Determination of Vertical Fluxes of Sulfur Dioxide and Dimethyl Sulfide in The

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Determination of Vertical Fluxes of Sulfur Dioxide and Dimethyl Sulfide in the

Remote Marine Atmosphere by Eddy Correlation and an Airborne Isotopic Dilution

Atmospheric Pressure Ionization Mass Spectrometer

A Thesis

Submitted to the Faculty

Drexel University

Glenn M. Mitchell

in partial fulfillment of the

requirements for the degree

Doctor of Philosophy

May 2001

DEDICATIONS

To my loving wife Rachel without whom my life would not be complete.

Love Always and Forever. iii

ACKNOWLEDGEMENTS

I would like to begin by thanking my research advisor Professor Dr. Alan R. Bandy for his guidance and support over the course of this work, as well as communicating his vast knowledge to me as a mentor. I would also like to thank Dr. Bandy for his moral encouragement that continued my drive and ambition to complete this work. I would like to thank Dr. Donald C. Thornton for his hard work and dedication during the development, testing, and field deployment of the instrument and techniques used in this work. Through his efforts, Dr. Thornton has taught me how to strive for excellence as an analytical chemist.

I would also like to thank Dr. Donald H. Lenschow of the National Center for Atmospheric

Research (NCAR) for his assistance in interpreting the field data, as well as his guidance in helping me understand the micrometeorological nature of my experiments.

Dr. Bryon W. Blomquist was a critical part of the field deployments of the instrument and techniques used in this work, and I would like to thank him for his involvement. I would also like to thank Dr. Blomquist for many meaningful discussions related to the work presented in this dissertation. Special thanks goes to the NCAR

Research Aviation Facility scientists, staff, and crew of the C-130 for their involvement and support during the field deployments described in this work.

Wolfgang Nadler and Maryanne Fitzpatrick also have my gratitude for their involvement in the creation of many of the electronic components needed for the success of instrument used in this work. Nikolaus Kwasnjuk, Zoltan Boldy, and Mark Schriber of the

Drexel University machine shop also deserve a word of thanks for their involvement in manufacturing components to create an aircraft deployable instrument. I would also like to iv thank the faculty, staff, and students of the Department of Chemistry at Drexel University for their support during my graduate career.

Last, but definitely not least, I would like to thank my family and friends for their support and encouragement during my tenure as a graduate student. I would like to thank

Merlin and Chester for their unconditional love and significant role in my mental well-being.

Finally, I would like to especially thank my wife Rachel, who has been a part of my life since the start of my collegiate education, for all of her love, support, compassion, and understanding, without whom all my efforts would have been in vain.

TABLE OF CONTENTS

LIST OF TABLES ...... VIII

LIST OF FIGURES...... X

ABSTRACT...... XIV

CHAPTER 1 – INTRODUCTION...... 1 1.1 Research Significance...... 1 1.2 Thesis Objectives...... 4 1.3 Background...... 5 1.3.1 Previous Airborne SO2 Measurement Techniques...... 5 1.3.2 Previous Airborne DMS Measurement Techniques ...... 7 1.3.3 Methods for Determining Airborne Trace Gas Fluxes ...... 10 1.4 Thesis Structure...... 13 1.5 References...... 15

CHAPTER 2 – AIRBORNE ISOTOPIC DILUTION ATMOSPHERIC PRESSURE IONIZATION MASS SPECTROMETER INSTRUMENTAL DESIGN AND LABORATORY EXPERIMETATION...... 21 2.1 Introduction...... 21 2.2 Airborne Isotopic Dilution APIMS Instrumental Design and Operation...... 21 2.2.1 General Instrumental Schematics and Layout...... 22 2.2.2 APIMS Chamber Design and Vacuum Pumps...... 26 2.2.3 63Ni Ionization Source and Atmospheric Pressure Declustering Region ...... 27 2.2.4 Ion Optics, Mass Analyzer, and APIMS Tuning ...... 29 2.2.4.1 Focusing and Acceleration Lenses (Ion Optics) ...... 29 2.2.4.2 Quadrupole Mass Analyzer ...... 30 2.2.4.3 APIMS Tuning...... 31 2.2.5 Ion Signal Detection ...... 33 2.2.6 Power Distribution...... 33 2.2.7 Mass Flow Control...... 34 2.2.8 APIMS Data Acquisition...... 36 2.2.8.1 Computer Hardware and Software...... 36 2.2.8.2 Data Acquisition Boards...... 37 2.2.8.3 Data Acquisition Programs (VIs)...... 38 2.2.8.3.1 MS_SPECTRUM_SCANNING VI ...... 38 2.2.8.3.2 SIM VI...... 40 2.2.8.3.3 FLOW_DATA VI...... 41 2.3 Isotopic Dilution Application to APIMS ...... 42 2.3.1 Advantages of Isotopic Dilution APIMS Measurements...... 43 2.3.2 Isotopic Standards for Airborne APIMS Measurements ...... 45

2.4 APIMS Ion Molecule Reactions for the Determination of DMS and SO2...... 47 2.4.1 DMSH+ ...... 48 - 2.4.2 SO5 ...... 49 2.4.3 Further Issues Concerning the APIMS Detection of DMS and SO2 ...... 51 vi

2.5 Laboratory Experiments to Obtain High Sensitivity Determinations of DMS and

SO2 using the Airborne Isotopic Dilution APIMS...... 53 2.5.1 Laboratory Manifold Design and Additional Apparatus...... 53 2.5.1.1 Ozone Generation and Measurement ...... 55 2.5.1.2 Water Vapor Generation and Measurement...... 56 2.5.1.3 Nafion Dryers ...... 57 2.5.2 General APIMS Instrumental Sensitivity Enhancements ...... 59 2.5.3 Results of Laboratory Experiments performed on the Airborne APIMS ...... 59

2.5.3.1 Ozone Addition for the Airborne APIMS Detection of SO2 ...... 60 2.5.3.2 Water Vapor Addition and the Effect on the Airborne APIMS Response for SO2 ...... 64 2.5.3.3 Water Vapor Addition and the Effect on the Airborne APIMS Response for DMS ...... 67 2.6 Conclusion...... 77 2.7 References ...... 79

CHAPTER 3 – FAST MEASUREMENTS AND AIRBORNE INSTRUMENTAL INTERCOMPARISON OF SULFUR DIOXIDE IN THE ATMOSPHERE...... 82 3.1 Introduction...... 82 3.2 Experimental ...... 84 3.2.1 Airborne Isotopic Dilution APIMS Test Flights ...... 84 3.2.2 APIMS Instrumental Parameters...... 85 3.2.3 Airborne Manifold Design...... 86 3.2.4 APIMS Data Acquisition...... 89 3.3 Airborne Sulfur Dioxide APIMS-GC/MS/ILS Intercomparison ...... 89 3.3.1 Test Flight 2 (105TF02) Intercomparison ...... 90 3.3.2 Test Flight 3 (105TF03) Intercomparison ...... 93 3.3.3 Combined Airborne SO2 Instrumental Intercomparison ...... 94 3.4 Utility of the Fast Measurement of Airborne Isotopic Dilution APIMS ...... 97 3.5 Eddy Correlation Feasibility and Preliminary Marine Boundary Layer Tests.....101 3.6 Conclusions ...... 103 3.7 References ...... 105

CHAPTER 4 – EDDY CORRELATION FLUX MEASUREMENTS OF DIMETHYL SULFIDE AND SULFUR DIOXIDE IN THE MARINE BOUNDARY LAYER USING AN AIRBORNE ISOTOPIC DILUTION ATMOSPHERIC PRESSURE IONIZATION MASS SPECTROMETER...... 107 4.1 Introduction...... 107 4.2 Experimental ...... 110 4.2.1 Fast Counting System ...... 110 4.2.1.1 Fast Counter Mass Command...... 111 4.2.1.2 Fast Counter Pulse Counting...... 112 4.2.1.3 Fast Counter Timer Circuit...... 112 4.2.1.4 Fast Counter Data Ready Bit ...... 114 4.2.2 Real-Time Data Acquisition ...... 114 4.2.2.1 Drexel APIMS Computer System ...... 115 4.2.2.2 NSF/NCAR/RAF Aircraft Data System (ADS2)...... 117 4.2.2.3 Instrumental Parameters ...... 118 4.2.2.4 Data Reduction Technique...... 120

vii

4.3 Eddy Correlation Flux Studies ...... 123 4.3.1 Field Studies...... 123 4.3.2 Airborne Dimethyl Sulfide APIMS-GC/MS/ILS Intercomparison...... 128 4.3.3 Eddy Correlation Fluxes of Dimethyl Sulfide...... 130 4.3.4 Eddy Correlation DMS Flux Gradient ...... 148 4.3.5 Fast Time Series Analysis...... 154 4.3.6 Ambient DMS Vertical Concentration Profiles...... 156 4.3.7 Eddy Correlation Flux of Sulfur Dioxide...... 164 4.4 Conclusions ...... 167 4.5 References ...... 169

CHAPTER 5 – THESIS SUMMARY AND FUTURE RESEARCH...... 175 5.1 Thesis Summary ...... 175 5.2 Future Research ...... 181 5.3 References ...... 185

APPENDIX A – ABBREVIATIONS...... 188

APPENDIX B – APIMS INSTRUMENTAL CONTROL AND DATA ACQUISITION LABVIEW VIRTUAL INSTRUMENT PROGRAMS (VIS)...... 189

APPENDIX C – FAST COUNTING SYSTEM PANEL AND CIRCUIT DIAGRAMS ...... 204

APPENDIX D – MATLAB M-FILES FOR DATA REDUCTION IN THE EDDY CORRELATION FLUX CALCULATIONS...... 210

VITA ...... 234

viii

LIST OF TABLES

Table 2.1 Mass flow controller and meter specifications and calibration constants for airborne isotopic dilution APIMS...... 36

Table 2.2 Isotopic percentage constants used in Equation 2.1 for ambient air...... 45

Table 2.3 Isotopic standard cylinder concentrations and isotopic percentages...... 46

Table 3.1 Flight information for airborne instrumental intercomparison of isotopic

dilution AIPMS and GC/MS/ILS for SO2...... 84

Table 3.2 Airborne isotopic dilution APIMS instrumental setting for SO2 detection...... 86

Table 4.1 Typical airborne APIMS instrument, flow and pressure settings during the

measurements for the eddy correlation fluxes of DMS and SO2 in the marine boundary layer...... 119

Table 4.2 Flight information containing comments on when measurements for DMS and

SO2 eddy correlation fluxes were performed. Flights without comments were not flown in the marine boundary layer...... 124

Table 4.3 Ambient DMS flux measurement information for level 30 m altitude of flight 186rf02. MRLA is the water vapor mixing ratio...... 140

Table 4.4 Combined flux segments for DMS and MRLA flux calculations of flight 186rf02...... 141

Table 4.5 Ambient DMS flux measurement information for level 50 m altitude of flight 186rf03. MRLA is the water vapor mixing ratio...... 143

Table 4.6 Combined flux segments for DMS and MRLA flux calculations of flight 186rf03...... 144

Table 4.7 Total marine boundary layer leg fluxes of ambient DMS and MRLA for flight 186rf03...... 144

Table 4.8 Comparison chart of APIMS measured DMS surface layer flux and literature DMS surface layer flux values...... 147

Table 4.9 Ambient DMS flux measurement information for varying altitudes of flight 186rf05. MRLA is the water vapor mixing ratio...... 149

Table 4.10 Summarized ambient DMS flux data at varying marine boundary layer altitudes in flight 186rf05...... 151

Table 4.11 Ambient DMS surface flux, entrainment flux, and entrainment velocity from DMS flux gradient in flight 186rf05...... 153

Table 4.12 Ambient SO2 eddy correlation measurement information for flight 186rf05. ... 165

LIST OF FIGURES

Figure 2.1 Schematic illustration of the airborne isotopic dilution APIMS...... 23

Figure 2.2 Proportional scale CAD drawing (cut away side view) of the airborne isotopic dilution APIMS...... 24

Figure 2.3 Aircraft rack layout of the isotopic dilution APIMS...... 25

Figure 2.4 CAD drawing of the isotopic dilution APIMS 63Ni ionization source...... 28

Figure 2.5 SIMION 3-D rendering of APIMS declustering, focusing, and entrance lenses...32

Figure 2.6 Laboratory manifold for ozone and water addition to airborne APIMS...... 54

Figure 2.7 Ozone addition to negative ion airborne APIMS background...... 61

34 34 - Figure 2.8 Ozone addition to isotopic standard SO2 showing conversion from SO4 to 34 - SO5 in negative ion mode of an airborne APIMS...... 61

- Figure 2.9 Determination of manifold ozone concentration for maximum CO3 ion signal..63

Figure 2.10 Determination of manifold ozone concentration for complete formation of 34 - SO5 ...... 63

Figure 2.11 Effect of increasing dew point on the APIMS response for SO2 using a Nafion dryer...... 65

° - 34 - Figure 2.12 Effect of 25 C dew point on the APIMS response for CO3 and SO5 without a Nafion dryer...... 66

° - 34 - Figure 2.13 Effect of 25 C dew point on the APIMS response for CO3 and SO5 with a Nafion dryer...... 66

Figure 2.14 Sample air heater schematic for increased APIMS DMS sensitivity in moist air.70

+ Figure 2.15 Determination of optimal air temperature for maximum APIMS d6-DMSH response...... 71

Figure 2.16 Airborne APIMS response for decreasing d6-DMS manifold concentrations in humidified air with an air temperature of 391°C. No Nafion dryers were used...... 72

Figure 2.17 Plot of APIMS response (counts per second) versus d6-DMS manifold concentration in humidified air with and air temperature of 391°C. Error bars are the standard deviations of the APIMS response for a given DMS concentration. No Nafion dryers were used...... 72 xi

Figure 2.18 Airborne APIMS response for decreasing d6-DMS manifold concentrations in humid air (22.6°C dew point) with an air temperature of 425°C. A single strand Nafion dryer was also used...... 74

Figure 2.19 Plot of APIMS response (counts per second) versus d6-DMS manifold concentration in humid air (22.6°C dew point) with and air temperature of 425°C. Error bars are the standard deviations of the APIMS response for a given DMS concentration. A single strand Nafion dryer was used...... 75

Figure 2.20 Airborne APIMS response for random d6-DMS manifold concentration changes in humid air (25.25°C dew point) with an air temperature of 425°C. A single strand Nafion dryer was also used...... 75

Figure 2.21 Plot of APIMS response (counts per second) versus random d6-DMS manifold concentration changes in humid air (25.25°C dew point) with and air temperature of 425°C. Error bars are the standard deviations of the APIMS response for a given DMS concentration. A single strand Nafion dryer was used...... 76

Figure 2.22 Residuals from the linear regression of Figure 2.21 indicating no apparent trend in the APIMS response for DMS concentration changes in humid air using a heated air stream and Nafion dryer...... 76

Figure 2.23 Airborne APIMS DMS sensitivity for the data in Figure 2.20. Error bars are the standard deviations of the APIMS sensitivity for a given DMS concentration. No significant changes in sensitivity were noticed at different DMS manifold concentrations with a dew point of 25.25°C using a heated air stream and Nafion dryer...... 77

Figure 3.1 Manifold design for SO2 measurements using an airborne isotopic dilution atmospheric pressure ionization mass spectrometer (APIMS) and GC/MS/ILS.88

Figure 3.2 Altitude profile and flight track for SO2 airborne APIMS-GC/MS/ILS intercomparison during Test Flight 2 (105TF02), November 1, 1999...... 91

Figure 3.3 Test Flight 2 (105TF02) APIMS-GC/MS/ILS SO2 intercomparison data...... 92

Figure 3.4 Test Flight 2 (105TF02) regression residuals...... 92

Figure 3.5 Altitude profile and flight track for SO2 airborne APIMS-GC/MS/ILS intercomparison during Test Flight 3 (105TF03), November 3, 1999...... 93

Figure 3.6 Test Flight 3 (105TF03) APIMS-GC/MS/ILS SO2 intercomparison data...... 95

Figure 3.7 Test Flight 3 (105TF03) regression residuals...... 95 xii

Figure 3.8 Comparison of SO2 determined by isotopic dilution APIMS and GC/MS/ILS by combining continental boundary layer and free troposphere data from Test Flights 2 and 3 (Nov. 1 & Nov. 3 1999)...... 96

Figure 3.9 Fast airborne APIMS response (1 Hz) and GC/MS/ILS response (3 min. integration) while porpoising through a thin layer on Test Flight 2 (105TF02), November 1, 1999...... 98

Figure 3.10 Expanded plot of Figure 3.2 showing the interception of the thin moist layer between 3.3 and 3.8 km pressure altitude on Test Flight 2 (105TF02), November 1, 1999...... 98

Figure 3.11 1 Hz responses for airborne isotopic dilution APIMS SO2 and laser absorption spectroscopy water vapor mixing ratio (LAS MR) during the moist thin porpoises on Test Flight 2...... 99

Figure 3.12 Airborne APIMS and GC/MS/ILS SO2 responses through power plant plumes on Test Flight 2 (105TF02), November 1, 1999...... 101

Figure 3.13 Airborne test of eddy correlation feasibility with isotopic dilution APIMS 34 - during Test Flight 4. (a). Raw m/e 114 (isotopic standard SO5 response) (b). 10 point forward-reverse zero phase Fourier transform filter applied to (a)...... 102

Figure 4.1 Manifold design for marine boundary layer eddy correlation flux measurements using an airborne isotopic dilution atmospheric pressure ionization mass spectrometer (APIMS)...... 125

Figure 4.2 Airborne intercomparison of isotopic dilution APIMS and GC/MS/ILS for DMS over the open ocean...... 129

Figure 4.3 Subsection of ambient DMS intercomparison of flight 186rf02 (7/9/2000). The bars on the GC/MS/ILS data denote the integration period. The large spike in the middle range of the APIMS data is a file break...... 130

Figure 4.4 Flight track of marine boundary layer leg of PELTI flight 186rf02 (first St. Croix local) on July 9, 2000...... 131

Figure 4.5 Phase angle between ambient DMS concentration and water vapor mixing ratio (MRLA). (a). No time lag correction is applied to the DMS data. First full phase between sensors is at 1.2 Hz, or 833 ms (b). 833 ms time lag correction applied to DMS data...... 134

Figure 4.6 Power spectrum densities (PSD) of ambient DMS (a) and water vapor mixing ratio (MRLA) and vertical wind velocity (w) (b). Linear contribution to PSD of DMS is due to white noise, and is not correlated to the eddy correlation flux... 136 xiii

Figure 4.7 Co-spectral densities (CSD) of ambient DMS concentration (a) and MRLA (b) with vertical wind velocity. The ambient DMS concentration data was time lag corrected by 833 ms...... 136

Figure 4.8 Coherence plot (a &b) and phase angle plots (c & d) of ambient DMS concentration (a &c) and MRLA (b &d) with vertical wind velocity. The ambient DMS concentration data was time lag corrected by 833 ms...... 137

Figure 4.9 Normalized co-spectrum density of ambient DMS and vertical wind velocity. The co-spectrum was smoothed to obtain better estimates of the limits. The normalized frequency, n, is based on the original frequency, the boundary layer

height (zi), the aircraft speed (U), and a mixed layer scaling factor...... 138

Figure 4.10 Flight track of marine boundary layer legs of PELTI flight 186rf03 (second St. Croix local) on July 11, 2000...... 142

Figure 4.11 Altitude profile and flight track of marine boundary layer legs of PELTI flight 186rf03 (fourth St. Croix local) on July 16, 2000...... 148

Figure 4.12 Plot of altitude and fractional boundary layer height versus ambient DS flux measured by an airborne isotopic dilution APIMS during flight 185rf05 (July 16, 2000)...... 151

Figure 4.13 7.2 minute segment of flight 186rf02 (July 9. 2000) demonstrating the fast APIMS response to DMS as compared with other meteorological variables.. 155

Figure 4.14 Expanded plot of Figure 4.13 to show increased concentrations of ambient DMS and MRLA with thermals...... 156

Figure 4.15 Altitude profiles for ambient DMS concentration, water vapor mixing ratio (MRLA), potential temperature, and vertical wind velocity gusts for flight 186rf02...... 162

Figure 4.16 Altitude profiles for ambient DMS concentration, water vapor mixing ratio (MRLA), potential temperature, and vertical wind velocity gusts for flight 186rf03...... 162

Figure 4.17 Altitude profiles for ambient DMS concentration, water vapor mixing ratio (MRLA), potential temperature, and vertical wind velocity gusts for flight 186rf04...... 163

Figure 4.18 Altitude profiles for ambient DMS concentration, water vapor mixing ratio (MRLA), potential temperature, and vertical wind velocity gusts for flight 186rf05...... 163

xiv

ABSTRACT Determination of Vertical Fluxes of Sulfur Dioxide and Dimethyl Sulfide in the Remote Marine Atmosphere by Eddy Correlation and an Airborne Isotopic Dilution Atmospheric Pressure Ionization Mass Spectrometer Glenn M. Mitchell Dr. Alan R. Bandy

Vertical fluxes of dimethyl sulfide (DMS) and sulfur dioxide (SO2) were determined

by eddy correlation and an isotopic dilution atmospheric pressure ionization mass

spectrometer (APIMS) on an aircraft platform. The sampling frequency of the isotopic

dilution APIMS ranged from 1 Hz to 25 Hz for real-time measurements. Measurements

were made near the surface in the marine boundary layer to over 6 km in the free

troposphere. The APIMS demonstrated an average sensitivity of 80 cps/pptv for DMS and

SO2 with a signal-to-noise (S/N) ratio > 5. A lower limit of detection of 0.1 pptv in a one

second integration period was also determined for DMS and SO2 in airborne atmospheric

measurements.

Use of the isotopic dilution technique provided an internal calibration of every

ambient sample along with manifold conditioning for rapid and efficient transport of the

ambient species through the manifold. As a result the eddy correlation flux determinations

were accurate, precise, and reproducible.

Laboratory results suggest the addition of ozone in excess of 45 ppbv to the APIMS

- sampling manifold to ensure unaltered formation of SO5 ion (the ion detected for SO2).

Use of a Nafion dryer was warranted for sensitive APIMS detection of SO2 in humid air. A

Nafion dryer along with an air temperature of 400°C was required for sensitive APIMS detection of DMS in humid air. xv

Results from an initial airborne test deployment in continental air include a successful

SO2 intercomparison between the APIMS and a GC/MS/ILS (isotopically labeled standard- gas chromatography/mass spectrometer). Fast SO2 measurements in thin moist layers and pollution plumes demonstrated the utility of the fast isotopic dilution APIMS technique.

Results from a second airborne test deployment in the remote marine boundary layer

include a successful DMS intercomparison and eddy correlation fluxes of DMS and SO2 from isotopic dilution APIMS measurements. The average DMS flux near the surface was

(1.7±0.2)x1013 molecules m-2 s-1. A flux gradient for DMS generated a DMS surface flux of

(2.3±0.7)x1013 molecules m-2 s-1 and a DMS entrainment flux of (7.5±4.4)x1012 molecules m-2 s-1. High resolution altitude profiles of DMS allowed for the determination of the DMS

± ± 12 -2 -1 entrainment velocity (1.9 1.1 cm/s). A SO2 flux of (4.3 1.9)x10 molecules m s and a deposition velocity of 2.4 ± 1.1 mm/s also was determined. All values reported are in good agreement with the literature. 1

CHAPTER 1 – INTRODUCTION

1.1 Research Significance

The sea-to-air transfer of dimethyl sulfide (DMS) from the oceans constitutes the largest flux of biogenic reduced sulfur into the atmosphere.1, 2 Atmospheric DMS originates from the release of dimethylsulfoniopropionate (DMSP) in phytoplankton cells3, 4 in the ocean, which is transformed into DMS. DMS is then outgassed or expelled into the atmosphere through turbulent ocean surface mixing.

DMS is the major precursor of sulfur dioxide (SO2) in the background marine

3, 5 boundary layer. While DMS has been postulated to be a marine source of SO2 through

4 oxidation, and subsequently sulfuric acid (H2SO4) through further oxidation , it is only

6-8 recently that this connection has been demonstrated. Although SO2 could be formed in

9 the free troposphere by DMS oxidation , a majority of the SO2 comes from anthropogenic

10 sources. Because of the eventual impact on the global climate, DMS and SO2 are arguably the most important sulfur gases in the atmosphere.

The importance of atmospheric SO2 and DMS in affecting the global climate has been suggested by Charlson et al. (1987, 1992).4, 11 In their hypothesis, the authors argue that

the oxidation of DMS to SO2 to sulfuric acid (H2SO4) is a natural source of aerosols that could modify climate. Sulfate-containing particles produced can scatter the Sun’s radiation and absorb the Earth’s radiation, ultimately altering the radiation budget of the Earth.

Moreover, these aerosols contribute to the formation of new atmospheric particles that can grow into cloud condensation nuclei (CCN).4 The CCN control the number density and size 2

distribution of cloud droplets, thus indirectly affecting the optical properties of clouds. In

turn, there is a direct effect on the Earth’s radiation budget. The modification of the Earth’s

radiation budget would alter the Earth’s temperature, especially cooling down the

temperature of the upper ocean. This could affect the metabolism of the phytoplankton

producing DMSP, eventually affecting the production of DMS.

The processes that govern the steps to creating sulfate-containing aerosols, and

eventually CCN are poorly understood.12 These processes could be partially resolved by

investigating the atmospheric chemistry of DMS and SO2.

The budget of any species, s, in the atmosphere can be described by a continuity equation13-16:

d < s > = J (s) − J (s) + F(s) − D(s) . Equation 1.1 dt 0 h

d < s > Here is the time rate of change of the column concentration. J (s) is the surface dt 0

flux, Jh(s) is the entrainment flux, F(s) is the column formation rate, and D(s) is the column

destruction rate of s. Terms pertaining to the mean wind in the Equation 1.1 were removed assuming measurement patterns are advected with the mean wind. Budget investigations are usually performed on aircraft platforms where repeated measurements of the same air volume can be made (i.e. Lagrangian).

Direct determinations of the surface and entrainment flux are difficult to achieve.

Because of this fact, the formation and destruction rates, which contain the chemical information, are non-direct and based on estimated parameters.17-19 Creation of a technique 3

to provide high quality direct flux data for DMS and SO2 would allow unique extraction of chemical data from atmospheric measurements.

As demonstrated by Equation 1.1, direct determination of the surface and entrainment fluxes would provide the data needed to complete the atmospheric budgets of

DMS and SO2, as well as directly determine the formation and destruction rates if the time rate of change for those species is provided. In 1989, a report of the Global Tropospheric

Chemistry workshop20 jointly convened by the National Science Foundation (NSF), the

National Aeronautics and Space Administration (NASA), and the National Oceanic and

Atmospheric Administration (NOAA), assigned the following recommendation as the first priority in atmospheric research: “A major limitation to research on surface exchange and flux measurements is the lack of sensitive, reliable, and fast-response chemical species sensors that can be used for eddy correlation [i.e. direct flux] measurement. Therefore, we recommend that continued effort and resources be expended in developing chemical species sensors with the responsiveness and sensitivity required for direct eddy correlation flux measurements.” No known technique is available to determine direct airborne eddy

correlation fluxes of DMS and SO2.

1.2 Thesis Objectives

Presented here is a detailed account of the development, testing, and application of an

instrumental apparatus called the isotopic dilution atmospheric pressure ionization mass

spectrometer (APIMS) to determine the direct atmospheric fluxes of DMS and SO2 from aircraft platforms. The objectives presented in this work were the following:

1) Design and construction of an isotopic dilution APIMS that can be deployed on

aircraft platforms for in situ atmospheric studies

2) Development of methods yielding high APIMS sensitivity for DMS and SO2 under

ambient conditions, particularly moist air environments such as the marine boundary

layer

3) Validate the accuracy of the isotopic dilution APIMS measurements for atmospheric

studies through an airborne intercomparison with a well-proven isotopically labeled

standard-gas chromatography/mass spectrometer (GC/MS/ILS) technique21, 22

≥ 4) Obtain fast airborne measurements ( 1 Hz) of DMS and SO2 in the atmosphere

using the isotopic dilution APIMS to illustrate the utility of the technique

5) Determine the direct eddy correlation fluxes of DMS and SO2 in the atmosphere ≥ through the real-time ( 10 Hz) mixing ratio measurements of DMS and SO2 by an

airborne isotopic dilution APIMS.

1.3 Background

Although significant background and theory is provided in subsequent chapters when

warranted, a brief summary of previous airborne DMS and SO2 measurement techniques in relation to making direct eddy correlation flux determinations is presented here. A brief account of the most common airborne flux determination methods is also presented (details of the eddy correlation method are described in Chapter 4). Hopefully, providing this information will put the work presented here into perspective.

General APIMS theory and background have been thoroughly discussed in two recent Ph.D. dissertations.23, 24 Several recent review articles on APIMS and atmospheric pressure mass spectrometer technology are also available.25-28 Only information pertaining directly to the airborne isotopic dilution APIMS technique is discussed in future chapters, and the reader is encouraged to review the other references for a more general discussion.

1.3.1 Previous Airborne SO2 Measurement Techniques

Most of the prior methods for measuring SO2 in the atmosphere from aircraft platforms were based on sample preconcentration or filter collection with post flight analysis. Those techniques termed continuous usually require long averaging times to be

able to detect low concentration of SO2. Unfortunately, these techniques cannot be used for direct flux determinations since their sampling and processing times are much longer than the minimum 10 Hz sampling frequency requirement for aircraft-based eddy correlation flux studies.14, 29 These methods include the aforementioned GC/MS/ILS technique30, gas chromatography employing flame photometric detection (GC-FPD)31, continuous flame 6 photometric detection32, chemiluminescent analysis (continuous and filter pack)33, filter collection with ion chromatography analysis34, and pulsed fluorescence.35 Recent airborne intercomparisons have been performed for the instruments listed above36-38, and provide immense detail on their sampling and detection abilities. The reader is referred to those publications and references therein for more detail.

Although most of the methods stated above can produce a low limit of detection for

37 SO2 (~30 pptv) , most are plagued by interferences from other sulfur gases, water, aerosols and oxidants. Also, many of the techniques provide no means of correcting for changing instrument sensitivity due to sampling losses. Only the GC based methods have specificity.

Out of all the listed techniques, the GC/MS/ILS technique is the only method providing an internal calibration in every sample through use of the isotopic dilution method, guaranteeing insensitivity to changing instrument response or losses in the sampling manifold.

Airborne mass spectrometric techniques have been developed to obtain fast, sensitive, reliable measurements of many trace gas species.39, 40 The ionization schemes for many of these instruments usually employ ions generated from alpha or beta sources at pressures of less than 100 millibars to near atmospheric pressure. These ionization schemes usually rely on secondary ions resulting from ion-molecule reactions in complicated flow tubes and reactors that are controlled by the judicious use of chemical reagents at concentrations sufficient to dominate the ion chemistry. These techniques are generally referred to as CIMS (chemical ionization mass spectrometry). It should be noted that the

APIMS technique could be considered a related technique to CIMS. However, the creation of ions at atmospheric pressure in a small reaction volume typically without the addition of large amounts of chemical reagents differentiates the APIMS technique from CIMS. 7

41 In 1992, CIMS was investigated for the atmospheric detection of SO2. Creation of

- - the SO5 ion from SO2 is accomplished through various ion-molecule reactions with CO3 ,

- - O3 , and O2 within a flow tube. These same reactions are used in this work for the detection

of SO2 in the atmosphere with the isotopic dilution APIMS (discussed in Chapter 2), but

applied in a simpler way. The CIMS technique has been used to measure SO2 in the upper

41-44 troposphere and stratosphere , obtaining detection limits for SO2 (~ 20 pptv) comparable

to the other airborne SO2 measurement techniques recently mentioned. However, the

sampling time is much shorter (10- 15 seconds) for the CIMS technique. Nevertheless, the

sampling frequency on the CIMS is still inadequate for direct airborne flux determinations by

the eddy correlation method. Moreover, the quantitation by the CIMS technique has been

lacking because of the reliance on the measured or estimated reaction rate constants to

44, 45 determine the SO2 concentration. The CIMS instrument has recently been used over the

Indian Ocean during the INDOEX (Indian Ocean Experiment) campaign46 for the detection

of SO2. A detailed report has not yet been published.

1.3.2 Previous Airborne DMS Measurement Techniques

Similar to SO2 measurements, atmospheric DMS measurements have also been restricted to preconcentration methods analyzed in flight or post flight. This is primarily due to low atmospheric concentrations and the irreversible adsorption of DMS on many analytical systems. Preconcentration of ambient DMS on gold wool with post flight analysis by thermal desorption GC-FPD 2 to 24 hours after collection is common in many airborne studies.5, 34, 47-50 These various gold wool adsorption techniques differ primarily in the oxidant

scrubbers employed upstream of the preconcentration area to prevent destruction of DMS 8

adsorbed on the gold. The lower limit of detection for this type of preconcentration

technique is < 2 pptv DMS.51 Preconcentration of DMS in Teflon tubing submersed in

liquid argon52, 53 is also possible, as long as the Teflon tubing containing the sample is sealed

and preferably cryogenically stored before analysis. Due to the nature of these techniques,

direct airborne eddy correlation flux determinations are not possible. Moreover, delayed

analysis of the collected samples can result in significant loss of precision and accuracy in the

DMS measurement.

In-flight analysis of cryogenic preconcentrated ambient DMS samples is typically

performed by a GC method. These techniques include the GC/MS/ILS technique54

developed in our laboratory, GC with fluorination-electron capture detection55, and GC with

flame photometric detection.56 Loss of temporal resolution due to sampling times (> 1

minute) and chromatographic processing time (> 5 minutes) prevent the use of these

techniques for eddy correlation flux determinations. These in-flight GC-based techniques

typically have a lower limit of detection of ≈ 5 pptv DMS.51 Again, the GC/MS/ILS technique is the only method providing an internal calibration with every sample.

An airborne intercomparison of the techniques stated above has been reported in the literature.51 Again, detailed information on the sampling and detection abilities of the

airborne DMS measurement techniques is provided and the reader is referred to that

publication and references therein for more detail.

Airborne measurements of DMS by atmospheric pressure mass spectrometers have

been recently reported in the literature. Spicer et al. (1996)57 used a benzene additive with a

corona discharge source for the atmospheric pressure chemical ionization mass spectrometry

(APCIMS)58 detection of ambient DMS. They report a lower limit of detection of 2 pptv

DMS in dry air at a sampling frequency of 1 Hz. Calibration of their instrument was 9

periodically checked in flight by standard addition. However, the sensitivity of the APCIMS

was so significantly reduced in the presence of large ambient water vapor concentrations that

it was not useful under these conditions.

Use of water as a chemical ionization reagent in an APIMS type instrument has also

been reported for the determination of ambient DMS. Crutzen et al. (2000)59 and Williams

et al. (2001)60 report measuring ambient DMS using an airborne proton transfer reaction

mass spectrometer (PTR-MS)61 over the tropical forests of Surinam. Ionization of DMS was achieved through proton transfer from interactions with protonated water or water clusters62

(this is the same type of ion chemistry used in the isotopic dilution APIMS discussed in

Chapter 2). Although most of the measurements were in mass scanning mode lasting tens

of seconds, it was reported by Crutzen et al. that single ions could be monitored at a 2 Hz

sampling frequency. However, quantitation by the PTR-MS technique is questionable

because of the reliance on the reaction rate constants to determine the DMS concentration.

Moreover, effects of large ambient water vapor concentrations were not discussed.

Both APCIMS and PTR-MS show promising capabilities for measuring low-level

concentrations of ambient DMS. However, the sampling frequencies for both techniques

preclude the use of direct eddy correlation methods for determining the flux of DMS.

Moreover, periodic instrument calibrations or reliance on reaction rates for determination of

ambient DMS concentrations is not suitable for APIMS type measurements in the marine

boundary layer, especially since it has been demonstrated that the APIMS sensitivity is

significantly affected by changing water vapor concentrations.24, 58

1.3.3 Methods for Determining Airborne Trace Gas Fluxes

Many excellent reviews20, 29, 63-69 exist in the literature describing numerous direct and indirect methods for determining trace gas fluxes from tower and aircraft platforms. The brief discussion presented here is tailored to only the most common airborne flux determination methods, namely eddy correlation, eddy accumulation, and mixed-layer gradient and variance techniques, and is a summary of the aforementioned reviews. Again, a more thorough discussion of the eddy correlation technique is presented in Chapter 4.

The eddy correlation (covariance) technique is the most direct flux determination method. The name of the technique is derived from the fact that fluxes are determined directly from the measurements of the covariance of a scalar, s, (such as chemical concentration or mixing ratio) with vertical wind velocity, w, within the turbulent motion (i.e. eddies) of the atmosphere. The instantaneous flux, F(t),of s at time t can be written as:

F(t) = w(t)s(t) . Equation 1.2

The average flux over a sampling period is given by the time average:

F = ws . Equation 1.3

Equation 1.3 can be broken into its mean and fluctuating parts:

F = ws + w's' . Equation 1.4

If horizontal homogeneity in the mean flux is assumed, the flux over a flat surface would be

F = w's' . Equation 1.5

As it can be seen in Equation 1.5, the eddy correlation flux is determined from the time average of the fluctuations of the scalar (s’) and vertical wind velocity (w’) caused by eddies in the atmosphere. A meaningful flux average requires sufficiently long sampling periods in order to obtain robust statistics.70, 71

The eddy correlation method for airborne flux determinations places three critical constraints on the chemical measurement instrumentation for the scalar: fast sampling rate, specificity to the species of interest, and sufficient sensitivity.14, 29 For aircraft measurements, an instrument must respond fast enough to measure the changes in analyte concentration caused by the smallest eddies that transport the scalar. Typically, this value is ≈ 0.1 seconds.

Because of the fast response needed, very few compounds have had fluxes determined by aircraft with the eddy correlation method.20 However, recent ground-based mass spectrometric eddy correlation flux determinations of some volatile organic compounds at 8

Hz72 and 10 Hz73 show promise for the use of mass spectrometry in airborne eddy correlation flux determinations.

A variation of the eddy correlation method is the eddy accumulation technique.74 This technique is used when fast response sensors for scalars are not available. Also known as a conditional sampling technique, the method is based on collecting samples of the scalar of interest into reservoirs determined by some property of the vertical velocity. For eddy accumulation, the air is collected into two reservoirs—one for w’>0 and the other when w’<0. The rate at which samples are collected into the reservoirs is directly related to the 12 magnitude of w. The flux of the scalar is then the difference in mass collected in each reservoir divided by the collection time. Fast response measurements of the scalar are not required, but fast control of the flow into the reservoirs is required.

A simplification of the eddy accumulation technique, called the relaxed eddy accumulation technique75, collects the scalar sample at the same rate in each reservoir depending only on the sign of w. The flux is then given by the product of the standard deviation of the vertical velocity, the difference in mass in the reservoirs, and a scaling factor dependent on the probability distribution of the vertical velocity. Relaxed eddy accumulation methods are beginning to be used on aircraft, but mostly remain untested for such applications.76-78 Although this method has promise, there are numerous difficulties in its employment.79

There is some skepticism of this direct eddy accumulation flux method based on some of the problems associated with the technique. Differences in the mean concentration between two reservoirs are typically very small. Therefore, storage and handling problems are concerns when dealing with the reservoirs. Moreover, fast control of flow rates is non- trivial, and the required speed, accuracy, and range needed for this technique is difficult.

