Bounty samples. Components 67 are described in detail in the text. (*A) indicates that all river samples are variant from lake samples; (*W) West river varies

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from West lake; (*E) East river varies from East lake. Comparisons are at a level of statistical significance (2-tailed, unpaired t-test, unequal variance, p < 0.05). Error bars indicate standard error.

Figure 2.8. A) Initial absorbance of Arctic samples (5 mg DOC/L) prior to 72 UV exposure; variation from East lake to the remaining groups is statistically significant. B) Percent reduction in absorbance over 48 hours of simulated solar exposure; variance between a) West river and lake, b) East river and lake are statistically significant. (2-tailed, unpaired t-test, unequal variance, p < 0.05). Error bars indicate standard error.

Figure 3.1. Comparison of HPSEC-NMR techniques (SRNOM, 100 99 mg/mL, 100 μL injection). Column I (rows A-C): pseudo-2D chromatograms from online analyses, elution profiles along the y-axes. Column I (row D): sum profiles from selected NMR regions from column I, row A (indicated by dashed lines and colored regions): red, aromatics, 6.5-7.8 ppm; green, carbohydrates, 3.2-4.5 ppm; blue, carboxyl-rich alicyclic molecules (CRAM), 1.6-3.2 ppm; purple, material derived from linear terpenoids (MDLT), 0.6-1.6 ppm (as highlighted in column I, row A). These profiles are discussed later in the paper but included here so that the reader can visualize how the sum profiles are created from 2D HPLC-NMR data sets. Rows A, B, and C: NMR spectra from stopped-flow, slow continuous- flow, and fast continuous-flow, respectively. Row D (columns II-IV): NMR spectra from offline fraction collection. Columns II, III, and IV: NMR spectra of material eluted at 50%, 75%, and 95% of the total sample elution volume. Readers should be aware that under continuous-flow conditions (rows B and C) the sample is only in the NMR cell for a finite amount of time; hence, the number of scans cannot be increased. However, in the case of stopped-flow (row A) and fraction collection (row D) the number of scans can be increased substantially, permitting the detection of components at much lower concentrations. The asterisk indicates carbohydrates; methoxyl from lignin also contributes to this region.

Figure 3.2. Comparison at varying concentrations (stopped-flow, SRNOM, 103 100 μL injection). Rows A, B, and C: NMR spectra of 100, 20, and 5 mg/mL, respectively. Columns I, II, and III: material eluted at 25%, 50%, and 75% of the total sample elution volume. The arrow is discussed in the text.

Figure 3.3. Comparison of DOM samples (stopped-flow, 100 mg/mL, 100 μL 105 injection). Rows A, B, and C: SRNOM, NRNOM, and LSNOM,

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respectively. Column I: whole samples run in a traditional 5 mm NMR tube. Columns II, III, and IV: material eluted at 25%, 50%, and 75% of the total sample elution volume (100 mg/mL, stopped- flow). The insets in column I (rows A-C) illustrate elution profiles from HPSEC-NMR; the axes are the retention volume (mL). (Refer to the Figure 3.1 caption for color and chemical shift assignments.) The asterisk indicates carbohydrates; methoxyl from lignin also contributes to this region.

Figure 4.1. A) Chromatogram of HILIC separation. Blue line: DAD, 280nm, 123 units on left axis. Red line: fluorescence, 320/430 nm ex/em, units on right axis. Dashed lines: HPLC fraction intervals. Arrow: signal predominated by tryptophan. B) PCA plot of the scores for the NMR data. C) Major structural groups with increasing polarity; assignments explained in the main text. Correlations have a significance of p<0.0005 except aromatics (p=0.578). (avg%) indicates average percentage of NMR signal for all fractions.

Figure 4.2. High resolution 1D 1H NMR spectra of A) SRDOM, C) HILIC 126 fraction 9 (H09), and zoomed regions for B) SRDOM, D) H09. Axes indicate chemical shift of each spectrum. Assignments are as follows: arom, 6.5-7.8 ppm; carb (lignin methoxyl also resonates under this region), 3.2-4.5 ppm; CRAM, 1.6-3.2 ppm; MDLT, 0.6- 1.6 ppm; lig (lignin),* 6.57 ppm; (a) formic acid, 8.44 ppm; (b) residual water, 4.84 ppm; has lactic acid (quartet), 4.02 ppm; (d) glycolic acid, 3.94 ppm; (e) methanol, 3.33 ppm; (f) succinic acid, 2.38 ppm; (g) acetic acid, 1.90 ppm; (h) lactic acid (doublet), 1.33 ppm. *See Appendix.

Figure 4.3. 2D COSY45 NMR spectra of HILIC-simplified fraction (H09); left: 128 zoomed region from 0.5-4.5 ppm, right: zoomed aromatic region (6.3-8.5 ppm). Assignments made from reference database (see main text); (*) indicates assignment from previous work using a database of lignin components [Simpson et al., 2004].

Figure 4.4. PCA plots of the (A) loadings and (B) scores of the combined 131 NMR and PARAFAC data set. The loadings for PARAFAC are highlighted in orange and labeled accordingly; circled regions are discussed in the main text.

Figure 5.1. 1D 1H NMR spectra of 2D-HLIC/HILIC fractions and the HPLC/NMR 144 system blank. Assignments are as follows: arom, 6.5-7.8 ppm; carb (lignin methoxyl also resonates under this region), 3.2-4.5 ppm; CRAM, 1.6-3.2 ppm; MDLT, 0.6-1.6 ppm; lig (lignin), 6.57 ppm.

Figure 5.2. Diagram of HPLC apparatus illustrating how the 2D-HILIC/HILIC 145

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system was achieved.

Figure 5.3. A) Chromatogram of the 1st dimension HILIC separation 146 (diol stationary phase). B) 3D surface representation of the 2D- HILIC/HILIC separation.

Figure 5.4. 2D COSY NMR spectrum of 2D-HILIC/HILIC fraction 16. 152 Assignments made from reference database and confirmed with other 2D NMR experiments (see main text).

Figure 5.5. DIPSI profiles of SRDOM fractions along with profiles of 155 standards: cholic acid and human serum albumin (HSA) with sucrose.

Figure 5.6. Diffusion profiles generated from DOSY experiments for 4 157 fractions plus a cholic acid standard. DC indicates diffusion coefficient and numbers on each profile specify the apex of each peak. Fraction 109 has a broad diffusion profile in part because of much lower signal to noise.

Figure 5.7. Zoomed region of a selective TOCSY experiment overlaid by the 157 1D 1H NMR spectrum for fraction 16. Selective excitation of the O-R region (3.77 ppm) generated significant signal within the 1.7- 2.5 ppm region.

Figure 5.8. Selective TOCSY spectrum generated by the excitation of the CH3 160 region (1.06 ppm); minimal signal is apparent from other chemical shift regions.

Figure 5.9. A) 2D HSQC spectrum produced from a F2-F3 slice at 1.85 ppm 161 in the F1 dimension for the 3D HSQC-TOCSY experiment on fraction 16. B) Corresponding 2D spectrum generated from spectral predictions for a main cyclic component of sterol-type structures (cholic acid, minus side chain, see text for discussion).

Figure 5.10. Multiplicity edited 2D HSQC spectrum displaying the methyl 164 and methine functional groups in fraction 16. The prominent methyl region is indicated (~1 ppm , 1H and ~25 ppm, 13C).

Figure 5.11. Zoomed regions (1.3-3.0 ppm) of 2D NMR spectra for A) COSY 164 and B) TOCSY experiments on 2D-HILIC/HILIC fraction 16.

Figure 5.12. A) 2D TOCSY spectrum produced from a F1-F3 slice at the apex 166 of the material from the DOSY dimension for the 3D DOSY-TOCSY experiment on fraction 16; B) Corresponding 2D spectrum generated from spectral predictions on cholic acid.

Figure 5.13. Zoomed region (1.55-2.9 ppm, 1H) of 2D HMBC spectrum for 167 fraction 16. Chemical structure is cholic acid and blue highlighted regions indicate how variations of this structure can account for data using spectral predictions, see text for discussion.

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LIST OF ABBREVIATIONS

(Ad/Al)s : syringyl to syringyl aldehyde (µg/g DOC)

(Ad/Al)v : vanillyl to vanillyl aldehyde (µg/g DOC) ALD : active layer detachment

C : cinnamyl species of lignin-derived phenols

CBAWO : Cape Bounty Arctic Watershed Observatory

COSY : correlation spectroscopy

CRAM : carboxyl-rich alicyclic molecules

CuO : copper (II) oxide

DAD : diode array detector

DEAE : diethylaminoethyl

DOC : dissolved organic carbon

DOM : dissolved organic matter

DOSY : diffusion ordered spectroscopy

EEMs : excitation-emission matrices

FLD : fluorescence detector

FT-ICR-MS : Fourier transform ion cyclotron resonance mass spectrometry

GC-MS : gas chromatography-mass spectrometry

HILIC : hydrophilic interaction chromatography

HMBC : heteronuclear multiple bond correlation

HMQC : heteronuclear multiple quantum correlation

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HPLC : high performance liquid chromatography

HPSEC : high performance size exclusion chromatography

HSA : human serum albumin

HSQC : heteronuclear single quantum coherence

IHSS : International Humic Substances Society

LOD : level of detection

MDLT : material derived from linear terpenoids

NMR : nuclear magnetic resonance spectroscopy

NOM : natural organic matter

PARAFAC : parallel factor analysis

PCA : principle component analysis

PURGE : presaturation using relaxation gradients and echoes

PVDF : polyvinylidene difluoride

RP-HPLC : reversed phase high performance liquid chromatography

S : syringyl species of lignin-derived phenols

SOM : soil organic matter

TOC : total organic carbon

TOCSY : total correlation spectroscopy

V : vanillyl species of lignin-derived phenols

VSC : total lignin-derived phenol content (µg/g DOC)

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Preface

This dissertation is comprised of a series of manuscripts that are published in peer- reviewed journals (Chapters 2-4) or being prepared for submission (Chapter 5). Published manuscripts may contain unavoidable repetition. Contributions from authors are as follows:

CHAPTER ONE

Introduction

Written by Gwen C. Woods with critical comments from André J. Simpson

CHAPTER TWO

Evidence for the enhanced lability of dissolved organic matter following permafrost slope disturbance in the Canadian High Arctic

Published as: Woods G. C., Simpson M. J., Pautler B. G., Lamoureux S. F., Lafrenière M. J. and Simpson A. J. (2011) Evidence for the enhanced lability of dissolved organic matter following permafrost slope disturbance in the Canadian High Arctic. Geochim. Cosmochim. Acta 75 (22), 7226-7241.

The experimental design was created by Gwen C. Woods and André J. Simpson. The fieldwork was conducted by Gwen C. Woods with guidance from Melissa J. Lafrenière. The lab experiments were conducted by Gwen C. Woods with guidance from Myrna J. Simpson, Brent G. Pautler and André J. Simpson. GC-MS analysis was specifically performed by Brent G. Pautler. Data interpretation was performed by Gwen C. Woods with guidance from Myrna J. Simpson and André J. Simpson. The manuscript was written by Gwen C. Woods with critical comments from André J. Simpson, Myrna J. Simpson, Brent G. Pautler, Melissa J. Lafrenière and Scott F. Lamoureux.

CHAPTER THREE

Online high-performance size exclusion chromatography-nuclear magnetic resonance for the characterization of dissolved organic matter

Published as: Woods G. C., Simpson M. J., Kelleher B. P., McCaul M., Kingery W. L. and Simpson A. J. (2010) Online high-performance size exclusion

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chromatography-nuclear magnetic resonance for the characterization of dissolved organic matter. Environ. Sci. Technol. 44 (2), 624-630.

The experimental design was created by Gwen C. Woods and André J. Simpson. The lab analyses were conducted by Gwen C. Woods with help from André J. Simpson. Data interpretation was performed by Gwen C. Woods and André J. Simpson. The manuscript was written by Gwen C. Woods with critical comments from André J. Simpson.

CHAPTER FOUR

HILIC-NMR: toward the identification of individual molecular components in dissolved organic matter

Published as: Woods G. C., Simpson M. J., Koerner P. J., Napoli A. and Simpson A. J. (2011) HILIC-NMR: toward the identification of individual molecular components in dissolved organic matter. Environ. Sci. Technol. 45 (9), 3880- 3886.

The experimental design was created by Gwen C. Woods and André J. Simpson. The lab analyses were conducted by Gwen C. Woods with help from André J. Simpson. Data interpretation was performed by Gwen C. Woods with guidance from André J. Simpson. The manuscript was written by Gwen C. Woods with critical comments from André J. Simpson.

CHAPTER FIVE

Oxidized sterols as a significant component of dissolved organic matter: evidence from 2D HPLC in combination with 2D and 3D NMR spectroscopy

Content in this chapter has been submitted in: Water Research

The experimental design was created by Gwen C. Woods and André J. Simpson. The lab analyses were conducted by Gwen C. Woods with help from André J. Simpson. Data interpretation was performed by Gwen C. Woods with guidance from André J. Simpson. The manuscript was written by Gwen C. Woods with critical comments from André J. Simpson.

CHAPTER SIX

Conclusion and future directions

Written by Gwen C. Woods with critical comments from André J. Simpson.

Chapter 1

Introduction: Structural characterization of dissolved organic matter, global implications and analytical approaches

1.1 Introduction

Natural organic matter (NOM) largely originates from the decay of living organisms

[Hatcher, 2004], resides globally in soils, sediments and aquatic ecosystems and plays vital roles in many environmental processes. Only minor amounts of the material within NOM can be distinguished as biomolecules in the form as they are present within living organisms (e.g. carbohydrates, lipids, proteins) [Hedges, 2002: Hatcher, 2004]. While biomolecules in living organisms are dictated by a genetic code, the bulk of the constituents residing in NOM have undergone a series of abiotic and biotic reactions, resulting in a diverse pool of molecular structures that likely exceeds the numbers in living organisms by several orders of magnitude

[Hertkorn et al., 2007]. Among the competing views proposed by researchers is that the formation of this elusive material occurs via degraded biomolecules that undergo condensation polymerization, forming large macromolecular structures [Hatcher, 2004].

Other groups have argued that the molecularly uncharacterized fraction of NOM originates from living materials with only minor alterations and is the product of the selective preservation of more structurally robust and/or physically inaccessible material; remaining biomolecules (that potentially constitute a sizeable portion of living biomass) are labile organic constituents that are quickly degraded, reincorporated into biomass or mineralized

[Hedges et al., 2000; Hatcher, 2004]. A final emerging viewpoint is that the refractory material present in NOM does not retain the signatures of precursors as much as this material is simply a complex array of structures that are the result of the fundamentals of chemical binding and may represent a sizable fraction of molecular structures that are theoretically possible [Hertkorn et al., 2007]. The debates over structural formations and what structures are actually present in any great abundance remain topics of great interest to researchers.

These “supermixtures” or “nonrepetitive complex systems” are amongst the most complex substances on Earth and pose substantial analytical challenges in not only characterizing but further understanding reaction processes and how this dynamic pool of carbon interacts with other global carbon reservoirs.

This dissertation presents studies of the molecular characterization of NOM from aqueous environments, dissolved organic matter (DOM). For all studies, the versatile and powerful analytical instrument, nuclear magnetic resonance (NMR) spectroscopy was the primary analytical tool used. Online and offline hyphenated techniques were applied as were other complimentary analytical and statistical techniques. This chapter provides background information, motivation for the studies conducted and justification for analytical approaches.

1.1.1 Global significance and motivation for DOM research

DOM is operationally defined as the organic material that will pass through filters with pore sizes ranging from 0.2-1.0 µm [Benner, 2002], is typically present in concentrations range from ~0.5 to 10 mg/L [Thurman, 1985] and has a multitude of important functions in the natural environment. This material is a key component in nutrient cycling, microbial food webs, metal speciation, contaminant binding and subsequent bioavailability, regulating global surface temperatures, and furthermore provides a molecular record of natural history [Ogner and Schnitzer, 1970; Gjessing, 1976; Means and Wijayaratne, 1982; MacCarthy, 1989;

Gunderson et al., 1997; Ma and Graham, 1999; Wershaw, 1999; Hedges et al., 2000; Benner,

2002; Helbing and Zagarese, 2003; Thiele-Bruhn, 2003]. The aquatic pool of organic matter

(mostly comprised of DOM) is not only an actively moving interface between soils, sediments and air, but furthermore represents a sizeable pool of global NOM. As much as 1600 Pg

3 organic carbon are thought to reside in soils, 1000 Pg organic carbon in recently deposited marine sediments [Hedges and Keil, 1995; Hedges et al., 2000], and roughly 700 Pg organic carbon are believed to exist as DOM throughout the world’s oceans [Druffel et al., 1992;

Hedges and Keil, 1995; Hedges et al., 2000; McNichol and Aluwihare, 2007; Hansell et al.,

2009]. For comparison, land plants contain some 600 Pg organic carbon while marine organisms are estimated at 3 Pg organic carbon [Hedges and Keil, 1995; Hedges et al., 2000].

Yet, despite the sizeable reservoir of organic carbon present in the world oceans, the flux of

DOM in the marine environment is believed to constitute only ~ 0.1 Pg organic carbon per year [Druffel et al., 1992; McNichol and Aluwihare, 2007]. Understanding the processes involved in the formation, preservation and cycling of this reservoir is of great importance for a more complete understanding of carbon fluxes between reservoirs as well as being able to predict how this reservoir will be affected by perturbations such as climate change.

1.1.2 Global significance and motivation for Arctic DOM research

Northern ecosystems are estimated to contain anywhere from 25-50% of world’s organic carbon stored in soils [Billings, 1987; Dixon et al., 1994; Oechel and Vourlitis, 1995;

Dittmar and Kattner, 2003; Tarnocai et al., 2009] with as much as 14% of global soil organic carbon believed to reside in tundra ecosystems [Billings, 1987]. Organic constituents that are liberated from these landscapes as DOM are further carried via rivers and streams to the

Arctic Ocean where 10% of freshwater is estimated to enter on a global scale [Opsahl et al.,

1999]. Terrestrial inputs into Arctic surface waters account for approximately a third of

DOM present while comparative terrestrial inputs into temperate oceans are believed to only constitute about 1-2% of DOM present [Opsahl et al., 1999; Hernes and Benner, 2006]. The

4 sensitivity of polar environments to temperature change are therefore of great concern; changes in the transport of NOM from tundra and northern soils to aquatic and marine environments could significantly impact the marine DOM reservoir.

Arctic tundra has historically been a net sink for CO2 [Miller et al., 1983; Gorham,

1991] and thus enhanced warming is of concern in that CO2 could potentially become a source in these ecosystems with the advent of accelerated organic matter decomposition

[Oechel et al., 1993]. Polar environments are currently experiencing some of fastest rates of warming globally [Maxwell, 1992; Mikan et al., 2002; Boddy et al., 2008]. Over the past 40 years, unprecedented warming has occurred within the Arctic, resulting in greater depth of yearly thaw as well as significant melting of permafrost [Chapin et al., 2005]; such warming is speculated to significantly enhance NOM decomposition [Dittmar and Kattner, 2003;

Sjögersten et al., 2003; Turner et al., 2004; White et al., 2004; Boddy et al., 2008] and predicted to enhance the release of CO2 into the atmosphere [Miller and Zepp, 1995; Beer,

2008; Ping et al., 2008; Schuur et al., 2009]. Monitoring of DOM in the Arctic is therefore of importance so as to better understand if climate change could result in a system of positive feedback. While substantial thawing of permafrost and the subsequent degradation of previously frozen NOM are speculated to result in the large-scale flux of carbon from terrestrial ecosystems to the atmosphere [Schuur et al., 2008], accurate predictions can only be made if the quality and reactivity of this material is better understood. Chapter 2 of this dissertation examines DOM collected from High Arctic watersheds and specifically examines how permafrost disturbance may be affecting DOM in this remote environment.

1.2 Analytical approaches to DOM

The global numbers and environmental significance of DOM signify that our understanding of this material at the molecular-level is of vital interest. Elemental analyses have been conducted and demonstrate that DOM is largely comprised of carbon (43-50%) and oxygen (30-41%) with smaller concentrations of hydrogen (3-5%), nitrogen (0.5-7%) and sulfur (0.2-2%) [Frimmel et al., 2000], but more than 75% of this material remains molecularly uncharacterized [Hedges et al., 2000; Leenheer and Croué, 2003]. DOM researchers typically approach molecular-level characterization with varied and/or novel analytical approaches. Multifaceted data sets facilitate a more comprehensive chemical representation of DOM and the interpretation of confusing and often convoluted data signals is best confirmed with multiple techniques and approaches. Among the greatest challenges associated with the characterization of this material is the absence of well-defined reference materials [Hedges et al., 2000; Hertkorn et al., 2007]. Comparison of signals arising from reference standards to DOM samples is complicated by the complexity and aggregation at the molecular scale. Small differences arising from hydrophobic effects, hydrogen bonding, and weak intermolecular forces may significantly impact traditional methods of detection that rely on aspects such as retention time or spectral signatures [Hertkorn et al., 2007] not to mention that reference standards only exist for known constituents. Molecular components present within these mixtures are thus likely to be unidentified or misidentified with many targeted approaches and data must subsequently be handled carefully. Further problems with many analytical approaches include limited resolution or peak capacity; the subsequent lack of signal detail translates into considerable signal averaging, bulk-level descriptors and difficulty in differentiating between constituents present in a given sample as well as between bulk

6 samples [Hertkorn et al., 2007]. Thus techniques with superior spectral resolution are desirable for optimal signal detail as are varied approaches to confirm or refute the interpretation of complex signal sets.

To date, nuclear magnetic resonance (NMR) spectroscopy and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) have been identified as the most powerful techniques for the investigation of complex environmental samples [Hertkorn et al.,

2007; Dittmar and Paeng, 2009]. Both methods, however, are still hindered by the complexity of DOM such that NMR spectra become near featureless and FT-ICR-MS cannot readily provide connectivity and hence isomer-specific information [Baker et al., 2007].

Expanding either of these analyses, however, with mathematical analyses and/or chromatographic separations permits another analytical “dimension” from which information can be gained [Hertkorn et al., 2007]. While NMR is particularly hindered by the complexity of whole DOM samples, sample fractionation has been shown to greatly enhance the level of detail and subsequent information that can be obtained from more homogenous fractions

[Leenheer and Rostad, 2004]. Extensive fractionation would greatly benefit these analyses but DOM simplification has largely been hindered by the lack of successful chromatographic separations [Piccolo, 2001; Benner, 2002; Koch et al., 2008; Dittmar and Paeng, 2009].

Chromatographic applications on DOM, however, have typically used conventional organic chemistry techniques that are not well suited for the polar constituents suspended in aqueous solution. Exploration of more tailored chromatographic techniques might therefore prove useful to DOM analyses. High performance liquid chromatography (HPLC), has large separation capacity, is nondestructive, and if sufficient resolution can be achieved, greatly simplifies complex mixtures. Thus one of the major goals of the research presented in

7 chapters 3 through 5 was to find appropriate HPLC techniques from which adequate separation might be achieved prior to obtaining the unsurpassed molecular detail that can be provided by NMR [Hertkorn et al., 2007].

1.2.1 The application of high performance liquid chromatography with DOM

High performance liquid chromatography (HPLC) operates on the principal that analytes partition between a stationary phase (e.g. column packing) and mobile phase (e.g. solvent). Slight variations in affinity for the stationary vs. mobile phase will resolve analytes into discrete segments from which a detector (e.g. UV) can determine the extent of separation/resolution. Hydrophilic interaction chromatography (HILIC) is an emerging field of HPLC that is considered ideal for polar constituents. The need for this HPLC technique came about owing to the predominance of reversed phase (RP)-HPLC applications among organic chemists; this technique utilizes a hydrophobic stationary phase (e.g. C18) with a mobile phase of aqueous solutions of water-miscible organic solvents [Hemström and Irgum,

2006]. RP-HPLC has long been plagued, however, by the lack of retention of polar compounds that experience considerable solvophilic effects (i.e. polar functional groups permit the molecule to become solvated). These compounds subsequently remain associated with the liquid (mobile) phase and elute within the void volume as these compounds demonstrate little to no interaction with the stationary phase [Hemström and Irgum, 2006].

This lack of retention has largely been demonstrated with HPLC applications of DOM where

RP-HPLC is one of the most commonly employed techniques [Caron et al., 1996; Wu et al.,

2002; Hutta and Góra, 2003; Whelan et al., 2003; Wu et al., 2003; Simpson et al., 2004a;

Koch et al., 2008; Dittmar and Paeng, 2009]. The resulting chromatograms are generally poorly resolved with short retention times.

The evolution of “reversed RP” or HILIC instead utilizes a polar stationary phase that not only attracts polar analytes but further attracts the aqueous part of the mobile phase. This water-enriched layer adjacent to the hydrophilic stationary phase provides a setting for partitioning between the aqueous layer and the less polar organic layer [Hemström and Irgum,

2006]. Due to the possibility that smaller molecules may further penetrate the water layer and interact with the stationary phase, a variety of further retention mechanisms have been proposed including hydrogen bonding, ion-exchange, hydrophobic and hydrophilic interactions [Naidong, 2003; Hemström and Irgum, 2006; Hao et al., 2008]. Although the mechanisms involved have not been fully defined, this multimodal retention method is finding increasing applications with complex environmental samples [Stoll et al., 2007; Wang et al., 2008; Liu et al., 2009; Li et al., 2011; van Nuijs et al., 2011] but had not previously been demonstrated with DOM. Chapters 4 and 5 of this dissertation illustrate the application of this technique for Suwannee River DOM (Florida) and further illustrate the usefulness of this method for polar environmental samples.

1.2.2 The application of nuclear magnetic resonance spectroscopy with DOM

Nuclear magnetic resonance (NMR) spectroscopy is an analytical technique that utilizes the magnetic properties of nuclei to generate information about the molecular structures present in a given sample. Atomic nuclei precess or resonate at specific frequencies when placed in a strong magnetic field (B0) and can be identified and measured by further applying a second oscillating field until absorption of energy is detected. The

9 frequency of radiation needed to induce this absorption, or resonance, depends on the magnetic properties of a given nucleus (γ: gyromagnetic ratio) as well as the extent of electronic shielding experienced by the nucleus. The rate of precession is proportional to the strength of the magnetic fields of both the nuclei and of the external magnet:

v0 = γ B0/2π

Connectivity information of nuclei is elucidated by the extent to which neighboring and directly bound nuclei affect the electron distribution. Even slight variations in structure and bonding will result in very different shielding and subsequently give rise to unique resonance frequencies (i.e. chemical shifts) [Jacobsen, 2007; Levitt, 2001]. A considerable variety of

NMR techniques and experiments are currently available to take advantage of this phenomenon and can be applied to unknowns and sample mixtures so as to probe for the arrangement of chemical bonds, stereochemistry, dynamics and even reactivity of constituents that would largely go unidentified via most analytical tools [Hertkorn et al., 2007].

All studies presented in this dissertation specifically utilize solution state NMR for the analysis of DOM and fractionated DOM. The next two paragraphs rationalize the use of solution state NMR for DOM studies: Solution state NMR is known to be an excellent technique for highly complex mixtures and provides a greater level of detail than corresponding solid state or cross polarization magic angle spinning (CP-MAS) techniques

[Simpson et al., 2011]. Solid state NMR is best suited for samples with low solubility and is plagued by very broad resonances due to the fixed position of nuclei; signal attenuation further becomes problematic in the presence of paramagnetic species [Cardoza et al., 2004].

Broadening of signal peaks can be reduced by a technique known as CP-MAS but this technique is still hindered by broad band widths in comparison to solution-state methods

[Simpson et al., 2011] and has poor quantitative reliability [Cardoza et al., 2004]. Perhaps the greatest advantage of solution state NMR for complex environmental samples, however, is simply the ability to conduct multidimensional experiments [Cook et al., 2003; Simpson et al.,

2011]. In contrast, multidimensional solid-state NMR experiments are hindered by very strong 1H-1H dipolar interactions, making direct detection of the X nucleus almost always necessary and translating into considerable loss of sensitivity [Simpson et al., 2011].

With solution state, 1H NMR is not as widely employed as 13C NMR among DOM researchers due to broadened lines relative to the chemical shift range as well as a large residual water peak that must have an adequate solvent suppression technique applied in order to obtain suitable signal to noise. Used in conjunction, both techniques may provide complimentary information (e.g. 13C NMR permits the direct detection of ketone and carboxylate functional groups) [Bortiatynski, 1996; Cardoza et al., 2004]. The advantages that 1H NMR has over 13C NMR, however, is that the 1H isotope is present at 100% abundance (i.e. for all hydrogen isotopes) in comparison to only 1.1% for the 13C isotope (i.e. for all carbon isotopes) and that the 1H isotope has a sensitivity factor of 1.0 in comparison to only 0.016 for 13C; what these numbers translate into is a 5700 times greater sensitivity of 1H than 13C experiments and considerably shorter acquisition time [Preston, 1996; Jacobsen,

2007]. This sensitivity advantage is well illustrated by the application of “direct NMR” or the application of in situ detection of DOM without sample alteration as has been employed by both Lam and Simpson [2008] for samples from Lake Ontario and Pautler et al. [2011] for samples of thawed glacial ice. Furthermore for purposes of sample screening, the 1H NMR

11 technique is much more efficient and was used for 1D analyses in samples presented here.

Following sample screening with 1D experiments, more in-depth and information-rich multidimensional experiments were carried out. 1D 1H NMR provides a better means from which to selectively choose samples for these more time-intensive analyses.

Humic substances generate 1D 1H NMR spectra that are typically broad mounds without discretely resolved resonances. Regions of resonance are divided into categories for

1H NMR as follows: ~0.5-2 ppm, from methyl and methylene resonances, largely associated with aliphatic groups; ~2-3.5 ppm, acetyl groups, peptide aliphatics and carboxyl-rich alicyclic molecules (CRAM); ~3.5-5 ppm, from methyl and methylene attached to heteroatoms as well as protons adjacent to OH groups (mostly carbohydrates with contributions from CH-α of peptides); and ~6-8 ppm, for aromatic structures [García et al.,

1994; Hertkorn et al., 2006]. All studies presented in this dissertation used 1D 1H NMR experiments for DOM samples and DOM fractions are defined in accordance with these chemical shift regions.

1.2.3 The application of multidimensional NMR spectroscopy with DOM

The application of multidimensional NMR experiments has proved very useful for the structural characterization of DOM. The use of multidimensional NMR provides greater diversity in structural information for humic substances including proton covalent networks, exchange dynamics, spatial interactions and heteronuclear linkages [Cardoza et al., 2004].

To date, multidimensional NMR applications have identified a variety of evidence for material derived from lignin, protein, lipids, carbohydrates and tannins as major contributors to humic substances [Cook et al., 2003; Fan et al., 2000; Haiber et al., 2001; Hertkorn et al.,

2002; Mao et al., 2001; Simpson et al., 2001; Simpson et al., 2002; Simpson et al., 2003;

Simpson et al., 2004b; Simpson et al., 2007]. The extensive spectral overlap with DOM samples, however, complicates the identification of exact structures and prohibits the precise identification of the highly altered and refractory material that is present in significant contributions. This “black box” or elusive fraction of DOM has long hindered molecular- level analyses of humic substances and has the potential to hold a wealth of source and reactivity information of the organic constituents present. Utilizing a variety of multidimensional NMR experiments and allied techniques, however, proves useful in corroborating or refuting interpretations of these complex signals. Fractionation and subsequent reduction in heterogeneity further provides much more useful data from which to determine differences between samples as well as from which to elucidate precursor materials. Leenheer and Rostad [2004] found that fractionation of DOM provided NMR data that evidenced terpenoid-type structures as dominant constituents in DOM. Recent multidimensional NMR data coupled with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) has further provided strong evidence for carboxyl-rich alicyclic molecules as likely structures present in DOM [Hertkorn et al., 2006]. Believed to be derived from cyclic terpenoids, these structures were further verified by Lam et al. [2007] with multidimensional NMR and spectral prediction techniques which further provided evidence for linear analogs of CRAM: material derived from linear terpenoids (MDLT). Through the use of multidimensional NMR and complimentary analytical techniques, the complexities of

DOM may be reduced to highlight the major contributors and processes that give rise to this complex material. Chapters 3 through 5 of this dissertation utilized a number of

13 multidimensional NMR experiments from which structural information was obtained and are described briefly below.

