MEASUREMENT OF FOREST ECOSYSTEM-ATMOSPHERE …MEASUREMENT OF FOREST ECOSYSTEM-ATMOSPHERE EXCHANGE OF...

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MEASUREMENT OF FOREST ECOSYSTEM-ATMOSPHERE EXCHANGE OF δ 13 C – CO 2 USING FOURIER TRANSFORM INFRARED SPECTROSCOPY AND DISJUNCT EDDY COVARIANCE By MARIA OBIMINDA L. CAMBALIZA A dissertation submitted in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY WASHINGTON STATE UNIVERSITY Department of Civil and Environmental Engineering MAY 2010

Transcript of MEASUREMENT OF FOREST ECOSYSTEM-ATMOSPHERE …MEASUREMENT OF FOREST ECOSYSTEM-ATMOSPHERE EXCHANGE OF...

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MEASUREMENT OF FOREST ECOSYSTEM-ATMOSPHERE EXCHANGE OF

δ13C – CO2 USING FOURIER TRANSFORM INFRARED SPECTROSCOPY

AND DISJUNCT EDDY COVARIANCE

By

MARIA OBIMINDA L. CAMBALIZA

A dissertation submitted in partial fulfillment of therequirements for the degree of

DOCTOR OF PHILOSOPHY

WASHINGTON STATE UNIVERSITYDepartment of Civil and Environmental Engineering

MAY 2010

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To the Faculty of Washington State University:

The members of the Committee appointed to examine the dissertation of MARIA

OBIMINDA CAMBALIZA find it satisfactory and recommend that it be accepted.

___________________________________George H. Mount, Ph.D., Chair

____________________________________Brian K. Lamb, Ph.D.

____________________________________Halvor H. Westberg, Ph.D.

____________________________________John D. Marshall, Ph.D.

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DEDICATION

To Alfredo and Lagrimas Cambaliza

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ACKNOWLEDGMENT

I first learned about the environmental engineering program at Washington State

University through Fr. Daniel McNamara, SJ, a priest and a scientist. He was chair of the

Physics department of the Ateneo de Manila University where I was working as an

instructor. When I was seriously considering graduate school back in 2001, Fr. Dan

recommended that I look into the research of Dr. George Mount, a long-time friend from

graduate school days. Since then, my life has changed and the next seven years saw me

working on a very exciting, inter-disciplinary biosphere-atmosphere research that always

kept me on my toes. I would like to thank Fr. Dan for giving me that gentle push that

started this wonderful experience for me.

I would like to thank my faculty adviser, Dr. George Mount, and his family. I feel

very lucky and blessed that I ended up working with Dr. Mount. He is very passionate

with both teaching and research and his enthusiasm is contagious. I encountered many

challenges in my research, sometimes seemingly insurmountable problems, but he

believed in me and encouraged me to charge on even when I doubted myself. I cannot

thank him enough for the once-in-a-lifetime, very positive, enriching experience, as well

as for opening opportunities for me after graduate school. It is very rare to have an

adviser who goes out of his way to make sure that his graduate students are successful

during and after graduate school.

I also thank my committee members, Drs. Brian Lamb, Hal Westberg, and John

Marshall. Together with my advisor, they all helped me achieve my goals and made sure

that I receive the critical guidance I needed along the way. I thank Dr. John Marshall for

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guiding me with the biological aspect of my research and for coming down to Boardman,

Oregon in 2005. I enjoyed the many long discussions on carbon isotopes and ecosystem

processes as we looked closely at my FTIR results. Many thanks also go to Dr. Brian

Lamb for the very helpful discussions especially when I was having difficulties with my

flux analysis. I would also like to thank him for the RA support in the spring semester of

2007. I also thank Dr. Hal Westberg, who despite being retired, remained interested in

my work and never failed to encourage me to continue on especially when I encountered

an unforeseen problem with calibration.

To my friends who helped me with my fieldwork: Robert Gibson, Steve Nelson,

Brian Rumburg Jenny Filipy and Jacob McCoskey, thank you all! I am very thankful to

my friend Rob who spent two summers with me in Boardman, Oregon. He made our

fieldwork experience much more interesting. We shared some unforgettable, hilarious

experiences in Boardman and Hermiston that must be documented someday. Many

thanks also go to Brian and Jenny not only for working with me in the field but most

especially for their friendship, support, and encouragement. I have gotten to know them

quite well and vice versa, and their friendship helped me through some very challenging

times.

I would also like to thank Mr. Richard Winkler for working long hours with me in

the laboratory. Wink helped me with the software control of the instrument. Without his

generosity and expertise, it would probably take me longer to complete my degree. Wink

is one of the smartest, most focused persons I have had the pleasure to work with. I am

also thankful for his friendship.

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I also thank Dr. Dave Griffith of the School of Chemistry, University of

Wollongong, Australia, for the many helpful discussions on data analysis and FTIR

instrumentation, as well as for the use of the MALT program. I also thank him for his

very helpful recommendation on how to clean the FTIR gas cell.

Many thanks also go to Gary Held and Kurt Hutchinson of the WSU Engineering

Instrument Shop for helping me build the DEC instrument, and all other auxiliary

instruments needed for mounting various sensors in the field. Both Gary and Kurt are

very competent, capable, and skillful, and the College of Engineering is very fortunate to

have them.

I would also like to thank Ben Harlow and Dr. Dave Evans of the WSU Stable

Isotopes Core Laboratory for the exciting, collaborative work I have done in their

laboratory. I also thank the Idaho Stable Isotope Laboratory for the analysis of bulk leaf

and soil respiration samples. Many thanks also go to my friend Dr. Nerea Ubierna for the

many helpful and fun conversations over the phone, over lunch, and over an ice cream

sundae. My discussions with her clarified many isotope concepts and helped me

understand the implications of my measurements. I thoroughly enjoyed working with her

and Ben Harlow in the laboratory figuring out how to make gas measurements with the

CF-IRMS even more precise and accurate.

I also thank Dr. Shelley Pressley for all her help with the flux data analysis and

for the many insightful exchange of ideas. She was always available for consultation. I

thank her for her generosity with her time and expertise and for being a great role model

for other female scientists in LAR. I would also like to thank Lee Bamesberger, LAR’s

hard working retired technician. I cannot thank him enough for helping me prepare the

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dilutions of my calibration tanks, for helping choose the right vacuum pumps, and for all

the many laboratory preparations that go into a successful field campaign. Many thanks

also go to past and present graduate students of LAR: Erik Velasco, Jack Chen, Tara

Strand, Steve Edburg, Doris Montecastro, Rasa Grivicke, Jacob McCoskey and Kara

Yedinak for their friendship and support. Thank you also to the CEE staff: Vicki

Ruddick, Maureen Clausen, Lola Gillespie, Cyndi Whitmore, Glenda Rogers and Tom

Webber for their assistance and for making sure that everything runs like a well-oiled

machine so that the rest of us can focus on research. I would also like to thank my best

friend Dr. Gemma Narisma for the many phone conversations while she was still in the

United States.

Many thanks also go to my Filipino friends: Nelly and Cesar Zamora, Rhoda

Rimando, Ed and Julie Cenzon, Suzette and Greg Galinato, Jave Pascual, and Dan and

Medy Villamor, who all became my surrogate family in Pullman. Thank you for your

friendship and support.

I also thank Greenwood Resources for the use of the Boardman poplar site, with

special thanks to Mr. Mike Berk, caretaker of the plantation.

Finally, I thank my family: mom, dad, my sister Genalin and nanay Lucing for

their love, prayers, and encouragement. I am thankful to my parents for teaching me the

importance of education and for instilling in me the values of patience, hard work, and

perseverance. I would not be able to go this far without my family’s unwavering support.

This research was supported by the National Science Foundation.

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ATTRIBUTION

The research presented in this dissertation is a compilation of results from field

measurements during the summers of 2004 to 2006 in a managed poplar forest near

Boardman, Oregon, as well as laboratory measurements at the WSU Stable Isotope Core

Laboratory and Idaho Stable Isotope Laboratory, University of Idaho. This dissertation

has three important chapters: a description of the development of Fourier Transform

Infrared Spectroscopy – disjunct eddy covariance (FTIR – DEC) instrument for the

measurement of the vertical and temporal distribution of CO2 – δ13C in a forest ecosystem

(chapter 2), isotopic CO2 flux measurements using the FTIR – DEC approach (chapter 3),

and improvement of analysis of low-concentration gas samples with continuous flow

isotope ratio mass spectrometry (CF – IRMS) (chapter 4). Although Maria Obiminda

Cambaliza is the first author of these manuscripts, the results presented in this

dissertation are products of the ideas and efforts from many collaborators. M. O.

Cambaliza was responsible for all data analysis (except for those reported in chapter 4),

and for organizing and facilitating field measurements during the summers of 2004 to

2006.

Drs. Brian Lamb, Hal Westberg, and George Mount were the co-principal

investigators in a biosphere-atmosphere research proposal that was funded by the

National Science Foundation (NSF). They received funding for the development of a

suite of instruments that quantify the biosphere-atmosphere interaction in a forest. One

of these instruments was the FTIR – DEC, which M. O. Cambaliza helped to develop

together with G. Mount. The DEC sampling strategy was suggested by Drs. Brian Lamb

and Hal Westberg. The FTIR – DEC instrument underwent several modifications, as M.

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O. Cambaliza and G. Mount learned more about how to optimize isotopic CO2

measurements. Many of these modifications were the result of sensitivity analyses and

many brainstorming discussions. Modifications were carried out by Gary Held and Kurt

Hutchinson at the WSU Engineering Instrument shop. Mr. Richard Winkler helped with

the automation and control of the FTIR – DEC instrument. Dr. John Marshall provided

invaluable guidance with the interpretation of FTIR – DEC isotope measurements and the

design of the soil respiration chambers. He also helped with the field campaign in 2005

where he measured very important supporting data such as the carbon isotope ratios of

bulk leaf and phloem sap samples. Drs. Brian Lamb and George Mount helped with the

design of the experimental observations in the field (vertical profile and fixed height

measurements). Dr. Brian Lamb also provided invaluable guidance with the analysis of

the flux data as well as the vertical profile measurements.

Benjamin Harlow, and Drs. Nerea Ubierna, Dave Evans, John Marshall and

George Mount are the co-authors of the manuscript presented in chapter 4. Ben Harlow

and Nerea Ubierna helped with the laboratory measurements, which were conducted at

the WSU Stable Isotope Core Laboratory. They also helped with the data analysis,

organization, and writing of the manuscript. Dr. Dave Evans provided guidance with the

design of the experiments. Dr. George Mount provided initial helpful comments on the

first version of the paper. Both Drs. John Marshall and Dave Evans gave detailed

insightful comments that helped improve and polish the final version of the paper.

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MEASUREMENT OF FOREST ECOSYSTEM-ATMOSPHERE EXCHANGE OF

δ13C – CO2 USING FOURIER TRANSFORM INFRARED SPECTROSCOPY

AND DISJUNCT EDDY COVARIANCE

Abstract

By Maria Obiminda L. Cambaliza, Ph.D.Washington State University

May 2010

Chair: George H. Mount

The measurement of the stable isotopic content and isotopic flux of atmospheric

carbon dioxide is important for understanding the carbon budget on ecosystem, regional,

and global spatial scales. Conventional measurements of the isotopic composition of

atmospheric CO2 involve laboratory mass spectrometry analysis of grab samples from the

field, which limits the location, collection frequency and throughput of samples. More

technologically advanced methods (e.g. tunable diode laser spectroscopy) suffer from

interferences with other chemical species. We have developed a new measurement

method based on Fourier-transform infrared spectroscopy (FTIR) and disjunct eddy

covariance (DEC) for fast, continuous, real-time measurement of the carbon isotopic

composition of atmospheric CO2. Molecular absorption is measured in the 2100 to 2500

cm-1 spectral region of the 13CO2 and 12CO2 vibration-rotation bands with concentrations

of both isotopologues used to determine δ13C. We demonstrate the capability of this new

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technique in a managed poplar forest near Boardman, Oregon with measurements during

the summers of 2005 and 2006 from a 22-meter tower in a 16-m forest canopy. Long-

term calibration using reference gas cylinders yielded field accuracy and precision for the

forest measurements of 0.5‰ and 0.8‰, respectively, for the 45-second cycle time

between samples. The signature of ecosystem respiration derived from the nighttime

vertical profile measurements of CO2 - δ13C was –26.6‰, about 2‰ more enriched than

the isotopic composition of measured bulk leaf samples from the forest. Ecosystem

respired CO2 was ~1.6‰ more enriched than soil-respired CO2.

A comparison of the FTIR – DEC total CO2 fluxes against standard eddy

covariance measurements showed excellent (10%) agreement. FTIR – DEC

measurement of the CO2 isoflux enabled the estimation of the mean carbon isotope ratio

of the photosynthetic flux (δP). The average δP (-24.9‰) was 13C – enriched compared

with the isotopic composition of bulk leaf samples. These results are consistent with

observations using conventional methods. The FTIR – DEC technique can

simultaneously measure several important trace gases, which will make it a powerful tool

for application in forest, agricultural and urban ecosystems.

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TABLE OF CONTENTS

DEDICATION...............................................................................................................iii

ACKNOWLEDGMENT ................................................................................................ iv

ATTRIBUTION...........................................................................................................viii

Abstract .......................................................................................................................... x

LIST OF TABLES........................................................................................................ xv

LIST OF FIGURES ..................................................................................................... xvi

CHAPTER 1: INTRODUCTION AND LITERATURE REVIEW ................................. 1

1.1. Overview and Objectives ....................................................................................... 1

1.2. Background on the carbon isotope ratio.................................................................. 5

1.2.1. Definitions and isotopic composition of common sources ................................. 5

1.2.2. Stable carbon isotopes and ecosystem processes ............................................... 7

1.2.3. Determination of the carbon isotope ratio of ecosystem respired CO2............... 8

1.2.4. Determination of canopy – scale photosynthetic discrimination ..................... 11

1.3. Measurement of Carbon isotope ratio ................................................................... 13

1.4. Measurement of biosphere-atmosphere exchange of isotopic CO2 ........................ 17

1.5. Summary ............................................................................................................. 23

1.6. References ........................................................................................................... 24

CHAPTER 2: A NEW FOURIER-TRANSFORM INFRARED INSTRUMENT FOR

MEASUREMENT OF TEMPORAL AND VERTICAL DISTRIBUTION OF δ13C – CO2

1. AN OVERVIEW OF RESULTS FROM MEASUREMENTS IN A MANAGED

POPLAR FOREST IN NORTHERN OREGON............................................................ 34

2.1. Abstract ............................................................................................................... 34

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2.2. Introduction ......................................................................................................... 36

2.3. Methodology........................................................................................................ 40

2.3.1. Site Description .............................................................................................. 40

2.3.2. Description of experiment............................................................................... 41

2.3.3. Leaf sampling, soil respiration measurement and isotope analysis................... 44

2.3.4. Analysis of Measured Spectra ......................................................................... 45

2.3.5. Calibration...................................................................................................... 47

2.4. Results and discussion.......................................................................................... 47

2.5. Conclusions ......................................................................................................... 52

2.6. References ........................................................................................................... 54

CHAPTER 3: A NEW FOURIER TRANSFORM INFRARED INSTRUMENT FOR

MEASUREMENT OF TEMPORAL AND VERTICAL DISTRIBUTION OF δ13C – CO2

2. MEASUREMENT OF ECOSYSTEM-ATMOSPHERE EXCHANGE OF ISOTOPIC

CARBON DIOXIDE USING DISJUNCT EDDY COVARIANCE............................... 69

3.1. Abstract ............................................................................................................... 69

3.2. Introduction ......................................................................................................... 71

3.3. Theory and Methods ............................................................................................ 75

3.3.1. Canopy-scale photosynthetic discrimination.................................................... 75

3.3.2. Site description and meteorological conditions................................................ 77

3.3.3. Flux measurement and spectroscopic analysis of air samples .......................... 78

3.3.4. Leaf sampling and isotopic analyses ............................................................... 81

3.4. RESULTS AND DISCUSSION........................................................................... 82

3.4.1. FTIR – DEC and IRGA – EC Measurements of CO2 and H2O fluxes .............. 82

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3.4.2. Isoflux estimates, isotopic flux partitioning, and discrimination estimates ....... 83

3.5. Conclusions ......................................................................................................... 86

3.6. References ........................................................................................................... 88

CHAPTER 4: ANALYSIS OF LOW-CONCENTRATION GAS SAMPLES WITH

CONTINUOUS-FLOW IRMS: ELIMINATING SOURCES OF CONTAMINATION

TO ACHIEVE HIGH PRECISION ............................................................................. 103

4.1. Abstract ............................................................................................................. 103

4.2. Introduction ....................................................................................................... 104

4.3. Methods ............................................................................................................. 106

4.3.1. Isotopic analysis ........................................................................................... 106

4.3.2. Description of gas sample preparation methods............................................. 109

4.3.3. Experiment 1: Identification of sources of contamination............................. 111

4.3.4. Experiment 2: Application to gas samples of near-ambient CO2 concentrations

............................................................................................................................... 112

4.4. Results .............................................................................................................. 113

4.4.1. Identification of sources of contamination..................................................... 113

4.4.2. Application to gas samples of near-ambient CO2 concentrations ................... 115

4.5. Discussion.......................................................................................................... 115

4.6. References ......................................................................................................... 119

CHAPTER 5 ............................................................................................................... 127

SUMMARY AND CONCLUSIONS .......................................................................... 127

5.1. Summary ........................................................................................................... 127

5.2. Personal thoughts and recommendations for future measurements...................... 132

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LIST OF TABLES

CHAPTER 1

Table 1.1. Isotopic composition of various carbon sources. .......................................... 31

Table 1.2. Summary of capabilities of various spectroscopic techniques........................ 32

CHAPTER 2

Table 2. 1. Concentrations and carbon isotope ratios of four reference tanks used in the

field. ...................................................................................................................... 59

Table 2. 2. Estimate of the isotopic composition of respired carbon as a function of length

of averaging time. .................................................................................................. 60

Table 2. 3. CO2 concentrations and carbon isotope composition of soil pool measured

from the headspace of the static closed chambers.. ................................................. 61

CHAPTER 3

Table 3. 1. Concentrations and carbon isotope ratios of four reference cylinders used in

the field for calibration of FTIR derived CO2 and δ13C........................................... 93

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LIST OF FIGURES

CHAPTER 1

Figure 1.1. Carbon isotopic discrimination by C3 photosynthesis.. ................................. 33

CHAPTER 2

Figure 2. 1. Schematic diagram of field experiment set-up. ........................................... 62

Figure 2. 2. Examples of typical measured, fit and residual spectrum ........................... 63

Figure 2. 3. Long-term on-line calibration measurements taken in the field from August

11 to 17, 2006........................................................................................................ 64

Figure 2. 4. Nighttime vertical and temporal distributions of a) CO2 and b) δ13C. .......... 65

Figure 2. 5 . Daytime vertical and temporal distribution of a) CO2 and b) δ13C. ............ 66

Figure 2. 6. Estimate of the signature of ecosystem respiration, δ13CR........................... 67

Figure 2. 7. Carbon isotopic composition of leaves as a function of height. ................... 68

CHAPTER 3

Figure 3. 1. Diurnal averages of (a) temperature, (b) photosynthetically active radiation,

(c) friction velocity. ............................................................................................... 94

Figure 3. 2. Wind speeds and wind directions observed during the duration of field

experiments.. ......................................................................................................... 95

Figure 3. 3. Schematic diagram of the field experiment set-up....................................... 96

Figure 3. 4. Comparison of FTIR – DEC and IRGA – EC measurements of CO2 fluxes.97

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Figure 3. 5. Comparison of FTIR – DEC Latent Heat flux measurements against IRGA –

EC estimates.......................................................................................................... 98

Figure 3. 6. The isoflux of 13CO2. .................................................................................. 99

Figure 3. 7. Temperature dependence of the nighttime CO2 flux. ................................ 100

Figure 3. 8. The total CO2 flux and its photosynthetic assimilation (FA) and respiratory

(FR) components .................................................................................................. 101

Figure 3. 9. Half hour averages of the canopy-scale carbon isotope discrimination ..... 102

CHAPTER 4

Figure 4. 1. Schematic diagram of the vacuum backfill manifold................................ 121

Figure 4. 2. Maximum contamination on low-concentration CO2 gas samples as a

function of the various gas filling treatments.. ..................................................... 122

Figure 4. 3. Carbon isotope ratio as a function of the measured area for all Helium-filled

replicates of all treatments.. ................................................................................. 123

Figure 4. 4. Residual contamination as a function of the ten treatments.. .................... 124

Figure 4. 5. Two-source mixing model calculation of the uncertainty in the carbon

isotope ratio of a low-concentration sample.. ....................................................... 125

Figure 4. 6. Effect of three sample preparation techniques in air samples of identical CO2

concentrations but distinctly different carbon isotope ratios. ................................ 126

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CHAPTER 1

INTRODUCTION AND LITERATURE REVIEW

1.1. Overview and Objectives

Unrestrained use of fossil fuels and widespread land cover changes since the

industrial revolution have added significant amounts of CO2 in the atmosphere with

mixing ratios rising from 280 ppmv prior to the industrial revolution to ~385 ppmv today.

This excessive anthropogenic contribution has overwhelmed the natural carbon absorbing

capacity of oceans and terrestrial ecosystems. For the years between 2000 and 2005,

carbon emissions due to fossil fuel burning and cement production were estimated to be

7.2 ± 0.3 GtC/yr (Denman, et al., IPCC AR4, 2007). Approximately 60% of these

emissions, 4.1 ± 0.1 GtC/yr, remain airborne and the remaining 40% is partitioned

between the ocean and land reservoirs. There has been negligible change in the capacity

of the oceans (2.2 ± 0.5 GtC/yr) to absorb carbon between the 1990’s and 2005.