The mixed-layer gradient and variance flux determination techniques are not direct methods, but are empirically based on top down/bottom up80 mixed-layer scaling parameters determined from large-eddy simulation.81, 82 For fluxes from mixed-layer gradients, concentration differences of a scalar from a minimum of three altitude levels can be used in conjunction with scaling parameters to estimate the surface and entrainment fluxes of the scalar in the boundary layer. Real-time measurements of the scalar concentration are not necessary with mixed-layer gradient flux estimation. Similarly, the variances of the scalars can be used with the mixed-layer scaling parameters to estimate surface and entrainment 13 fluxes by the mixed-layer variance technique. Both methods have recently been applied to trace gas fluxes, including DMS, in the marine boundary layer over the equatorial Pacific

Ocean.83

1.4 Thesis Structure

The chapters in this thesis are logically divided into laboratory development and airborne field deployments of the isotopic dilution APIMS instrument.

Chapter 2 presents a detailed account of the instrumentation and components used to

create an isotopic dilution APIMS for the measurement of DMS and SO2 in the atmosphere on aircraft platforms. Original instrument control and data acquisition programs are also discussed. Results from laboratory experiments designed to develop methods for increased

isotopic dilution APIMS sensitivity for DMS and SO2 in simulated marine boundary layer atmospheres are also presented.

Chapter 3 presents the results of the first airborne test deployment in continental air of the isotopic dilution APIMS in November 1999. Results from an airborne

intercomparison for SO2 with the GC/MS/ILS are discussed. Also, the utility of the fast measurement capability of the isotopic dilution APIMS is illustrated for SO2 in various ambient air situations. A short flight in marine boundary layer air provided useful information for a future airborne test deployment over open ocean. Finally, an initial data collection close to 10 Hz established the feasibility of eddy correlation flux determinations with the isotopic dilution APIMS. 14

Chapter 4 presents the results from the second airborne test deployment over open

ocean East of St. Croix (U. S. Virgin Islands) in July 2000. The measurement accuracy of the

isotopic dilution APIMS is again validated through an airborne intercomparison with the

GC/MS/ILS, this time for DMS. The first ever results of the direct airborne eddy

correlation flux determination of DMS and SO2 in the marine boundary layer are also presented in this chapter using the real-time mixing ratio capabilities of the isotopic dilution

APIMS. A flux gradient was also measured for DMS in the marine boundary layer, allowing for the direct determination of the surface flux, entrainment flux, and entrainment velocity

of DMS. An SO2 deposition velocity over open ocean was also calculated from the

measured eddy correlation flux.

The thesis results are summarized in Chapter 5. Future research and suggestions are

also presented in this chapter. The appendix contains information cited in the various thesis

chapters. Appendix A contains abbreviations used throughout this work. Appendix B

presents the original LabVIEW programs written for instrument control and data

acquisition. Appendix C contains the panel drawings and circuit diagrams for the fast

counting system developed for > 10 Hz data acquisition. Appendix D contains the original

Matlab m-files used in the data reduction to calculate the eddy correlation from the raw

aircraft flight data. The appendices are provided as a reference to the reader.

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CHAPTER 2 – AIRBORNE ISOTOPIC DILUTION ATMOSPHERIC PRESSURE IONIZATION MASS SPECTROMETER INSTRUMENTAL DESIGN AND LABORATORY EXPERIMETATION

2.1 Introduction

This chapter presents the method development for the airborne isotopic dilution atmospheric pressure ionization mass spectrometer (APIMS) used to investigate the direct

eddy correlation flux determinations of dimethyl sulfide (DMS) and sulfur dioxide (SO2) in the atmosphere. The chapter is divided into two sections. The first section describes the isotopic dilution APIMS instrumental design and operation as applied to an aircraft platform for airborne studies. The second section presents the results of laboratory experiments performed during the course of this dissertation work. The experiments performed were primarily concerned with obtaining the best possible response for the APIMS instrument for

DMS and SO2 in conditions similar to real atmospheric scenarios as encountered on aircraft.

Thus, the laboratory experiments were both in preparation for airborne field tests and as a response to the results of those field tests.

2.2 Airborne Isotopic Dilution APIMS Instrumental Design and Operation

The use of an isotopic dilution APIMS is not a new technique for our laboratory. Xu

(1999)1, in her Ph.D. dissertation, describes the use of an APIMS instrument for laboratory

and ground-based measurements of DMS and SO2, as well as other gaseous sulfur species.

Although the instrument performed well for these types of measurements, the instrumental 22 design and components were not suitable for use on an aircraft platform for direct airborne studies.

In that respect, the APIMS instrument presented in this dissertation work is a second- generation APIMS instrument. The APIMS instrument was designed and constructed with the sole purpose of deploying the instrument on research aircraft for airborne measurements, which is entirely different than instruments used solely for ground-based or laboratory measurements. The specifications and quality of instrumental components, structure, and design are more stringent due to the to the safety regulations of deploying such instrumentation on aircraft. Moreover, the ever-changing experimental conditions during airborne studies, such as pressure, humidity, and mass flow, presented new obstacles to overcome in order for the APIMS instrument to become a robust aircraft-deployable instrument. To build an APIMS instrument of this caliber, most of the original APIMS design was either re-engineered or rebuilt, or was entirely new. The information presented in following sections details the design and operation of an isotopic dilution APIMS instrument specifically for aircraft deployment.

2.2.1 General Instrumental Schematics and Layout

Before proceeding directly to the discussion of the airborne isotopic dilution APIMS components and operation, a brief presentation of the instrumental schematics and aircraft layout is needed to facilitate these future discussions. Figure 2.1 and 2.2 show a schematic representation and a CAD drawing of the airborne APIMS design, respectively. Dr. Donald

C. Thornton of our research group created the drawings shown in those figures. The CAD drawing in Figure 2.2 presents proportional scales for the components shown, while the 23 illustrative schematic of Figure 2.1 does not show proportionate scales and is slightly exaggerated in certain components, particularly the API source, skimmers, and ion optics.

For this reason, Figure 2.1 will mostly be referenced in instrumental discussions.

Air Inlet (b)

(n) (o) Air Exhaust

Figure 2.1 Schematic illustration of the airborne isotopic dilution APIMS. Label descriptions: (a) API source region; (b) 63Ni ionization source; (c) aperture skimmer; (d) Decluster skimmer; (e) API ion energy skimmer; (f) ion optics lens 1 (L1); (g) ion optics lens 2 (L2); (h) ion optics lens 3 (L3); (i) entrance lens; (j) Prefilter; (k) quadrupoles; (l) exit lens; (m) ion multiplier; (n) decluster region rough pump; (o) turbo pumps.

Focusing Quadrupole Electron Multiplier Lenses Mass Filter Detector API Declustering Lenses

63Ni Source

Differential Turbomolecular Chamber Pumps

Figure 2.2 Proportional scale CAD drawing (cut away side view) of the airborne isotopic dilution APIMS.

An aluminum aircraft rack built by the National Center for Atmospheric Research

(NCAR, Boulder, CO) was used to house the APIMS chamber and its ancillary components.

The rack is specifically constructed for use on the NCAR C-130 research aircraft. The rack had two bays (51.5” x 17.625” x 25”) with pre-drilled holes consistently spaced (1/4”-5/8”–

5/8”-1/2” series) for standard panel fronts for aircraft component boxes. The APIMS aluminum aircraft component boxes (Techmar Omniclosure, Marina Del Rey, CA) were secured to the rack with standard aircraft hardware (Genuine Aircraft Hardware Co., Paso

Robles, CA; Aero-Missile Components, Inc., Bensalem, PA), with aluminum rails supporting the heavier boxes. Miscellaneous hardware was obtained from McMaster-Carr Supply

Company (Dayton, NJ). Care was taken to ensure proper balance/center of mass requirements when arranging the APIMS instrument components, chamber, and pumps.

Caster wheels on the rack enabled the racked APIMS instrument to be mobile, but were 25 removed for aircraft installation. Figure 2.3 presents the aircraft rack layout of the APIMS used during the airborne studies discussed in Chapters 3 and 4.

NCAR C-130 Aircraft Rack NCAR C-130 Aircraft Rack

Power Distribution Filament Nafion Dryer Lens Control Computer Ozone Generator

Turbo Fast Data Control Acquisition Mass Flow Control Turbo Turbo Gas Cylinder Braces Control Control

Quadrupole RF DC Power

APIMS Chamber Keyboard

Turbo Pumps Fans

Gas Cylinder Braces Rough Pumps Fans UPS

Front Back

Figure 2.3 Aircraft rack layout of the isotopic dilution APIMS.

2.2.2 APIMS Chamber Design and Vacuum Pumps

The chamber design for the airborne isotopic dilution APIMS is based on a design of a differentially pumped quadrupole mass spectrometer (Dr. Donald Thornton). The chamber was manufactured out of thin-walled stainless steel by A & N Corporation

(Williston, FL) from the CAD design shown in Figure 2.2. Hardware for flange attachments to the chamber was obtained from McMaster-Carr Supply Company. The initial high vacuum regime (source region) in direct contact to the low vacuum API declustering section, which also contains the focusing ion optics, and was maintained at a stagnation pressure of

5x10-5 Torr. The source region of the vacuum chamber is evacuated by two Alcatel (Annecy,

France) ATP 900 turbomolecular pumps controlled by two Alcatel ACT 1000T turbo

controllers. The pumping speed for each of these pumps was 900 L/s for N2 buffer gas.

The high vacuum region containing the quadrupole mass analyzer and ion multiplier detector (analyzer region) is typically held at a stagnation pressure of 7x10-6 Torr. The analyzer region is evacuated with one Varian (Leini, Italy) Turbo-V 550 turbomolecular pump controlled by a Varian Turbo-V 550 controller. A typical pumping speed of 550 L/s

in N2 was obtainable with this pump. All turbomolecular pumps were backed with an

Edwards (West Sussex, UK) EM218 rotary (roughing) pump having a 6.9 L/s pumping speed. Cold cathode ionization vacuum sensors (MKS Series 903, MKS Instruments,

Boulder, CO) monitored the chamber pressure in both the source and analyzer regions. A

MKS Series 907 Pirani gauge monitored the pressure in the fore line to the rough pump.

2.2.3 63Ni Ionization Source and Atmospheric Pressure Declustering Region

The atmospheric pressure ionization (API) source for the APIMS is similar to the design of an electron capture detector2, 3 used in gas chromatography (i.e. Horning et al.4-8 and Grimsrud et al.9). The APIMS source contained a nickel-alloy foil coated with 10 millicuries of 63Ni (Nicomed Amersham, Chicago, IL). The foil was approximately 1 X 3 cm, and was coiled to form a cylinder of 0.24 cm3 volume. The source was designed to provide 1 – 10 ms transit times to the aperture of the declustering region. The foil was placed inside a stainless steel housing, which was directly attached to the API declustering lens flange by stainless steel screws. A Viton o-ring sealed the connection between the API source housing and the vacuum flange. The 63Ni was retained in place by a stainless steel

Swagelok® (Solon, OH) fitting that was connected to the air source to be analyzed. Air inside the source volume was in constant interaction with the radioactive foil, where it was ionized by the spontaneous β particles from the decay of the foil. A detailed description of the ionization chemistry is addressed in a future section. The construction of the API source is shown in Figure 2.4 (Dr. Donald C. Thornton).

The API source housing was heated by two Omega cartridge heaters (Chromalox

CLR-1013, Stamford, CT) to a temperature of 150 - 180°C, and was controlled by a K-type thermocouple feedback using a CN132 Omega temperature controller. The temperature controller was part of the power distribution system, which is discussed in a subsequent section. The exterior of the heated ion source was insulated with fiberglass insulation.

Heating of the ion source helps to reduced clustering before entering the declustering section.10 There were no adverse effects on the 63Ni foil through heating.11

Air Out 63Ni

Aperture Skimmer/Lens Air In

Figure 2.4 CAD drawing of the isotopic dilution APIMS 63Ni ionization source.

Ions formed in the API source traveled into the declustering section (ABB Extrel,

Model 813302, Pittsburgh, PA), portrayed by Figure 2.1 parts (c)-(e). A portion of the gas entering the ion source was removed before it encountered the aperture. However, 600 -

800 mL/min (depending on aperture orifice diameter and temperature) of the ionized gas passed through the 275 µm diameter aperture orifice into the declustering region. Ions and residual gas were drawn into the declustering section by viscous flow as well as the electric field generated by aperture skimmer. The aperture had an applied potential and acts as an attracting lens for the ions. The ambient pressure gas exiting the aperture forms a sonic expansion into the declustering region at 1 – 7 Torr (a detailed description of this expansion process can be found in Zook (1990)10 and references therein). In the declustering region, ion clusters go through the collision induced dissociation process that removes weakly 29

bound neutral molecules, such as H2O. The pressure in the declustering region is manually controlled with a ball valve attached to an Edwards EM218 rough pump and measured with a MKS Series 907 Pirani gauge. The sonic jet produced behind the aperture skimmer is sampled by a 1 mm diameter decluster skimmer/lens. As the ions become focused and accelerated from the potential on the declustering lens, a further reduction in background gas load occurs by sampling the gas stream through the 250 µm diameter orifice API ion energy skimmer/lens. The ions in the sample stream obtain most of their kinetic energy from the potential applied to this final API lens prior to entering the high vacuum regime of the ion optics and mass analyzer. The voltage control for the API lenses is discussed in the next section.

2.2.4 Ion Optics, Mass Analyzer, and APIMS Tuning

2.2.4.1 Focusing and Acceleration Lenses (Ion Optics)

The focusing ion optics (Figure 2.1 (f)-(h)) for the airborne isotopic dilution APIMS was purchased from ABB Extrel. This region of the APIMS is termed the source region and is the first high vacuum section of the APIMS. The lenses in this region focus the ion beam

(now relatively free of fluid effects from the buffer gas) into the mass analyzer. Lens 1 (L1) is a cylindrical mesh basket 0.92” long and 0.38” in diameter. The mesh grid is fine so that the cylinder appears solid to the ions. L1 is attached to a solid 3/64” thick stainless steel plate with an outer diameter of approximately 2”. The entrance orifice diameter into the remaining electrostatic lenses is 0.12”. Lens 2 (L2) and lens 3 (L3) are flat electrostatic lenses 30 of similar size to the base stainless steel plate of lens 1. The orifice diameter for both plates was 0.25”. The separation distance between the lens plates was approximately 1/32”.

Following the focusing lenses was another set of flat electrostatic lenses that determine the energy of the ions entering the quadrupole mass analyzer. These lenses are shown in Figure 2.1 (i) and (j). The entrance lens (i) is similar in size, shape, and separation distance to L2 and L3, but is directly attached to the quadrupole mass analyzer housing. The prefilter lens (j) was also similar in size and shape to L2 and L3, but is located within the quadrupole housing. An exiting flat electrostatic lens (Figure 2.1 (l)) further focused the mass filtered ion beam so that ion beam passed through the exit aperture of the quadrupole housing. Electrical connections to the lenses (including API declustering lenses) were made using a ceramic sealed QF-25 type electrical feedthrough (A & N Corporation) in the source region of the vacuum chamber.

Control for the API declustering lenses, focusing lenses, acceleration lenses, and quadrupole pole bias was accomplished using a commercial voltage control system (Model

TD 10200, Spectrum Solutions, Inc., Russelton, PA). Ten ± 200 V power supplies controlled all of the lens potentials. Two rows of precision potentiometers (one row for positive ions, the other row for negative ions) provided set points for these potentials.

2.2.4.2 Quadrupole Mass Analyzer

The quadrupole mass analyzer also was purchased from ABB Extrel. The quadrupoles were ¾” cylindrical rods with a 0 - 300 amu mass range capable of better than 1 amu mass resolution. The mass filter was powered by a 1.2 MHz, 300 W rf supply (ABB

Extrel, Model 150 QC). The quadrupole mass analyzer resolves the focused ion beam either 31

by means of a mass sweep over a given range or through selected ion monitoring by means

of manual tuning in either positive or negative ion mode.

2.2.4.3 APIMS Tuning

Initial tuning and testing of the isotopic dilution APIMS were performed using

electron impact ionization. The filament was controlled by a commercial filament control

system (Model FPS-1, Spectrum Solutions, Inc.). The filament and control system was a

permanent part of the airborne APIMS and was used to diagnose instrument problems.

Initial tuning using the 63Ni ionization source was accomplished using the SIMION ion optics modeling program (SIMION 3-D Version 6, David A. Dahl, Idaho National

Engineering Laboratory, Idaho Fall, ID). A precise model of the API declustering, focusing, and entrance lenses was constructed in SIMION. Figure 2.5 is a three-dimensional rendering of the constructed ion optics in SIMION. Using predicted ion energies produced from the 63Ni source allowed for an estimation of the potentials that needed to be applied to the various lenses for proper focusing and acceleration into the quadrupole mass analyzer.

Although SIMION neglects contributions from gas dynamics, the potential estimates produced were within 20% of the final potential values currently used.

Tuning and optimization of the isotopic dilution APIMS was checked on a daily

basis since the tuning and sensitivity depended highly on the potentials applied to the various

lenses, the pressures inside the declustering region and chamber, and the gas flow rate into

the source. Lens tuning voltages are presented within the experimental descriptions of

subsequent chapters for airborne field tests of the isotopic dilution APIMS.

An interesting effect of the decluster pressure on the sensitivity and tuning of the

APIMS was noticed routinely and should be mentioned. There seems to exist multiple high 32

ion throughput regions when adjusting the decluster pressure setting. These high sensitivity

regions were stable and reproducible, and were used during experimentation with the

APIMS, including airborne studies. Although this effect has not been thoroughly

investigated, it is believed to be related to the Mach disc location10 in the sonic expansion in

the declustering region. Xu (1999)1 also noticed such an effect with decluster pressure on

APIMS sensitivity and briefly discusses this observation.

API Ion Decluster Energy L2 L3 Entrance

Aperture L1

Figure 2.5 SIMION 3-D rendering of APIMS declustering, focusing, and entrance lenses.

2.2.5 Ion Signal Detection

The isotopic dilution APIMS utilized a continuous dynode electron multiplier detector (Model 402 A-H, DeTech, Palmer, MA). This detector was positioned on axis to the ion beam from the mass analyzer and was operated in pulse counting mode by means of a pulse amplifier (i.e. preamplifier) (Model MTS-100, Advanced Research Instruments

Corporation, Boulder, CO) coupled to the detector. The signal from the amplifier was sent to the APIMS data acquisition system, which is discussed in a future section of this chapter.

The DC high voltage for the electron multiplier was obtained from two 5 kV power supplies

(one positive, one negative) inside the TD 10200 Spectrum Solutions lens control system.

The high voltage power supplies could be programmed for the detection of either positive or negative ions. The high voltage DC power supplies were operated at a sufficiently high bias for a plateaued ion counting response. The discriminator voltage on the counting preamplifier was set to eliminate most of the background counting noise from the detector.

Modifications were made to the original detector housing and flange stand-offs on the quadrupole mass analyzer assembly to extend the entire assembly for use in the vacuum chamber of the APIMS. No adverse effect on the detectability of ion signals was noticed from this modification.

2.2.6 Power Distribution

A power distribution system meeting aircraft safety standards was developed to accommodate the power requirements necessary for the operation of the isotopic dilution

APIMS. The major AC power requirements for instrument operation are the two Edwards 34 roughing pumps and the metal bellows manifold pump (Model MB-601, Parker Hannifin

Corp., Sharon. MA). Each pump had its own power source either from a split three-phase

400 Hz line or three independent 60 Hz lines by modifying jumper locations inside the power distribution box. For laboratory and aircraft use, the 60 Hz line choice was routinely used for the pumps. The remaining 120 V/ 60 Hz requirements for the instrument were satisfied using another 60 Hz power line. In total, the airborne APIMS used four 120 V / 60

Hz power lines for normal operation.

Additional features of the APIMS power distribution system are two Omega CN132 temperature controllers for controlling the API source temperature and inlet heater temperature (later replaced by the sample air heater temperature discussed in this chapter).

Hourly rate meters were also supplied on the power distribution system as a gauge for determining when to replace the roughing pump oil and other maintenance procedures. For those items requiring low DC voltage, an ABB Extrel Model U-1272 DC power supply was also employed. A battery operated uninterruptible power supply (Model UPS 400, American

Power Conversion, West Kingston, RI) powered critical components, such as the mass flow control system, data acquisition computer, and turbomolecular controllers, during periods of power loss or unstable aircraft power.

2.2.7 Mass Flow Control

A mass flow control system (i.e. flow box) was constructed for the airborne APIMS to serve as a centralized area for flow control and mass spectrometer enable (MS on) on the instrument. A dual readout was designed to display the command and response signals of the mass flow controllers as well as pressure responses on the cold cathode and Pirani 35 gauges. These responses were connected to a terminal board located inside the flow box for acquisition of the flow and pressure signals, which is discussed in the next section. The TTL signal from the pulse counting detector/preamplifier was also brought into the flow box and wired to the data acquisition terminal board. An independent ± 15 V DC power supply was used in the flow box to supply power to the mass flow controllers and meter, as well as the cold cathode and Pirani pressure gauges. The cold cathode pressure gauges are enabled through a toggle switch on the front panel of the flow box. The mass spectrometer interlock (MS on) is also located on the front panel and can either use the source or analyzer cold cathode set-point to enable operation of the quadrupole rf supply and high voltage supplies on the APIMS.

There are four mass flow controllers and one mass flow meter in the mass flow control box, all manufactured by Unit Instruments, Inc. (Yorba Linda, CA). The mass flow controller command voltages were set by precision potentiometers mounted on the front panel of the box. Each flow controller had a stainless steel Swagelok toggle valve to quickly stop the flow from the controller and to determine zero readings for the controllers. The manifold flow was measured by a mass flow meter and was controlled by a ball valve during aircraft operation. The manifold pressure was controlled manually using a two-way valve or dynamically through the use of an absolute pressure sensor and voltage controlled value

(Series 145 Vacuum Sentry, MKS Instruments). The mass flow controllers and meter were calibrated before and after each airborne field test using a NIST traceable flow calibrator and flow cells (Drycal DC-2M, Bios International Corporation, Pompton Plains, NJ). Table 2.1 is a list of the Unit mass flow controllers/meter used in the APIMS mass flow control system and the latest flow calibration slopes. The zero reading voltages are subtracted from the flow signal voltage before determining the flow value. The mass flow controllers/meter 36 were calibrated with air and corrections were applied to the standard gas mass flow controllers.

Table 2.1 Mass flow controller and meter specifications and calibration constants for airborne isotopic dilution APIMS.

Calibration Slope Name Model # Maximum Flow (Signal Voltage/Standard Flow)

API (source flow) UFC-8100 10 SLM (Air) 0.4994

Manifold UFM-3100 80 SLM (Air) 0.0619

ZA (zero or ultrapure air) UFC-3050A 80 SLM (Air) 0.0611

Cal 1 (SO2 standard) UFC-8100 100 SCCM (He) 0.0495

Cal 2 (DMS standard) UFC-8100 100 SCCM (N2) 0.0497

2.2.8 APIMS Data Acquisition

2.2.8.1 Computer Hardware and Software

Data acquisition was performed using data acquisition cards installed in a computer system suitable for aircraft rack use. The computer was built using a standard Pentium II class motherboard containing 128 MB RAM. Features such as a floppy, ZIP, and CDrom drive, as well as parallel, serial and USB ports were included in the system. The operating system on the APIMS computer was Windows NT 4.0 Workstation (Microsoft, Seattle,

WA). All data acquisition was accomplished using programs based in LabVIEW (Version 37

5.1, National Instruments, Austin, TX), which was run in real-time mode under Windows

NT.

2.2.8.2 Data Acquisition Boards

Data acquisition was accomplished through the use of two data acquisition boards.

An AT-MIO-16DE-10 (National Instruments) board was used for all analog input, digital input/output, and event counting and timing (such as the TTL pulses from the detector/preamplifier). This board featured 16 single ended 12-bit analog input channels

(A/D), four 8-bit digital input/output channels (DIO), and two 12-bit digital-to-analog

(D/A) channels. Two 24-bit up/down counters/timers are available with 20 MHz and 100 kHz base clock frequencies of 0.01% accuracy and were used for event counting and timing.

The CIO-DAC 02/16 (Computer Boards, Inc., Middleboro, MA) board having two 16-bit

D/A channels was used to provide the mass command signal to the quadrupole 150-QC rf supply unit.

The accuracy and precision of the counting/timing control of the AT-MIO-16DE-

10 card was tested for both the 20 MHz and 100 kHz onboard clocks using a 20 kHz signal generated by a BK Precision 2MHz sweep/function generator (Model 3002) and verified via a Heath-Schlumberger counter (Model SM-4100). It was concluded that the 100 kHz clock was suitable for signal integration times from 100 ms and greater, while the 20 MHz was suitable for integration times below 850 ms. This is in agreement with the predicted overrun time for the 20 MHz clock based on the resolution, namely 224/20 MHz which was approximately 840 ms. For most studies, the 20 MHz clock was utilized.

2.2.8.3 Data Acquisition Programs (VIs)

To operate the airborne APIMS instrument and instrumental control systems, various LabVIEW programs (called a VI) were designed and compiled. A LabVIEW VI program was written to log mass flow rate and pressure command and response signals

(FLOW_DATA VI), as well as control two analog output signals (for pressure control) and four digital input/output controls (for mass flow control). The pressure and mass flow control VIs were never implemented. Mass spectrum scanning (or sweep)

(MS_Spectrum_Scanning VI) and selected ion mode scanning (time series) (SIM VI) were based on existing low data rate VIs (Tim Wade). These programs were modified to provide more accurate and precise counting and timing in fast data acquisition applications. Both programs incorporate time stamping routines and data logging capabilities. All VIs were programmed to use 10% or less of the CPU time and have accurate timing. Multiple VIs could run simultaneously without degrading timing accuracy. The following sections briefly describe the three main VIs used for data acquisition.

2.2.8.3.1 MS_SPECTRUM_SCANNING VI

Figures B.1 (a) and (b) in Appendix B presents the front panel (user interface to the program) and wiring diagram (programming code) for the most recent revision of the mass spectrum scanning VI. This VI uses the AT-MIO-16DE-10 data acquisition board and performed the event counting and timing of the incoming TTL pulses from the detector/preamplifier based on the various user inputs. The user can specify the number of total mass sweeps (number of scans), the mass scan range, the mass resolution (bins per amu), the integration time per bin, and the clock frequency. These control data were written as header information in the ASCII data file generated by this VI. 39

The ion signal data acquisition was accomplished by a set of two FOR loops. The number of iterations in the first FOR loop is controlled by the number of scans selected on the front panel. This loop determines the total number of complete mass scans that are collected. The number of masses to scan, the number of bins per amu, and the integration time, governs the second FOR loop iteration values. Inside of this FOR loop is a sequence that starts by sending values from a mass scan range array to the CIO-DAC 02/16 D/A channel that provides a voltage to the quadrupoles for the appropriate mass amu (or fractional amu). The second part of the sequence is a 1 ms wait time for settling of the quadrupole voltage. The final part of the sequence is the simultaneous starting of the event and timing counters. The event counter counts the number of TTL pulses from the detector/preamplifier from the mass that is currently being allowed through the quadrupoles, and the timing counter counts the number of cycles from the base clock frequency during the mass integration time. When the integration time is complete, the event counts are divided by the timing counts and multiplied by the base clock frequency to obtain the counts per second (cps) for the current mass value. This information is stored in a memory indexing array until the FOR loop iterations are complete. This sequence repeats for all masses in the mass scan range. Once all masses have been scanned, and their ion signals counted, the memory indexing array is written to file as well as displayed as a mass spectrum scan on the front panel. This initiates the next iteration of the initial FOR loop.

The displayed mass spectrum can either be refreshed each FOR loop iteration or a summation of the ion signal intensity for each scan. New data is continually appended to the same data file since only one ASCII data file is created per complete program execution.

This program continues until all of the total scans are complete or the user stops the program. Incomplete scans due to stopping the program prematurely are not written to file. 40

The mass spectrum scanning VI is primarily used for tuning the APIMS. This VI can write a calibration file, which is basically a look-up table used in SIM mode. The user can build a master calibration file from smaller mass scan ranges to eliminate the effect of the piecewise linear nature of the quadrupole mass filter. The calibration offset input on the front panel assists in obtaining the best (i.e. centered) mass calibration for the specific mass range. The mass spectrum scanning VI is also useful for exploratory analysis.

2.2.8.3.2 SIM VI

Figures B.2 (a) and (b) in Appendix B presents the front panel and wiring diagram for the most recent version of selected ion monitoring VI. This VI has the same basic operation of the MS_Scanning_Mode VI with respect to the event counting and timing (AT-

MIO-16DE-10 board) and mass command voltage control (CIO DAC 02/16 board). What is different is that a table of specific masses (masses to scan) is used in place of a mass range.

The number of masses is not limited; however, more masses create an overall slower sampling frequency per amu. The calibration file must contain the masses entered into the table. Fractional mass values can be used if the calibration file was created having the same resolution. A mass value entered without a corresponding calibration value will result in null value for the ion signal. Again, the ion signal is integrated over the mass integration time user input value is displayed and written to file as counts per second for the mass of interest.

Only one data file is created and is continually appended with new information. The user input information is again placed as header information in the ASCII data files. The delay time is used as a settling time on the quadrupole if the masses in the table span a wide range.

This value can be set to zero ms if only one mass is in the masses to scan table. 41

Operational experience demonstrated that the minimum integration time that can be used for one mass is 100 ms (10 Hz sampling frequency, 5 Hz Nyquist frequency). This is sufficient for fast measurements using the airborne isotopic dilutions APIMS, but not for real-time direct eddy correlation flux determinations using the APIMS. A FAST_SIM VI developed to obtain real-time ion signals is discussed in Chapter 4.

2.2.8.3.3 FLOW_DATA VI

The mass flow command and signal voltage values for the mass flow controllers and meter, pressure gauge signal voltage values, and TTL pulses from the detector/preamplifier are wired to a National Instruments SCB-100 terminal block inside the mass flow control box. Output from the terminal block to the AT-MIO-16DE-10 data acquisition board is transferred using a National Instrument SH100100 100 pin shielded cable. The flow and pressure voltage values were connected to the 16 single ended 12-bit A/D channels on the data acquisition board. The FLOW_DATA VI is a 1 Hz data logger for all of the A/D channels. Figures B.3 (a) and (b) in Appendix B presents the front panel and wiring diagram for this VI.

Polling the A/D channels inside a WHILE loop collects the analog data. The initial

A/D channel configuration was performed outside the WHILE loop to reduce the CPU usage. The WHILE loop iteration is governed by a 1000 ms wait command. Since this VI is usually run in conjunction with one of ion signal data acquisition VIs, the 1 Hz A/D data is continuously acquired into a circular buffer and written to file when the processing time is available. The buffer size is adjustable but is limited to the amount of internal buffer memory. Too much data in buffer memory will cause older data to become overwritten resulting in a program halt. This buffered data approach significantly reduced operating 42 system calls, and therefore has little effect on the faster data acquisition programs. User input and analog data were written to an ASCII file that is continually appended with new analog data. This VI will run continuously until the user stops the program or an error occurs.

2.3 Isotopic Dilution Application to APIMS

The ability to reproducibly measure a trace compound in the atmosphere does not mean that the measurements are necessarily accurate.12 Reliable methods are required that not only measure the amount of a trace compound, but also provide a means to determine the accuracy of the analytical method (an accurate analytical method is a method in which the systematic errors are understood and controlled). Isotopic dilution mass spectrometry

(IDMS) is a method of proven accuracy especially for trace compound measurements.13

Several recent reviews12, 14, 15 are available on the IDMS method, including the reviews by

Bandy et al.16, 17 for use in analytical atmospheric chemistry. The next section briefly discusses the advantages of the IDMS technique as applied to airborne APIMS measurements, followed by the specific details of the isotopic standards used for airborne

APIMS measurements of DMS and SO2.

2.3.1 Advantages of Isotopic Dilution APIMS Measurements

The same principles behind the success of the GC/MS/ILS technique16, 17 for airborne

measurements of DMS and SO2 are directly applicable to the airborne APIMS measurements of those same compounds. The basic principle behind this isotopic dilution method is the addition of an isotopically labeled analyte of the trace compound of interest to the ambient sample as an internal standard. Only stable isotopes are used. Some of major advantages of

the isotopic dilution technique for the airborne APIMS measurement of DMS and SO2 are as follows:

• Insensitivity to sampling losses

• Insensitivity to changes in the APIMS instrumental sensitivity

• Same chemistry as analyte compound

• Isotopically labeled standard is measured along with every ambient sample providing

a calibration in every sample

• High sensitivity with low detectable limits

• High manifold analyte concentration allowing for efficient and rapid transport of

ambient species to the instrument.

Perhaps the most important feature of the isotopic dilution technique is the inclusion of the isotopomer of the analyte in every measurement allowing for an internal calibration on every sample. Changes in instrument performance or ambient conditions, such as an increase in water vapor concentration, affect both the standard and analyte compound equally, thereby having no effect on the accuracy of the measurement. Moreover, the 44

APIMS sensitivity and lower limit of detection can be calculated accurately in real-time, allowing immediate information concerning the performance of the instrument to be checked with every measurement.

Another key benefit to the isotopic dilution technique with an APIMS is the fact that the manifold always has a high concentration of the analyte. Since the internal standard is an isotopomer of the ambient analyte, the chemistry is the same, allowing for preconditioning of the manifold tubing by a similar compound. Thus, small changes in ambient analyte concentrations are easily detected with the APIMS since the ambient analyte compound is interacting with conditioned tubing in the manifold. Losses of sample after the isotopic standard addition also decreases the measurement error of the ambient analyte due to the presence of a preexisting large analyte concentration in the manifold.

The concentration of the ambient analyte is determined relative to the internal standard after correcting for the ambient isotopomers in the standard cylinder and isotopic standard isotopomers present in ambient air through use of the following equation17:

K R − K C = C ss as a s − Equation 2-1 K aa K sa R

where Ca is the ambient concentration, Cs is the manifold concentration of the isotopic standard, R is the ratio of ambient analyte signal counts to isotopic standard signal counts, or

th th Ia/Is, and the Kij values are the percentage contribution of the i species in the j species.

The percentage isotope contribution for the ambient analyte and standard in ambient air is known and can be calculated. The percentage of isotopic material in the standard, as well as 45

ambient isotopomer in the standard is based in the purity of the standard gas samples used.

Table 2.2 gives the isotope percentages in ambient air for DMS and SO2.

Table 2.2 Isotopic percentage constants used in Equation 2.1 for ambient air.

Species Kaa Ksa

DMS 0.9280 0.0000

SO2 0.9457 0.0457

From Table 2.2 it can be seen that there is no standard isotopomer contribution to the

ambient air for DMS when using d6-DMS or d3-DMS as the internal standard.

2.3.2 Isotopic Standards for Airborne APIMS Measurements

Gas phase isotopic standards for DMS and SO2 were prepared in gas cylinders by

Scott-Marrin, Inc. (Riverside, CA) from >99% purity isotopically labeled samples of those

34 species. The SO2 standard was supplied from Icon Services (Mount Marion, NY) and the deuterated DMS samples were supplied from Aldrich Chemical Company (Milwaukee, WI).

Two different DMS standards were used during airborne APIMS experimentation. The d6-

DMS standard was used in the laboratory up to and including the first airborne field test.

However, it was determined that significant contamination was present at the APIMS

protonated mass for the perdeuterated DMS standard (m/e 69) from use of the Nafion

dryers during those experiments (this topic is discussed later in this chapter). The d3-DMS

standard was created to replace the d6-DMS standard since there were no interfering ions at 46

the protonated d3-DMS APIMS mass (m/e 66). The d6-DMS was still used by the

GC/MS/ILS instrument during the airborne DMS intercomparison discussed in Chapter 4.

34 The d6-DMS and SO2 cylinder concentrations were determined by comparison to

permeation tubes of DMS and SO2 (isotopically labeled and non-labeled) (VICI Metronics,

Inc., Santa Clara, CA) using the GC/MS/ILS in our laboratory. The d3-DMS cylinder concentration was determined during the second airborne field test by comparison to the d6-

DMS cylinder using the GC/MS/ILS. Table 2.3 presents the cylinder concentrations and

isotopic percentage constants for the standards used during the airborne APIMS

experiments.

Table 2.3 Isotopic standard cylinder concentrations and isotopic percentages.

Concentration Species Cylinder # Kss Kas (ppbv)

34 SO2 CA01130 177 0.9939 0.0017

d6-DMS CC102828 111 0.924 0.000

d3-DMS CA01132 99 0.929 0.000

Although not an isotopic dilution standard, it should be noted that a non-labeled

DMS cylinder (h6-DMS) was being used in laboratory experimentation when it was

discovered that there was significant contamination at the perdeuterated APIMS mass peak

(m/e 69 amu) from the Nafion dryer. The non-labeled cylinder was calibrated by the same

method used for the perdeuterated DMS standard. The cylinder number for the h6-DMS is

CC94460 and had a concentration of 100 ppbv. 47

Gas delivery from the isotopic standard gas cylinders was accomplished using

stainless steel regulators (Model 2SS75-330-1/8”, Scott-Marrin, Inc.) both in the laboratory

and airborne field studies. The regulators were used only for this purpose and were

meticulously sealed when not in use to avoid contamination or misuse. The regulator for the

ultrapure zero air (Scott-Marrin, Inc.) was purchased from Scott Specialty Gases (Model 18B,

Plumsteadville, PA), and was treated in a similar fashion as the regulators used for standards.

2.4 APIMS Ion Molecule Reactions for the Determination of DMS and SO2

The main goal of this work was to make fast measurements from which real-time

vertical fluxes of DMS and SO2 could be determined by the eddy correlation method. The

success of these measurements depended on the ability of the airborne APIMS to determine

DMS and SO2 in the atmosphere at a sufficient sensitivity with negligible effects from changing ambient conditions. Thus, the primary goal of the laboratory work presented later

in this chapter was to verify that the APIMS had the required sensitivity for DMS and SO2

and was not sensitive to changing ambient conditions. To fully understand the nature of the

APIMS measurements of ambient DMS and SO2, a brief discussion is warranted of the ion-

+ - molecule chemistry used to form the ions measured, namely DMSH and SO5 .

