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Microbiology and geochemistry of neutral pH waste rock from the Antamina mine, Peru Dockrey, John William 2010

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MICROBIOLOGY AND GEOCHEMISTRY OF NEUTRAL PH WASTE ROCK FROM THE ANTAMINA MINE, PERU  by John William Dockrey B.S., University of Wisconsin Madison, 2007  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Geological Sciences)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  April 2010  © John William Dockrey, 2010  Abstract The microbial populations of waste rock piles and field cells producing neutral pH drainage at the Antamina mine were characterized to better understand processes which affect current and future drainage quality. Naturally weathered waste rock samples were collected and examined using a variety of high resolution imaging, geochemical, mineralogical, and microbiological techniques. Despite the relatively young age of the waste rock piles (1.5 years), populations dominated by neutrophilic sulfur oxidizers as large as 108 bacteria per gram were found. An exponential relationship was found between the size of microbial communities and the contemporary sulfate loadings.  These results indicate that the microbial populations  rapidly grow to reach a mass which is proportional to the rate of substrate release, and then decrease as the host rocks reactivity diminishes. One sample from a field cell producing pH 6.2 drainage had a mixed population of neutrophiles and acidophiles capable of both S0 and Fe2+ oxidation.  A mini-column study was conducted to determine the catalytic effect of  microbiology in various rock types. No catalysis was identified in the sulfur concentrations of most mini-column series, with the exception of the one mini-column series constructed of material containing acidophilic S0 and Fe2+ oxidizing bacteria, which demonstrated strong microbial catalysis. It was also determined that concentrations of Mo as low as 10mg/l were toxic to these acidophilic bacteria, and dissolved Mo may inhibit the establishment of these geochemically important bacteria.  A massive sulfide from this material was thoroughly  examined using high resolution imaging techniques. Biofilms of bacteria were found upon and within a porous schwertmannite precipitate.  The mixed population of neutrophilic and  acidophilic bacteria and circumneutral drainage pH implies that the bacteria are living in acidic microenvironments surrounding sulfide minerals in which ferric iron leaching can take place. The large microbial populations and the close correlation between geochemistry and biology described in this study, emphasize the importance of biological processes in determining current and future drainage quality emanating from the mine waste. .  ii  Table of Contents Abstract ...................................................................................................................................... ii Table of Contents ...................................................................................................................... iii List of Tables ............................................................................................................................ viii List of Figures............................................................................................................................. ix Acknowledgements .................................................................................................................... x Dedication ................................................................................................................................. xi Co-Authorship Statement ......................................................................................................... xii 1  Chapter 1: Introduction .......................................................................................................1 1.1  Problem Description ....................................................................................................1  1.2  Project Overview .........................................................................................................4  1.3  Research Objectives .....................................................................................................5  1.4  Site Description ...........................................................................................................6  1.5  Literature Review ........................................................................................................6  1.5.1  Acid Generation .....................................................................................................6  1.5.2  Heterogeneity ........................................................................................................8  1.5.2.1  Material Heterogeneity ...................................................................................9  1.5.2.2  Flow Processes................................................................................................9  1.5.2.3  Geochemical Heterogeneity ..........................................................................10  1.5.3  Assessing the Reactivity of Mine Waste at the Antamina Mine ............................11  1.5.3.1  Field Experiments .........................................................................................13  1.5.3.2  Microbiology in field experiments .................................................................14  1.6  Approach ...................................................................................................................14  1.7  Figures .......................................................................................................................17  1.8  References .................................................................................................................19  2 Chapter 2: Structure and Chemistry of Bacterially Populated Acidic Microenvironments in Circumneutral pH Waste RockI ..................................................................................................24 2.1  Introduction...............................................................................................................24  2.2  Site Description .........................................................................................................26  2.2.1  Antamina .............................................................................................................26  2.2.2  Experimental Waste Rock Piles and Field Cells .....................................................26  2.3  Methods ....................................................................................................................27 iii  2.3.1  Microbiological Sampling .....................................................................................27  2.3.2  Inoculum ..............................................................................................................27  2.3.3  MPN and Live/dead Baclight™ .............................................................................28  2.3.4  Material Characterization ....................................................................................29  2.3.5  FIB-FEG-SEM-EDS .................................................................................................30  2.4  Results .......................................................................................................................30  2.4.1  Mineralogy and Geochemistry .............................................................................30  2.4.2  Microbial Community Results ..............................................................................31  2.4.3  Structure and FIB-FEG-SEM-EDS ...........................................................................32  2.4.4  Cell Morphologies ................................................................................................33  2.5  Discussion ..................................................................................................................33  2.5.1  Iron Mineralogy and Structure .............................................................................33  2.5.2  Porosity of Schwertmannite .................................................................................35  2.5.3  Pitting ..................................................................................................................35  2.5.4  Transitional Microbial Community .......................................................................36  2.5.5  Microbial Habitat .................................................................................................37  2.5.6  Development and Occurrence of Acidic Microenvironments................................37  2.6  Figures .......................................................................................................................40  2.7  Tables ........................................................................................................................47  2.8  References .................................................................................................................49  3 Chapter 3: Neutral pH Biological Catalysis of Sulfide Mineral Oxidation and Toxicity Effects of Molybdenum2 .......................................................................................................................55 3.1  Introduction...............................................................................................................55  3.2  Methods ....................................................................................................................57  3.2.1  Material Sampling and Characterization...............................................................57  3.2.2  NAG Testing .........................................................................................................58  3.2.3  Analytical Methods ..............................................................................................58  3.2.4  Molybdate Toxicity in Liquid Media......................................................................59  3.2.5  Mini-Column Study ..............................................................................................59  3.2.5.1  Construction .................................................................................................59  3.2.5.2  Influent Media ..............................................................................................60  3.2.5.3  Effluent Analysis ...........................................................................................61  3.2.5.4  Geochemical Modeling .................................................................................61 iv  3.2.5.5 3.3  SEM-EDS .......................................................................................................61  Results .......................................................................................................................61  3.3.1  NAG Testing .........................................................................................................61  3.3.2  XRF ......................................................................................................................62  3.3.3  Effluent Volume ...................................................................................................62  3.3.4  Sulfur Loading ......................................................................................................63  3.3.5  pH ........................................................................................................................63  3.3.6  Phreeqc ...............................................................................................................64  3.3.7  Liquid Substrate Molybdate Toxicity Test .............................................................65  3.3.8  Micro-Column Molybdate Toxicity Test ................................................................65  3.3.9  SEM-EDS ..............................................................................................................65  3.4  Discussion ..................................................................................................................66  3.4.1  Metal Attenuation ...............................................................................................66  3.4.2  pH ........................................................................................................................67  3.4.3  Sulfate Loadings ...................................................................................................68  3.4.4  Molybdate Toxicity ..............................................................................................70  3.5  Conclusion .................................................................................................................72  3.6  Figures .......................................................................................................................73  3.7  Tables ........................................................................................................................78  3.8  References .................................................................................................................83  4 Chapter 4: Relationship between Geochemistry Microbiology and Material Composition of Antamina Mine Waste Rock3 .....................................................................................................86 4.1  Introduction...............................................................................................................86  4.2  Site Description .........................................................................................................88  4.2.1 4.3  Antamina .............................................................................................................88  Methods ....................................................................................................................88  4.3.1  Experimental Waste Rock Piles and Field Cells .....................................................88  4.3.2  Material Characterization ....................................................................................89  4.3.3  Geochemical Analysis...........................................................................................89  4.3.3.1 4.3.4 4.4  Geochemical Modeling .................................................................................90  Microbiology ........................................................................................................90  Results .......................................................................................................................90  4.4.1  Material Characterization ....................................................................................90 v  4.4.2  Geochemistry.......................................................................................................92  4.4.2.1  pH and Buffering Minerals ............................................................................92  4.4.2.2  Sulfate Loadings ............................................................................................93  4.4.2.3  Metal Loadings .............................................................................................94  4.4.2.4  Nutrient Availability ......................................................................................95  4.4.3  Microbiology ........................................................................................................95  4.4.3.1  Estimating Total Microbial Populations .........................................................96  4.4.3.2  Microbial Correlations to Sulfate Loadings ....................................................97  4.4.3.2.1 Thermodynamic Limitations .....................................................................97 4.5  Discussion ..................................................................................................................99  4.5.1  4.5.1.1  Initial Colonization of Waste Rock ...............................................................101  4.5.1.2  Thermodynamic Constraints .......................................................................101  4.5.1.3  Nutrient Availability ....................................................................................101  4.5.1.4  Microbial Populations in Field Cells .............................................................102  4.5.1.5  Acidophiles at Neutral pH ...........................................................................103  4.5.2  5  Growth and Size of Microbial Populations ............................................................99  Geochemical Implications ..................................................................................104  4.5.2.1  Armoring.....................................................................................................104  4.5.2.2  Metal Leaching ...........................................................................................105  4.6  Conclusion ...............................................................................................................106  4.7  Figures .....................................................................................................................108  4.8  Tables ......................................................................................................................120  4.9  References ...............................................................................................................126  Chapter 5: Data Integration and Outlook .........................................................................131 5.1  Integration of Results...............................................................................................131  5.2  Strengths and Weaknesses of Data ..........................................................................133  5.2.1  Sampling and Transport .....................................................................................134  5.2.2  Inoculum ............................................................................................................134  5.2.3  Data Processing .................................................................................................135  5.2.4  Microcolumns ....................................................................................................136  5.2.5  Geochemistry.....................................................................................................136  5.2.6  SEM ...................................................................................................................136  5.3  Contribution ............................................................................................................137 vi  5.4  Future Work ............................................................................................................138  5.5  Concluding Remarks ................................................................................................140  5.6  References ...............................................................................................................141  Appendix 1: Sampling ..........................................................................................................142 Appendix 2: XRD ..................................................................................................................152 Appendix 3: Net Acid Generating Test .................................................................................174 Appendix 4: MPN Procedure ................................................................................................180 Appendix 5: Mini-Column Sulfur Loadings and Construction ................................................182 Appendix 6: Particle Size Distribution ..................................................................................191 Appendix 7: ICP-OES ............................................................................................................200 Appendix 8: Phreeqc ............................................................................................................202  vii  List of Tables Table 2.1. Results of XRD and SEM-EDS analysis ........................................................................47 Table 2.2. Microbial populations measured using light m ..........................................................48 Table 3.1. List of current and prior studies of toxicity limits.......................................................78 Table 3.2. Numbering scheme of mini-columns with ................................................................78 Table 3.3 NAG results on sample material. ................................................................................79 Table 3.4. XRF results of fine-grained waste rock <1.18 mm in diameter. ..................................80 Table 3.5 Total sulfur loadings. ..................................................................................................81 Table 3.6 Liquid medium MoO42- toxicity results. ......................................................................82 Table 3.7. Molybdenum attenuation in mini-columns ...............................................................82 Table 4.1. Results of quantitative XRD given in wt%. ...............................................................120 Table 4.2. XRF results and estimated chemical composition. ...................................................121 Table 4.3. Field pH values and yearly loadings of field cells .....................................................122 Table 4.4. Nutrient concentrations in the outwash..................................................................123 Table 4.5. The % of alive bacteria as measured by the Live/Dead Baclight™ ............................124 Table 4.6. Redox reactions from which energy can potentially be conserved. .........................124 Table 4.7. Theoretical microbial populations ...........................................................................125  viii  List of Figures Figure 1.1. Five experimental waste rock piles. .........................................................................17 Figure 1.2. Field cells consisting of 55gallon drums. ..................................................................17 Figure 1.3. Location of Antamina Mine, scale bar is 200 km. .....................................................18 Figure 2.1. Linear combination of Fe-EXAFS fitting. ...................................................................40 Figure 2.2. (A) Overview of area containing monolayer and agglomerate biofilms ...................40 Figure 2.3. Ball and whisker morphology of schwertmannite ....................................................42 Figure 2.4. (A) Two bacterial ‘footprints’ on chalcopyrite. .........................................................43 Figure 2.5. (A) Porous schwertmannite surrounded by crust.. ...................................................44 Figure 2.6. Bacteria are present within porosity of schwertmannite ..........................................45 Figure 2.7. (A) Thin film of iron oxyhydroxides covering pyrrhotite surface.. .............................45 Figure 2.8. (A) Area where microbially populated schwertmannite has been peeled away. .......46 Figure 3.1. pH results from mini-column experiment.................................................................73 Figure 3.2. Zn and Cu concentrations as measured in the effluent.............................................74 Figure 3.3. ICP-OES validation of Mo concentrations. ................................................................75 Figure 3.4. EDS spectra and image of gypsum identified in material from mini-column 3-3-2A..76 Figure 3.5. Metal molybdate precipitate in 4-Pile2 on a silicate mineral.. ..................................76 Figure 3.6. Metal molybdate precipitate in 3-2-2B.. ..................................................................77 Figure 3.7. Metal molybdate precipitate in 3-1-3A ....................................................................77 Figure 4.1. Pitted pyrite sample ...............................................................................................108 Figure 4.2. Precipitates found covering sulfide ........................................................................108 Figure 4.3. Molybdenite surface from Pile-2. ...........................................................................109 Figure 4.4. Bismuthenite sample from Pile-3.. .........................................................................109 Figure 4.5. Pyrite sample from Pile-1 .......................................................................................110 Figure 4.6 Chemistry of field cell drainage. ..............................................................................111 Figure 4.8 Solubility indices of metal molybdates in FC-07 as modeled by Phreeqc. ...............112 Figure 4.7. Gypsum saturation in FC-3-2A as modeled by Phreeqc. .........................................112 Figure 4.9. Solubility indices of metal molybdates in FC-2-2B as ..............................................113 Figure 4.10. Solubility indices of smithsonite (ZnCO3) in FC-3-2A .............................................113 Figure 4.11. Solubility Indices of smithsonite (ZnCO3) in FC-1-3A .............................................114 Figure 4.12. Saturation indices of effecting copper solubility in FC-2-3A ..................................114 Figure 4.13. The modeled maximum concentration of dissolved P. .........................................115 Figure 4.14. Sulfate loadings vs. wt. % sulfide as determined by XRF analysis. .........................116 Figure 4.15. Relationship between the estimated bacteria per gram .......................................117 Figure 4.16 MPN results not fixed for % dead or GSD graphed against sulfate loadings.. .........118 Figure 4.17 Molybdenum concentrations of field cells and waste rock pile .............................119  ix  Acknowledgements This research was made possible by funding provided by a collaborative research and development (CRD) grant funded by the Natural Sciences and Engineering Research Council of Canada (NSERC), Teck, and Compañia Minera Antamina S.A. I would like to thank my advisors Roger Beckie, Ulrich Mayer, and Gordon Southam for the guidance and foresight. I would like to express my greatest gratitude to my examining committee, Roger Beckie, Ulrich Mayer, and Leslie Smith for putting in the time and effort to see me through my defence. I particularly enjoyed the thoughts and anecdotes which were in constant supply from my officemates who have become too numerous to name, thanks for being cool. I would like to thank everyone involved on the Antamina project, particularly Danny Bay, Sharon Blackmore, and Holly Peterson who are three of the most resilient and hard working people that I have ever met. I would like to thank my parents, and I would especially like to thank my loving wife Morgan for being her wonderful self.  x  DEDICATION  Judge a man by his questions rather than his answers. -Voltaire  xi  Co-Authorship Statement This research program was initiated by Dr. Roger Beckie, Dr. Klaus Ulrich Mayer, and Dr. Leslie Smith as part of a larger research project studying the hydrogeology and geochemistry of neutral pH waste rock at the Antamina Mine, Peru. The current research approach was designed by John Dockrey with the guidance of R. Beckie, K.U. Mayer, and Dr. Gordon Southam. The laboratory work was performed by John Dockrey under the guidance of G. Southam. Synchrotron analysis was conducted by Dr. Ben Kocar. The data was analyzed by John Dockrey under the guidance of R. Beckie, K.U. Mayer, and G. Southam. The manuscript was written by John Dockrey, and edited by R. Beckie, K.U. Mayer, and G. Southam. A version of Chapter 2 will be submitted for publication, a version of Chapter 3 will be submitted for publication, and a version of Chapter 4 will be submitted for publication.  xii  1  Chapter 1: Introduction  1.1 Problem Description Mine wastes constitute the largest volume of material handled in the world and present a variety of environmental concerns and logistical difficulties (ICOLD, 1996). Due to the sheer volume of material, environmental hazards associated with mine waste disposal are global in scale. At most mines the primary environmental concern associated with mine waste is poor water quality.  Although the scale of the problem has increased dramatically with the  development of modern industrialized societies, the problem itself has been recognized for centuries. One of the oldest surviving texts devoted to the art of mining, De Re Metallica, (Agricola, 1556) states as much:  “Further, when the ores are washed, the water which has been used poisons the brooks and streams, and either destroys the fish or drives them away. Therefore the inhabitants of these regions, on account of the devastation of their fields, woods, groves, brooks and rivers, find great difficulty in procuring the necessaries of life…”  Valuable metals are typically associated with sulfide minerals, which produce acid and release metals through oxidative dissolution when exposed to atmospheric conditions. If the waste material contains insufficient buffering capacity, the drainage will become acidic. The poisoning of brooks and streams above mentioned by Agricola (1556) is likely referring to acid rock drainage (ARD). The effects of ARD can be easily recognized, ARD-affected streambeds may become smothered with iron oxyhydroxides colloquially known as ‘yellow boy’. Today ARD is one of the most widespread environmental problems in the world, and billions of dollars are spent on mine waste remediation (Feasby et al., 1991). Over the past 30 years, researchers have produced a plethora of studies pertaining to the geochemistry, hydrogeology, and microbiology involved in the oxidation of mine waste (Nordstrom, 2000). Most of this research has focused on ARD, only addressing processes taking 1  place at neutral pH in the context of ARD development. Relatively little work has addressed water quality issues associated with neutral rock drainage (NRD) compared to ARD. Although NRD does not have the potential to acidify streams and waterways, it is no guarantee of a benign drainage. Mine waste containing an abundance of pH buffering minerals may remain at a neutral pH indefinitely despite ongoing acid generation. The principle concern of NRD is leaching of metals which are either weakly hydrolyzing, e.g., Zn, Ni, and to a lesser extent Cu (Nicholson and Rinker, 2000; Hiekenin et al., 2008), or oxyanion forming metals such as Mo, As, Se, and Cr (Blowes et al., 1995; 1998). Metal oxyanions are only leached from mine waste while the pH remains neutral, as oxyanions are strongly sorbed in acidic environments. Metals are typically leached from oxidative dissolution of sulfide minerals, the same process responsible for acid generation. Studying the processes controlling metal release at neutral pH is relevant to both predicting water quality of NRD and understanding the initiation of ARD. One of the most critical factors in predicting the water quality emanating from mine waste is predicting the rate of sulfide mineral oxidation. The rate of sulfide oxidation is affected by many factors including: pH, redox potential, oxidant supply, temperature, mineral coatings, grain size, and microbiology (Blowes et al., 2003). The importance of microbiology in the kinetics of sulfide mineral oxidation has been established for decades (Singer and Stumm, 1970).  It has been estimated that  chemoautotrophic microorganisms are responsible for the bulk of ARD produced in mine waste (Silverman and Ehrlich, 1964; Baker and Banfield, 2003). This is largely due to the fact that bacteria are responsible for the production of ferric iron, which in turn aggressively leaches sulfide minerals at acidic pH values. Sulfur oxidizing bacteria clean sulfide mineral surfaces of intermediate sulfur species which slows mineral armoring (Dopson and Lindstrom, 1999). The presence of iron oxidizing bacteria has been found to increase the rate of acid generation in mine waste by a factor of 10-100 under acidic conditions (Olson, 1991; Ritchie, 1994). The vast majority of the experiments concerning the effect of microbiology on sulfide mineral oxidation have been conducted under acidic conditions  2  The degree to which microbiology can catalyze sulfide mineral oxidation and metal leaching under neutral pH conditions is somewhat ambiguous as most studies concerning the microbiology of mine waste examine acidic conditions. Neutrophilic sulfur oxidizing bacteria typically dominate the microbial community under neutral pH conditions. These bacteria will have a similar effect as acidophilic sulfur oxidizing bacteria, in that they prevent armoring of sulfide minerals by oxidizing intermediate sulfur compounds.  The few studies that have  addressed the issue have found that these bacteria can increase weathering rates by 30-100%; however, further studies are clearly needed (Elberling et al., 2000; Hollings et al., 2001). The paucity of data on this topic makes it impossible to determine the reproducibility of this catalytic effect. The large populations of these acid generating microorganisms found in both neutral and acidic mine wastes makes it impossible to produce a complete model of the geochemistry of mine waste without accounting for microbiology at any pH. Neutrophilic sulfur oxidizing bacteria typically dominate the microbial community in neutral pH mine waste; however, they are by no means the only phenotype present. Numerous studies have cultured acidophilic iron and sulfur oxidizing bacteria from neutral pH waste rock and tailings environments, typically in much lower numbers than their neutrophilic counterparts (Blowes et al., 1995; Blowes et al., 2003; Moncur et al., 2005). This was initially surprising because these bacteria require an acidic environment to grow. It has been proposed that  acidophilic  iron  oxidizing  bacteria  form  Fe-oxyhydroxide  bounded  acidic  microenvironments on mineral surfaces in which ferric iron is soluble enough to allow ferric leaching of sulfide minerals to proceed in neutral ambient pH environment (Southam and Beveridge, 1992; Nordstrom and Southam, 1997; Mielke et al., 2003). This process is of particular concern as it will lead to significantly faster leaching of metals by allowing ferric iron leaching to occur which would otherwise be inhibited due to the high pH. Despite the fact that the importance of microbiology is well established, little of this knowledge is applicable to drainage quality predictions or mine closure plans. This is largely due to the fact that microbial constraints are not typically considered in the primary analysis used to make these predictions. Accounting for factors which effect microbiology, such as nutrient availability, temperature, and metal toxicity may help constrain ARD and NRD metal 3  leaching predictions. Accounting for microbiological sensitivities may have resulted in a more accurate prediction. This work contributes to bridging the gap between the accumulated knowledge on the microbiology of sulfide mineral oxidation and application of this knowledge by correlating microbial communities with mineralogy and geochemistry of drainage from well characterized field cells and experimental waste rock piles.  1.2 Project Overview This thesis is a part of a broader project to study the physicochemical processes that control drainage quality from neutral pH waste rock at the Antamina mine. The project is a collaboration between Teck, Compañia Minera Antamina S.A., the University of British Columbia (UBC), and the University of Western Ontario (UWO). Five large scale experimental waste rock piles have been built at Antamina (Figure 1.1). The piles are 10 m-high with a base of 34 m x 34 m and are composed of approximately 25,000 tons of rock. Instrumentation to monitor hydrology, pore gasses, and geochemistry of pore water was installed during construction and all drainage is captured and metered by basal lysimeters. As the piles were being constructed, material was set aside for tandem field cell experiments using 55 gallon drums (Figure 1.2). Silica sand was packed in the bottom of each drum, more than 300 kg of waste rock material was placed on top. The field cells were left uncovered in the field so that natural weathering processes could take effect. The field cells address the leaching behavior of a single material on a small scale and provide insight into the natural heterogeneity of material from which the experimental waste rock piles are constructed. The field cells and experimental waste rock piles were created to examine the geochemistry and hydrogeology of different types of waste rock. Prior to the commencement of this collaborative effort numerous kinetic cells, and acid base accounting tests were conducted, which lead to the conclusion that >85% of the waste rock generated at the Antamina mine has little or no potential for acid generation (Klohn Crippen 1998). The principal concern regarding water quality is the loading of metals that are soluble at neutral pH and alkaline conditions. This work is focused on the microbiological characterization of the waste rock. The overall goal of this research project is to improve our understanding of NRD in order  4  to produce more accurate predictions of drainage quality from various waste rock types and minimize environmental impact of waste rock disposal in the most cost effective manner.  1.3 Research Objectives Currently, the role of microbiology in neutral pH waste rock is relatively unknown. This research project provides an opportunity to observe the microbial communities that have developed in geochemically and mineralogically well characterized waste rock. The overarching objective of this research is to contribute to bridging the gap between accumulated knowledge of microbiology in mine waste environments, and applied microbiology which can be incorporated into predictions of mine waste reactivity by determining the occurrence and geochemical implications of acid generating microorganisms. To do this, a focus is placed on establishing a qualitative relationship between microbial community occurrence, structure, and size in relation to weathering rates, waste rock type, and drainage quality. The following research objectives have been formulated and addressed with this goal in mind: o What is the size and structure of the microbial community in the field cells and piles? o Are the microbial communities that have developed in the field cells similar in size and structure to the microbial communities that have developed in the experimental waste rock piles? o Is the size of the microbial community controlled by material composition, age of waste rock, geochemistry, or a combination of the above? o What is the relationship between size and structure of microbial communities, weathering rates, and metal leaching? Can a relationship between geochemistry and microbiology independent of rock type be established? o Are acidophilic iron oxidizing bacteria present in the neutral pH waste rock? If they are present, do they have a distinct effect on weathering rates, is it possible to identify the conditions under which acidophiles thrive at neutral pH in the bulk pore water, and can the structure and chemistry of microbially populated acidic microenvironments be identified and described? o Does molybdenum inhibit the growth of bacteria isolated from the Antamina Mine? If so, can high concentrations of molybdate inhibit the weathering of mine waste? 