However, there is an inter-annual and inter-decadal variability in the growth rate of

atmospheric CO2, which tracks the changing magnitude of the biospheric sink. Although

the global carbon cycle is reasonably well understood, it still remains difficult to predict

the future response of the terrestrial ecosystem to changing environmental drivers such as

increased temperature, precipitation, and nutrient fertilization. Therefore, there is a

strong need to characterize ecosystem response to different environmental conditions

during various seasons to understand the complex flow of carbon between the biosphere

and atmosphere.

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The net ecosystem exchange (NEE) of carbon dioxide is now continuously

monitored in over 400 ecosystems via FLUXNET, an international network of

micrometerological tower sites that make use of eddy covariance techniques to measure

the exchange of CO2, H2O and energy between the terrestrial ecosystem and the

atmosphere (FLUXNET, http://daac.ornl.gov/FLUXNET/, accessed August 19, 2009).

However, net flux measurements are not able to separate the two opposing contributions,

photosynthesis and respiration, which are differentially influenced by changing

environmental conditions (Yakir and Sternberg, 2000).

The measurement of the isotopic composition of atmospheric CO2 has emerged as

a very important tool for understanding the flow of carbon between the biosphere and

atmosphere. The measurement of stable carbon isotopes enabled the investigations of

complex interactions that are not possible with the analysis of partial pressures alone.

Keeling’s defining research in the 1950’s (Keeling 1959, 1961) clearly showed the

influence of ecosystem processes on the isotopic composition of atmospheric carbon

dioxide. It has been recognized that photosynthesis and respiration impart unique

isotopic signatures in the atmosphere. The process of photosynthesis discriminates

against the 13CO2 isotopologue of carbon dioxide and preferentially fixes the lighter 12CO2

into plant biomass, leaving the air in the canopy isotopically enriched. On the other hand,

ecosystem respiration returns to the atmosphere isotopically depleted carbon dioxide.

In recent years, there has been an increase in the application of stable carbon

isotope measurements to understand ecosystem carbon exchange. Variations in the

carbon isotopic signature of ecosystem respiration, δ13CR, have been linked to changing

environmental drivers such as precipitation, vapor pressure deficit, and soil moisture

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content (e.g., Bowling et al., 2002, McDowell et al., 2004, Knohl et al., 2005, Lai et al.,

2005, Pataki et al., 2003). Information from the carbon isotopic composition of net

ecosystem exchange (NEE) has provided an additional constraint to facilitate the

partitioning of NEE into its photosynthetic and respiratory components (Yakir and Wang,

1996, Bowling et al., 2001, Knohl and Buchmann, 2005, Ogee et al., 2003 and 2004,

Zhang, 2006). Isotopic flux partitioning is important, as it will help in understanding the

sensitivity of these isolated fluxes to climatic changes in moisture, temperature and

precipitation. On a global scale, flux partitioning has been also used to partition CO2 into

its land and ocean uptake components (Ciais, et al., 1995a &b, Keeling, et al., 1995,

Battle, et al., 2000).

The main objective of this work is to develop a Fourier transform infrared

spectroscopy (FTIR) instrument for fast, real-time, in-situ measurement of isotopic CO2

exchange (concentration, carbon isotope ratio, and isotopic CO2 flux) between the

terrestrial ecosystem and atmosphere. This dissertation is divided into five chapters. The

first chapter provides (1) background on the concept of carbon isotope ratio and its

application to ecosystem scale processes such as photosynthesis and respiration, (2)

discussion of the conventional and spectroscopic techniques for δ13C – CO2

measurements, (3) description of the development of flux methods for isotopic CO2 flux

measurements, and (4) recent advances in the application of stable isotope measurements.

The next two chapters are manuscripts that will be submitted for publication in Journal of

Agricultural and Forest Meteorology. The first manuscript is entitled “A new Fourier

transform infrared instrument for measurement of temporal and vertical distribution of

δ13C – CO2 1. An overview of results from measurements in a managed poplar forest in

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northern Oregon”. It describes in detail the development of an FTIR system for

continuous, real-time measurement of isotopic CO2 in forest ecosystem. The third

chapter is entitled “A new Fourier transform infrared instrument for measurement of

temporal and vertical distribution of δ13C – CO2 2. Measurement of ecosystem-

atmosphere exchange of isotopic carbon dioxide using disjunct eddy covariance”. It

describes the development of a new approach combining FTIR spectroscopy with

disjunct eddy covariance, a direct flux measurement technique, for simultaneous

measurement of NEE of CO2, latent heat flux, and isotopic CO2 fluxes. The fourth

chapter is entitled “Analysis of low-concentration gas samples with continuous-flow

IRMS: eliminating sources of contamination to achieve high precision” (Cambaliza et al.,

2009, copyright Wiley-Blackwell). This manuscript resulted from the observation that

there is still no standard procedure/protocol amongst isotope laboratories in analyzing gas

standards using continuous flow isotope ratio mass spectrometry (CF-IRMS). The lack

of a protocol presented a time consuming challenge in analyzing the working gas

standards that were used to calibrate our field measurements. Thus, we first developed a

procedure for preparing and analyzing gas samples on a CF – IRMS. The optimized

procedure was used to ascertain the isotopic composition of our reference cylinders that

were used during field measurements. The fourth chapter discusses (1) sources of

contamination in CF-IRMS analysis of near-ambient CO2 concentration gas samples, (2)

analytical procedures that reduce the contamination, and (3) the identification of a

precise, efficient method for introducing gas samples into vials. The last chapter of this

thesis is a summary of insights gained from this research work and outlines in detail

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recommendations to improve the instrumentation for future measurements of isotopic

CO2 in forest ecosystems.

1.2. Background on the carbon isotope ratio

This section is focused on providing a theoretical framework for the concept of

stable carbon isotopes as it applies to ecosystem processes, beginning with a discussion

of typical isotopic compositions of various carbon sources. A theoretical framework on

isotopic flux partitioning is given followed by a discussion on photosynthetic

discrimination (Δ) and isotopic composition of ecosystem respiration (δR), their

importance to the science of global change and their measurement.

1.2.1. Definitions and isotopic composition of common sources

Carbon isotope measurements are usually expressed in terms of the “delta

notation” (in per mil or ‰), which gives the ratio of the heavier to the lighter

isotopologue relative to the Vienna Pee Dee Belemnite (VPDB) standard:

δ13C =R13sampleR13standard

−1⎡

⎣ ⎢

⎦ ⎥ ×1000 (1)

where R13 = 13C16O2/12C16O2 is the ratio of the abundances of 13C16O2 and 12C16O2 in the

sample and in the standard, respectively.

The natural variations in the carbon isotopic content of different compounds

(called the isotope effect) are observed whenever chemical bonds are created or destroyed

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during an enzymatic reaction (termed kinetic fractionation) or during molecular diffusion

where the heavier isotopologue diffuses much slower than the lighter isotopic form

(termed diffusion fractionation). Photosynthesis, as will be discussed later for the C3

photosynthetic pathway, is a clear example where both diffusion and kinetic

fractionations are observed.

Table 1.1 shows the isotopic composition of common carbon sources such as air,

fossil fuels and plant biomass. Emissions from fossil fuel combustion (coal and natural

gas burning as well as flaring) and cement production range from about –24 to –44 ‰

(Ciais et al., 1997) and slowly change the isotopic composition of the air we breathe. The

mean carbon isotope ratio of fossil fuels used by Ciais et al., (1997) in their inverse

model to partition atmospheric CO2 between the land and ocean reservoirs was –28.4 ‰.

They observed a small influence on the inferred ocean/land partitioning when they varied

the isotopic signature of fossil fuels by about 1‰, which corresponded to a change in the

northern hemisphere terrestrial uptake of 0.3 GTC.

Depending on the photosynthetic pathway, plant biomass has an isotopic

signature ranging from either –12 to –15 ‰ (for plants following the C4 photosynthetic

pathway, e.g., grass) or –21 to –35 ‰ (for plants utilizing the C3 pathway, e.g., trees and

shrubs). C3 and C4 photosynthesis are the two most important photosynthetic pathways in

the study of global environmental change with the C3 pathway more widely distributed

globally than C4.

Background CO2 such as found in the unperturbed region above a forest canopy

currently has a molar concentration and carbon isotope ratio of ~385 ppmv (Carbon

dioxide information analysis center, http://cdiac.ornl.gov/trends/co2/sio-mlo.html,

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accessed August 17, 2009), and ~ -8‰ (Bowling, et al., 2008), respectively. The carbon

isotopic composition of atmospheric CO2 was –6.4 ‰ before the industrial revolution

(Fung et al., 1997) and has since continually decreased to ~-8‰. The dilution of the

heavier isotopologue of carbon dioxide in atmospheric CO2 is due to the continued

uncontrolled use of fossil fuels, which are isotopically lighter than background CO2

(Table 1).

1.2.2. Stable carbon isotopes and ecosystem processes

This section discusses the carbon isotopic composition of ecosystem processes:

photosynthesis and respiration. During daytime, the carbon isotope ratio of air within the

canopy is determined by the combined effects of photosynthetic discrimination against

13CO2 as well as the turbulent mixing of background air with CO2 respired by the

ecosystem. From conservation of mass, the net ecosystem exchange of total CO2 is given

as

F = Fe + ρdCa

dt= FR + FA (2)

where

Fe = ρw 'Ca' is the eddy CO2 flux, ρ is the density of air, Ca is the ambient carbon

dioxide concentration,

ρdCa dt is the storage term, FR is the total ecosystem respiration,

and FA is the net photosynthetic assimilation term (we follow here the notations of

Bowling et al., 2003b). Multiplying each term in equation (2) by its respective isotope

ratio and converting into isotopic notation, we obtain the equation for the isoflux of 13CO2

(Bowling et al., 2001, 2003b, see also Zobitz, et al., 2008)

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δNEEF = δeFe + ρdδaCa

dt= δRFR + δPFA (3)

Fδ = δRFR + δ pFA (4)

where

Fδ = δNEEF is the isoflux of 13CO2, defined as the product of the net ecosystem

exchange and its isotopic composition (δNEE). We note that the isoflux is analogous to, but

mathematically distinct from, the net ecosystem exchange of 13CO2. δR is the carbon

isotope ratio of ecosystem respiration derived from the nighttime Keeling plot regression

of δ13C versus [CO2]-1 (Keeling, 1958, 1961), δeFe is the eddy isoflux term, and

ρdδaCa

dt

is the isotopic storage flux. The carbon isotope ratio of photosynthetic assimilation, δP, is

expressed as

δP = δ13Ca − Δcanopy (5)

where δ13Ca is the carbon isotope ratio of ambient CO2, and Δcanopy is the canopy-

integrated photosynthetic discrimination (discussed below). When the ecosystem is in

isotopic disequilibrium, i.e., δR ≠ δP, new information can be obtained from equation (3)

and equations (2), (4), and (5) form the basis for either partitioning the net ecosystem

exchange of total CO2 into FR and FA, or for estimating the canopy-scale photosynthetic

discrimination, Δcanopy. Determination of canopy scale photosynthetic discrimination and

its importance in the science of global change is discussed below.

1.2.3. Determination of the carbon isotope ratio of ecosystem respired CO2

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The carbon isotope ratio of ecosystem respiration, δR, is determined using the

Keeling plot approach (Keeling, 1958, 1961) or the flux ratio method developed by

Griffis et al. (2004). Here we discuss the classic Keeling plot regression, as it is still as

widely used today. The relationship between variation in the mixing ratio and

corresponding change in the carbon isotopic composition of atmospheric carbon dioxide

was first presented in Charles Keeling’s seminal works in the 1950’s (1958, 1961).

Using results from mass spectrometric analysis of air samples collected in 5-L flasks

from the Pacific coast of North America, he determined a systematic linear relationship

between the carbon isotope ratio, δ13C, and the reciprocal of the CO2 partial pressure of

ambient air:

δ13C = i + m 1CO2[ ]

⎝ ⎜ ⎞

⎠ ⎟ (6)

Keeling used equation (6) to explain changes in the carbon isotopic content of

atmospheric air and to identify the sources that bring about the gradients on a regional

scale. Equation (6) is derived using a two-source mixing model that is based on the

assumption that the atmospheric concentration of species in the ecosystem (CC) is a mass

balance combination of a background concentration (CB) and a contribution from sources

or sinks in the ecosystem (CS) (Pataki et al., 2003, Yakir and Sternberg, 2000):

CC = CB + CS (7)

Multiplying both sides of the equation with the carbon isotope ratio of each component,

we obtain

δ13CC( )CC = δ13CB( )CB + δ13CS( )CS (8)

Combining equations (7) and (8) and rearranging terms

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δ13CC =CB δ13CB − δ

13CS( )CC

+ δ13CS (9)

Equation (9) is applied in the field by taking simultaneous measurements of the carbon

isotope ratios and partial pressures of carbon dioxide at varying heights in the canopy.

Each sample represents the combined contribution from a background CO2 component

and an additional source component. The Keeling plot regression assumes that the

carbon isotope ratio of the background air and the additional source are constant at the

time of measurement. Pataki et al., (2003) recommends that the Keeling plot approach be

applied only at night when the conditions are stable, photosynthesis has ceased, and the

only additional CO2 source that modifies the concentration of canopy CO2 is from

ecosystem respiration. Application of the Keeling plot during daytime may be difficult,

as the background CO2 may continually change as the ground heats up and the convective

boundary layer develops. Recycling of respired CO2 inside the canopy may also occur

during daytime (i.e., re-fixation of respired CO2, see Sternberg et al., 1997). Under stable

nighttime conditions, the CO2 mixing ratio builds up inside the canopy due to ecosystem

respiration. When all the CO2 is mathematically exhausted from the ecosystem ([CC] →

∝ in equation 9), the Keeling plot intercept represents the integrated carbon isotopic

composition of respiration from all of the aboveground and belowground respiring

sources in the ecosystem, i.e. i = δ13CS = δR.

The main difficulty in applying the Keeling plot approach is that the intercept is

far removed from the measurement data. Small errors in the CO2 and δ13C measurements

may result in significant uncertainty in the intercept. Thus, this difficulty imposes high

precision requirements for the measurement of CO2 and δ13C. Except for the quantum

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cascade laser absorption spectroscopy (QCLAS), most current spectroscopic techniques

for in situ stable isotope measurements have poorer precisions (on the order of 0.2 to 0.8

‰) relative to conventional isotope ratio mass spectrometry. However, because of the

abundance of data obtained by spectroscopic techniques, averaging of δ13C and CO2

measurements is possible, which significantly improves the precision and accuracy of

determination of the Keeling plot intercept. The ability of spectroscopic techniques to

measure the isotopologues of CO2 independently also enables the use of the concentration

ratio method (Griffis et al., 2004), which relies on the slope of the 13CO2 versus 12CO2

regression line (and not on the intercept) to determine the carbon isotope ratio of

ecosystem respiration, and this will be discussed later.

1.2.4. Determination of canopy – scale photosynthetic discrimination

This section briefly discusses the process of photosynthesis with particular focus

on the C3 photosynthetic pathway. As will be discussed in chapter 2, field experiments

were conducted in a managed poplar forest, a C3 ecosystem. The term C3 photosynthesis

originated from the observation that the first product of this photosynthetic pathway is a

3-carbon molecule. The process of photosynthesis discriminates against the heavier

13CO2 isotopologue of carbon dioxide and preferentially fixes the lighter 12CO2 in the

process of carboxylation. Figure 1 shows a diagram of the C3 photosynthetic pathway.

In this metabolic pathway, isotope effects or fractionations are most strongly expressed in

two major steps: first, during the diffusion of CO2 through the stomatal pathway, and

second, during the enzymatic reaction of CO2 with the enzyme Rubisco (Ribulose

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Bisphosphate Carboxylase Oxygenase). The discrimination by C3 photosynthesis

(Farquhar et al., 1989) is expressed as:

Δ = a + b − a( ) pipa

(10)

where a = 4.4‰ is the fractionation due to the differential diffusivity of 13CO2 and 12CO2

through the stomates, b = ~27.5‰ (Fung, et al., 1997) is the fractionation due to the

discrimination by Rubisco, pi and pa are the intercellular and ambient partial pressures of

CO2, respectively. The magnitude of the discrimination against 13CO2 largely depends on

the intercellular partial pressure of CO2 (pi), which in turn is controlled by the rate of

photosynthesis and stomatal conductance. The stomates are like apertures that are

sensitive to environmental parameters such as light, and moisture. Hence, the

discrimination is an effective indicator of plant response to changing environmental

conditions. Farquhar et al. (1989) provides a comprehensive discussion on the theory of

and environmental effects on carbon isotope discrimination during photosynthesis.

Direct measurement of canopy-scale discrimination (Δcanopy) is not trivial, as it

involves the measurement of isotopic CO2 flux exchanged between the biosphere and

atmosphere. The canopy-integrated discrimination has been determined in the past using

an indirect approach that involves the estimation of the canopy-scale conductance using

the Penman-Montieth equation (Bowling et al., 2001). It is only in recent years that

advances in instrumentation have made it possible to continuously measure the isotopic

composition of carbon dioxide in situ at relatively high time resolution (Bowling et al.,

2003b, Griffis et al., 2008). The ecosystem carbon isotope discrimination is an important

parameter that constrains global estimates of the carbon budget. An overestimation in the

mean global magnitude of Δ by about 3‰ translates to about a 20% underestimation in

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the strength of the biospheric sink (Fung et al., 1997). We note that from the canopy

scale discrimination, the carbon isotope ratio (δA) of photosynthetic assimilation (FA) can

be estimated using equation (5).

1.3. Measurement of Carbon isotope ratio

The C-stable isotopes of atmospheric CO2 have been traditionally determined by

collecting whole air samples in 2-L flasks followed by off-line cryogenic CO2 extraction

and subsequent analysis on a dual-inlet isotope ratio mass spectrometer (IRMS). While

the use of large sample size has made it possible to achieve very high measurement

precision, it also severely limits the number and location of samples that can be collected

in the field. Cryogenic extraction is also not able to separate contaminant N2O from CO2

(same mass), and a mass balance correction for N2O must be applied to dual-inlet

measurements of CO2 isotopes (Ferretti et al., 2000, Ghosh and Brand, 2003).

The development of continuous flow IRMS in the 1980s and its commercial

appearance in the 1990s made possible the high-precision compound-specific isotope

measurement of a large number of small organic and inorganic samples. Brenna, et al.,

(1997) provided a detailed review of the various continuous flow techniques interfaced to

the IRMS. In 1998, Ehleringer and Cook described the use of the PreCon, an automated

pre-GC concentration device that was originally designed for the analysis of N2O and

CH4 trace gases (Brand, 1995) but was demonstrated to work well for separating and

condensing small volumes of CO2. The PreCon was interfaced to a continuous flow

IRMS to analyze CO2 in air samples collected in small 100-ml vials. In Ehleringer and

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Cook’s method, a 250-µL sub-sample was extracted with a gas-tight syringe from the

septum-fitted 100-ml vial and injected into the helium carrier stream for analysis.

Because of the small volume requirement, it was possible to sub-sample the flasks for

repeated CO2 analysis leading to reported precisions of < 0.05 ‰. The automated pre-

concentration process and the use of smaller flasks made possible the collection and

analysis of more samples, and the presence of a GC column separated N2O from the CO2,

eliminating the contamination issues described above.

More than a decade ago, Prosser, et al., (1991) described an automated method

for rapid isotopic analysis of high-concentration CO2 from breath samples collected in

13-ml septum-capped vials. The technique involved the use of an autosampler where the

headspace gas was transferred to the helium carrier stream using a double-holed needle

probe. More recently, Tu et al. (2001) described an analogous method, but focused on

the isotopic analysis of near-ambient CO2 samples from 10-ml septum-capped vials for

Keeling plot applications. The concentration of ambient CO2 was sufficiently high that a

direct headspace measurement with the autosampler interfaced to a continuous-flow

IRMS (CF – IRMS) was possible without cryogenic pre-concentration. They found that

this relatively inexpensive method achieves a precision of 0.08 ‰ δ13CVPDB (standard

deviation) and allows for higher collection throughput from the field. While the

continuous-flow method increased the throughput from the field relative to the

conventional dual-inlet IRMS, it was still based on grab samples rather than continuous

measurement of the temporal and spatial distribution of the CO2 concentration and carbon

isotope ratio.

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A number of spectroscopic techniques for field use in-situ stable isotope

measurements of atmospheric CO2 have recently been developed or evaluated (Bowling,

et al., 2003a, Esler, et al., 2000a & b, Tuzson, et al., 2008, Mohn, et al., 2007, 2008,

Griffith, et al., 1997 & 2006). Table 1.2 shows a summary of these techniques and their

reported precision for δ13C. To date, the quantum cascade laser based absorption

spectroscopic (QCLAS) technique (Tuzson, et al., 2008) has the highest precision and

approaches that of the traditional IRMS method. The tunable diode laser absorption

spectroscopic (TDLAS) technique has been recently applied to investigate ecosystem-

atmosphere interactions (Bowling, et al., 2005, Griffis et al., 2004, 2005, 2008) and to

identify urban CO2 sources (Pataki, et al., 2006). Both the TDLAS and QCLAS make

use of single spectral line pairs for quantifying 12CO2 and 13CO2. Thus, both methods are

limited only to the measurement of single chemical species and are susceptible to

interferent absorbances, as there are numerous uncharacterized trace gases absorbing in

the mid-infrared region in the atmosphere.

In this work, we have considered an alternative optical technique, Fourier

transform infrared spectroscopy (FTIR). FTIR spectroscopy simultaneously measures the

molecular absorption of many atmospheric trace gases using their unique rotational-

vibrational absorption bands in the mid-infrared region, and so is not limited to the

measurement of single species. The large wavelength coverage allows simultaneous

measurement of several important trace gases such as CO2, N2O, CH4, CO, and H2O in a

single air sample. Isotopic substitution changes the distribution of the vibrational and

rotational energy states of a molecule so that independent measurement of a number of

isotopomers, including the 12C16O2 and 13C16O2 isotopomers is possible (Esler, et al.,

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2000b). Absorption from other interfering species has little effect on the result, as their

spectra merely form part of the residual spectrum after data reduction, thus making the

FTIR spectroscopy approach much less susceptible to interferent gases.