2.4.1 DMSH+

The formation of the protonated form of DMS, DMSH+ (m/e 63 ambient, m/e 66

d3-based internal isotopic standard) occurs through proton transfer reactions with other

protonated species, namely water. The positive ion reaction chemistry to form protonated

water and water clusters in an APIMS is well documented in the literature.1, 4, 18-22 The positive ion chemistry is similar for both the corona discharge and 63Ni ionization sources.6

The dominant gas phase APIMS reactions forming protonated water are

+ N2 + e N2 +2e Reaxn. 2.1

+ + N2 + 2N2 N4 + N2 Reaxn. 2.2

+ + N4 + H2O H2O + 2N2 Reaxn. 2.3

+ + H2O + H2O H3O + OH Reaxn. 2.4

+ + H3O + H2O + N2 H (H2O)2 + N2 Reaxn. 2.5

+ + H (H2O)n-1 + H2O + N2 H (H2O)n + N2. Reaxn. 2.6

+ 23, 24 In humid air the formation of H (H2O)n is aided by additional reactions :

+ + e + O2 O2 + O2/M O4 Reaxn. 2.7

+ + On=2,4 + H2O O2 (H2O) + O2 Reaxn. 2.8

+ + O2 (H2O) + H2O H3O (OH) + O2 Reaxn. 2.9

+ + H3O (OH) + H2O H3O (H2O) + OH Reaxn. 2.10

Then Reaxn. 5 and Reaxn. 6. Reaxn. 2.11

Species with a gas phase proton affinity25 greater than water (691 kJ/mol, 165 kcal/mol), such as DMS (831 kJ/mol, 199 kcal/mol) can abstract a proton from the protonated water cluster by the following reaction24, 26:

+ + Reaxn. 2.12 H3O (H2O)n + X XH (H2O)m + (n+1-m)H2O

+ + Reaxn. 2.13 YH (H2O)n + X XH (H2O)m + (n+1-m)H2O

27 Here X has a higher proton affinity species than H2O or Y , and n>m. However for some species, such as DMS, the hydration energetics of the newly formed protonated species (i.e.

DMSH+) are small and thus Reaction 2.12 will shift to the left. Also, an overabundance of water vapor will also shift Reaction 2.12 to the left. Therefore, to create a long lived

DMSH+ ion, the amount of water must be reduced and the water clusters dehydrated, either through drying the air1 or heating the air28, or both to achieve high yields for the protonated

DMS molecule. This is the goal of some of the laboratory experiments discussed later in this chapter regarding the detection of ambient DMS with an airborne isotopic dilution APIMS in moist air, such as the marine boundary layer of the atmosphere.

- 2.4.2 SO5

The initial production of negative ions in an APIMS measurement begins with the

- 4, 21 electron capture process, performed primarily by O2, to create the O2 ion. The reactions

- 34 to form SO5 (m/e 112 ambient, m/e 114 internal S isotopic standard), which is the measured SO2 ion species with the airborne APIMS, are from ion-molecule reactions with

- 29-33 CO3 . The following reactions are a summary of this procedure : 50

- O2 + e + (O2 or N2) O2 + (O2 or N2) Reaxn. 2.14

- - O2 + O2 +M O4 + M Reaxn. 2.15

- ν - O4 + O (from O2 + h ) O3 + O2 Reaxn. 2.16

- - O2 + O3 O3 + O2 Reaxn. 2.17

- - O3 + CO2 CO3 + O2 Reaxn. 2.18

- - CO3 + SO2 SO3 + CO2 Reaxn. 2.19

- - SO3 + O2 + N2 SO5 + N2 Reaxn. 2.20

- Without additional ozone added for the creation of CO3 , Reaction 2.16 is the only source of

- O3 and is limited. The primary reactions that occurs in the absence of additional ozone

are32:

- - O2 + SO2 SO2 + O2 Reaxn. 2.21

- - SO2 + O2 + N2 SO4 + N2 Reaxn. 2.22

- The SO4 (m/e 96) ion was the ion measured for most of the experimentation done by Xu

- (1999) in our laboratory using a laboratory/ground-based APIMS. Measurements of SO5 in

ambient air at the South Pole were hindered by the lack of ambient ozone and also the

fluctuations in the ozone concentration that was present. Internal zeros could not be

- performed on the SO5 ion or its internal standard isotopomer, since there is no ozone

present in the ultrapure air. Again, these problematic situations spurred the laboratory

- investigations discussed later in this chapter of providing a stable, unfluctuating SO5 ion

signal from the airborne APIMS during ambient air measurements.

- - - - 32, 34 Hydrates of CO3 , O3 , and O2 play major roles in the ionization of SO2 to SO5 .

- - CO3 (H2O)n + SO2 SO3 (H2O)m + CO2 + (n-m)H2O Reaxn. 2.23

- - SO3 (H2O)m + O2 SO5 (H2O)p + (m-p)H2O Reaxn. 2.24

However, a large excess of water vapor can shift Reactions 2.23 and 2.24 to the left, favoring

- - the formation of CO3 clusters, and hindering the formation of SO5 . Again, laboratory experiments in the next section investigated the effect of excess water vapor on the creation

- and detection of SO5 in the atmosphere by APIMS.

2.4.3 Further Issues Concerning the APIMS Detection of DMS and SO2

As stated previously, compounds with a gas phase proton affinity larger than water can extract a proton from protonated water or water clusters to form a positive ion. The same principle is also true for coexisting analytes27 that have a larger proton affinity than

DMS. Species such as ammonia (NH3) have the ability to compete with DMS for protons from protonated water, as well as extract protons from the DMSH+ ion.27 Even species with a lower proton affinity than DMS, but with higher gas phase hydration energies (i.e. stability), can interfere with DMSH+ production.24 Molecules such as acetone and methanol have lower proton affinities than DMS (but higher proton affinities than water), and are formed more readily than DMS, especially in moist air. Unfortunately, use of a Nafion dryer to remove excess water from the air stream during APIMS analysis also adds contaminants such as acetone and methanol. These contaminants and their hydrate clusters reduce the 52 sensitivity for the detection of DMS using APIMS. For some species they also present a

• + background signal for use in isotopic dilution APIMS (such as CH3OH H (H2O)2 at m/e 69

+ which is also the mass for d6-DMSH ).

- - One of the reasons for forming the SO5 ion instead of using SO4 is the existence of

- a varying background signal from CO3 (H2O)2 at m/e 96. However, the background problem is still possible at m/e 112 and 114 if care is not taken to dehydrate and decluster

- • - ions such as CO4 and H2O2 CO3 . Proper APIMS source flow rate, lens tuning, and decluster pressure eliminate the effects of larger hydrates of coexisting analytes. Therefore,

- the influence of the CO3 (H2O)3 at m/e 114 has been negligible in our experiments. During internal calibrations, a species with m/e 112 was found in zero air that was not present in

- - ambient air. This species, CO4 (H2O)2, is formed from the reaction of O4 and CO2 in the

- 21 ultrapure air (to form CO4 ) and excess water vapor concentrations. However, the effect of

- this species on the background signal for ambient SO5 was minimized by optimizing the

APIMS reaction conditions stated above, and had little effect on the results of the experiments.

2.5 Laboratory Experiments to Obtain High Sensitivity Determinations of DMS and

SO2 using the Airborne Isotopic Dilution APIMS

Several laboratory experiments were conducted to increase the airborne APIMS

response for DMS and SO2 in simulated ambient air conditions. These experiments focused

on obtaining high sensitivity and insensitivity to manifold conditions in the APIMS. These

experiments took place before and after the first airborne test (November 1999). The

34 isotopic standards d6-DMS and SO2 were primarily used during these experiments, except

for later experiments on DMS where h6-DMS was used in place of the isotopic standard due

to contaminant interferences from the dryer.

The laboratory experiments were divided into three main categories: ozone addition

to SO2, water vapor addition to SO2, and water vapor addition to DMS. These experiments

provided information needed to obtain highly accurate and precise measurements of

atmospheric DMS and SO2 using isotopic dilution APIMS on aircraft platforms.

2.5.1 Laboratory Manifold Design and Additional Apparatus

A manifold was constructed in the laboratory to test the effects of ozone and water

vapor additions on the DMS and SO2 responses from the airborne APIMS (Figure 2.6).

Mass Flow Control Box

Man In

P Exhaust Man Out

API In API Out Pump

Cal/ZA Out

ZA In

Vent O3 Dew Point Concentration Meter Nafion Dryer

Dryer Bypass MFM H2O O3 Air Generation Generation Pump

MFM APIMS

Heater

MFM Cap

Figure 2.6 Laboratory manifold for ozone and water addition to airborne APIMS.

The main manifold was constructed of various sizes of perfluorinated ethylene- propylene Teflon tubing with stainless steel Swagelok fittings. All Swagelok fittings in contact with sulfur-based compounds also were stainless steel. Polypropylene tubing and brass Swagelok pieces were used for the exhaust lines. Flow through the manifold was kept turbulent to have efficient mixing. All stainless steel used in conjunction with the laboratory

APIMS experimentation was 316 stainless steel. All of the above laboratory manifold specifications apply to the aircraft manifolds reported in Chapters 3 and 4.

The manifold flow was slightly pressurized during the laboratory experiments using manifold pressure control methods described in Section 2.2.7. Flow through the 63Ni source was controlled by the API mass flow controller (also described in Section 2.2.7). Vacuum 55

was supplied by a diaphragm pump (Model 2727CA39, Thomas Industries, Sheboygan, WI)

on the output of the API flow controller. The flow into the APIMS source could be

completely isolated from the manifold using a stainless steel three-way valve that was capped

on one input. This allowed for manifold changes to be made without affecting the APIMS.

Also, it allowed the APIMS to pump down completely during idle times. For most

laboratory experiments, the API flow was > 2 L/min with the ionization source temperature

housing maintained at 175°C.

The air source for zero air (ZA), water vapor generation, and ozone generation

varied according to the compound being investigated. For SO2 investigation, standard

“medical” air was used (BOC Group, Inc., Murray Hill, NJ). For DMS investigation, boil-

off gas from a liquid N2 cylinder was used due to it low water vapor content (BOC Group,

Inc.).

2.5.1.1 Ozone Generation and Measurement

Ozone was created by flowing air through a quartz tube irradiated by a standard Hg ultraviolet lamp source. The UV intensity could be varied using a metal shield that was slid over portions of the lamp. Maximum intensities were achieved by having the full lamp exposed to the quartz tube. The entire quartz-UV lamp apparatus was housed in a steel box, with openings for flow in and out, and the lamp shield, thus reducing UV exposure to the analyst. Flow into the ozone generator was regulated using a Hastings 500 SCCM flow meter (Teleydyne-Hastings-Baydist, Hampton, VA) and a Swagelok needle value. A toggle valve was used to shut flow off completely to the ozone generator. The point at which ozone enters the manifold was kept close to the exit of the isotopic standard (Cal)/ZA gases 56

from the mass flow control box to have a long enough residence time in the manifold for

mixing.

The manifold ozone concentration was determined downstream of the ozone mixing

point through use of a Dasibi O3 concentration meter (Model 1003-AH, Highland Springs,

VA). An internal pump controlled by a needle valve and measured with an internal mass

flow meter accomplished flow through the ozone concentration meter.

2.5.1.2 Water Vapor Generation and Measurement

Water vapor generation into the manifold was accomplished by flowing air through a

one foot single strand Nafion tube (1/8” I.D., PermaPure, Inc., Toms River, NJ) that was

submersed in doubly deionized water in a Teflon container. This was the same apparatus

used by Xu (1999)1 in our laboratory for elementary studies of the effect of water vapor on

DMS sensitivity. The doubly deionized water was chlorine treated to avoid biological

growth. Flow through the Nafion tube was governed by a stainless steel needle valve and

measured using a Hastings 10 SLM flow meter. A flow rate as large as 10 L/min through

the Nafion tubing still created a water-saturated flow out of the apparatus. A stainless steel

three-way valve was used to direct the flow emanating from the Nafion tubing either into the

manifold or out to room vent. A toggle valve was not used to shut off flow since it was

discovered that water vapor still permeated out of the Nafion tubing into the manifold even

without flow.

To obtain high levels of water vapor in the manifold (i.e. dew points above 15°C), the watertight container containing the double deionized water and Nafion tubing was submersed in warm water (~50°C). All manifold tubing and fittings subjected to the 57 elevated water vapor concentration were heated with a heat lamp to avoid condensation.

The entry point for the water vapor addition to the manifold occurred close to the standard/ZA mass flow control box exit to ensure proper mixing.

The manifold water vapor concentration was measured as the dew point downstream of the water vapor entry point by an EG&G Model 911 Dew Point Analyzer (Waltham,

MA). Flow through the humidity analyzer was controlled by a needle value (not shown in

Figure 2.6) and measured with a Hastings 1 SLM flow meter. A constant flow rate was maintained through the humidity analyzer using a small pump (Thomas Industries,

Sheboygan WI) on the output of the flow meter.

2.5.1.3 Nafion Dryers

Nafion is a polymer consisting of a Teflon backbone and perfluorinated ether side chains ended with hydrophilic sulfonic acid sites.35 The sulfonic acid group can absorb up to

13 molecules of water, allowing Nafion to absorb up to 22% by weight of water. Nafion has a high selectivity for water, a low selectivity for other gaseous species (such as DMS and

SO2), and is resistant to chemical degradation.

The driving force in the drying effect of Nafion tubing is a water vapor pressure gradient across the Nafion membrane. The dryers purchased from PermaPure all have the ability to send a purge gas flow across the outside of the Nafion tubing in the opposite direction of the flow inside the Nafion tubing. When the purge gas flow contains less water vapor than the flow inside the Nafion tubing, water vapor transverses the tubing and the air stream inside the tubing is consequently dried. However, the reverse effect is also possible, which allows water vapor to enter a relatively dry air sample passing through the Nafion tubing if the purge gas contains more water vapor. This is typically seen if the dryer has 58 been used in a moist air environment and then kept inline when a dryer air sample is analyzed. Therefore, care must be taken to bypass the dryer if low water vapor air is being analyzed. The excess flow from the API source was used to purge the Nafion tubing since it had already been dried and subjected to more dehydration through the heated source. A general review of Nafion dryer technology is available in the literature.36

Two different types of PermaPure dryers were used in this study. Single strand dryers of 6 and 12 foot lengths were used (Models MD-070-72P (6’), MD-070P-144P (12’)), as well as multiple strand (100 strands) dryers of two feet in length (PD-1000-24SS). The single strand dryers worked well for low dew points, but the drying capacity was quickly diminished at higher dew points due to an over saturation of the drying sites in the Nafion tubing. The multiple strand dryers tolerated a larger water vapor concentration in the air stream at similar flow rates to the single strand dryers. Therefore, the multiple strand Nafion dryers were used for higher dew point laboratory investigations, as well as for all airborne field measurements.

The major drawback of the use of Nafion dryers is the contamination that appears in positive and negative ion spectra. The contaminations present in negative ion mode seem to

- have no effect on the APIMS response for SO5 . However, the presence of contaminations in positive ion does slightly reduces the APIMS response for DMSH+, and caused our

laboratory to abandon the use of d6-DMS as the internal standard for DMS due to interfering ions at the mass.

2.5.2 General APIMS Instrumental Sensitivity Enhancements

Two sensitivity enhancements occurred on the airborne isotopic dilution APIMS

during experimentation and are noteworthy to mention at this point. After the first airborne

field test, and before the experimentation of high dew points on the APIMS response for

DMS and SO2, it was determined that the differential pumping capabilities of the airborne

APIMS were not optimized due to gaps surrounding the quadrupole housing. Unnecessary vents in the focusing lens stack were also discovered. Consequently, the vents were covered, and gaps filled, resulting in an overall increase of signal response for the airborne APIMS due to reduced ion-molecules collisions in the quadrupole area.

While deployed on the second airborne field test of the APIMS, it was determined

that an unpressurized manifold at high altitudes significantly reduced the ion transmission

due to insufficient backing pressure in the API source. To overcome this obstacle, the

aperture orifice diameter in the API declustering lens section was enlarged from the original

275 µm to 310 µm. Although this did not have many effects at low altitudes, the APIMS

performance at higher altitudes was enhanced.

2.5.3 Results of Laboratory Experiments performed on the Airborne APIMS

The following sections provide the results of ozone and water vapor addition on the

airborne isotopic dilution APIMS response for DMS and SO2. It should be reiterated that

these experiments were designed to establish the best working environment and parameters

for the most sensitive APIMS detection of atmospheric DMS and SO2 from aircraft 60

platforms. Although the experiments were quantitative, and the results directly applicable to

real airborne ambient measurements, the results should also be used as guides for the best

APIMS optimization. More detailed work needs to be done to fully understand all the

nuances of the APIMS response for the detection of DMS and SO2. As a reminder, Section

2.5.1 provides the experimental information for results in the next sections.

2.5.3.1 Ozone Addition for the Airborne APIMS Detection of SO2

1 - Xu’s (1999) results indicate that monitoring ambient SO2 in the form of the SO4 ion

(formed from Reactions 2.21 and 2.22) suffers from inconsistent backgrounds and low ion

- yields. Monitoring SO5 based on ambient O3 concentrations can yield fluctuating responses

if ambient O3 changes. Moreover, internal calibrations (i.e. zeros) could not be performed

- on the SO5 ion since O3 is not present in the ultrapure air.

Experiments were performed on the airborne APIMS response for SO2 by adding

known amounts of O3 to the manifold to validate Reactions 2.17 to 2.20 for the formation

- of SO5 . These experiments were conducted before the first airborne field-test in November

1999. No water vapor was added to the manifold during these experiments. Any water

vapor present is from the medical air cylinder.

Figure 2.7 presents mass scans of without the addition of the isotopic standard of

SO2 with increasing amounts of O3 added to the manifold. Figure 2.8 is the response of the isotopic standard SO2 in the same O3 addition range. As it can be seen in Figure 2.7, the

- primary ion in the background mass scan was m/e 68, or O2 (H2O)2. Upon addition of O3

- to the manifold, m/e 60 (CO3 ) becomes the primary ion, which is consistent with Reactions

34 - 34 - 2.17 and 2.18. Figure 2.8 demonstrates the conversion from SO4 to SO5 with addition of

ozone to the manifold, again consistent with Reactions 2.19 and 2.20. 61

Ozone Addition to Negative Ion APIMS Background

60000

50000

0 ppb O3 40000 4 ppb O3 9 ppb O3

cps 17 ppb O3 30000 32 ppb O3 41 ppb O3 20000 49 ppb O3 61 ppb O3

10000

0 49 ppb O3 149 145 141

32 ppb O3 137 133 129 125 121 117 113

9 ppb O3 109 105 97 101 93 89 85 81

77 m/e 0 ppb O3 73 69 65 61 57 53 49 45

Figure 2.7 Ozone addition to negative ion airborne APIMS background.

34 - 34 - SO4 Conversion to SO5 with Ozone Addition

m/e 98

25000

m/e 114 20000

95 0 ppb O3 97.2 15000 8 ppb O3 99.4 19 ppb O3 cps 101.6 10000 32 ppb O3 44 ppb O3 103.8 61 ppb O3 106 5000 m/e 108.2 0 110.4

112.6

114.8 61 ppb O3 44 ppb O3 32 ppb O3 19 ppb O3 8 ppb O3 0 ppb O3

34 34 - 34 - Figure 2.8 Ozone addition to isotopic standard SO2 showing conversion from SO4 to SO5 in negative ion mode of an airborne APIMS

Figures 2.9 is a plot of the ion signals for the APIMS negative ion background with

increasing ozone concentration in the manifold to determine the amount of ozone needed

- for CO3 to become the primary negative ion species. Figures 2.10 presents a plot of the

isotopic standard SO2 ion signals to also determine the ozone concentration that allows

34 - complete conversion of the isotopic standard to SO5 . As it can be seen from the plot in

- Figures 2.9, CO3 becomes the primary negative ion species when the ozone concentration in

- the manifold is above 30 ppbv. A maximum for the CO3 ion signal appears around 50 ppbv

ozone addition and begins to decrease with larger ozone concentrations. Information from

34 - Figure 2.10 also shows that a majority conversion to SO5 occurs with ozone

concentrations greater than 30 ppbv, with an apparent maximum ion signal around 50 ppbv

- ozone addition, which is in good agreement with the CO3 results. However, without more

information, it is difficult to determine whether a significant decrease occurs in the isotopic

- standard SO2 and CO3 ion signals with ozone concentrations greater than 50 ppbv. For

future experiments regarding the measurement of SO2 with the airborne APIMS, ozone is

added in excess of 45 ppbv, which removed any dependence on ambient ozone

concentrations (especially in the marine boundary layer) and allowed for the SO2 background

signals to be obtained during an internal (zero) calibration. Brief tests of the addition of

ozone on the positive ion chemistry of DMS show that there are no obvious effects on the

formation of DMSH+ in the presence of ozone. There were also no apparent effects on the

APIMS response for DMSH+ in the presence of ozone.

- Determination of Maximum CO3 Ion Signal with O3 Additions 70000

60000

50000

CO3- 40000 CO3-*H2O CO3-+CO3-*H2O cps O2-*H2O 30000 O2-*(H2O)2 O2-*H2O+O2-*(H2O)2

20000

10000

0 0 10203040506070

O3 Manifold Concentration (ppb)

- Figure 2.9 Determination of manifold ozone concentration for maximum CO3 ion signal.

34 Conversion of Isotopic Standard SO2 with O3 Additions

22000

m/z 98 cts/s 20000 m/z 114 cts/s

18000

16000

14000

12000 cps 10000

8000

6000

4000

2000

0 0 102030405060

O3 Manifold Concentration (ppb)

34 - Figure 2.10 Determination of manifold ozone concentration for complete formation of SO5 .

2.5.3.2 Water Vapor Addition and the Effect on the Airborne APIMS Response for SO2

Studies on the effects of water vapor addition on the APIMS response for SO2 were conducted both before and after the first airborne field test. Ozone was added during these experiments at concentrations above 45 ppbv per the suggestions of Section 2.5.3.1. These

- experiments concluded that the SO5 sensitivity without the use of a Nafion dryer decreased approximately linearly with dew points between 12°C and 20°C. Dew points greater than

° - 18 C resulted in negligible sensitivity (i.e. SO5 ion signal approached background signal

37 levels). The use of a Nafion dryer restored the SO2 sensitivity.

Early experimentation with Nafion dryers relied on two multiple strand Nafion

dryers used in series to reduce the effect of high dew points on the APIMS SO2 sensitivity.

Cryogenic cooling of the purge gas of the Nafion dryers appeared not to have a significant

enhancement on the sensitivity for SO2. Under marine boundary layer dew point

° ° 34 - simulations (>20 C at 25 C air temperature), the APIMS sensitivity for the SO5 ion was 5

to 10 cps per pptv.

After the differential pumping chamber enhancement on the APIMS, it became clear

that much of the reduced sensitivity for the detection of SO2 was caused by ion-molecule

collisions in the quadrupole area due to improper instrument optimization. In fact, after the

instrumental alteration described in Section 2.5.2, single strand dryers could be used in high

dew point manifolds to significantly reduce the effect of water vapor on the detection of SO2

34 - with an APIMS. Figure 2.11 presents a plot of SO5 ion signal and dew point changes over

time with the use of a single strand Nafion dryer (MD-070-144P). As it can be seen in

34 - Figure 2.11, the SO5 ion signal decreases linearly with increasing dew point, but does not

go to background levels, which was approximately 500 to 1000 cps. At a dew point of

° 34 - ~25 C, the APIMS sensitivity for SO5 is approximately 20 cps/pptv. Use of a multiple 65

strand Nafion dryer increases the sensitivity on average by a factor of four under similar dew

points.

Figures 2.12 and 2.13 better illustrate the dramatic effect that Nafion dryers have on

the APIMS response for SO2 under high dew point conditions. Figure 2.12 (a) is a mass

scan showing the m/e 114 peak with no additional water vapor added to the manifold and

no Nafion dryer present. Figure 2.12 (b) shows the results of adding water vapor to the

manifold to increase the dew point to ~25°C. Attention is drawn to the disappearance of

- the m/e 114 peak and the m/e 60 peak (CO3 ) under extremely moist air conditions. Figure

2.13 (a) is the same scenario as Figure 2.12 (a) except that a single strand MD-070-144P

Nafion dryer has been placed inline to the flow going to the APIMS source. Figure 2.13 (b)

again is the addition of water vapor to the manifold to bring the dew point up to ~25°C.

Attention is now drawn to the fact that both the m/e 114 and m/e 60 peaks are still present, albeit quite reduced from their former levels. However, this is proof that the use of Nafion

- dryers preserves the ion chemistry necessary for the formation of SO5 , and in turn allows for

a sufficient response by the APIMS for the detection of SO2 in moist air environments.

000316_data/s100040.txt, H2O addition, MD-077-144P PermaPure dryer, API source 175°C, 34 API Flow 2 L/min, SO2 avg. conc. 1.6 ppb

140000 30

m/z 114 dew point 120000 25

100000 20

80000

m/z 114 cps 60000 dew point (°C)

10 40000

5 20000

0 0 38379 38579 38779 38979 39179 39379 39579 39779 time (sec)

Figure 2.11 Effect of increasing dew point on the APIMS response for SO2 using a Nafion dryer. 66

No H2O addition, no dryer, 34SO2 con. 1.6 ppbv 500000 (a) 400000

300000 cps 200000

100000

0 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100105110115 120125130 mass

70000 Dew point ~+25C, no dryer, 34SO2 conc 1.6 ppb 60000 (b) 50000 40000

cps 30000 20000 10000 0 15 20 25 30 35 40 45 50 55 60 65 70mass 75 80 85 90 95 100 105110115120125130

- 34 - Figure 2.12 Effect of 25°C dew point on the APIMS response for CO3 and SO5 without a Nafion dryer.

400000 No H2O, MD-070-144P Nafion dryer, 34SO2 conc. 1.6 ppbv (a) 300000

200000 cps

100000

0 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100105110115 120125130 mass

Dew point ~+25C, MD-070-144P Nafion dryer, 34SO2 conc. 1.6 ppbv 50000 (b)

40000

30000 cps 20000

10000

0 15 20 25 30 35 40 45 50 55 60 65 70mass 75 80 85 90 95 100 105110115120125130

- 34 - Figure 2.13 Effect of 25°C dew point on the APIMS response for CO3 and SO5 with a Nafion dryer.

2.5.3.3 Water Vapor Addition and the Effect on the Airborne APIMS Response for DMS

Results of laboratory experiments conducted by Xu (1999)1 show that the APIMS response for DMS is significantly diminished in the presence of water vapor. Her results show that the DMSH+ sensitivity approached zero at a dew point of -10°C. Xu’s work is

corroborated by the laboratory work of Spicer et al. (1996).38 Examination of Reaction 2.12,

and the results of Sunner et al. (1988)24 regarding the hydration energetics of protonated

DMS, also support these findings. Xu also reported that use of a multiple strand Nafion

dryer (or two in series) did not enhance the sensitivity for DMS for dew points above -10°C.

Results discussed in Chapter 3 for initial airborne measurements of DMS in the marine boundary layer support her findings.

Since DMS is primarily evolved from open ocean, measurement of this species with an airborne APIMS will take place in the marine boundary layer where the dew point is greater than 20°C. Because the problem with the diminished DMS APIMS sensitivity lies in the hydration energetics of the protonated DMS molecule, means other than removing water vapor from the sample through a Nafion dryer need to be developed. Sunner et al. (1988)28

described sensitivity enhancements for DMS through the dehydration of water clusters in

the declustering region of their APIMS. A simple Nichrome wire spiral heater glowing red

in the declustering region of their APIMS increased the relative DMS response in their

experiment (where the relative humidity of their air samples was 21%, which is a dew point a

little over 1°C at an air temperature of 25°C) by a factor of 104 to 106 by heating the sample

to 430°C. In essence, their approach to the low DMSH+ hydration energetics was to

dehydrate the water clusters in their reaction volume, leaving Reaction 2.12 to produce a

non-hydrated DMSH+ ion. A similar approach to Sunner et al.’s findings was undertaken in 68 our laboratory to increase the sensitivity for DMS with our airborne APIMS DMS at dew points greater than 20°C (at 25°C air temperature).

Modification of our APIMS source/declustering region is non-trivial and not recommended. This eliminated the possibility of placing a heater directly into the declustering area of our APIMS. An alternate heating method was developed where the air coming into the source and declustering region of the APIMS was heated to temperatures greater than 300°C. Initially, air containing DMS and water vapor was passed though a heated stainless steel tube. Poor insulation, poor thermal conductivity to the air sample, and a low temperature range eliminated this procedure. A Nichrome resistance wire was wrapped around a 1/8” glass rod and sheathed by sealed ¼” glass tube was tried next.

Alumina rods and tubes of the same size replaced the glass rod and tubes in later experiments. This heating tube was then placed inside a 3/8” stainless steel tube. The sample air flowed over the glass or alumina heating tube into the APIMS source. Although this procedure worked and improved the DMS sensitivity in moist air, significant contamination of the air stream occurred, most notably the presence of bare and protonated potassium and sodium ions, their oxides, and hydrates. The contaminants competed with

DMS for available protons, limiting the DMS sensitivity gain. The APIMS DMS sensitivity increase was limited at temperatures above 300°C since this is the temperature at which the contaminants started to appear.

To remove the contamination effect above 300°C, the ¼” alumina heater was enclosed in a 3/8” stainless steel tube (0.049” wall thickness) that was hermetically sealed with a stainless steel plug (the plug was later welded onto the 3/8” tube). A stainless steel

Swagelok ½” to 3/8” port connector-reducing union was carefully bored to an inner diameter of 3/8” so that the stainless steel heating tube sheath could fit through with ease. 69

A stainless steel ferrule was used to secure the heating tube in place to a ½” stainless steel

Swagelok tee. Sample air was brought in perpendicular to the heating tube inside the tee, where the air transversed the length of the 3/8” stainless steel heating tube. The flow from the heating apparatus then passed into a ¼” stainless steel tee containing a K-type surface thermocouple (Omega) secured in the middle of the air stream to measure the air stream temperature before entering the APIMS source. Sustained temperatures above 400°C were not possible with the Nichrome resistance wire. Thus, the alumina tube heater containing the Nichrome resistance wire was replaced with a Watlow 100 W cartridge heater (Model

Firerod E3A48, St. Louis, MI). Figure 2.14 graphically presents the above sample air heater description. The apparatus shown in Figure 2.14 was the sample air heater apparatus deployed on the second airborne APIMS field test in July 2000 and is still employed as a sample air heater today.

The sample air heater shown in Figure 2.14 was insulated by wrapping the stainless steel with various widths of high temperature ceramic insulation tape (Cotronics, Brooklyn,

NY). Once wrapped with the ceramic tape, the entire apparatus was enclosed in 1” thick

(3/4” I.D.) calcium silicate pipe insulation (McMaster-Carr). This ensemble was covered with adhesive aluminum tape to keep the insulation constrained and eliminate dust from the calcium silicate insulation. The insulated sample air heater was warm to the touch at air stream temperatures of 400°C, allowing this apparatus to be safely deployed on aircraft.

Temperature regulation of the air stream was accomplished through the use of an Omega

CN77342-CX temperature controller with thermocouple feedback in the laboratory, and by the Omega CN132 temperature controller with thermocouple feedback on the APIMS power distribution system while in the field. Keeping an ionization source temperature near

175°C helped to sustain the sample air temperature. Incidentally, there is no apparent effect 70 or degradation of the 63Ni ionization source at air temperatures of 400°C, which was consistent with reports in the literature.11

1/2” to 3/8” Port Connector-Reducing Union bored out to 3/8” ID ~ 9”

Silicon Tape 1/2” Tee Heated Air Out to Sample Air Thermocouple and 63Ni APIMS Source

Expanded 2” Long 1/2” to 1/4” Reducing Union bored out to 1/4” ID View of 1/2” OD, 0.035” Wall Heating Tube SS 316 Tube

Air In

Cartridge Heater

Ground Wire 8.5” Long 3/8” OD, 0.049” Wall SS 316 Tube Welded SS Plug 0.035”

3/8” Electrical Connector End View of Heating Tube Inside 1/2” Tube

Note: Not drawn to scale. All pieces are SS Swagelok unless otherwise noted. 1/2”

Figure 2.14 Sample air heater schematic for increased APIMS DMS sensitivity in moist air.

An experiment was performed to determine the optimal air temperature for

maximum APIMS response for DMS in semi-humid air. A d6-DMS mixing ratio of 900 pptv was maintained in a manifold having a dew point of 0°C without the use of a Nafion dryer. The air temperature was slowly raised, and the APIMS response for DMS recorded.

Figure 2.15 presents the result of that experiment. As it can be seen in Figure 2.15, the 71 maximum APIMS response for DMS occurred around 400°C, which is consistent with the report of Sunner et al. (1988).28 The APIMS response increase for DMS at 400°C is also on the same order of magnitude reported by Sunner et al. (1988).28 An air temperature close to

400°C was used for the remaining laboratory experiments as well as during the second airborne APIMS field test in July 2000.

Figure 2.16 shows the response of decreasing the DMS concentration in a 0°C dew point manifold with an air temperature of 391°C. Figure 2.17 presents a calibration curve

(background subtracted) for the concentration decreases in Figure 2.16. A ~5000 cps background is due to contaminants eluting into the sample air from the Nafion tubing used for water vapor generation.1 The data in Figure 2.17 demonstrate a linear APIMS response

+ to changing DMS concentration. Notably, the APIMS sensitivity for d6-DMSH was approximately 25 cps/pptv at a dew point of 0°C without the use of a Nafion dryer at an air temperature of 391°C.

900 pptv d6-DMS Response to Varying Air Temperatures at Dew Point 0°C

30000 m/z 69

25000

20000

15000 d6-DMS cps

10000

5000

0 30 80 130 180 230 280 330 380 430 480 Air Stream Temperature (°C)

+ Figure 2.15 Determination of optimal air temperature for maximum APIMS d6-DMSH response. 72

d6-DMS Signal Response in Humid Air January 14, 2000 35000

909 pptv DMS

m/z 69 DMS signal 30000 745 pptv DMS Dew Point 0°C Air Stream Temperature 391°C 646 pptv DMS 25000 607 pptv DMS

20000 377 pptv DMS cps

15000 190 pptv DMS

102 pptv DMS 10000

25 pptv DMS

5000

background

0 75200 75300 75400 75500 75600 75700 75800 75900 76000 76100 Time (sec)

Figure 2.16 Airborne APIMS response for decreasing d6-DMS manifold concentrations in humidified air with an air temperature of 391°C. No Nafion dryers were used.

d6-DMS Background Corrected Calibration Curve in Humid Air January 14, 2000

m/z 69 signal 25000 Linear (m/z 69 signal) y = 27.515x + 722.9 R2 = 0.9958

20000

15000 cps

10000

5000

0 20 120 220 320 420 520 620 720 820 920 d6-DMS conc. (pptv)

Unfortunately, the APIMS DMS sensitivity decreases almost linearly with increasing dew

° ° points above 0 C, similar to that seen for SO2 in Figure 2.11. At dew points >20 C, the

DMS response was not different than the background measurement.

To measure DMS in high humidity air (i.e. dew points >20°C), a combination of a heated air stream and Nafion dryers was used. Due to the significant contamination

produced by the Nafion dryer at m/e 69, the isotopic dilution standard d6-DMS could not be

used for these experiments. The new isotopic dilution standard d3-DMS was not yet

available. Instead, non-labeled h6-DMS was used. Figure 2.18 presents another decreasing

DMS concentration profile, this time in manifold having a dew point of 22.6°C. A single

strand Nafion dryer (MD-070-144P) was used to dry the air before it was heated to a

temperature of 425°C. A calibration plot from the data in Figure 2.18 is shown in Figure

2.19. The APIMS response is linear with DMS concentrations. Sensitivity over 40 cps/pptv

was obtained for DMS under these conditions.

Figure 2.20 presents a more comprehensive and randomized DMS concentration

profile under similar conditions as Figure 2.18. Figure 2.21 is the calibration plot for the

data in Figure 2.20. This plot is similar to Figure 2.19, and the coefficients of the linear

model in both figures are similar. No background subtraction was performed on the data,

and the intercepts on Figure 2.18 and 2.21 indicate a minor background of 300 to 500 cps.

Inspection of residuals of the calibration plot in Figure 2.21, as shown in Figure 2.22, show

random variations and indicate no apparent trends in the APIMS response for changing

DMS concentrations. Figure 2.23 is a plot of the APIMS sensitivity for the data presented in

Figure 2.20. As it can be seen in Figure 2.23, the APIMS sensitivity was constant at

approximately 42 cps/pptv for changing DMS concentrations in a manifold with a dew 74 point of 25.25°C employing the use of a single strand Nafion dryer and heated air stream to a temperature of 425°C. The APIMS sensitivity for DMS increases on average by a factor of two when using a multiple strand Nafion dryer under similar dew points and air temperatures.

000314_data/s100035.txt Dew Point +22.6C, Tair 425C, Tsource 175C, API Flow 2 L/min, MD-070-144P Nafion Dryer

30000

611 pptv m/z 63 cps 25000

497 pptv

20000

377 pptv

15000 cps

252 pptv

10000

123 pptv

5000 66 pptv

17 pptv background 0 34587 34637 34687 34737 34787 34837 34887 34937 34987 35037 35087 35137 time (secs)

Figure 2.18 Airborne APIMS response for decreasing d6-DMS manifold concentrations in humid air (22.6°C dew point) with an air temperature of 425°C. A single strand Nafion dryer was also used. 75

000314_data/s100035.txt Dew Point +22.6C, Tair 425C, Tsource 175C, API Flow 2 L/min, MD-070-144P Nafion Dryer 25000 m/z 63 y = 39.748x + 455.55 Linear (m/z 63) R2 = 0.9991

20000

15000 cps

10000

5000

0 0 50 100 150 200 250 300 350 400 450 500 550 600 650 h6-dms conc (pptv)

000315_data/s100037.txt Dew Point +25.25C, MD-077-144P PermaPure dryer, Tair 425C, Tsource 175C, API Flow 2 L/min

16500 m/z 63 15000

13500

12000

10500

9000 cps 7500

6000

4500

3000

1500

0 38148 38348 38548 38748 38948 39148 39348 39548 39748 39948 time (sec)

Figure 2.20 Airborne APIMS response for random d6-DMS manifold concentration changes in humid air (25.25°C dew point) with an air temperature of 425°C. A single strand Nafion dryer was also used.

000315_data/s100037.txt Dew Point +25.25C, MD-077-144P PermaPure dryer, Tair 425C, Tsource 175C, API Flow 2 L/min

12000 m/z 63 y = 40.167x + 295.88 R2 = 0.9924 Linear (m/z 63)

10000

8000

6000 cps

4000

2000

0 0 50 100 150 200 250 300 h6-dms conc (pptv)

000315_data/s100037.txt Dew Point +25.25C, MD-077-144P PermaPure dryer, Tair 425C, Tsource 175C, API Flow 2 L/min

1000

800

600

400

200

0 m/z 63 cps Residuals 0 50 100 150 200 250 300

-200

-400

-600 h6-dms conc (pptv)

000315_data/s100037.txt Dew Point +25.25C, MD-077-144P PermaPure dryer, Tair 425C, Tsource 175C, API Flow 2 L/min

m/z 63

40 m/z 63 sensitivity (cps/pptv)

20 10 60 110 160 210 260 310 h6-dms conc (pptv)

2.6 Conclusion

This chapter presented detailed accounts of the instrumentation and components used

to create an isotopic dilution APIMS for the measurement of DMS and SO2 in the atmosphere on aircraft platforms. A discussion on the benefits of the isotopic dilution method to the airborne APIMS measurements, as well as relevant ion chemistry for the

APIMS detection of DMS and SO2, was also presented. Results of laboratory investigations for obtaining the maximum APIMS sensitivity for SO2 indicate the ozone should be added in excess of 45 ppbv to the APIMS manifold in order to ensure complete formation of the 78

- detected SO5 ion. Simulations of moist marine boundary layer air indicate that a Nafion

dryer should be used in order to obtain the optimal APIMS SO2 sensitivity in this environment. Maximum airborne APIMS sensitivity for DMS in moist air was accomplished through the use of a Nafion dryer and sample air heater at an air temperature of 400°C. 79

2.7 References

(1) Xu, N. H., Drexel University, Philadelphia, 1999.