5  1.4 Site Description The Antamina mine is a Cu-Zn skarn deposit with ore-grade mineralization surrounding a quartz-monzonite porphyry hosted in Cretaceous limestone. Antamina is one of the largest copper and zinc mines in the world and also produces molybdenum and lead. The Antamina mine is located approximately 270 km (473 km by road) northeast of Lima, Peru within the Andes Mt. at an elevation between 4200 and 4800 m (above sea level) (Figure 1.3). Despite the high elevation, the temperature is moderate with a yearly average of 8oC due to its tropical latitude. The average annual rainfall ranges from 1200 – 1500 mm (Evans et al., 2005), with 80% of the precipitation falling within the summer wet season (October – April) (Klohn Crippen, 1998). The Antamina mine is in the upper Amazon River Basin, near the headwaters of two contributory rivers of the Amazon River, the Quebrada Carash, and the Quebrada Ayash. The Antamina mine began operations in 2001, mine closure is currently scheduled for 2029 (Golder, 2007). It is estimated that 1.5 billion tons of waste rock will be generated in the mine's lifetime (Golder, 2007). The waste rock is composed of 75% limestone, 15% intrusives, and 10% skarn (Klohn and Crippen, 1998). The abundance of limestone and other carbonates has alleviated most concern of drainage from waste rock piles becoming acidic, however 20% of the waste rock is potentially acid generating (Klohn and Crippen, 1998).  1.5 Literature Review 1.5.1 Acid Generation The geochemical effect of microbiology in mine waste is fundamentally mechanistic, in that they affect the surface properties and oxidant supply of sulfide minerals which are responsible for acid generation and metal leaching. Two oxidants are responsible for pyrite oxidation in the environment, O2 and microbially produced ferric iron (Fe3+). As pyrite is the most ubiquitous sulfide mineral, it is commonly used as a proxy for acid generation in mine waste environments. Acid generation from sulfide minerals is the result of a complex series of reactions. Under neutral pH conditions pyrite oxidation can be summarized with the following two reactions: + 3.5O +  ⇒ Fe  + 2  + 2  (1) 6  +  O +  ⇒ Fe(OH) + 2  (2)  Reaction 1 expresses the aerobic oxidation of pyrite. The ferrous iron produced by reaction 1 is rapidly oxidized to ferric iron and precipitates, often as ferrihydrite (reaction 2) depending on pH and sulfate concentration. Reaction 1 and 2 are known as the initiator step of acid mine drainage, as they can take place immediately when pyrite minerals are brought into contact with atmospheric oxygen and water. The ability of O2 to get to sulfide mineral surfaces and the systems pH is considered the rate determining step in the initiator step of acid mine drainage. Williamson and Rimstidt (1994) compiled data available in the literature to determine the following rate law for abiotic pyrite oxidation by dissolved oxygen: = 10  .  ± .  .  ∗  ± .  .  (3)  ± .  Where DO is dissolved oxygen in moles L-1, R is the rate of pyrite oxidation in units of moles m-2 s-1. This rate expression is applicable between a pH of 2-10. Note that the [H+] term is in the denominator of this equation, hence the rate of pyrite oxidation by O2 decreases with decreasing pH. The oxidation and precipitation of iron in reaction 2 becomes kinetically inhibited as the pH decreases below approximately 3.5. In addition, reaction 1 is relatively slow at this pH and the initiatior step (reaction 1 and 2) cease to explain sulfide oxidation. Under acidic conditions the oxidation of pyrite can be summarized into the following two reactions: 4 + 14  +  +  + 8  ⇒ 2  +  ⇒ 15  + 2  (4) + 16  (5)  Due to a more efficient electron transfer chain, ferric iron is more effective oxidant of most sulfide minerals than oxygen (Luther, 1987). The rate expression as compiled by Williamson and Rimstidt for the abiotic oxidation of pyrite by ferric iron with the presence of DO is: = 10  .  ± .  ∗  .  ± .  .  ± .  (6)  This rate expression is applicable to a pH range of 0.5-3, and Fe concentrations over a range of 6 orders of magnitude. Singer and Stumm (1970) identified reaction 4 as the rate determining step in sulfide oxidation in AMD settings, as is mediated by iron oxidizing bacteria. They found 7  that iron oxidizing bacteria increase the rate of reaction 4 from 3 x 10-12 mol L-1 s-1 to 3 x 10-7 mol L-1 s-1, potentially increasing the rate of pyrite oxidation by five to six orders of magnitude. The cycling of iron between reaction 4 and 5 can lead to much more rapid pyrite oxidation than reaction 1 and 2, and is commonly referred to as the propagation cycle (Singer and Stumm, 1970). Most waste rock at the Antamina mine is rich in carbonate minerals. Calcite can effectively buffer the pH in mine waste and prevent acidification, despite ongoing acid generation. The buffering reaction of calcite can be expressed as: +  ⇒  +  ⇒  + +  (7) (  )  (8)  Reaction 7 and 8 describe pH-buffering reactions of due to calcium carbonate dissolution under unsaturated conditions.  Maintaining the pH at circumneutral conditions can prevent the  propagation cycle (reactions 4 and 5) from occurring. 1.5.2 Heterogeneity One of the primary difficulties in working with waste rock environments is the degree of chemical and physical heterogeneity. Geochemical variations within waste rock are important because they affect the mechanisms and rates by which acid is generated or neutralized, what metals are soluble, what precipitates will form, and what microbial biota can be supported. Heterogeneous flow processes are important because they produce drainage waters that are not representative of the geochemistry that the reactive surface area is exposed to. Also, the wide range of particle sizes that are incorporated into waste rock environments lead to hydrological regimes dominated by a combination of macropore and matrix flow. This variation in flow regimes leads to variations in the dissolved load between the two regimes. The distribution of reactive minerals within a given rock type, and mixing of rock types can exacerbate the problem. Several processes can contribute to geochemical heterogeneity; mixing of fine and coarse grained material which leads to complex flow processes, mineralogical variation, and biogenic microenvironments.  8  1.5.2.1 Material Heterogeneity Grain sizes in waste rock piles often ranges over six orders of magnitude, containing particles between 1 µm and >1 m in diameter (Fala et al., 2005). Within the waste rock pile the grain sizes are segregated due to the depositional techniques. The waste rock is typically deposited by push-dumping or end-dumping, both methods resulting in internal structures with zones of fine-grained material, and zones of relatively coarse grained material. A vast majority of the reactive surface area is in the fine-grained material. Waste rock piles are mineralogically and chemically heterogeneous as a variety of rock types are typically deposited together. Stromberg and Banwart (1998) found that sulfur and Cu varied by a factor of 3, and Zn by a factor of 8, while other elements varied by at most 25% within a waste rock pile. This indicates the distribution of sulfides is more variable than other minerals that typically make up the bulk of the sample. This is not surprising as sulfides are typically a small percentage of the bulk waste rock but may be enriched in veins and fractures cutting through the gangue material. 1.5.2.2 Flow Processes Waste rock hydrology is complicated due to the segregation of particle sizes and the unsaturated conditions which typically prevail in large waste rock piles. The two dominant flow mechanisms in waste rock are fast macropore flow, which quickly channels water through the waste rock, and slow matrix flow, in which water is retained within fine-grained material through capillary pressures. Material in which >35% of the mass is <4.75mm can be expected to be dominated by matrix flow (Yazdani et al., 2000). Coarser grained areas will be dominated by faster macro-pore flow. A majority of the reactive surface area is in the fine-grained material which comprises the matrix flow zone of a waste rock pile (Stromberg and Banwart, 1999). The matrix flow water moves slowly compared to macro pore water allowing a longer reaction period with a material which has a relatively higher specific surface area due to the smaller grain size before the water is discharged. This results in two distinct geochemical signatures due to the accumulation of weathering products in the matrix water relative to macro pore water. The mixing of the two flow mechanisms results in a diluted outflow which does not represent the geochemistry which occurs at the sulfide mineral surfaces. The extent of 9  dilution depends on the relative contribution of the two flow mechanisms (Harvey, 1993; Nichol, 2002; Richards and Kump, 2003). 1.5.2.3 Geochemical Heterogeneity Heterogeneous distribution of reactive minerals and segregation of particle sizes leads to multiple flow mechanisms causing a comparable heterogeneity in geochemistry within a waste rock pile. This can potentially lead to local zones of acid generation. Wisotzky and Obermann (2001) found pH ranging between 3.7 and 7.3, and sulfate concentrations ranging over more than two orders of magnitude in a waste rock dump.  Nowhere is the development of  geochemical heterogeneity more obvious than in the several unsuccessful attempts to mix acid neutralizing with acid generating waste rock (Morin and Hutt, 2000).  Mixing has resulted in  waste rock piles that produce both acidic and neutral pH at different discharge points despite an abundance of acid neutralizing potential (Morin and Hutt, 1997). If acidic zones develop, they will be a major source of metal release, even if the drainage is neutralized prior to discharge. Geochemical heterogeneity also exists at the micron scale.  Biogenic acidic micro  environments can exist within extremely small volumes of water between cell membranes and sulfide mineral surfaces (Mielke et al., 2003). These environments are inferred to exist due to the presence of acidophilic bacteria and the presence of acidic iron-oxyhydroxide sulfates in ambient neutral pH environments. Numerous studies have cultured acidophilic iron oxidizing bacteria from neutral pH waste rock and tailings environments (Southam and Beveridge, 1992; Blowes et al., 1995; Blowes et al., 2003; Moncur et al., 2005). It has been proposed that acidophilic bacteria form acidic iron-oxyhydroxide bounded microenvironments on mineral surfaces in which ferrous iron can be dissolved and oxidized allowing the aggressive propagation cycle (ferric iron leaching) to proceed in overall neutral pH environments (Southam and Beveridge, 1992; Nordstrom and Southam, 1997; Mielke et al., 2003). Geochemistry of pH sensitive iron oxyhydroxides and iron oxyhydroxide sulfates has provided empirical evidence of acidic micro-environments. For example, Kawano and Tomita, (2001) found jarosite was forming in mine waste despite a bulk pH did not promote its formation. Mielke et al., 2003 documented the colonization and growth of Acidithiobacillus 10  ferrooxidans, an obligate acidophile, on pyrite under ambient pH of 6.5 in a laboratory experiment. The acidic iron oxyhydroxide-sulfate, jarosite, was identified on the pyrite surface after the bacteria had been removed. Jarosite only forms at a pH <3, jarosite’s presence confirms that acidic conditions are created on the sulfide surface (Bigham et al., 1996). The presence of jarosite on the pyrite surface beneath the acidophilic bacterium confirms the formation of an acidic microenvironment. This could lead to higher weathering rates of iron bearing sulfide minerals such as; pyrrhotite (Fe1-xS (X=0-0.2)), pyrite (FeS2) chalcopyrite (CuFeS2), arsenopyrite (FeAsS), bornite (Cu5FeS4), femolite ((Mo, Fe)S2)and to a lesser extent sphalerite ((Zn, Fe)S). These acidic micro-environments could become a major point source of metal release and acid generation in otherwise slowly weathering waste rock. 1.5.3 Assessing the Reactivity of Mine Waste at the Antamina Mine The overarching objective of the current research is an improved understanding of longterm evolution of water quality and metal loading from waste rock dumps. Prior to the mines construction, 242 samples were analyzed with static and/or kinetic laboratory tests to determine acid generating and metal leaching potential of the predominant rock types (Klohn and Crippen, 1998). The static test methodologies included acid base accounting (ABA) (Sobek et al., 1978), and net acid generation tests (NAG) (Miller et al., 1997). In summary, these analyses predicted that 85% of the waste rock is non-acid generating, while 15% is potentially acid generating.  The kinetic test methodologies include humidity cells and leach tests.  Laboratory tests provide reliable and timely data; however, they cannot account for the numerous site-specific variables, including microbiology. Metal sulfide oxidation is essentially a microbially mediated process; however, none of the above mentioned laboratory procedures explicitly accounts for microbiology. As acid generating microorganisms are ubiquitous in mining environments, it is typically assumed that an indigenous population is present in an environmental sample used in a kinetic test. Nonetheless some kinetic cell protocols include instructions for the inoculation of A. ferrooxidans (Sobek et al, 1978; ASTM, 1996).  The inoculation of this bacterium at the  beginning of an experiment either leads to a temporary increase in sulfide oxidation rates, compared to non-inoculated samples. This has lead to the conclusion that biological effects can 11  be regarded as a constant in kinetic cells (Morin and Hutt, 1997). Therefore no effort is made to ensure that the microbial communities which develop in kinetic tests are similar to those which the material will be exposed to in the environment. This assumption has led to a lack of awareness of biological sensitivities, which may affect the development of microbial communities in laboratory settings.  Not acknowledging biological sensitivities, such as  temperature, nutrient availability, presence of toxic metals, and water content can lead to biased data. Growing environmental bacteria in a laboratory setting is difficult even when that is the explicit purpose of an experiment (Joseph et al., 2003).  The numerous difficulties in  reproducing a natural microbial community in a laboratory suggest that the microbial effects in the laboratory will be inconsistent with microbial effects in the field, whether inoculated or not. Bacteria require unrealistic geochemistry to be successfully grown in laboratory settings, e.g. A. ferrooxidans cannot grow in culture media containing less than 6.9mg L-1 PO42- , a concentration dramatically higher than could be expected in kinetic cells or under field conditions. Their growth is also sensitive to toxic metals, temperature, and the moisture content. Also, these bacteria cannot tolerate dry conditions. Significant drying of acidic kinetic cells has lead to orders of magnitude decreases in weathering rates (Sapsford et al., 2009). The numerous difficulties in cultivating environmental bacteria in a laboratory setting make it unclear if reproduction of the microbial community present in mine waste is even possible in a laboratory setting.  Many of these difficulties can be avoided by adopting field-based methods for  determining mine waste weathering rates. At the Antamina mine biological sensitivities could be particularly important due to the abundance of the microbially toxic metal, molybdenum. The abundance of molybdenite in the Antamina waste rock has implications for biooxidation of metal sulfides. With many biologically essential trace metals, such as Mo, an over abundance can be as deadly as an absence. Molybdate has been found to inhibit the activity and growth of iron oxidizing bacteria at concentrations as low as 5 mg/l (Tuovinen et al., 1971). Molybdates ability to stop the growth of A. ferrooxidans under laboratory conditions has been examined in numerous studies, (Tuovinen et al., 1971; Jack et al., 1980; Pistacio et al., 1994; Yong et al., 1997). 12  It is empirically evident that molybdenum mines in British Columbia tend to break predictions of ARD generation. The Boss Mt., Kitsault, and Trout lake Mines have produced little ARD, while the Endako, Brenda and Highland Valley copper mine have produced no ARD, despite all six mines having samples which are theoretically net acid generating based on acid base accounting (ABA) testing (Morin, et al., 2001). Morin et al., (2001) concluded that molybdenum mines violate common ABA predictive rules.  While this may reflect the  limitations of the ABA method, the coincidence is does suggest a biological answer. No studies of microbial activity were conducted at any of these mines. The relatively high pH and lack of bacterial activity at the mine tailings in El Salvador, Chile has been attributed to molybdate concentrations being well above toxicity limits to A. ferrooxidans (Dold and Fontbote, 2001). Microbial samples from the El Salvador mine tailings had no ability to oxidize iron in laboratory experiments. Molybdate may work as a naturally occurring bactericide in the Antamina waste rock, possibly inhibiting microbial catalysis of sulfide oxidation. 1.5.3.1 Field Experiments In order to eliminate the intrinsic biases of lab based tests, 32 Field cells and 5 experimental waste rock piles have been constructed at the Antamina mine. Field cells were constructed out of 55 gallon drums containing 260-350kg of waste rock in the current research program. Particle sizes of <4” were included in the field cell construction. The field cells are left uncovered at the mine site so that natural weathering processes and microbial colonization can take effect. The outflow is metered, and geochemically analyzed. Field cells are small enough so that the drainage geochemistry is not severely complicated by preferential flow or gas transport as it is in larger waste rock piles. Field cells remove many of the difficulties associated with laboratory tests, as they expose mine waste to markedly more realistic weathering conditions, such as natural precipitation. In addition the mass of rock used in the field cells (>260kg) is more than two orders of magnitude larger than that which is used in humidity cells (1-2kg). Also, a greater range of grain sizes is incorporated into the field cells as particles up to two inches in diameter are included, while the material used for the kinetic cells is typically crushed. Field cells offer a more realistic alternative to kinetic cells to determine acid generation potential and metal leaching of various mine waste materials. 13  The experimental waste rock piles are the most comprehensive means of studying drainage quality. The piles are 10 m-high with a base of 34 m x 34 m and are composed of approximately 25,000 tons of rock. The piles have instrumentation to monitor hydrology, pore gasses, and geochemistry of pore water installed during construction. All drainage is captured and metered by basal lysimeters. The grain size distribution is not manipulated, with only the largest boulders being removed (>1m in diameter). The piles are large enough for complex flow processes and mineralogical heterogeneity to take place and be studied. Experimental waste rock piles mimic the waste rock dumps at the Antamina mine in every way except scale. The piles provide an environment for microbiological growth that is as close to the mines waste rock dumps as can reasonably be achieved in a scientific study. The waste rock piles will not experience the day to night temperature swings of the field cells, rather the temperature will be mediated by air convection and exothermic oxidation reactions. Although waste rock piles will not provide the timely data which laboratory experiments can, they provide the most natural conditions for waste rock weathering which can reasonably be obtained in a scientific study. 1.5.3.2 Microbiology in Field Experiments One of the greatest advantages to field based experiments is that they increase confidence that a microbial community representative of the larger waste rock piles will develop. The well characterized geochemistry and the removal of many of the biases of labbased techniques provides an excellent opportunity to empirically relate naturally developed microbial populations with sulfide mineral weathering rates.  The well constrained  geochemistry and the ability for microbiological processes to proceed under natural conditions allow a quantitative relationship between weathering of mine waste material and size and type of microbial community to be determined.  1.6 Approach This thesis employs a variety of laboratory techniques to gain insight into field data collected from geochemically and mineralogically well characterized field cells and experimental waste rock piles. Little work has been done regarding microbiology of neutral pH 14  waste rock environments. In order to characterize the microbial community present at the Antamina mine samples collected from the field cells and waste rock piles were enumerated using the most probable number (MPN) technique, and the Live/Dead Baclight™ technique. The MPN method provides cultures for the examination of various phenotypes of bacteria, and the opportunity to test the effect of Mo toxicity, while the Live/Dead Baclight™ technique will provide an independent estimate of microbial populations. The material composition was characterized by X-ray diffraction (XRD) and X-ray fluorescence (XRF) and a particle size distribution (PSD) curve was developed, this data was integrated with the geochemical and microbiological data in order to determine the relationship between weathering rates and microbiology. The occurrence of bacteria on sulfide minerals is examined using a focused ion beam field emission scanning electron microscope with an electron dispersive spectrometer (FIB-FESEM-EDS). The focused ion beam is used to cut into iron oxide coatings, the field emission electron microscope is used to image the structure of the coatings and bacteria, while the EDS is used to determine chemical composition. In this study XRD was the primary technique used to determine mineralogy; however it is unable to identify iron-oxyhydroxides and ironoxyhydroxide sulfates due to their nanocrystalline nature. These minerals are of particular interest as they are pH indicators that identify the general pH at which sulfide mineral oxidation is taking place. Synchrotron methods were employed to identify iron oxyhydroxide and iron oxyhydroxide sulfate precipitates on sulfide mineral surfaces. A special emphasis was placed on examining samples that possessed large populations of acidophilic bacteria. The catalytic effect of bacteria on neutral pH waste rock is analyzed by comparing the weathering of sterile and non-sterile mini-columns. Mini-columns were constructed from 6-9 grams of fine-grained waste rock sampled from the experimental piles and selected field cells. This experiment provided an opportunity to examine the geochemical effect that toxic concentrations of Mo may have in mine waste, by determining if the geochemical evolution of the Mo receiving mini-columns is more similar to the sterile or non-sterile set of mini-columns. It is the aim of this research to contribute to bridging the gap between accumulated knowledge of microbiology in mine waste, and applied microbiology which can be incorporated into 15  predictions of mine waste reactivity by determining the occurrence, effect, and possible remedies to acid generating microorganisms in mine waste.  16  1.7 Figures  Figure 1.1. Five experimental waste rock piles have been constructed at the Antamina mine to evaluate long-term water quality and metal leaching.  Figure 1.2 Field cells consisting of 55gallon drums filled with waste rock were constructed in tandem with experimental waste rock piles.  17  Peru  Figure 1.3 Location of Antamina Mine, scale bar is 200 km. Modified from Conlan (2009)  18  1.8 References Agricola, G. (1556). De Re Metallica. Translated by HC Hoover and LH Hoover. The Mining Magazine (1912). Baker J.B. and Banfield J.W. (2003). Microbial Communities in acid mine drainage. Fems microbiology ecology, 33, 139-152. Bigham J.M., Schwertmann U., Traina S.J., Winland R.L. and Wolf M. (1996). Schwertmannite and the chemical modeling of iron in acid sulfate waters. Geochemica et Cosmochimica Acta, 60, 2111-2121. Blowes D.W., Al T.A., Lortie L., Gould W.D. and Jambor J.L. (1995) Microbiological, chemical and mineralogical characterization of the Kidd Creek Mine Tailings Impoundment, Timmins area, Ontario. Geomicrobiology Journal, 13, 13-31. Blowes D.W., Jambor J.L., Hanton-Fong C.J., Lortie L. and Gould W.D. (1998). Geochemical, mineralogical and microbiological characterization of a sulphide-bearing carbonate-rich gold mine tailings impoundment, Joutel, Quebec. Applied geochemistry, 13, 687-705. Blowes D.W., Ptacek C.J., Jambor J.L. and Weisener C.B. (2003). The geochemistry of acid mine drainage. 149-204. In B. Sherwood Lollar (Ed.), Treatise on geochemistry (Vol. 9, pp 149204). Environmental geochemistry. Elsevier, Toronto. Dopson, M. and Lindstrom, E.B. (1999) Potential role of Thiobacillus caldus in arsenopyrite bioleaching. Applied and Environemntal Microbiology, 65, 36-40 Elberling, B., Schippers, A. and Sand, W. (2000). Bacterial and chemical oxidation of pyritic mine tailings at low temperatures. Journal of Contaminant Hydrology, 41, 225-238. Evans, D., Letient, H. and Aley, T. (2005). Aquifer vulnerability napping in karstic terrain at Antamina Mine, Peru. Robertson GeoConsultants Inc. Technical Report, Vancouver, BC. Fala, O., Molson, J., Aubertin, M. and Bussiere, B. (2005). Numerical modeling of flow and capillary barrier effects in unsaturated waste rock piles. Mine water and the environment. 24, 172-185. Feasby, D.G., Blanchette, M. and Tremblay, G. (1991). The mine environment neutral drainage program. IN: Proceedings in the 2nd International Conference of Abatement of Acidic Drainage, Montreal, Canada.  19  Golder Associates (2007). Environmental Impact Assessment: Expansion of the open pit and processing optimization, Compañia Minera Antamina S.A. July, 2007. Golder Associates., S.A., Lima, Peru. Harvey, J.W. (1993). Measurement of variation in soil solute tracer concentration across a range of effective pore sizes. Water Resources research. 29, 1831-1837. Heikenin, P.M. and Raisanen, M.L. (2008). Mineralogical and geochemical alteration of Hitura sulphide mine tailings with emphasis on nickel mobility and retention. Journal of Geochemical Exploration, 87, 1-20. Hollings, P., Hendry, M.J., Nicholson, R.V. and Kirkland, R.A. (2001). Quantification of oxygen consumption and sulphate release rates for waste rock piles using kinetic cells: Cluff lake uranium mine, northern Saskatchewan, Canada. Applied Geochemistry, 16, 1215-1230. ICOLD, 1996. A guide to tailings dams and impoundments: Design, construction, use and rehabilitation. International commission on Large Dams, Bulletin (United Nations Environmental Program). No. 106-239. Jack, T.R., Sullivan E.A. and Zajic, J.E., (1980). Growth inhibition of Thiobacillus thiooxidans by metals and reductive detoxification of Vanadium(V). European J. Appl. Microbiol. Bioptechnol., 9, 21-30. Joseph S.J., Hugenholtz, P., Sangwan, P., Osborne, C.A. and Janssen P.H. (2003). Laboratory cultivation of widespread and previously uncultured soil bacteria. Applied and Environmental Microbiology. 69, 7210-7215. Kawano M., and Tomita K. (2001). Geochemical modeling of bacterially induced mineralization of schwertmannite and jarosite in sulfuric acid spring water. American Mineralogist, 86, 1156-1165. Klohn-Crippen, S.S.A., (1998). Antamina Environmental impact assessment, March 1998, Klohn Crippen, S.A., Lima, Peru. Luther, G. (1987). Pyrite oxidation and reduction-molecular orbital theory considerations. Geochimica et Cosmochimica Acta, 51, 3193-3199.  20  Mielke R.E., Pace D.L., Porter T. and Southam, G. (2003). A critical stage in the formation of acid mine drainage: Colonization of pyrite by Acidithiobacillus ferrooxidans under pH-neutral conditions. Geobiology 1, 81-90. Miller, S.D., Robertson, A. and Donohue, T. (1997). Advances in acid drainage prediction using the net acid generation (NAG) test. IN: proceedings of the fourth international conference on acid rock drainage, Vancouver, Canada.. Moncur, M.C., Ptacek, C.J., Blowes, D.W. and Jambor, J.L. (2004). Release, transport and attenuation of metals from an old tailings impoundment. Applied Geochemistry. 20, 639-659. Morin, K.A., Hutt, N.M., Price, W.A., and Coffin, V. (2001). Violation of common ABA prediction rules by molybdenum-related minesites in British Columbia, Canada. IN: Proceedings of Securing the future: International Confrerence on Mining and the Environment. June 25July 1 2001, Skelleftea, Sweden. Morin, K.A. and Hutt, N.M. (2000). Discrete-zone mixing of net-acid-neutralizing and net-acidgenerating rock: Avoiding the argument over appropriate ratios. IN: Proceedings from the Fifth International Conference on Acid Rock Drainage, May 20-26, Denver, USA, Volume II, P. 797-803. Morin, K.A. and Hutt, N.M. (1997). Control of Acidic drainage in layered waste rock at the Samatosum mine site: Laboratory studies and field monitoring. Canadian MEND Program Report 2.37.3. Nichol C.R., (2002). Transient flow and transport in unsaturated heterogeneous media: field experiments in mine waste rock. PHD Thesis, University of British Columbia, Vancouver. Nicholson R.V. and Rinker M.J. (2000). Metal leaching from sulphide mine waste under neutral pH conditions. ICARD 2000: proceedings from the fifth international conference on acid rock drainage volume II. Chapter 7-Prevention and remediation of problematic minewaste drainage, 41, doi:10.1029/ 2004WR003035 Nordstrom D.K. (2000). Advances in the hydrogeochemistry and microbiology of acid mine waters. Int. Geol. Rev. 42, 499-515.  21  Olson G.J. (1991). Rate of bioleaching by Thiobacillus ferrooxidans; results of an interlaboratory comparison. Applied Microbiology, 57, 642-644. Pistacio, L., Curutchet, G., Donati, E. and Tedesco, P. (1994). Analysis of molybdenite bioleaching by Thiobacillus ferrooxidans in the absence of iron (II). Biotechnology letters, 16, 189-194. Richards, P.L. and Kump, L.R. (2003). Soil pore-water distributions and the temperature feedback of weathering in soils. Geochemica et Cosmochimica acta, 67, 3803-3815. Ritchie A.I.M. (1994). Rates of mechanisms that govern pollutant generation from pyritic waste. Environmental geochemistry of sulfide oxidation: Amer. Chem. Soc. Symp. Ser. 550, 108122. Sapsford, D.J., Bowell, R.J., Dey, M., and Williams, K.P. (2009). Humidity cell tests for the prediction of acid rock drainage. Minerals Engineering. 22, 25-36. Silverman, M.P. and Ehrlich, H.L. (1964). Microbial formation and degredation of minerals. Adv. Appl. Microbiol., 6, 153-206. Singer P.C. and Stumm W. (1970). Acidic mine drainage: the rate determining step. Science, 167, 1121-1123. Sobek, A.A., Schuller, W.A., Freeman, J.R. and Smith, R.M. (1978). Field and laboratory methods applicable to overburdens and minesoils. IN: U.S. Environmental protection agency environmental protection technology, EPA-600/2-78-054, Cincinnati, OH. Southam G. and Beveridge T.J. (1992) Enumeration of thiobacilli within pH-neutral and acidic mine tailings and their role in the development of secondary mineral soil. Applied and Environmental Microbiology 58, 1904-1912. Nordstrom E.K. and G. Southam (1997). Geomicrobiology of sulfide mineral oxidation. In J.F. Banfield and K.H. Nealson (Eds.), Geomicrobiology: Interactions Between Microbes and Minerals (Vol. 35, pp. 361-390), Mineralogical society of America, Washington, D.C. Stromberg B. and Banwart, S.A. (1999). Experimental study of acidity-consuming processes in mining waste rock: some influences of mineralogy and particle size. Applied geochemistry, 14, 1-16.  22  Tuovinen, O.H., Niemela, S.I. and Gyllenberg, H.G. (1971). Tolerance of Thiobacillus ferrooxidans to some metals. Antonie van Leeuwenhoek Journal of microbiology and serology, 37, 489-496. Williamson, M.A. and Rimstidt, J.D. (1994). The kinetics and electrochemical rate-determining step of aqueous pyrite oxidation. Geochimica et Cosmochimica Acta, 58, 5443-5454. Wisotzky, F. and Obermann, P. (2001). Acid mine groundwater in lignite overburden dumps and its prevention-the Rhineland lignite mining area (Germany). Ecological Engineering, 17, 115-123. Yazdani, J. (1995). Soil water characteristic curve for mine waste rock containing coarse material. M. Eng. Thesis, University of Saskatchewan, Saskatoon, Canada. Yong N.K., Oshima, M., Blake, R,C and Sugio, T. (1997). Isolation and some properties of an ironoxidizing bacterium Thiobacillus ferrooxidans resistant to molybdenum ion. Biosci. Biotech. Biochem., 61, 1523-1526.  23  2  Chapter 2: Structure and Chemistry of Bacterially Populated Acidic Microenvironments in Circumneutral pH Waste RockI  2.1 Introduction Mining operations produce immense significant amounts of waste material in the form of tailings or waste rock. Valuable metals are typically associated sulfide minerals, e.g., pyrite and pyrrhotite being the two most abundant.  Many sulfide minerals, including pyrite and  pyrrhotite, become unstable when exposed to atmospheric conditions and undergo oxidation. Waste material containing insufficient neutralization capacity may result in acid rock drainage (ARD), which produces acidity, increases iron and sulfate concentrations in the pore water, and contains heavy metals.  Water treatment may be required for prolonged times to avoid  acidification of aquatic ecosystems and other detrimental impacts on the environment. Mine waste containing an abundance of carbonate minerals may remain at a neutral pH indefinitely despite ongoing acid generation. Under neutral rock drainage (NRD) conditions the principal threat to water quality is leaching of metals which are either weakly hydrolyzing, e.g., Zn, Ni, and to a lesser extent Cu (Nicholson and Rinker, 2000; Hiekenin et al., 2008), or oxyanion forming metals such as Mo, As, Se, and Cr (Blowes et al., 1995; 1998).  Leaching of metal-  oxyanions is only an issue under NRD conditions as oxyanions are strongly sorbed to iron oxides in acidic environments. Metals are typically leached from oxidative dissolution of sulfide minerals, the same process responsible for acid generation. Studying the processes controlling sulfide mineral weathering at neutral pH is relevant to both predicting water quality of NRD and understanding the initiation of ARD. The rate of sulfide oxidation and subsequent pH-buffering reactions are affected by many factors including pH, redox potential, oxidant transport, temperature, mineral coatings, geochemistry, grain size, and microbiology (Blowes et al., 2003). The ability of mine waste to produce ARD is essentially due to the activity of chemolithotrophic bacteria (Singer and Stumm, 1970).  Microbially mediated ferrous iron  oxidation has been termed the ‘rate determining step’ of acid generation as it leads to ferric leaching of sulfide minerals, which is an extremely aggressive reaction (Singer and Stumm, I  A version of this chapter will be submitted for publication. Dockrey, J., Beckie, R., Mayer, K. and G. Southam (2010) Structure and Chemistry of Bacterially Populated Acidic Microenvironments in Circumneutral pH Waste Rock.  24  1970; Williamson and Rimstidt, 1994). Iron oxidizing bacteria are obligatory acidophiles due to the solubility controls of their substrate in oxidizing conditions. Acidophilic iron oxidizing bacteria have been found to increase the rate of acid generation by a factor of 10-100, and are the dominant bacteria in ARD environments (Olson, 1991; Ritchie, 1994). The degree to which bacteria can catalyze sulfide mineral oxidation and metal leaching under neutral pH conditions is somewhat ambiguous. Neutrophilic bacteria capable of oxidizing intermediate sulfur species, such as thiosulfate and elemental sulfur, typically dominate the microbial community under neutral pH conditions. It has been hypothesized that biological oxidation of these intermediate sulfur compounds is essential to prevent armoring of reactive sites on sulfide mineral surfaces (Dopson and Lindstrom, 1999). Neutrophilic sulfur oxidizing bacteria have been found to increase the rate of acid generation by a factor of 2, but little work has been reported on the subject (Hollings et al., 2001). Numerous studies have cultured acidophilic iron oxidizing bacteria from neutral pH waste rock and tailings environments, typically in much lower numbers then their neutrophilic counterparts (Blowes et al., 1995; Blowes et al., 2003; Moncour et al., 2005). It has been proposed that acidophilic iron oxidizing bacteria form acidic iron-oxyhydroxide bounded microenvironments on mineral surfaces in which ferric leaching of sulfide minerals can take place in bulk neutral-pH environments (Southam and Beveridge, 1992; Nordstrom and Southam, 1997; Mielke et al., 2003). It is important to understand the conditions that allow the growth of acidic iron oxidizing bacteria at neutral pH as they can rapidly speed the onset of ARD. This study investigates the bacterial populations of waste rock producing neutral drainage using culture-dependent as well as culture–independent enumeration, and imaging techniques. The objective of this study is to characterize microbial communities living in neutral pH mine waste and examine the structure and chemistry of acidophilic microenvironments in which iron redox cycling may occur.  25  2.2 Site Description 2.2.1  Antamina The Antamina mine is a Cu-Zn skarn deposit with ore-grade mineralization found in  quartz-monzonite porphyry hosted in Cretaceous limestone. Antamina is one of the largest copper and zinc mines in the world and also produces molybdenum and lead. The abundance of carbonates in the surrounding country rock has alleviated most concern of the mine drainage becoming acidic. The Antamina mine is located approximately 270 km northeast of Lima, Peru within the Andes Mt. at an elevation between 4200 and 4800 m (above sea level). Despite the high elevation, the temperature is moderate with a yearly average of 8oC due to its tropical latitude. The average annual rainfall ranges from 1200-1500 mm, approximately 80% of which occurs within the summer wet season (November – April). 2.2.2 Experimental Waste Rock Piles and Field Cells Five large scale experimental waste rock piles have been built at Antamina for the purpose of monitoring drainage of three dominant classes of waste rock. Pile-1 was built in 2006, piles 2 and 3 in 2007, and piles 4 and 5 at the beginning of 2009. The piles are 10 m-high with a base of 35 m x 35 m and are composed of approximately 25,000 – 30,000 metric tons of waste rock per pile, placed in three distinct discharge events.  