Mercury Isotope Fractionation by Environmental Transport and Transformation Processes. Paul Gijsbert Koster van Groos

Transcription

1 Mercury Isotope Fractionation by Environmental Transport and Transformation Processes By Paul Gijsbert Koster van Groos A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Engineering Civil and Environmental Engineering in the Graduate Division of the University of California, Berkeley Committee in charge: Professor James R. Hunt, Chair Professor David L. Sedlak Professor Donald J. DePaolo Dr. Bradley K. Esser Spring 2011

2 Mercury Isotope Fractionation by Environmental Transport and Transformation Processes Copyright 2011 by Paul Gijsbert Koster van Groos

3 Abstract Mercury Isotope Fractionation by Environmental Transport and Transformation Processes by Paul Gijsbert Koster van Groos Doctor of Philosophy in Engineering Civil and Environmental Engineering University of California, Berkeley Professor James R. Hunt, Chair Mercury is a toxic metal with well known health risks, but uncertainties regarding its environmental fate remain. Analytical tools capable of distinguishing small variations in mercury isotope composition have recently become available and there is considerable interest in applying these to help improve understanding of mercury s complex biogeochemical cycle, and to identify specific sources to remediate. In this dissertation, mercury isotope fractionation by three environmental transport and transformation processes mercury diffusion through a polymer, thermal decomposition of HgS(s), and mercury diffusion through air are investigated. Clear understanding of processes that affect mercury isotopes, such as these, is needed to ensure field scale isotopic data are interpreted correctly. A new analytical method for measuring mercury isotopes with high precision was developed to pursue the work described here. In this method, both mercury and thallium (for instrumental mass bias corrections) are introduced to a multi-collector inductively coupled plasma mass spectrometer (MC-ICP-MS) as a liquid aerosol. The addition of cysteine to liquid samples effectively controlled mercury memory effects. A purge and trap sample preparation technique, using KMnO4 or HOCl as mercury oxidants, was used in this work to prepare mercury in a common matrix. The long-term reproducibility of the method was approximately 0.3 for δ 202 Hg, which is similar to other contemporary methods. Mercury diffusion through a polymer was found to have a very large isotope effect. This effect was determined by measuring Hg 0 that permeated PVC tubing and matching this with models of the rate and isotopic composition of this gas. The isotope fractionation factor for this process, α202 = ± , is the largest factor yet determined for mercury near ambient conditions. This fractionation factor represents the relative diffusion coefficients of 198 Hg and 202 Hg in the polymer. There have been recent observations of mercury isotope variations at mercury mines that were speculated to have resulted from heating of mercury ores. In experiments 1

4 described here, thermal decomposition of HgS(s) did not result in bulk isotope fractionation of the remaining HgS(s). This was evaluated by heating HgS(s) particles in an argon gas flow for different periods of time and measuring the mass and isotopic composition of remaining HgS(s). A model of congruent evaporation from a solid explained this lack of bulk isotope fractionation well. This model indicates that, while changes in the isotopic composition of a thin surface layer are possible, isotopic changes of the bulk material are very small. Mercury diffusion in air was found to have a large isotope effect that can be predicted by kinetic gas theory using only the molecular masses of mercury isotopes and air. This effect was determined by observing mercury remaining in a well mixed reservoir that was depleted by diffusion through a set of hypodermic needles. The ratio of 198 Hg to 202 Hg diffusion coefficients in air was determined to be ± Kinetic theory predicts this ratio to be The fractionation factor of this fundamental and common environmental process is similar to the larger isotope fractionation factors documented previously. The determination of mercury isotopic variations with new analytical tools offers a promising approach for examining mercury in the environment. Interpretations of field measurements will need to be guided by mechanistic understanding developed under controlled conditions. The work described in this dissertation enables better understanding of mercury isotope fractionation by environmental transport and transformation processes that lead to isotopic variations throughout the environment. These isotopic differences suggest not only a means of interpreting environmental transport and transformation processes but also determining the dominant sources of mercury where there have been multiple releases. 2

5 To my family i

6 Table of Contents List of Figures... v List of Tables... x Acknowledgements... xi Chapter 1 Introduction Problem Description Background Mercury Isotopes Mercury Isotope Fractionation Processes Previous Mercury Isotope Research Field Studies Experimental Studies Present Work... 7 Chapter 2 High Precision Mercury Isotope Measurements Introduction Multi-collector ICP-MS Measurements Sample Introduction Signal Integrity Mass Bias Effect Measuring Delta Values Measurements of Standards Sample Preparation Potassium Permanganate Trapping Hypochlorous Acid Trapping Summary Chapter 3 Elemental Mercury Diffusion in a PVC Polymer ii

7 3.1 Introduction Preliminary Observations Experimental Methods Setup and Procedure Analytical Methods Numerical Modeling Results Mercury Permeation Isotope Values Calculation of Isotope Effects Numerical Modeling Discussion Permeation Isotope Effects Temperature Effects Summary Chapter 4 Thermal Decomposition of Mercury Sulfide Introduction Experimental Methods Setup and Procedure Analytical Methods Results Thermal Decomposition Isotope Values Discussion Thermal Decomposition Isotope Effects iii