(2) Hill Jr., H. H.; Baim, M. A. Trends Anal. Chem. 1982, 1, 232.

(3) Pellizzari, E. D. J. Chromatogr. 1974, 98, 323.

(4) Horning, E. C.; Horning, M. G.; I., C. D.; Dzidic, I.; Stillwell, R. N. Anal. Chem. 1973,

45, 936.

(5) Horning, E. C.; Horning, M. G.; Carroll, D. I.; Stillwell, R. N.; Dzidic, D. I. Life Sci.

1973, 13, 1331.

(6) Dzidic, I.; carroll, D. I.; Stillwell, R. N.; Horning, E. C. Anal. Chem. 1976, 48, 1763.

(7) Carroll, D. I.; Dzidic, I.; Stilwell, R. N.; Horning, M. G.; Horning, E. C. Anal. Chem.

1974, 46, 706.

(8) Carroll, D. I.; Dzidic, I.; Stillwell, R. N.; Horning, E. C. Anal . Chem. 1975, 47, 1956.

(9) Grimsrud, E. P.; Kim, S. H.; Gobby, P. L. Anal. Chem. 1979, 51, 223.

(10) Zook, D. R., Montana State University, Bozeman, 1990.

(11) Simmonds, P. G.; Fenimore, D. C.; Pettitt, B. C.; Lovelock, J. E.; Zlatkis, A. Anal.

Chem. 1967, 39, 1429.

(12) Heumann, K. G. Int. J. Mass Spectrom. Ion Process. 1992, 118/119, 579.

(13) De Bievre, P. Fresenius J. Anal. Chem. 1990, 337, 766.

(14) Fassett, J. D.; paulsen, P. J. Anal. Chem. 1989, 61, 643.

(15) Smith, D. H. Practical Spectroscopy 2000, 23, 223.

(16) Bandy, A. R.; Thornton, D. C.; Ridgeway Jr., R. G.; Blomquist, B. W. In ACS Symp.

Ser., 1992; Vol. 502, pp 409-422.

(17) Bandy, A. R.; Thornton, D. C.; Driedger III, A. R. J. Geophys. Res. 1993, 98, 23423. 80

(18) Good, A.; Durden, D. A.; Kebarle, P. J. Chem. Phys. 1970, 52, 212.

(19) Kambara, H.; Kanomata, I. Anal. Chem. 1977, 49, 270.

(20) Ketkar, S. N.; Ridgeway Jr., R. G.; Martinez de Pinillos, J. V. J. Electrochem. soc. 1992,

139, 3675.

(21) Siegel, M. W.; Fite, W. L. J. Phys. Chem. 1976, 80, 2871.

(22) Stimac, R. M.; Cohen, M. J.; Wernlund, R. F. Int. Reliability Physics Symp. 1982, 20th,

260.

(23) Good, A.; Durden, D. A.; Kebarle, P. J. Chem. Phys. 1970, 52, 222.

(24) Sunner, J.; Nicol, G.; Kebarle, P. Anal. Chem. 1988, 60, 1300.

(25) Hunter, E. P. L.; Lias, S. G. J. Phys. Chem. Ref. Data 1998, 27, 413.

(26) Arnold, S. T.; Thomas, J. M.; Viggiano, A. A. Int. J. Mass Spectrom. 1998, 179, 243.

(27) Ketkar, S. N.; Penn, S. M.; Fite, W. L. Anal. Chem. 1991, 63, 924.

(28) Sunner, J.; Ikonomou, M. G.; Kebarle, P. Anal. Chem. 1988, 60, 1308.

(29) Albritton, D. L.; Dotan, I.; Streit, G. E.; Fahey, D. W.; Fehsenfeld, F. C.; Ferguson,

E. E. J. Chem. Phys. 1983, 78, 6614.

(30) Benoit, F. M. Anal. Chem. 1983, 55, 2097.

(31) Eisele, F. L.; Berresheim, H. Anal. Chem. 1992, 64, 283.

(32) Mohler, O.; Reiner, T.; Arnold, F. J. Chem. Phys. 1992, 97, 8233.

(33) Arnold, S. T.; Morris, R. A.; Viggiano, A. A. J. Geophys. Res. 1995, 100, 14141.

(34) Seeley, J. V.; Morris, R. A.; Viggiano, A. A. Geophys. Res. Lett. 1997, 24, 1379.

(35) Nafion gas sample dryers; PermaPure Inc.: Toms River, 1997.

(36) Leckrone, K. J.; Hayes, J. M. Anal. Chem. 1997, 69, 911.

(37) Thornton, D. C.; Driedger III, A. R.; Bandy, A. R. Anal. Chem. 1986, 58, 2688. 81

(38) Spicer, C. W.; Kenny, D. V.; Chapman, E. G.; Busness, K. M.; Berkowitz, C. M. J.

Geophys. Res. 1996, 101, 29137. 82

CHAPTER 3 – FAST MEASUREMENTS AND AIRBORNE INSTRUMENTAL INTERCOMPARISON OF SULFUR DIOXIDE IN THE ATMOSPHERE

3.1 Introduction

The detection of the parts per trillion by volume (pptv) range of atmospheric sulfur

species, such as dimethyl sulfide (DMS) and sulfur dioxide (SO2), has primarily been

performed by isotopically labelled standard gas chromatography/mass spectrometry

(GC/MS/ILS), developed in our laboratory since 1986.1-4 Bandy et al.5, 6 have reviewed the

general aspects of this technique. The GC/MS/ILS instrument has been involved in

numerous field mission deployments7-18, such as the Pacific Exploratory Missions (PEM), and Aerosol Characterization Experiments. A synthesis of several years of these field

missions for the determination of SO2 using the GC/MS/ILS technique was written by

Thornton et al. (1996).19

Although the GC/MS/ILS technique was a robust instrument capable of making

precise measurements of DMS and SO2, the relatively long cycle time of 4-6 minutes limits

the use of the instrument for fast real-time measurements of these sulfur species.

Consequently, the GC/MS/ILS instrument cannot be used for direct flux measurements.

The isotopic dilution atmospheric pressure ionization mass spectrometer (APIMS)

developed by our research group20 has the potential to make the fast measurements of DMS

20 and SO2 that are necessary for real-time concentration and flux determinations. Xu

performed the first ground field deployment of the isotopic dilution APIMS to the South

Pole as part of the ISCAT mission in 1998. Her results show that the APIMS instrument

can detect DMS and SO2 with high sensitivity. Although her experiments were conducted 83 with a sampling frequency of 1 Hz or less, her results showed that the APIMS was capable of real-time measurements.

To effectively use the APIMS in ground or airborne measurements, the accuracy of the instrument needs to be compared to an existing similar technique. The GC/MS/ILS

technique was subject to double blind intercomparison studies of DMS and SO2, which validated the accuracy of the GC/MS/ILS instrument.21, 22 Xu (1999)20 performed a laboratory based instrumental intercomparison between the APIMS and GC/MS/ILS

technique for DMS and SO2. The results of her study indicate that the two instruments agree well in the laboratory. To date, no intercomparison exists between these two instruments for either ground or airborne studies.

One of the primary objectives of this chapter is to describe an airborne

intercomparison for SO2 using the isotopic dilution APIMS and the GC/MS/ILS. The goal of this intercomparison was to validate the airborne measurements of SO2 by the isotopic dilution APIMS technique. The airborne intercomparison for DMS is presented in Chapter

4. The utility of fast airborne measurements (1 Hz or greater) of SO2 with an APIMS is also demonstrated by examining the results of an interception of a pollution plume and moist layer in the atmosphere. Finally, an initial airborne real-time measurement test (~10 Hz) for eddy correlation flux determinations is discussed, along with sensitivity enhancement issues

for DMS and SO2 in marine boundary layer conditions.

It should be stressed that the information obtained by this initial airborne test of the isotopic dilution APIMS was needed to establish the feasibility of the using the APIMS for

eddy correlation flux determinations. The successful results of the airborne SO2 intercomparison and fast airborne measurements presented in this chapter directly led to the 84

second airborne test where direct eddy correlation fluxes of DMS and SO2 were measured in the marine boundary layer.

3.2 Experimental

In this section, experimental details regarding the first airborne test of the isotopic dilution APIMS are presented. Information regarding the test flights, in particular aircraft used and location, are presented. Detailed instrumental settings and parameters, manifold design, and data acquisition are also discussed.

3.2.1 Airborne Isotopic Dilution APIMS Test Flights

Aircraft time was granted by the National Center for Atmospheric Research

(NCAR)/Research Aviation Facility (RAF). We used the RAF Hercules EC-130Q to

conduct airborne testing of the isotopic dilution APIMS. Five flights were performed and

are summarized in Table 3.1.

Table 3.1 Flight information for airborne instrumental intercomparison of isotopic dilution AIPMS and GC/MS/ILS for SO2.

Date Flight Number Description Comments 29 October 1999 105TF01 Boulder Local General APIMS and GC/MS/ILS Testing 1 November 1999 105TF02 Boulder Local SO2 Intercomparison; Pollution Plume Interception; Moist Layer Study 3 November 1999 105TF03 Boulder Local SO2 Intercomparison 4 November 1999 105TF04 Boulder to San Diego Brief Eddy Correlation Testing 5 November 1999 105TF05 San Diego to Boulder Marine Boundary Layer Testing 85

The RAF is located in Broomfield, Colorado and was the departure point for the C-

130 aircraft. As seen in Table 3.1, the first three flights were local flights (termed Boulder

Local for simplicity) and averaged 176 minutes. The last two flights were transits to and

from San Diego, where a preliminary marine boundary layer flight was conducted. The

average transit time for these flights was 294 minutes.

The instruments were located in the forward right cabin of the aircraft. The APIMS

was located in the first right forward cabin position, with the GC/MS/ILS directly behind

the APIMS in the second right forward cabin position. The instruments were kept close in

position due to a shared manifold.

3.2.2 APIMS Instrumental Parameters

Detailed descriptions of the operating parameters for the airborne APIMS were

given in Chapter 2. Table 3.2 presents a list of the settings for the APIMS during the

measurements for the airborne instrumental intercomparison for SO2. Typically in the

continental boundary layer and free troposphere, the APIMS had an average sensitivity of 10 ≥ counts per second (cps)/pptv with a signal-to-noise ratio 5 for SO2. The lower limit of detection was determined to be 1 pptv for a one second integration time. An eight-fold

improvement in sensitivity was obtained for SO2 in the second airborne test (Chapter 4)

through modifications of the quadrupole and lens housings in the APIMS chamber, as well

as enlarging the aperture lens orifice by 13%. Operation and settings for the GC/MS/ILS

system are similar to those mentioned in the literature.5, 6

Table 3.2 Airborne isotopic dilution APIMS instrumental setting for SO2 detection.

Lenses (Volts) Flows

Aperture -2.3 Manifold (L/min) 8 to 20

Decluster -8.6 API (L/min) 5 to 7

API Ion Energy -3.5 Isotopic Standard (mL/min) 50 to 100

L1 +6.0 Pressures (Torr)

L2 +90.2 Source 6x10-5

L3 +65.5 Analyzer 1x10-5

Entrance -12.0 Decluster 7

Prefilter +9.5 Manifold (mb) 1100

Exit +44.5

Pole Bias -1.7 Source Temperature (°C) 150

- SO5 (m/e 112) Multiplier (kVolts) +3.5 Masses Detected 34 - SO5 (m/e 114)

The reader is referred back to Chapter 2, Reactions 2.17 to 2.20 for a discussion of the

- relevant ion-molecule reactions leading to the formation of ambient SO5 (m/e 112) and

34 - isotopic standard SO5 (m/e 114).

3.2.3 Airborne Manifold Design

The manifold for the tropospheric measurements of SO2 for the airborne intercomparison between the isotopic dilution APIMS and GC/MS/ILS is shown in Figure 87

3.1. This manifold was similar to the general laboratory manifold discussed in Chapter 2.

The APIMS and GC/MS/ILS sampled from the same ambient air manifold.

Ambient air was drawn into a Teflon sampling line that extended through an inlet pipe attached to a window plate on the aircraft wall. The sampling line extended beyond the

34 aircraft boundary layer and was backwards facing. The SO2 internal standard was added to the air sampling line about 40 cm from the entrance to the air inlet. The air sampling manifold was pressurized to about 1100 mb with a metal bellows pump6(Model MB-601,

Parker Hannifin Corp., Sharon, MA). The manifold pressure was dynamically controlled with an absolute pressure sensor and a voltage controlled valve (MKS, Inc., Sharon, MA).

The declustering region was maintained near 7 Torr with a manually controlled valve.

Details of the GC/MS/ILS cryogenic sampling technique and additional sample drying methods are discussed in detail in the literature.5, 6

The concentration of the ambient SO2 was determined relative to the internal standard after correcting for the ambient isotopomers in the standard cylinder using

34 Equation 2.1 in Chapter 2. The SO2 standard cylinder (cylinder # CA01130) was prepared

34 by Scott-Marrin Inc. (Riverside, CA) from SO2 containing >99% mol S supplied by Icon

Services (Mount Marion, NY). The coefficients used in Equation 2.1 are the following:

Kss=0.9939; Kas=0.0017; Kaa=0.9457; Ksa=0.0457. The concentration of the standard cylinder was 177 ppbv determined by comparison to permeation tubes using the

GC/MS/ILS technique.

Forward

To GC/MS/ILS ½ Metal ½ Metal Bellows Pump Nafion Dryers Nafion

Mass Flow Mass Flow Manifold Controller Controller Mass Flow Meter Ambient Ionizer APIMS SO2 34 Ultrapure Air S O2 O3

Mass Flow Controller

½ Metal Bellows Venturi Pump Exhaust

Aircraft Wall

Figure 3.1 Manifold design for SO2 measurements using an airborne isotopic dilution atmospheric pressure ionization mass spectrometer (APIMS) and GC/MS/ILS.

The flow through the 63Ni source region was controlled with a mass flow controller

(Unit Instruments, Inc., Model UFC-8100, Yorba Linda, CA) downstream from the source.

Ozone in excess of ambient concentrations was added to the air flow into the 63Ni source.

The addition of ozone removed any dependence on ambient ozone concentration

(particularly in the marine boundary layer) and allowed the SO2 background signals to be obtained using ultrapure air (Scott-Marrin, Inc., Riverside, CA). The metal bellows pump

14 contributes a small SO2 background. Two Nafion dryers (Model PD-1000-24SS, 89

PermaPure, Inc., Toms River, NJ) were used in series to reduce the interference of water

vapor in the marine boundary layer or moist layers.1 The dryers could be bypassed through

use of two three-way valves. The purge gas through the exterior of the dryer was the API

flow excess from the 63Ni source.

3.2.4 APIMS Data Acquisition

The ion multiplier detection for the APIMS was operated in counting mode. The

mass selection voltage and the ion counting were performed with software developed using

LabVIEW 5.1 (National Instruments, Austin, TX). The flow and pressure information from

Table 3.2 was recorded at 1 Hz. The selected ion monitoring (SIM), mass spectrum

scanning, and flow/pressure logging software was previously described in Chapter 2, and are

shown in Appendix B. The integration period could be varied from 5 ms to >5 sec in either

mass scanning mode or selected ion monitoring mode. During the flights on the NCAR C-

130, typical integration periods were 25 ms for scanning mode and 500 ms per mass in

selected ion mode. For one test period, sampling for 100 ms in selected ion mode indicated

that eddy correlation measurements of SO2 were feasible.

3.3 Airborne Sulfur Dioxide APIMS-GC/MS/ILS Intercomparison

This section discusses the airborne instrumental intercomparison for SO2. Table 3.1 indicates the flights used in the intercomparison. The section is divided into three parts— one part for each test flight used in the intercomparison and a third part for combined 90 results. As mentioned previously, both the isotopic dilution APIMS and GC/MS/ILS

34 sampled from the same manifold that was continuously spiked with SO2 from a standard gas cylinder. The same algorithm (Equation 2.1) was used to correct each instrument’s response for the isotopic abundances of the ambient and standard materials. The 1 Hz

APIMS response was averaged over the integration period of the GC/MS/ILS (3 – 4

minutes depending on flight). The average background for both instruments is 25 pptv SO2 and was determined to be from the metal bellows pump at high altitudes.14 On this basis, no zero subtraction was performed for either instrument response during the intercomparison.

Moreover, the metal bellow zeros measurements were used as part of the intercomparison due to the fact that no data were available below 100 pptv.

3.3.1 Test Flight 2 (105TF02) Intercomparison

The second airborne isotopic dilution APIMS test flight (105TF02) occurred on

November 1, 1999. The flight track covered an area of 40 - 42°N, 103 - 105°W, which is along the border between Wyoming and Nebraska with Colorado (US). Multiple level legs at varying altitudes within the continental boundary layer and free troposphere were performed. Data used for the intercomparison was from the duration 18:44:10 – 21:21:30

UTC, and consisted of 22 sets of measurements. Regions in the data corresponding to ascents and descents were excluded from the intercomparison. The GC/MS/ILS integration time for this flight was three minutes; therefore, the APIMS 1 Hz response was averaged over the GC/MS/ILS integration time. Figure 3.2 illustrates the flight track and altitude for the intercomparison duration.

SO2 Airborne Instrument Intercomparison Test Flight 2 (105TF02) OctoberNovember 29, 1 1999, 1999 5000

4000

3000

2000

Pressure Altitude (m) 1000 18.5 19 19.5 20 20.5 21 21.5 UTC (Hours) 42.5

42 ) ° 41.5

Latitude ( Latitude 40.5

40 18.5 19 19.5 20 20.5 21 21.5 UTC (Hours) -102 )

° -103

-104

-105 Longitude (

-106 18.5 19 19.5 20 20.5 21 21.5 UTC (Hours)

Figure 3.2 Altitude profile and flight track for SO2 airborne APIMS-GC/MS/ILS intercomparison during Test Flight 2 (105TF02), November 1, 1999.

Figure 3.3 presents the SO2 intercomparison data for Test Flight 2. Inspection of

Figure 3.3 reveals that isotopic dilution APIMS and GC/MS/ILS responses for SO2 were

very linear having a correlation coefficient of 0.966. The intercept was –3.9 ± 12.0 pptv

suggesting that there may be a minor, but not significant, offset between the two methods.

This offset was probably due to residual ambient SO2 contributions in the GC/MS/ILS

column or tubing or differences in sensitivity between the instruments at lower

concentrations. The slope of the regression was 0.91 ± 0.04. Examination of the residual

plot in Figure 3.4 indicates no apparent trend in the data.

SO2 Airborne Instrument Intercomparison Test Flight 2 (105TF02), November 1, 1999 550

500

450

400 (pptv) 2 350

300

250

200

150

Averaged APIMS SO 100

0 0 50 100 150 200 250 300 350 400 450 500 550

GC/MS/ILS SO2 (pptv)

Figure 3.3 Test Flight 2 (105TF02) APIMS-GC/MS/ILS SO2 intercomparison data.

Regression Residuals for SO2 Instrument Intercomparison Test Flight 2 (105TF02), November 1, 1999 100.0

80.0

60.0

40.0

20.0 - Regression 2 0.0

-20.0

APIMS SO -40.0

-60.0

-80.0 0 50 100 150 200 250 300 350 400 450 500 550

GC/MS/ILS SO2

Figure 3.4 Test Flight 2 (105TF02) regression residuals.

3.3.2 Test Flight 3 (105TF03) Intercomparison

The third airborne isotopic dilution APIMS test flight (105TF03) occurred on

November 3, 1999. The flight track covered an area of 40 - 42°N, 102 - 104°W, which is a

similar geographic area to Test Flight 2. Only two level free troposphere flight legs were

performed. Data used for the intercomparison was from the duration 19:23:30 – 20:53:00

UTC, and consisted of 11 sets of measurements. The region in the data for the descent was

excluded from the intercomparison. The GC/MS/ILS integration time for this flight was

four minutes. Again, the APIMS 1 Hz response was averaged over the GC/MS/ILS

integration time. Figure 3.5 illustrates the flight track and altitude for the intercomparison

duration of Test Flight 3.

SO2 Airborne Instrument Intercomparison Test Flight 3 (105TF03) November 3, 1999 5500

5000

4500

4000

3500

Pressure Altitude (m) 3000 19.4 19.6 19.8 20 20.2 20.4 20.6 20.8 UTC (Hours) 42 )

° 41.5

41 Latitude ( Latitude

40.5 19.4 19.6 19.8 20 20.2 20.4 20.6 20.8 UTC (Hours) -101 )

° -102

-103

-104 Longitude (

-105 19.4 19.6 19.8 20 20.2 20.4 20.6 20.8 UTC (Hours)

Figure 3.5 Altitude profile and flight track for SO2 airborne APIMS-GC/MS/ILS intercomparison during Test Flight 3 (105TF03), November 3, 1999. 94

Figure 3.6 presents the SO2 intercomparison data for Test Flight 3. Inspection of

Figure 3.6 reveals once again that the isotopic dilution APIMS and GC/MS/ILS responses

± for SO2 were very linear having a correlation coefficient of 0.993. The intercept was –18.7

8.6 pptv once again suggesting that there may be a minor offset between the two methods.

This intercept, however, is within the statistical range of the Test Flight 2 intercept value.

Examination of the data reveals a general trend for the GC/MS/ILS SO2 response to be

slightly larger than the APIMS response during Test Flight 3. Since the isotope dilution

method always gives the upper bound without zero correction5, 6, the lower signal (i.e.

APIMS) is probably correct, suggestion that a true background exits in the GC/MS/ILS.

This fact is less significant once the two test flight data are combined to create an overall

± airborne SO2 intercomparison profile. The slope of the regression was 0.95 0.03.

Examination of the residual plot in Figure 3.7 once again indicates no apparent trend in the

data.

3.3.3 Combined Airborne SO2 Instrumental Intercomparison

To obtain a better perspective on the airborne isotopic dilution APIMS-

GC/MS/ILS intercomparison of SO2, the data from Test Flight 2 and Test Flight 3 were

combined. Similarity in geographical test area, altitudes, experimental, and instrumental

conditions allows for the combination of the data. Furthermore, combining the data creates

a large number of sample periods. Figure 3.8 presents the combined plot of the two test

flight data.

SO2 Airborne Instrument Intercomparison Test Flight 3 (105TF03), November 3, 1999 650 600 550 500 450 (pptv) 2 400

350 300

250 200 150

Averaged APIMS SO 100 50 0 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700

GC/MS/ILS SO2 (pptv)

Figure 3.6 Test Flight 3 (105TF03) APIMS-GC/MS/ILS SO2 intercomparison data.

Regression Residuals for SO2 Instrument Intercomparison Test Flight 3 (105TF03), November 3, 1999 40

0 - Regression 2

-10

-20 APIMS SO

-30

-40 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700

GC/MS/ILS SO2

Figure 3.7 Test Flight 3 (105TF03) regression residuals.

SO2 Airborne Instrument Intercomparison Combined Test Flights (105TF02, 105TF03), Nov. 1 & Nov. 3, 1999 650 Test Flight 3 600 Test Flight 2 550 Regression

500

450 (pptv) 2 400

350

300

250

200

150 Averaged APIMS SO 100

0 0 50 100 150 200 250 300 350 400 450 500 550 600 650 700

GC/MS/ILS SO2 (pptv)

Figure 3.8 Comparison of SO2 determined by isotopic dilution APIMS and GC/MS/ILS by combining continental boundary layer and free troposphere data from Test Flights 2 and 3 (Nov. 1 & Nov. 3 1999).

Inspection of Figure 3.8 reveals that the APIMS and GC/MS/ILS responses for SO2

were very linear having a correlation coefficient of 0.979. The intercept was –10.7 ± 7.8 pptv

suggesting that there was no significant offset between the two methods for a larger number

of data sets. The slope of the regression was 0.93 + 0.02. From Figure 3.8, the two methods agree to within 3 pptv at 25 pptv indicating that the agreement of the two methods may be much better than the data from this experiment suggests due to the fact that all of the ambient (i.e. non zero) measurements were above 100 pptv. A general conclusion that can be drawn from Figure 3.8 and its regression is that the isotopic dilution APIMS and

GC/MS/ILS agree very well, validating the accuracy of the APIMS measurements on aircraft platforms.

3.4 Utility of the Fast Measurement of Airborne Isotopic Dilution APIMS

In section 3.3, the fast airborne isotopic dilution APIMS SO2 response was averaged over the integration time of the GC/MS/ILS to intercompare similar time durations. By doing so, the resolution of the APIMS response is lost. Part of the utility of the APIMS instrument is its ability to rapidly measure the response to ambient species, with the ultimate

goal of conducting eddy correlation flux determinations of DMS and SO2. However, the prospect of this instrument can be clearly demonstrated even at 1 Hz in comparison to the

GC/MS/ILS technique.

The one second response time of the APIMS is illustrated in Figure 3.9 for a period when the NCAR C-130 was porpoising through a thin moist layer in the free troposphere between 3.3 and 3.8 km pressure altitude. Figure 3.10 is an expanded region of the pressure altitude plot of Figure 3.2 to demonstrate the vertical profiles through the moist layer. 98

Thin Layer between 3.3 & 3.8 km Pressure Altitude on Test Flight 2 (105TF02), November 1, 1999 800 APIMS SO2

GC/MS SO2 & sample period 700

600

500

(pptv) 400 2 SO 300

200

100

0 20:12 20:14 20:16 20:18 20:20 20:22 20:24 20:26 20:28 20:30 UTC

Figure 3.9 Fast airborne APIMS response (1 Hz) and GC/MS/ILS response (3 min. integration) while porpoising through a thin layer on Test Flight 2 (105TF02), November 1, 1999.

3.9

3.8

3.7

3.6

3.5

3.4

3.3 Pressure altitude (km) 3.2

3.1 20:12 20:14 20:16 20:18 20:20 20:22 20:24 20:26 20:28 20:30

Figure 3.10 Expanded plot of Figure 3.2 showing the interception of the thin moist layer between 3.3 and 3.8 km pressure altitude on Test Flight 2 (105TF02), November 1, 1999.

700 SO2 1.4 MR 600 1.2

500 1

400 0.8

300 0.6 SO2 (pptv) LAS MR (g/kg) 200 0.4

100 0.2

0 0 20:12 20:14 20:16 20:18 20:20 20:22 20:24 20:26 20:28 20:30 UTC 01 Nov 99

Figure 3.11 1 Hz responses for airborne isotopic dilution APIMS SO2 and laser absorption spectroscopy water vapor mixing ratio (LAS MR) during the moist thin porpoises on Test Flight 2.

The wealth of detail provided by the fast response of the APIMS illustrated the

advantage the isotopic dilution technique has of supplying an internal standard calibration

for each sample. Although the GC/MS/ILS response followed the general trend of the

data, it is clear that the detail is lost, particularly in respect to the range of SO2 concentrations

over short time periods.

During the flights on the NCAR C-130, fast water vapor mixing ratio measurements

were obtained by laser absorption spectrometry (LAS) by Bruce Gandrud23 of NCAR.

Figure 3.11 shows the responses of the airborne isotopic dilution APIMS for SO2 and the

LAS H2O for the same time period as Figure 3.9 and Figure 3.10. The mixing ratio (MR) data was averaged to 1 second. Each ambient SO2 point was determined from the response

of the APIMS for the ambient ion integrated for 500 ms followed by the internal standard 100

for 500 ms, yielding 1 Hz data. Inspection of Figure 3.11 reveals that the LAS MR and SO2

are well correlated on time scales as short as 1 - 3 sec. Nearly every increase in water vapor

had a concomitant increase in SO2. The SO2 and water vapor probably had the same source, which was most likely boundary layer air that was transported to the study region. Even at 1

Hz, the APIMS capability to perform correlated measurements is well established, suggesting that eddy correlation flux determinations are feasible with an airborne isotopic dilution

APIMS at fast sampling rates.

Additional fast response data was obtained for SO2 with the APIMS system during

Test Flight 2 that intercepted power plant plumes. At 300 m above ground over Wyoming

(see Figure 3.2), the flight path was oriented along a power plant plume. Figure 3.12

presents the data collected during the plume interception. The integration times were again

500 ms for the ambient ion internal standard ion yielding 1 Hz data. Signal to noise ratios

were sufficient to have obtained sampling rates a factor of ten faster. Also shown on this

figure is the averaged APIMS and GC/MS/ILS response for portions of the plume. Once

again, the GC/MS/ILS followed general trend but failed to resolve the fine structure in the

plume. 101

C-130 Test Flight through Power Plant plume at 300 m above ground level 100000 APIMS SO2 Response GC/MS SO2 average APIMS SO2

10000

1000 SO2 (pptv)

100

10 20:29 20:31 20:33 20:35 20:37 20:39 20:41 20:43 UTC 01 Nov 99

Figure 3.12 Airborne APIMS and GC/MS/ILS SO2 responses through power plant plumes on Test Flight 2 (105TF02), November 1, 1999.

3.5 Eddy Correlation Feasibility and Preliminary Marine Boundary Layer Tests

During Test Flight 4 (105TF04, November 4, 1999), en route to San Diego, initial

tests were conducted at 6.1 km pressure altitude at a sampling rate of 9.7 Hz (Nyquist

frequency 4.85 Hz) to establish the feasibility of performing real-time eddy correlation flux

determinations of trace atmospheric species using the isotopic dilution APIMS technique.

Since the data acquisition system was not designed to take real-time data on the order of 10

Hz, only the isotopic standard mass was monitored during the test. Figure 3.13

demonstrates the isotopic standard response to the 9.7 Hz sampling rate.

102

Airborne Eddy Correlation Feasibility Test on Isotopic Standard m/e 114 4 x 10 Test Flight 4 (105TF04), November 4, 1999 3 (a) 2.8

2.6

m/e 114 m/e 2.4

2.2 Counts Per Second (cps)

2 18.2 18.21 18.22 18.23 18.24 18.25 18.26 UTC (Hours)

4 x 10 3 (b) 2.8

2.6

2.4 m/e 114 m/e

2.2 Counts Per Second (cps)

2 18.2 18.21 18.22 18.23 18.24 18.25 18.26 UTC (Hours)

Figure 3.13 Airborne test of eddy correlation feasibility with isotopic dilution APIMS during Test Flight 4. (a). 34 - Raw m/e 114 (isotopic standard SO5 response) (b). 10 point forward-reverse zero phase Fourier transform filter applied to (a).

Examination of Figure 3.13 (a) reveals a near constant signal for the isotopic standard with little noise associated with the signal (it is possible that Poisson white noise may affect the signal at higher sampling frequencies due to the nature of the pulse counting detection system). The variations in the standard in Figure 3.13 (b) (10 point zero-phase Fourier transform filter applied to raw m/e 114 signal), were due to meteorological changes (such as changing water vapor concentration), are expected and normal and were also reflected in the ambient measurement. Hence, there is a continuous correction for changes in the instrument sensitivity, which is a strong benefit in the isotopic dilution technique.5, 6

Although this would not be the data acquisition system deployed for eddy correlation flux determinations, it is evident that the isotopic dilution APIMS technique can provide 103 adequate stable measurements of the internal standard for use in continuous calibration of real-time flux determinations.

Marine boundary layer flight legs were conducted off the coast of San Diego during the initial part of Test Flight 5 (105TF05, November 5, 1999). This provided a first look

into the responses of SO2 and DMS with the airborne isotopic dilution APIMS in marine boundary layer air. As expected, the APIMS SO2 response was significantly reduced in the marine boundary layer without the use of the Nafion dryers. The SO2 response was satisfactory with the use of the dryers but was still reduced compared to the free tropospheric response. The response for DMS using the APIMS in the marine boundary layer was significantly diminished with and without the Nafion dryers for both the isotopic standard and ambient DMS. DMS signal was not detectable above the background level.

Frequent aperture orifice blockage also occurred during the marine boundary layer flights, which also significantly reduced the sensitivity of the APIMS. The results of this test prompted changes in the quadrupole and lens housing inside the APIMS chamber, enlargement of the aperture orifice, and the invention of a sample air heater (discussed in

Chapter 2) all in effort to increase the sensitivity of the APIMS response for DMS and SO2 in the marine boundary layer.

3.6 Conclusions

Validity of the accuracy of the isotopic dilution APIMS measurements for SO2 was obtained through a successful airborne intercomparison with the well-proven GC/MS/ILS

method. The application of APIMS for the airborne determination of SO2 with a sampling frequency of 1 Hz was also demonstrated. Temporal resolution for ambient SO2 104 measurements is gained with the APIMS technique, as demonstrated through the probing of a thin moist layer and power plant plume interception during the test flights. The inclusion of the isotopically labeled standard provided a continuous reference for the performance of

the instrument and the transmission of the ambient SO2 through the air sampling manifold.

Brief tests of sampling at a Nyquist frequency of 4.85 Hz indicate that eddy correlation flux

determinations of SO2 appear feasible with this instrument design. However, water vapor reduction is necessary to maintain sensitivity in the marine boundary layer.

105

3.7 References

(1) Thornton, D. C.; Driedger III, A. R.; Bandy, A. R. Anal. Chem. 1986, 58, 2688.

(2) Driedger III, A. R.; Thornton, D. C.; Lalevic, M.; Bandy, A. R. Anal. Chem. 1987, 59,

1196.

(3) Thornton, D. C.; Bandy, A. R.; Ridgeway, R. G.; Driedger III, A. R.; Lalevic, M. J.

Atmos. Chem. 1990, 11, 299.

(4) Ridgeway Jr., R. G.; Bandy, A. R.; Thornton, D. C. Mar. Chem. 1991, 33, 321.

(5) Bandy, A. R.; Thornton, D. C.; Ridgeway Jr., R. G.; Blomquist, B. W. In ACS Symp.

Ser., 1992; Vol. 502, pp 409-422.

(6) Bandy, A. R.; Thornton, D. C.; Driedger III, A. R. J. Geophys. Res. 1993, 98, 23423.

(7) Bandy, A. R.; Scott, D. L.; Blomquist, B. W.; Chen, S. M.; Thornton, D. C. Geophys.

Res. Lett. 1992, 19, 1125.

(8) Blomquist, B. W.; Bandy, A. R.; Thornton, D. C.; Chen, S. J. Atmos. Chem. 1993, 16,

23.

(9) Thornton, D. C.; Bandy, A. R.; Beltz, N.; Driedger III, A. R.; Ferek, R. J. Geophys.

Res. 1993, 98, 23459.

(10) Thornton, D. C.; Bandy, A. R. J. Atmos. Chem. 1993, 17, 1.

(11) Bandy, A. R.; Thornton, D. C.; Blomquist, B. W.; Chen, S.; Wade, T. P.; Ianni, J. C.;

Mitchell, G. M.; Nadler, W. Geophys. Res. Lett. 1996, 23, 741.

(12) Blomquist, B. W.; Bandy, A. R.; Thornton, D. C. J. Geophys. Res. 1996, 101, 4377.

(13) Thornton, D. C.; Bandy, A. R.; Blomquist, B. W.; Davis, D. D.; Talbot, R. W. J.

Geophys. Res. 1996, 101, 1883. 106

(14) Thornton, D. C.; Bandy, A. R.; Blomquist, B. W.; Talbot, R. W.; Dibb, J. E. J.

Geophys. Res. 1997, 102, 28489.

(15) Thornton, D. C.; Bandy, A. R.; Blomquist, B. W.; Bradshaw, J. D.; Blake, D. R. J.

Geophys. Res. 1997, 102, 28501.

(16) Russell, L. M.; Lenschow, D. H.; Laursen, K. K.; Krummel, P. B.; Siems, S. T.;

Bandy, A. R.; Thornton, D. C.; Bates, T. S. J. Geophys. Res. 1998, 103, 16411.

(17) Lenschow, D. H.; Paluch, I. R.; Bandy, A. R.; Thornton, D. C.; Blake, D. R.;

Simpson, I. J. Geophys. Res. 1999, 104, 16275.

(18) Wang, Q.; Lenschow, D. H.; Pan, L.; Schillawski, R. D.; Kok, G. L.; Prevot, A. S. H.;

Laursen, K. K.; Russell, L. M.; Bandy, A. R.; Thornton, D. C.; Suhre, K. J. Geophys.

Res. 1999, 104, 21767.

(19) Thornton, D. C.; Bandy, A. R.; Blomquist, B. W.; Driedger III, A. R.; Wade, T. P. J.

Geophys. Res. 1999, 104, 5845.

(20) Xu, N. H., Drexel University, Philadelphia, 1999.

(21) Gregory, G. L.; Davis, D. D.; Beltz, N.; Bandy, A. R.; Ferek, R. J.; Thornton, D. C. J.

Geophys. Res. 1993, 98, 23325.

(22) Gregory, G. L.; Warren, L. S.; Davis, D. D.; Andreae, M. O.; Bandy, A. R.; Ferek, R.

J.; Johnson, J. E.; Saltzman, E. S.; Cooper, D. J. J. Geophys. Res. 1993, 98, 23373.

(23) Gandrud, B.; Personal Communication / Unpublished Data, 1999. 107

4.1 Introduction

Local budgets for atmospheric scalars require accurate determinations of vertical fluxes of these scalars. For example, consider the studies of the budgets of dimethyl sulfide

1 (DMS) and sulfur dioxide (SO2) by Bandy et al. (1996) conducted at Christmas Island,

Kiribati in 1994. They made time series determinations of DMS and SO2 from which the

efficiency of the conversion of DMS to SO2 was uniquely computed. Inspection of their

work reveals that important parameters such as the rate of mass of DMS converted to SO2

could not be determined because the vertical fluxes of these species were not directly

measured. In fact, much effort was devoted to estimating vertical fluxes using chemical

models that provide closure. To decrease the uncertainty of these budgets, precise

atmospheric determinations of vertical fluxes need to be made. Thus, we set out to make

precise determinations of the vertical fluxes of DMS and SO2 using the well-known eddy

correlation method with an airborne isotopic dilution atmospheric pressure ionization mass

spectrometer (APIMS).

Eddy correlation fluxes are determined from the continuous real-time measurement

of a scalar quantity (such as the mixing ratios of DMS or SO2 in the atmosphere) and vertical

wind velocity.2-4 To briefly restate the principles of eddy correlation from Chapter 1, fluxes

are directly determined by measuring the covariance of a scalar, s, with the vertical wind

velocity, w. The instantaneous eddy correlation flux, F, at a given point in time is the 108 product of instantaneous fluctuations (deviations from the mean) in the scalar, s’, and the vertical wind velocity, w’,

F = w's' . Equation 4.1

The flux is defined to be positive upwards. The time-averaged flux is then determined by

1 t2 F = w's' = ∫ w'(t)s'(t)dt Equation 4.2 − t1 t2 t1

where t1 and t2 are the initial and final times, respectively, for a given time length determined by the existing meteorological conditions and instrumentation capabilities.4, 5 The above equations effectively demonstrate that the eddy correlation method is a preferred method for determining the atmospheric fluxes of trace gases since the method is direct and makes no assumptions. However, a crucial requirement for this method is the fast and accurate measurement of both the vertical wind velocity and the scalar of interest.

