Florida State University Libraries

Electronic Theses, Treatises and Dissertations The Graduate School

2011

Novel Ionization Methods forCharacterization of Natural Organic Matterby Fourier Transform Ion CyclotronResonance Mass SpectrometryDavid Christopher Podgorski

Follow this and additional works at the FSU Digital Library. For more information, please contact lib-ir@fsu.edu

THE FLORIDA STATE UNIVERSITY

COLLEGE OF ARTS AND SCIENCES

NOVEL IONIZATION METHODS FOR CHARACTERIZATION OF NATURAL

ORGANIC MATTER BY FOURIER TRANSFORM ION CYCLOTRON

RESONANCE MASS SPECTROMETRY

By

DAVID CHRISTOPHER PODGORSKI

A Dissertation submitted to the

Department of Chemistry and Biochemistry in partial fulfillment of the

requirements for the degree of Doctor of Philosophy

Degree Awarded:

Fall Semester, 2011

Copyright © 2011

David Christopher Podgorski All Rights Reserved

ii

David C. Podgorski defended this dissertation on August 11, 2011.

The members of the supervisory committee were:

William T. Cooper

Professor Directing Dissertation

Markus Huettel University Representative

Naresh Dalal

Committee Member

John G. Dorsey Committee Member

Alan G. Marshall

Committee Member

The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with university requirements.

iii

To my family,

and

To my son,

Ezekiel Isaac Podgorski

whose unconditional love is my source of inspiration to strive for excellence.

iv

ACKNOWLEDGEMENTS

First and foremost, I thank Dr. William T. Cooper. He was

enthusiastic about me joining his group from the first day we met. Dr.

Cooper made me feel wanted when no one else seemed to want a student

from a small liberal arts university with an average G.P.A. and minimal

research experience. Furthermore, Dr. Cooper brought me in early and

immediately placed me on projects which emphasized his confidence in

my abilities. I was provided opportunities to travel to several different

countries and many conferences. There is no doubt that he and I had our

highs and lows and the occasional, rather, frequent falling out. Even

after our worst moments he came in the next morning with a positive

attitude and as if nothing happened when other advisors would have

asked me to leave their group. I have the utmost respect for him and am

grateful for all I was given.

I am most grateful to Dr. Alan Marshall for his willingness to

essentially invite me to become part of his group (unofficially). I will never

forget when he listed the grades on the board the day after our first mass

spectrometry test. If the grades were written in proportional intervals

from the top of the board, mine would have been under the building.

That was probably my all time low of graduate school. Yet, he never gave

up on me. He always made time to meet with me several times a week to

answer questions and with his help I survived. Even after I proved to him

how much I lacked in book smarts, he invited me to come out to the

Magnet Lab and find someone in his group to learn how to obtain quality

data. He invited me to come to subgroup meetings, group meetings at his

house, paid my way to conferences, and even provided support for my

last two semesters (unofficially). Dr. Cooper took the chance with me by

planting the seed, and Dr. Marshall provided the fertilizer, water, and

sunlight. Without either of them I would not be who I am today. Thank

v

you, Dr. Marshall. I think the phrase I will never forget for the rest of my

life was sent to me by Dr. Marshall in an email. It said, “You need to

become intament with one of the members in my group”, referring to his

desire to have me shadow someone in his group to learn FT-ICR MS.

This brings me to Amy McKenna. Amy became my intament FTMS

partner. The only word I can use to describe Amy when I first started

coming to the Magnet Lab regularly is…….. I’ll chose not to use that

word, but she was not kind. A “dirt sprayer” invaded her world and there

was nothing she could do about it because I was sent by the boss. After a

month or so of sitting next to her day after day without a word spoken

between us, I think she realized that I was not going anywhere and that

maybe if she started talking to me about the instrument and all of the

different setting, etc. that maybe I would leave. Unfortunately for her, I

would nod my head and pretended like I had the slightest clue what she

was talking about, although I didn’t, and stuck around. Eventually over

time, I gathered the courage to ask questions and began to learn. Now, I

owe almost everything I know about FTMS, operation of the mass

spectrometer and APPI to Amy McKenna. I even think she is glad that

she gave me a chance, although the she was reluctant at best. Amy even

trusts me with her favorite baby and it’s not Charleigh, Sammy or Joey,

it’s her APPI source. Through everything Dr. McKenna and I are now

great friends. Our families spend time together and we talk trash about

hockey. Amy thanks for giving this “dirt sprayer” a shot and for teaching

me all that I know.

Thanks to all of the members of the Cooper group. Rasha, I always

enjoy you tough questions when one of us presents at group meeting.

Malak, I will always be angry and jealous that you give such phenomenal

presentations and English is not even your native language. O, keep

asking Dr. Cooper for money. Those are always fun conversations to

hear. You will get it one day, just stay persistent. To the Pollack at USF,

when is the next FAME Meeting? Dan, are you almost finished with that

vi

dissertation of Warchant? Alli, thank you for all of your support and

always being there for me. I would not have made it without you.

I thank Ryan Rodgers for his eventual acceptance of me. Ryan took

a little longer to come around than Amy, but has been a huge advocate

for me. Ryan, thank you for your help and support.

I have never met anyone who made me think as hard a Chris

Hendrickson. The thing that is most impressive about Chris is that he

makes you think without being in direct contact with him. I am going to

have a rubber bracelet made that reads, “WWCA”. What will Chris ask?

Before you even think you have something worthwhile to show Chris he

forces you to ask yourself questions that you normally would not think

about asking. Then once you think you have everything figured out and

can answer any questions he may ask, you show him the data and ten

questions come that bring you right back to Earth. My favorite thing

about Chris is that he shows now favoritism. Chris drills me the same

way he does, Amy, Ryan, or even Dr. Marshall. Thank you Chris for

making me a better analytical chemist.

John Quinn, for all of your patience and assistance over the years

and for teaching me the workings of a true instrumentation lab. There

was never a time where John said, I’m too busy to help you with your

(trivial) task, although I know he had 100 more important things to do.

John you have my utmost appreciation and gratitude. The same is true

for Nate Kaiser and Josh Savory. Thank you both for your time and help

when I was confused and frustrated

I would also like to thank the entire Marshall Research group for

accepting me as a part of your family. You could have made things a lot

more difficult for me; rather, you accepted me into your family and often

went out of your way to help when it was not necessary.

I thank Dr.s Brooks, Eddins and Totten for all that you did for me

at Withrow University. All of you do a phenomenal job with the little you

are provided from the university. I may have not had experience on

vii

advanced instrumentation, but I honestly can say that I could not have

been more prepared for what to expect in graduate school. You took an

ex-football jock that was very rough around the edges and polished him

into a research diamond. I thought that at best, I would finish school

with a B.S. and that would be a huge accomplishment. I never thought

that I could go on to earn a Ph.D. You provided me with the knowledge,

skills and confidence that have allowed me to succeed in graduate school

I would also like to thank my family. Mom, words cannot even

describe how much I love you. Without your unconditional love and

support who knows where I would be. You were always there for me and

never turned your back on me no matter how much I deserved it. You

made countless sacrifices that I am still realizing as an adult. You have

always been there to help me with Zeke and you are honestly the best

parent a child could ever hope to have. I hope that I can be half the

parent to Zeke and my children in the future as you have been to me.

Finally, Zeke. Zeke I hope you read this one day and realize that

you are what made me get out of bed some mornings. There were times

in this whole process when I wanted to give up, when the hours of work,

multiple jobs, and stress had worn me down to the breaking point. You

are the reason I started this road to earn a Ph.D. and you are the reason

why I finished it. I hope that you realize one day that all of the nights

when you did not see me, the mornings I was loading trucks at UPS, or

the weekends at the Magnet Lab were for our future. You are my

motivation in life and no matter how bad my day is at work or how tired I

am, you always bring a smile to my face. I love you Zeke and I will always

be there for you.

viii

TABLE OF CONTENTS

LIST OF TABLES ............................................................................ XII LIST OF FIGURES..........................................................................XIII ABSTRACT ................................................................................. XVIII

CHAPTER 1. ORGANIC MATTER ....................................................... 1 Dissolved organic matter ................................................................ 1

DOM ORIGIN AND COMPOSITION ..................................................... 2 DISSOLVED ORGANIC NITROGEN ..................................................... 4

Urea .............................................................................................. 5 DCAA ............................................................................................ 5

DFAA ............................................................................................. 5 Humic and fulvic substances .......................................................... 6 Additional DON compounds ............................................................ 6

BLACK CARBON ................................................................................ 7

BC formation ................................................................................. 7 CHARACTERIZATION OF NATURAL ORGANIC MATTER ................... 9

NMR spectroscopy ........................................................................ 11 EEMS .......................................................................................... 11 FT-ICR MS ................................................................................... 12

CHAPTER 2. CHARACTERIZATION OF DISSOLVED ORGANIC MATTER BY FOURIER TRANSFORM ION CYCLOTRON RESONANCE MASS SPECTROMETRY ................................................................... 14

IONIZATION TECHNIQUES .............................................................. 15 Electrospray ionization ................................................................. 16 Atmospheric pressure photoionization ........................................... 18 Positive ion APPI ........................................................................... 19 Negative ion APPI ......................................................................... 20 Dopant-assisted APPI ................................................................... 21

FT-ICR MASS SPECTROMETRY: THEORY ....................................... 22

9.4 TESLA FT-ICR MASS SPECTROMETER AT THE NHMFL ........... 23 DOM ANALYSIS BY FT-ICR MASS SPECTROMETRY ........................ 24

ix

Kendrick mass sorting .................................................................. 25 Mass resolution ............................................................................ 27 Spectral complexity ...................................................................... 30 Isotope signatures ........................................................................ 31

Mass accuracy ............................................................................. 32 Dynamic range ............................................................................. 34

CONCLUSION ................................................................................... 36 CHAPTER 3. SELECTIVE IONIZATION OF DISSOLVED ORGANIC NITROGEN BY POSITIVE ION ATMOSPHERIC PRESSURE PHOTOIONIZATION COUPLED WITH FT-ICR MS ............................. 37

SUMMARY ....................................................................................... 37 INTRODUCTION ............................................................................... 37

Atmospheric pressure photoionization ........................................... 38 EXPERIMENTAL METHODS............................................................. 40

Samples ....................................................................................... 40

Mass spectrometry ....................................................................... 40 RESULTS AN DISCUSSION............................................................... 41

Lake Bradford DOM...................................................................... 42 Deep-sea marine DOM .................................................................. 44 Various DOM samples .................................................................. 46 Na+ adduct formation by (+) ESI .................................................... 47

CONCLUSION ................................................................................... 48 CHAPTER 4. APPI FT-ICR MS CHARACTERIZATION OF WASTEWATER-DERIVED DISSOLVED ORGANIC NITROGEN AFTER ADVANCED OXIDATION TREATMENT AND ALGAL BIOREMEDIATION ........................................................................... 50 SUMMARY ....................................................................................... 50

INTRODUCTION ............................................................................... 50 EXPERIMENTAL METHODS............................................................. 53

Samples ....................................................................................... 53 Advance oxidation treatment ......................................................... 54 Mass spectrometry ....................................................................... 54

RESULTS AND DISCUSSION ............................................................ 55 Untreated vs. treated DON ............................................................ 55

x

Untreated and treated wastewater remediated by algae .................. 61 CONCLUSION ................................................................................... 67

CHAPTER 5. CHARACTERIZATION OF REACTIVE AND REFRACTORY DISSOLVED ORGANIC NITROGEN IN A STORMWATER TREATMENT AREA BY APPI FT-ICR MS ............................................................... 69 SUMMARY ....................................................................................... 69 INTRODUCTION ............................................................................... 70

EXPERIMENTAL METODS ............................................................... 72 Samples ....................................................................................... 72 Bioassays..................................................................................... 72 Extraction .................................................................................... 73 Mass spectrometry ....................................................................... 74

RESULTS AND DISCUSSION ............................................................ 75

Bioassay results ........................................................................... 75

Characterization of DON by APPI FT-ICR MS ................................. 76 Kendrick analysis ......................................................................... 79 van Krevelen analysis ................................................................... 76

CONCLUSION ................................................................................... 85 CHAPTER 6. CHARACTERIZATION OF PYROGENIC BLACK CARBON BY DESORPTION ATMOSPHERIC PRESSURE PHOTOIONIZATION

FOURIER TRANSFORM ION CYCLOTRON RESONANCE MASS SPECTROMETRY ............................................................................. 87 SUMMARY ....................................................................................... 87 INTRODUCTION ............................................................................... 88 EXPERIMENTAL METHODS............................................................. 90

Samples ....................................................................................... 90 DAPPI source ............................................................................... 91 Mass spectrometry ....................................................................... 92 Data analysis ............................................................................... 94 Nuclear magnetic resonance spectroscopy ..................................... 95 Elemental analysis ....................................................................... 96

RESULTS AND DISCUSSION ............................................................ 96

Parent oak ................................................................................... 96 Oak combusted at 250 °C ............................................................. 98

xi

Oak pyrolyzed at 400 °C .............................................................. 100 DBE distribution ......................................................................... 102 Oxygen class distribution............................................................. 104

CONCLUSION .................................................................................105 REFERENCES ................................................................................107 BIOGRAPHICAL SKETCH...............................................................127

xii

LIST OF TABLES Table 1.1. Elemental Composition of SRFA, PLFA and NLFA ....................................... 4 Table 1.2. Properties of black carbon as a function of increased temperature .............. 8 Table 5.1. Three nitrogen-containing homologous series identified from m/z 432.00-

432.30. All three series exhibit the substitution of CH4 for O and the corresponding 36.4 mDa mass difference ....................................................................................79

xiii

LIST OF FIGURES

Figure 1.1. Structure of black carbon as a function of increased temperature. Highly

aromatic char eventually forms planar graphite sheets. Graphite sheets may either for randomly distributed stacks or bowled structures ............................................ 9

Figure 2.1. Negative ion electrospray ionization FT-ICR broadband mass spectrum of

Lake Bradford DOM. Here, ions are detected for 5.6 s providing the resolving power needed to resolve and assign exact molecular formulas to the more than 15,000 peaks in the spectrum .........................................................................................15

Figure 2.2. Schematic of electrospray ionization (Figure from

www.bris.ac.uk/nerclsmsf/techniques/hplcms.html.). 1-2 kV are applied to the tip of a capillary. Ions desolvate and undergo a series of columbic explosions to form intact gas-phase ions ...........................................................................................17

Figure 2.3. Two-dimensional schematic of the APPI ion source coupled to the 9.4 Tesla

FT-ICR mass spectrometer at the NHMFL (Figure modified from Purcell et al. 2006). The krypton vacuum ultraviolet gas discharge lamp is drawn on the z-axis along with the heated metal capillary. In practice, the three assemblies are mutually orthogonal ...........................................................................................................19

Figure 2.4. Schematic of the 9.4 Tesla FT-ICR mass spectrometer located at the

National High Magnetic Field Laboratory, Tallahassee, Florida. Six stages of differential pumping are used to reduce the base pressure in the ICR cell to 10-10

Torr to minimize collisions between ions during excitation/detection. (Figure provided by the Marshall Research group courtesy of John Paul Quinn) ................25

Figure 2.5. Expanded mass spectral segments of Suwannee River fulvic acid produced

by negative ESI FT-ICR MS. 2.0157 Da spacings represent compounds that differ by two hydrogen atoms, indicative of compounds that differ by the addition of one non aromatic ring or double bond (DBE values). 14.01565 Da spacings (bottom) represent members of a homologous series which differ only in alkylation (CH2) ....27

Figure 2.6. Theoretical resolving power for FT-ICR mass spectrometry (Figure modified

from Marshall et al. 1998). Because of the complexity DOM, a minimum resolving power much be achieved to facilitate separation and correct identification of isobaric species. The 3.4 mDa split occurs between species with 36 Da nominal mass, but differing by SH4 and C3. The overlap between SH3

13C and C4 occurs between species weighing 48 Da ....................................................................................................28

Figure 2.7. Broadband positive ion APPI FT-ICR MS at 9.4 Tesla. 26,359 mass spectral

peaks above 6σ the signal-to-noise ratio baseline rms noise are observed from 400 < m/z < 1100 with m/∆m 50% = 900,000 at m/z 687, currently the world record for resolving power at 9.4 Tesla of a complex mixture.................................................29

Figure 2.8. Broadband negative ion APPI 9.4 T FT-ICR mass spectrum of Lake Bradford

DOM. More than 25,000 mass spectral peaks are resolved at 6σ baseline rms noise at an average resolving power, m/∆ 50% > 600,000. Inset: m/z ~ 0.1 expanded mass spectral segment that shows the spectral complexity of DOM ................................31

Figure 2.9. An m/z ~ 2 Da expanded mass spectral segment of negative ion APPI FT-

ICR mass spectrum of Lake Bradford DOM at m/z 423 showing the monoisotopic

xiv

peak for [C20H23O10-H]- with corresponding 13C1 and 18O1 isotopic signatures. The signatures of heavy isotopes are used to confirm the assignment of molecular formulas ..............................................................................................................32

Figure 2.10. Internal calibration mass accuracy for more than 10,000 mass spectral

peaks observed at 10 times the signal-to-noise ratio baseline rms noise collected by APPI FT-ICR MS at 9.4 T for European crude. Calculation of the rms mass error for all observed peaks across 350 < m/z <1025 was 260 ppb .....................................34

Figure 2.11. Expanded mass spectral segment of Lake Bradford DOM by negative ESI

FT-ICR. The dynamic range of FT-ICR MS enables simultaneous detection of peaks with low and high signal-to-noise peaks (zoom inset) ............................................35

Figure 3.1. Broadband negative electrospray 9.4 T FT-ICR mass spectrum of Lake

Bradford DOM. Inset: m/z ~ 0.3 expanded mass spectral segment at m/z 412.0 ..43 Figure 3.2. Broadband positive ion atmospheric pressure photoionization 9.4 T FT-ICR

mass spectrum of Lake Bradford DOM. Inset: m/z ~ 0.3 expanded mass spectral segment at m/z 414.0 ..........................................................................................44

Figure 3.3. FT-ICR MS m/z expanded mass spectral segments for deep-sea marine

DOM produced by positive ion APPI (top) and negative ESI (bottom). For APPI, the most abundant have even nominal mass, e.g., [CcHhN1Oo + H]+. For ESI, the most abundant ions have odd nominal mass, e.g., [CcHhOo - H]- ....................................45

Figure 3.4. Two m/z ~ 0.01 expanded mass spectral segments for deep-sea marine

DOM produced by APPI (left) and ESI (right). Compounds with a common neutral formula were selected. Note that S/N ratio is more than 10-fold (top) or (5-fold (bottom) higher for APPI than ESI for the same neutral compound ........................46

Figure 3.5. Histogram depicting the percent relative abundances for all nitrogen-

containing species representative of five distinct DOM sources for positive ion APPI and negative ion ESI ............................................................................................47

Figure 3.6. An m/z ~1 expanded segment of the positive ion ESI 9.4 T FT-ICR mass

spectrum of SRFA. Each sodium adduct is separated by 2.4 mDa from the compound of the same nominal mass, but differing in composition by substitution of NaH for C2. The elemental compositions that contain Na are highlighted with an (*) ........................................................................................................................48

Figure 4.1. van Krevelen diagram of wasterwater-derived DON compounds before (top)

and after (bottom) treatment by AOP. AOP degrades the aromatic (yellow) and condensed aromatic (red) DON compounds .......................................................... 57

Figure 4.2. DBE vs. carbon number plots of wastewater-derived DON before (top) and

after (bottom) AOP. A shift to lower DBE is observed after AOP caused by degradation of aromatic compounds. The increase of compounds at low DBE and high carbon number may be the product of reactions between partially oxidized compounds ..........................................................................................................58

Figure 4.3. Class graph of the most abundant DON species in wastewater before and

after treatment. A shift to lower heteroatom number is indicative of degradation of large compounds .................................................................................................59

xv

Figure 4.4. van Krevelen diagrams of individual nitrogen classes before (top) and after (bottom) AOP. N2 and N3 compounds are removed after AOP. The removal of N2 and N3 compounds are consistent with degradation of large compounds by AOP. The N1 class remains mostly unchanged although there is a slight addition of compounds with high H:C and low O:C. Although N1 compounds are most likely degraded after AOP it is likely that degraded N2 and N3 compounds have the same composition as the original N1 compounds before AOP. Therefore, the N1 class has similar compositional coverage before and after AOP ........................................................ 60

Figure 4.5. van Krevelen diagrams of wastewater-derived DON untreated (black),

untreated after algal remediation (green), after treatment (blue), and after treatment and algal remediation (red). No change is observed for untreated wastewater before and after algal remediation. After treatment, formulas with relatively high H:C and low O:C are lost and formulas with low H:C and high O:C are added ..................... 62

Figure 4.6. van Krevelen diagrams of the formulas that appear only in treated

wastewater before algal remediation (top) and formulas only detected in the sample after algal remediation (bottom). Algae remove compounds with relatively high H:C and low O:C and release compounds with low H:C and high O:C........................... 63

Figure 4.7. Class graph of the most abundant nitrogen species in treated wastewater

before and after algal remediation. After remediation, a shift to higher oxygen class is observed. Furthermore, there is a significant decrease in the abundant N1O1 and N1O5 classes observed in the treated sample before algae ...................................... 64

Figure 4.8. FT-ICR MS enables characterization of individual heteroatom classes. A

shift to lower O:C of the N1O5 class is observed in the treated sample after algal remediation (bottom). The trend to lower O:C in the N1O5 class differs from the overall trend observed for all N classes; however, the compounds removed after remediation (top) are not aromatic in nature and may be bioavailable to algae.......65

Figure 4.9. Class graph of the oxygen species in treated wastewater before and after

algal remediation. There is an increase in relative abundance of O5-O12 classes indicative of a release of highly oxygenated DOC compounds by algae ...................66

Figure 4.10. van Krevelen diagram of DOC formulas unique to the treated sample

before (top) and after (bottom) algal remediation. The trend of removal of compounds with relatively high H:C and low O:C, and input of compounds with low H:C and high O:C see in the plots for DOC are similar to those observed for DON ...............67

Figure 5.1. Percentage of total assigned formulas verse nitrogen class comparison of

positive and negative ion atmospheric pressure photoionization in generating organic nitrogen ions ...........................................................................................77

Figure 5.2. a) Broadband positive ion APPI FT-ICR mass spectrum of Caloosahatchee

River DOM and; b) A m/z = 0.3 expanded mass spectral segment at m/z 432 with formulas containing N1 (●) and N3 (■) labeled .......................................................78

Figure 5.3. a) Kendrick plot of the nitrogen-containing formulas assigned in both the

original (T0) and incubated sample (T5) from the Caloosahatchee River during the wet season; and b) Kendrick plot of assigned formulas only in T0 (♦) and those only in T5 (■) ...............................................................................................................81

Figure 5.4. a) A van Krevelen diagram of nitrogen-containing formulas identified in

both the original (T0) and incubated sample (T5) from the Caloosahatchee River. B)

xvi

A van Krevelen diagram of nitrogen containing formulas identified only in T0 (♦) and those only in T5 (■) ...............................................................................................84

Figure 6.1. Schematic of Thermo LCT source converted for DAPPI experiments (Figure

from modified from Purcell et al. 2006). The sample is placed directly the path of the heated solvent spray for thermal and chemical desorption. Desorbed neutrals undergo either direct photoionization, proton transfer, or charge exchange and enter the mass spectrometer through a heated metal capillary .......................................92

Figure 6.2. Broadband (-) DAPPI LTQ mass spectra of the parent oak, oak combusted

at 250 °C, and oak pyrolyzed at 400 °C. The optimum solvent plume temperature was determined for each sample. Top: The mass spectrum is typical of fresh, labile organic biomass. Middle and Bottom: The mass spectra exhibits a broad pseudo-Gaussian distribution as the biomass is thermally degraded. ................................93

Figure 6.3. van Krevelen diagram of elemental H:C vs. O:C ratios. Molecular formulas

with similar chemical characteristics tend to aggregate in specific regions. van Krevelen plots of different samples may be compared to determine changes in chemical composition. Formulas with Aromaticity Index (AI) values > 0.5 are considered aromatic, and those with AI > 0.67 condensed aromatic. .....................95

Figure 6.4. (Top) van Krevelen diagram for the elemental compositions assigned to

parent oak by DAPPI FT-ICR MS. The molecular formulas aggregate in regions of the diagram typical of wood, i.e., lignin, protein, and cellulose. A few formulas are associated with aromatic compounds, i.e., A.I. > 0.5. (Bottom) NMR spectrum for parent oak. The spectrum is dominated by the O-alkyl peak, 60-110 ppm, with only minor contribution from the aromatic peak, 110-160 ppm. (* bulk O:C and H:C ratios determined by elemental analysis) ..............................................................98

Figure 6.5. (Top) van Krevelen diagram for the elemental compositions assigned to oak

combusted at 250 °C by DAPPI FT-ICR MS. Molecular formulas characteristic of aromatic and condensed aromatics, i.e., AI > 0.5 and AI ≥ 0.67, are formed relative to the parent oak. Although the elemental compositions associated with proteins disappear relative to parent oak, compounds with high O:C and H:C associated with cellulose remain in oak 250. (Bottom) NMR spectrum of oak 250, showing a decrease in the O-alkyl peak and increase in the aromatic peak relative to the parent oak. (* bulk O:C and H:C ratios determined by elemental analysis) ........... 100

Figure 6.6. (Top) van Krevelen diagram of the molecular formulas assigned to oak

pyrolyzed at 400 °C. Molecular formulas exhibit lower O:C and H:C ratios relative to parent oak and oak 250, due to depolymeriztion of cellulose and dehydration and deactylation of lignin and cellulose. Approximately half of elemental compositions assigned for to oak 400 have an AI > 0.55. (Bottom) NMR spectrum of oak 400. The spectrum is dominated by the aromatic peak. There almost no O-alkyl contribution relative to the parent oak and oak 250. (* denotes bulk O:C and H:C ratios determined by elemental analysis) ...................................................................... 101

Figure 6.7. Double bond equivalents (DBE) relative abundance distribution for parent

oak, oak 250, and oak 400. The parent oak has a relatively low DBE range, and oak 250 exhibits a bimodal distribution. Oak 400 is characterized by elemental compositions with relatively high DBE, the result of further thermal degradation of lignin and cellulose ............................................................................................ 103

Figure 6.8. Percent relative abundance for various oxygen classes. Oak 250 has a

bimodal distribution with the first distribution, and formulas in the O4-O11 classes

xvii

are most likely thermally degraded lignin and cellulose compounds. Formulas in the O12-O20 classes represent residual cellulose that is not thermally degraded and partially oxidized lignin and cellulose. Oak 400 formulas in the lower oxygen classes are the result of deactylation caused by thermal degradation .............................. 104

xviii

ABSTRACT Natural organic matter (NOM) exists as a highly functionalized,

polydisperse and complex mixture of organic compounds derived from

decaying plan and animal detritus. NOM has been characterized by

Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR

MS) for approximately the past 10 years. Over that time advancements in

transfer optics and ICR cell technology have resulted in improvements in

sensitivity, dynamic range, mass accuracy, and signal-to-noise; however,

ionization techniques for NOM characterization have not improved

significantly. Typically, NOM is ionized by negative ion electrospray (ESI).

ESI is amenable to NOM characterization because the majority of NOM is

highly polar; however, important fractions of NOM are not ionizable by

ESI and are therefore remain uncharacterized at the molecular level.

The work presented is devoted to novel ionization methods for two

of the most under characterized fractions of NOM by FT-ICR MS.

Dissolved organic nitrogen (DON) may be selectively ionized by positive

ion atmospheric pressure photoionization. Typically, DON is not

characterized by FT-ICR MS because ESI does not efficiently ionize DON

relative to the C, H, and O component of NOM. Black carbon, including

biochar may be ionized by desorption atmospheric pressure

photoionization. Biochar has defied molecular level characterization by

FT-ICR MS because, as temperature of thermal degradation increases,

the solubility of char in common solvents decreases.