8 4.5 Summary Chapter 5 Elemental Mercury Diffusion Through Air Introduction Experimental Methods Setup and Procedure Analytical Methods Results Mercury Diffusion Calculation of Measured Diffusion Coefficients Isotope Values Calculation of Measured Isotope Effects Discussion Diffusion Coefficient Diffusive Isotope Effect Summary Chapter 6 Conclusion Summary Recommendations References Appendix A Visual Basic Code Appendix B Additional Data iv

9 List of Figures Figure 1.1 The abundances of mercury isotopes for NIMS-1 (or NIST 3133). (Meija et al. 2010)... 2 Figure 1.2 Illustration of Rayleigh fractionation model system. R indicates isotope ratios and α is the fractionation factor Figure 1.3 Experimentally observed fractionation factors, α202. An asterisk indicates that MIF was reported for the process. α202 for equilibrium effects are given as the ratio of the first term to second term. For all other effects, α202 is a defined in the text. Sources are indicated by letter: a), Estrade et al. (2009), b) Wiederhold et al. (2010), c) W. Zheng et al. (2007), d) Yang & Sturgeon (2009), e) Bergquist & Blum (2007), f) W. Zheng & H. Hintelmann (2009), g) Kritee et al. (2008), h) Kritee et al. (2009), and i) Rodríguez- González et al. (2009)... 7 Figure 2.1 Simplified schematic of IsoProbe MC-ICP-MS Figure 2.2 Mercury signals for different sample introduction systems. Cysteine effectively reduces the memory effect Figure 2.3 Mercury signals using the IsoProbe and different solution chemistries. The range from smallest to largest measured value is indicated by the whiskers and the box indicates the range from the 25 th to 75 th percentile Figure 2.4 Thallium signals using the IsoProbe and different solution chemistries. The range from smallest to largest measured value is indicated by the whiskers and the box indicates the range from the 25 th to 75 th percentile Figure 2.5 Tailing effect in LLNL IsoProbe at analyzer vacuum of 7.9x10-8 mbar Figure 2.6 Relative signals of mass 197 to mass 198 during the analytical runs. The range from smallest to largest measured value is indicated by the whiskers and the box indicates the range from the 25 th to 75 th percentile Figure 2.7 Log plot of measured 202 Hg/ 198 Hg vs measured 205 Tl/ 203 Tl for two standards illustrating the mass bias correction approach Figure 2.8 Measured δ 202 Hg values for the UM-Almaden mercury standard Figure 2.9 Measured δ 202 Hg values for the In-House LLNL mercury standard. The asterisk indicates an outlier discussed further in the text Figure 2.10 Measured δ 202 Hg values for the UC-Berkeley mercury standard Figure 2.11 Illustration of the purge and trap system used for sample preparation v

10 Figure 2.12 Recoveries of mercury standards using the trapping solutions indicated. The outlier, indicated by asterisk, among HOCl trapping had lower concentrations as indicated in the text Figure 2.13 Difference in δ 202 Hg resulting from sample preparation techniques. The outlier, indicated by asterisk, among HOCl trapping had lower concentrations as indicated in the text Figure 3.1 Observed mercury fractionation in stored centrifuge tubes. The concentrations indicated are the initial concentrations of mercury stored in the tubes Figure 3.2 The setup for diffusion in PVC experiments Figure 3.3 The cumulative mass of mercury trapped after permeating through tube walls. The data are for the three experiments (80 C, 68 C, and 23 C) Figure 3.4 The estimated fraction of mercury remaining in the system (tubing and reservoir) Figure 3.5 Linearized time series indicating first order loss in all three experiments (80 C, 68 C, and 23 C) Figure 3.6 Isotope composition of mercury trapped after permeating tubing Figure 3.7 δ 202 Hg of individual samples at different temperatures plotted by fraction remaining in tubing and reservoir. All three different temperatures appear to behave similarly Figure 3.8 Multi-isotope plot indicating that fractionation is mass dependent as anticipated. All data from the experiments (80 C, 68 C, and 23 C) are plotted Figure 3.9 Linearized plot of δ 202 Hg for all experiments (80 C, 68 C, and 23 C) indicating Rayleigh isotope fractionation Figure 3.10 Analytical and model determination of the relationship between Kpoly and Dpoly for 23 C permeation of tubing Figure 3.11 Modeled Hg 0 mass rate permeating tubing compared to experimental data for different Kpoly at 23 C Figure 3.12 Modeled Hg 0 mass rate permeating tubing compared to experimental data for different Kpoly at 23 C Figure 3.13 Model runs for isotope effects for 23 C permeation comparing model run with α202= and different Kpoly Figure 3.14 Model runs for isotope effects for 23 C permeation comparing model run with α202= and different Kpoly vi