The measurement of vertical wind velocity by gust probes6, 7 on aircraft has been well documented. Airplanes can readily supply adequate sampling speed and accuracy. A measurement method for a scalar that utilizes the eddy correlation method should have a sufficient sampling frequency for aircraft measurements (>10 Hz), be specific to the compound(s) whose flux is being determined, and have sufficient sensitivity to measure the flux rate.2, 8 It is because of these constraints that the eddy correlation method has been

limited to a small number of atmospheric gases, such as H2O, CH4, CO, CO2, O3, NO, NOx,

N2O, and SO2, whose fluxes were primarily determined over land and are limited in their sampling frequency and sensitivity.2 109

In addition to lowering the uncertainty in budget measurements, DMS is an ideal molecule for studying and quantifying the vertical exchange in the marine boundary layer.9

What makes DMS ideal is that its only known source over the open ocean is at the surface, it is virtually insoluble in water, and its lifetime is about 1-2 days.9, 10 Because of this relatively short lifetime compared to overturning time scales of the troposphere, DMS concentrations above the marine boundary layer top are generally small. However, this lifetime is still rather large compared to the boundary layer turbulence timescales which are on the order of 10 minutes and timescales associated with advection over small-scale variations in surface production. Hence, we expect to find reasonably homogeneous concentrations of DMS in the marine boundary layer, with sharp well-defined jumps across the top of the marine boundary layer. Measurements of the DMS flux by eddy correlation will enable the direct calculation of the entrainment velocity between layers, which will then enable the calculation of other species fluxes by analogy using the calculated DMS entrainment velocity.9, 10

The remaining sections of this chapter demonstrate the results of using an airborne isotopic dilution atmospheric pressure mass spectrometer for the direct eddy correlation flux determinations of dimethyl sulfide and sulfur dioxide in the marine boundary layer. Vertical concentration profiles of DMS also are provided to demonstrate the real-time measurement capability of the APIMS instrument, as well as the usefulness of DMS as a tracer molecule for meteorological investigations. The next section briefly describes the instrumental

changes needed to successfully measure real-time mixing ratios of DMS and SO2, as well as the data reduction techniques used to compute the eddy correlation flux from the aircraft and instrument data.

110

4.2 Experimental

In Chapter 2, we presented a basic description of the instrumentation and

methodology needed to measure atmospheric dimethyl sulfide and sulfur dioxide with an

isotopic dilution APIMS. Also in Chapter 2, ancillary instrumentation was described, such as

the inclusion of an ozone generator, Nafion dryer, and sample air heater, which enabled the

direct measurement of DMS and SO2 in the marine boundary layer with an APIMS. This

section describes the fast counter and data acquisition system required to perform the fast

real-time measurements of DMS and SO2 in the atmosphere necessary for computing the vertical flux of those species using the eddy correlation technique. Information on the instrumental parameters is also provided in this section. The section concludes with a description of the data reduction techniques used to compute the eddy correlation flux values and errors.

4.2.1 Fast Counting System

The data acquisition system described in Chapter 2 is based on the internal timing

chips of the National Instruments AT-MIO-16DE-10 data acquisition card, which is not fast

enough for sampling data above 10 Hz. Therefore, a fast counting system was constructed

that could control the mass switching and count rate collection up to 100 Hz per channel.

The fast counting system deployed contained two 12-bit mass command channels and two

16-bit binary data channels. The information from the mass and data channels can be

acquired simultaneously by two independent data acquisition systems. The data are acquired

as alternating sets of mass and pulse counts from the two mass command channels. The 111 system also has a remote mode that allows low rate data acquisition using LabVIEW programs to be performed in place of the real-time data acquisition. Wolfgang Nadler built the system in-house at Drexel University.

4.2.1.1 Fast Counter Mass Command

The two mass commands are developed and set using a precision voltage source and

10-turn potentiometers mounted on the front panel (Figure C.1, Appendix C). An approximate voltage setting is obtained using the approximate conversion of 1 amu = 25 mV. The mass control voltage range is 0 - 10 Volts. Introducing a constant amount of analyte to the APIMS and then adjusting the mass command so that the signal was maximized obtained the actual command voltage used in data acquisition. The actual command voltage entered into the fast counter is verified with a modified spectrum scanning program (Figure B.1, Appendix B) that is run under LabVIEW 5.1 for Windows NT

(National Instruments, Austin, TX). This VI displays counts per second on the ordinate and voltage on the abscissa. This program is similar to the spectrum scanning VI explained in

Chapter 2.

The command voltages are connected to a two input analog multiplexer (Maxim

DG419, Figure C.3, Appendix C). The mass select control signal, derived from the timer board, selects the appropriate mass command voltage at the appropriate time. The output of the multiplier is buffered using a Texas Instrument TLE 2024 buffer amplifier (gain of +1), and then used as the mass command input for the Model 150 QC quadrupole rf supply

(ABB Extrel). The mass command signals were digitized using a 12-bit analog-to-digital

(A/D) converter (Maxim 574 AJ) and latched out using a Texas Instruments 74LS 374 latch/buffer. For the 12-bit A/D, the minimum mass resolution that can be obtained is 2.4 mV, or 1/10 of a mass unit; thus, the 12-bit A/D can provide adequate resolution for this 112 study. The output of this latch buffer was connected to a DB-37 male connector, where the upper 4 bits of the 16-bit word were grounded. The latches are duplicated to run two data acquisition systems (local computer and aircraft computer) without the possibility of interference. A timer board determined when to switch the multiplexer to select the next mass to be samples and begin integrating the count rate.

4.2.1.2 Fast Counter Pulse Counting

Pulses emitted from the DeTech Dynode Electron Multiplier (Model 420 A-H,

Palmer, MA) are amplified, shaped, and stretched by a Model MTS-100 preamplifier/discriminator (Advanced Research Instruments Corporation, Boulder, CO), thereby producing a corresponding train of TTL pulses. The TTL pulses are integrated using a circuit consisting of two counters operating in parallel (Figure C.4, Appendix C).

One counter, a binary coded decimal (BCD) counter (National 74 F160), produces the integrated count rate displayed on the instrument front panel. The other counter (National

74 F161) also integrates the count rate producing a signal that is directly sent to the data port, which is split buffered for two simultaneous data acquisition systems, and connected to two DB-37 male connectors on the instrument back panel (Figure C.2, Appendix C).

4.2.1.3 Fast Counter Timer Circuit

The timer circuit in the fast counter system drives the mass voltage control analog multiplexer, the binary data counter, and the data ready flag (bit). This circuit consists of four “down counters” (Fairchild 74LS 190) arranged in a ring or circular pattern, so that after each counter completes it count, it will trigger the next counter (Figure C.5, Appendix 113

C). Two sets of counters control the integration time for the two mass command channels

(mass A, mass B); the remaining two counters control the delay or settling time between the switching of the mass command voltage. Mass A and B timers are independently adjustable on separate sets of thumbwheel switches. The two delay times are not independent and share one common thumbwheel input. All switches preload the down counters with the integration times and delay times.

The first counter counts the time for the mass A integration. After this count is complete, the mass voltage signal is switched to the second channel. The delay timer is activated to allow the voltage to settle before pulse counting begins. Once that counter completes its count, the mass B timer is started. Every time the mass command timers are active, the appropriate gate signal is sent to the counter gate and latches, and pulse counting occurs for the current mass value being timed. This procedure repeats ad infinitum until the operator switches the control to remote mode.

The down counters are decremented from a common TTL crystal clock (DigiKey

SPG 8640 BN) signal of 1 kHz ± 100 part-per-million (ppm). The error on the clock translates into ± 8.5 seconds/day. This is important if the fast counting system is to be used as an absolute time reference; however, this is not the case for our data collection, which is stored on an aircraft data system under the same time base as other meteorological variables.

The process of synchronizing our APIMS data with the aircraft data system is accomplished through the use of a data ready bit.

114

4.2.1.4 Fast Counter Data Ready Bit

A 1-bit binary data ready signal (flag) is provided to notify data acquisition systems that the mass voltage and corresponding pulse counts from the last integration period are ready to be acquired. This data ready flag is derived from the end of count of the current mass integration time down counter. The data collection is intended to be a true interrupt driven system; thus, the data can be read as soon as 30 µs after the down counters have completed their tasks.

However, interrupt driven data acquisition systems are not always available. To facilitate data acquisition, the data ready pulse has been extended to 5 ms in order to provide ample time for a polling, non-interrupt driven data acquisition systems to recognize data is available to be recorded. The flag is reset after the 5 ms duration. A binary high (TRUE) on the data ready bit is indicative of data being available.

4.2.2 Real-Time Data Acquisition

Two data acquisition systems were employed to collect the fast real-time data from the fast counting system described previously. Both data acquisition systems are non- interrupt driven systems that require the continuous polling of the data ready bit to become aware of data being available for recording. The two data acquisition systems used were the

Drexel University airborne APIMS computer system and the National Science Foundation

(NSF)/National Center for Atmospheric Research (NCAR)/ Research Aviation Facility

(RAF) Hercules EC-130Q Aircraft Data System II (ADS2).

115

4.2.2.1 Drexel APIMS Computer System

The computer hardware for this data acquisition system was described in Chapter 2.

In addition to the National Instruments (Austin, TX) data acquisition card for <10 Hz data recording, a Computer Boards, Inc. (Middleboro, MA) CIO-DI48 digital input data acquisition card was installed to record the real-time binary data from the fast counting system. The CIO-DI48 card is a 48-bit non-programmable TTL digital input only card split into six eight-bit ports. The card can read signals from other TTL devices as well as solid- state relays.

A LabVIEW program was created to record the binary data entering the digital input card from the fast counting system. The FAST_SIM VI logged to file the incoming mass voltage and integrated count rate from the fast counting system at user specified intervals.

This program also provides a display of that information to the instrument operator through waveform plots on the program front panel (Figure B.4 (a), Appendix B). The header file, which is a collection of all of the user specified information along with date and time stamping for the start of the acquisition, is collected as a text file independently of the data files. The data files are stored as successive binary files using the procedure described below.

Data retrieval from the CIO-DI48 card is based on nested FOR loops that govern the way the digital information is collected and stored to file or displayed. To access the information on the digital ports, digital port configuration subroutines are called. These subroutines are called outside of the FOR loops in order to decrease the access time for the data. Once configured, data acquisition from the digital ports is performed within the FOR loops.

As seen in wiring diagram for the FAST_SIM VI (Figure B.4 (b), Appendix B), the user specified inputs of data/display scan rate, sample collection time, and data frequency 116

(via the mass integration time) dictate the iteration values for the FOR loops collecting the data. The primary FOR loop controls the number of times information is stored to a file or displayed. The secondary FOR loop controls the number of times an internal sequence for polling the data ready bit and reading the digital port information is executed. The first part of this sequence is a WHILE loop polling the data ready bit. This loop continues until a binary high (TRUE) is detected. Once the TRUE bit is detected, the second section of the sequence is executed that acquires the mass and pulse counting data from the digital ports, which is to be stored in an indexing array in memory until the secondary FOR loop iterations are complete.

Since the data acquired from the digital input is indexed by the mass command value, a method was developed to separate the integrated rate data going to the display so that it was indexed to a specific mass. The user supplied input of the Mass 1 and Mass 2 voltages from the front panel of the vi is compared to the actual voltage acquired from the digital input, and the pulse count data is sorted accordingly. This information is also placed on memory indexing arrays until the secondary FOR loop sequence completes.

The last section of the sequence is simply a 10 ms wait command that reduced computer overhead caused by polling of the data ready bit. This loop dramatically increases the accuracy and precision of the timing for the VI.

When the secondary FOR loop is complete, the data collected in the indexing arrays are sent to both the display and file. Each secondary FOR loop iteration produces a separate binary data file with an automatic appendage of an incremental numeric value. These separate binary files are later concatenated using the MS-DOS COPY command within the

Windows NT operating system and converted to ASCII format through another LabVIEW program. 117

To illustrate the function of this VI, suppose a typical data collection period consists

of a 20 minute segment of 50 Hz data (20 ms mass integration time). Based on that

information, 60000 data points are collected. Using a 500 scan display/data rate, 120

iterations are used in the primary FOR loop, while the original scan rate of 500 is used to

control the secondary FOR loop that controls how many times the internal digital port

reading sequence is executed. Thus, the data is collected and displayed every ten seconds for

the 20 minute period, and 120 separate data files are collected.

The primary function of this fast pulse counting VI is to obtain a real-time display of

the data being collected, as well as provide a data set that can be used to examine fast DMS

and SO2 data that does not depend on the aircraft data system. Along with the simultaneous execution of the flow and pressure data logging vi (FLOW_DATA VI, Figure B.3, Appendix

B) described in Chapter 2, ambient concentrations, instrument sensitivity, and background concentrations can be quickly determined without the need for importing and extracting information from the aircraft data system. The files collected from this VI also allow for the determination of internal calibration areas (zeros) during a flight that can later be excluded from the data collected on the aircraft data system. Although the data collected on the

APIMS computer system has good accuracy and precision, it is not synchronized to other meteorological variables, and is therefore not on an identical time base with those variables.

Thus, this data cannot be used for eddy correlation flux calculations. That is primary purpose of collecting the APIMS real-time data on the RAF aircraft data system.

4.2.2.2 NSF/NCAR/RAF Aircraft Data System (ADS2)

The 50 Hz output from the Fast Counting System on the airborne APIMS is logged

by the NSF/NCAR/RAF Hercules EC-130Q Aircraft Data System 2 (VME Motorola 118

68040 based with vxWorks). A Distributed Sampling Module (DSM) located in the forward

cabin of the aircraft collects the 50 Hz data from the APIMS system. The DSM is capable

of sampling up to 64 16-bit channels, all of which are synchronized at a fixed rate of 10K

samples per second (sps).11-13 Since the data collection system is not interrupt driven, a C based program was written to acquire the 50 Hz data by polling the data ready bit on the

APIMS fast counting system. The data was collected on a common time base with the rest of the metrological variables.

The aircraft data was processed in-house at NCAR through the use of the NIMBUS data processing software. The output data files are in Network Common Data Form

(netCDF) format (Unidata Program, UCAR, Boulder, CO). Data files for each flight are available in low-rate (1 Hz) and high-rate (25 Hz and 50 Hz) formats.

4.2.2.3 Instrumental Parameters

Detailed descriptions of the operating parameters for the airborne APIMS are contained in Chapter 2. Table 4.1 is a list of the settings for the APIMS during the

determinations of the eddy correlation fluxes of DMS and SO2. For operation in the marine

boundary layer, the APIMS demonstrated an average sensitivity of 80 counts per second

(cps)/pptv with a signal-to-noise ratio ≥ 5 for both species.

119

Table 4.1 Typical airborne APIMS instrument, flow and pressure settings during the measurements for the eddy correlation fluxes of DMS and SO2 in the marine boundary layer.

DMS SO2

Lenses (Volts)

Aperture -0.4 +2.3

Decluster +11.7 -12.3

API Ion Energy +5.1 -8.4

L1 -5.0 +14.3

L2 -77.3 +60.7

L3 -147.9 +2.2

Entrance -1.2 +2.8

Prefilter -7.2 +14.6

Exit -11.5 +37.5

Pole Bias +0.7 0.0

Multiplier (kVolts) -3.3 +3.3

Flows and Pressures

Manifold Flow (L/min) 50 32

API Flow (L/min) 4 to 5 3

Standard Gas Flow (mL/min) 100 100

Source Pressure (Torr) 8x10-5 5x10-5

Analyzer Pressure (Torr) 6x10-6 5x10-6

Decluster Pressure (Torr) 3 3

Manifold Pressure (Torr) Ambient Ambient

Source Temperature (°C) 175 175

Air Stream Heater (°C) 400 N/A

+ - DMSH (m/e 63, 25 Hz) SO5 (m/e 112, 25 Hz) Masses Detected/Sampling Rate + 34 - d3-DMSH (m/e 66, 25 Hz) SO5 (m/e 114, 25 Hz) 120

4.2.2.4 Data Reduction Technique

The computation of the eddy correlation fluxes of DMS and SO2 is complex and warrants a detailed discussion of the mathematics and associated software. The fast time series data from the APIMS computer system was used to examine the quality of the ambient and isotopic standard data and to find areas where internal calibrations were performed (zeros).

Matlab Release 12 Version 6.0 (Mathworks Inc., Natick, MA) routines (Appendix D) were written to sort the raw concatenated ASCII data from the corresponding ambient and standard masses. The background counts for each mass were computed and subtracted from the raw integrated counts. These zero areas were then removed from the sorted data.

A manifold concentration was calculated for the isotopic standard from the 1 Hz flow data

collected simultaneously with the fast data. The ambient concentrations of DMS or SO2 were then computed based on algorithms of the isotopic abundance existing in the ambient and standard species, as seen in Equation 2.1 in Chapter 2.14 Sensitivities were also computed based on the integrated counts of the isotopic standard.

Programs were developed based on the netCDF Matlab toolbox (Dr. Charles R.

Denham, U.S. Geological Survey, Woods Hole, MA) to extract the 50 Hz APIMS data, 25

Hz meteorological variables, and 1 Hz meteorological and position data from the netCDF data sets collected by the RAF ADS2 (Appendix D). A list of time segments to be studied was produced from the data collected by the APIMS computer in order to remove areas of internal zeros. This list consists of 10 second segments.

An error in the NCAR RAF ADS2 data acquisition software produced a data corruption approximately every ten minutes on the 50 Hz APIMS data. This error made the sorting the 50 Hz APIMS data into their two respective masses and counts very difficult and 121

dramatically shortened the time segments from which flux data were calculated. Fortunately,

10 minute segments are adequate for flux determinations.2, 4 To obtain eddy flux data the data corruption areas were identified, and a list of time segments (with continuous uncorrupted data) was produced that was used to extract data from the larger aircraft data

sets. For subsets of uncorrupted data, the ambient DMS or SO2 concentration was

calculated and stored as a separate concentration time series. The needed corresponding

meteorological variables (25 Hz and 1 Hz) also were extracted from the main aircraft data set

and stored to file.

Once these data segments were generated, another Matlab program (Appendix D)

was written to calculate the power spectral density (PSD), co-spectral density (CSD),

15 coherence, and phase angle of the ambient concentration of DMS or SO2 with meteorological variables. The data used in the spectral calculations are mean removed (i.e. fluctuations only) and have a linear detrend applied. The CSD of the ambient compound concentration and water vapor mixing ratio (MRLA) from the aircraft Lyman-alpha hygrometer16 were calculated to estimate the time lag between the two sensors based on the

phase angle (which is computed from the CSD).4, 15, 17-19 Once the time lag was determined,

the PSD and CSD of the ambient compound concentration with vertical wind velocity and

MRLA were computed. The covariance of the CSD of the ambient concentration and

vertical wind velocity is the eddy correlation flux value for that segment.

σ Random (sampling) errors in the calculated flux values, F(L), were computed using the following equations5, 20:

1/ 2 1/ 2 σ (L)  2L  1+ r 2  F =  f   ws     2  Equation 4.3 F  L   rws  122

where F is the flux, Lf is the integral length scale of the flux measurement based on the maximum spectral peak of the PSD of the flux (w’s’),4, 21 L is the length of the flux segment,

and rws is the correlation coefficient of the flux defined as

F r ≡ ws σ σ Equation 4.4 w s

σ σ where w and s are the variances in the vertical wind velocity and analyte concentration, respectively. The average air speed for the flight leg was used to compute lengths in meters when derived from the frequency spectrum. The flux integral scale length can also be estimated from the empirically determined relationship22

= ()1/ 3 L f 0.16zi z* Equation 4.5

where z* is defined as the ratio of the height of the flux measurement (z) and the marine boundary layer top (zi), namely z/zi.

The vertical concentration profiles of DMS were extracted from the real-time count data collected by the APIMS computer. The same approach was used to determine the

standard d3-DMS manifold concentration, background counts, and calibrations areas as to what was stated above for the flux calculations. After the ambient DMS concentration was computed for the time series of the vertical profile, the data was filtered using a 25 point zero-phase forward and reverse filter and decimated (i.e. reduced) to 1 Hz. 1 Hz altitude data was plotted against the ambient DMS concentration data, as well as other 1 Hz 123 meteorological variables to demonstrate the effectiveness of DMS as a tracer for entrainment studies.

4.3 Eddy Correlation Flux Studies

4.3.1 Field Studies

The data presented in this section were taken on the NSF/NCAR/RAF C-130 aircraft during the Passing Efficiency of the Low Turbulence Inlet (PELTI) program in July

2000. The main research flights were concentrated East of St. Croix, U.S. Virgin Islands, approximately spanning the region 17° to 19° N, 60° to 65° W. There were 11 flights, four of which were ferry flights between Boulder, Miami, and the St. Croix research area. Since the PELTI flights were not designed for flux studies, only three flights were concentrated in the marine boundary layer and were suitable for eddy correlation flux calculations. These flights also provided an opportunity to measure the vertical profile of DMS in and above the marine boundary layer.

Table 4.2 presents details of the flight information. More detailed description of the relevant flux data flight legs are presented in subsequent sections.

The APIMS and GC/MS/ILS instruments were located in the forward left cabin of the aircraft. The APIMS was located in the first left forward cabin position, while the

GC/MS/ILS was located in the first left mid-section position. Spacing between the instruments was not important since separate inlets and manifolds were used. 124

Table 4.2 Flight information containing comments on when measurements for DMS and SO2 eddy correlation fluxes were performed. Flights without comments were not flown in the marine boundary layer.

Date Flight Number Description Comments 5 July 2000 186FF01 Transit Boulder to Miami 6 July 2000 186RF01 Transit Miami to St. Croix DMS Intercomparison 9 July 2000 186RF02 St. Croix Local Flight Leg 5 MBL Vertical Profile DMS; Leg 6 (30m) MBL DMS Flux Measurements; DMS Intercomparison 11 July2000 186RF03 St. Croix Local Flight Legs 2 to 5 (50m) MBL DMS Flux Measurements; Leg 6 MBL Vertical Profile DMS 15 July 2000 186RF04 St. Croix Local Flight 16 July 2000 186RF05 St. Croix Local Flight Leg 1 MBL SO2 Flux Measurements; Legs 3, 4 and 6 MBL DMS Flux Gradient Measurements; Leg 2 to 3 MBL Vertical Profile DMS; DMS Intercomparison 16 July 2000 186RF06 St. Croix Local Flight 19 July 2000 186RF07 St. Croix Local Flight DMS Intercomparison 21 July 2000 186RF08 St. Croix Local Flight DMS Intercomparison 22 July 2000 186RF09 Transit St. Croix to Miami 23 July 2000 186FF02 Transit Miami to Boulder

The manifold used in the flux studies is shown in Figure 4.1, and is similar to the manifolds described in previous chapters. However, there are some changes and additions made to the manifold design to permit a faster APIMS response (i.e. smaller time lag) for

fluxes of DMS and SO2 in the moist marine boundary layer. As seen previously, a

PermaPure Nafion dryer (Model PD-1000-24SS, Toms River, NJ) was added just upstream of the APIMS ion source to remove the high level of moisture associated with marine boundary layer measurements. As discussed in Chapter 2, an in-house built sample air heater is placed before the source of the APIMS to heat the incoming air stream to 400°C to increase the DMS sensitivity in moist air by shifting the hydrate equilibrium in favor of production of DMSH+.23 Ozone was also added before the source to ensure an abundance 125

- 24, 25 of ozone for the reactions that create SO5 . This was done to eliminate SO2 signal variability due to fluctuating ozone concentrations in the marine boundary layer and to achieve a proper zero.

Forward Nafion Dryer Nafion

P (DMS Only) Heater Mass Flow Mass Flow Manifold Controller Controller Mass Flow Meter Ambient Ionizer APIMS DMS, SO2 34 DMS-d3 S O2 O3

Mass Flow (SO2 Only) Controller Mass Flow Controller Zero Air

Metal Bellows Venturi Pump Exhaust

Aircraft Wall

Figure 4.1 Manifold design for marine boundary layer eddy correlation flux measurements using an airborne isotopic dilution atmospheric pressure ionization mass spectrometer (APIMS).

Ambient air was pulled into the APIMS manifold using a Venturi exhaust plate

located on a window plate in the rear section of the aircraft. The manifold flow was 126

maintained at approximately 30-50 L/min during boundary layer flights by manually

adjusting a valve on the Venturi line. The manifold flow was measured with a Unit

Instruments, Inc. mass flow meter (Model UFM-310, Yorba Linda, CA). The manifold was

not pressurized and was approximately at the ambient pressure. The benefit of an

unpressurized manifold is that there are no perturbations on the incoming airflow from a

sampling pump, which is a critical factor in flux determinations. However, to minimize the

effects of reduced pressure in the source of the APIMS, and maintain sufficient ion flow and

sensitivity at higher altitudes, the aperture orifice was enlarged to 310 µm.

34 Isotopically labelled standards for DMS (d3-DMS) and SO2 ( SO2) were added into

the manifold within the sampling tube extending through the aircraft wall. Details on the

cylinder concentrations and purity of the isotopic standards were described in Chapter 2.

The standard additions were controlled by Unit mass flow controllers (Models UFC-8100).

The standard was added in excess of the ambient DMS or SO2 concentrations in order to

provide continuous conditioning of the manifold, which in turn provides high efficiency in

analyte transfer in the manifold.26 Since the concentration of the standard was known, the

ambient concentration was calculated using Equation 2.1. Changes or losses in sampling, or

changes in instrument sensitivity were canceled by this approach. Thus, there is little effect

on the precision, and the precision of the measurement is very high.26

The flow into the source of the APIMS was drawn from the main manifold line by a

Metal Bellows pump (Model MB-601, Parker Hannifin Corp., Sharon, MA) and controlled by a mass flow controller (Unit, Model UFC-8100). Care was taken to maintain turbulent flow through the sample line (above 3 L/min) to prevent attenuation of the fluxes of DMS

27 or SO2 through the introduction of laminar flow. 127

The sampling line to the source of the APIMS was as short as possible to reduce the

time lag between the APIMS measurements and the vertical wind velocity and water vapor

mixing ratio, whose sensors were located near the nose of the aircraft. A ten meter

horizontal sensor displacement exists between the APIMS and these sensors due to their

respective locations on the aircraft. However, the time lag can be calculated through the

spectral phase angle between the APIMS measurements and the water vapor measurements,

and corrected before an eddy correlation flux is calculated.4

As mentioned previously, effective measurements of the flux of a scalar from an

aircraft require fast instrument response, low noise (or noise which is uncorrelated to the

flux), and high selectivity to the scalar of interest.2, 4, 8, 28 The airborne isotopic dilution

APIMS has all of these requirements for both DMS and SO2. Using a flux integral scale

range of 30 to 100 m, and an air speed of approximately 100 m/s, the instrument response

time must be greater than the range 2 to 7 Hz (two times the Nyquist frequency) to yield

accurate fluxes occurring at these length scales. The 25 Hz APIMS measurement (12.5 Hz

Nyquist frequency) is sufficient to measure the fluxes of DMS and SO2.

White noise is common when pulse counting is used for detection. However, if the

noise is not correlated with the vertical wind velocity, it affects only the error in the flux

value, not the value itself.28 If the count rate exceed about 103 counts per flux integral length

scale, the white noise due to counting statistics is small enough that fluxes can be

28 measured. For an average concentration value of 50 pptv for both DMS and SO2, and an average sensitivity of 80 cps/pptv, the APIMS response meets the necessary 103 counts per

integral scale requirement.

Sufficient instrumental selectivity for measuring scalars of interest, especially

concentration or mixing ratio measurements, is needed so that the background level for the 128 measurement will be low. This affects the ability of the instrument to measure the differences required for flux calculations. For the airborne isotopic dilution APIMS measurements, care was taken to select standards whose masses correspond to little or no background from interfering species. Also, experimental conditions were met to reduce any background present for the ambient masses. The result is an increase in precision for the flux measurement.

4.3.2 Airborne Dimethyl Sulfide APIMS-GC/MS/ILS Intercomparison

To verify the accuracy of the APIMS DMS measurements, the APIMS was intercompared with an airborne isotopically labelled standard gas chromatography/mass

spectrometry (GC/MS/ILS). The GC/MS/ILS is the same instrument used in the SO2

APIMS-GC/MS/ILS intercomparison discussed in Chapter 3, and was part of a double blind intercomparison conducted by NSF and NCAR with other airborne instruments for its accuracy in measuring DMS.29 The GC/MS/ILS performed well in the intercomparison, and is considered the method of choice for all low level determinations of sulfur gases. The

GC/MS/ILS instrumentation and manifold design has been described previously.14, 26 The

intercomparison experiment design is similar to the SO2 intercomparison, except for the fact that the GC/MS/ILS had a separate inlet and manifold than the APIMS. Using two separate inlets and manifolds creates two independent measurements.

Table 4.2 lists the flights used for the DMS intercomparison. Due to detection malfunctions, no useful GC/MS/ILS information was obtained for flight 186rf03. Data from the remaining flights was filtered to remove GC/MS/ILS data points with a S/N < 4 for two reasons. First, GC/MS/ILS data with a S/N < 4 demonstrated large errors in the 129

concentration measurement. Second, data with S/N ratios ≥ 4 are comparable to the quality of data from the APIMS, where the S/N was ≥ 5 for DMS measurements. The 25

Hz APIMS response for ambient DMS concentration was averaged over the GC/MS/ILS integration period (excluding gaps for zero/calibration measurements and file breaks) for comparison to the GC/MS/ILS response. The result of the APIMS-GC/MS intercomparison for ambient DMS concentrations over the ocean is shown in Figure 4.2.

Airborne Dimethy Sulfide (DMS) Intercomparison between an Isotopic Dilution Atmospheric Pressure Ionization Mass Spectrometer (APIMS) and Isotopically Labelled Standard Gas Chromatography/Mass Spectrometer (GC/MS)

APIMS DMS Concentration (pptrv) y = 1.06±0.07x - 2.6±3.5 20 R2 = 0.9048

Data is from PELTI flights over 10 open ocean around St. Croix, USVI, in July 2000

0 0 1020304050607080 GCMS DMS Concentration (pptrv)

Figure 4.2 Airborne intercomparison of isotopic dilution APIMS and GC/MS/ILS for DMS over the open ocean.

Inspection of Figure 4.2 reveals that the APIMS and GC/MS/ILS responses for DMS over

the ocean are very linear having a correlation coefficient of 0.905. The intercept was –2.5 ±

3.5 pptv, suggesting that there was no significant offset between the two methods. The

slope of the linear fit was 1.06 ± 0.07. The two instruments agree to within 2 pptv at 17

pptv, suggesting that there were no significant background issues in either method. Figure 130

4.3 shows a small subsection of flight 186rf02 for the DMS intercomparison. The APIMS

DMS concentration data is 25 Hz, while the GC/MS/ILS is integrated over 3 minutes (the horizontal bars on the GC/MS/ILS data denote the integration period). Although the

GC/MS/ILS data followed the general trend of the APIMS data, it is clear that much of the detail is lost.

Airborne Ambient DMS APIMS-GC/MS/ILS Intercomparison PELTI Flight 186rf02, 7/9/2000 90 Isotopic Dilution APIMS (25 Hz) GC/MS/ILS (3 min.) 85

65 Ambient DMS concentration (pptv) 60

50 61370 61470 61570 61670 61770 61870 61970 UTC (seconds past midnight)

4.3.3 Eddy Correlation Fluxes of Dimethyl Sulfide

The first series of measurements of DMS eddy correlation fluxes in the marine boundary layer were obtained from the first PELTI St. Croix local flight (186rf02) on July 9,

2000. The marine boundary layer leg of the flight was 66.83 minutes long at a constant 131 altitude of 30 m with an average aircraft air speed of 120 m/s. Figure 4.4 shows the flight track for the boundary layer leg.

PELTI Flight 186rf02, First St. Croix Local, 7/9/2000

18.5 ) ° 18

Latitude ( Latitude 17.5

17 -65 17.8 -64.5 17.6 -64 17.4 -63.5 17.2 -63 17 -62.5 ° 16.8 Longitude ( ) -62 UTC (Hours) -61.5 16.6 -61 16.4

Figure 4.4 Flight track of marine boundary layer leg of PELTI flight 186rf02 (first St. Croix local) on July 9, 2000.

Due to the data corruption problem during the collection of the real-time DMS count data on the NCAR RAF aircraft data system, continuous data for the entire leg is not available.

Subsequently, the flight leg was split into 14 segments of varying lengths. Care was taken to create segments as long as possible (the longest being 8.33 minutes). The absolute minimum sampling time can be estimated from a plot of the flux cospectrum versus a normalized frequency, n,4

132

n=0.2 f zi / U Equation 4.6

where f is the frequency range of the cospectrum, zi is the boundary layer height, and U is

the aircraft speed. Selecting a value of n =0.005, the fraction of the flux cospectrum not

included in the integral is approximately 4%. Using zi = 500 m and U = 100 m/s, the

measurement period for n = 0.005 is 200 seconds (3.33 minutes). Thus, for sampling

periods 200 seconds or longer, the systematic error in the flux determination would be less

than about 4%.30

Alternatively, the peak wavelength in the flux cospectrum can be used to determine

the minimum sampling time.30 Within the mixed layer, the peak of the cospectrum is

approximately equal to the depth of the mixed layer.2, 4 In order to resolve most of the flux,

the sampling period should encompass about 10 times the peak wavelength of the

cospectrum. An estimate of 5 km at an aircraft speed of 100 m/s yields a minimum

sampling period of 50 seconds. However, this approach is not conservative and most likely

underestimates the flux by 10% to 15%.30

Therefore, time periods shorter than one minute were not used, and short time

period segments were combined in order to obtain better flux statistics.5, 31 Preferably,

combined segment lengths 10 minutes or greater should be used to obtain multiple samples

of the largest eddies contributing to the flux.4 Combined segments shorter than these

lengths are subject to larger errors in the calculated flux value.5

A weighted average approach was used to obtain the flux values and errors from combined segments32:

133

t F + t F + t F + F = 1 1 2 2 3 3 L Equation 4.7 avg + + + t1 t2 t3 L

where t1, t2, t3, … are the time lengths of the individual flux segments, F1, F2, F3, … are the

flux contributions from the individual segments, and Favg is the weighted average flux from the combined segments. The calculated flux value from combined segments is not affected by gaps between the segments except for the added contribution in the random (sampling) error variance from multiple periods.5 Averages of other parameters associated with the flux

measurement are also computed using a similar weighted average.

To determine the appropriate time lag correction factor to apply to the real-time

DMS data, the co-spectral density was calculated for ambient DMS concentration and water

vapor mixing ratio (MRLA) for the longest time segment available. At a flight altitude of 30

m, DMS and MRLA share the same source and should be strongly correlated. Through

examination of the phase angle between DMS and MRLA, the time lag was determined.

This time lag correction also was applicable to vertical wind velocity since the sensors for

MRLA and vertical wind velocity are located in the same area on the aircraft, and should

have little time lag. Inspection of Figure 4.5 yields a phase angle for DMS and MRLA of 833

ms for the DMS data.

134

PELTI Flight 186rf02, First St. Croix Local, 7/9/2000 UTC 16:59:05 - 17:07:25 200

150 (a). No Time Lag Correction

100

-50

-100

Phase Angle (degrees) Angle Phase -150

-200 -3 -2 -1 0 1 2 10 10 10 10 10 10 Frequency (Hz)

200

150 (b). Ambient DMS Concentration Data 100 is Time Lag Corrected by 833 ms

-50

-100

Phase Angle (degrees) Angle Phase -150

-200 -3 -2 -1 0 1 2 10 10 10 10 10 10 Frequency (Hz)

As illustrated in Figure 4.5 (a), the first 360° phase between the DMS and MRLA sensors

occurs at 1.2 Hz, or 833 ms. This is the time lag correction that was applied to the ambient

DMS concentration data for all subsequent flux calculations during the marine boundary

flight leg of flight 186rf02.

The power spectral densities (PSD) of DMS and MRLA are shown in Figure 4.6.

The PSD (multiplied by frequency) for DMS exhibits a unit slope for frequency greater than

0.4 Hz, which is indicative of the presence of Poisson white noise in the DMS

measurement.15, 28 However, this white noise is not correlated to vertical velocity and thus does not affect the value of the flux measurement. Its effect on the error in the flux value is small due to the count rate maintained per flux integral length scale.28 The same effect 135 observed previously in 20 Hz ozone data made no observable contributions to the flux.17-19

The PSDs (multiplied by frequency) of vertical velocity (w) and MRLA show inertial range scaling (–2/3 slope) for turbulence below 0.4 Hz.33, 34 The spectral peak in the vertical velocity PSD occurs near 0.2 Hz, which is a length scale of approximately 80 m using an air speed value of 120 m/s. This is consistent with a boundary layer depth on the order of 500 m for this flight.30

The flux values for ambient DMS and MRLA were calculated from the covariance obtained from the co-spectral density (CSD) of DMS and MRLA with vertical wind velocity, respectively. Figure 4.7 shows the CSDs for DMS (time lag corrected by 833 ms) and

MRLA with vertical velocity. Note the similarity of the two plots below 2 Hz, indicating that both ambient DMS and MRLA have similar sources and fluxes at 30 m in the boundary layer. Figures 4.8 (a) and (b) demonstrate the coherence of ambient DMS and MRLA with vertical velocity. The coherences, which are a measure of the correlation of the scalars with vertical velocity as a function of frequency, have similar ranges. The DMS and vertical velocity coherence, however, is heavily affected below 1 Hz by the white noise contribution in the DMS measurement. Figures 4.8 (c) and (d) are the phase angle plots of ambient DMS concentration and vertical wind velocity and MRLA and vertical velocity, respectively.