Esler et al. (2000a and b) first described the development of an in situ method for

high-precision stable carbon isotope measurement that is based on Fourier transform

infrared spectrometry. They reported a precision of the order of 0.1 ‰ δ13C with 15-

minute integration times for ambient CO2 samples (Table 2). Recent application of their

system in the field (Griffith et al., 2006) achieved a precision of 0.2 to 0.5 ‰ for

acquisition times of several minutes. More recently, Mohn et al. (2007) developed and

laboratory-tested a calibration strategy for an FTIR system that yielded a precision of

0.15 ‰ obtained at spectral averaging times of 12 (256 scans) to 24 minutes (512 scans).

They tested their system in the field (Mohn et al., 2008) and measured continuously at 1-

minute cycle time (16 scans) where the uncertainty was about 5 times more (~0.75 ‰)

than those obtained at 512 scans. An inherent trade-off exists between precision and

speed of spectral acquisition.

We have developed a new instrument for fast, continuous real-time stable carbon

isotope measurement based on FTIR spectroscopy. Short cycle times between samples

were necessary since the system was developed not only to measure the spatial and

temporal distribution of CO2 – δ13C but also the rapid exchange of isotopic CO2 between

the atmosphere and the terrestrial ecosystem. This was made possible by combining the

FTIR approach with a direct flux measurement method called disjunct eddy covariance

(DEC). The DEC technique is briefly discussed in the next section. Fast field

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measurements were conducted using four co-added scans corresponding to 15-second

acquisition time per sample.

1.4. Measurement of biosphere-atmosphere exchange of isotopic CO2

Information obtained from isotopic CO2 flux measurements has been used to

partition the net ecosystem exchange (NEE) of CO2 into its photosynthetic (Fa) and

respiratory (Fr) components (Yakir and Wang, 1996, Bowling et al., 2001, Knohl et al.,

2005, Ogee et al., 2003 and 2004, Zhang, 2006). The ability to partition NEE is

important, as it will help facilitate understanding of the sensitivity of these opposing

components to changes in moisture, temperature and nutrient fertilization. Measurement

of the isotopic CO2 fluxes can also constrain to inverse models that estimate the flow of

carbon into various reservoirs (Ciais et al., 1995, Fung et al., 1997). Recently, isotopic

CO2 flux measurements have enabled the development of a flux ratio method that

estimates the signature of ecosystem respiration (δ13CR) directly from the slope of the

nighttime 13CO2 versus 12CO2 fluxes. Thus, the δ13CR calculated from this approach is

directly related to the flux footprint (Griffis et al., 2004).

The application of stable isotope measurements to biosphere – atmosphere

exchange has been limited mainly because of the lack of instrumentation for fast, direct

and continuous measurement of the isotopic CO2 concentrations. In the past, the net

ecosystem exchange of 13CO2 has been estimated using indirect flux measurement

approaches such as the gradient technique (Yakir and Wang, 1996, Bowling, et al.,

2003b, Griffis, 2004), hyperbolic relaxed eddy accumulation (Bowling, et al., 1999a &b,

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2003b), and the eddy covariance/flask method (Bowling, et al., 1999, 2001). Griffis, et

al. (2008) and Bowling, et al. (2003b) provide detailed reviews of the advantages and

disadvantages of these approaches specifically when applied to isotopic CO2 flux

measurements. The three methods are briefly described here. Notations in equations

(11) to (18) follow Bowling et al. (2003b).

In the flux gradient approach, the flux of some atmospheric constituent (e.g. CO2)

is estimated from the vertical gradient of its concentrations

Fe = ρK C1 − C2

z1 − z2(11)

where ρ is the density of air (mol m-3), C1 and C2 are the concentrations (µmol mol-1) at

the two measurement heights (m), z1 and z2, and K is the eddy diffusivity (m2 s-1), which

can be empirically determined from the Monin-Obukhov similarity theory. This

approach assumes similarity in the eddy diffusivities for momentum, energy, and scalars

such as CO2 and its isotopomers.

The isotopic flux is determined by multiplying the concentration terms by the

carbon isotope ratio at each height. Thus, for the gradient technique, the eddy isoflux

term is given as

δeFe = ρKδ13C1( )C1 − δ13C2( )C2

z1 − z2(12)

Prior to the application of spectroscopic techniques to isotopic flux gradient

measurements, the isotopic flux was estimated by taking flask measurements at two

heights above the canopy (Yakir and Wang, 1996, Bowling et al., 2003b). The flasks

were taken to the laboratory for analysis at a later time, making the method labor

intensive and with limited the throughput from the field. More recently, Griffis et al.

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(2004) combined the flux gradient approach with TDLAS, which allowed for more

convenient and continuous measurement of the isotopic CO2 fluxes. However, the

gradient technique still has a number of concerns: (1) it requires empirical corrections to

account for atmospheric stability (Moncrieff, et al., 2000), (2) there is a possible disparity

in the flux footprint associated with the two inlets (Griffis et al., 2008), (3) it remains

difficult to discern small gradients in daytime concentrations (Griffis et al., 2008), and (4)

the approach assumes that the vertical profiles of the concentration are well-defined,

which requires that the inlets must be positioned much higher than the roughness

sublayer. This requirement limits the application of the technique mostly to short

canopies such as those of grasslands and crops (Bowling, et al., 2003b).

The hyperbolic relaxed eddy accumulation (HREA) method is a modification of

the conditional sampling concept of the standard REA technique (Bowling et al., 1999a

and b). While the standard REA method conditionally sampled updrafts/downdrafts

based on vertical wind velocity, the HREA sampling threshold is based on both w and

CO2. Just as in the REA technique, the CO2 flux is estimated from the difference in the

concentration between the updrafts (Cup) and downdrafts (Cdown)

Fe = ρbσ w CUP − CDOWN( ) (13)

where σw is the standard deviation in the vertical wind speed (m s-1), and b is an empirical

coefficient. Updraft and downdraft samples are initially collected in flexible bags and

then transferred to flasks for later analysis. Equation 13 can be modified to quantify the

biosphere-atmosphere exchange of 13CO2

δeFe = ρbσ w δ13CUP( )CUP − δ13CDOWN( )CDOWN( ) (14)

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where δ13CUP and δ13CDOWN are the corresponding carbon isotope ratios of the updraft and

downdraft samples. The main concern with the REA method is the ability to detect small

isotopic differences in the updrafts and downdraft samples. Modifying the standard REA

method enabled the detection of as much as 8 – 10 ppmv differences in the CO2

concentrations of the updraft/downdrafts samples. However, to enable detection of small

isotopic differences in the updrafts/ downdrafts, a large fraction of air is discarded (as

much as 80%) because of the relatively high threshold (Bowling, 2003b). Thus, most of

the sampling is limited only to the larger flux containing eddies (Griffis et al., 2008).

In the EC/flask method, fast 10-Hz measurement of the CO2 concentration is

combined with flask measurements to quantify the isoflux (Bowling et al., 1999b, 2001,

2003). In the standard eddy covariance approach, the flux of CO2 and the eddy isoflux

are given as

Fe = ρ ′ w Ca′ (15)

δeFe = ρ ′ w δ13Ca( )Ca[ ]′ (16)

where the primes represent fluctuations from the mean, and the overbars denote Reynolds

averaging. The carbon isotope ratio of ambient air, δ13Ca, is estimated from flask

measurements taken at various heights in the canopy. For a small range of CO2, a linear

relationship can be established between the CO2 concentration and the carbon isotope

ratio

δ13Ca = mCa + a (17)

where m and a are the slope and y-intercept of the regression line. Thus, equation (9) is

now expressed as

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δeFe = ρ ′ w mCa + a( )Ca[ ]′ (18)

This method is attractive mainly because of its simplicity. However, the approach

depends on a limited number of flasks measurements to estimate the fluctuations in 13CO2

and it also implicitly assumes that the relationship between δ13Ca and Ca (equation 17) is

valid at all turbulent time scales.

Recently, Griffis et al. (2008) evaluated the use of the eddy covariance (EC)

technique in combination with tunable diode laser absorption spectroscopy (TDLAS) for

direct and continuous measurement of the isotopic CO2 fluxes. Their long-term

measurements showed very good agreement between the net CO2 flux measured with the

EC-IRGA and the EC-TDLAS systems. The EC approach is the most direct flux

measurement technique and has the fewest theoretical assumptions (for a detailed review

of the EC approach, see Aubinet, et al., 2000). However, its application is limited to a

few atmospheric compounds because it requires instrumentation that has a very fast

response (usually 10 Hz). The TDLAS approach easily satisfies this criterion, as it is

able to scan the single absorption lines of the CO2 isotopomers at a very high rate (500

Hz).

In this dissertation, we combine FTIR spectroscopy with disjunct eddy covariance

(DEC), a direct flux measurement approach to quantify the isoflux in forest ecosystems.

FTIR spectroscopy is a scanning measurement technique that simultaneously measures

the absorption of several infrared-active species. Although the FTIR system cannot scan

at very high speed, it is an attractive alternative, as it is able to detect multiple

atmospheric molecules at the same time. Thus, the fluxes of CO2 and its 12C16O2 and

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13C16O2 isotopomers, as well as other greenhouse gases (e.g. H2O, N2O, CH4) can be

simultaneously quantified in each air sample.

The DEC approach is a direct flux measurement method but it relaxes the demand

for very fast instrumentation and allows use of a subset of the continuous time series to

accurately measure the fluxes of species of interest (Rinne et al., 2000, 2001, 2008,

Lenschow et al., 1994). In the DEC method, grab samples of air are taken very quickly

(within tenths of a second) but analyzed within tens of seconds to accommodate the use

of only moderately fast instrumentation. Grab sampling occurs at a constant time interval

Δt so that s' and w' are discrete functions of

ti = to + iΔt . The flux is determined by

Fs = ′ w ′ s =1N

′ w to + iΔt( ) ′ s to + iΔt( )i=1

N

∑ (12)

where the brackets denote averaging within the disjunct time series. Rinne, et al., (2008)

tested the robustness of this new approach by conducting the first field inter-comparison

measurement between the DEC method and the classic eddy covariance technique. They

found very good agreement of the results with slight overestimation (<10%) of daytime

fluxes measured by the DEC technique and underestimation of the nighttime fluxes

relative to the EC results. In recent years, the DEC method has been successfully applied

to measure fluxes of biogenic volatile organic compounds (Rinne, et al., 2001, Karl, et

al., 2003, Karl, et al., 2004, Grabmer, et al., 2004). An up-to-date detailed resource on

the theory and application of disjunct sampling is found in Turnipseed et al., (2009).

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1.5. Summary

A general overview of stable carbon isotope measurement and its importance to the

science of global environmental change has been given. Because of the importance of

stable isotope measurements in ecosystem and global-scale processes, we can foresee

continued development of spectroscopic instrumentation for fast, continuous, real-time

measurement of ecosystem-atmosphere exchange of isotopic carbon dioxide. Long-term

measurement of δ13C – CO2 in many different types of ecosystems will provide key

information on photosynthetic discrimination and ecosystem respiration carbon isotope

ratio, which were both shown to be sensitive to climatic drivers and hence, can be useful

indicators of ecosystem response to changing environmental conditions. Such long-term,

wide-range measurements will provide important constraints to global inverse models

that partition atmospheric CO2 into its land and ocean uptake components. This

dissertation describes in detail the development of instrumentation for fast, continuous,

real-time measurement of δ13C – CO2 in forest ecosystems that is based on Fourier

transform infrared spectroscopy and disjunct eddy covariance. The results are

encouraging and future long-term measurements will help elucidate the complex

exchange of carbon between the biosphere and atmosphere.

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Griffis, T. J., Baker, J. M., Zhang, J., 2005. Seasonal dynamics and partitioning ofisotopic CO2 exchange in a C3/C4 managed ecosystem. Agricultural and ForestMeteorology 132, 1 – 19.

Griffis, T. J., S. D. Sargent, J. M. Baker, X. Lee, B. D. Tanner, J. Greene, E. Swiatek, andK. Billmark, 2008, Direct measurement of biosphere-atmosphere isotopic CO2exchange using the eddy covariance technique, J. Geophys. Res., 113, D08304,doi:10.1029/2007JD009297.

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Table 1.1. Isotopic composition of various carbon sources.

Source δ13C (‰)

Air ~ -8

C3 Plant Biomass (leaves, roots, stem, etc.) ~ -21 to –35

(typically between –25 to –28)

C4 Plant Biomass ~ -12 to –15

Fossil Fuels (oil, coal, natural gas, flaring

and cement production)

~ -24 to –44 (typical mean magnitude used

in global inverse models is –28.4)

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Table 1.2. Summary of capabilities of various spectroscopic techniques recently

developed or evaluated for continuous, in-situ stable carbon isotope measurements.

Research Group System Precision Remarks

Bowling et al.(2003)

Tunable DiodeLaser Absorptionspectroscopy

0.25 ‰ δ13C

Tuzson et al.(2008)

Quantum CascadeLaser BasedAbsorptionspectroscopy(QCLAS)

0.03 ‰ δ13C, 0.05‰ δ18O

Esler et al.(2000b)

Fourier TransformInfraredSpectroscopy(FTIR)

0.15 ‰ δ13C 15-min integration time(256-coadded scans)

Griffith et al.(2006)

FTIR 0.2 to 0.5 ‰ δ13C Recent field application ofthe same FTIR systemdescribed by Esler et al.(2000b)

Mohn et al.(2007)

FTIR 0.15 ‰ δ13C 12 to 24-min integrationtime (256 to 512 co-addedscans)

Mohn et al.(2008)

FTIR ~0.7 ‰ δ13C; 24-min averages of 1-min measurementswere done to obtain0.15 ‰ δ13Cprecision similar tolaboratory results

1-min spectral averagingtime (16 scans); Fieldtesting results of systemdescribed by Mohn et al.(2007);

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Figure 1.1. Carbon isotopic discrimination by C3 photosynthesis. This image was taken

from the Biosphere-atmosphere stable isotope network website (www.basinisotopes.org).

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CHAPTER 2

A NEW FOURIER-TRANSFORM INFRARED INSTRUMENT FORMEASUREMENT OF TEMPORAL AND VERTICAL DISTRIBUTION

OF δ13C – CO2 1. AN OVERVIEW OF RESULTS FROM MEASUREMENTSIN A MANAGED POPLAR FOREST IN NORTHERN OREGON.

2.1. Abstract

Stable isotopic analysis of atmospheric carbon dioxide is essential for

understanding the complex carbon exchange between the biosphere and the atmosphere.

Isotopic abundances have traditionally been measured by laboratory mass spectrometric

analysis of grab samples taken from field sites. This approach has limited the throughput

and frequency of measurements. We describe an alternative approach that is based on

Fourier-transform infrared spectroscopy (FTIR) for fast, real-time, in-situ measurement

of the stable carbon isotope ratio of carbon dioxide in ambient air. Molecular absorption

was measured in a 4-pass 1-meter length cell in the 2100 to 2500 cm-1 region of the 13CO2

and 12CO2 isotopic vibration-rotation bands. From the absorption data, concentrations of

both isotopomers were measured; from the ratio of their concentrations, δ13C was

determined. We demonstrate the capabilities of this technique using measurements in a

poplar forest plantation near Boardman, Oregon. Measurements were made over several

summers at six levels on a 22-meter tower erected in a 16-m canopy. Long-term

measurement of our reference gas in the field yielded accuracy for the forest

measurements of 0.5 ‰ and precision of 0.8 ‰, respectively, at 45-second cycle time

between samples. Nighttime and daytime vertical profiles of δ13C and CO2 were

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measured. The signature of ecosystem respiration, δ13CR, derived from the Keeling-plot

and concentration-ratio methods, was -26.6‰ and -25.8‰, respectively, about 2‰ more

enriched than the carbon isotope ratio of bulk leaf samples. CO2 respired from leaves has

been previously reported to be enriched relative to bulk tissue δ13C. Comparison of δ13CR

with the isotopic signature of soil respiration, δ13CSR, showed that ecosystem respired CO2

was also more enriched than soil-respired CO2. These results are consistent with recent

findings using conventional methods. Because δ13CR represents the overall contribution

from all respiring sources, we can expect from mass balance calculations that the isotopic

composition of ecosystem respiration will have a value that is intermediate between the

isotopic ratios of respired CO2 from above and below ground components. We describe

here (1) the details of the experiment, (2) concentration determination, (3) isotopic ratio

determination, and (4) initial results from the Boardman poplar forest.

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2.2. Introduction

The stable isotopic composition of carbon dioxide exchanged between the

biosphere and atmosphere provides useful information about the physiological and

ecological processes controlling the carbon cycle. The measurement of stable carbon

isotopes (e.g., Keeling, 1958) enabled the investigations of complex interactions between

the biosphere and atmosphere that are not possible from the analysis of mixing ratios

alone. Since Keeling’s seminal work fifty years ago (Keeling, 1958, 1961), stable carbon

isotope measurements have been used to study the response of ecosystems to changing

environmental drivers (e.g., Bowling et al., 2002, McDowell et al., 2004, Knohl et al.,

2005, Lai et al., 2005, Pataki et al., 2003) and to investigate the effects of land use change

on ecosystem respiration (Griffis, et al., 2005). Information from the carbon isotopic

composition of net ecosystem exchange (NEE) has provided an additional constraint to

facilitate the partitioning of NEE into its photosynthetic and respiratory components

(Yakir and Wang, 1996, Bowling et al., 2001, Knohl et al., 2005, Ogee et al., 2003 and

2004, Zhang et al., 2006). On a global scale, stable carbon isotope measurements have

been used to partition CO2 into its land and ocean uptake components (Ciais et al., 1995a

&b, Keeling et al., 1995, Fung et al., 1997). All these investigations were possible

because photosynthesis discriminates against the heavier 13CO2 isotopologue and

preferentially fixes the lighter 12CO2 into plant biomass. The stable carbon isotope ratio

can therefore be used as a natural tracer for ecosystem processes such as photosynthesis

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and respiration. A comprehensive resource on the theory of carbon isotope

discrimination is found in Farquhar et al. (1989).

The stable isotopes of CO2 in air have conventionally been measured by taking

grab samples from the field followed by analysis in the laboratory with an isotope ratio

mass spectrometer (IRMS). Traditionally, the samples were collected in 2-L flasks

followed by cryogenic CO2 extraction and subsequent analysis on a dual-inlet IRMS.

This method provides the best precision for isotope measurements (< 0.1 ‰). However,

the cryogenic pre-concentration process is tedious, time-consuming, and requires an

experienced operator, as fractionation and contamination may occur at any step (Brenna,

et al, 1997). The entire process limits the number of samples that can be collected from

the field. Cryogenic pre-concentration is also unable to separate N2O from CO2 because

of the similar vapor pressure versus temperature relationships of these two species

(Ferretti, et al., 2000). The N2O must be separated on a column or eliminated with a mass

balance correction.

In recent years, Tu et al. (2003) and Knohl et al. (2004) demonstrated that it is

possible to use continuous-flow isotope ratio mass spectrometry (CF-IRMS) for the

analysis of small grab samples collected in inexpensive 10 or 12-ml septum-capped vials

without compromising precision. While this method increased the sample throughput

from the field relative to the traditional method, it still does not allow for continuous

measurement of the spatial and temporal distributions of the carbon isotope ratio.

A number of spectroscopic techniques for in-situ stable isotope measurements of

atmospheric CO2 have recently been developed. Bowling et al. (2003) evaluated a

tunable diode laser absorption spectrometer (TDLAS) for measuring the carbon isotopic

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composition of air at ambient CO2 mixing ratios of 350 - 700 ppmv and δ13C abundance

of –6 to – 16 ‰. They reported a precision of 0.25 ‰ δ13C and 0.4% for CO2 mole

fraction (1.6 ppmv at 400 ppmv). The TDLAS method has since then been applied to

investigate ecosystem-atmosphere interactions (Bowling et al., 2005, Griffis et al., 2004,

2005, 2008). More recently, Tuzson, et al. (2008) described a quantum cascade laser

based absorption spectrometer (QCLAS) designed for high-precision, in-situ monitoring

of atmospheric CO2 with a precision of 0.03 ‰ δ13C and 0.05 ‰ δ18O, respectively. To

date, the QCLAS technique has the highest precision and approaches that of the

traditional IRMS method. Both the TDLAS and QCLAS make use of single spectral line

pairs for quantifying 12CO2 and 13CO2. Thus, both methods are limited only to the

measurement of carbon dioxide, and single line methods are always susceptible to

interferent absorbances from the real atmosphere.

We have considered an alternative optical technique, Fourier transform infrared

spectroscopy (FTIR), which measures simultaneously the molecular absorption of

atmospheric trace gases using their unique rotational-vibrational absorption bands in the

mid-infrared region. FTIR spectroscopy is an attractive method as it is not limited to the

measurement of carbon dioxide; the large wavelength coverage allows simultaneous

measurement of important trace gases such as CO2, N2O, CH4, CO, and H2O in a single

air sample. Because isotopic substitution alters the distribution of the vibrational and

rotational energy states of a molecule, the 12C16O2 and 13C16O2 isotopomers of carbon

dioxide can be independently and simultaneously measured (Esler, et al., 2000b).

Absorption from other interfering species does not affect the result, as their spectra

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merely form part of the residual spectrum after data reduction. Thus, the reduction of

FTIR spectra is much less susceptible to interferent gases.

Esler, et al. (2000a and b) first described the development of an in situ method for

high-precision carbon isotope measurement that is based on Fourier transform infrared

spectrometry. They reported a precision of the order of 0.1 ‰ δ13C with 15-minute

integration times for ambient CO2 samples. A recent application of their system in the

field (Griffith et al., 2006) achieved a precision of 0.2 to 0.5 ‰ for spectral acquisitions

of several minutes. More recently, Mohn, et al. (2007) developed and laboratory-tested a

calibration strategy for an FTIR system and yielded a precision of 0.15 ‰ obtained at

spectral averaging times of 12 (256 scans) to 24 minutes (512 scans). They tested their

system in the field (Mohn et al., 2008) and measured continuously at 1-minute cycle time

(16 scans) where the uncertainty was about five times more (~0.75 ‰) than those

obtained using 24-min periods (512 scans). An inherent trade-off exists between

precision and speed of spectral acquisition. To address this, they took 24-minute

averages of their 1-minute measurements to improve their precision similar to that

obtained in the laboratory.