11 Figure 3.15 The sensitivity of isotope effects to equilibrium isotope fractionation between polymer and air at 23 C. αpart is the Rpoly/Rair. Evaluated with Kpoly=50, αdiff= Figure 3.16 Isotope composition of permeating mercury illustrating the uncertainty in αdiff at 23 C Evaluated with Kpoly=50, αpart= Figure 3.17 Comparison of β between this experiment and Agrinier et al. (2008). The minor gas isotopologues for the measurements were C- 18 O-O, 36 Ar, 18 O-O, 19 N-N, D-H Figure 3.18 Temperature-dependence of mercury diffusion through PVC tubing with the range of Dpoly estimates as given in Table Figure 4.1 Schematic of quartz decomposition tube used for thermal decomposition Figure 4.2 Temperature inside quartz tube measured by thermocouple Figure 4.3 Hg remaining after thermal decomposition and representative temperature in the decomposition tube Figure 4.4 δ 202 Hg measurements at different levels of mercury remaining for thermal decomposition experiments Figure 4.5 Multi-isotope plot indicating that behavior is as expected for all isotopes Figure 4.6 Linearized isotope fractionation of δ 202 Hg shows very limited effects for thermal decomposition experiments Figure 4.7 Partial pressures of Hg and S in equilibrium with phases given. The time necessary to remove 10 µg of Hg assuming vapor equilibrium with HgS at the given temperature and experimental flow rates is also given. Based on (Ferro, Piacente, and Scardala 1989; Weast 1999; Peng 2001) Figure 4.8 Evolution of δ values in the solid mineral. Time 0 is the initial condition. Time 2 is later than time 1. The surface moves inward at a velocity of Uevap, and the surface layer thickness is defined by the diffusion coefficient and the evaporation velocity, 2Ds/Uevap Figure 4.9 δbulk 202 Hg evolution of a hypothetical particle with Rinitial=5µm, Uevap = 8.6*10-6 cm/s,and Ds=10-11 cm 2 /s Figure 5.1 The diffusion reactor used for determining mercury diffusion coefficients and fractionation factors Figure 5.2 Mercury mass remaining in reactors with time for air diffusion experiments Figure 5.3 Illustration of the expected steady-state Hg 0 concentration profile in the needles Figure 5.4 Linearized time series Figure 5.5 Change in isotope composition with time. The asterisk identifies an outlier vii

12 Figure 5.6 δ 202 Hg of mercury remaining in the reservoir. The asterisk identifies an outlier Figure 5.7 Multi-Isotope Plot indicating that fractionation is mass dependent as anticipated. The asterisks identify outliers Figure 5.8 Linearized isotope fractionation of δ 202 Hg. The asterisk identifies an outlier Figure 5.9 isotope effects associated with Henry's partitioning. Data from Benson & Krause (1980), Beyerle et al. (2000), and Klots & Benson (1963) Figure 5.10 Comparison of experimental results and kinetic theory for Hg 0 diffusion in air Figure 5.11 Isotope composition of Hg 0 (l) recovered and Hg 0 (g) lost to atmosphere relative to the composition of the initial Hg 0 (g) if diffusion controlled condensation Figure 6.1 Experimental results for all three processes examined. Lines represent mechanistic models using the determined isotope fractionation factors Figure A.1 Comparison of analytical solution for plane sheet and numerical model for large, thin hollow cylinder. Dpoly = cm 2 /min, thickness = 1 cm, radius = 500 cm, initial mass = 1 µg Figure A.2 Comparison of analytical solution for plane sheet and numerical model for large, thin hollow cylinder. thickness = 1 cm, radius = 500 cm Figure B.1 δ 201 Hg in individual samples at different temperatures plotted by fraction remaining in polymer tubing and reservoir Figure B.2 δ 200 Hg in individual samples at different temperatures plotted by fraction remaining in polymer tubing and reservoir Figure B.3 δ 199 Hg in individual samples at different temperatures plotted by fraction remaining in polymer tubing and reservoir Figure B.4 Linearized isotope fractionation of δ 201 Hg variations during experiments of mercury permeation of polymer tubing Figure B.5 Linearized isotope fractionation of δ 200 Hg variations during experiments of mercury permeation of polymer tubing Figure B.6 Linearized isotope fractionation of δ 199 Hg variations during experiments of mercury permeation of polymer tubing Figure B.7 Model mass rate permeating tubing compared to experimental data for different Kpoly at 68C viii

13 Figure B.8 Model mass rate permeating tubing compared to experimental data for different Kpoly at 80C Figure B.9 Model runs for isotope effects for 68 C permeation comparing model run with α202= and different Kpoly Figure B.10 Model runs for isotope effects for 80 C permeation comparing model run with α202= and different Kpoly Figure B.11 δ 201 Hg observed in reservior during experiments of mercury diffusion in air Figure B.12 δ 200 Hg observed in reservior during experiments of mercury diffusion in air Figure B.13 δ 199 Hg observed in reservior during experiments of mercury diffusion in air Figure B.14 Linearized isotope fractionation of δ 201 Hg variations during experiments of mercury diffusion in air Figure B.15 Linearized isotope fractionation of δ 200 Hg variations during experiments of mercury diffusion in air Figure B.16 Linearized isotope fractionation of δ 199 Hg variations during experiments of mercury diffusion in air ix

14 List of Tables Table 2.1 Literature δ 202 Hg values for UM-Almaden standard. Errors are 2SD Table 3.1 Evaluated rates of mercury loss from reservior and tubing at different temperatures Table 3.2 Observed Isotope fractionation factors in the polymer permeation experiments 40 Table 3.3 Results of diffusion experiments and calculated diffusion Coefficients Table 4.1 Rayleigh fractionation factors for thermal decomposition experiments Table 5.1 Results of air diffusion experiments and calculated diffusion coefficients Table 5.2 Observed Isotope fractionation factors in the air diffusion experiments Table 5.3 Hg 0 diffusion coefficients through air x