1.2.3.1 Two dimensional correlation spectroscopy

Two dimensional correlation spectroscopy (COSY) is a relatively simple NMR experiment that correlates nuclei of a single isotope (usually 1H) via a single J-coupling (i.e. adjacent spins). A diagonally symmetric plot is generated where the one dimensional spectrum is on the diagonal and peaks generated off of the diagonal (cross peaks) represent the proton-proton couplings. These cross peaks are a result of magnetization transfer and indicate the chemical shifts of the two coupled nuclei. A final pulse of 90° is generally applied to these experiments but certain samples may be better suited to a reduced pulse angle. COSY45, for example employs a final pulse of 45° and may be used to simplify an otherwise crowded spectrum. The reduced angle reduces the intensity of spectral splitting and results in the overall simplification of spectra; the diagonal is further narrowed such that greater detail near the diagonal becomes apparent. With COSY45 experiments some loss in sensitivity is essentially traded for a cleaner spectrum and is better suited for highly complex data sets [Jacobsen, 2007; Levitt, 2001].

1.2.3.2 Two dimensional total correlation spectroscopy

Two dimensional total correlation (TOCSY) spectroscopy is a technique similar to

COSY in that magnetization transfer of 1H-1H is measured but rather than measuring only neighboring spins, coherence or magnetization transfer is measured from multiple “jumps” throughout an entire spin network. Such measurement enables the detection of all coupled

14 protons within a molecule. A useful 1D version of TOCSY is the selective TOCSY experiment wherein a single resonance or range of resonances are selectively excited and the resulting coherence is then transferred to all other protons within the spin system [Jacobsen,

2007; Levitt, 2001]. Such selective probing is appropriate for mixture analysis and may provide very useful information for the investigation of structures in DOM.

1.2.3.3 Heteronuclear single quantum correlation

Heteronuclear single quantum correlation (HSQC) experiments yield crosspeaks representing 1H-13C coupling over a single bond (or may have other nuclei such as 15N measured instead but for DOM the 1H-13C relationship is generally most useful). The 13C is in the indirection dimension on the F1 axis while the 1H is the direct dimension on the F2 axis

[Jacobsen, 2007]. This technique is very similar to a technique known as heteronuclear multiple quantum correlation (HMQC) with the exception that during the evolution time,

HMQC allows both proton and the heteronuclei (e.g. 13C) magnetization to evolve while

HSQC permits only the heteronuclei magnetization to evolve. The resulting data from

HMQCs contain proton J-coupling resulting in broadened peaks in the F1 dimension. HSQC spectra do not contain proton couplings and are therefore more resolved [Simpson, 2001]. A further variation on the HSQC experiment is to “edit” the data such that crosspeaks provide information on the number of protons attached to heteronuclei. With 1H-13C experiments, for example, CH3 and CH crosspeaks are positive while CH2 crosspeaks are negative [Jacobsen,

2007].

1.2.3.4 Heteronuclear multiple bond connectivity

Heteronuclear multiple bond connectivity (HMBC) is similar to HSQC in terms of correlating 1H to a heteronuclei (e.g. 13C) except that in this experiment long range correlations are measured. HMBC spectra contain crosspeaks that arise from 2, 3 and sometimes 4 bonds away while the directly coupled 1H-X that appear in HSQC spectra are missing [Jacobsen, 2007].

1.2.4 Analysis of lignin-derived phenol biomarkers in DOM

While DOM is a complex heterogeneous mixture, techniques such as NMR may provide excellent quantitative and qualitative information but, pending substantial reduction in heterogeneity, is often limited to bulk structural characteristics. More targeted approaches are therefore frequently used to probe information into precursor materials and degradation processes. Biomarkers, for example, are organic indicators that provide source and process information and for DOM studies, one of the most revolutionary biomarker applications has been the analysis of lignin-derived phenols [Hedges and Mann, 1979; Hedges and Ertel,

1982; Opsahl and Benner, 1998; Hernes and Benner, 2003; Hernes et al., 2007; Hood et al.,

2009; Spencer et al., 2009]. Lignin is the second most abundant biopolymer after cellulose in vascular plants and is an excellent tracer for material of terrestrial origin. CuO chemolysis is most often used to break lignin down into smaller units such that these lignin-derived phenols may be derivatized and analyzed via gas chromatography [Hedge and Ertel, 1982]. The resulting lignin-derived phenols are classified into three structural groups: vanillyls, syringyls and cinnamyls. These phenols can be used to gain insight into plant origins: vanillyl species

(V) are ubiquitous throughout lignin samples, syringyl phenols (S) are unique to angiosperms

16 and cinnamyl phenols (C) are derived from nonwoody plant tissues [Hedges and Mann,

1979]. These lignin-derived phenols are further useful in that the extent of degradation is often indicated by increasing contributions from the oxidized species of each group [Hedges and Weliky, 1989; Opsahl and Benner, 1995]. Acid to aldehyde ratios are therefore frequently used as proxies for the diagenetic state of DOM samples. Chapter 2 of this dissertation utilizes lignin-derived phenol biomarkers to probe the variability in samples across two High Arctic watersheds.

1.2.5 Analysis of DOM with 3D EEMs and parallel factor analysis

A further targeted approach that can be used to investigate variability within a sub- fraction of DOM is fluorescence spectroscopy [Stedmon and Bro, 2008]. Fluorescence spectroscopy is a technique that excites the molecules present in a sample and measures the subsequent emission of light. Constituents present that are excited to the singlet state are rapidly returned to the ground state and the energy emitted during this transition is typically lower than the energy absorbed (i.e. Stokes shift). The resulting excitation and emission spectra may provide valuable information on fluorophores present (i.e. functional groups capable of fluorescing) as well as the nature of the surrounding environment [Lakowicz,

2006]. Further techniques examining a range of emission spectra resulting from a range of excitation wavelengths may provide a three dimensional data set. These 3D excitation- emission matrices (3D EEMs) are frequently applied to studies of DOM for both the sensitivity of the technique a well as rapid analysis time. Deconvolution of these complex emission maps has enabled researchers to identify distinct components within DOM samples that have origins of terrestrial, marine, microbial and anthropogenic origins. [Coble et al.,

1990; Coble, 1996; Baker, 2001; Stedmon et al., 2003; Cory and McKnight, 2005]. The process of simplifying 3D EEMS into distinct components has traditionally been a matter of

“peak picking” or identifying local maxima across the emission map [Coble, 1996; McKnight et al., 2001], but recent studies have further applied statistical analyses to obtain more detailed information [Stedmon et al., 2003; Boehme et al., 2004; Hall et al., 2007; Fellman et al., 2008; Balcarczyk et al., 2009]. Parallel factor analysis (PARAFAC) is a technique now widely employed on 3D EEMS and utilizes the following equation to model the data:

xijk = Σ aif bif ckf + εijk , i = 1,.,I; j = 1,.,J; k = 1,.,K; f = 1

xijk : fluorescence intensity i : a given sample j : measured emission wavelength k : excitation wavelength εijk : residuals (noise and un-modeled variation) a : relative contribution of each component to total fluorescence b : emission spectra c : excitation spectra

The model is generated by fitting three-way data to the above equation and minimizing the sum of squared residuals (εijk) [Bro, 1997; Stedmon and Bro, 2008]. With simple mixtures of known fluorophores, the correct number of “components” or fluorophores can readily be achieved. With the complex nature of DOM, containing unknown numbers of fluorophores and unknown interactions between constituents present, finding an appropriate number of components that represent the major fluorescence phenomena can be quite challenging. The general approach is to increase the number of components until an appropriate percentage of

18 variance is explained (~99% or higher) and then to determine that the following criteria are satisfied: a) residual analysis, i.e. are the residuals relatively consistent without demonstrating major peaks or troughs?; b) do the excitation and emission spectra look like plausible fluorescence spectra?; c) split half analysis, i.e. does randomly split data generate the same model? (but this criteria is amongst the hardest to satisfy with DOM sample sets and will not be validated if too few samples are available and/or there is too great variability between samples); and d) random initialization, i.e. models are fitted with random numbers and then compared to determine that the data are not a local minimum [Stedmon and Bro, 2008].

The complex 3D fluorescence map may be effectively separated into simpler fluorescence phenomena and due to the lack of rotational freedom provided by PARAFAC modeling, specific excitation and emission spectra are preserved [Bro, 1997]. This statistical analysis has proven useful in increasing the dispersity of data made possible by 3D EEMs by effectively identifying major components or fluorescence phenomena occurring within samples of DOM. Identification of such components has proven very useful to field researchers who have used this technique to characterize and quantify changes in DOM fluorescence across aquatic ecosystems [Cory and McKnight, 2005; Stedmon and Markager,

2005; Murphy et al., 2006] and has proven particularly useful for the identification of amino acid inputs [Yamashita and Tanoue, 2003a; Yamashita and Tanoue, 2003b; Yamashita and

Tanoue, 2004; Fellman et al., 2008; Balcarczyk et al., 2009; Hood et al., 2009]. Chapters 2 and 4 of this dissertation use 3D EEMs and PARAFAC analysis to characterize DOM fluorescence from both Arctic landscapes and HPLC-simplified DOM fractions.

1.3 Study objectives

The molecular composition of DOM from temperate and polar climates is still largely unknown [Hedges et al., 2000; Leenheer and Croué, 2003]. If researchers had a more detailed depiction of the molecular constituents present, models and predictions of global carbon cycling and impacts of predicted warming could be more accurately determined. This dissertation aims to further understand the composition of this material via multifaceted and novel approaches. Field research enabled extensive sampling of unique samples from the

Canadian High Arctic from watersheds representative of stable and permafrost-disturbed systems. Further laboratory research explored chromatographic separations to simplify this complex substance prior to a variety of multidimensional NMR experiments. The specific objectives were:

To examine the structural variability between two High Arctic watersheds, one with

heavy permafrost disturbance and one with relatively intact permafrost, so as to

determine possible alterations to this stored pool of organic carbon under warming

conditions.

To develop techniques for better structural characterization of DOM samples with

hyphenated NMR techniques both online and offline.

To develop better HPLC techniques for the separation of DOM into more homogenous

fractions from which analytical detection methods such as NMR are more effective.

To attempt to piece together complex data sets so as to determine the plausible

refractory structures present in samples of DOM.

1.4 Thesis summary

CHAPTER 1: Introduction: Structural characterization of dissolved organic matter, global implications and analytical approaches

CHAPTER 2: Evidence for the enhanced lability of dissolved organic matter following permafrost slope disturbance in the Canadian High Arctic

This chapter has been published in Geochimica et Cosmochimica Acta. The contents of this chapter reveal an extensive sampling scheme conducted in the summer of 2007 on

Melville Island, Nunavut, Canada. Samples from two watersheds were collected with novel passive samplers that were placed throughout most of the tributaries, the main stem rivers and multiple locations and depths in the receiving lakes. One watershed was characterized by extensive permafrost disturbance while the second, neighboring watershed was relatively intact and used as a proxy for a control watershed. Samples from both catchments were molecularly characterized by 1D 1H NMR spectroscopy, 3D fluorescence excitation-emission matrices, PARAFAC statistical modeling, CuO oxidation techniques to determine dissolved lignin-derived phenols and lab photolysis studies to determine the relative photodegradability of samples. Results from these experiments provide evidence that permafrost disturbance has altered the biogeochemistry of organic carbon in the Arctic and results in more photochemically and biologically labile material.

CHAPTER 3: Online high performance size exclusion chromatography-nuclear magnetic resonance for the characterization of dissolved organic matter

This chapter was published in Environmental Science and Technology. This work explored the potential for using directly hyphenated HPLC and NMR for the structural

21 elucidation of DOM. High performance size exclusion chromatography (HPSEC) was found to be a very suitable HPLC technique for the direct hyphenation due to the possibility of a single solvent system and also for the potential of heavy sample loading such that sufficient material is available for NMR detection. Multiple sample concentrations, flow speeds and samples from different locations were analyzed. The resulting interpretation of NMR data revealed significant structural variability both with size and with samples collected from various water sources.

CHAPTER 4: HILIC-NMR: toward the identification of individual molecular components in dissolved organic matter

This chapter was published in Environmental Science and Technology. This research explored more suitable chromatographic techniques for the separation of DOM and resulted in the application of HILIC for DOM separations. The resulting DOM fractions provided substantially enhanced signal detail with NMR detection. HILIC fractions were analyzed via

1D 1H NMR analysis and fractions demonstrating greatest signal detail were further analyzed with a number of 2D NMR experiments including COSY45, TOCSY, HSQC and edited

HSQC. Further analysis with fluorescence and PARAFAC techniques were applied to link structural information to fluorescence phenomena. Data from these analyses evidenced that more signal detail is available both with the PARAFAC and NMR analyses following the reduction in DOM complexity with HILIC.

CHAPTER 5: Oxidized sterols as a significant component of dissolved organic matter: evidence from 2D HPLC in combination with 2D and 3D NMR spectroscopy

This chapter is in preparation for submission to Water Research and presents the findings from extensive DOM fractionation followed by in-depth multidimensional NMR analyses. A 2D-HILIC/HILIC system was carefully developed and subsequent fractions were screened with 1D 1H NMR. The extent of signal detail surpassed 1D HILIC and provided fractions from which more meaningful NMR data could be obtained. Extensive NMR analysis with select fractions was conducted with the following NMR experiments: 1D experiments – selective TOCSY and DIPSI; 2D experiments - DOSY, COSY, TOCSY,

HSQC, edited HSQC, HMBC; 3D experiments – HSQC-TOCSY and DOSY-TOCSY. The resulting compilation of data provided strong evidence for the presence of highly oxidized sterols as a major component present in DOM.

CHAPTER 6: Conclusions and future directions

Insights into the molecular characterization of DOM are discussed in the 6th and final chapter of this dissertation. Further perspectives into future approaches and use of this information are summarized as well.

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Chapter 2

Evidence for the enhanced lability of dissolved organic matter following permafrost slope disturbance in the Canadian High Arctic

Reproduced with permission from Geochimica et Cosmochimica Acta, 2011, 75: 7226-7241. © Copyright Elsevier.

2.1 Abstract Arctic landscapes are believed to be highly sensitive to climate change and accelerated disturbance of permafrost is expected to significantly impact the rate of carbon cycling.

While half the global soil organic matter (SOM) is estimated to reside in Arctic soils, projected warmer temperatures and permafrost disturbance will release much of this SOM into waterways in the form of dissolved organic matter (DOM). The spring thaw and subsequent flushing of soils releases the highest contributions of DOM annually but has historically been undersampled due to the difficulties of sampling during this period. In this study, passive samplers were placed throughout paired High Arctic watersheds during the duration of the 2008 spring flush in Nunavut, Canada. The watersheds are very similar with the exception of widespread active layer detachments (ALDs) that occurred within one of the catchments during a period of elevated temperatures in the summer of 2007. DOM samples were analyzed for structural and spectral characteristics via nuclear magnetic resonance

(NMR) and fluorescence spectroscopy as well as vulnerability to degradation with simulated solar exposure. Lignin-derived phenols were further assessed utilizing copper (II) oxide

(CuO) oxidation and gas chromatography-mass spectrometry (GC-MS). The samples were found to have very low dissolved lignin phenol content (~ 0.07% of DOC) and appear to originate from primarily non-woody angiosperm vegetation. The acid/aldehyde ratios for dissolved vanillyl phenols were found to be high (up to 3.6), indicating the presence of highly oxidized lignin. Differences between DOM released from the ALD vs. the undisturbed watershed suggest that these shallow detachment slides have significantly impacted the quality of Arctic DOM. Although material released from the disturbed catchment was found to be highly oxidized, DOM in the lake into which this catchment drained had chemical

40 characteristics indicating high contributions from microbial and/or primary productivity. The resulting pool of dissolved carbon within the lake appears to be more biologically- and photochemically-labile than material from the undisturbed system. These disturbances may have implications for projected climate warming; sustained elevated temperatures would likely perpetuate widespread ALDs and further affect carbon cycling in this environment.

2.2 Introduction

Dissolved organic matter (DOM) is comprised of a myriad of organic molecules that can be found ubiquitously throughout aquatic systems. Research in recent years has drawn attention to DOM in the Arctic as an estimated 10% of global freshwater enters the Arctic

Ocean [Opsahl et al., 1999 and references therein] and yet this ocean constitutes only 1% of total marine waters [Menard and Smith, 1966]. While terrestrial dissolved organic carbon

(DOC) is estimated to constitute some ~1-2% of total DOC in temperate oceans [Hernes and

Benner, 2006] terrestrial DOC is believed to constitute up to a third of total DOC in Arctic surface waters [Opsahl et al., 1999]. With the risk of global warming, factors such as increasing temperature, receding sea ice, increasing UV exposure from stratospheric ozone depletion, permafrost thaw and intensified release of organic matter from soils will all affect

DOM cycling in the Arctic. Arctic catchments are estimated to contain approximately half of the global organic carbon stored in soils [Dixon et al., 1994; Dittmar and Kattner, 2003;

Tarnocai et al., 2009], nearly double that present in the atmosphere [Schuur et al., 2008], and are predicted to be amongst the most strongly affected environments by climate change

[Dittmar and Kattner, 2003]. Since CO2 is a major degradation product of DOM [Miller and

Zepp, 1995] and substantial CO2 is predicted to be released from soils into the atmosphere with continued permafrost thaw [Schuur et al., 2009], the fate of DOM in the Arctic has important implications for atmospheric processes and future global temperatures. The sensitivity of these high-latitude environments to global climate change has led researchers to speculate that changing temperatures in the Arctic will have considerable impact on carbon cycling [Frey and Smith, 2005; Striegl et al., 2005; Holmes et al., 2008].

Although DOM has been investigated within Arctic marine environments [Opsahl and

Benner, 1997; Opsahl et al., 1999; Dittmar and Kattner, 2003] and to some extent in Arctic freshwater [Lobbes et al., 2000; Cory et al., 2007; Raymond et al., 2007; Holmes et al., 2008;

Spencer et al., 2008; Balcarczyk et al., 2009; Spencer et al., 2009a], research conducted during the spring flush - an event that arguably releases the most important pulse of DOM at the onset of snowmelt - is historically unrepresented in Arctic rivers [Raymond et al., 2007;

Spencer et al., 2009a]. The spring flush encompasses a short period of thaw wherein snow, ice and the soil above permafrost melt, releasing water to percolate through tundra soils and ultimately entering streams and rivers [Dittmar and Kattner, 2003]. The spring flush is found to release more labile material, both to microbes and photolysis, than that found later in the season [Holmes et al, 2008; Spencer et al, 2008; Osburn et al., 2009] and is further found to release upwards of 50% of annual riverine inputs during this short period [Dittmar and

Kattner, 2003; Finlay et al., 2006; Raymond et al., 2007; Holmes et al., 2008]. The challenges associated with sampling at the spring flush include personal safety as well as logistics of reaching waterways that are surrounded by a landscape of deep slush, overhanging snow embankments and continuously changing hydrological conditions.

The Cape Bounty Arctic Watershed Observatory (CBAWO) is a collaborative research program that began in 2003 on the south-central coast of Melville Island, Nunavut to monitor and study hydrological and ecosystem conditions in two similar Arctic watersheds, draining into two similar lakes. The Cape Bounty watersheds are of particular interest since the summer of 2007, when a significant episode of slope disturbances occurred within the

West catchment [Lamoureux and Lafrenière, 2009]. These disturbances, referred to as active layer detachments (ALDs), occur when shear strength in saturated soils and sediments above

43 the permafrost fail and result in mass movement downslope along the base of the thawed active layer [Lewkowicz, 2007]. The formation of ALDs was monitored by Lamoureux and

Lafrenière [2009] in July of 2007 following a period of unusually high temperatures and two moderate rainfall events. Substantial ALDs were mapped in the West catchment but only minimal disturbance was noted in the East. Following the rainfall events, considerable suspended sediment transport was observed in the West river and substantial erosion was anticipated to continue to occur during the following spring flush [Lamoureux and Lafrenière,

2009] - the period during which sampling for the current study was undertaken. The paired watersheds are thus suitable to contrast the effects of slope disturbance on DOM. Previous research on soil organic matter (SOM) from these watersheds has provided evidence of elevated levels of microbial activity and subsequent enhanced degradation of SOM at ALD- affected vs. undisturbed sites at Cape Bounty [Pautler et al., 2010b]. The occurrence of

ALDs disturbs the top layer of permafrost, releases old carbon and nutrients and is likely to affect primary productivity, microbial activity and subsequently pools of organic carbon

[Hobbie et al., 2005; Lewkowicz, 2007; Nowinski et al., 2008; Lamoureux and Lafrenière,

2009; Pautler et al., 2010b].

The objectives of the current research were to qualitatively examine the structural characteristics of DOM over the duration of the spring flush as well as to compare watersheds; the disturbed watershed may provide insight into the effects of ALD on pools of

DOM and may provide evidence of enhanced productivity. With the use of uniquely- designed passive samplers [Lam and Simpson, 2006], we were able to obtain Arctic DOM from tributaries and main stem rivers of the two small watersheds (< 20 km2), along with the two lakes into which the basins respectively drain. The small size of the watersheds enabled a

44 more comprehensive analysis at the onset of the spring flush than might be logistically possible with much larger systems. Samples were analyzed for structural groups via 1H nuclear magnetic resonance spectroscopy (NMR), for lignin-derived phenol biomarkers by copper (II) oxide (CuO) oxidation and gas chromatography-mass spectrometry (GC-MS), for fluorescence characteristics with excitation-emission matrices (EEMs) and parallel factor analysis (PARAFAC), and for relative susceptibility to photolysis with simulated sun- exposure studies. With both the disturbed and undisturbed catchments, we were able to obtain DOM as the tributaries began to flow and to collect samples representative of a variety of different landscapes over the duration of the spring flush. Frequent ground access permitted samplers to be placed in the majority of tributaries and thus we were able to analyze and characterize the differences between samples across landscapes as well as between watersheds.

2.3 Experimental

2.3.1 Study area and sampling

DOM samples were obtained from two Arctic watersheds located at CBAWO on southern Melville Island, Nunavut, Canada (74˚ 54’ N, 109˚ 35’ W). Briefly, the observatory is comprised of the East and West catchments (unofficial names), that drain 11.6 km2 and 8.0 km2 areas, respectively. Both catchments contain tributaries and main rivers that drain into similar small lakes, referred to informally as the West and East lakes (Figure 2.1). Both lakes are entirely freshwater, exhibit persistent ice cover (10-11 months) and are isothermal. The region is gently incised broad plateau topography that rises to a maximum of 125 m above sea level. The region is underlain by continuous permafrost above which a seasonal active layer

WP EP

NF SF PT MO MWR G Car LG

Plat

WLP ER WLL WLM ELP

WLD ELD ELM Contour Interval 10 m 0 0.5 1 2 km

Cape Bounty

Figure 2.1. Map of study area at Cape Bounty, Melville Island (above) and regional location map (below). Catchments are outlined by heavy lines and sampler site locations are indicated by circles.

(~0.5-1 m deep) develops during the short runoff season between June and August

[Lamoureux et al., 2006; Lafrenière and Lamoureux, 2008; Lamoureux and Lafrenière,

2009].

The vegetation at Cape Bounty is characterized by scattered prostrate dwarf-shrub tundra with an abundance of graminoid and forb species. Dry and mesic habitats are dominated by prostrate dwarf-shrubs with such species as Dryas integrifolia and Salix arctic as well as forb species (i.e. flowering, non-woody plants; e.g. Saxifraga oppositifolia,

Papaver corwallisense). Low-lying wetlands are in turn largely dominated by graminoids

(i.e. grasses and sedges; e.g. Luzula spp., Eriophorum spp.) as well as some forbs (e.g.

Saxifraga spp.) [Walker et al., 2002; Walker et al., 2005; Laidler et al., 2008].

Passive samplers were placed throughout catchments and lakes in both the East and

West as indicated in Figure 2.1. DOC concentrations at Cape Bounty have previously been observed to be highest at the initial summer snowmelt (~mid-June) when some 75% of the seasonal snowmelt is released within the two weeks following initial flow and then decreases by a factor of approximately 2 to 3.5 during recession and baseflow in July [Lafrenière and

Lamoureux, 2008]. DOC concentrations have been found to be as high as 7 mg/L DOC at the onset of melt and drop to ~2 mg/L DOC over the course of the next two weeks [Lafrenière and Lamoureux, 2008]. As such, passive samplers were placed within 12-48 hours of stream formation to obtain this initial pulse of DOM (June 14th-17th). Attempts were made to place samplers in as many tributaries as were safe to reach by foot. Samplers were placed in a site

(LG) affected by a historic ALD that occurred sometime prior to 2003. Placement of samplers in the region of an extensive ALD (mid-West catchment, west of the river) was unfortunately not possible due to deep slush, but another site (Pt), downslope of a 2007 ALD,

47 was accessible. Passive samplers were maintained in the main flow of all reachable tributaries and main stem rivers and were recovered on July 3rd-5th. Twenty to fifty samplers were placed at each site to ensure sufficient material was obtained. At the two river stations, near the outflow into lakes, samplers were replaced every five days over a period of twenty days to collect time series samples (June 14th-July 5th). These stations also contained a set of samplers that were left for the entire duration of the study. Lake samplers were deployed via auger holes in the lake ice (June 9th/10th) and retrieved on July 4th. The mid-lake stations contained samples at two depths: 5 and 20 meters, measured from the ice surface. The remaining lake stations were at 5 meters depth.

The DOM samples acquired in the summer of 2008 were isolated via the use of freshwater passive samplers, designed to concentrate DOM on diethylaminoethyl (DEAE)- cellulose encased in a semi-permeable membrane [Lam and Simpson, 2006]. Extraction efficiencies will vary depending on salt content within a given system as negative ions will compete with DOM for binding sites on the DEAE resin. Previous laboratory studies have demonstrated that percent recovery varied from 72-89% for DOM isolated from both small and large rivers [Lam and Simpson, 2006]. Further field research, encompassing a marshland, a fast-flowing creek and a large freshwater lake, have provided evidence that these passive samplers provide nearly equal recovery of all functional groups with the exception of a reduction in low molecular weight carbohydrates [Lam and Simpson, 2006]. These carbohydrates are highly soluble in water and are predicted to readily move back into the water column. The original polyvinylidene difluoride (PVDF) dialysis tubing used by Lam and Simpson [2006] is no longer manufactured. Consequently, dialysis tubing was replaced with Millipore PVDF filters (0.22 µm) which were filled with DEAE-cellulose and heat

48 sealed. Samplers were encased in high-density polyethylene (HDPE) bottles, drilled with dozens of holes for circulation, and deployed in mesh bags into the center of flow in streams or tied lengthwise to other bottles for deployment down lake auger holes. When collected, samplers were placed into containers with 0.05% sodium azide solution to prohibit microbial growth during transportation. Upon arrival in the laboratory, samplers were carefully opened, resin removed and extracted with 0.1 M NaOH under N2. Supernatant was further filtered through 0.22 µm PVDF filters to remove remnants of the DEAE-cellulose, ion exchanged with AMBERJET 1200 Na resin to remove Na+, freeze dried and stored in the dark.

2.3.2 Solution-state 1H NMR analysis

A Bruker AvanceTM 500 MHz spectrometer equipped with a 1H-13C-15N, 1.7 mm microprobe with an actively shielded z-gradient was used to analyze all Arctic samples at 298

K. Two mgs of lyophilized samples were dissolved into 50 µl of D2O with 1% NaOD (to facilitate dissolution) and transferred into 1.7 mm microtubes. For all samples, 256 scans were acquired with 32 k time domain points and a recycle delay of 2 s. Presaturation utilizing relaxation gradients and echoes (PURGE) [Simpson and Brown, 2005] was used to suppress the 1H signal from water at ~4.7 ppm. Spectra were apodized with an exponential multiplication factor of 0.3 Hz and processed using a zero filling factor of 2. Spectra were additionally referenced externally to DSS (4,4-dimethyl-4-silapentane-1-sulfonic acid) at 0 ppm.

2.3.3 Organic carbon content and lignin-derived phenol extraction

Dissolved organic carbon (DOC) content of lyophilized samples was determined by a

Shimadzu TOC-V Series analyzer. The lyophilized samples were reconstituted in water, acidified to pH 2 and sparged to remove dissolved inorganic carbon. The samples were then exposed to high temperature catalytic oxidation to form CO2 which was measured with a nondispersive infrared detector and used to quantify the DOC present. This process was repeated until measurements were within 1 standard deviation for each sample (generally 2 measurements, 3 maximum).

Select DOM samples were analyzed for lignin-derived phenols using a modified version of the CuO oxidation methods described by Hedges and Ertel [1982], Goñi and

Hedges [1992], and Louchouarn et al. [2000]. Briefly, lignin-derived phenols were extracted from DOM samples (~5 mg DOC) with 0.5g CuO, 100mg ammonium iron (II) sulfate hexahydrate and 5 mL of 2M NaOH in N2-purged Teflon-lined bombs at 170°C for 2.5 h.

Extracts were acidified to pH 1 with 6M HCl and kept for 1 hour at room temperature in the dark. After centrifugation (2500 rpm for 5 min), supernatants were liquid-liquid extracted 3 times with 30 mL of diethyl ether, dried with anhydrous Na2SO4, concentrated by rotary evaporation, transferred to 2 mL glass vials and dried under N2. The CuO oxidation extracts were converted to trimethylsilyl (TMS) derivatives by reaction with 100 μL N,O-bis-

(trimethylsilyl)trifluoracetamide (BSTFA) and 10 μL anhydrous pyridine for 1 h at 70°C.

After cooling, 100 μL of hexane was added to dilute the extracts.

GC-MS analysis of derivatized extracts was performed using an Agilent model 6890N chromatograph coupled to an Agilent model 5973N quadrupole mass selective detector; details of operating parameters may be found in Pautler et al. [2010a]. The only modification

50 was the spectrometer was operated in selected ion monitoring (SIM) mode with an electron ionization energy (EI) of 70eV. Compounds were identified and externally quantified by comparison of mass spectra to standards of the eight lignin-derived phenol monomers: vanillic acid, vanillin, acetovanillone, syringic acid, syringaldehyde, acetosyringone, ferulic acid, p-coumaric acid (all as TMS esters). These values were normalized to DOC content

[Hedges and Ertel, 1982; Goñi and Hedges, 1992; Louchouarn et al., 2000].

The limit of detection (LOD) for all standards was calculated to be less than 1 pg

(much lower than sample values reported here). The LOD’s were estimated based upon 4- point calibration curves and the calculation presented by Louchouarn et al. [2000]. (LOD =

(3σ)/m, where σ is the variability of repeated blank injections and m is the slope from the calibration.) To further verify the accuracy of the very lowest phenol concentrations measured in this study (~30 ng), a five-point calibration curve was measured with vanillin in the concentration range of 0.5 to 100 ng/µL (i.e. 1-200 ng was injected) and the curve verified that strong linearity (r2 = 0.97) exists even at the lowest concentrations reported.

Reproducibility was tested using repeat samples, each with multiple injections; values for all phenols were found to reproduce with a standard error ranging from 0.3 to 1.2%.

2.3.4 EEMs and PARAFAC analyses

All excitation-emission matrices (EEMs) were collected on an Agilent 1200 series fluorescence detector (G1321A), equipped with a xenon flash lamp and an offline cuvette for

EEMs acquisition. Samples were prepared fresh daily by reconstituting lyophilized samples to 5 mg/L DOC with HPLC-grade water. All samples were adjusted to pH 7 with µl additions of 0.1 M NaOH and 0.1 M HCl and measured via a pH meter. EEMs were collected using

51 excitation ranging from 230-450 nm with 5 nm increments, and emission ranging from 350-

550 nm with 2 nm increments. Instrumental bias caused by lamp fluctuations, wavelength- dependent output, daily fluctuations and inner-filter effects was corrected following procedures used previously [McKnight et al., 2001; Stedmon et al., 2003; Woods et al., 2011].