Again, a time lag of 833 ms was applied to the DMS data. The procedure used to estimate the time lag for DMS from the phase of DMS and MRLA is consistent with the lack of phase shift between MRLA and vertical velocity. 136

PELTI Flight 186rf02, First St. Croix Local, 7/9/2000 UTC 16:59:05 - 17:07:25 3 10 DMS )

-1 (a) 2 s

2 10

1 10

0 10 f*PSD of (pptvDMS

-1 10 -3 -2 -1 0 1 2 10 10 10 10 10 10 Frequency, f (Hz)

-1 10

) w -1 MRLA s ) -2

-3 (b)

s -2 kg 2

2 10

-3 10 f*PSD of w (m w of f*PSD f*PSD of MRLA (g MRLA of f*PSD -4 10 -3 -2 -1 0 1 2 10 10 10 10 10 10 Frequency, f (Hz)

PELTI Flight 186rf02, First St. Croix Local, 7/9/2000 UTC 16:59:05 - 17:07:25 0.25 )

-1 DMS X w 0.2 0.15 (a)

0.1

0.05

-0.05

-0.1

f*CSD of DMS and w (pptv m s m (pptv w DMS f*CSD of and -0.15 -3 -2 -1 0 1 2 10 10 10 10 10 10 Frequency, f (Hz)

-3 x 10 ) 10 -1 MRLA X w

m s 8 -1 (b) 6

-2

f*CSD of MRLA and w (g kg (g w and MRLA f*CSD of -3 -2 -1 0 1 2 10 10 10 10 10 10 Frequency, f (Hz)

Figure 4.7 Co-spectral densities (CSD) of ambient DMS concentration (a) and MRLA (b) with vertical wind velocity. The ambient DMS concentration data was time lag corrected by 833 ms. 137

PELTI Flight 186rf02, First St. Croix Local, 7/9/2000 UTC 16:59:05 - 17:07:25 0.7 200 0.6 (a) 150 (c) 100 0.5 50 0.4 0 0.3 -50 0.2 -100 Coherence of DMS w and of Coherence 0.1 -150

0 w DMSPhase and of Angle (degrees) -200 -3 -2 -1 0 1 2 -3 -2 -1 0 1 2 10 10 10 10 10 10 10 10 10 10 10 10 Frequency, f (Hz) Frequency, f (Hz)

0.7 200

0.6 150 (b) 100 (d) 0.5 50 0.4 0 0.3 -50 0.2 -100 Coherence of MRLA and w MRLA and of Coherence 0.1 -150

0 w MRLAPhase and of Angle (degrees) -200 -3 -2 -1 0 1 2 -3 -2 -1 0 1 2 10 10 10 10 10 10 10 10 10 10 10 10 Frequency, f (Hz) Frequency, f (Hz)

To obtain robust statistics for the flux determinations, averaging times need to be long enough to sample the largest eddy contributing to the flux at least about 10 times.2, 4

Furthermore, the sampling frequency must to be high to obtain flux measurements from the smallest scales contributing to the flux.2, 4 An estimate of these values was obtained by normalizing the co-spectral density of ambient DMS concentration and vertical wind velocity. Figure 4.9 is a plot of the normalized CSD of DMS and vertical wind velocity and

normalized frequency (using the scaling factor for the mixed layer, where zi=500 m and

U=100 m/s).4

138

PELTI Flight 186rf02, First St. Croix Local, 7/9/2000

0.3

0.25

0.2 wDMS n C 0.15

0.1

0.08

0.06

0.04

0.02

0 -2 -1 0 1 2 10 10 10 10 10 n=0.2 f z /U i

Figure 4.9 is essentially the same co-spectrum seen in Figure 4.7 (a) with the exception of the smoothing and use of the normalized frequency, n. The co-spectrum was smoothed to estimate the limits without the interference from white noise.

The dashed vertical lines on the plot indicate where the co-spectrum is ~20% of the maximum value. The flux contained between these limits (0.03

10 minutes or greater were used to improve flux statistics. The minimum sampling frequency that can be used is based on twice the highest frequency obtained in Figure 4.9, which is 6 Hz.

The calculated ambient DMS and MRLA flux values and errors for the individual segments for flight 186rf02 are shown in Table 4.3. Segments were combined in sequential order from Table 4.3 to achieve lengths greater than 10 minutes (minimum 12.50 minutes, average 15.57 minutes). Table 4.4 shows the combined segment flux calculations for DMS and MRLA, as well as the total flight leg averages.

140

Table 4.3 Ambient DMS flux measurement information for level 30 m altitude of flight 186rf02. MRLA is the water vapor mixing ratio.

(g/kg m/s) (g/kg (pptv m/s) (m) (m) (g/kg) (pptv) (m/s) dms Flux_DMS MR LA Flux_MRLA w f_DMS f_MRLA wDMS wMRLA r L (m) L Segment UTC Start UTC Stop Ambient DMS Conc. (pptv) L r DMS Flux m/s) (pptv (g/kg) MRLA MRLA FluxMRLA (g/kg m/s) 1 16:33:50 16:35:50 86.18 7.10 0.809 0.132 14.15 0.24 0.0360 0.0072 0.342 0.33 0.43 14299 19 45 2 16:36:10 16:41:50 89.94 8.15 0.659 0.087 14.66 0.25 0.0265 0.0040 0.338 0.24 0.31 40699 19 41 3 16:43:40 16:46:20 73.21 5.65 0.657 0.085 15.15 0.17 0.0206 0.0046 0.316 0.37 0.39 19099 19 62 4 16:50:00 16:52:40 64.80 6.20 0.642 0.095 15.49 0.20 0.0203 0.0070 0.315 0.33 0.32 19099 20 105 5 16:55:30 16:56:50 65.99 3.94 0.208 0.075 15.56 0.16 0.0160 0.0079 0.296 0.18 0.34 9499 19 118 6 16:59:05 17:07:25 65.72 5.18 0.654 0.043 15.51 0.23 0.0251 0.0046 0.308 0.41 0.35 59899 19 107 7 17:07:40 17:13:20 59.76 4.68 0.388 0.047 15.75 0.18 0.0187 0.0036 0.315 0.26 0.34 40699 19 77 8 17:14:30 17:18:00 55.22 7.40 0.718 0.099 15.81 0.19 0.0285 0.0044 0.330 0.29 0.47 25099 19 53 9 17:18:20 17:19:50 61.56 4.44 0.697 0.115 15.81 0.26 0.0335 0.0069 0.370 0.42 0.35 10699 22 26 10 17:21:00 17:26:20 58.59 8.88 0.725 0.152 15.99 0.29 0.0321 0.0048 0.330 0.25 0.34 38299 49 44 11 17:27:00 17:28:30 50.29 5.96 0.725 0.175 15.97 0.23 0.0327 0.0073 0.340 0.36 0.41 10699 35 39 12 17:29:00 17:36:20 56.87 5.01 0.691 0.048 16.10 0.25 0.0326 0.0026 0.326 0.42 0.40 52699 19 23 13 17:38:00 17:39:10 44.91 4.48 0.941 0.123 16.22 0.22 0.0306 0.0092 0.347 0.61 0.41 8299 19 53 14 17:39:20 17:40:40 47.32 6.24 0.634 0.208 16.35 0.25 0.0256 0.0082 0.319 0.32 0.33 9499 47 48

141

Table 4.4 Combined flux segments for DMS and MRLA flux calculations of flight 186rf02.

(g/kg m/s) (pptv m/s) Flux_DMS Flux_MRLA Segments UTC Start UTC Stop DMS Flux m/s) (pptv MRLA FluxMRLA m/s) (g/kg > 10 Minute Averages 1-3 16:33:50 16:46:20 0.688 0.095 0.027 0.005 4-6 16:50:00 17:07:25 0.603 0.058 0.023 0.005 7-10 17:07:40 17:26:20 0.601 0.100 0.027 0.004 11-14 17:27:00 17:40:40 0.715 0.091 0.032 0.005

Entire Flight Average All 16:33:50 17:40:40 0.65 0.09 0.027 0.005

The average DMS flux for the entire marine boundary leg of flight 186rf02 was

0.65±0.09 pptv m/s ((1.7±0.2)x1013 molecules m-2 s-1 or 2.5±0.3 µmol m-2 d-1). The average ambient DMS concentration for the entire flight leg was 63±13 pptv. The average flux integral length scale for DMS was 24 m, which is in agreement with the estimate of 31 m based on Equation 4.5 using 500 m as the boundary layer depth. The correlation coefficient,

rwDMS, for the entire flight leg was 0.33, which is also consistent with estimation of the correlation coefficient for a scalar flux in the lower part of the boundary layer to be close to

0.4.4, 35 The relative error for the DMS flux is 14%, indicating that the white noise contribution did not have a significant impact on the error of the flux. A detailed comparison of the DMS flux to the second marine boundary layer flight (186rf03) and to the literature is provided in later sections.

The second series of measurements of DMS eddy correlation fluxes in the marine boundary layer are from the second PELTI St. Croix local flight (186rf03) on July 11, 2000.

The flight consisted of four marine boundary layer legs of total time 243.83 minutes, with an 142 average length of 61 minutes per leg, at a level altitude of 50 m with an average aircraft air speed of 115 m/s. The four legs were flown over the same geographical area. Figure 4.10 shows the flight track for the boundary layer legs.

PELTI Flight 186rf03, 2nd St. Croix Local, 7/11/2000

18.75

) 18.7 ° Leg 4

18.65 Leg 3 Leg 5 Latitude ( Latitude

18.6 -64 -63.5 -63 Leg 2 -62.5 22 21.5 -62 21 -61.5 20.5 20 ° -61 Longitude ( ) 19.5 -60.5 19 -60 18.5 18 -59.5 17.5 UTC (Hours) -59 17

Figure 4.10 Flight track of marine boundary layer legs of PELTI flight 186rf03 (second St. Croix local) on July 11, 2000.

The calculated ambient DMS and MRLA flux values and errors for the individual segments for flight 186rf03 are shown in Table 4.5. Again, segments were combined in sequential order from Table 4.5 for each leg to achieve lengths greater than 10 minutes

(minimum 14.83 minutes, average 18.14 minutes). Table 4.6 shows the combined segment flux calculations for DMS and MRLA for each leg. Table 4.7 shows the leg averages as well as the combined marine boundary layer flight average for flight 186rf03.

143

Table 4.5 Ambient DMS flux measurement information for level 50 m altitude of flight 186rf03. MRLA is the water vapor mixing ratio. (g/kg m/s) (g/kg (pptv m/s) (m) (m) (g/kg) (pptv) (m/s) dms Flux_DMS MRLA Flux_MRLA w f_MRLA f_DMS wDMS wMRLA DMS Flux (pptv m/s) FluxDMS (pptv MRLA FluxMRLA (g/kg m/s) Segment UTC Start UTC Stop AmbientDMS Conc. (pptv) r L (m) L MRLA (g/kg) MRLA r L LEG 2 1 17:17:20 17:23:20 35.97 3.70 0.608 0.046 13.72 0.25 0.0234 0.0061 0.384 0.43 0.24 39508 17 76 2 17:23:40 17:32:10 36.42 4.45 0.814 0.050 14.47 0.32 0.0279 0.0057 0.403 0.45 0.22 54903 17 52 3 17:36:50 17:44:20 38.15 3.89 0.585 0.044 15.42 0.21 0.0224 0.0030 0.397 0.38 0.27 49408 17 31 4 17:44:30 17:49:40 38.96 4.86 0.464 0.141 15.53 0.17 0.0177 0.0055 0.363 0.26 0.28 34008 102 121 5 17:50:20 17:54:40 37.74 4.20 0.533 0.055 15.73 0.14 0.0145 0.0035 0.349 0.36 0.30 28508 17 70 6 17:55:00 18:04:50 39.12 4.56 0.630 0.042 15.78 0.19 0.0204 0.0040 0.369 0.37 0.30 64808 17 100 7 18:05:10 18:12:50 35.92 5.56 0.610 0.094 15.92 0.15 0.0189 0.0038 0.384 0.29 0.33 50508 46 99 LEG 3 1 18:17:00 18:25:00 35.78 5.15 0.646 0.104 15.94 0.21 0.0236 0.0045 0.402 0.31 0.28 58458 67 75 2 18:25:20 18:30:50 39.90 5.87 0.682 0.073 16.08 0.15 0.0227 0.0041 0.382 0.30 0.40 40158 19 89 3 18:32:50 18:35:00 38.87 5.25 0.954 0.263 16.23 0.17 0.0249 0.0080 0.395 0.46 0.38 15758 105 101 4 18:35:20 18:44:50 37.96 5.49 0.765 0.075 16.22 0.29 0.0216 0.0039 0.388 0.36 0.19 69438 38 38 5 18:45:10 18:54:40 35.22 4.08 0.621 0.102 16.34 0.16 0.0227 0.0037 0.401 0.38 0.36 68213 117 107 6 18:55:40 19:04:30 33.60 3.82 0.458 0.049 16.09 0.19 0.0178 0.0027 0.384 0.31 0.25 64558 33 41 7 19:04:50 19:14:10 37.28 5.03 0.668 0.054 16.21 0.23 0.0242 0.0057 0.434 0.31 0.24 68218 19 104 LEG 4 1 19:16:54 19:22:34 35.29 5.49 0.490 0.120 16.26 0.28 0.0204 0.0068 0.400 0.22 0.18 37308 53 68 2 19:22:44 19:23:54 36.39 4.40 0.994 0.316 16.38 0.15 0.0253 0.0113 0.418 0.54 0.41 7608 87 111 3 19:24:14 19:33:24 32.67 4.31 0.592 0.093 16.08 0.17 0.0205 0.0035 0.367 0.37 0.34 60408 92 89 4 19:33:44 19:43:04 32.91 3.47 0.529 0.035 16.27 0.13 0.0184 0.0030 0.362 0.42 0.39 61508 21 110 5 19:43:24 19:52:34 39.92 5.17 0.607 0.047 16.65 0.20 0.0204 0.0035 0.355 0.33 0.28 60408 17 67 6 19:52:54 20:02:04 40.54 6.90 0.601 0.067 16.56 0.27 0.0199 0.0053 0.393 0.22 0.19 60408 17 75 7 20:03:14 20:11:34 39.63 4.97 0.700 0.059 16.36 0.17 0.0205 0.0043 0.380 0.37 0.32 54908 23 112 8 20:11:54 20:21:04 37.31 5.89 0.542 0.107 16.36 0.15 0.0180 0.0032 0.375 0.25 0.31 60408 67 86 9 20:23:14 20:26:54 39.76 4.68 0.677 0.070 16.15 0.16 0.0191 0.0054 0.367 0.39 0.33 24108 17 96 LEG 5 1 20:31:00 20:40:10 39.70 3.85 0.552 0.038 16.30 0.17 0.0182 0.0027 0.381 0.38 0.29 66998 19 58 2 20:41:10 20:49:40 36.77 6.08 0.533 0.132 16.28 0.15 0.0153 0.0032 0.368 0.24 0.27 62122 102 96 3 20:51:20 20:59:50 42.99 5.80 0.862 0.136 16.49 0.19 0.0223 0.0044 0.387 0.38 0.31 62118 100 104 4 21:00:10 21:09:50 40.21 5.26 0.592 0.046 16.64 0.23 0.0181 0.0041 0.357 0.32 0.22 70658 19 89 5 21:10:10 21:19:50 34.24 4.51 0.641 0.096 16.54 0.19 0.0184 0.0034 0.342 0.42 0.29 70658 116 95 6 21:20:10 21:29:50 38.00 4.98 0.834 0.116 16.44 0.18 0.0250 0.0043 0.387 0.43 0.35 70658 107 117 7 21:30:00 21:32:10 41.74 5.26 0.722 0.110 16.57 0.20 0.0250 0.0089 0.398 0.34 0.31 15758 19 90

144

Table 4.6 Combined flux segments for DMS and MRLA flux calculations of flight 186rf03.

(g/kg m/s) (g/kg (pptv m/s) (pptv Flux_DMS Flux_MRLA Segments UTC Start UTC Stop m/s) FluxDMS (pptv MRLA Flux m/s) MRLA (g/kg LEG 2 1-2 17:17:20 17:32:10 0.728 0.048 0.0260 0.0059 3-5 17:36:50 17:54:40 0.535 0.076 0.0190 0.0039 6-7 17:55:00 18:12:50 0.621 0.065 0.0197 0.0039 LEG 3 1-3 18:17:00 18:35:00 0.701 0.115 0.0234 0.0048 4-5 18:35:20 18:54:40 0.693 0.089 0.0222 0.0038 6-7 18:55:40 19:14:10 0.565 0.052 0.0211 0.0042 LEG 4 1-3 19:16:54 19:33:24 0.585 0.119 0.0208 0.0052 4-5 19:33:44 19:52:34 0.568 0.041 0.0194 0.0033 6-7 19:52:54 20:11:34 0.648 0.063 0.0202 0.0048 8-9 20:11:54 20:26:54 0.581 0.096 0.0183 0.0038 LEG 5 1-2 20:31:00 20:49:40 0.543 0.083 0.0168 0.0030 3-4 20:51:20 21:09:50 0.718 0.088 0.0201 0.0043 5-7 21:10:10 21:32:10 0.736 0.106 0.0220 0.0044

Table 4.7 Total marine boundary layer leg fluxes of ambient DMS and MRLA for flight 186rf03.

(g/kg m/s) (g/kg (pptv m/s) (pptv Flux_DMS Flux_MRLA Segments UTC Start UTC Stop m/s) Flux DMS (pptv MRLA Flux MRLA m/s) (g/kg Leg Averages LEG 2 ALL 17:17:20 18:12:50 0.623 0.064 0.0213 0.0045 LEG 3 ALL 18:17:00 19:14:10 0.652 0.084 0.0222 0.0043 LEG 4 ALL 19:16:54 20:26:54 0.596 0.077 0.0197 0.0043 LEG 5 ALL 20:31:00 21:32:10 0.671 0.093 0.0198 0.0039

Entire Flight Average ALL 17:17:20 21:32:10 0.63 0.08 0.021 0.004

145

The average DMS flux for the entire marine boundary legs was 0.63±0.08 pptv m/s

((1.7±0.2)x1013 molecules m-2 s-1 or 2.4±0.3 µmol m-2 d-1). The average ambient DMS

concentration for flight legs 2 to 5 were 37.5±1.4 pptv, 37.6±2.3 pptv, 37.2±3.0 pptv, and

39.1±3.0 pptv. The total marine boundary layer flight leg ambient DMS concentration

average was 37.8±2.5 pptv. Incidentally, the ambient DMS concentration average of 45.5 ±

12.3 pptv for all local St. Croix flights in the marine boundary layer are in good agreement

with previous DMS measurements in similar geographic areas.36-42

The average flux integral length scale for DMS was 50 m for all legs, which is in good agreement with the estimate of 37 m based on Equation 4.5 using 500 m as the

boundary layer depth. The correlation coefficient, rwDMS, for the all legs was 0.35, which also

is consistent with estimation of the correlation coefficient for a scalar flux in the lower part

of the boundary layer to be close to 0.4.4, 35 The relative error for the DMS flux is 13%, once again indicating that the white noise contribution did not have a significant impact on the error of the flux.

Since the marine boundary flight legs for flight 186rf03 were flown over the same geographical region, an estimate of the reproducibility of the flux determinations was possible. A significance test was performed on the mean values of the flux for each leg using a standard Student’s t statistic and pooled variance.43 It was assumed that the minor

variations in time length for the individual segments comprising the total leg do not have

different variances. The mean fluxes for all legs were statistically part of the same

population at the 95% confidence interval, suggesting excellent reproducibility in measuring

the flux with the APIMS over several hours. A significance test also was performed between

the entire flight leg means of flights 186rf02 and 186rf03 since the flux determinations for 146

each flight were in the same general geographical area. Although the flights were separated

by two days, the mean fluxes for the two flights were part of the same population at the 95%

confidence interval. This result suggests that there is substantial day-to-day reproducibility in

determining the flux values of DMS with an APIMS over the same geographical area.

The DMS flux data reported here agree well with other flux estimates. However, it is

important to emphasize that the eddy correlation DMS flux data presented in this work are

the only direct determinations available. DMS flux measurements cited in the literature are

based on air and sea measurements of ambient DMS (with relatively longer sampling times

than the APIMS) applied to various models and parameters44-47 to estimate the flux.

Although empirical, the fluxes are derived values. Table 4.8 is a comparison of the surface layer DMS flux measured from flights 186rf02 and 18rf03 with the current surface layer

DMS flux values from the literature. Since the altitudes for the DMS flux flights were within the surface layer of the marine boundary layer (≤ 50 meters), DMS surface flux values were also compared with the current flux measurement. DMS flux values similar to the current research area are cited, which includes both the Atlantic and Pacific tropical and equatorial regions. Inspection of Table 4.8 reveals that the current determined DMS flux values are either within range or within a factor of two from the 16 cited literature values with only one exception.48 Based on the fact that the literature DMS flux values are estimated from various

models and parameterizations, errors creating a factor of two difference between the

measured and estimated flux values are not uncommon and are acceptable. However,

differences greater than a factor of two between the measured and estimated flux suggests

shortcomings in the model and parameters used in estimating DMS fluxes. 147

Table 4.8 Comparison chart of APIMS measured DMS surface layer flux and literature DMS surface layer flux values.

Sampling DMS Flux X 1013 General Location Reference Period (molecules m-2 s-1)

Caribbean / West Indies July 1.7±0.2 This Work

North Atlantic 4.11 Andreae and Raendonck (1983)49

Northern Tropical Pacific Summer 3.10 Bates et al. (1987)50

Northern Equatorial Pacific Spring 3.36 Bates et al. (1987)50

Southern Equatorial Pacific Spring 2.23 Bates et al. (1987)50

Northern Tropical Atlantic July 3.1 - 3.5 Erickson et al. (1990)51

North Atlantic Spring 2.4 Berresheim et al. (1991)52

Northern Tropical Atlantic October 3.5 Putaud et al. (1993)38

Northern Tropical Pacific Feb.-March 4.9±2.8 Bates et al. (1993)53

Northern Tropical Pacific Feb.-March 5.4±2.6 Bates et al. (1993)53

Southern Tropical Atlantic Feb.-March 1.4 - 3.5 Andreae et al. (1994)54

North Atlantic June 0.70 – 9.1 Blomquist et al. (1996)41

Northern Equatorial Pacific July-August 3.8±0.8 Bandy et al. (1996)1

Global Northern Tropical 1.0 Chin et al. (1996)55

Global Average 1.3 - 2.2 Chin et al. (1998)56

Northern Tropical Pacific Sept.-Oct. 0.42 – 2.1 Chen et al. (1999)57

Northern Tropical Pacific Feb.-March 0.98 – 1.3 Chen et al. (1999)57

Northern Tropical Pacific August 2.5±1.2 Davis et al. (1999)47

Northern Tropical Pacific August 6.7±2.1 Lenschow et al. (1999)48

Caribbean Sea September 1.25±0.14 Sharma et al. (1999)58

Northern Tropical Pacific 3.4±1.2 Chen et al. (2000)59

148

4.3.4 Eddy Correlation DMS Flux Gradient

The third and final series of real-time eddy correlation DMS flux measurements used data from the fourth PELTI St. Croix local flight (186rf05) on July 16, 2000. This flight consisted of short (~10 minute) legs at multiple altitudes in the marine boundary layer.

Three of these altitude legs were used to calculate a flux gradient for ambient DMS in the marine boundary layer using the eddy correlation method to compute the fluxes. Figure 4.11 shows the altitude profile and flight track for the flux gradient.

PELTI Flight 186rf05, 4th St. Croix Local, 7/16/2000 600 (a). 500 Leg 6 400 Leg 3 300 200 100 Leg 4

Pressure Altitude (m) 0 18.2 18.4 18.6 18.8 19 19.2 UTC (Hours) 18.8 (b). 18.6 ) ° 18.4

18.2

Latitude ( Latitude 18

17.8 18.2 18.4 18.6 18.8 19 19.2 UTC (Hours) -65.2 (c). )

° -65.4

-65.6

-65.8 Longitude (

-66 18.2 18.4 18.6 18.8 19 19.2 UTC (Hours)

Figure 4.11 Altitude profile and flight track of marine boundary layer legs of PELTI flight 186rf03 (fourth St. Croix local) on July 16, 2000.

149

Table 4.9 Ambient DMS flux measurement information for varying altitudes of flight 186rf05. MRLA is the water vapor mixing ratio. (g/kg m/s) (g/kg (pptv m/s) (pptv (g/kg) (pptv) (m/s) (m) (estimate) Flux_MRLA w dms Flux_DMS MR LA f wDMS wMRLA MRLA Flux m/s) MRLA (g/kg DMS Flux (pptv m/s) FluxDMS (pptv r L (m) L Segment UTC Start UTC Stop Altitude (m) (pptv) Ambient DMS Conc. r MRLA (g/kg) MRLA LEG 3 1 18:15:00 18:16:10 284 49.48 4.99 0.410 0.272 17.10 0.14 0.0238 0.0082 0.417 0.20 0.40 8251 68 2 18:17:30 18:24:10 282 47.56 10.66 0.632 0.265 17.10 0.26 0.0201 0.0066 0.463 0.13 0.16 47851 68 LEG 4 1 18:28:20 18:34:00 34 53.15 6.03 0.540 0.080 17.27 0.27 0.0211 0.0036 0.314 0.29 0.25 40651 34 2 18:34:10 18:36:20 34 59.01 7.94 1.365 0.186 17.34 0.28 0.0293 0.0060 0.312 0.55 0.34 15451 34 3 18:37:20 18:38:20 34 51.66 6.28 0.877 0.214 17.63 0.14 0.0231 0.0048 0.321 0.44 0.53 7051 34 LEG 6 1 18:55:00 19:03:00 440 42.89 8.46 0.340 0.158 17.43 0.25 0.0108 0.0046 0.354 0.11 0.12 57451 79 2 19:06:00 19:07:30 439 39.33 3.47 0.237 0.144 17.45 0.23 0.0159 0.0096 0.335 0.20 0.20 10651 79

150

A linear fit of the flux values at these three different altitudes in the marine boundary

layer allows for the calculation of the surface and entrainment fluxes by extrapolation.

Knowing the ambient DMS concentration jump across the boundary layer top provides an

estimate of the entrainment velocity.

The raw flux segment data is shown in Table 4.9. Fortunately, all segments had the

minimum time required for use in a flux calculation, and combined segment lengths were

approximately 10 minutes in length. However, due to the short time lengths and minimal

number of lengths at each altitude, an estimate for the flux integral length scale was used in

the flux error calculation based on Equation 4.5. Based on the comparison of flux integral

length scales from flights 186rf02 and 186rf03, the error calculated from the flux integral

length scale estimation is within 10% of the value that would be based on the spectral peak

of the power spectrum density of the product of the fluctuations of ambient DMS

concentration and vertical wind velocity. Table 4.10 shows the reduced data for the

combined segments at each altitude. Inspection of Table 4.10 reveals that the DMS flux

decreases linearly with increasing altitude. The correlation coefficient at 34 m is comparable

to the previous flights of DMS flux measurements, and the resulting relative error of 15% is

also reasonable. However, the correlation decreases to less than half of the surface layer

value, causing the relative errors in the flux values to increase to 50% close to the top of the

boundary layer. The ambient DMS concentration is slightly larger at 34 m (~54 pptv), but

appears constant at higher altitudes (45±4 pptv), which is consistent for scalars in a well-

mixed turbulent boundary layer.60 Figure 4.12 is a plot of altitude and fractional boundary layer height versus DMS flux from the values in Table 4.10

151

Table 4.10 Summarized ambient DMS flux data at varying marine boundary layer altitudes in flight 186rf05.

(g/kg m/s) (pptv m/s) Flux_DMS Flux_MRLA wDMS wMRLA Segments UTC Start UTC Stop Altitude (m) Ambient DMS Conc. (pptv) m/s) FluxDMS (pptv r r MRLA (g/kg)MRLA Flux m/s) (g/kg MRLA LEG 3 ALL 18:15:00 18:24:10 282 47.84 0.60 0.27 17.10 0.0207 0.0068 0.14 0.20 LEG 4 ALL 18:28:20 18:38:20 34 54.42 0.78 0.12 17.33 0.0233 0.0043 0.37 0.30 LEG 6 ALL 18:55:00 19:07:30 440 42.33 0.32 0.16 17.43 0.0116 0.0054 0.13 0.14

PELTI Flight 18rf05, 4th St. Croix Local, 7/16/2000

500 1

450 0.9

400 0.8 Fraction Boundary Layer Height (z/z

350 0.7

300 0.6

250 0.5

Altitude (m) 200 0.4

150 0.3 i )

100 0.2

50 0.1

0 0 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 DMS Flux (pptv m/s)

Figure 4.12 Plot of altitude and fractional boundary layer height versus ambient DS flux measured by an airborne isotopic dilution APIMS during flight 185rf05 (July 16, 2000).

152

The top of the boundary layer (zi) was estimated at 500 m based on the vertical profile of

ambient DMS concentration during flight 185rf05. This deduction is discussed in a later

section of this chapter. The model fit of fractional boundary layer height versus DMS flux is

linear (r2=0.94) and produces the equation

± ± (z/zi)=(-1.72 0.43)*DMS flux + 1.48 0.26. Equation 4.8

The linear fit for the flux gradient of DMS in the boundary layer is in agreement with other

estimates.61, 62 Based on Equation 4.8, the surface and entrainment flux for DMS in the marine boundary layer can be extrapolated. Errors for the extrapolated values were obtained from the error of the linear fit. A better estimate of the error in the flux gradient is available from Mann and Lenschow (1994)20 and should be used when more altitude legs are used to

obtain the gradient. Since the concentration difference across the boundary layer is known

from vertical profiles of ambient DMS concentration during this flight, the entrainment

velocity can be calculated from the entrainment flux and the mean concentration difference

across the top by the equation63

F W = − zi Equation 4.9 e ∆DMS

∆ where We is the entrainment velocity, Fzi is the entrainment flux of DMS, and DMS is the mean concentration difference of ambient DMS across the top of the boundary layer. An error estimate41 for the entrainment velocity was propagated using the error of the

entrainment flux and a conservative error of 1 pptv on the 15 pptv DMS concentration 153 difference. The DMS surface flux, entrainment flux, and entrainment velocity are shown in

Table 4.11. Although an entrainment velocity close to 2 cm/s is high compared to previously measured and estimated values of 0.3-0.7 cm/s64-66, it is not unreasonable since it can range from 1 to 20 cm/s in the mixed layer.60 Also, the surface flux value of

(2.3±0.7)x1013 molecules m-2 s-1 is in range with the values presented in Table 4.8.

Table 4.11 Ambient DMS surface flux, entrainment flux, and entrainment velocity from DMS flux gradient in flight 186rf05.

∆DMS 15±1 pptv

Zi 500 m

DMS Surface Flux 0.86±0.26 pptv m s-1 ((2.3±0.7)x1013 molecules m-2 s-1)

DMS Entrainment Flux 0.28±0.17 pptv m s-1 ((7.5±4.4)x1012 molecules m-2 s-1)

DMS Entrainment Velocity 1.9±1.1 cm/s

Again, it should be stressed that the PELTI program was not optimized for flux measurements. An improved flux gradient for DMS would be obtained by careful flight planning with a significant number of legs of different altitudes in the boundary layer. The reader is referred to Figure 4 in Lenschow et al. (1999)9 for an example of the type of flight planning required for optimal flux gradient measurements. 154

4.3.5 Fast Time Series Analysis

A look at a short time series of ambient DMS concentration, water vapor mixing

ratio (MRLA), and vertical wind velocity fluctuations (w’) reveals meteorological structure,

especially the presence of thermals. Thermals are columns of buoyant warm air from the

surface, and are the main driving force for turbulence in the boundary layer.60 Typically,

thermals can range from 100 m to several km in diameter, and typically have vertical

updrafts of 1 to 2 m/s associated with them.60

Figure 4.13 shows a 7.2 minute segment of flight 186rf02. All data are filtered 25 Hz

data. It is evident from Figure 4.13 that DMS and MRLA are highly correlated where both

scalars follow the same trend. This correlation brings about two conclusions. First, it was

assumed earlier that DMS and MRLA shared the same source (i.e. the ocean), and it can now

be shown that this is true. Second, it was demonstrated in Chapter 2 that increased water

vapor concentration had a negative effect on the DMS sensitivity if a Nafion dryer and

sample air heater were not employed. Figure 4.13 does not show evidence of anti-

correlation between DMS and MRLA, indicating that the ambient water vapor level did not

noticeably affect the DMS sensitivity while using the dryer and air stream heater, which is

consistent with the laboratory results of Chapter 2. This second conclusion also verifies the

principle stated in Chapter 1 about not needing to use the Webb67 correction for changing

water vapor density in the calculation of a scalar flux if the sample is decoupled from the

ambient moisture through drying and utilizes a mixing ratio determination.4 Another conclusion brought about by the inspection of Figure 4.13 is the stability of the isotopic standard. As it is shown in the figure, the standard is relatively unchanging and is not 155 correlated to meteorological changes, which provides greater confidence for use of the isotopic dilution technique in eddy correlation flux determinations with an airborne APIMS.

To see the effect of thermals more clearly, Figure 4.14 is an expanded segment (1.8 minutes) of Figure 4.13. The line drawn at 0 m/s on the vertical velocity plot is the mean value for all vertical wind velocity fluctuations. It can be seen in Figure 4.14 that there are extended regions were w’ is close to 1 m/s, indicating the presence of thermals. Using an average air speed of 100 m/s, some of the proposed thermals are on the order of 300 m in diameter. Greater evidence that the updrafts are thermals from the surface is in the increased amount of ambient DMS concentration and MRLA found during these upwelling periods.

PELTI Flight 186rf02 7/9/2000 190

3 180

170

160 Concentration (pptv) Concentration DMS-d 17 17.02 17.04 17.06 17.08 17.1 17.12 90

Concentration (pptv) Concentration 17 17.02 17.04 17.06 17.08 17.1 17.12 Ambient DMS 16.5

15.5

15 MRLA (g/kg) MRLA 14.5 17 17.02 17.04 17.06 17.08 17.1 17.12 1

-1 17 17.02 17.04 17.06 17.08 17.1 17.12 Vertical Wind Velocity Wind Vertical w' (m/s) UTC (Hours)

Figure 4.13 7.2 minute segment of flight 186rf02 (July 9. 2000) demonstrating the fast APIMS response to DMS as compared with other meteorological variables. 156

PELTI Flight 186rf02 7/9/2000 190

3 180

170

160 Concentration (pptv) Concentration DMS-d 17.02 17.025 17.03 17.035 17.04 17.045 17.05 90

Concentration (pptv) Concentration 17.02 17.025 17.03 17.035 17.04 17.045 17.05 Ambient DMS 16.5

15.5

15 MRLA (g/kg) MRLA 14.5 17.02 17.025 17.03 17.035 17.04 17.045 17.05 1

-1 w' (m/s)

Vertical Wind Velocity Wind Vertical 17.02 17.025 17.03 17.035 17.04 17.045 17.05 UTC (Hours)

Figure 4.14 Expanded plot of Figure 4.13 to show increased concentrations of ambient DMS and MRLA with thermals.

4.3.6 Ambient DMS Vertical Concentration Profiles

It has been demonstrated in the preceding sections that a direct determination of the eddy correlation flux of DMS is possible through the use of an airborne isotopic dilution

APIMS. The determined surface layer flux of DMS with this technique is in good agreement with the estimates from the literature in similar geographic areas. Calculations of entrainment fluxes of DMS, as well as the entrainment velocity were also possible from the determined flux of DMS at varying altitudes. In this section, the real-time response characteristics of the APIMS were used to investigate the vertical (altitude) concentration 157 profile of ambient DMS to establish the feasibility of using DMS as a tracer molecule for the study of the meteorology of the marine boundary layer, particularly entrainment.

Entrainment is defined as the rate at which a turbulent fluid, such as the marine boundary layer, incorporates a non-turbulent fluid adjacent to it (i.e. the free troposphere or buffer layer) through impinging eddies, the result of which is growth of the turbulent layer.9,

60 In this respect, the entrainment velocity is an important variable in models of the boundary layer and cloud systems. The entrainment velocity is of particular importance in models describing the evolution and stabilization of stratiform clouds. Current models predict drastically different entrainment rates, which ultimately affects the quality of the estimation of cloud cover and content.9 Entrainment velocity is also important for the investigation of boundary layer development in trade wind and fair weather cumulus regimes9, as well as intermittent buffer layers between the boundary layer and the free troposphere.68 For this area of meteorological research, the direct and accurate determination of entrainment velocity is critical.

As discussed in Section 4.3.4, one method for determining entrainment from measurements of a passive scalar is to form the ratio between the change in a scalar’s concentration across the top of the boundary layer, and the flux of that scalar just below the boundary layer top (Equation 4.9). The success of this method depends on the accurate measurement of this concentration change, and determination of the vertical flux. Although ozone and water vapor have been used extensively to make such measurements65, 69,both are problematic and can generate large errors in the calculated entrainment velocity.9

A primary difficulty with using ozone and water as entrainment tracer molecules is that it they are often poorly suited to obtain an accurate estimation of the jump, which is the change in concentration of these species across the top of the boundary layer.64, 65, 69 One 158

reason for this is that both ozone and water have substantial concentrations above the

boundary layer top that can have significant horizontal and vertical variability.9 The typical

result is large horizontal variability in the concentration difference for these species across

the boundary layer top.

Although ozone entrainment fluxes have been routinely measured17-19, 69, it has been historically very difficult to estimate the flux of water near the top of the boundary layer.

Part of this difficulty stems from the fact that the top of many boundary layers contains a cloud layer. Due to problems with probe wetting, the Lyman-alpha (which is typically used to make fast measurements of water vapor) often performs poorly in clouds. In addition, the added source/sink of water associated with drizzle, and the need to estimate liquid water fluctuations (to estimate the total water flux), makes estimates of total water flux at the top of the boundary layer extremely difficult.

Because of these difficulties with ozone and water, a number of other tracer molecules were considered for use in determining entrainment. The characteristics of an ideal tracer molecule are the following: horizontally homogenous in terms of concentration and flux; a lifetime of 104 to 106 seconds (preferably with no other sources except for

oceanic emission) so that the jump across the boundary layer can be measured; negligible

solubility in clouds; and a fast response measurement for the concentration of the tracer.9, 10

Of all the tracers considered to date, DMS appears to be the most ideal over the ocean.

Because of the relatively short lifetime of DMS (1-2 days) compared to overturning time

scales of the troposphere, DMS concentrations above the marine boundary layer are

expected to be small. However, this lifetime is still rather large compared to the boundary

layer turbulence timescales on the order of 10 minutes and timescales associated with

advection over small-scale variations in surface production. Hence, rather homogeneous 159 concentrations of DMS are found in the marine boundary layer, with sharp well defined jumps across the top of the boundary layer.

Several attempts have been made to measure the vertical concentration profile of

DMS37, 42, 70-74 in the atmosphere. However, limitations on the sampling integration time and sensitivity loss due to moisture effects have prevented a direct measurement of the concentration difference across the top of the boundary layer. The results of these measurements are concentration averages at various altitudes with little time resolution, allowing only for general trends to be established. Notwithstanding this limitation, this type of DMS profile has been used to investigate various meteorological regimes.48, 68 To date, the information in this section is the only known report of high-resolution vertical concentration profiles of DMS, effectively demonstrating changing meteorological conditions within the boundary layer and above, and also providing a means for calculating the entrainment velocity.

Four altitude concentration profiles for ambient DMS are shown in Figures 4.15 -

4.18. Each of the flights mentioned in previous sections of this chapter (186rf02, 186rf03, and 186rf05) have a vertical profile of DMS that established the top of the boundary layer for the DMS flux measurement, in particular flight 186rf05 where the flux gradient of DMS was measured. The other vertical profile, from the third PELTI flight on July 15, 2000

(186rf04), is presented due to its extended height into the free troposphere. Along with ambient DMS concentration, altitude profiles for water vapor mixing ratio (MRLA), potential temperature, and vertical wind velocity fluctuations are also shown as a comparison to DMS. The ambient DMS data is 25 Hz APIMS data collected and processed according to the information presented in section 4.2.2.4. The 25 Hz data was filtered to remove white 160

noise contributions and decimated to 1 Hz. The meteorological variables were all extracted

from the low-rate (1 Hz) data file produced from the NCAR RAF Nimbus data system.