15 Acknowledgements My success in graduate school is owed to many wonderful role models and colleagues found in Berkeley and at Lawrence Livermore National Laboratory. This work would have been impossible without the steady guiding hand of my advisor, James Hunt. His confidence and support were invaluable as I worked to develop suitable analytical methods and experiments for examining mercury isotopes. His calm demeanor often helped relieve stress when progress seemed difficult. Brad Esser and Ross Williams at Livermore were not only instrumental to this work by facilitating the use of the IsoProbe ICP-MS, but were also wonderful resources that I often turned to with problems that arose. They always found time in their busy schedules to meet with me and I am very grateful. I am further indebted to Brad Esser for providing valuable feedback that helped improve this dissertation greatly. I am thankful for the excellent faculty and staff within the Department of Civil and Environmental Engineering as well as across campus. I thank David Sedlak, Mark Stacey, Kara Nelson, and Rob Harley for serving on various committees during my time at Cal. I would further like to thank David Sedlak for reviewing this dissertation. His feedback helped clarify several topics. Shelley Okimoto and Dee Korbel both aided my time at Berkeley greatly by smoothly addressing whatever administrative needs existed. The isotope courses on campus taught by Donald DePaolo, Todd Dawson, and Ron Amundson provided a wonderful foundation for this work. These afforded me an opportunity to explore the field of isotope geochemistry, and taught me many of the basic principles used in this work. I greatly appreciate the time Donald DePaolo took to read and contribute to this dissertation. I am extremely grateful to the U.S. EPA STAR fellowship and the Jane Lewis fellowship programs for financial assistance during my time in graduate school. My time at Berkeley was made much more enjoyable by the many wonderful students and post-docs I have met. I cannot think of a better lab-mate than Patrick Ulrich, who always was ready to give a hand with whatever experiments I was attempting and his suggestions saved me many headaches. I am grateful to all the students and post-docs in O Brien Hall, and particularly the Environmental Fluid Mechanics group, for keeping things fun. I thank my family for their never ending support and encouragement. Finally, I am grateful for the unwavering support of Janet Casperson. She, most of all, helped make my time in graduate school successful. xi

16 Chapter 1 Introduction 1.1 Problem Description Mercury is highly toxic. At low concentrations, its distribution in the environment greatly impacts human and ecological well-being. As evidence of the significant concern mercury pollution causes, consider that more than 65% of lake area and 80% of river distance with fish advisories in the United States are due, at least in part, to elevated mercury concentrations (USEPA 2009). Interest in identifying and removing sources of mercury continues to grow as public awareness of potential mercury exposure increases. The most relevant forms of mercury in the environment are elemental Hg 0, Hg(II), and methylmercury, CH3Hg +. Hg 0 in the atmosphere can oxidize to Hg(II), which readily deposits over land and water. If mercury is methylated, it may bioaccumulate in animal tissues. Humans are primarily exposed to mercury through fish that have accumulated methylmercury. To effectively address mercury pollution, it is essential to accurately assess its complex biogeochemical cycle. Great resources are being invested to do so. The recent discovery of small mercury isotope variations in the environment introduces a new powerful tool to help evaluate components of the mercury cycle. This includes the potential of differentiating and quantifying anthropogenic and natural sources. The work described in this dissertation focuses on improving knowledge of mercury isotope effects such that observed variations in mercury isotope composition can be interpreted better, leading to more accurate understanding of mercury fate at many scales, from local to global. 1.2 Background Mercury Isotopes Mercury belongs to a set of non-traditional stable isotope systems that have been investigated intensively only during the past decade. This recent activity is due to advances in mass spectrometry instrumentation, primarily the development of multi-collector inductively coupled plasma mass spectrometers (MC-ICP-MS). In the case of mercury, these new instruments have enabled accurate observations of small environmental variations in mercury isotope composition for the first time. There are seven stable isotopes of mercury: 196 Hg, 198 Hg, 199 Hg, 200 Hg, 201 Hg, 202 Hg, and 204 Hg. Two radioactive isotopes, 197 Hg and 203 Hg with respective half lives of 2.7 and 47 days, complement these seven, and are used with some frequency as tracers in experiments. The abundances of mercury stable isotopes are listed and illustrated in Figure 1.1 (Meija et al. 2010). These abundances are best estimates for a mercury standard, NIST 3133 SRM, which was certified for isotope composition as NIMS-1. The uncertainty of the abundances is on the order of 10-3 (for 198 Hg). 1

17 30 Stable Hg Isotopes (%) (Meija et al. 2010) 196 Hg = ± Hg = ± Hg = ± Hg = ± Hg = ± Hg = ± Hg = ±0.006 Abundance (%) Figure 1.1 The abundances of mercury isotopes for NIMS-1 (or NIST 3133). (Meija et al. 2010) The uncertainties in mercury abundances given in Figure 1.1 are greater than many observed variations in mercury isotope composition. As such, it is useful to describe variations relative to a standard measured at the same time. This is done with standard delta(δ) notation and is reported on a per mil ( ) basis: where the 198 Hg isotope is used to set ratios because it is the lightest isotope with reasonable abundance. This is consistent with nomenclature used historically in other isotope systems, where ratios of heavy to light isotopes are typically used (J.D. Blum & Bergquist 2007). With this convention, larger δ values correspond to isotope compositions relatively enriched in the heavier isotope. This dissertation uses the δ notation above with NIST 3133 as the common reference standard, as suggested by Blum and Bergquist (2007). Most relative changes in stable isotope composition, or fractionations, observed to date are mass dependent fractionations (MDF). With this type of fractionation, variations in 2