The Agilent detector has a reference diode that adjusts for intensity drift as well as a quartz diffuser that effectively reduces light. Data were normalized for wavelength-dependent output, and these corrections were verified via use of the standard rhodamine B [Karstens and

Kobs, 1980]. Daily water blanks were subtracted from sample EEMs and the resulting EEMs were normalized to the area under the water Raman peak [Lawaetz and Stedmon, 2009].

Inner filter effects were accounted for following procedures by Tucker et al. [1992]. As found previously in Woods et al. [2011], the inner filter effect was negligible with adjustment factors ranging from 1.0 to 1.1, likely owing to the very narrow cuvette (0.5 mm) on the

Agilent fluorescence detector; adjustment factors were nevertheless applied and triplicates of the samples were reproducible within a standard error of < 3.0%. Absorbance measurements were necessary to verify the absence of the inner-filter effect and were conducted on a

Unicam (UV 2-200) Spectrometer, also in triplicate. All samples were within < 1% standard error.

The materials involved in the collection of passive samplers were tested for potential contributions to fluorescence and absorbance measurements. Using a standard of IHSS

Suwannee River DOM, triplicate untreated samples were compared to triplicate samples that were collected and extracted with passive samplers. The untreated samples were found to have 2% greater absorbance at 280 nm, 5% greater absorbance at 320 nm and 15% greater fluorescence at emission maxima than treated samples. The overall excitation-emission

52 profile, however, did not change suggesting that material was not significantly altered by the samplers.

Parallel factor analyses (PARAFAC) were generated in MATLAB 7.11 via use of the toolbox and procedures provided in Appendix 1 of Stedmon and Bro [2008]. For PARAFAC analysis of samples from all 30 sites (i.e. 30 composite samples from 20-50 samplers at each site) the model was run from 1-15 components with nonnegativity constraints in each dimension. To reduce the variability of fluorescence intensity, samples were standardized to maximum intensity [Murphy et al., 2008; Dubnick et al., 2010] and subsequently no outliers were removed. The PARAFAC model was validated using a combination of residual analysis indicating how well a given model fit all samples, differences in sum of squared error between models, examination of the loadings for viable excitation and emission spectra, and random initialization to insure that the least squares result was not a local minimum [Stedmon and Bro, 2008]. An 8-component model best satisfied all of these criteria and explained

>99.9% of variation within the data. Split-half analysis was attempted but could not be accomplished for any of the models via the 4-way split provided in the toolbox of Stedmon and Bro [2008]. Large variation in fluorescence and sample size are likely responsible for the lack of split-half validation [Stedmon and Bro, 2008].

2.3.5 Photolytic degradation experiments

A Hanau Suntest solar simulator (80 mW/cm2) was used to analyze the effects of UV- vis radiation on Arctic DOM samples. Samples from both catchments were reconstituted to 5 mg/L DOC with HPLC-grade water, sonicated to ensure dissolution and 0.22 µm filtered.

Triplicate 60 mL square glass bottles were filled with each sample and one of the triplicates

53 was used as a dark control. Dark controls were wrapped several times (~6) in tin foil and also placed in the solar simulator; the effectiveness of this technique at preventing UV-exposure and subsequent loss of absorbance over 48 hrs was found to be > 99%, as assessed with chemical actinometery using potassium ferrioxalate. The glass bottles used in this analysis absorb light in the UVB and UVA (~5% T below 300 nm, <50% T below 325, <85% T below

360) and are similar but more conservative (~15 nm) than the absorbance spectrum of Pyrex glass that was reported by Wetzel [1992] and Wetzel et al. [1995] to closely resemble natural sunlight. Although designed to mimic natural sunlight, the Suntest unit does irradiate at significantly higher intensities and lower wavelengths within UVB and UVA than global radiation averages (Hanau Suntest manual) and thus to some extent this difference will be offset by absorption from the sample bottles. The unit is equipped with a parabolic reflector to generate uniform radiation and was found to heat exposed samples to an average of 44.6°C

(SD=0.9) while dark controls were found to average 43.3°C (SD=0.6). The bottles were placed on side for maximum lamp exposure; the base of the unit has dimensions of 20x28 cm so that up to 10 bottles at a time could be run. Samples were rearranged 4 times throughout the analysis to accommodate for variations in temperature and radiation. All samples were prepared immediately prior to light exposure and all absorbance measurements were taken immediately after collection. Sample aliquots (4 mL) were taken for absorbance measurements at 0, 12, 24 and 48 hours. Absorbance measurements were taken on a Unicam

(UV 2-200) Spectrometer in triplicate (standard error < 1%) at 280 and 320 nm.

Reproducibility of duplicate exposed samples was within 3% standard error across all sample sets.

A) MDLT River CRAM carb *

arom

Lake

8 6 4 2 ppm Chemical shift B) 45.0 West river 40.0 West lake *W, A 35.0 East river *W, A

NMR NMR signal 30.0 East lake H H

1 25.0 20.0 15.0 10.0 *E, A 5.0

Percentageoftotal 0.0 arom carb CRAM MDLT

Figure 2.2. A) Example NMR spectra of DOM obtained at Cape Bounty; arom, aromatics; carb, carbohydrates (*note: methoxyl groups from lignin also contribute to this region); CRAM, carboxyl-rich alicyclic molecules; MDLT, material previously described as originating from linear terpenoids. B) Statistical data from integration of all NMR spectra. (*A) indicates that all river samples are variant from lake samples; (*W) West river varies from West lake; (*E) East river varies from East lake. Comparisons are at a level of statistical significance (2-tailed, unpaired t-test, unequal variance, p < 0.05). Error bars represent standard error.

2.4 Results and discussion

2.4.1 Solution-state 1H NMR

The NMR data reveal that significant structural variability was found between river and lake samples (Figure 2.2). Previous research by Hertkorn et al. [2006] and Lam et al.

[2007] has provided evidence from multidimensional NMR, Fourier transform ion cyclotron

55 resonance mass spectrometry (FT-ICR-MS) and structural prediction techniques to suggest that major components present in DOM are oxidation products of cyclic and linear terpenoids

(carboxyl-rich alicyclic molecules (CRAM) and material derived from linear terpenoids

(MDLT), respectively). Components giving rise to such signals in addition to aromatic and carbohydrate-type structures are detailed in Woods et al. [2010, 2011] and illustrated in

Figure 2.2.A.

In examining mean differences between river and lake sites at a statistically- significant level, structural differences are apparent between rivers and lakes in the East, in the West and also in the combined data set of all Cape Bounty samples (illustrated in Figure

2.2.B by *E, *W and *A, respectively). Mean sample values indicate that aromatic components were found to decrease from river to lake within the East; further analysis of all sites reveals a statistically-significant decrease from river to lake sites. (Figure 2.2.B and

Table 2.1; all comparisons in this section are at a level of statistical significance unless stated otherwise, 2-tailed, unpaired t-test, unequal variance, p < 0.05). Carbohydrate (carb) signals were found to decrease from river to lake sites in the West and from river to lake sites overall.

MDLT-type material increased from rivers to lakes, both in the West and with the samples overall. CRAM signals, in turn, were found not to vary at a level of statistical significance across both watersheds. Cory et al. [2007] likewise found that aromatic inputs were greater in stream than in lake fulvic acids in the Arctic and found evidence to suggest that more photolabile material was typical of streams while lake material had higher microbial inputs and was less susceptible to photolysis. The NMR results reported here further corroborate this trend as the aromatics were higher in stream sites (statistically only in the East) and the

Table 2.1. Summary of major structural groups, given as the percentage of total NMR signal.

West river sitesa West lake sitesb

NMRc WR2 WR2 WLM2 WP EP Pt MWR G LG WR WR30 WR5 WLP WLL WLM5 WLD 0 5 0 Arom 4.5 3.6 5.8 4.5 7.9 5.5 6.6 4.1 5.3 4.5 4.2 2.1 3.3 4.5 4.4 4.8 Carbs 18.2 20.7 28.7 24.6 25.4 25.1 27.0 26.2 23.3 26.8 30.3 21.3 25.0 22.6 18.0 17.4 CRAM 39.4 42.1 32.3 37.0 38.5 36.2 37.6 37.3 36.9 39.0 39.8 41.7 38.7 38.5 44.5 47.6 MDLT 37.9 33.6 33.2 33.9 28.3 33.3 28.8 32.4 34.5 29.7 25.8 34.9 33.0 34.4 33.1 30.2

East river sitesa East lake sitesb

NMRc NF SF MO Car Plat ER ER19 ER24 ER29 ER4 ELP ELM5 ELM20 ELD Arom 5.3 4.4 3.5 4.4 8.4 6.7 5.3 5.2 4.2 6.4 2.1 2.1 3.7 3.2 Carbs 25.7 26.9 31.3 29.7 27.3 28.5 18.5 29.4 34.1 22.0 22.3 28.8 22.7 24.2 CRAM 37.6 38.7 39.3 35.0 36.4 40.2 45.4 38.6 40.5 44.9 35.6 41.1 39.1 38.7 MDLT 31.4 30.0 25.9 30.9 27.9 24.6 30.8 26.8 21.3 26.7 40.1 28.0 34.5 33.9

Site locations are indicated in Figure 2.1. (a) Numbers in site names indicate date of collection for time series data; (WR: June 20th/25th/30th and July 5th; ER: June 19th/24th/29th and July 4th). (b) Numbers in site names indicate depth in meters. (c) arom, aromatics; carbs, carbohydrates; CRAM, carboxyl-rich alicyclic molecules; MDLT, material believed to be derived from linear terpenoids.

57 region characteristic of aliphatic material (i.e. MDLT) was greater in lake sites, providing evidence of increased microbial inputs.

2.4.2 Characteristics of lignin-derived phenols in Arctic DOM

Copper oxidation methods are used to gain insight into both terrestrial source material as well as the diagenetic state of natural organic matter via examination of lignin-derived phenols present [Ertel and Hedges, 1984; Hernes and Benner, 2003]. Increasing acid to aldehyde ratios of vanillyl (Ad/Al)V and syringyl (Ad/Al)S phenols have previously been shown to indicate greater oxidation of lignin-derived phenols either through microbial

[Hedges et al., 1988; Opsahl and Benner, 1998] or photochemical [Hernes and Benner, 2003;

Spencer et al., 2009b] oxidation. Total syringyl to vanillyl phenols (S/V) and total cinnamyl to vanillyl phenols (C/V) have traditionally been used as taxonomic indicators of source plant material for vascular plants. High S/V values are to some extent indicative of abundant angiosperm tissues while high C/V ratios indicate non-woody materials [Hedges and Mann,

1979]. The interpretation of these ratios is complicated by the fact that S/V and C/V values are found to decrease with microbial degradation [Hedges et al., 1988; Opsahl and Benner,

1998] but evidence is conflicting as to how these ratios are affected by photochemical processes within aquatic environments. Opsahl and Benner [1998] and Hernes and Benner

[2003] suggest that S/V should decrease with solar exposure while Spencer et al. [2009b] argue that S/V is shown to increase with very prolonged exposure and oxidation. C/V ratios are proposed to increase [Opsahl and Benner, 1998] or not change [Spencer et al., 2009b] with photooxidation. Research suggests that sorption processes may further play a role in removing select dissolved phenols and altering lignin ratios, ultimately increasing both C/V

58 and S/V ratios within aquatic environments [Hernes et al., 2007]. Data from lignin-derived phenols must therefore be examined carefully and interpretations must take into consideration source, removal and diagenetic processes.

Tables 2.2 and 2.3 list the lignin-derived phenol results for DOM samples from Cape

Bounty. High C/V and S/V ratios, a mean of 0.76 and 1.64 respectively (Table 2.3), indicate that vascular plant inputs primarily consisted of non-woody angiosperm tissues. Dittmar and

Kattner [2003] have reported that S/V > 0.7 is typical of 100% tundra drained terrain. The

C/V and S/V values from Cape Bounty were higher than those generally reported for freshwater DOM, even for other northern latitude rivers (Table 2.3). Lobbes et al. [2000] reported higher C/V and S/V values for 100% tundra-drained landscapes than for partial to

100% taiga landscapes, but even these values are much lower than those reported here (Table

2.3). Higher (Ad/Al) ratios for both syringyl and vanillyl were found at the Cape Bounty sites than in previous high latitude studies (Table 2.3). The (Ad/Al)V ratios are reported here to be as high as 3.6 (Table 2.2) which approaches the 4.0 value indicated by Opsahl and Benner

[1998] to be at the upper end of what has been reported for DOM and therefore amongst the most oxidized dissolved lignin phenols reported to date. Pautler et al. [2010a] likewise found high (Ad/Al)S and (Ad/Al)V ratios (upwards of 2.0 and 3.5 respectively) in littoral sediments collected from the East lake in 2006. These findings suggest that organic matter present in water and soils at Cape Bounty is considerably oxidized and contains amongst the most degraded lignin-derived phenols found globally. The high C/V and S/V values further signify that despite highly oxidized material, the DOM collected at Cape Bounty maintained very high levels of cinnamyl and syringyl phenols either through high content in parent plant tissues and/or the selective removal of vanillyl species. Hernes et al. [2007], for example,

Table 2.2. Summary of lignin-derived phenol concentrations (µg/g DOC) and molar ratios.

West river sitesa W. lake sites Lignin-derived WP EP Pt G LG WR WR20 WR25 WR30 WR5 WLP WLD phenols b Vanillin 14.4 22.3 345.9 16.6 68.5 63.0 45.9 27.3 45.1 30.8 11.8 24.3 Acetovanillone 31.6 30.4 101.4 11.6 86.5 71.6 85.5 66.9 106.2 54.9 28.0 31.7 Vanillic Acid 33.5 35.7 64.1 33.6 36.1 75.0 53.3 76.3 96.1 35.6 42.0 38.9 Syringaldehyde 12.6 19.3 126.1 37.7 65.0 53.4 31.6 36.3 62.9 45.1 31.5 39.2 Acetosyringone 22.2 14.9 78.0 47.4 142.4 61.8 92.6 114.0 167.7 64.7 54.9 44.2 Syringic acid 47.1 65.5 164.3 64.9 231.1 212.8 154.4 147.0 230.0 129.4 87.8 66.6 p-Coumaric acid 35.5 18.4 15.4 37.4 20.0 33.9 14.0 43.5 77.0 19.8 18.7 31.0 Ferulic acid 36.4 21.8 124.1 37.2 164.2 99.7 28.4 97.9 142.0 107.3 60.8 85.4 VSC 233.3 228.3 1019.3 286.4 813.8 671.2 505.7 609.2 927.0 487.6 335.5 361.3 C/V 0.90 0.45 0.27 1.21 0.96 0.64 0.23 0.83 0.89 1.05 0.97 1.23 S/V 1.03 1.13 0.72 2.43 2.29 1.56 1.51 1.74 1.86 1.97 2.13 1.58

(Ad/Al)v 2.33 1.60 0.19 2.02 0.53 1.19 1.16 2.79 2.13 1.16 3.56 1.60 (Ad/Al)s 3.74 3.39 1.30 1.72 3.56 3.99 4.89 4.05 3.66 2.87 2.79 1.70 East river sitesa E. lake sites Lignin-derived NF SF MO Car Plat ER ELP ELD phenols b Vanillin 24.9 24.2 32.3 144.9 58.1 163.8 210.0 102.2 Acetovanillone 59.7 54.4 69.0 136.9 93.5 181.3 215.4 120.5 Vanillic Acid 40.0 56.7 62.0 69.1 63.2 109.9 96.4 80.5 Syringaldehyde 31.2 29.4 58.2 97.2 70.7 130.3 230.1 128.3 Acetosyringone 60.4 36.1 88.0 119.6 195.6 133.3 199.9 125.5 Syringic acid 138.9 132.1 132.0 219.8 266.6 351.8 269.8 152.3 p-Coumaric acid 7.0 14.4 25.4 11.4 54.7 28.5 35.9 31.6 Ferulic acid 62.7 23.0 126.4 95.8 208.8 113.0 466.4 273.7 VSC 424.8 370.3 593.3 894.7 1011.2 1211.9 1723.9 1014.6 C/V 0.56 0.28 0.93 0.31 1.23 0.31 0.96 1.01 S/V 1.85 1.46 1.70 1.24 2.48 1.35 1.34 1.34

(Ad/Al)v 1.61 2.34 1.92 0.48 1.09 0.67 0.46 0.79 (Ad/Al)s 4.45 4.49 2.27 2.26 3.77 2.70 1.17 1.19 Site locations are indicated in Figure 2.1. (a) Numbers in site names indicate date of collection for time series data; (WR: June 20th/25th/30th and July 5th; ER: June 19th/24th/29th and July 4th).

(b) VSC, total lignin-derived phenol content in µg/g DOC; C/V, cinnamyl to vanillyl phenols; S/V, syringyl to vanillyl phenols; (Ad/Al)V, vanillyl acid to vanillyl aldehyde; (Ad/Al)S, syringyl acid to syringyl aldehyde.

Table 2.3. Comparison of lignin-derived phenols from Cape Bounty, Melville Island to other high latitude freshwater [Lobbes et al., 2000; Spencer et al., 2008] as well as samples from typical freshwater environments [Perdue and Ritchie, 2003].

Study C/V S/V (Ad/Al)v (Ad/Al)s %DOC

2 small High Arctic watersheds (tundra) Melville Island, Nunavut, Canada 0.76 1.64 1.48 3.00 0.07

3 Russian rivers (tundra) [Lobbes et al., 2000] 0.46 0.70 1.32 - 0.36

12 Russian rivers (tundra and taiga) [Lobbes et al., 2000] 0.26 0.40 1.17 - 0.24

Yukon River Basin (tundra and taiga) [Spencer et al., 2008] 0.27 0.59 1.30 1.00 0.24

Numerous freshwater studies [Perdue and Ritchie, 2003] ~0.11 ~0.50 - - ~1.0%

Ratios indicate sample mean of each study.

Molar ratios: C/V, cinnamyl to vanillyl phenols; S/V, syringyl to vanillyl phenols; (Ad/Al)V, vanillyl acid to vanillyl aldehyde; (Ad/Al)S, syringyl acid to syringyl aldehyde; %DOC, concentration of all 8 phenols expressed as % of total DOC by mass.

61 demonstrated that C/V and S/V ratios in freshwater can be increased by selective sorption onto soils; this process may explain the elevated ratios found at Cape Bounty. Finally, the average % of lignin-derived phenol content (VSC) in total DOC (%DOC, Table 2.3) was much lower for Cape Bounty samples (0.07%) than is typical for freshwater (~1.0%) or other

Arctic samples (0.24-0.36%, Table 2.3), and is closer to values reported for heavily glaciated

Arctic watersheds (~0.1% in Hood et al. [2009]).

The data for the time series station in the West catchment are presented in Figure 2.3 and strongly suggest that material was increasingly less oxidized with time (i.e. lower Ad/Al ratios, higher C/V and S/V ratios). The one exception to this trend is shown by week 1 with a low (Ad/Al)V that then increased the second week and continued to decrease the last two weeks. This anomaly is likely the result of inconsistent flow of the watershed within the first

C/V 5 S/V (Ad/Al)v 4 (Ad/Al)s

2 Ligninphenol ratios 1

0 June 20th June 25th June 30th July 5th

Figure 2.3. Lignin-derived phenol ratios from the time series station in the West river (WR), June 15th-July 5th, 2008. Samplers were deployed for 5 day intervals and collected on the date indicated. C/V, cinnamyl to vanillyl phenols; S/V, syringyl to vanillyl phenols; (Ad/Al)V, vanillyl acid to vanillyl aldehyde; (Ad/Al)S, syringyl acid to syringyl aldehyde.

62 week. Field notes confirm that enough exposed water was flowing only in certain sites to place samplers by June 15th (the start of week 1) and included the sites G, LG and Pt. Where two of the three flowing sites had (Ad/Al)V lower than ~0.5, heavier flow from these early sites likely explains the vanillyl anomaly.

Regardless of whether these sites are responsible for the observed trend, the first week does not represent flow from the entire catchment and thus should not be directly compared with the other weeks. All of the samplers in the West catchment were placed by the 19th, so that material collected in subsequent weeks was a more accurate representation of the entire catchment. The subsequent weeks do contain a clear trend of increasing C/V and S/V with a decreasing (Ad/Al)S and (Ad/Al)V suggesting that material released during the spring flush was characterized by oxidized material during initial melt of surface materials but was followed by the release of less oxidized material. It has been reported previously that more photochemically- and microbially-labile material is typically released from watersheds at the spring flush [Holmes et al, 2008; Spencer et al, 2008; Osburn et al., 2009]. This study offers higher temporal resolution of the spring flush event and evidence presented here indicates a trend of decreasing oxidation over the duration of the spring flush as melting water percolated deeper into the soils. It is possible that following the spring flush event the oxidation trend reverses, but this idea would require further monitoring to confirm.

Further variability of the lignin-derived biomarkers is evident both between the two catchments as well as between upslope vs. downslope sites. The total lignin-derived phenol content (VSC) was much higher for the East catchment than the West. The tributaries sampled in the East averaged 751 µg VSC/g DOC and in the West averaged 578 µg VSC/g

DOC; the quantity leaving the tributary systems and entering the lakes (ER/WR) was nearly

63 double in the East compared to the West (Table 2.2: 1212 vs. 671 µg VSC/g DOC, respectively). The lake proximal sites (ELP/WLP) further demonstrate that the VSC concentration was more than 5 times greater in the East than in the West lake (Table 2.2: 1724 vs. 336 µg VSC/g DOC, respectively). Differences in vegetation cover likely explain the elevated VSC concentrations within the East. Upslope Arctic landscapes are typically dominated by tussock-type vegetation, mid-slope can be characterized by high contributions from willow-type communities while downslope high-moisture communities are typically wet sedge-dominated [Zak and Kling, 2006]. Upslope tussock and willow vegetated landscapes contain plant species with appreciably greater lignin content than downslope wet sedge tundra

[Hobbie, 1996; Robinson et al., 1999; Larter and Nagy, 2001]. The West catchment of Cape

Bounty has previously been cited as having significantly greater percent coverage of wet sedge vegetation than the East [Lafrenière and Lamoureux, 2008 and reference therein;

Lamoureux and Lafrenière, 2009 and reference therein]. Thus, the higher concentrations of

VSCs in the East catchment of Cape Bounty might be explained by a greater percent coverage of tussock and willow-type communities resulting in greater lignin-derived phenol production.

The trend from upslope to downslope in both catchments is illustrated in Figure 2.4 and suggests that VSCs generally increased downslope in both catchments with a particularly clear trend in the East. Despite the greater contribution of upslope, dry vegetation communities to lignin-derived phenols, soluble phenolics have been suggested to ultimately accumulate downslope [Zak and Kling, 2006]. The West catchment, however, did not demonstrate as clear a trend with elevation as the East. The sites Pt and LG clearly have elevated VSC content, do not follow the trend with elevation and are both sites of permafrost

1200 West river East river *Pt 1000

800

*LG . 600

400

200 Total lignin phenols (µg/g DOC)lignin Total 0 WP/NF EP/SF Pt/MO G/Car LG/Plat WR/ER

Decreasing elevation

Figure 2.4. Total lignin-derived phenol content (µg/g DOC) for riverine sites in the West and East. Sites are displayed in order of decreasing elevation within respective catchments. (*Pt), recent slope disturbance; (*LG), historic disturbance.

2.5 A

1.5

S/V a 1 rivers 0.5 lakes time series 0 G g 0 0.5 1 1.5 C/V

Figure 2.5. C/V vs. S/V plot of Cape Bounty samples. Letters indicate sample means with ranges for data obtained by Hedges and Mann [1979] (adapted from a figure in Ertel and Hedges [1984]). (A), woody angiosperm; (a), non-woody angiosperm; (G), woody gymnosperm; (g), non-woody gymnosperm.

65 disturbance. While the West tributaries overall had lower VSC concentrations than the East, these sites of elevated VSCs within the West are suggestive that slope disturbances may be releasing elevated concentrations of lignin-derived phenols.

C/V versus S/V plots have been used to describe the origins of natural organic matter

[Ertel and Hedges, 1984; da Cunha et al., 2001]. Figure 2.5 depicts the lignin source ratios for the DOM samples collected at Cape Bounty in comparison to fresh plant tissue data from

Hedges and Mann [1979]. In comparing river to lake sites, river samples encompassed a greater range of material and the lake sites had amongst the highest C/V and S/V values of the

Cape Bounty samples. The river sites that most nearly match lake values include: G, LG, MO and Plat, which are all characterized by low elevation and greater wet sedge-type vegetation

(Table 2.2 and Figure 2.1). Further examination of the time series collected from the West river station (WR) indicate that the initial 5 days of melt (June 20th-25th) had the lowest C/V and S/V values and that the subsequent 3 time periods (June 25th-July 5th) had C/V and S/V ratios closer to those measured within lake samples. The results are suggestive that dissolved lignin-derived phenols present in lake water were most comparable to tributary water entering the lake not at the initial spring flush but a week or two after initial melt and that overall the lignin present in the lakes most closely resembled that of low-lying, wet sedge-laden regions.

2.4.3 PARAFAC components derived from fluorescence EEMS

From the PARAFAC analysis of the EEMs, an 8-component model was generated and verified. These components represent fluorophores or fluorescence phenomena that are largely responsible for the total fluorescence signal of samples taken from both watersheds and the excitation/emission spectra are illustrated in Figure 2.6. These PARAFAC

0.2 0.2 0.3

0.25 0.15 C1* 0.15 C4* TRP 0.2

0.1 0.1 0.15

0.1 0.05 0.05 0.05

0 0 0

0.2 0.2 0.3 0.25 0.15 C2* 0.15 C5* TYR 0.2

0.1 0.1 0.15

0.1 0.05 0.05 0.05

0 0 0 230 280 330 380 430 480 530 0.2 0.2 Wavelength (nm) C3 † C6 †

0.15 0.15 Spectral Loadings (R.U.)SpectralLoadings 0.1 0.1

0.05 0.05

0 0 230 280 330 380 430 480 530 230 280 330 380 430 480 530 Wavelength (nm) Wavelength (nm)

Figure 2.6. PARAFAC loadings of Cape Bounty samples. C1-C6 are detailed in the text; TRP, similar to fluorescence from free tryptophan; TYR, similar to fluorescence from free tyrosine; (*), similar to components previously linked to terrestrial material; (†), similar to components previously linked to microbial material; R.U., Raman units.

50 West river *E,A

EEMs *W,E,A West lake *A 40 East river East lake 30

20 *E,A *W,E,A *A *A

10 Percentage of totalofmodeled Percentage 0 C1 C2 C3 C4 C5 C6 TRP TYR PARAFAC component

Figure 2.7. PARAFAC scores of Cape Bounty samples. Components are described in detail in the text. (*A) indicates that all river samples are variant from lake samples; (*W) West river varies from West lake; (*E) East river varies from East lake. Comparisons are at a level of statistical significance (2-tailed, unpaired t-test, unequal variance, p < 0.05). Error bars indicate standard error.

67 components have similar excitation and emission to components previously identified as of terrestrial, microbial and amino acid origin. Components C1 and C4, were substantially more prominent in the stream samples than in the lake samples (Figure 2.7) and are similar to components described as terrestrially-derived signals in Arctic stream waters (C1 and C4 in

Balcarczyk et al. [2009]), Arctic marine surface waters (BERC1 and BERC3 in Walker et al.

[2009]) and in numerous marine and freshwater studies [Coble et al., 1998; Stedmon and

Markager, 2005; Williams et al., 2010]. Two other components, C3 and C6, were likewise found to decrease from river to lake environments. Both C3 and C6 have similar fluorescence maxima to components previously found in Arctic streams (C3 and C6 in Balcarczyk et al.

[2009]) and Arctic soil water (components 6 and 3 in Fellman et al. [2008]) and are similar to components linked to microbial inputs [Balcarczyk et al., 2009]. C4 was here found to be correlated with both C1 (R2 = 0.69, p < 0.0005) and C6 (R2 = 0.38, p < 0.0005), while C3 was found not associated with any of the other components. C3 is identical to a component found previously to be relatively consistent throughout 80 fractions of Suwannee River DOM, differentiated by a gradient of hydrophilicity [Woods et al., 2011]. C2 and component C5 were found to be weakly correlated (R2 = 0.29, p < 0.002) and C2 is comparable to terrestrially-linked components found in Arctic soil and stream waters (component 4 in

Fellman et al. [2008] and C8 in Balcarczyk et al. [2009]). Finally, component TYR is readily identified as fluorescence likely resulting from tyrosine fluorescence while TRP has the appropriate excitation and emission maxima to suggest the fluorescence of free tryptophan in water [Coble et al., 1998; Stedmon and Markager, 2005; Fellman et al., 2008; Balcarczyk et al., 2009; Walker et al., 2009; Williams et al., 2010; Woods et al., 2011]. These amino acid signals were most prominent in lake samples.

Of particular interest to the fluorescence contributions reported here is that the very highest contributions appeared in amino acid-type signals from lake proximal sites (Table 2.4;

64.3% from tyrosine at the East lake proximal site (ELP) and 88.6% from the West lake proximal site (WLP)), values significantly higher than those typically reported for freshwater environments. The amino acid content of DOM is high in freshly produced material [Davis and Benner, 2007], is suggested to indicate an inherent bioavailability [Davis and Benner,

2007] and amino acid-type fluorescence has been found highly correlated to the bioavailability of DOM [Fellman et al., 2008; Balcarczyk et al., 2009; Hood et al., 2009].

Tryptophan-type signals have been found associated with less degraded proteins [Yamashita and Tanoue, 2003a; Yamashita and Tanoue, 2003b; Yamashita and Tanoue, 2004] and have been linked to surface waters containing high primary productivity [Yamashita and Tanoue,

2003b]. Heterotrophic productivity has further been found associated with tryptophan-type signals and such signals have been found to decrease once stationary phase growth is reached

[Cammack et al., 2004]. Finally, recent research from Hood et al. [2009] on Arctic coastal watersheds with high glacial inputs had comparable amino acid-type fluorescence (77%) to those reported here. Interestingly, the study provided evidence that enhanced glacial discharge was not only linked to older carbon inputs but also to more biologically labile DOM

[Hood et al., 2009].

The high amino acid-type fluorescence of both proximal sites at Cape Bounty is thus suggestive of high primary and/or microbial productivity and the predominance of tryptophan-type fluorescence at the WLP site (the site closest to the outlet of the slide- disturbed tributaries) is further suggestive that this site may have experienced enhanced productivity during the period samples were collected. Figure 2.7 illustrates the difference in

Table 2.4. Parallel factor analysis (PARAFAC) components in Arctic DOM. Components are presented as percentage of total modeled excitation-emission matrices (EEMs).