Instrumentation to monitor  hydrology, pore gas, and geochemistry of pore water were installed between the three discharge events. All drainage is captured and metered by basal lysimeters. The principal concern regarding the drainage water is release of Zn, As, Se, and Mo. This study focuses on samples obtained from waste rock present in Piles 1-3. Pile-1 is composed of marble and hornfels and is classified by the mine as mildly reactive. Pile-2 is composed of intrusive rocks and Pile-3 is composed mostly of exoskarn; these rock types are classified by the mine as reactive and low quality drainage that requires water treatment is expected. All three waste rock piles contain calcite and are producing neutral pH drainage. For a more complete description of hydrogeology and geochemistry of the experimental waste rock piles see Bay et al. (2009).  26  Waste rock from each discharge event, was collected for tandem field cell experiments using 208 L (55 gallon) drums. Silica sand was packed in the bottom 20 cm of each drum and 260 – 350 kg of waste rock was placed on top. The field cells were left uncovered at the mine site so natural weathering processes and microbial colonization could take effect. With the exception of FC-07, the first number in the Antamina field cell nomenclature designates the corresponding pile, whereas the second number relates to the pile discharge event (e.g., field cell 1-3A is composed of Pile-1 material, discharge #3). Note that the letter found after the discharge number relates to a duplicate sample (e.g., A and B).  For a more complete  description of field cell construction and drainage see Aranda et al., (2009).  FC-07 was  constructed before the initiation of this research project; it is composed of endoskarn and was constructed by Golder Associates Ltd. Drainage water from the field cells was collected in buckets and the volume measured when more than 4 L had accumulated.  The pH, pe,  temperature, and electrical conductivity were measured in the field.  2.3 Methods 2.3.1 Microbiological Sampling Rock samples were collected from field cells and waste rock piles.  Samples were  extracted from field cells by cutting a hole near the base of the field cell immediately above the quartz sand. Samples were collected from the experimental waste rock piles by excavating a 1m deep hole on top of Piles 1, 2, and 3. Samples were collected on Feb 12th, 2009, at the height of the rainy season. Samples ranging from 200 – 250 g were collected in 200 ml Nalgene bottles. Sub-samples for imaging were washed three times with DI water, and transported to Canada in a 1% glutaraldehyde solution in 50 ml Falcon tubes. Additional weathered sulfide minerals found within the 200 ml Nalgene bottles were fixed with 1% glutaraldehyde in the laboratory. 2.3.2 Inoculum Due to the gravel texture and variable grain size distribution between samples, a wash was taken to characterize the microbial community. Forty ml of DI water was added to the sample bottles containing between 200 – 250 g of waste rock. The sample was shaken vigorously and the wash was captured in 50 ml falcon tubes. The sample was vigorously shaken 27  again and one ml was taken and vortexed for 30 s and used as inoculum. Another milliliter of wash was removed and dried to determine its try weight. 2.3.3 MPN and Live/dead Baclight™ Both culture-dependent and culture-independent techniques were used to quantify the microbial community. The most probably number technique (MPN) as described by Cochran (1950) was used to enumerate viable, culturable iron-oxidizing acidophiles, sulfur-oxidizing acidophiles, and thiosulfate-oxidizing neutrophiles. The culture media for the iron oxidizers contained per liter: 0.4 g (NH4)2SO4, 0.1 g K2HPO4, 0.4 g MgSO4∙7H2O, 33.3 g FeSO4∙7H2O, the pH was adjusted to 2.3 using H2SO4. The culture media for sulfur-oxidizing acidophiles contained: 0.3 g (NH4)2SO4, 0.1 g KH2PO4, 0.4 g MgSO4∙7H2O, 0.33 g CaCl2∙2H2O, 18 mg FeSO4∙7H2O, the pH was set to 2.3 using H2SO4. A thin film of S0 was amended as substrate after tubes were inoculated. The culture media for thiosulfate oxidizing neutrophiles contained per liter: 0.3 g (NH4)2SO4, 0.1 g KH2PO4, 0.4 g MgSO4∙7H2O, 0.33 g CaCl2∙2H2O, 4.93 g Na2S2O3 ∙5H2O, the pH was set to 7 using NaOH. The inoculum was vortexed for 30 seconds before each transfer while performing serial dilutions. One ml from each of nine 10-fold serial dilutions of a sample was then inoculated into 5 tubes containing 5 ml of culture medium. One milliliter of the primary wash was placed directly into the first 5 tubes to enumerate microbial populations as small as 100 per ml. Results were tabulated after 6 weeks of incubation at room temperature (approx. 21°C). Total (live and dead) bacteria were counted using a Live/Dead Baclight™ bacterial viability kit (Molecular Probes Inc., Eugene, Oregon (USA)). The kit contains both SYTO 9 and propidium iodide stains. SYTO 9 is a green fluorescent nucleic acid stain which binds to cell membranes and generally labels all cells in a population. Propidium iodide labels only cells with damaged membranes, causing a reduction in the SYTO 9 stain, and the cell to fluoresce red. Six µl of inoculum was added to 1 ml of DI water. The sample was allowed to incubate in the propidium iodide and SYTO 9 stains for at least 15 min. The sample was then filtered through a 0.1 µm filter and counted with a Nikon Optiphot-2 fluorescent photomicroscope. The integrated criteria of shape, size, and fluorescence were used to distinguish stained bacteria from autofluorescence and non-specific binding of fluorophores to clay particles. 28  2.3.4 Material Characterization Sub-samples of 40 – 50 g were taken from the sample bottles and analyzed by quantitative x-ray diffraction (XRD) to determine their mineralogy. The samples were first crushed to <1 mm with a mortar and pestle and thoroughly mixed. A 3 g sub-sample was then ground to < 5 µm in a McCrone micronizing mill, mounted, and step-scanned over from 3° – 80° 2θ with CuKα X-radiation in a Siemens D5000 Bragg-Brentano diffractometer (Raudsepp and Pani, 2003). The Scan data was refined using the Rietveld program Topas 3.0. X-Ray absorption spectroscopy (XAS) was used to probe the local atomic environment of iron-oxyhydroxides and iron-oxyhydroxy-sulfates, which were observed with FIB-FEB-SEM-EDS analysis. Extended X-ray adsorption fine structure (EXAFS) for iron were collected at beamline 11-2 at the Stanford Synchrotron Radiation Laboratory.  A double crystal Si(220)  monochromator was utilized for energy selection. Scans were conducted from 100ev to 1000ev above the K-edge at 7111 eV. The data reduction software SIXPACK/IFEFFIT was used to isolate backscattering contributions by subtracting a spline function from the EXAFS data region (Webb, 2005). The results were then converted from eV to A-1 and weighted by  K  3  and  windowed from 3 to 14 Å-1. Samples from the waste rock piles field cells were examined using a Philips XL30 electron microscope equipped with a Princeton Gamma-Tech energy-dispersion X-ray spectrometer (SEM-EDS) to identify geochemically important minerals, such as sulfides, carbonates and phosphates, which were insufficiently abundant to be detected by XRD analysis. Fine-grained samples of waste rock were mounted on aluminum stubs using adhesive double sided tape, and then sputter coated with carbon. Elemental composition was determined by x-ray fluorescence (XRF) analysis of samples formed into a pressed powder disk. Two grain size fractions (GSF) were analyzed by this technique, relatively coarse grain samples (CG) of 0.3-1.18 mm diameter, and particles of <0.3 mm in diameter, which will be referred to as the fine-grained (FG) fraction. Only small grain sizes were analyzed because most of the reactive surface area in waste rock has been found to be in the sand and silt particle sizes (Strömberg and Banwart, 1999).  29  2.3.5 FIB-FEG-SEM-EDS Weathered sulfide samples from the three waste rock piles and FC-2-3A were processed for scanning electron microscopy (SEM); samples were dehydrated through a graded ethanol series (25, 50, 75 and 100% v/v ethanol for 30 minutes each) and critical point-dried in a SamDri® Critical Point Drier (Tousimis Research Corp., Rockville, MD, USA) to preserve the structure of the cells. The mineral surfaces were coated with Pt to reduce sample charging and imaged with the SE detector at 3.0 kV using a LEO 1540XB Field Emission Gun (FEG) SEM (Carl Zeiss SMY AG, Oberkochen, Germany). The SEM was equipped with an EDAXTM Energy dispersive X-ray spectrophotometer (EDS) for qualitative elemental analysis. For EDS analysis the beam energy was increased to 10 kV. The Focused Ion Beam (FIB) was used to cut into secondary precipitates and the exposed cross section was imaged with the FEG-SEM. The EDX was used to determine relative elemental abundance with spot analysis of approx. 1 μm2 diameter (volume approx. 0.5 μm3).  2.4 Results 2.4.1 Mineralogy and Geochemistry The effluent from all experimental waste rock piles and field cells has remained >7, with two notable exceptions. The pH of FC-2-3A dropped from 8.2 to 6.1 within three months of installation. The pH has remained between 6 and 6.5 since then. The pH of FC-3-2A dropped from 7.6 to 4.5 within the first 27 days which was accompanied by a spike in the total Fe concentration. The pH subsequently rebounded to above 7 three months later where it has remained. This is interpreted as the flushing of acidic oxide minerals such as jarosite or siderite. The XRD analysis detected calcite in all samples except for those composed of intrusive material (FC-2-2B, FC-2-3A, and Pile-2) although prior studies have found calcite in quantities of <1% in intrusive waste rock which is below the detection limit of XRD (Klohn Crippen, 1998). The geochemistry as monitored in the field reflects a 1:1 molar ratio of Ca2+ to SO42- in all samples except for FC-2-3A, which has a ratio of 1:1.5. The 1:1 molar ratio is indicative of an open calcium carbonate buffered system. Gypsum precipitation does not affect this ratio as it removes both species from solution at a 1:1 ratio. The dissolution of the Ca-rich plagioclase 30  anorthite can produce the 1:1 molar ratio of Ca to SO42- observed in the drainage of Pile-2 and FC-2-2B. However, it is unlikely that anorthite would be the sole buffering mineral if calcite was depleted. Therefore calcite is considered the dominant buffering mineral in these two systems despite its absence from the XRD pattern. The 1:1.5 Ca2+ to SO42- ratio and non-alkaline pH in FC-2-3A indicates that a Ca-bearing silicate, likely a Ca-plagioclase species is the dominant buffering mineral. The sulfide minerals detected during SEM-EDS and XRD analysis in the field cells and waste rock piles samples are listed in Table 2.1. In addition, the XRF results for two notable elements, S and Mo are included. A large diffuse XRD diffraction peak was present at 7° 2θ angle in FC-07, indicating significant clay content. The Fe-XAFES results from the synchrotron analysis revealed that lepidocrocite, and schwertmannite were the dominant iron bearing weathering products on the ocherous coating covering the massive sulfide sample from FC-2-3A (Figure 2.1). Including K-Jarosite in the refinement improves the goodness of fit of, however it is near the detection limit of and therefore cannot be definitively identified. 2.4.2 Microbial Community Results MPN results indicate the microbial population in all samples ranged between 3.6 x 106 to 2.2 x 108 bacteria g-1 of washed sediment (Table 2.2).  Acidophilic thiobacilli were only  culturable from Pile-2, and FC-2-3A. FC-2-3A had high (> 106 bacteria g-1) populations of culturable acidophilic and neutrophilic bacteria.  All cultured acidophilic iron and sulfur  oxidizers were capable of oxidizing both sulfur and iron, which is indicative of the species Acidithiobacillus ferrooxidans as opposed to other common acidophiles (e.g., Acidithiobacillus thiooxidans, Leptospirillum ferrooxidans).  Direct counts using the Live/dead Baclight™ kit  produced counts between 1.0 x 107 – 3.4 x 108 bacteria g-1. With the exception of FC-07, populations measured by the Live/dead Baclight™ kit were within an order of magnitude of the populations measured by the MPN technique. Difficulties exist in distinguishing fluorescing clay particles from fluorescing bacteria (Boivin-Jahns et al., 1996; Lawrence et al., 2000), which may lead to the large discrepancy observed in FC-07 In 6 of the 8 samples the bacteria were >75% alive, indicating that microbial populations were presumably active at the mine site. The two 31  samples with highest populations as measured by both Live/dead Baclight™ and MPN techniques also had the lowest percent alive (FC-3-2A, Pile-3). This result suggests the most populous samples may have been stressed in the waste rock or in their Nalgene bottle-bounded environment during transport. 2.4.3 Structure and FIB-FEG-SEM-EDS Weathered sulfide samples from the three waste rock piles and FC-2-3A, were examined using FEG-SEM. Bacteria were sparse, but found on weathered pyrite and chalcopyrite samples from Pile-1 and Pile-2. No bacteria were found attached to weathered pyrite and chalcopyrite samples from Pile-3, despite having the highest population of thiosulfate oxidizing bacteria. Biofilms were found on a massive sulfide composed of chalcopyrite, pyrrhotite and pyrite with ocherous iron oxides from FC-2-3A was extensively imaged. The most densely populated areas were in proximity too, but not upon an exposed pyrrhotite surface (Figure 2.2 B, Figure 2.2 C). The bacteria in this sample were typically associated with an iron sulfate precipitate with the ‘ball-and-whisker’ or ‘sea urchin’ structure typical of schwertmannite, the occurrence of which is discussed below (Figure 2.3). Unless stated otherwise, all images presented in this study are from the massive sulfide sample from FC-2-3A. A FIB cut was made into the coating of the massive sulfide sample from FC-2-3A in proximity to an exposed pyrrhotite surface and a biofilm of bacteria (Figure 2.2 D). Three distinct layers of different brightness’s and textures were observed.  The top layer was  composed of Fe, S, O, and minor amounts of Si. It had an Fe:S ratio of 8:2.4, indicative of schwertmannite (Fe8O8(OH)6-4.5(SO4)1-1.75) which has a molar ratio of Fe:S of 8:(1-1.75). The middle layer is darker and composed of Fe, O, and had a Si content of ~20% in addition to small amounts of Al. A sharp boundary denotes the transition from the dark middle Fe:O:Si layer to the light bottom layer which is composed solely of Fe and O.  A non-porous, smooth crust is  observed on top of porous schwertmannite over much of sample (Figure 2.2G, Figure 2.6). . EDS, which would sample to depth of approx. ~0.5 µm into the surface, did not show any elemental difference between the crusted and un-crusted material. The upper schwertmannite layer is porous, while the deeper two layers lack significant porosity. A 2D binary image analysis of the schwertmannite layer produced a porosity estimate 32  of 8.2%. Approximately 50% of this porosity is due to the macropore in the upper left of Figure 2.2 D, which is the focus of Figure 2.2 E. The porosity is presumably much higher in the upper schwertmannite which was not preserved during ion beam milling (Upper right of Figure 2.2 D). In addition the surface of the schwertmannite surrounding the bacteria in Figure 2.5 B is porous as well. 2.4.4 Cell Morphologies Three dominant cell morphologies were observed; slightly curved rods, highly curved rods, and filamentous bacteria. The most abundant cell morphology was the slightly curved rod typical of Acidithiobacillus sp. identified during electron microscopy and the Live/dead Baclight™ procedure.  Less abundant were highly curved rod-shaped bacteria similar in  morphology to Leptospirillum sp. observed in Figure 2.2 B and Figure 2.7 B in sample FC-2-3A during electron microscopy (Chapana and Tributsch 2003). Filamentous bacteria were detected in samples FC-2-2B, FC-3-2A, FC-1-3A, and Pile-3 during Live/Dead Baclight™ procedure. These filamentous bacteria are similar to Thermothrix thiopara, which is capable of neutral pH thiosulfate oxidation (Brannan and Caldwell, 1980). No filamentous bacteria were observed during electron microscopy.  2.5 Discussion 2.5.1 Iron Mineralogy and Structure In iron sulfide oxidizing environments, poorly ordered ferrihydrite and schwertmannite are the first iron minerals to precipitate (Jang et al., 2003; Regenspurg et al., 2004). At a pH above 3, both of these phases will evolve to the more stable goethite, whereas they will transform to jarosite at a pH below ~3 (Schwertmann, et al., 1995; Yu et al., 1999; Jang et al., 2003). Lepidocrocite is an intermediate of ferrihydrite transformation to goethite, its formation and stability is promoted by the presence of Fe2+ and sulfate ions (Schwertmann and Taylor 1971; Jang et al., 2003). The presence of lepidocrocite as identified in the Fe-XAFES spectrum indicates that both ferrihydrite and goethite may be present, while the abundance of lepidocrocite relative to these two end products indicates the environment is high in both sulfate and Fe2+. Although schwertmannite evolves to goethite as well, lepidocrocite has not 33  been reported as an intermediate product (Knorr and Blodau, 2007; Regenspurg et al., 2004; Jonsson et al., 2005; Schwertmann and Carlson, 2005).  The abundance of lepidocrocite  indicates that at one point in the past, conditions favored ferrihydrite precipitation, however the lack of ferrihydrite and abundance of schwertmannite indicate that conditions had shifted from those favoring ferrihydrite precipitation to those favoring schwertmannite precipitation. The precipitation of ferrihydrite is favored when the pH is above 6.5 (Bigham et al., 1996). When the pH dropped below 6.5, the concurrent precipitation of ferrihydrite and schwertmannite is thermodynamically possible, while schwertmannite occurs without ferrihydrite at a pH < 4.5 (Bigham et al., 1996; Majzlan et al., 2004). Therefore if the conditions at the sulfide surface were the same as the drainage pH (6.2) both ferrihydrite and goethite would be present. The absence of ferrihydrite indicates that the pH at which these precipitates formed is <4.5, while the absence of goethite and the possible presence of jarosite indicates the pH is <3. A thin ‘non-porous’ crust-like structure overlies the schwertmannite (Figure 2.2 C, Figure 2.7, and Figure 2.6). There are four possible explanations, individually or in combination for its formation. (1) The crust is possibly a relic of the SEM sample preparation process. Sample dehydration may cause compaction of the upper layer of secondary precipitates and thus form a crust. This seems unlikely as the crust is often in proximity to well preserved bacteria (Figure 2.2 B, Figure 2.2 C, Figure 2.5 A. and Figure 2.6), and a monolayer of bacteria appear to be colonizing the crust in Figure 2.2 B, where there is a sharp transition between the crusted and biofilm covered region. (2) The crust may represent the physical boundary created by the shrinking film of water surrounding the grain at the end of the previous dry season, driving precipitation by evaporation. (3) The crust may be the result of a sharp geochemical gradient. Lower pH and higher SO42- concentrations closer to sulfide mineral surfaces encourage schwertmannite precipitation and stability, while the higher pH and lower SO42- concentrations further from the mineral surface would encourage ferrihydrite precipitation and faster Ostwald ripening of schwertmannite to goethite (Jönsson et al., 2004).  Ostwald ripening of  schwertmannite to goethite has been found to reduce specific surface area and not produce needle shaped crystals typical of goethite (Jönsson et al., 2004). (4) The crust may be a 34  biogenically produced boundary to geochemically isolate the microbial habitat within the porous schwertmannite from the ambient pore water. 2.5.2 Porosity of Schwertmannite The contrast in porosity between the upper schwertmannite layer and bottom two ironoxyhydroxide layers in Figure 2.2 D has implications for reactant transport through sulfide mineral coatings.  The macropore in Figure 2.2 E contains ~50% of the porosity in the  schwertmannite exposed in the FIB cut. It is impossible to analyze the connectivity of the pore spaces to sulfide surfaces in the FIB cut, however such macropores have the potential to allow significant transport of reactants. In addition, there is evidence that bacteria exist within the macropore itself. Distinctive objects the size and shape of bacterial cross sections are evident in Figure 2.2 E. Although it is impossible to assess the health or viability of these bacteria, their existence within the deep macropore space of the schwertmannite supports the conclusion of a strong association between microbiology and the formation of the porous schwertmannite. If the porosity within the schwertmannite is truly amenable to microbial growth it would mean that significant reactant transport through the pore space is taking place. Sulfide surfaces covered with the non-porous lower two layers exposed in the FIB cut may quickly become armored, while surfaces covered by porous schwertmannite may remain more reactive due to oxidant diffusion through the pore spaces. If sulfide mineral surface continue to be oxidized beneath the porous schwertmannite, acidic conditions would rapidly develop within the pore space of the schwertmannite. 2.5.3 Pitting Pitting is observed where a thin veneer of schwertmannite is detached from the pyrrhotite surface (Figure 2.8). Very little pitting is observed on the adjacent uncovered pyrrhotite surface. This indicates that the iron which forms the schwertmannite is coming from the sulfide surface which it has precipitated upon. The thickness of the schwertmannite in some areas indicates that pyrrhotite is being oxidized more aggressively beneath the schwertmannite than on the uncovered surfaces (Figure 2.2 B). The schwertmannite would create a barrier for oxygen diffusion to sulfide mineral surfaces; however, if the pH in the 35  porosity of the schwertmannite is conducive to iron solubility, ferric iron leaching would rapidly accelerate weathering rates. Ferric iron leaching is much more aggressive than oxygen-driven oxidation of sulfide minerals and may significantly increase the weathering rates despite the reduced oxygen content that would develop in the pore space of the schwertmannite. 2.5.4 Transitional Microbial Community It is widely accepted that a transition in microbial communities takes place during acidification of mine waste environments, whereby the microbial community changes from one dominated by neutrophilic sulfur oxidizers to one dominated by acidophilic sulfur and iron oxidizers. This transition has been found to begin prior to acidification of mine waste (Southam and Beveridge, 1993; Blowes et al., 1995; Blowes et al., 2003; Moncur et al., 2005). FC-2-3A is undergoing such a transition, as it contains similar numbers of acidophilic and neutrophilic bacteria. It has been hypothesized that acidophiles grow at neutral pH by forming acidic microenvironments bound to sulfide mineral surfaces by iron-oxyhydroxides and extracellular polysaccharides (Southam and Beveridge, 1992; Nordstrom and Southam, 1997; Mielke et al., 2003).  While the micron-scale nature of the acidic environment cannot be verified, the  coexistence of neutrophilic and acidophilic bacteria indicates the presence of steep pH gradients within the 250g sample from which MPN wash was collecting, indicative of a more acidic pH surrounding acid generating minerals then is measured in the bulk solution which is circumneutral. The structure and occurrence of a transitional microbial community has never been demonstrated in an environmental sample. The presence of acidic iron precipitates, and the bacterial associations with schwertmannite which is known to be the predominant iron precipitate formed by A. ferrooxidans (Schwertmann et al., 1995; Kawano and Tomito, 2001; Fukushi et al., 2003; Liao et al., 2009a, Liao et al., 2009b), and the presence of bacteria with the morphology of Leptospirillium sp. which are obligate acidophiles(Figure 2.7 B, and Figure 2.2 B), all points toward an acidic microenvironment on the massive sulfide sample imaged in from FC2-3A. This is the first field documentation of the structure and chemistry associated with the development of biogenic acidic microenvironments surrounding acid generating mineral surfaces in otherwise circumneutral pH mine waste. 36  2.5.5 Microbial Habitat Bacteria were typically found upon or within the porous schwertmannite which was often covered with a non-porous crust (discussed above). Bacteria were also found in porous schwertmannite on the fringe of the crusted region (Figure 2.5 A), and buried within schwertmannite between the crust and the sulfide mineral surface (Figure 2.2 C, Figure 2.6, Figure 2.7). The crust overlying the porous schwertmannite is extremely thin but non-porous, which will increase the geochemical isolation of the porous schwertmannite and may shelter the underlying sulfide from oxygen. The crust only develops where schwertmannite is relatively thick (Figure 2.2 C), whereas thin schwertmannite coatings are not covered by the non-porous crust Figure 2.6. Maintaining acidic conditions presumably becomes more difficult as the schwertmannite thickens, i.e., at greater distances from the sulfide mineral. The abundance of acid generating microorganisms beneath the crust within the porous schwertmannite indicates that both features are important in forming the microbial habitat; the most obvious function would be to provide geochemical isolation. 2.5.6 Development and Occurrence of Acidic Microenvironments Sample FC-2-3A has demonstrated that acidophilic bacteria can thrive under bulk, circumneutral pH conditions, a key finding of the present study. The direct attachment model is often used to explain the colonization of acidophilic bacteria in neutral pH environments (Mielke et al., 2003; Southam et al., 1993). This model describes the development of acidic micro-environment at the bacteria (outer membrane/lipopolysaccharide)-mineral interface, within an approximately 0.0009 µm3 volume between the cell and the sulfide surface and surrounded with iron-oxyhydroxides where iron redox cycling and ferric leaching may proceed (Mielke et al., 2003). This does not seem to be required in the massive sulfide sample from FC2-3A. However, a feature that is interpreted as a microbial ‘footprint’ cast in porous iron oxyhydroxide was found on a chalcopyrite sample from Pile-2 is believed to represent such an initiation step (Figure 2.4). The secondary precipitate surrounding the bacterial footprint have a tubular structure similar to schwertmannite which is in distinct contrast to the precipitate observed several microns above the microbial footprint in Figure 2.4A. Iron oxidizing bacteria are known to 37  promote the precipitation of iron oxyhydroxides and iron oxyhydroxide sulfate minerals on or near the cell surface (Fortin and Langley, 2005). The surrounding un-weathered chalcopyrite surface indicates that the bacteria were actively oxidizing the sulfide mineral rather than passively consuming intermediate sulfide species. Therefore they were likely involved in iron oxidation which actively leaches sulfide mineral surfaces. Acidophilic iron oxidizing bacteria were not abundant, but they were culturable from Pile-2. The bacterium was directly attached to the sulfide surface, and then was either washed away during the sample preparation procedure, or unfavorable conditions developed and the bacteria left on their own accord. Direct attachment may be essential in the initial development of an acidophilic microenvironment; however the more successful acidophilic microbial community in the massive sulfide sample from FC-2-3A do not require direct attachment, rather living within the geochemically protected environment provided by the porous schwertmannite and overlying crust structure. The well defined geochemistry and mineralogy of the field cells and piles can grant insight into the conditions necessary for the growth of acidophiles in neutral pH waste rock. Samples from Pile-3 and FC-3-2A have the largest microbial communities and highest sulfide contents, but the growth of acidophiles has been inhibited. FC-07, FC-1-3A, and Pile-1 samples are all older than FC-2-3A, but the development of acidophilic microbial communities has not occurred. Samples from FC-2-2B and Pile-2 are similar in elemental and chemical composition to FC-2-3A; however acidophiles were only culturable from Pile-2 in very low numbers. One of the distinctive features of FC-2-3A is that its average pH 6.2, whereas the other samples were above a pH of 7.3. Numerous studies have found associations between schwertmannite formation with the activity of At. ferrooxidans (Schwertmann et al., 1995; Kawano and Tomito, 2001; Fukushi et al., 2003; Liao et al., 2009a, Liao et al., 2009b). An analysis of ocherous sediments from 28 mine drainage sites by Bigham et al. (1996) only found schwertmannite in samples with a pH < 6.5. This result when combined with the close association between iron oxidizing bacteria and schwertmannite, implicitly states that iron oxidizing bacteria are inactive at a pH above 6.5.  38  The results of this study demonstrate that these iron oxidizing bacteria can become abundant at a bulk drainage pH of 6.1, while they are inhibited at an alkaline pH. FC-2-3A revealed that biogenic acidic microenvironments which allow ferric leaching of sulfide minerals may become active in the Antamina waste rock at a bulk pH of 6-6.5 and is closely tied to the occurrence of schwertmannite. Whether or not the schwertmannite and the overlying crust are biogenically derived, bacteria thrived in the protected environment they provided.  The precipitation and stability of schwertmannite may be essential for the  proliferation of acidophilic bacteria in neutral pH waste rock and tailings environments. Although pH conditions in the drainage water from the experimental waste rock piles remained alkaline, acidophilic bacteria may thrive in local environments at a larger scale in zones of calcite depletion. Our results indicate that Iron oxidizing bacteria produce a porous schwertmannite precipitate. If the bulk geochemistry is amenable to schwertmannite stability, they will be able to create a geochemically and physically protected environment. Iron oxidizing bacteria can then proliferate within the porous schwertmannite accelerating the sulfide oxidation by ferric leaching of the underlying sulfide surface. However the ambient geochemical conditions must be accommodating to the stability of schwertmannite for the acidic micro-environments to expand beyond the immediate vicinity of the bacterium during the initiation step shown in Figure 2.4. Waste rock environments are extremely (mineralogically and geochemically) heterogeneous (Nichol et al., 2005). Although the pH of FC-2-3A is somewhat anomalous compared to the other field cells, it is important to understand the processes going on in this field cell as it represents an end member of waste rock types and geochemistry. Such end members are the predominant concern regarding water quality and are the most important to understand. The geochemical conditions conducive to growth of acidophilic iron oxidizing bacteria which are known to rapidly accelerate sulfide oxidation and metal leaching have already developed in circumneutral, relatively young intrusive material of FC-2-3A.  39  2.6 Figures  x(k)*k3  Data Fit  -7 3 4 5 6 7 8 9 10 11 12 13 14 k(Å-1)  Figure 2.1. Linear combination of Fe-EXAFS fitting of solid phase products from ocherous coating of massive sulfide sample from FC-2-3A.  Figure 2.2. C  D &E B  Figure2.2 A. Overview of area containing monolayer and agglomerate biofilms (B and C), location of FIB cut is in the center right of the image (D & E), note the exposed pyrrhotite surface in the upper right of the image. Letters denote location of figures 2B-2E. Scale bar is 50 µm  40  Figure 2.2 B. Monolayer biofilm which is covering crusted region, note that no bacteria are attached to the adjacent exposed pyrrhotite surface in the upper left of the image. Scale bar is 5 µm..  Figure 2.2 C. Agglomerate biofilm surrounded by porous schwertmannite with non-porous crust present at the top of the image. Note that no bacteria are directly attached to surrounding pyrrhotite surface. Scale bar is 2 µm.  41  Figure 2.2 D. (D) Three different layers exposed in FIB cut. The white box denotes the location of Figure 2 E. Scale bar is 5 µm.  Figure 2.2 E. (E) Within macropore there are objects the size and shape of bacterial cross sections (arrows). Scale bar is 0.5 µm.  Figure 2.3. Ball and whisker morphology of schwertmannite observed over much of the sample, schwertmannite is often associated with bacteria. Scale bar is 3 µm  42  Figure 2.4. (A) Two bacterial ‘footprints’ on chalcopyrite sample from Pile-2. Note the contrast in morphology between the weathering products in the top of the image, and the precipitates around the microbial ‘footprint’. White box denotes location of figure 4 B. Scale bar is 3 µm. (B) Note porous nature of iron oxide precipitates around microbial footprint. Scale bar is 0.5 µm.  Figure 2.4 A.  Figure 4 B.  43  Figure 2.5. (A) Porous schwertmannite surrounded by crust. Morphology of schwertmannite evident beneath crust. White box denotes location of Figure 5 B. Scale bar is 10 µm. (B) Bacteria are abundant upon porous schwertmannite. The white objects in the center right and upper left of the image are detrital silicates. Scale bar is 1 µm.  Figure 2.5 A.  Figure 2.5 B.  44  Figure 2.6. Bacteria are present within porosity of schwertmannite beneath crust. Scale bar is 1 µm.  Figure 2.7. (A) Thin film of iron oxyhydroxides covering pyrrhotite surface. White box denotes location of figure 7 B. Scale bar is 50 µm. (B) Bacteria are abundant upon edge of film. Scale bar is 3µm.  Figure 2.7 A  45  Figure 2.7 B  Figure 2.8 (A) Area where microbially populated schwertmannite has been peeled away. The white box denotes the location of figure 8 B. Scale bar is 1µm. (B) Severe pitting evident beneath schwertmannite, adjacent to relatively un-pitted surface. Scale bar is 0.5 µm  Figure 2.8 A.  Figure 2.8 B s  46  2.7 Tables  Mineral  Rock type  FC-1-3A  Marble-hornfels  Pyrite X  Marble-hornfels  X  Intrusive  X  X  FC-2-3A  Intrusive  X  X  Pile-2  Intrusive  X  FC-3-2A  exoskarn  X  X  X  Pile-3  exoskarn  X  X  X  FC-07  endoskarn  Pile-1 FC-2-2B  Chalcopyrite  Arsenopyrite  Pyrrhotite  ü ü  Sphalerite  Molybdenite ü  Galena X  Bismuthinite  wt% S (XRF)  X  X  0.06  ü  X  0.59  ü  X  1.88  X  1.21  0.06  5.85 ü X  11.25 0.33  Table 2.1. Results of XRD and SEM-EDS analysis of sulfide minerals present in waste rock samples. Minerals detected by XRD are marked with an X, while minerals detected only with SEM-EDS are marked with ü.  47  Age  Average pH  Year to  (08-09 wet  date  season)  FC-07  6.1  FC-2-2B  Sample  Live/dead  MPN  Total  %Alive  Fe2+  S0  S2O32-  8.1  1.24 X 108  88.1  0  0  3.59 X 106  1.5  7.9  4.60 X 107  100  0  0  5.97 X 106  FC-1-3A  2.6  7.9  3.98 X 107  95.2  0  0  6.90 X 106  FC-2-3A  1.4  6.2  1.05 X 107  98.6  6.50 X 106  Pile-1  2.5  8.1  1.01 X 107  75.6  0  0  2.62 X 107  Pile-2  1.5  7.8  2.15 X 107  100  4.90 X 101  0  4.28 X 107  FC-3-2A  1.4  7.3  1.33E X 107  28.4  0  0  2.05 X 108  Pile-3  1.5  7.5  3.43 X 107  17.2  0  0  2.22 X 108  Designation  1.11 X 107 6.50 X 106  Table 2.2. Microbial populations measured using light microscopy combined the Live/Dead Baclight™ kit, and the MPN method. The reported numbers are per gram wash sediment.  48  2.8 References Aranda C.A., Klein B., Beckie R.D. and Mayer K.U. (2009). 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Water Resources Research, 41, doi:10. 1029/ 2004WR003035 Nicholson R.V. and Rinker M.J. (2000). Metal leaching from sulphide mine waste under neutral pH conditions. ICARD 2000: proceedings from the fifth international conference on acid rock drainage volume II. Chapter 7-Prevention and remediation of problematic minewaste drainage, 41, doi:10.1029/ 2004WR003035 Nordstrom E.K. and G. Southam (1997). Geomicrobiology of sulfide mineral oxidation. In J.F. Banfield and K.H. Nealson (Eds.), Geomicrobiology: Interactions Between Microbes and Minerals (Vol. 35, pp. 361-390), Mineralogical society of America, Washington, D.C. Olson G.J. (1991). Rate of bioleaching by Thiobacillus ferrooxidans; results of an interlaboratory comparison. Applied Microbiology, 57, 642-644. Raudsepp M. and Pani E. (2003). Environmental Aspects of Mine wastes. In J.L. Jambor, D.W. Blowes and A.I.M. 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The first occurrence of schwertmannite in a natural stream environment. European Journal of Mineralogy, 7, 546-552. Schwertmann, U. and Carlson, L. (2005) The pH dependent transformation of schwertmannite to goethite at 25°C. Clay Minerals, 40, 63-66. Singer P.C. and Stumm W. (1970). Acidic mine drainage: the rate determining step. Science, 167, 1121-1123. Southam B. and Beveridge T.J. (1992). Enumeration of thiobacilli within pH-neutral and acidic mine tailings and their role in the development of secondary mineral soil. Applied and Environmental Microbiology, 58, 1904-1912. Southam G. and Beveridge T.J. (1993). Examination of lipopolysaccharide (O-Antigen) populations of Thiobacillus ferrooxidans from two mine tailings. Applied and Environmental Microbiology, 59, 1283-1288. Stromberg B. and Banwart, S.A. (1999). Experimental study of acidity-consuming processes in mining waste rock: some influences of mineralogy and particle size. Applied Geochemistry, 14, 1-16 Veglio F., Beolchini F., Nardini A., Toro L. (2000). Bioleaching of pyrrhotite ore by a sulfooxidans strain: kinetic analysis. Chemical Engineering Science. 55, 783-795. Webb, S. (2005). Sixpack v. 0.53. Stanford SynchrotronRadiation Laboratory; Menlo Park, Ca, USA. Williamson, M.A. and Rimstidt, J.D. (1994). The kinetics and electrochemical rate-determining step of aqueous pyrite oxidation. Geochimica et Cosmochimica Acta, 58, 5443-5454.  53  Yu, J.Y., Heo, B., Choi, I.K., Cho, J.P. and Chang, H.W. (1999). Apparent solubilities o fschwertmannite and ferrihydrite in natural stream waters polluted by mine drainage. Geochimica et Cosmochimica Acta, 63, 3407-3416.  