18 isotope composition vary by relative difference in isotope mass, Δm/m, where m indicates the isotope mass. For mercury δ values, this can be approximated as: where δ 204 Hg should yield the largest and most accurate representation of MDF. However, because it is difficult to accurately measure 204 Hg due to an interference with 204 Pb, δ 202 Hg is customarily reported to describe variations in the MDF of mercury isotopes. Uncertainties for δ 202 Hg measurements in the literature currently range from 0.1 to 0.3. Measurements in this work have an uncertainty of approximately 0.3. There is considerable excitement in the isotope community due to observed deviations from MDF, as in equation 1.2, for mercury. This deviation has been termed both mass independent fractionation, MIF (used here), and non-mass dependent fractionation, NMF (Bergquist & J.D. Blum 2007; Estrade et al. 2009). In either case, a capital Δ value, reported on a per mil basis ( ), has been defined to describe this deviation (J.D. Blum & Bergquist 2007): Processes that produce MIF appear to change Δ 199 Hg and Δ 201 Hg and not Δ 200 Hg. MIF appears indicative of more specific processes than MDF, and there is hope it will help characterize specific aspects of the mercury cycle (Bergquist & J.D. Blum 2007). Because it provides an additional dimension of isotopic information, MIF will also be helpful for source identification. As MIF was not observed in this work, it will not receive much further discussion Mercury Isotope Fractionation Processes All observed variations in stable mercury isotope composition result from isotope fractionation processes. This is in sharp contrast to variations among lead isotopes, where three of the four stable isotopes are daughter products of radioactive decay. Knowledge of fractionation processes is essential for understanding the information isotopes may provide. Isotope fractionation processes separate isotopes into two or more fractions with different isotope compositions. Slight property differences among isotopes introduce isotope effects that lead to this fractionation. Usually, isotope effects result directly from differences in isotope mass, leading to MDF, but other differences, such as in nuclear volume or nuclear spin, can lead to MIF. Isotope effects may occur at equilibrium or not, in which case they are most often termed kinetic isotope effects. One model of isotope fractionation frequently encountered is the Rayleigh fractionation model. In this model system, two fractions exist, as illustrated in Figure

19 One fraction serves as a reservoir and the other as an outflow, which flows from the system with no back flow. If isotope ratios of the outflow are inversely proportional to the composition of the reservoir by a constant fractionation factor α, one finds: 1.6 where R is the isotope ratio of the reservoir, the subscript i indicates the initial condition, and F is the mass fraction of the initial reservoir remaining. The definition of the fractionation factor, α, here is such that fractionation factors greater than unity correspond to outflows with smaller isotope ratios than the reservoir. In this text, a subscript is used to identify the specific fractionation factor for a given mercury isotope ratio. For example, α202 indicates the fractionation factor for the 202 Hg/ 198 Hg ratio. Natural and engineered systems that closely match the Rayleigh model are common, especially with kinetic fractionation where back reactions are often inhibited. The catalyzed transformation of organic compounds can match this model, for example. Most of the mercury isotope effects examined experimentally, as described in section 1.3.2, and throughout this dissertation were quantified using the Rayleigh model. R Reservoir R outflow =R/α Outflow Figure 1.2 Illustration of Rayleigh fractionation model system. R indicates isotope ratios and α is the fractionation factor. 1.3 Previous Mercury Isotope Research Mercury isotope research has been approached in two complementary ways: through field work and through laboratory experiments. The pace of developments on both fronts is increasing and a review of literature is quickly outdated. Brief or more exhaustive reviews of contemporary research are available (Yin et al. 2010; Bergquist & J.D. Blum 2009). I will mention some results briefly to place the current work in context. 4

20 1.3.1 Field Studies Many mercury isotope field studies to date have been exploratory. Variations in excess of 6 in δ 202 Hg and 6 in Δ 201 Hg have been observed (Bergquist & J.D. Blum 2009). These relatively large variations are encouraging because they greatly exceed current measurement uncertainties and should enable the discrimination of informative patterns, be they for source identification or transformation processes. Some noteworthy cases where observations of 1 or greater in δ 202 Hg and Δ 201 Hg were observed are coal (Biswas et al. 2008), fish (Bergquist & J.D. Blum 2007), and arctic snow (Sherman et al. 2010). Mercury ores and areas of mining have been a significant focus of mercury isotope work to date. Foucher and Holger Hintelmann (2009) investigated sediments originating from the Idrija mercury mine in Slovenia and observed mercury with the same isotopic composition extending from the mine into the Gulf of Trieste. This mercury isotope composition was distinct from background values in the Adriatic Sea, supporting the application of mercury isotopes as a tracer for this mine. San Francisco Bay (SF Bay), which is heavily impacted by historic mercury and gold mining has also been the subject of mercury isotope research. Gehrke et al. (2011) reported systematic variation of δ 202 Hg values in sediments along the length of SF Bay, with values ranging from -0.3 in the south to -1.0 in the north. This trend, along with some MIF, was mirrored in mercury isotope variations of fish caught at similar locations (Gretchen E Gehrke et al. 2011). Smith et al. (2008) measured isotope compositions of mercury ores in California and found δ 202 Hg variations of approximately 5 with no MIF. These observed differences among ores provide one potential explanation for variations in SF Bay. Gehrke et al. (2011) offered an alternative explanation. They suggested that incomplete processing of Hg ores at mines may have led to the isotope fractionation observed in SF Bay. The largest mercury mine that operated in North America, the New Almaden mine, was located upstream of the southern end of SF Bay. The general supposition is that processing at mines led to Hg 0 (l) products with more negative δ 202 Hg values and processed mine tailings, often termed calcines, with more positive δ 202 Hg values. Evidence of more positive δ 202 Hg values in calcines was reported by both Stetson et al. (2009) and Gehrke et al. (2011). A simple interpretation of these calcines may be difficult, however, as Stetson observed colocated mercury-bearing minerals that also exhibited larger δ 202 Hg values. Uncertainties regarding the potential role of isotope fractionating processes at mercury mines provided much of the inspiration for the work in this dissertation Experimental Studies Interpretation of observed variations in mercury isotope composition requires knowledge of mercury isotope fractionation processes. While understanding of these processes can be informed by knowledge developed in other isotope systems, significant uncertainties exist and experimental studies of mercury isotope effects are necessary. To date, most studies examining mercury isotope effects have been phenomenological. 5