West river sitesa West lake sitesb

WR2 WR2 WR3 WLM2 Component WP EP Pt MWR G LG WR WR5 WLP WLL WLM5 WLD 0 5 0 0 C1 48.1 52.9 35.2 47.2 19.8 43.9 44.1 46.6 29.6 21.0 42.6 2.0 19.3 16.5 6.2 13.0 C2 4.2 5.0 0.0 0.0 4.1 7.5 3.4 2.9 0.6 10.3 7.8 2.2 7.4 5.9 18.2 10.9 C3 6.9 4.8 7.1 4.4 58.0 3.4 4.8 4.4 7.2 12.8 3.6 0.3 0.2 2.7 0.6 2.4 C4 10.2 8.4 11.5 13.6 6.0 15.3 12.6 12.9 7.5 7.5 14.5 0.7 7.1 5.5 5.8 7.0 C5 6.0 6.3 9.0 8.3 2.7 8.8 7.7 6.8 3.6 7.1 10.3 1.3 11.1 9.3 15.2 14.4 C6 7.8 6.2 8.4 10.3 5.2 12.1 10.5 9.5 6.7 6.5 12.1 0.5 8.0 6.8 9.2 7.5 TRP 7.0 9.1 10.3 6.2 1.4 5.6 7.4 4.3 1.2 7.0 6.6 88.6 43.1 37.4 11.8 23.1 TYR 9.9 7.2 18.5 10.0 2.9 3.4 9.5 12.6 43.5 27.7 2.5 4.3 3.9 15.8 32.9 21.7 All a East lake sitesb East river sites sitesc

Component NF SF MO Car Plat ER ER19 ER24 ER29 ER4 ELP ELM5 ELM20 ELD Avg. C1 33.4 46.7 37.5 43.0 30.5 39.7 38.1 37.1 14.0 0.0 9.1 9.3 4.3 0.0 27.7 C2 7.8 5.6 0.0 3.5 0.0 0.0 1.7 5.3 14.6 51.5 2.0 8.7 10.9 12.6 7.2 C3 9.6 5.8 7.3 3.6 4.5 6.8 2.0 5.0 6.8 4.8 0.0 4.2 4.7 6.1 6.5 C4 11.7 11.2 9.5 14.9 10.1 12.9 11.6 12.1 7.4 9.0 3.4 5.5 4.4 3.4 9.1 C5 9.1 7.8 7.2 9.3 6.1 8.3 5.8 7.8 8.5 14.2 10.1 16.4 13.8 10.0 8.8 C6 10.4 9.4 22.4 12.5 16.9 19.2 24.8 19.7 7.7 8.7 3.8 6.1 5.2 5.4 10.0 TRP 6.7 2.7 8.6 6.7 14.6 6.7 9.6 7.0 33.1 7.4 7.3 24.8 18.7 11.9 14.5 TYR 11.2 10.8 7.4 6.5 17.3 6.4 6.3 6.0 7.8 4.3 64.3 24.9 38.0 50.6 16.3 Site locations are indicated in Figure 2.1. (a) Numbers in site names indicate date of collection for time series data; (WR: June 20th/25th/30th and July 5th; ER: June 19th/24th/29th and July 4th). (b) Numbers in site names indicate depth in meters. (c) Avg., mean % contribution of each component.

70 tryptophan-type fluorescence between the West vs. East lakes. Although the data were too variable across the lakes and the number of lake sites too limited for these differences to be significant at the statistical level, the data in Table 2.4 clearly indicate that the West lake sites had much higher tryptophan contributions than the East. If the East catchment and lake can be used as a control for an undisturbed system, the West lake may have experienced elevated microbial and/or primary productivity due to the downslope accumulation of nutrients and previously sequestered material from the permafrost-disturbed West catchment.

2.4.4 Effects of simulated solar radiation on Arctic samples

The aromatic content of a given sample will largely affect the absorption of UV light and subsequent availability for photochemical reactions and as such terrigenous DOM, enriched in aromatics, has been found to exhibit greater photoreactivity than algal/microbially-derived DOM [Cory et al., 2007; Osburn et al., 2009; Sulzberger and

Durisch-Kaiser, 2009]. This trend is clearly seen within the East catchment and lake where aromatic content shown by the NMR data (Figure 2.2.B) decreased from tributary to lake environment, initial absorbance of samples prior to UV-exposure was lower in the East lake vs. catchment (Figure 2.8.A and Table 2.5), and susceptibility to photolysis was lowest in these lake sites (Figure 2.8.B and Table 2.5). From the West tributaries to lake, however,

Figure 2.2.B illustrates that the mean aromatic content decreased but not as much as within the East and not at a level of statistical significance. West river and lake sites were further very similar in initial absorbance measurements (Figure 2.8.A) but Figure 2.8.B illustrates that the West lake sites had an approximate 15% greater loss of absorbance than samples collected from West tributaries. These data provide evidence, therefore, that the West lake

A) 0.1 280 nm

0.08 320 nm

0.06

0.04

0.02 Initial absorbane hour) absorbane (0 Initial

0 West river West lake East river East lake

B) 45

40 280 nm

35 320 nm

10 % absorbance absorbance over lost hrs. 48 % 5

0 West river West lake East river East lake

Figure 2.8. A) Initial absorbance of Arctic samples (5 mg DOC/L) prior to UV exposure; variation from East lake to the remaining groups is statistically significant. B) Percent reduction in absorbance over 48 hours of simulated solar exposure; variance between a) West river and lake, b) East river and lake are statistically significant. (2-tailed, unpaired t-test, unequal variance, p < 0.05). Error bars indicate standard error.

samples were the most susceptible to photolysis despite having equitable absorbance and aromatic content to riverine sites.

The absorption of UV by the glass bottles used will offset some of the difference between the solar simulator and natural light (see methods) and may represent conservative results. It is difficult to speculate, however, as to how this irradiation would compare to the occasionally intensely lit, often dimly lit and frequently ice/snow covered waters of the High

Table 2.5. Summary of percent loss of absorbance at 280 and 320 nm over 48 hours of simulated solar exposure.

West river sitesa West lake sitesb

WR2 WR2 WR3 WLM2 nm hr. WP EP Pt MWR G LG WR WR5 WLP WLL WLM5 WLD 0 5 0 0 280 12 16 15 12 9 9 7 10 9 10 9 12 15 9 14 12 13 24 22 19 14 14 17 18 16 15 16 17 18 20 17 20 19 19 48 22 23 15 18 22 23 22 19 20 20 22 32 31 27 33 37

320 12 16 15 14 10 10 7 9 7 9 9 13 15 14 19 15 17 24 22 20 16 23 18 21 15 10 18 17 25 22 29 30 22 30 48 28 26 18 26 24 29 27 21 26 24 31 40 44 34 40 42

East river sitesa East lake sitesb nm hr. NF SF MO Car Plat ER ER19 ER24 ER29 ER4 ELP ELM5 ELM20 ELD 280 12 10 11 7 6 12 14 14 12 12 12 12 5 9 8 24 18 16 14 16 21 22 21 19 20 18 13 8 15 13 48 18 17 15 16 25 31 29 23 25 23 16 9 15 14

320 12 15 15 13 11 15 16 23 14 16 9 9 9 12 11 24 23 23 15 21 24 27 32 25 27 14 12 13 17 16 48 24 24 17 24 31 42 43 29 35 30 15 16 18 19

Site locations are indicated in Figure 2.1. Change in absorbance from 0 hr. controls were within 6% for all sites. (a) Numbers in site names indicate date of collection for time series data; (WR: June 20th/25th/30th and July 5th; ER: June 19th/24th/29th and July 4th). (b) Numbers in site names indicate depth in meters.

Arctic. Future studies of Arctic DOM photolysis might take into consideration conditions more similar to that environment and ideally with a radiation source that can be wavelength- and temperature-controlled.

2.5. DOM biogeochemistry in paired Arctic watersheds

During the majority of the year, low irradiance, cold temperatures and snow and ice cover limit primary production and subsequent autochthonous production of DOM in the

Arctic [Dittmar and Kattner, 2003]. In spring, the pulse of flowing water and subsequent release of DOM from soil and snow generates arguably the most important contribution of material to the Arctic DOM pool. Arctic riverine DOM has previously been speculated to be refractory and behave conservatively, particularly material released from landscapes characterized by 100% tundra [Dittmar and Kattner, 2003; Kawahigashi et al., 2004;

Balcarczyk et al., 2009]. The lignin-derived phenol data from the samples collected at Cape

Bounty provide evidence that the DOM is highly degraded and that permafrost-disturbed locations release qualitatively different material from sites of intact permafrost. Further evidence provided by fluorescence data demonstrates that the lake environments into which both watersheds drain experienced higher microbial and/or primary productivity than catchments as indicated by prominent amino acid signals. The pronounced tryptophan-type signals from the West lake are further suggestive of elevated productivity over the period sampled and could feasibly be caused by permafrost disturbance, which is likely to release nutrients and previously sequestered DOM into waterways. The low rate of autochthonous

DOM production that is typical of Arctic waters may in part be due to the very low input of inorganic nutrients from Arctic terrains which have been reported to be amongst the lowest

74 values worldwide [Dittmar and Kattner, 2003; Lafrenière and Lamoureux, 2008].

Interestingly, the occurrence of extensive ALDs in the West catchment of Cape Bounty resulted in elevated electrical conductivity (i.e. enhanced inorganics) during the summer of

2007 [Lamoureux and Lafrenière, 2009]. With substantial erosion expected and visually obvious during the following spring pulse, it would be expected that elevated inorganics continued to be released during the summer of 2008. Evidence from soils in the 2008 season indicated that soil from ALD sites in the West catchment supported elevated microbial activity, likely facilitated by the release of nutrients [Pautler et al., 2010b]. Recent research on glaciated Arctic watersheds further demonstrated that older, sequestered material released from glacial discharge was dramatically more biologically labile than younger Arctic riverine

DOM [Hood et al., 2009]. Thus the high amino acid fluorescence reported here could very well be the result of nutrients and/or sequestered organics released from permafrost disturbance promoting increased biological activity.

Further evidence from the photolysis experiments suggests that DOM from the West lake at Cape Bounty was also more photolytically labile than DOM from either of the catchments or the East lake. The fluorescence data suggest that algal-derived DOM may be a major constituent of the West lake; such DOM is generally biologically available

[Obernosterer and Benner, 2004; Sulzberger and Durisch-Kaiser, 2009]. Autochthonous material, however, is typically much less susceptible to photolysis than terrigenous DOM, largely owing to a lower aromatic content [Obernosterer and Benner, 2004; Sulzberger and

Durisch-Kaiser, 2009]. The West lake sites were found to have nearly the same aromatic content and equivalent absorbance in both catchments, suggesting that significant terrigenous inputs were present. The results may therefore indicate some combination of nutrient inputs,

75 enhanced biological activity and/or release of sequestered permafrost carbon that has resulted in what appears to be more photochemically- and biologically-labile material. Further sampling and monitoring is necessary to elucidate the processes involved as well as to determine how the material varies temporally over a larger timescale.

Substantial erosion and sediment transport are expected to continue some years after the ALDs of 2007 [Lamoureux and Lafrenière, 2009] and will likely continue to transport organic and inorganic material from catchment soils to waterways. The long-term impact of this transport requires further monitoring. If increasing temperatures in the Arctic persist,

ALDs are certain to become more widespread, substantially altering the diagenetic state as well as quality of DOM released from Arctic soils. This research demonstrates that to accurately predict the long-term implications of increasing temperatures in the Arctic, it is imperative to continue monitoring permafrost disturbances as well as organic matter cycling within polar regions. Of particular interest is measuring the extent to which previously unavailable carbon is mineralized vs. the extent to which young, labile carbon is simply recycled. Only through continued monitoring of carbon cycles in the Arctic can we more accurately assess if positive feedback loops dominate in polar environments, ultimately resulting in more release than uptake of atmospheric carbon.

2.6 Acknowledgements

The authors would like to thank the Natural Sciences and Engineering Research

Council (NSERC) of Canada and the Government of Canada International Polar Year program for funding provided as well as the Polar Continental Shelf Project (Natural

Resources Canada) for logistical support. A.J.S. would like to thank the government of

Ontario for an Early Researcher Award and the NSERC Strategic Program for additional support. Appreciation is extended to L. Bosquet, J. Chudiak, H. Dugan, J. Fletcher, E.

Laurin, T. Lewis and C. McGoldrick for field assistance. The authors would further like to extend gratitude to Dr. Carol Arnosti and the anonymous reviewers whose time and efforts have helped to shape this manuscript. This article is PCSP contribution number 2510.

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Chapter 3

Online high performance size exclusion chromatography-nuclear magnetic resonance for the characterization of dissolved organic matter

Published as: Woods G. C., Simpson M. J., Kelleher B. P., McCaul M., Kingery W. L. and Simpson A. J. (2010) Online high-performance size exclusion chromatography-nuclear magnetic resonance for the characterization of dissolved organic matter. Environ. Sci. Technol. 44 (2), 624-630.

3.1 Abstract

The substantial heterogeneity of dissolved organic matter (DOM) inhibits detailed chromatographic analysis with conventional detectors as little structural information can be obtained in the presence of extensive coelution. Here we examine the direct hyphenation of high performance size exclusion chromatography (HPSEC) with nuclear magnetic resonance

(NMR) spectroscopy to determine how size-distinguished fractions differ in composition.

The results support the applicability of using HPSEC to generate more homogeneous fractions of DOM prior to NMR analysis and demonstrate that structure is significantly altered with size. The largest fractions are enriched in carbohydrate- and aromatic-type structures. The midsized material is substantial and is representative of carboxyl rich alicyclic molecules

(CRAM). The smallest material has strong signatures of material derived from linear terpenoids (MDLT). Both CRAM and MDLT have been recently hypothesized as major components of DOM, and detection by HPSEC-NMR confirms their existence as unique and separable entities. This preliminary work focuses on NMR hyphenation to HPSEC due to widespread use of HPSEC to characterize DOM. Online hyphenation is useful not only for time-efficient analysis of DOM but also for that of other highly complex samples such as those found in many environmental analyses.

3.2 Introduction

Dissolved organic matter (DOM) constitutes one of the largest reservoirs of actively cycling organic carbon on Earth [Amon and Benner, 1994]. As CO2 is a primary product of

DOM mineralization, an intimate link exists between this dissolved pool of carbon and the atmosphere. It has been suggested that enhanced oxidation of marine DOM brought the planet out of a series of severe glaciations and into the Cambrian explosion of life [Peltier et al.,

2007]. Understanding DOM mineralization processes is thus critical to assessing environmental issues such as climate change, but despite considerable research designated to the characterization of DOM, the individual molecular structures, and to some extent the major structural components, are still not well understood. Access to such molecular-level information would provide insight into origins of water masses, clarity of DOM interactions with environmental contaminants, and a better understanding of DOM significance in the global carbon cycle. Through the use of multidimensional NMR, predictions, and simulations, our research group has been able to describe the major categories of components present in freshwater DOM [Lam et al., 2007] but have concluded that new analytical techniques will be required to elucidate exact structures.

Originating at least in part from the degradation of biomolecules, DOM is comprised of a myriad of degraded and reworked products and is subsequently one of the most complex natural mixtures. In laboratory analyses, excessive signal overlap with most detectors limits the amount of detailed information that can be obtained. In attempting to decipher information, researchers frequently employ multiple techniques and instruments, either hyphenated into a single analytical run or conducted offline. HPLC is frequently hyphenated to mass spectrometry (MS) [Reemtsma, 2001; Dittmar et al., 2007; Peuravuori et al., 2007].

Ultra-high resolution Fourier transform ion cyclotron resonance MS (FT-ICR-MS) can be used to calculate the molecular formula of thousands of constituents in DOM and is complemented by offline NMR analyses [Dittmar and Paeng, 2009]. Spectral information from UV-vis and fluorescence is frequently used to characterize sources of DOM, and these techniques have been used in combination with high performance size exclusion chromatography (HPSEC) [Wu et al., 2003]. Despite such multifaceted approaches, detailed structures remain indiscernible. Even when using advanced multidimensional NMR experiments that have successfully unraveled components in soil organic matter [Simpson et al., 2003; Kelleher and Simpson, 2006; Kelleher et al., 2006; Simpson et al., 2007a-b], the lack of appropriate standards for DOM and extensive spectral overlap result in only basic information of major structural components [Lam et al., 2007]. Individual structures themselves remain elusive and warrant further research.

NMR is a powerful detector for unknown constituents in mixtures, but hyphenated

NMR is a fitting solution when faced with the unprecedented complexity of DOM. If suitable separation can be obtained with an instrument online with NMR, more detailed information can be obtained than with offline analyses. Online HPLC-NMR finds application in such areas as the analysis of pharmaceuticals, food products, and contaminants, with the intent of resolving mixtures of metabolites and degradation products [Levsen et al., 2000; Albert, 2002;

Duarte et al., 2003; Kitayama and Ute, 2008]. Sample mixtures are simplified chromatographically and directly detected by NMR without extensive laboratory procedures.

The greatest advantage is simply that samples are analyzed quickly and frequent data acquisition is possible (i.e., “slices” can be taken more often than in comparable offline techniques, translating into less coelution per fraction). The drawbacks to the online system

92 include sample “dilution” during HPLC separation, leading to sensitivity issues with NMR detection as well as problems with solvent signal suppression of nondeuterated HPLC solvents. The consequence of these drawbacks is that compromises must be made when designing experimental parameters, but the end result is unparalleled detection capabilities with only a single chromatographic run.

Online HPLC-NMR has found widespread use in pharmaceutical screening but rarely in environmental applications [Levsen et al., 2000], with only a preliminary online analysis of

DOM to date [Simpson et al., 2004]. The development of HPLC-NMR techniques is significant not only for DOM elucidation but also for analysis of other complex environmental samples such as organic matter in soils, sediments, and the atmosphere. Here we examine the structural components of DOM with NMR hyphenated to HPSEC (a commonly used chromatographic technique with DOM). As such this work describes the successful hyphenation of HPSEC with NMR to examine the size-distinguished structural variability in DOM.

3.3 Materials and methods

3.3.1 Sample collection and preparation

Three DOM samples were used for this study. Nordic Reservoir natural organic matter

(NRNOM) and Suwannee River natural organic matter (SRNOM) were purchased from

IHSS. Both samples were isolated via reverse osmosis following 0.4 μm filtration (details on the IHSSWebsite, http://ihss.gatech.edu/ihss2). The third sample was collected from a wetland in the Lynde Shores Conservation Area, Whitby, Ontario, in August of 2007. The

Lynde Shores sample (LSDOM) was collected via pressure filtration with 0.45 μm PVDF

93 membranes and isolated on DEAE cellulose. Samples were extracted under N2 with 0.1 M

NaOH, ion exchanged to remove Na+, and lyophilized [Simpson et al., 2004; Lam and

Simpson, 2006]. All samples were prepared in the HPSEC mobile phase, adjusted to pH 12 with NaOH, and syringe filtered (0.45 μm).

3.3.2 HPSEC separation

HPSEC separation and HPSEC-NMR analyses were conducted on an Agilent HP1100

HPLC system, equipped with a column heater, diode array detector (DAD), and fraction collector (Foxy Jr., ISCO) and controlled with Hystar software (Bruker), version 3.0. Readers should note that the DAD signal was in most cases swamped due to the large and concentrated injections required for NMR spectroscopy. As such the DAD data are not considered here. Future studies designed to use both the DAD and NMR as parallel detectors would need a software-controlled splitter and dilutor prior to DAD. Two columns,

Ultrahydrogel 250 and 120 (Waters, rated pH 2-12), were used in series. Column performance was assessed daily using a mixture of poly-(styrenesulfonic acid) standards, and no qualitative or quantitative changes were noted to occur over the duration of this study.

Additionally, quantitative recovery of DOM was assessed by collecting all eluate after injections both with and without the columns, and 100.1 +/- 2.3% recovery was noted on the basis of multiple injections. Although the column was calibrated with PSS standards, molecular weight estimates of DOM are not presented. The hydrodynamic radii as well as interactions with the stationary phase will differ between common commercial standards and humic substances as debated heavily in the literature [Perminova, 1999]. Appropriate calibration standards for DOM are difficult to assess pending better insight into molecular

94 structures and aggregation of this complex material [Lam and Simpson, 2009]. HPSEC is used here as a means to size-separate DOM, and molecular weights are not presented due to concerns as to the accuracy of such data.

An isocratic solvent system at 40 °C was used for HPSEC separations with an aqueous buffer comprised of 0.1 M NaCl and 0.03 M NH4Cl, adjusted to pH 11 with NH4OH. A similar buffer has been cited elsewhere [Samburova et al., 2005], but a lower NaCl concentration was used here for purposes of NMR compatibility (which is particularly crucial for fraction collection where salts are concentrated during drying). Separation was not significantly affected by this reduction in salts. For directly coupled HPLC-NMR experiments, a 90:10 H2O/D2Oversion of the buffer was used.

3.3.3 Fraction collection

HPSEC-eluted fractions were acquired for comparison to online techniques. Fractions of the DOM were taken in 2 min intervals over the duration that DOM eluted from the

HPSEC column. This process was repeated 30 times, and combined fractions were lyophilized and stored for later analysis. Lyophilized samples were reconstituted with D2O and analyzed via 1H NMR.

3.3.4 Solution 1H NMR

HPSEC-NMR analyses were achieved on a Bruker Avance 500 MHz spectrometer at

298 K. A dual tuned 1H-19F flow probe (120 μL) fitted with an actively shielded z-gradient was used for NMR detection. For continuous-flow analyses run at 0.5 mL/min, 16 scans were acquired, while at 0.05 mL/min 88 scans were acquired. Stopped-flow experiments were

95 performed using 96 scans. Fraction-collected and whole DOM samples were further analyzed in D2O (with 10 μL of NaOD added to whole samples to ensure solubility) with a 5 mm, triple-resonance broad-banded inverse (TBI) probe using 128 scans. For all NMR experiments 16 384 time domain points were used with a recycle delay of 2 s. NOESY presaturation (pulse program NOESYPR1D) was used with a 400 μs mixing time to suppress the signal from the mobile phase (water at 4.7 ppm).

During Fourier transform the residual water signal was reduced using a Gaussian function centered at the water frequency (~4.7 ppm) and corresponding to a bandwidth in the transform spectrum of 0.7 ppm [Marion et al., 1989]. Reduced contributions from water allow the profiles and projections from the DOM signal to be more reliably discerned. The disadvantage is that the region around the water appears artificially “smoothed” and is seen most clearly in samples with low S/N ratio (e.g., Figure 3.2, row C). Readers should recognize this as an artifact from the processing employed.

Spectra were apodized through multiplication with an exponential decay corresponding to 1Hz and processed using a zero filling factor of 2. All 1D spectra generated from online experiments were compiled into pseudo-2D NMR chromatograms (1D 1H NMR spectra along the x-axis and HPSEC elution volume on the y-axis) using Bruker Topspin

(Bruker), version 2.1.

3.4 Results and discussion

3.4.1 Online techniques

Hyphenated HPLC-NMR may be accomplished in either continuous-flow (also called on-flow) or stopped-flow mode, with advantages and disadvantages to both. Both methods

96 include directly flowing eluate from the HPLC to the NMR flow cell. Continuous-flow is accomplished with the HPLC pump and NMR running simultaneously, whereas stopped-flow involves stopping the pump in intervals during which time NMR experiments are run on the static sample (controlled here via Hystar software and stopped for ~10 min per fraction).

Continuous-flow is limited in the number of scans per spectrum (translating into reduced sensitivity) due to limited time in the NMR cell. The process of continuously flowing also causes inhomogeneities within the magnetic field which can lead to reduced spectral resolution. In continuous-flow, however, chromatographic separation is not interrupted and the influence of diffusion is much less compared to that in stopped-flow. Thus, despite drawbacks, continuous-flow is useful for fast screening of samples and/or comparison to stopped-flow. In contrast, stopped-flow allows the sample to be shimmed, generating a more homogeneous field inside the NMR cell, as well as the possibility to collect more scans per slice, hence improving detection limits [Albert, 2002]. Due to the potential for complementary information and/or for comparison, we examined online analyses in both stopped- and continuous-flow. Stopped-flow experiments were run in time-slice mode, which essentially stops flow when the detection cell is refreshed with new material, ensuring that all material is detected. With continuous-flow, a limited number of scans can be collected, and thus, we examined the effect of a reduced flow by dropping the rate by 10-fold (i.e., run at both 0.5 and 0.05 mL/min). Readers should keep in mind throughout the remainder of the text that compromised chromatography is a necessity of successful online HPLC-NMR. The reduced sensitivity of NMR compared to traditional HPLC detectors requires that conditions such as large sample load and slower flow are used to achieve sufficient material within the

NMR flow cell. HPLC is essentially a means of separation prior to NMR analysis, and

97 optimal separation, although desirable, is not necessary to obtain structural information from

NMR [Levsen et al., 2000].

Figure 3.1 illustrates the chromatographic variability of conducting the above- mentioned modes on SRNOM. Rows A, B, and C depict stopped-flow, slow continuous- flow, and fast continuous-flow, respectively. For these rows, column I illustrates 2D NMR chromatograms. The y-axes of these 2D chromatograms contain profiles of SRNOM elution with mobile-phase volume and are constructed from the sum of NMR signals (excluding solvent signal from water). Examination of these y-axes illustrates that continuous-flow eluted material over larger volumes than stopped-flow trials and that continuous-flow resulted in later apex elution (see also Table 3.1). Theoretically it would be expected that peaks in stopped-flow would be broader due to the substantial diffusion that is permitted to occur from both motionless conditions and considerably longer run times (e.g., here ~17 h for stopped- flow vs. ~4 h for slow and ~1 h for fast continuous-flow). Diffusion is also likely to promote

DOM disaggregation, and thus, stopped-flow should result in smaller material at larger

Table 3.1. Parameters and apex retention volume for HPSEC-NMR analyses

flow rate concentration apex retention method sample (ml/min) (mg/ml) volume (ml) stopped-flow 0.5 SRNOM 100 14.25 continuous-flow 0.05 SRNOM 100 16.83 continuous-flow 0.5 SRNOM 100 16.08 fraction collection 0.5 SRNOM 100 16.14 stopped-flow 0.5 SRNOM 20 13.88 stopped-flow 0.5 SRNOM 5 13.63 stopped-flow 0.5 NRNOM 100 14.25 stopped-flow 0.5 LSNOM 100 14.63

I II CRAM III IV

Stopped-flow ml MDLT

carb* 18

A 16 aromatics

Ret. volume Ret. 14

Slow ml

continuous-flow 18

B 16

Ret. volume Ret. 14

Fast ml

continuous-flow 18

C 16

Ret. volume Ret. 14

9 8 7 6 5 4 3 2 1 0 9 7 F2 Chemical5 Shift (ppm) 3 1 ppm Chemical shift

13 14 15 16 ml 9 7 5 3 ppm 9 7 5 3 ppm 9 7 5 3 ppm Retention volume Chemical shift Chemical shift Chemical shift 50% elution (apex) 75% elution 95% elution

Figure 3.1. Comparison of HPSEC-NMR techniques (SRNOM, 100 mg/mL, 100 μL injection). Column I (rows A-C): pseudo-2D chromatograms from online analyses, elution profiles along the y-axes. Column I (row D): sum profiles from selected NMR regions from column I, row A (indicated by dashed lines and colored regions): red, aromatics, 6.5-7.8 ppm; green, carbohydrates, 3.2-4.5 ppm; blue, carboxyl-rich alicyclic molecules (CRAM), 1.6-3.2 ppm; purple, material derived from linear terpenoids (MDLT), 0.6-1.6 ppm (as highlighted in column I, row A). These profiles are discussed later in the paper but included here so that the reader can visualize how the sum profiles are created from 2D HPLC-NMR data sets. Rows A, B, and C: NMR spectra from stopped-flow, slow continuous-flow, and fast continuous- flow, respectively. Row D (columns II-IV): NMR spectra from offline fraction collection. Columns II, III, and IV: NMR spectra of material eluted at 50%, 75%, and 95% of the total sample elution volume. Readers should be aware that under continuous-flow conditions (rows B and C) the sample is only in the NMR cell for a finite amount of time; hence, the number of scans cannot be increased. However, in the case of stopped-flow (row A) and fraction collection (row D) the number of scans can be increased substantially, permitting the detection of components at much lower concentrations. The asterisk indicates carbohydrates; methoxyl from lignin also contributes to this region.

99 retention volumes. Stopped-flow instead generated a sharper peak and earlier apex than continuous-flow, contrary to what was expected.

A plausible explanation for these elution differences is the effects of sample viscosity.

As noted to occur in preparative HPLC, “viscous fingering” occurs with large sample injections, causes a delay in retention volume, and is known to generate false banding after apex material has passed [Cherrak et al., 1997; Mayfield et al., 2005]. In simple terms, viscous fingering occurs when a large plug of sample is injected into a less viscous mobile phase and is essentially “run over” by the mobile phase, resulting in poor separation, broadened peaks, and false “bands” at the tail (which was noted to occur with samples run in continuous-flow mode). Stopped-flow, permitting abundant diffusion, would be expected to generate ample time for the sample edges to mix with the mobile phase and therefore generate fewer viscous fingering effects. The early elution and tighter sample bands produced by stopped-flow indeed suggest that viscous fingering did not occur nearly as much as with continuous-flow conditions. To further test the role viscosity might play, much less concentrated/viscous samples were also run and found to elute earlier than more concentrated samples (Table 3.1). This finding further suggests that viscosity acts to delay and broaden eluting material in the more concentrated samples. An alternative theory might be that late elution is the result of a reduction in conformational size with increasing concentration, but

NMR research has shown that not to be the case with SRNOM [Lam and Simpson, 2009], and thus, it is not likely to be the case here. Further comparison of the two continuous-flow experiments reveals that slow flow resulted in broader and later elution than fast flow trials

(Figure 3.1 (column I, row C, and column I, row B); see also Table 3.1), suggesting that both diffusion and viscosity affect samples run under slow continuous-flow conditions.

100

Along with differences in chromatographic profiles, the NMR data provide information as to the separation of structural entities in the fast, slow, and stopped-flow experiments. In all cases the NMR spectrum for the major fraction eluting at the apex is dominated by carboxyl-rich alicyclic molecules (CRAM), signified by the dominant signal from ~1.6 to 3.2 ppm (Figure 3.1, column II, rows A-C). By the latest fractions (95% elution), however, CRAM is depleted in the stopped-flow and fast continuous-flow experiments but are still prominent in the slow continuous-flow. The reduced variability apparent in the slow trial compared to the others suggests that slow continuous-flow provides the least effective separation.

Offline fraction collection of SRNOM is illustrated in Figure 3.1 (row D, columns II-

IV). These spectra are similar to the online spectra, but differences exist that are likely due to the 8x greater sample volume collected for the offline fractions (1.00 mL vs. 0.125 mL elution volume). These offline spectra represent 240 times more material (8x larger fractions and 30

HPLC runs), 240 times higher salt content, longer NMR experiments, and material freeze- dried following fraction collection. Excess repetitions of HPLC runs were conducted to acquire more sample material from the most dilute front and tail fractions in hopes of obtaining information unobtainable from online analyses. No structural information was gained that was not discernible in online experiments. Fraction collection is, however, more readily available to researchers, and depending on the type of separation or research goals, fraction collection may in many instances be more practical than online HPLC-NMR. The goal of research presented here is to develop online techniques that are in many ways complementary to offline techniques as both have advantages and disadvantages. For example, the online experiments result in a degree of carryover within the NMR flow cell

101

(dependent upon the volume and design of the flow cell and connections). Fraction collection, in turn, is limited in the sense that increasing the numbers of fractions becomes an issue of resources and labor. From our research experience, 100 fractions could be accomplished within ~1h (continuous-flow) and ~17h (stopped-flow) while online. The comparable offline technique took weeks to obtain and run 25 fractions. Improved HPLC separations in the future (with potentially hundreds to thousands of peaks) would make fraction collection extremely laborious to achieve the sort of resolution (i.e., minute fractions) capable with online HPLC-NMR. Additionally, salts from buffers are concentrated while fractions are collected, which can make accurate matching and tuning difficult for NMR.

Of the online methods compared in this study, stopped-flow provided the best separation (due to reduced viscous fingering) and has the potential for enhanced sensitivity over other methods (as one can increase the number of scans). Though diffusion could be problematic, evidence suggests that more time for equilibration between the sample and solvent system in stopped-flow reduces undesirable viscosity effects which at least in this study outweigh negative effects from diffusion. Environmental samples are frequently very heterogeneous and due to the vast numbers of constituents present in very small quantities require large HPLC injections. The effects of viscosity are therefore an important consideration of online HPLC-NMR with environmental applications.