In this work, we describe in detail the development of an FTIR system for

continuous real-time measurement of isotopic CO2 in forest ecosystems for determination

of [CO2], δ13C, and isotopic CO2 fluxes. Fast measurements were done at four co-added

scans equivalent to short spectral acquisition time of 15 seconds, yielding an accuracy

and precision of 0.5 ‰ and 0.8 ‰, respectively. Fast measurements and short cycle

times between samples were necessary, as the system was developed not only to measure

the temporal and spatial variation of δ13C – CO2 but also to capture the rapid exchange of

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isotopic CO2 by combining FTIR with disjunct eddy covariance (DEC), a direct flux

measurement method (Rinne and Guenther, 2001, Rinne et al., 2008, Turnipseed, et al.,

2009). Detailed results of our isotopic CO2 flux measurements will be reported in a

companion paper. In this paper, we demonstrate the capability of our system to

simultaneously measure the temporal and spatial distributions of carbon dioxide and δ13C

in real time in a forest ecosystem.

2.3. Methodology

2.3.1. Site Description

Measurements were made from a 22-meter tower erected in a poplar forest

plantation located near Boardman, Oregon (45.8°N, 119.7°W). Experiments were

conducted for 4 weeks in each of the summers of 2005 and 2006. The poplar forest,

managed by Greenwood Resources (http://www.greenwoodresources.com/),

encompassed an area of about 100 km2 in flat terrain with an elevation 202 meters a.s.l.

The site was drip irrigated six days a week during the growing season. The poplar trees

were genetically identical and grew as much as 3 m per year for at least 7 years before

they were harvested and chipped for pulp. The average canopy height was 16 m in 2006.

The soil was sandy with little or no organic matter. There were almost no understory

plants growing under the canopy and there was little accumulation of litter.

On sampling days, the daylight hours were sunny, clear, and warm with

maximum temperatures generally greater than 30°C. Prevailing winds showed a diurnal

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pattern. During days characterized by slow wind speeds (< 4 m/s), the winds came from

the southeast and southwest at night (between 165 and 240°) and shifted to northeast and

northwest during the day (between 350 and 60°). During days characterized by high

wind speeds (> 4 m/s with wind gusts as much as 12 m/s), the nighttime prevailing wind

direction was consistently from the southwest between 210 and 240° while daytime wind

direction was mainly from the west between 255 and 285°.

2.3.2. Description of experiment

Air samples were analyzed at 0.5 cm-1 spectral resolution using a Bruker Optics

V22 FTIR spectrometer (Bruker Optik GmbH, Ettlingen, Germany) with a globar light

source and a liquid nitrogen-cooled InSb detector (Teledyne Judson Technologies,

Montgomeryville, PA). The InSb detector is sensitive in the mid-infrared region from

1800 – 4000 cm-1 (5.5 µm – 2.5 µm) with specific peak detectivity of 2.11x1011 cm Hz1/2

W-1. The FTIR system was enclosed in a plexiglass housing that was temperature-

controlled at 27 OC ± 0.1 OC. The 135-ml sample gas cell was enclosed within the FTIR

itself with a heating sleeve that was controlled to a temperature of 33°C ± 0.1 OC.

Two steps were taken to increase the precision of our spectrometric data. First, it

was necessary to eliminate CO2 absorption in that part of the optical path outside the

sample gas cell. This was accomplished by continuously purging the interferometer and

detector compartments with nitrogen gas at 6L min-1. Second, sample gas pressure and

gas cell temperature were monitored using a solid-state pressure transducer (Scientific

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Technologies, Inc., Logan, UT.) and a Pt RTD temperature sensor (Omega Engineering,

Inc., Stamford, CT.). Analyses were conducted at pressures of about 700 mbar.

Using molecular absorption in a 4-pass 1-meter length cell in the 2060 to 2310

cm-1 region of the 12CO2 and 13CO2 isotopic vibration-rotation bands, concentrations of

both isotopomers were derived from the measured spectra. From the concentration ratio

of the two isotopomers, the carbon isotope ratio (δ13C) was determined. This region was

used to minimize nonlinearity and deviations from the Beer-Lambert law. In the middle

of this window (2310 cm-1), maximum peak absorbance for 12CO2 was ~ 0.15. Esler et al.

(2000a) experimentally determined for a similar FTIR set-up that the degree of

nonlinearity was not a source of systematic error as long as the analyzed spectra included

only rotational-vibrational lines with measured absorbances less than 0.4. The spectral

window we used for our analysis has rotational-vibrational lines well within this

absorbance limit.

A schematic diagram of the field experiment set-up is shown in Figure 2.1. Air

samples from the canopy were introduced into the instrument by pulling air from inlet

valves at various heights on a 22-meter tower so that vertical profiles or fixed-height

measurements could be performed. Inlet valves were installed in the tower sampling line

(2.5-cm i.d. PVC pipe system) and positioned at 0.3, 3.9, 7.3, 10.7, 13.7, and 20.4 m. A

blower running at 190 l/min flow pulled air from the tower toward the system at all times.

The measurement system was composed of the disjunct eddy covariance (DEC)

sampler and the FTIR spectrometer. The DEC sampler consisted of a 1-L stainless steel

intermediate sampling reservoir (ISR) and a set of fast valves to control and direct the

airflow. The DEC sampler took fast grab samples either for isotopic CO2 flux

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measurements or for vertical profile and fixed-height δ13C – CO2 measurements. From a

continuous air flow of 190 L min-1 in the 2.5-cm PVC sampling line, a fast time section

of rapidly moving air was trapped into the 1-L ISR using fast valves with ~15 ms

response times (Parker Hannifin Corp, New Britain, CT and Leybold Vakuum,

Germany). When the valves were engaged (closed), the fast moving air was diverted into

a by-pass line to prevent overheating of the high-volume pump (The Spencer Turbine

Company, Windsor, CT). The isolated air sample in the ISR was then introduced into the

evacuated FTIR gas cell. Analysis relied on four co-added FTIR scans corresponding to

a 15-second measurement integration time. While the air sample in the gas cell was

analyzed, the fast valves at each end of the reservoir were activated (opened) to allow the

rapidly moving air to flow continuously through the ISR. After analysis in the FTIR, the

gas cell was flushed with zero air and evacuated in preparation for the next cycle. The

total cycle time between samples was about 45 seconds. RMS noise in the system was ~3

parts in 105 for a 45-second cycle time (4 co-added scans, 15-second measurement

integration time). Sample acquisition and recording of data were computer-controlled

using a program written in the LabView language (National Instruments, Austin, TX).

During vertical profile measurements, two quick, “snap shot” samples were taken

from each height, cycling progressively from the lowest inlet to the highest intake valve.

One vertical profile was completed every 15 minutes for the six-valve system utilized.

Two background lamp spectra and a calibration spectrum from a calibrated gas bottle of

CO2 were analyzed approximately every 50 minutes. In addition, a background spectrum

was taken after completing one vertical profile. When analyzing background or

calibration samples, the gas cell was initially purged by rinsing with either instrument

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grade zero air or reference tank air before filling to approximately 700-mb pressure with

the analyte gas.

Environmental parameters such as air temperature, relative humidity,

photosynthetically active radiation (PAR), net radiation, soil temperature and volumetric

water content were also monitored at the site. All environmental parameters were stored

at 1-min temporal averages in the computer using two Campbell data loggers (Campbell

Scientific, Inc., UT).

2.3.3. Leaf sampling, soil respiration measurement and isotope analysis

Leaves from various heights in the canopy were collected and transported to the

laboratory, dried at 65°C for 48 hours and subsequently analyzed for δ13C. Eighteen static

closed chambers (Mora and Raich, 2007) were installed on the ground in a radial pattern,

extending out from the flux tower in clusters of three chambers. The CO2 in the chamber

headspace was allowed to equilibrate for a period of 24 hours. Following the method of

Mora and Raich (2007), a gas sample was withdrawn from the chamber headspace and

analyzed within 48 hours. At equilibrium, the carbon isotopic composition of the

chamber headspace is assumed to be equal to the isotopic content of the CO2 pool in the

soil. The CO2 concentration and isotopic content of the soil pool was then used to

calculate for the isotopic composition of the soil respiration flux using the theory

developed by Ubierna et al., (2009) and Cernusak et al., (2004):

δ13CSR = (δ p − a) ×Cp

Cp − Catm

⎣ ⎢

⎦ ⎥ − δatm − a( ) × Catm

Cp − Catm

⎣ ⎢

⎦ ⎥ (1)

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where δ13CSR is the isotopic composition of soil respiration, δp and Cp are the carbon

isotopic composition and CO2 concentrations of the soil pool, respectively, (measured

from the chamber head space), a is the kinetic fractionation term due to molecular

diffusion (equal to 4.4‰), and δatm and Catm are the carbon isotope ratio and CO2

concentration of atmospheric CO2, respectively (assumed to be –8‰ and 380 ppmv,

respectively).

All isotopic analyses of leaf and soil respiration samples were performed at the

Idaho Stable Isotopes Laboratory (ISIL) in the Department of Forest Resources,

University of Idaho. Gas samples were analyzed for δ13C using a Finnigan GasBench II

interfaced to a Delta XP IRMS (Finnigan MAT, Bremem, Germany). Leaf samples were

analyzed for δ13C on a NC 2500 EA (Carlo Erba Instrument, Milan, Italy) interfaced to a

Delta+ IRMS (Finnigan, MAT, Bremen, Germany). Carbon isotope measurements are

usually expressed in terms of the delta notation (in per mil or ‰) relative to the Vienna

Pee Dee Belemnite (VPDB) standard:

δ13C =R13sampleR13standard

−1⎡

⎣ ⎢

⎦ ⎥ ×1000 (2)

where R13 = 13C/12C is the ratio of the abundances of 13C and 12C in the sample and in the

standard.

2.3.4. Analysis of Measured Spectra

The mixing ratios of 13CO2 and 12CO2 and other IR active species (H2O, N2O, and

CO) were determined from the measured single beam spectrum using an iterative least

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squares fitting technique (MALT: Multiple Atmospheric Layer Transmission developed

by Griffith at the University of Woolongong, Australia, see Griffith, 1996, Esler, et al.,

2000a and b). The multi-component FTIR spectrum was analyzed by first calculating a

set of reference spectra for all species based on their HITRAN (Rothman, et al., 2008)

molecular line parameters at the measured temperature and pressure for the observation.

MALT incorporates the FTIR instrument line shape parameters such as the apodization,

field of view, and resolution as well as the environmental parameters such as gas

temperature and pressure so that the reference spectrum closely simulates the actual

spectrum measured by the instrument. The reference spectrum was then fitted to the

measured spectrum using non-linear least squares quantitative analysis with MALT

(Griffith, et al., 2003 conference, Griffith, et al., 2006) to separately determine the

concentrations of the species by minimizing the residual spectrum in an iterative fitting

process. This quantitative analysis provides molecular mixing ratios with accuracies

better than ± 5% depending on the accuracy of the molecular line parameters in the

HITRAN database (Griffith, et al., 2006) and accurate characterization of the instrument

line shape function (Griffith, et al., 2003). Higher accuracy is achieved by regular

calibration of the FTIR system using reference gases. Figure 2.2 shows a typical example

of the measured, fit and residual spectra where we observe a small average peak-to-peak

variation of about 5 parts in 104 (RMS level ~ 0.02%) in the residual spectrum after

iteratively fitting the measured spectrum with the reference spectrum using the non-linear

least squares technique. This exhibits a very high quality fit to the observational data for

both [CO2] and δ13C.

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2.3.5. Calibration

Four calibration cylinders were used in the field. Table 2.1 shows the

concentration and carbon isotope ratios of the standards. One reference tank was

dedicated for online calibration measurements. When performing fixed-height

experiments, online calibration measurements were as frequent as every half-hour. As

stated above, online calibration was conducted every 50 minutes during vertical profile

measurements. In addition, offline calibrations were also carried out in the field with all

four tanks analyzed on a daily basis.

2.4. Results and discussion

Analytical precision in the field was determined from the results of the online

calibration runs. Figure 2.3 shows the carbon isotope ratio of calibration tank #1 over a

period of approximately five days from July 23 to 27, 2006. The average carbon isotope

ratio was determined to be –9.6 ‰ with a standard deviation of ± 0.8 ‰ (n = 182). The

actual carbon isotope ratio of tank #1 was determined from isotope ratio mass

spectrometry measurements to be –10.14 ± 0.04 ‰. Based on the results of these long-

term measurements, we determined our analytical precision and accuracy to be 0.8 and

0.5 ‰, respectively, at 45-second measurement cycle time. The corresponding precision

for CO2 concentrations was determined to be 1.3 ppmv at 380 ppmv. On the ecosystem

scale, the simultaneous measurement of δ13C and CO2 is particularly critical for

partitioning the net ecosystem exchange (NEE) into its photosynthetic assimilation (FA)

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and ecosystem respiration (FR) components. To determine the importance of

uncertainties of the order of 0.8‰, a sensitivity analysis was conducted on the net

photosynthetic assimilation flux, FA. Using the theory developed by Bowling, et al.

(2001), and their noontime values for the NEE, isoflux, and stomatal conductance, we

determined that a change of 0.8 ‰ in the carbon isotope ratio of ecosystem respiration

(δ13CR) results in a change in the assimilation flux of the order of 1%. On the global

scale, Badeck et al. (2005) determined that a change in the signature of respiration of the

order of 0.7‰ results in a change in the biospheric sink by about 5%. These are small

changes.

The nighttime vertical profiles of CO2 and δ13C were measured at the Boardman,

OR site to determine the carbon isotope ratio of ecosystem respiration. Figures 2.4a) and

b) show the simultaneous nighttime vertical and temporal profile measurements of CO2

and δ13C on July 22, 2006 from 12:00 midnight to 5:30 AM. Winds generally came from

the south, with magnitudes ranging from 1 – 3 ms-1 at the time of measurement. The

vertical profiles of CO2 and δ13C were the result of taking one-hour running averages of

the “snap shot” measurements taken at each height. A one-hour average is equivalent to

the average of eight spectra at each height (equivalent to 32 co-added scans). The dashed

line in the contour plots marks the canopy height (~16 m). High CO2 concentrations were

observed within the canopy because of the combined effects of vegetation and soil

respiration, stable nighttime conditions, and slow winds. The ecosystem becomes a

source of CO2 at night and the slow winds and the shallow boundary layer allow for the

accumulation of the respired CO2 within the canopy. The highest CO2 concentrations

were observed closest to the ground where the corresponding carbon isotope ratios were

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most depleted (more negative). The CO2 concentrations progressively decreased with

increasing height and approached background values above the canopy. Conversely, the

corresponding carbon isotope ratios increased with height. A clear inverse relationship

was observed between the CO2 concentrations and δ13C. Figures 2.4c) and d) show the

contour plots of the standard deviations resulting from the running averages in Figures

2.4a) and b).

Because of photosynthesis, daytime contour plots of CO2 and δ13C (Figure 2.5a)

and b), respectively) show that the air within and above the canopy was isotopically

enriched (δ13C was less negative) and that the carbon dioxide concentrations were lower

relative to background values. The CO2 concentrations and the corresponding δ13C values

were also uniformly distributed within and above the canopy indicating that there was

good air mixing due to the unstable conditions. At the time of measurement, the winds

generally came from the north and northeast direction ranging from 1 - 3 ms-1 and high

air temperature rising from 29ºC at noon to 32ºC at 14h PST. Contour plots of the

corresponding standard deviations of the one-hour running averages are shown in Figures

2.5c) and d). Because of air turbulence, the natural variation in daytime CO2 was small

and the standard deviation in δ13C was essentially determined by the instrument precision.

Following the approach of Keeling (1958, 1961), the carbon isotopic composition

of ecosystem respiration, δ13CR, was obtained from the y-intercept (the source term) of

the nighttime δ13C versus [CO2]-1 plot (Figure 2.6a). The y-intercept represents the

integrated carbon isotope ratio of the respired CO2 from all sources in the ecosystem. To

investigate the effect of data averaging on the isotopic composition of ecosystem

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respiration, we applied various averaging times on our nighttime data set (i.e., no

averaging, 30-min, 1-hr, 2-hr, and 3-hr running average) and determined δ13CR using the

Keeling plot approach. The result of the calculation is shown in Table 2.2. As expected,

the Pearson correlation coefficient increased with averaging time but the isotopic

signature of respired carbon was essentially stable and insensitive to the smoothing

process. For further analysis, we have chosen the intermediate averaging period of one

hour to smooth the noise without losing the details and natural variations in the data.

The Keeling plot has been traditionally used to determine δ13CR because

conventional mass spectrometry directly compares the isotopic ratio of samples to that of

the standards. Because techniques such as FTIR spectroscopy directly measure the

concentrations of the isotopologues of carbon dioxide, the signature of ecosystem

respiration can be alternatively calculated from the slope of the linear fit of the 13CO2

versus 12CO2 concentration plot (Figure 2.6b) using the following relationship derived

from equation (1) (Griffis, et al., 2004)

13CO2 = 1+δ13CR

1000⎛ ⎝ ⎜

⎞ ⎠ ⎟ × Rstandard ×

12CO2 (3)

Both methods yielded similar values for δ13CR within the limits of their standard

deviations, which were –26.6 ± 0.5 ‰ and –25.8 ± 0.5 ‰ for the Keeling plot and

concentration ratio methods, respectively.

We compared the signature of ecosystem respiration to the carbon isotopic

composition of leaves collected from various heights in the canopy (Figure 2.7). As

expected, a strong vertical gradient in the carbon isotopic composition of leaves was

observed. Leaf δ13C was about 3 ‰ more enriched (less negative) close to the top (at 12

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m) relative to the bottom (~ 2 m) of the canopy. This result is consistent with the findings

of other groups (Duursma and Marshall, 2006, Ometto et al., 2006, Berry et al., 1997,

Broadmeadow et al., 1992) and has been attributed to gradients in light intensity and

nitrogen concentration within the canopy. The controls over Ci/Ca and leaf δ13C have

been discussed in detail in the literature (Farquhar, et al., 1989) where Ci/Ca is the ratio

of the intercellular and ambient partial pressures of carbon dioxide. Greater Ci/Ca was

observed for leaf samples deeper into the canopy where light intensities were weaker and

N concentrations lower relative to the top. Higher Ci/Ca in turn results in more depleted

leaf carbon isotope ratios. The dashed line in Figure 2.7 marks the average leaf carbon

isotope ratio (-27.6 ‰) in the canopy. We find that the respired ecosystem carbon

dioxide (δ13CR = -26.6 ± 0.5 ‰) was enriched relative to the bulk leaf carbon isotopic

composition by as much as ~ 2 ‰. Although the carbon isotope ratio of ecosystem

respiration represents the integrated contributions from various components (above and

below ground compartments), our result is consistent with recent findings that respired

CO2 is enriched relative to its substrates (Ghashghaie et al., 2001, Ocheltree and

Marshall, 2004, Klump et al., 2005).

We also compared the signature of ecosystem respiration against the carbon

isotope ratio of soil respiration. Table 2.3 shows the average concentration and isotopic

composition of CO2 in the soil pool for the clusters of three static closed chambers and

the corresponding isotopic signature of the soil respiration flux. The resulting average

isotopic composition of soil respiration flux was δ13CSR = –28.2 ± 0.5 ‰. We find that

the respired ecosystem carbon dioxide (δ13CR = -26.6 ± 0.5 ‰) was also enriched by

about 1.6 ‰ relative to soil respired CO2. This result was consistent with the findings of

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Klump, et al., (2005). They found that above ground respiration (from shoots) was

isotopically enriched relative to below ground respiration (from roots) by about 4 ‰ on

average. Because the carbon isotope composition of ecosystem respiration represents the

combined contributions from all sources, we expect from mass balance calculations that

δ13CR will have a value that is intermediate between the respired fluxes from above- and

below-ground compartments.

2.5. Conclusions

We describe in detail a new in-situ, real-time measurement technique for stable

isotope measurements that is based on Fourier Transform Infrared spectroscopy (FTIR).

Based on long-term measurement of our gas standard in the field, we report an accuracy

and precision for isotopic ratios of 0.5 and 0.8 ‰, respectively, corresponding to a 15-

second spectral acquisition time. The instrument can run virtually unattended for

24h/day taking data at various heights in a canopy or at a single height on time centers

less than one minute. This time resolution is sufficient for application of disjunct eddy

correlation to determine the biosphere-atmosphere exchange of isotopic CO2 fluxes. We

deployed the FTIR system in a managed poplar forest for three summers where temporal

and spatial distributions of CO2 and δ13C were simultaneous measured. The signature of

ecosystem respiration, δ13CR, derived from the Keeling plot and concentration ratio

method was more enriched than the carbon isotope ratio of bulk leaf samples by as much

as 2 ‰. This result was consistent with current findings that the respired CO2 is more

enriched than its organic substrates. Comparison of δ13CR with the isotopic signature of

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soil respiration, δ13CSR, showed that ecosystem respired CO2 was also more enriched than

soil respired CO2. Recent findings have shown that above ground respiration is

isotopically enriched relative to below ground respiration. Because δ13CR represents the

overall contribution from all respiring sources, we can expect from mass balance

calculations that the isotopic composition of ecosystem respiration will have a value that

is intermediate between the isotopic ratios of respired CO2 from above- and below-

ground components. The ability of the system to measure the temporal and spatial

distribution of CO2 and δ13C with a temporal resolution of less than one minute

continuously 24h/day is encouraging, and future long-term measurements will allow us to

investigate the complex exchange of carbon between the atmosphere and the terrestrial

ecosystem.