Chapter 1 is a brief introduction to natural organic matter

including a short overview of two major components of DOM that remain

largely uncharacterized at the molecular level, dissolved organic nitrogen

and black carbon. Chapter 2 is a brief introduction to FT-ICR MS

principles and establishes why FT-ICR MS is necessary for

characterization of complex mixtures such as DOM.

xix

In Chapter 3, positive ion APPI is established as a selective

ionization method for DON. DON has an important role in

biogeochemistry; however, it remains largely uncharacterized by FT-ICR

MS due to inefficient ionization relative to dissolved organic carbon

(DOC). Positive ion APPI dramatically increases S/N of DON ions

compared with negative ion ESI. Extensive molecular characterization of

DON may now be conducted, including tandem mass spectrometry to

reveal structural information about DON ions of a single m/z.

Chapter 4 and 5 are applications of positive ion FT-ICR MS for

characterization of wastewater-derived DON before and after treatment

by advanced oxidation processes and algal remediation, and

characterization of DON treated by microbes. An important factor in the

bioavailability of organic nitrogen is composition. Large, aromatic

compounds that are not available to algae for uptake in the untreated

sample are degraded to more labile compounds that are bioavailable.

Furthermore, labile DON may be used by microbes and converted to

refractory compounds.

Chapter 6 focuses on biochar, an important natural product for

the agricultural and fuel industries. Black carbon also represents a

significant long-term sink for atmospheric carbon. Characterization of

biochar is important for understanding how it interacts in the

environment. Many questions are yet to be answered about how char is

degraded after initial formation. To date, only the water-soluble fraction

of char is characterized at the molecular level by FT-ICR MS. As the

temperature of char formation increases, chars become insoluble in

common solvents. In Chapter 5, the implementation of desorption

atmospheric pressure photoionization (DAPPI) to characterize intact

chars is described. DAPPI is an ambient ionization method that does not

require sample preparation or separation. The elemental composition of a

parent oak, oak combusted at 250 °C, and oak 400 °C are determined by

DAPPI coupled to FT-ICR MS. The data show the parent material is

xx

mostly composed of lignin- and cellulose-like compounds. As the oak is

thermally degraded, the compounds become more aromatic. At 400 °C

the oak has lost all of its original identifiable components and is

composed of mostly aromatic compounds.

1

CHAPTER 1

ORGANIC MATTER

Natural organic matter (NOM) exists as a highly functionalized,

polydisperse and complex mixture of organic compounds derived from

decaying plant and animal detritus. Dissolved organic matter (DOM) is

operationally defined as the fraction of NOM that passes through a 0.2-

1.0 µm filter.1 DOM may be subcategorized into humic and nonhumic

substances. Nonhumic substances are those that contain biochemically

identifiable compound classes, e.g. simple sugars, fatty acids,

carbohydrates, and peptides.2-3 Humic substances are the

uncharacterized portion of DOM composed of individual compounds, i.e.

degraded plant and animal detritus. Humic substances are the

recalcitrant fraction of DOM that are resistant to additional microbial

degradation. Humic substances may be further classified as humic acids

(HA), fulvic acids (FA) and humin. HA is base-soluble and acid-insoluble,

FA is soluble at any pH, and humin is insoluble across the pH scale.

DOM interacts in many biogeochemical processes in marine and

terrestrial aquatic ecosystems, e.g. metal redox cycling, contaminant

transport, microbial growth, gas exchange in surface waters, and the

carbon cycle.4-10 The total amount of carbon stored in DOM represents

one of the largest reservoirs on Earth. Dissolved organic carbon (DOC)

accounts for an active carbon pool that is approximately equal to

atmospheric carbon dioxide (6.8 x 1017 g C).11-12

Due to its complex nature, DOM has defined complete molecular

characterization. Chromatographic methods such as reversed-phase

liquid chromatography and capillary electrophoresis are able to

characterize less than 10% of DOM as intact amino acids, sugars and

2

small aromatic and phenolic constituents. Size exclusion

chromatography may provide bulk information about molecular size and

polarity of DOM.13 Nuclear magnetic resonance spectroscopy only

provides information about functional groups associated with DOM.

Ultrahigh resolution Fourier transform ion cyclotron resonance mass

spectrometry provided the first insight about the composition of DOM at

the molecular level.14-18 Ultrahigh resolving power (m/∆m50% > 600,000 at

m/z 500) and sub-ppm mass accuracy enable the ability to assign

unique molecular formulas to thousands of DOM ions in an individual

mass spectrum.

DOM origin and composition

DOM is a product of several biogeochemical processes. The

composition of DOM is dependent upon these processes and the source

from which it is derived. Biomass produced from primary producers such

as terrestrial and aquatic plants, algae, and photosynthetic bacteria are

significant precursors for DOM. However, DOM is also derived from

secondary producers. Bacterial biomass produced by heterotrophic

bacteria in secondary production processes, exceeds plant biomass on

Earth.19 Furthermore, fungi are another secondary production source.

Therefore, DOM derived from secondary production sources is as

significant as DOM derived from primary production sources.

The proximately to the source is another important factor on DOM

composition. There are two classifications of DOM based on origin.

Autochthonous DOM originates at the source and allochthonous DOM is

derived far from a source and transported to the source by rivers,

streams, etc. Autochthonous sources of DOM are more significant in

samples where DOM inputs from allochthonous sources are minimal,

e.g., a lake with algal blooms. Allochthonous DOM is subject to more

degradation and removal processes during transport.

3

The trophic status of an aquatic environment affects DOM

concentration and composition. Aquatic systems may be either

eutrophic, oligotrophic, or mesotrophic. Eutrophic aquatic systems are

rich in plant nutrients and are therefore highly productive. Vast

quantities of suspended algae are present that serve as the base for

production of organic matter and subsequent formation of DOM. Due to

the productivity and subsequent decomposition of organic matter, much

of the oxygen is depleted at the lower depths from microbial respiration.

Eutrophic systems typically contain 10 mg L-1 DOM. Oligotrophic aquatic

systems are the contrast to eutrophic systems. Oligotrophic systems

contain very low concentrations of nutrients required for plant growth

and therefore the productivity of these systems is very low. Only small

quantities of organic matter are produced from minimal aquatic life

present in the system. Oligotrophic systems typically contain 2 mg L-1

DOM. Mesotrophic aquatic systems are the intermediate to eutrophic

and oligotrophic systems. Typical DOM concentrations are 2-4 mg L-1.

The major elements found in DOM include hydrogen, oxygen,

nitrogen; minor elements include halogens, sulfur, and phosphorus.

Table 1.1 shows the elemental compositions of Suwannee River fulvic

acid (SRFA), Nordic Lake fulvic acid (NLFA) and Pony Lake fulvic acid

(PLFA) provided by the International Humic Substances Society. The

Suwannee River flows through the forests and swamps of South Georgia

and North Florida before it empties into the Gulf of Mexico. The DOM is

rich in terrestrial derived DOM consisting of degraded lignin and tannin

with DOC concentrations ranging from 25 to 75 mg L-1. NLFA is isolated

from Lake Hellrudmyra located outside of Oslo, Norway. Lake

Hellrudmyra is a relatively small glacial lake (3.2 acres) located on the

side of a mountain. DOC concentrations range from 10 to 25 mg L-1.

Lake Pony is a saline pond located on Cape Royds in the McMurdo

Sound area of Antarctica. PLFA is formed entirely from lignin-free

biomass. The composition is representative of aquatic fulvic acids with

4

negligible input of terrestrial derived organic matter. Here, DOC

concentration may be very high, ~100 mg L-1. Although the amount of

carbon and hydrogen in each sample is similar, the amount of oxygen,

nitrogen, and sulfur differ drastically from the two aquatic systems with

input of terrestrial organic matter compared with the system without

terrestrial input, representative of the source dependency of DOM.

Table 1.1. Elemental Composition of SRFA, PLFA and NLFA.

Property SRFA NLFA PLFA

Elemental Analysis (wt%) Carbon 52.44 52.31 52.47

Hydrogen 4.31 3.98 5.31 Oxygen 42.20 45.12 31.38 Nitrogen 0.72 0.68 6.51 Sulfur 0.44 0.46 3.03 Phosphorus <0.01 <0.01 0.55

Dissolved Organic Nitrogen

Dissolved organic nitrogen (DON) represents less than 10% of the

total organic matter pool. Until recently, dissolved organic nitrogen (DON)

was believed to be largely unreactive, therefore, studies focused on the

dissolved inorganic fraction.20 However, it is now known that

approximately 60% to 69% of the total dissolved N in lakes, rivers, and

ocean waters is in the form of DON.21 Furthermore, a large fraction of

DON is bioavailable.22-26 There is a crucial need to understand the

composition and movement of DON through the biogeochemical cycle.

DON is a heterogeneous mixture of compounds composed of labile

functionalities which turn over on the order of days to weeks and

recalcitrant compounds which may exist for months to hundreds of

years.21 Labile DON is less abundant in the environment than

recalcitrant DON compounds; however, labile DON is far more important

5

as a source of N in the biogeochemical cycles of aquatic systems. A

number of compounds are identified within the DON pool including,

urea, dissolved combined amino acids (DCAA), dissolved free amino acids

(DFAA), humic and fulvic substances, and nucleic acids. The remainder

of the DON pool is a mixture of unidentified compounds.

Urea

Urea is a low molecular weight organic compound which is a

product of organic matter decomposition and excretion from organisms.

Concentrations of urea range widely from 0 to 3 µM. Open ocean systems

tend to contain the least urea (<0.3 µM). Coastal systems tend to contain

slightly higher concentrations (<0.7 µM). Estuary and river systems

contain the most urea (<3 µM). Urea is extremely labile with turnover

rates on the timescale of days.27

DCAA

The chemical structure of DCAA is largely unknown but can

include proteins and oligopeptides,28 amino acids bound to humic and

fulvic substances,29-30 and amino acids adsorbed to clays and minerals.31

DCAA are the largest identifiable pool of DON in aquatic systems.32

Concentrations range from 0.15 to 4.20 µM and represent approximately

7% of the total DON pool. The turnover rates for these for DCAAs are on

annual timescales.33

DFAA

Primary produces, many of which have large intracellular pools of

amino acids that may be released are the main source of DFAA in

aquatic systems.34-35 Diatoms show the highest rates of DFAA excretion

during exponential growth.36 Furthermore, the types of amino acids may

change in some diatoms with growth stage, e.g. exponential vs.

6

stationary growth.36 DFAA concentrations range from 0.001 to 0.7 µM

and account for approximately 5% of the total DON pool.

Humic and fulvic Substances

Humic substances are the recalcitrant and most hydrophobic

component of the DON pool. Humic substances isolated from aquatic

systems originate from either terrestrial or marine environments. Humic

substances of terrestrial origin are mostly aromatic and have a higher

C:N than marine humics which have a more aliphatic character.37 The

building blocks of terrestrial humic substances are mostly lignin-like

products while marine humics are believed to be biosynthetic

compounds such as amino acids, sugars, amino-sugars, and fatty

acids.38 The percentage of nitrogen ranges from 1 to 6% in humic

substances.39-41 It is unknown exactly how N is associated with humic

substances. Schnitzer suggested that there are two types of N

associations.39 The first group contains N compounds that have distinct

and identifiable characteristics, i.e. amino acids, amino sugars,

ammonium, nucleic acid and bases, and purines. The second group

includes compounds that have N integrated into the actual humic

compound. According to Schnitzer, the total DON associated with each

group accounts for 50% of the humic associated N; however, the first

group of compounds is the most likely source of bioavailable N.

Additional DON compounds

Nucleic acids, purines, pytimidines, pteridines, methylamines, and

creatine are identified in natural aquatic systems. Dissolved

deoxyribonucleic acids (D-DNA) and dissolved ribonucleic acids (D-RNA)

are produced when bacteria die. Purines and pyrimines are heterocyclic

bases. The major purine bases found in nucleic acids are adenine and

quinine and the major pytimide bases are thymine, cytosine, and

7

Uracil.21 Some purines and pteridines are excreted as end products of N

catabolism.42 Methyamines are produced by diatoms and come in

primary, secondary and tertiary methylated homologues of NH3.21 All of

the compounds mentioned in this section are minor constituents of the

total DON pool (<1%).21

Black Carbon

Black Carbon (BC) is a product of incomplete combustion of fossil

fuels and vegetation.43 Although there is no general agreement on a

clear-cut definition or boundary, BC may be understood as a continuum

from partly charred biomass through char and charcoal to graphite and

soot particles recondensed from the gas phase (Table 1.2).44 BC has

potential importance in a wide range of biogeochemical process. BC may

represent a significant sink in the global carbon cycle,45-47 effect the

Earth’s radiative heat balance,48-49 a tracer for Earth’s fire history,50-51 a

significant source of carbon buried on soils and sediments,52-56 and act

as an important carrier of organic pollutants or heavy metals.57-60

Charred particles from the burning of biomass and fossil fuel combustion

share a relative lack of biochemical reactivity and are therefore, strongly

resistant to decomposition over a geological timescale.

BC formation

Formation of BC may occur by two different processes. The

volatiles recondense to highly graphitized, sott-BC, whereas the solid

residues form char-BC. Soot-BC forms via small molecules that are

released by pyrolysis and subsequently recombine by free radical

reactions. The randomness of these reactions results in a characteristic,

but widely varying, spectrum of products, including polycyclic aromatic

hydrocarbons and graphitic moieties. Char-BC forms during the flaming

and smoldering phases of combustion, when oxygen reacts with carbon

8

that builds on solid fuel surfaces. At this stage, fuel gases produced by

pyrolytic reaction are insufficient to maintain the flame envelope, and

oxygen must diffuse to the fuel surface to maintain combustion.

Turbulence in the combustion zone enhances this transport and

oxidation at the fuel surface provides heat evolution and heat feedback to

accelerate pyrolytic reactions and volatilization of fuel gases.61-62 BC is

formed in an exothermic reaction at temperatures greater than 250 °C.

Table 1.2. Properties of black carbon as a function of increased

temperature.

Property Slightly Charred Char Charcoal Soot Biomass

Formation Low High Temperature Plant Structures Abundant Significant Few None Reactivity High Low Size mm and larger mm to submicron submicron

The chemical structure of BC is highly aromatic relative to the

parent biomass. Carbon may form different structures including planar

graphite structures, curved or possibly closed spheres, and randomly

oriented stacks of few graphitic layers (Figure 1.1).63 The short- and

long-term order of BC depends on combustion conditions such as

temperature and moisture content of the biomass. Slightly charred and

charred biomasses retain significant identifiable characteristics of plant

structures, while charcoal and soot do not.

9

Figure 1.1. Structure of black carbon as a function of increased temperature. Highly aromatic char eventually forms planar graphite sheets. Graphite sheets may either for randomly distributed stacks or

bowled structures.

Characterization of Natural Organic Matter

Dissolved organic matter exists as a polyfunctional, heterogeneous,

polyelectrolytic complex mixture with varying molecular weight and

concentration. Therefore, identification of individual components in DOM

poses a significant challenge with most analytical methods. Until

recently, analysis was limited to characterization of bulk properties or

limited fractions not representative of the entire sample. Bulk property

measurements are useful but are not true molecular descriptors because

10

there is not an average molecule that defines the entire DOM pool. Early

bulk measurements focused on DOC and DON quantification in

seawater. The first DOC and DON measurements were reported in 1934

by Krogh and Keys.64 A sulfuric acid hydrolysis method was applied to

quantify DON and wet oxidation in aqueous chromic acid to quantify

DOC.64 For the next 60 years what was known about DOM came from

bulk measurements.

It was not until the 1980s that efficient isolation 65-66 and methods

for advanced characterization were developed for DOM analysis,

particularly in the area of NMR spectroscopy. The first high quality 1H

and 13C NMR spectra of isolated marine DOM were published in 1983.67-

68 The spectra presented evidence for highly branched alkyl chains and

aromatic carbon as the major structural components of seawater DOM.

Advanced characterization methods revealed that NOM is composed of

compounds with identifiable classes e.g., lignin, amino acids, sugars,

proteins, and nucleic acids; however, these compounds are not

representative of the complete composition of DOM. A significant fraction

of NOM is termed “uncharacterized”. The uncharacterized fraction of

NOM is the portion that is significantly degraded and does not retain

chemical properties associated with one particular class.

A variety of analytical methods are now implemented to describe

the chemical properties of marine and terrestrial DOM including, low

resolution mass spectrometry and size-exclusion chromatography for

molecular weight distributions, elemental analysis and Fourier-transform

ion cyclotron resonance mass spectrometry for elemental composition,

nuclear magnetic resonance spectroscopy, and UV/Vis absorbance and

excitation emission matrix fluorescence spectroscopy for information

about the core composition and functional groups of NOM. No single

analytical technique produces both bulk and detailed molecular

information on DOM. The three most promising analytical techniques for

comprehensive DOM characterization are nuclear magnetic resonance

11

(NMR) spectroscopy, excitation emission matrix fluorescence

spectroscopy (EEMS), and Fourier transform ion cyclotron resonance

mass spectrometry (FT-ICR MS).

NMR spectroscopy

NMR is a common analytical method applied for structural

characterization of NOM. One-dimensional 1H and 13C NMR was first

applied to DOM in 1976 and yielded a spectrum consisting of many

unresolved peaks.69 Increases in resolution due to larger magnets and

new techniques have advanced the capability of NMR to produce data

that are useful for NOM characterization. Solid-state cross polarization

magic-angle spinning (CP-MAS) NMR was first used to characterized

marine and terrestrial DOM in 1983.70 The advent of CP-MAS NMR

provided the foundation for comparative analysis of marine and

terrestrial DOM signatures as a function of source and depth.71-73

EEMS Composition, concentration, distribution, and dynamics of the

fraction of DOM that absorbs light, chromophoric DOM (CDOM), may be

inferred from Fluorescence spectroscopy.74 CDOM from marine and

terrestrial sources is composed of aromatic rings and unsaturated

aliphatic chains. Therefore, a fluorescent method such as excitation

emission matrix fluorescence spectroscopy (EEMS) is useful for

characterization of CDOM. In EEMS, repeated emission scans are

collected at numerous excitation wavelengths, resulting in excitation-

emission matrices which provide highly detailed information that may be

used to identify fluorescent compounds in a complex mixture.75 Three-

dimensional plots of excitation and emission wavelength as a function of

intensity enable direct visualization of fluorophores in a DOM sample.76-

79 EEMS was applied to infer chemical composition,76, 80-86 environmental

12

interactions,87 and degradation88-92 of DOM from both marine and

terrestrial systems.74 Coble and Parlanti et al. determined marine DOM

to have marine-humic like and tryptophan-like fluorophores with varying

terrestrial characteristics depending on near-shore proximity and

depth.76, 93 Meanwhile, freshwater DOM from natural terrestrial sources

is predominantly composed of humic-like and fulvic-like fluorophores

with distinct maxima compared to marine DOM.76, 93-94 DOM from both

sources are affected by autochthonous and allochthonous contributions

and fluctuations in human activity. EEMS provides a high throughput

means to continuously monitor DOM in dynamic environments.

FT-ICR MS

Fourier transform ion cyclotron resonance mass spectrometry (FT-

ICR MS) is currently the only analytical method capable of achieving the

resolution and mass accuracy required to assign unique, unambiguous

molecular formulas to each peak across an entire molecular weight

distribution (200 < m/z < 1500). The application electrospray ionization

coupled to FT-ICR MS for DOM characterization began in the last decade

to typically characterize the carbon, hydrogen, and oxygen component of

DOM.15-18, 95 Advancements in sensitivity, mass range, mass resolving

power, m/∆m 50% > 600,000 at m/z 500),96 and part-per-billion mass

accuracy (<200 ppb)97 have increased the information inferred from

ultra-high resolution mass spectra. In the past decade, a typical DOM

spectrum contained ~5,000 resolved peaks. Currently, typical DOM

spectra contain greater than 15,000 resolved peaks which may be

assigned unambiguous molecular formulas. Information about the

chemical composition, aromaticity, and structure (tandem MS) may be

obtained from the custom-built 9.4 Tesla FT-ICR mass spectrometer

located at the National High Magnetic Field Laboratory, Tallahassee,

Florida.

13

The application of “novel” ionization methods coupled to FT-ICR

MS for characterization of NOM is the theme of this dissertation.

Historically, only the C, H, and O component of DOM was characterized

by FT-ICR MS. Other components of DOM such as nitrogen, which are

equally important in biogeochemical processes, are neglected due to

relatively low concentration and disparities in ionization efficiency. Still

other fractions of NOM, such as black carbon are insoluble in common

solvents and defy molecular level characterization by FT-ICR MS. Here, I

address these problems and explore methods to efficiently ionize and

characterize fractions of NOM that were uncharacterized or under

characterized. Furthermore, I apply these methods to characterize NOM

to studies that are topics of interest in the field of biogeochemistry.

14

CHAPTER 2

CHARACTERIZATION OF DISSOLVED ORGANIC

MATTER BY FOURIER TRANSFORM ION CYCLOTRON RESONANCE MASS SPECTROMETRY

Dissolved organic matter is a complex mixture of components with

a variety of chemical and physical properties. The majority of DOM

species are polar, although water soluble compounds with hydrophobic

functionalities are also present. Various analytical methods, including,

gas chromatography-mass spectrometry (GC-MS), liquid

chromatography-mass spectrometry (LC-MS), fluorescence spectroscopy,

and nuclear magnetic resonance spectroscopy (NMR) have been utilized

in an effort to characterize DOM.98-99 These methods only enable

characterization of a small fraction of the total DOM pool. Fourier-

transform ion cyclotron resonance mass spectrometry (FT-ICR MS) at

high magnetic field (> 9 Tesla) is the only analytical method that enables

characterization of DOM at the molecular level.

A typical DOM mass spectrum has more than 15,000 individual

spectral peaks that range from 200 <m/z < 1500 (Figure 2.1). The

assignment of individual molecular formulas to each spectral peak

requires ultrahigh resolution and high mass accuracies due to the

spectral complexity and close peak proximity. The 9.4 Tesla FT-ICR mass

spectrometer located at the National High Magnetic Field Laboratory

routinely provides resolving power (m/∆m 50%) >600,000 at m/z 500 and

200 ppb mass accuracy.

15

Figure 2.1. Negative ion electrospray ionization FT-ICR broadband mass spectrum of Lake Bradford DOM. Here, ions are detected for 5.6 s providing the resolving power needed to resolve and assign exact molecular formulas to the more than 15,000 peaks in the spectrum.

Electrospray ionization (ESI) is the most common ionization

method utilized for DOM analysis. 3, 15-17, 100-104 DOM is well suited for

ESI because ESI occurs at atmospheric pressure, ionizes a wide range of

polar, hydrophilic molecules with acidic and basic functional groups, and

may generate positive or negative ions. Recently, atmospheric pressure

photoionization (APPI) was implemented to ionize the less polar fraction

of DOM. Like ESI, APPI is a relatively soft ionization method that ionizes

an analyte based on its ionization energy and gas-phase acidity rather

than the pH of an analyte in solution.

Ionization Techniques

16

Electrospray ionization

John Fenn earned the Nobel prize in 2001 for electrospray

ionization. The applicability of ESI expanded rapidly because it is able to

transform analyte species in solution to free ions in the gas phase

continuously. Furthermore, multiple charges enable detection of large

compounds that are outside the analytical window of some mass

analyzers. Moreover, fragile compounds may be ionized without

fragmentation. Electrospray quickly became the ionization technique of

choice for large polar molecules and is the most common ion source used

to couple a liquid chromatograph to a mass spectrometer.

Figure 2.2 is a schematic of an electrospray source. Solution-

phase anions or cations, depending on the sign of the applied potential,

create charged droplets by application of an electric field. Dilute sample

solution is pushed by a syringe pump through a needle where 2-4 kV

electric potential is applied. As the drops evaporate, they reach their

Rayleigh limit. Gas-phase ions are produced after a series of Columbic

explosions. A small portion of the ions enter the mass spectrometer at

atmospheric pressure through a capillary that is coupled to the first

pumping stage of the instrument that is at a few mTorr of pressure.

17

Figure 2.2. Schematic of electrospray ionization. 1-2 kV are applied to the tip of a capillary. Ions desolvate and undergo a series of columbic explosions to form intact gas-phase ions. (Figure from www.bris.ac.uk/nerclsmsf/techniques/hplcms.html.)

Fievre et al., first applied electrospray ionization to FT-ICR MS for

characterization of humic and fulvic acids in 1997.95 The ability of

electrospray to ionize the most polar species in a complex mixture poses

a problem for characterization of the component of DOM that contains

other heteroatom classes besides C, H, and O. Negative electrospray

ionizes the C, H, and O component very efficiently because these

compounds are the most acidic species in a DOM mixture. Less polar

and basic species such as the N-containing component of DOM are not

ionized as efficiently. The nitrogen-containing compounds that are

ionized have very low signal magnitude relative to the compounds with

18

only C, H, and O. Therefore, nitrogen compounds are typically ignored

and left uncharacterized. It is critical to determine an ionization method

for characterization of the nitrogen species in DOM. Atmospheric

pressure photoionization (APPI) ionizes polar and nonpolar analytes, and

ionization is independent of the chemistry of an analyte in solution which

makes APPI an ideal candidate to efficiently ionize nitrogen species in

DOM.

Atmospheric pressure photoionization One of the main limitations of electrospray is limited ability to

efficiently ionize less polar and nonpolar species. Positive ESI was used

previously in an attempt to characterize the nitrogen component of DOM.

However, little information was gained and abundant sodium adducts

were present throughout the spectra. Atmospheric pressure

photoionization (APPI) is tolerant of salts, forms both positive and

negative ions simultaneously, and ionization is independent of the

chemistry of an analyte in solution. For these reasons, APPI is perceived

to be the method of choice for characterization of the N-containing

fraction of DOM.

Figure 2.3 is a schematic of the APPI source used at NHMFL. A

custom-built adapter was used to interface the APPI source to the first

stage of pumping in the mass spectrometer. The sample solution is

dissolved in methanol to 250 μg mL-1 and toluene (10% v/v) is added as

a dopant to assist in ionization. The sample is supplied to a fused silica

capillary by a syringe pump at a rate of 50 μL min-1. The sample mixes

with a nebulization gas, typically N2, at approximately 50 psi inside a

heated chamber. The nebulization temperature is controlled by an

external heating supply which can be operated between 200-500 ˚C .

Once nebulized, the sample exits the chamber as a confined jet and

passes orthogonally to a vacuum gas UV-Krypton discharge lamp where

19

photoionization occurs at atmospheric pressure. The ions are then swept

into the mass spectrometer through a resistively heated capillary into the

mass spectrometer.

Figure 2.3. Two-dimensional schematic of the APPI ion source coupled to the 9.4 Tesla FT-ICR mass spectrometer at the NHMFL (Figure modified from Purcell et al. 2006).105 The krypton vacuum ultraviolet gas discharge lamp is drawn on the z-axis along with the heated metal

capillary. In practice, the three assemblies are mutually orthogonal.

Positive ion APPI. APPI relies on the absorption of a photon by an

analyte causing the ejection of an electron and the formation of a

molecular radical cation (Eq. 2.1). Direct photoionization occurs if the

photon energy is greater than the ionization potential (IP) of the molecule.

The probability of this occurring is very low, since photons collide with

20

gases and other molecules in the source before they reach the analyte. As

a result, an easily photoionizable reagent called a dopant is added in

excess to increase ionization efficiency. Equations 2.2-2.4 are the

pathways of positive ion formation by APPI. Ionization of the dopant (Eq.

2.2) followed by subsequent charge exchange or proton transfer

increases ionization efficiency of the analyte. If the proton affinity of the

analyte is greater than that of the dopant, the analyte is ionized through

proton transfer to form [M+H]+ (Eq. 2.3). If the ionization potential of the

analyte is much less than that of the dopant, a molecular radical cation

is formed through charge exchange (Eq. 2.4). Although, some species in a

complex mixture form both protonated and radical ions, other

preferentially form one or the other which may provide additional

information regarding the nature and structure of the analyte.