Upon inspection of Figures 4.15 - 4.18, several general comments can be made.

First, the boundary layer structure present in each of the figures is consistent with what is

expected in a tropical trade wind regime.75, 76 Second, the response of ambient DMS concentration within the boundary layer is fairly constant as expected, and the jumps in concentration across the top of boundary layer are pronounced and well defined. Also, the

DMS concentration in the free troposphere is much less than the boundary layer, which is consistent with the notion that the transport of air into the free troposphere has a longer time scale than the DMS chemical lifetime. Finally, the vertical profile of ambient DMS is also comparable to physico-chemical modeling of vertical DMS profiles over the open ocean.61, 62, 77

A closer examination of Figures 4.15 - 4.18 demonstrates that DMS is sensitive to

changes in meteorological conditions and generally has more prominent changes than the

other two meteorological variables. Attention to abscissa scales is important at this point in

order to show the magnitude of change of the scalars to meteorological conditions. In

Figure 4.15, the DMS concentration profile indicates the existence of many layers. A small

layer between the surface layer and convective boundary layer can be seen at 200 m.

Although MRLA also shows this change, the net change is mixing ratio is on the order of

4%, while the net change in DMS concentration is on the order of 30%. The boundary layer

top at 500 m is barely noticeable in the fluctuations in MRLA, but again is pronounced by a

significant concentration change in DMS. Changes in meteorological layers such as these are

also difficult to discern from the vertical velocity gust (fluctuation) profile. Inspection of the

other figures in this set demonstrates similar DMS sensitivity to various atmospheric layers. 161

Additional features, such as the presence of a buffer layer between the marine boundary layer and free troposphere, as shown in Figures 4.16 to 4.18, or a possible small cloud layer

(500-600 m) in Figure 4.15, again demonstrate the sensitivity of DMS as tracer molecule in meteorological studies.

Several points can be concluded from this section. First, the airborne isotopic dilution APIMS demonstrated the ability to provide real-time high-resolution ambient DMS concentration profile data within the marine boundary layer and the lower free troposphere.

This is the first report in the literature of such information as well as an instrument that can provide such data. Next, DMS appears to be a suitable molecular tracer for meteorological studies, in particular entrainment. Finally, the entrainment velocity, under a planned flux gradient experiment, could be obtained with high accuracy and precision if the DMS flux and concentration profile were measured with the airborne isotopic dilution APIMS.

162

PELTI Flight 186rf03, 2nd St. Croix Local, 7/11/2000 Ascent, UTC 21:32:18 - 21:39:35 1800 1800 1600 1600

1400 1400

1200 1200

1000 1000 800 800

600 600 Pressure Altitude (m) Altitude Pressure Pressure Altitiude (m) Altitiude Pressure 400 400

200 200

0 0 0 10 20 30 40 50 60 14.5 15 15.5 16 16.5 17 17.5 Ambient DMS Concentration (pptv) MRLA (g/kg)

1800 1800 1600 1600

1400 1400

1200 1200 1000 1000

800 800

600 600

Pressure Altitude (m) Altitude Pressure 400 (m) Altitude Pressure 400

200 200

0 0 -1 -0.5 0 0.5 1 1.5 298 299 300 301 302 303 304 Vertical Wind Velocity (w', m/s) Potential Temperature (θ, K)

Figure 4.15 Altitude profiles for ambient DMS concentration, water vapor mixing ratio (MRLA), potential temperature, and vertical wind velocity gusts for flight 186rf02.

PELTI Flight 186rf02, First St. Croix Local, 7/9/2000 Descent, UTC 16:24:50 - 16:33:29 1400 1400

1200 1200

1000 1000

800 800

600 600

400 400 Pressure Altitude (m) Altitude Pressure (m) Altitude Pressure 200 200

0 0 20 40 60 80 100 120 6 8 10 12 14 16 Ambient DMS Concentration (pptv) MRLA (g/kg)

1400 1400

1200 1200

1000 1000

800 800

600 600

400 400 Pressure Altitude (m) Altitude Pressure (m) Altitude Pressure 200 200

0 0 -2 -1 0 1 2 298 299 300 301 302 303 304 Vertical Wind Velocity (w', m/s) Potential Temperature (θ, K)

Figure 4.16 Altitude profiles for ambient DMS concentration, water vapor mixing ratio (MRLA), potential temperature, and vertical wind velocity gusts for flight 186rf03.

163

PELTI Flight 186rf04, 3rd St. Croix Local, 7/15/2000 Ascent, UTC 19:42:00 - 19:54:00 2000 2000

1500 1500

1000 1000

500 500 Pressure Altitude (m) Altitude Pressure (m) Altitude Pressure

0 0 0 20 40 60 80 100 10 12 14 16 18 Ambient DMS Concentration (pptv) MRLA (g/kg)

2000 2000

1500 1500

1000 1000

500 500 Pressure Altitude (m) Altitude Pressure (m) Altitude Pressure

0 0 -1.5 -1 -0.5 0 0.5 1 1.5 298 300 302 304 306 308 310 312 Vertical Wind Velocity (w', m/s) Potential Temperature (θ, K)

Figure 4.17 Altitude profiles for ambient DMS concentration, water vapor mixing ratio (MRLA), potential temperature, and vertical wind velocity gusts for flight 186rf04.

PELTI Flight 186rf05, 4th St. Croix Local, 7/16/2000 Descent, UTC 18:10:00 - 18:13:20 800 800

700 700

600 600

500 500

400 400

300 300 Pressure Altitude (m) Altitude Pressure (m) Altitude Pressure 200 200

100 100 10 20 30 40 50 60 70 15 15.5 16 16.5 17 17.5 Ambient DMS Concentration (pptv) MRLA (g/kg)

800 800

700 700

600 600

500 500

400 400

300 300 Pressure Altitude (m) Altitude Pressure (m) Altitude Pressure

200 200

100 100 -1.5 -1 -0.5 0 0.5 1 299 299.5 300 300.5 301 301.5 Vertical Wind Velocity (w', m/s) Potential Temperature (θ, K)

Figure 4.18 Altitude profiles for ambient DMS concentration, water vapor mixing ratio (MRLA), potential temperature, and vertical wind velocity gusts for flight 186rf05.

164

4.3.7 Eddy Correlation Flux of Sulfur Dioxide

The time series for the eddy correlation flux measurement of SO2 is also from the fourth PELTI St. Croix Local flight (186rf05) on July 16, 2000. The flux measurement was made around 18.6°N, 63°W at an altitude of 266 m with an average air speed of 120 m/s. It is important to repeat that the PELTI mission was not intended for flux studies.

Consequently, most of the information during boundary layer flights was concentrated on

DMS fluxes since switching to SO2 required cooling down the air stream heater to produce

- SO5 ions that were proportional to ambient SO2 levels (as discussed in Chapter 2).

However, it was important to determine the SO2 flux in the boundary layer in order to

compute a deposition velocity that is widely needed in models. Previously, only crude

estimates of the deposition velocity were available.

The manifold and instrumental parameters for SO2 were stated in a previous section

- of this chapter. Ozone was added to the manifold in excess to convert SO2 to SO5 as

24, 25 described in Chapter 2 (Reactions 2.17 - 2.20) for both ambient SO2 and the isotopic

34 standard SO2. The data reduction was also the same as stated previously for DMS. A time

lag correction of 1717 ms was applied to the ambient SO2 concentration data through

inspection of the phase angle between MRLA and SO2 (similar to Figure 4.5).

The eddy correlation flux measurement information is presented in Table 4.12.

Again, small time segments were used in a weighted average to obtain an approximate 10

minute flux measurement segment to produce robust statistics. The minimum length of a

segment was over one minute, meeting the requirements needed for good accuracy and

precision of the flux determination. 165

The average eddy correlation flux of SO2 in the marine boundary of this region was

–0.16 ± 0.07 pptv m/s ((4.3 ± 1.9)x1012 molecules m-2 s-1). The negative value for the flux indicates that the scalar has a downward flux, which is what is expected for a species such as

SO2. Although this is not a surface flux measurement, this direct flux measurement of SO2 by the airborne isotopic dilution APIMS at 266 m agrees within a factor of 2 with a rough

12 -2 -1 78 estimate of SO2 flux to the surface of 8.4x10 molecules m s of Pszenny et al. (1990) over tropical oceans. The correlation coefficient for the flux measurement is 0.09 and is in agreement with the correlation coefficient for DMS at a similar altitude (as seen in section

± 4.3.4). The average ambient SO2 concentration for this measurement was 69 2 pptv,

which is in agreement with prior boundary layer measurements of SO2 conducted in similar geographic areas.38, 41, 79-81

Table 4.12 Ambient SO2 eddy correlation measurement information for flight 186rf05. Conc. (pptv) Conc. 2 (pptv m/s) (m) (pptv) Flux (pptv m/s) Flux (pptv 2 (m/s) SO2 Flux_SO2 w f_SO2 f_SO2 wSO2 Segment UTC Start UTC Stop Ambient SO L (m) L SO r 1 15:58:30 15:59:40 70.26 4.88 -0.402 0.104 0.301 0.27 8194 19 2 16:02:35 16:05:05 71.47 4.84 -0.099 0.102 0.455 0.05 17794 19 3 16:19:07 16:24:57 67.59 4.33 -0.144 0.057 0.439 0.08 41794 19 (pptv m/s) Flux (pptv m/s) Flux (pptv 2 Flux_SO2 Segments UTC Start UTC Stop SO Average ALL 15:58:30 16:24:57 -0.16 0.07

166

A deposition velocity can be calculated using the following equation82:

FSO (w'SO ') v = − 2 = − 2 z . Equation 4.10 dSO2 c SO2 [SO2 ]z

Here FSO2 is the average SO2 flux at height z in the boundary layer, and cSO2 is the average

SO2 concentration at the same height. Using the direct SO2 flux data provided in Table 4.12,

± a deposition velocity of 2.4 1.1 mm/s was calculated for SO2 over the ocean.

Although eddy correlation has not been used to determine oceanic boundary layer

83-85 fluxes of SO2, it has been used over land to obtain fluxes and to estimate dry deposition velocities for SO2. Deposition velocities for SO2 have been cited in literature by Erisman and Baldocchi (1994 and references therein)86 in a range between 0.1 to 2 cm s-1 for many types of surfaces, including water. A range of 0.2 to 0.7 cm s-1 has been used in model

1, 59, 87 calculations of concentrations of species related to SO2, including DMS. More applicable to the present direct calculation, Chen et al. (2000)59 calculated an oceanic

deposition velocity for SO2 for use in a model simulation of DMS oxidation that was compared to the experimental data obtained by Bandy et al. (1996)1 at Christmas Island.

Taking into account the non-sea-salt sulfate (NSS) balance88 over the open ocean, Chen et al. estimate that the deposition velocity should be on the order of 2.5 mm/s, which is in excellent agreement with the deposition velocity calculated based on the direct flux

measurements of SO2.

Although the data set was limited, the airborne isotopic dilution APIMS has

demonstrated the ability to determine the direct eddy correlation fluxes of SO2 in the marine boundary layer that are comparable to literature values. The quality of this measurement is 167

reinforced through comparison of the direct calculation of SO2 deposition velocity over open ocean with an estimate taking into account sulfate aerosol balance.

4.4 Conclusions

Although information was presented in Chapter 3 regarding the airborne intercomparison of the isotopic dilution APIMS with the GC/MS/ILS system for sulfur dioxide, as well as initial testing of the fast data collection for that species, this chapter presents the answers to the initial hypotheses stated in Chapter 1.

The accuracy for determining dimethyl sulfide in marine boundary layer with an airborne isotopic dilution APIMS was established through an intercomparison with a previously tested airborne GC/MS/ILS instrument used to measure DMS in marine boundary layers. More importantly, it was shown that direct eddy correlation surface layer flux determinations can be made for DMS with high accuracy and precision using an airborne isotopic dilution APIMS, an accomplishment that has not been seen before in this field. Moreover, a rudimentary but still useful flux gradient for DMS was produced, allowing for the precise determinations of surface and entrainment fluxes. Real-time high-resolution altitude profiles of DMS concentration have also been obtained, providing a means to calculate entrainment velocities and investigate various meteorological conditions. These vertical profiles of DMS also establish DMS as a useful tracer molecule for meteorological

studies. Finally, eddy correlation marine boundary layer flux determinations of SO2 were presented. Although limited, the data set provided sufficient information to establish that

direct SO2 flux determinations can also be made using an isotopic dilution APIMS. 168

Calculation of the SO2 deposition velocity and comparison to estimated values again reinforces the accuracy of this measurement. 169

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CHAPTER 5 – THESIS SUMMARY AND FUTURE RESEARCH

5.1 Thesis Summary

Real-time mixing ratio measurements and eddy correlation flux determinations of

dimethyl sulfide (DMS) and sulfur dioxide (SO2) were obtained in situ using an isotopic

dilution atmospheric pressure ionization mass spectrometer (APIMS) on an aircraft

platform. The sampling frequency of the isotopic dilution APIMS ranged from 1 Hz to 25

Hz per ambient sample for real-time measurements of DMS and SO2. Measurements were

made from near the surface (~30 m) in the marine boundary layer to over 6 km in the free

troposphere. The airborne isotopic dilution APIMS demonstrated an average sensitivity of

80 cps/pptv for DMS and SO2 with a signal-to-noise (S/N) ratio > 5. A lower limit of detection of 0.1 pptv in a one second integration period was also determined for DMS and

SO2 in airborne atmospheric measurements.

Use of the isotopic dilution technique in conjunction with the APIMS measurements

provided an internal calibration of every ambient sample, eliminating factors such as changes

in instrument sensitivity and manifold sample loss from the determination of the ambient

concentrations. Moreover, manifold conditioning provided by larger amounts of analyte

present in the manifold (from the internal isotopic dilution standard) provided rapid and

efficient transport of the ambient species through the manifold, allowing for accurate and

precise determinations of small concentration fluctuations with the APIMS technique. As a

result of the aforementioned information, eddy correlation flux determinations for DMS and

SO2 were accurate, precise, and reproducible. 176

Chapter 2 presented detailed accounts of the instrumentation and components used in

the creation of an isotopic dilution APIMS for the measurement of DMS and SO2 on aircraft

platforms. The APIMS instrument control and basic data acquisition through original

programs was also discussed. A more in-depth discussion of the benefits of the isotopic

dilution method to the airborne APIMS measurements, as well as relevant ion chemistry for

the APIMS detection of DMS and SO2, were also presented. Laboratory experiments of the

isotopic dilution APIMS were planned around actual airborne test deployments. These

experiments simulated ambient air conditions and allowed for methods to be developed in

order to obtain the most sensitive isotopic dilution APIMS response for DMS and SO2 in

ambient air environments.

Results of laboratory investigations for obtaining the maximum APIMS sensitivity for

SO2 indicate that ozone should be added in excess of 45 ppbv to the APIMS manifold in

- order to ensure complete formation of the detected SO5 ion. Adding ozone to the manifold

- causes the airborne isotopic dilution APIMS response to the SO5 ion to be independent of ambient ozone concentrations. Also, addition of ozone to the manifold allows for internal

calibrations (zeros) to be performed on SO2 in ultrapure air to determine background

contributions to the ambient signal. Ozone addition during the determination of DMS

appears to have no influence on the positive ion chemistry or APIMS sensitivity.

Simulations of moist marine boundary layer air indicate that a Nafion dryer should be

1 used in order to obtain the optimal APIMS SO2 sensitivity in this environment. Fast source flow rates (> 2 L/min) and high decluster pressures (> 5 Torr) improve the ion chemistry conditions in moist air environments by reducing water cluster formation. These instrumental conditions also provide the lowest backgrounds for the ambient and isotopic

- standard SO5 ions. 177

A sample air heater was developed to improve the APIMS sensitivity for DMS in humid air environments by dehydrating the water cluster formed in the API source.2

Maximum airborne APIMS sensitivity of 42 cps/pptv for DMS in humid air (dew point

~25°C at an air temperature of 25°C) was accomplished through the use of a single strand

Nafion dryer and sample air heater at air temperature of 400°C. This sensitivity improved on average by a factor of two in the airborne field tests through the use of a multiple strand

Nafion dryer.

Chapter 3 presented the results of the first airborne test of the isotopic dilution

APIMS performed in November 1999. Validity of the accuracy of the isotopic dilution

APIMS measurements for SO2 was obtained through a successful airborne intercomparison with the well proven isotopically labelled standard-gas chromatography/mass spectrometry

(GC/MS/ILS)3, 4 method. The application of the APIMS for the airborne determination of

SO2 with a sampling frequency of 1 Hz was also demonstrated. Fast temporal resolution for ambient SO2 measurement was demonstrated through probing a thin moist layer and power plant plume interception during the airborne test flights.

Test flights off the coast of San Diego, CA, during the first airborne deployment provided the first experimental test of the isotopic dilution APIMS in marine boundary layer

environment. The APIMS sensitivity for SO2 was significantly decreased in the presence of large amounts of ambient water vapor. However, use of a Nafion dryer restored the APIMS

sensitivity for SO2.

Initial tests for the APIMS detection for DMS in the marine boundary layer were also performed. As seen before in the laboratory5 and field6, the APIMS response for DMS in the presence of large amounts of water vapor is reduced to background levels. Use of a

Nafion dryer during the marine boundary layer flights to aid the detection of ambient DMS 178 with the isotopic dilution APIMS was not beneficial at high dew points. This prompted the development of the previously mentioned sample air heater to aid the APIMS detection of

DMS..

Brief tests of sampling at a Nyquist frequency of 4.85 Hz also were conducted during

the test flights. The results indicated that eddy correlation measurements of SO2 appeared feasible with the isotopic dilution APIMS instrument design.

Chapter 4 presented the results from the second airborne test deployment of the isotopic dilutions APIMS. This deployment was based out of St. Croix (U. S. Virgin Islands) during July 2000. The isotopic dilution APIMS accuracy for DMS measurements in the marine boundary layer was established through an airborne intercomparison with the

GC/MS/ILS instrument, similar to that described for SO2 in Chapter 3. More importantly,

Chapter 4 presented the results for the primary objective stated in Chapter 1—the direct

determination of eddy correlation fluxes of DMS and SO2 from the measurements obtained with the airborne isotopic dilution APIMS method.

It was shown that direct eddy correlation flux determinations can be made for atmospheric DMS with high accuracy and precision using an airborne isotopic dilution

APIMS, an accomplishment that has not been seen before in this field. From an aircraft altitude of 30 m, the average ambient DMS flux was determined to be 0.65±0.09 pptv m/s

((1.7±0.2)x1013 molecules m-2 s-1 or 2.5±0.3 µmol m-2 d-1) over a ~67 minute level flight in the marine boundary layer east of St. Croix (U. S. Virgin Islands). Another flight two days later in a similar geographic area at an aircraft altitude of 50 m produced an average DMS flux of 0.63±0.08 pptv m/s ((1.7±0.2)x1013 molecules m-2 s-1 or 2.4±0.3 µmol m-2 d-1) from an

~244 minute level flight consisting of four flight legs that crossed over the same geographical area. A significance test on the means at the 95% confidence level of each 179 flight leg determined that there was no statistically significant difference between the DMS flux determinations for each leg. This indicates excellent reproducibility in the eddy correlation flux determinations using the isotopic dilution APIMS within one flight.

The reproducibility for eddy correlation flux determination using the APIMS between flights was also tested. The two average DMS flux values stated above in the same geographic area were compared and determined to be statistically identical at the 95% confidence level. It can be stated with certainty that eddy correlation flux determinations from an aircraft platform were reproducible using measurements from an isotopic dilution

APIMS.

Based on the flux integral scale obtained from the power spectral density of the DMS flux, the precision on the DMS flux values presented above is on the order of 13%. Based on the sampling time for the flux, the accuracy of the APIMS determined DMS flux values are within 10%.7 The direct eddy correlation DMS flux values determined from the airborne isotopic dilution APIMS DMS measurements also agreed well with the literature values of estimated surface and marine boundary layer DMS fluxes. The DMS concentrations determined by the airborne isotopic dilution APIMS were also in good agreement with pervious measurements of atmospheric DMS in similar areas.

A rudimentary but still effective flux gradient for DMS was produced using the direct eddy correlation flux determinations allowing for the precise determinations of surface and entrainment fluxes. Eddy correlation DMS fluxes were determined from flight legs at three different altitudes in the marine boundary layer over the same geographical area. Plotting the altitude versus the DMS flux values, and fitting a linear model to the data, the DMS surface flux was determined to be 0.86±0.26 pptv m s-1 ((2.3±0.7)x1013 molecules m-2 s-1) through extrapolation to the surface. Similarly, the entrainment flux for DMS was 180 determined to be 0.28±0.17 pptv m s-1 ((7.5±4.4)x1012 molecules m-2 s-1) by extrapolating to the boundary layer top. Again, these surface and entrainment fluxes, determined from direct eddy correlation fluxes using the isotopic dilution APIMS DMS measurements, are comparable to estimates from the literature.

Real-time high-resolution altitude profiles of DMS concentration have also been demonstrated with the airborne isotopic dilution APIMS, providing a means to calculate entrainment velocities and investigate various meteorological conditions. These vertical profiles of DMS also establish DMS as a useful tracer molecule for meteorological studies.

Based on the entrainment flux mentioned above, and the DMS concentration difference across the boundary layer top determined by the APIMS during those flight legs, an entrainment velocity of 1.9±1.1 cm/s was determined for DMS.

Finally, the eddy correlation marine boundary layer flux determinations of SO2 were presented in Chapter 4. Although limited, the data set provided sufficient information to

establish that direct SO2 flux measurements can be determined from isotopic dilution

APIMS measurements. During a level flight leg at 266 m in the boundary layer, the SO2 eddy correlation flux was determined to be –0.16 ± 0.07 pptv m/s ((4.3 ± 1.9)x1012 molecules m-2 s-1). Although not a surface flux determination, the eddy correlation flux value

is within a factor of two of literature values for estimated SO2 surface fluxes over open ocean. Using the eddy correlation SO2 flux determination, and the average SO2

± concentration at that altitude, the SO2 deposition velocity was determined to be 2.4 1.1 mm/s, which is highly comparable to an estimate of 2.5 mm/s by Chen et al. (2000)8 used in

studying DMS oxidation in the Tropics. Incidentally, the concentration values of SO2 measured in the marine boundary layer by the isotopic dilution APIMS also agreed well with values in the literature. 181

5.2 Future Research

The results of this work provide adequate proof that fast in situ atmospheric

measurements and eddy correlation flux determinations of trace gases are possible through

the use of an isotopic dilution APIMS on aircraft platforms. The ability to obtain fast

measurements and direct eddy correlation flux determinations of DMS and SO2 in the atmosphere open up a wide venue of new research areas. It is also possible to extend the results of this study to other atmospheric compounds of interest. Some of these new possibilities are discussed below.

Since the results were limited in this study, a more detailed investigation of SO2

fluxes in the marine boundary layer should be conducted with the airborne isotopic dilution

APIMS. High resolution vertical profiles of SO2 should also be investigated. These studies

could be conducted as an auxiliary part of a future field mission with the APIMS instrument.

Detailed studies of fundamental aspects of APIMS should also be investigated.

Different radioactive ionization sources, aperture orifice diameters, and source flow rates

should be tried to determine the optimal APIMS response. Also, different sample air

heating methods should be developed to try to improve the APIMS response for DMS

beyond its current status.

Fast SO2 measurements could allow study of SO2 transport along with other

chemical species (for example, ozone and nitrogen oxides (NOx)) generated from

anthropogenic activity. Since SO2 has a water solubility much greater than O3 or NOx, the

vertical transport of SO2 in convective clouds can have a different impact on the spatial

distribution of SO2 than for O3 or NOx. Fast SO2 measurements provided by an isotopic 182

dilution APIMS in and around clouds would be very useful data in evaluating models of

heterogeneous removal of SO2.

Anthropogenic SO2 is also often correlated with other anthropogenic tracers, such as

9-12 O3, NOx, and hydrocarbons, in the middle and upper free troposphere. Vertical profiles

of SO2 and O3 with high spatial resolution (similar to that demonstrated with the isotopic dilution APIMS for DMS in the marine boundary layer) would be very useful in determining

the fate of SO2 and its role in new particle production in the free troposphere. Correlation

of fast measurements of SO2 and O3 would also be very useful for study of stratosphere –

troposphere exchange.13, 14

Trace measurements at various altitudes of nitric acid, hydrogen peroxide, formic

acid, acetic acid, and hydrogen cyanide in the atmosphere have already been demonstrated

using airborne chemical ionization mass spectrometric techniques14-21 similar to the APIMS

detection of SO2. However, the isotopic dilution advantages, in combination with the fast

measurement capabilities of our APIMS, would allow for accurate and precise real-time

measurements, and possibly direct flux determinations of those species. If methods were

- investigated to optimize the isotopic dilution APIMS response to CO3 (as well as eliminate

possible backgrounds interferences), atmospheric CO2 flux determinations are also quite

plausible with the isotopic dilution APIMS technique.

Atmospheric trace measurements at various altitudes of acetone, acetonitrile,

acetaldehyde, and methanol have been reported using proton transfer reactions in the

atmospheric sources of airborne mass spectrometers14, 19-27, similar to the method described

in this work for DMS. Again, the isotopic dilution capabilities of a real-time airborne

APIMS measurement could improve the quantitation of these species and possibly

determine their direct flux in the atmosphere. Ammonia fluxes are also feasible, but like 183

CO2 fluxes, may take some time to develop quantitation methods before flux determinations

become routine.

The ability to measure accurate and precise atmospheric concentrations and eddy

correlation fluxes DMS and SO2 should allow for updated studies of the quantitative

boundary layer chemistry of DMS and SO2. For example, if the isotopic dilution APIMS were deployed in a remote area, free of anthropogenic contributions, a study similar in nature to that of Bandy et al. (1996)28 could be performed. In such experiments, closure

methods based on models for flux estimates29-31 would no longer have to be used since the

vertical fluxes of DMS and SO2 could be directly determined. Thus, important parameters

such as the rate of mass of DMS converted to SO2 could be established through the use

direct flux determinations. Moreover, the accuracy of the models and parameters used to

estimate DMS fluxes in the closure methods could also be investigated.

One of the more promising features of the direct eddy correlation flux determination

of DMS is the correlation of the DMS flux with another parameter to broaden the use of the

flux determination. Thompson et al. (1990)32 describe a technique to determine the sea-to-

air flux of DMS through comparison with satellite ocean color. To be able to use ocean

color as a DMS surface flux indicator, an assumption is made that the phytoplankton

chlorophyll a pigment concentrations are related to the DMS flux activity. If direct eddy

correlation DMS fluxes determined through measurements made by the isotopic dilution

APIMS correlate to satellite ocean color, then this correlation could be used to predict

precise global DMS flux. Furthermore, databases of atmospheric DMS concentration

measurements could be examined with respect to the eddy correlation DMS flux-satellite

ocean color correlation to provide retrospective DMS flux determinations. 184

The results presented in this work have been used to obtain NASA and NSF funding for participation in three major atmospheric chemistry programs. The same airborne isotopic dilution APIMS presented in this work is currently deployed in the NASA sponsored Transport and Chemical Evolution over the Pacific (TRACE-P) program, where

real-time measurements of SO2 are being obtained in the polluted Asian Pacific area. This is the first non-test aircraft deployment for fast measurements of SO2 with the isotopic dilution

APIMS. A near identical duplicate airborne isotopic dilution APIMS (used in the ISCAT field mission in Antarctica in 2000) has also been simultaneously deployed in the NSF sponsored Asian Pacific Regional Aerosol Characterization Experiment (ACE Asia) for a

similar SO2 study.

In July 2001, the NSF sponsored Dynamics and Chemistry of the Marine

Stratocumulus II (DYCOMS II) program will be conducted off the coast of San Diego, CA.

The DYCOMS II field program is designed to collect data to test the large-eddy simulations of marine stratocumulus. Our isotopic dilution APIMS will contribute real-time airborne

DMS measurements that will be used to calculate direct eddy correlation DMS flux values

(similar to what is presented in this work) for use in determining DMS entrainment fluxes and velocities at the marine stratocumulus boundary layer top. This will be the first non-test aircraft deployment of the isotopic dilution APIMS for the determination of direct eddy correlation fluxes.

185

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(32) Thompson, A. M.; Esaias, W. E.; Iverson, R. L. J. Geophys. Res. 1990, 95, 20551.

188

APPENDIX A – ABBREVIATIONS

Abbreviation Meaning amu Atomic Mass Unit API Atmospheric Pressure Ionization Atmospheric Pressure Ionization Mass APIMS Spectrometer/Spectrometry cps Counts per Second DMS Dimethyl Sulfide Isotopically Labeled Standard-Gas GC/MS/ILS Chromatograph/Mass Spectrometer / Spectrometry m/e Mass-to-Charge Ratio MS Mass Spectrometer/Spectrometry NCAR National Center for Atmospheric Research NSF National Science Foundation Performance Evaluation of the Low PELTI Turbulence Inlet ppbv Parts per Billion by Volume pptv Parts per Trillion by Volume RAF Research Aviation Facility (NCAR) SCCM Standard Cubic Centimeters per Minute SIM Selected Ion Monitoring SLM Standard Liters per Minute

SO2 Sulfur Dioxide VIs LabVIEW Virtual Instrument Programs

189

APPENDIX B – APIMS INSTRUMENTAL CONTROL AND DATA ACQUISITION LABVIEW VIRTUAL INSTRUMENT PROGRAMS (VIS)

This appendix contains the LabVIEW virtual instrument programs (VIs) for the airborne isotopic dilution atmospheric pressure ionization mass spectrometer (APIMS) instrumental control and data acquisition. These VIs were referenced in Chapters 2 and 4.

190

Figure B.1 (a) MS_Spectrum_Scanning VI front panel.

191

Figure B.1 (b) MS_Spectrum_Scanning VI wiring diagram.

192

Figure B.1 (b) (continued) MS_Spectrum_Scanning VI wiring diagram.

193

Figure B.2 (a) SIM VI front panel.

194

Figure B.2 (b) SIM VI wiring diagram.

195

Figure B.2 (b) (continued) SIM VI wiring diagram.

196

Figure B.2 (b) (continued) SIM VI wiring diagram.

197

Figure B.2 (b) (continued) SIM VI wiring diagram.

198

Figure B.2 (b) (continued) SIM VI wiring diagram. 199

Figure B.3 (a) FLOW_DATA VI front panel.

200

Figure B.3 (b) FLOW_DATA VI wiring diagram.

201

Figure B.4 (a) FAST_SIM VI front panel.

202

Figure B.4 (b) FAST_SIM VI wiring diagram.

203

Figure B.4 (b) (continued) FAST_SIM VI wiring diagram. 204

APPENDIX C – FAST COUNTING SYSTEM PANEL AND CIRCUIT DIAGRAMS

This appendix contains the panel and circuit diagrams for the fast counting system developed for the real-time data acquisition with the airborne isotopic dilution atmospheric pressure ionization mass spectrometer (APIMS) instrument. The fast counting system was used to obtain the data for the direct eddy correlation flux determinations. These diagrams were referenced in Chapter 4. 205

Fast Counting System - Front Panel

Local Remote Display Update Integrated Signal Count

10 9 9 9 9 9 1 M1 Display Select 100 M2 Mass A (M1) Display Mass Command Voltage Mass Command Voltage 1 0 ... 0 0 0 Mass B (M2) 10-Turn Potentiometers

LED Indicators M1 M2 Reset M1 Both Data Switch M2 Thumbwheel 9 9 9 9 9 9 Switches

Mass A Mass B Delay X 10 ms X 10 ms X 1 ms Power

Figure C.1 Front panel of fast counting system. 206

Fast Counting System - Back Panel

Mass In Mass Out TTL (Remote) Counts In 5 V Signal BNC 2 A Common

A B Twinex +15 V Fuses 1 A

-15 V 1 A Dual Data Acquisition

DB-37 DB-37 Output

DC Power In

Figure C.2 Back panel of fast counting system. 207

Fast Counter Mass Command Circuit

Buffer Amps TLE2024 + Mass G=1 Out +10 V Mass A Maxim 574AJ Latch 74LS374 Maxim DG419 Bit 0 Analog + Mux G=1 +10 V 16-Bit Data Acquisition A/D 12-Bit DB-37

Mass B Bit 11 Mass Bits Select 12-15

Control Logic

From Timer Circuit

Figure C.3 Fast counter mass command circuit diagram.

208

Fast Counter Pulse Counting Circuit

Display HP 5082 99999 44 44 4 Update Latch Latch

44444 BCD Counter Reset 74F160 From Gate TTL Pulses Timer Circuit

In From Count Time Control Logic Discriminator Control

Count Time Binary Counter Reset Control 74F161 4 44 4

Latch Data Latch 74LS374 Bit 0 Bit 15 Data Ready Bit

16-Bit Data Acquisition

Figure C.4 Fast counter pulse counting circuit diagram. 209

Fast Counter Timer Circuit

TTL Crystal 1 KHz Control Logic DigiKey SPG8640BN

In Out Data Ready A/B Pulse Counting Circuit

Mass A Timer Mass B Timer To Mass Command Circuit /1074LS190 Delay /10 74LS190 Delay A B Scaler Scaler 44 444 4

0-9 0-9 0-9 0-9 0-9

Thumbwheel Switches

Figure C.5 Fast counter timer circuit diagram. 210

APPENDIX D – MATLAB M-FILES FOR DATA REDUCTION IN THE EDDY CORRELATION FLUX CALCULATIONS

This appendix contains the various Matlab m-files (or scripts) written for the data

reduction procedures to calculate the eddy correlation flux measurements for dimethyl

sulfide (DMS) and sulfur dioxide (SO2) measured in situ in the atmosphere with an airborne isotopic dilution APIMS. These scripts were referenced in Chapter 4. Description of the m- file is available with the script itself as comments. Matlab Release 12 (Version 6), along with the Signal Processing Toolbox for Matlab Release 12 (Version 6) was used to create the m- files. Therefore, some internal functions called in the m files may be specific to this version of the Signal Processing Toolbox.

The netCDF nomenclature refers to the Unidata Program (www.unidata.ucar.edu,

UCAR, Boulder, CO) Network Common Data format, which is the file system used for data collected on the NCAR C-130 aircraft. Extraction of information from the netCDF file system was made possible through the use of netCDF Matlab toolbox (Dr. Charles R.