21 It is perhaps interesting to note that one of the earliest observations of intentional isotope fractionation for all elements was with mercury (Brönsted & Hevesy 1920). In their experiments, Brönsted and Hevesy (1920) distilled liquid mercury under vacuum and observed significant differences in mercury density between condensates and the initial reservoir. Similar experiments under vacuum were recently performed by Estrade et al. (2009), who found a large fractionation factor, α202, of ±0.0011, indicating large kinetic isotope effects associated with the evaporation of Hg 0 (l) into vacuum. This is a large mercury isotope effect, but smaller than the theoretical maximum, given the inverse square root of mercury isotope masses. That is, in this case: where m indicates the mass of the isotope indicated by the subscript. This inverse square root relationship is revisited in Chapter 5. In contrast to the vacuum experiments described above, most experiments examining mercury isotope effects have been performed near ambient pressures and better represent effects expected in natural systems. Figure 1.3 shows many, if not all, of the experimentally determined mercury isotope effects to date. It is interesting to observe significant equilibrium isotope effects for mercury, despite its large mass (Estrade et al. 2009; Wiederhold et al. 2010). These equilibrium effects have been attributed to differences in the nuclear volume of mercury, as this effect is predicted to be significant for very heavy isotopes (Schauble 2007). Most of the isotope effects studied are kinetic effects. The isotope effect of Hg 0 volatilization from water likely indicates a diffusive isotope effect of Hg 0 (aq), as this controls Hg 0 volatilization under most conditions (Kuss et al. 2009). Many experiments use gas sparging to separate the two fractions for subsequent measurement and do not explicitly address mercury isotope fractionation potentially caused by this process. Several experiments exhibited MIF after exposure to ultraviolet (UV) light. The mechanisms leading to this effect are still unclear, but they appear to involve interactions between nuclear spin and radical pair intermediates (Bergquist & J.D. Blum 2009). Observed MIF is indicated in Figure 1.3 by an asterisk. Photochemical processes leading to MIF have drawn significant attention as potential explanations for observed MIF in the environment. Photodemethylation, in particular, has been shown to lead to methylmercury with large MIF, which can then bioaccumulate in fish. With the exception of the vacuum distillation experiment described earlier, α202 of experiments ranged from to The fractionation factors determined in this dissertation work are also indicated on Figure 1.3 and are described further below. 6

22 Hg0(l)/Hg0(g) -a HgCl2/Hg(thiol) - b Hg(OH)2/Hg(thiol) - b Vacuum evaporation - a Hg0 volatilization from water - c Hg0 volatilization from water - c Chemical reduction and sparging - d Chemical reduction and sparging - d UV reduction and sparging -d UV reduction and sparging -e UV reduction and sparging -f UV reduction and sparging -f Dark abiotic reduction and sparging - e Dark abiotic reduction and sparging - e NaBEt4 ethylation and sparging - d Bacteria reduction and sparging - g Microbial demethylation and sparging - h UV demethylation and sparge - e UV demethylation and sparge - e Fermentive methylation - i Fermentive methylation - i THIS WORK -Hg0 diffusion of PVC polymer THIS WORK - Thermal decompostion of THIS WORK -Hg0(g) diffusion in air * * * * * * * Equilibrium effects Kinetic effects * * Fractionation factor, α 202 Figure 1.3 Experimentally observed fractionation factors, α 202. An asterisk indicates that MIF was reported for the process. α 202 for equilibrium effects are given as the ratio of the first term to second term. For all other effects, α 202 is a defined in the text. Sources are indicated by letter: a), Estrade et al. (2009), b) Wiederhold et al. (2010), c) W. Zheng et al. (2007), d) Yang & Sturgeon (2009), e) Bergquist & Blum (2007), f) W. Zheng & H. Hintelmann (2009), g) Kritee et al. (2008), h) Kritee et al. (2009), and i) Rodríguez-González et al. (2009) 1.4 Present Work Variations among mercury isotope compositions observed in the environment were noted above, along with experimentally determined fractionation factors. It is important to have a comprehensive understanding of mercury isotope effects to interpret environmental observations. Furthermore, knowledge of isotope effects can help future experiments and field work by guiding expectations. The present work expands knowledge of mercury isotope effects by experimentally determining new fractionation factors, and in one case, matching this factor with fundamental mechanistic theory. These new fractionation factors are more mechanistic in nature than many described previously. Mechanistic factors are particularly useful because they can be applied in more cases. Chapter 2 describes the development of a new analytical method, using liquid sample introduction, for measuring mercury isotopes with sufficient precision for the work 7