The main advantage of FTIR spectroscopy relative to other existing spectroscopic

techniques is its ability to simultaneously measure CO2 and its isotopomers, as well as

other important greenhouse gases.  This contrasts, e.g., with a TDLAS and QCLAS

systems, which measure only a single absorption feature of a single isotopomer (a

minimum of two absorption lines is required to obtain a ratio).  With the many poorly

understood trace gases present in the real atmosphere, as opposed to laboratory

conditions, the possibility of interfering spectral absorbance from those other species at

the wavelengths chosen for the measurement is very real and will change the isotopic

ratio determined. By measuring many spectral absorption features of the different

isotopomers simultaneously, it is easy to discriminate against any interferent species.

 When combined with the disjunct eddy covariance flux measurement technique, the

FTIR method becomes a powerful tool for quantifying not only the concentrations of

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molecular isotopes and their ratios, but also the rapid exchange of these important trace

gases between terrestrial ecosystems and the atmosphere. This approach offers the

exciting possibility of applying FTIR spectroscopy not only to forest ecosystems but also

to agricultural and urban environments. 

2.6. References

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Berry, S. C., Varney, G. T., Flanagan, L. B., 1997. Leaf δ13C in Pinus resinosa Trees andUnderstory Plants: Variation Associated with Light and CO2 Gradients. Oecologia,109, pp. 499-506.

Bowling, D. R., Tans, P. P., Monson, R. K., 2001. Partitioning net ecosystem exchangewith isotopic fluxes of CO2. Global Change Biology, 7, 127 – 145.

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Bowling, D. R., Sargent, S. D., Tanner, B. D., Ehleringer, J. R., 2003. Tunable diodelaser absorption spectroscopy for stable isotope studies of ecosystem-atmosphereCO2 exchange. Agricultural and Forest Meteorology, 118, 1 – 19.

Bowling, D. R., Burns, S. P., Conway, T. J., Monson, R. K., White, J. W. C., 2005.Extensive observations of CO2 carbon isotope content in and above a high-elevationsubalpine forest. Global Biogeochemical Cycles, 19, GB3023,doi:10.1029/2004GB002394.

Brenna, J. T., Corso,T. N., Tobias, H. J., Caimi, R. J., 1997. High – precision continuous-flow isotope ratio mass spectrometry. Mass Spectrometry Reviews, 16, 227–258.

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Cernusak, L.A., Farquhar, G.D., Wong, S.C., Stuart-Williams, H., 2004. Measurementand interpretation of the oxygen isotope composition of carbon dioxide respired byleaves in the dark. Plant Physiology, 136, 3350-3363.

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Ciais, P., Tans, P. P., White, J. W. C., Trolier, M., Francey, R. J., Berry, J. A., Randall,D. R, Sellers, P. J., Collatz, J. G., Schimel, D. S., 1995b. Partitioning of ocean andland uptake of CO2 as inferred by δ13C measurements from the NOAA ClimateMonitoring and Diagnostics Laboratory Global Air Sampling Network. J. Geophys.Res. 100, 5051 – 5070.

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Esler, M. B., Griffith, D. W. T., Wilson, S.R., Steele, L.P., 2000a. Precision Trace GasAnalysis by FT-IR Spectroscopy. 1. Simultaneous analysis of CO2, CH4, N2O andCO in Air. Anal. Chem., 72, 206 - 215.

Esler, M. B., Griffith, D. W. T., Wilson, S.R., Steele, L.P., 2000b. Precision Trace GasAnalysis by FT-IR Spectroscopy. 2. The 13C/12C Isotope Ratio of CO2. Anal. Chem.,72, 216 – 221.

Farquhar, G. D., Ehleringer, J. R., Hubick, K. T., 1989. Carbon isotope discriminationand photosynthesis. Annu. Rev. Physiol. Plant Mol. Biol., 40, 503 – 537.

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Fung, I., Field, C. B., Berry, J. A., Thompson, M. V., Randerson, J. T., Malmstrom, C.M., Vitousek, P.M., James Collatz, G., Sellers, P. J., Randall, D. A., Denning, A. S.,Badeck, F., John J., 1997. Carbon 13 exchanges between the atmosphere andbiosphere. Global Biogeochem. Cycles, 11, pp. 507 – 533.

Ghashghaie, J., Duraceau, M., Badeck, F.W., Cornic, G., Adeline, M. T., Deleens, E.,2001. δ13C of CO2 respired in the dark in relation to δ13C of leaf metabolites:comparison between Nicotiana sylvestris and Helianthus annuus under drought.Plant, Cell and Environment 24, 505 – 515.

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Griffis, T. J., Baker, J. M., Sargent, S. D., Tanner, B. D., Zhang, J., 2004. Measuringfield-scale isotopic CO2 fluxes with tunable diode laser absorption spectroscopy andmicrometeorological techniques. Agricultural and Forest Meteorology 124, 15 – 29.

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Griffis, T. J., S. D. Sargent, J. M. Baker, X. Lee, B. D. Tanner, J. Greene, E. Swiatek, andK. Billmark, 2008, Direct measurement of biosphere-atmosphere isotopic CO2exchange using the eddy covariance technique, J. Geophys. Res., 113, D08304,doi:10.1029/2007JD009297.

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Griffith, D. W. T., Jamie, I., Esler, M., Wilson, S. R., Parkes, S. D., Waring, C., Bryant,G. W., 2006. Real-time field measurements of stable isotopes in water and CO2 byFourier transform infrared spectrometry. Isotopes in Environmental and HealthStudies, 42, 9–20.

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Table 2. 1. Concentrations and carbon isotope ratios of four reference tanks used in the

field.

Tank No. Concentration (ppmv, ± 1%) Carbon isotope Ratio (‰)

1 378.7 -9.98 ± 0.08

2 383.7 -39.15 ± 0.03

3 324.7 -39.7 ± 0.12

4 449.5 -39.87 ± 0.06

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Table 2. 2. Keeling plot estimates of the isotopic composition of respired carbon as a

function of length of averaging time.

Length of RunningAverage

Y-intercept, δ13CR Slope Pearson’s correlation,Pr

No averaging -26.2 ± 1 7172 ± 420 0.73

30-min -26.7 ± 0.5 7426 ± 238 0.89

1-hr -26.6 ± 0.5 7360 ± 199 0.93

2-hr -26.0 ± 0.3 7110 ± 130 0.97

3-hr -25.8 ± 0.2 7062 ± 108 0.98

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Table 2. 3. CO2 concentrations and carbon isotope composition of soil pool measured

from the headspace of the static closed chambers. The carbon isotope ratio of soil

respiration flux, δ13CSR, is calculated from equation (2). Values are shown as the average

for the clusters of three chambers. Standard deviations are shown in the parenthesis.

Cluster Number Soil pool CO2

(ppmv)

Soil pool δ13C (‰) δ13CSR (‰)

1 6330 (1401) -22.5 (1.1) -27.8 (0.9)

2 3336 (1413) -22.1 (1.2) -28.6 (0.5)

3 4408 (2743) -22.3 (1.0) -28.4 (0.2)

4 4437 (229) -22.8 (0.1) -28.5 (0.1)

5 4683 (336) -22.0 (0.1) -27.7 (0.1)

6 5281 (639) -22.5 (0.6) -28.1 (0.5)

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Figure 2. 1. Schematic diagram of field experiment set-up.

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Figure 2. 2. Examples of typical measured, fit and residual spectrum resulting from the

application of the nonlinear least squares analysis to reduce for the 12CO2 and 13CO2

concentrations in the actual data. The RMS fit was ~ 0.02%.

1.1

1.0

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Figure 2. 3. Long-term on-line calibration measurements taken in the field from August

11 to 17, 2006. Analytical precision and accuracy were determined to be 0.8 and 0.5 ‰,

respectively.

-12

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Figure 2. 4. Nighttime vertical and temporal distributions of a) CO2 and b) δ13C. The corresponding standard deviations to the

one-hour running averages are shown in c) for CO2, and d) for δ13C.

20

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Figure 2. 5 . Daytime vertical and temporal distribution of a) CO2 and b) δ13C. The corresponding standard deviations

to the one-hour running averages are shown in c) for CO2, and d) for δ13C.

20

15

10

5

Hei

ght

(m)

12:30 PM7/20/06

1:00 PM 1:30 PM 2:00 PM

PSTA)

364

363 363

363

362

362

362

362 362

361

361

361

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359

359 359

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358 358

357

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Hei

ght

(m)

12:30 PM7/20/06

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PSTB)

-6.8 -6.9

-6.9

-6.9 -6.9

-7

-7

-7

-7

-7

-7

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-7.

1

-7.1

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-7.1 -7.2

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-7.

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1:00 PM 1:30 PM 2:00 PM

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8.5 8

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6 5.5

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PSTD)

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Figure 2. 6. Estimate of the signature of ecosystem respiration, δ13CR, calculated from

(a) Keeling plot approach (δ13CR = –26.6 ± 0.5 ‰, y-intercept), and (b) concentration

ratio method (δ13CR = –25.8 ± 0.5 ‰).

5.6

5.4

5.2

5.0

4.8

4.6

13CO

2 (p

pmv)

47046045044043042041012CO2 (ppmv)

B)

y = a + bx, Pr = 0.99a = 0.082927 ± 0.00229b = 0.011348 ± 5.17e-06

δ13CR = -25.8 ± 0.5

July 22, 200612 midnight to 5:30 a.m.

-12

-11

-10

-9

-8

-7

-6δ

13C

(‰)

2.50x10-32.452.402.352.302.252.202.15

CO2-1 (ppmv-1)

A)

y = a + bx, Pr = 0.93a = -26.6 ± 0.5b = 7360 ± 199

From 12 midnight to 5:30 a.m.July 22, 20061-Hr Ave data

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Figure 2. 7. Carbon isotopic composition of leaves as a function of height. Dashed line

indicates the average leaf δ13C. Error bars represent the precision of the isotope ratio

mass spectrometer (± 0.1‰).

14

12

10

8

6

4

2

0

Heig

ht (

m)

-30 -29 -28 -27 -26 -25 -24

δ13C of leaves (‰)

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CHAPTER 3

A NEW FOURIER TRANSFORM INFRARED INSTRUMENT FORMEASUREMENT OF TEMPORAL AND VERTICAL DISTRIBUTION OF δ13C –

CO2 2. MEASUREMENT OF ECOSYSTEM-ATMOSPHERE EXCHANGE OFISOTOPIC CARBON DIOXIDE USING DISJUNCT EDDY COVARIANCE

3.1. Abstract

Techniques for ecosystem carbon isotopic flux measurements include the gradient

method, hyperbolic relaxed eddy accumulation, flask/eddy covariance method, and more

recently, a combination of the tunable diode laser absorption spectroscopy with eddy

covariance, a direct flux measurement technique. We describe an alternative approach

that is based on Fourier-transform infrared spectroscopy and disjunct eddy covariance

(FTIR – DEC). FTIR – DEC is a direct flux measurement technique that allows for the

simultaneous determination of the fluxes of several important trace gases such as CO2

and its isotopologues, and H2O, N2O, CO and CH4. Flux measurements of CO2 and its

isotopologues were conducted in a poplar forest plantation near Boardman, Oregon in the

summers of 2005 and 2006. A comparison of the CO2 fluxes derived from FTIR – DEC

derived against conventional eddy covariance measurements using an infrared gas

analysis (IRGA - EC) showed very good agreement between the two approaches (within

10%, Pr = 0.89), which lends credibility to the isotopic fluxes. The main source of

statistical uncertainty in our flux measurements is the slow time resolution of the FTIR –

DEC approach relative to continuous EC measurements. This resolution issue can be

addressed in future measurements by decreasing the cycle time between samples or

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increasing the averaging interval. The measurement of the biosphere-atmosphere

exchange of isotopic carbon dioxide provides constraints that enable (1) partitioning of

the net ecosystem exchange (NEE) of CO2 into its one-way flux components:

photosynthetic assimilation and ecosystem respiration, or (2) direct determination of the

canopy-integrated photosynthetic discrimination. Simultaneous FTIR measurements of

CO2 and its isotopic composition enabled the direct determination of the isoflux of 13CO2

in the forest. Information from the isoflux and the NEE were used to estimate the canopy

scale discrimination, Δcanopy, and the carbon isotope ratio (δP) of the photosynthetic

assimilation flux. We find that the average δP (-24.6 ± 5.1 ‰) was 13C – enriched

compared with the mean isotopic composition (-27.6 ± 1 ‰) of bulk leaf organic matter,

which agrees with observations that the carbon isotope ratio of recent assimilates are

more enriched then the bulk leaf material from which they were extracted. Initial results

with the FTIR – DEC approach are encouraging and future measurements will find

applications not only in forest ecosystems but also in agricultural and urban

environments.

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3.2. Introduction

The measurement of the isotopic composition and fluxes of carbon dioxide is an

important tool for understanding the complex cycling of carbon in the biosphere and

atmosphere. On the ecosystem scale, the measurement of the net ecosystem exchange of

isotopic CO2 provides additional constraints to partition the daytime total CO2 flux into

its components: gross photosynthetic assimilation (FA), and ecosystem respiration (FR)

(Yakir and Wang, 1996, Bowling et al., 2001, Knohl and Buchmann, 2005, Ogee et al.,

2003, Zhang et al., 2006). The competing processes of photosynthesis and respiration

impart unique isotopic effects in the atmosphere. Photosynthesis preferentially

assimilates the lighter isotopologue of carbon dioxide, leaving the ambient air

isotopically enriched in the canopy. Respiration, on the other hand, releases isotopically

depleted carbon dioxide. The ability to distinguish these two processes through isotopic

flux partitioning is important, as it allows for the independent investigation of the

sensitivity of photosynthesis and respiration to changing environmental conditions (Yakir

and Sternberg, 2000).

The measurement of isotopic CO2 fluxes also enables the direct determination of

the canopy-scale discrimination (Δcanopy) (Griffis et al., 2005, Bowling et al., 2003a),

which characterizes the isotopic fractionation against 13CO2 during photosynthesis. At

the leaf level, Farquhar et al., (1989) described the discrimination as

Δ = a + b − a( ) pipa

(1)

where a is the fractionation associated with gaseous diffusion through the stomates

(4.4‰), b is the enzymatic fractionation during carboxylation (~27‰), pi and pa are the

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partial pressures of CO2 inside the leaf and in the ambient atmosphere. Quantifying the

canopy-scale discrimination is not trivial, especially for heterogeneous ecosystems. In

the past, it has been indirectly estimated using a big-leaf model, which involves the

estimation of the canopy-scale conductance using the Penman-Montieth equation

(Bowling et al., 2001). The measurement of ecosystem discrimination is important, as it

provides much needed constraint to global inversion models that partition anthropogenic

CO2 emissions into their land and ocean uptake components (Fung et al., 1997, Ciais et

al., 1995, Randerson et al., 2002). Fung et al., (1997) showed that a 3‰ overestimation

of the global- averaged discrimination translates to a 20% underestimation of the strength

of the biospheric sink.

The biosphere-atmosphere interaction of isotopic CO2 has been measured in the

past using indirect flux measurement techniques such as the gradient method (Yakir and

Wang, 1996, Griffis et al. 2004), hyperbolic relaxed eddy accumulation (Bowling et al.,

1999, 2003a), and a combination of flask and eddy covariance (flask/EC) methods

(Bowling et al., 1999, 2001, 2003a, Knohl and Buchmann, 2005). Each of these

approaches has advantages and disadvantages, which are discussed in detail by Bowling

et al (2003a) and Griffis et al. (2008).

More recently, the isoflux (defined as the product of the flux and its isotopic

composition) has been directly measured using the eddy covariance approach combined

with tunable diode laser absorption spectroscopy (TDLAS) (Griffis et al. 2008). The eddy

covariance (EC) method is the most direct and robust approach for measuring vertical

fluxes of atmospheric constituents in the turbulently mixed planetary boundary layer. In

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the EC method, the flux of a scalar property s is the integral of the covariance of the

fluctuations of s and the vertical wind speed w within the time interval [tA, tB]

Fs = ′ w ′ s =1

t2 − t1′ w t( )

t1

t2∫ ′ s t( )dt (2)

where

′ w t( ) = w t( ) − w and

′ s t( ) = s t( ) − s , and the over bar refers to the time average.

It requires very fast instrumentation to capture the fluctuation in w and s at all significant

frequencies. The TDLAS measures the concentration of CO2 at a very fast rate (2 ms

sampling time) using single absorption lines for each of the isotopologues of carbon

dioxide (Bowling et al., 2003b). Thus, the TDLAS easily satisfies the instrument

requirement of the eddy covariance approach. However, because a single TDLAS uses

only single spectral lines for quantifying 12CO2, 13CO2, 12C18O16O, it is limited only to the

measurement of the isotopic CO2 fluxes.

Here we present an alternative flux measurement approach that is based on

Fourier transform infrared spectroscopy (FTIR) and disjunct eddy covariance (DEC)

(Rinne and Guenther, 2001). The DEC method makes direct eddy covariance

measurements but relaxes the demand for instantaneous concentration measurements. In

the DEC method, grab samples of air are taken very quickly (with tenths of a second time

resolution) but analyzed within tens of seconds to accommodate the use of only

moderately fast instrumentation. Grab sampling occurs at a constant time interval Δt so

that s' and w' are discrete functions of

ti = to + iΔt . The flux is determined by

Fs = ′ w ′ s =1N

′ w to + iΔt( ) ′ s to + iΔt( )i=1

N

∑ (3)

where the brackets denote averaging within the disjunct time series. Rinne, et al., (2008)

tested the robustness of this new approach by conducting the first field intercomparison

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measurement between the DEC method and the classic eddy covariance technique and

found very good agreement of the results with slight overestimation (<10%) of daytime

fluxes measured by the DEC technique and underestimation of nighttime fluxes relative

to the EC results. In recent years, the DEC method has been successfully applied to

measure fluxes of biogenic volatile organic compounds (Rinne and Guenther, 2001, Karl,

et al., 2003, 2004, Grabmer, et al., 2004).

FTIR spectroscopy measures the molecular absorption of IR – active species

using multiple rotational – vibrational lines over a wide wavelength region, which allows

for the simultaneous measurement of several species such as CO2 and its isotopologues,

and H2O, N2O, CO, and CH4 (Griffith et al., 2006, Esler et al., 2000a & b, Mohn et al.,

2007, 2008). Spectral integration times may range from seconds to several minutes,

depending on the number of co-added scans chosen for the analysis. For disjunct eddy

covariance flux measurements, there is only a small increase in the statistical error (≤

8%) if the time interval between grab samples is less than the integral time scale of

turbulence (Lenschow et al., 1994), which is usually between 15 to 60 seconds in a forest

(Turnipseed et al., 2009).

In this work, we (1) compare the measured fluxes from the FTIR – DEC approach

with the results from conventional IRGA – EC method for (nonisotopic) carbon dioxide

and water vapor, (2) report the isoflux of 13CO2 measured by the FTIR – DEC technique,

and (3) estimate the canopy scale photosynthetic discrimination, Δcanopy, in a genetically

uniform poplar forest using the measured NEE and isoflux. We discuss the main sources

of errors in our isotopic CO2 measurements and suggest procedures to increase accuracy

in the future.

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3.3. Theory and Methods

3.3.1. Canopy-scale photosynthetic discrimination

From conservation of mass, the net ecosystem exchange of total CO2 is given as

F = Fe +dCa

dt= FR + FA (4)

where

Fe = w 'Ca' is the total CO2 flux measured by eddy covariance or disjunct eddy

covariance,

dCa dt is the storage term, FR is the total ecosystem respiration, and FA is the

net photosynthetic assimilation term (for convenience, we follow here the notations of

Bowling et al., 2003a). Multiplying each term in equation (4) by its respective isotope

ratio and converting into isotopic notation, we obtain the isoflux of 13CO2 (Bowling et al.,

2001, 2003a, see also Zobitz, et al., 2008)

δNEEF = δeFe +dδaCa

dt= δRFR + δPFA (5)

Fδ = δRFR + δ pFA (6)

where

Fδ = δNEEF is the isoflux of 13CO2, δR is the carbon isotope ratio of ecosystem

respiration derived from the nighttime Keeling plot regression of δ13C versus [CO2]-1

(Keeling, 1958, 1961), δeFe is the eddy isoflux term measured by the FTIR – DEC

approach, and

dδaCa

dt is the isotopic storage flux. The carbon isotope ratio of

photosynthetic assimilation, δP, is expressed as

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δP = δ13Ca − Δcanopy (7)

where δ13Ca is the carbon isotope ratio of ambient CO2 (assumed to be -8‰), and Δcanopy is

the canopy-integrated photosynthetic discrimination. When the ecosystem is in isotopic

disequilibrium, i.e., δR ≠ δA, new information can be obtained from equation (5) and

equations (4), (6), and (7) form the basis for either partitioning the net ecosystem

exchange of total CO2 into FR and FA, or for estimating the canopy-scale photosynthetic

discrimination, Δcanopy. In this work, we estimated Δcanopy by first partitioning F into its

components (FR and FA) using conventional regression of nighttime flux with temperature

for well-mixed conditions (u* > 0.1 ms-1) where nighttime

F = FR = f T( ) (Goulden et al.,

1996, 1997, Bowling et al., 2003a, Griffis et al., 2005). The daytime photosynthetic

assimilation was then calculated from

FA = F − FR . Because of windy and well-mixed

conditions for both daytime and nighttime measurements during our summer

observations, we assume that the storage CO2 flux and the storage isoflux terms are

negligible in the succeeding calculations (Aubinet et al., 2000) and the net ecosystem

exchange of CO2 is equivalent to the measured eddy flux. Under this assumption, the

isoflux of 13CO2 is simply equivalent to the eddy isoflux term

Fδ = δeFe = w ' δ13CO2 × CO2( )' . We note that the isoflux is analogous to, but

mathematically distinct from, the net ecosystem exchange of 13CO2 (Bowling et al.,

2003a). The carbon isotope ratio of ecosystem respiration, δ13CR, was taken to be – 26.6 ±

0.5 ‰, which was the intercept of the Keeling plot regression of our nighttime vertical

profile measurements on July 22, 2006.