54  3  Chapter 3: Neutral pH Biological Catalysis of Sulfide Mineral Oxidation and Toxicity Effects of Molybdenum2  3.1 Introduction The importance of microbiology in the geochemistry of acid rock drainage (ARD) has been known for decades (Singer and Stumm 1970). However, the effect of microbiology on neutral pH mine waste is poorly understood. It has been established that iron oxidizing bacteria, such as Acidithiobacillus ferrooxidans, can increase sulfide oxidation rates by a factor of 10-100 under acidic conditions (Olson, 1991; Ritchie, 1994). Iron oxidizing bacteria can also be active in neutral rock drainage (NRD) systems by forming acidic microenvironments on sulfide mineral surfaces (Southam and Beveridge, 1992; Nordstrom and Southam, 1997; Mielke et al., 2003). Bacteria capable of oxidizing intermediate sulfur species, such as thiosulfate, are typically the dominant phenotype when the ambient pH is neutral. However few studies have compared the weathering of sterile vs. non-sterile neutral pH mine waste. Sterilizing neutral pH waste rock has been shown to decrease oxidation rates by a factor of 2 in laboratory analysis (Hollings et al., 2001). In a calorimetric study of mine tailings, Elberling et al., (2000) found that biological activity was responsible for about a third of pyrite oxidation at neutral pH. Although there is a relatively small body of work regarding neutral pH biological catalysis, it is evident that both biotic and abiotic factors are important. Understanding acid generating mechanisms under neutral pH conditions is important in predicting both metal release at neutral pH, and the potential for ARD to develop. Some mine waste may be more or less conducive to microbial colonization, this means oxidation rates are affected by nutrient availability and the presence of toxic metals. Relating the affinity of waste rock to colonization of specific microbial consortia at neutral pH could provide a new tool in assessing the liability of mine waste. It will also show the degree of sensitivity of sulfide mineral oxidation to biological factors, such as nutrient availability, and the presence of toxic compounds. The most poisonous metals to the heavily studied iron and sulfur oxidizing  2  A version of this chapter will be submitted for publication. Dockrey, J., Beckie, R., Mayer, K. and G. Southam (2010) Neutral pH Biological Catalysis of Sulfide Mineral Oxidation and Toxicity Effects of Molybdenum.  55  bacteria, A. ferrooxidans, are: Ag(2mg/l), Hg(2mg/l) and Mo(5mg/l) ( Mahapatra and Mishra, 1984; Tuovinen, 1971). Out of these three toxic metals, only Mo forms a highly soluble oxyanion (molybdate, MoO42-) at neutral pH. Molybdate can be attenuated by precipitating with various divalent metal cations, i.e., Ca, Zn, Cu, Pb, Fe, and Mn (Smith et al., 1997). In a series of batch experiments, Conlan (2009) found that the precipitation of ZnMoO4 and CuMoO4 is kinetically inhibited despite their low solubility, and identified powellite (CaMoO4) as the primary means of molybdate attenuation in the absence of Pb. The precipitation of these metal molybdates is important to the understanding of both metal attenuation, and the mobility of this biologically and environmentally significant metal. The release of molybdenum as molybdate has the potential to serve as a naturally occurring bactericide that is soluble prior to the onset of ARD. There is a relatively small body of literature concerning the toxicity of Mo to A. ferrooxidans despite the fact that the susceptibility of this heavily studied microorganism to molybdenum has been known for decades (Bhappu et al., 1965). This is because oxyanions such as molybdate, sorb strongly at a pH < 7 (Goldbert et al., 1996; Conlan, 2009), and most studies concerning these bacteria focus on acidic environments. To date, there is little field evidence which can verify or discount the inhibitory effect of molybdate on this microorganism. However, it is empirically evident that molybdenum mines in British Columbia tend to break predictions of ARD generation. The Boss Mt., Kitsault, and Trout lake Mines have produced little ARD, while the Endako, Brenda and Highland Valley copper mine have produced no ARD, despite mine waste from all six mines being theoretically acid generating based on acid base accounting (ABA) tests (Morin, et al., 2001). Morin et al., (2001) concluded that molybdenum mines violate common ABA predictive rules. While this may reflect the limitations of the ABA method, the coincidence does suggest a biological answer. The relatively high pH and inability of batch reactors to oxidize iron at the mine tailings in El Salvador, Chile is attributed to molybdate concentrations being well above toxicity limits to A. ferrooxidans  (Dold and  Fontbote, 2001). Molybdenite is stable under acidic conditions, and unlike pyrite it is not subject to rapid bioleaching (Vlek and Lindsay, 1976). The molybdate oxyanion species dominates the pe-pH 56  stability field for this metal (Vlek and Lindsay, 1976). The oxidation of molybdenite to molybdate can be stated as: MoS2 + 3H2O + 4.5O2 → MoO42+ +2SO42- + 5H+  (1)  Molybdenum is present in the aqueous phase under neutral aerobic conditions as the Mo6+ anion molybdate, MoO42- , not the toxic Mo5+ ion. Molybdate oxidizes Fe2+, producing Mo5+, which strongly binds to the plasma membrane of A. ferrooxidans where it interferes with the iron oxidase enzyme system (Yong et al., 1997). Since the Mo5+ ion directly attacks the iron oxidizing enzyme system in A. ferrooxidans, it is likely toxic to other iron oxidizing bacteria possessing similar enzymes. As can be expected there is a range of tolerance limits reported in the literature for A. ferrooxidans (Table 3.1). In a study of molybdate resistance in 75 strains of A. ferrooxidans by Yong et al. (1997), the growth of most strains were inhibited by concentrations as low as 50 mg/l; the most resistant strain could only grow at concentrations as high as 125 mg/l. A. ferrooxidans has enzymes to both reduce and oxidize Mo, which form the basis of its resistance to this metal (Yong et al., 1997). Other studies have shown molybdate toxicity limits to A. ferrooxidans as low as 5mg/l. The purpose of this study is first, to quantify the catalytic effect of bacteria in neutral pH waste rock by comparing the drainage from sterile vs. non-sterile waste rock. Secondly, it will be evaluated if molybdenum can serve as an in-situ bactericide, stopping or slowing this catalytic effect. Metal molybdate precipitates which can control the concentration of this biologically significant metal and other metals at neutral pH are also examined.  3.2 Methods 3.2.1 Material Sampling and Characterization Samples were collected from field cells and waste rock piles. These samples were taken from field cells by cutting a hole near the base of the field cell immediately above the quartz sand. Gravel sized samples were collected from the experimental waste rock piles by excavating approximately a 1 m deep hole on top of Piles 1, 2, and 3. Samples ranging from 200-250 g were collected from Feb 12th-14th, 2009 in sterilized 200 ml Nalgene bottles. The field cells and waste 57  rock piles contain the four dominant waste rock types present at the Antamina Mine; Marble Hornfels (Pile-1 and FC-1-3A), intrusive (Pile-2, FC-2-2B, FC-2-3A), exoskarn (Pile-3, FC-3-2A), and endoskarn (FC-07).  The samples were then mineralogical and microbiologically  characterized, for a detailed discussion of microbial and mineralogical results see Dockrey et al., (2010).  Briefly, the dominant phenotype in most samples was neutrophilic thiosulfate  oxidizers, with acidophilic iron oxidizing bacteria only culturable from FC-2-3A and Pile-2, and acidophilic sulfur oxidizers only culturable from FC-2-3A. The exoskarn material had the largest populations of >108 bacteria per gram, while microbial numbers were approximately 106 to 107 in the intrusive, marble hornfels, and endoskarn for a more detailed discussion of site conditions and microbial results see Dockrey et al., (2010). 3.2.2 NAG Testing Net acid generation (NAG) tests were performed based on the procedure presented by Southam and Beveridge (1993) with slight modifications. The NAG test procedure involved placing six grams of material used to construct the mini-columns (discussed below) in 1.5L flasks containing 350 ml of 17% (vol/vol) H2O2 (aq). The samples were allowed to react for 6 days, after which time there were still visible unreacted sulfides. A 45ml aqueous sample from the resulting liquor was filtered (0.45µm-pore-size filter), boiled for 1 h to remove residual H2O2. The liquor was then cooled to room temperature at which point the pH was determined, and two sub-samples were taken. One was used for ICP-OES analysis of dissolved sulfur to determine total sulfuric acid generation, and the other was back titrated using 1N NaOH to determine net acid generation. 3.2.3 Analytical Methods Inductively coupled plasma optical emission spectroscopy (ICP-OES) was used to analyze the geochemistry of micro column effluent (see below), NAG liquor, and media at the end point of the aqueous molybdate toxicity tests. Litmus paper was used to qualitatively determine the pH of the effluent water of the mini-columns. The pH was measured in effluent immediately after amending influent.  58  Elemental composition was determined by x-ray fluorescence (XRF) analysis of a pressed powder disk. Two grain size fractions (GSF) were analyzed by this technique, relatively coarse grain samples (CG) of 0.3-1.18 mm diameter, and particles of <0.3 mm in diameter, which will be referred to as the fine-grained (FG) fraction. Only small grain sizes were analyzed because most of the reactive surface area in waste rock has been found in the sand and silt particle sizes (Strömberg and Banwart, 1999). 3.2.4 Molybdate Toxicity in Liquid Media Molybdate toxicity to iron and sulfur oxidizing bacteria cultured from Antamina waste rock was analyzed in ideal 9K growth media with variable concentrations of molybdate. The culture media for the iron oxidizers used for molybdate toxicity test contained per liter: 0.4 g(NH4)2SO4, 0.1 g K2HPO4, 0.4 g MgSO4·7H2O,33.3g FeSO4·7H2O; the pH was set to 2.3 using H2SO4. The culture media for sulfur oxidizing acidophiles contained: 0.3 g (NH4)2SO4, 0.1 g KH2PO4, 0.4 g MgSO4·7H2O, 0.33 g CaCl2·2H2O, 18 mg FeSO4·7H2O; the pH was set to 2.3 using H2SO4. A thin film of S0 was poured into culture tube as substrate after tubes were inoculated. The culture media for thiosulfate oxidizing neutrophiles contained per liter: 0.3 g (NH4)2SO4, 0.1 g KH2PO4, 0.4 g MgSO4·7H2O, 0.33 g CaCl2·2H2O, 4.93 g Na2S2O3 ·5H2O; the pH was set to 7 using NaOH. Concentrations ranging from 5 mg/l and 2 g/l were achieved by amending Na2MoO4. After growth was marked as positive or negative, solution samples were taken from culture tubes to determine final molybdenum concentration by ICP-OES to verify that molybdenum was conserved. 3.2.5 Mini-Column Study 3.2.5.1 Construction Mini-columns were constructed using 5 ml syringes and 6-9 g dry weight of fine-grained waste rock material. There is some variation in grain sizes depending on the type of waste rock from which they were constructed. The samples were not sieved as that would require drying the sample, which would have killed much of the indigenous microbial biota, therefore, there was some variability in grain size between samples. For instance, Pile-2 material is finer grained then Pile-3 material, while Pile-1 material is the coarsest grained. Glass wool was stuffed into 59  the bottom of the syringes to prevent the waste rock material from spilling out. For the first 50 days 1 ml of media was amended to each mini-column once a week. After 50 days the amendment regiment was increased to 1 ml amendment twice a week due to a change in laboratory conditions which resulted in greater evaporation leading to no effluent water being produced in most of the columns. Over the course of 91 days 19 or 20 ml of media was amended to columns depending on if they were oven-dried to sterilize the samples. Outflow was collected in 1.5 ml micro-centrifuge tubes, the samples were analyzed for pH and dissolved metals (see analytical methods). The mini-column material was composed of waste rock which had been microbially populated under in-situ conditions and remained moist between sampling and mini-column construction.  Mini-column 5 was sterilized by autoclaving, while mini-  columns 6-8 were sterilized by oven drying. Eight mini-columns were constructed from each sample of waste rock. From this point on, ‘system’ refers to the 8 mini-columns made from an individual sample. For instance system 2-3A refers to the 8 mini-columns composed of material from Field cell 2-3A. The individual mini-columns in each system are numbered 1-8, the number signifies what type of influent media the mini-column is receiving (described below), and whether or not the material was sterilized prior to the experiment. For instance 3-2-3A, refers to mini-column number 3 from system 2-3A. Table 3.2 describes the media and sterile or non-sterile conditions of the various mini-columns. 3.2.5.2 Influent Media Three variations upon a M9K basal salt media were used as mini-column influent, containing 0.3 g (NH4)2SO4, 0.1 g KH2PO4, 0.4 g MgSO4·7H2O, 0.33 g CaCl2·2H2O, Na2S2O3 ·5H2O; the pH was set to 5.6 using H2SO4 to mimic the pH of rain water (Mielke et al., 2003) (Table 3.2). The M9K basal salts media was used with no adjustments in columns 1 & 2 and 5 & 6 to examine waste rock weathering in microbially populated, versus sterile conditions. In columns 3 & 4 the 319 mg/l of Na2MoO4 was amended to the M9K basal salts media to achieve a concentration of 150 mg/l Mo to examine molybdate inhibition of microbial growth and catalysis of sulfide mineral oxidation. An influent composed of M9K basal salts without PO42-  60  was used to examine abiotic sulfide oxidation in the absence of phosphate inhibition (Elsetinow et al., 2001). 3.2.5.3 Effluent Analysis The pH of the effluent was measured immediately after influent was amended. It was not necessary to analyze samples using ICP-OES on a weekly basis to achieve the desired resolution. Rather samples over several weeks were combined with a greater emphasis of achieving higher resolution in the later dates. Hence, the first four weeks of samples were mixed before analysis, and the last week of samples were analyzed individually. Samples were kept on ice prior to sampling rather than acidified because oxyanions were the dominant species of interest, and acidification does not preserve oxyanion species. 3.2.5.4 Geochemical Modeling Phreeqc and the minteqV4 database were used to construct an equilibrium model to analyze the speciation and solubility indices of solutes measured within the column effluent (Parkhurst and Appelo, 1999). 3.2.5.5 SEM-EDS Waste rock samples from select columns were examined using a Philips XL30 electron microscope equipped with a Princeton Gamma-Tech energy-dispersion X-ray spectrometer (SEM-EDS). Material from mini-columns receiving molybdate media (columns 3 & 4) were examined as they are most likely to contain metal molybdate precipitates which may affect metal loadings, in addition to sulfate and calcium-bearing precipitates which are used as indicators of sulfide mineral weathering.  At the end of the experiment the columns were  deconstructed and samples were collected from near the center of the columns and mounted on aluminum stubs using adhesive double-sided tape, and then sputter coated with carbon.  3.3 Results 3.3.1 NAG Testing The pH in samples composed of Pile-2 and Pile-3 material dropped to <7 during the NAG test indicating that the calcite buffering capacity is unable to maintain an alkaline pH (Table 61  3.3). Exoskarn material has the highest total acid generation, however it has a low AP/NP ratio. The Pile-2 (intrusive) material had the highest AP/NP ratio and produced an acidic liquor (pH <4); however, it has only intermediate total acid generation when compared to exoskarn material.  Pile-1 (marble diopside) and FC-07 (endoskarn) material have no potential for acidic  conditions to develop, and relatively little leachable sulfate. The NAG results indicate that five of the eight samples are capable of producing sub-neutral to acid rock drainage. 3.3.2 XRF There was no systematic trend in sulfur or trace metal content between the two grain sizes analyzed by XRF. Therefore XRF results are averaged between the two grain sizes and presented as <1.18 milimeter grain size fraction (GSF) by using the following equation wt% M = (GSF<0.3) x ((GSF<1.18) x (wt% Mfg))-1+ (GSF 0.3-1.18) x ((GSF<1.18)x(wt% Mcg))-1 (2) Where wt% M is the weight % of a given element, Mfg is the elemental content of the finegrained fraction, and Mcg is the elements content in the coarse grained fraction. The GSF <0.3 is the grain size fraction which is less than 0.3mm, and so on. The results of wt% Mo and S are listed in Table 3.4. In general the exoskarn material (Pile-3, FC-3-2A) had the highest S content; the intrusive material (Pile-2, FC-2-2B and FC-2-3A) had intermediate S content, while the marble hornfels material (Pile-1, FC-1-3A) and endoskarn (FC-07) had the lowest sulfide content. The intrusive material has the highest Mo content, while the marble hornfels and exoskarn have similarly low Mo contents. 3.3.3 Effluent Volume Between 8 and 65% of the volume of the amended media was produced as effluent (Table 3.5). This large range is due to variation in mass and grain size of the column material leading to different drainage characteristics and evaporation rates. Columns constructed of marble diopside (pile 1, 1-1-3A) and endoskarn (FC-07) produced a higher percentage of effluent (46% and 45% of influent respectively), then Pile-2 (37%) and Pile-3 (34%) due to the smaller fine-grained fraction. The range in effluent quantities complicated comparison of  62  results between samples. Preferential flow paths were in the wetting pattern observed when influent was amended to the mini-columns. 3.3.4 Sulfur Loading There was negative net sulfur loading in 36 of the 64 mini-columns (Table 3.5). A negative value is produced when the sulfate in the influent media added to the columns over the course of the experiment is greater than the sulfate that is collected in the effluent. Most of the systems with large negative sulfate loadings had very small fractions of influent reporting as effluent. This is most likely caused by evaporation preventing the percolation of water through the mini-column, leading to a buildup of sulfate within the mini-column and gypsum precipitation. The mini-columns composed of exoskarn (Pile-3, 3-2A) had positive sulfate loadings; however, these sulfate loadings strongly correlate to percent of influent reporting as effluent, making interpretation of microbial catalysis of sulfide oxidation difficult. This trend is less evident in system 2-3A (Table 3.5). The highest sulfate loadings were in mini-columns 1-4 of system 2-3A. The sulfate loadings in this system are not controlled by effluent volume. Minicolumns 5 and 7 have similar effluent volumes as reactors 1-4, yet have negative sulfate loadings. The XRF and NAG results show that system 2-3A has only an intermediate wt.% and H2O2 leachable sulfur compared to the other systems (Table 3.4). There is little difference in sulfate loadings between mini-columns 5 and 6, which received M9K media and mini-columns which received the phosphate free M9K media. Most variations in sulfate production between these reactors can be related to variations in effluent volume. This, along with the positive sulfate production in mini-columns 5 and 6 may indicate that abiotic phosphate inhibition of sulfide oxidation is relatively unimportant; however, the inconsistency in effluent production relegates this to speculation. 3.3.5 pH The pH of the initial effluent water in all samples was >7.5. A decrease in pH occurred in all of the columns when the amendment schedule was increased from one to two mm per week on day 50 of the experiment. This decrease in pH is caused by the reduced residence time in the column and increased significance of preferential flow paths, which were visually observed 63  in the mini-columns, causing the effluent chemistry to be more similar to the original influent chemistry. The pH of the drainage decreased below that of the influent media in reactors 1-4 of systems 2-2B and 2-3A (Figure 3.1). This pH decrease was reflected in the Zn and Cu metal loadings in systems 2-2B and 2-3A (Figure 3.2). The pH in the other 6 systems remained above the pH of the influent for the duration of the experiment. Notable trends in pH occurred within system 1-3A, in which mini-columns 1-4 had a lower pH than mini-columns 5-8, and system 07 in which the sterile mini-columns 6 and 7 produced lower pH effluent then the other minicolumns within the system. The pH remained above the pH of the influent in these systems, indicating that no net acid production occurred. 3.3.6 Phreeqc Phreeqc models found that gypsum was under saturated in the effluent with two exceptions, no solubility controls on sulfate were found despite its non-conservative nature as demonstrated by negative sulfate loadings in many of the mini-columns. Gypsum solubility was approached in mini-column 6-Pile-3 and 8-23-A. Both of these columns had <20% of influent reporting as effluent, leading to evaporative concentration of dissolved solutes. All reactors were initially saturated with respect to calcite.  Calcite ceased to be  saturated in the mini-column outflow when the pH of the effluent dropped below 7.5. The pH drop corresponds to when the amendment regime was increased from one milliliter per week to two milliliters per week on day 50, with the exception of system 2-3A which was saturated with respect to calcite for the first 30 days. Calcium concentrations were also controlled by several Ca-phosphate species, which were above saturation in the effluent from all reactors. Phreeqc modeling suggested several solubility controls may be affecting metal loadings in mini-column; the dominant metals in the outflow were Mo, Cu, and Zn. Powellite (CaMoO4) is oversaturated in all outflow from reactors receiving Mo media. Most mini-columns, which received molybdate media, and produced detectable Zn concentrations were oversaturated with respect to ZnMoO4. In addition 3-2-3A is oversaturated with respect to CuMoO4.  64  3.3.7 Liquid Substrate Molybdate Toxicity Test Molybdate was shown to be toxic to sulfur and iron oxidizing bacteria in liquid media at concentrations as low as 5 mg/l for sulfur oxidizers and 25 mg/l for iron oxidizers (Table 3.6). At a concentration of 20 mg/l iron oxidizers were inhibited as the culture tube only turned faintly orange as opposed to the deep red oxides seen in the other positive tubes. Under acidic conditions molybdate sorbs strongly to solid surfaces (Goldbert et al., 1996). To validate the molybdate concentrations at the end point of the experiment the liquid media of select tubes was filtered and analyzed using ICP-OES (Figure 3.1). The results show that concentrations of molybdate were maintained in tubes that were negative for growth with iron as substrate. The reduced molybdate concentration in tubes positive for growth demonstrates that the molybdate was sorbing to ferric oxides formed as a result of biological activity. Molybdenum was not conservative in the S0 toxicity tests. This is because S0 is provided as a solid substrate which molybdate can sorb to, biological activity is not prerequisite to molybdate sorption. 3.3.8 Micro-Column Molybdate Toxicity Test Amending molybdate to the mini-column influent had no obvious effects on pH trends or sulfate loadings in the mini-columns. Only three systems had any sign of microbial catalysis of sulfate loadings, 2-3A, 2-2B, and 1-3A. Of these systems, the mini-columns receiving the molybdate influent behaved more similarly to the other live reactors than the sterile minicolumns. Low concentrations of Mo were measured in the effluent of system 2-3A (<4.5 mg/l), which were below toxicity limits.  Molybdate concentrations in mini-columns 3 and 4 of the  other systems were between 100-130 mg/l. 3.3.9 SEM-EDS Gypsum was identified in mini-columns 3-3-2A, 4-2-3A, 3-Pile-3, 4-Pile-2 and 1-2-3A (Figure 3.4). Metal molybdate precipitates were identified in 3-3-2A, 3-2-2B, 3-Pile-3, 4-Pile-2, and 3-1-3A, all of which received Mo media (Figure 3.5, Figure 3.6, and Figure 3.7). No metal molybdate precipitates were found in mini-column 3-2-3A or 4-2-3A material despite thorough examination. Because the EDS signal is sensitive to the composition of the hemispherical volume below the surface point, the presence of an element in any precipitate is only certain if 65  it is absent from the EDS spectra of the primary mineral which the precipitate formed on. The metal molybdate featured in Figure 3.6 is on a Si-O bearing mineral, possibly quartz, providing a relatively easily interpreted spectrum. This precipitate contains Mn, Cu, Zn, Ca, and Mo, the only anion forming metal being Mo suggesting the presence of CuMoO4, ZnMoO4, MnMoO4, and CaMoO4. It is possible that these metals are present in their hydrolyzed form (i.e. Zn(OH)2) or present as metal carbonates (i.e. ZnCO3). However no metal precipitates were found in the absence of Mo. The dominant Ca and Mn peak indicate that this precipitate is predominantly composed of the latter two species. No other Zn or Cu species besides ZnMoO4 and CuMoO4 were predicted to be oversaturated by Phreeqc. It is also possible that this precipitate is MnMoO4 and CaMoO4 with Zn and Cu impurities formed through co-precipitated. The metal molybdate precipitates which were ubiquitous in some systems had variable composition. No Mn is present in the molybdate precipitate featured in Figure 3.7, and no Ca or Zn is present in the precipitate featured in Figure 3.5. Also of note during SEM-EDS analysis, calcite was identified in mini-column 3-1-3A despite a sub-neutral pH.  3.4 Discussion 3.4.1 Metal Attenuation Most of the molybdate provided to the mini-columns was attenuated over the course of the experiment (Table 3.7). This can be considered an underestimation of Mo attenuation, as Mo was present within the waste rock prior to mini-column construction as revealed by XRF analysis (Table 3.4). The results of Conlan (2009) indicate that powellite should be the only significant source of molybdenum attenuation in a carbonate buffered NRD system in the absence of Pb.  The white spherical precipitate featured in Figure 3.6 is very similar in  morphology to a precipitate identified as powellite by Conlan (2009). In this study, precipitates identified as powellite and MnMoO4 were identified which contained Cu and Zn impurities. No kinetic data on the precipitation of MnMoO4 is available, however it is relatively insoluble with a Ksp similar to ZnMoO4 (Reddy, 1990). The presence of Zn and Cu associated with molybdate precipitates is unexpected as both metals were typically at very low concentrations even in mini-columns which did not 66  receive Mo bearing media. Zinc concentrations in the effluent were only sporadically above the detection limit of 0.0004 mg/l in most mini-columns, while Cu and Mn were below the detection limit, 0.0016 mg/l and 0.0072 mg/l respectively, in all mini-columns with the exception of systems 2-2B and 2-3A. The only source of Zn, Cu and Mn in the effluent is from leaching of the waste rock. The precipitation of powellite or MnMoO4 may create nucleation sites for these metal species. Alternatively, Zn, Mn, and Cu could sorb to powellite and replace Ca in the crystal structure. Leaching of Zn, Cu, and Mo is the primary concern regarding water quality at the Antamina mine, a precipitate which can attenuate all three metals is deserving of further study. However the concentrations of Mo in the microcolumn effluent were extremely high, between 130-200mg/l, therefore these precipitates may not be reproducible in the field. No molybdenum bearing precipitates were found on mini-columns 3-2-3A or 4-2-3A despite Phreeqc indicating that CaMoO4, ZnMoO4, and CuMoO4 were above saturation, and the >99% of the molybdenum amended to these mini-columns was attenuated (Table 3.7). This is probably due to the acidic pH of this system leading to strong molybdate sorption, which was not accounted for by Phreeqc.  In addition, the high sulfate loadings indicate that iron-  oxyhydroxides are abundant providing strong sorption sites (Goldbert et al., 1996). 3.4.2 pH A decreasing trend in pH was observed in the non-sterile mini-columns (1 to 4) in systems 2-3A, 2-2B, and to a lesser extent 1-3A (Table 3.5). Mini-columns 1 to 4 in systems 23A and 2-2B produced drainage that was at a lower pH than the influent, therefore net acid generation took place. In system 1-3A a similar trend is evident, however, the pH of the outflow is still above that of the influent indicating that no net production of acidity is taking place and the pH difference could be explained by preferential flow paths mixing alkaline pH pore water with fresh media, which is pH 5.6. Calcite dropping below saturation in this system was not due to an exhaustion of the calcite’s buffering capacity; SEM-EDS identified CaCO3 in this mini-column at the end of the experiment. This is probably due to a combination of preferential flow paths and calcite mineral armoring by gypsum and molybdenum bearing precipitates as seen in Figure 3.7.  67  The suppressed pH in mini-columns 1-4 relative to the pH of reactors 5-8 show that microbial catalysis of acid production is taking place in system 2-3A, and 2-2B. It is somewhat surprising that these two systems produced acidic drainage while system Pile-2 remained neutral. The Pile-2 intrusive material contained the highest acid generating to acid neutralizing ratio, and the lowest acid neutralizing potential (Table 3.3). This system also contained a larger microbial population than 2-3A; however it did not contain a large population of acidophilic iron oxidizing bacteria as system 2-3A did. The contradiction in the NAG results and the minicolumn results between 2-3A and Pile-2 samples demonstrates the importance of the microbiology community in predicting the drainage of mine waste. 3.4.3 Sulfate Loadings The inconsistency in production of effluent, phosphate inhibition of sulfide oxidation, and the sulfate attenuation through gypsum precipitation within most mini-columns complicated the interpretation of sulfide oxidation rates. The Phreeqc speciation model did not indicate that any sulfate bearing species were saturated; however gypsum was identified using SEM-EDS in mini-column 3-3-2A, 1-2-3A, 4-2-3A, 4-Pile-2 and 3-Pile-3 (Figure 3.4). The presence of gypsum in the mini-columns shows that gypsum is saturated within them, which is supported by the outflow geochemistry that shows sulfate is not conservative as demonstrated by negative sulfate loadings in many mini-columns (Table 3.5). It is likely that gypsum is present in other columns as well, as only a small percentage of the material in the column is able to be examined using SEM-EDS. The existence of preferential flow paths which were observed in the mini-columns, will lead to the effluent being relatively dilute compared to the reacted pore water in the mini-column. This has lead to an under estimation of the saturation indices predicted by modeling the effluent geochemistry. The large contrasts in sulfate production between the live and sterile reactors in system 2-3A demonstrates that microbial catalysis is taking place. This divergence in sulfate production was evident within the first 30 days. This divergence in sulfate production took place prior to acidification of the non-sterile mini-columns indicating that microbial catalysis was taking place at a pH >6. No microbial catalysis of sulfide oxidation was observed in the sulfate loadings of  68  any other system, despite system 3-2A, Pile-3, and Pile-2 having significantly larger microbial populations than 2-3A. According to the NAG results 3-2A, Pile-3, and FC-2-2B material have the largest amount of H2O2 leachable sulfate (Table 3.3), however the sulfate loadings in the mini-column experiment do not reflect this. The high sulfate loadings of mini-columns 1-4 of system 2-3A relative to the other field cells, indicate that the occurrence of acidophilic bacteria is more important in determining weathering rates then total acid generating potential, or the occurrence of neutrophilic bacteria. The lack of sulfate production in many of the mini-columns may be due to phosphate inhibition of sulfide mineral oxidation at a pH >4 (Elsetinow et al., 2001). Phosphate does not bind strongly to bare pyrite surfaces, only to ferric hydroxides and oxides which form on the pyrite surface. The mini-columns were composed of weathered material taken from the field cells, so the surfaces would already contain weathering products. Phosphate is theorized to inhibit pyrite oxidation at pH >4 in one of two ways. By preventing oxygen adsorption to reactive sites on ferric iron oxide by competitive inhibition, or by electrically modifying the surfaces to which it binds creating a barrier to electron transport. In either case phosphate prevents electron transfer between Fe2+ sites in pyrite and O2 (Elsetinow et al., 2001). Sulfide oxidation was evident in the sulfate loadings of some systems besides 2-3A, showing that phosphate did not completely inhibit sulfide oxidation. Furthermore mini-columns receiving the phosphate free media (7 and 8) did not demonstrate greater sulfide oxidation rates then mini-columns receiving phosphate media (5 and 6). Neutral pH sulfur oxidizing bacteria do not directly weather sulfide minerals; they catalyze the oxidation of sulfide minerals by cleaning sulfide surfaces of the intermediate sulfur species. In the absence of Fe2+ oxidizing bacteria they rely on the abiotic oxidation by O2 to release intermediate sulfur species. If the oxidation of sulfides by O2 is slowed by phosphate inhibition, neutral pH sulfur oxidizing bacteria capable of cleaning sulfide mineral surfaces will have a reduced catalytic effect. Iron oxidizing bacteria living in ambient neutral pH conditions form acidic microenvironments on sulfide mineral surfaces, where they can strongly catalyze sulfide 69  mineral oxidation by iron redox cycling and ferric leaching. The pH within the acidic microenvironments must be pH <3.5 for ferric iron to be sufficiently soluble for ferric iron oxidation of sulfide minerals to take place - at this pH phosphate does not bind to sulfide surfaces and inhibit sulfide oxidation. The abundance of acidophilic iron oxidizing bacteria, and acidic microenvironments (described in the previous chapter) in sample 2-3A prevented the inhibitory effects of phosphate seen in the other samples, and led to strong catalyzation of sulfide oxidation. No iron oxidizing bacteria were culturable from 2-2B material. This means that iron oxidizing bacteria are not particularly abundant in this sample. Iron oxidizing bacteria had the greatest affinity for Intrusive material which system 2-2B is composed of although none were found in system 2-2B itself. The MPN technique only measures the number of bacteria in about 100 mg of material, whereas several grams of this heterogeneous material is likely to contain a greater diversity of bacteria.  The lag time present in system 2-2B reflects not only the  consumption of acid neutralization potential, but may also be due to the time required for the growth of an iron oxidizing microbial community. 3.4.4 Molybdate Toxicity The molybdate toxicity test in liquid media confirmed the results of numerous other studies, which demonstrated the sensitivity to acidophilic iron oxidizing bacteria to molybdate (Bhappu et al., 1965 Tuovinen, 1971, Yong et al., 1997).  In addition, it was found that  molybdate is more toxic to acidophilic bacteria in sulfur oxidizing conditions (10mg/L) then iron oxidizing conditions (25mg/L), and neutrophilic sulfur oxidizing bacteria were tolerant to concentrations of Mo up to 2g/l. This is similar to the results of Tuovinen, (1971) who found that A. ferrooxidans was more sensitive to most metals under sulfur oxidizing conditions then iron oxidizing conditions. However, biological catalyzation of sulfide mineral oxidation was not inhibited by influent media containing 150 mg/l of Mo in mini-columns which were dominated by acidophilic sulfur and iron oxidizing bacteria. This result may be due to the fact that the only mini-column system dominated by acidophilic bacteria sensitive to molybdenum rapidly acidified, leading to Mo being insoluble. The highest concentrations measured in the effluent of mini-columns 3-2-3A and 4-2-3A was 4.5 mg/l, which is below the toxicity limit determined in 70  the liquid media toxicity test. The only other minicolumn system to demonstrate biological catalyzation was 2-2B, bio-catalysis in which was not inhibited by 150mg/l Mo media. This was not surprising as it is dominated by neutrophilic bacteria which were not sensitive to molybdate in liquid media test; it is uncertain if iron oxidizing bacteria developed in this mini-column series. The strong biological catalysis observed in the molybdate inoculated mini-columns of system 2-3A cannot be attributed solely to neutral pH sulfur oxidizing bacteria, which are not sensitive to molybdate (Table 3.6). Neutral pH thiosulfate oxidizing bacteria demonstrated no ability to catalyze sulfide oxidation in most systems. Acidophiles may or may not have been influential in the pH drop observed in the non-sterile system 2-2B mini-columns. System 2-3A mini-columns receiving the molybdate media (3 and 4) actually had higher sulfate loadings then mini-columns 1 and 2, indicating that molybdate may in fact stimulate sulfide oxidation. This is surprising as this is the only system that is dominated by an acidophilic microbial community that was quite sensitive to molybdate based on the liquid toxicity test. Molybdenum may stimulate biological activity as it is an important micro-nutrient. Mo is required for 30 known cellular enzymes, the most important of which being nitrogenase, an enzyme used for nitrogen fixation (Liermann et al. 2007). A 2007 study demonstrated that starving microbial cultures of molybdenum inhibits cellular growth of bacteria under nitrogen fixing conditions, but had no effect on growth when NH3 was provided in solution (Liermann et al. 2007). In this experiment NH3 was provided in the influent media, so it seems unlikely that Mo stimulated microbial activity in this way. Molybdate is also an essential micronutrient under thiosulfate oxidizing conditions; however, the trace amounts of Mo required (9.5 µM) could easily be obtained from the host rock as molybdenite is abundant in the intrusive waste rock (Table 3.4) (Friedrich et al., 1986). The increased sulfate loadings could be due to the precipitation of powellite, or complexation of molybdate with Ca, lowering the activity of Ca in solution, decreasing the precipitation of gypsum, which would lead to increased mobility of sulfate released from sulfide mineral oxidation. However powellite was not found in this minicolumn during SEM-EDS analysis. This relatively small variation in sulfate loadings could also be due to natural heterogeneity of the material within the mini-columns. 