3.4.2 Effects of concentration

Concentrations as low as 5 mg/mL are known to result in the aggregation of DOM into conformationally larger material [Lam and Simpson, 2009]. Thus, it is important to investigate whether increased concentration leads to reduced separation, either from sample

102 aggregation or from column overload, which in turn leads to a loss of information via NMR detection. Figure 3.2 illustrates strong similarities in the eluting material between the three tested concentrations (5, 20, and 100 mg/mL). At 5 mg/mL (Figure 3.2, row C), the early- and late-eluting material produced spectra with low S/N ratio and sample peaks are overshadowed by large artifacts resulting from water suppression during data processing (see the Materials and Methods). At such low concentrations, distortions from the Gaussian filter used to reduce the water signal are emphasized and weak sample signals are difficult to discern. This is best exemplified by the region at ~3.7 ppm adjacent to the water and

I II III

100 mg/mL

20 mg/mL

5 mg/mL

9 7 5 3 ppm 9 7 5 3 ppm 9 7 5 3 ppm Chemical shift Chemical shift Chemical shift 25% elution 50% elution (apex) 75% elution

Figure 3.2. Comparison at varying concentrations (stopped-flow, SRNOM, 100 μL injection). Rows A, B, and C: NMR spectra of 100, 20, and 5 mg/mL, respectively. Columns I, II, and III: material eluted at 25%, 50%, and 75% of the total sample elution volume. The arrow is discussed in the text.

103 highlighted with an arrow in Figure 3.2 (column I, row A). This signal arises from a combination of lignin methoxyl and carbohydrates [Lam and Simpson, 2009], is clearly seen in the more concentrated sample, is likely present in the 20 mg/mL separation, and is not discernible in the 5 mg/mL separation. Figure 3.2 demonstrates that eluting material appears similar at all tested concentrations but also illustrates the importance of using large sample quantities to obtain sufficient NMR signal for more detailed and robust identifications.

3.4.3 Structural information

Recent work on the structural identification of DOM has identified material thought to be derived from cyclic terpenoids known as CRAM [Hertkorn et al., 2006; Lam et al., 2007] and material derived from linear terpenoids (MDLT) [Lam et al., 2007] as major components of DOM. Recent 1H and 13C NMR research on DOM and humic acids provides evidence of aromatics and carbohydrates as major constituents of conformationally larger DOM, while

CRAM and MDLT generally constitute smaller material [Conte et al., 2006; Conte et al.,

2007; Lam and Simpson, 2009]. For the analyses of spectra described in this study, constituents present in 1D 1H NMR spectra were assigned the following chemical shifts: aromatics (6.5-7.8 ppm), carbohydrates (3.2-4.5 ppm), CRAM (1.6-3.2 ppm), and MDLT

(0.6-1.6 ppm).

The 1D slices from all experiments reveal that the bulk of carbohydrate- and aromatic- type material eluted in the early slices (Figures 3.2, column I, and 3, column II). The apex material was in turn characterized by a strong CRAM signature (Figures 3.2, column II, and

3, column III), and the late-eluting material was characterized by strong signals from the

MDLT region as well as sharp peaks in the carbohydrate region suggestive of small sugars

104

(Figure 3.1, column IV). To analyze the elution order of major components present in each sample, 1D profiles were generated from selected regions of the pseudo-2D NMR chromatograms using Advanced Chemistry Development (ACD laboratories) Spectrum

Manager (version 11.0). As indicated by the colored regions in Figure 3.1 (column I, row A), profiles of the aromatics, carbohydrates, CRAM, and MDLT were generated by compiling the sums of these regions. An example of the resulting profiles is illustrated in the bottom left corner of Figure 3.1 (column I, row D). The insets in Figure 3.3 illustrate the component

I II III CRAM IV

MDLT carb*

A 13 14 15 ml aromatics SRNOM

B 13 14 15 ml NRNOM

C 13 14 15 16 ml LSNOM

9 7 5 3 ppm 9 7 5 3 ppm 9 7 5 3 ppm 9 7 5 3 ppm Chemical shift Chemical shift Chemical shift Chemical shift Whole samples 25% elution 50% elution (apex) 75% elution

Figure 3.3. Comparison of DOM samples (stopped-flow, 100 mg/mL, 100 μL injection). Rows A, B, and C: SRNOM, NRNOM, and LSNOM, respectively. Column I: whole samples run in a traditional 5 mm NMR tube. Columns II, III, and IV: material eluted at 25%, 50%, and 75% of the total sample elution volume (100 mg/mL, stopped-flow). The insets in column I (rows A-C) illustrate elution profiles from HPSEC-NMR; the axes are the retention volume (mL). (Refer to the Figure 3.1 caption for color and chemical shift assignments.) The asterisk indicates carbohydrates; methoxyl from lignin also contributes to this region.

105 order of elution for three DOM samples (SRNOM, NRNOM, and LSNOM) and are discussed in detail below. The maxima of the four material types eluted independently and with the general elution volumes of aromatics < carbohydrates < CRAM < MDLT. This trend was typical for all experiments and samples with the exception of LSNOM. LSNOM did not separate well as indicated by an elution order of aromatics=carbohydrates=CRAM

(further discussion below).

3.4.4 Variability of environmental samples

To assess environmental variability, DOM samples isolated from three very different bodies of water were compared. Figure 3.3 illustrates whole DOM samples (column I) and

HPSEC-eluted material at 25%, 50%, and 75% of the total sample elution volume (columns

II-IV). The whole sample NMR spectra of all three samples consist of similar broad NMR profiles (characteristic of DOM). The profiles of size-distinguished fractions, however, reveal clear molecular differences between samples. This variability is key to elucidating and understanding the origins of the organic material as well as the physical and biological processes that act to degrade and mineralize DOM. The fractions displayed in Figure 3.3 are merely representative of the types of material eluted prior to, during, and after the chromatographic apex of each sample; these fractions do not add up to make the whole of the bulk samples but do illustrate the variability in composition between samples. With the largest material, the NRNOM sample has the strongest carbohydrate contribution (methoxyl from lignin may also resonate here) compared to the other samples. With the midsized fraction, NRNOM has the weakest contribution of CRAM, while the smallest fraction has the strongest MDLT component. The SRNOM and LSNOM samples appear most similar to each

106 other by HPSEC-NMR, but differences not apparent from the total NMR spectra are apparent.

For example, the SRNOM contains more MDLT in the smaller fraction than the LSNOM, despite the fact that LSNOM contains more MDLT as a whole. Further analysis of apex retention volumes (Table 3.1) reveals that the LSNOM eluted later than the other samples, indicative of smaller material (or greater viscosity). The SRNOM and NRNOM samples, in turn, eluted earlier than the LSNOM sample but with retention volumes equal to one another.

Finally, the profiles of aromatics, carbohydrates, CRAM, and MDLT for all three samples are illustrated in the insets of Figure 3.3, column I. From these insets it is apparent that the

LSNOM sample separated the least, with nearly all component maxima coeluting while the

SRNOM and NRNOM samples had distinct elution volumes for the four major groups. The inset in column I (row B) further illustrates that the NRNOM sample had the most successful chromatographic separation with a second major peak after apex elution. This second peak is rich in MDLT-type material and is structurally very distinct from the earlier material (hence the higher contributions from the purple trace in the second peak).

The variation in HPSEC elution could be the result of a number of factors. The samples originated from diverse water sources and were collected in different years with varying methods of isolation. The SRNOM and NRNOM samples originated from a black water river and drinking reservoir, respectively, and were both concentrated via reverse osmosis (in 1999 and 1997). The LSNOM sample was collected from a productive marshland in 2007 and concentrated on DEAE cellulose followed by alkaline extraction. All samples were stored in an identical fashion (freeze-dried, sealed, and in the dark) but factors such as origin, parent vegetation, microbial inputs, degradation processes, mentioned isolation techniques, and age could contribute to the variability in HPSEC separations. The LSNOM

107 sample was notably the most difficult to separate into chemically distinct fractions, while the

NRNOM sample was the easiest and even produced a second major chromatographic peak

(refer to the inset in Figure 3.3, column I, row B). More research is necessary to determine the reasons for elution variability between samples. These differences do, however, demonstrate that while all samples are similar in terms of their 1D NMR (column I), HPSEC-

NMR provides additional information on the subcomponents within DOM which may prove useful in understanding how components vary temporally and spatially and their role in the formation and persistence of DOM in the environment.

Despite the compromises necessary for online HPSEC-NMR, analyses of slices eluted at regular intervals indicate variations in material from the largest to the smallest-sized fractions, and material generally eluted in the order of (1) aromatics, (2) carbohydrates, (3)

CRAM, and (4) MDLT. A noteworthy phenomenon is the strong NMR signal of CRAM-type material during apex elution (i.e., the biggest fraction of the sample). This supports the existence of CRAM as a separable and unique entity in DOM that has only recently been proposed in the literature [Hertkorn et al., 2006]. Similarly, MDLT dominates the smaller size fraction, supporting the hypothesis that MDLT also contributes significantly to DOM and is a unique and separable entity. Comparison of whole sample spectra to size-separated fractions suggests that DOM separates into structurally different material that varies between samples. It has been previously proposed that while biomolecules undergo similar biogeochemical processes, it seems likely that there are similarities between samples that have undergone significant degradation [Hertkorn et al., 2006]. Whether the differences found in the separations here are caused more by source material or degradation warrants further research.

108

With this initial HPSEC-NMR hyphenation, we have shown that online HPLC-NMR can be used for the structural analyses of DOM. This technique is useful and perhaps advantageous over other analytical techniques in that samples can be analyzed very quickly and that large quantities of data and detailed information are made available in a short period of time. Of particular interest for further research is to examine stationary phases better suited for the separation of DOM constituents. Improved chromatography online with NMR has the potential to provide unsurpassed structural characterization of DOM and is a technique applicable to analyses of other complex natural samples.

3.5 Acknowledgements

The authors thank the Natural Sciences and Engineering Research Council of Canada

(Discovery Grant, A.J.S), the International Polar Year (IPY), the Science Foundation of

Ireland (GEOF509) and the Irish Environmental Protection Agency (STRIVE program) for providing funding. The authors also thank the government of Ontario for providing an Early

Researcher Award (A.J.S).

109

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Chapter 4

HILIC-NMR: toward the identification of individual molecular components in dissolved organic matter

114

4.1 Abstract

This paper presents research targeted toward the isolation and detection of unique molecular structures from what is believed to be the world’s most complex organic mixture: dissolved organic matter (DOM). Hydrophilic interaction chromatography (HILIC) was used to separate Suwannee River DOM (SRDOM) into 80 fractions, simplified to the extent that detection with nuclear magnetic resonance spectroscopy (NMR) results in many sharp signals that are indicative of individual compounds some of which are identifiable with multidimensional NMR. Parallel factor analysis (PARAFAC) of fluorescence excitation- emission matrices (EEMs) was additionally employed on HILIC-simplified fractions to further confirm the effectiveness of the HILIC separations as well as draw insight into how structural characteristics relate to DOM fluorescence signals. Findings suggest that material believed to be derived from both cyclic and linear terpenoids was dominant in the most hydrophobic fractions as were the majority of the fluorescence signals while hydrophilic material was highly correlated with carbohydrate-type structures as well as high contributions from amino acid fluorescence. NMR spectra of DOM, typically featureless mounds, are substantially more detailed with HILIC-simplified fractions to the point where hundreds of signals are present and 2D NMR correlations permit significant structural identifications.

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4.2 Introduction

Dissolved organic matter (DOM) is ubiquitous throughout the world’s aquatic ecosystems and is thought to be one of the most complex natural substances on Earth

[Hertkorn et al., 2007]. Despite research dating back more than a century [Stephens, 1982],

DOM has proven difficult to characterize at the molecular-level; it has been estimated that a mere 1-10% of DOM can be resolved into specific chemical structures [Leenheer and Croué,

2003] using conventional analytical approaches. Fourier transform ion cyclotron resonance- mass spectrometry (FT-ICR-MS) and nuclear magnetic resonance (NMR) spectroscopy have been cited as amongst the most promising for obtaining the molecular-resolution necessary for DOM elucidation [Hertkorn et al., 2007; Dittmar and Paeng, 2009]. The application of

FT-ICR-MS with DOM has the potential for extreme resolution and research in recent years has enabled the identification of 1000’s of molecular formulas [Hertkorn et al., 2007; Koch et al., 2007; Dittmar and Paeng, 2009]. The limitation, however, is that distinct structural formulas are not elucidated and become progressively more difficult to ascertain with increasing molecular mass. Even with the resolving power of FT-ICR-MS, constituents of

~600 Da result in 15 possible molecular formulas which in turn represent on the order of

100,000-1,000,000’s of possible structural formulas [Koch et al., 2007]. Multidimensional, advanced NMR techniques have the capacity to solve bonding and structural inquiries and have enabled researchers to identify plausible types of structures present in marine and freshwater DOM such as carboxyl-rich alicyclic molecules (CRAM) [Hertkorn et al., 2006] and material derived from linear terpenoids (MDLT) [Lam et al., 2007]; both structural classifications likely originate from terpenoids and have been estimated to account for as much as 75% of DOM [Lam et al., 2007]. NMR analysis of DOM, in turn, is limited in that

116 severe signal overlap from the many constituents present hinders extensive structural assignments. Further developments with both these analytical techniques as well as novel research approaches are therefore necessary to reach the resolution necessary for the assignment of substantial structural formulas.

A lack of sufficient chromatographic resolution of DOM has been cited as a reason for the shortage of molecular information [Benner, 2002] and substantial improvements with chromatography is likely critical in order to obtain the homogeneity necessary for substantial identifications via both NMR and FT-ICR-MS. This material is believed to be a complex mixture ranging in polarity that is aggregated into large, supramolecular structures [Piccolo,

2001; Peuravuori, 2005; Lam and Simpson, 2009], and thus chromatographic separations have proven to be challenging. High performance liquid chromatography (HPLC), for example, typically results in very few resolved signals [Piccolo, 2001; Koch et al., 2008;

Dittmar and Paeng, 2009; Woods et al., 2010] indicating that the 1000’s of DOM compounds present are not substantially separated. The HPLC techniques used to date, however, have primarily focused on high performance size exclusion chromatography (HPSEC) or reverse phase HPLC (RP-HPLC). Surface adsorption chromatography, such as RP-HPLC, is best accomplished if analytes have varying affinities for the stationary and mobile phases. If constituents present have little affinity for the stationary phase they will elute quickly and be poorly resolved. Hydrophilic interaction chromatography (HILIC) is similar to normal-phase

HPLC with a polar stationary phase but utilizes partial aqueous mobile phase. HILIC separations are appropriate for polar retention [Hemström and Irgum, 2006] while in complex environmental samples the less polar constituents will interact much less with the polar stationary phase and subsequently elute with the earliest material. Retention of constituents

117 are achieved via multiple mechanisms, the most influential being analyte partitioning between organic-rich eluent and a thin water layer near the polar stationary surface. Charge interaction, hydrogen-bonding, dipole-dipole interactions and hydrophobic effects further affect analyte selectivity [Hemström and Irgum, 2006; Hao et al., 2008]. HILIC is well suited for HPLC separations of highly oxidized environmental samples and has the added advantage of permitting high sample loads (via partitioning mechanisms) [Hemström and Irgum, 2006].

To the best of our knowledge, this research is the first application of HILIC with natural organic matter and we here report the separation of IHSS Suwannee River DOM

(SRDOM) into simplified fractions prior to offline NMR detection. It is important to note that HILIC is based on hydrophilic, or polar, interactions and is hence ideally suited for the separation of polar material which are prolific in the aquatic environment and which are challenging to separate using more conventional chromatographic approaches. Excitation- emission matrices (EEMs) were further collected on fractions and deconvoluted with parallel factor analysis (PARAFAC) to draw insight into fluorescence characteristics. Fluorescence techniques are rapid, highly sensitive, widely employed and useful for the identification of source material such as terrestrially-derived fluorescence vs. microbially-derived amino acid fluorescence [Stedmon et al., 2003; Cory and McKnight, 2005; Fellman et al., 2009]. The two techniques are complimentary in that they target different types of information: NMR overall structures present and fluorescence the minor constituents present that fluoresce most strongly. Understanding how structural information is linked to fluorescence signals is therefore useful and applied here to further characterize the separation of DOM with HILIC.

PARAFAC has generally been used for the characterization of bulk DOM samples, but is

118 considered ideal for DOM samples collected along a gradient [Stedmon and Bro., 2008] and is applied here to a gradient of simplified fractions.

The objectives of this current research thus include: A) to assess the effectiveness of

HILIC as a novel method applied to the separation of DOM and to utilize more than one analytical method to determine the variability and subsequent success of the material separated, B) to determine how structural information from NMR varies with polarity, C) to determine how PARAFAC components vary with polarity, and finally D) to link the results of the widely used PARAFAC methods with the less accessible NMR-derived structural information in hopes of drawing insight into how structural groups relate to DOM fluorescence phenomena.

4.3 Experimental

4.3.1 HILIC separation

HILIC was used to separate SRDOM into 80 fractions that were repeatedly collected over 60 runs and lyophilized. HILIC separations were achieved on an Agilent 1200 series system with a diode array detector (DAD, Agilent model G1315B) monitored at 254, 280 and

320 nm, a fluorescence detector (FLD, Agilent model G1321A) monitored at λexc=320 nm and

λem=430 nm, and an analytical fraction collector (Agilent model G1364C). A Phenomenex,

LUNA column was used (4.6x15 mm, 3µm particle size, comprised of silica derivatized with crosslinked diol functional groups, fitted with a prefilter and guard column). A complex gradient of acetonitrile/aqueous was used for the mobile phase. Further details of the separation and fraction collection are provided in the Appendix.

119

4.3.2 NMR analysis

HILIC fractions were analyzed on a Bruker AvanceTM 500 MHz spectrometer at 298K with a 1H-13C-15N 1.7mm microprobe fitted with an actively shielded z-gradient. Lyophilized fractions were prepared (2mg per 50µl) in D2O with 1% NaOD, sonicated to ensure complete dissolution and injected into 1.7mm microtubes. For most 1D experiments, 1024 scans were acquired with 16k time domain points and a recycle delay of 2s. Select samples were rerun with 32k time domain points to obtain spectra with high resolution (referred to here as high resolution spectra). For non-high resolution experiments, presaturation utilizing relaxation gradients and echoes (PURGE) [Simpson and Brown, 2005] was used to suppress the signal from water at ~4.7 ppm. Due to improved peak shape when processed without line broadening, high resolution experiments were acquired using composite pulse presaturation

[Bax, 1985]. Most 1D spectra were apodized with an exponential multiplication factor of

1Hz; the high resolution experiments were Fourier transformed directly without the use of a window function. All NMR spectra were processed using a zero filling factor of 2 and referenced externally to DSS (4,4-dimethyl-4-silapentane-1-sulfonic acid) at 0 ppm.

2D correlation spectroscopy with a 45˚ pulse (COSY45) spectra were obtained with

512 transients, with increments of 2048 and 256 for F2 and F1, respectively. Each spectrum was zero-filled by a factor of 2 and with 5500Hz spectral widths. The recycle delay was 1s for all COSY45 experiments and the data were processed in absolute value mode. A range of additional NMR experiments including TOCSY, HSQC, HSQC-TOCSY and HMBC were also collected and assignments were confirmed with these additional data sets. Experimental parameters along with discussion of the TOCSY and HSQC experiments may be found in the

Appendix.

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4.3.3 Fluorescence analysis

For all fluorescence analyses, aliquots of 5µl were taken from freshly prepared NMR samples (just prior to addition into NMR tubes) and diluted to ~5mg/l DOC with HPLC-grade water. All EEMs were collected on an Agilent 1200 series fluorescence detector (G1321A), equipped with a xenon flash lamp and an offline cuvette for EEMs acquisition. EEMs were collected using excitation from 230-450nm (5nm increments) to generate emission spectra from 280-550nm (2nm increments). Instrument corrections were accounted for following procedures from previous studies [McKnight et al., 2001; Stedmon et al., 2003] and are outlined in the Appendix along with procedures for the verification of the PARAFAC model.

All samples were run in duplicate and found to be reproducible within a standard error of

<3.0%. HILIC fractions were further spiked with various standards (e.g. model quinones, tryptophan) to aid in identifying possible fluorophores. Further details are provided in the

Appendix.

4.3.4 Statistical Analyses

The 1H NMR data sets of all 80 HILIC fractions were statistically analyzed with principal component analysis (PCA). PCA is a technique that is used to reduce the number of variables in a complex system to obtain significant principal components and has previously been applied to DOM research to ascertain similarities/dissimilarities between DOM samples

[Peuravuori, 2005; Zbytniewski et al., 2005]. Calculations were made using an 80 x 142 matrix (80 samples x 142 data points resulting from the integration of the processed 1H NMR spectra from 0.5-8 ppm into buckets of ~0.05 ppm; the region from 4.8-5.2 ppm was

121 selectively excluded to eliminate any residual signal from water). A second matrix was generated from the first with the addition of the loadings from the PARAFAC components

(80 x 149 matrix with 7 identified PARAFAC components). Previous research has applied data from multiple types of experiments on the same sample set for PCA [Peuravuori, 2005;

Zbytniewski et al., 2005] and this technique was used here to relate how a given PARAFAC component might relate to the structural classifications provided by NMR. Both data sets were standardized to increase the influence of variables with little variance as well as to render the data dimensionless. The models and all associated calculations were generated in

MATLAB 7.11.

4.4 Results and discussion

4.4.1 HILIC separation and NMR analyses

The HILIC chromatogram of SRDOM is illustrated in Figure 4.1.A and despite continuous signal overlap (which is expected for a “supermixture”) the significant variation in signal and long elution time (220 minutes) suggests that HPLC, and specifically HILIC, can be used to pull apart DOM into more chemically distinct fractions. This assumption is further substantiated by the HPLC detectors, NMR data, and PARAFAC analysis (discussed further below). The chromatogram in Figure 4.1.A has an overlay of both DAD and fluorescence signals and demonstrates that the mid-eluted material (~30 ml elution volume) fluoresced significantly more than other fractions while UV absorption was stronger over a greater range of material.

The scores of the PCA model produced from the 1D 1H NMR data are further illustrated in Figure 4.1.B. The first three principal components (PC1, PC2 and PC3)

122

A) 600 150 Flow rate: 0.3 ml/min. 400 Run time: 220 min. 100

mAU mAU mAU 200 50

0 0 0 15 30 45 60 Elution volume (ml) 8 B) 6 arom B) C) 4 8 R2 = 0.00 2 6 arom 60 CRAM avg% = 4.4 0 0.1 4 2 40 2 60 R = 0.00 R = 0.55 carb 2 20 40 PC3 PC3 ( avg% = 4.4 avg% = 39.7 0.05 2 0 0 20 R = 0.83 avg% = 30.9 0 9.0%) 0 60 carb 40 MDLT60 CRAM -0.05 40 40 R2 = 0.55 R2 = 0.83 20 R2 = 0.53 20

-0.1 20 avg% = 39.7

% of total NMR signal signal signal signal NMR NMR NMR NMR total total total total %%%%of of of of % of total NMR signal NMR total %of avg% = 30.9 avg% = 25.1 signal NMR total %of 0 Increasing polarity 0 0 0.8 40 MDLT 0.4 0 20 40 60 80 0 0.6 20 40 60 80 0.4 20 R2 = 0.53 0.2 HILIC0.2 fraction # avg% = 25.1 0 0 0 -0.2 -0.2 0 20 40 60 80 -0.4 HILIC fraction #

Figure 4.1. A) Chromatogram of HILIC separation. Blue line: DAD, 280nm, units on left axis. Red line: fluorescence, 320/430 nm ex/em, units on right axis. Dashed lines: HPLC fraction intervals. Arrow: signal predominated by tryptophan. B) PCA plot of the scores for the NMR data. C) Major structural groups with increasing polarity; assignments explained in the main text. CRAM Correlations have a significance of p<0.0005 except aromatics (p=0.578). (avg%) indicates average percentage of NMR signal for all fractions.

123 generated by the model can explain 77.8% of the total variance in structural functionality.

The scores were found to follow a path across the PCA plot from the earliest to last-eluted

HILIC fractions. The overall trend of the PCA scores demonstrates a progression of the least polar material within the positive region of PC1, PC2 and PC3 and curves down and around the z-axis where the scores from the most polar material are found within the negative PC1 and PC3 and positive PC2 region. The very earliest and very latest fractions are more disperse with regards to neighboring fractions, but the remainder of the plotted scores displays a numerical progression across the PCA plot, demonstrating that the material eluted from the

HILIC column was structurally similar to neighboring fractions but ultimately very unique from start to finish.

The information provided by 1D 1H NMR alone has limitations as to identification of discrete structures but proves useful as a screening technique as well as identifying broad structural classifications. 1D 1H NMR is useful in that it is a much more sensitive and therefore faster technique than 1D 13C NMR or 2D NMR analyses. As such, the HILIC fractions were analyzed first by 1D 1H NMR to screen the fractions for select samples to run with more laborious 2D NMR experiments as well as provide broad structural information of all 80 fractions. Individual chemical structures are identified later in this manuscript from the

2D NMR data. The 1D spectra were divided into four sections as illustrated in Figure 4.2.A: aromatics (arom), carbohydrates (carb), carboxyl-rich alicyclic molecules (CRAM, or the region characterized by groups such as ester, amide, methyl ketones and carboxylic acids) and material derived from linear terpenoids (MDLT, a region dominated by aliphatics). These structural groups were further examined for changes with polarity throughout the 80 fractions and are illustrated in Figure 4.1.C. With increasing elution volume, more hydrophilic

124 material was eluted and was found to be well correlated with carbohydrate contributions

(R2=0.83). With hydrophilicity, aromatics were found to be most abundant in mid-polarity fractions (Figure 4.1.C). CRAM and MDLT-type material were varied in the earliest- and latest-eluting material but largely decreased with polarity (R2=0.55 and R2=0.53, respectively). Previous research on DOM has provided evidence for carbohydrates present in more hydrophilic fractions of DOM [Guggenberger and Zech., 1994; Kaiser et al., 2004;

Henderson et al., 2008] and for MDLT (i.e. aliphatic) and CRAM constituents as more typical of hydrophobic material [Wong et al., 2002; Kaiser et al., 2004; Hertkorn et al., 2006;

Henderson et al., 2008; Lam and Simpson, 2009]. Aromatics, however, have often been reported as associated with hydrophobic fractions [Dilling and Kaiser, 2002; Wong et al.,

2002; Kaiser et al., 2004] but are here shown to be most prominent in mid-polar material and may be due to the extensive fractionation and subsequent detail of the structural differences over a gradient of polarity. Previous research has also provided evidence that the CRAM- region of NMR spectra is the most susceptible to aggregation with the MDLT-region representing constituents that are also somewhat prone to aggregation [Lam and Simpson,

2009]. Figure 4.1.C illustrates that CRAM and MDLT signals represent ~90% of the most hydrophobic material and thus these regions appear to be hydrophobic constituents that are driving forces in the aggregation of DOM [Lam and Simpson, 2009].

Figures 4.2.A and 4.2.B illustrate typical NMR spectra of an unfractionated DOM sample with near featureless signals due to severe overlap. In comparison, Figures 4.2.C and

4.2.D reveal the extent to which the HILIC fractions reduce chemical heterogeneity and permit distinct signals to be detected. These sharp signals are indicative of many discrete

125

MDLT A) CRAM carb arom lig

8 6 4 2 ppm

2.4 2.2 2.0 ppm

e g C) d f h a c lig b

8 6 4 2 ppm

2.4 2.2 2.0 ppm

Figure 4.2. High resolution 1D 1H NMR spectra of A) SRDOM, C) HILIC fraction 9 (H09), and zoomed regions for B) SRDOM, D) H09. Axes indicate chemical shift of each spectrum. Assignments are as follows: arom, 6.5-7.8 ppm; carb (lignin methoxyl also resonates under this region), 3.2-4.5 ppm; CRAM, 1.6-3.2 ppm; MDLT, 0.6-1.6 ppm; lig (lignin),* 6.57 ppm; (a) formic acid, 8.44 ppm; (b) residual water, 4.84 ppm; (c) lactic acid (quartet), 4.02 ppm; (d) glycolic acid, 3.94 ppm; (e) methanol, 3.33 ppm; (f) succinic acid, 2.38 ppm; (g) acetic acid, 1.90 ppm; (h) lactic acid (doublet), 1.33ppm. *See Appendix.

126 chemical compounds but present still in such numbers that overlap inhibits extensive structural assignments. Despite complexity, the direct assignment of simple molecules is still possible and a variety of carboxyl and alpha hydroxy acids are readily identified.

Additionally, a characteristic triangular peak from lignin is apparent at ~6.5 ppm.

Assignments are indicated in the caption of Figure 4.2 and full details of assignment verifications are given in the Appendix.

A variety of multidimensional NMR experiments were run to further characterize select HILIC fractions, chosen from 1D 1NMR analyses. Post-separation, 2D NMR data provide a range of connectivity and chemical shift information that is not apparent from the unfractionated sample. For brevity, only COSY45 experiments are shown (Figure 4.3) but information and example spectra for additional 2D experiments may be found in the

Appendix. COSY provides information on the connectivity between protons on neighboring carbons. COSY45 varies in that a final pulse of 45˚ is employed to reduce fine splittings, ultimately simplifying cross-peaks and narrowing the region lost under the diagonal. The level of detail provided by this HILIC-simplified fraction is much greater than the whole

DOM sample (see Appendix) such that multidimensional NMR experiments enable the identification of numerous chemical structures within the simplified material; such identifications are not possible in the unfractionated material. Pattern matching with AMIX

(version 3.8.7, Bruker BioSpin) against the Bruker Biofluid Reference Compound Database

(version 2-0-0 through version 2-0-3) was performed sequential starting with COSY, then

TOCSY and then HSQC for each assignment. All assignments were consistent across all 3 data sets and showed a strong correlation (r2 > 0.99) for all chemical shifts between the

127

14 11 6.5 8 3 13 9 15 1 5 5 1 7.0 20 6 19 24 12 11 13 25 13 3 16 5 3 7.5 4 2 26 3 4 5 10 21 6 25 25 6 3 24 8 8.0 27 4 25 2 9 14 22 4 5 7 22 13 19 8 27 6 5 22 8 16 6 8.0 7.5 7.0 6.5 21 27 21 1 - 1,2-Propanediol 14 - Cinnamic Acid moieties (double bond)* 27 6 27 22 27 3 2 - 1,3-Propanediol 25 1 21 3 - 2-Hydroxybutyric Acid 15 - Fumaric Acid 10 4 - 2-Hydroxyglutaric Acid 16 - Glutaric Acid 23 1 5 - 2-Methylglutaric Acid 17 - Glyceric Acid 2 chemicalF1 shift (ppm) 17 1 7 6 - 2-Methylsuccinic Acid 18 - Glycolic Acid 4 7 - 3-Hydroxypropionic Acid 19 - Isobutyric Acid 25 3 8 - 3-Methylglutaric Acid 20 - Lactic Acid 17 20 9 - 4-Hydroxybenzoic Acid 21 - Levulinic Acid 18 22 22 25 10 - 4-Hydroxybutyric Acid 22 - Malic Acid 11 - 4-Methylphenol* 23 - Methanol 12 - Acetic Acid 24 - Propionic Acid 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 13 - Butyric Acid 25 - Quinic Acid 26 - Succinic Acid F2 chemical shift (ppm) 27 - Tricarballylic Acid

Figure 4.3. 2D COSY45 NMR spectra of HILIC-simplified fraction (H09); left: zoomed region from 0.5-4.5 ppm, right: zoomed aromatic region (6.3-8.5 ppm). Assignments made from reference database (see main text); (*) indicates assignment from previous work using a database of lignin components [Simpson et al., 2004].

128 proposed database structure and the resonances in DOM (detail provided in the Appendix).

The confirmed structural assignments are presented in Figure 4.3 and are comprised of a variety of carboxyl and hydroxy acids. The additional detail provided by 2D NMR after fractionation is likely due to a combination of: 1) enhanced homogeneity, 2) reduced interaction within microenvironments giving rise to sharper signals, and 3) separation removed paramagnetics that enhance relaxation.

4.4.2 Fluorescence analysis

Validation of the PARAFAC model resulted in a 7-component system; Figure A4 (in the Appendix) illustrates these 7 components as well as an example of the modeling results of a HILIC fraction. Detailed discussion of these components may be found in the Appendix but in summation: 4 components were found to contain signals influenced by the addition of a variety of model quinones (Q1, Q2, Q3, Qb), one of these specifically to p-benzoquinone

(Qb). The remaining 3 components are all attributed to amino acid fluorescence: a tyrosine component (TYR), and 2 tryptophan components (TRP1, TRP2). The 2 tryptophan signals are likely the result of the anisotropy of tryptophan fluorescence so that TRP1 results in most environments while TRP2 only occurs in association with very apolar constituents.