2.1) M + hν → M+• Direct Photoionization if 10 eV > IP (M)

2.2) D + hν → D+• Photoionization of Dopant if 10 eV > IP (D)

2.3) D+• + M → [D-H]• + [M + H]+ Proton Transfer if PA (M) > Pa (D+•)

2.4) D+• + A → D + M+• Charge Exchange if IP (M) << IP (D+•)

Negative ion APPI. Negative ions are formed by an analyte with

either high gas-phase acidity, positive electron affinity, or both. An

analyte with one or both of these properties may be ionized in negative

ion mode by proton transfer to form a deprotonated molecule, or by

electron capture or charge exchange to form a molecular radical anion.

The process of negative ion formation is the same as positive ion APPI.

Equations 2.5-2.9 are the main pathways for negative ion formation by

APPI. Dopant molecules are photoionized which release an abundant

amount of electrons in the source (Eq. 2.5). Oxygen, which has a

relatively high electron affinity (EA), scavenges the electrons to form

superoxide radicals (Eq. 2.6). Proton transfer from an analyte to the

21

superoxide radical is possible if the proton affinity of the analyte is less

than the superoxide radical (Eq. 2.7). Charge exchange between the

analyte and superoxide radical is possible if the EA of the analyte is

greater than that of oxygen (Eq. 2.8). Finally, the analyte may capture an

electron if the EA of the analyte is greater than 0 eV (Eq. 2.9).

2.5) D + hν → D+• Photoionization of Dopant if 10 eV > IP (D)

2.6) O2 + e- → O2-• Formation of Superoxide

2.7) M + O2-• → [M-H]- + HO2

• Proton Transfer if PA (M) < PA (O2-•)

2.8) M + O2-• → M-• + O2 Charge Exchange if EA (M) > EA (O2)

2.9) M + e- → M-• Electron Capture if EA (M) > 0 eV

Dopant-assisted APPI. One of the main limitations of APPI is

that ionization occurs at atmospheric pressure, where collisions between

photons and atmospheric gases can occur and limit analyte ionization

efficiency. Robb et al. developed a technique called dopant-assisted APPI

to help increase analyte ionization through the addition of an easily

ionizable reagent in excess relative to the analyte.106 Benzene and

toluene are two commonly used dopants because they are photoionizable

with a 10 eV lamp, and have relatively high PA and IE. Other solvents

including acetone, anisole, substituted anisole, substituted benzene, and

tetrahydrofuran, have been used as dopants.

FT-ICR Mass Spectrometry: Theory In 1973, Alan Marshall and Melvin Comisarow combined Fourier

transforms, ion cyclotron resonance and mass spectrometry to create FT-

ICR mass spectrometry. A fixed magnetic field and an rf pulse applied

excited trapped ions to cyclotron motion through electrodes parallel to

the magnetic field. Coherent ion packets were excited close enough to

another pair of detection electrodes to induce an “image” current that

22

was measured as a time-varying differential voltage. Sinusoidal signals

were subjected to Fourier transformation after conversion from analog to

digital. The first FT-ICR mass spectrum was collected on methane ions

in 1973 at the University of British Columbia.107

Ion cyclotron motion occurs when a charged particle enters a

static, uniform, magnetic field. As an ion enters the magnetic field, it

encounters a force which bends the ion’s path into a circle. This is the

Lorentz force (FL), and the applied force on the ion is always

perpendicular to the ion motion and is expressed mathematically by Eq

(2.10), in which q is ion charge, v is ion velocity and Bo is magnetic field

strength.

FL = mass x acceleration = q v x Bo (2.10)

The cross product indicates that the force is perpendicular to the

velocity and the magnetic field. The angular acceleration of uniform

circular motion is shown in Eq. (2.11) where v and r are velocity and

radius.

a = v 2 / r (2.11)

Substituting Eq. (2.12) into Eq. (2.10)

m v 2/ r = q v Bo (2.12)

Angular velocity (ω) is equal to

ω r = v (2.13)

Substitution of Eq. (2.13) into Eq. (2.12) and simplification

produces the conventional form of the cyclotron equation Eq. (2.14)

where ω is the cyclotron frequency.

ω = q Bo / m (2.14)

23

A more useful form of the cyclotron equation is given in Eq. (2.15)

where vc is the cyclotron frequency in Hertz, Bo is the magnetic field

strength in Tesla, m is the ion mass in Da and z is multiples of

elementary charge.

zm

B101.535611

2πω

ν 0

7

cc

×== (2.15)

Ion cyclotron motion is independent of ion velocity and is what

makes ion cyclotron resonance a valuable attribute for mass

spectrometry.

9.4 Tesla FT-ICR Mass Spectrometer at the NHMFL

Figure 2.4 is a schematic of the custom-built FT-ICR mass

spectrometer equipped with a passively-shielded 22 cm room

temperature bore 9.4 Tesla superconducting magnet (Oxford

Instruments, Abingdon, Oxfordshire OX13 5QX United Kingdom)

controlled by a modular ICR data station. Ions generated at atmospheric

pressure in the external ionization region (ESI or APPI) enter the skimmer

region operated at ~2 Torr through a heated metal capillary into the first

rf-only octopole. Ions then pass through a quadrupole to a second rf-only

octopole where they are accumulated for ~50-1000 ms before they are

collisional cooled with helium gas and transferred through another rf-

only octopole to an open cylindrical Penning ion trap. Octopole ion guides

are operated at 2.0 MHz and 240 Vp-p rf amplitude. Broadband frequency

chirp excitation at a sweep rate of 50 Hz µs-1 accelerate the ions to a

detectable cyclotron orbital radius by the differential current induced

between two opposed electrodes within the ICR cell. Multiple (50-200)

time-domain acquisitions are summed for each sample, Hanning-

24

apodized, and zero-filled once before fast Fourier transform and

magnitude calculation.

DOM Analysis by FT-ICR Mass Spectrometry

FT-ICR MS is the only analytical method available to separate and

identify formulas for the tens of thousands of individual compounds

typically observed in a single mass spectrum of DOM. Mass spectrometry

techniques provided little information for characterization of DOM prior

to the 1990’s; however, in the past 10 years the technique has been

successfully applied to characterize organic matter at the molecular level.

FT-ICR MS was first applied to the analysis of humic and fulvic acids in

1997.95 Since then, FT-ICR mass spectrometry has become a standard

for molecular characterization of NOM. The study of the natural cycles is

called “Geomics” by some. The primary focus of Geomics is to determine

the origin and fate of organic molecules in the environment, e.g.

geochemical cycles. However, the challenge is that the beginning and end

points of these cycles are not well defined. Often there are many small

cycles within larger cycles. For example, to understand the role of

dissolved organic nitrogen (DON) in an estuary the elemental

composition of DON must be determined as it enters the estuary, as it

moves through various geochemical cycles within the estuary, and then

as it exits to the open ocean. Different biogeochemical processes may be

inferred based on the composition at each point. Understanding the

bioavailability of thermogenic organic matter (e.g. black carbon, biochar)

and determinig which fractions are inert it is another topical scientific

question that can be addressed by FT-ICR MS.

25

Figure 2.4. Schematic of the 9.4 Tesla FT-ICR mass spectrometer located at the National High Magnetic Field Laboratory, Tallahassee, Florida. Six stages of differential pumping are used to reduce the base pressure in the ICR cell to 10-10

Torr to minimize collisions between ions during excitation/detection. Figure provided by the Marshall

Research group courtesy of John Paul Quinn.

Kendrick mass sorting

Figure 2.5 includes two expanded mass spectral segments for

Suwannee River fulvic acid. A 30 Da expanded mass spectral segment

shows the spacing of 2.0157 Da which is compounds differing in

elemental composition by two hydrogens, equivalent to a increase or

decrease in one double bond equivalent (DBE). DBE represents the

number of double bonds or non aromatic rings (Eq. 2.7). A 100 Da

expanded mass spectral segment depicts the 14.01565 Da spacing

26

representative of members of a homologous series, differing in CH2 units

with the same heteroatom content and DBE (bottom).

DBE = C – 0.5H + 0.5N + 1 (2.16)

Even with high mass accuracy, assignment of correct molecular

formulas above m/z 400 becomes difficult because the number of

possible elemental combinations increase with higher m/z. Kendrick

mass sorting may be used to assign formulas to ions of higher m/z by

extending the mass range of a homologous series from low m/z to span

the entire molecular weight distribution. The Kendrick mass scale is

derived through normalization of molecular weights by the integer value

of the molecular weight of a CH2 unit (14.00000 Da versus the IUPAC

weight of CH2, 14.01565 Da) Eq (2.17).

Kendrick Mass = IUPAC Mass X (14.0000/14.01565) (2.17)

Complex natural mixtures, such as organic matter and crude oil, benefit

by using the Kendrick scale because compounds that differ only in the

degree of alkylation make up homologous series and may be sorted by

their Kendrick mass defect Eq. (2.18).

Kendrick Mass Defect = (exact Kendrick Mass –

Nominal Kendrick Mass) (2.18)

Kendrick normalization and Kendrick mass sorting then identify

homologous series that span the entire molecular weight distribution of a

sample. Accurate mass alone can assign elemental formulas up to m/z

400 and extension of the series allows for identification of all the other

members of that series. Kendrick mass sorting extends elemental

formula assignment to formulas up to nearly m/z 1400.

27

Figure 2.5. Expanded mass spectral segments of Suwannee River fulvic acid produced by negative ESI FT-ICR MS of. 2.0157 Da spacings represent compounds that differ by two hydrogen atoms, indicative of compounds that differ by the addition of one non aromatic ring or

double bond (DBE values). 14.01565 Da spacings (bottom) represent members of a homologous series which differ only in alkylation (CH2).

Mass resolution

Ultrahigh resolution (m/∆m 50% > 350,000, where ∆m 50% is the

magnitude mode mass spectral peak width and half-maximum peak

height) is essential for separation of isobaric species complex mixtures. A

minimum resolving power must be achieved in order to separate signals

from ions with the same nominal mass but differing in Kendrick mass.

For example, the 3.4 mDa split between isobars which differ in elemental

composition by SH4 vs. C3, both having a nominal mass of 36 Da. To

28

accurately assign compositions in complex mixtures, these species must

be resolved, and separation requires a minimum resolving power. The

sulfur species in DOM cannot be correctly assigned if the 3.4 mDa split

is not resolved. In other complex mixtures such as crude oil ionized by

APPI, the overlap between SH313C and C4 (1.1 mDa split) occurs between

a protonated and radical cation, both with 48 Da nominal mass. Correct

elemental assignment requires sufficient resolving power to separate and

identify these isobaric species.

Figure 2.6. Theoretical resolving power for FT-ICR mass spectrometry

(Figure modified from Marshall et al. 1998).108 Because of the

complexity of DOM, a minimum resolving power much be achieved to

facilitate separation and correct identification of isobaric species. The

3.4 mDa split occurs between species with 36 Da nominal mass, but

differing by SH4 and C3. The overlap between SH313C and C4 occurs

between species weighing 48 Da.

29

Figure 2.6 is the theoretical resolving power in FT-ICR MS and the

minimum resolving power required to separate the 3.4 mDa split and the

1.1 mDa split. Separation of the 1.1 mDa and 3.4 mDa isobaric overlap

is required to correctly assign elemental formulas to mass spectral

peaks.

Figure 2.7 Broadband positive ion APPI FT-ICR MS at 9.4 Tesla. 26,359 mass spectral peaks above 6σ the signal-to-noise ratio baseline rms noise are observed from 400 < m/z < 1100 with m/∆m 50% = 900,000 at m/z 687, currently the world record for resolving power at 9.4 Tesla of a petroleum sample.

Figure 2.7 shows broadband APPI FT-ICR MS at 9.4 Tesla of a

crude oil. 26,359 mass spectral peaks from 350 < m/z < 1000 were

observed at 6 times the signal-to-noise ratio baseline rms noise, at an

30

average m/∆m50% = 900,000 at m/z 687. The mass spectrum represents

the highest resolving power at 9.4 Tesla for a broadband mass spectrum

of a complex mixture by FT-ICR MS.

Spectral complexity

Spectral complexity may hinder correct identification of elemental

compositions if sufficient resolution is not achieved. Routinely, FT-ICR

MS of DOM results in more than 15,000 spectral peaks in a single mass

spectrum. As DOM weathers and ages, spectral complexity increases.

Furthermore, APPI ionizes polar and non polar species, and forms

protonated/deprotonated and radical molecular ions which increase

spectral complexity. Approximately 50 peaks per single nominal mass are

common for APPI spectra of DOM. Figure 2.8 shows a broadband

negative ion APPI FT-ICR mass spectrum at 9.4 tesla for DOM isolated

from Lake Bradford (Tallahassee, FL). The mass spectrum contains more

than 25,000 peaks (each with signal magnitude higher than at least 6σ

baseline noise) between 200 and 1000 Da, at a mass resolving power

m/Δm50% (in which Δm50% denotes the full mass spectral peak width at

half-maximum peak height) greater than 600,000 at m/z 500. Mass

spacings as small as 1.8 mDa (C2N13C vs. H3O3) are observed in this

particular mass spectrum.

31

Figure 2.8. Broadband negative ion APPI 9.4 T FT-ICR mass spectrum of Lake Bradford DOM. More than 25,000 mass spectral peaks are resolved at 6σ baseline rms noise at an average resolving power, m/∆50% > 600,000. Inset: m/z ~ 0.1 that shows the spectral complexity of DOM.

Isotopic signatures

To ensure that elemental compositions are assigned correctly,

isotopic signatures are used in conjunction with mass accuracy. One

commonly used isotopic signature is 13C. Since NOM is composed of

compounds containing carbon and hydrogen, the 13C peak may be

detected and identified for nearly every compound. The exact mass

difference between 12C and 13C is 1.0033 Da at an abundance of 1%;

therefore, once a molecular formula is assigned it can be further

validated from its 13C isotope. DOM also contains compounds with high

oxygen content. The 18O signatures may also be used to confirm a

32

molecular formula assignment. Isotopomers, compounds with the same

elemental composition differing by an isotope, such as 16O and 18O differ

in mass by 2.0042 Da at O.2% abundance and are routinely used in FT-

ICR MS to confirm molecular formula assignment. Figure 2.9 shows the

isotopic signatures for a compound containing ten oxygens, its 12C, 13C1,

16O, and 18O1 isotopomers.

Figure 2.9. An m/z ~2 expanded mass spectral segment of negative ion APPI FT-ICR mass spectrum of Lake Bradford DOM at m/z 423 showing the monoisotopic peak for [C20H23O10-H]- with corresponding 13C1 and 18O1 isotopic signatures. The signatures of heavy isotopes are used to confirm the assignment of molecular formulas.

Mass accuracy

Inside of the ICR cell, the act of trapping ions inside an

electrostatic cell shifts their natural cyclotron frequency slightly. A

33

frequency-to-m/z calibration can be applied to correct the m/z

measurement across the molecular weight distribution. The most widely

used calibration equation is shown in Eq. (2.19).

m/z = A/f + B/f2 (2.19)

A and B are constants that are obtained by fitting at least two ICR

frequencies of ions of known m/z to the equation. Internal calibration

produces mass accuracies of less than 1 ppm because calibrant and

analyte ions experience the same electric field inside the ICR cell during

detection. Internal calibration in a DOM mass spectrum is based on

calibration on a homologous, highly abundant alkylation series of ions

differing in mass by 14.01565 Da, the mass of a CH2 unit, across the

entire molecular weight distribution of the sample.

Internal calibration with Eq (2.19) yields mass accuracies between

100-400 ppb for complex mixtures and allows for unambiguous

elemental formula assignments. However, recently it was shown that the

application of a two term Eq. (2.20) or three term Eq. (2.21) “walking

calibration” routinely produces sub 100 ppb mass accuracy.97

m/z = Ai/f2 + Bi/f2 (2.20)

m/z = Ai/f2 + Bi/f2 + Ci*I/f2 (2.21)

34

Figure 2.10. Internal calibration mass accuracy for more than 10,000

mass spectral peaks observed at 10 times the signal-to-noise ratio

baseline rms noise collected by APPI FT-ICR MS at 9.4 Tesla for

European crude. Calculation of the rms mass error for all observed

peaks across 350 < m/z <1025 was 260 ppb.

Dynamic range

Dynamic range is the concentration range of an analyte over which

an analyzer responds linearly. In mass spectrometry, it is the ratio

between the largest and smallest signals simultaneously present in a

mass spectrum and allows measurement of the smaller signal to a given

degree of uncertainty. FT-ICR mass spectrometry has a high dynamic

range therefore making it uniquely sorted for complex mixture analysis,

since less abundant ions are able to be resolved along with highly

abundant ions in the same spectrum. Other techniques with a lower

35

dynamic range have difficulty identifying the less abundant species in a

sample.

Figure 2.11. An m/z ~ 0.25 expanded mass spectral segment of Lake Bradford DOM by negative ESI FT-ICR (bottom). The dynamic range of

FT-ICR MS enables simultaneous detection of peaks with low and high signal-to-noise peaks (top).

Often, the species of interest in DOM are those in low

concentration relative to the overall composition of the sample or

compounds that do not ionize as efficiently, e.g., nitrogen. Figure 2.11

visually represents the advantage of dynamic range across a 250 mDa

window of a FT-ICR mass spectrum. The peak with the greatest

magnitude is at m/z 411.129671 with a signal-to-noise ratio of 222 and

a -40 ppb mass error in elemental composition assignment. A 12 mDa

36

window shows three peaks above six times the baseline rms signal-to-

noise level with much lower signal to noise. However, these peaks are

detected and exact molecular formulas may be assigned with high mass

accuracy due to high dynamic range.

Conclusion

FT-ICR MS is a powerful technique for characterization of complex

mixtures. The enormous complexity of NOM thus makes it well-suited for

the characterization by FT-ICR MS. High mass accuracy and dynamic

range are needed to assign exact molecular formulas to the tens of

thousands of peaks in a single mass spectrum of DOM. High mass

accuracy alone can assign elemental compositions below ~400 Da.

Kendrick mass sorting exploits patterns in DOM and extends the upper

mass limit based on homologous series.

37

CHAPTER 3 SELECTIVE IONIZATION OF DISSOLVED ORGANIC

NITROGEN BY POSITIVE ION ATMOSPHERIC PRESSURE PHOTOIONIZATION COUPLED WITH FT-

ICR MS

Summary

Dissolved organic nitrogen (DON) comprises a heterogeneous

family of organic compounds that includes both well known biomolecules

such as urea or amino acids as well as more complex, less characterized

compounds such as humic and fulvic acids. Typically, DON represents

only a small fraction of the total dissolved organic carbon pool and

therefore presents inherent problems for chemical analysis and

characterization. Here, we demonstrate that DON may be selectively

ionized by atmospheric pressure photionization (APPI) and characterized

at the molecular level by Fourier transform ion cyclotron resonance mass

spectrometry. Unlike electrospray ionization (ESI), APPI ionizes polar and

nonpolar compounds, and ionization efficiency is not determined by

polarity. APPI is tolerant to salts, due to the thermal treatment inherent

to nebulization, and thus avoids salt-adduct formation that can

complicate ESI mass spectra. Here, for dissolved organic matter from

various aquatic environments, we selectively ionize DON species that are

not efficiently ionized by other ionization techniques, and demonstrate

significant increase of signal to noise for APPI relative to ESI.

Introduction

38

Although dissolved organic nitrogen (DON) is an important

component of aquatic ecosystems, most studies focus on dissolved

inorganic nitrogen because that fraction is directly available for biological

uptake.109-110 In fact, DON accounts for a considerable portion of the

total N pool in most environments and represents a potential bioavailable

source of N for phytoplankton and bacteria.21, 111-114 Historically, DON

was believed to be composed predominantly of refractory compounds

resistant to biological degradation.21, 115 However, more recent studies

suggest that DON may have a wide temporal range in turnover, ranging

from hours to decades.21 The roles of DON in the global nitrogen cycle

are identified in fields such as water purification, soil chemistry,

wastewater treatment, and atmospheric chemistry.116-119 Therefore,

complete molecular level characterization of DON is essential to develop a

more complete understanding of the global nitrogen cycle of DON as a

mixture of labile and refractory compounds with significantly different

roles.120-121

Previous molecular characterization of DON has been based on

negative electrospray ionization (ESI) or negative/positive-ESI coupled to

ultrahigh resolution mass spectrometry.122-128 However, ion formation in

ESI is based on the relative acidity (negative ESI) or basicity (positive ESI)

of the analyte in solution. Because DOM is composed of molecular

species with predominately highly polar, carboxylic functionalities,

oxygen-containing compounds are most efficiently ionized by negative

ESI and thus suppress ionization of less acidic species, e.g., nitrogen-

containing compounds.129 Basic nitrogen species may be selectively

ionized by positive ESI; however, less polar nitrogen species are not

efficiently ionized and Na+ adducts often complicate positive ESI mass

spectra.

Atmospheric pressure photoionization

39

Atmospheric pressure photoionization (APPI) is described in detail

elsewhere.130 Briefly, ionization relies on photon absorbtion, which

excites an electron and forms a molecular radical cation (direct

photoionization-see Equation 3.1).106 This process is referred to as direct

photoionization Eq. (3.1). The energy of the photon, hυ, must be greater

than the ionization energy of the analyte. However, in practice, the

radiation output of conventional krypton UV-lamps used for APPI

analysis is too low for efficient direct photoionization of analytes. As a

result, Bruins et al. developed dopant-assisted APPI,106 in which an

excess of photoionizable reagent, D, provides D+• ions (Equation 3.2) that

can react with analyte, M, by charge exchange or proton transfer to

generate M+• or [M+H]+ ions (Equations 3.3 and 3.4). Dopant-assisted

APPI can increase analyte ionization by 2-3 orders of magnitude.131

M + hν → M+˙ + e- (3.1)

D + hν → D+˙ + e- (3.2)

D+˙ + M → [M+H]+ + [D-H]˙ (3.3)

D+˙ + M → M+˙+ D (3.4)

Unlike ESI, in which the sign of the applied potential determine the

charge of the ion, APPI forms both positive and negative ions

simultaneously. As noted above, positive APPI ions are of two ion types,

M+• (if analyte ionization energy is far below that of the dopant) and

[M+H]+ (if the analyte proton affinity is higher than that of the

dopant.106,130 Here, we present data from a broad range of DOM pools to

demonstrate the advantages of positive ion APPI for selective ionization of

nitrogen-containing species, coupled with ultrahigh resolution Fourier

transform ion cyclotron resonance mass spectrometry (FT-ICR MS) for

40

determination of elemental compositions (CcHhNnOo) of thousands of

DON components.

Experimental Methods

Samples

A Suwannee River fulvic acid (SRFA) standard, obtained from the

International Humic Substance Society (IHSS), was diluted to a final

concentration of 500 μg mL-1 with HPLC grade methanol (Sigma-Aldrich,

St. Louis, Missouri). A deep-sea marine DOM sample was collected from

the sea floor of the Gulf of Mexico. Approximately 15 g of Tripsacum

floridanum (Gamma grass without stems or seeds) was dried thoroughly,

cut into 1x1x5 cm pieces and combusted in an oven at a heating rate of

10-12 ° C min-1 and held at a peak temperature of 250 °C for 3 h. Non-

rinsed biochar (1.5 g) was added to 35 mL of deionized water and shaken

for 4 days to obtain the water-soluble leachate. Another sample was

collected from the mouth of the Ochlockonee River, Florida, prior to its

terminus in the Ochlockonee Bay. Finally, a fifth sample was collected

from Lake Bradford, Florida. All samples except the SRFA were filtered

through 0.45 µm Whatman Polycap 150TC filter, acidified to pH 2, and

concentrated by solid-phase extraction as previously reported.132 Each

sample was further diluted with methanol to a final concentration of 500

µg mL-1 in 100% MeOH for mass spectral analysis. Toluene (Sigma-

Aldrich, St. Louis, Missouri) was added as a dopant directly to samples

(10% v/v) prior to APPI analysis.133

Mass spectrometry FT-ICR mass spectra were acquired with a custom-built FT-ICR

mass spectrometer with a passively shielded 9.4 tesla superconducting

magnet (Oxford Instruments, Abingdon, Oxfordshire OX13 5QX United

41

Kingdom) located at the National High Magnetic Field Laboratory,

Tallahassee, Florida.134 A modular ICR data acquisition station was used

for data acquisition, collection, and processing.135 Negative ions were

produced at atmospheric pressure by an external electrospray source

and positive ions were produced with an external atmospheric pressure

photoionization source.105, 136 Electrosprayed negative ions were

accumulated in the first of two radio frequency (rf)-only octopoles for

300-1000 ms. Positive ions produced by APPI were accumulated directly

into the second rf-only octopole (250-500 ms) prior to collisional cooling

with helium gas before transfer to an open cylindrical Penning ion trap.

Broadband frequency sweep ("chirp") excitation (~90-700 kHz at a sweep

rate of 50 Hz µs-1 and 400 V peak-to-peak amplitude at m/z 600)

accelerated the ions to a detectable cyclotron orbital radius. Multiple

(150-200) time-domain acquisitions were summed for each sample,

Hanning-apodized, and zero-filled once prior to fast Fourier transform

and magnitude calculation137 and detected frequencies converted to m/z

by the quadrupolar electric trapping potential approximation.138-139 Mass

spectra were internally calibrated from extended (20-30 peaks)

homologous alkylation series (compounds that differ in elemental

composition by integer multiples of CH2) of high relative abundance. An

average mass resolving power, m/∆m50% > 600,000 at m/z 500 with 100-

400 ppb mass error was achieved for all samples.

Results and Discussion Negative electrospray ionization (ESI) is routinely applied to

characterize DOM by FT-ICR MS.3, 16-17, 124, 140-142 The presence of

carboxylic acid moieties renders negative ESI especially efficient for

ionizing CcHhOo and CcHhOoSs compounds.133, 143 However, many

CcHhOoNn compounds are not ionized efficiently by negative ion ESI

42

(Figure 3.1). Although nitrogen-containing DOM compounds often have

carboxylic acid groups readily available for deprotonation by negative

ESI, the nitrogen may exist as a basic moiety or a relatively less polar

compound.144 Furthermore, nitrogen-containing compounds are always

present at lower concentration than CHO compounds in DOM.

Therefore, comprehensive characterization of DON by (-) ESI is not

feasible without prior separation. Here, we present data to demonstrate

the advantages of positive ion APPI for selective ionization of nitrogen-

containing species, coupled with ultrahigh resolution Fourier transform

ion cyclotron resonance mass spectrometry (FT-ICR MS) for

determination of elemental compositions (CcHhNnOo) of thousands of

DON components without prior separation of the nitrogen-containing

component.

Lake Bradford DOM We compared samples of marine- and terrestrial-derived DOM by

negative ion ESI and positive ion APPI to determine the selectivity of both

ionization types for nitrogen-containing species. Figure 3.1 is a

broadband FT-ICR mass spectrum and a 0.3 Da (all species reported

here are singly charged) mass-scale expanded region (insert) of a

terrestrial DOM sample ionized by negative ion ESI. The sample was

obtained from a fresh water lake (Lake Bradford, FL) for which the DOM

is primarily allochthonous detritus from a surrounding hardwood forest.

The most abundant ions are [CcHhOo - H]- of odd nominal mass. The

most abundant species of even nominal mass are [13C12Cc-1HhOo - H]-.

According to the "nitrogen rule", an even-electron ion (e.g., [M-H]-, as for

negative ESI) containing an odd (even) number of nitrogens will have

even (odd) mass.145

43

Figure 3.1. Broadband negative electrospray 9.4 T FT-ICR mass spectrum of Lake Bradford DOM. Inset: m/z ~ 0.3 expanded mass spectral segment at m/z 412.0.

In contrast, the positive ion APPI FT-ICR mass spectrum of Lake

Bradford DOM exhibits a marked increase in the number of nitrogen

species (Figure 3.2). Although [CcHhOo + H]+ are the most abundant ions

throughout the mass spectrum, [CcHhOoN1 + H]+ are the most abundant

ions at even nominal mass.145 The four most abundant nitrogen-

containing species are labeled in each expanded segment of Figures 3.1

and 3.2. Five-nine fold higher S/N are observed by APPI for the two

parent compounds common to both ionization modes, C18H23N1O10 and

C19H27N1O9. Similarly higher APPI S/N of the nitrogen-containing ions

are observed across the entire mass spectrum.