71  3.5 Conclusion Microbial catalysis is evident in the pH trends of two of the eight mini-column systems, both of which were composed of intrusive material. The sample that generated the largest amount of sulfuric acid during the mini-column experiment (2-3A) had a relatively small total acid generating potential as predicted by the NAG test. This was the only sample with an established population of acidophilic bacteria capable of iron oxidation. Samples dominated by neutrophilic sulfur oxidizing bacteria did not demonstrate biological catalysis in sulfate loadings, metal leaching, or pH trends indicating the occurrence of neutrophilic sulfur oxidizing bacteria is geochemically less significant than the occurrence of acidophilic iron and sulfur oxidizing bacteria. A liquid toxicity tests demonstrated that Mo is toxic to acidophilic sulfur oxidizing bacteria at concentrations <10mg/l and acidophilic iron oxidizing bacteria at concentrations <25mg/l, while neutrophilic sulfur oxidizing bacteria were tolerant of concentrations as high as 2g/l. Biological catalysis was not inhibited in mini-columns containing acidophilic bacteria receiving influent containing 150mg/l. This is likely due to Mo attenuation causing Mo to remain below toxic concentrations. Between 39-99% of the Mo amended to the mini-columns was attenuated. Powellite and MnMoO4 were the dominant metal molybdate precipitates. These metal molybdate precipitates contained impurities of Zn and Cu. This is particularly significant as Zn and Cu are two of the primary metals of concern in Antamina waste rock. This indicates that the precipitation of CaMoO4 can lead attenuate of Zn and Cu even when these metals are at very low concentrations. However, the mini-columns were given an influent media containing 150mg/l, which is a much higher concentration than measured in the field. This may have lead to unrealistic precipitates. Nonetheless, the leaching of Mo, Zn and Cu are the primary concern regarding water quality in many NRD systems. Any precipitate that is capable of attenuating all three metals is deserving of further study.  72  3.6 Figures  1-3A  9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0  22-1-3A  11-2-3A 22-2-3A  9.0  33-1-3A  33-2-3A  8.0  44-1-3A  44-2-3A 55-2-3A  20  40  60  80  6.0  77-2-3A  5.0  11-2-2B  10.0  22-2-2B  9.0  33-2-2B  8.0  44-2-2B  7.0  55-2-2B  6.0  66-2-2B  5.0  77-2-2B  4.0  88-2-2B 40  77-1-3A  4.0 20  40  60  80  100  Day  2-2B  20  66-1-3A  0  100  Day  0  55-1-3A  7.0  66-2-3A 88-2-3A 0  pH  11-2-3A  10.0  pH  pH  2-3A  60  80  Figure 3.1. pH results from mini-column experiment. The other five systems experienced no notable trends in pH. Note that reactors receiving 15mg/l Mo in their influent (#3 and 4) behaved more similarly to the live experimental mini-columns 1 and 2 than the sterile control mini-columns 5 and 8.  100  Day  73  2-3A Zn loading  33-2-3A FC-23A 44-2-3A FC-23A  0.04 0.03  55-2-3A FC-23A  0.02  66-2-3A FC-23A  0.01  77-2-3A FC-23A  0 0  50  100  22-2-3A FC-23A 33-2-3A FC-23A  0.3  44-2-3A FC-23A  0.2  55-2-3A FC-23A  0.1  66-2-3A FC-23A 77-2-3A FC-23A  0  88-2-3A FC-23A  0  50  days  1-3A Zn loading  11-2-2B FC-22B 22-2-2B FC-22B  88-2-3A FC-23A  0.0004  11-1-3A 13A 22-1-3A 13A  33-2-2B FC-22B 44-2-2B FC-22B 55-2-2B FC-22B  0.01  66-2-2B FC-22B 0.005  77-2-2B FC-22B  33-1-3A 13A  0.0003 mg Zn/g  0.015 mg Zn / g  100  days  2-2B Zn loading  0.02  11-2-3A FC-23A  0.4 mg Cu /g  0.05 mg Zn / g  2-3A Cu loading  11-2-3A FC-23A 22-2-3A FC-23A  44-1-3A 13A  0.0002  55-1-3A 13A  0.0001  66-1-3A 13A 77-1-3A 13A  88-2-2B FC-22B  0 0  50 Days  100  0 0  20  40 Days 60  80  100  Figure 3.2. Zn and Cu concentrations as measured in the effluent from systems 2-3A, 2-2B and 1-3A. Note that system 2-3A was the only system to produce detectable quantities of Cu.  74  2-3A Fe2+  25  30  20 Mo mg/L  Mo mg/L  25 20 15  15 10  10 5  5 0  0 3  4  5  6  7  8  9  2  Tube #  30  3  4 5 Tube #  6  7  measured Measuredafter after21 21days days Original concentration  Pile-2 Fe2+  25 Mo mg/L  Figure 3.3. ICP-OES validation of Mo concentrations for liquid molybdate toxicity test for Fe2+ and S0 oxidizing bacteria. Note that when growth is positive molybdate begins to drop out of solution through sorption to colloidal material which forms due to microbial growth.  2-3A S0  Tube #  mg/l Mo  2  5  3  10  5  4  15  0  5  20  6  25  7  30  20 15 10  3  4  5  6 Tube #  7  8  9  75  A  B A  Si  S  O  Ca O Al K Ti  Figure 3.4. EDS spectra and image of gypsum identified in material from mini-column 3-3-2A.  Fe  Cu  Si  O Al K Ti Mn Fe  Cu  Mo  Figure 3.5. Metal molybdate precipitate in 4-Pile2 on a silicate mineral. (A) is an EDS spectra of the primary mineral, while (B) is the EDS spectra of the white precipitate upon the mineral. This precipitate contains Mn, Mo, and possibly Fe and Cu. 76  B  A  A  B  C  Si  O  O  Mo  D  Ca  A  Si  Mo  B  A  Ca  B  Ca  Si  O  Mn  O  Cu Zn  Mo  Figure 3.6. Metal molybdate precipitate in 3-2-2B on Si-O mineral. (A) is an EDS spectra of the primary mineral, while (B) is the EDS spectra of the white precipitate upon the mineral. This preciptiate contains Mn, Cu, Zn and Mo.  Si Mn  Cu  Zn  Mo  Figure 3.7. Metal molybdate precipitate in 3-13A on a calcium bearing precipitate. (A) is an EDS spectra of the primary mineral, while B is the EDS spectra of the white precipitate upon the mineral. This precipitate contains Cu, Zn, Mo and possibly Ca and Fe.  77  3.7 Tables  Reference  pH  Young et al., 1997  2.5  190  growth inhibited most strains stopped most resistant strain stopped inhibited stopped  3  49.4  stopped  Tuovinen et al., 1971  2-2.8  5  stopped  This study  2.3  20  inhibited  25  stopped  Pistacio et al., 1994  1.8  Jack et al., 1980  mg/l 47.5 95 125 9.5  Table 3.1. List of current and prior studies of toxicity limits of molybdenum to iron oxidizing bacteria. Column #  Sterilized  Influent Media  1&2  No  9K basal salts  3&4  No  9K basal salts + 319mg/l Na2MoO4  5&6  Yes  9K basal salts  7&8  Yes  9K basal salts without PO4  Table 3.2. Numbering scheme of mini-columns with corresponding influent and microbiological conditions.  78  Material type  Net acid generation  Total acid generation  Acid neutralizing  (NAG)  (ICP)  potential of calcite  Final pH  7  Endoskarn  0  1.45  >1.45  8.4  Pile-1  Marble Diopside  0  3.09  >3.09  8.6  1-3A  Marble Diopside  0  9.49  >9.49  8.7  Pile-2  Intrusive  7.15  12.03  4.88  3.9  2-2B  Intrusive  3.68  12.62  8.95  3.8  2-3A  Intrusive  6.34  13.53  8.17  3.2  Pile-3  Exoskarn  15.81  79.45  63.64  2.9  3-2A  Exoskarn  11.99  89.70  78.69  6.7  Table 3.3 NAG results on sample material used for mini-column construction. Numbers reported in mg CaCO3 per gram of sample. Note that additional acid neutralization capacity exists in silica minerals, which will increase the total neutralization potential beyond the acid neutralizing potential of calcite reported.  79  Sample  S wt% Mo (ppm)  FC-1-3A  0.06  68  FC-3-2A  5.85  78.37179  FC-2-3A  1.88  887.571  FC-2-2B  0.79  2200  FC-07  0.33  2712.361  Pile-1  0.06  265.567  Pile-2  1.21  1674.531  Pile-3  11.25  92.83405  Table 3.4. XRF results of fine-grained waste rock <1.18 mm in diameter.  80  mgSO4/g Column #  % inflow  final  as outflow  pH  mgSO4/g  FC-2-3A  % inflow as  final  outflow  pH  mgSO4/g  FC-3-2A  % inflow  final  as outflow  pH  mgSO4/g  Pile-3  % inflow as  final  outflow  pH  FC-1-3A  1  0.93  37  4.2  0.06  28  7.1  0.20  45  7.1  0.27  39  6.7  2  0.92  31  4.2  0.15  23  7.1  0.30  35  7.0  0.10  31  6.5  3  1.02  44  4.2  0.52  38  6.9  0.16  31  7.0  0.20  55  6.5  4  1.32  48  4.3  0.32  33  6.9  -0.02  31  7.0  0.16  57  6.5  5  -0.12  36  5.6  0.49  36  7.0  0.33  38  6.8  0.11  48  7.4  6  -0.36  21  6.1  0.82  47  6.6  -0.41  14  6.7  0.13  44  7.2  7  -0.36  44  6.5  -0.13  27  6.6  0.63  57  6.0  -0.30  32  7.2  8  -0.85  17  6.1  -0.25  26  6.6  -0.21  28  6.6  NA  NA  NA  FC-07  FC-2-2B  Pile-1  Pile-2  1  0.05  38  7.1  -0.06  24  5.7  -0.22  52  7.1  -0.21  35  7.8  2  0.09  46  6.1  -0.49  20  5.2  -0.26  53  7.1  -0.14  56  6.7  3  -0.32  49  6.7  -0.01  45  5.3  -0.56  41  6.9  -0.13  65  6.8  4  -0.17  58  5.9  -0.31  53  5.3  -0.59  44  7  -0.17  53  6.8  5  -0.05  53  7.1  -0.41  27  7.3  -0.41  33  7.2  -0.20  40  7.4  6  0.03  48  5.6  NA  NA  NA  -0.21  58  7.1  -0.24  22  6.7  7  0.07  52  5.9  -0.41  52  6.8  -0.82  35  7  -0.26  37  6.7  8  NA  NA  NA  -0.98  20  7.3  -0.89  34  7.3  -0.24  36  7.4  Table 3.5 Total sulfur loadings (mg of SO4 leached per gram of sample), percent of influent reporting as effluent, and final pH of minicolumn experiment. 81  Substrate pH S0  2.3  Fe2+  2.3  S2O32-  7  Concentration mg/l  Growth  <10  Stopped  20  Inhibited  25  Stopped  2000  +  Table 3.6 Liquid medium MoO42- toxicity results.  Mini-  % Mo  column  attenuated  3-2-3A*  99.5  4-2-3A*  99.9  3-Pile-3  87  4-Pile-3  87.8  3-Pile-1  77.8  4-Pile-1  77.8  3-3-2A  76.5  4-3-2A  76.4  3-07  71.3  4-07  64.2  3-1-3A  70.7  4-1-3A  63.9  3-2-2B  61.0  4-2-2B  64.5  3-Pile-2  38.9  4-Pile-2  51.8  Table 3.7. Molybdenum attenuation in mini-columns. Ca loadings are provided for comparison as powellite is identified as the dominant Molybdenum bearing precipitate. 82  3.8 References Bhappu, R.B., Reynolds, D. H. and Roman, R.J. (1965). Molybdenum recovery from sulfide and oxide ores. J. Metals, 17, 119-1205. Conlan, M.J. (2009). Attenuation mechanisms for Molybdenum in neutral rock drainage. MASc Thesis. University of British Columbia, 2009. Dockrey, J.W., Mayer, K.U., Beckie, R. and Southam, G. (2010) Structure and chemistry of bacterially populated microenvironments on neutral pH waste rock. Manuscript submitted for publication. Dold, B. and Fontbote, L. (2001). Element cycling and secondary mineralogy in porphyry copper tailings as a function of climate, primary mineralogy, and mineral processing. Journal of geochemical exploration, 74, 3-55. Dold, B., (2005). Basic concepts in environmental geochemistry of sulfide mine waste. XXIV Curso Latinoamericano de metalogenia UNESCO-SEG. Elsetinow, A.R., Schoonen, M.A. and Strogin, D.R. (2001). Aqueous geochemical and surface science investigation of the effect of phosphate on pyrite oxidation. Environmental Science and Technology, 35, 2252-2257. Friedrich, C.G., Meyer, O. and Chandra, T.S. (1986). Molybdenum-dependent sulfur oxidation in facultatively lithautotrophic thiobacteria, Fems Microbiology, 37, 105-108. Goldbert, S., Forster, H.S. and Godfrey, C.L. (1996). Molybdenum adsorption on oxides, Clay minerals, and soils. Soil Science Society of America journal, 60, 425-432. Hollings, P., Hendry, M.J., Nicholson, R.V. and Kirkland, R.A. (2001). Quantification of oxygen consumption and sulphate release rates for waste rock piles using kinetic cells: Cluff lake uranium mine, northern Saskatchewan, Canada. Applied Geochemistry, 16, 1215-1230. Liermann, J., Hausrath, E., Anbar, A. and Brantley, S. (2007). Assimilatory and dissimulatory processes of microorganisms affecting metals in the environment. Journal of Analytical Atomic Spectrometry. 22, 867-877. Lortie L., Gould W., Stichbury M., Blowes D.W. and Thurel A. (1999). Inhibitors for the prevention of acid mine drainage. In: Proceedings from Sudbury ’99 Mining and the Environment II Conference, Sudbury, Canada. 83  Mahapatra, S.S. and Mishra, A.K., (1984). Inhibition of iron oxidation in Thiobacillus ferrooxidans by toxic metals and its alleviation by EDTA. Current Microbiology, 11, 1-6. Mielke R.E., Pace D.L., Porter T. and Southam, G. (2003). A critical stage in the formation of acid mine drainage: Colonization of pyrite by Acidithiobacillus ferrooxidans under pH-neutral conditions. Geobiology 1, 81-90. Nordstrom E.K. and G. Southam (1997). Geomicrobiology of sulfide mineral oxidation. In J.F. Banfield and K.H. Nealson (Eds.), Geomicrobiology: Interactions Between Microbes and Minerals (Vol. 35, pp. 361-390), Mineralogical society of America, Washington, D.C. Olson, G.J. (1991). Rate of bioleaching by Thiobacillus ferrooxidans; results of an interlaboratory comparison. Applied Microbiology. 57, 642-644. Parkhurst, D.L. and Appelo, C.A.J. (1999). Users Guide to PHREEQC—A Computer Program for Speciation, Reaction-path, 1D-transport and Inverse Geochemical Calculations. US Geol. Surv. Water Resour. Invest. Reddy, K.J., Want, L. and Lindsay, W.L. (1990). Molybdenum supplement to Technical Bulletin 134: Selection of standard free energy of formation for use in soil chemistry. Technical Bulletin LTB90-4. Ritchie A.I.M., 1994. Rates of mechanisms that govern pollutant generation from pyritic waste. Environmental geochemistry of sulfide oxidation , IN Blowes, D.W. and Alpers, N., and K.H. Nealson (Eds.), Alpers and D.W. Blowes, pp. 108-122. Amer. Chem. Soc. Symp. Ser. 550. Smith K.S., Balistrieri, L.S., Smith, S.M. and Severson, R.C. (1997). Distribution and mobility of molybdenum in the Terrestrial environment, In: U.G. Gupta (Ed.), Molybdenum in Agriculture (pp. 23-46), Cambridge university Press, New York. Southam G. and Beveridge T.J. (1992) Enumeration of thiobacilli within pH-neutral and acidic mine tailings and their role in the development of secondary mineral soil. Applied and Environmental Microbiology 58, 1904-1912. Southam G. and Beveridge T.J. (1993) Examination of lipopolysaccharide (O-Antigen) populations of Thiobacillus ferrooxidans from two mine tailings. Applied and Environmental Microbiology 59, 1283-1288. 84  Strömberg B. and Banwart, S.A. (1999). Experimental study of acidity-consuming processes in mining waste rock: some influences of mineralogy and particle size. Applied geochemistry, 14, 1-16. Tuovinen, O.H., Niemela, S.I. and Gyllenberg, H.G. (1971). Tolerance of Thiobacillus ferrooxidans to some metals. Antonie van Leeuwenhoek Journal of microbiology and serology, 37, 489-496. Vlek, P.L.G. and Lindsay, W. L. (1976). Thermodynamic Stability and Solubility of Molybdenum Minerals in Soils. Soil Science Society of America Journal, 41, 42-46 Yong N.K., Oshima, M., Blake, R,C and Sugio, T. (1997). Isolation and some properties of an ironoxidizing bacterium Thiobacillus ferrooxidans resistant to molybdenum ion. Biosci. Biotech. Biochem., 61, 1523-1526.  85  4  Chapter 4: Relationship between Geochemistry Microbiology and Material Composition of Antamina Mine Waste Rock3  4.1 Introduction Mining operations produce immense amounts of waste material in the form of tailings or waste rock. Valuable metals are often associated with sulfides. The biologically mediated oxidation of sulfide minerals is the primary concern to the quality of drainage waters as it may lead to heavy metal loading of the environment and acidic drainage, referred to as acid rock drainage (ARD). If an abundance of pH buffering minerals is present, ARD may never develop despite ongoing acid generation. Understanding the processes responsible for acid generation at neutral pH is important for both predicting the water quality of neutral rock drainage (NRD), and the potential for ARD to develop. With a mind for minimizing environmental impact and mine closure expenses it is important to assess early in the mines life the potential of ARD. The reactivity of mine waste can be assessed by lab studies, field investigation, and supplementary numerical modeling. Laboratory experiments provide timely and cost effective data, however the numerous biases inherent in laboratory analysis. Most laboratory tests can be categorized as static tests or kinetic tests. Static tests are good indicators of whether the potential for ARD does or does not exist; however, they provide little information as to time until, or extent of acidification and a wide range of results indicate uncertain ARD potential. The primary means of further assessing material with uncertain ARD potential are kinetic tests. Kinetic tests grant insight into the rate of acid generation and neutralization, the time until acidification of drainage, and the potential metal loadings to the environment (Sapsford et al., 2009).  A variety of kinetic test  methodologies have been developed with the goal of reproducing natural weathering conditions. However, little is known about how reliably kinetic tests can reproduce a natural microbial biota, which is an essential component of mine waste weathering. It is interesting to note that weathering rates of kinetic tests typically follow classic Monod growth equations, which are used to model microbial growth and substrate consumption (Bowell et al., 2006).  3  A version of this chapter will be submitted for publication. Dockrey, J., Beckie, R., Mayer, K. and G. Southam (2010) Relationship between Geochemistry Microbiology and Material Composition of Antamina Mine Waste Rock.  86  Growing a natural microbial consortium in a laboratory setting is difficult even when that is the explicit purpose of an experiment (Joseph et al., 2003). Numerous difficulties in reproducing a natural microbial community in a laboratory suggest that the effect of bacteria on geochemistry in laboratory tests will be inconsistent with their effect in the field. Bacteria require unrealistic geochemistry to be successfully grown in laboratory settings, e.g., A. ferrooxidans cannot grow in culture media containing less than 6.9 mg L-1 PO42-(Personal communication, G. Southam 2009), a concentration dramatically higher than could be expected in kinetic cells or under field conditions. Not acknowledging biological sensitivities can lead to unrealistic data. For instance, many bacteria cannot tolerate dry conditions. Significant drying of kinetic cells during the dry air cycle has lead to orders of magnitude decreases in weathering rates (Sapsford et al., 2009). The numerous difficulties in cultivating environmental bacteria in laboratory settings imply that laboratory kinetic tests will not typically reproduce the microbial community that the material will be exposed to in the field. Many of these difficulties can be avoided by adopting field-based methods for determining mine waste weathering rates. Field cells remove many of the intrinsic biases associated with laboratory tests, as they expose mine waste to markedly more realistic weathering conditions (Brown et al., 2006). Field cells are typically composed of open drums filled with several hundred kilograms of waste rock or tailings. The field cells are left uncovered at the mine site so that natural weathering processes and microbial colonization can take effect.  The outflow is metered, and  geochemically analyzed. Field cells are small enough so that the drainage geochemistry is not severely complicated by preferential flow or gas transport as it is in larger waste rock piles. One of the greatest advantages to field cells is that they increase confidence that a microbial community representative of the larger waste rock piles will develop. However, this hypothesis awaits verification. Numerous questions remain concerning the role and significance of microbiology in NRD and in the initiation of ARD.  The well constrained geochemistry and the ability for  microbiological processes to proceed under natural conditions allow a relationship between weathering of mine waste material and size and type of microbial community to be established. Microbial samples were taken from field cells and experimental waste rock piles at the 87  Antamina mine to determine if similar microbial communities developed in field cells and waste rock piles composed of the same rock type, and to relate the size and structure of the microbial communities to the geochemical development of the various field cells. This work contributes to bridging the gap between the accumulated knowledge on microbiology of sulfide mineral oxidation and application of this knowledge by reporting empirical correlations between microbial populations and geochemistry of drainage in geochemically and mineralogically well characterized field cells weathered under field conditions.  4.2  Site Description  4.2.1  Antamina The Antamina mine is a Cu-Zn skarn deposit with ore grade minerals found in quartz-  monzonite porphyry hosted in Cretaceous limestone. It is one of the largest zinc and copper mines in the world and also contains economic quantities of molybdenum and lead. The abundance of carbonates in the surrounding country rock has alleviated most concern of the mine drainage becoming acidic. The Antamina mine is located approximately 270 km northeast of Lima, Peru within the Andes Mt. at an elevation between 4200 and 4800 m (above sea level). Despite the extreme elevation, the temperature is moderate with an average of 8oC due to its tropical latitude. The average annual rainfall ranges from 1100 – 1300 mm, with most of the precipitation falling within the summer wet season (November – April).  4.3 Methods 4.3.1 Experimental Waste Rock Piles and Field Cells Five large-scale experimental waste rock piles were built at Antamina for the purpose of monitoring drainage from the three dominant classes of waste rock. The principal concern regarding the drainage water is Zn, As, Se, Cu and Mo loading. For more detailed description of waste rock piles see Chapter 2. During each discharge phase, material was set aside for grain size distribution (GSD) analysis and tandem field cell experiments. GSD analysis was conducted on each tipping phase by Golder Associates using the ASTM D 5519 methodology. Field cells were composed of 208 L (55 gallon) drums packed with silica sand in the bottom 20 cm, upon 88  which 260 – 350 kg of waste rock was placed. For more detailed description of field cells see Chapter 2. 4.3.2 Material Characterization Sub-samples of 40 – 50 g of material <1 cm in diameter were analyzed by quantitative xray diffraction (XRD) to determine their mineralogy. The samples were first crushed to <1 mm with a mortar and pestle and thoroughly mixed. A 3 g sub-sample was then ground to < 5 µm in a McCrone micronizing mill, mounted, and step scanned over from 3° – 80° 2θ with CuKα Xradiation in a Siemens D5000 Bragg-Brentano diffractometer (Raudsepp and Pani, 2003). The scan data was refined using the Rietveld program Topas 3.0. Elemental composition was determined by x-ray fluorescence (XRF) analysis of samples formed into a pressed powder disk. Two grain size fractions (GSF) were analyzed by this technique, relatively coarse-grained samples (CG) of 0.3-1.18 mm diameter, and particles of <0.3 mm in diameter, which will be referred to as the fine-grained (FG) fraction. Only small grain sizes were analyzes because most of the reactive surface area in waste rock has been found to be in the sand and silt particle sizes (Strömberg and Banwart, 1999). Samples from the waste rock piles field cells were examined using a Philips XL30 electron microscope equipped with a Princeton Gamma-Tech energy-dispersion X-ray spectrometer (SEM-EDS) to identify geochemically important minerals, such as sulfides and phosphates, which were insufficiently abundant to be detected by XRD analysis. Fine-grained samples of waste rock were mounted on aluminum stubs using adhesive double sided tape, and then sputter coated with carbon. Sulfide mineral surfaces were examined using a LEO 1540XB Field Emission Gun (FEG) SEM (Carl Zeiss SMY AG, Oberkochen, Germany). The SEM was equipped with an EDAXTM Energy dispersive X-ray spectrophotometer (EDS) for qualitative elemental analysis. Mineral surfaces were coated with Pt to reduce sample charging and imaged with the SE detector at 3.0 kV. 4.3.3 Geochemical Analysis The drainage from the field cells and piles was measured for dissolved metals and nutrients. Metal concentrations were determined by inductively coupled plasma-atomic emission 89  spectroscopy. Nitrogen was measured calorimetrically; total phosphorus and phosphate were measured by the ascorbic acid method. In 2007 the sampling regime was changed and only total P was measured as opposed to total P and phosphate. All analytical geochemistry was conducted by EnviroLab, Peru S.A.C. Loadings were calculated by multiplying the concentration of sulfate measured in the drainage, by the volume of the drainage for that time period. 4.3.3.1 Geochemical Modeling For a better understanding of the processes controlling the geochemistry of drainage waters a Phreeqc speciation model was employed. The minteqV4 database was used to construct an equilibrium model to analyze the speciation and solubility indices (SI) (Parkhurst and Appelo, 1999). Geochemical data reported by Envirolab Peru, was used as input. The primary solubility controls of interest are those affecting SO42-, Ca2+, MoO42-, and Zn.  The dissolved P  concentration remained below the detection limit of 0.3mg/l in all field cells. Due to the high concentrations of Ca and the alkaline pH, Ca-phosphate precipitation can be expected to prevent significant P concentrations from developing.  Therefore Phreeqc was used to  determine the maximum concentration of P that can be reached before CaHPO4 saturation. 4.3.4 Microbiology Both culture-dependent and culture-independent techniques were used to quantify the microbial community. The most probably number technique (MPN) as described by Cochran (1950) was used to enumerate viable, culturable iron-oxidizing acidophiles, sulfur-oxidizing acidophiles, and thiosulfate-oxidizing neutrophiles. Total (live and dead) bacteria were counted using a Live/Dead Baclight™ bacterial viability kit (Molecular Probes Inc., Eugene, Oregon (USA)). For detailed description of microbiological sampling and analysis methods see Chapter 2.  4.4 Results 4.4.1 Material Characterization XRD analysis revealed that Pile-1, Pile-3, FC-1-3A, FC-07, FC-3-2A, all contain significant quantities of calcite (Table 4.1).  Quantitative Rietveld analysis indicates that the marble 90  hornfels material is composed of >44 wt% calcite. Although calcite was not detected in the intrusive waste rock using XRD, SEM-EDS, or 1% HCl, calcite has been reported as <1 wt.% of the material in prior studies (Klohn Crippen, 1998), a concentration too low to be reliably detected by XRD techniques. If the calcite is becomes depleted in these samples, silicate minerals such as biotite, chlorite, or calcium rich plagioclase will become the dominant buffering mineral. Plagioclase is the only one of these minerals which is identified in significant quantities in the intrusive material. Pyrite was the dominant sulfide mineral present in the exoskarn and marble hornfels, while chalcopyrite was the dominant sulfide mineral in the intrusive material. Molybdenite was the only sulfide mineral detectable in the older endoskarn material in FC-07. Gypsum was detected in FC-2-3A, FC-3-2A and Pile-1. A large diffuse peak was present at 7° 2θ angle in FC-07, indicating significant clay content indicative of aluminosilicate weathering. Smithsonite (ZnCO3) was detected in FC-3-2A. No P bearing mineral was detected in any sample by XRD. Several sulfide bearing minerals were identified by SEM-EDS to supplement those identified with XRD (Table 4.1). A Ca-P-F bearing mineral, probably apatite, was identified by SEM-EDS in FC-2-2B. There was no systematic trend in sulfur or trace metal content between the two grain sizes analyzed by XRF. Therefore XRF results are averaged between the two grain sizes and presented as <1.18 mm grain size fraction (GSF) by using the following equation: wt% M = (GSF<0.3) x ((GSF<1.18) x (wt% Mfg))-1+ (GSF 0.3-1.18) x ((GSF<1.18)x(wt% Mcg))-1 (2) Where wt% M is the weight % of a given element, Mfg is the elemental content of the fine grain fraction, and Mcg is the elemental content in the coarse grained fraction. The results for elements of interest are listed in Table 4.2. In general the exoskarn material (Pile-3, FC-3-2A) had the highest S content; the intrusive material (Pile-2, FC-2-2B and FC-2-3A) had intermediate S content, while the marble hornfels material (Pile-1, FC-1-3A) and endoskarn (FC-07) had the lowest sulfide content. Calcium was most abundant in the marble hornfels material, due to the high calcite content. Significant Ca is present in the exoskarn, which contains some calcite, while little Ca is present in the intrusive waste rock. Phosphorus was in similarly low wt% for all 91  rock types. Iron can be used as a proxy for acid generating iron sulfides, but is also present in garnets, such as andradite identified in FC-2-3A during XRD analysis. Iron is most abundant in the exoskarn material, while its abundance in the marble hornfels and intrusive waste rock was roughly 2 wt% with the exception of FC-2-3A which had iron content of 5 wt%. The GSD results show that in general, Pile-1 material is coarser grained then Pile-2 (intrusive), Pile-3 (exoskarn), and FC-07 (endoskarn) material, which have similar GSDs. Imaging of the sulfide mineral surfaces with the FEG-SEM-EDS showed that silica-rich iron oxyhydroxide; probably ferrihydrite, was the dominant weathering product on iron sulfide minerals from the waste rock piles (Figure 4.1). In contrast, the dominant weathering product found on FC-2-3A was a porous schwertmannite. Two additional iron-oxyhydroxides were identified in a FIB cut. A silica rich iron oxyhydroxide layer beneath the schwertmannite and a pure iron oxyhydroxide bottom layer (Figure 4.2). Fe-XAFES identified schwertmannite and lepidocrocite as the dominant iron weathering products with minor amounts of K-jarosite near the detection limit (see Chapter 2). A sample of molybdenite from Pile-2 had no precipitates forming on its surface (Figure 4.3). A sample of Bismuth from Pile-3 was thoroughly weathered, covered with a Bi:O containing coating (Figure 4.4Error! Reference source not found.). A pyrite sample from Pile-1 was coated with an oxide, probably ferrihydrite, and associated with isolated bacteria which had precipitates formed on the cell wall (Figure 4.5). 4.4.2 Geochemistry 4.4.2.1 pH and Buffering Minerals The effluent from all experimental waste rock piles and field cells has remained >7, with two notable exceptions. The pH of FC-2-3A dropped from 8.2 to 6.1 within three months of installation. The pH has remained between 6 and 6.5 since then. The pH of FC-3-2A dropped from 7.6 to 4.5 within the first 27 days which was accompanied by a spike in the total Fe concentration. The pH subsequently rebounded to above 7 three months later where it has remained. This is interpreted as the flushing of acidic oxide minerals such as jarosite or siderite. The geochemistry as monitored in the field reflects a 1:1 molar ratio of Ca2+ to SO42- in all samples except for FC-2-3A, which has a ratio of 1:1.5. The 1:1 molar ratio is indicative of an 92  open calcium carbonate buffered system. Gypsum precipitation does not affect this ratio as it removes both species from solution at a 1:1 ratio. No carbonate minerals were detected in FC2-2B or Pile-2 intrusive material in this study, however prior studies have identified it in the intrusive material as discussed above. The dissolution of the Ca-rich plagioclase anorthite can produce the 1:1 molar ratio of Ca to SO42- observed in the drainage of Pile-2 and FC-2-2B. However, it is unlikely that anorthite would be the sole buffering mineral if calcite was depleted. Therefore calcite is considered the dominant buffering mineral in these two systems. The 1:1.5 Ca2+ to SO42- ratio and lack of carbonates in the XRD spectra in FC-2-3A indicates that a Ca-bearing silicate, likely a Ca-plagioclase species is the dominant buffering mineral as opposed to other quickly weathering silicates such as biotite or chlorite which do not contain Ca. 4.4.2.2 Sulfate Loadings The sulfate concentrations in all field cells show high seasonal variability (Figure 4.6). Highest concentrations are observed at the beginning of the wet season due to the flushing of sulfate accumulated in the dry season. Similarly, the lowest sulfate concentrations were observed at the end of the wet season when the field cells have been well flushed. In contrast to the sulfate concentrations, the highest sulfate loadings take place during the middle and end of the wet season showing a strong correlation to drainage volume. In general, yearly sulfate loadings in the field cells are decreasing with time (Table 4.3). The sulfate concentrations and loadings are controlled by gypsum precipitation in the experimental waste rock piles and cannot be considered an accurate proxy of weathering rates (Bay et al., 2009). The only field cells to approach gypsum saturation with an SI of > -0.5 are FC-3-2A and FC-2-3A (Figure 4.7). The SI of FC-3-2A remains at ~-0.5 for the duration of the first wet season and half way through the second. After which the SI of gypsum begins to steadily decrease. FC-2-3A is near gypsum saturation briefly during the first wet season, but the SI subsequently drops, reaching as low as -2 during at the end of the second wet season. Some gypsum has been identified in the XRD analysis of FC-3-2A and FC-2-3A from samples taken in the middle of the wet season. Due to the low gypsum SI at the end of the second wet season and the kinetically fast nature of gypsum dissolution and precipitation, yearly sulfate loadings are considered a good qualitative 93  indicator of weathering rates in the field cells. They may however provide a larger underestimate of sulfate loadings in FC-2-3A and FC-3-2A than in the other field cells. Schwertmannite was identified in FC-2-3A. Assuming only pyrite oxidation is taking place, and all the iron oxidized in the pyrite is precipitated as schwertmannite, 11% of the sulfur would be precipitated in schwertmannite. Therefore sulfate loadings from FC-2-3A can be considered a larger under-estimate then in the other field cells. 4.4.2.3 Metal Loadings Metal concentrations and loadings show a similar seasonal variability to sulfate loadings, indicating that the primary mechanism of metal release is sulfide oxidation (Table 4.3). The highest mass loadings were at the end of the wet season, and show a strong correlation to drainage volume. The highest metal concentrations in both piles and field cells are in the beginning of the wet season when the pore water that has incubated over the summer is washed out (Aranda et al., 2009; Bay et al., 2009). The primary metals being leached from the field cells and pile is Mo and Zn. The highest Mo concentrations observed were 57.15 mg/l and 22.3 mg/l in FC-07 and FC-2-2B respectively. Phreeqc results demonstrate that ZnMoO4 and CaMoO4 are oversaturated in these field cells indicating that precipitation is taking place (Figure 4.8, Figure 4.10), but is kinetically inhibited from significantly lowering Mo concentrations (Conlan, 2009). FC-07 leached the lowest concentrations of Mo during the first wet season (<2.3 mg/l). Conversely, Mo concentrations from FC-07 in the second wet season increased significantly to >40 mg/l. To date, FC-07 Mo concentrations have remained above 10 mg/l. Significant concentrations of Zn were leached from FC-1-3A, FC-3-2A, and FC-2-3A (14.22, 41.4, and 61.66 mg/l, respectively). Smithsonite is near saturation in FC-3-2A and FC-1-3A (Figure 4.9, Figure 4.12). In addition, smithsonite was identified in XRD analysis of FC-3-2A material indicating that it is precipitating and may be a significant mechanism of Zn attenuation in alkaline pH field cells, but not important in the sub-neutral pH of FC-2-3A despite higher Zn concentrations. FC-2-3A leached high concentrations of Zn, despite XRF analysis showing that relatively little Zn is present in the field cell, and sphalerite not being detected during XRD analysis. This shows that the leaching of Zn is more sensitive to pH then oxidation rates or material composition. FC-2-3A was the only field cell to leach significant quantities of Cu and 94  reached a peak concentration of 38 mg/l at the beginning of the 2008 wet season. Phreeqc analysis indicates that both malachite and azurite are controlling the Cu concentration in this field cell (Figure 4.11). 4.4.2.4 Nutrient Availability The highest concentrations of fixed N (ammonia, nitrite, and nitrate) occurred shortly after field cell installation, and sharply decreased within the first three months (Table 4.4). Nitrate is the dominant nitrogen species in all field cells except for FC-07. Nitrite composed a minor component of the biologically available N in solution. Very high concentrations of nitrogen concentrations were found in the experimental waste rock piles (>50 mg/l), while variable amounts were found in the field cells. Total phosphorus was consistently below the detection limit of 0.3 mg/l. Phosphate measurements, which have a lower detection limit, indicate that the concentrations are decreasing in time in FC-07. No phosphorous minerals were identified during XRD analysis, and XRF results show that P is in similarly low concentrations (0.070-0.026 wt.%) in all rock types. Apatite was identified in FC-2-2B during SEM-EDS and is probably the primary P source. To provide insight into why concentrations of dissolved phosphate consistently remain below detection limits, a Phreeqc model was constructed to predict the solubility of P, as controlled by CaHPO4 precipitation (Figure 4.13). The maximum concentration of dissolved P is predicted to remain < 0.05 µg/l by this precipitation reaction. 4.4.3 Microbiology MPN results estimated total microbial populations between 106 to 108 bacteria/g of washed sediment (Table 4.5). Acidophilic bacteria were only culturable from Pile-2, and FC-23A. FC-2-3A had a higher number of acidophilic bacteria then neutrophilic bacteria despite a circumneutral pH. All cultured acidophilic iron and sulfur oxidizing bacteria were capable of oxidizing both sulfur and iron, which is indicative of the species Acidithiobacillus ferrooxidans as opposed to other common acidophiles (e.g., Acidithiobacillus thiooxidans, Leptospirillum ferrooxidans). For a more detailed discussion of this sample see Chapter 2. . 95  4.4.3.1 Estimating Total Microbial Populations In order to compare the size of the microbial populations in the various field cells to the geochemistry of the drainage, it is necessary to convert the estimates of bacteria per gram wash sediment to bacteria per gram of waste rock. To do this, results from the MPN technique, Live/dead Baclight™ method, and the GSD must be integrated. A large percentage of the total microbial population is not alive in the Pile-3 and FC-3-2A samples. Therefore it is necessary to correct the MPN estimates for the % dead to produce a total microbial population per gram of fine-grained sediment using the following equation:  ((% dead) x (MPN total)) + (MPN total) = Total Bacteria / (g wash sediment)  (3)  The estimate of microbial numbers produced by the above equation is only valid for grain sizes capable of passing through the pipette tip (<1 mm). This is the most important grain size fraction as it contains a large percentage of the reactive surface area (Strömberg and Banwart, 1998). To compare microbial numbers to the geochemistry of waste rock, an estimate of bacteria/kg waste rock must be produced. To do this the grain size distribution must be taken into account. A qualitative estimate of the bacteria per kg can be produced by the equation:  (% material <1mm) x (Total Bacteria / (g wash sediment)) = Estimated Bacteria / (g waste rock) (4) Where % material <1mm is the fraction of the GSD smaller than 1 mm. Calculations were also done using the 2 mm grain size fraction for comparison. The results are presented in Table 4.5, note that whether the 1 mm or the 2 mm grain size fraction is selected the trends change relatively little. Equation 2 produces a qualitative estimate of microbial numbers in the waste rock.  The estimate produced by equations 3 and 4 are a comparative metric to relate  differences in microbial populations to the geochemical characteristics of their respective environments.  96  4.4.3.2 Microbial Correlations to Sulfate Loadings An exponential relationship exists between microbial populations in field cells and yearly sulfate loadings (Figure 4.15). To verify that this relationship is not an artifact of the data processing done in equation 3 and 4, the raw MPN data (bacteria per gram wash sediment) is graphed against sulfate loadings in Figure 4.16.  In Figure 4.16 the same exponential  relationship remains, and is actually has a larger R2 then the processed data in Figure 4.15. Therefore, this exponential relationship is not an artifact of data preparation. 