Correlation data indicate that all quinone-influenced signal (except Qb) and TRP2 are correlated with the most hydrophobic material while TYR and TRP1 are in turn most prominent in hydrophilic fractions (see the Appendix).

Much like the NMR data, the PARAFAC components were found to vary with polarity with the exception of Qb which was found more uniformly throughout all 80 fractions (more information in the Appendix). The variation of the remaining components

129 illustrates that the fractions were chemically distinct and this chemical distinction was further verified by two fractions that were dominated by single PARAFAC components. The 18th fraction (H18) was found to be predominantly Q1, quinone-influenced signal (80%) while the much more hydrophilic fraction (H69) was identifiable as tryptophan-derived (TRP1: 97%).

The isolation of TRP1 is further illustrated in the chromatogram (Figure 4.1.A). The arrow indicates the elution of H69 and a prominent, isolated peak is evident within this fraction.

The dominance of single components within fractions demonstrates that the HILIC fractions are considerably more homogenous than start material.

4.4.3 Statistical analysis of NMR and PARAFAC

The PCA loadings were examined for the combined NMR and PARAFAC data set to determine how fluorescence phenomena relate to the structures present throughout the 80

HILIC fractions. PC1, PC2 and PC3 account for 45.2%, 20.4% and 8.7% of the data, respectively. To simplify the graphical presentation, the loadings and scores are plotted with just the PC1 and PC2 dimensions (65.6% of variance explained, no loss of information by omitting the 3rd dimension) and are presented in Figure 4.4. The plots are used as a means of pattern matching to determine how the scores relate to the loadings. Points furthest from zero have the greatest impact on variance so that scores and loadings in approximate collocations are indicative of a link between samples and variables. The scores plot illustrates that the

HILIC fractions wrap counterclockwise around the plot from the PC1(-)/PC2(-) to the PC1(-

)/PC2(+) region from the least polar to most polar material. Across this arc in the loadings plot, the PARAFAC components follow in the order of

Q3→TRP2→TYR→Q2→Q1→TRP1, while NMR groups follow from:

130

A) Loadings B) Scores .16 20 TRP1 Q1 carb ~ 6.3-7.7 ppm 0.12 arom 15

0.08 10 H65-H75 ~ 3.7-4.1 ppm H38-60 0.04 5

0 0 Qb Q2 ~ 2.3-3.0 ppm H23-H28

-0.04 -5

PC2 (20.4%)PC2 TYR H14-H22

-0.08 CRAM -10

~ 1.5-1.7 ppm TRP2 H10-H13 -0.12 H04-H09 ~ 2.1-2.3 ppm -15 Q3 INC. ~ 1.3-1.9 ppm POLARITY -0.16 -20 -0.16 -.12 -0.08 -0.04 0 0.04 0.08 .12 0.16 -20 -15 -10 -5 0 5 10 15 20 MDLT PC1 (45.2%)

Figure 4.4. PCA plots of the (A) loadings and (B) scores of the combined NMR and PARAFAC data set. The loadings for PARAFAC are highlighted in orange and labeled accordingly; circled regions are discussed in the main text.

MDLT→CRAM→arom→carb. Qb was not near any other loading and in a region of sparse scores. The proximity of Q3 and TRP2 with MDLT and in a region associated with the most apolar fractions suggests that material high in MDLT-type structures is responsible for the nonpolar tryptophan signal as well as Q3. TYR and Q2 are in turn clustered near NMR resonances within the CRAM region as well as shown to be associated with fractions ~H14-

H28 in the scores plot. Q1 is linked to mid-late fractions as are resonances from the aromatic region. TRP1 is in turn associated with the most polar fractions as are resonances from the carbohydrate region. The overall trend would thus appear to be that quinone-influenced signals are linked to apolar constituents and largely associated with the resonances for CRAM

131 and MDLT-type material. The tryptophan signals were found to occupy opposite regions of the PCA plot so that the polar TRP1 and nonpolar TRP2 are nicely associated with the polar and nonpolar scores, respectively. The PCA results provide evidence that tyrosine-type fluorescence is weighted in the PC1(+)/PC2(-) in association with the scores from more apolar fractions despite the tyrosine signal having a positive correlation with polarity

(R2=0.55, p<0.005). This finding suggests that the tyrosine signal is prominent in polar fractions but is also an important factor in more apolar material.

The methods used to analyze the HILIC-separated fractions of SRDOM provide evidence that HILIC is an effective approach for DOM fractionation. 1D 1H NMR spectra are characterized by dozens of sharp signals, indicative of individual molecules and such detail is not possible without substantial simplification of DOM. Major structural groups are shown to either increase or decrease with polarity such that PCA analysis demonstrates that there is a progression of material that differs slightly from fraction to fraction and that apolar material is structurally very distinct from polar constituents. PARAFAC analysis of fluorescence signals further demonstrates variability across the polarity gradient with quinone-influenced signals most apolar and amino acid-type fluorescence most prominent in polar fractions. Fractions dominated by single fluorescence components further provide evidence for the homogeneity of the material. Finally, and perhaps most notably, the material is now sufficiently resolved within NMR spectra that discrete structural assignments can be made with multidimensional experiments – a feat not readily accomplished on DOM samples. Such identifications can aid in determining the abundance of unknown molecules present in DOM as well as aid in understanding source material and processes that form DOM. This novel application of

HILIC for DOM separation reveals that molecular-level elucidation is possible with NMR and

132 that further development of these techniques and particularly further improvements in mobile/stationary phase choices for DOM separation may prove indispensable to molecular- level identifications.

4.5 Acknowledgements

The authors thank the Natural Sciences and Engineering Research Council of Canada

(Discovery Grant, A.J.S) and the International Polar Year (IPY) for providing funding. A.J.S. would further like to thank the government of Ontario for providing funding in the form of an

Early Researcher Award.

133

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137

Chapter 5

Oxidized sterols as a significant component of dissolved organic matter: evidence from 2D HPLC in combination with 2D and 3D NMR spectroscopy

Content in this chapter has been submitted as: Woods G. C., Simpson M. J. and Simpson A. J. (2011) Oxidized sterols as a significant component of dissolved organic matter: evidence from 2D HPLC in combination with 2D and 3D NMR spectroscopy. Water Res.

Reproduced with permission from Water Research, 2012, In Press. © Copyright Elsevier.

138

5.1 Abstract

The elucidation of molecular structures present in dissolved organic matter (DOM) has the potential to unlock many of the queries associated with organic precursors, diagenetic processes and reactivity of this highly complex material. Suwannee River DOM was extensively fractionated by two dimensional hydrophilic interaction chromatography

(HILIC)/HILIC and fractions were analyzed via a suite of two and three dimensional NMR.

HILIC provided more greatly resolved fractions with a second dimension and enabled extensive and in-depth NMR analyses. The composite NMR experiments provide strong evidence for highly oxidized sterols as major structural components present in one of the most fractionated and subsequently resolved fractions. Further data on other fractions across the polarity gradient likewise support the presence of alicyclic structures present with considerable hydroxyl groups, carboxylic acids and methyl groups associated with quaternary carbon suggesting that further sterol- and hopanoid-type structures are potentially dominant throughout DOM.

139

5.2 Introduction

The abundance and diversity of molecules present in dissolved organic matter (DOM) have the potential to provide a wealth of information on the origins and processes that have affected these molecules upon moving through aquatic environments [Hedges, 2002]. This carbon reservoir is estimated to be as large as 700 Pg C [Bolin, 1986; Hedges et al., 1992;

Hansell et al., 2009], or roughly as much carbon as is contained in all atmospheric CO2

[Hansell et al., 2009]. The substantial pool of actively cycling DOM originates from living material and is subject to alteration via processes such as photooxidation, aggregation and biodegradation [Engel et al., 2004; Obernosterer and Benner, 2004; Sulzberger and Durisch-

Kaiser, 2009]; little is known, however, about the actual chemical structures present, particularly for the greater fraction of refractory material that is believed to persist for hundreds to thousands of years [Druffel et al., 1992; Benner, 2002]. DOM has traditionally been proposed to contain compounds derived from lignin, tannins, proteins and carbohydrates

[del Rio et al., 1996; ; Hernes et al., 1996; Opsahl and Benner, 1997; Hernes and Hedges,

2000; Hernes et al., 2001; Benner, 2002; Hernes and Benner, 2002; Benner, 2003; Leenheer et al. 2003] but more recent studies suggest that an additional major contribution to the formation of DOM is terpenoid precursors [Leenheer et al. 2003; Leenheer and Rostad, 2004;

Hertkorn et al., 2006; Lam et al., 2007]. Among the difficulties in determining the structures present is the sheer complexity of this “supermixture” that is comprised of tens of thousands of compounds, many of these structures yet unknown [Hertkorn et al., 2007; Koch et al.,

2007; Dittmar and Paeng, 2009]. Targeted approaches can isolate known biomolecules or degradation products but are hindered by the larger group of completely unidentified constituents. Non-targeted analytical approaches are plagued by sample complexity and

140 generally only provide basic compositional information; one of the largest challenges for humic substance researchers is thus to make sense of the relationships between the chemical composition of DOM, biogeochemical processes and fate of this material.

Variable and novel approaches are necessary to overcome such a substantial analytical challenge. Multidimensional NMR techniques have been used in recent years to reveal structural information and prominent functional group connectivity in humic substances

[Schmitt-Kopplin et al., 1998; Haiber et al., 2001; Simpson, 2001; Simpson et al., 2001;

Hertkorn et al., 2002; Simpson, 2002; Simpson et al., 2002; Cook et al., 2003; Simpson et al.,

2003; Simpson et al., 2004; Hertkorn et al., 2006; Deshmukh et al., 2007; Lam et al., 2007;

Simpson et al., 2011; Woods et al., 2011]; while the bulk of the research to-date has utilized two dimensions, 3D-NMR applications have further increased spectral dispersion for more detailed chemical assignments but the overlap of signals in NMR spectra still inhibit identification of distinct structures present [Simpson, 2002; Simpson et al., 2003].

Multidimensional NMR in conjunction with FT-ICR-MS has been used to characterize cyclic polycarboxylates as probable structures present in abundance within marine DOM (carboxyl- rich alicyclic molecules (CRAM)) [Hertkorn et al., 2006] and further work with multidimensional experiments in conjunction with spectral predictions provided evidence for linear analogs as prominent structures in freshwater DOM (material derived from linear terpenoids (MDLT)) [Lam et al., 2007]. Both of these structural types are predicted to originate from terpenoids and further NMR research has likewise evidenced the occurrence of terpenoid precursors in DOM [Leenheer et al. 2003; Leenheer and Rostad, 2004].

The objective of the research presented here was to further probe the molecularly uncharacterized constituents present in DOM with multidimensional NMR experiments. The

141 current spectral overlap, even with 3D-NMR techniques, indicates that NMR identification of

DOM constituents is hindered pending substantial simplification of this mixture prior to NMR experiments. With very complex biological and natural samples, 2D-HILIC/HILIC systems are gaining popularity as a very promising fractionation technique for polar compounds [Stoll et al., 2007; Wang et al., 2008; Liu et al., 2009]. Recently, hydrophilic interaction chromatography (HILIC) has been demonstrated to be an efficient means to reduce the heterogeneity of DOM [Woods et al., 2011], resulting in NMR spectra with much more resolved and detailed signals. Here, we report the application of further fractionation via 2D-

HILIC/HILIC for the extensive separation of Suwannee River DOM (SRDOM) prior to detection with a suite of 1D, 2D and 3D NMR applications. Our findings provide strong evidence for highly-oxidized sterols as a major group of structures present in DOM.

5.3 Experimental

5.3.1 2D-HILIC/HILIC separation

HILIC separations were achieved on an Agilent high performance liquid chromatography (HPLC) system (Agilent 1200, Agilent Technologies) and consisted of a quaternary pump, a binary pump for the second column, degassers for both pumps, an autosampler, a column heater, a diode array detector (DAD, Agilent model G1315B) monitored at 254, 280 and 320 nm, a fluorescence detector (FLD, Agilent model G1321A) monitored at λexc=250 nm and λem=410 nm, and an analytical fraction collector (Agilent model G1364C). An online 2D-HLIC/HILIC system was developed for the separation of

SRDOM and was found to pose many challenges for subsequent NMR detection. High orthogonality in 2D-HPLC generally leads to higher resolving ability but can be problematic

142 if mobile phases are not compatible [Hemström and Irgum, 2006; Stoll et al., 2007; Liu et al.,

2009] and column bleed was here found to be a substantial hurdle when faced with subsequent NMR detection of analytes at very low concentration. Numerous HPLC stationary phases were tested and the final system was to some extent a compromise in orthogonality in exchange for a clean NMR spectrum (Figure 5.1, blank; the only NMR signals from the blank are residual solvent signals from the sample solution and mobile phase). A silica column with crosslinked diol functional groups (Phenomenex, LUNA,

4.60x150 mm, 3µm particle size) was used for the first dimension and has been previously used and described for 1D separations of SRDOM [Woods et al., 2011]. The second dimension was achieved using an unbound silica stationary phase (Phenomenex, Kinetex,

4.60x150mm, 2.6µm particle size) and was specifically chosen for the second dimension due to the lack of detection of column bleed (silica) with either 1H or 13C NMR. Both columns were fitted with prefilters and guard columns. The gradient mobile phase for the first dimension consisted of a 95% acetonitrile, 5% 100mM deuterated ammonium acetate in

HPLC-grade water, with a step-wise gradient applied of increasing aqueous contributions; the details of this gradient elution are provided in Table 5.1. The mobile phase for the second dimension consisted of an isocratic solution of 50% acetonitrile, 50% 10mM deuterated ammonium acetate in HPLC-grade water. The flow rate was held at 0.3 ml/min for both pumps and both columns were maintained at 30°C with the column heater. 50 mg of

Suwannee River DOM (SRDOM, purchased from the International Humic Substances

Society) was dissolved in a solution of 0.6M NaOH in HPLC-grade water, 50 µl of dimethyl sulfoxide (DMSO) was added, the solution was sonicated for 3 hours and 0.22 µm syringe filtered into HPLC vials. 100 µl of the sample solution was injected directly onto the first

143

CRAM MDLT Frac. 10 carb

arom lig

Frac. 16

Frac. 66

Frac. 104

Blank DMSO H2O Acetic Acid

8.0 6.0 4.0 2.0 ppm Chemical shift

Figure 5.1. 1D 1H NMR spectra of 2D-HLIC/HILIC fractions and the HPLC/NMR system blank. Assignments are as follows: arom (aromatics), 6.5-7.8 ppm; carb (carbohydrates – the traditional assignment for this region, lignin methoxyl also resonates under this region), 3.2-4.5 ppm; CRAM, 1.6-3.2 ppm; MDLT, 0.6-1.6 ppm; lig (lignin), 6.57 ppm.

144

Table 5.1. Mobile phase composition for HILIC separations, 1st dimension; flow rate of 0.3 ml/min.

% CD COOND (100 elution time (min.) elution volume (ml) % H O % ACN 3 4 2 mM) 0.0 0.0 0.0 95.0 5.0 20.0 6.0 0.0 95.0 5.0 40.0 12.0 20.0 75.0 5.0 90.0 27.0 29.0 66.0 5.0 160.0 48.0 45.0 50.0 5.0 189.7 56.9 0.0 5.0 95.0 210.0 63.0 0.0 5.0 95.0 216.7 65.0 0.0 95.0 5.0 250.0 75.0 0.0 95.0 5.0

Column 1 Instrument Apparatus: Column 1 position

100 mM deut. H2O ACN solution Pump 2 Valve Column 2

Quaternary Pump (1) Sample Injector Loop

Waste

HILIC 1 Column 2 position (diol)

10 mM deut. ACN solution

Binary Pump (2) Valve Detectors HILIC 2 Sample (silica) Loop Fraction Collector

Waste

Figure 5.2. Diagram of HPLC apparatus illustrating how the 2D-HILIC/HILIC system was achieved.

145 dimension column, separated and then passed through a 500 µl sample loop (I.D. 0.5mm) prior to complete injection (i.e. 500 µl) on the second dimension column. (A diagram of the instrument and valve apparatus may be found in Figure 5.2). The first dimension eluted material over ~200 minutes, with the bulk of the material eluted within a 1 hour period

(Figure 5.3.A); the second dimension was run for 3.4 minutes between injections of material from the first dimension (i.e. 1.7 minutes loading the sample loop from the first dimension and 1.7 minutes loading the filled sample loop onto the second dimension). The second dimension was found to not retain material for longer than a few minutes and it is furthermore advantageous to operate with short run times on the second dimension so as to limit diffusion on the first [Wang et al., 2008]. A comprehensive “3D chromatogram” of the final elution

0.0 40.0 80.0 120.0 1600.0 200.0 240.0 Retention time (minutes) 1st dimension: silica column

185.2 175 126 112.7 99.1 85.5 71.9 58 29 19.3 Retention time (minutes) 9.1 0 1.0 2.0 3.0 1st dimension: diol column Retention time (minutes) nd 2 dimension: silica column

Figure 5.3. A) Chromatogram of the 1st dimension HILIC separation (diol stationary phase); B) 3D surface representation of the 2D-HILIC/HILIC separation.

146 from the second dimension is illustrated in Figure 5.3.B. From this dimension, 126 fractions

(max. permitted by the fraction collector tray) were collected at regular intervals across the regions of eluted peaks and the run (250 minutes total) was repeated 70 times to collect sufficient material from each fraction. Comprehensive fractionation of all of the material was not attempted for the following reasons: A) with multiple, lengthy multidimensional NMR techniques, only a handful of samples can realistically be analyzed in depth and thus a few highly simplified fractions from different points along the run are suitable for the detection methods used here, B) if the sample loop delivered all of the material from the first to second dimension, pump 1 would need to be stopped and subsequent diffusion on the column would negate some of the benefits in resolution from a second dimension, and C) more homogenous fractions are theoretically collected (i.e. only ~half of the material is collected overall translating into an ~2 fold reduction in heterogeneity per fraction). In theory, SRDOM was simplified to the extent that each fraction represents ~0.4% of the original material. The eluate of each fraction was dried via the use of a SpeedVac (Savant, AES 2000) and further stored in a desiccator. The mass balance of the 2D-HILIC/HILIC system was tested via collection of the material leaving both columns 1 and 2. Triplicate injections of the sample were conducted with the HILIC columns connected as well as triplicate with no columns present and the eluates were collected, dried on the SpeedVac and massed on an analytical balance. The mean percent recovered was 102.4% with a standard error of 2%. Triplicate runs were further conducted without injection, both with and without columns present, and the eluates from these trials confirmed that negligible contributions from the columns, mobile phase and/or HPLC system occurred (<0.5%).

147

5.3.2 NMR analysis

All NMR analyses were conducted on an AvanceTM 500 MHz spectrometer at 298K with a 1H-13C-15N 1.7mm microprobe fitted with an actively shielded z-gradient. Solution state 1D 1H NMR experiments were performed using 1024 scans, a recycle delay of 2 s, 65k time domain points, and an acquisition time of 3.2 s. For all 1D 1H experiments, composite pulse presaturation [Bax, 1985] was used to suppress the signal from water at ~4.7 ppm.

Relaxation filtered 1D experiments were performed using a decoupling in the presence of scalar interactions (DIPSI) spin-lock, 1024 scans, a time domain of 65k, and a variable mixing time of between 0-1000 ms. All 1D spectra were apodized with an exponential multiplication factor of 0.3 Hz and chemical shift was calibrated according to the Bruker

Biofluid Reference Compound Databases (versions 2-0-0 through 2-0-3). 1D solution-state

13C was further attempted but fractions were unfortunately too dilute.

2D correlation spectroscopy (COSY) spectra were obtained with digital quadrature detection, 1024 transients, 8192 time domain points (F2), 196 increments (F1), with a total delay of 1.7 s between scans. Each spectrum was zero-filled by a factor of 2 and with 6000Hz spectral widths; the data were processed with an unshifted sine-squared functions in the F1 and F2 dimensions and projected in magnitude mode. Total correlation spectroscopy

(TOCSY) was performed with Time-Proportional Phase Incrementation (TPPI) in the F1 dimension, 512 scans, 4096 time domain points in the F2 dimension, 256 increments in the F1 dimension and a mixing time of 500 ms. Spectra were processed using sine-squared functions with a 90° phase shift as well as a zero-filling factor of 2. 1D selective TOCSY experiments were further run to target specific regions of the 1H chemical shift range. Spectra were acquired utilizing digital quadrature detection, 8k scans, 65k time domain points, a mixing

148 time of 500 ms and a total delay between scans of 3.9 s. 1D selective TOCSY spectra were apodized with an exponential function corresponding to 0.3 Hz line broadening in the transformed spectrum. Heteronuclear single quantum coherence (HSQC) and DEPT phase- edited HSQC experiments were performed using echo/antiecho-gradient selection [Cicero et al., 2001] and the following parameters: 512 scans, 2048 time domain points (F2), 128 increments (F1), a 1J (1H-13C) value of 145 Hz and a delay between scans of 1.3 s. The data were processed such that the F2 dimension was apodized with an exponential multiplication factor of 15 Hz line broadening; the F1 dimension was processed using a sine-squared function with a 90° phase shift and a zero-filling factor of 2. Two dimensional heteronuclear multiple bond correlation (HMBC) experiments were performed in phase-sensitive mode using echo/antiecho-gradient selection [Cicero et al., 2001] and the following parameters:

4096 scans, 4096 time domain points (F2), 96 increments (F1), and a delay of 1.4 s between scans. The F2 dimension was apodized with an exponential multiplication factor of 10 Hz line broadening and the F1 dimension was processed with a zero-filling factor of 2. Diffusion ordered spectroscopy (DOSY) experiments were performed utilizing a bipolar pulse longitudinal encode–decode sequence [Wu et al, 1995] with a 2.5 ms sine-shaped encoding/decoding gradient pulse, ramped from 0.98 to 49 gauss/cm in 24 linear increments.

Acquisition parameters included: 256 scans, a diffusion time of 200 ms, and 32k time-domain points (F2). Viscosity was accounted for with all DOSY spectra by using the diffusion coefficient of the free water signal as an internal calibrant [Simpson, 2002]. Spectra were apodized with an exponential multiplication factor of 1 Hz in the F2-dimension and a zero- filling factor of 2. All DOSY spectra were processed using Bruker Topspin™ (Bruker) version 2.1 using monoexponential fitting of each datapoint, a noise-sensitivity factor of 1,

149 and a spike-suppression factor of 4. The 24 slices were processed with 65k points in the F2

(chemical shift) dimension and the diffusion axis was created with 512 points in the F1 dimension. 3D DOSY-TOCSY was acquired using MLEV, 96 scans, a mixing time of

125ms, and 128 points in the indirect 1H dimension (F1) and presaturation for solvent suppression. All other diffusion parameters were identical to the 2D DOSY experiments. The

3D cube was processed using a filling factor of 2 in the proton dimension and monoexponential fitting of the diffusion decays to create a diffusion dimension of 256 processed points.

A three-dimensional HSQC-TOCSY experiment was conducted using 64 scans, 4096 time domain points (F3) and increments of 64 (F1), and 64 (F2), a 1J (1H-13C) value of 145

Hz, a TOCSY mixing time of 125 ms with States-TPPI in the F1 dimension and echo/antiecho-gradient selection in the F2 dimension. The 3-D cube was processed with a zero-filling factor of 2, with a sine-squared function with a 90° phase shift in the F1 and F2 dimensions and with an exponential multiplication factor of 15 Hz line broadening in the F3 dimension.

Spectral predictions were carried out using Advanced Chemistry Development’s

ACD/SpecManager and ACD/2D NMR Predictor with Neural Network Prediction algorithms

(version 12.0). Predictions were made by comparing spectral resolution and base frequency so as to match predicted structures with the real data.

150

5.4 Results and discussion

5.4.1 One dimensional 1H NMR

The high sensitivity of proton NMR permits detailed variations in DOM signals and can provide critical quantitative information of the structural groups present [Hertkorn et al.,

2006]. 1D 1H NMR spectra from all 126 collected fractions reveal a range of differences both in abundance of varying structural groups and spectral detail. Figure 5.1, for example, illustrates 4 fractions collected from different points across the polarity gradient, or 2D-

HILIC/HILIC run. The earlier fractions were found both to be characterized by greater contributions from the upfield chemical shift region (e.g. the regions previously characterized as CRAM- [Hertkorn et al., 2006] and MDLT- [Lam et al., 2007] or aliphatic-type regions) as well as finer splittings apparent in 1D 1H NMR spectra (see Figure 5.7 later for a more detailed zoom region). With subsequent fractions, aromatics and a peak characteristic of lignin-derived material (~6.5 ppm) [Woods et al., 2011] were most prominent in mid-polarity fractions while carbohydrate-type material was greatest in late fractions and the spectral resolution was reduced. The reduction in line shape in the later fractions may arise due to:

A) aggregation with the lignin-derived material that are prominent in these later fractions, B) that the material in these later fractions have larger molecular weight, and/or C) that despite the extensive fractionation, later fractions are still sufficiently complex to result in broadened spectral overlap. Chemical structures were difficult to assign with 1D NMR even in the more highly resolved fractions and thus 4 fractions were chosen across the elution period for further comprehensive 2D NMR experiments. Multidimensional proton techniques permit liquid- state NMR experiments to reduce sample complexity via spreading signals into more resolved peaks as well as providing information as to how structural groups are linked.

151

1 - 1,2-Propanediol 2 - 1,3-Propanediol 3 - 2-Hydroxyglutaric acid

4 - 2-Methylglutaric acid 11 16 8 1111 16 5 - 2-Methylsuccinic acid 4 88 18 19 23 44 24 17 1 6 - 3-Hydroxybutyric acid 6 5 17 18 11 26 10 4 1 7 - 3-Hydroxypropionic acid 1819 16 2223 4 2324 2 13 3 66 55 251617 44 4 16 8 - 3-Methylglutaric acid 3 11 2526 10 9 20 5 11 10 11 26 5 4 24 18 9 - 4-Hydroxybutyric acid 2616 28 13 4 4 26 2 3 2 13 4 33 6 2 21 10 - Acetic acid 21 7 2425 11 44 17 168 28 5 21 99 33 8 13 4 11 - Butyric acid 25 1920 5 55 28 5 11 26 20 5 2324 1718 12 - Fumaric acid 2526 27 21 204 25 2 28 28 5 28 284 26 23 3 13 - Glutaric acid 26 33 1 16 1 20 4 2021 66 2021 9 1111 8 14 - Glyceric acid 77 22 4 1617 14 2728 1655 8 24 1 15 - Glycolic acid 2021 1 7 8 1313 5 19 2728 5 16 – Isobutyric acid 26 20 6 4 14 6 1920 15 2021 19 17 - Isovaleric acid 21 21 26 22 2728 55 2728 2728 23 3 18 - Lactic acid 25 1 16 1 1920 4.5 264.0 1 3.51 3.0 2.5 2.0 1.5 1.0 0.5 19 – Levulinic acid 99 2122

20 - Malic acid (ppm)shift ChemicalF1 1414 16 22 1 21 - Methanol 11 77 22 - Methylmalonic acid 23 - Propionic acid 2526 6 4 24 - Pyruvic acid 1414 66 1819 1515 2526 25 - Quinic acid 2021 2021 26 - Succinic acid 27 - Tricarballylic acid 4.54.5 4.04.0 3.53.5 3.03.0 2.52.5 2.02.0 1.5 1.0 0.5 F2 Chemical shift (ppm)

Figure 5.4. 2D COSY NMR spectrum of 2D-HILIC/HILIC fraction 16. Assignments made from reference database and confirmed with other 2D NMR experiments (see main text).

152

Table 5.2. Structural assignments in 2D-HILIC/HILIC fractions provided by COSY and confirmed with other multidimensional NMR experiments; references indicate previous studies with evidence for these compounds in humic substances.

Structural Fractions 1H Chemical Shift (ppm) References Assignments

1,2-Propanediol 10, 16, 66 1.14, 3.45, 3.55, 3.88 [Templier et al., 2005] 1,3-Propanediol 10, 16 1.80, 3.68 [Bories et al., 2005] 2-Hydroxyglutaric acid 10, 16 1.84, 1.99, 2.26, 4.02 [Graham et al., 2002; Claeys et al., 2004] 2-Methylglutaric acid 16 1.08 1.60, 1.76, 2.16, 2.25 [Lehtonen, 2005; Kawamura et al., 2010] 2-Methylsuccinic acid 10, 16 1.08, 2.13, 2.52, 2.63 [Graham et al., 2002; Lehtonen, 2005; Templier et al., 2005; Kawamura et al., 2010] 3-Hydroxybutyric acid 16 1.20, 2.31, 2.41, 4.15 3-Hydroxypropionic acid 10, 16, 66 2.43, 3.79 3-Methylglutaric acid 10, 16 0.93, 1.99, 2.22 4-Hydroxybutyric acid 10, 16, 66 1.80, 2.23, 3.60 [Templier et al., 2005] Acetic acid 10, 16, 66 1.91 [Wilson et al., 1988; Fischer et al., 1994; Herzog et al., 1997; Bertilsson and Tranvik, 2000; Bories et al., 2005; Templier et al., 2005] Butyric acid 10, 16 0.90, 1.56, 2.16 [Bertilsson and Tranvik, 2000; Bories et al., 2005] Fumaric acid 10, 16, 66 6.52 [Graham et al., 2002; Lehtonen, 2005; Kawamura et al., 2010] Glutaric acid 10, 16, 66 1.78, 2.18 [Graham et al., 2002; Kawamura et al., 2010] Glyceric acid 10, 16, 66 3.72, 3.82, 4.08 [Fischer et al., 1994; Graham et al., 2002] Glycolic acid 10, 16, 66 3.94 [Fischer et al., 1994; Graham et al., 2002] Isobutyric acid 10, 16 1.06, 2.39 Isovaleric acid 10, 16 0.91, 1.95, 2.05 [Gralapp et al., 2001] Lactic acid 10, 16, 66 1.33, 4.11 [Fischer et al., 1994; Bertilsson and Tranvik, 2000; Graham et al., 2002; Bories et al., 2005] Levulinic acid 10, 16, 66 2.23, 2.41, 2.78 [Lehtonen, 2005; Templier et al., 2005] Malic acid 10, 16, 66 2.36, 2.67, 4.31 [Fischer et al., 1994; Graham et al., 2002; Claeys et al., 2004; Bories et al., 2005; Kawamura et al., 2010] Methanol 10, 16, 66 3.36 [Wilson et al., 1988 ] Methylmalonic acid 10, 16 1.25, 3.17 [Graham et al., 2002; Kawamura et al., 2010] Propionic acid 16 1.06, 2.18 [Wilson et al., 1988; Fischer et al., 1994; Bories et al., 2005] Pyruvic acid 10, 16, 66 2.37 [Fischer et al., 1994; Bertilsson and Tranvik, 2000; Graham et al., 2002; Kawamura et al., 2010] Quinic acid 10, 16 1.88, 1.97, 2.05, 3.55, 4.02, 4.14 [Guggenberger et al., 1989] Succinic acid 10, 16, 66 2.46 [Wilson et al., 1988; Fischer et al., 1994; Graham et al., 2002; Lehtonen, 2005; Templier et al., 2005; Kawamura et al., 2010] Tricarballylic acid 16, 66 2.22, 2.48, 2.90 [Graham et al., 2002; Lehtonen, 2005]

153

5.4.2 Identification of small acids

2D COSY NMR experiments are used to probe through-bond connections of protons up to 3 bonds away. A variety of simple structures are readily identifiable on COSY spectra via database matching (AMIX, version 3.8.7, including Bruker Biofluid Reference Compound

Databases, versions 2-0-0 through 2-0-3) and are indicated in Figure 5.4 and Table 5.2. The more highly resolved early fractions from 2D-HILIC/HILIC generated COSY spectra with dozens of carboxylic acids, hydroxy acids and diols and even a later analyzed fraction (66) was found to have appreciable numbers of these small organics. All assigned structures were further verified via analysis of 2D HSQC (direct coupling of 1H-13C), DEPT-HSQC (an edited

HSQC experiment where CH and CH3 resonances are negatively phased and CH2 signals are positive) and TOCSY (shows protons not directly coupled but within the same spin system) and this method of structural verification has been demonstrated previously [Woods et al.,

2011]. The limitation of this assignment method, however, largely includes that current NMR databases are comprised of known biomolecules and therefore cannot elucidate the many unknowns postulated to be present in DOM [Hertkorn et al., 2007; Koch et al., 2007; Dittmar and Paeng, 2009].