23 that follows. Mercury isotope effects associated with Hg 0 diffusion through a polyvinyl chloride (PVC) polymer is described in Chapter 3. As shown in Figure 1.3, this is the largest mercury isotope effect yet observed at ambient pressures. This suggests that large mercury isotope fractionation may be associated with systems where polymer or polymer-like materials are present, such as with mercury permeation of cell walls and membranes, plant cuticles, or even geomembrane liners used at landfills. The potential of mercury isotope fractionation resulting from processing at mercury mines inspired the experiments described in Chapter 4 and Chapter 5. The thermal decomposition of HgS(s), or cinnabar, was an essential component of mercury production. This process is described in Chapter 4, and resulted in negligible isotope fractionation. As such, this process is unlikely to be the cause of larger δ 202 Hg values for calcines as observed by Stetson et al. (2009) and Gehrke et al. (2011). Chapter 5 describes a significant isotope effect due to Hg 0 (g) diffusion through air. This likely led to isotope differences between Hg 0 (l), produced through condensation, and ore materials at mercury mines. The observed isotope effect of Hg 0 (g) diffusion through air matched kinetic gas theory well. The final chapter summarizes the findings of this dissertation and suggests future research questions these findings prompt. 8

24 Chapter 2 High Precision Mercury Isotope Measurements 2.1 Introduction This chapter describes the analytical methods used to measure relative isotope ratios of mercury with high precision. After a brief description of multi-collector inductively coupled plasma mass spectrometers (MC-ICP-MS) presently needed to make these precise isotope measurements, I detail the sample introduction method used, factors affecting the accuracy and precision of measurements, and corrections made to counter instrumental mass bias. The long-term reproducibility of standards measured using this approach is evaluated and compared to contemporary methods. The development and performance of the two sample preparation methods used in this work are also described. 2.2 Multi-collector ICP-MS Measurements All methods used today to observe small differences in the isotope composition of mercury rely on MC-ICP-MS instruments. Previous to the development of these instruments, larger differences were observed with techniques such as density measurements or neutron activation analysis (Kumar et al. 2001). Most investigations, however, utilized mass spectrometers. A. O. Nier (1937) made early measurements of mercury isotopes using electron impact gas source mass spectrometry, which remained the predominant method of examining mercury isotopes until recently. The recent advent of ICP-MS instruments allowed for more efficient ionization of mercury and better measurements. Single-collector ICP-MS instruments are capable of very accurate and precise mercury concentration measurements through isotope dilution methods (Mann et al. 2003; Christopher et al. 2001). Contemporary single-collector ICP-MS instruments, however, do not provide enough precision to measure variations in mercury isotopes. Natural variations in the isotope composition of mercury are too small to investigate with these tools, and require the high precision made available by collecting multiple isotopes simultaneously with more expensive MC-ICP-MS instruments (Ridley & Stetson 2006). All mass spectrometers operate using the same general design: 1) ions are generated at an ion source, 2) an ion beam is accelerated, shaped, and separated by their charge to mass ratio electromagnetically, and 3) ions are collected and counted. Figure 2.1 is a simplified schematic illustrating the IsoProbe (GV Instruments) MC-ICP-MS instrument, located at Lawrence Livermore National Laboratory (LLNL), used for this work. Like all ICP-MS instruments, it uses an argon inductively coupled plasma (ICP) torch to produce ions. Because the ICP is very energy rich and operates at very high temperatures, it effectively ionizes most atoms, including mercury. However, as a result of the high first ionization potential of mercury, ev, its ionization is usually incomplete resulting in smaller signals relative to other elements such as thallium. 9

25 PlasmaIon Source Ion accelerationand separation Ioncollection and counting Figure 2.1 Simplified schematic of IsoProbe MC-ICP-MS Ions produced in the plasma are near atmospheric pressures and enter the high vacuum mass spectrometer through an interface consisting of a set of cones designed to sample ions in the plasma torch, while maintaining a vacuum. The plasma produces ions with a relatively wide range of energies and this must be reduced to effectively separate the ions by mass. This is accomplished with an energy filter. The IsoProbe instrument used in this work is unique in using a hexapole collision cell for this energy filter. In contrast to many ICP-MS instruments that use a quadrupole filter to separate ions by mass, MC-ICP-MS instruments use a magnetic sector to separate the ions to be collected by multiple collectors. Because ions of interest are collected simultaneously with multicollector instruments, instrumental fluctuations affect all isotopes similarly, allowing much greater precision. In this work, all nine Faraday collectors of the IsoProbe were used to measure signals corresponding to singly charged atomic ions with masses 195, 197, 198, 199, 200, 201, 202, 203, and 205 simultaneously Sample Introduction The introduction of samples to the plasma occurs in the form of a gas mixture, often with fine aerosols containing the analyte(s) of interest. The analyte is usually sourced from liquid samples, and aerosols are easily generated with a nebulizer and, most often, a spray chamber. The spray chamber selects for the smallest aerosols, enhancing atomization and subsequent ionization within the plasma. Because it is desirable to maximize the delivery of analyte atoms rather than those of water, systems have been developed to dry aerosols. 10