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3.3.2. Site description and meteorological conditions

Flux measurements were made from a 22-meter tower installed in a managed

poplar forest near Boardman, Oregon (45.8ºN, 119.7ºW). The site has an elevation of

202 meters a.s.l., and encompassed an area of approximately 100 km2 of genetically

identical poplars in a nearly flat terrain. Trees were drip irrigated six days/week during

the growing season. The soil was sandy and there were almost no understory growing

under the canopy. Viable flux measurements were conducted for a period of

approximately seven days (July 25 – 29, August 15 – 17) in the summer of 2006. The

average canopy height was 16 m. The site was managed by Greenwood Resources

(http:://www.greenwoodresources.com/).

Figures 3.1 and 3.2 show the meteorological conditions at the time of

measurement given in terms of the diurnal averages of the air temperature,

photosynthetically active radiation (PAR), friction velocity (u*), and polar plots of wind

speed and wind direction. Daylight hours were sunny and warm (Figures 3.1a and b),

with maximum average temperature as high as 30ºC, and midday PAR was ~1000 µmol

m-2 s-1. Average diurnal low temperature was ~17ºC. The measurement period was also

characterized by high wind speeds with magnitudes ranging from 3 ms-1 to ≥ 6 ms-1 at

least 60% of the time (Figures 3.2a and b). The prevailing winds also displayed a diurnal

pattern. Daytime wind direction was consistently from the west between 255º and 285º

while nighttime prevailing winds were mainly from the southwest between 210º and

240º. Strong turbulent mixing was also observed at the time of measurement as indicated

by the large magnitudes of the average diurnal friction velocity (u*), which ranged from

0.4 to 0.9 ms-1 (Figure 3.1c).

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3.3.3. Flux measurement and spectroscopic analysis of air samples

Figure 3.3 shows the schematic diagram of the field experiment set-up

(Cambaliza et al, 2010, to be submitted to Agricultural and Forest Meteorology). The

conventional eddy covariance (EC) system was composed of a 3-D acoustic anemometer

(WindMaster Pro Ultrasonic Anemometer, Gill Instruments Ltd., Hampshire, England)

and an open-path infrared gas analyzer (IRGA) (OP-2 Open-path CO2/H2O Analyser,

ADC BioScientific Ltd., Herts, England). The anemometer and IRGA were installed at

the top of the tower at the end of a 3-meter boom. To avoid flow distortions from the

tower, the boom was mounted towards the west, as the prevailing wind directions were

mainly from the west and southwest. The IRGA was calibrated for CO2 once a week

using two Scott-Marin Inc gas standards (331 and 430 ppmv ± 2%). The water vapor

signal was compared with measurements from a Vaisala HMP45A humidity sensor on

the tower. The comparison was used as an approach for calibration of the IRGA water

vapor response. Both the DEC and EC systems used the data from the sonic

anemometer.

The disjunct eddy covariance (DEC) system was composed of a DEC sampler and

the FTIR spectrometer (Figure 3.3, Cambaliza et al 2010). The DEC sampler consisted

of a 1-L intermediate sampling reservoir (ISR) and a set of fast valves that control and

direct the airflow from the sampler to the spectrometer. Using a high-volume blower (The

Spencer Turbine Company, CT, USA) running continuously at 190 L min-1, air samples

from the canopy were introduced into the DEC system by pulling air from inlet valves

installed at six positions (0.3, 3.9, 7.3, 10.7, 13.7, and 20.4 m) on the tower sampling line

(2.5-cm i.d. PVC pipe system). The set of six intake valves positioned at various heights

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on the tower and under computer control coordinated with air sampling enabled us to

carry out either vertical profile or fixed height measurements. CO2 flux and isoflux

measurements were performed by sampling air from the highest inlet valve positioned at

20.4 m (1.3 x canopy height). The total length of the sampling line from the inlet close to

the acoustic anemometer to the FTIR spectrometer was 42.5 m. From a continuous air

flow of 190 L min-1 in the PVC sampling line, a time section of the rapidly moving air

was trapped in the ISR using fast valves with ~ 15 ms response times (Parker Hannifin

Corp, New Britain, CT, USA and Leybold Vakuum, Germany). When the valves were

engaged (closed), the fast moving air was diverted into a by-pass line to avoid heating the

blower. The isolated air trapped in the ISR was then transferred to the FTIR gas cell for

analysis using four co-added FTIR spectral scans corresponding to a 15-s measurement

integration time. While the air sample in the gas cell was analyzed, the fast valves

positioned at each end of the ISR were activated (opened) to allow the rapidly moving air

to flow through the reservoir. After analysis in the FTIR, the gas cell was evacuated in

preparation for the next sample. The total cycle time between samples was 45 seconds

due to cell flushing, cell filling, pressure stabilization, and measurement. FTIR

calibrations were performed using standard gases every 30 minutes.

Spectroscopic measurement of air samples was performed at 0.5 cm-1 spectral

resolution using a Bruker Optics V22 FTIR spectrometer (Bruker Optik GmbH,

Ettlingen, Germany) with a globar light source and a liquid nitrogen-cooled Indium

Antimonide detector (Teledyne Judson Technologies, Montgomeryville, PA). The FTIR

spectrometer was enclosed in a plexiglass housing that was temperature controlled at

27ºC. The 135-mL gas cell within the FTIR was enclosed in a heating sleeve and

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temperature controlled at 33 ± 0.1ºC. CO2 absorption in the optical path outside the gas

cell was eliminated by continuously purging the interferometer and the detector

compartments with nitrogen gas at 6L min-1. Sample pressure and temperature was

monitored and recorded using a solid-state pressure transducer (Scientific Technologies,

Inc., Logan, UT) and a Pt RTD temperature sensor (Omega Engineering, Inc., Stamford,

CT). Concentrations of the 12CO2 and 13CO2 isotopomers and other IR active species such

as H2O, CO, and N2O were derived from the single beam spectrum using molecular

absorption in the 4-pass 1-m length cell in the 2060-2310 cm-1 region. The spectra were

analyzed using a program called MALT (Multiple Atmospheric Layer Transmission)

(developed by Griffith, 1996, 2006, see also Esler et al., 2000a and b), which employs an

iterative non-linear least squares fitting approach. From the individual concentrations of

the isotopomers of CO2, the carbon isotope ratio (δ13C) was determined. Calibration for

CO2 and δ13C was achieved using four calibration cylinders (Table 1). One reference tank

was dedicated for online calibration measurements performed every half-hour. In

addition, offline calibrations were also carried out in the field with all four cylinders

analyzed on a daily basis. Tanks 1 and 2 are standard gas mixtures from Scott – Marin,

Inc (Riverside, CA). Tanks 3 and 4 are reference cylinders prepared from the dilution of

carbon isotope reference gases (Oztech Trading Corp., Safford, AZ) with instrument

grade zero air. The carbon isotope ratios of the reference gases were determined using

continuous-flow isotope ratio mass spectrometry following the method of Cambaliza, et

al (2009).

The lag time between the sonic anemometer and the DEC sampler was

determined by releasing puffs of high-CO2 sample gas at the highest inlet and recording

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the time it takes for the puffs to be detected by the FTIR system. The recorded lag time

was 7.4 ± 0.5 seconds. DEC and EC fluxes were averaged over 30 minutes and standard

density corrections were applied (WPL corrections, Webb et al., 1980). For details of

the experimental instrumentation, see Cambaliza et al, (2010).

3.3.4. Leaf sampling and isotopic analyses

Leaves from various heights in the canopy were collected and transported to the

laboratory, dried at 65°C for 48 hours and subsequently analyzed for δ13C. Isotopic

analyses for δ13C of leaf samples were performed at the Idaho Stable Isotopes Laboratory

(ISIL) in the Department of Forest Resources, University of Idaho, on a NC 2500 EA

(Carlo Erba Instrument, Milan, Italy) interfaced to a Delta+ IRMS (Finnigan, MAT,

Bremen, Germany). Carbon isotope measurements are usually expressed in terms of the

delta notation (in per mil or ‰) relative to the Vienna Pee Dee Belemnite (VPDB)

standard:

δ13C =R13sampleR13standard

−1⎡

⎣ ⎢

⎦ ⎥ ×1000 (8)

where R13 = 13C/12C is the ratio of the abundances of 13C and 12C in the sample and in the

standard.

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3.4. RESULTS AND DISCUSSION

3.4.1. FTIR – DEC and IRGA – EC Measurements of CO2 and H2O fluxes

Figure 3.4a shows the half-hour averaged CO2 fluxes measured by the FTIR –

DEC and IRGA – EC systems for a period of approximately seven days. The measured

fluxes from the two systems track each other well; this is clearly observed in the diurnal

averages (Figure 3.4b). The maximum daytime total CO2 flux was about –10 µmols m-2

s-1, which roughly balanced the nighttime respiration. The errors bars shown in Figure

3.4b represent the standard deviations about the half-hour mean values. The FTIR – DEC

measurements displayed larger standard deviations, which are attributed to the reduced

time resolution of the disjunct sampling method compared to the continuous sampling EC

approach (45 second FTIR – DEC cycle time versus 100 ms IRGA – EC time resolution).

Turnipseed et al. (2009) empirically determined the error associated with disjunct

sampling at various sampling time intervals (Δt = 1 to 60s) relative to continuous eddy

covariance measurement. As Δt increased, a larger spread in the data was observed as

expected, since fewer points were used to estimate the covariance. At Δt = 45s, these

authors predicted errors as large as 45% in the DEC – derived fluxes. We plotted the

FTIR – DEC diurnal averages versus the IRGA – EC estimates and found that the

measured fluxes from the two systems agree to within 10% (Figure 3.4c), which is well

within the statistical error predicted by Turnipseed et al. (2009).

Figure 3.5 shows the latent heat flux measured by the two systems. The FTIR –

DEC approach consistently underestimated the IRGA – EC derived fluxes. A

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comparison of the diurnal averages (Figure 3.5b) from the two systems reveals that the

FTIR – DEC captures about 75% of the IRGA – EC estimates (Figure 3.5c). Our result is

consistent with the observations of other groups that have used long sampling tubes for

the measurement of water flux (Ammann et al., 2006, Goulden et al., 1996). Using a

closed path IRGA – EC system, Goulden et al., (1996) observed the damping of high-

frequency turbulent fluctuations for H2O (but not for CO2 fluxes) and attributed it to

adsorption and desorption of water vapor within the sampling line. Ammann et al. (2006)

applied an empirical ogive approach to correct for the high-frequency losses due to the

adsorption effects and found excellent agreement between their closed-path DEC

measurements with conventional open-path IRGA – EC estimates. The use of a sampling

tube by itself already adds uncertainty in the flux measurements because the radial

gradient in the air velocity dampens the fluctuations in the gas concentrations (Leuning

and Judd, 1996). However, the excellent agreement between the FTIR – DEC and IRGA

– EC estimates for CO2 demonstrates that the low-pass filtering effect of the sampling

tube does not add considerable uncertainty in the DEC derived flux measurements. But

for the case of water vapor, there is always an additional uncertainty due to tube-related

dampening effects.

3.4.2. Isoflux estimates, isotopic flux partitioning, and discrimination estimates

The half-hour diurnal average of the isoflux is shown in Figure 3.6. Maximum

daytime isoflux was ~ 200 µmols m-2 s-1 ‰. We observe a diurnal pattern that mirrors the

total CO2 flux (shown in Figure 3.4). Daytime isoflux was positive, which is consistent

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with positive discrimination against 13CO2 during photosynthesis. Nighttime isoflux was

negative, which corresponded with the release of depleted (more negative) CO2 during

respiration. As expected, the isoflux also displayed considerable variation about the half-

hour diurnal averages and this observation can be attributed to the lack of a large number

of disjunct sampling points within the 30-minute averaging interval.

Using conventional regression of nighttime CO2 flux against air temperature

(Figure 3.7), the total daytime CO2 flux was partitioned into its photosynthetic and

respiratory components (Figure 3.8). Following the approach of Bowling et al., (2003a),

Figure 3.7 shows the daily nighttime CO2 flux plotted against air temperature (open

circles) as well as the average CO2 flux binned in 1ºC intervals (solid circles). The error

bars represent the standard deviations of the averaging process. In general, we observe a

very weak exponential relationship despite the wide range of temperature values. When

we include the outlier at T = 24ºC, the correlation was 0.77; excluding the outlier

significantly improved the correlation (R2 = 0.94) between FR and air temperature. To

optimize the partitioning of F into FA and FR, we used the exponential relationship

resulting from the exclusion of the outlier in the succeeding calculations.

The total CO2 flux and its components are shown in Figure 3.8. As expected, the

respiration flux, FR, showed the same diurnal pattern as the air temperature in Figure 3.1a.

The maximum respiration flux (~ 16 µmol m-2 s-1) occurred at around 16:00 PST. The

assimilation flux, FA, closely mirrors the diurnal average of the photosynthetically active

radiation (PAR) shown in Figure 3.1b. FA was essentially zero at 19:30h until about

5:00h and peaked at approximately noontime with maximum magnitude of about –25

µmol m-2 s-1. We propagated the errors (for this case, the standard deviation of the

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diurnal averages of F and FR) using the method of Taylor (1997) to calculate the

corresponding standard deviations of the diurnal average of FA shown in Figure 3.8.

Using equations (4), (6) and (7), we calculated the canopy scale discrimination

(Figure 3.9). We did not observe a trend or a diurnal pattern in the discrimination but

found the hourly daytime values to be randomly scattered about an average daytime

magnitude of 16.6 ± 5.1 ‰. We find the average canopy discrimination to be lower than

the mean discrimination reported by Bowling et al., (2003a) for an irrigated alfalfa field

in Utah by about 1 ‰. It was also lower than the mean C3 discrimination estimated by

Pataki et al., (2003) for 33 sites of coniferous and deciduous forests in North and South

America by about 1.4 ‰. However, it is in closer agreement with the flux-weighted

canopy discrimination estimated by Bowling et al. (2001) using a big-leaf analogue

model for the canopy. Their modeled values ranged from 16.8 to 17.1 ‰ for a

deciduous forest in eastern Tennessee (with uncertainties between 2.7 and 4.7 ‰).

Using equation (7),

δP = δ13Ca − Δcanopy , the mean carbon isotope ratio of

photosynthetic assimilation was estimated to be δP = –24.6 ± 5.1 ‰. δP represents the

average isotopic composition of the carbon dioxide that was drawn by photosynthesis and

is likely to reflect the carbon isotope ratio of recent assimilates (i.e. sugars, as they are the

first products of photosynthesis). We compared the carbon isotope ratio of the

assimilation flux,δP, with the isotopic composition of leaves collected from various

heights in the poplar canopy. Leaf δ13C was 3 ‰ more enriched close to the top (at 12 m)

relative to the isotopic composition of leaves deeper in the canopy (~ 2 m), consistent

with the findings of other groups (Duursma and Marshall, 2006, Ometto, et al., 2006,

Berry, et al., 1997, Broadmeadow, et al., 1992). Variation in the isotopic composition of

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bulk leaf with canopy height has been attributed to gradients in light intensity (Farquhar

et al., 1989) and nitrogen concentration within the canopy (Duursma and Marshall, 2006).

The average bulk leaf isotopic composition was –27.6 ± 1 ‰. We find that the mean

carbon isotope ratio of the assimilation flux (δP = –24.9 ‰) was enriched relative to the

isotopic composition of bulk leaf samples. This result is in agreement with observations

of other groups that the carbon isotope ratio of recent assimilates are more enriched

relative to whole leaf organic matter (Duranceau et al., 1999, Ocheltree and Marshall,

2003, Badeck et al., 2005, see also Bowling et al., 2008 for a detailed review). We find

that the enrichment was about 3.0 ‰, which is within the range (2.2 to 3.0 ‰) reported

by Ocheltree and Marshall (2003) when comparing the carbon isotope ratio of plant

tissue (mature leaves and growing tips) against soluble carbohydrates at various light

treatments.

3.5. Conclusions

We have demonstrated the capability of combining Fourier-transform infrared

spectroscopy (FTIR) with disjunct eddy covariance (DEC) for the simultaneous

determinations of the biosphere-atmosphere exchange of total CO2, H2O, and carbon

dioxide isoflux in a poplar forest. There was very good agreement (~ 10%) between the

total CO2 flux derived from the FTIR – DEC approach and the conventional IRGA – EC

method. This is well within the statistical uncertainty empirically determined by

Turnipseed et al., (2009) for a sampling interval of 45 seconds. Disjunct sampling of the

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water vapor flux constantly underestimated the IRGA – EC measurements. This result is

consistent with observations by other groups and has been attributed to the damping of

high frequency fluctuations due to the adsorption and desorption of water vapor on the

inner walls of the sampling line.

We observed considerable variations (standard deviations of the diurnal averages)

in the FTIR – DEC flux estimates that are chiefly attributed to the limited temporal

resolution of the disjunct sampling technique relative to the conventional IRGA – EC

approach (45 second time resolution versus 100 ms sampling interval). We consider the

reduced time resolution to be the main source of statistical uncertainty in the DEC –

derived fluxes. Increasing the temporal resolution is therefore a priority for future

measurements with the FTIR – DEC system. The uncertainty can be considerably

reduced either by decreasing the cycle time between samples or increasing the averaging

interval.

Simultaneous measurement of CO2 and its isotopic composition enabled the

measurement of the isoflux using the FTIR - DEC approach. Information from the

isoflux of 13CO2 and the net ecosystem exchange of total CO2 were used to directly

determine the canopy-scale photosynthetic discrimination. We found that the carbon

isotopic composition of the assimilation flux was 13C – enriched relative to whole leaf

samples by about 3.0 ‰. This result agrees with findings that recent assimilates are

enriched relative to the whole organic matter from which they were removed.

The ability of Fourier transform infrared spectroscopy to simultaneously measure

the concentrations of several important trace gases gives it a unique advantage over other

existing spectroscopic techniques. When combined with disjunct eddy covariance, a

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direct flux measurement method, it becomes a powerful tool for investigating the

complex exchange of various infrared-active species in forest, urban and agricultural

ecosystems.

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Table 3. 1. Concentrations and carbon isotope ratios of four reference cylinders used in

the field for calibration of FTIR derived CO2 and δ13C.

Tank No. Concentration (ppmv, ± 1%) Carbon isotope Ratio (‰)

1 324.7 -39.7 ± 0.12

2 449.5 -39.87 ± 0.06

3 378.7 -9.98 ± 0.08

4 383.7 -39.15 ± 0.03

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Figure 3. 1. Diurnal averages of important meteorological parameters measured from

July 25 – 29 and August 15 – 17, 2006: (a) temperature, (b) photosynthetically active

radiation, (c) friction velocity.

1.2

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Figure 3. 2. Wind speeds and wind directions observed during the duration of field

experiments (July 25 – 29, August 15 – 17, 2006). Wind speed magnitudes ranged from

about 3 to 6 ms-1. Wind direction was consistently from the west (255º to 285º) during

daytime while nighttime prevailing wind direction was southwest (210º to 240º).

0

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Figure 3. 3. Schematic diagram of the field experiment set-up (Cambaliza et al., 2010).

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Figure 3. 4. Comparison of FTIR – DEC and IRGA – EC measurements of CO2 fluxes.

(a) Time series data measured by the two approaches. (b) Diurnal average of the data

shown in figure 4(a). (c) Linear regression between the FTIR – DEC and the IRGA – EC

estimate showing that the two approaches agree to within 10%.

-60

-40

-20

0

20

40

60

CO2

Flux

(µm

ol m

-2 s

-1)

7/25/06 7/27/06 7/29/06

PST

8/15/06 8/17/06

FTIR - DEC IRGA - EC

4A)

-30

-20

-10

0

10

20

CO2

Flux

(µm

ol m

-2 s

-1)

20151050Hour

Diurnal Average

IRGA - EC FTIR - DEC

4B)

-20

-15

-10

-5

0

5

10

15

FTIR

- DE

C CO

2 Fl

ux (µm

ol m

-2 s

-1)

1050-5-10IRGA - EC CO2 Flux (µmol m-2 s-1)

y = a + bx, Pr = 0.89a = -4.161 ± 0.615b = 0.897 ± 0.067Diurnal Average

4C)

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Figure 3. 5. Comparison of FTIR – DEC Latent Heat flux measurements against the

IRGA – EC estimates. (a) Time series data measured by the two approaches. (b) Diurnal

average of the data shown in Figure 5 (a). (c) Linear regression between the two

approaches showing that FTIR – DEC underestimates the IRGA – EC measurement for

latent heat by about 26%, which is attributed to the damping of the high frequency

1000

800

600

400

200

0

-200

Late

nt H

eat

Flux

(W

m-2

)

7/25/06 7/27/06 7/29/06

PST

8/15/06 8/17/06

FTIR - DEC IRGA - EC

5A)

800

600

400

200

0

-200

Late

nt H

eat

Flux

(W

m-2

)

20151050

Hour

IRGA - EC FTIR - DECDiurnal Average

5B)

500

400

300

200

100

0

-100

FTIR

- DE

C La

tent

Hea

t Fl

ux (

W m

-2)

400350300250200150100

IRGA - EC Latent Heat Flux (W m-2)

Diurnal Average

y = a + bx, Pr= 0.86a = -18.7 ± 14.6b = 0.74 ± 0.06

5C)

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fluctuations due to the adsorption and desorption of water vapor in the inner walls of the

pvc sampling line.

Figure 3. 6. The isoflux of 13CO2 derived from the covariance of the fluctuations of the

vertical wind speed and the product of CO2 and its isotopic composition.

400

200

0

-200

-400

Isof

lux

( µm

ol m

-2 s

-1 ‰

)

20151050hr

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Figure 3. 7. Temperature dependence of the nighttime CO2 flux (unfilled circles). FR

was averaged and grouped in 1ºC intervals (solid circles). A weak exponential

relationship was obtained. Excluding the outlier at T = 24ºC, the resulting exponential fit

was FR(T) = 5.5014e0.0352T, R2 = 0.94.