44

Figure 3.2. Broadband positive ion atmospheric pressure photoionization 9.4 T FT-ICR mass spectrum of Lake Bradford DOM. Inset: m/z ~ 0.3 expanded mass spectral segment at m/z 414.0.

Deep-sea marine DOM

A more dramatic increase in the S/N of DON ions from (+) APPI

relative to (-) ESI is observed for deep-sea marine DOM (Figure 3.3). In

fact, across an eight Da mass window, the most abundant ions formed

by (+) APPI are N1 species at even nominal mass, e.g., [CcHhN1Oo + H]+

(Figure 3.3 (top)). However, the same DOM sample analyzed by negative

ESI yields the most abundant ions at odd nominal mass, e.g., [CcHhOo -

H]- (Figure 3.3 (bottom)), with little to no signal from [CcHhN1Oo - H]-. It is

important to note that ions formed by positive ion APPI may be an even-

(e.g., [M+H]+) or odd-electron (e.g., M+•). However, one can in fact

45

distinguish even- from odd-electron nitrogen-containing ions based on

the calculated half-integer or integer DBE for the ion.146

Figure 3.3. FT-ICR MS m/z expanded mass spectral segments for deep-sea marine DOM produced by positive ion APPI (top) and negative ESI (bottom). For APPI, the most abundant have even nominal mass, e.g., [CcHhN1Oo + H]+. For ESI, the most abundant ions have odd nominal mass, e.g., [CcHhOo - H]-.

Next, we randomly chose two deep-sea DOM neutral compounds

ionized by APPI and ESI, to compare ionization efficiency (Figure 3.4).

C20H29N1O5 has a 13-fold higher S/N (Figure 3.4 (top)) and C16H21N1O5

has nearly a 6-fold increase in S/N with (+) APPI compared with (-) ESI

(Figure 3.4 (bottom)). The increase in S/N is particularly important for

isolation of ions of a single m/z for subsequent dissociation (MS/MS) to

46

provide structural information: e.g., as previously reported for (-) ESI FT-

ICR MS/MS of CHO compounds.147

Figure 3.4. Two m/z ~ 0.01 expanded mass spectral segments for deep-sea marine DOM produced by APPI (left) and ESI (right). Compounds with a common neutral formula were selected. Note that S/N ratio is

more than 10-fold (top) or (5-fold (bottom) higher for APPI than ESI for the same neutral compound.

Various DOM Samples

Deep sea marine DOM, Lake Bradford DOM, a DOM leachate from

combusted biomass (e.g. biochar, or black carbon), DOM from the

Ochlockonee River, and Suwannee River fulvic acid (SRFA) were

compared by positive APPI and negative ESI. Those samples represent a

wide variety of unique DOM pools, and all exhibit markedly higher

47

relative abundance for all nitrogen-containing classes by (+) APPI relative

to (-) ESI, e.g., CcHhN1-3Oo and CcHhN1OoS1 (Figure 3.5). The exception is

Suwannee River fulvic acid standard (SRFA). It should be noted that the

fulvic acid extract does not necessarily represent a comprehensive

molecular fraction of the DOM in the Suwannee River.

Figure 3.5. Histogram depicting the percent relative abundances for all nitrogen-containing species representative of five distinct DOM sources for positive ion APPI and negative ion ESI.

Na+ adduct formation by (+) ESI. Positive ion ESI has previously

characterized DON in algae and rainwater.128, 148 Although positive ion

ESI can access the more basic nitrogen species, Na+ adducts complicate

the spectrum without providing addition information, as illustrated by

the Na+ adduct species in the (+) ESI FT-ICR mass spectrum of Figure

48

3.6. Moreover, such an adduct ion frequently occurs at a mass

separation of only 2.4 mDa from an ion of identical composition except

for substitution of NaH for C2. We observe that (+) APPI of DOM yields [M

+ H]+ but not [M + Na]+. Moreover, less polar and non-polar nitrogen

species are not ionized by (+) ESI. Therefore, (+) APPI is recommended for

DON characterization because it does not complicate spectra with

unwanted adducts and ionizes nitrogen in all forms, i.e., basic, less-

polar, non-polar.

Figure 3.6. An m/z ~1 expanded segment of the positive ESI 9.4 T FT-ICR mass spectrum of SRFA. Each sodium adduct is separated by 2.4 mDa from the compound of the same nominal mass, but differing in composition by substitution of NaH for C2. The elemental compositions that contain Na are highlighted with an (*).

Conclusion

49

Changes in DON composition are paramount in understanding

DON cycling and environmental interactions. Although DON has an

important role in biogeochemical processes, DON remains largely

uncharacterized by FT-ICR MS due to inefficient ionization relative to

DOC. Here, for the first time, we show that positive ion APPI ionizes DON

more efficiently that negative ESI. Positive ion APPI coupled to FT-ICR

MS opens new doors for the molecular characterization of DON. DON

may be characterized without separation. Finally, High signal to noise

and relative abundance of DON peaks enable possible MS/MS

characterization of DON ions at a single m/z unlocking structural

information about the incorporation of nitrogen in DOM.

50

CHAPTER 4

APPI FT-ICR MS CHARACTERIZATION OF

WASTEWATER-DERIVED DON AFTER ADVANCED OXIDATION TREATMENT AND ALGAL

BIOREMEDIATION

Summary

Wastewater treatment facilities (WWTF) are equipped with

nitrification and denitrification systems that may decrease the

concentration of dissolved inorganic nitrogen (DIN) by more than 95%.

However, the removal of dissolved organic nitrogen (DON) is significantly

less efficient. DON accounts for approximately 65% of the total dissolved

nitrogen (TDN) in conventional WWTF effluent and may compose up to

80% of the TDN in effluent from WWTF with efficient nitrification-

denitrification systems. Previous studies suggest that anywhere from 2 to

70% of DON in surface water is bioavailable. The variability in DON

bioavailability in natural waters is most likely related to differences in

DON composition. Here, the elemental composition of DON in wastewater

before and after treatment by advanced oxidation processes (AOP) and

algal remediation was determined by positive ion atmospheric pressure

photoionization coupled to Fourier transform ion cyclotron resonance

mass spectrometry. The data show that AOP degrades DON compounds.

DON is mostly unavailable to algae prior to treatment by AOP. After AOP

degraded compounds are more labile and are available for uptake by the

algae.

Introduction

51

Wastewater-derived nitrogen accounts for approximately 12 to 33%

of the nitrogen pollution in rivers worldwide, while agriculture and

fertilizer runoff account for the remainder of the anthropogenic nitrogen

release to rivers.149 Wastewater treatment facilities (WWTF) are equipped

with nitrification and denitrification systems that may decrease the

concentration of dissolved inorganic nitrogen (DIN) by more than 95%;

however, the removal of dissolved organic nitrogen (DON) is significantly

less efficient. DON accounts for approximately 65% of the dissolved

nitrogen in conventional WWTF effluent and may compose up to 80% of

the total dissolved nitrogen (TDN) in effluent from WWTF with efficient

nitrification-denitrification systems.150

The relatively high contribution of DON to the TDN content of

treated wastewater effluent is significant for watershed protection

because most total daily load permits use TDN as the nitrogen parameter

and do not consider the possibility that DON and DIN may have different

potentials to cause cultural eutrophication. Both nitrate and ammonium

are known to stimulate primary production; however, the bioavailability

of DON is uncertain.42 Previous studies suggest that from 2 to 70% of

DON in surface water is bioavailable.25, 151-155 The variability of DON

bioavailability in natural waters is most likely related to differences in

DON composition. Free amino acids, urea, and nucleic acids are readily

available for uptake by heterotrophic bacteria and algae. DON in other

forms, such as humic substances, are not as available to support algal

growth in N-limited systems.114, 153, 156 However, photochemical reactions

in natural waters may convert DON that is not readily available to more

labile compounds such as primary amines152 or ammonia,157-158 although

photochemical reactions may also adversely affect the bioavailability of

DON.158

Although wastewater-derived DON is a significant contributor to

anthropogenic nitrogen input in receiving waters, there is a lack of

52

information about its bioavailability. Previously, the bioavailability of

wastewater-derived DON was determined to range from 0 to 60% by

measuring the uptake of nitrogen by activated sludge bacteria over 60

days.159 In another study, DON that was exposed to bacteria for 42 days

prior to algal uptake experiments, did not support algal growth.160 Two

factors may have resulted in underestimation of the bioavailability of

DON. First, gravimetric methods used for determination of algal growth

may not have been sensitive enough to detect small changes in algal

biomass. Second, bacterial uptake is needed to reincorporate dissolved

organic matter from primary production161 and evidence is provided that

bacteria play role in the availability of DON to algae in natural waters.153,

162-163 The absence of bacteria in the algal cultures used by Parkin and

McCarty may have underestimated the availability of wastewater-derived

DON to algae. The importance of bacteria in the wastewater-derived DON

cycle was also indicated by algal bioavailability experiments conducted in

the absence and presence of bacteria.164 Approximately 10% of the

wastewater-derived DON was available to algae in the absence of

bacterial compared with 60% in the presence of bacteria.

Wastewater-derived DOM may also serve as precursors to

disinfection by-products when wastewater is treated with chlorine.

Although disinfection of water with chlorine offers protection against

waterborne diseases, chlorination forms disinfection by-products (DBP)

which are linked to other illnesses such as cancer.165 Organic

compounds in water, including humic substances, amino acids, and

proteins, are known to form trihalomethanes (THM) and dihaloacetic

acids (DHAA) when treated with chlorine.166-168 In addition to acting as a

precursor for THM and DHAA, wastewater-derived DON may form a

variety of DBP with a nitrogen functional group such as haloacetonitriles,

cyanogen halides, and N-nitrosodimethylamine. Chlorinated wastewater

effluent is toxic to aquatic organisms and must undergo dechlorination

prior to discharge. DON affects the efficiency the chlorination and

53

dechlorination process because DON reacts with chlorine to form organic

chloramines.

DON is typically characterized by bulk property measurements.

These measurements do not provide information about the individual

nitrogen components in a complex DOM mixture. Recently, positive ion

atmospheric pressure photoionization (APPI) showed selective ionization

of the nitrogen-containing component of DOM.169-170 Here, untreated

wastewater and wastewater treated by advanced oxidation processes

(AOP), i.e., a combination of ultra-violet (UV) radiation and ozone are

characterized APPI coupled to a Fourier transform ion cyclotron

resonance (FT-ICR) mass spectrometer. AOP provide an alternative

method for disinfection of wastewater without formation of chlorinated

DBP. Furthermore, AOP may break down refractory organic compounds

such as humic substances to more labile compounds. To better

understand if wastewater DON is more bioavailable after AOP we added

algae to treated and untreated water and characterized the changes in

elemental composition by positive ion APPI FT-ICR MS.

Experimental Methods

Samples

A wastewater sample was collected from the secondary clarifier at

the Tallahassee Municipal Wastewater Treatment Facility, Tallahassee,

Florida in a 5 gallon carboy. Fats, oils, and grease are skimmed from the

surface in the secondary clarifier while solids settle to the bottom. The

secondary clarifier is prior to disinfection in the treatment process. The

sample was immediately filtered through a 0.7 μm Whatman GF/F filter.

An untreated and treated wastewater sample was inoculated with algae.

After 14 days, each sample (including treated and untreated wastewater

without algae) was filtered through a 0.2 μm Whatman Polycap TC150

54

filter and acidified to pH 2. Varian Bond-Elute PPL cartridges were used

for solid-phase extraction of DOM.132

Advance oxidation treatment

Approximately 1 L of filtered wastewater was treated for 90 min. by

UV radiation and ozone pumping chamber made from 4 inch diameter

PVC pipe. A Maxi-Jet 1200 submersible pump and power head is located

within the chamber for continuous circulation of the sample. Ozone was

pumped into the reaction chamber by an air-cooled corona discharge

ozone generator at a rate of ~0.33 g ozone min-1. A Mazzei injector

entrained ozone into the water column. The wastewater sample was

simultaneously irradiated with an 8-Watt, UV-C germicidal lamp inserted

down the center line of the reaction chamber. Aliquots were removed for

analyses at 15, 30, 60, 90, and 120 min. intervals to determine when the

sample was fully treated. Samples were considered fully treated when

absorption, measured by a Cary Varian 100 dual beam UV/VIS

spectrometer, no longer changed as a function of exposure time. It was

found that absorption did not change after 90 min. of exposure for any of

the samples.

Mass spectrometry

FT-ICR mass spectra were acquired with a custom-built FT-ICR

mass spectrometer with a passively shielded 9.4 tesla superconducting

magnet (Oxford Instruments, Abingdon, Oxfordshire OX13 5QX United

Kingdom) located at the National High Magnetic Field Laboratory,

Tallahassee, Florida.96 A modular ICR data acquisition station was used

for data acquisition, collection, and processing.135 Positive ions were

produced with an external atmospheric pressure photoionization

source.105, 136 Ions were accumulated directly into the second rf-only

octopole (250-500 ms) prior to collisional cooling with helium gas before

55

transfer to an open cylindrical Penning ion trap.136 Broadband frequency

sweep (“chirp”) excitation (~90-700 kHz at a sweep rate of 50 Hz µs-1 and

a 400 V peak-to-peak amplitude at m/z 600) accelerated the ions to a

detectable cyclotron orbital radius. Multiple (150-200) time-domain

acquisitions were summed for each sample, Hanning-apodized, and zero-

filled once prior to fast Fourier transform and magnitude calculation137

and detected frequencies converted to m/z by the quadrupolar electric

trapping potential approximation.138-139 Mass spectra were internally

calibrated from extended (20-30 peaks) homologous alkylation series

(compounds that differ in elemental composition by integer multiples of

CH2) of high relative abundance. An average mass resolving power,

m/∆m50% > 600,000 at m/z 500 with 100-400 ppb mass accuracy was

obtained for all samples.

Results and Discussion

Fourier transform ion cyclotron resonance mass spectrometry (FT-

ICR MS) is the only analytical method with the resolving power and mass

accuracy required to assign exact molecular formulas to each peak in a

mass spectrum of a complex mixture such as dissolved organic matter

(DOM). The elemental composition of the DOM extracted from untreated

wastewater, wastewater treated by advanced oxidation processes (AOP),

and untreated and treated wastewater after 14 days of algal growth is

determined by FT-ICR MS. As mentioned previously, reports show that

the bioavailability of DON is dependent on composition. Therefore, the

data presented specifically focuses on the changes in DON as a function

of treatment and algal growth.

Untreated vs. treated DON Molecular formulas for DON in untreated and treated wastewater

are shown in Figure 4.1. Prior to treatment, wastewater is composed of a

56

relative high amount of compounds with aromatic (yellow) and

condensed aromatic (red) character (top). The formulas reside in regions

of the diagram associated with lignin-, aminosugar-, protein-, and lipid-

like compounds.100 Although the bulk of the DON composition remains

the same, the aromatic compounds are removed after treatment by AOP

(bottom). The removal of aromatic compounds after AOP are consistent

with previous reports of DOM degradation by UV radiation124 and

ozone.171

Formulas for DON in treated and untreated wastewater are plotted

as a function of double bond equivalents (DBE) and carbon number

(Figure 4.2). Prior to treatment, wastewater has a DBE range from ~1

to18 and a carbon number distribution from ~11 to 30. After treatment

the maximum DBE decreases to ~13 and more formulas are formed at

lower DBE. Furthermore, there is a slight shift to lower carbon number;

however, compounds with low DBE, but high carbon number are formed.

A possible explanation for the formation of compounds with high carbon

number and low DBE may be that the compounds are products of

reactions between multiple partially oxidized DON compounds.

Although FT-ICR MS is only semi-quantitative, formula class

graphs are useful for visualizing differences in mass spectra. The

dominant DON heteroatom classes in untreated and treated wastewater

are shown in Figure 4.3. Wastewater-derived DON is predominately

composed of N1O4-9 and N2O4-7 classes. The most abundant class is the

treated and untreated sample is N1O5. The relative abundance of the

N1O5 class increases following AOP. Furthermore, an abundance of N1O1

species are formed. There is an overall decrease in relative abundance of

N1 and N2 classes after treatment.

57

Figure 4.1. van Krevelen diagram of wasterwater-derived DON compounds before (top) and after (bottom) treatment by AOP. AOP degrades the aromatic (yellow) and condensed aromatic (red) DON compounds.

58

Figure 4.2. DBE vs. carbon number plots of wastewater-derived DON before (top) and after (bottom) AOP. A shift to lower DBE is observed after AOP caused by degradation of aromatic compounds. The increase of compounds at low DBE and high carbon number may be the product of reactions between partially oxidized compounds.

Before AOP

After AOP

59

Figure 4.3. Class graph of the most abundant DON species in wastewater before and after treatment. A shift to lower heteroatom number is indicative of degradation of large compounds.

The decrease in relative abundance of N2 classes in Figure 4.3 is

believed to be indicative of degradation of larger DON compounds to

smaller more labile compounds. Figure 4.4 is a series of van Krevelen

diagrams for individual N1, N2, and N3 DON classes before (top) and after

(bottom) AOP. The van Krevelen diagrams for the N2 and N3 classes show

a significant loss of compounds after AOP. The majority of the elemental

composition of the N1 class remains relatively unchanged although there

is a loss of aromatic compounds (relatively low H:C and O:C) and a minor

increase of compounds with high H:C and low O:C. The relatively

consistency in N1 composition after AOP may be explained by the

degradation of N2 and N3 compounds to N1 compounds.

60

Figure 4.4. van Krevelen diagrams of individual nitrogen classes before (top) and after (bottom) AOP. N2 and N3 compounds are removed after AOP. The removal of N2 and N3 compounds are consistent with degradation of large compounds by AOP. The N1 class remains mostly

unchanged although there is a slight addition of compounds with high H:C and low O:C. Although N1 compounds are most likely degraded after AOP it is likely that degraded N2 and N3 compounds have the same composition as the original N1 compounds before AOP. Therefore, the N1 class has similar compositional coverage before and after AOP.

The data from untreated and treated wastewater show that large

and aromatic DON compounds are degraded by AOP. Presumably, the

compounds are degraded to labile compounds that may be available for

uptake by algae. The second phase of this work is to characterize

changes in DON composition of untreated and treated wastewater by

algal remediation.

61

Untreated and treated wastewater remediated by algae The elemental compositions of DON in untreated and treated

wastewater before and after 14 days of bioremediation by algae is

summarized in the van Krevelen Diagrams of Figure 4.5. The elemental

composition of untreated wastewater-derived DON remediated by algae

does not significantly change relative to the original untreated sample

(top). However, after AOP treatment and bioremediation, compounds with

relatively high H:C and low O:C are removed and those with relatively low

H:C and high O:C are formed. Figure 4.6 is a van Krevelen Diagram of

the DON formulas that are only present in the treated sample without

algae (top) and the DON formulas only present in the treated sample

after algal bioremediation (bottom). The algae uptake nitrogen

compounds with high H:C and low O:C and release DON compounds

with relatively low H:C and high O:C.

The relative abundance of DON compounds with relatively high

oxygen increase after remediation while DON with relatively low oxygen

decrease (Figure 4.7). The relative abundance of N1O1 and N1O5 classes

are significantly decreased after bioremediation. The formulas for the

N1O5 class are plotted in Figure 4.8. The trend of the N1O5 class is

different than the trend observed for all DON compounds. The N1O5

compounds shift to lower O:C and remain at a relatively constant H:C

after algal remediation. The shift to lower O:C is not easily explained;

however, the compounds that are removed after remediation (top) are not

aromatic in nature and therefore may be available to algae.

62

Figure 4.5. van Krevelen diagrams of wastewater-derived DON untreated (black), untreated after algal remediation (green), after treatment (blue), and after treatment and algal remediation (red). No change is observed for untreated wastewater before and after algal remediation. After treatment, formulas with relatively high H:C and low O:C are

lost and formulas with low H:C and high O:C are added.

63

Figure 4.6. van Krevelen diagrams of the formulas that appear only in treated wastewater before algal remediation (top) and formulas only detected in the sample after algal remediation (bottom). Algae remove compounds with relatively high H:C and low O:C and release compounds with low H:C and high O:C.

64

Figure 4.7. Class graph of the most abundant nitrogen species in treated wastewater before and after algal remediation. After remediation, a shift to higher oxygen class is observed. Furthermore, there is a significant decrease in the abundant N1O1 and N1O5 classes observed in the treated sample before algae.

Although the work here is devoted to characterization of DON, it is

important to note the changes in dissolved organic carbon. The relative

abundance of O5-O12 classes drastically increase after remediation

compared with the treated sample before remediation (Figure 4.9). The

increase implies that a significant amount of highly oxygenated DOC

compounds are released by the algae. Figure 4.10 shows that the oxygen

compounds removed (top) and added (bottom) follow a similar trend to

the DON compounds. The data show that algae are not only significant

participants in the organic nitrogen cycle, but also participate in the

carbon cycle as well.

65

Figure 4.8. FT-ICR MS enables characterization of individual heteroatom classes. A shift to lower O:C of the N1O5 class is observed in the treated sample after algal remediation (bottom). The trend to lower

O:C in the N1O5 class differs from the overall trend observed for all N classes; however, the compounds removed after remediation (top) are not aromatic in nature and may be bioavailable to algae.

66

Figure 4.9. Class graph of the oxygen species in treated wastewater before and after algal remediation. There is an increase in relative abundance of O5-O12 classes indicative of a release of highly oxygenated DOC compounds by algae.

67

Figure 4.10. van Krevelen diagram of DOC formulas unique to the treated sample before (top) and after (bottom) algal remediation. The trend of removal of compounds with relatively high H:C and low O:C, and input of compounds with low H:C and high O:C see in the plots for DOC are similar to those observed for DON.

Conclusion

Here, we show that positive ion APPI coupled to FT-ICR MS enables

characterization of wastewater-derived DON before and after AOP and

algal remediation. AOP degrades large compounds that are refractory in

the environment into labile compounds. Further evidence is provided

that AOP beaks apart N2 and N3 compounds into smaller N1

components. Some of these degraded compounds are then bioavailable to

algae for uptake. Undoubtedly, algae continuously remineralize DON in

68

untreated aquatic systems. However, it is difficult to discern which

compounds are removed and released by algae in untreated water. After

treatment, it became clear which compounds were used and released by

the algae. Furthermore, we show that algae not only use labile DON, but

also labile DOC, further emphasizing the key role of algae in the organic

nitrogen and organic carbon cycles. As suggested by others,

bioavailability of DON to algae is dependent on DON composition. AOP

degrades DON that is not available to algae to more labile compounds

that are bioavailable.

69

CHAPTER 5

CHARACTERIZATION OF REACTIVE AND

REFRACTORY DISSOLVED ORGANIC NITROGEN IN A STORMWATER TREATMENT AREA BY APPI FT-

ICR MS

Summary

Dissolved organic nitrogen (DON) represents a significant fraction

of the total dissolved nitrogen pool in most surface waters. While

traditionally phytoplankton and bacteria were thought to prefer inorganic

nitrogen as a substrate, recent work indicates that DON also serves as

an important nitrogen source. We have coupled atmospheric pressure

photoionization (APPI) with ultrahigh resolution Fourier transform-ion

cyclotron resonance mass spectrometry (FT-ICR MS) to examine DON

from the Caloosahatchee Estuary located in southwest Florida as part of

a larger bioassay study to determine DON bioavailability. FT-ICR mass

spectra were obtained on samples before and after a 5-day bioassay with

natural microbial communities. Positive ion APPI FT-ICR MS yielded

more nitrogen-containing DOM ions than negative ion APPI, an

indication that positive APPI selectively ionizes DON. Less than 5% of

DON was removed after 5 days of microbial exposure. Mass spectral data

confirmed that the majority of formulas found in the original sample were

also present in the degraded sample, and most of these compounds,

which we define as refractory, had molecular compositions representative

of lignin-like molecules. In contrast, lipid-like and protein-like molecules

were the primary compounds removed from the original sample during

70

the bioassay, suggesting that they may be the small reactive component

of the DON pool.

Introduction

Transport of nitrogen to coastal waters leads to coastal

eutrophication, often resulting in harmful algal blooms, reductions in

submerged aquatic vegetation, hypoxia, and anoxia.172 The

Caloosahatchee River, in southwest Florida, USA, transports water,

nutrients, and suspended solids from Lake Okeechobee and the Northern

Everglades Watershed to the Caloosahatchee River Estuary and then into

the Gulf of Mexico. This region of the Gulf is plagued with annual blooms

of the red tide forming dinoflagellate, Karenia brevis.173 Karenia brevis,

like many harmful algal bloom species, uptake dissolved organic nitrogen

(DON).174 Riverine input of nitrogen to the Gulf of Mexico may support

near shore blooms and some suggest that the reduction of terrestrial

derived nitrogen sources may aid in reducing red tides.

The South Florida Water Management District is developing a

water quality treatment area (WQTA) to demonstrate a wetland-based

technology for reducing dissolved nitrogen in the Caloosahatchee River,

of which DON comprises approximately 90% of the total dissolved

nitrogen (TDN). Unlike treatment of dissolved inorganic nitrogen (DIN),

where successful treatment results in dramatic reduction of the

concentration of DIN, DON reduction is feasible only to some background

refractory concentration. The design of nitrogen WQTA’s must thus focus

on; 1) removal of DON by sequestering it into particles that will

eventually sink and be removed from the system or 2) transformation of

DON into recalcitrant forms that will not be further degraded while

within the river or coastal zone.

Although DON plays an important role in microbial productivity,

over 75% of the total marine DON pool remains uncharacterized at the

71

molecular level.175 Characterization of DON at the molecular level

enables identification of DON compounds based on molecular formula,

and insight into the types of DON compounds that are bioavailable or

refractory. Therefore, designing a WQTA requires molecular-level insight

into DON bioavailability and the development of cost-effective methods to

determine the specific transformations of DON that occur here.

Molecular-level characterization of DON for the work presented

was accomplished by atmospheric pressure photoionization (APPI)

Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR

MS). The DON component of natural organic matter (NOM) is extremely

complex and has resisted molecular-level characterization because of a

lack of suitable analytical methods.175 FT-ICR MS is a powerful method

for advanced characterization of dissolved organic matter (DOM).3, 15, 104,

176 Recent reports suggest that FT-ICR MS has significant potential for

the molecular-level characterization of DON in a variety of ecosystems.133,

177-179

Unlike electrospray ionization (ESI), APPI may ionize non-polar and

polar analytes by proton transfer or charge exchange between a

photoionized dopant (e.g., toluene) and an analyte.131 Furthermore, APPI

is more resistant to chemical noise from solvents and salts (particularly

beneficial for DOM characterization), and exhibits less ion suppression

from matrix effects.131, 180 In a recent study of DOM, we noted that APPI

generated nitrogen-containing ions with significantly higher S/N relative

to negative ESI, providing evidence that positive ion APPI may be more

appropriate for studies of DON bioavailability than negative ESI.169

Here, we conduct a detailed study of DON composition and

bioavailability in waters sampled from the Caloosahatchee River and

adjacent stormwater treatment areas. Bulk DON bioavailability was

assessed through bioassays with inoculum from downstream, mid-

salinity (salinity of 15) sites in the Caloosahatchee River. Sampling was

carried out at the end of the dry (April/May) and during the wet (end of

72

June) seasons to determine if “fresh” DON washed into the river during

the rainy season was more bioavailable than “older” DON that had

accumulated during the dry season.