Denham, U. S. Geological Survey, Woods Hole, MA). The toolbox is available for download from the Internet at http://crusty.er.usgs.gov/~cdenham/MexCDF/nc4ml5.html. 211 flux1.m

% This m file creates sets of data for eddy correlation flux calculations % of DMS derived from an original set of netcdf data files t=input('enter spm data variable: '); % spm refers to second past midnight time sets for flux calcaultions m63z=input('enter ambient zero (counts): '); m66z=input('enter standard zero (counts): '); mand3dms=input('enter standard manifold concentration (pptv): '); xx=input('enter drxdata variable: '); ww=input('enter fast var variable: '); zz=input('enter lat-long variable: ');

N=max(size(t)); for i=1:1:N t1=t(i,1); t2=t(i,2); %start splitdrxdata which splits extracted netcdf 50 Hz mass and count data into %two 25 Hz sets x=xx; t11=find(x(:,1)==t1); if isempty(t11)==1 t11=find(x(:,1)<=t1 & x(:,1)>(t1-0.05)); end t22=find(x(:,1)==t2); if isempty(t22)==1 t22=find(x(:,1)<=t2 & x(:,1)>(t2-0.05)); end x=x(t11:t22,:); if mod((max(size(x))/2),2)>0 %in case there is an odd number of rows x(end,:)=[]; end d=diff(x(:,3)); d=[1;d]; tbad=find(d==0); %test to see if there is data corruption if isempty(tbad)==1 ln=max(size(x)); %start actual mass/count data split n=1:1:ln; n=n'; x(:,5)=n; n1=find(mod(x(:,5),2)>0); n2=find(mod(x(:,5),2)<1); x1=x(n1,:); x2=x(n2,:); if x1(1,3)<1.6 m63=x1; else m66=x1; end if x2(1,3)<1.6 m63=x2; else m66=x2; end m63(:,5)=[]; m66(:,5)=[]; % the next section computes the ambient DMS data and stores files based on % each spm set in the original spm time file 212 x=m63; y=m66; r=x(:,1); x(:,5)=x(:,4)-m63z; y(:,5)=y(:,4)-m66z; r(:,2)=x(:,5)./y(:,5); r(:,3)=((mand3dms*0.929).*r(:,2))./0.928; rr=['r6366_',int2str(i),' = r;']; eval(rr); mm63=['m63_',int2str(i),' = x;']; eval(mm63); mm66=['m66_',int2str(i),' = y;']; eval(mm66); % the next section truncates the extracted netcdf variable data for the % current spm set w=ww; t11=find(w(:,1)==t1); if isempty(t11)==1 t11=find(w(:,1)<=t1 & w(:,1)>(t1-0.05)); end t22=find(w(:,1)==t2); if isempty(t22)==1 t22=find(w(:,1)<=t2 & w(:,1)>(t2-0.05)); end w=w(t11:t22, :); if mod((max(size(w))/2),2)>0 w(end,:)=[]; end www=['var_',int2str(i),' = w;']; eval(www); % the next section truncates the 1 Hz GLAT,GLON data to the current spm set z=zz; t11=find(z(:,1)==t1); if isempty(t11)==1 t11=find(z(:,1)<=t1 & z(:,1)>(t1-0.05)); end t22=find(z(:,1)==t2); if isempty(t22)==1 t22=find(z(:,1)<=t2 & z(:,1)>(t2-0.05)); end z=z(t11:t22, :); if mod((max(size(z))/2),2)>0 z(end,:)=[]; end zzz=['ll_',int2str(i),' = z;']; eval(zzz); else ['DATA CORRUPTION IN TIME SERIES SPM',int2str(i)] %skips the spm set if the data is bad end end clear x* y* ln t* d* m63 m66 mm63 mm66 w* z* N n* r rr;

213 flux2.m

% This m file creates sets of data for eddy correlation flux calculations % of DMS derived from an original set of netcdf data files % Only use this file if there is one (1) spmset in the spmset list! t=input('enter spm data variable: '); m63z=input('enter ambient zero (counts): '); m66z=input('enter standard zero (counts): '); mand3dms=input('enter standard manifold concentration (pptv): '); xx=input('enter drxdata variable: '); ww=input('enter fast var variable: '); zz=input('enter lat-long variable: ');

t1=t(1,1); t2=t(1,2); %start splitdrxdata which splits extracted netcdf 50 Hz mass and count data into %two 25 Hz sets x=xx; t11=find(x(:,1)==t1); if isempty(t11)==1 t11=find(x(:,1)<=t1 & x(:,1)>(t1-0.05)); end t22=find(x(:,1)==t2); if isempty(t22)==1 t22=find(x(:,1)<=t2 & x(:,1)>(t2-0.05)); end x=x(t11:t22,:); if mod((max(size(x))/2),2)>0 %in case there is an odd number of rows x(end,:)=[]; end d=diff(x(:,3)); d=[1;d]; tbad=find(d==0); %test to see if there is data corruption if isempty(tbad)==1 ln=max(size(x)); %start actual mass/count data split n=1:1:ln; n=n'; x(:,5)=n; n1=find(mod(x(:,5),2)>0); n2=find(mod(x(:,5),2)<1); x1=x(n1,:); x2=x(n2,:); if x1(1,3)<1.6 m63=x1; else m66=x1; end if x2(1,3)<1.6 m63=x2; else m66=x2; end m63(:,5)=[]; m66(:,5)=[]; % the next section computes the ambient DMS data and stores files based on % each spm set in the original spm time file x=m63; 214 y=m66; r=x(:,1); x(:,5)=x(:,4)-m63z; y(:,5)=y(:,4)-m66z; r(:,2)=x(:,5)./y(:,5); r(:,3)=((mand3dms*0.929).*r(:,2))./0.928; rr=['r6366_1 = r;']; eval(rr); mm63=['m63_1 = x;']; eval(mm63); mm66=['m66_1 = y;']; eval(mm66); % the next section truncates the extracted netcdf variable data for the % current spm set w=ww; t11=find(w(:,1)==t1); if isempty(t11)==1 t11=find(w(:,1)<=t1 & w(:,1)>(t1-0.05)); end t22=find(w(:,1)==t2); if isempty(t22)==1 t22=find(w(:,1)<=t2 & w(:,1)>(t2-0.05)); end w=w(t11:t22, :); if mod((max(size(w))/2),2)>0 w(end,:)=[]; end www=['var_1 = w;']; eval(www); % the next section truncates the 1 Hz GLAT,GLON data to the current spm set z=zz; t11=find(z(:,1)==t1); if isempty(t11)==1 t11=find(z(:,1)<=t1 & z(:,1)>(t1-0.05)); end t22=find(z(:,1)==t2); if isempty(t22)==1 t22=find(z(:,1)<=t2 & z(:,1)>(t2-0.05)); end z=z(t11:t22, :); if mod((max(size(z))/2),2)>0 z(end,:)=[]; end zzz=['ll_1 = z;']; eval(zzz); else ['DATA CORRUPTION IN TIME SERIES SPM',int2str(i)] %skips the spm set if the data is bad end clear x* y* ln t* d* m63 m66 mm63 mm66 w* z* N n* r rr;

215 flux3.m

% This m file creates sets of data for eddy correlation flux calculations % of SO2 derived from an original set of netcdf data files t=input('enter spm data variable: '); m112z=input('enter ambient zero (counts): '); m114z=input('enter standard zero (counts): '); manso2conc=input('enter standard manifold concentration (pptv): '); xx=input('enter drxdata variable: '); ww=input('enter fast var variable: '); zz=input('enter lat-long variable: ');

N=max(size(t)); for i=1:1:N t1=t(i,1); t2=t(i,2); %start splitdrxdata which splits extracted netcdf 50 Hz mass and count data into %two 25 Hz sets x=xx; t11=find(x(:,1)==t1); if isempty(t11)==1 t11=find(x(:,1)<=t1 & x(:,1)>(t1-0.05)); end t22=find(x(:,1)==t2); if isempty(t22)==1 t22=find(x(:,1)<=t2 & x(:,1)>(t2-0.05)); end x=x(t11:t22,:); if mod((max(size(x))/2),2)>0 %in case there is an odd number of rows x(end,:)=[]; end d=diff(x(:,3)); d=[1;d]; tbad=find(d==0); %test to see if there is data corruption if isempty(tbad)==1 ln=max(size(x)); %start actual mass/count data split n=1:1:ln; n=n'; x(:,5)=n; n1=find(mod(x(:,5),2)>0); n2=find(mod(x(:,5),2)<1); x1=x(n1,:); x2=x(n2,:); if x1(1,3)<2.75 m112=x1; else m114=x1; end if x2(1,3)<2.75 m112=x2; else m114=x2; end m112(:,5)=[]; m114(:,5)=[]; % the next section computes the ambient SO2 data and stores files based on % each spm set in the original spm time file x=m112; 216 y=m114; r=x(:,1); x(:,5)=x(:,4)-m112z; y(:,5)=y(:,4)-m114z; r(:,2)=x(:,5)./y(:,5); r(:,3)=((manso2conc.*((0.933.*r(:,2))-0.0012)))./(0.9457-(0.0457.*r(:,2))); rr=['r112114_',int2str(i),' = r;']; eval(rr); mm112=['m112_',int2str(i),' = x;']; eval(mm112); mm114=['m114_',int2str(i),' = y;']; eval(mm114); % the next section truncates the extracted netcdf variable data for the % current spm set w=ww; t11=find(w(:,1)==t1); if isempty(t11)==1 t11=find(w(:,1)<=t1 & w(:,1)>(t1-0.05)); end t22=find(w(:,1)==t2); if isempty(t22)==1 t22=find(w(:,1)<=t2 & w(:,1)>(t2-0.05)); end w=w(t11:t22, :); if mod((max(size(w))/2),2)>0 w(end,:)=[]; end www=['var_',int2str(i),' = w;']; eval(www); % the next section truncates the 1 Hz GLAT,GLON data to the current spm set z=zz; t11=find(z(:,1)==t1); if isempty(t11)==1 t11=find(z(:,1)<=t1 & z(:,1)>(t1-0.05)); end t22=find(z(:,1)==t2); if isempty(t22)==1 t22=find(z(:,1)<=t2 & z(:,1)>(t2-0.05)); end z=z(t11:t22, :); if mod((max(size(z))/2),2)>0 z(end,:)=[]; end zzz=['ll_',int2str(i),' = z;']; eval(zzz); else ['DATA CORRUPTION IN TIME SERIES SPM',int2str(i)] %skips the spm set if the data is bad end end clear x* y* ln t* d* m112 m114 mm112 mm114 w* z* N n* r rr;

217 fluxcheck1.m

% This m file checks the fast Drexel data for corruption % before being used for eddy correlation flux calculations. % Gaps in the spm input are due to zero/cal off removals t=input('enter spm data variable: '); xx=input('enter drxdata variable: ');

fluxcheck2.m

% This m file checks the fast Drexel data for corruption % before being used for eddy correlation flux calculations. % Gaps in the spm input are due to zero/cal off removals % FOR USE WITH ONLY ONE ROW IN SPM SET! t=input('enter spm data variable: '); xx=input('enter drxdata variable: ');

t1=t(1,1); t2=t(1,2); %start splitdrxdata which splits extracted netcdf 50 Hz mass and count data into %two 25 Hz sets x=xx; 218 t11=find(x(:,1)==t1); if isempty(t11)==1 t11=find(x(:,1)<=t1 & x(:,1)>(t1-0.05)); end t22=find(x(:,1)==t2); if isempty(t22)==1 t22=find(x(:,1)<=t2 & x(:,1)>(t2-0.05)); end x=x(t11:t22,:); if mod((max(size(x))/2),2)>0 %in case there is an odd number of rows x(end,:)=[]; end d=diff(x(:,3)); d=[1;d]; tbad=find(d==0); %test to see if there is data corruption if isempty(tbad)==1 else ['DATA CORRUPTION IN TIME SERIES SPM'] %skips the spm set if the data is bad end clear x* t* d* i N;

fluxerrors.m

% This m file creates variables used in determining the error in the DMS eddy correlation flux through maximum peak of the power spectrum density of the flux w’DMS’ % a file is written out in ASCII text with the results % use this m file only if you have used fluxcheck1.m to eliminate corrupt data % and have built a spmset that is corruption free fluxerror=[1 1 1 1 1 1 1]; %creat dummy first row s1=input('number of spm sets: ','s'); s2=eval(s1); s3=input('name of output file: ', 's'); as=input('avg. air speed (m/s): '); tsstring=input('time shift of compound variable (ms): ','s'); ts=eval(tsstring); pc=fix((ts/1000)/(1/25)); for i=1:1:s2 y=['wp=var_',int2str(i),';']; eval(y); yy=['sp=r6366_',int2str(i),';']; eval(yy); yyy=['mp=var_',int2str(i),';']; eval(yyy); wpc=wp; spc=sp(pc+1:end, :); wpc=wpc(1:end-pc, :); Ls=(wpc(end,1)-wpc(1,1))*as; Lm=(wp(end,1)-wp(1,1))*as; s11=max(size(wp)); s11pc=max(size(wpc)); wp=wp(:,6)-mean(wp(:,6)); 219 wpc=wpc(:,6)-mean(wpc(:,6)); spc=spc(:,3)-mean(spc(:,3)); mp=mp(:,4)-mean(mp(:,4)); b=ones(1,25)/25; spcy=fftfilt(b,spc,1); sigmaw=std(wpc); sigmas=std(spcy); sigmam=std(mp); wsp=wpc.*spc; wmp=wp.*mp; [ys,fs]=pwelch(wsp,128,[],s11pc,25); % power spectrum density of flux t1s=find(fs>0 & fs<1.005); sp1=max(ys(t1s).*fs(t1s)); t2s=find((ys(:).*fs(:))==sp1); Lfs=(1/fs(t2s)); Lfs=(Lfs*as)/(2*pi); [ym,fm]=pwelch(wmp,128,[],s11,25); t1m=find(fm>0 & fm<1.005); sp2=max(ym(t1m).*fm(t1m)); t2m=find((ym(:).*fm(:))==sp2); Lfm=(1/fm(t2m)); Lfm=(Lfm*as)/(2*pi); fluxerror=[fluxerror; sigmaw sigmas sigmam Ls Lm Lfs Lfm]; %output is stdw stds stdmrla length(m) fluxlengths(m) fluxlengthmrla(m) end fluxerror(1,:)=[]; %delete dummy first row dlmwrite(s3,fluxerror,'\t'); clear y yy yyy wp sp mp wsp wmp sp1 sp2 s11 as spcy spc wpc sigma* Ls Lm Lfs Lfm ys fs ym fm t1* t2* s1 s2 s3 fluxerror i

fluxerrorsso2.m

% This m file creates variables used in determining the error in the SO2 eddy correlation flux through maximum peak of the power spectrum density of the flux w’SO2’ % a file is written out in ASCII text with the results % use this m file only if you have used fluxcheck1.m to eliminate corrupt data % and have built a spmset that is corruption free fluxerror=[1 1 1 1 1 1 1]; %creat dummy first row s1=input('number of spm sets: ','s'); s2=eval(s1); s3=input('name of output file: ', 's'); as=input('avg. air speed (m/s): '); tsstring=input('time shift of compound variable (ms): ','s'); ts=eval(tsstring); pc=fix((ts/1000)/(1/25)); for i=1:1:s2 y=['wp=var_',int2str(i),';']; eval(y); yy=['sp=r112114_',int2str(i),';']; eval(yy); yyy=['mp=var_',int2str(i),';']; eval(yyy); 220 wpc=wp; spc=sp(pc+1:end, :); wpc=wpc(1:end-pc, :); Ls=(wpc(end,1)-wpc(1,1))*as; Lm=(wp(end,1)-wp(1,1))*as; s11=max(size(wp)); s11pc=max(size(wpc)); wp=wp(:,6)-mean(wp(:,6)); wpc=wpc(:,6)-mean(wpc(:,6)); spc=spc(:,3)-mean(spc(:,3)); mp=mp(:,4)-mean(mp(:,4)); b=ones(1,25)/25; spcy=fftfilt(b,spc,1); sigmaw=std(wpc); sigmas=std(spcy); sigmam=std(mp); wsp=wpc.*spc; wmp=wp.*mp; [ys,fs]=pwelch(wsp,128,[],s11pc,25); t1s=find(fs>0 & fs<1.005); sp1=max(ys(t1s).*fs(t1s)); t2s=find((ys(:).*fs(:))==sp1); Lfs=(1/fs(t2s)); Lfs=(Lfs*as)/(2*pi); [ym,fm]=pwelch(wmp,128,[],s11,25); t1m=find(fm>0 & fm<1.005); sp2=max(ym(t1m).*fm(t1m)); t2m=find((ym(:).*fm(:))==sp2); Lfm=(1/fm(t2m)); Lfm=(Lfm*as)/(2*pi); fluxerror=[fluxerror; sigmaw sigmas sigmam Ls Lm Lfs Lfm]; %output is stdw stds stdmrla length(m) fluxlengths(m) fluxlengthmrla(m) end fluxerror(1,:)=[]; %delete dummy first row dlmwrite(s3,fluxerror,'\t'); clear y yy yyy wp sp mp wsp wmp sp1 sp2 s11 as spcy spc wpc sigma* Ls Lm Lfs Lfm ys fs ym fm t1* t2* s1 s2 s3 fluxerror i

getdrxdata.m function x=getdrxdata(filename)

%getdrxdata.m--A function to open a NCAR RAF netcdf file, %read the hour, minute, second, XDCNT, and XDMASS %values, calculate the seconds past midnight and UTC %decimal time, and make all variables accessible %You need to cd to the path where the file is %and use single quotes around filename. %Also, don't forget the semicolon! if (~ischar(filename)) error('you must supply a filename') end 221

ncquiet; nc=netcdf(filename); %open netcdf file in nowrtie mode hour=nc{'HOUR'}; %reads HOUR variable hour=hour(:); %translates ncvar to array minute=nc{'MINUTE'}; %you get the drift minute=minute(:); seconds=nc{'SECOND'}; seconds=seconds(:); spm_start=(hour(1).*3600)+(minute(1).*60)+seconds(1); %calculate start seconds past midnight spm_end=(hour(end).*3600)+(minute(end).*60)+seconds(end); %calculate end seconds past midnight spm=spm_start:0.02:(spm_end+1); %calculates 50Hz time intervals mass=nc{'XDMASS'}; %reads mass voltages mass=mass(:); d=size(mass); %gets dimensions of mass d=d(1,1).*d(1,2); %calculates row size for reshape mass=mass'; %prepares mass variable for reshape mass=reshape(mass,d,1); %reshapes mass for single column data counts=nc{'XDCNT'}; %reads counts counts=counts(:); e=size(counts); e=e(1,1).*e(1,2); counts=counts'; counts=reshape(counts,e,1); spm=spm'; %transform time array spm=spm(1:d,:); %truncates time variable utc=spm./3600; %calculates decimal utc time x=[spm utc mass counts]; nc=close(nc); %closes data file %done

getdrxvardata3.m function x=getdrxvardata3(filename)

%getdrxvardata3.m--A function to open a NCAR RAF low rate netcdf file, %read the hour, minute, second, GLAT, GLONG, RSTB, UVB and UVT %values, calculate the seconds past midnight and UTC %decimal time, and make all variables accessible %You need to cd to the path where the file is %and use single quotes around filename. %Also, don't forget the semicolon! if (~ischar(filename)) error('you must supply a filename') end ncquiet; nc=netcdf(filename); %open netcdf file in nowrtie mode hour=nc{'HOUR'}; %reads HOUR variable 222 hour=hour(:); %translates ncvar to array minute=nc{'MINUTE'}; %you get the drift minute=minute(:); seconds=nc{'SECOND'}; seconds=seconds(:); spm_start=(hour(1).*3600)+(minute(1).*60)+seconds(1); %calculate start seconds past midnight spm_end=(hour(end).*3600)+(minute(end).*60)+seconds(end); %calculate end seconds past midnight spm=spm_start:1:(spm_end+1); %calculates 1Hz time intervals glat=nc{'GLAT'}; %reads latitude glat=glat(:); d=size(glat); %gets dimensions of latitude d=d(1,1).*d(1,2); %calculates row size for reshape glat=glat'; %prepares GLAT variable for reshape glat=reshape(glat,d,1); %reshapes GLAT for single column data glon=nc{'GLON'}; %reads longitude glon=glon(:); e=size(glon); e=e(1,1).*e(1,2); glon=glon'; glon=reshape(glon,e,1); rstb=nc{'RSTB'}; %reads radiometric surface temperature rstb=rstb(:); f=size(rstb); f=f(1,1).*f(1,2); rstb=rstb'; rstb=reshape(rstb,f,1); uvb=nc{'UVB'}; %reads ultraviolet irradiance, bottom uvb=uvb(:); g=size(uvb); g=g(1,1).*g(1,2); uvb=uvb'; uvb=reshape(uvb,g,1); uvt=nc{'UVT'}; %reads ultraviolet irradiance, top uvt=uvt(:); h=size(uvt); h=h(1,1).*h(1,2); uvt=uvt'; uvt=reshape(uvt,h,1); spm=spm'; %transform time array spm=spm(1:d,:); %truncates time variable utc=spm./3600; %calculates decimal utc time x=[spm utc glat glon rstb uvb uvt]; nc=close(nc); %closes data file %done

getdrxvardata6.m function x=getdrxvardata6(filename)

%getdrxvardata6.m--A function to open a high rate NCAR RAF netcdf file, %read the hour, minute, second, ATX, MRLA, PALT, WI, WIC, IRBC, IRTC, THETA, THETAE, THETAV %values, calculate the seconds past midnight and UTC 223

%decimal time, and make all variables accessible %You need to cd to the path where the file is %and use single quotes around filename. %Also, don't forget the semicolon! if (~ischar(filename)) error('you must supply a filename') end ncquiet; nc=netcdf(filename); %open netcdf file in nowrtie mode hour=nc{'HOUR'}; %reads HOUR variable hour=hour(:); %translates ncvar to array minute=nc{'MINUTE'}; %you get the drift minute=minute(:); seconds=nc{'SECOND'}; seconds=seconds(:); spm_start=(hour(1).*3600)+(minute(1).*60)+seconds(1); %calculate start seconds past midnight spm_end=(hour(end).*3600)+(minute(end).*60)+seconds(end); %calculate end seconds past midnight spm=spm_start:0.04:(spm_end+1); %calculates 25Hz time intervals (can be changed to 1 Hz by using a step size of 1—must use low rate data for 1 Hz) atx=nc{'ATX'}; %reads temperature atx=atx(:); d=size(atx); %gets dimensions of temperature d=d(1,1).*d(1,2); %calculates row size for reshape atx=atx'; %prepares mass variable for reshape atx=reshape(atx,d,1); %reshapes mass for single column data mrla=nc{'MRLA'}; %reads fast mixing ratio data mrla=mrla(:); e=size(mrla); e=e(1,1).*e(1,2); mrla=mrla'; mrla=reshape(mrla,e,1); palt=nc{'PALT'}; %reads altitude data palt=palt(:); f=size(palt); f=f(1,1).*f(1,2); palt=palt'; palt=reshape(palt,f,1); wi=nc{'WI'}; %reads vertical velocity wi=wi(:); g=size(wi); g=g(1,1).*g(1,2); wi=wi'; wi=reshape(wi,g,1); wic=nc{'WIC'}; %reads corrected vertical velocity wic=wic(:); h=size(wic); h=h(1,1).*h(1,2); wic=wic'; wic=reshape(wic,h,1); irbc=nc{'IRBC'}; %reads corrected infrared irradiance, bottom irbc=irbc(:); k=size(irbc); k=k(1,1).*k(1,2); irbc=irbc'; 224 irbc=reshape(irbc,k,1); irtc=nc{'IRTC'}; %reads corrected infrared irradiance, top irtc=irtc(:); l=size(irtc); l=l(1,1).*l(1,2); irtc=irtc'; irtc=reshape(irtc,l,1); theta=nc{'THETA'}; %reads potential temperature theta=theta(:); m=size(theta); m=m(1,1).*m(1,2); theta=theta'; theta=reshape(theta,m,1); thetae=nc{'THETAE'}; %reads equivalent potential temperature thetae=thetae(:); n=size(thetae); n=n(1,1).*n(1,2); thetae=thetae'; thetae=reshape(thetae,n,1); thetav=nc{'THETAV'}; %reads virtual potential temperature thetav=thetav(:); o=size(thetav); o=o(1,1).*o(1,2); thetav=thetav'; thetav=reshape(thetav,o,1); spm=spm'; %transform time array spm=spm(1:d,:); %truncates time variable utc=spm./3600; %calculates decimal utc time x=[spm utc atx mrla palt wi wic irbc irtc theta thetae thetav]; nc=close(nc); %closes data file %done

makenetcdf4.m

% makes netcdf file from mat file for NCAR NCPLOT program d=input('Variable Name >'); t=input('Record Length >'); %divide # rows by 25 (may need to remove rows to get integer) % puts variables in proper matrix format for netcdf file spm2=d(:,1); spm2=reshape(spm2,25,t); spm2=spm2'; mrla2=d(:,2); mrla2=reshape(mrla2,25,t); mrla2=mrla2'; wi2=d(:,3); wi2=reshape(wi2,25,t); wi2=wi2'; cr6366=d(:,4); cr6366=reshape(cr6366,25,t); cr6366=cr6366'; % creation of netcdf dimensions, variables, and global attributes cdfname=input('netcdf filename (string w/sq) >'); 225 f=netcdf(cdfname,'noclobber'); f('Time')=t; f('sps25')=25; ti=input('Time Interval (formatted string/w sq) >'); f.TimeInterval=ncchar(ti); pna=input('Project Name (formatted string/w sq) >'); f.ProjectName=ncchar(pna); pnu=input('RAF Project Number (formatted string/w sq) >'); f.ProjectNumber=ncchar(pnu); fn=input('RAF Flight Number (formatted string/w sq) >'); f.FlightNumber=ncchar(fn); fd=input('Flight Date (formatted string/w sq) >'); f.FlightDate=ncchar(fd); f{'base_time'}=nclong; f{'time_offset'}=ncfloat('Time'); f{'spm'}=ncfloat('Time','sps25'); f{'spm'}.units=ncchar('Seconds\0'); f{'spm'}.long_name=ncchar('Seconds Past Midnight\0'); spm=f{'spm'}; spm(:)=spm2; f{'mrla'}=ncfloat('Time','sps25'); f{'mrla'}.units=ncchar('g/kg\0'); f{'mrla'}.long_name=ncchar('Mixing Ratio, Lyman-alpha\0'); mrla=f{'mrla'}; mrla(:)=mrla2; f{'wi'}=ncfloat('Time','sps25'); f{'wi'}.units=ncchar('M/s\0'); f{'wi'}.long_name=ncchar('Vertical Wind Velocity\0'); wi=f{'wi'}; wi(:)=wi2; f{'ambdms'}=ncfloat('Time','sps25'); f{'ambdms'}.units=ncchar('pptv\0'); f{'ambdms'}.long_name=ncchar('Ambient DMS\0'); ambdms=f{'ambdms'}; ambdms(:)=cr6366; base_time=f{'base_time'}; bt=input('Base Time >'); base_time(:)=bt; n=0:1:t-1; n=n'; time_offset=f{'time_offset'}; time_offset(:)=n; close (f); clear all

pcpsdspt.m function Ppc=pcpsdspt(xpc,ypc,Fspc)

%PCPSDSPT A function used to calculate phase corrected eddy correlation fluxes. % PCPSDSPT=Phase Corrected Power Spectrum Density using % the Signal Processing Toolbox. 226

% % Warning! This function uses routines called from the signal processing % toolbox in Matlab version 6.0. DO NOT USE UNLESS THE TOOLBOX IS INSTALLED. % % *PLEASE SEE PSDSPT FOR MORE INFORMATION.* % % PCPSDSPT returns the array % P=[ fpc psdxpc psdypc copc qupc chpc papc] % where % fpc = frequency % psdxpc = X-signal power spectral density, units of (x)^2 / unit of Fs % psdypc = Y-signal power spectral density, units of (y)^2 / unit of Fs % copc = Co-spectrum, units of xy / unit of Fs % qupc = Quad-spectrum, units of xy / unit of Fs % chpc = Coherence of x & y % papc = Phase angle of x & y % % A non signal processing toolbox spectral estimation function is also % available. PSDNOSPT is mostly the original program HSPEC by Harvey Chen % with the addition of coherence and phase angle calculations, and streamlined % graphics.

% References % % - HSPEC.m (Version 1.0) by Harvey Chen, 09/02/92 % - Bendat, Julius and Piersol, Allan, Random Data, John Wiley % & Sons, Inc., 1986.

% Version 1.1 - 01/31/2001 % Written by Glenn M. Mitchell ([email protected]) % Drexel University, Philadelphia, PA, USA

% set phase correction (time shift) of x variable tsvar=input('which variable do you want to time shift (x or y)?: ','s'); tsstring=input('time shift of x variable (ms): ','s'); ts=eval(tsstring); pc=fix((ts/1000)/(1/Fspc)); if tsvar=='x' xpc=xpc(pc+1:end); ypc=ypc(1:end-pc); else xpc=xpc(1:end-pc); ypc=ypc(pc+1:end); end

% Set number of samples and sample rate

Npc=max(size(xpc)); SRpc=Fspc; tmpc=Npc/SRpc; dfpc=1/tmpc; Nqpc=Fspc/2; %Nyquist Frequency

% Set Frequency axis fpc=0:dfpc:Fspc; 227

xpc=xpc-mean(xpc); ypc=ypc-mean(ypc); xpc=xpc(:); ypc=ypc(:);

% Calculation of Fourier transform and Power Spectral Density

[xxpc,fpc]=pwelch(xpc,512,[],Npc,Fspc); psdxpc=xxpc;

% Calculated variance from PSD varxpc=sum (psdxpc)*dfpc %variance w/out DC component if ypc~=0

% Calculate y signal Power Spectral Density yypc=pwelch(ypc,512,[],Npc,Fspc); psdypc=yypc; varypc=sum (psdypc)*dfpc %variance w/out DC component

% Calculate cross spectrum and separate into co and quad spectrums.

[crosspc,f2pc]=csd(xpc,ypc,Npc,Fspc,512,256,'linear'); copc=real(crosspc); qupc=imag(crosspc);

% Correction factor for normalization factor in csd.m kpc=fix((Npc-256)/256); kkpc=kpc*512^2; cfpc=Npc^2/kkpc; cvpc=(sum(copc)*dfpc)/Nqpc %variance w/out DC component

% Calculate coherence

[chpc,f3pc]=cohere(xpc,ypc,Npc,Fspc,512,256,'linear');

% Calculate phase angle papc=atan2(qupc,copc)*(180/(pi)); fltnum=input('Flight #: ', 's'); fltdate=input('Flight Date: ','s'); flttime=input('Flight Period (UTC): ','s'); namx=input ('Name of signal x:','s'); namy=input ('Name of signal y:','s');

%plot of psdx, psdy figure subplot(2,1,1) loglog(fpc, fpc.*psdxpc,'r') 228 title(['Flight ',fltnum,' ', fltdate,' UTC ', flttime, ' Phase Corrected by ',tsstring,' ms']) xlabel ('Frequency (Hz)') ylabel (['PSD of ',namx]) subplot(2,1,2) loglog(fpc,fpc.*psdypc,'r') xlabel ('Frequency (Hz)') ylabel (['PSD of ',namy])

%plot of copsd, quad figure subplot(2,1,1) semilogx(fpc,(fpc.*copc)./Nqpc,'r') title(['Flight ',fltnum,' ', fltdate,' UTC ', flttime, ' Phase Corrected by ',tsstring,' ms']) xlabel ('Frequency (Hz)') ylabel (['Co-spectrum of ',namx,' and ',namy]) subplot(2,1,2) semilogx(fpc,(fpc.*qupc)./Nqpc,'r') xlabel ('Frequency (Hz)') ylabel (['Quad-spectrum of ',namx,' and ',namy])

%plot of coherence, phas angle figure subplot(2,1,1) semilogx(fpc,chpc,'r') title(['Flight ',fltnum,' ', fltdate,' UTC ', flttime, ' Phase Corrected by ',tsstring,' ms']) xlabel ('Frequency (Hz)') ylabel (['Coherence of ',namx,' and ',namy]) subplot(2,1,2) semilogx(fpc,papc,'r') xlabel ('Frequency (Hz)') ylabel (['Phase of ',namx,' and ',namy])

% Make sure each variable is a column vector and output the array. fpc=fpc(:); psdxpc=psdxpc(:); psdypc=psdypc(:); copc=copc(:); qupc=qupc(:); chpc=chpc(:); papc=papc(:);

end

% Make sure each variable is a column vector and output the array. fpc=fpc(:); psdxpc=psdxpc(:); if ypc==0 Ppc=[ fpc psdxpc]; end if ypc~=0 Ppc=[ fpc psdxpc psdypc copc qupc chpc papc]; end 229 psdspt.m function P=psdspt (x,y,Fs)

%PSDSPT A function used to calculate eddy correlation fluxes. % PSDSPT=Power Spectrum Density using the Signal Processing Toolbox. % % Warning! This function uses routines called from the signal processing % toolbox in Matlab version 6.0. DO NOT USE UNLESS THE TOOLBOX IS INSTALLED. % % Power, co-, and quad-spectrum estimate, coherence, phase angle % of one or two data sequences. The power and co-spectrum is defined % such that its integral is the variance and co-variance (without the mean), % respectively. If Y=0 then the FFT analysis is done only on signal X. % % The x variable should be the variable that has a known time lag or is % colleceted independent of a data collection system (i.e., an aircraft % data system). This is just convention to show the phase angle change a % little more clearly for the first 360 degree phase shift. Making the independent % variable the y variable has no change on the co-spectrum and covariance. % % The power spectra are computed using pwelch.m, a FFT based algorithm % utilizing a 512 point Hamming window with a 256 point overlap. % The cross spectrum is computed using csd.m, utilizing a 512 point Hanning % window with 256 point overlap, and a linear detrend. % The cross spectrum is then broken into % the co-spectrum (real) and quad-spectrum (imaginary). % The coherence is computed using cohere.m with a 512 point Hanning % window, 256 point overlap, and a linear detrend. % The Phase angle is computed directly from the co- and quad-spectra. % A minor correction factor is applied to the co- and quad-spectra due to % a normalizing factor applied by the csd.m function. % % A phase correction (time shift) function is also available, and is called % PCPSDSPT. It is vitually identical to PSDSPT except for the input % of a time shift factor. The function will then truncate the variables % appropiately and compute everyting identical to PSDSPT. % % PSDSPT returns the array % P=[ f psdx psdy co qu ch pa] % where % f = frequency % psdx = X-signal power spectral density, units of (x)^2 / unit of Fs % psdy = Y-signal power spectral density, units of (y)^2 / unit of Fs % co = Co-spectrum, units of xy / unit of Fs % qu = Quad-spectrum, units of xy / unit of Fs % ch = Coherence of x & y % pa = Phase angle of x & y % % A non signal processing toolbox spectral estimation function is also % available. PSDNOSPT is mostly the original program HSPEC by Harvey Chen % with the addition of coherence and phase angle calculations, and streamlined % graphics.

% References % 230

% - HSPEC.m (Version 1.0) by Harvey Chen, 09/02/92 % - Bendat, Julius and Piersol, Allan, Random Data, John Wiley % & Sons, Inc., 1986.

% Version 1.1 - 01/31/2001 % Written by Glenn M. Mitchell ([email protected]) % Drexel University, Philadelphia, PA, USA fltnum=input('Flight #: ', 's'); fltdate=input('Flight Date: ','s'); flttime=input('Flight Period (UTC): ','s'); namx=input ('Name of signal x:','s'); namy=input ('Name of signal y:','s');

% Set number of samples and sample rate

N=max(size(x)); SR=Fs; tm=N/SR; df=1/tm; Nq=Fs/2; %Nyquist Frequency

% Set Frequency axis f=0:df:Fs; x=x-mean(x); y=y-mean(y); x=x(:); y=y(:);

% Calculation of Fourier transform and Power Spectral Density

[xx,f]=pwelch(x,512,[],N,Fs); psdx=xx;

% Calculated variance from PSD varx=sum (psdx)*df %variance w/out DC component

%plot of psdx if y==0 loglog(f, f.*psdx) title(['Flight ',fltnum,' ', fltdate,' UTC ', flttime]) xlabel ('Frequency (Hz)') ylabel (['PSD of ',namx]) else subplot(2,1,1) loglog(f, f.*psdx) title(['Flight ',fltnum,' ', fltdate,' UTC ', flttime]) xlabel ('Frequency (Hz)') ylabel (['PSD of ',namx]) end if y~=0

% Calculate y signal Power Spectral Density 231

yy=pwelch(y,512,[],N,Fs); psdy=yy; vary=sum (psdy)*df %variance w/out DC component

% Calculate cross spectrum and separate into co and quad spectrums.

[cross,f2]=csd(x,y,N,Fs,512,256,'linear'); co=real(cross); qu=imag(cross);

% Correction factor for normalization factor in csd.m k=fix((N-256)/256); kk=k*512^2; cf=N^2/kk; cv=(sum(co)*df)/Nq %variance w/out DC component

% Calculate coherence

[ch,f3]=cohere(x,y,N,Fs,512,256,'linear');

% Calculate phase angle pa=atan2(qu,co)*(180/(pi));

%plot of psdy subplot(2,1,2) loglog(f,f.*psdy) xlabel ('Frequency (Hz)') ylabel (['PSD of ',namy])

%plot of copsd, quad figure subplot(2,1,1) semilogx(f,(f.*co)./Nq) title(['Flight ',fltnum,' ', fltdate,' UTC ', flttime]) xlabel ('Frequency (Hz)') ylabel (['Co-spectrum of ',namx,' and ',namy]) subplot(2,1,2) semilogx(f,(f.*qu)./Nq) xlabel ('Frequency (Hz)') ylabel (['Quad-spectrum of ',namx,' and ',namy])

%plot of coherence, phas angle figure subplot(2,1,1) semilogx(f,ch) title(['Flight ',fltnum,' ', fltdate,' UTC ', flttime]) xlabel ('Frequency (Hz)') ylabel (['Coherence of ',namx,' and ',namy]) subplot(2,1,2) semilogx(f,pa) xlabel ('Frequency (Hz)') ylabel (['Phase of ',namx,' and ',namy]) 232

% Make sure each variable is a column vector and output the array. f=f(:); psdx=psdx(:); psdy=psdy(:); co=co(:); qu=qu(:); ch=ch(:); pa=pa(:); end

% Make sure each variable is a column vector and output the array. f=f(:); psdx=psdx(:); if y==0 P=[ f psdx]; end if y~=0 P=[ f psdx psdy co qu ch pa]; end end

sumzonevar.m

% writes average variable information for segements produced with flux1.m or flux2.m (DMS) for use in eddy correlation flux calculation of DMS % file is written as ASCII text % use this m file only if you have used fluxcheck1.m to eliminate corrupt data % and have built a spmset that is corruption free sumvar=[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]; %creat dummy first row s1=input('number of spm sets: ','s'); s2=eval(s1); s3=input('name of output file: ', 's'); for i=1:1:s2 y=['x=var_',int2str(i),';']; eval(y); yy=['xx=r6366_',int2str(i),';']; eval(yy); yyy=['xxx=ll_',int2str(i),';']; eval(yyy); sumvar=[sumvar; mean(xxx(:,3)) mean(xxx(:,4)) mean(xxx(:,5)) mean(xxx(:,6)) mean(xxx(:,7)) mean(xx(:,3)) mean(x(:,3)) mean(x(:,4)) mean(x(:,5)) mean(x(:,6)) mean((x(:,6)-mean(x(:,6)))) mean(x(:,8)) mean(x(:,9)) mean(x(:,10)) mean(x(:,11)) mean(x(:,12))]; %output is glat glon rstb uvb uvt amb_dms_conc atx mrla palt wi wi(mean removed) irbc irtc theta thetae thetav end 233

sumvar(1,:)=[]; %delete dummy first row dlmwrite(s3,sumvar,'\t'); clear y yy yyy x xx xxx s1 s2 s3 sumvar i

sumzonevarso2.m

% writes average variable information for segements produced with flux3.m (SO2) for use in eddy correlation flux calculation of SO2 % file is written as ASCII text % use this m file only if you have used fluxcheck1.m to eliminate corrupt data % and have built a spmset that is corruption free sumvar=[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]; %creat dummy first row s1=input('number of spm sets: ','s'); s2=eval(s1); s3=input('name of output file: ', 's'); for i=1:1:s2 y=['x=var_',int2str(i),';']; eval(y); yy=['xx=r112114_',int2str(i),';']; eval(yy); yyy=['xxx=ll_',int2str(i),';']; eval(yyy); sumvar=[sumvar; mean(xxx(:,3)) mean(xxx(:,4)) mean(xxx(:,5)) mean(xxx(:,6)) mean(xxx(:,7)) mean(xx(:,3)) mean(x(:,3)) mean(x(:,4)) mean(x(:,5)) mean(x(:,6)) mean((x(:,6)-mean(x(:,6)))) mean(x(:,8)) mean(x(:,9)) mean(x(:,10)) mean(x(:,11)) mean(x(:,12))]; %output is glat glon rstb uvb uvt amb_so2_conc atx mrla palt wi wi(mean removed) irbc irtc theta thetae thetav end sumvar(1,:)=[]; %delete dummy first row dlmwrite(s3,sumvar,'\t'); clear y yy yyy x xx xxx s1 s2 s3 sumvar i

234

VITA

GLENN M. MITCHELL

PLACE AND DATE OF BIRTH Jan. 20th, 1972 Philadelphia, PA Citizenship United States

EDUCATION 2001 Drexel University Philadelphia, PA Ph.D. Analytical Chemistry 1996 Drexel University Philadelphia, PA M.S., Analytical & Physical Chemistry 1994 Drexel University Philadelphia, PA B.S., Chemistry Graduated Summa Cum Laude.

HONORS AND AWARDS

American Institute of Chemists Foundation Award for Outstanding Achievement in Chemistry, 1994.

Alexander V. Kornilew Award for Unusual and Fundamental Research in Chemistry, 1994.

Second Honors in Chemistry Award, Drexel University, 1994.

PUBLICATIONS

Mitchell, G. M., Thornton, D. C.; Bandy, A. R.; Fast airborne dimethyl sulfide and sulfur dioxide measurements by isotopic dilution atmospheric pressure ionization mass spectrometry, in preparation, 2001.

Hensel R. R.; Mitchell, G. M.; Zhu, L.; Ciraolo, J.; Owens, K. G. Electrospray sample preparation for quantitative analysis by MALD/I TOFMS 46th ASMS Conference on Mass Spectrometry and Allied Topics, Orlando, FL 1998, 792.

Laprade, B.; Owens, K. G.; Mitchell, G. M.; Ciraolo, J. High mass sensitivity enhancements for microchannel plate-based detectors 46th ASMS Conference on Mass Spectrometry and Allied Topics, Orlando, FL 1998, 918.

Xiong, Y.; Mitchell, G. M.; Owens, K. G. Investigations of MALDI/TOFMS analysis of synthetic polymers 45th ASMS Conference on Mass Spectrometry and Allied Topics, Palm Springs, CA 1997, 1043.

Bandy, A. R.; Thornton, D. C.; Blomquist, B. W.; Chen, S.; Wade, T. P.; Ianni, J. C.; Mitchell, G. M.; Nadler, W. Chemistry of dimethyl sulfide in the equatorial Pacific atmosphere Geophys. Res. Lett. 1996, 23(7), 741.