20

18

16

14

12

10

8

6

Resp

iratio

n ra

te, F

R ( µ

mol

m-2

s-1

)

2624222018Air temperature (ºC)

including outlier at T = 24 ºCFR(T) = 5.9516e0.0309T

R2 = 0.77

not including outlier at T = 24º CFR(T) = 5.5014e0.0352T

R2 = 0.94

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Figure 3. 8. The total CO2 flux and its photosynthetic assimilation (FA) and respiratory

(FR) components after applying the conventional regression of the nighttime CO2 flux

against temperature (Figure 3.7).

-30

-20

-10

0

10

20

CO2

Flux

(µm

ol m

-2 s

-1)

20151050hr

1816141210

Diurnal Average

F Fa Fr

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Figure 3. 9. Half hour averages of the canopy-scale carbon isotope discrimination

calculated from FR and FA in Figure 3.8. Average daytime canopy discrimination was

about 16.6 ± 5.1 ‰.

40

30

20

10

0

Disc

rimin

atio

n ∆

(‰

)

18161412108hr

∆AVE = 16.6 ± 5.1 ‰

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CHAPTER 4

ANALYSIS OF LOW-CONCENTRATION GAS SAMPLES WITHCONTINUOUS-FLOW IRMS: ELIMINATING SOURCES OF

CONTAMINATION TO ACHIEVE HIGH PRECISION

4.1. Abstract

Developments in continuous-flow isotope ratio mass spectrometry have achieved

rapid analysis of δ13C in CO2 of small-volume gas samples with precisions ≤ 0.1 ‰.

Prior research has validated the integrity of septum-capped vials for collection and short-

term storage of gas samples. However there has been little investigation into the sources

of contamination during preparation and analysis of low concentration gas samples. In

this study we determined (1) sources of contamination on a Gasbench II, (2) developed

an analytical procedure to reduce contamination, and (3) identified an efficient, precise

method for introducing sample gas into vials.

We investigated three vial filling procedures: (1) automated flush-fill (AFF), (2)

vacuum back-fill (VBF), and (3) hand-fill (HF). Treatments were evaluated based on

time required for preparation, observed contamination, and multi-vial precision. Worst-

case observed contamination was 4.5 % of sample volume. Our empirical estimate

showed that this level of contamination results in error of 1.7 ‰ for samples with near-

ambient CO2 concentrations and isotopic values that followed a high-concentration

carbonate reference with an isotope ratio of –47 ‰ (IAEA-CO-9). This carry-over

contamination on the Gasbench can be reduced by placing a helium-filled vial between

the standard and succeeding sample or by ignoring the first two of five sample peaks

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generated by each analysis. High-precision (SD ≤ 0.1 ‰) results with no detectable

room-air contamination were observed for AFF and VBF treatments. In contrast

precision of HF treatments was lower (SD ≥ 0.2 ‰). VBF was optimal for the

preparation of gas samples, as it yielded faster throughput at similar precision when

compared to AFF.

4.2. Introduction

The development of continuous flow isotope ratio mass spectrometry (CF-IRMS)

in the 1980s and its commercial appearance in the 1990s made possible the high-

precision isotope measurement of large numbers of samples.1 In 1991, Prosser et al.2

described an automated method for rapid isotopic analysis of high-concentration CO2

from breath samples collected in 13-mL septum-capped vials. The technique involved the

use of an autosampler where the headspace gas was transferred to the helium carrier

stream using a double-holed needle probe. More recently, Tu et al.3 described an

analogous method but focused on the isotopic analysis of near-ambient CO2 samples

from 10-mL septum-capped vials for Keeling plot applications. Without cryogenic pre-

concentration, Tu et al.3 achieved precision of 0.08 ‰ δ13C for ambient concentration air

samples using a Gasbench II headspace sampler coupled to an IRMS. This application of

CF-IRMS techniques to low concentration gas samples allows for higher throughput and

reduced sampling costs compared to traditional dual-inlet IRMS.

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Despite several studies3-5 using continuous flow preparation devices for CF-IRMS

analysis of low concentration gas samples, methods for sample introduction to vials are

not yet standardized. Several methodological developments have focused on improving

vial integrity and storage3-7 but all had different parameters for sample introduction to the

IRMS. Common methods are through a flushing technique or glove bags. Flushing

durations and flow rates vary among laboratories. Tu et al.3 flushed their vials manually

with a double-holed needle for 10 s at a flow rate of ~ 1 L min-1. Midwood et al.5 flushed

their specially designed metal vials for 190 s at a flow rate of 80 mL min-1 while Knohl et

al.4 did not mention the flush-fill duration and flow rate during preparation of their

calibration gases. Standardized methods for vial filling are required not only for

unknown gas samples, but also for true internal standards and quality control samples.

For flush-filling techniques, it is essential that the procedure result in complete

volumetric turnover.8 Residual room air in the vial clearly results in inaccurate

measurement. To date, contamination due to insufficient flushing times has been

discussed in detail only for the analysis of high-concentration carbonate samples8.

Improving precision in the analysis of low concentration gas samples using CF-

IRMS techniques requires optimization of all steps of the analysis; vial preparation and

filling, instrument method, and normalization. We compared two sources of

contamination: memory effects on the Gasbench II and room air contamination for three

sample preparation methods. These vial-filling methods were: (1) automated flush-fill

(AFF), (2) vacuum back (VBF), and (3) hand fill (HF). The specific objectives of this

work were: (1) analyze and eliminate contamination during preparation and analysis, (2)

compare methods for the preparation of gas samples, and (3) determine the optimal

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sample preparation method evaluated by the absence of contamination, speed of

preparation, and vial-to-vial uncertainty.

4.3. Methods

4.3.1. Isotopic analysis

Isotopic measurements were performed at the Laboratory for Biotechnology and

Bioanalysis II, Stable Isotope Core at Washington State University, Pullman, WA, USA.

The carbon isotope ratio of low concentration CO2 gas samples was measured using a

Gasbench II interface (ThermoFinnigan, Bremen, Germany) with GC-PAL autosampler

(CTC analytics, Zwingen, Switzerland) connected to a Delta plus XP IRMS

(ThermoFinnigan, Bremen, Germany). The autosampler was equipped with a double-

holed needle that samples the headspace from 12-mL borosilicate vials (Labco Limited,

High Wycombe, UK #938W) capped with butyl rubber septa. Total run time for each

sample was 875s. Included in the total run time was a 20-s transfer time to allow the

analyte gas and helium carrier to fill the sampling line. These gasses then passed through

an inline nafion water trap and into a 100-µL sampling loop for 80s. During the load

cycle of the sampling loop, 3 pulses of a pure CO2 tank were introduced into the mass

spectrometer through an open split as an external reference. The 3rd peak of the

monitoring gas was used for all initial calculation of isotope ratios by arbitrarily defining

the monitoring gas as 0‰ δ13CVPDB. After 80s of filling the sampling loop, an eight-port

valve (VICI Valco, Houston, TX) was switched to inject for 60s. This process was

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repeated five times, to ultimately provide five sample peaks. During the inject phase, gas

samples passed through a Poraplot Q GC column (25m l x 0.32 mm ID, 10 µm thickness,

Varian, Inc., Walnut Creek, CA, USA) held at 40°C followed by another nafion trap.

After the GC column, analyte gas and helium entered the IRMS through a movable

sample open split. Moving the open-split capillary could be achieved through the

software, diluting the sample entering the IRMS. We used the split out/dilution setting to

achieve consistent signal among samples and standards, and to remove earlier eluting

N2O which has isobaric interference for mass-to-charge ratio, m/z 44, 45, 46. For

experiments where the purpose was to observe maximum contamination in the system

and for blank vials, the dilution was turned to off/split in.

Prior to every sample sequence, stability of the instrument was verified to be <

0.06 ‰ δ13CVPDB from ten or more injections of the CO2 monitoring tank, and at least five

conditioning samples from the one blank vial were run through the system. Linearity was

verified to be within the manufacturers recommendations of < 0.06 ‰ per volt for the

range of 500-8000mV m/z 44. Isotopic values were expressed in terms of the

delta notation (in ‰) relative to the Vienna Pee Dee Belemnite (VPDB) standard:

δx =Rx

Rstd−1

⎛ ⎝ ⎜

⎞ ⎠ ⎟ (1)

where Rx and Rstd are the ratios of 13C relative to 12C in the CO2 in the sample and in the

VPDB standard, respectively.

Three carbonate standards that covered a range of delta values were used to

reduce uncertainty in isotope measurements.9-11 The span of calibration standards was

–47.32 ‰ (IAEA-CO-9) to +1.95 ‰ (NBS19). NBS18 (-5.01 ‰) was used as a quality

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control (QC). The carbonate standards were prepared according to Revesz et al.12:

standards were transferred to 12-mL septum-capped exetainers, flush-filled with helium

for 5 minutes, digested with 100 % H3PO4 in a CO2 – free environment, and allowed to

equilibrate for 24 hours. Vials were flush-filled in the gas bench with tank air at 26 mL

min-1 using the double-holed needle. The internal pressures of all vials were equilibrated

with atmospheric pressure to reduce variability of results.3 The flush-fill needle was left

in the vial for 60 seconds after shutting off the supply of the flushing gas to achieve

atmospheric equilibrium. A pressure gauge (model HHP701-2, Omega Engineering,

Stanford CT) fitted with a needle was used to verify internal vial pressure.

All sequences were normalized using the Laboratory Information Management

System (LIMS) for Light Stable Isotopes (USGS, Reston VA) with NBS19 and IAEA

CO-9 as anchor points. LIMS uses ordinary least squares regression (OLS) to normalize

isotopic values and perform hourly drift corrections if needed. An hourly drift correction

was applied to the data only if all references and QC samples showed improvement.

Final isotopic composition of each sample was evaluated from the best three of five

sample peaks, with vial precision typically < 0.1 ‰ (1SD). The first two peaks were

typically ignored because they were most likely to be affected by carryover from the

previous sample. Occasionally the last peak was ignored due to helium dilution of the

signal. In cases where the goal was to measure prior sample carryover, the area and

δ13CVPDB of all peaks were used. We chose 10mV as a minimum peak detection

parameter because it was the smallest peak above typical background fluctuations our

system could reliably detect. Since diagnostic tests encompassed 500-8000mV only, it

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should be noted that some additional bias might exist for sample peaks <500mV. We

present only the mean values for such peaks, normalized the same way as other samples.

4.3.2. Description of gas sample preparation methods

We compared three filling techniques to prepare gas samples: (1) automated

flush-fill (AFF), (2) vacuum back- fill (VBF), and (3) hand-fill (HF). All vials were

initially open to room air and caps tensioned prior to all treatments. In the AFF method,

vials were flush-filled using the GC-PAL autosampler on the Gasbench II for 5 and 10

minutes at a flushing rate of 26 mL min-1 . The autosampler was programmed to leave the

flushing needle in the vial for 60s after turning off the flush gas. This was determined

previously to keep the vials at only a slightly positive pressure. We combined AFF with

vial pre-cleaning treatments, where individual vials were pre-evacuated and filled with

helium one or three times.

For the VBF technique, vials were evacuated and filled via a vacuum backfill

manifold (Figure 4.1). The line was constructed of 1/4” stainless steel tubing with 8

valved ports designated V1 (1/4 turn Swagelok plug valves, part # SS-4P4T, The

Swagelok Company, Solon, OH, USA) which reduce into 28G needles. Both ends of the

line have separate valves, designated V2 (Swagelok 1/4” integral bonnet needle valve,

part #SS-1RS4) which allows the user to control connection to a vacuum pump (Edwards

RV 1.5, Edwards, Tewksbury, MA, USA) and the flush gas. The flush gas for the VBF

technique was helium in the case of blank preparation or a gas tank filled with actual

sample. The entire VBF manifold and the connection to the regulator of the flushing tank

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were initially evacuated. Vacuum was measured with a Varian thermocouple type 0531

(Varian, Inc., Walnut Creek, CA, USA) sending unit and model 801 analog gauge.

Pressure was measured with a pressure transducer and digital readout (MKS 122A-1123,

PDR-D-1, MKS Instruments, Andover, MA, USA). After each evacuation, the manifold

was isolated from the vacuum pump and vials were filled with the tank gas to

atmospheric pressure by slowly adjusting the tank regulator. The VBF method was

applied in two treatments: 1 evacuation/backfill, and 3 evacuations/backfills.

In the HF method, vials were evacuated with the VBF line, left under vacuum and

manually filled with the tank gas using a 30-mL syringe. The syringe was used to pierce

an empty septum-fitted Swagelok 1/4” nut that was attached to the tank regulator, set at

138 kPa (20 psi). After three rinses of the syringe, sample vials were initially over-

pressurized by injecting 20 mL of sample gas into the 12-mL exetainers. Excess vial

pressure was released by briefly puncturing the vial septum with a needle, allowing for

the pressure to equilibrate with ambient pressure. The HF method was applied in two

treatments: one evacuation/handfill, and three evacuations/handfills.

In summary, the three sample introduction methods (AFF, VBF, HF) in

combination with vial pre-filling procedures (no evacuation, 1 evacuation, and 3

evacuations) yielded a total of 10 treatments. These treatments were: 5-min AFF with no

evacuation, 5-min AFF with 1 evacuation, 5-min AFF with 3 evacuations, 10-min AFF

with no evacuation, 10-min AFF with 1 evacuation, 10-min AFF with 3 evacuations, HF

with 1 evacuation, HF with 3 evacuations, VBF with 1 evacuation, VBF with 3

evacuations. The HF and VBF filling techniques were not used with the no-evacuation

procedure because these techniques always require an evacuated vial.

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4.3.3. Experiment 1: Identification of sources of contamination

Contamination was assessed using He-filled vials (blanks) for the 10 treatments

previously described. Three replicates (designated as blanks 1, 2, and 3, respectively)

were prepared for each treatment and successively positioned after an IAEA-CO-9 (-

47.32‰) carbonate standard. The default minimum peak detection parameter was

lowered from 50 to 10 mV. The [CO2] for gas samples can be determined from the area

of the voltage signal peak13-14. Therefore the magnitude of contamination was estimated

by taking the ratio of the maximum sample area of the blanks over the maximum area of

a 380-ppm CO2 sample for this instrument. The source of contamination was identified

using the δ13C values of the blank vials. Memory effects (carry-over contamination) in

the Gasbench were associated with the depleted δ13C values of the IAEA-CO-9 standard;

in contrast, contamination due to laboratory air had a significantly more enriched δ13C (≈

-9‰).

The CF-IRMS sampled each vial and analyzed it five times. The gas in the vial

was diluted as He was injected to transfer the sample into the mass spectrometer.

Therefore, our ability to detect small contaminations in the blanks decreased over the

analysis time required for each sample: room air contamination might have been detected

in the first sample peak but most likely would disappear in the remaining peaks. A

common practice for post-processing isotope data is to discard the first sample peak

because it is subject to larger errors. We decided to use the third peak in our analysis of

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blanks 2 and 3 to identify the treatments with room air contamination that would still be

present even after post-processing.

4.3.4. Experiment 2: Application to gas samples of near-ambient CO2

concentrations

The results obtained from Experiment 1 (blanks) were used to select among each

category (AFF, HF and VBF) the treatment that (1) produced minimum contamination,

and (2) required the least preparation time. The selected treatments were 5-min AFF with

1 evacuation/He fill, VBF with 3 evacuations/backfills, HF with 3 evacuations/handfills.

Those three treatments were used for further testing with the CO2 tanks to evaluate the

best method for gas standard preparation. Three sample vials were prepared per filling

treatment. To avoid carry-over contamination, a blank (helium-filled) vial was positioned

between the carbonate standard and the CO2-filled sample vials. This experimental set-up

enabled us to attribute any observed residual contamination to the sample preparation

method.

The CO2 tanks were dilutions prepared by mixing carbon isotope reference gases

with instrument grade zero air. Carbon isotope reference gases were: tank 1, –23.5‰

(Cambridge Isotope Laboratories Inc., Andover, MA, USA) and tank 2, –38.9‰ (Oztech

Trading Corporation, Safford, AZ, USA). The CO2 concentrations were 396.4 ppmv (±

1%) and 383.7 ppmv (± 1%) for tanks 1 and 2, respectively.

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4.4. Results

4.4.1. Identification of sources of contamination

The highest contamination (~ 4.5 % of the area of a 380-ppm sample) occurred in

the 5-min AFF method when no vial pretreatment was done (Figure 4.2). Contamination

was reduced (< 4%) by either flushing for 10 minutes or rinsing the vials with He before

flushing. No improvement was observed by rinsing the vials three times instead of one.

For both VBF and HF methods contamination was about 4% and was not reduced by

applying more evacuations (Figure 4.2). One replicate for the 10-min AFF with 1

evacuation was not included in Figures 4.2 and 4.3, as the observed voltage area for this

treatment was almost twice as large as those observed for the other treatments. An outlier

test using orthogonal distance regression on the δ13C and peak areas of all treatments

confirmed that the result for the 10-min AFF with 1 evacuation was an outlier in the data

set.

To identify the sources of contamination we analyzed the δ13C values of the

blanks. For each filling technique three replicates (blanks 1, 2 and 3) were placed in

sequential order after an IAEA CO-9 standard. Blank 1, positioned immediately after

IAEA CO-9, had more negativeδ13CVPDB and larger voltage areas (Figure 4.3). We found

contamination in blank 1 for nine treatments (data for 10-min AFF with 1 evacuation was

not included in the figure). For blank 2 only four treatments presented contamination and

for blank 3 only three treatments had detectable contamination (Figure 4.3). In cases

where we still observed contamination in blanks 2 or 3, the δ13CVPDB values were similar

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to laboratory room air (Figure 4.3). These results clearly show that: (1) the carry-over

contribution from IAEA CO-9 was observed in the sample positioned immediately after

the carbonate (blank 1), and (2) the residual room-air contamination in blanks 2 and 3

were only observed for some treatments, i.e., not all treatments were susceptible to room

air contamination. When we analyzed the third peak of blanks 2 and 3, only the two HF

and the 5-min AFF methods exhibited presence of contamination (Figure 4.4). This

residual contamination in the HF and 5-min AFF methods was clearly an artifact of the

sample preparation method. Flush filling for five minutes (no evacuation) was

insufficient to turn over the gas in the vial, while doubling the flush fill time to 10

minutes or rinsing the vial with helium before filling completely removed the residual

room air. Puncturing the HF vials to release excess pressure may have introduced room

air during pressure equilibration.

Simulation using a two-source mixing model predicts that a 4.5% contamination

(maximum contamination measured in Experiment 1) translates to as much as ~1.7 ‰

error. This error would result when the differential isotopic composition of the sample

gas and the contaminant is about –37 ‰ (Figure 4.5), as in the case where the

contamination is carry-over from IAEA CO-9 (-47 ‰) and the sample gas is room air

with an isotopic signature of –9.3 ‰ (Figure 4.5). As the stable isotope composition of

contamination becomes more similar to that of the sample, there is less added

uncertainty. The error decreased when the carbon isotope ratio of the contaminant

approached that of the sample. This result suggests that carry-over contamination can be

masked whenever the carbon isotope ratio of the contaminant is similar to the sample.

For the case where the contamination is isotopically very different (e.g. -47 ‰) and of

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very low concentration (nominally equal to the minimum peak detection limit of 10 mV),

the contamination contribution is about 0.4% of the area of a 380-ppm sample.

Simulation predicts that this contribution translates to an error of ~ 0.15 ‰, which is of

the same order as the precision of continuous - flow IRMS systems.

4.4.2. Application to gas samples of near-ambient CO2 concentrations

The AFF and VBF techniques yielded similar precisions (≤ 0.1‰) while the

largest variability in results was observed for the HF method (≥ 0.2‰) (Figure 4.6). The

carbon isotope ratio for tank 2 obtained with the HF method was slightly enriched (-

38.3‰) relative to the AFF (-38.9 ‰) and VBF (-38.8‰). This result is consistent with

the introduction of room air (~ -9.3 ‰) into the more depleted HF samples (~-38 ‰)

during pressure equilibration. We did not observe the same isotopic enrichment for tank 1

samples prepared with the HF technique. This observation is consistent with the result of

the two-source mixing model simulation shown in Figure 4.5. Since the carbon isotope

ratio of the sample (tank 1, -23.5‰) was closer to that of the ambient samples, the small

contamination introduced by the HF technique did not significantly alter the δ13C

estimated for the sample.

4.5. Discussion

Continuous flow isotope ratio mass spectrometry is now frequently used to

investigate the carbon isotopic exchange between the biosphere and atmosphere3, 14-16,

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making use of septum-capped vials to collect gas samples from remote field sites

followed by laboratory analysis. To reduce errors associated with gas collection and

storage, earlier studies focused on improving the reliability of septum-capped vials prior

to application in the field3-4. Previous studies have investigated the flushing times

required for carbonate standards preparation8, but there is no standard protocol for

introducing low-concentration gas samples into vials for analysis on the same sampling

device. Through improved vial preparation and analysis, we found that error can be

reduced in low-concentration samples.

Carry-over contamination associated with the laboratory analysis can add

uncertainty to the results. The carry-over contribution was attributed to the transfer of a

small portion of sample from the previous to the current vial. Significance of carryover

depends on both prior sample concentration and its stable isotope composition. When we

diluted our carbonate standard with helium, carry-over contamination was not observed

in any of the blank vials (data not shown). Therefore, sequence design must consider

sample concentration and expected isotopic composition, and similar samples should be

grouped together in the sequence. We found errors due to carry-over as large as 1.7 ‰.

Errors in the isotopic ratio of collected gas samples of the order of 1.7 ‰ can potentially

introduce significant uncertainty when used in Keeling plot applications to determine

signatures of ecosystem respiration. For example, Badeck et al.17 reported that on the

global scale, an uncertainty in the autotrophic respiration of the order of 0.7 ‰ translates

to as much as 5 % change in the estimate of the biospheric sink.