Experimental Methods

Samples

Water samples were collected by Professor Deborah Bronk from the

Virginia Institute of Marine Sciences. Waters from the Caloosahatchee

River and adjacent stormwater treatment areas in southwest Florida

were sampled during the dry season on April 29, 30 and May 1, 2009,

and the wet season on June 23, 24, and 25, 2009. In this report we will

focus on data from the northern Caloosahatchee River C-43 site, located

at 26.79199° N, 81.29789 W. Samples obtained during both seasons

were handled in a similar fashion. The water was collected in acid

washed (10% HCl) 9 L polycarbonate carboys and stored in the dark and

kept cool to limit biological activity during transportation. The water was

filtered within through a Pall Life Sciences 142 mm A/D binder-free glass

fiber filter (GFF), which was pre-combusted for 2 hours at 450°C. A

similarly pre-combusted Whatman 142 mm GF/F was used as a second

filtration and the final filtration step was with a pre-cleaned Pall Gelman

Acropak 500 0.2 µm capsule filter. The resulting filtrate was stored

refrigerated (~4oC) in the dark until use in bioassay experiments.

Bioassays Bioassays were carried out at the Virginia Institute of Marine

Sciences under the supervision of Professor Bronk. The water from the

sampling site was fresh. However, the interest of this study was to

determine the bioavailability of the DON downstream of the sampling

site. Therefore, the inoculum was collected from a site on the river with a

73

salinity of 15, and was subsequently passed through 150 µm mesh to

remove grazers. The salinity of the fresh water sampling sites was

increased to 15 with pre-combusted sodium chloride, magnesium

sulfate, and sodium bicarbonate. Once the salinities were properly

adjusted, incubations were set up using 400 mL of site water and 125

mL of inoculum. The initial samples (designated T0) were filtered with two

Whatman 25 mm GF/F, which were pre-combusted for 2 hours at 450°C,

immediately after the inoculum was added, and the filtrate was stored in

the dark at 4°C. Samples were incubated on a 12:12 light:dark cycle at

ambient temperature, which was 24°C in the dry season and 26°C in the

wet season. After 120 hours, the samples (designated T5) were removed

from the incubators and filtered as described for the T0 samples. Some of

the filtrate was collected in polypropylene tubes for later analysis of total

dissolved nitrogen (TDN), ammonium (NH4+), nitrite (NO2

-), and nitrate

(NO3-) concentrations. Additional filtrate was stored in high-density

polyethylene (HDPE) bottles, which was washed with 7% HCl and de-

ionized water, before the extractions for APPI FT-ICR MS analyses.

Extraction DON in water samples was extracted for FT-ICR MS analysis with

100 g, 1 mL Varian Bond Elut PPL solid phase extraction (SPE)

cartridges that were first rinsed with HPLC grade methanol.132

Optimization experiments were carried out to determine the effect of

acidity on DON extraction efficiency. Each sample was extracted after; (1)

addition of concentrated HCl to a final pH ≤ 2.5, (2) addition of NH4OH to

a final pH of > 10, and (3) no pH adjustment. Analysis of subsequent FT-

ICR mass spectra indicated that the neutral extraction was slightly

superior, although the differences between methods were within the

experimental variability (~4%) of the overall method. However, the acid

extraction is surprisingly efficient at isolating organic-N, with ~ 22% of

74

the assignable compounds containing nitrogen. Because the acid

extraction greatly increases the total number of compounds we observe

(i.e. those that do and do not contain N) we chose the acid extraction

method.

Prior to extraction each acidified sample was pre-filtered through a

sequence of 3.0 and 0.2 μm Nuclepore filter cartridges. One L of water

was pumped through the SPE PPL cartridge at a flow rate of <50

mL/min. The cartridges were rinsed with 100 mL of ultrapure water at

pH 2 to remove any remaining salts, dried with a stream of N2 and eluted

with 30 mL of HPLC grade methanol at a flow rate of <10 mL/min. DON

extractions were stored in the dark at -18ºC. The quantitative reliability

of this method for extracting DOM is estimated to be ~60% for DOM in

fresh water,132 but there is no data available on DON extraction

efficiency. Toluene (10 v/v) was added as the APPI dopant to the SPE

extracts prior to FT-ICR MS analysis.

Mass Spectrometry FT-ICR mass spectra were acquired with a custom-built FT-ICR

mass spectrometer with a passively shielded 9.4 tesla superconducting

magnet (Oxford Instruments, Abingdon, Oxfordshire OX13 5QX United

Kingdom) located at the National High Magnetic Field Laboratory,

Tallahassee, Florida.96 A modular ICR data acquisition station was used

for data acquisition, collection, and processing.135 Ions were produced

with an external atmospheric pressure photoionization source105 and

accumulated directly into the second rf-only octopole (250-500 ms) prior

to collisional cooling with helium gas before transfer to an open

cylindrical Penning ion trap. Broadband frequency sweep ("chirp")

excitation (~90-700 kHz at a sweep rate of 50 Hz µs-1 and a 0.75 V peak-

to-peak amplitude) accelerated the ions to a detectable cyclotron orbital

radius. Multiple (150-200) time-domain acquisitions were summed for

75

each sample, Hanning-apodized, and zero-filled once prior to fast Fourier

transform and magnitude calculation137 and detected frequencies

converted to m/z by the quadrupolar electric trapping potential

approximation.138-139 Mass spectra were internally calibrated from

extended (20-30 peaks) homologous alkylation series (compounds that

differ in elemental composition by integer multiples of CH2) of high

relative abundance. An average mass resolving power, m/∆m 50% >

600,000 at m/z 500 with 100-400 ppb mass error was achieved for all

samples.

Results and Discussion

Bioassay results For the site chosen for this study, DON uptake during the

bioassays was negligible (1.2% for the dry season and 4.1% for the wet

season). Similar results were obtained from bioassays on samples from

all sites sampled. The low rates of DON removal may be attributed to a

variety of factors including availability of alternative nitrogen sources,

failure of the inoculum to grow, or the refractory nature of the DON at

the sampling site.181 Phytoplankton generally favor inorganic nitrogen as

a growth substrate, and NH4+ is preferred by both phytoplankton and

bacteria.23, 182 Approximately 96% of the TDN was determined to be DON,

and therefore, only a relatively small fraction of the nitrogen available

existed as inorganic nitrogen. Bioassays are completely dependent on the

ability of the inoculum to thrive, not only so that the microbes can

remove DON, but also so that dying cells do not contribute to the

accumulation of DON. Although the changes were minimal, the

decreasing DON values do indicate that as least some portion of the DON

was reactive. However, it is likely that the labile fraction is very small

relative to the refractory fraction of the DON pool. Therefore, the majority

76

of the DON compounds in the C-43 water are not readily available for

uptake by the microbes during the time-scale of this study, which in turn

resulted in a relatively small change in the bulk DON concentrations.

Characterization of DON by APPI FT-ICR MS

All assigned DON ions contained carbon, hydrogen, and oxygen,

and thus we define classes based on the number of nitrogen atoms

contained in each molecular formula. Compounds in the N1 class are the

most frequently observed in both the negative and positive ion mass

spectra, with a steady decline in the number of molecules in the N2 and

N3 classes (Figure 5.1). No ions could be confirmed with more than three

nitrogen atoms.

It is clear from these data that positive ion APPI is significantly

more efficient at ionizing DON molecules than negative ion APPI for all

three nitrogen classes. Approximately 62% of the formulas identified in

the positive ion mass spectrum contained at least one nitrogen atom;

38%, 18% and 6% for the N1, N2 and N3 classes, respectively.

Furthermore, approximately 31% of the identified formulas in the

negative ion mass spectrum contained at least one nitrogen; 22%, 8%

and <1% for the N1, N2 and N3 classes, respectively. It should also be

noted that the data in Figure 1 represent relative numbers of identifiable

molecules observed. The total number of formulas identified containing

nitrogen was almost twice as high for the positive ion mass spectrum

(9403 for positive ion APPI to 4990 for negative ion APPI). Moreover,

positive ion APPI is also selective for nitrogen. We found that the N1, N2

and N3 classes comprised 35.22 ± 2.40%, 16.21 ± 2.19%, and 4.74 ±

2.32% of the identified formulas, respectively. With positive mode APPI,

over 50% of the molecules identified contained nitrogen. Thus, the

remainder of the ICR data presented are from positive ion APPI mass

spectra.

77

Figure 5.1. Percentage of total assigned formulas verse nitrogen class comparison of positive and negative ion atmospheric pressure

photoionization in generating organic nitrogen ions.

The mass distribution of the positive ion spectra of the dry season

C-43 stormwater treatment area extract ranges from m/z 200 to 800,

and contain 12722 indentified molecular formulas (Figure 5.2a). The

apex of the mass distribution is approximately m/z 353, with signal-to-

noise 339.

78

Figure 5.2. a) Broadband positive ion APPI FT-ICR mass spectrum of Caloosahatchee River DOM and; b) An m/z = 0.3 expanded mass spectral segment at m/z 432 with formulas containing N1 (●) and N3 (■) labeled.

Figure 5.2b is a 0.3 Da (all ions are singly charged) expanded

mass spectral zoom inset from the broadband spectrum in Figure 5.2a.

This small inset exhibits the spectral complexity of the sample and the

need for ultrahigh resolution, as it contains 38 peaks above a

conservative noise threshold of 6σ greater than the RMS baseline noise

Of the 38 peaks, 18 are formulas that contain at least one nitrogen atom.

In this m/z = 0.3 even nominal mass expanded zoom inset, both N1 (solid

circle) and N3 (solid square) compound classes are present; N2 ions are

located at odd nominal mass.145 Even in this very narrow zoom expanded

mass spectral segment, three distinct homologous series (two N1 and one

N3) may be identified. In each case, these series are defined by the

79

substitution of CH4 for O in the molecular formula, which results in the

36.4 mDa mass difference that is commonly observed in DOM (Table

5.1).

Table 5.1. Three nitrogen-containing homologous series identified from m/z 432.00-432.30. All three series exhibit the substitution of CH4 for

O and the corresponding 36.4 mDa mass difference.

Series Measured Mass (m/z)

Molecular Formula

1st N1 432.09264 C20H17N1O10 1st N1 432.12901 C21H21N1O9

1st N1 432.16540 C22H25N1O8 1st N1 432.20179 C23H29N1O7 1st N1 432.23817 C24H33N1O6 1st N1 432.27456 C25H37N1O5 2nd N1 432.11379 C17H21N1O12 2nd N1 432.15014 C18H25N1O11 2nd N1 432.18653 C19H29N1O10 2nd N1 432.22291 C20H33N1O9

2nd N1 432.25930 C21H37N1O8 N3 432.14021 C20H21N3O8 N3 432.17663 C21H25N3O7 N3 432.21306 C22H29N3O6

Kendrick analysis The power of ultrahigh resolution mass spectrometry is that

sample comparisons are possible on a molecular level. However, the large

quantities of data produced makes molecule-by-molecule analysis

unreasonable. Therefore, graphical representations are used to condense

and visualize the data. One of the most useful of these data reduction

strategies is the Kendrick Mass Analysis.183 This method rescales the

measured masses from their IUPAC masses based on the IUPAC CH2

mass of 14.01565 Da to Kendrick masses based on CH2=14.00000 Da

(Eq. 5.1).

80

Kendrick Mass = IUPAC mass x (14/14.01565) (5.1)

Members of the same homologous series (e.g. molecules that are similar

except for the addition of –CH2) have Kendrick masses differing by

exactly 14 Da and have the same Kendrick Mass Defect (KMD). The

KMD is the calculated difference between the Kendrick mass and the

IUPAC nominal mass (Eq. 5.2).

KMD = (Nominal Mass – Kendrick Exact mass) x 1000 (5.2)

Kendrick Plots of FT-ICR MS data are formulas reduced to their KMD

plotted against nominal mass. This allows visualization of a large data

sets, with an emphasis on the characteristics of formulas at each

nominal mass.

The elemental composition for the dry season samples are

remarkably similar, with over 95% of the formulas identified appearing in

both samples (data not shown). This molecular-based qualitative data

supports the conclusions from quantitative measurements that suggest

the dry-season DON in the river and STAs is essentially refractory.

Significant differences are exhibited in the mass spectra for the wet

season C-43 STA DON sample. We highlighted both the similarities and

differences in DON composition with the Kendrick plots in Figure 5.3.

Figure 5.3a includes all 5367 of the nitrogen-containing formulas that

were present in both the original C-43 STA wet season sample (T0) and

the sample that was incubated for 5 days (T5). The fact that these

formulas are found in both of these samples suggests that these

molecules are refractory. The vast majority of these refractory

components are located within a broad, elliptical distribution with

nominal masses between approximately 200 and 800 Da and KMD

values ranging from around 100 to 600. This is the same general area of

the Kendrick plot where the largely refractory dry season DON molecules

81

were located. There are a small number of formulas that fall outside of

that broad distribution, with KMD values <100.

Figure 5.3. a) Kendrick plot of the nitrogen-containing formulas assigned in both the original (T0) and incubated sample (T5) from the Caloosahatchee River during the wet season; and b) Kendrick plot of

assigned formulas only in T0 (♦) and those only in T5 (■). Figure 5.3b is a Kendrick plot that includes DON molecules

identified in either the original or in the sample after the five day

bioassay. The 1976 formulas identified only in the T0 sample are

represented as red diamonds and are referred to as removed formulas

because they were present in the T0 but not in T5. We consider the

removed formulas the labile fraction of the DON pool in the wet season

C-43 STA sample. The 945 formulas that appear in the T5 sample, but

not the T0 sample, are referred to as formed formulas. The most notable

82

characteristic of the distribution of removed formulas is that presence of

an elliptical pattern between about 400 and 820 Da that occurs at lower

KMD values than the patterns for refractory DON (Figure 5.3b). There are

two ways to view this distribution relative to the refractory distribution.

First is that the labile fraction occurs at higher nominal masses along a

common KMD line, an indication that molecules within a given

homologous series (i.e. longer or more abundant alkyl chains) are more

readily available for use by the microbes. The second way to examine the

labile distribution relative to the refractory molecules is by looking at a

common nominal mass. From this perspective, the labile components of

the DON pool have lower KMD values than their refractory counterparts.

There are two features of organic molecules that result in the decrease of

KMD at a given nominal mass; either a decrease in the number of oxygen

atoms or an increase in the number of hydrogen atoms. The exact mass

of oxygen is 15.994915 Da, and the removal of oxygen decreases KMD.

Conversely, hydrogen has an atomic mass of 1.007825. Therefore, an

increase of hydrogen content (i.e., increasing saturation) results in a

decrease in KMD at a given nominal mass. Unfortunately, it is not

possible to determine through the Kendrick analysis which of these

factors contributes to the decreased KMD of reactive of DON molecules.

There is another group of molecules present in the labile region of

the plot. These components have nominal masses from approximately

200 to 700 Da, with KMD values greater than 150, and they lie parallel

to the horizontal axis. Hatcher et al. identified molecules in this portion

of the Kendrick plot as fatty acids and have used them as internal

standards to calibrate FT-ICR spectra.184 Fatty acids are composed of

long alkyl chains and a relatively low number of oxygen molecules. These

characteristics are consistent with our earlier assessment of the first

distribution of reactive DON components.

Finally, there are removed formulas scattered throughout the

region that we considered mostly refractory molecules. However, toward

83

the low nominal mass, low KMD edge of that distribution there appears

to be a more concentrated grouping of these types of molecules.

Therefore, it is possible that low molecular weight versions of typically

refractory molecules may be consumed by the microbes during

incubation.

The formed formulas identified in Figure 5.3b occupy the same

region of the Kendrick plot that is linked to refractory components

(Figure 5.3a). These formed molecules span nearly the same mass range,

but they are more concentrated in the mid to high nominal mass region

(~400-800 Da). While it might appear from Figure 5.3a that virtually all

possible KMDs occurs in the refractory DON pool, these newly formed

formulas clearly prove that is not the case. Moreover, it is noteworthy

that the newly formed formulas generally have KMD values greater than

the labile DON KMDs, suggesting that the removed formulas were

transformed by microbes into these newly formed formulas through

either addition of oxygen or loss of hydrogen (unsaturation) of the

original compounds.

van Krevelen analysis While Kendrick plots are useful for identifying trends in mass

effects in ultrahigh resolution MS data, comparisons of actual formulas

are best visualized with van Krevelen diagrams that reduce molecular

formulas to elemental ratios. A typical van Krevelen diagram includes

O/C ratios (x-axis) plotted against H/C ratios (y-axis). Different classes of

biomolecules, such as lipids, proteins, lignin, and carbohydrates, tend to

aggregate in distinct regions of the van Krevelen diagram.100 Therefore, it

is possible to examine changes to specific classes of DON compounds

with these plots.

As noted previously, 5367 formulas containing nitrogen were

present in both the T0 and T5 wet season C-43 samples, and Figure 5.4a

84

depicts the O/C and H/C of these formulas. The overwhelming majority

of these formulas fall within the region of the diagram associated with

lignin- and tannin-like compounds, the most refractory types of DOM

molecules. These formulas are the same group that forms the large

elliptical distribution in Figure 5.3a. A small group of formulas is also

apparent in the upper left corner of Figure 5.4a, an area which is

associated with lipids. This group represents the small number of

formulas in Figure 5.3a with KMD values less than 100.

Figure 5.4. a) A van Krevelen diagram of nitrogen-containing formulas identified in both the original (T0) and incubated sample (T5) from the

Caloosahatchee River. B) A van Krevelen diagram of nitrogen containing formulas identified only in T0 (♦) and those only in T5 (■).

Similar to the Kendrick plot in Figure 5.3b, the formulas identified

in the van Krevelen diagram in Figure 5.4b are distinguished as removed

85

or formed formulas. The most striking aspect of these data are the large

number of labile formulas with O/C ranging from approximately 0.1 to

0.3 and H/C of approximately 1.5 to 1.9, an area of the diagram

representative of protein-like compounds. Molecules in the upper left-

hand corner of this distribution have O/C < 0.1 and an H/C ~ 2.0,

typically associated with lipid- and fatty acid-like compounds. These

molecules can therefore be correlated with molecules with low KMD

values arranged horizontally in Figure 5.3b.

The van Krevelen plot analysis indicates that nearly all the newly

formed formulas are located in one characteristic region of the graph

associated with lignin- and tannin-like molecules. The position of these

formed formulas relative to the labile group is particularly noteworthy.

The labile formulas generally have lower O/C and higher H/C than their

formed counterparts, an indication of less oxygenated, more saturated

molecules that are more bioavailable. These data also suggest that the

microbes are converting these lipid- and protein-like compounds into

more oxygenated and less hydrogenated (unsaturated) structures.

Conclusion

The role DON plays in estuarine systems is relatively unknown,

which can largely be attributed to the lack of qualitative measurements

available. Physical separation of DON components from their complex

sample matrices is currently not possible, and therefore, mass spectral

analysis is the most suitable technique for characterization of DON

species on a molecular level. However, the inherent heterogeneity of both

the sample as a whole and, specifically, the DON pool requires ultrahigh

resolution and mass accuracy that is only achievable by FT-ICR MS. The

results of this study suggest that positive ion APPI is a highly efficient

method for the ionization of DON molecules. Bulk DON measurements

show that a limited amount of DON was removed during the course of a

86

5-day bioassay. Kendrick plots and van Krevelen diagrams yielded

molecular level information regarding the DON pool and were used to

confirm that the majority of the DON exists as a refractory component of

the DOM pool. However, it appears that molecules with a high degree of

saturation and low oxygen content are available for uptake by microbes

and converted to refractory type compounds.

87

CHAPTER 6

CHARACTERIZATION OF PYROGENIC BLACK

CARBON BY DESORPTION ATMOSPHERIC PRESSURE PHOTOIONIZATION FOURIER

TRANSFORM ION CYCLOTRON MASS SPECTROMETRY

Summary We present a new method for molecular characterization of intact

biochar directly, without sample preparation or pretreatment, based on

desorption atmospheric pressure photoionization (DAPPI) coupled to

Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry.

Conventional ionization methods (e.g., electrospray or atmospheric

pressure photoionization) for characterization of natural organic matter

have limited utility for the characterization of chars due to incomplete

solubility in common solvents. Therefore, direct ionization techniques

that do not require sample dissolution prior to analysis are ideal. Here,

we apply DAPPI FT-ICR mass spectrometry to enable the first molecular

characterization of uncharred parent oak biomass, and after combustion

(250 °C), or pyrolysis (400 °C). Parent oak is primarily composed of

cellulose-, lignin-, and protein-like compounds. Oak combusted at 250

°C contains condensed aromatic compounds with low H:C and O:C ratios

while retaining compounds with high H:C and O:C ratios. The bimodal

distribution of aromatic and aliphatic compounds observed in the

combusted oak sample is attributed to incomplete thermal degradation of

lignin and hemicellulose. Pyrolyzed oak constituents exhibit lower H:C

and O:C ratios: approximately three-quarters of the identified species are

aromatic. DAPPI FT-ICR MS results agree with bulk elemental

88

composition as well as functional group distribution determined by

elemental analysis and solid state 13C NMR spectroscopy. With the

detailed molecular fingerprint and molecular transformation that occur

in biomass after combustion and pyrolysis, the relationship between

biomass composition and thermal degradation processes may be better

understood. Complete molecular characterization of biomass upon

thermal transformation may also provide insight into the biogeochemical

cycles of biochar and future renewable energy sources, particularly for

samples currently limited by solubility, separation, and sample

preparation.

Introduction

Combustion products from thermally degraded vegetation (i.e.,

“black carbon” or BC) range from slightly charred biomass (biochar), to

charcoal, soot, and graphite according to the degree of thermal

degradation. Terrestrial soil and sediments contain BC, and groundwater

runoff transports BC to marine sediments.185 Although BC is a

heterogeneous mixture with a wide range of chemical and physical

properties, most BC remains in the environment and is resistant to

biological or chemical degradation.186-188 Characterization and

quantitation of BC in the global carbon budget is of recent interest

because BC may act as a significant carbon sink, moving carbon from

the relatively rapid bio-atmospheric cycle to the slower sedimentary

cycle.44, 48, 189

Contrary to previous reports that all BC is inert, Baldock and

Smerick reported on the presence of reactive BC, and concluded that the

degree of subsequent degradation depends on the extent of thermal

alteration of certain organic components in the post-fire residue.190

Degradation of BC occurs through two main processes: microbial and

photochemical.43, 190-191 Shneour found that 2% of artificial graphite is

89

oxidized in non-sterile soils.192 Scott et al. and Hofrichter et al. identified

several fungi able to decompose low-grade coals.193-194 They provided

evidence that BC undergoes some degradation in the environment, but

on a time scale ranging from a few to thousands of years.50, 191, 195-196

Previous characterization of char has been based on nuclear

magnetic resonance (NMR) spectroscopy,197-199 which provides structural

and bulk property measurements, but does not identify molecular

rearrangements that occur upon release into the environment. FT-ICR

mass spectrometry has been routinely applied to characterize complex

organic mixtures, due to high mass accuracy (< 200 ppb) and ultrahigh

resolution (m/Δm50% = 400,000 at m/z 400), required for accurate

elemental composition assignment.105, 200-202 Previous molecular

characterization of BC by FT-ICR MS examined and identified the

changes in elemental composition of the water soluble fraction after it

enters and is transported through an aquatic environment.126, 203-204

However, they were unable to characterize the starting BC material due

to low solubility. Here, we present the direct ionization and molecular

characterization of solid biochar and and its compositional changes of

oak before and after combustion or pyrolysis at 400 °C.

Direct ionization techniques enable the rapid analysis of solid

samples with little or no sample pretreatment, by placing them in an

atmospheric pressure chamber interface to the mass spectrometer inlet.

The most common direct ionization methods are desorption electrospray

ionization (DESI)205 and direct analysis in real time (DART) based on a

corona discharge.206 DESI and ESI mainly ionize polar analytes. Here we

present the first coupling of DAPPI to FT-ICR MS, thereby taking

advantage of all of the inherent benefits of APPI to enable analysis of

compounds spanning a wide range of polarity.131 DAPPI was first

introduced in 2007, and detailed mechanistic characterization has been

reported.106, 207 In DAPPI, a heated nebulizer produces a plume of hot

gas/solvent that is directed to the surface of a sample to desorb neutral

90

analyte molecules into the gas phase through a combination of thermal

and chemical processes.207 The desorbed analytes are then ionized by the

same mechanism as for APPI.106 APPI can ionize an analyte molecule

directly, resulting in loss of an electron to generate a molecular radical

cation, M+•. The energy of the photon must be greater than the ionization

energy of the analyte. However, the radiation output of a conventional

krypton UV lamp used for APPI is too low for efficient direct

photoionization.106 As a result, Bruins et al. developed dopant-assisted

APPI106 in which an excess of photoionizable reagent (dopant) provides

D+• ions that can easily react with analyte, M, by proton transfer or

charge exchange to generate M+• or [M+H]+ ions. Dopant-assisted APPI

can increase ionization efficiency by 2-3 orders of magnitude.131 The

choice of dopant for DAPPI determines the type/distribution of analyte

ion generated: [M+H]+ vs. M+• or [M-H]- vs. M-•, and should be

appropriately selected to increase ionization efficiency for compounds of

interest.180 DAPPI has been applied to the analysis of pharmaceutical

tablets,207 illicit drugs,208 polycyclic aromatic hydrocarbons in soil,209

pesticides on produce,209 and MS imaging of biological tissue.210

Here, for the first time we present molecular changes in solid

biochar for parent, combustion (at 250 °C), and pyrolysis (at 400 °C)

products of oak (henceforth denoted as parent oak, oak 250, and oak

400) by DAPPI FT-ICR MS without sample preparation or preseparation.

Our method provides an experimental framework for enhanced molecular

level characterization of biochar and its reactivity in the environment.

Experimental Methods

Samples

Quercus laurifolia (laurel oak) without bark was dried thoroughly

and cut into 1x1x5 cm pieces. Portions of approximately 1.5 g were

91

loosely wrapped in foil and baked in a 0.04 m3 combustion oven or

heated in a pyrolyzer (5.5 cm diameter x 50 cm length pipe). In the

pyrolysis experiment, N2 gas flowed over the sample at a rate of

approximately 2.3 oven volumes min-1. The heating rate was 10-12 °C

min-1 and peak temperature hold time was 3 h for combustion and

pyrolysis.

DAPPI source

A ThermoFisher IonMaxx™ ion source equipped with a krypton UV

lamp was used for all linear ion trap MS experiments, and a modified

ThermoFisher LCQ APPI (ThermoFisher Corp., Bremen, Germany) source

was used for all 9.4 Tesla FT-ICR MS experiments (Figure 6.1).105

Parent and charred biomass were held ~1 mm from the exit of the heated

ceramic nebulization tube by use of tweezers. The sample was 10 mm

from the MS inlet. Gas-phase neutrals were produced through thermal

and chemical desorption. Nitrogen was used as nebulizer gas at 100 psi

with toluene as dopant at a flow rate of 50 uL min-1. The temperature of

the heated nebulizer gas/solvent plume ranged from 100-500 °C

depending upon the sample. The temperature of the MS inlet capillary

was 200 °C and the tube lens voltage was set at -35 V.

92

Figure 6.1. Schematic of Thermo LCT source converted for DAPPI Experiments (Figure from modified from Purcell et al. 2006).105 The sample is placed directly the path of the heated solvent spray for thermal and chemical desorption. Desorbed neutrals undergo either direct photoionization, proton transfer, or charge exchange and enter

the mass spectrometer through a heated metal capillary.

Mass spectrometry Molecular weight distributions were determined with a

ThermoFisher linear ion trap (LTQ ThermoFisher Corp., Bremen,

Germany) mass spectrometer. LTQ mass spectra were obtained for

molecular weight determination and for desorption temperature

optimization based on signal intensity measurements across a

temperature range (100-500 °C). Negative ion mass spectra were

acquired with automatic gain control.211 Data were acquired with

Xcalibur version 2.0 software at a maximum injection period of 2000 ms

93

and a scan speed of 3 scans/spectrum. 50 scans were acquired for each

sample. For parent oak, 100 °C produced the highest signal intensity,

oak 250 at 200 °C, and oak 400 at 350 °C (Figure 6.2).

Figure 6.2. Broadband (-) DAPPI LTQ mass spectra of the parent oak,

oak combusted at 250 °C, and oak pyrolyzed at 400 °C. The optimum

solvent plume temperature was determined for each sample. Top: The mass spectrum is typical of fresh, labile organic biomass. Middle and Bottom: The mass spectra exhibits a broad pseudo-Gaussian distribution as the biomass is thermally degraded.