5.4.3 Evidence of sterols from DIPSI, DOSY and selective TOCSY experiments

The COSY experiments readily identify a handful of small compounds but the 1D 1H

NMR spectra of SRDOM fractions are still composed of hundreds if not thousands of overlapping signals. Looking at Figure 5.1, fraction 16, for example, it is not clear whether the complex “humps” in DOM arise from sheer overlap (i.e. the base of each lorentzian peak compounds to create a hump-like distribution) or that small molecules such as those identified

154

sucrose HSA & sucrose Frac.Frac 10. 10 Frac.Frac 16. 16 HSA

0 ms 0 ms 0 ms

50 ms 50 ms 50 ms

250 ms 250 ms 250 ms

10.0 8.0 6.0 4.0 2.0 ppm 10.0 8.0 6.0 4.0 2.0 ppm 10.0 8.0 6.0 4.0 2.0 ppm

CholicCholic acid acid Frac.Frac 66. 66 Frac.Frac 104. 104

0 ms 0 ms 0 ms

50 ms 50 ms 50 ms

250 ms 250 ms 250 ms

10.0 8.0 6.0 4.0 2.0 ppm 10.0 8.0 6.0 4.0 2.0 ppm 10.0 8.0 6.0 4.0 2.0 ppm Chemical shift

Figure 5.5. DIPSI profiles of SRDOM fractions along with profiles of standards: cholic acid and human serum albumin (HSA) with sucrose.

155 with COSY are sitting on top of a broader background arising from macromolecular material.

This phenomenon can be tested by employing relaxation filters and as such, a DIPSI spin-lock was employed so as to selectively filter macromolecular signals. Figure 5.5 demonstrates such experiments applied to human serum albumin (HSA) and sucrose to demonstrate the decay of broad signal from large molecules (HSA, 67 kDa) while small molecules (sucrose,

342 Da) still maintain prominent, sharp signals. In examining Figure 5.5, however, the decay in signal for fraction 16 is relatively consistent across all chemical shifts thus evidencing that the broad humps in the normal 1D 1H NMR spectrum result from the extreme overlap of a large number of relatively small molecular weight compounds (a true macromolecular signal cannot survive the spin-lock and disappears completely as is demonstrated by HSA at 250 ms). Identical DIPSI experiments were run with fractions 10, 66, 104 and a cholic acid standard; these profiles demonstrate that fraction 10, fraction 16 and cholic acid have similar decay profiles in terms of loss of signal intensity while fractions 66 and 104 are similar but slightly more suppressed and thus may have larger or more highly aggregated material. The higher aromatic contributions present in these latter fractions would further suggest that lignin-derived material is present and would corroborate the presence of larger material. The fractions therefore appear to not contain significant macromolecular signature but to verify the relative size of the various fractions, further experiments must be conducted.

To assess the relative size of the various fractions tested, DOSY experiments may be used to elucidate diffusion coefficients and the subsequent average sample hydrodynamic radii [Simpson, 2002; Lam and Simpson, 2009]. DOSY experiments on selected fractions from 2D-HILIC/HILIC and cholic acid reveal that even the largest material is diffusing on average at a rate consistent with relatively small material. Figure 5.6 illustrates that diffusion

156

Frac. 10

- 9.44

Frac. 16

- 9.42

Frac. 66

- 9.60

Frac. 104

- 9.76

Cholic acid

-9.49

2 -1) --9.0 --9.5 - 10.0 Log (DC), DC m (m2s-1 s Figure 5.6. Diffusion profiles generated from DOSY experiments for 4 fractions plus a cholic acid standard. DC indicates diffusion coefficient and numbers on each profile specify the apex of each peak. Fraction 109 has a broad diffusion profile in part because of much lower signal to noise.

cyclic + OH groups

1D 1H NMR

Selective TOCSY

3.0 2.5 2.0 1.5 1.0 ppm Chemical Shift

Figure 5.7. Zoomed region of a selective TOCSY experiment overlaid by the 1D 1H NMR spectrum for fraction 16. Selective excitation of the O-R region (3.77 ppm) generated significant signal within the 1.7-2.5 ppm region.

157 of the earlier fractions (10 and 16) is similar to free cholic acid (409 Da) while the latter fractions are either comprised of larger and/or more highly aggregated material. It should be noted that the broad peak arising from fraction 104 is to some extent due to a significantly lower signal-to noise than the other samples analyzed (hence the standard deviation which is encoded into the larger peak width). The signal-to-noise is still sufficient for quantitative and qualitative analyses and what is important for comparisons with this fraction is the location of the apex of the peak, demonstrating that the bulk of the material has a much lower diffusion coefficient than the other samples. What the trend overall demonstrates is that the earlier fractions have diffusion characteristics similar to cholic acid while late-eluting material appears to have a significantly larger hydrodynamic radii. The late fractions appear to have lignin contributions and as such, the average diffusivity for the sample, which is measured by monoexponential fitting, appears to fall somewhere between the larger molecular weight lignin components and the smaller molecular weight entities. Extensive DOSY analyses on

SRDOM has previously demonstrated that log(DC) < -9.5 m2s-1 are associated with highly concentrated and subsequently aggregated material [Lam and Simpson, 2009], and the data presented here, therefore provide evidence for aggregation of smaller components and/or larger constituents such as those likely introduced by lignin inputs.

Selective TOCSY experiments permit selective excitation of a chemical shift range.

Spins within the excited range then couple to other protons within the same molecule (spin- spin system). Spin-spin couplings in TOCSY cannot relay across quaternary carbons as the

1H-1H spin system is interrupted but in unbroken systems couplings can relay through > 7 bonds. The resulting spectra contain signals from protons that are coupled to protons in the excited region and provide useful structural information. Selectively excited-regions

158 analyzed include sections centered at 1.06 ppm (CH3), 2.18 ppm (CRAM region), 3.77 ppm

(O-R region), and 6.5 ppm (lignin peak). Of these selective TOCSY experiments, the O-R region proved most interesting. In this case, the region arising from OH/OR groups was selectively excited (~0.9 ppm wide excitation centered at 3.77 ppm) and protons that are in the same molecules as these groups appear in the selective TOCSY spectrum. Figure 5.7 demonstrates that for fraction 16, chemical moieties resonating from about 3.3 to 4.2 ppm are within the same spin system as a multitude of prominent signals spanning 1.5-2.5 ppm. These couplings contain all the same splittings as those that can be seen in the 1D NMR and could arise from the side chains of amino acids and/or cyclic structures attached to OH groups.

Strong amino acid contributions, however, seem unlikely as strong coupling to CH3 groups was not demonstrated (see further below) and no CHα signals are present in the HSQC data

(see section 5.4.4, Figure 5.9-A; CHα groups from amino acids would produce signal from

~4.0-4.5 ppm (F2) and ~50-60 ppm (F1) but no such signals were found throughout fractions tested). All of these “hair-like” structures from 1.5-2.5 ppm are, however, consistent with spin systems that contain OH groups and cyclic structures (e.g. see cyclized rings with OH groups on cholic acid structure, Figure 5.5) and evidence of these couplings was further present at lower resolution in all of the fractions tested. Of further interest to the data presented here is that the selective TOCSY experiment targeting the 1.06 ppm (CH3) region does not significantly excite any other parts of the spectrum (Figure 5.8) in any of the fractions tested suggesting that the CH3’s are largely associated with quaternary carbons (e.g. see CH3 groups attached to rings on cholic acid structure, Figure 5.5). When compiled, the evidence provides an interesting portrait:

159

The mixture has a strong component of OH/OR substituted carbon that predominantly

couple within the 1.5-2.5 ppm region.

The 1.5-2.5 ppm region in 1D 1H NMR spectra of DOM samples is somewhat unusual

and has been previously demonstrated to likely arise from cyclic structures [Hertkorn

et al., 2006; Lam et al., 2007] that are believed to be from terpenoid-derived

structures. Extensive spectral simulations could not identify any other types of

structures that can account for these signals [Lam et al., 2007].

There is a major CH3 component in the samples (see further below) but according to

the selective TOCSY data, these methyl groups lack coupling to other moieties and

therefore are likely associated with quaternary carbon (structural connectivities

common to terpenoid-type structures such as sterols).

4.0 3.0 2.0 1.0 ppm Chemical shift

Figure 5.8. Selective TOCSY spectrum generated by the excitation of the CH3 region (1.06 ppm); minimal signal is apparent from other chemical shift regions.

160

88 A) Data III 16 I 24 No long 32

chain (CH2)n 40

II 64 (ppm) Shift Chemical F1 F2 Chemical(ppm) F2 shift 72

80 4.5 4.04.0 3.5 33.0.0 2.5 2.02.0 1.5 1.01.0 0.5 F2 Chemical Shift (ppm) F3 Chemical shift (ppm)

B) Predictions 88 OH CH3 H [3.67 [0.71/ 73.79] [1.53/ 13.89] [1.32/ 32.77] / - ] 16 [1.56; 1.69 / 30.12] [1.66; 1.77 / 22.94] CH H IV 3 [1.04;[0.93 1.85 [ // -22.63]36.04] / 36.98][1.62 [/ -- /] 47.74][1.36; 2.02 / 25.75] 24 [1.45; 1.69 / 30.12] [ - / 35.26] H H [3.50 / 72.70][ - / 39.32][2.25 [3.95/ - ] [1.76/ 72.23] / - ] HO [1.73; 2.15[1.61; / 39.48] 1.92O / H36.67] 3232 H [1.44 / - ] 40

5656 F1 Chemical Shift (ppm) Shift Chemical F1

V 64 F2 Chemical shift (ppm) Chemicalshift F2 72 8080 4.5 4.04.04 3.5 3.03.03 2.5 2.02.02 1.5 1.01.01 0.5 0 0 F2 Chemical Shift (ppm) F3 Chemical shift (ppm)

Figure 5.9. A) 2D HSQC spectrum produced from a F2-F3 slice at 1.85 ppm in the F1 dimension for the 3D HSQC-TOCSY experiment on fraction 16. B) Corresponding 2D spectrum generated from spectral predictions for a main cyclic component of sterol-type structures (cholic acid, minus side chain, see text for discussion).

161

The molecules in the early 2D-HILIC/HILIC fractions demonstrate relaxation and

average diffusivities similar to free sterol structures.

Considering the combined evidence, more extensive NMR experiments with additional dispersion and connectivity information would be useful to confirm the presence of sterols.

5.4.4 Evidence of sterols with three dimensional NMR

In attempting to simplify the complex and overlapping signals of DOM, 3D NMR is extremely useful for the additional connectivity information and spectral dispersion afforded by the 3rd dimension. Fraction 16 was chosen for the lengthy 3D NMR analyses due to the exceptionally detailed splittings apparent with 1D 1H NMR (Figures 5.1 and 5.7); such fine splittings provide more detailed structural information and where multidimensional NMR experiments are very lengthy (on the order of several days to weeks), a sample with more resolved signals is ideal to obtain maximum information. A slice from a 3D TOCSY-HSQC experiment on fraction 16 is illustrated in Figure 5.9.A. Of the three dimensions, F1 and F3 are proton dimensions while F2 is the carbon dimension. Detailed information is most easily viewed in slices or planes through this cube and as such, Figure 5.9.A shows an F2-F3 slice at

1.85 ppm on the F1 axis. This point on the F1 dimension corresponds to the main CH2 region expected for sterol-type structures. In this case, the corresponding F2-F3 plane represents a

2DHSQC spectrum of just the components attached to CH2’s on cyclic rings. If this region is dominated by sterols, the resulting F2-F3 plane should provide evidence for OH groups substituted directly onto rings. Quaternary CH3’s, also prominent structural groups of sterols, should not appear as a TOCSY relay will not pass through the quaternary resonance. The acquired data satisfies all of these characteristics. In Figure 5.9.A, region I is consistent with

162 a wide range of couplings and chemical shifts from cyclic CH2’s and reveals strong couplings to a second region (II) where carbon bound to OH would appear. No couplings are seen back to the CH3 region suggesting that any methyl groups can only be bound to quaternary carbon which further confirms the selective TOCSY data (for example structure, see cholic acid,

Figure 5.5 but in this case the side chain must be absent to satisfy the lack of CH3 coupling to other protons). The absence of CH3 groups in this spectrum is likely explained by coupling to quaternary carbon and is further supported by the edited HSQC for fraction 16 (Figure 5.10) in which significant CH3 content is apparent. Furthermore, if straight chain lipids and alcohols were present in abundance in the mixture they would all couple back to ~1.3 ppm

(proton) and 13~32ppm (carbon) to create a very strong resonance in the 3D correlation

(Figure 5.9.A, region III). In soil extracts, these correlations are from dominant straight chain species that are by far the most intense correlations in the 3D data (Figure 5.3 in Simpson et al. [2003]). In the fraction of SRDOM presented here, however, these correlations are

1 completely missing and all the CH2 resonances are shifted considerably to ~2 ppm ( H) and

~35 ppm (13C). COSY and TOCSY data further show strong self coupling within the ~1.7-

2.5 ppm region (Figure 5.11); in previous research discussed by Lam et al. [2007] and

Hertkorn et al. [2006], such spectral characteristics are quite unusual and can only be fit with the CH2’s that are cyclized into ring structures.

5.4.5 NMR spectral predictions for sterols

Terpenoids fit the NMR data presented above and more specifically sterols, originating from terpenoid precursors, fit well into the NMR data and NMR spectral predictions of fraction 16. Spectral predictions of highly oxidized sterol-type structures, such

163

CH3

100

150 (ppm)shift Chemical F1

10 8 6 4 2 0 F2 Chemical shift (ppm) Figure 5.10. Multiplicity edited 2D HSQC spectrum displaying the methyl and methine functional groups in fraction 16. The prominent methyl region is indicated (~1 ppm , 1H and ~25 ppm, 13C).

A) B) 1.5 1.5

2.0 2.0

2.5 2.5

F1 Chemical shift (ppm)shift Chemical F1 F1 Chemical shift (ppm)shift Chemical F1

2.5 2.0 1.5 2.5 2.0 1.5 F2 Chemical shift (ppm) F2 Chemical shift (ppm) Figure 5.11. Zoomed regions (1.3-3.0 ppm) of 2D NMR spectra for A) COSY and B) TOCSY experiments on 2D-HILIC/HILIC fraction 16.

164 as the structure in Figure 5.5 (cholic acid, a sterol found in bile and used here as a generic standard for a highly oxidized sterol), fit quite well with the correlations present in the F2-F3 slice from the 3D HSQC-TOCSY spectrum of fraction 16 (Figure 5.9). The spectral predictions for this particular slice, however, work best without the sidechain. This removal may occur, for example, via microbially-mediated processes [Szentirmai, 1990], but the resulting structure is simply a generic representation of the main cyclic component predicted to be present for sterol-type structures. There is variability throughout all of the 3D HSQC-

TOSCY slices and structural diversity is further evident in HMBC data, discussed below.

This variability indicates that there is likely to be significant variation in substitution patterns within this fraction but that overall this main cyclic component (i.e. cholic acid, minus sidechain) is a proxy for the main structural features present. For this main cyclic component, the CH3 groups will not appear in the 3D experiment leaving two main regions: one arising mainly from the CH2 cyclic structure (region IV) and the other from the associated OH groups

(region V). The two spectra cannot be expected to match perfectly due to the following: A) the structure is used as a generic proxy of just one example of 100’s to 1000’s of potential compounds; B) the slice is just a single slice from many in the 3D cube, and while all slices around this region show the characteristic resonances consistent with sterol-derived structures, all slices vary slightly indicating a range of different types of sterol-type structures; and C) the chemical shift will vary slightly based on solute-solute interactions, pH, salt, etc., so that simulations represent the general resonances of such structures but should not be over- interpreted.

Further analysis of data from a 3D DOSY-TOCSY experiment of fraction 16 is presented in Figure 5.12.A (illustrating a slice from the TOCSY dimension). F1 and F3 are

165

ppm A) Data 1.01.0

1.5

2.02.0

2.5

3.0

3.5 F1 Chemical(ppm) F1 shift 4.04.0

4.5 4.5 4.04.0 3.5 33.0.0 2.5 2.02.0 1.5 1.01.0 ppm F3 Chemical shift (ppm)

B) Predictions 1.0

1.5

2.0

2.5

3.0 F1 Chemical Shift (ppm) Shift Chemical F1 3.5

4.04.0 Chemical(ppm) shiftF1

4 3 2 1 4.0 F23 Chemical.0 Shift2.0 (ppm) 1.0 F3 Chemical shift (ppm)

Figure 5.12. A) 2D TOCSY spectrum produced from a F1-F3 slice at the apex of the material from the DOSY dimension for the 3D DOSY-TOCSY experiment on fraction 16; B) Corresponding 2D spectrum generated from spectral predictions on cholic acid.

166

H3C OH O CH3 H

H CH3

H H HO OH H

CH3 Cyclic 5050 OH

100100

C=C 150150

COOH Chemical(ppm) F1shift 200200 C=O

2.75 2.52.5 2.25 2.02.0 1.75 F2 Chemical shift (ppm)

Figure 5.13. Zoomed region (1.55-2.9 ppm, 1H) of 2D HMBC spectrum for fraction 16. Chemical structure is cholic acid and blue highlighted regions indicate how variations of this structure can account for data using spectral predictions, see text for discussion.

167 the TOCSY dimension, F2 is the dimension for the diffusion coefficient and the F1-F3 slice in Figure 5.12.A is taken at the apex of the main diffusivity from the DOSY dimension at -

9.42 m2s-1. Figure 5.12.B demonstrates the corresponding spectral predictions for the cholic acid standard and predictions are here shown to nicely line-up with the actual data from fraction 16, providing further evidence that this fraction is comprised of an abundance of sterol-type structures.

Finally, data generated from HMBC experiments (providing coupling information on protons and carbons of 2-3 bonds) and further spectral predictions may lend insight into the variations of chemical structures present within this proposed class of molecules. Figure 5.13 illustrates HMBC data from fraction 16 of the 2D-HILIC/HILIC separations and provides chemical shift data consistent with oxidized sterols. Within this highly simplified SRDOM fraction, significant variability in structures is still likely to be present and as such, extensive spectral predictions of varying sterol structures may explain all of the regions displayed in

Figure 5.13. Using the generic cholic acid structure as a proxy, the highlighted regions are satisfied via alterations of this structure and composite variations subsequently satisfy the actual data collected. The top three highlighted regions on the HMBC spectrum are readily explained by the CH3 groups present, the occurrence of fused cyclic structures and multiple

OH groups. The highlighted region at ~130 ppm (F1) may arise from a C=C double bond on the sterol as indicated in Figure 5.13 at positions C-5/C-6 (green oval); this double bond is among the most commonly found products from autoxidation [Smith, 1981; Parish, 1991] and is typical of many sterols found in nature [Smith, 1981; Christodoulou et al., 2009; Sarma et al., 2009]. Another possibility for this chemical shift region includes the presence of a complete aromatic ring such as is known to occur to the A-ring of cholic acid under anaerobic

168 conditions [Owen and Bilton, 1983] and is further known to occur with terpenoid structures with both biodegradation and thermal degradation [McGraw et al., 1999; Leenheer, 2009].

The range of chemical shifts in the 1H dimension for the carboxylic acid region in the HMBC spectrum (~180 ppm, F1) evidences that COOH may be present on the side chain or plausibly may be substituted for either of the two methyl groups indicated on the structure (red ovals).

Although these methyl groups are very stable owing to bonds with quaternary carbons in cyclic structures, there are enzymes known to functionalize such methyl groups with the addition of an oxygen atom [Summons et al., 2006], and these positions are found substituted with OH and COOH groups in naturally-occurring sterols such as arise in soft corals [Smith,

1981; Sarma et al., 2009]. Finally, the highlighted region at ~215 ppm (F1) is explained if the cholic acid sidechain is removed and replaced with a ketone (orange oval), a process that readily occurs with microbially-mediated processes [Szentirmai, 1990], and is common in sterols found in nature [Smith, 1981].

5.4.6 Variability in structures

The multidimensional experiments and spectral predictions overall support the presence of highly oxidized sterols. The data provide strong evidence that appreciable amounts of OH groups on an abundance of cyclic structures and numerous quaternary methyl groups are further present in the fractions analyzed. In examining what types of molecules are likely to be abundant in aquatic environments, previous studies have provided strong evidence for terpenoid-type structures as major components present in DOM from landfills, surface water, groundwater, lakes and marine ecosystems [Leenheer et al., 2003; Leenheer and Rostad, 2004; Hertkorn et al., 2006; Lam et al., 2007]. Evidence has been presented for

169 the abundance of polycarboxylated fused-ring structures in DOM [Leenheer et al., 2003;

Hertkorn et al., 2006; Lam et al., 2007] that appear to originate from sterols and hopanoids

[Hertkorn et al., 2006]. Terpenoids are a diverse class of molecules that constitute the largest family of natural products [Dubey et al., 2003; Zwenger and Basu, 2008] and are most stable as functionalized cyclic structures [Leenheer et al., 2003; Hertkorn et al., 2006]. Within sediments, sterols have a very long geological record with identifiable species found several thousand meters deep [Gagosian et al., 1982]. Sterol structures have further been used as biomarkers throughout aquatic environments due to the stability and refractory nature of these cyclic structures [Schwendinger and Erdman, 1964; Saliot and Barbier, 1973; Gagosian,

1975; Laureillard and Saliot, 1993; Rontani and Marchand, 2000; Saliot et al., 2001 ; Koch et al., 2003; Lütjohann, 2004; Marchand et al., 2005; Christodoulou et al., 2009; Rontani et al., 2009; Jeanneau et al., 2011; Rontani et al., 2011]; throughout these studies, however, highly oxidized sterols have not been targeted and thus may be largely overlooked by current analytical techniques. Of the SRDOM fractions presented here, the fractions that exhibited smaller hydrodynamic size (fractions 10 and 16) provide evidence for sterols as an explicit and abundant class of molecules present. The data furthermore imply that the sterols are highly oxidized, as might be expected for species that remain in the water column. Processes involving biological activity, photolysis, heat and autoxidation are known to enhance oxidation and subsequent polarity of sterol species [Smith, 1981; Rontani and Marchand,

2000; Lütjohann, 2004; Marchand et al., 2005; Christodoulou et al., 2009; Rontani et al.,

2009; De Fabiani et al., 2010; Rontani et al., 2011] and triols and other highly oxidized sterols have been found throughout plants, corals and natural waters [Smith, 1981;

Christodoulou et al., 2009; Sarma et al., 2009].

170

The diffusion data, 2D NMR data and 3D NMR data coupled with spectral predictions compositely suggest that the sterols present in fraction 16 are varied. It is evident that some

COOH groups are present, but the exact number and substitution pattern cannot be determined absolutely. Considering that this fraction eluted early, the material is likely more hydrophobic than later material and thus likely to contain fewer COOH groups than later material. The diffusion and DIPSI data demonstrate that late-eluting fractions (66 and 104) are larger and/or more highly aggregated; larger structures may include the occurrence of hopanoid-type structures, sterol dimers and/or greater carboxylation. The greater aggregation and/or larger size of the late-eluting material makes NMR interpretations more difficult due to broadening of sample signal and this broadening highlights the challenges associated with analyzing this highly complex material. Taking into consideration, however, that early material provides strong evidence for oxidized sterols and that the general spectral profiles of

1D and 2D spectra from all tested fractions are similar (albeit less resolved), the later material may likely contain a very similar structural backbone (e.g. sterols/hopanoids) but with greater

COOH/OH functionalization to permit greater hydrophilicty. The overall variability across fractions, however, demonstrates not only the effectiveness of the 2D-HILIC/HILIC system for fraction simplification, but also demonstrates that fractionation of this highly complex material provides an easier matrix from which to determine precursor materials [Leenheer and

Rostad, 2004].

5.4.7 Further considerations

While biomolecules are dictated via a genetic code and will experience similar biogeochemical processes throughout the environment, it has been previously speculated that

171

DOM will exhibit similar structural characteristics globally [Hertkorn et al., 2006; Lam et al.,

2007], particularly for the refractory material that likely accounts for the predicted ~6000 year average lifespan of molecules present [Druffel et al., 1992; Benner, 2002]. The NMR evidence presented here demonstrates that oxidized sterols are likely a major component of

DOM but the estimate of quantitative contributions is hindered by a number of factors: a) not all of the material was collected from the first HILIC dimension, b) not all of the fractions collected could be analyzed with extensive NMR analysis, and c) it is difficult to quantitaviely assess just the sterol contributions within NMR spectra. The data, however, provide overwhelming support for these structures as abundantly present in 2D-HILIC/HILIC fractions and the signals are further supported by the preservation and subsequent use of this class of molecules as biomarkers in soils, sediments and waterways [Schwendinger and

Erdman, 1964; Saliot and Barbier, 1973; Gagosian, 1975; Laureillard and Saliot, 1993;

Rontani and Marchand, 2000; Saliot et al., 2001 ; Koch et al., 2003; Lütjohann, 2004;

Marchand et al., 2005; Rontani et al., 2009; Jeanneau et al., 2011; Rontani et al., 2011].

Laureillard and Saliot [1993], for example, examined 17 readily-known sterol structures in

DOM and found them in concentrations of 1.2-1.6 µg/L. If this were expanded to include

100’s to 1000’s of sterol structures (as is evidenced by the extremely crowded regions that are consistent with cyclic sterols in NMR spectra as well as the variability evident from HMBC data), the contribution from sterols could easily account for as much as 1 mg/L, a sizeable contribution to DOM. To verify these structures and further quantify, targeted approaches need to be developed to isolate, identify and calculate the contributions of oxidized sterols to

DOM. NMR here has been critical in demonstrating that a large portion of signals from

DOM can be assigned to sterol-type structures. The key to future research may include

172 reading the sterol fingerprint and relating it back to origin, cycling, aging, in natural waters.

As sterols are often specific to a certain species and/or can be linked to degradation processes

[Christodoulou et al., 2009], sterols likely hold a great wealth of information yet to be fully realized.

5.5 Acknowledgements

The authors thank the Natural Sciences and Engineering Research Council of Canada

(Discovery Grant, A.J.S) for providing funding. A.J.S. would further like to thank the government of Ontario for providing funding in the form of an Early Researcher Award.

173

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(58) Rontani J.-F., Zabeti N. and Wakeham S. G. (2011) Degradation of particulate organic

matter in the equatorial Pacific Ocean: biotic or abiotic? Limnol. Oceanogr. 56, 333-

349.

(59) Saliot A. and Barbier M. (1973) Sterols from sea water. Deep-Sea Res. 20, 1077-1082.

(60) Saliot A., Mejanelle L., Scribe P., Fillaux J., Pepe C., Jabaud A. and Dagaut J. (2001)

Particulate organic carbon, sterols, fatty acids and pigments in the Amazon River

system. Biogeochemistry 53, 79-103.

(61) Sarma N. S., Krishna M. S., Pasha S. G., Rao T. S. P., Venkateswarlu Y. and

Parameswaran P. S. (2009) Marine metabolites: the sterols of soft coral. Chem. Rev.

109, 2803-2828.

(62) Schmitt-Kopplin P., Hertkorn N., Schulten H.-R. and Kettrup A. (1998) Structural

changes in dissolved soil humic acid during photochemical degradation processes

under O2 and N2 atmosphere. Environ. Sci. Technol. 32, 2531-2541.

181

(63) Schwendinger R. B. and Erdman J. G. (1964) Sterols in recent aquatic sediments.

Science, 144, 1575-1576.

(64) Simpson A. J. (2001) Multidimensional solution state NMR of humic substances: a

practical guide and review. Soil Sci. 166, 795-809.

(65) Simpson A. J. (2002) Determining the molecular weight, aggregation, structures and

interactions of natural organic matter using diffusion ordered spectroscopy. Magn.

Reson. Chem. 40, S72-S82.

(66) Simpson A. J., Burdon J., Graham C. L., Hayes M. H. B., Spencer N. and Kingery W.

L. (2001) Interpretation of heteronuclear and multidimensional NMR spectroscopy of

humic substances. Eur. J. Soil Sci. 52, 495-509.

(67) Simpson A. J., Salloum M. J., Kingery W. L. and Hatcher P. G. (2002) Improvements

in the two-dimensional nuclear magnetic resonance spectroscopy of humic substances.

J. Environ. Qual. 31, 388-392.

(68) Simpson A. J., Kingery W. L. and Hatcher P. G. (2003) The identification of plant

derived structures in humic materials using three-dimensional NMR spectroscopy.

Environ. Sci. Technol. 37, 337-342.

(69) Simpson A. J., Lefebvre B., Moser A., Williams A., Larin N., Kvasha M., Kingery W.

L. and Kelleher B. (2004) Identifying residues in natural organic matter through

spectral prediction and pattern matching of 2D NMR datasets. Magn. Reson. Chem.

42, 14-22.

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Magn. Reson. Spectrosc. 58, 97-175.

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comprehensive two-dimensional liquid chromatography. J. Chromatogr. A 1168, 3–43.

(73) Sulzberger B. and Durisch-Kaiser E. (2009) Chemical characterization of dissolved

organic matter (DOM): A prerequisite for understanding UV-induced changes of DOM

absorption properties and bioavailability. Aquat. Sci. 71, 104-126.

(74) Summons R. E., Bradley A. S., Jahnke L. L. and Waldbauer J. R. (2006) Steroids,

triterpenoids and molecular oxygen. Philos. Trans. R. Soc. London, Ser. B 361, 951-

968.

(75) Szentirmai A. (1990) Microbial physiology of sidechain degradation of sterols. J. Ind.

Microbiol. 6, 101-115.

(76) Templier J., Derenne S., Croué J.-P. and Largeau C. (2005) Comparative study of two

fractions of riverine dissolved organic matter using various analytical pyrolytic

methods and a 13C CP/MAS NMR approach. Org. Geochem. 36, 1418-1442.

(77) Wang Y., Lu X. and Xu G. (2008) Development of a comprehensive two-dimensional

hydrophilic interaction chromatography/quadrupole time-of-flight mass spectrometry

system and its application in separation and identification of saponins from Quillaja

saponaria. J. Chromatogr. A 1181, 51-59.

(78) Wilson M. A., Collin P. J., Malcolm R. L., Perdue E. M. and Cresswell P. (1988) Low

molecular weight species in humic and fulvic fractions. Org. Geochem. 12, 7-12.

(79) Woods G. C., Simpson M. J., Koerner P. J., Napoli A. and Simpson A. J. (2011)

HILIC-NMR: toward the identification of individual molecular components in

dissolved organic matter. Environ. Sci. Technol. 45, 3880-3886.

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(80) Wu D., Chen A. and Johnson C. S. (1995) An improved diffusion-ordered

spectroscopy experiment incorporating bipolar-gradient pulses. J. Magn. Reson., Ser.

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(81) Zwenger S. and Basu C. (2008) Plant terpenoids: applications and future potentials.

Biotechnol. Mol. Biol. Rev. 3, 1-7.