4.4.3.2.1 Thermodynamic Limitations A strong correlation between the number of microbes and weathering rates during the wet season of sampling implies that the size of the microbial populations is linked to substrate availability. This suggests that thermodynamic constraints are important in determining the microbial population. Although the microbial populations estimated in the preceding section are only qualitative, the apparent correlation between substrate supply and microbial populations compels investigation of thermodynamic equilibrium between substrate and biomass. For a given environment, the maximum microbial population is reached when the available energy is consumed solely for cell maintenance, which can be estimated by the cell maintenance coefficient (ME). The ME is the minimum amount of energy required to maintain the vital functions of a microorganism, independent of growth conditions (Pirt, 1965).  A  species independent equation for ME can be used to estimate this value (Tijhuis et al., 1993; Harder, 1997; Hoehler, 2004): ME=Ae-Ea/RT  (5)  Where ME is in units of kJ (g dry wt.)-1 d-1, R is the universal gas constant, T is Kelvin, Ea is the activation energy (69.4 kJ mol-1) (Tijhuis et al., 1993; Harder, 1997), and A is an empirically derived constant (Harder, 1997). experiments to be 8.23 x 10  12  The value of A has been determined in chemostat  kJ mol-1 day-1 for aerobic bacteria. There are a number of  uncertainties in this equation, and it has only been applied to the natural environment with limited success (Kolb et al., 2005). There is evidence that the value of the A coefficient may be several orders of magnitude smaller than that determined by Harder (1997) in the natural environment (Hoehler, 2004). The parameters in this equation have been defined during 97  growth with an abundance of mineral nutrients. Scarceness in nutrients has been found to lead to an increase in ME (Thingstad et al., 1987).  Despite the numerous difficulties, the strong  correlation between substrate availability and size of microbial populations is compelling evidence for a ME related explanation for the observed trends. Using these experimentally defined variables for equation 5, a maintenance energy of 474 J (g dry wt.)-1 d-1 is estimated for aerobic bacteria at 5°C (Antamina average temperature). This can be related to the available energy in sulfur produced in the field cells to produce an estimate of the maximal potential dry weight of the biomass (Harder, 1997; Kolb et al., 2005): -ΔG° S (d[S]/dt) * ME-1 = biomass (g dry wt. (bact)-1)  (6)  Where d[S]/dt is moles of substrate produced per day, ΔGS is Gibbs free energy released per mole of substrate. Iron and a variety of intermediate sulfur species are available as substrate in sulfidic mine waste.  The dominant metastable intermediate sulfur compounds formed from sulfide  oxidation under abiotic neutral pH conditions are S2O32- and S4O62-, while approximately 40% of sulfur released goes straight to the stable end product SO42- (Goldhaber, 1983). The complete oxidation of these compounds, and the Gibbs free energy yielded by those reactions are listed in Table 4.6. Note that the oxidation of these species can involve multiple steps, often resulting in SO32- and S0 formation (Kelly, 1999). Iron oxidation yields relatively little energy compared to the oxidation of sulfur compounds. The iron oxidizing bacteria isolated in MPNs were capable of both Fe2+ and S0 oxidation. The metabolic process of Fe2+ oxidation is primarily a means of increasing the supply of the primary substrate, intermediate sulfur species. The presence of bacteria capable of gaining energy from iron oxidation in FC-2-3A does not lead to a major drift from the exponential relationship observed between the biomass and the sulfate loadings in the field cells and can be excluded from thermodynamic calculations. The sulfate loadings for the month prior to sampling were used in equation (6) to determine the sulfate loading rate contemporary to sampling. It is necessary to use recent sulfur loadings as the ME is typically measured on a scale of days when applied in previous studies, yearly averages may not be reliable. It is then assumed that 60% of sulfate measured in the outflow was released from sulfide minerals as S2O32- or S4O62- and available as a microbial 98  growth substrate (Goldhaber, 1983). Note that the oxidation of these substrates leads to several intermediate products, such as insoluble S0. The thermodynamic data being used in Table 4.6 is calculated for the complete oxidation of these compounds.  Inserting these  parameters into equation 6 produces a prediction of the dry weight of the maximal microbial biomass. The dry weight of bacteria can then be related to the number of bacteria by the equation: Biomass / (g dry wt.(bact-1)) = Bact g-1 theoretical  (7)  Assuming a g dry wt.(bact)-1 of 3x10-13 g, estimated microbial populations can be produced (Enders et al., 2006).  Potential energy provided by iron oxidation can be ignored as it yields  only a small amount of energy compared to sulfur oxidation as discussed above. The results are presented in Table 4.7. This method greatly overestimates the microbial populations in most field cells. The results closely predict the population of FC-3-2A and to a lesser extent FC-07. The results indicate that FC-1-3A, FC-2-2B, and FC-2-3A are still growing, while populations in FC-07 and FC-3-2A have reached the thermodynamic limits of microbial growth. The ME approach produces a linear relationship between sulfate loadings and maximal microbial populations, and does not reproduce the exponential relationship observed. Therefore parameters other then maintenance energy are important in determining microbial populations.  4.5 Discussion 4.5.1 Growth and Size of Microbial Populations An exponential relationship exists between microbial populations in field cells and yearly sulfate loadings, while a linear relationship exists between the sulfide mineral content, and the yearly sulfate loadings. This relationship is consistent in samples of various age, and rock types. This exponential relationship is similar to a microbial growth curve; however, the varying age of the waste rock indicates that growth kinetics are not the controlling factor in determining the size of microbial populations. There are several possible non-exclusive explanations that have been postulated for this observed relationship. (1) Thermodynamic calculations show that microbial populations in FC99  1-3A, FC-2-2B, and FC-2-3A are still growing, while FC-07 and FC-3-2A have reached a maximum population.  Assuming the ME approach is correct, growth kinetics may be important in  determining the microbial populations in FC-1-3A, FC-2-2B and FC-2-3A. In which case the observed exponential relation between sulfate loadings and microbial populations may be a coincidental, and a linear relationship will be reached with time.  However the strong  correlation between sulfate loadings and microbial populations is difficult to ignore and attribute to chance.  (2) A linear relationship between sulfate loadings and microbial  populations in fact does exist, but a solubility control not evident in the drainage geochemistry is reducing the sulfate loadings in the more reactive field cells. Gypsum has been identified in FC-2-3A and FC-3-2A despite the outwash water not being saturated with gypsum. Local zones of gypsum saturation may be leading to significant under-estimates of sulfide weathering. However, it is difficult to imagine that potential solubility controls could affect sulfate loading estimates by orders of magnitude required to produce a linear relationship. (3) The larger populations of bacteria could extract energy from the oxidation of sulfide to sulfate with greater efficiency. Several intermediates are formed in the oxidation of sulfide to sulfate, including elemental sulfur, thiosulfate, trithionate, tetrathionate, and pentathionate. The larger populations of sulfur oxidizing bacteria may be extracting energy from more steps of the sulfide to sulfate oxidation process. (4) Larger microbial populations can form macrostructures using extracellular polymeric substances, such as biofilms and pod structures (Norlund et al., 2009). These macrostructures can regulate geochemical environments and lead to syntrophic benefits, such as micro-scale geochemical gradients and nutrient cycling.  (5) Larger microbial  populations may become more effective at scavenging biologically essential nutrients, such as P, from the host rock. It is impossible to definitively answer the question of what has led to the exponential relationship between microbial populations and weathering rates. However, this observed relationship has several implications concerning, time required for colonization, constraints on microbial growth, and the geochemical role of microbiology in neutral pH waste rock  100  4.5.1.1 Initial Colonization of Waste Rock The oldest waste rock material (FC-07) has the smallest total population, whereas the some of the youngest waste rock (Pile-3) has the largest population. The rapid pace at which populations of younger, more reactive waste rock have exceeded those of the older, less reactive waste rock indicates that the populations are not strongly dependent on growth kinetics. The waste rock of FC-07 has leached higher concentrations and masses of sulfate over its lifetime than FC-1-3A and FC-2-2B, indicating that microbial populations are more dependent on weathering rates contemporary to sampling than total sulfate loadings.  This  also implies that the microbial population of FC-07 has decreased since its peak in sulfate loadings experienced in the 2003-2004 wet season. These results indicate that the microbial populations rapidly grow to reach a mass which is proportional to contemporary substrate release, and then decrease as the host rocks reactivity diminishes. 4.5.1.2 Thermodynamic Constraints The correlation between sulfate loadings and microbial populations strongly suggest a thermodynamic control. However calculations based on the ME requirements of bacteria fail to predict the size microbial populations and exponential nature of the relationship. Although there are numerous uncertainties in applying the ME equation to this system, it does indicate that microbial populations are not controlled solely by substrate availability such as nutrient availability, temperature, moisture content,  toxic metals, and mineralogy.  The large  uncertainties in the ME calculations, and the limited success that has been met in applying this method in past field studies make this results based on ME calculations highly speculative (Kolb et al., 2005). 4.5.1.3 Nutrient Availability High concentrations of fixed N observed in the first year can be attributed to the washing out of explosive residues from the blasting of ammonium nitrate fuel oil (ANFO) used in mining (Morin and Hutt, 2009). Nitrogen is not an essential nutrient for chemolithotrophic bacteria as they are capable of fixing their own N from atmospheric N2.  However nitrogen  fixation is an energy intensive activity, high concentrations of nitrogen observed shortly after 101  the waste rocks deposition may stimulate microbial growth and colonization the waste rock. This is supported by a correlation between microbial populations and observed nitrogen concentrations. The concentrations total P typically remained below the detection limit of 0.3 mg/l with few exceptions. The high sensitivity PO43- data that is available indicates that its concentration is decreasing with time. Phosphorous is a critical nutrient for chemolithotrophic bacteria, which must be obtained from the host rock. No P-bearing silicate minerals were identified during XRD analysis; however SEM-EDS identified apatite in FC-2-2B. XRF results indicate that a minor amount of P is present in the waste rock. There is no clear correlation between wt% P and microbial population. The release of P from silicate minerals will be inhibited in a calcium mineral buffered mine waste system in two ways. (1) Silicate mineral weathering rates are strongly related to pH, and typically weather relatively slowly at alkaline compared to acidic conditions (Schooner and Stumm, 1986). (2) The P that is released may be quickly removed from solution due the low solubility of Ca-phosphate phases at neutral pH, such as (Ca5(PO4)3OH) and CaHPO4 (Feng et al., 2000). The low concentrations of P in the outwash and inferred solubility controls indicate that the microbial populations may have been limited by P availability. 4.5.1.4 Microbial Populations in Field Cells The microbial populations within the piles were found to be higher than in field cells composed of the same rock types. This may be due to the fact that the field cells temperature closely resembles ambient air leading to large day to night temperature swings typical of alpine climates.  Waste rock piles provide a more stable environment by protecting microbial  populations from large temperature variations and prevent excessive drying out during the dry season. The microbial communities in the field cells were similar in structure to the piles. Piles and field cells tended to be dominated by neutrophilic bacteria capable of oxidizing sulfur. The Pile-2 intrusive material was more amenable to colonization by acidophilic bacteria, as FC-2-3A and Pile-2 were the only samples with culturable acidophilic bacteria. These results are similar to a study conducted by Ardau et al., (2009), in which microbial communities in kinetic cells were smaller, but dominated by similar phenotypes as field samples of the same material. The 102  relationship between microbial populations and sulfate loadings found in the field cells indicates that the piles are weathering at a faster rate in the vicinity that the microbial sample was taken from than the corresponding field cell material. 4.5.1.5 Acidophiles at Neutral pH There is little variation in the chemical or mineralogical composition between FC-2-3A which had abundant acidophiles, and the other intrusive waste rock samples (FC-2-2B and Pile2) (Table 4.1, Table 4.2).  The geochemistry suggests that minor amounts of calcite are  maintaining an alkaline pH in Pile-2 and FC-2-2B, while any calcite buffering potential in FC-22B. Compositionally the most notable difference is FC-2-3A had a considerably higher iron and sulfide content, and garnets were relatively abundant in FC-2-3A compared to the other intrusive material. Andradite is a relatively quickly weathering garnet; however, it can become armored with iron-oxyhydroxides similarly to iron-bearing sulfide minerals (Velbel, 1999). Caplagioclase is thought to be the dominant buffering mineral in FC-2-3A as discussed above. The pH at which silicate minerals can buffer a system is determined by the rate of acid generation and neutralization (Schooner and Stumm, 1986). It seems likely that the lower pH in FC-2-3A has lead to an environment more accommodating to the growth of acidophiles, and alkaline pHs seem to be inhibiting their growth. It is also of note that Mo is relatively low in the material composing FC-2-3A as compared to the other intrusive sample. Molybdate is toxic to iron oxidizing bacteria and will inhibit their establishment in both FC-2-2B and Pile-2. The concentration of molybdate has reached as higher than 20 mg/l in both FC-2-2B and Pile-2, while the highest Mo concentration seen in FC-2-3A is 0.06 mg/l (Figure 4.17) (Dockrey et al., 2010b). The presence of iron oxidizing bacteria in FC-2-3A did not lead to a deviation from the linear relationship between sulfide content and sulfate loading of the 2008-09 wet season observed in the field cells (Figure 4.14). However, it did lead to a rapid peak, and then decrease in weathering rates between the first two wet seasons (Table 4.3). The inability of the iron oxidizing bacteria to more effectively oxidize the waste rock in the 2008-09 wet season could be due to sulfide mineral armoring and/or exhaustion of easily weathered sulfide minerals in the first wet season. A decrease in sulfate loadings of 43% was observed in FC-2-3A between the 103  first and second wet season the FC was installed. This is analogous to past experiments of inoculation of kinetic cells with A. ferrooxidans. Inoculation of kinetic cells with iron oxidizing bacteria leads to peak weathering rates being realized more rapidly (Morin and Hutt, 1997). While this may be due to the flushing of soluble sulfate minerals formed prior to field cell installation, no other field cell in this study experienced such a marked decrease in year to year sulfate loadings. 4.5.2 Geochemical Implications 4.5.2.1 Armoring Sulfur oxidizing bacteria catalyze sulfide mineral oxidation by cleaning sulfide mineral surfaces (Dopson and Lindstrom, 1999). By preventing mineral armoring these bacteria may increase the time span over which neutral pH mine waste leaches metals. Therefore their effect may not be seen in fresh waste rock, but only over time as sulfide mineral surfaces become armored. The bacterium featured in Figure 4.5 is serving as a nucleation site mineral precipitation, which may eventually encapsulate the bacterium. This bacterium does not appear to be entirely preventing mineral armoring in its vicinity; however the precipitates are less thick in its vicinity then in the upper left of the image. Note the difference in texture between the iron oxides featured in Figure 4.1 and Figure 4.5. Although these sulfide minerals are from different piles (Pile-1 and Pile-3) they are carbonate-buffered, and pH is the dominant control of ferric iron precipitates. The iron oxides in the vicinity of the bacteria are needle like in texture (Figure 4.5), as opposed to the solid crust forming over the pyrite sample from Pile-3 in Figure 4.1. Presumably the solid crust will be more difficult for oxidants to diffuse through then the needle like precipitates, leading to more effective armoring of the sulfide mineral. The exponential relationship between microbial populations and sulfide content indicates that microbiology is increasingly effective at cleaning sulfide mineral surfaces in more quickly weathering waste rock. The relative importance of armoring by intermediate sulfide species compared to insoluble metal oxides is unknown, and undoubtedly varies depending on the metal cation in the sulfide mineral. However, the fact that these bacteria catalyze sulfide mineral oxidation by slowing armoring implies that they will increase the time span required for 104  pacification of reactive sulfide surfaces. This implicates that the sulfide minerals will be more thoroughly oxidized in the exoskarn (Pile-3 and FC-3-2A) before they are pacified by metal oxide precipitation. The variation in structure of iron precipitates observed using FEG-SEM-EDS shows that the mineralogy of iron precipitates will enhance or inhibit mineral armoring. The fibrous texture of schwertmannite, and porosity observed within the schwertmannite indicate that schwertmannite is less effective at armoring sulfide mineral surfaces then ferrihydrite. The formation of acidic micro-environments by acidophilic iron oxidizing bacteria promotes the formation of schwertmannite as discussed in (Dockrey et al., 2010a). Schwertmannite tends to appear in mine waste environments when the pH drops below 6.5 (Bigham et al., 1996). 4.5.2.2 Metal Leaching Metal leaching is largely controlled by oxidation rates and pH in the Antamina waste. For instance the circumneutral pH FC-2-3A is leaching much higher concentrations of Zn than any other field cell which are at an alkaline pH, despite a much higher Zn content in FC-3-2A, FC-1-3A and similar Zn contents in FC-07 and FC-2-2B (Table 4.2). The concentration of Zn in calcite buffered field cells is controlled by smithsonite precipitation in FC-3-2A and FC-1-3A. Smithsonite has been identified by XRD in FC-3-2A, and is at or below saturation in FC-3-2A and FC-1-3A. This strongly indicates that this precipitate is preventing significant concentrations of Zn from leaching from alkaline pH field cells. The highest Zn concentrations are in FC-2-3A, the sub-neutral pH and low alkalinity are preventing smithsonite precipitation, allowing much higher concentrations of Zn to develop. Azurite and Malachite precipitation is strongly pH dependent, and appears to be controlling Cu concentrations in FC-2-3A. In the alkaline pH field cells Cu is insoluble due to the low solubility of Azurite and Malachite. In addition it is removed from solution by sorption.  Metal molybdates are ineffective at controlling molybdenum  concentrations as powellite and ZnMoO4 are far above saturation. The continuous production of Mo from FC-07 indicates that no mechanisms exist to sufficiently armor molybdenite. This is not surprising as both Mo and S are soluble at neutral pH and metal molybdate precipitation is kinetically inhibited. The lack of any precipitates forming on the surface of the molybdenite sample from Pile-2 featured in Figure 4.3 supports this conclusion. 105  As there was relatively little variation in microbial community structure in the field cells it is difficult to relate variability in microbiology to variability in metal leaching patterns in most field cells. The only sample colonized by acidophilic bacteria had a pH accommodating to Zn and Cu mobility making any comparison with alkaline pH field cells difficult. Acidophilic iron oxidizing bacteria will preferentially attach to iron bearing sulfide minerals, both chalcopyrite and to a lesser extent sphalerite contain iron, while avoiding non-iron bearing sulfides, and sulfides containing toxic metals such as molybdenite. It can be speculated that Cu and Zn will be preferentially leached as compared to non-iron bearing sulfide minerals once acidophilic iron oxidizing bacteria become established, but the current data set cannot confirm this. The sample of bismuthinite analyzed from Pile-3 was heavily weathered, and thick Bi bearing precipitates had formed on its surface.  If the pH drops and becomes more  accommodating to Bi solubility, a large release Bi can be expected. The lack of bacteria on this heavily weathered bismuthinite sample indicates that bismuthinite weathering proceeds at neutral pH without microbial mediation.  4.6 Conclusion Microbial colonization and growth in Antamina waste rock takes place rapidly after deposition. Large populations can be reached within the first 1.5 years, the sizes of which are primarily controlled by nutrient and substrate availability. However the physical factors of temperature and moisture content appear to exert an influence as seen be larger microbial populations in waste rock piles than in field cells composed of the same material.  An  exponential relationship between sulfate loadings and microbial populations was found. This indicates that little biological catalysis is taking place in slowly weathering waste rock as compared to relatively rapidly weathering waste rock. Maintenance energy calculations of the thermodynamic limitations to microbial growth fail to produce the exponential trend observed in the field cells. Therefore either growth kinetics are controlling the microbial population of some field cells, or symbiotic relationships present in larger microbial communities allow greater efficiency in substrate use. The nutrient phosphorus was similarly low in all waste rock material, and it is relatively insoluble in calcite buffered waste rock due to Ca-phosphate 106  precipitation. Assuming the size of microbial populations is proportional to biological catalysis of sulfide oxidation, rapidly weathering waste rock may be strongly affected by biological sensitivities, such as toxic metals, temperature and nutrient depletion, while slowly weathering waste rock would be relatively unaffected. A field cell with a mixed population of acidophiles and neutrophiles did not deviate from the exponential relationship between microbial populations and sulfate loadings. However, a unique weathering trend was produced in this field cell, the sulfate loadings peaked in the first wet season and was significantly lower in the second wet season; this result is analogous to inoculation of iron oxidizing bacteria in kinetic cells which leads to a rapid peak in sulfate loadings compared to non-inoculated material. The microbial populations in the field cells tend to have similar phenologies but smaller total populations than the much larger experimental waste rock piles. This is likely due to the day to night temperature swings experienced in the field cells, more stable moisture content, and the longer time scales required for nutrient depletion in the larger waste rock piles. Although microbial communities affect sulfide weathering rates, the characteristics of which metals are leaching is controlled by pH, and precipitation reactions.  107  4.7 Figures  Figure 4.1. Pitted pyrite sample covered with non-porous iron oxide from Pile-3. Scale bar is 3 µm.  Fe:O:Si:S  Figure 4.2. Precipitates found covering sulfide sample and EDS composition, from FC-2-3A. Note the porosity in the upper schwertmannite layer. Scale bar is 1 µm.  Fe:O:Si  Fe:O 108  Figure 4.3. Molybdenite surface from Pile-2. Note that although mineral has broken apart no precipitates have formed on its surface, indicating that no armoring is taking place, or the mineral is not being weathered. Scale bar is 3 µm.  Figure 4.4. Bismuthenite sample from Pile-3. This sample is coated with Bi:O bearing precipitate featured in this image. Few fresh bismuthenite surfaces were observed. Scale bar is 3 µm.  109  Figure 4.5. Pyrite sample from Pile-1 covered with Fe:O:Si bearing precipitate. Note the mineralized bacteria in the center of the image. Scale bar is 1 µm.  110  40 1000 20 0 Jan-04  May-05  Oct-06  2000  FC-3-2A  Feb-08  60  SO4  Jul-09  FC-2-3A  2000  40  40  1000  1000 20  20 0 Sep-07 60  Zn, Cu, Mo mg/L  Zn  0 Apr-08  Oct-08  0  Sep-07 Jan-08 Apr-08 Jul-08 Oct-08 Feb-09 May-09  May-09  FC-1-3A  0  2000  60  FC-2-2B  2000  40  40  1000  1000 20  20 0 Aug-06  0 Jan-08  May-09  0 Sep-07  SO42- mg/L  Zn, Cu, Mo mg/L  60  Mo  SO42- mg/L  0 Sep-02  Cu  2000  FC-07  SO42- mg/L  Zn, Cu, Mo mg/L  60  0 Apr-08  Oct-08  May-09  Figure 4.6 Chemistry of field cell drainage.  111  SI Gypsum  SI Gypsum 0 -0.2 -0.4 -0.6 -0.8 -1 -1.2 -1.4 -1.6 -1.8 -2  Figure 4.7. Gypsum saturation in FC3-2A as modeled by Phreeqc.  Series2 FC-3-2A si_Gypsum FC-2-3A  4/28/07 11/14/07 6/1/08 12/18/08 7/6/09 Axis Title  FC-07  2.00  SI  1.50 1.00  si_ZnMoO4 si_CaMoO4  0.50 0.00 9/1/02  1/14/04  5/28/05  10/10/06  2/22/08  7/6/09  11/18/10  Date  Figure 4.8 Solubility indices of metal molybdates in FC-07 as modeled by Phreeqc. 112  FC-2-2B  2  0.5  1 0  FC-32A SI smithsonite  -1  SI  SI  0 si_ZnMoO4 si_CaMoO4  -0.5  -2 -1  -3 11/14/07 2/22/08  6/1/08  9/9/08  12/18/08 3/28/09  7/6/09  Date  Figure 4.10. Solubility indices of metal molybdates in FC-2-2B as modeled by Phreeqc.  11/14/07 2/22/08  6/1/08  9/9/08  12/18/08 3/28/09  7/6/09  Date  Figure 4.9. Solubility indices of smithsonite (ZnCO3) in FC-3-2A as modeled by Phreeqc. Note that smithsonite was detected in XRD analysis of this field cell.  113  0.50  FC-1-3A SI smithsonite  FC-2-3A  6 4 2  0.00 SI  SI  si_Malachite  -0.50  0  si_Azurite  -2 -4  -1.00 3/24/06 10/10/06 4/28/07 11/14/07 Date  6/1/08  12/18/08  7/6/09  -6 4/28/07  11/14/07  6/1/08  12/18/08  7/6/09  Figure 4.12. Solubility Indices of smithsonite (ZnCO3) in FC-1-3A as Figure 4.11. Saturation indices of effecting copper solubility in FC-2-3A, as modeled by Phreeqc. modeled by Phreeqc.  114  5.E-05  P mg/L  4.E-05  FC-3-2A Modeled maximum P concentration  3.E-05 2.E-05 1.E-05 0.E+00 11/14/07 2/22/08 6/1/08  9/9/08 12/18/08 3/28/09 7/6/09 Date  Figure 4.13. The modeled maximum concentration of dissolved P which can be reached before CaHPO4 precipitation.  115  Sulfate loadings vs. wt% S 7.00  6.00  y = 0.011x - 1.0 R² = 0.95  5.00 wt% S grain sizes <1.18mm  FC-32A  4.00  3.00  FC-23A  2.00  FC-22B  1.00  FC-07 FC-13A  0.00 0  100  200  300  400  500  600  700  Sulfate loadings mg/Kg 2008-09 wet season  Figure 4.14. Sulfate loadings vs. wt. % sulfide as determined by XRF analysis. 116  5.00E+08  Bacteria per gram waste rock vs. sulfate loading FC-32A  y=  3E+05e0.0098x R² = 0.97  Bacteria/g  5.00E+07  5.00E+06  FC-23A  FC-22B  FC-07  FC-13A 5.00E+05 0  100  200  300  400  500  600  700  Sulfate loading 2008-09 wet season  Figure 4.15. Relationship between the estimated bacteria per gram and sulfate loadings in the field cells. The microbial estimates incorporating the 1mm grain size fraction of the GSD are used in this graph.  117  Unprocessed MPN results vs. sediment vs sulfate loading 5.00E+08  y=  FC-32A  2E+06e0.0085x R² = 0.98  Bacteria/g  5.00E+07  FC-23A FC-13A FC-22B 5.00E+06  FC-07  5.00E+05 0  100  200  300  400  500  600  700  sulfate loading /Kg waste rock  Figure 4.16 MPN results not fixed for % dead or GSD graphed against sulfate loadings. Note that the data processing behind the microbial estimates in Figure 4.15 does not affect the overall trend. 118  25  Toxicity Limit Fe2+ substrate Mo mg/L  20  Pile-2  15  FC-2-3A 10  FC-2-2B  Toxicity Limit S0 substrate 5 0 Nov-07  Jan-08  Feb-08  Apr-08  Jun-08  Jul-08  Sep-08  Oct-08  Dec-08  Feb-09  Mar-09  Figure 4.17 Molybdenum concentrations of field cells and waste rock pile composed of intrusive material. Note that both Pile-2 and FC-2-2B have experienced concentrations that are toxic to acidophilic Fe2+ and S0 oxidizing bacteria. Toxicity limits from Dockrey et al., (2010b).  119  4.8 Tables Mineral Calcite Quartz Phlogopite Talc Orthoclase Albite Oligoclase Andradite Spessartine Grossular Almandine Augite Diopside Pyrite Chalcopyrite Arsenopyrite Pyrrhotite Sphalerite Molybdenite Galena Bismuthenite Apatite Gypsum Smithsonite Magnesite Bernalite Amorpheous  Chemical Formula CaCO3 SiO2 KMg3[(OH,F)2(AlSi3O10] MgSi4O10(OH)2 K[AlSi3O8] NaAlSi3O8 (Na0.5Ca0.5)AlSi3O8 Ca3Fe2(SiO4)2 Mn3Al2(SiO4)3 Ca3Al2(SiO4)3 Fe3Al2(SiO4)3 (Ca,Na)(Mg,Fe,Al,Ti)(Si,Al)2O6 CaMg(Si2O6) FeS2 CuFeS2 FeAsS Fe1-xS (x=0-0.2) ZnS MoS PbS Bi2S3 Ca5(PO4)3(OH,F,Cl) CaSO4*2H2O ZnCO3 MgCO3 Fe(OH)3 ???  FC-1-3A 67.08 2.72 2.44 5.40  Pile-1 52.82 4.84 1.54 0.92 7.16 1.86  8.47  16.50 1.34  6.91 1.37  11.88 0.83  ü 0.20  0.71 0.29  FC-2-2B  FC-2-3A  Pile-2 39.16 4.42  FC-3-2A 9.09 4.06 3.10  Pile-3 2.99 8.61 2.60  39.09 3.54  31.00 3.22  41.46  38.11  40.96  3.59  6.49  11.99  9.61 13.69  8.38 0.92  45.39 1.99  48.24 1.99  FC-07 2.09 3.77 0.86  14.87 14.07  Table 4.1. Results of quantitative XRD given in wt% of sample and minerals identified with SEMEDS. Minerals only detected by SEM are marked with ü.  19.22  0.48 0.79 ü ü 0.02  1.73 2.56 ü ü 0.16  0.29  20.13 8.63 0.92  2.80 11.52 13.03 3.74  5.11  1.10  8.90  0.12 ü  ü 0.59 1.49 3.91  2.54 0.28 0.00  0.69 1.24 3.52  2.62  0.00  0.00  0.00  0.00  0.19  120  Sample FC-1-3A Pile-1 FC-2-2B FC-2-3A Pile-2 FC-3-2A Pile-3 FC-07  Analysis S wt% Si wt% Ca wt% Fe wt% P wt% Zn (ppm) Cu (ppm) Mo (ppm) XRF 0.06 5.97 29.12 1.99 0.03 1338 735 68 XRD 0.76 4.82 30.40 0.64 0 NA NA NA XRF 0.06 8.70 28.99 1.82 0.04 3282 1587 265 XRD 0.68 10.91 27.76 0.39 0 NA NA NA XRF 0.79 36.86 0.97 2.03 0.04 434 4785 2200 XRD 0.59 35.27 0.89 0.51 0 NA NA NA XRF 1.88 32.53 2.62 5.87 0.05 475 9991 887 XRD 2.18 31.18 2.42 5.36 0 NA NA NA XRF 1.21 37.66 1.04 2.17 0.04 157 4304 1674 XRD 0.58 34.24 0.64 0.47 0 NA NA NA XRF 5.85 14.08 17.64 15.08 0.05 28947 16808 78 XRD 6.85 11.57 18.30 14.34 0 NA NA NA XRF 11.25 15.35 10.88 19.69 0.04 5120 6745 92 XRD 8.92 14.31 15.17 18.11 0 NA NA NA XRF 0.33 24.18 11.51 3.45 0.07 479 1442 2712 XRD 0.96 24.95 9.85 3.09 0 NA NA NA  Table 4.2. XRF results and estimated chemical composition based on XRD results. For most major elements the XRF results verify the interpretation of XRD pattern. Iron is typically underestimated when interpreting XRD as compared to the XRF results due to amorphous iron-oxyhydroxides which do not produce detectable peaks.  121  FC-1-3A FC-3-2A FC-2-3A FC-2-2B  FC-07  year 2006-07 2007-08 2008-09 2007-08 2008-09 2007-08 2008-09 2008* 2008-09 2003* 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09  pH high pH low pH average SO4 8.26 7.11 7.76 8.21 7.42 7.78 8.09 7.42 7.85 7.59 4.52 6.84 7.71 7.08 7.32 8.26 6.02 6.64 6.68 5.8 6.18 9.01 7.83 8.24 8.19 6.74 7.88 8.42 7.44 7.96 8.55 7.23 7.94 8.54 7.19 8.12 8.61 7.73 8.19 8.71 7.84 8.19 8.66 7.45 8.12 8.57 7.67 8.14  Zn 34.73 122.36 137.60 514.89 592.32 544.24 312.70 188.11 168.21 81.77 209.59 135.11 119.54 111.76 87.50 57.72  Mo 0.52 2.85 2.56 12.99 14.59 15.32 16.90 0.08 0.28 0.02 0.07 0.06 0.07 0.14 0.05 0.02  Cu 0.005 0.011 0.014 0.009 0 0.001 0.001 1.365 7.076 0.109 12.203 11.978 12.474 13.282 7.821 6.355  0.027 0.087 0.076 0.119 0.119 12.988 12.872 0.003 0.002 0 0 0 0.009 0.010 0.007 0.002  Table 4.3. Field pH values and yearly loadings of field cells in mg/kg waste rock. *FC-07 was installed in the early 2003 and was not exposed for the entire wet season.  122  Sample Designation year 2006-07 FC-1-3A 2007-08 2008-09 2007-08 FC-3-2A 2008-09 2007-08 FC-2-3A 2008-09 2007-08 FC-2-2B 2008-09 20032003-04 2004-05 FC-07 2005-06 2006-07 2007-08 2008-09 2006-07 Pile-1 2007-08 2008-09 2007-08 Pile-2 2008-09 2007-08 Pile-3 2008-09  N high N low N average P-total P -dissolved 5.175 0.070 0.994 0.003 <0.3 0.216 0.033 0.083 NA <0.3 0.101 0.029 0.089 NA <0.3 49.852 0.744 8.413 NA <0.3 0.180 0.053 0.109 NA <0.3 9.657 0.086 1.866 NA <0.3 0.885 0.0247 0.119 NA <0.3 4.029 0.238 1.053 NA <0.3 0.148 0.047 0.100 NA <0.3 22.751 1.693 7.398 0.355 <0.3 16.211 0.323 5.421 0.270 <0.3 0.198 0.008 0.069 0.151 <0.3 0.058 0.033 0.048 0.051 <0.3 0.068 <0.02 0.027 0.006 <0.3 0.016 <0.02 0.013 NA <0.3 0.035 <0.02 0.035 NA <0.3 68.26 0.02 18.79 0.008 <0.3 68.26 0.10 7.01 NA <0.3 21.98 <0.02 2.88 NA <0.3 20.56 1.17 5.63 NA <0.3 7.13 <0.02 1.27 NA <0.3 49.38 0.09 10.05 NA <0.3 26.16 <0.02 2.99 NA <0.3  Table 4.4. Nutrient concentrations in the outwash of the field cells and experimental piles in mg/l. Note that total P stopped being measured in 2007, after which only dissolved P measurements are available which remained below the detection limit.  123  Live/Dead Baclight™ MPN/(g wash sediment) Estimated Bacteria /(g waste rock) Sample Designation % Alive Fe2+ S0 S2O321mm correction 2mm correction 6 FC-07 88.1 0 0 3.59 X 10 9.13 X 105 1.13E X 106 6 6 FC-2-2B 100 0 0 5.97 X 10 1.38 X 10 1.62 X 106 FC-1-3A 95.2 0 0 6.90 X 106 1.09 X 106 1.23 X 106 FC-2-3A 98.6 6.50 X 106 1.11 X 107 6.50 X 106 4.46 X 106 5.20 X 106 Pile-1 75.6 0 0 2.62 X 107 1.83 X 106 1.98 X 106 Pile-2 100 4.90 X 101 0 4.28 X 107 8.48 X 106 9.34 X 106 8 8 FC-3-2A 28.4 0 0 2.05 X 10 1.36 X 10 1.68 X 108 Pile-3 17.2 0 0 2.22 X 108 1.83 X 108 2.39 X 108 Table 4.5. The % of alive bacteria as measured by the Live/Dead Baclight™ technique. Microbial populations as measured by MPN results for each phenotype. The estimated bacteria/(g waste rock) are estimates corrected for the GSD. The 1mm correction is the estimated microbial population using the 1 mm correction, and the 2 mm correction as discussed in the results section. Note that incorporating the GSD affects the absolute numbers, but not the overall trend.  Reaction S2O32- + 2O2 + H2O → 2SO42- + 2H+ S4O62- + 3.5O2 + 3H2O → 4SO42- + 6H+ SO32- + O2 →SO424 Fe2+ + O2 + 4H+ = 4 Fe3+ +2H2O  ΔG° [kJ (mol S or Fe)-1] -369 -311 -258 -27.2  Potential moles CO2 fixed 0.738 0.622 0.516 0.054  Table 4.6. Redox reactions from which energy can potentially be conserved by chemolithotrophic bacteria. The Gibbs free energy released from each reaction, and the potential moles of CO2, which can be fixed as biomass per mole substrate.  124  Field Cell  Substrate  Bact. g-1 theoretical  (Bact. g-1 theor)/ (Bact. g-1est 1mm)  (Bact. g-1 theor)/ (Bact. g-1est 2mm)  FC-07 FC-07 FC-1-3A FC-1-3A FC-2-2B FC-2-2B FC-2-3A FC-2-3A FC-3-2A FC-3-2A  S2O32S4O62S2O32S4O62S2O32S4O62S2O32S4O62S2O32S4O62-  1.13 x 107 9.56 x 106 4.48 x 107 3.78 x 107 8.18 x 107 6.89 x 107 2.54 x 108 2.14 x 108 3.04 x 108 2.57 x 108  12.4 10.5 41.2 34.8 59.1 49.8 56.8 47.9 1.9 2.2  10.1 8.49 36.4 30.7 50.6 42.6 48.7 41.1 1.5 1.8  Table 4.7. Theoretical microbial populations using the maintenance energy equation and ratio of theoretical bacterial populations per gram to estimated bacteria per gram for both 1mm and 2mm GSD corrections in Table 4.5.  125  4.9 References APHA-AWWA-WEF. (2005). Standard methods for the examination of water and wastewater, 21st ed. Washington, DC: American Public Health Association. 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The maintenance energy of bacteria in growing cultures. In: Proc. Of the Royal Society of London 163 B:224-231. Raudsepp M. and Pani E. (2003). Environmental Aspects of Mine wastes. In J.L. Jambor, D.W. Blowes and A.I.M. Ritchie (Eds.), Application of Rietveld analysis to environmental 129  mineralogy (Vol 31, pp. 165-180), Mineralogical association of Canada Short Course, Ottawa, Ontario, Canada. Sapsford, D.J., Bowell, R.J., Dey, M., and Williams, K.P. (2009). Humidity cell tests for the prediction of acid rock drainage. Minerals Engineering. 22, 25-36. Schooner, J.L. and Stumm, W. (1986). The role of chemical weathering in the neutralization of acidic deposition. Schweiz Z. Hydrol. 48, 170-195. Singer P.C. and Stumm W. (1970). Acidic mine drainage: the rate determining step. Science, 167, 1121-1123. Stevens, T.O. and McKinley, J.P. (1995). Lithoautotrophic microbial ecosystems in deep basalt aquifers. Science, 270, 450–454. Strömberg B. and Banwart, S.A. (1999). Experimental study of acidity-consuming processes in mining waste rock: some influences of mineralogy and particle size. Applied Geochemistry, 14, 1-16 Temple, K.L. and Delchamps, E.W. (1951) The autotrophic oxidation of iron by a new bacterium: Thiobacillus ferrooxidans. J. Bacteriol., 62, 605-611. Thingstad, T.F., (1987). Utilization of N, P, and organic C by heterotrophic bacteria. I. Outline of a chemostat theory with a consistent concept of ‘maitenance’ metabolism. Marine ecology, 35, 99-109. Tijhuis, L., Van Loosdrecht, M.C.M. and Heijnen, J.J. (1993). A thermodynamically based correlation for maintenance gibbs energy requirements in aerobic and anaerobic chemotrophic growth. Biotechnology and Bioengineering, 42, 509-519. Tuovinen, O.H., Niemela, S.I. and Gyllenberg, H.G. (1971). Tolerance of Thiobacillus ferrooxidans to some metals. Antonie van Leeuwenhoek Journal of microbiology and serology, 37, 489-496. U.S. EPA, (1994). Technical Document: Acid Mine Drainage Prediction. United States Environmental Protection Agency (U.S. EPA), EPA530-R-94-036. Velbel, M.A. (1999). Bond Strength and the relative weathering rates of simple orthosilicates. American Journal of Science, 229, 679-696.  