FT-ICR mass spectra were acquired with a passively shielded 9.4

tesla superconducting magnet (Oxford Instruments, Abingdon,

Oxfordshire OX13 5QX United Kingdom) located at the National High

Magnetic Field Laboratory in Tallahassee, Florida.212 Time-domain

transient signals were collected and processed by a modular ICR data

acquisition system.213 Negative ions were accumulated (50-500 ms)

94

externally214 in the second rf-only octopole and collisionally cooled with

helium prior to transfer through an rf-only octopole to a seven segment

open cylindrical cell with capacitively coupled excitation electrodes

similar to the configuration of Tolmachev et al.215 Chirp excitation (~700-

90 kHz at a sweep rate of 50 Hz µs-1 and 360 Vp-p amplitude) accelerated

the ions to a detectable cyclotron radius. Approximately 10-20 time-

domain acquisitions were co-added, Hanning-apodized, and zero-filled

once prior to fast Fourier transform and magnitude calculation.

Frequency was converted to m/z by the quadrupolar electric trapping

potential approximation.138, 216 Spectra were internally calibrated from

extended homologous alkylation series (compounds that differ in

elemental composition by integer multiples of CH2) of high relative

abundance.

Data analysis

Each peak in the mass spectrum was assigned a unique molecular

formula. In a van Krevelen diagram; namely, a plot of H:C vs. O:C ratio

(Figure 6.3),100, 196, 217 compounds with similar chemical properties tend

to locate in specific regions. The double bond equivalents (DBE = number

of rings plus double bonds to carbon) measures hydrogen deficiency,

calculated from the elemental composition, CcHhNnOoSs (DBE = C – h/2 +

n/2 + 1), determined by FT-ICR mass spectrometry.201, 218 DBE and

oxygen class are plotted as a function of percent relative abundance to

illustrate compositional changes in aromaticity and oxygen number

under different thermal conditions. Each elemental formula was assigned

an aromaticity index (AI) based on the system proposed by Koch and

Dittmar.219 Here, assignments are separated into non-aromatic (AI < 0.5),

aromatic (AI > 0.5) and condensed aromatics (AI ≥ 0.67) (Figure 2). AI

along with van Krevelen diagrams are efficient tools to visualize changes

95

in the elemental composition of organic materials resulting from thermal

degradation.

Figure 6.3. van Krevelen diagram of elemental H:C vs. O:C ratios. Molecular formulas with similar chemical characteristics tend to aggregate in specific regions. van Krevelen plots of different samples may be compared to determine changes in chemical composition. Formulas with Aromaticity Index (AI) values > 0.5 are considered aromatic, and those with AI > 0.67 condensed aromatic.

Nuclear magnetic resonance spectroscopy Solid-state 13C NMR spectra were obtained with a widebore Varian

Inova 500 MHz spectrometer operated at 125 MHz. Each sample (~100

mg) was packed in a 4 mm O.D. zirconium rotor and sealed with KEL-F

caps, followed by Ramped-Cross Polarization (CP) and Magic Angle

96

Spinning (MAS) at 14 kHz. Ramped CP overcomes the inefficiency of

cross polarization between 1H and 13C in high-speed MAS and decrease

in sensitivity from magnetic field inhomogeneity.220 A 3 s pulse delay five

times longer than the longest 1H spin lattice relaxation times minimizes

saturation effects. 20,000 free induction decays were summed for each

sample, zero-filled once, and processed with 50 Hz Lorentzian line

broadening. Functional group distributions were determined by

integrating over defined chemical shift regions; 0-50 ppm (alkyl C), 50-60

ppm ((N-alkyl and methoxy), 60-110 ppm (O-alkyl C including

carbohydrates), 110-160 ppm (aromatic C), 160-220 ppm (carbonyl C in

carboxylic acids, esters, amides, ketones, and aldehydes).

Elemental analysis Bulk elemental analyses were performed with a ThermoFinnigan

(Milan, Italy) Elemental Analyzer (Flash EA 1112) for total C, H, N, S, and

O content. Each sample (~1-2 mg) was weighed into a silver container for

oxygen determination (CE Elantech, Inc., Lakewood, NJ) or a tin

container (ThermoFinnigan Italia, Milan, Italy) for CHNS analysis,

crushed into a sphere, placed in an autosampler, and analyzed in

quadruplicate. Calibration is based on elemental analysis of

sulfanilamide (Thermo Electron S.p.A., Milan, Italy, CAS# 63-74-1, C:

±0.210, H: ±0.030, O: ±0.0834) and all quadruplicate runs included a

separate, independent standard not used in the initial calibration.

Results and Discussion

Parent oak

Figure 6.4 (top) shows the van Krevelen diagram for molecular

formulas detected for parent oak determined by DAPPI FT-ICR MS. Most

of assigned molecular formulas have high H:C and O:C with AI < 0.5,

97

indicative of aromatic structures based on the Koch/Dittmar method.219

Few formulas exhibit AI > 0.5, indicating that aromatic compounds

constitute a minor fraction of the overall composition of the parent oak,

as expected for untreated biomass. Most molecular formulas lie in

regions associated with cellulose-, aminosugar-, lignin-, and protein-like

compounds, abundant in cell walls and wood. Wood is composed of

~50% cellulose and ~30% lignin; therefore, a large contribution of

formulas with cellulose- and lignin-like molecular formulas is

expected.221 Results are in good agreement with bulk elemental analysis:

O:C = 0.51 +/- 0.003 and H:C = 1.51 +/- 0.010.

To determine if DAPPI ionization is representative of the native

sample, molecular compositions obtained by FT-ICR MS were compared

to 13C NMR results. Figure 6.4 (bottom) shows the RAMP CP 13C NMR

spectrum for the parent oak. The predominant peaks in the NMR

spectrum of parent oak are between 60-110 ppm, the region associated

with O-alkyl functionality. O-alkyl compounds have high O:C ratio, in

accord with DAPPI FT-ICR MS and bulk elemental analysis. Only a minor

contribution is observed in the aromatic region of the NMR spectrum

(110-160 ppm), consistent with MS results. The average O:C and H:C

ratios of parent oak are 0.51 ± 0.003 and 1.51 ± 0.010, which fall directly

in the center of the van Krevelen diagram distribution and further

validate that the DAPPI FT-ICR MS represent the parent material. High

H:C, and O:C of the parent material are consistent with prior 13C NMR

spectra of higher-plant biomass which are dominated by O-alkyl species,

a reflection of the dominance of cellulose.190, 217, 222

98

Figure 6.4. (Top) van Krevelen diagram for the elemental compositions assigned to parent oak by DAPPI FT-ICR MS. The molecular formulas aggregate in regions of the diagram typical of wood, i.e., lignin, protein, and cellulose. A few formulas are associated with aromatic compounds, i.e., A.I. > 0.5. (Bottom) NMR spectrum for parent oak. The spectrum is dominated by the O-alkyl peak, 60-110 ppm, with only minor contribution from the aromatic peak, 110-160 ppm. (* bulk O:C and H:C ratios determined by elemental analysis)

Oak 250 Combusted at 250 °C

A shift to formulas of lower H:C is evident in the van Krevelen

diagrams of oak 250 (Figure 6.5 (top)). The region associated with

protein-like compounds is lost and a significant number of formulas with

AI > 0.5 are formed. The appearance of aromatic and condensed aromatic

structures after the combustion of biomass is expected because of

degradation followed by dehydration of cellulose from 200-300 °C has

99

been reported to be responsible for the accumulation of new aryl

compounds.61 Furthermore, molecular formulas with AI ≥ 0.67, i.e.,

condensed aromatics, are observed for oak 250. Although elemental

formulas associated with protein-like compounds disappear after

combustion at 250 °C relative to the parent oak, formulas with H:C and

O:C associated with cellulose-like compounds remain. Elemental

compositions with high H:C and O:C in oak 250 may indicate multiple

processes. First, the elemental compositions that remain in that region

may show that a fraction of cellulose resists degradation by combustion

at 250 °C and remains mostly intact. Second, those compounds may be

by-products of incomplete combustion.

The DAPPI FT-ICR MS data for oak 250 again with RAMP CP 13C

NMR data (Figure 6.5 (bottom)), showing a relative decrease in the O-

alkyl signal and a relative increase in the aromatic region of the NMR

spectrum of oak 250 relative to the parent oak. From the van Krevelen

diagram, it is obvious that some of the compounds with high H:C and

O:C ratio are removed after combustion. The formation of aromatic

compounds after combustion is confirmed by the relative increase in the

aromatic peak in the NMR spectrum. Finally, the average O:C and H:C

ratios for oak 250 obtained by bulk elemental analysis are 0.34 ± 0.003

and 0.87 ± 0.017, suggesting that oak 250 consists primarily of

aromatic-like compounds. However, DAPPI FTMS enables the

simultaneous identification of the aromatic compounds and the high H:C

and O:C compounds that are not well represented by bulk elemental

analysis.

100

Figure 6.5. (Top) van Krevelen diagram for the elemental compositions

assigned to oak combusted at 250 °C by DAPPI FT-ICR MS. Molecular formulas characteristic of aromatic and condensed aromatics, i.e., AI > 0.5 and AI ≥ 0.67, are formed relative to the parent oak. Although

the elemental compositions associated with proteins disappear relative to parent oak, compounds with high O:C and H:C associated with cellulose remain in oak 250. (Bottom) NMR spectrum of oak 250, showing a decrease in the O-alkyl peak and increase in the aromatic peak relative to the parent oak. (* bulk O:C and H:C ratios determined by elemental analysis)

Oak 400 pyrolyzed at 400 °C A van Krevelen diagram from FT-ICR MS data for oak pyrolyzed at

400 °C shows a marked increase in the number of formulas with AI

values > 0.5, consistent with aromatic and condensed aromatic

compounds (Figure 6.6 (top)). Molecular formulas exhibit lower O:C and

H:C relative to the parent oak and oak 250. A complete loss of elemental

101

compositions associated with cellulose-like compounds is observed in the

oak 400 char. Significant depolymerization of cellulose occurs from 300-

350 °C, so a loss of most of the cellulose-like compounds observed in oak

400 is consistent with prior reports.223

Figure 6.6. (Top) van Krevelen diagram of the molecular formulas

assigned to oak pyrolyzed at 400 °C. Molecular formulas exhibit lower

O:C and H:C ratios relative to parent oak and oak 250, due to

depolymeriztion of cellulose and dehydration and deactylation of

lignin and cellulose. Approximately half of elemental compositions

assigned for to oak 400 have an AI > 0.55. (Bottom) NMR spectrum of

oak 400. The spectrum is dominated by the aromatic peak. There

almost no O-alkyl contribution relative to the parent oak and oak 250.

(* denotes bulk O:C and H:C ratios determined by elemental analysis)

102

The DAPPI FT-ICR MS data for oak 400 again agree with RAMP CP

13C NMR data (Figure 6.6 (bottom)). Here, the predominant NMR peak is

in the aromatic region. The peak in the O-alkyl region of the pyrolyzed

oak NMR spectrum is almost completely gone relative to the parent oak

and oak 250. Only a few of the compounds with high H:C and O:C ratios

remain. Moreover, the van Krevelen diagram for oak 400 identifies more

than 75% of the assigned formulas as aromatic or condensed aromatic.

Finally, the average O:C and H:C ratios for oak 400 from bulk elemental

analysis are 0.20 ± 0.002 and 0.78 ± 0.001. The bulk elemental analysis

reflects the predominance of aromatic compounds in oak 400 and

generally agrees with the formulas plotted in the van Krevelen diagram.

DBE distribution Figure 6.7 shows DBE distributions for all assigned elemental

compositions for negative DAPPI ions from parent oak, oak 250, and oak

400. Parent oak has relatively lower DBE than oak 250 and 400. Its

DBE classes range from DBE = 2 to 14 with the most abundant classes

from DBE = 3 to 6. The high relative abundance of classes with low DBE

is expected for parent material dominated by O-alkyl functionality and

high O:C and H:C.

The DBE distribution for oak 250 is bimodal, ranging from DBE =

1 to 21, with maxima at DBE = 7 and 13. The first distribution is slightly

higher in DBE than that for the parent oak, and the second distribution

is slightly lower than that for oak 400. The DBE distributions are

consistent with the van Krevelen plots. Elemental compositions with

relatively high H:C and O:C correspond to the low DBE maximum and

are associated with compounds that are either partially oxidized or not

thermally degraded. The higher DBE maximum is associated with the

species of AI > 0.5 that are formed after combustion.

103

The DBE distribution for oak 400 is dramatically higher than that

for the parent oak. High DBE values likely result from cleavage of

aliphatic chains from aromatic rings, with an onset at approximately 300

°C. Furthermore, high DBE in addition to lower O:C and H:C for oak 400

could result from loss of acetic acid (e.g., the deactylation of

hemicellulose at 200-300 °C). Although the contribution from thermally

unmodified lignin- and cellulose-like compounds make it unclear,

deactylation of hemicellulose may also explain the shift to lower DBE of

oak 250.

Figure 6.7. Double bond equivalents (DBE) relative abundance distribution for parent oak, oak 250, and oak 400. The parent oak has a relatively low DBE range, and oak 250 exhibits a bimodal distribution. Oak 400 is characterized by elemental compositions with relatively high DBE, the result of further thermal degradation of lignin

and cellulose.

104

Oxygen class distribution

Figure 6.8 shows On class relative abundances for parent oak, oak

250, and oak 400. Oxygen classes for the parent material range from O4

to O16. The two dominant oxygen classes are O9 and O10, with significant

O8 and O11 contributions. The narrow diversity in On classes is typical of

fresh, labile biomass.

As for the DBE distribution, the On class distribution for oak 250

is bimodal, centered at ~O8 and ~O13. The bimodal distribution is

consistent with the van Krevelen diagram for oak 250, in which

compounds with distributions centered at relatively low and high high

O:C and H:C. The relatively lower oxygen components may be attributed

to dehydration of hemicellulose and lignin, beginning at ~200 °C.223 The

higher oxygen species (~O12-O20) may arise from partial oxidation of

lignin due to incomplete combustion or thermally undegraded cellulose.

Additional evidence for deactylation of hemicellulose is evidenced

from the oxygen class graph for oak 400. The oxygen classes for oak 400

range from O2-O12, centered at O5. The lower oxygen numbers suggest

that the lower O:C ratios observed in Figure 3 are likely due to a

combination of deactylation and dehydration. The net change in O:C

ratio from deactylation (loss of H2CO) is negative (provided that, as in

this case, there are more carbons than oxygens in the molecule), and the

net change in O:C ratio is always negative for dehydration because one

oxygen is lost, but no carbons.

105

Figure 6.8. Percent relative abundance for various oxygen classes. Oak 250 has a bimodal distribution with the first distribution, and formulas in the O4-O11 classes are most likely thermally degraded lignin and cellulose compounds. Formulas in the O12-O20 classes represent residual cellulose that is not thermally degraded and

partially oxidized lignin and cellulose. Oak 400 formulas in the lower oxygen classes are the result of deactylation caused by thermal degradation.

Conclusion

We present data here that demonstrates for the first time that

molecular formula information for solid biochar may be determined by

DAPPI coupled to a FT-ICR MS. The results obtained by DAPPI FT-ICR

MS for parent oak, oak combusted in the presence of O2 at 250 °C, and

oak pyrolyzed at 400 °C in the absence of O2 are in agreement with bulk

106

data obtained by NMR spectroscopy and elemental analysis.

Furthermore, the DAPPI FT-ICR mass spectra are in agreement with

previous studies of parent and charred biomass reported in the

literature. Thes results provide the framework for molecular-level

characterization of a wide range of chars and complex mixtures where

solubility, separation, and sample preparation are limiting factors.

Finally, the data presented in this paper speaks for the versatility of

DAPPI as an ionization method for complex mixture analysis.

107

REFERENCES

1. R. Benner, in Biogeochemistry of Marine Dissolved Organic Matter, ed. D. A. Hansell, C. A. Carlson. Academic Press: New York, 2002, pp 59-90.

2. D. A. Hansell, C. A. Carlson, Marine Dissolved Organic Matter and the Carbon Cycle. 2001, 14. 9.

3. B. P. Koch, M. R. Witt, R. Engbrodt, T. Dittmar, G. Kattner, Molecular formulae of marine and terrigenous dissolved organic matter detected by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry. Geochim Cosmochim Ac 2005, 69. 3299-3308, DOI: DOI 10.1016/j.gca.2005.02.027.

4. J. P. Hassett, M. A. Anderson, Effects of Dissolved Organic-Matter on Adsorption of Hydrophobic Organic-Compounds by River-Borne and Sewage-Borne Particles. Water Res 1982, 16. 681-686.

5. P. H. Santschi, J. J. Lenhart, B. D. Honeyman, Heterogeneous processes affecting trace contaminant distribution in estuaries: The role of natural organic matter. Mar Chem 1997, 58. 99-125.

6. A. D. Redman, D. L. Macalady, D. Ahmann, Natural organic matter

affects arsenic speciation and sorption onto hematite. Environ Sci Technol 2002, 36. 2889-2896, DOI: Doi 10.1021/Es0112808.

7. W. V. Sobczak, J. E. Cloern, A. D. Jassby, A. B. Muller-Solger, Bioavailability of organic matter in a highly disturbed estuary: The role of detrital and algal resources. P Natl Acad Sci USA 2002, 99. 8101-8105, DOI:

DOI 10.1073/pnas.122614399.

8. B. C. Crump, G. W. Kling, M. Bahr, J. E. Hobbie, Bacterioplankton community shifts in an arctic lake correlate with seasonal changes in organic matter source. Appl Environ Microb 2003, 69. 2253-2268, DOI: Doi 10.1128/Aem.69.4.2253-2268.2003.

9. J. I. Hedges, G. Eglinton, P. G. Hatcher, D. L. Kirchman, C. Arnosti, S. Derenne, R. P. Evershed, I. Kogel-Knabner, J. W. de Leeuw, R. Littke, W. Michaelis, J. Rullkotter, The molecularly-uncharacterized component of nonliving organic matter in natural environments. Org Geochem 2000, 31. 945-958.

10. R. Benner, J. D. Pakulski, M. Mccarthy, J. I. Hedges, P. G. Hatcher, Bulk Chemical Characteristics of Dissolved Organic-Matter in the Ocean. Science 1992, 255. 1561-1564.

108

11. J. I. Hedges, Global Biogeochemical Cycles - Progress and Problems. Mar Chem 1992, 39. 67-93.

12. D. A. Hansell, C. A. Carlson, N. R. Bates, A. Poisson, Horizontal and vertical removal of organic carbon in the equatorial Pacific Ocean: A mass balance assessment. Deep-Sea Res Pt Ii 1997, 44. 2115-2130.

13. T. Dittmar, G. Kattner, Recalcitrant dissolved organic matter in the

ocean: major contribution of small amphiphilics. Mar Chem 2003, 82. 115-123, DOI: Doi 10.1016/S0304-4203(03)00068-9.

14. T. L. Brown, J. A. Rice, Effect of experimental parameters on the ESI FT-ICR mass spectrum of fulvic acid. Anal Chem 2000, 72. 384-390.

15. A. C. Stenson, W. M. Landing, A. G. Marshall, W. T. Cooper, Ionization and fragmentation of humic substances in electrospray ionization Fourier transform-ion cyclotron resonance mass spectrometry. Anal Chem 2002, 74. 4397-4409, DOI: Doi 10.1021/Ac020019f.

16. A. C. Stenson, A. G. Marshall, W. T. Cooper, Exact masses and chemical formulas of individual Suwannee River fulvic acids from ultrahigh resolution electrospray ionization Fourier transform ion cyclotron resonance mass spectra. Anal Chem 2003, 75. 1275-1284, DOI: Doi 10.1021/Ac026106p.

17. E. B. Kujawinski, M. A. Freitas, X. Zang, P. G. Hatcher, K. B. Green-Church, R. B. Jones, The application of electrospray ionization mass spectrometry (ESI MS) to the structural characterization of natural organic matter. Org Geochem 2002, 33. 171-180, DOI: Pii S0146-6380(01)00149-8.

18. E. B. Kujawinski, P. G. Hatcher, M. A. Freitas, High-resolution Fourier

transform ion cyclotron resonance mass spectrometry of humic and fulvic acids: Improvements and comparisons. Anal Chem 2002, 74. 413-419, DOI: Doi 10.1021/Ac0108313.

19. S. J. Gould, Planet of the bacteria: Full house. Harmony Books: New York, 1996.

20. W. S. Gardner, J. A. Stephens, Stability and Composition of Terrestrially Derived Dissolved Organic Nitrogen in Continental-Shelf Surface Waters. Mar Chem 1978, 6. 335-342.

21. D. A. Bronk, in Biogeochemistry of marine dissolved organic matter, ed. D. A. Hansell, C. A. Carlson. Academic Press, 2002.

22. E. S. A. Badr, A. D. Tappin, E. P. Achterberg, Distributions and seasonal variability of dissolved organic nitrogen in two estuaries in SW England. Mar

Chem 2008, 110. 153-164, DOI: DOI 10.1016/j.marchem.2008.04.007.

109

23. D. A. Bronk, J. H. See, P. Bradley, L. Killberg, DON as a source of bioavailable nitrogen for phytoplankton. Biogeosciences 2007, 4. 283-296.

24. G. M. Berg, D. J. Repeta, J. Laroche, Dissolved organic nitrogen hydrolysis rates in axenic cultures of Aureococcus anophagefferens (Pelagophyceae): Comparison with heterotrophic bacteria. Appl Environ Microb 2002, 68. 401-404.

25. S. P. Seitzinger, R. W. Sanders, R. Styles, Bioavailability of DON from natural and anthropogenic sources to estuarine plankton. Limnol Oceanogr 2002, 47. 353-366.

26. N. O. G. Jorgensen, L. J. Tranvik, G. M. Berg, Occurrence and bacterial cycling of dissolved nitrogen in the Gulf of Riga, the Baltic Sea. Mar Ecol-Prog

Ser 1999, 191. 1-18.

27. D. A. Bronk, P. M. Glibert, T. C. Malone, S. Banahan, E. Sahlsten, Inorganic and organic nitrogen cycling in Chesapeake Bay: autotrophic versus heterotrophic processes and relationships to carbon flux. Aquat Microb Ecol

1998, 15. 177-189.

28. C. Lee, J. L. Bada, Dissolved Amino-Acids in Equatorial Pacific, Sargasso Sea, and Biscayne Bay. Limnol Oceanogr 1977, 22. 502-510.

29. C. R. Lytle, E. M. Perdue, Free, Proteinaceous, and Humic-Bound Amino-Acids in River Water Containing High-Concentrations of Aquatic Humus. Environ Sci Technol 1981, 15. 224-228.

30. E. L. Poutanen, R. J. Morris, Comparison of the Structures of Humic Acids from Marine-Sediments and Degraded Field Diatoms by C-13-Nmr and

H-1-Nmr Spectroscopy. Mar Chem 1985, 17. 115-126.

31. J. I. Hedges, P. E. Hare, Amino-Acid Adsorption by Clay-Minerals in Distilled Water. Geochim Cosmochim Ac 1987, 51. 255-259.

32. J. H. Sharp, in Nitrogen in the Marine Environment, ed. D. G. Capone, E. J. Carpenter. Plenum: New York, 1983, pp 101-120.

33. J. Fuhrman, Close Coupling between Release and Uptake of Dissolved Free Amino-Acids in Seawater Studied by an Isotope-Dilution Approach. Mar

Ecol-Prog Ser 1987, 37. 45-52.

34. M. R. Brown, The Amino-Acid and Sugar Composition of 16 Species of Microalgae Used in Mariculture. J Exp Mar Biol Ecol 1991, 145. 79-99.

110

35. P. Marsot, A. D. Cembella, J. C. Colombo, Intracellular and Extracellular Amino-Acid Pools of the Marine Diatom Phaeodactylum-Tricornutum (Bacillariophyceae) Grown on Unenriched Seawater in High-Cell-Density Dialysis Culture. J Phycol 1991, 27. 478-491.

36. S. Myklestad, O. Holmhansen, K. M. Varum, B. E. Volcani, Rate of Release of Extracellular Amino-Acids and Carbohydrates from the Marine Diatom Chaetoceros-Affinis. J Plankton Res 1989, 11. 763-773.

37. D. H. Stuermer, K. E. Peters, I. R. Kaplan, Source Indicators of Humic Substances and Proto-Kerogen - Stable Isotope Ratios, Elemental Compositions and Electron-Spin Resonance-Spectra. Geochim Cosmochim Ac 1978, 42. 989-997.

38. R. B. Gagosian, C. Lee, in Marine Organic Chemistry: Evolution, Composition, Interactions and Chemistry of Organic Matter in Seawater, ed. E. K. Duursma, R. Dawson. Elsevier: Amsterdam, 1981, pp 91-124.

39. M. Schnitzer, in Humic Substances in Soil, Sediment, and Water, ed. G. R. Aiken, D. M. McKnight, R. L. Wershaw. Wiley: New York, 1985, pp 303-328.

40. E. M. Thurman, Organic Geochemistry of Natural Waters. Niyhoff/Junk: Boston, 1985.

41. J. I. Hedges, in Humic Substances and Their Role in the Environment, ed. F. H. Frimmel, R. F. Christman. Wiley: New York, 1988, pp 45-58.

42. N. J. Antia, P. J. Harrison, L. Oliveira, The Role of Dissolved Organic Nitrogen in Phytoplankton Nutrition, Cell Biology and Ecology. Phycologia 1991, 30. 1-89.

43. E. D. Goldberg, Black carbon in the Environment: Properties and Distribution. John Wiley and Sons: New York, 1985.

44. W. Seiler, P. J. Crutzen, Estimates of Gross and Net Fluxes of Carbon between the Biosphere and the Atmosphere from Biomass Burning. Climatic Change 1980, 2. 207-247.

45. T. A. J. Kuhlbusch, Black carbon and the carbon cycle. Science 1998,

280. 1903-1904.

46. J. Lehmann, J. Skjemstad, S. Sohi, J. Carter, M. Barson, P. Falloon, K. Coleman, P. Woodbury, E. Krull, Australian climate-carbon cycle feedback reduced by soil black carbon. Nat Geosci 2008, 1. 832-835, DOI: Doi 10.1038/Ngeo358.

111

47. P. M. Fearnside, N. Leal, F. M. Fernandes, Rain-Forest Burning and the Global Carbon Budget - Biomass, Combustion Efficiency, and Charcoal Formation in the Brazilian Amazon. J Geophys Res-Atmos 1993, 98. 16733-16743.

48. P. J. Crutzen, M. O. Andreae, Biomass Burning in the Tropics - Impact on Atmospheric Chemistry and Biogeochemical Cycles. Science 1990, 250. 1669-1678.

49. A. Marinoni, P. Cristofanelli, P. Laj, R. Duchi, F. Calzolari, S. Decesari, K. Sellegri, E. Vuillermoz, G. P. Verza, P. Villani, P. Bonasoni, Aerosol mass and black carbon concentrations, a two year record at NCO-P (5079 m, Southern Himalayas). Atmos Chem Phys 2010, 10. 8551-8562, DOI: DOI 10.5194/acp-10-8551-2010.

50. M. I. Bird, J. A. Cali, A million-year record of fire in sub-Saharan Africa. Nature 1998, 394. 767-769.

51. F. Thevenon, D. Williamson, E. Bard, F. S. Anselmetti, L. Beaufort, H. Cachier, Combining charcoal and elemental black carbon analysis in

sedimentary archives: Implications for past fire regimes, the pyrogenic carbon cycle, and the human-climate interactions. Global Planet Change 2010, 72. 381-389, DOI: DOI 10.1016/j.gloplacha.2010.01.014.

52. M. W. I. Schmidt, J. O. Skjemstad, E. Gehrt, I. Kogel-Knabner, Charred organic carbon in German chernozemic soils. Eur J Soil Sci 1999, 50. 351-365.

53. J. O. Skjemstad, P. Clarke, J. A. Taylor, J. M. Oades, S. G. McClure, The chemistry and nature of protected carbon in soil. Aust J Soil Res 1996, 34. 251-271.