184

Chapter 6

Conclusions and future directions

185

6.1 Conclusions and future directions

A variety of NMR techniques and complimentary analytical approaches have been used to characterize DOM from both Arctic and temperate climates. Alterations to Arctic

DOM inputs evidences that the biogeochemical processes have been altered by permafrost disturbance brought about by warming temperatures. The interpretation of data presented here offer evidence that DOM from the High Arctic is impacted by permafrost disturbance and results in pools of labile organic carbon in conjunction with enhanced microbial/primary productivity. To some extent this enhanced productivity may result from the release of previously stored nutrients, a resource that is typically depleted in high Arctic environments

[Dittmar and Kattner, 2003]. Additional studies aimed toward better molecular-level characterization of DOM revealed that the chromatographic separations achievable with

HILIC (1D and 2D) may reduce DOM heterogeneity such that meaningful NMR data is obtained. Following extensive reduction in heterogeneity, a DOM sample from Suwannee

River, Florida revealed strong evidence for cyclic structures with numerous hydroxyl groups attached. Major structural finds within these studies further corroborate the existence of

CRAM and terpenoid-derived materials [Leenheer et al., 2003; Leenheer and Rostad, 2004;

Hertkorn et al., 2006; Lam et al., 2007] as likely constituents present in DOM. Evidence including an abundance of cyclized structures, quaternary carbons on the cyclized structures,

OH groups attached to cyclized structures and associated carboxyl groups generate a picture of sterol/hopanoid-type molecules. Additional interpretation of data from spectral predictions and diffusion NMR experiments revealed that highly oxidized sterol structures were explicitly indicated as major constituents in DOM. The inherently robust nature of these polycyclic

186 structures would additionally corroborate this finding. Future research may take the following points into consideration:

Future Arctic research: more extensive sampling, spatially and temporally, in the

High Arctic as well as studies focused on radiocarbon isotope analyses and

measurement of the bioavailable fraction of DOM

Theoretical approaches: Theoretical approaches need to be taken as to the constituents

likely to be present in conjunction with targeted experimental techniques

Sterol oxidation to identify possible end-members: Further exploration into the biotic

and abiotic degradation of sterols needs to be undertaken so that possible end-

members can be identified

Targeted approaches: Methods need developing that target specific structures based

upon theoretical models and data from oxidation studies

6.2 Future Arctic research

The Cape Bounty fieldwork covered the spring flush for a single season, comparing

DOM from a heavily permafrost-disturbed catchment to a relatively intact catchment. More comprehensive monitoring of both watersheds and other High Arctic systems will provide a better picture of the extent that warming might impact the organic carbon reservoirs. One of the major concerns that need addressing is whether net CO2 is affected by the release of organic carbon and nutrients from disturbed and melting permafrost. If sufficient negative feedback loops are in place (e.g. plankton consumption of CO2), polar environments may have mechanisms by which the elevated release of CO2 does not occur or occurs only in small doses. To predict and model the complexities of these environments, more collection of data

187 is necessary; specifically, regions of disturbance and melting should be compared to intact regions to determine the impact of these disturbances. Permafrost melting and detachments also need careful monitoring to determine the rate of permafrost loss. Radiocarbon isotope analyses of organic matter from soils and water may further provide insight into the age and diagentic state of constituents present in the Arctic. Coupled with lab incubation studies to determine the bioavailability of DOM released, radiocarbon isotope analysis may provide insight into whether younger material is simply recycled or whether old carbon, preserved in permafrost, is preferentially used by microbes. Such analyses on distinct fractions and/or biomarkers of DOM may be used to target questions such as the source of the material being consumed, age of labile fractions, etc.

6.3 Theoretical approaches

Within the various propositions of how DOM is formed and which compounds are eventually incorporated into the refractory backbone, there is likely some wisdom in some or all of the proposed evolutions of these molecules. The most chemically robust to biotic and abiotic aggressors will certainly persist longer within the environment as will structures that have been altered from DNA-dictated molecules into structures that are favorably preserved.

In the process of analyzing this complex and largely uncharacterized material, examining the laws of thermodynamics as well as kinetics may help guide the analytical research toward compounds that a) are likely to originate from biomolecules, c) are likely to be found in an aqueous environment and b) are likely to persist in the environment.

While scientific progress has historically occurred via both experimental and theoretical studies, the creation of new hypotheses is a critical measure [Saxena and

188

Hildemann, 1996]. Theoretical approaches are likely essential to further solving the vast array of unknown structural components in DOM. Considerations such as knowledge of biomolecules released into the environment, degradation processes, types of structures that are inherently robust, and solubility in aqueous solutions are all factors that need to be taken into account. Solubility in water, for example, is dependent on the number of carbon atoms, functional group content and spatial orientation. A multitude of slightly to highly soluble organics and inorganics are expected to be present and associated into aggregates and this multicomponent system further causes the solubility of individual components to differ from free components. More hydrophobic constituents are expected to have greater tendencies of aggregation and the supramolecular nature of these aggregates may to some extent provide hydrophobic regions that inherently limit bacterial and abiotic processes due to physical isolation [Hedges et al., 2000]. Examination of chemical properties in the context of complex systems is perhaps a much-needed avenue to be explored with theoretical approaches.

Significant development in theoretical modeling of such heterogeneous microsystems in addition to improved spectral predictions for complex molecular environments may greatly enhance our understanding of DOM and subsequently advance this field of study. Such theoretical analyses may provide better guidance for future experimental design and greatly impact the difficult task of interpreting these complex molecular signatures.

6.4 Sterol oxidation to identify possible end-members

With strong evidence presented here for sterol- and possible hopanoid-type structures that appear to be and theoretically are expected to be quite oxidized, targeted approaches need additional development. One approach may be to undertake extensive laboratory analyses on

189 sterol and hopanoid structures. Specific goals would include the controlled degradation of these structures in a laboratory setting with specific biotic and abiotic influences. When examining the major degradation products arising from further laboratory studies, researchers might take into consideration that some degradation products have known standards but that highly altered species may require more in-depth identification. Identification of such species is best undertaken via use of FT-ICR-MS and NMR. Once identified, methods might then be developed such as derivatization followed by analysis with GC-MS such that the identified species could subsequently be identified with more routine laboratory techniques. Once plausible end-members have been recognized and appropriate verification techniques have been established, these products might then be targeted within environmental samples.

Identification of major end-members will distinguish the most likely end-members in the environment and may subsequently be probed within the more complex matrix (DOM).

6.5 Targeted approaches

Methods that target specific structures based upon theoretical models and data from oxidation studies need to be developed. Following the oxidation of sterols via a variety of biotic and abiotic pathways will reveal information on the end products as well as specific processes that are responsible for said products. To some extent this is already being used with biomarker analysis of sterols in seawater. It is known, for example, that free radical oxidation may add hydroperoxy groups that are then reduced to alcohol groups on the C7 position of Δ5 sterols. Singlet oxygen-mediated photoprocesses in turn are characterized by a rearrangement of the double bond, addition of hydroperoxy groups to either C5 or C6 and further reduction of the hydroperoxy groups to alcohol groups [Marchand et al., 2005;

190

Rontani et al., 2007; Christodoulou et al., 2009]. Studies have provided evidence that aerobic bacterial hydrogenation leads to removal of the double bond or the rearrangement of the double bond in conjunction with conversion of the alcohol group at C3 to a ketone with Δ5 sterols [Christodoulou et al., 2009; Rontani et al., 2011]. Hence sterols have already found good use with researchers as stable biomarkers that enable tracing of parent material, food chains and diagenetic transformations.

What may be of future interest is to probe a wider variety of sterol structures in addition to hopanoids as well as to subject structures to prolonged degradation experiments in an attempt to mimic prolonged and harsh conditions in the environment. Upon having a wider variety of biomarkers to identify or “molecular signatures,” much more information may become available as to source and diagenetic processes of DOM. Such signatures may be used to gain insight into natural history, environmental processes, movement of water masses and trends in carbon cycling.

The more that these structures are verified, variations identified and other major constituents discovered, the more of the refractory backbone of DOM that may be understood.

These further studies may enhance our understanding of this highly complex substance and ultimately help researchers better understand global carbon cycling.

191

6.6 References

(1) Christodoulou S., Marty J.-C., Miquel J.-C., Volkman J. K. and Rontani J.-F. (2009) Us

of lipids and their degradation products as biomarkers for carbon cycling in the

northwestern Mediterranean Sea. Mar. Chem. 113, 25-40.

(2) Dittmar T. and Kattner G. (2003) The biogeochemistry of the river and shelf ecosystem

of the Arctic Ocean: a review. Mar. Chem. 83, 103-120.

(3) Hedges J. I., Eglinton G., Hatcher P. G., Kirchman D. L., Arnosti C., Derenne S.,

Evershed R. P., Kögel-Knabner I., de Leeuw J. W., Littke R., Michaelis W. and

Rullkötter J. (2000) The molecularly-uncharacterized component of nonliving organic

matter in natural environments. Org. Geochem. 31, 945-958

(4) Hertkorn N., Benner R., Frommberger M., Schmitt-Kopplin P., Witt M., Kaiser K.,

Kettrup A. and Hedges J. I. (2006) Characterization of a major refractory component of

marine dissolved organic matter. Geochim. Cosmochim. Acta 70, 2990-3010.

(5) Lam B., Baer A., Alaee M., Lefebvre B., Moser A., Williams A. and Simpson A. J.

(2007) Major structural components in freshwater dissolved organic matter. Environ.

Sci. Technol. 41, 8240-8247.

(6) Leenheer J. A. and Rostad C.E. (2004) Tannins and terpenoids as major precursors of

Suwannee River fulvic acid. U.S. Geological Survey Scientific Investigations Report

2004-5276, 16 p.

(7) Leenheer J. A., Nanny M. A. and McIntyre C. (2003) Terpenoids as major precursors

of dissolved organic matter in landfill leachates, surface water, and groundwater.

Environ. Sci. Technol. 37, 2323-2331.

192

(8) Marchand D., Marty J.-C., Miquel J.-C. and Rontani J.-F. (2005) Lipids and their

oxidation products as biomarkers for carbon cycling in the northwestern Mediterranean

Sea: results from a sediment trap study. Mar. Chem. 95, 129-147.

(9) Rontani J.-F., Jameson I., Christodoulou S. and Volkman J. K. (2007) Free radical

oxidation (autoxidation) of alkenones and other lipids in cells of Emiliania huxleyi.

Phytochemistry 68, 913-924.

(10) Rontani J.-F., Zabeti N. and Wakeham S. G. (2011) Degradation of particulate organic matter in the equatorial Pacific Ocean: Biotic or abiotic? Limnol. Oceanogr. 56, 333- 349. (11) Saxena P. and Hildemann L. M. (1996) Water-soluble organics in atmospheric

particles: a critical review of the literature and application of thermodynamics to

identify candidate compounds. J. Atmos. Chem. 24, 57-109.

193

Appendix

Supplementary information for Chapter 4

194

A.1 HILIC sample preparation and separation

SRDOM was prepared at 50mg/ml in HPLC-grade water with the addition of NaOH to 0.3M to promote disaggregation and the addition of 1% DMSO, experimentally found to improve separations. Samples were 0.45µm syringe filtered and 50µl was injected each run.

Acetonitrile is the most commonly used organic solvent for HILIC as many other common

HPLC solvents are polar protic, more actively compete for polar active sites and subsequently disrupt the water-layer [Hao et al., 2008]. Gradient mobile phase separation with acetonitrile/water was moreover found to work best for DOM. Separations of SRDOM were achieved with a complex gradient system, accomplished by first running a simple gradient from 97 to 50% acetonitrile with aqueous buffer (continuous 5mM ammonium acetate in

HPLC-grade water). The gradient was adjusted by shortening or lengthening segments until large sample peaks were spread into roughly continuous elution. Table A.1 demonstrates the final mobile phase composition with time. Fractions were collected every 2.5 minutes at 0.3 ml/min (i.e. every 0.75ml) over the duration of 220 minutes with a column temperature of 50º

Celsius. Following collection, fractions were dried under N2 to eliminate most acetonitrile.

Remaining acetonitrile was further diluted by increasing the volume 5 fold or greater with

HPLC-grade water and fractions were then lyophilized.

Pressure was monitored throughout the 60 HPLC runs and not found to change suggesting that build-up of material within the pre-filter or column did not occur. This assumption was further verified by assessment of the quantitative recovery of material.

Triplicate injections of the sample were conducted with the HILIC column connected as well as triplicate with no column present. Injected samples were individually collected, freeze

195 dried and an analytical balance (Shimadzu model AW120) was used to obtain the mass of both sets of samples. The mean percent recovered was 102% with a standard error of 3%.

A.2 Methods for additional 2D NMR analyses

Heteronuclear single quantum coherence (HSQC) was implemented using echo/antiecho- gradient selection with 2560 scans, 1k data points in the F2 dimension and 256 increments in the F1. The 1J 1H-13C value was set to 145 Hz and the relaxation delay was 0.6 s. Total correlation spectroscopy (TOCSY) was performed with Time-Proportional Phase

Incrementation (TPPI), 640 scans, 2048 points in the F2 dimension and 400 increments in the

F1 dimension and a mixing time of 103 ms. Both HSQC and TOCSY experiments were processed using sine-squared functions with a 90° phase shift as well as a zero-filling factor of 2.

A.3 Fluorescence instrument corrections and PARAFAC methods

Instrument corrections were accounted for with all excitation-emission matrices as outlined in previous studies [McKnight et al., 2001; Stedmon et al., 2003]. Lamp spectral fluctuations are accounted for on the Agilent fluorescence detector (G1321A) with a reference diode that corrects for intensity drift and with a quartz diffuser to reduce light. The detector also normalizes data that would otherwise vary due to the wavelength-dependent output. The built-in corrections provided by the instrument were verified running a solution of rhodamine

B [Karstens and Kobs, 1980]. Further instrument bias was avoided by subtracting daily water blanks. For each blank-subtracted sample, intensities were normalized to the area under the water Raman peak [Lawaetz and Stedmon, 2009]. With the Agilent detector, inner filter

196 effects are negligible at environmentally-relevant sample concentrations owing to a very narrow cuvette (0.5mm). This assumption was checked by adjusting for inner filter effects as illustrated in Tucker et al. [1992]. All fractions were found to be nearly void of such effects

(adjustment factors ranged from 1.0-1.1) but were nevertheless adjusted. The Raman normalization and instrument corrections result in spectra that are in Raman units. All samples were run in duplicate and found to be reproducible within a standard error of <3.0%.

Absorbance measurements were taken to verify the absence of the inner-filter effect on a

Unicam (UV 2-200) Spectrometer in triplicate; all samples were found to be <1% standard error.

PARAFAC analyses were run in MATLAB 7.11 according to the procedures outlined in Appendix 1 of Stedmon and Bro [2008]. The model was run with one outlier removed and with nonnegativity constraints in each dimension. The model was run with random initialization to verify the accuracy of each rank (i.e. number of components applied to the model). Verification of the correct number of components was accomplished using split-half and residual analyses as well as examination of loadings to ensure that the final spectra looked like plausible excitation and emission spectra [Stedmon and Bro, 2008]. To aid in verifying chemical identities of possible fluorophores, a HILIC fraction was spiked with various standards, independently and at low concentration (0.1-0.5 mg/L). New EEMs were collected, the model was rerun with the original data plus a spiked sample, and finally the spiked sample was compared to the original sample. If the model has generated the right number of components and a prominent fluorophore is present then if a small amount of this fluorophore is added to a sample, the model should have the same loadings (i.e. fluorescence spectra) but vary in scores (i.e. concentration) in comparison to the unspiked sample.

197

A.4 1D 1H solution-state NMR: structural assignments

Figure 4.2.C in the main text of Chapter 4 illustrates the variety of alpha hydroxy acids that are readily identifiable with 1D 1H NMR. These assignments are based on pattern matching to NMR databases (AMIX, version 3.8.7, including Bruker Biofluid Reference

Compound Database, version 2-0-0 through version 2-0-3, Bruker BioSpin) and have been confirmed by previously published NMR assignments of humic substances [Wilson et al.

1988; Herzog et al., 1997]. The authors also here identify glycolic acid at ~3.9 ppm [Males and Herring, 1999; Kählig et al., 2009]. Glycolic acid is a main product excreted from algae

[Berman and Holm-Hansen, 1974], and has been shown to be released with the oxidation of

DOM [Kulovaara, 1996].

Evidence for the assignment of the lignin peak at ~6.5 ppm is provided by the analysis of 2 lignin samples and three dissolved organic matter (DOM) samples that were analyzed via

1D 1H NMR (Figure A.1). The peak at 6.5 ppm is prominent in lignin samples and also appears in terrestrially-derived DOM samples. The lignin samples, Kraft lignin and

Organosolv lignin, were obtained from Aldrich. Two of the DOM samples were purchased from the International Humic Substances Society (IHSS): Suwannee River DOM (SRDOM) and Nordic Reservoir DOM (NRDOM). Details about the collection of these samples can be found on the IHSS website. A sample was isolated from Lynde Shores Conservation Area, a productive marshland adjacent to Lake Ontario (LSDOM). Details on the collection of the

LSDOM sample can be found in Woods et al. [2010]. All samples were prepared in D2O with

1% NaOD and analyzed on a Bruker AvanceTM 500 MHz spectrometer at 298K. All NMR spectra were externally referenced to DSS (4,4-dimethyl-4-silapentane-1-sulfonic acid).

198

With terrestrially-derived DOM samples, there is frequently a triangular-shaped peak at the upfield region of the aromatics at roughly 6.5 ppm. This peak mimics a prominent peak in lignin samples and Figure A.1 illustrates how well the apexes of these peaks align. Lignin is a significant precursor material for terrestrial DOM, lignin-derived phenols are found in varying quantities throughout DOM samples and lignin phenols are frequently used in techniques to trace sample origins and/or degradation processes of DOM. As the lignin aromatic peak aligns with the triangular-shaped peak in multiple DOM samples from various terrestrial origins, the evidence suggests that this peak largely arises from lignin and/or degraded lignin products in DOM.

A.5 2D 1H solution-state NMR: detail and structural assignments

Figure A.2 illustrates the level of NMR detail provided by a HILIC-simplified fraction vs. the bulk SRDOM sample utilizing the COSY45 experiment. After fractionation, considerably more cross-peaks are observed, including those very close to the diagonal (see zoomed regions, Figure A.2.C and A.2.D). The correlations near the diagonal and within the

2-2.3 ppm range are not apparent in the unfractionated sample but are prominent within the fractionated spectrum as are a number of prominent cross-peaks at a distance from the diagonal. These cross-peaks enable identification of structures using multidimensional NMR and structural assignments were subsequently made utilizing a variety of 2D experiments.

Assignments were only accepted after verification by COSY, TOCSY (where available),

HSQC and DEPT phase-edited HSQC. Chemical shifts from the databases were correlated against the DOM resonances for all nuclei in each spectrum and strong correlations existed for all assigned structures (R2 > 0.99).

199

Assignments of possible structures from 2D experiments were first generated with data from the COSY45 experiments run on HILIC fraction 9 (H09: refer to Figure 4.3 in

Chapter 4). All assignments were generated in AMIX (version 3.8.7, Bruker BioSpin) using the Bruker Biofluid Reference Compound Database (version 2-0-0 through version 2-0-3,

Bruker BioSpin) to match peaks in the 2D spectrum with database structures generated from standards run in known solutions and known pH. Figure A.3.A, for example, illustrates the

COSY45 assignment of 2-hydroxyglutaric acid. Matches were considered if all peaks in a proposed structure were present in the H09 spectrum. Assignments were further analyzed for how well the proposed database structure correlated to the actual DOM spectrum; proton resonances from the database vs. actual resonances were correlated for all nuclei and subsequent R2-values were > 0.99 for all assigned structures.

Structures consistent with COSY45 were then verified using a TOCSY of H09.

Figure A.3.B illustrates that database proton resonances were matched to actual peaks in the

TOSCY spectrum in the same manner as the COSY45. All final assignments confirmed with

TOCSY spectra were correlated within R2 > 0.99. (Note: In some case TOCSY spectra were not available for structures in the databases).

HSQC experiments were further used to confirm the presence of the proposed structures in H09. A normal HSQC was used to confirm if database structures were present and a DEPT phase-edited HSQC was further used to distinguish between multiplicities where cross-peaks with CH and CH3 signals are positive and CH2 signals are negative. The DEPT phase-edited HSQC adds multiplicity information as another constraint that must be fulfilled for assignment of molecular fragments in the DOM mixture. Figures A.3.C and A.3.D illustrate how the two HSQC experiments were used. Matches were only accepted for

200 database structures that fell within the regions of DOM signal in the normal HSQC (Figure

A.3.C) and the edited spectrum confirmed or refuted whether expected multiplicities were present (Figure A.3.D). All final assignments were confirmed in the HSQC spectra and are indicated in Figure 4.3 in Chapter 4 as well as in Table A.2. Table A.2 further provides literature references where evidence has previously been provided for the presence of these structures within humic substances. Two additional assignments (4-methylphenol and resonances from double bonds in cinnamic acid-derived moieties) were made based upon previous NMR predictions of structures present in lignin [Simpson et al., 2004].

A.6 Discussion of PARAFAC components

The HILIC fractions were found to generate a PARAFAC model with 7 components present meaning that the fractions are largely characterized by 7 excitation/emission spectra that represent single fluorophores, groups of similar fluorophores, or averaged phenomenon resulting from interactions such as quenching or charge-transfer processes [Stedmon and Bro,

2008]. Components from PARAFAC analyses of DOM have previously been proposed to resemble the fluorescence of tryptophan, tyrosine, quinones, and compounds unique to DOM

[Cory and McKnight, 2005; Fellman et al., 2009]. To aid in indentifying possible fluorophores, a HILIC fraction was spiked with varying standards, modeled with the rest of the data and compared to the original, unspiked fraction. Two components were found to increase with the addition of small quantities of tyrosine and tryptophan (Figure A.4-TYR and

TRP1). Tryptophan was additionally found to affect a second component (Figure A.4-TRP2) and is further discussed below. Quinones, believed to be important electron shuttles and active fluorescing components in DOM [Cory and McKnight, 2005; Fellman et al., 2009],

201 were also individually spiked into the HILIC fraction and resulted in more complex results.

Model quinones anthraquinone-2,6-disulfonate (AQDS), Lawsone, and Juglone were found to affect three of the PARAFAC components (Figure A.4-Q1,Q2,Q3). The smaller p- benzoquinone was found to affect Q2 to some extent but largely affected a unique component

(Figure A.4-Qb). The role that quinones, as good electron acceptors, play in DOM is likely to be complicated. Previous research has provided evidence that energy transfer plays a dominant role in the photochemistry of humic substances and such results have led to the suggestion that charge-transfer interactions between hydroxy-aromatic donors and quinoid or other acceptors are common in DOM and other humic substances [Del Vecchio and Blough,

2004; Boyle et al., 2009]. With the preliminary spiking of samples here, quinones are not attempted to be positively identified but rather components are referred to as quinone- influenced PARAFAC components.

In analyzing why tryptophan-spiking influenced two PARAFAC components, TRP1 and TRP2, the absorption anisotropy of tryptophan is examined. The chemical structure in

1 1 Figure A.4, column II illustrates the two electronic transitions, La and Lb states, that

1 1 contribute to tryptophan fluorescence. Compared to Lb, the La state is known to absorb at longer wavelengths and is believed to be largely responsible for tryptophan fluorescence in

1 most environments. The Lb state has only been found to emit when the local environment is nonpolar [Lakowicz, 2006]. Figure A.4 illustrates that component TRP1 and TRP2 have similar excitation and emission maxima but that TRP1 has an excitation profile that extends to longer wavelengths. Correlations indicate that TRP1 is weakly correlated with polarity while

TRP2 has a weak negative correlation with polarity. (The R2-values are low but nevertheless significant; p<0.0005 for both). Evidence would suggest that TRP1 and TRP2 are the result

202

1 of the anisotropy of tryptophan fluorescence; TRP1 is the result of fluorescence from the La

1 state while TRP2 is from the Lb state due to association with hydrophobic constituents.

The correlation data provided in Figure A.4 illustrate how the quinone-influenced components Q1 and Q3 were found associated with the more hydrophobic material while tyrosine (TYR) was more prominent in hydrophilic fractions. TRP1 was weakly correlated with polarity; Q2 and TRP2 were weakly negatively correlated with polarity and component

Qb was found consistently throughout all fractions. The overall trend from the various components would suggest that quinone-influenced fluorescence is associated with hydrophobic constituents while amino acid fluorescence is more prominent in polar fractions.

203

9 8 7 6 5 4 3 2 ppm

Chemical Shift

Figure A.1. 1D 1H NMR spectra of A) Kraft lignin, B) Organosolv lignin, C) SRDOM, D) LSDOM, E) NRDOM. Dashed lines indicate the alignment of the proposed peak from lignin found in DOM.

204

A) B) 1 1

2 2

3 3

4 4

4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5

C) 1.5 D) 1.5 F1 chemical (ppm) chemical shift F1 2.0 2.0

2.5 2.5

2.30 2.25 2.20 2.15 2.10 2.05 2.00 1.95 2.30 2.25 2.20 2.15 2.10 2.05 2.00 1.95

F2 chemical shift (ppm)

Figure A.2. 2D COSY45 NMR spectra of A) SRDOM, B) HILIC fraction 9 (H09), and the zoomed regions for C) SRDOM, D) H09. All spectra acquired at the same concentration and conditions and plotted just under the noise floor.

205

ppm ppmppmppm ppmppmppmppm ppm A) B)

1.001.00 1.001.00

25.0025.00 25.0025.00

2.002.00 2.002.00

50.0050.00 50.0050.00

3.003.00 3.003.00

75.0075.00 75.0075.00

4.004.00 4.004.00

ppmppm 4.00 3.004.004.004.00 3.003.003.00 2.00 2.002.002.00 1.001.001.004.00 ppmppmppm 3.004.004.004.00 3.003.003.00 2.00 2.002.002.00 1.001.001.00 ppm ppmppm C) D) OH O 8 1 3 5 O OH 2 4 16

OH chemical F1 shift 25.0025.00 24

3 32

48 50.0050.00 2

56 F1 Chemical F1 ShiftChemical (ppm)

72 75.0075.00

4 80

4.0 3.5 3.0 2.5 2.0 1.5 1.0 F2 Chemical Shift (ppm)

4.004.004.00 3.003.003.00 2.002.002.00 1.001.00 ppmppm F2 chemical shift

Figure A.3. Assignment of 2-hydroxyglutaric acid using 2D NMR spectra of HILIC fraction 9 (H09): A) COSY45, all database protons match peaks; B) TOCSY, all database protons match peaks; C) HSQC, all CH-multiplicities match signals from DOM; D) DEPT phase- edited HSQC plotted at very low threshold so that signal from 2-hydroxyglutaric acid are

clearly displayed. Green = signals from CH2, Red = signals from CH3 and CH. Black are the chemical shifts of 2-hydroxyglutaric acid from the reference database. (Note: the CH2 at position 3 is split into two different signals as the CH group at position 4 is rigid due to hydrogen bonding between the OH at 4 and the COOH at 5; as such the CH at 4 splits the

CH2 at 3 into two separate resonances.)

206

I II III

0.18 0.30 550 0.20 0.16 R2=0.61(-) Q1 TYR 0.16 0.14 %=26.0 0.20 0.12

450 0.1 0.10 2 0.08 0.10 R =0.55 0.08 %=22.5 0.06 350 0.04 0 0 Measured 0.02 R2=0.26 0.30 1 0 0.00 0.20 (-) Q2 L a TRP1 250 350 450 %=8.6 0.20 550 0.18 0.10 R2=0.25 0.160.16 0.10 0.14

%=13.4 0.12 450 0 0 0.1 0.080.08 0.20 2 1 0.06 R =0.59(-) Q3 0.20 L b TRP2 % =9.2 350 Modeled 0.04

0.02

Emission (nm) (nm) Emission Emission

Spectral Loading Loading Spectral Spectral

Spectral Loading Loading Spectral Spectral 0 0.10 2 0.00 0.10 R =0.21(-) 250 350 450 %=8.2

550 8 0 0 0.008 300 400 500 6 Qb 4 0.20 Wavelength (nm) 450 2 0 2 0.000 R =0.04 HO -2 0.10 %=12.2 1 -4 L 350 -6 b O Residual -8

-3 x 10-0.008 0 N NH2 300 400 500 H 250 350 450 1L Tryptophan Wavelength (nm) a Excitation (nm)

Figure A.4. Column I and II: PARAFAC component loadings; pink lines indicate excitation; blue lines indicate emission; chemical structure demonstrates the absorption anisotropy of tryptophan; R2-values correlate percent contribution with fraction number (i.e. polarity); (-) indicates a negative correlation; correlations have a significance of p<0.005 except Qb (p=0.099); (%) indicates the average percent contribution to total fluorescence. Column III: example of measured, modeled and residual EEMs for fraction #20. Intensities are in Raman units.

207

Table A.1. Mobile phase composition for HILIC separations.

elution volume (ml) % H2O % ACN % CH3COONH4 0.0 2.5 97.0 0.5 3.0 2.5 97.0 0.5 3.6 9.5 90.0 0.5 6.0 20.5 79.0 0.5 7.5 20.5 79.0 0.5 10.5 20.8 78.7 0.5 11.4 21.2 78.3 0.5 11.7 21.5 78.0 0.5 12.3 23.4 76.1 0.5 12.9 24.2 75.3 0.5 15.0 24.6 74.9 0.5 22.5 27.5 72.0 0.5 24.0 29.7 69.8 0.5 29.7 30.3 69.2 0.5 32.3 32.0 67.5 0.5 32.7 33.1 66.4 0.5 35.4 35.4 64.1 0.5 36.6 39.5 60.0 0.5 42.0 46.4 53.1 0.5 43.5 46.4 53.1 0.5 53.1 46.5 53.0 0.5 54.0 49.5 50.0 0.5 54.6 99.5 0.0 0.5 57.0 99.5 0.0 0.5 57.6 2.5 97.0 0.5 66.0 2.5 97.0 0.5

208

Table A.2. Structural assignments in HILIC fraction H09 provided by multidimensional NMR experiments along with references that provide evidence for these compounds in humic substances.

Structural Assignments References 1,2-Propanediol [Templier et al., 2005] 1,3-Propanediol [Bories et al., 2005] 2-Hydroxybutyric Acid [Fischer et al., 1994] 2-Hydroxyglutaric Acid [Graham et al., 2002; Claeys et al, 2004] 2-Methylglutaric Acid [Lehtonen, 2005; Kawamura et al., 2010] 2-Methylsuccinic Acid [Graham et al., 2002; Lehtonen, 2005; Templier et al., 2005; Kawamura et al., 2010] 3-Hydroxypropionic Acid 3-Methylglutaric acid 4-Hydroxybenzoic Acid [Graham et al., 2002; Lehtonen, 2005; Templier et al., 2005; Carletti et al., 2009] 4-Hydroxybutyric Acid 4-Methylphenol [Gadel and Bruchet, 1987; Joly et al., 2000; Simpson et al., 2004; Templier et al., 2005] Acetic Acid [Wilson et al., 1988; Fischer et al., 1994; Herzog et al., 1997; Bertilsson and Tranvik, 2000; Bories et al., 2005; Templier et al., 2005] Butyric Acid [Bertilsson and Tranvik, 2000; Bories et al., 2005] Cinnamic Acid moieties (double bond) [Simpson et al., 2004; Templier et al., 2005; Louchouarn e al., 2010] Fumaric Acid [Graham et al., 2002; Lehtonen, 2005; Kawamura et al., 2010] Glutaric Acid [Graham et al., 2002; Kawamura et al., 2010] Glyceric Acid [Fischer et al., 1994; Graham et al., 2002] Glycolic Acid [Fischer et al., 1994; Graham et al., 2002] Isobutyric Acid Lactic Acid [Fischer et al., 1994; Bertilsson and Tranvik, 2000; Graham et al., 2002; Bories et al., 2005] Levulinic Acid [Lehtonen, 2005; Templier et al., 2005] Malic Acid [Fischer et al., 1994; Graham et al., 2002; Claeys et al, 2004; Bories et al., 2005; Kawamura et al., 2010] Methanol [Wilson et al., 1988] Propionic Acid [Wilson et al., 1988; Fischer et al., 1994; Bories et al., 2005] Quinic Acid [Guggenberger et al., 1989] Succinic Acid [Wilson et al., 1988; Fischer et al., 1994; Bertilsson and Tranvik, 2000; Graham et al., 2002; Lehtonen, 2005; Templier et al., 2005; Kawamura et al., 2010] Tricarballylic Acid [Graham et al., 2002; Lehtonen, 2005]

209

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