Carry-over can cause significant errors in the analysis of low-concentration

samples, such as those at ambient [CO2]. Carry-over can be reduced by placing a blank

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(Helium-filled) vial between a standard and the gas sample or by discarding the first two

sample peaks during post-processing of data. We hypothesize that carry-over is a

potential artifact of the continuous flow system. Unlike a traditional off-line analysis

where pure gases are transferred in controlled amounts via dual inlet, and then evacuated

between samples, the gas bench system relies on a continuous flow of helium to transfer

the analyte gas to the IRMS. In the gas bench itself, we see the sample gas continually

diluted as it is transferred to the IRMS. In a separate test, we compared blanks

immediately following carbonates with the sample open split in the “out” position.

Performing this test reduced the carbonate signal in the IRMS compared to the split “in”

position, but it did not reduce the blank effect of a subsequent vial. Therefore, we think

that the carryover observed is occurring in the gasbench and not in the IRMS. Memory

effects in CF-IRMS systems must therefore be addressed in future efforts to design

systems with high reliability and precision.

Ambient air contamination was shown to be an artifact of the sample preparation

method. We found that a basic (<10 minutes or without vial pretreatment) flush-fill

method can lead to incomplete volumetric turnover of gas in the vial. This result is

consistent with recent findings of Paul and Skrzypek8 who recommended flushing times

of ≥ 600 s to completely remove air in the vials. Results indicated that either the 5-min

AFF with He pre-cleaning or the VBF with 3 sample fills was a satisfactory method for

analyzing low-concentration CO2 gas tanks. Accordingly, either technique can be used to

prepare gas standards for Gasbench applications. However, we found the VBF technique

to be a more efficient method with higher throughput. With the use of the vacuum

backfill manifold, it took approximately the same time to evacuate and fill eight vials as it

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did to fill one vial on the Gasbench using the AFF method. The variation in the HF data

was associated with less uniformity due to manual fills.

We recommend VBF, or AFF with vial pretreatment, for filling low-concentration

gas samples into vials. The VBF method can be more readily applied to field

measurements. The procedure is a slight modification to the method described by Knohl

et al.15 Vials fitted with Kel-F disks can be simultaneously evacuated using the vacuum

backfill (VBF) manifold and a vacuum pump. The VBF set-up will allow for several

vials to be pre-rinsed with canopy air before filling. The approach can facilitate a larger

throughput from the field, increasing the statistical robustness of analysis of samples.

The vacuum line system could easily be deployed in the field.

All of our procedures sampled gas from pressurized tanks, which may not be

practical for all field samples. Where low pressure, low volume samples are all that is

available (and a VBF system is inaccessible), the HF method is the most suitable outlined

here. Procedures we tested provide insight into the differences between the HF technique

and what we consider the gold standard, the VBF approach. Therefore, techniques

presented here could be used to prepare multiple gas standards in exetainers or to test HF

methods before implementing them in the field. Precision of low-concentration gas

samples with CF-IRMS devices can also be improved by taking steps to reduce carryover

in the device itself. It is recommended to build sequences with similar samples

(concentration and stable isotope composition), incorporate helium blanks, and use post-

run data processing to reduce external sources of variation.

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4.6. References

1. Brenna JT, Corso TN, Tobias HJ, Caimi RJ. Mass Spectrom. Rev. 1997; 16: 227.

2. Prosser SJ, Brookes ST, Linton A. Biol. Mass Spectrom. 1991; 20: 724.

3. Tu KP, Brooks PD, Dawson TE. Rapid Commun. Mass Spectrom. 2001; 15: 952.

4. Knohl A, Werner RA, Geilmann H, Brand WA. Rapid Commun. Mass Spectrom.

2004; 18: 1663.

5. Midwood AJ, Gebbing T, Wendler R, Sommerkorn M, Hunt JE, Millard P. Rapid

Commun. Mass Spectrom. 2006; 20: 3379.

6. Nelson ST. Rapid Commun. Mass Spectrom. 2000; 14: 293.

7. Laughlin RJ, Stevens RJ. Soil Sci. Soc. Am. J. 2003; 67: 540.

8. Paul D, Skrzypek G. Rapid Commun. Mass Spectrom. 2006; 20: 2033.

9. Coplen TB, Brand WA, Gehre M, Groning M, Meijer HAJ, Toman B,

Verkouteren RM. Anal. Chem. 2006; 78: 2439.

10. Coplen TB, Brand WA, Gehre M, Groning M, Meijer HAJ, Toman B,

Verkouteren RM. Rapid Commun. Mass Spectrom. 2006; 20: 3165.

11. Paul D, Skrzypek G, Forizs I. Rapid Commun. Mass Spectrom. 2007; 21: 3006.

12. Revesz KM, Landwehr JM. Rapid Commun. Mass Spectrom. 2002; 16: 2102.

13. Steinmann KTW, Siegwolf R, Saurer M, Korner C. Oecologia. 2004; 141: 489.

14. Mora G, Raich JW. Rapid Commun. Mass Spectrom. 2007; 21: 1866.

15. Knohl A, Werner RA, Brand WA, Buchmann N. Oecologia. 2005; 142: 70.

16. Knohl A, Buchmann N. Global Biogeochem. Cycles. 2005; 19: 4008.

DOI:10.1029/2004GB002301.

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17. Badeck FW, Tcherkez G, Nogues S, Piel C, Ghashghaie J. Rapid Commun. Mass

Spectrom. 2005; 19: 1381.

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Figure 4. 1. Schematic diagram of the vacuum backfill manifold. The line consists of

1/4” stainless steel tubing with 8 valved ports designated V1 which reduce into 28G

needles. Both ends of the line have separate valves, designated V2 which allows the user

to control connection to a vacuum pump and the flush gas.

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Figure 4. 2. Maximum contamination on low-concentration CO2 gas samples as a

function of the various gas filling treatments. The three sample introduction methods

(AFF = automated flush fill, VBF = vacuum backfill, HF = hand fill) were combined with

vial pre-filling procedures (no evacuation, 1 evacuation, and 3 evacuations) for a total of

ten treatments. The HF and VBF filling techniques were not used with the no-evacuation

procedure because these techniques always require an evacuated vial. Data for the 10-min

AFF with 1 evacuation were not included as the observed voltage area for this treatment

was almost twice as large as those observed for the other treatments. An outlier test

using orthogonal distance regression on the δ13C and peak areas of all treatments

confirmed that the result for the 10-min AFF with 1 evacuation was an outlier in the data

set.

6

5

4

3

2

1

Cont

amin

atio

n (%

)

5-m

in A

FF

10-m

in A

FF

5-m

in A

FF

10-m

in A

FF

VBF HF

5-m

in A

FF

10-m

in A

FF

VBF HF

No evacuation 1 evacuation with 1 He fill 3 evacuations with 3 He fills

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Figure 4. 3. Carbon isotope ratio as a function of the measured area for all Helium-filled

replicates of all treatments. Blank vials 1, 2, and 3 are the helium-filled replicates

sequentially positioned after the IAEA CO-9 standard.

-50

-40

-30

-20

-10

0δ 13

C of

firs

t pe

ak (

x100

0)

0.110.100.090.080.070.060.05

Area of first peak (Vs-1)

vial1 vial2 vial3

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Figure 4. 4. Residual contamination as a function of the ten treatments. Peaks one and

two out of five were omitted. Three treatments (5-min AFF no evacuation, HF with 1

evacuation, and HF with 3 evacuations) still showed contamination following data post

processing.

3.5

3.0

2.5

2.0

1.5

1.0

0.5

Resi

dual

Con

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inat

ion

(%)

5-m

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FF

10-m

in A

FF

5-m

in A

FF

10-m

in A

FF

VBF HF

5-m

in A

FF

10-m

in A

FF

VBF HF

No evacuation 1 evacuation with 1 He fill 3 evacuations with 3 He fills

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Figure 4. 5. Two-source mixing model calculation of the uncertainty in the carbon

isotope ratio of a low-concentration sample. The model was based on the difference in

the isotopic signatures of the sample gas and the contaminant for the worst-case

contamination of 4.5 %. The isotopic signature of the sample was assumed to be -9.3 ‰,

which was the average measured δ13C of room air in our laboratory.

-2.0

-1.5

-1.0

-0.5

0.0

0.5

Erro

r in

δ 13

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sam

ple

(x10

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-35 -30 -25 -20 -15 -10 -5

Differential δ13C (x1000)

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Figure 4. 6. Effect of three sample preparation techniques in air samples of identical

CO2 concentrations but distinctly different carbon isotope ratios (δ13C of tank1 = -23.5

‰, and δ13C of tank2 = -38.9 ‰).

-24.0

-23.5

-23.0

δ 13C

(x10

00)

5-m

in A

FF +

1He

fill

VBF

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ls

HF_3

sam

ple

fills

-39.0

-38.5

-38.0

tank 1 (-23.5 ‰) tank 2 (-38.9 ‰)

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CHAPTER 5

SUMMARY AND CONCLUSIONS

5.1. Summary

This dissertation described the development of a novel approach to measure the

exchange of isotopic CO2 in a forest ecosystem. The first chapter provided background

on the measurement of the isotopic composition of atmospheric carbon dioxide, its

importance in the study of global environmental change, and a detailed discussion on the

various techniques for concentration, stable isotopes, and isotopic flux measurements.

Results of field and laboratory measurements as well as scientific conclusions were

presented in chapters 2, 3, and 4.

The uncontrolled use of fossil fuels has significantly increased the atmospheric

concentration of carbon dioxide since the industrial revolution. Although the distribution

of CO2 into its land, ocean, and atmospheric reservoirs is relatively well known, there

remain many uncertainties in the global carbon budget. Quantification of the changing

partial pressure of CO2 is not sufficient to understand its flow into and out of the various

carbon reservoirs. The measurement of the stable isotopic composition of CO2 provides

additional constraints that aid in understanding the partitioning of atmospheric carbon

dioxide into its land and ocean components.

The traditional method for isotopic CO2 measurement makes use of laboratory

isotope ratio mass spectrometry (IRMS). While this method provides the best precision,

it does not allow for continuous measurement of the isotopic content of carbon dioxide.

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This approach limits long-term investigations of ecosystem response to changing patterns

in environmental drivers such as temperature, precipitation, and nutrient availability.

Several spectroscopic methods have been proposed in the literature as an alternative to

IRMS for continuous, real-time measurement of isotopic CO2. These spectroscopic

techniques rely on the strong isotopic absorption of carbon dioxide in the infrared. In this

work, we have chosen to develop an approach that is based on Fourier Transform

Infrared spectroscopy (FTIR). FTIR is a scanning method that is not as fast or as precise

as tunable diode laser absorption spectroscopy (TDLAS) or quantum cascade laser

absorption spectroscopy (QCLAS), but its ability to measure without ambiguity and

simultaneously a number of important trace gases (many of which are greenhouse gases)

makes it an attractive and flexible method that will find application in forest, urban, and

agricultural ecosystems. Since QCLAS and TDLAS make use of a single spectral line

feature to measure the concentration of an isotopologue of CO2, they are inherently

susceptible to interferences from the many poorly understood compounds that absorb in

the infrared and are present in a forest ecosystem or urban environment. The use of large

wavelength coverage in FTIR spectroscopy makes it easy to distinguish any interferent

features in the absorption spectra.

The first manuscript (“A new Fourier-transform infrared instrument for

measurement of temporal and vertical distribution of δ13C – CO2 1. An overview of

results from measurements in a managed poplar forest in northern Oregon”) described in

detail a new FTIR approach for in-situ, real-time stable isotope measurement in forest

ecosystems. The instrument can run virtually unattended for 24h/day taking data at

various heights in a canopy or at a single height on time centers less than one minute. We

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deployed the FTIR system in a managed poplar forest in the summers of 2004 to 2006

where temporal and vertical distributions of CO2 and δ13C were measured. Based on our

long-term measurement of a gas standard in the field, we report accuracy for the forest

measurements of 0.5 ‰ and a precision of 0.8 ‰, respectively, corresponding to a 45-

second cycle time between samples. This time resolution was sufficient for application of

disjunct eddy correlation to determine the biosphere-atmosphere exchange of isotopic

CO2 fluxes.

The nighttime vertical profile measurements of δ13C and CO2 contrasted

significantly with the daytime results. The nighttime measurements revealed high

accumulations of CO2 within the canopy because of the combined effects of ecosystem

respiration, stable nighttime conditions, and slow winds. The highest CO2 concentrations

were observed closest to the ground; they progressively decreased with increasing height

and approached background values above the canopy. Conversely, the corresponding

carbon isotope ratios increased with height. A clear inverse relationship was observed

between the CO2 concentrations and δ13C.

Because of photosynthesis, the daytime contour plots of CO2 and δ13C show that the

air within and above the canopy was isotopically enriched and that the CO2

concentrations were lower relative to background values. The CO2 concentrations and

the corresponding carbon isotope ratios were also uniformly distributed within and above

the canopy indicating that there was good air mixing due to unstable conditions.

The signature of ecosystem respiration (δ13CR) was derived from the application of

the Keeling plot regression on nighttime δ13C – CO2 vertical profile measurements. The

resulting estimate was more enriched than the carbon isotope ratio of bulk leaf samples

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by as much as 2 ‰. This result was consistent with observations that the respired CO2 is

more enriched than its organic substrates. Comparison of δ13CR with the isotopic

signature of soil respiration, δ13CSR, showed that ecosystem respired CO2 was also more

enriched than soil respired CO2. Recent findings have shown that above ground

respiration is isotopically enriched relative to below ground respiration. We can expect

from mass balance calculations that the isotopic composition of ecosystem respiration

should be intermediate between the isotopic ratios of respired CO2 from above and below

ground components.

The second manuscript was entitled “A new Fourier transform infrared instrument

for measurement of temporal and vertical distribution of δ13C – CO2 2. Measurement of

ecosystem-atmosphere exchange of isotopic carbon dioxide using disjunct eddy

covariance”. This chapter focused on the combination of FTIR spectroscopy with

disjunct eddy covariance for the simultaneous determinations of the biosphere-

atmosphere exchange of total CO2, H2O, and isoflux in the poplar forest. We first

compared the total CO2 flux and the Latent Heat flux derived from the FTIR – DEC

approach and the conventional eddy covariance method using an infrared gas analyzer

(IRGA – EC). The FTIR – DEC CO2 flux measurements agreed with the IRGA – EC

estimates to within 10%, which is well within the statistical uncertainty empirically

determined by Turnipseed et al., (2009) for a sampling interval of 45 seconds. Disjunct

sampling of the water vapor flux constantly underestimated the IRGA – EC

measurements. This result is consistent with observations by other groups and has been

attributed to the damping of high frequency fluctuations due to the adsorption and

desorption of water vapor on the inner walls of the sampling line.

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Simultaneous measurement of CO2 and its isotopic composition enabled the

measurement of the isoflux using the FTIR - DEC approach. Information from the

isoflux of 13CO2 and the net ecosystem exchange of total CO2 were used to directly

determine the canopy-scale photosynthetic discrimination. We found that the carbon

isotopic composition of the assimilation flux was 13C – enriched relative to whole leaf

samples by about 2.7‰. This result agrees with findings that recent assimilates are

enriched relative to the whole organic matter from which they were extracted.

We observed considerable variations in the FTIR – DEC flux estimates that are

chiefly attributed to the limited temporal resolution of the disjunct sampling technique

relative to the conventional IRGA – EC approach (45 second time resolution versus 100

ms sampling interval). We consider the reduced time resolution to be the main source of

statistical uncertainty in the DEC – derived fluxes. Increasing the temporal resolution is

therefore a main consideration for future measurements with the FTIR – DEC system.

The uncertainty can be considerably reduced either by decreasing the cycle time between

samples or increasing the averaging interval.

The ability of Fourier transform infrared spectroscopy to simultaneously measure

the concentrations of several important trace gases is an advantage over other existing

spectroscopic techniques. When combined with disjunct eddy covariance, FTIR – DEC

becomes a powerful tool not only for quantifying the concentrations of molecular

isotopologues and their ratios, but also for investigating the rapid exchange of various

infrared-active species in forest, urban and agricultural ecosystems.

The fourth chapter (“Analysis of low-concentration gas samples with continuous-

flow IRMS: eliminating sources of contamination to achieve high precision”) is already

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published in Rapid Communications in Mass Spectrometry. This manuscript resulted

from the fact that there is still no standard procedure/protocol amongst isotope

laboratories in analyzing gas samples using continuous flow isotope ratio mass

spectrometry (CF-IRMS). The lack of a protocol presented a major challenge in

analyzing the working gas standards that were used during our field measurements.

Thus, we first developed a procedure for preparing and analyzing gas samples on a CF –

IRMS. The optimized procedure was then used to ascertain the isotopic composition of

the four reference cylinders that were used during field measurements. The chapter

discussed (1) sources of contamination in CF-IRMS analysis of near-ambient CO2

concentration gas samples, (2) analytical procedures that reduce the contamination, and

(3) the identification of a precise, efficient method for introducing gas samples into vials.

5.2. Personal thoughts and recommendations for future measurements

This research was a multi-disciplinary project and it gave me the opportunity to be

exposed to various disciplines such as spectroscopy, instrument development, plant

physiology, biosphere-atmosphere interactions, and carbon cycling. Apart from finishing

the dissertation, the most challenging aspect of this project was the high precision

measurement of the carbon isotope ratio, a quantity that is inherently small in magnitude

(i.e. detection of changes smaller than 1 part in 104 was not trivial). From 2005 to 2006,

we learned how to modify our experimental methods to increase the sensitivity of the

instrument for δ13C measurements such as purging the spectrometer, detector and sample

compartments with nitrogen gas. When the background CO2 dropped to ~2 ppmv, we

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saw a considerable improvement in our measurements. Frequent online calibration with

a reference tank as well as continuous measurement of gas cell pressure and temperature

also enabled us to track any experimental irregularities such as instrument drift.

However, there are still several ways to further improve the measurement of the isotopic

composition of carbon dioxide for future measurements. Here is a short list of

recommendations:

1) To reduce the cycle time between samples, and increase the time resolution of the

FTIR – DEC samples, replace the platinum RTD temperature sensor and the

Omega pressure transducer for fast response but accurate sensors that have short

equilibration times. This will effectively reduce the “dead time” in between

samples.

2) To improve the retrievals of δ13C, use two reference cylinders with a range of

carbon isotope ratios from –5 to –12 ‰, with corresponding concentrations of 320

to 450 ppmv respectively, for the online calibration of samples. Cambridge

isotope laboratory carries isotopic CO2 gas standards with the appropriate range

that can be mixed with instrument grade zero air to obtain the corresponding

concentrations.

3) Our sensitivity analysis showed a 1.4‰ uncertainty in the retrieved δ13C for every

0.1ºC change in gas cell temperature. Therefore, a more stringent control of gas

cell temperature and feed lines is also recommended because of the temperature

dependence of δ13C.

4) If funding is available for a new FTIR instrument, then it is best to purchase a

vacuum system. This modification will remove the need to transport around 15

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T-sized cylinders of nitrogen gas to a distant site to purge the instrument for a

two-week field campaign.

5) To improve the measurement of the lag time between the acoustic anemometer

and the FTIR spectrometer, a flow meter can be installed in the tower sampling

line. As we have done in the field, puffs of high concentration CO2 samples can

be released at the highest intake point in the tower and the time it takes for the

puffs to be detected by the FTIR spectrometer recorded. With the addition of the

flow meter, the dependence of the lag time on the flow rate can be determined.

6) The instrument was computer controlled using a National Instruments FieldPoint

(FP) computer. The FP also recorded data from all the auxiliary sensors (Pt RTD

sensors, pressure transducers, and GPS) aside from instrument control. The

increase in sensors in the summer of 2006 taxed the memory of the FP, and was

probably the reason why the instrument would occasionally stop data acquisition

at random times. As more sensors are added to the system, it would be best to

replace the FP with an updated acquisition system with much larger memory and

higher speed.

7) It would also worthwhile to invest in a detector that does not require cooling with

liquid nitrogen but has comparable or higher sensitivity and signal to noise ratio

(SNR) in the 2100 to 2600 cm-1 region as the InSb detector we used in the field.

The use of an InSb detector with Peltier cooling with excellent SNR that can

operate at room temperature will remove the need to haul liquid nitrogen to

remote field sites.

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Apart from implementing the suggested recommendations, where do we go from

here? There are many exciting science problems that can be addressed by long-term

measurements of CO2 and δ13C. Here is a short list of some of the possible science

problems that can be investigated with the FTIR – DEC system:

a. How does canopy discrimination change with changing seasons? How does an

ecosystem’s primary productivity and discrimination against 13CO2 vary with

changing environmental conditions such as temperature and moisture?

b. When measurements are conducted in a mix C3 – C4 ecosystem, is it possible to

partition the overall contribution of C3 and C4 photosynthesis to the

ecosystem’s productivity?

c. When there is a land cover change in an ecosystem, e.g. a change from a forest

to an agricultural system or a change from C3 to C4-dominated species, how

much will be the change in the ecosystem’s discrimination against 13CO2 and

its net productivity?

d. As stated previously, the FTIR-DEC system simultaneously measures the rapid

exchange of several important trace gases such as N2O, H2O, and CH4. If there

is an increase of nitrogen fertilization in an ecosystem, is it possible to

correlate N2O emissions with canopy discrimination?

e. Apart from the possible future projects suggested above, the existing data set

obtained from the poplar forest is also an important resource that must be

further explored and can potentially be an exciting project for another graduate

student.

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This project exposed me to the exciting experience of instrument development,

fieldwork, and data analysis. It was interesting to be immersed in various disciplines and

to see how this project brought together seemingly unrelated topics. The magnitude of

the work was often overwhelming and it would not have been possible to complete the

various tasks without the expertise and time of many people who were part of this

project. Every step of the way presented a different challenge that undoubtedly enriched

my research experience as a graduate student. Ever since I came on board the project in

2003, there was never a dull moment.