54. C. A. Masiello, E. R. M. Druffel, Black carbon in deep-sea sediments. Science 1998, 280. 1911-1913.

55. M. Llorente, B. Glaser, M. B. Turrion, Storage of organic carbon and Black carbon in density fractions of calcareous soils under different land uses.

Geoderma 2010, 159. 31-38, DOI: DOI 10.1016/j.geoderma.2010.06.011.

56. A. Grossman, U. Ghosh, Measurement of activated carbon and other black carbons in sediments. Chemosphere 2009, 75. 469-475, DOI: DOI 10.1016/j.chemosphere.2008.12.054.

57. O. Gustafsson, F. Haghseta, C. Chan, J. MacFarlane, P. M. Gschwend, Quantification of the dilute sedimentary soot phase: Implications for PAH speciation and bioavailability. Environ Sci Technol 1997, 31. 203-209.

112

58. D. A. Hiller, G. W. Brummer, Electron microprobe studies on soil samples with varying heavy metal contamination .2. Contents of heavy metals and other elements in aggregations of humic substances, litter residues and charcoal particles. Z Pflanz Bodenkunde 1997, 160. 47-55.

59. J. H. Zhang, M. C. He, Effect of Surfactants on Sorption and Desorption of Phenanthrene onto Black Carbon. Water Environ Res 2011, 83. 15-22, DOI: Doi 10.2175/106143010x12681059116536.

60. X. S. Wang, H. H. Miao, W. He, H. L. Shen, Competitive Adsorption of Pb(II), Cu(II), and Cd(II) Ions on Wheat-Residue Derived Black Carbon. J Chem Eng Data 2011, 56. 444-449, DOI: Doi 10.1021/Je101079w.

61. F. Shafizadeh, The Chemistry of Pyrolysis and Combustion. Adv Chem

Ser 1984. 491-529.

62. D. E. Ward, in Fire in the tropical biota : ecosystem processes and global challenges, ed. J. G. Goldammer, Volkswagenstiftung. Springer-Verlag: Berlin ; New York, 1990, pp xvii, 497 p.

63. M. W. I. Schmidt, A. G. Noack, Black carbon in soils and sediments: Analysis, distribution, implications, and current challenges. Global Biogeochem Cy 2000, 14. 777-793.

64. A. Krogh, A. Keys, Methods for the determination of dissolved organic carbon and nitrogen in sea water. Biol Bull-Us 1934, 67. 132-144.

65. T. Fu, R. Pocklington, Quantitative Adsorption of Organic-Matter from Seawater on Solid Matrices. Mar Chem 1983, 13. 255-264.

66. B. Little, J. Jacobs, A comparison of two techniques for the isolation of adsorbed dissolved organic material from seawater. 1985, 8. 27-33.

67. M. A. Wilson, A. H. Gillam, P. J. Collin, Analysis of the Structure of

Dissolved Marine Humic Substances and Their Phytoplanktonic Precursors by H-1 and C-13 Nuclear Magnetic-Resonance. Chem Geol 1983, 40. 187-201.

68. G. R. Harvey, D. A. Boran, L. A. Chesal, J. M. Tokar, The Structure of Marine Fulvic and Humic Acids. Mar Chem 1983, 12. 119-132.

69. D. H. Stuermer, J. R. Payne, Investigation of Seawater and Terrestrial Humic Substances with C-13 and Proton Nuclear Magnetic-Resonance. Geochim Cosmochim Ac 1976, 40. 1109-1114.

70. P. G. Hatcher, I. A. Breger, L. W. Dennis, G. E. Maciel, in Aquatic and Terrestrial Humic Materials, ed. R. F. Christman, E. T. Gjessing. Ann Arbor Science: Ann Arbor, MI, 1983, pp 37-82.

113

71. K. J. Dria, J. R. Sachleben, P. G. Hatcher, Solid-state carbon-13 nuclear magnetic resonance of humic acids at high magnetic field strengths. J Environ Qual 2002, 31. 393-401.

72. P. Conte, R. Spaccini, A. Piccolo, State of the art of CPMAS C-13-NMR spectroscopy applied to natural organic matter. Prog Nucl Mag Res Sp 2004, 44. 215-223, DOI: DOI 10.1016/j.pnmrs.2004.02.002.

73. R. L. Cook, Coupling NMR to NOM. Anal Bioanal Chem 2004, 378. 1484-1503, DOI: DOI 10.1007/s00216-003-2422-z.

74. N. Hudson, A. Baker, D. Reynolds, Fluorescence analysis of dissolved organic matter in natural, waste and polluted waters - A review. River Res Appl 2007, 23. 631-649, DOI: Doi 10.1002/Rra.1005.

75. C. D. Christian, J. B. Callis, E. R. Davidson, in Modern fluorescence spectroscopy, ed. E. L. Wehry. Plenum Press: New York, 1976, pp 111-165.

76. P. G. Coble, Characterization of marine and terrestrial DOM in seawater using excitation emission matrix spectroscopy. Mar Chem 1996, 51. 325-346.

77. A. Baker, Fluorescence excitation-emission matrix characterization of some sewage-impacted rivers. Environ Sci Technol 2001, 35. 948-953.

78. M. M. D. Sierra, M. Giovanela, E. Parlanti, E. J. Soriano-Sierra, Fluorescence fingerprint of fulvic and humic acids from varied origins as viewed by single-scan and excitation/emission matrix techniques. Chemosphere 2005, 58. 715-733, DOI: DOI 10.1016/j.chemosphere.2004.09.038.

79. W. Guo, L. Yang, H. Hong, C. A. Stedmon, F. Wang, J. Xu, Y. Xie, Assessing the dynamics of chromophoric dissolved organic matter in a subtropical estuary using parallel factor analysis. 2011, 124. 125-133.

80. S. Mounier, N. Patel, L. Quilici, J. Y. Benaim, C. Benamou, Three-dimensional fluorescence of the dissolved organ carbon in the Amazon River. Water Res 1999, 33. 1523-1533.

81. C. D. Clark, J. Jimenez-Morais, G. Jones, E. Zanardi-Lamardo, C. A. Moore, R. G. Zika, A time-resolved fluorescence study of dissolved organic

matter in a riverine to marine transition zone. Mar Chem 2002, 78. 121-135, DOI: Pii S0304-4203(02)00014-2.

82. N. Her, G. Amy, D. McKnight, J. Sohn, Y. M. Yoon, Characterization of DOM as a function of MW by fluorescence EEM and HPLC-SEC using UVA, DOC, and fluorescence detection. Water Res 2003, 37. 4295-4303, DOI: Doi

10.1016/S0043-1354(03)00317-8.

114

83. Y. Yamashita, E. Tanoue, Chemical characterization of protein-like fluorophores in DOM in relation to aromatic amino acids. Mar Chem 2003, 82. 255-271, DOI: Doi 10.1016/S0304-4203(03)00073-2.

84. W. K. L. Cammack, J. Kalff, Y. T. Prairie, E. M. Smith, Fluorescent dissolved organic matter in lakes: Relationships with heterotrophic metabolism. Limnol Oceanogr 2004, 49. 2034-2045.

85. R. Jaffe, J. N. Boyer, X. Lu, N. Maie, C. Yang, N. M. Scully, S. Mock,

Source characterization of dissolved organic matter in a subtropical mangrove-dominated estuary by fluorescence analysis. Mar Chem 2004, 84. 195-210, DOI: DOI 10.1016/j.marchem.2003.08.001.

86. S. Elliott, J. R. Lead, A. Baker, Thermal quenching of fluorescence of

freshwater, planktonic bacteria. Anal Chim Acta 2006, 564. 219-225, DOI: DOI 10.1016/j.aca.2006.01.087.

87. M. M. D. Sierra, O. F. X. Donard, M. Lamotte, Spectral identification and behaviour of dissolved organic fluorescent material during estuarine mixing processes. Mar Chem 1997, 58. 51-58.

88. R. M. W. Amon, R. Benner, Photochemical and microbial consumption of dissolved organic carbon and dissolved oxygen in the Amazon River system. Geochim Cosmochim Ac 1996, 60. 1783-1792.

89. A. Skoog, M. Wedborg, E. Fogelqvist, Photobleaching of fluorescence and the organic carbon concentration in a coastal environment. Mar Chem 1996, 55. 333-345.

90. M. A. Moran, W. M. Sheldon, R. G. Zepp, Carbon loss and optical

property changes during long-term photochemical and biological degradation of estuarine dissolved organic matter. Limnol Oceanogr 2000, 45. 1254-1264.

91. R. Del Vecchio, N. V. Blough, Photobleaching of chromophoric dissolved organic matter in natural waters: kinetics and modeling. Mar Chem 2002, 78. 231-253, DOI: Pii S004-4203(02)00036-1.

92. S. Bertilsson, P. Carlsson, W. Graneli, Influence of solar radiation on the availability of dissolved organic matter to bacteria in the Southern Ocean. Deep-Sea Res Pt Ii 2004, 51. 2557-2568, DOI: DOI 10.1016/j.dsr2.2000.07.001.

93. E. Parlanti, K. Worz, L. Geoffroy, M. Lamotte, Dissolved organic matter fluorescence spectroscopy as a tool to estimate biological activity in a coastal zone submitted to anthropogenic inputs. Org Geochem 2000, 31. 1765-1781.

115

94. M. Katsuyama, N. Ohte, Determining the sources of stormflow from the fluorescence properties of dissolved organic carbon in a forested headwater catchment. J Hydrol 2002, 268. 192-202, DOI: Pii S0022-1694(02)00175-0.

95. A. Fievre, T. Solouki, A. G. Marshall, W. T. Cooper, High-resolution Fourier transform ion cyclotron resonance mass spectrometry of humic and fulvic acids by laser desorption/ionization and electrospray ionization. Energ Fuel 1997, 11. 554-560.

96. N. K. Kaiser, J. P. Quinn, G. T. Blakney, C. L. Hendrickson, A. G. Marshall, A novel 9.4 Tesla FT-ICR mass spectrometer with improved sensitivity, mass resolution, and mass range, for petroleum heavy crude oil analysis. 2011, 22.

97. J. J. Savory, N. K. Kaiser, A. M. McKenna, F. Xian, G. T. Blakney, R. P. Rodgers, C. L. Hendrickson, A. G. Marshall, Parts-Per-Billion Fourier Transform Ion Cyclotron Resonance Mass Measurement Accuracy with a "Walking" Calibration Equation. Anal Chem 2011, 83. 1732-1736, DOI: Doi 10.1021/Ac102943z.

98. P. G. Hatcher, K. J. Dria, S. Kim, S. W. Frazier, Modern analytical studies of humic substances. Soil Sci 2001, 166. 770-794.

99. J. A. Leenheer, J. P. Croue, Characterizing aquatic dissolved organic matter. Environ Sci Technol 2003, 37. 18a-26a.

100. S. Kim, R. W. Kramer, P. G. Hatcher, Graphical method for analysis of ultrahigh-resolution broadband mass spectra of natural organic matter, the van Krevelen diagram. Anal Chem 2003, 75. 5336-5344, DOI: Doi 10.1021/Ac034415p.

101. S. Kim, A. J. Simpson, E. B. Kujawinski, M. A. Freitas, P. G. Hatcher, High resolution electrospray ionization mass spectrometry and 2D solution NMR for the analysis of DOM extracted by C-18 solid phase disk. Org Geochem 2003, 34. 1325-1335, DOI: Doi 10.1016/S0146-6380(03)00101-3.

102. S. W. Kim, L. A. Kaplan, R. Benner, P. G. Hatcher, Hydrogen-deficient molecules in natural riverine water samples - evidence for the existence of black carbon in DOM. Mar Chem 2004, 92. 225-234, DOI: DOI 10.1016/j.marchem.2004.06.042.

103. N. Hertkorn, R. Benner, M. Frommberger, P. Schmitt-Kopplin, M. Witt, K.

Kaiser, A. Kettrup, J. I. Hedges, Characterization of a major refractory component of marine dissolved organic matter. Geochim Cosmochim Ac 2006, 70. 2990-3010, DOI: DOI 10.1016/j.gca.2006.03.021.

116

104. E. B. Kujawinski, J. W. Farrington, J. W. Moffett, Evidence for grazing-mediated production of dissolved surface-active material by marine protists. Mar Chem 2002, 77. 133-142, DOI: Pii S0304-4203(01)00082-2.

105. J. M. Purcell, C. L. Hendrickson, R. P. Rodgers, A. G. Marshall, Atmospheric pressure photoionization Fourier transform ion cyclotron resonance mass spectrometry for complex mixture analysis. Anal Chem 2006, 78. 5906-5912, DOI: Doi 10.1021/Ac060754h.

106. D. B. Robb, T. R. Covey, A. P. Bruins, Atmospheric pressure photoionisation: An ionization method for liquid chromatography-mass spectrometry. Anal Chem 2000, 72. 3653-3659.

107. Comisaro.Mb, A. G. Marshall, Fourier-Transform Ion-Cyclotron

Resonance Spectroscopy. Chem Phys Lett 1974, 25. 282-283.

108. A. G. Marshall, C. L. Hendrickson, G. S. Jackson, Fourier transform ion cyclotron resonance mass spectrometry: A primer. Mass Spectrom Rev 1998, 17. 1-35.

109. L. Brand, in Florida Bay and Adjacent Marine Systems Science Conference. 1999, pp 78-79.

110. C. Tomas, B. Bendus, L. Houchin, in Florida Bay Science Conference.

1998.

111. J. N. Boyer, J. W. Fourqurean, R. D. Jones, Spatial characterization of water quality in Florida Bay and Whitewater Bay by multivariate analyses: Zones of similar influence. Estuaries 1997, 20. 743-758.

112. P. J. Wangersky, Dissolved Organic-Carbon Methods - a Critical-Review. Mar Chem 1993, 41. 61-74.

113. S. E. Davis, D. L. Childers, J. W. Day, D. T. Rudnick, F. H. Sklar,

Nutrient dynamics in vegetated and unvegetated areas of a southern Everglades mangrove creek. Estuar Coast Shelf S 2001, 52. 753-768.

114. P. Carlsson, E. Graneli, Availability of Humic Bound Nitrogen for Coastal Phytoplankton. Estuar Coast Shelf S 1993, 36. 433-447.

115. D. A. Bronk, P. M. Glibert, B. B. Ward, Nitrogen Uptake, Dissolved Organic Nitrogen Release, and New Production. Science 1994, 265. 1843-1846.

116. B. Xu, T. Ye, D. P. Li, C. Y. Hu, Y. L. Lin, S. J. Xia, F. X. Tian, N. Y. Gao,

Measurement of dissolved organic nitrogen in a drinking water treatment plant: Size fraction, fate, and relation to water quality parameters. 2011, 409. 1116-22, DOI: S0048-9697(10)01319-7 [pii]

117

117. J. B. Fellman, E. Hood, R. G. Spencer, Fluorescence spectroscopy opens new windows into dissolved organic matter dynamics in freshwater ecosystems: A review. 2010, 55. 2452-2462.

118. M. Urgun-Demirtas, C. Sattayatewa, K. R. Pagilla, Bioavailability of dissolved organic nitrogen in treated effluents. Water Environ Res 2008, 80. 397-406, DOI: Doi 10.2175/106143007x221454.

119. M. A. Wedyan, K. G. Fandi, S. Al-Rousan, Bioavailability of atmospheric

dissolved organic nitrogen in the marine aerosol over the Gulf of Aqaba. 2007, 1. 208-212.

120. J. C. Neff, F. S. Chapin, P. M. Vitousek, Breaks in the cycle: dissolved organic nitrogen in terrestrial ecosystems. Front Ecol Environ 2003, 1. 205-211.

121. E. N. J. Brookshire, H. M. Valett, S. A. Thomas, J. R. Webster, Coupled cycling of dissolved organic nitrogen and carbon in a forest stream. Ecology 2005, 86. 2487-2496.

122. F. Schmidt, M. Elvert, B. P. Koch, M. Witt, K. U. Hinrichs, Molecular characterization of dissolved organic matter in pore water of continental shelf sediments. Geochim Cosmochim Ac 2009, 73. 3337-3358, DOI: DOI 10.1016/j.gca.2009.03.008.

123. T. Reemtsma, A. These, M. Linscheid, J. Leenheer, A. Spitzy, Molecular and structural characterization of dissolved organic matter from the deep ocean by FTICR-MS, including hydrophilic nitrogenous organic molecules. Environ Sci Technol 2008, 42. 1430-1437, DOI: Doi 10.1021/Es7021413.

124. M. Gonsior, B. M. Peake, W. T. Cooper, D. Podgorski, J. D'Andrilli, W. J.

Cooper, Photochemically Induced Changes in Dissolved Organic Matter Identified by Ultrahigh Resolution Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. Environ Sci Technol 2009, 43. 698-703, DOI: Doi 10.1021/Es8022804.

125. J. J. Mosher, G. C. Klein, A. G. Marshall, R. H. Findlay, Influence of

bedrock geology on dissolved organic matter quality in stream water. Org Geochem 2010, 41. 1177-1188, DOI: DOI 10.1016/j.orggeochem.2010.08.004.

126. A. Stubbins, R. G. M. Spencer, H. M. Chen, P. G. Hatcher, K. Mopper, P. J. Hernes, V. L. Mwamba, A. M. Mangangu, J. N. Wabakanghanzi, J. Six, Illuminated darkness: Molecular signatures of Congo River dissolved organic

matter and its photochemical alteration as revealed by ultrahigh precision mass spectrometry. Limnol Oceanogr 2010, 55. 1467-1477, DOI: DOI 10.4319/lo.2010.55.4.1467.

118

127. B. P. Koch, K. U. Ludwichowski, G. Kattner, T. Dittmar, M. Witt, Advanced characterization of marine dissolved organic matter by combining reversed-phase liquid chromatography and FT-ICR-MS. Mar Chem 2008, 111. 233-241, DOI: DOI 10.1016/j.marchem.2008.05.008.

128. K. E. Altieri, B. J. Turpin, S. P. Seitzinger, Composition of Dissolved Organic Nitrogen in Continental Precipitation Investigated by Ultra-High Resolution FT-ICR Mass Spectrometry. Environ Sci Technol 2009, 43. 6950-6955, DOI: Doi 10.1021/Es9007849.

129. J. B. Fenn, M. Mann, C. K. Meng, S. F. Wong, C. M. Whitehouse, Electrospray Ionization-Principles and Practice. Mass Spectrom Rev 1990, 9. 37-70.

130. S. J. Bos, S. M. van Leeuwen, U. Karst, From fundamentals to applications: recent developments in atmospheric pressure photoionization mass spectrometry. Anal Bioanal Chem 2006, 384. 85-99, DOI: DOI 10.1007/s00216-005-0046-1.

131. D. B. Robb, M. W. Blades, State-of-the-art in atmospheric pressure

photoionization for LC/MS. Anal Chim Acta 2008, 627. 34-49, DOI: DOI 10.1016/j.aca.2008.05.077.

132. T. Dittmar, B. Koch, N. Hertkorn, G. Kattner, A simple and efficient method for the solid-phase extraction of dissolved organic matter (SPE-DOM) from seawater. Limnol Oceanogr-Meth 2008, 6. 230-235.

133. J. D'Andrilli, T. Dittmar, B. P. Koch, J. M. Purcell, A. G. Marshall, W. T. Cooper, Comprehensive characterization of marine dissolved organic matter by Fourier transform ion cyclotron resonance mass spectrometry with electrospray and atmospheric pressure photoionization. Rapid Commun Mass Sp 2010, 24. 643-650, DOI: Doi 10.1002/Rcm.4421.

134. M. W. Senko, C. L. Hendrickson, L. PasaTolic, J. A. Marto, F. M. White, S. H. Guan, A. G. Marshall, Electrospray ionization Fourier transform ion cyclotron resonance at 9.4 T. Rapid Commun Mass Sp 1996, 10. 1824-1828.

135. G. T. Blakney, D. E. Robinson, N. V. Ly, N. L. Kelleher, C. L. Hendrickson, A. G. Marshall, Predator Data Station: A Fast Data Acquisition System for Advanced FT-ICR MS Experiments. 2011, 000. 000-000.

136. M. W. Senko, C. L. Hendrickson, M. R. Emmett, S. D. H. Shi, A. G.

Marshall, External accumulation of ions for enhanced electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry. J Am Soc Mass Spectr 1997, 8. 970-976.

119

137. A. G. Marshall, F. R. Verdun, Fourier transforms in NMR, optical, and mass spectrometry : a user's handbook. Elsevier: Amsterdam ; New York, 1990; p xvi, 450 p.

138. S. D. H. Shi, J. J. Drader, M. A. Freitas, C. L. Hendrickson, A. G. Marshall, Comparison and interconversion of the two most common frequency-to-mass calibration functions for Fourier transform ion cyclotron resonance mass spectrometry. Int J Mass Spectrom 2000, 196. 591-598.

139. E. B. Ledford, D. L. Rempel, M. L. Gross, Space-Charge Effects in Fourier-Transform Mass-Spectrometry - Mass Calibration. Anal Chem 1984, 56. 2744-2748.

140. L. B. Tremblay, T. Dittmar, A. G. Marshall, W. J. Cooper, W. T. Cooper,

Molecular characterization of dissolved organic matter in a North Brazilian mangrove porewater and mangrove-fringed estuaries by ultrahigh resolution Fourier Transform-Ion Cyclotron Resonance mass spectrometry and excitation/emission spectroscopy. Mar Chem 2007, 105. 15-29, DOI: DOI 10.1016/j.marchem.2006.12.015.

141. E. B. Kujawinski, K. Longnecker, N. V. Blough, R. Del Vecchio, L. Finlay, J. B. Kitner, S. J. Giovannoni, Identification of possible source markers in marine dissolved organic matter using ultrahigh resolution mass spectrometry. Geochim Cosmochim Ac 2009, 73. 4384-4399, DOI: DOI 10.1016/j.gca.2009.04.033.

142. M. P. Bhatia, S. B. Das, K. Longnecker, M. A. Charette, E. B. Kujawinski, Molecular characterization of dissolved organic matter associated with the Greenland ice sheet. Geochim Cosmochim Ac 2010, 74. 3768-3784, DOI: DOI 10.1016/j.gca.2010.03.035.

143. M. M. Mapolelo, L. A. Stanford, R. P. Rodgers, A. T. Yen, J. D. Debord, S.

Asomaning, A. G. Marshall, Chemical Speciation of Calcium and Sodium Naphthenate Deposits by Electrospray Ionization FT-ICR Mass Spectrometry. 2009, 23. 349-355.

144. D. A. Bronk, in Biogeochemistry of marine dissolved organic matter, ed. D. A. Hansell, C. A. Carlson. Academic Press: Amsterdam ; Boston, 2002, pp 153–

247.

145. F. W. McLafferty, Interpretation of mass spectra. 2d ed.; W. A. Benjamin: Reading, Mass.,, 1973; p xix, 278 p.

146. J. M. Purcell, R. P. Rodgers, C. L. Hendrickson, A. G. Marshall, Speciation of Nitrogen Containing Aromatics by Atmospheric Pressure Photoionization or Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. 2007, 18. 1265-1273.

120

147. M. Witt, J. Fuchser, B. P. Koch, Fragmentation Studies of Fulvic Acids Using Collision Induced Dissociation Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. Anal Chem 2009, 81. 2688-2694, DOI: Doi 10.1021/Ac802624s.

148. R. Sipler, S. Seitzinger, Use of electrospray ionization (ESI) mass spectrometry to investigate complex dissolved organic matter (DOM) and its potential applications in phytoplankton research. Harmful Algae 2008, 8. 182-187, DOI: DOI 10.1016/j.hal.2008.08.022.

149. R. W. Howarth, Human acceleration of the nitrogen cycle: drivers, consequences, and steps toward solutions. Water Sci Technol 2004, 49. 7-13.

150. S. R. Qasim, Wastewater treatment plants: Planning, design, and

operation. Technomic: Lancaster, PA, 1999.

151. S. P. Seitzinger, R. W. Sanders, Contribution of dissolved organic nitrogen from rivers to estuarine eutrophication. Mar Ecol-Prog Ser 1997, 159. 1-12.

152. K. L. Bushaw-Newton, M. A. Moran, Photochemical formation of biologically available nitrogen from dissolved humic substances in coastal marine systems. Aquat Microb Ecol 1999, 18. 285-292.

153. P. Carlsson, E. Graneli, A. Z. Segatto, Cycling of biologically available nitrogen in riverine humic substances between marine bacteria, a heterotrophic nanoflagellate and a photosynthetic dinoflagellate. Aquat Microb Ecol 1999, 18. 23-36.

154. H. W. Paerl, D. R. Whitall, Anthropogenically-derived atmospheric

nitrogen deposition, marine eutrophication and harmful algal bloom expansion: Is there a link? Ambio 1999, 28. 307-311.

155. R. Stepanauskas, N. O. G. Jorgensen, O. R. Eigaard, A. Zvikas, L. J. Tranvik, L. Leonardson, Summer inputs of riverine nutrients to the Baltic Sea: Bioavailability and eutrophication relevance. Ecol Monogr 2002, 72. 579-597.

156. P. Carlsson, E. Graneli, P. Tester, L. Boni, Influences of Riverine Humic Substances on Bacteria, Protozoa, Phytoplankton, and Copepods in a Coastal Plankton Community. Mar Ecol-Prog Ser 1995, 127. 213-221.

157. K. L. Bushaw, R. G. Zepp, M. A. Tarr, D. SchulzJander, R. A. Bourbonniere, R. E. Hodson, W. L. Miller, D. A. Bronk, M. A. Moran, Photochemical release of biologically available nitrogen from aquatic dissolved organic matter. Nature 1996, 381. 404-407.

121

158. A. V. Vahatalo, R. G. Zepp, Photochemical mineralization of dissolved organic nitrogen to ammonium in the Baltic Sea. Environ Sci Technol 2005, 39. 6985-6992, DOI: Doi 10.1021/Es050142z.

159. G. F. Parkin, P. L. Mccarty, A Comparison of the Characteristics of Soluble Organic Nitrogen in Untreated and Activated-Sludge Treated Wastewaters. Water Res 1981, 15. 139-149.

160. G. F. Parkin, P. L. Mccarty, Characteristics and removal of soluble

organic nitrogen in treated effluents. 1975, 7. 435-445.

161. L. J. Tranvik, in Aquatic humic substances : ecology and biogeochemistry, ed. D. O. Hessen, L. J. Tranvik. Springer: Berlin ; New York, 1998, pp 259-279.

162. R. Stepanauskas, H. Edling, L. J. Tranvik, Differential dissolved organic nitrogen availability and bacterial aminopeptidase activity in limnic and marine waters. Microbial Ecol 1999, 38. 264-272.

163. R. Stepanauskas, L. Leonardson, L. J. Tranvik, Bioavailability of

wetland-derived DON to freshwater and marine bacterioplankton. Limnol Oceanogr 1999, 44. 1477-1485.

164. E. Pehlivanoglu, D. L. Sedlak, Bioavailability of wastewater-derived organic nitrogen to the alga Selenastrum Capricornutum. Water Res 2004, 38.

3189-3196, DOI: DOI 10.1016/j.watres.2004.04.027.

165. R. D. Morris, A. M. Audet, I. F. Angelillo, T. C. Chalmers, F. Mosteller, Chlorination, Chlorination by-Products, and Cancer - a Metaanalysis. Am J Public Health 1992, 82. 955-963.

166. L. Hureiki, J. P. Croue, B. Legube, Chlorination Studies of Free and Combined Amino-Acids. Water Res 1994, 28. 2521-2531.

167. J. D. Plummer, J. K. Edzwald, Effect of ozone on disinfection by-product

formation of algae. Water Sci Technol 1998, 37. 49-55.

168. J. D. Plummer, J. K. Edzwald, Effect of ozone on algae as precursors for trihalomethane and haloacetic acid production. Environ Sci Technol 2001, 35. 3661-3668.