26 One such system, an Aridus desolvating nebulizer (Cetac, Omaha, NE, USA) was used in this mercury work. By contrast, most contemporary mercury isotope measurements have used gaseous elemental mercury, Hg 0 (g), generated in-line for introduction to the plasma (Stetson et al. 2009; Lauretta et al. 2001; D. Foucher & H. Hintelmann 2006; Estrade et al. 2009). However, because most of these cases used a thallium aerosol to correct for instrumental biases, liquid introduction systems were still necessary, leading to complex hybrid sample introduction systems. One difficulty encountered in handing mercury using liquid introduction systems is its ability to sorb to many system components. This causes what has been termed a mercury memory effect. The memory effect is of concern, because it can affect the integrity of sample signals by reducing their overall contribution to measured signals. Thiol containing compounds, such as cysteine, have been used to effectively address this memory effect (Harrington et al. 2004; Y. Li et al. 2006; Malinovsky et al. 2008). Figure 2.2 shows mercury signals measured using the IsoProbe instrument at LLNL during sample introduction, and subsequent washout, using an Aridus desolvating system, with and without cysteine, and a cyclonic spray chamber. It is evident in Figure 2.2 that the Aridus desolvating system with cysteine performed best, with little evidence of a memory effect. The mercury signal decreased to less than 1% of the sample signal in less than three minutes using a solution of approximately 200 mg/l cysteine. Because this concentration of cysteine appeared effective, it was added to most standards, samples, and washout solutions throughout this work. Some later analytical runs were performed using washout solutions without cysteine to help minimize the buildup of materials on instrumental cones, but this was found to adversely affect the accuracy of blank solutions. 11

27 Mercury Signal (mv/ppb) Aridus with 200 ppm cysteine Aridus Desolvating Nebulizer Cyclonic Spray Chamber Measurement Period Washout Period Time (seconds) Figure 2.2 Mercury signals for different sample introduction systems. Cysteine effectively reduces the memory effect. The precision of isotope measurements is a function of the number of ions counted. Large counts lead to more precise measurements. One challenge faced in maximizing ion counts is the solution chemistry of aerosols introduced to the plasma. Figure 2.3 and Figure 2.4 show box and whisker plots of the LLNL IsoProbe signals for mercury and thallium over the analytical runs performed. Indicated on the figures are periods where potassium (K), manganese (Mn), and sodium (Na) were present in solutions as part of the sample preparation process. While the mechanism of signal suppression is unknown (be it reduced ionization in the plasma, or poor transmission through the cones and the remainder of the instrument), it is clear that more complex matrices reduce the overall signal. This decreased the precision of measurements from more complex samples. Later sample preparation procedures minimized these affects as much as possible by lowering the level of salts in the samples. Not all the variability in signal response can be attributed to the solution chemistry and may be a factor of cone cleanliness. High ion counts can be achieved by increasing either the concentration of solutions or analysis time. It is difficult to increase the aerosol production rate without drastically altering other factors. Some efforts during sample preparation were aimed at increasing mercury concentrations. The concentrations of mercury and thallium used in this work ranged from 10 to 100 µg/l and 10 to 15 µg/l, respectively. Sample uptake rates were 50 to 90 µl/min and individual measurements were integrated for 5 minutes. Each measured sample reflects the measurement of 3 to 50 ng of mercury and 3 to 8 ng of thallium. As a 12

28 result of more efficient ionization in the plasma, and their relative abundances, thallium isotope measurements yield larger signals and more precise isotope measurements than mercury. The least common thallium isotope, 203 Tl, and the most common mercury isotope, 202 Hg, both have abundances of approximately 30%. Mercury Signal (mv/ppb) Mercury Cysteine Cysteine, K, and Mn Cysteine and Na Figure 2.3 Mercury signals using the IsoProbe and different solution chemistries. The range from smallest to largest measured value is indicated by the whiskers and the box indicates the range from the 25 th to 75 th percentile. 13

29 Thallium Signal (mv/ppb) Thallium Cysteine Cysteine, K, and Mn Cysteine and Na Figure 2.4 Thallium signals using the IsoProbe and different solution chemistries. The range from smallest to largest measured value is indicated by the whiskers and the box indicates the range from the 25 th to 75 th percentile Signal Integrity It is important for calculating isotope ratios that signals truly represent only isotopes of interest. Factors that adversely affect these signals can be viewed as affecting the integrity of the intended signal. To optimize the accuracy and precision of isotope measurements, it is important to evaluate the integrity of each isotope signal and to mitigate, as much as possible, factors affecting this integrity. The three primary corrections applied to maximize signal integrity were: 1) on-peak zero, 2) isobaric interference, and 3) tailing corrections. While adding cysteine to the liquid sample introduction system reduced the memory effect greatly, it was still important to control for background mercury concentrations and other interferences. This was primarily achieved through on-peak zero corrections. This correction was performed by observing background signals at all masses produced from blank solutions bracketing samples or standards. The observed on-peak zero signals were subtracted from sample or standard signals. Many factors are relatively constant among blanks, samples, and standards and this correction largely addresses these. Most mercury isotope measurements are reported relative to the 198 Hg isotope (e.g., 202 Hg/ 198 Hg). This is advantageous because it allows for a large relative mass difference with relatively abundant isotopes. However, of the isotopes typically examined for mercury 14