169. D. C. Podgorski, D. M. Osborne, A. M. McKenna, R. P. Rodgers, A. G. Marshall, W. T. Cooper, in Goldschmidt 2010: Earth, Energy, and the Environment. Knoxville, TN, 2010.

122

170. D. C. Podgorski, A. M. McKenna, R. P. Rodgers, A. G. Marshall, W. T. Cooper, Selective ionization and molecular characterization of dissolved organic nitrogen by positive ion atmospheric pressure photoionization Fourier transform ion cylotron resonance mass spectrometry. 2011.

171. E. Gilbert, Biodegradability of Ozonation Products as a Function of Cod and Doc Elimination by Example of Substituted Aromatic Substances. Water Res 1987, 21. 1273-1278.

172. D. J. Conley, H. W. Paerl, R. W. Howarth, D. F. Boesch, S. P. Seitzinger, K. E. Havens, C. Lancelot, G. E. Likens, Eutrophication: Time to Adjust Expectations Response. Science 2009, 324. 724-725.

173. D. G. Redalje, S. E. Lohrenza, M. J. Natter, M. D. Tuel, G. J. Kirkpatrick,

D. F. Millie, G. L. Fahnenstiel, F. M. Van Dolah, The growth dynamics of Karenia brevis within discrete blooms on the West Florida Shelf. Cont Shelf Res 2008, 28. 24-44, DOI: DOI 10.1016/j.csr.2007.04.011.

174. G. A. Vargo, C. A. Heila, K. A. Fanning, L. K. Dixon, M. B. Neely, K. Lester, D. Ault, S. Murasko, J. Havens, J. Walsh, S. Bell, Nutrient availability

in support of Karenia brevis blooms on the central West Florida Shelf: What keeps Karenia blooming? Cont Shelf Res 2008, 28. 73-98, DOI: DOI 10.1016/j.csr.2607.04.008.

175. S. P. Seitzinger, H. Hartnett, R. Lauck, M. Mazurek, T. Minegishi, G. Spyres, R. Styles, Molecular-level chemical characterization and bioavailability

of dissolved organic matter in stream water using electrospray-ionization mass spectrometry. Limnol Oceanogr 2005, 50. 1-12.

176. K. Mopper, A. Stubbins, J. D. Ritchie, H. M. Bialk, P. G. Hatcher, Advanced instrumental approaches for characterization of marine dissolved organic matter: Extraction techniques, mass spectrometry, and nuclear

magnetic resonance spectroscopy. Chem Rev 2007, 107. 419-442, DOI: Doi 10.1021/Cr050359b.

177. D. A. Bronk, Q. N. Roberts, M. P. Sanderson, E. A. Canuel, P. G. Hatcher, R. Mesfioui, K. C. Filippino, M. R. Mulholland, N. G. Love, Effluent Organic Nitrogen (EON): Bioavailability and Photochemical and Salinity-

Mediated Release. Environ Sci Technol 2010, 44. 5830-5835, DOI: Doi 10.1021/Es101115g.

178. P. G. Hatcher, R. Mesfioui, G. Mckee, Identification of amides involved in Nitrogen sequestration in dissolved organic matter and sediments by ESI-FTICR-MS. Geochim Cosmochim Ac 2010, 74. A386-A386.

123

179. G. A. McKee, P. G. Hatcher, Alkyl amides in two organic-rich anoxic sediments: A possible new abiotic route for N sequestration. Geochim Cosmochim Ac 2010, 74. 6436-6450, DOI: DOI 10.1016/j.gca.2010.08.004.

180. K. A. Hanold, S. M. Fischer, P. H. Cormia, C. E. Miller, J. A. Syage, Atmospheric pressure photoionization. 1. General properties for LC/MS. Anal Chem 2004, 76. 2842-2851.

181. D. A. Bronk, K. J. Flynn, in Algal cultures, analogues of blooms and applications, ed. D. V. Subba Rao. Science Publishers: Enfield, NH, 2006, pp 2 v. (xxi, 971 p.).

182. D. L. Kirchman, in Microbial Ecology of the Oceans, ed. D. L. Kirchman.

Wiley: New York, 2000, pp x, 542 p.

183. E. Kendrick, A Mass Scale Based on Ch2=14.0000 for High Resolution Mass Spectrometry of Organic Compounds. Anal Chem 1963, 35. 2146-&.

184. P. G. Hatcher, R. L. Sleighter, G. A. McKee, Z. Liu, Naturally present fatty acids as internal calibrants for Fourier transform mass spectra of dissolved organic matter. Limnol Oceanogr-Meth 2008, 6. 246-253.

185. L. A. Ziolkowski, E. R. M. Druffel, Aged black carbon identified in marine dissolved organic carbon. Geophys Res Lett 2010, 37.

186. M. I. Bird, D. R. Grocke, Determination of the abundance and carbon isotope composition of elemental carbon in sediments. Geochim Cosmochim Ac 1997, 61. 3413-3423.

187. M. J. Cope, W. G. Chaloner, Fossil Charcoal as Evidence of Past Atmospheric Composition. Nature 1980, 283. 647-649.

188. B. Glaser, L. Haumaier, G. Guggenberger, W. Zech, The 'Terra Preta' phenomenon: a model for sustainable agriculture in the humid tropics.

Naturwissenschaften 2001, 88. 37-41.

189. T. A. J. Kuhlbusch, P. J. Crutzen, Toward a Global Estimate of Black Carbon in Residues of Vegetation Fires Representing a Sink of Atmospheric Co2 and a Source of O-2. Global Biogeochem Cy 1995, 9. 491-501.

190. J. A. Baldock, R. J. Smernik, Chemical composition and bioavailability of thermally, altered Pinus resinosa (Red Pine) wood. Org Geochem 2002, 33. 1093-1109, DOI: Pii S01466-6380(02)0062-1.

191. U. Hamer, B. Marschner, S. Brodowski, W. Amelung, Interactive priming of black carbon and glucose mineralisation. Org Geochem 2004, 35. 823-830, DOI: DOI 10.1016/j.orggeochem.2004.03.003.

124

192. E. A. Shneour, Oxidation of Graphitic Carbon in Certain Soils. Science 1966, 151. 991-&.

193. C. D. Scott, G. W. Strandberg, S. N. Lewis, Microbial Solubilization of Coal. Biotechnol Progr 1986, 2. 131-139.

194. M. Hofrichter, D. Ziegenhagen, S. Sorge, R. Ullrich, F. Bublitz, W. Fritsche, Degradation of lignite (low-rank coal) by ligninolytic basidiomycetes

and their manganese peroxidase system. Appl Microbiol Biot 1999, 52. 78-84.

195. M. I. Bird, C. Moyo, E. M. Veenendaal, J. Lloyd, P. Frost, Stability of elemental carbon in a savanna soil. Global Biogeochem Cy 1999, 13. 923-932.

196. C. M. Preston, M. W. I. Schmidt, Black (pyrogenic) carbon: a synthesis of current knowledge and uncertainties with special consideration of boreal regions. Biogeosciences 2006, 3. 397-420.

197. M. A. Alexis, C. Rumpel, H. Knicker, J. Leifeld, D. Rasse, N. Pechot, G.

Bardoux, A. Mariotti, Thermal alteration of organic matter during a shrubland fire: A field study. Org Geochem 2010, 41. 690-697, DOI: DOI 10.1016/j.orggeochem.2010.03.003.

198. B. T. Nguyen, J. Lehmann, W. C. Hockaday, S. Joseph, C. A. Masiello, Temperature Sensitivity of Black Carbon Decomposition and Oxidation. Environ

Sci Technol 2010, 44. 3324-3331, DOI: Doi 10.1021/Es903016y.

199. A. V. McBeath, R. J. Smernik, Variation in the degree of aromatic condensation of chars. Org Geochem 2009, 40. 1161-1168, DOI: DOI 10.1016/j.orggeochem.2009.09.006.

200. A. G. Marshall, C. L. Hendrickson, G. S. Jackson, Fourier Transform Ion Cyclotron Resonance Mass Spectrometry: A Primer. 1998, 17. 1-35.

201. E. B. Kujawinski, R. Del Vecchio, N. V. Blough, G. C. Klein, A. G.

Marshall, Probing molecular-level transformations of dissolved organic matter: insights on photochemical degradation and protozoan modification of DOM from electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry. Mar Chem 2004, 92. 23-37, DOI: DOI 10.1016/j.marchem.2004.06.038.

202. A. G. Marshall, R. P. Rodgers, Petroleomics: Chemistry of the Underworld. 2008, 105,. 18090-18095.

203. R. W. Kramer, E. B. Kujawinski, P. G. Hatcher, Identification of black carbon derived structures in a volcanic ash soil humic acid by Fourier transform ion cyclotron resonance mass spectrometry. Environ Sci Technol

2004, 38. 3387-3395, DOI: Doi 10.1021/Es030124m.

125

204. T. Dittmar, B. P. Koch, Thermogenic organic matter dissolved in the abyssal ocean. Mar Chem 2006, 102. 208-217, DOI: DOI 10.1016/j.marchem.2006.04.003.

205. Z. Takats, J. M. Wiseman, B. Gologan, R. G. Cooks, Mass spectrometry sampling under ambient conditions with desorption electrospray ionization. Science 2004, 306. 471-473.

206. R. B. Cody, J. A. Laramee, H. D. Durst, Versatile new ion source for the

analysis of materials in open air under ambient conditions. Anal Chem 2005, 77. 2297-2302, DOI: Doi 10.1021/Ac050152j.

207. M. Haapala, J. Pol, V. Saarela, V. Arvola, T. Kotiaho, R. A. Ketola, S. Franssila, T. J. Kauppila, R. Kostiainen, Desorption atmospheric pressure

photoionization. Anal Chem 2007, 79. 7867-7872, DOI: Doi 10.1021/Ac071152g.

208. T. J. Kauppila, V. Arvola, M. Haapala, J. Pol, L. Aalberg, V. Saarela, S. Franssila, T. Kotiaho, R. Kostiainen, Direct analysis of illicit drugs by desorption atmospheric pressure photolonization. Rapid Commun Mass Sp

2008, 22. 979-985, DOI: Doi 10.1002/Rcm.3461.

209. L. Luosujarvi, S. Kanerva, V. Saarela, S. Franssila, R. Kostiainen, T. Kotiaho, T. J. Kauppila, Environmental and food analysis by desorption atmospheric pressure photoionization-mass spectrometry. Rapid Commun Mass Sp 2010, 24. 1343-1350, DOI: Doi 10.1002/Rcm.4524.

210. J. Pol, V. Vidova, G. Kruppa, V. Kobliha, P. Novak, K. Lemr, T. Kotiaho, R. Kostiainen, V. Havlicek, M. Volny, Automated Ambient Desorption-Ionization Platform for Surface Imaging Integrated with a Commercial Fourier Transform Ion Cyclotron Resonance Mass Spectrometer. Anal Chem 2009, 81. 8479-8487,

DOI: Doi 10.1021/Ac901368q.

211. T. J. Kauppila, T. Kotiaho, R. Kostiainen, A. P. Bruins, Negative ion-atmospheric pressure photoionization-mass spectrometry. J Am Soc Mass Spectr 2004, 15. 203-211, DOI: DOI 10.1016/j.jasms.2003.10.012.

212. N. K. Kaiser, G. E. Skulason, C. R. Weisbrod, J. E. Bruce, A Novel Fourier Transform Ion Cyclotron Resonance Mass Spectrometer with Improved Ion Trapping and Detection Capabilities. J. Am. Soc. Mass Spectrom. 2009, 20. 755-762, DOI: 10.1016/j.jasms.2008.12.022.

213. G. T. Blakney, C. L. Hendrickson, A. G. Marshall, Predator data station: A fast data acquisition system for advanced FT-ICR MS experiments. In Press, Corrected Proof. DOI: 10.1016/j.ijms.2011.03.009.

126

214. M. W. Senko, C. L. Hendrickson, M. R. Emmett, S. D.-H. Shi, A. G. Marshall, External Accumulation of Ions for Enhanced Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. 1997, 8. 970-976.

215. A. V. Tolmachev, E. W. Robinson, S. Wu, H. Kang, N. M. Lourette, L. Pasa-Tolic, R. D. Smith, Trapped-ion cell with improved DC potential harmonicity for FT-ICR MS. J Am Soc Mass Spectr 2008, 19. 586-597, DOI: DOI 10.1016/j.jasms.2008.01.006.

216. E. B. Ledford, Jr., D. L. Rempel, M. L. Gross, Space Charge Effects in Fourier Transform Mass Spectrometry. Mass Calibration. 1984, 56. 2744-2748.

217. K. Hammes, R. J. Smernik, J. O. Skjemstad, A. Herzog, U. F. Vogt, M. W.

I. Schmidt, Synthesis and characterisation of laboratory-charred grass straw (Oryza saliva) and chestnut wood (Castanea sativa) as reference materials for black carbon quantification. Org Geochem 2006, 37. 1629-1633, DOI: DOI 10.1016/j.orggeochem.2006.07.003.

218. A. C. Stenson, A. G. Marshall, W. T. Cooper, Exact Masses and Chemical

Formulas of Individual Suwannee River Fulvic Acids from Ultrahigh Resolution Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectra. 2003, 75. 1275-1284.

219. B. P. Koch, T. Dittmar, From mass to structure: an aromaticity index for high-resolution mass data of natural organic matter. Rapid Commun Mass Sp

2006, 20. 926-932, DOI: Doi 10.1002/Rcm.2386.

220. B. Glaser, J. Lehmann, W. Zech, Ameliorating physical and chemical properties of highly weathered soils in the tropics with charcoal - a review. Biol Fert Soils 2002, 35. 219-230, DOI: DOI 10.1007/s00374-002-0466-4.

221. E. Sjostrom, Wood Cemistry. Fundamentals and Applications. 2 ed.; Academic Press: San Diego, 1993.

222. C. M. Preston, J. A. Trofymow, J. Niu, C. A. Fyfe, (13)CPMAS-NMR

spectroscopy and chemical analysis of coarse woody debris in coastal forests of Vancouver Island. Forest Ecol Manag 1998, 111. 51-68.

223. R. H. White, M. A. Dietenberger, in Encyclopedia of materials : science and technology, ed. K. H. J. Buschow, R. W. Cahn, M. C. Flemings, B. Ilschner,

E. J. Kramer, S. Mahajan, P. Veyssiere. Elsevier, 2001, pp 9712-9716.

127

BIOGRAPHICAL SKETCH

Current Address: Department of Chemistry and Biochemistry, Florida State

University, Tallahassee, Florida 32306-4390.

Education

Gardner-Webb University: B.S. in Chemistry, 2007

Florida State University: Graduate Student, Department of Chemistry &

Biochemistry, Analytical Chemistry Program, June, 2007 – present.

Graduate Advisor: W.T. Cooper, Department of Chemistry & Biochemistry,

Florida State University

Previous International Experience

Moscow and St. Petersburg Russia, September 14-19, 2008: Presented poster

and lecture at the 14th Meeting of the International Humic Substances

Society.

Current Research Activities

Amy M. McKenna, Ryan P. Rodgers, David C. Podgorski, Christopher Reddy, Robert Nelson, Mmilli Mapolelo; Molecular Level Characterization and

Archive for the 2010 BP Oil Spill. National Science Foundation Rapid Response Grant No. CHE-1049753.

Christopher Reddy, Karin Lemkau, Amy M. McKenna, Ryan P. Rodgers, David Valentine, David C. Podgorski; Molecular Characterization of the Cosco Busan oil spill in the San Francisco Bay 2007. National Science Foundation Grant No. OCE-0960841

William T. Cooper, David C. Podgorski, Ralph Mead, Robert Kieber;

Characterization of the Reactivity of CDOM in Rainwater from Ethanol, Gasoline and Diesel Emissions. National Science Foundation Grant No. AGS-1003078

William T. Cooper, David C. Podgorski, Rasha Hamdan, Andrew R. Zimmerman; Effects of Biomass Type and Combustion Conditions on the Molecular Properties of Biochar-derived Dissolved Organic Matter as Determined by Ultrahigh Resolution Mass Spectrometry. National Science Foundation Grant No. EAR-0819811.

Honors and Awards

2008. International Humic Substances Society Travel Bursary

2007. Hoffman Merit Award, Department of Chemistry & Biochemistry, Florida

State University

128

2007. M.A. Moseley Chemistry Award, Florida State University

2006. Gardner-Webb University Community Service Award

2005-06. Gardner-Webb University Academic Scholarship

Publications

Podgorski, D. C., McKenna, A. M., Nyadong, L., Rodgers, R. P., Marshall, A. G., Cooper, W. T.; Characterization of pyrogenic black carbon by desorption atmospheric pressure photoionization Fourier transform ion cyclotron

resonance mass spectrometry. Anal. Chem., in preparation. Podgorski, D. C., McKenna, A. M, Osborne, D. M., Hendrickson, C. L.,

Marshall, A. G., Cooper, W. T.; Detection and unique molecular formula assignment of doubly charged negative dissolved organic matter ions extracted from natural sources by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry. Anal. Bioanal. Chem., in preparation.

Osborne, D. M., Podgorski, D. C., Roberts, Q., Bronk, D. A., Austin, D., Spiler, R., Bays, J.; Characterization of reactive and refractory dissolved organic nitrogen compounds in a stormwater treatment area by atmospheric pressure photoionization Fourier transform ion cyclotron resonance mass spectrometry. Environ. Sci. Technol., in preparation.

Podgorski D. C., McKenna, A. M., Rodgers, R. P., Marshall, A. G., Cooper, W. T.; Selective ionization and molecular characterization of dissolved

organic nitrogen by positive ion atmospheric pressure photoionization Fourier transform ion cyclotron resonance mass spectrometry. Submitted to Rapid Commun. Mass Spectrom., 2011.

Tfaily, M. M., Podgorski, D. C., Corbett, J. E., Chanton, J. P., Cooper, W. T.; Influence of acidification on the optical properties and molecular composition of dissolved organic matter. Submitted to Anal. Chim. Acta, 2011.

Gonsior, M., Peake, B. M., Cooper W. T., Podgorski D. C., D’Andrilli, J.,

Dittmar, T., Cooper, W.J.; Characterization of dissolved organic matter across the Subtropical Convergence off the South Island, New Zealand. Mar. Chem, 2011, 123, 99-110.

Chipman, L., Podgorski, D. C., Green, S., Kostka, J., Cooper, W.T., Huettel, M.; Decomposition of plankton-derived dissolved organic matter in permeable coastal sediments. Limnol. Oceanogr., 2010, 55, 857-871.

Gonsior, M., Cooper, W. J., Cooper, W. T., Podgorski D. C., D’Andrilli, J.,

Peake, B. M.; Photochemically-induced Changes in Dissolved Organic Matter Identified by Ultrahigh Resolution Fourier Transform - Ion Cyclotron Resonance - Mass Spectrometry. Environ. Sci. Technol., 2009, 43 (3), pp 698–703.

Oral Conference Presentations

129

Podgorski, D. C., Osborne, D. M., McKenna, A. M., Rodgers, R. P., Marshall, A. G., Cooper, W. T.; “The molecular characterization of dissolved organic nitrogen by atmospheric pressure photoionization Fourier-transform ion cyclotron resonance mass spectrometry”. Goldschmidt 2010: Earth,

Energy, and the Environment, Knoxville, TN, June 13-18, 2010. Podgorski, D. C., Osborne, D. M., Cooper, W. T.; “The Detection and Exact

Molecular Formula Assignment of Multiply-Charged Ions in Complex Mixtures by ESI FT-ICR MS.” Pittsburgh Conference on Analytical Chemistry and Applied Spectroscopy, Orlando, FL, February 28-March 5, 2010.

Podgorski, D. C., Zimmerman, A. R., Cooper, W. T.; “Molecular Composition of

Dissolved Pyrogenic Carbon by Ultrahigh Resolution Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry and its Relationship to Bioavailability”, 238th American Chemical Society National Meeting, Washington, D.C., August 16-20, 2009.

Podgorski, D. C., Zimmerman, A. R., Cooper, W. T.; “Molecular Characterization of Dissolved Pyrogenic Carbon by Ultrahigh Resolution Mass Spectrometry.” North American Biochar Conference, Boulder, CO,

August 9-12, 2009. Podgorski, D. C., Zimmerman, A. R., Cooper, W. T.; “Effects of Biomass Type

and Combustion Conditions on the Molecular Properties of Biochar-Derived Dissolved Organic Matter as Determined by Ultrahigh Resolution Mass Spectrometry”, 85th Florida Annual Meeting and Exposition, Orlando, FL, May 14-16, 2009.

Podgorski, D. C., Huettel, M., Chipman, L., Magen, C., Cooper, W. T.; “Characterization of Microbiological Effects on the Composition and

Photochemical Properties of DOM in Coastal Sands Using Ultrahigh Resolution Mass Spectrometry”, 14th Meeting of the International Humic Substances Society, Moscow, Russia, September 14-19, 2008

Conference Abstracts Cooper, W. T., Osborne, D. M., Podgorski, D. C.; Ultrahigh Resolution Mass

Spectrometry of Dissolved Organic Nitrogen in Water Quality Treatment

Areas. 2011 IWA Specialty Conference on Natural Organic Matter: From Source to Tap and Beyond, Costa Mesa, CA, July 26-29, 2011.

McKenna, A. M., Rodgers, R. P., Nelson, R., Reddy, C., Podgorski, D. C., Savory, J. T., Kaiser, N. K., Hendrickson, C. L., Marshall, A. G.; Catastrophe in the Gulf of Mexico: The Deepwater Horizon Mississippi Canyon Mocondo Well 252 Oil Spill Characterized by FT-ICR Mass Spectrometry and Comprehensive Two-Dimensional GC x GC. Petrophase 2011, London, U.K., July 10-14, 2011.

Nikhil, J., Lim, F., Juyal, P., McKenna, A. M., Podgorski, D. C., Ho, V., Yen, A. T., Rodgers, R. P., Allenson, S. J., Marshall, A. G.; Characterization of Crude Oil and Asphaltenes from an Elevated GOR Production Well in the Gulf of Mexico. Petrophase 2011, London, U.K., July 10-14, 2011.

130

McKenna, A. M., Rodgers, R. P., Nelson, R., Reddy, C., Podgorski, D. C., Savory, J. T., Kaiser, N. K., Hendrickson, C. L., Marshall, A. G.; Catastrophe in the Gulf of Mexico: Molecular Characterization of the Deepwater Horizon Oil Spill by FT-ICR MS and Comprehensive GC x GC.

59th ASMS Conference on Mass Spectrometry and Allied Topics, Denver, CO, June 5-9, 2011.

Ruddy, B. M., McKenna, A. M., Podgorski, D. C., Rodgers, R. P., Huettel, M., Marshall, A. G.; Compositional Analysis of BP Deepwater Horizon Oil Contaminated Pensacola Beach Sand by Ultrahigh Resolution FT-ICR MS. 59th ASMS Conference on Mass Spectrometry and Allied Topics, Denver, CO, June 5-9, 2011.

McKenna, A. M., Podgorski, D. C., Corilo, Y. E., Ruddy, B. M., Kaiser, N. K.,

Savory, J. T., Rodgers, R. P., Hendrickson, C. L., Marshall, A. G.; Advanced Characterization of Environmental Samples by FT-ICR MS: Dissolved Organic Matter to Petroleum. 8th North American FT MS Conference, Key West, FL, May 1-5, 2011.

Cooper, W. T., Witowski, C., Podgorski, D. C., Wetz, M., Kostka, J.; Optimization and Lipid Profiling of Algae Biofuel Feedstocks Grown in Wastewater. 2010 Florida Energy Systems Consortium, Orlando, FL,

September 28-29, 2010. Huettel, M., Chipman, L., Podgorski, D. C., Green, S. J., Magen, C.,

Niggemann, J., Ziervogel, K., Arnosti, C., Berg, P., Cooper, W. T., Dittmar, T., Kostka, J. E., Hallas, K.; Organic Matter Degradation and Nutrient Remobilization in Permeable Costal Sands. Goldschmidt 2010: Earth, Energy, and the Environment, Knoxville, TN, June 13-18, 2010.

Osborne, D. M., Cooper, W. T., Podgorski, D. C.; Dissolved Organic Nitrogen:

Not Just a Number Anymore? 86th Florida Annual Meeting and Exposition, Innisbrook, FL, May 13-16, 2010.

Witowski, C., Podgorski, D. C., Cooper, W. T.; Growth Optimization and Lipid Profiling of Algae Biofuel Feedstocks. 86th Florida Annual Meeting and Exposition, Innisbrook, FL, May 13-16, 2010.

Huettel, M., Chipman, L., Laschet, M., Podgorski, D. C., Green, S. J., Kostka, J. E., Cooper, W. T.; Dissolved Organic Carbon Degradation in

Sublittoral Sands. ASLO/TOS/AGU 2010 Ocean Sciences Meeting, Portland, OR, February 22-26, 2010.

Magen, C., Huettel, M., Podgorski, D. C., Cooper, W. T.; Advection of Labile Dissolved Organic Carbon (DOC) Through Permeable Sands Induces the Release of Sorbed DOC to the Overlying Water. ASLO/TOS/AGU 2010 Ocean Sciences Meeting, Portland, OR, February 22-26, 2010.

Osborne, D. M., Podgorski D. C., Cooper, W. T.; The Characterization of

Dissolved Organic Nitrogen by Ultrahigh Resolution Mass Spectrometry. 2009 Fall Meeting of the American Geophysical Union, San Francisco, CA, December 14-18, 2009.

Cooper, W. T., Tfaily, M. M., Podgorski, D. C., Osborne, D. M., Paul, A. L., Corbett, J. E., Chanton, J. P.; Relationship between Molecular

131

Composition and Optical Properties of Dissolved Organic Matter. 2009 Fall Meeting of the American Geophysical Union, San Francisco, CA, December 14-18, 2009.

Chipman, L., Podgorski, D. C., Green, S., Kostka, J., Cooper, W. T., Huettel,

M.; DOM Decomposition in Permeable Coastal Sediments. Chemical Oceanography in a Changing World Symposium, Savannah, GA, February 22-24, 2009.

D’Andrilli, J., Cooper, W. T., Podgorski, D. C., Magen, C., Huettel, M. and Kostka, J.;“Characterization of the Effects of Microbial Processing in Gulf of Mexico Coastal Sands on the Composition of Dissolved Organic Matter Using Ultrahigh Resolution Mass Spectrometry”, 2008 Fall Meeting of

the American Geophysical Union, San Francisco, CA, December 14-19, 2008.

Kloecking, R., Helbig, H., Kinne, M., Kleiner, C., Gorecki, T., Poerschmann, J., Podgorski, D. C. and Cooper, W.T.; “Characterization of Synthetic (Core) Humic Substances Made from Dihydroxylated Phenylpropanoids” 14th Meeting of the International Humic Substances Society, Moscow, Russia, September 14-19, 2008.

Cooper, W. T., D’Andrilli, J., Podgorski, D. C., Dittmar, T., Huettel, M., Kostka, J., Chipman, L. and Gonsior, M.; “Ultrahigh Resolution Mass Spectrometry of Dissolved Organic Matter in Estuaries”, National Science Foundation Workshop, St. Petersburg, FL, May 6-8, 2008.

Cooper, W. T., D’Andrilli, J., Podgorski, D. C., Dittmar, T., Huettel, M. and Chipman, L.; “Ultrahigh Resolution Mass Spectrometry of Dissolved Organic Matter: The Path to Geomics”, American Society of Limnology and

Oceanography (ASLO) 2008 Ocean Sciences Meeting, Orlando, FL, March 2-7, 2008.

Field Experience April 2008: Research assistant on R/V Bellows. Participated in water sample

collection and preparation for FT-ICR MS analysis. Chief Scientist: Nicolas Wienders

June 2010: Research assistant on multiple research trips to sample rivers and

estuaries over a period of 10 days in Brazil (Sepetiba Bay and Paraiba do Sul River). Collected and prepared water samples for FT-ICR MS analysis. Chief Scientist: William T. Cooper

Professional Societies

• American Chemical Society

• American Geophysical Union

• International Humic Substances Society

• American Society of Mass Spectrometry

• American Association for the Advancement of Science