130  5  Chapter 5: Data Integration and Outlook  In chapters 2-4 the most pertinent results of the current research are presented. The overall goal of which is to improve our understanding and predictive capabilities of drainage quality from Antamina mine waste rock by focusing on micron-scale processes. To do this, a variety of high-resolution imaging, geochemical, mineralogical and microbiological techniques have been employed to examine the processes taking place on sulfide mineral surfaces. In this section, the most compelling conclusions are re-iterated and integrated, to produce an outlook of drainage from Antamina mine waste rock. The strengths and weaknesses of the current work are discussed along with recommendations for future work.  5.1 Integration of Results The waste rock piles and field cells have microbial populations between 106 and 108 bacteria per gram. The dominant phenotype was neutrophilic bacteria capable of thiosulfate oxidation, with the exception of one field cell, which had a mixed population of acidophilic and neutrophilic bacteria. The largest microbial populations were found in the youngest, most reactive waste rock, the exoskarn material. The smallest microbial populations were found in a field cell that was six years old composed of endoskarn material and leaching a relatively small amount of sulfate. The lack of a positive correlation between waste rock age and microbial populations indicates that the observed microbial population trends are not a result of growth kinetics. The critical nutrient phosphorus was below detection limits in the drainage water from both field cells and waste rock piles. Geochemical modeling indicates that Ca-phosphate precipitation will prevent significant concentrations of phosphate from ever being in solution. This has been observed in prior studies (Feng et al., 2000), and may be an endemic limitation to microbial populations in calcium mineral buffered mine waste. Phosphorous is similarly low in all waste rock types and below detection limits in the drainage. The lack of this critical nutrient is likely inhibiting microbial growth. It is concluded that the size of microbial populations are largely controlled by both substrate availability as measured by sulfate and phosphorous  131  availability. The larger microbial populations observed in the waste rock piles implies that stability in temperature and moisture content are important as well. A logarithmic relationship exists between microbial populations and sulfate loadings, which is independent of waste rock age or rock type. Despite the strong correlation to substrate supply and population size, the logarithmic relationship cannot be described by thermodynamic maintenance energy requirements alone.  The logarithmic relationship is  probably due to a form of bacteria-mineral symbiosis present in the larger microbial communities. The observed relationship between sulfate loading, pH, and microbiology show that the size and type of microbial population can be determined based on geochemistry alone in the rock types analyzed at the Antamina mine. This also implies that factors affecting microbiology will affect geochemistry. The field cells have lower microbial populations then samples taken from the waste rock piles of the same material. This is probably due to large day to night temperature swings experienced by the field cells and dry conditions during the winter dry season, leading to a less amenable environment for microbial growth. If the strong correlation between weathering rates and microbial populations in the field cells is maintained in the piles, the field cells will produce an underestimate of sulfide mineral weathering rates in the waste rock piles. Acidophilic iron oxidizing bacteria were culturable from one waste rock pile and one field cell, both composed of intrusive waste rock. These bacteria are of particular concern as they imply the presence of biogenic acidic microenvironments in which ferric iron leaching can take place. The field cell that had a mixed population of acidophilic and neutrophilic bacteria was at a sub-neutral pH of 6.5, while all other samples were at an alkaline pH. This field cell also had unique weathering characteristics. The sulfate loading decreased by nearly 40% between the first and second wet season, a far larger decrease then experienced by any other field cell sampled in this study. This observation is similar to kinetic cell procedures which call for the inoculation of iron oxidizing bacteria, which results in the peak weathering rates being reached much more rapidly. Waste rock containing biogenic acidic microenvironments was thoroughly examined using FIB-FEG-SEM-EDS. The bacteria were found upon or within the porosity of an iron 132  oxyhydroxide sulfate identified as schwertmannite, which is contrary to previous studies that have theorized that acidophiles required direct attachment to sulfide mineral surfaces to grow at ambient neutral pH. Schwertmannite does not typically appear in waste rock until the pH drops below 6.5, the strong correlation between schwertmannite precipitation and acidophilic bacteria indicates that the appearance of schwertmannite may correspond to the establishment of acidophilic bacteria at ambient neutral pH. As schwertmannite is not detected in alkaline pH waste rock, acidophilic iron oxidizing bacteria are likely absent which is confirmed by the lack of acidophiles in the carbonate buffered waste rock found in this study. The leaching of molybdenite as molybdate is one of the primary concerns regarding water quality at the Antamina mine. The high concentrations of molybdate in the drainage water may have unforeseen consequences as low concentrations of this metal is toxic to acidophilic sulfur and iron oxidizing bacteria. Mine waste leaching molybdenum is known to violate predictions of acid generation in six major molybdenum mines in British Columbia (Morin et al., 2001). Molybdenum was found to be toxic to iron and sulfur oxidizing bacteria isolated in this study at concentrations as low as 10 mg/l. The abundance of molybdenum in the intrusive waste rock may inhibit biological catalysis. The one intrusive sample which has been colonized by acidophilic bacteria is molybdenum poor compared to the other two intrusive samples which are leaching Mo at concentrations well above the toxicity limit. This suggests that Mo toxicity may play a role in inhibiting the growth of acidophiles in this rock type.  5.2 Strengths and Weaknesses of Data Few studies have examined microbial populations of neutral pH waste rock, so the nature of the research is in many ways exploratory. Due to the lack of literature regarding methods and techniques for sampling, analyzing, and interpreting microbial samples, many established procedures were modified, or newly developed in this work. The lack of precedent is also a point of interest in this research. This research represents one of the first bodies of data on bacteria in neutral pH waste rock, and is complimented with antecedent drainage geochemistry available for comparison.  133  5.2.1 Sampling and Transport Waste rock provides a difficult sampling medium. It is impossible auger, or install sampling wells in existing waste rock piles due to the gravel and boulders sized particles present in the material. Sample selection is limited by the size of containers used in sampling. The material must be transported in a cooler to slow any changes in microbial communities caused by the change in environment. This places a logistical difficulty on shipping large samples. Therefore only modest amounts of material were sampled (≈0.5 Kg). The samples required approximately 1 month of transportation prior to arrival at the laboratory at University of Western Ontario where microbial analyses were conducted.  Changes to the microbial  community may have taken place over this timeframe. The bacteria from the exoskarn material may have been particularly affected by this as a majority of the microbial population was found to be dead using Live/dead BaclightTM. The cause of one of these bacterial samples faring worse than the others can only be speculated upon. The two exoskarn samples did have by far the largest populations which are speculated to be the cause of the die off, however it is impossible to determine this with certainty. 5.2.2 Inoculum Once the waste rock samples arrived at the laboratory, there was no published technique on how to sample bacteria from the heterogeneous material.  Most research  conducted on mine waste has focused on tailings, which is homogenous and a considerably less difficult of a material to work with. A wash was used as inoculum in microbial enumeration methods. The wash technique only measures the microbial population of the finest grain size fraction. This is acceptable because most of the reactive surface area; hence most of the microbial population is located in this grain size fraction. However the results are difficult to apply to waste rock in the field which incorporates boulder sized fragments, this required an added data processing step of reconciliation with the GSD.  This step is unnecessary when  working in a mine tailings environment. This technique may have biases, which make it difficult to compare to the more robust data set of microbiology of tailings environments.  134  5.2.3 Data Processing In order to produce microbial estimates it was necessary to integrate aspects of the Live/dead BaclightTM method, and the MPN method. The Live/dead BaclightTM method was expected to produce higher population estimates then the MPN technique because the Live/dead BaclightTM method is a direct count of all bacteria, while the MPN technique only quantifies bacteria which can grow in selected media. However the Live/dead BaclightTM method typically produced values much lower than those predicted by MPNs. This shows that the dominant phenotypes were able to grow in the selected growth media. The Live/dead BaclightTM technique produced estimates that varied between samples considerably less than the MPN technique. This could be due to the presence of fine-grained material in the inoculum which was being counted, ensuring a baseline population estimate. The Live/dead BaclightTM technique is also a less established technique then the MPN method for measuring microbial communities in waste rock. Therefore the MPN technique was selected as the better estimate of microbial populations. The large portion of the exoskarn bacteria which were found to be dead using the Live/dead BaclightTM had to be accounted for. Live/dead BaclightTM  Therefore aspects of the  technique results, the percent alive, were incorporated into the  estimates while the total numbers predicted by the Live/dead BaclightTM technique were not used. In order to relate the geochemical data with the microbiological data, it was necessary to produce a microbial estimate for the entire waste rock pile. To do this, novel techniques of integration of the GSD with the microbiological results had to be developed. Although only relative microbial numbers can be produced, they are sufficient for the purposes of comparison. The data processing had little effect on the overall trends, so the results are reproducible even if microbial population estimates are made by an alternative method. A logarithmic relationship between microbial populations and sulfate loadings is found in this research. The implications of this relationship are discussed; however no strong explanation for the relationship was presented. It is speculated that this relationship is a result of microbial ecology. which is beyond the scope of this thesis.  135  5.2.4 Microcolumns Numerous difficulties were encountered with the mini-column experiment, which again were largely due to the lack of prior studies done using the mini-column technique. In the design of this study it was decided not to sieve the waste rock prior to packing it into the sterile syringes used in microcolumn construction to avoid cross contamination of the samples, as it was deemed unfeasible to sterilize a sieve between samples which were time sensitive in nature. The heterogeneity in grain sizes led to preferential flow paths, which complicated the interpretation of effluent geochemistry.  The most pervasive problem was evaporation.  Evaporation increases the concentrations of dissolved species, which leads to precipitation reactions which attenuate species of interest. This was a particular problem with sulfate loading, which was attenuated due to gypsum precipitation. The rapid pace that the minicolumns receiving the molybdate medium dropped in pH in the FC-2-3A sample prevented molybdate toxicity from being analyzed in a higher pH environment in which molybdate would have been more soluble. 5.2.5 Geochemistry This research has a large data set of antecedent for comparison to microbial communities. The close marriage of geochemistry and microbiology found in this study make it abundantly clear that a complete understanding of mine waste environments requires attention to this relationship. The lack of detailed phosphate data was a significant hindrance; however, as phosphate is a critical nutrient. The phosphate concentrations remained low due to Caphosphate solubility controls; its availability was more than likely one of the controlling factors on microbial growth and size. 5.2.6 SEM A large part of this work is based on interpreting images and EDS spectra from scanning electron microscopes. This type of data requires interpretation and frequently produces nonunique explanations. For instance, some iron oxides have both indistinct EDS spectra and crystal structures, such as ferrihydrite and goethite, and are impossible to tell apart using an SEM. The interpretation of iron mineralogy can significantly affect the conclusions. The FIB136  FEG-SEM-EDS was available only for a limited amount of time. Therefore only samples from FC2-3A and the experimental waste rock piles were analyzed.  5.3 Contribution The contributions in this current research are owing to similar reasons as its weaknesses, lack of precedent. To the authors knowledge no study has measured microbial communities in geochemically and mineralogically well characterized neutral pH waste rock, examined the geochemical effect of toxic concentrations of molybdate, or described the structure and chemistry of microbially populated acidic microenvironments in an environmental sample. The primary shortfall of past research regarding microbiology of sulfide mineral oxidation is that it is often laboratory based, typically analyzing chemolithotrophic bacteria in pure cultures using ideal growth media, and not acknowledging microbially sensitivities in laboratory kinetic tests.  The contributions of this research is particularly  significant because microbiological analysis was conducted on naturally colonized waste rock, and the waste rock from which weathering trends are derived, was weathered under field conditions. The abundance of acidophilic iron oxidizing bacteria has significant implications in the initiation of ARD. This study found a strong correlation between schwertmannite precipitation and the presence of acidophilic thiobacilli. This association has been found in past studies of iron oxidizing bacteria (Liao et al., 2009), however none of the prior studies were analyzing iron oxidizing bacteria in neutral pH environments. This result has implications for numerical modeling of mine waste weathering. Prior models typically model neutral pH sulfide oxidation with the rate-determining step being oxidation by O2 and the ability of O2 to get to sulfide mineral surfaces. This research shows that the weathering of iron sulfide minerals may require a more complex model, in which ferric leaching begins to take place at a pH of 6.5. The strong association between schwertmannite and acidophiles indicates that the relative abundance of schwertmannite may be an indication of the degree of ferric leaching taking place at neutral pH. Microbiology in young mine waste requires time for growth and maturation of the microbial community. This research demonstrates that after 1.5 years bacteria will be exerting significant influence and fully integrated into the geochemical processes taking place. Although 137  the role of neutrophilic sulfur oxidizing bacteria is somewhat ambiguous, it is difficult to justify disregarding the geochemical effect of populations of bacteria as large as 108 per gram. Recent works has demonstrated that bacteria are more prevalent than previously thought in the deep earth (Maclean et al., 2008).  It should be assumed that bacteria are indigenous to the  subsurface in the vicinity of sulfide mineral ore bodies, prior to anthropogenic disturbance, especially if weathering horizons or processes have occurred prior to mining. The strong connection between geochemistry and microbiology has been evident to environmental scientists for decades. To the author’s knowledge, the weathering rate of neutral pH waste rock has never been compared to the size of the microbial community. Studies focusing on the relation between iron oxidizing bacteria and sulfide leaching rates are more common. Relating iron oxidizing bacteria to geochemistry is easier because they are directly involved in the oxidation of sulfide minerals. Virtually no work has attempted to develop relations between neutrophilic sulfur oxidizers and weathering rates. It is apparent that the size of a microbial population will correlate to the supply of its substrate, which is primarily sulfur in waste rock environments. The relationship of microbial populations and sulfate loadings is compelling because it is so consistent between samples of different age and rock types, and that the correlation itself is logarithmic. Based on the thermodynamics of cellular maintenance energy, the maximal population should have a linear relationship with sulfate loadings. The logarithmic relation shows that bacteria are increasingly involved in sulfide mineral oxidation as the supply of their substrate increases. This means that quickly weathering waste rock will be more responsive to microbial sensitivities, such as temperature, drying, toxic metals, or synthetic bactericides.  Defining a constant relationship between  weathering rates and microbiology independent of age and mineralogy, which can be universally applied to waste rock is essential if microbiology is to become incorporated in mine waste drainage predictions.  5.4 Future Work The primary thrust of future work will largely be to corroborate the results presented in this study. Due to the novelty of this research it is important to have independent studies verify the results.  If the field cells sampled in this study are sampled again when the sulfate loadings 138  have decreased it could be determined whether the microbial populations are in equilibrium with sulfate loadings.  If the microbial populations of the field cells have decreased  proportionally to sulfate loadings it will confirm that a maximal microbial population is present. No attempt was made in this study to measure fluctuations in the microbial consortia over the course of the year. The strongly seasonal variability in the weathering rates and geochemistry is certainly going to lead to seasonal variability in the microbial community. The measurement of microbial populations in field cells at other mine sites will show whether the trends found in this study are site specific, or universal. An association between schwertmannite and acidophiles has been described in past studies; however this is the first study that shows this association at bulk neutral pH. Schwertmannite is not typically the dominant iron precipitate in neutral pH mine waste, suggesting its presence at neutral pH could be a proxy for the amount of ferric leaching taking place. This potentially useful tool could be verified in a laboratory experiment in which sulfidic mine waste is weathered both biotically and abiotically at a pH of 6.5. Analysis of the resulting iron weathering products could indicate the applicability of using schwertmannite at neutral pH as a proxy for the abundance of acidic microenvironments.  Such a finding would be a  potentially useful tool in the modeling and prediction of mine drainage quality. One of the most conspicuous absences of data in the literature on this subject is regarding the catalytic effect of neutrophilic microbial communities. An attempt was made in this study to quantify such an effect, however unforeseen difficulties in experimental design made it impossible to determine sulfate loadings with sufficient sensitivity. It is recommended that kinetic tests which compare sterile mine waste to alkaline mine waste naturally colonized by neutrophilic sulfur oxidizers. Or the microcolumn study could be repeated using naturally colonized waste rock samples with the following refinements; influent water mimics the geochemistry present within the field cells and waste rock piles in all ways except for very low sulfate and calcium concentrations, and have the mini-columns maintained in a glove box to prevent variability in evaporation.  139  5.5 Concluding Remarks Vast improvements in the understanding of microbiology and geochemistry of mine waste have been made over the past two decades. The advent of high resolution scanning electron microscopy techniques and genomics has led to renewed interest in microbial consortias and their relationship with mineral coatings, and geochemistry. However little of this knowledge has been applied to prediction or prevention of metal leaching or ARD. The primary challenge of applying this knowledge is fundamentally an upscaling issue. Processes that take place at the micron scale, which are measured at the laboratory scale, cannot easily be applied to the field scale. Due to the intractable nature of this problem, the quality of mine drainage is predicted based solely on geology and climate.  The close marriage between mineral coatings,  microbiology, geochemistry, and mineralogy does not contradict the assumption that geology and climate are controlling factors. However predictions based on macro scale observations lead to a site-specific understanding, which cannot readily be applied to other localities. A mechanistic understanding of sulfide mineral oxidation, which involves microbiology, iron precipitate speciation, and hydrogeology are essential to develop more robust prediction and prevention strategies.  140  5.6 References Conlan, M.J. (2009). Attenuation mechanisms for Molybdenum in neutral rock drainage. MASc Thesis. University of British Columbia, 2009. Klohn-Crippen, S.S.A. (1998). “Antamina Environmental impact assessment”, March 1998, Klohn Crippen, SVS S.A., Lima, Peru Liao, Y., Zhou, L., Liang, J. and Xiong, H. (2009). Biosynthesis of schwertmannite by Acidithiobacillus ferrooxidans cell suspensions under different pH condition. Materials Science and Engineering C. 29, 211-215. Morin, K.A., Hutt, N.M., Price, W.A., and Coffin, V. (2001). Violation of common ABA prediction rules by molybdenum-related minesites in British Columbia, Canada. IN: Proceedings of Securing the future: International Confrerence on Mining and the Environment. June 25July 1 2001, Skelleftea, Sweden. Maclean L. C., Tyliszczak, T., Gilbert, P.U.P.A., Zhou, D., Pray, T.J., Onstott and T.C., Southam, G. (2008). A high-resolution chemical and structural study of framboidal pyrite formed within a low-temperature bacterial biofilm. Geobiology, 6, 471-480.  141  1  Appendix 1: Sampling  1.1 General Guidelines · · · · · ·  Keep bottle closed until you arrive at the specified site. Wipe/clean the scoop and sampling containers with 70% ethanol and Kimwipes. Allow to air dry. Do not touch the scoop/sample with your hands. Wear nitrile gloves. Take lots of pictures. After taking the sample, immediately close the tube and place in an ice chest (if possible). Transport at 4°C ideally.  1.2 Dig Hole at Least 1.5m Deep in Back Center of Pile The purpose is to get to a depth and position that is not subjected to strong temperature and moisture variations. The deeper we get the more representative of what’s going on in the piles, and the more chance of finding some secondary minerals. There are moisture probes 2m from the surface of the piles. 1. Dig a >1.5m hole 2m behind or to the side of the top of line 1 (back center of pile), do not dig in front!  1.3 Taking a Sample The idea is to get a representative soil sample, and a sample of sulfide minerals to look at weathering and surface colonization. Take samples from piles 1-3. 1. Use a stainless steel scoop to fill 2 Nalgene bottles with representative samples of soil and gravel. Alternatively just turn the bottles upside down and push them into the ground. 2. Look around for chunks of pyrite and any other shiny sulfides. Place them in Falcon tubes if they fit, if they’re too big you can use the wide mouth Nalgene bottles. Find at least 3-6 samples of pyrite. 3. Add some wet mud from the sample location to the sulfide mineral containers to prevent them from drying out. 4. Place samples in cooler with ice  1.4 Placing Teabags and Mineral Pucks We will look at mineral weathering and microbial colonization of the microcosm material at the end of the wet season. 1. Use the 2008 access tube material. 142  2. 3. 4. 5.  Keep them attached to the nylon thread so that they can be found. When placing material, make sure that samples are not lying on top of one another. Fill in hole completely; don’t leave any air pockets around the samples. Mark the location that they were inserted.  1.5 Sampling Notes Samples were Collected Between February 7th-12th 2009. The location of the samples was recorded with GPS, and the depth beneath the surface was measured with a tape measure after the sample had been collected. Images documenting sample collection along with the GPS location and depth of sampling are below.  1.6 Sample Location Description  Northing  Easting  Height  Depth (m)  Pila1  8945535.989  275423.59  4385.715  1.2  Pila2  8945491.554  275428.022  4385.466  1.25 m  Pila3  8945447.669  275436.046  4385.136  1.35 m  Figure 1. Global positioning system of sample location, and depth below pile surface.  143  1.7 Images Documenting Pile and Field Cell Sampling 1.7.1 Pile-1 Ø Red pole denotes location of sampling  Ø Sample being collected from 1.5m depth.  144  1.7.2 Pile-2 Ø Excavator digging hole for sample collection  Ø Red pole denotes location of sampling  Ø Sample being collected from 1.5m depth.  145  1.7.3 Pile-3  Ø Excavator digging hole for sample collection  Ø Red pole denotes location of sampling  Ø Sample being collected from 1.5m depth.  146  1.7.4 FC-1-3A Ø UBC field cell 1-3A.  Ø Image of the top of exposed material at the top of the field cell. Ø Note the material looks relatively un weathered with no visible iron staining  Ø Hole cut at base of Field cell from which sample was collected.  147  1.7.5 FC-2-2B Ø UBC field cell 2-2B  Ø Image of the top of exposed material at the top of the field cell. Ø Note that some weathering is evident in the orange hue of the material.  Ø Hole cut at base of Field cell from which sample was collected.  148  1.7.6 FC-2-3A Ø UBC field cell 2-3A  Ø Image of the top of exposed material at the top of the field cell. Ø Note that significant weathering is evident due to the iron staining of the material. However relatively unweathered sulfide minerals are present as well.  149  1.7.7 FC-3-2A Ø UBC field cell 3-2A  Ø Image of the top of exposed material at the top of the field cell. Ø Note that weathering appears to be extremely localized around the large particle on the right of the image. The rest of the material looks relatively un weathered.  150  1.7.8 FC-07 Ø UBC field cell 3-2A  Ø Image of the top of exposed material at the top of the field cell. Ø Note that little iron staining has occurred, the pinkish hue is due to an abundance of K-feldspar. XRD has shown this material to be approximately 32% microcline.  151  2  Appendix 2: XRD  2.1 Description Quantitative X-ray diffraction (XRD) was employed to determine the mineralogy of the Antamina waste rock. This method utilizes mono-chromatic X-rays directed at a powdered sample. X-rays will be diffracted at characteristic angles when Bragg’s law is satisfied: nλ=2d sin θ  Where n is the integer of the ‘order’ of reflection, λ is the wavelength of the X-rays, d is the interplanar spacing of the crystal structure, and θ is the angle between the incident X-ray and the plane of the crystal surface.  Bragg’s law essentially defines when  constructive interference will occur between X-rays diffracted from more than one crystal plane for any given crystal spacing (d). The only variable manipulated during sample analysis is the angle of the incident ray (θ). A powdered sample is rotated to manipulate θ, the angle at which the X-ray detector receives counts above background noise can be related to d spacing. From the angle at which X-rays are diffracted, and the intensity as measured by counts received by the X-ray detector, a crystal structure can be defined. Most common crystalline minerals have structures which have been well defined. By overlaying theoretical lines of where peaks should occur based on these defined crystal structures, the mineralogical makeup of a sample can be determined. Rietveld (1967,1969) developed a method from which a measured pattern can be used to not just identify the presence of mineral structures, but the relative abundance of a mineral in a sample.  By accounting for various structural and  experimental parameters which effect a pattern, a refinement of the fitted pattern is done using the least squares technique to minimize difference between an observed and theoretical diffraction pattern. In this way the Rietveld refinement can be used to quantify samples containing complex assemblages of minerals. 152  One drawback of the XRD based methods when applying to mine waste weathering environments is that secondary precipitates often precipitate as nanocrystalline material.  The XRD technique requires crystals is >5nm in diameter to  produce a recognizable pattern. XRD patterns have been produced for secondary ironoxyhydroxides and iron-oxyhydroxide sulfates, such as ferrihydrite and schwertmannite, which are of particular interest in mine waste studies. However they are typically analyzed as a near pure phase, other complex crystal structures will quickly crowd out the small diffuse peaks produced by material such as ferrihydrite, rendering them invisible in the pattern. Although this amorphous material cannot be identified, it can be quantified. By adding a known quantity of a foreign well crystalline mineral which is known to not exist in the sample material, an estimate of the quantity of material evading detection can be made.  2.2 Method Quantitative X-ray diffraction (XRD) was employed to determine the mineralogy of the Antamina waste rock. Sub-samples of 40 – 50 g of material <1cm in diameter were analyzed by quantitative x-ray diffraction (XRD) to determine their mineralogy. The samples were first crushed to <1 mm with a mortar and pestle and thoroughly mixed. A 3 g sub-sample was then ground to < 5 µm in a McCrone micronizing mill, mounted, and step scanned over from 3° – 80° 2θ with CuKα X-radiation in a Siemens D5000 BraggBrentano diffractometer (Raudsepp and Pani, 2003). The scan speed was 20seconds per 2θ . The Scan data was refined using the Reitveld program Topas 3.0. The ocherous coating of the massive sulfide sample from FC-2-3A which is the focus of chapter 2 is of particular interest. The coating was scraped off and decanted to separate the finest grained material from the larger grained silicates and sulfides which were entrained in the coating. The sample was smeared onto a quartz plate to minimize background noise, and step scanned from 3° – 80° 2θ with CuKα X-radiation in a Bruker D-8 Focus diffractometer. The scan speed was slowed down to 2minutes per 2θ so as to maximize the number of counts the X-ray detector would receive per step. A Rietveld  153  refinement was not performed on this sample as the only purpose of this analysis was to identify minerals, not quantify them.  2.3 Results The XRD results are presented below along with the Rietveld refinement results. The pattern produced by the XRD is the blue line. The red line is the fitted pattern from which material composition is determined.  The grey line below the scan is the  difference between the two patterns, ideally it is straight. Any missing peaks or poorly fit peaks will lead to peaks or troughs in the grey line, from which a qualitative estimate of goodness of fit can be derived. For independent verification of the XRD results, the results are compared to the XRF results. The mineralogical composition as predicted by XRD is converted to a chemical composition of elements which make up a high proportion of the total sample. This can also give some indication of the composition of the amorphous material. In all samples the XRF predicts a higher % iron then the XRD results. This indicates that most of the amorphous material is iron-oxyhydroxides, as is expected in mine waste environments. Note that the rocks analyzed in this study had been weathered for at least 1.5 years prior to sampling.  Therefore a significant quantity of secondary mineral  precipitates such as gypsum and ferrihydrite have accumulated in some samples, and quickly weathering minerals may be relatively depleted. The samples taken from Piles13 were taken from the back of the piles, which is composed of the first tipping phase. One peak of the iron-oxyhydroxide sulfate jarosite was identified in the FC-2-3A massive sulfide scraping. However it is impossible to verify the presence of a mineral based on one peak which is not very high above background.  154  Counts Counts  8,000 7,500 7,000 6,500 6,000 5,500 5,000 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1,000 500 0 -500 -1,000 -1,500 -2,000 -2,500 -3,000  JD-13A-spike.raw FC-13A  5  10  Fluorite Calcite Quartz Pyrite Phlogopite 1M Galena Grossular Orthoclase Magnesite Diopside Amor.  15  20  25  30  35  40  45  50  55  60  65  70  10.00 % 60.37 % 2.45 % 1.23 % 2.20 % 0.18 % 7.62 % 4.86 % 1.34 % 6.22 % 3.52 %  75  80  2Th Degrees  155  FC-1-3A  Chemical Formula  Pattern% wt%  Fluorite  CaF2  10.00  0.00  Calcite  CaCO3  60.37  67.08  Quartz  SiO2  2.45  2.72  Phlogopite  KMg3[(OH,F)2(AlSi3O10]  2.20  2.44  Orthoclase  K[AlSi3O8]  4.86  5.40  Grossular  Ca3Al2[SiO4]3  7.62  8.47  Diopside  CaMg[Si2O6]  6.22  6.91  Magnesite  MgCO3  1.34  1.49  Pyrite  FeS2  1.23  1.37  Galena  PbS  0.18  0.20  Amorpheous  ?????  3.52  3.91  Element XRD wt% XRF wt% Ca  30.40  29.12  Si  4.82  5.97  K  0.99  0.37  Mg  1.63  0.76  Al  1.69  1.69  Fe  0.64  1.99  S  0.76  0.06  156  5,000 4,500  JD-Pile1-spike.raw  Fluorite Calcite Quartz Phlogopite 1M Grossular Sphalerite Pyrite Molybdenite 2H Bernalite Orthoclase Albite low Diopside Talc 1A Magnesite Almandine Amor.  Pile-1  4,000 3,500 3,000 2,500  Counts Counts  2,000  10.00 % 47.54 % 4.36 % 1.39 % 14.85 % 0.64 % 0.75 % 0.26 % 0.25 % 6.44 % 1.67 % 10.69 % 0.83 % 2.29 % 1.21 % -3.16 %  1,500 1,000 500 0 -500  -1,000 -1,500 -2,000 -2,500 -3,000 -3,500 25  30  35  40  45  50  55  60  2Th Degrees  157  Pile-1  Chemical Formula  Pattern %  wt%  Fluorite  CaF2  10.00  0.00  Calcite  CaCO3  47.54  52.82  Quartz  SiO2  4.36  4.84  Phlogopite  KMg3[(OH,F)2(Al Si3O10]  1.39  1.54  Orthoclase  K[AlSi3O8]  6.44  Grossular  Ca3Al2[SiO4]3  Diopside  XRD Element wt%  XRF wt%  Ca  27.76  28.99  Si  10.91  8.70  K  1.15  0.54  Mg  2.34  0.96  Al  2.77  2.12  7.16  Fe  0.39  1.82  14.85  16.50  S  0.68  0.06  CaMg[Si2O6]  10.69  11.88  Magnesite  MgCO3  2.29  2.54  Pyrite  FeS2  0.75  0.83  Sphalerite  ZnS  0.64  0.71  Molybdenite  MoS  0.26  0.29  Almandine  Fe3Al2(SiO4)3  1.21  1.34  Albite  NaAlSi3O8  1.67  1.86  Bernalite  Fe(OH)3  0.25  0.28  Talc  MgSi4O10(OH)2  0.83  0.92  Amorpheous  ?????  -3.16  -3.51  158  Fluorite  10.00 %  Quartz  27.90 %  9,000  Andradite  12.32 %  8,000  Phlogopite 1M  2.90 %  Moly bdenite 2H  0.14 %  7,000  Chalcopyrite  2.30 %  Pyrite  1.56 %  Gypsum  0.53 %  Orthoclase  34.30 %  10,000  JD-2-3A-spike.raw  FC-23A  6,000  Counts Counts  5,000  Albite low, calcian 8.65 %  4,000  Amor.  -0.60 %  3,000 2,000 1,000 0 -1,000 -2,000 -3,000 -4,000 5  10  15  20  25  30  35  40  45  50  55  60  65  70  75  80  2Th Degrees  159  FC-2-3A  Chemical Formula  Pattern % wt%  Fluorite  CaF2  10.00  0.00  Quartz  SiO2  27.90  31.00  Phlogopite  KMg3[(OH,F)2(AlSi3O10]  2.90  3.22  Orthoclase  K[AlSi3O8]  34.30  38.11  Pyrite  FeS2  1.56  1.73  Molybdenite  MoS  0.14  0.16  Oligoclase  (Na0.75Ca0.5)AlSi3O8  8.65  9.61  Calchopyrite  CuFeS2  2.30  2.56  Andradite  Ca3Fe2(SiO4)2  12.32  13.69  Gypsum  CaSO4*2H2O  0.53  0.59  Amorpheous  ?????  -0.60  -0.67  XRD wt%  XRF wt%  Ca  4.12  2.42  Si  31.18  32.75  K  5.65  4.05  Mg  0.56  0.69  Al  4.86  4.23  Fe  4.76  5.36  S  2.18  1.88  160  JD-2-2B-spike.raw FC-22B  Fluorite Quartz Phlogopite 1M Chalcopyrite Pyrite Oligoclase Molybdenite 2H Orthoclase Amor.  Counts Counts  13,000 12,000 11,000 10,000 9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 -1,000 -2,000 -3,000 -4,000 -5,000  5  10  15  20  25  30  35  40  45  50  55  60  65  70  10.00 % 35.18 % 3.19 % 0.71 % 0.43 % 10.79 % 0.02 % 37.31 % 2.36 %  75  80  2Th Degrees  161  FC-2-2B  Chemical Formula  Pattern % wt%  Fluorite  CaF2  10.00  0.00  Quartz  SiO2  35.18  39.09  Phlogopite  KMg3[(OH,F)2(AlSi3O10]  3.19  3.54  Orthoclase  K[AlSi3O8]  37.31  41.46  Pyrite  FeS2  0.43  0.48  Molybdenite  MoS  0.02  0.02  Oligoclase  (Na0.5Ca0.5)AlSi3O8  10.79  11.99  Calchopyrite  CuFeS2  0.71  0.79  Amorpheous  ?????  2.36  2.62  XRD Element wt%  XRF wt%  Ca  0.89  0.97  Si  35.27  36.86  K  6.15  4.06  Mg  0.62  0.39  Al  5.44  4.39  Fe  0.51  2.07  S  0.59  0.79  162  JD-Pile2-spike.raw Pile-2  Fluorite 10.00 % Quartz 35.24 % Orthoclase 36.86 % Albite low 7.54 % Pyrite 0.26 % Chalcopyrite 0.83 % Molybdenite 2H 0.11 % Phlogopite 1M 3.98 % Amor. 5.18 %  Counts Counts  14,000 13,000 12,000 11,000 10,000 9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 -1,000 -2,000 -3,000 -4,000  5  10  15  20  25  30  35  40  45  50  55  60  65  70  75  80  2Th Degrees  163  Pile-2  Chemical Formula  Pattern % wt%  Fluorite  CaF2  10.00  0.00  Quartz  SiO2  35.24  39.16  Phlogopite  KMg3[(OH,F)2(AlSi3O10]  3.98  4.42  Orthoclase  K[AlSi3O8]  36.86  40.96  Pyrite  FeS2  0.26  0.29  Molybdenite  MoS  0.11  0.12  Oligoclase  (Na0.75Ca0.25)AlSi3O8  7.54  8.38  Calchopyrite  CuFeS2  0.83  0.92  Amorpheous  ?????  -0.60  -0.67  XRD Element wt%  XRF wt%  Ca  0.63  1.03  Si  34.24  37.86  K  6.16  3.53  Mg  0.77  0.33  Al  5.10  3.54  Fe  0.47  2.13  S  0.58  1.21  164  2,500  JD-3-2A-spike.raw  Fluorite  10.00 %  FC-32A  Quartz  3.65 %  Pyrite  7.77 %  Sphalerite  4.60 %  Andradite  40.85 %  Diopside  18.12 %  Calcite  8.18 %  Phlogopite 1M  2.79 %  Smithsonite  1.12 %  Gypsum  0.62 %  Chalcopyrite  2.78 %  Orthoclase  3.23 %  Spessartine  1.79 %  Amor.  -5.52 %  2,000 1,500  Counts Counts  1,000 500 0 -500 -1,000 -1,500 5  10  15  20  25  30  35  40  45  50  55  60  65  70  75  80  2Th Degrees  165  FC-3-2A  Chemical Formula  Pattern %  wt%  Fluorite  CaF2  10.00  Calcite  CaCO3  8.18  9.09  Ca  18.30  17.46  Quartz  SiO2  3.65  4.06  Si  11.57  14.03  Phlogopite  KMg3[(OH,F)2(AlSi 3O10]  2.79  3.10  K  0.79  0.37  Mg  2.80  1.79  Orthoclase  K[AlSi3O8]  3.23  3.59  Al  0.76  2.85  Diopside  CaMg[Si2O6]  18.12  20.13  Fe  14.34  15.08  Pyrite  FeS2  7.77  8.63  S  6.85  5.85  Sphalerite  ZnS  4.60  5.11  Calchopyrite  CuFeS2  0.83  0.92  Andradite  Ca3Fe2(SiO4)2  40.85  45.39  Gypsum  CaSO4*2H2O  0.62  0.69  Spessartine  Mn3Al2(SiO4)3  1.79  1.99  Smithsonite  ZnCO3  1.12  1.24  Amorpheous  ?????  -5.52  -6.13  XRD Element wt%  XRF wt%  166  2,500  JD-Pile3-spike.raw  Pile-3  2,000  Counts Counts  1,500  Quartz  7.75 %  Pyrite  11.73 %  Calcite  2.69 %  Microcline maximum  5.84 %  Augite  2.52 %  Andradite  43.42 %  Fluorite  10.00 %  Phlogopite 1M  2.34 %  Chalcopyrite  3.37 %  Sphalerite  0.99 %  Magnesite  3.17 %  Diopside  10.37 %  Amor.  -4.19 %  1,000 500 0 -500 -1,000  5  10  15  20  25  30  35  40  45  50  55  60  65  70  75  80  2Th Degrees  167  Pile-3  Chemical Formula  Pattern %  wt%  Fluorite  CaF2  10.00  0.00  XRD Element wt%  Calcite  CaCO3  2.69  2.99  Ca  15.17  10.78  Quartz  SiO2  7.75  8.61  Si  14.31  15.36  Phlogopite  KMg3[(OH,F)2(Al Si3O10]  2.34  2.60  K  1.15  0.94  Mg  3.02  0.95  Diopside  CaMg[Si2O6]  10.37  11.52  Al  1.77  2.82  Magnesite  MgCO3  3.17  3.52  Fe  18.11  19.64  Pyrite  FeS2  11.73  13.03  S  8.92  11.25  Sphalerite  ZnS  0.99  1.10  Calchopyrite  CuFeS2  3.37  3.74  Andradite  Ca3Fe2(SiO4)2  43.42  48.24  Spessartine  Mn3Al2(SiO4)3  1.79  1.99  Microcline  KAlSi3O8  5.84  6.49  Augite  (Ca,Na)(Mg,Fe,Al,Ti )(Si,Al)2O6  2.52  2.80  -5.52  -6.13  Amorpheous ?????  XRF wt%  168  Counts Counts  2,600 2,400 2,200 2,000 1,800 1,600 1,400 1,200 1,000 800 600 400 200 0 -200 -400 -600 -800 -1,000  JD-7-spike.raw FC-07  Quartz  3.39 %  Phlogopite 1M  0.77 %  Grossular  17.30 %  Fluorite  10.00 %  Molybdenite 2H  0.18 %  Calcite  1.88 %  Andradite  12.66 %  Albite low, calcian  13.38 %  Microcline maximum 32.26 %  5  10  15  20  25  30  35  40  45  50  55  60  65  70  Diopside  8.01 %  Amor.  0.17 %  75  80  2Th Degrees  169  FC-07  Chemical Formula  Pattern %  wt%  Fluorite  CaF2  10.00  0.00  Calcite  CaCO3  1.88  2.09  Quartz  SiO2  3.39  3.77  Phlogopite  KMg3[(OH,F)2(AlSi3O10] 0.77  0.86  Grossular  Ca3Al2[SiO4]3  17.30  19.22  Diopside  CaMg[Si2O6]  8.01  8.90  Molybdenite  MoS  0.18  0.20  Oligoclase  (Na0.75Ca0.25)AlSi3O8  13.38  14.87  Andradite  Ca3Fe2(SiO4)2  12.66  14.07  Microcline  KAlSi3O8  32.26  35.84  0.17  0.19  Amorpheous ????? XRD Element wt%  XRF wt%  Ca  9.85  11.51  Si  24.95  24.18  K  5.11  3.87  Mg  1.15  0.98  Al  7.34  7.92  Fe  3.09  3.42  S  0.96  0.33  170  FC-23A  12000  Scraping from massive Sulfide sample featured in Chapter 2  11000  10000  9000  Lin (Counts)  8000  7000  6000  5000  B 4000  3000  2000  1000  0 4  10  20  30  40  50  60  70  80  2-Theta - Scale 9M-decant - File: 9M-decant.raw 01-076-0824 (N) - Orthoclase - (K0.931Na0.055)( 03-065-0466 (A) - Quartz low, syn - O2Si 01-089-3061 (I) - Sphalerite, ferroan, syn - Zn0.78  01-071-5208 (N) - Pyrite, syn - FeS1.74 00-037-0471 (*) - Chalcopyrite - CuFeS2 01-089-5378 (*) - Actinolite - (Fe3.112Mn0.088Mg 01-072-0359 (I) - Bohmite - AlOOH  00-036-0427 (*) - Jarosite, hydronian - (K,H3O)Fe 01-074-1051 (A) - Marcasite - FeS2 01-087-2496 (*) - Clinochlore (IIb-4) - Mg4.882Fe0 01-085-2274 (I) - Phlogopite - K(Mg,Al,Fe)3Si2.5A  04-007-9876 (C) - Albite, calcian - Na0.6Ca0.4Al1.  171  4800  B  4700 4600 4500  FC-23A  4400 4300 4200 4100  Zoomed in view of box in previous figure. Jarosite is the only plausible mineral which can account for the circled peak.  4000 3900 3800 3700 3600 3500 3400 3300 3200 3100 3000 2900 2800  Lin (Counts)  2700 2600 2500 2400 2300 2200 2100 2000 1900 1800 1700 1600 1500 1400 1300 1200 1100 1000 900 800 700 600 500 400 300 200 100 0 17.1  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  2-Theta - Scale 9M-decant - File: 9M-decant.raw 01-076-0824 (N) - Orthoclase - (K0.931Na0.055)( 03-065-0466 (A) - Quartz low, syn - O2Si 01-089-3061 (I) - Sphalerite, ferroan, syn - Zn0.78  01-071-5208 (N) - Pyrite, syn - FeS1.74 00-037-0471 (*) - Chalcopyrite - CuFeS2 01-089-5378 (*) - Actinolite - (Fe3.112Mn0.088Mg 01-072-0359 (I) - Bohmite - AlOOH  00-036-0427 (*) - Jarosite, hydronian - (K,H3O)Fe 01-074-1051 (A) - Marcasite - FeS2 01-087-2496 (*) - Clinochlore (IIb-4) - Mg4.882Fe0 01-085-2274 (I) - Phlogopite - K(Mg,Al,Fe)3Si2.5A  04-007-9876 (C) - Albite, calcian - Na0.6Ca0.4Al1.  172  2.4 References Raudsepp, M., Pani, E., 2003. Application of Rietveld analysis to environmental mineralogy. In J.L. Jambor, D.W. Blowes, and A.I.M. Ritchie, Eds., Environmental aspects of mine wastes, 31, p. 165-180. Mineralogical Association of Canada Short Course, Ottawa, Ontario, Canada. Rietveld, H.M., 1967. Line profiles of neutron powder-diffraction peaks for structure refinement. Acta Crystallogr. 22, 151-152. Rietveld, H.M., 1969. A profile refinement method for nuclear and magnetic structures. J. Appl. Crystallogr. 2, 65-71.  173  3  Appendix 3: Net Acid Generating Test  3.1 Methodology of NAG Tests Net acid generation (NAG) test was performed with few modifications of the procedure presented by (Southam and Beveridge, 1993). The NAG test was conducted by placing 6 grams of sample in 1.5L flasks containing 350 ml of 17% (vol/vol) H2O2 (aq). The procedure calls for 24 hours of incubation prior to boiling to remove the H2O2. However it was evident that the reaction had not been completed as some samples were still fizzing at this time so the analysis was allowed to proceed until the fizzing stopped. It took 6 days for the fizzing to stop, at which point the samples were boiled for 1 hour to remove the remaining H2O2. A 50ml aqueous sample from the resulting slurry was filtered (0.45µm-pore-size filter), boiled for 1 h to remove residual H2O2. The sample was then cooled to room temperature at which point the pH was determined, and back titrated using 1N NaOH to determine net acid generation.  3.2 Gran Titration of NAG Liquor The Gran titration Curves conducted during the NAG leaching experiment are presented below. The results are presented in Chapter 3. Titrations were only performed on samples that had consumed the buffering potential of calcite. An example of the titration results are presented in Error! Reference source not found.. Note that no acid was generated in the marble-hornfels material, so no titration was performed. The formula for the Gran function which was used to determine the equivalence point is: Gran function =(Vs+Vb)^(10-14/10pH)  (1)  Where Vs is the volume of sample, Vb is the volume of 1N NaOH. The Gran function can then be plotted against the volume of base added to produce a curve from which the CO2 equivalence point can be determined. The CO2 equivalence point in this study equates to the acidity of the sample produced after calcite was depleted as acid generated before this will not contribute to acidity due to CO2 degassing. The point at which the Gran function represents the sensitivity of the pH in the solution to the base titrate. When the Gran function diverges from 174  the near zero value, it has surpassed the CO2 equivalence point. This equivalence point is then calculated by fitting a linear function to the points beyond the linear part of the curve as seen in Error! Reference source not found.. This gives a volume of 1N NaOH base required to consume the acid produced in the sample after calcite depletion. However a variable amount of volume was lost during the boiling step to remove the H2O2. The acidity produced is then corrected to be per gram of sample which corrects for variable amounts of volume lost during boiling. The Gran titration curves are presented below.  ml 1N NaOH sum of base volume pH-Pile-2 0 0 3.95 0.005 0.005 4.17 0.005 0.01 4.5 0.005 0.015 4.76 0.005 0.02 5.04 0.005 0.025 5.3 0.005 0.03 5.61 0.005 0.035 6.03 0.005 0.04 6.16 0.005 0.045 6.31 0.005 0.05 6.42 0.005 0.055 6.49 0.005 0.06 6.63 0.005 0.065 6.83 0.005 0.07 7.37 0.005 0.075 8.32 0.005 0.08 9.38 0.005 0.085 9.92 Example of titration results from back titration of Pile-2 NAG liquor.  175  NAG titration Pile 2  pH-pile 2 0.003  y = 0.3917x - 0.0305  10 pH  Linear (Series1)  0.0025  8  0.002  6  0.0015  4  0.001  2  0.0005  0  0 0  0.02  0.04  0.06  0.08  mls of 1N NaOH  0.1  Gran function  12  Series3  1N NaOH required to reach equivilence  Example of gran titration with linear function fit to the final two points.  Parameters Volume 17% H2O2: 350ml Sample Volume: 45ml Waste rock mass: 6 grams Boiled sample volume: 38.8ml Equivalence point: 0.0778 ml 1N NaOH CO2 acidity: 0.0019 meq/l CO2 acidity/mass 0.149 meq/g Acidity as H2SO4 : 7.301 mg/gL  176  NAG titration FC-22B 12  0.004 0.0035 0.003 0.0025 0.002 0.0015 0.001 0.0005 0  10 pH  8 6 4 2 0 0  0.01  0.02  0.03  0.04  Series1 Gran Function  Gran function  pH 2-2B  0.05  mls of 1N NaOH  Parameters Volume 17% H2O2: 350ml Sample Volume: 45ml Waste rock mass: 6 grams Boiled sample volume: 38.2ml Equivalence point: 0.0389 ml 1N NaOH CO2 acidity: 0.00102 meq/l CO2 acidity/mass 0.0000765 meq/g Acidity as H2SO4 : 3.748 mg/g  pH 2-2B  12  0.003  10  0.0025  8  0.002  6  0.0015  4  0.001  2 0  0.0005 0 0  0.02  0.04  0.06  0.08  Series1  Gran function  pH  NAG titration FC-23A  0.1  mls of 1N NaOH  Parameters 177  Volume 17% H2O2: 350ml Sample Volume: 45ml Waste rock mass: 6 grams Boiled sample volume: 39.1 ml Equivalence point: 0.0696 ml 1N NaOH CO2 acidity: 0.00176 meq/l CO2 acidity/mass 0.000132 meq/g Acidity as H2SO4 : 6.467 mg/g  NAG titration Pile 3 12  0.0025  10  0.002  pH  8  0.0015  6  0.001  4 2  0.0005  0  0 0  0.05  0.1  0.15  Series1  Gran function  pH 2-2B  0.2  mls of 1N NaOH  Parameters Volume 17% H2O2: 350ml Sample Volume: 45ml Waste rock mass: 6 grams Boiled sample volume: 38.5 ml Equivalence point: 0.169 ml 1N NaOH CO2 acidity: 0.00439 meq/l CO2 acidity/mass 0.000330 meq/g Acidity as H2SO4 : 16.128 mg/g  178  pH 2-2B  12 10 8 6 4 2 0  0.006 0.005 0.004 0.003 0.002 0.001 0 0  0.05  0.1  Series1  Gran function  pH  NAG titration FC-32A  0.15  mls of 1N NaOH  Parameters Volume 17% H2O2: 350ml Sample Volume: 45ml Waste rock mass: 6 grams Boiled sample volume: 38.8 ml Equivalence point: 0.169 ml 1N NaOH CO2 acidity: 0.00306 meq/l CO2 acidity/mass 0.000229 meq/g Acidity as H2SO4 : 11.228 mg/g  3.3 References Southam G. and Beveridge T.J. (1993) Examination of lipopolysaccharide (O-Antigen) populations of Thiobacillus ferrooxidans from two mine tailings. Applied and Environmental Microbiology 59, 1283-1288.  179  4  Appendix 4: MPN Procedure  Growth media for acidophilic iron oxidizing bacteria  Basal Salts (will keep ‘forever’) (NH4)2SO4,  0.4 g  K2HPO4,  0.1 g  MgSO4·7H2O  0.4 g  dH2O  900 ml  pH 2.3 with H2SO4  Filter sterilize.  Fe-Stock (needs to be made fresh) FeSO4·7H2O, pH 2.3 (final concentration, 33.3 g L-1)  Filter sterilize.  Basal medium:iron stock = 9:1. Combine 4.5 ml Basal Salts in sterile 13X100 mm test tubes + 0.5 ml Fe-Stock. When mixed you may get a white fluffy precipitate (FePO4), but the bugs grow fine.  Growth Medium for acidophilic elemental sulfur oxidizing bacteria  Basal Salts (will keep ‘forever’) (NH4)2SO4,  0.3 g  KH2PO4,  0.1 g  MgSO4·7H2O  0.4 g  180  CaCl2·2H2O  0.33 g  FeSO4·7H2O  18 mg  dH2O  1L  pH 2.3 with H2SO4  Filter sterilize.  Add 5 ml Basal Salts in sterile 13X100 mm test tubes. Add a thin film of S° after the bacteria have been inoculated. Growth Medium for Neutrophilic thiosulfate oxidizing bacteria  (NH4)2SO4,  0.3 g  KH2PO4,  0.1 g  MgSO4·7H2O  0.4 g  CaCl2·2H2O  0.33 g  Na2S2O3·5H2O  4.93 g  dH2O  1L  Filter sterilise. The unadjusted pH is approx. pH 7, which is fine for this growth medium. Dispense in 5 ml aliquots into sterile 13X100 mm test tubes.  181  5  Appendix 5: Mini-Column Sulfur Loadings and Construction  Mini-Column Construction Mini-columns were constructed using 5ml syringes and 6-9 g dry weight of fine-grained waste rock material. There is some variation in grain sizes depending on the type of waste rock from which they were constructed. For instance, Pile-2 material is finer grained then Pile-3 material, and Pile-3 material is finer grained then Pile-1 material. Glass wool was stuffed into the bottom of the syringes to prevent the waste rock material from spilling out. An image of the complete mini-columns is attached below. For the first 50 days 1ml of media was amended to each mini-column once a week. After 50 days the amendment regiment was increased to 1ml amendment twice a week due to a change in laboratory conditions which resulted in greater evaporation leading to no effluent water being produced in most of the columns. Over the course of 91 days 19 ml of media was amended to mini-columns 1-5, while 20ml was amended to mini-columns 6-8 to allow them to wet up faster as they were oven dried to sterilize the waste rock. Outflow was collected in 1.5ml micro-centrifuge tubes, the samples were analyzed for pH and dissolved metals. The mini-column material was composed of waste rock which had been microbially populated insitu and remained moist between sampling and mini-column construction. Columns 5-8 were sterilized by either autoclave or oven drying. The masses, initial water contents, and graphs of sulfate loadings are below.  182  Completed Mini-columns  183  Column  Mass (g)  Initial water content (ml)  1-07  7.67  0.99  2-07  7.96  1.03  3-07  7.65  0.99  4-07  7.13  0.92  5-07  6.81  0.88  6-07  7.54  0  7-07  5.98  0  8-07  6.00  0  1-1-3A  6.24  2.00  2-1-3A  6.14  1.96  3-1-3A  6.02  1.92  4-1-3A  5.77  1.85  5-1-3A  6.38  2.04  6-1-3A  5.84  0  7-1-3A  5.99  0  1-2-2B  6.42  1.51  2-2-2B  6.14  1.44  3-2-2B  6.42  1.51  4-2-2B  6.37  1.49  5-2-2B  6.41  1.51  6-2-2B  6.45  0  7-2-2B  5.71  0  8-2-2B  6.00  0  1-2-3A  6.57  1.23  2-2-3A  7.55  1.41  3-2-3A  7.18  1.35  4-2-3A  7.51  1.41  184  Column  Mass (g)  Initial water content (ml)  5-2-3A  7.07  1.32  6-2-3A  7.35  0  7-2-3A  5.66  0  8-2-3A  6.11  0  1-3-2A  9.00  1.46  2-3-2A  8.55  1.39  3-3-2A  8.70  1.41  4-3-2A  8.23  1.33  5-3-2A  8.69  1.41  6-3-2A  8.56  0  7-3-2A  5.9  0  8-3-2A  5.67  0  1-Pile-1  6.11  1.42  2-Pile-1  6.79  1.58  3-Pile-1  6.78  1.58  4-Pile-1  6.61  1.53  5-Pile-1  6.49  1.51  6-Pile-1  6.19  0  7-Pile-1  6.81  0  8-Pile-1  6.2  0  1-Pile-2  6.00  1.62  2-Pile-2  5.91  1.59  3-Pile-2  5.98  1.61  4-Pile-2  6.46  1.74  5-Pile-2  6.19  1.67  6-Pile-2  6.50  0  7-Pile-2  6.1  0  185  Column  Mass (g)  Initial water content (ml)  8-Pile-2  7.22  0  1-Pile-3  9.71  1.40  2-Pile-3  9.50  1.37  3-Pile-3  9.14  1.31  4-Pile-3  9.24  1.33  5-Pile-3  8.47  1.22  6-Pile-3  9.66  0  7-Pile-3  6.81  0  8-Pile-3  6.2  0  5.1 Sulfur loading results from Mini-columns  Pile 2 micro-columns S loading 0 -0.05  mg S / g  -0.1  1 pile2 2 pile2  -0.15  3 pile2 -0.2  4 pile2 5 pile2  -0.25  6 pile2 -0.3 0  20  40  60  80  100  days  186  mg S/g  Celda07 S loading  1 celda07  0.1  2 celda07  0.05  3 celda07  0  4 celda07  -0.05  5 celda07 6 celda07  -0.1  7 celda07  -0.15 0  20  40  60  80  100  days  mg S / g  Pile 1 S loading  Series1 1-Pile1  0  Series2 2-Pile1  -0.05  Series3 3-Pile1 Series4 4-Pile1  -0.1  Series5 5-Pile1  -0.15  Series6 6-Pile1  -0.2 -0.25 0  20  40  60  80  100  days  187  FC-23A S loading  0.5  1 FC-23A  0.4  2 FC-23A  mg S / g sample  0.3  3 FC-23A  0.2  4 FC-23A  0.1  5 FC-23A  0  6 FC-23A  -0.1  7 FC-23A  -0.2  8 FC-23A  -0.3 -0.4 0  20  40  60  80  100  days  FC-22B S loading  mg S /g  0.1 1 FC-22B  0.05 0 -0.05 -0.1 -0.15 -0.2 -0.25 -0.3 -0.35  2 FC-22B 3 FC-22B 4 FC-22B 5 FC-22B 6 FC-22B 0  20  40  60 days  80  100  7 FC-22B 8 FC-22B  188  mg S /g  Pile 3 S loading 1 Pile3  0.1 0.05 0 -0.05 -0.1 -0.15 -0.2 -0.25 -0.3 -0.35  2 Pile3 3 Pile3 4 Pile3 5 Pile3 6 Pile3 7 Pile3 8 Pile3 0  20  40  60  80  100  days  Axis Title  Pile 3 Zn loading  1 Pile3  0.00006  2 Pile3  0.00005  3 Pile3  0.00004  4 Pile3  0.00003  5 Pile3  0.00002  6 Pile3  0.00001  7 Pile3  0 0  20  40  60  80  100  8 Pile3  Axis Title  189  mg S /g  FC-32A S loading  Series1 1 FC-32A Series2 2 FC-32A  0.25 0.2 0.15 0.1 0.05 0 -0.05 -0.1 -0.15  Series3 3 FC-32A Series4 4 FC-32A Series5 5 FC-32A Series6 6 FC-32A Series7 7 FC-32A Series8 8 FC-32A 0  20  40  60  80  100  days  190  6  Appendix 6: Particle Size Distribution During each discharge phase, material was set aside for grain size distribution (GSD)  analysis and tandem field cell experiments. GSD analysis was conducted on each tipping phase by Golder Associates using the ASTM D 5519 methodology. Material was set aside from each tipping phase for field cell construction. Material >4” in diameter (101.6mm) was removed prior to field cell construction. With the exception of FC-1-3A and FC-07 PSDs were not conducted on the individual field cell material. Therefore the PSD produced for the tipping phase had to be corrected to account for the removal of material >4”. The pile samples were collected from material from the first tipping phase. The field cells were collected from tipping phases 1-3. The attached PSDs for Piles 1-3 are the PSDs of the first tipping phase. The PSDs attached for FC-2-2B, FC-23A, and FC-3-2A are corrected for the removal of material >4”. The PSD results show that in general, Pile-1 material is courser grained then Pile-2 (intrusive), Pile-3 (exoskarn), and FC-07(endoskarn) material which have similar PSDs. All field cells with the exception of FC-1-3A (marble hornfels) are the material composition is approximately 50% <10mm in diameter.  191  % PASSING  Pile-1 GRADATION  10000  1000  100  10  1  0.1  0.01  PARTICLE SIZE (mm) % PASSING 100 83.3 32.6 28.9 26.4 23.7 21.1 17.6 15.5 14.9 13.2 11.1 9.5 8.5 7.6 7.2 6.2 5.7 5.2 4.9 4.5 4.0 3.4  mm 2000 1000 406.4 304.8 254 203.2 152.4 101.6 76.2 63.5 50.8 38.1 25.4 19.05 12.7 9.525 4.75 2 0.84 0.42 0.25 0.148 0.075 192  % PASSING  Pile-2 Gradation  100  10  1  0.1  0.01  PARTICLE SIZE (mm)  Pile-2 mm % passing 914.40 100.00 406.40 100.00 254.00 96.20 152.40 92.20 101.60 89.00 76.20 87.40 25.40 65.70 19.05 60.20 12.70 52.00 9.53 47.60 4.76 36.80 0.595 26.000 0.420 24.400 0.149 19.000 0.074 16.200  193  % PASSING  Pile 3 GRADATION  100  10  1  0.1  0.01  PARTICLE SIZE (mm) Pile-3 mm % passing 914.4 100 406.4 95.5 254 92.5 152.4 88.6 76.2 82.5 25.4 63.5 19.05 57.9 12.7 48.9 9.525 43.5 4.76 30.7 0.595 12.4 0.42 10.4 0.149 5.9 0.074 4.1  194  % PASSING  FC-13A GRADATION  100  10  1  0.1  0.01  PARTICLE SIZE (mm) FC-1-3A mm % passing 101.6 100.0 76.2 94.5 50.8 85.1 38.1 72.0 25.4 58.2 19.05 50.0 12.7 38.5 9.525 33.9 4.76 24.3 2 18.7 0.85 15.4 0.42 13.6 0.25 12.1 0.149 10.0 0.074 7.3  195  FC-22B GRADATION 100.0 90.0  % PASSING  80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0 100  10  1  0.1  0.01  PARTICLE SIZE (mm) mm % passing %passing corrected 914.4 100 406.4 100 254 100 152.4 99.5 101.6 95.5 100.0 76.2 93.5 97.9 25.4 70 73.3 19.05 61.1 64.0 12.7 52.4 54.9 9.525 47.1 49.3 4.76 36.2 37.9 0.595 20.6 21.6 0.42 18.5 19.4 0.149 12.1 12.7 0.074 8.3 8.7  196  FC-23A GRADATION 100 90  % PASSING  80 70 60 50 40 30 20 10 0 100  10  1  0.1  0.01  PARTICLE SIZE (mm)  mm 914.4 406.4 254 152.4 101.6 76.2 25.4 19.05 12.7 9.525 4.76 0.595 0.42 0.149 0.074  FC-2-3A % passing %passing corrected 100 100 99.6 95.4 85.7 100.0 80.8 94.3 61.1 71.3 56.5 66.0 49.3 57.5 45.2 52.8 34.9 40.7 20 23.3 17.9 20.9 11.2 13.1 7.2 8.4  197  FC-32A GRADATION 100 90  % PASSING  80 70 60 50 40 30 20 10 0 100  10  1  0.1  0.01  PARTICLE SIZE (mm)  mm 914.4 406.4 254 152.4 101.6 76.2 25.4 19.05 12.7 9.525 4.76 0.595 0.42 0.149 0.074  FC-3-2A % passing %passing corrected 100 97.1 94.6 88.7 82.9 100.0 79.9 94.3 58.2 71.3 52.3 66.0 43.6 57.5 38.9 52.8 29.3 40.7 14.2 23.3 11.8 20.9 6.9 13.1 4.7 8.4  198  FC-07 GRADATION 100 90  % PASSING  80 70 60 50 40 30 20 10 0 100  10  1  0.1  0.01  PARTICLE SIZE (mm)  (mm) % Passing 101.6 100 50.8 95.7 25.4 79.3 19 65.4 6.3 46.3 1.18 24.1 0.425 17 0.075 6 <75 um 0  199  7  Appendix 7: ICP-OES  A Varian inductively coupled plasma optical emission spectrometer (ICP-OES) with auto sampler was used to analyze the mini-column effluent, and the NAG liquor. Samples were stored on ice prior to analysis. It was also used to confirm that molybdenum had not been attenuated in samples which were negative for growth during the molybdate toxicity test. It was decided not to acidify the samples as oxyanions were the primary species of interest in all samples. All samples were measured 3 times, with an internal standard of Eu as a control. As an additional means of quality control blanks, duplicates and standards were mixed in with the experimental samples. The sample preparation procedure and difficulties encountered are discussed in this section.  7.1 Mini-Columns Mini-column effluent was collected every week. The effluent over the course of several weeks was combined in order to reduce the number of ICP analyses required. None the less this combined volume was still relatively small (<1 milliliter). The auto sampler coupled with the Varian ICP-OES used in this study required at least 3.5 milliliters to run a complete analysis of a sample. Therefore, 3 milliliter of 30% nitric acid was used to dilute the samples. 30% nitric acid was selected because it is the concentration of the carrier fluid in which the Eu standard is mixed with the sample by the Varian ICP-OES. The variability in sample volume led to some variability in the detection limit of various samples. Standard concentrations were based on the known content of the influent media as a baseline minimum, and the first run high concentrations of standards were used. In the subsequent run standard concentrations were refined to bracket the expected concentrations of the effluent. A multi element standard was used to analyze the entire suite of metals. In addition, highly concentrated samples (>500ppm) of S, P, Ca, and Mo standards were used. Although Ca and Mo were present in the multi element standard, they were not in high enough concentrations. Standards were diluted with 30% nitric acid to achieve desired concentrations. The standards were re-ran after each batch of 60 experimental samples. As the auto sampler is capable of holding 180 samples, the standards were measured three times during an analysis.  200  7.2 Mo Toxicity and NAG Liquor A sub-sample of the filtered NAG liquor was taken for ICP-OES analysis. The samples were diluted by 400% with 10% nitric acid in order to ensure that the concentrations would be within the range offered by the available S standard. Samples from the Mo toxicity test were filtered in a 0.45µm filter paper and then directly measured with the ICP-OES.  201  8  Appendix 8: Phreeqc  For a better understanding of the processes controlling the geochemistry of the mini-column drainage a number of Phreeqc models were written. The minteqV4 database was used to construct an equilibrium model to analyze the speciation and solubility indices (Parkhurst and Appelo, 1999).  8.1 Field Cells Geochemical data reported by Envirolab Peru, was used as input, while O2 was set at atmospheric concentrations to mimic the unsaturated and well aerated environment of the field cells. The primary solubility control of interest is gypsum and those effecting metals of interest, Zn and Mo.  The SI of gypsum approached saturation in FC-3-2A and FC-2-3A.  However the SI dropped well below 0 by the end of the wet seasons, leading to the conclusion that yearly sulfate loadings is a good qualitative indicator of weathering rates. To determine maximum phosphate concentrations a phreeqc program was modified with the concentration of P rising until saturation of CaHPO4 is reached. An example of this phreeqC input and results are below. A concentration of 0.000001 mg L-1 of P is intered into the initial solution as a concentration of 0 crashes the model. -----------------------------------Reading input data for simulation 1. -----------------------------------DATABASE C:\Program Files\USGS\Phreeqc Interactive 2.15.0\minteq.v4.dat SELECTED_OUTPUT SOLUTION pe 12 units mg/l density Temp 8.3 pH 7.01 Alkalinity S(6) Al As Ba Ca Cu Fe K  1  # assumed  1 19.6  397.7333333 0.09 0 0.02 445.5 0.303 0 10.67  mg/l as Ca0.5(CO3)0.5 as SO4  202  Mg 15.34 Mn 3.984 Mo 0 Na 2.73 Ni 0.024 P 0 Se 0.019 as Se Si 0 as Si Zn 27.98 water 1 # kg EQUILIBRIUM PHASES 1 O2(g) -0.68 10.0  ------------------------------Saturation indices-----------------------------Phase Al(OH)3(am) Al2O3 Al4(OH)10SO4 AlOHSO4 Alunite Anhydrite Antlerite Aragonite Artinite Azurite Ba(OH)2:8H2O Barite BaSeO3 BaSeO4 Bianchite Birnessite Bixbyite Boehmite Brochantite Brucite Bunsenite Calcite CaSeO3:2H2O CaSeO4:2H2O CH4(g) Chalcanthite CO2(g) Cu(OH)2 Cu2Se(alpha) Cu2SO4 Cu3Se2 CuCO3 Cumetal CuOCuSO4 Cuprite CuSe CuSe2  SI log IAP -0.90 -0.22 4.66 -2.58 2.02 -0.85 -0.95 -1.09 -10.85 -1.39 -18.04 0.27 -15.32 -6.30 -4.79 2.97 3.46 1.26 -1.25 -7.52 -6.18 -0.87 -11.81 -6.24 -129.37 -5.93 -2.82 -1.02 -58.01 -31.43 -120.50 -1.27 -17.66 -12.13 -16.19 -47.13 -98.38  11.06 22.12 27.36 -5.81 2.81 -5.13 7.84 -9.27 0.00 -17.30 6.92 -9.95 -13.61 -13.99 -6.55 21.06 4.11 11.06 16.08 10.51 7.31 -9.27 -8.79 -9.17 -173.09 -8.64 -21.01 8.24 -106.04 -33.18 -187.53 -12.77 -27.16 -0.40 -16.31 -81.49 -133.20  log KT 11.95 22.34 22.70 -3.23 0.78 -4.29 8.79 -8.18 10.85 -15.92 24.96 -10.22 1.71 -7.69 -1.76 18.09 0.65 9.80 17.33 18.03 13.49 -8.40 3.02 -2.93 -43.72 -2.70 -18.19 9.26 -48.03 -1.75 -67.03 -11.50 -9.50 11.74 -0.12 -34.36 -34.83  Al(OH)3 Al2O3 Al4(OH)10SO4 AlOHSO4 KAl3(SO4)2(OH)6 CaSO4 Cu3(OH)4SO4 CaCO3 MgCO3:Mg(OH)2:3H2O Cu3(OH)2(CO3)2 Ba(OH)2:8H2O BaSO4 BaSeO3 BaSeO4 ZnSO4:6H2O MnO2 Mn2O3 AlOOH Cu4(OH)6SO4 Mg(OH)2 NiO CaCO3 CaSeO3:2H2O CaSeO4:2H2O CH4 CuSO4:5H2O CO2 Cu(OH)2 Cu2Se Cu2SO4 Cu3Se2 CuCO3 Cu CuOCuSO4 Cu2O CuSe CuSe2  203  CuSeO3:2H2O -13.19 -12.29 0.89 CuSeO3:2H2O CuSeO4:5H2O -10.23 -12.67 -2.44 CuSeO4:5H2O CuSO4 -12.33 -8.63 3.70 CuSO4 Diaspore 3.11 11.06 7.94 AlOOH Dolomite(disordered) -3.71 -19.77 -16.06 CaMg(CO3)2 Dolomite(ordered) -3.09 -19.77 -16.68 CaMg(CO3)2 Epsomite -4.12 -6.37 -2.25 MgSO4:7H2O Gibbsite 1.78 11.06 9.28 Al(OH)3 Goslarite -4.39 -6.55 -2.16 ZnSO4:7H2O Gypsum -0.51 -5.13 -4.62 CaSO4:2H2O H2Se(g) -84.93 -89.73 -4.80 H2Se Hausmannite 1.16 66.56 65.41 Mn3O4 Huntite -11.92 -40.77 -28.85 CaMg3(CO3)4 Hydromagnesite -25.01 -31.50 -6.50 Mg5(CO3)4(OH)2:4H2O K-Alum -13.83 -19.31 -5.48 KAl(SO4)2:12H2O K2SeO4 -13.43 -14.16 -0.73 K2SeO4 Langite -3.13 16.08 19.21 Cu4(OH)6SO4:H2O Lime -22.97 11.74 34.71 CaO Magnesite -2.83 -10.50 -7.67 MgCO3 Malachite 1.57 -4.53 -6.10 Cu2(OH)2CO3 Manganite 3.18 28.52 25.34 MnOOH Mg(OH)2(active) -8.29 10.51 18.79 Mg(OH)2 MgSeO3:6H2O -13.03 -10.03 3.00 MgSeO3:6H2O MgSeO4:6H2O -9.20 -10.40 -1.20 MgSeO4:6H2O Mirabilite -8.90 -10.84 -1.94 Na2SO4:10H2O Mn2(SO4)3 -42.50 -46.51 -4.01 Mn2(SO4)3 MnSe -84.74 -80.22 4.52 MnSe MnSeO3 -12.15 -11.02 1.13 MnSeO3 MnSeO3:2H2O -11.91 -11.02 0.89 MnSeO3:2H2O MnSeO4:5H2O -9.35 -11.40 -2.05 MnSeO4:5H2O MnSO4 -10.62 -7.36 3.26 MnSO4 Morenosite -7.30 -9.57 -2.27 NiSO4:7H2O Na2SeO3:5H2O -24.80 -14.50 10.30 Na2SeO3:5H2O Na2SeO4 -16.16 -14.88 1.28 Na2SeO4 Natron -12.98 -14.98 -2.00 Na2CO3:10H2O Nesquehonite -6.08 -10.50 -4.42 MgCO3:3H2O Ni(OH)2 -6.49 7.31 13.79 Ni(OH)2 Ni4(OH)6SO4 -19.65 12.35 32.00 Ni4(OH)6SO4 NiCO3 -7.26 -13.70 -6.44 NiCO3 NiSe -64.72 -82.42 -17.70 NiSe NiSeO3:2H2O -16.36 -13.22 3.14 NiSeO3:2H2O NiSeO4:6H2O -12.08 -13.60 -1.52 NiSeO4:6H2O Nsutite 3.56 21.06 17.50 MnO2 O2(g) -12.99 76.04 89.03 O2 Periclase -12.65 10.51 23.16 MgO Portlandite -12.40 11.74 24.14 Ca(OH)2 Pyrochroite -6.69 9.51 16.20 Mn(OH)2 Pyrolusite 3.33 47.53 44.21 MnO2 Retgersite -7.48 -9.57 -2.09 NiSO4:6H2O Rhodochrosite -0.93 -11.49 -10.56 MnCO3 Semetal(am) -44.49 -51.71 -7.22 Se Semetal(hex -43.84 -51.71 -7.87 Se SeO2 -20.64 -20.53 0.11 SeO2 SeO3 -43.48 -20.91 22.57 SeO3 Smithsonite -0.85 -10.68 -9.84 ZnCO3 Spinel -8.26 32.62 40.88 MgAl2O4 Tenorite -0.08 8.24 8.32 CuO  204  Thenardite -11.26 Thermonatrite -15.72 Witherite -5.48 Zincite -1.94 Zincosite -11.34 Zn(OH)2 -1.88 Zn(OH)2(am) -2.99 Zn(OH)2(beta) -2.29 Zn(OH)2(epsilon) -2.06 Zn(OH)2(gamma) -1.41 Zn2(OH)2SO4 -3.72 Zn3O(SO4)2 -24.37 Zn4(OH)6SO4 -3.98 ZnCO3:1H2O -0.42 Znmetal -55.08 ZnO(active) -1.79 ZnSe -64.74 ZnSeO4:6H2O -9.07 ZnSO4:1H2O -6.37  -10.84 0.42 -14.98 0.75 -14.09 -8.61 10.32 12.27 -6.55 4.79 10.32 12.20 10.32 13.31 10.32 12.62 10.32 12.38 10.32 11.73 3.78 7.50 -2.77 21.60 24.42 28.40 -10.68 -10.26 -27.70 27.38 10.32 12.11 -79.41 -14.67 -10.59 -1.52 -6.55 -0.18  Na2SO4 Na2CO3:H2O BaCO3 ZnO ZnSO4 Zn(OH)2 Zn(OH)2 Zn(OH)2 Zn(OH)2 Zn(OH)2 Zn2(OH)2SO4 Zn3O(SO4)2 Zn4(OH)6SO4 ZnCO3:1H2O Zn ZnO ZnSe ZnSeO4:6H2O ZnSO4:1H2O  Model to estimate maximum P concentration: -----------------------------------Reading input data for simulation 1. -----------------------------------DATABASE C:\Program Files\USGS\Phreeqc Interactive 2.15.0\minteq.v4.dat SELECTED_OUTPUT SOLUTION  1 pe 12 units mg/l density Temp 8.3 pH 7.01 Alkalinity S(6) Al As Ba Ca Cu Fe K Mg Mn Mo Na Ni  # assumed 1 19.6  397.7333333 0.09 0 0.02 445.5 0.303 0 10.67 15.34 3.984 0 2.73 0.024  mg/l as Ca0.5(CO3)0.5 as SO4  205  P 0 Se 0.019 as Se Si 0 as Si Zn 27.98 P 0.000001 CaHPO4 0 water 1 # kg EQUILIBRIUM PHASES 1 O2(g) -0.68 10.0  FC-1-3A Date 11/16/06 1/1/00 1/11/07 2/7/07 3/15/07 3/22/07 3/29/07 4/12/07 4/25/07 10/31/07 11/22/07 12/28/07 1/11/08 2/8/08 2/22/08 3/7/08 3/21/08 4/4/08 4/18/08 5/2/08 11/7/08 11/21/08 12/5/08 1/9/09 1/16/09 1/30/09 2/13/09  pH Ca mg/l 7.87 65.08 7.87 144.3 7.55 65.08 7.72 84.44 7.70 83.87 7.87 54.86 7.71 62.73 7.95 76.60 7.58 78.42 7.44 194.3 7.74 151.4 7.66 180.6 7.69 152.4 7.96 103.0 7.87 121.0 8.12 80.74 8.06 98.41 8.04 110.9 7.90 89.66 7.86 120.6 7.86 152.2 7.77 134.8 7.78 130.2 7.91 102.2 7.98 81.27 7.75 83.43 7.62 66.15  SO4 mg/l SI Calcite SI gypsum SI smithsonite Si CaHPO4 365.1 -0.43 -1.47 -0.92 NA 357.649 -0.03 -1.23 -0.87 NA 60.34286 -0.62 -2.17 -0.96 NA 178.7755 -0.27 -1.68 -0.43 NA 149.0939 -0.31 -1.72 -0.64 -2.30 100.702 -0.28 -2.01 -0.71 -2.61 106.8735 -0.39 -1.94 -0.70 -2.52 165.7469 -0.08 -1.71 -0.40 NA 169.9592 -0.35 -1.71 -0.54 NA 401.6327 -0.36 -1.11 -0.43 NA 344.9143 -0.21 -1.23 -0.46 NA 385.9592 -0.22 -1.14 -0.34 NA 354.0245 -0.30 -1.21 -0.15 NA 245.9755 -0.06 -1.46 -0.23 NA 298.7755 -0.14 -1.34 -0.33 NA 140.6694 0.15 -1.76 -0.09 NA 205.5184 0.10 -1.54 -0.13 NA 197.3878 0.11 -1.53 -0.16 NA 160.6531 -0.10 -1.67 -0.26 NA 264.6857 0.02 -1.41 -0.08 NA 357.8449 -0.16 -1.19 -0.37 NA 299.5592 -0.27 -1.29 -0.42 NA 360.9796 -0.34 -1.23 -0.49 NA 248.3265 -0.22 -1.44 -0.50 NA 152.6204 -0.16 -1.70 -0.46 NA 197.1918 -0.27 -1.60 -0.52 NA 170.7429 -0.55 -1.72 -0.82 NA 206  Date 2/27/09 3/7/09 3/20/09 4/3/09 4/17/09  Date 1/11/08 1/18/08 1/25/08 2/1/08 2/22/08 2/29/08 3/7/08 3/14/08 3/28/08 4/4/08 4/11/08 4/18/08 11/7/08 11/21/08 1/2/09 1/9/09 1/16/09 1/23/09 1/30/09 2/6/09 2/13/09 2/20/09 2/27/09 3/7/09 3/13/09 3/20/09 3/27/09 4/3/09 4/10/09 4/17/09  pH Ca mg/l 8.09 71.09 7.78 79.76 7.96 60.440 7.96 67.52 8.05 61.07  pH 7.95 7.87 7.95 8.24 8.09 8.59 8.81 8.4 8.51 8.71 9.01 7.83 7.86 7.84 7.87 8.0 8.13 8.13 7.85 8.1 8.19 8.2 7.2 7.83 8.010 8.020 7.89 8.01 6.7 8.1  SO4 1047.4 1070.0 515.9 375.5 401.8 197.7 197.3 204.9 259.9 303.4 210.8 231.7 826.8 651.8 378.0 374.8 233.4 186.4 244.0 196.1 185.9 144.1 166.4 157.0 137.4 121.8 129.0 148.2 121.4 136.2  FC-1-3A SO4 mg/l SI Calcite SI gypsum SI smithsonite Si CaHPO4 151.9347 -0.07 -1.75 -0.37 NA 154.1878 -0.30 -1.71 -0.59 NA 101.7796 -0.24 -1.96 -0.55 NA 122.351 -0.22 -1.85 -0.51 NA 113.0449 -0.15 -1.91 -0.46  Ca 441.5 394.6 206.5 168.8 168.6 98.16 85.46 96.46 121.0 136.8 99.09 92.60 308.2 268.8 160.2 130.7 99.53 81.51 98.17 80.67 65.10 59.37 65.53 70.01 63.54 44.89 54.06 58.48 52.19 47.96  FC-2-2B si_Calcite si_Gypsum 0.3616 -0.5632 0.2701 -0.599 0.156 -1.0146 0.349 -1.1809 0.1795 -1.1488 0.5529 -1.5763 0.6791 -1.6137 0.3893 -1.5701 0.6029 -1.4219 0.7645 -1.3272 0.9059 -1.5618 -0.1366 -1.5423 0.0887 -0.7455 0.094 -0.8696 -0.0263 -1.199 -0.0415 -1.2526 -0.0013 -1.5117 0 -1.6587 -0.2535 -1.4992 -0.0775 -1.6408 -0.0325 -1.7272 -0.0858 -1.8551 -1.0406 -1.7661 -0.3621 -1.7716 -0.1263 -1.8556 -0.4383 -2.0031 -0.5276 -1.9191 -0.4414 -1.837 -1.6503 -1.9558 -0.368 -1.9362  si_ZnMoO4 -2.1223 -1.6606 -1.6016 -1.3894 -0.5881 -0.8124 -1.3424 -0.5303 -0.4951 -0.4199 -1.0727 -0.0895 0.917 0.8191 0.5713 0.496 0.3737 0.2013 0.3544 0.2799 0.0871 0.0622 0.2006 0.1911 0.1194 -0.0019 0.0698 0.1351 0.1667 0.1012  si_CaMoO4 -0.8977 -0.4821 -0.366 -0.1056 0.6795 0.5758 0.5096 0.6874 0.8838 1.018 0.8006 0.7927 1.6571 1.5937 1.3315 1.2646 1.0785 0.933 1.0494 0.9393 0.7968 0.7322 0.7753 0.8053 0.7513 0.5431 0.6584 0.6971 0.6634 0.613  207  Date 11/29/07 12/21/07 12/28/07 1/11/08 1/18/08 2/22/08 2/29/08 3/7/08 3/14/08 3/28/08 4/4/08 4/11/08 4/18/08 11/7/08 11/21/08 12/26/08 1/2/09 1/9/09 1/16/09 1/23/09 1/30/09 2/6/09 2/13/09 2/20/09 2/27/09 3/7/09 3/13/09 3/27/09 4/3/09 4/10/09 4/17/09  pH 7.49 6.66 6.34 6.32 6.14 6.35 6.42 6.43 6.66 6.36 6.48 6.4 6.07 6.11 6.01 6.2 6.14 6.24 6.24 6.37 6.18 6.26 6.34 6.24 6.23 6.14 6.17 6.17 6.17 6.18 6.24  Ca 1576.7 1842.7 1815.7 1757.8 1605.2 1617.6 657.8 687.7 588.4 612.5 608.8 496.2 483.6 959.8 819.7 672.2 531.6 921.6 733.2 811.7 660.6 266.7 459.6 362.0 279.2 281.8 283.0 144.7 205.1 179.3 184.6  FC-2-3A SO4 si_Calcite si_Gypsum si_smithsonite 22.5 -0.2449 -0.3862 -0.7913 20.0 -1.6919 -0.3033 -1.8503 19.0 -1.9974 -0.3479 -1.996 19.0 -2.0871 -0.3444 -2.0669 18.4 -2.2124 -0.4313 -2.047 19.1 -2.5065 -0.4961 -2.2174 19.3 -2.4847 -0.9396 -2.1915 19.3 -2.3232 -0.9249 -2.0032 20.0 -2.4149 -1.0231 -2.024 19.1 -2.6734 -0.998 -2.3132 19.4 -2.3017 -0.9836 -2.0064 19.2 -2.6657 -1.0982 -2.3301 18.2 -2.7236 -1.1345 -2.3744 18.3 -2.8722 -0.7058 -2.4669 18.0 -2.9231 -0.8121 -2.4848 18.6 -2.7307 -0.9366 -2.2691 18.4 -2.7123 -1.0849 -2.2546 18.7 -3.0229 -0.9517 -2.5362 18.7 -4.0876 -1.0899 -3.5164 19.1 -3.9146 -1.0945 -3.3616 18.5 -3.9215 -1.1584 -3.3675 18.8 -5.5385 -1.5524 -5.0091 19.0 -3.6957 -1.3831 -3.1272 18.7 -3.0798 -1.5204 -2.51 18.7 -3.0152 -1.5827 -2.4808 18.4 -3.0918 -1.5858 -2.5403 18.5 -2.9442 -1.5912 -2.3878 18.5 -3.1973 -1.9409 -2.5604 18.5 -3.577 -1.7609 -3.0412 18.5 -3.2968 -1.8612 -2.7337 18.7 -3.2608 -1.8409 -2.7536  208  Date 2/22/08 2/29/08 3/7/08 3/14/08 3/21/08 3/28/08 4/4/08 4/11/08 5/2/08 11/7/08 12/26/08 1/2/09 1/9/09 1/16/09 1/23/09 1/30/09 2/6/09 2/20/09 2/27/09 3/7/09 3/13/09 3/20/09 3/27/09 4/3/09 4/10/09 4/17/09  Date 2/20/03 3/6/03 3/20/03 4/3/03 4/25/03 5/15/03 6/15/03 11/6/03 12/4/03  pH 7.01 7.35 7.35 7.44 7.42 7.21 7.35 7.58 7.15 7.29 7.36 7.17 7.36 7.59 7.08 7.18 7.27 7.5 7.25 7.18 7.58 7.13 7.38 7.63 7.71 7.42  SO4 mg/l 1193.2 1032.2 982.2 1171.5 1040 1068 1235.5 1022.5 1191.5 1240.3 1675.3 1454.9 1437.4 1338.7 1183.3 1174.3 1076.6 966.4 976.2 924.5 808.6 866.7 789.1 644 662.6 625.9  Ca mg/l 445.5 425.1 423.8 443.4 483.0 500.8 496.5 398.1 448.5 511.6 572.1 542.8 501.0 456.4 402.1 437.8 369.5 338.6 359.6 361.0 332.8 332.900 336.500 293.9 287.4 262.0  pH si_Calcite si_Gypsum 8.62 0.84 -0.77 8.38 0.45 -1.12 8.16 0.16 -1.07 8.52 0.41 -1.31 8.41 0.52 -0.53 8.51 0.52 -1.10 8.25 0.22 -1.25 8.22 0.17 -1.38 8.15 0.15 -1.16  FC-3-2A si_Calcite -0.8693 -0.4325 -0.5173 -0.3776 -0.2929 -0.3299 -0.3417 -0.2125 -0.5823 -0.3437 -0.2898 -0.4712 -0.313 -0.1101 -0.5733 -0.5169 -0.428 -0.2057 -0.4177 -0.4746 -0.0796 -0.5109 -0.3105 -0.0922 0.0066 -0.3537  si_Gypsum -0.5104 -0.5748 -0.5964 -0.5263 -0.5476 -0.5452 -0.4748 -0.596 -0.5283 -0.489 -0.3486 -0.4113 -0.4303 -0.4745 -0.5464 -0.5306 -0.5975 -0.6558 -0.6397 -0.6609 -0.7286 -0.6989 -0.7358 -0.8415 -0.8357 -0.8639  si_smithsonite -0.8485 -0.3411 -0.4313 -0.2479 -0.2131 -0.2369 -0.3204 -0.1456 -0.3691 -0.2376 -0.2491 -0.4188 -0.2549 -0.0439 -0.4815 -0.473 -0.3561 -0.1393 -0.325 -0.3563 0.027 -0.5128 -0.1883 -0.0309 0.0757 -0.3591  mg/l P* 5.97E-06 1.32E-05 1.30E-05 1.85E-05 1.48E-05 1.16E-05 1.32E-05 2.63E-05 1.13E-05 1.49E-05 1.76E-05 1.12E-05 1.80E-05 3.14E-05 1.00E-05 1.20E-05 1.58E-05 2.73E-05 1.49E-05 1.26E-05 3.20E-05 1.14E-05 2.00E-05 3.65E-05 4.48E-05 1.87E-05  FC-07 si_ZnMoO4 si_CaHPO4 si_CaMoO4 si_smithsonite 0.28 -1000.00 1.53 -1.05 0.37 -1000.00 1.47 -1.30 0.29 -1000.00 1.35 -1.54 0.27 -1000.00 1.34 -1.31 0.37 -1000.00 1.62 -1.39 0.14 -1000.00 1.40 -1.39 0.22 -1000.00 1.28 -1.48 0.23 -1000.00 1.27 -1.53 0.38 -1000.00 1.40 -1.52 209  Date 12/16/03 12/25/03 1/1/04 1/15/04 2/15/04 3/11/04 4/1/04 4/15/04 6/3/04 9/23/04 10/14/04 12/16/04 2/3/05 3/3/05 4/1/05 5/5/05 11/17/05 1/21/06 2/16/06 3/16/06 4/13/06 11/1/06 12/6/06 1/3/07 1/31/07 2/28/07 4/5/07 5/2/07 11/8/07 11/22/07 12/7/07 12/21/07 1/4/08 2/1/08 3/7/08 4/4/08 5/2/08 11/7/08 12/5/08  FC-07 pH si_Calcite si_Gypsum si_ZnMoO4 si_CaHPO4 si_CaMoO4 si_smithsonite 7.98 -0.17 -1.63 -0.03 -1000.00 0.99 -1.83 8.23 0.18 -1.52 0.10 -1000.00 1.16 -1.53 7.88 -0.12 -0.57 0.40 -1000.00 1.56 -1.94 7.90 -0.04 -0.60 0.42 -1000.00 1.54 -1.81 7.97 -0.10 -0.80 0.30 -1000.00 1.42 -1.86 7.75 -0.23 -0.79 0.10 -1000.00 1.61 -2.37 8.06 0.00 -1.02 0.33 -1000.00 1.46 -1.77 8.39 0.22 -1.19 0.17 -1000.00 1.32 -1.58 8.26 0.17 -1.41 -0.03 -1000.00 1.15 -1.65 8.34 0.24 -1.45 -0.08 -1000.00 1.11 -1.60 8.04 -0.01 -1.32 -0.21 -1000.00 0.84 -1.71 8.04 0.17 -0.76 0.22 -1000.00 1.48 -1.73 8.04 0.06 -0.91 0.19 -1000.00 1.40 -1.80 8.12 0.09 -1.18 -1000.00 -1000.00 1.28 -1000.00 8.15 0.04 -1.39 -1000.00 -1000.00 0.99 -1000.00 7.83 -0.26 -1.64 -1000.00 -1000.00 0.91 -1000.00 8.25 0.13 -1.67 -1000.00 -1000.00 0.92 -1000.00 8.62 0.84 -0.77 0.28 -1000.00 1.53 -1.05 8.38 0.45 -1.12 0.37 -1000.00 1.47 -1.30 8.16 0.16 -1.07 0.29 -1000.00 1.35 -1.54 8.52 0.41 -1.31 0.27 -1000.00 1.34 -1.31 8.41 0.52 -0.53 0.37 -1000.00 1.62 -1.39 8.51 0.52 -1.10 0.14 -1000.00 1.40 -1.39 8.25 0.22 -1.25 0.22 -1000.00 1.28 -1.48 8.22 0.21 -1.39 0.24 -1000.00 1.27 -1.47 8.15 0.19 -1.17 0.39 -1000.00 1.40 -1.47 7.98 -0.07 -1.65 0.00 -1000.00 0.99 -1.71 8.23 0.21 -1.53 0.10 -1000.00 1.16 -1.50 7.88 -0.01 -0.59 0.45 -1000.00 1.57 -1.78 7.90 0.04 -0.62 0.46 -1000.00 1.55 -1.70 7.97 0.01 -0.82 0.35 -1000.00 1.42 -1.71 7.75 -0.12 -0.81 0.15 -1000.00 1.61 -2.22 8.06 0.11 -1.04 0.38 -1000.00 1.47 -1.63 8.39 0.32 -1.21 0.20 -1000.00 1.32 -1.46 8.26 0.30 -1.43 0.01 -1000.00 1.15 -1.50 8.34 0.34 -1.47 -0.05 -1000.00 1.12 -1.48 8.04 0.06 -1.33 -0.19 -1000.00 0.84 -1.62 8.04 0.24 -0.77 0.25 -1000.00 1.48 -1.64 8.04 0.13 -0.92 0.21 -1000.00 1.40 -1.71 210  Date 1/9/09 2/6/09 3/6/09 4/3/09  FC-07 pH si_Calcite si_Gypsum si_ZnMoO4 si_CaHPO4 si_CaMoO4 si_smithsonite 8.12 0.16 -1.19 -1000.00 -1000.00 1.28 -1000.00 8.15 0.12 -1.41 -1000.00 -1000.00 0.99 -1000.00 7.83 -0.25 -1.64 -1000.00 -1000.00 0.91 -1000.00 8.25 0.13 -1.67 -1000.00 -1000.00 0.92 -1000.00  8.2 Mini-Columns The solubility controls effecting field cell geochemistry were measured by similar Phreeqc model as the field cells. The input parameters are defined by the measured geochemistry in the mini-column effluent, atmospheric CO2 concentrations were set as dissolved inorganic carbon concentrations will be in equilibrium with this value and pH. The concentration of Cl-1 was allowed to float to maintain the charge balance because this element cannot be measured by ICP-OES. This is justified by Cl-1 being present in the influent media recipe, and significant concentrations of this anion are being leached from the waste rock in the field. An example of the Phreeqc script used below. -----------------------------------Reading input data for simulation 1. -----------------------------------DATABASE C:\Program Files\USGS\Phreeqc Interactive 2.15.0\minteq.v4.dat SOLUTION 1 Ca of run 4 1-1-3A run 4 temp 25 pH 7 pe 4 redox pe units mg/l density 1 K 8.6 S(6) 219.6 P 64.7 Mg 78.3 Na 6.6 Ca 174 Cl 100 charge N(-3) 176.8 Mo 126.9 Zn 0 Cu 0 EQUILIBRIUM_PHASES 1  211  CO2(g) -3.5 10 end ------------------------------------------Beginning of initial solution calculations. -------------------------------------------  ------------------------------Saturation indices-----------------------------Phase  SI log IAP  log KT  Anhydrite -1.47 -5.83 -4.36 CaSO4 Aragonite -2.17 -10.47 -8.30 CaCO3 Artinite -9.17 0.43 9.60 MgCO3:Mg(OH)2:3H2O Brucite -5.80 11.04 16.84 Mg(OH)2 Ca3(PO4)2(beta) 2.96 -25.96 -28.92 Ca3(PO4)2 Ca4H(PO4)3:3H2O 2.56 -44.52 -47.08 Ca4H(PO4)3:3H2O CaHPO4 0.71 -18.57 -19.27 CaHPO4 CaHPO4:2H2O 0.43 -18.57 -19.00 CaHPO4:2H2O Calcite -1.99 -10.47 -8.48 CaCO3 CaMoO4 2.03 -5.92 -7.95 CaMoO4 CH4(g) -66.14 -107.18 -41.05 CH4 CO2(g) -3.50 -21.65 -18.15 CO2 Dolomite(disordered) -4.53 -21.07 -16.54 CaMg(CO3)2 Dolomite(ordered) -3.98 -21.07 -17.09 CaMg(CO3)2 Epsomite -3.84 -5.96 -2.13 MgSO4:7H2O Gypsum -1.22 -5.83 -4.61 CaSO4:2H2O H2MoO4 -4.22 -17.10 -12.88 H2MoO4 H2S(g) -60.87 -68.88 -8.01 H2S Halite -7.05 -5.45 1.60 NaCl Huntite -12.32 -42.29 -29.97 CaMg3(CO3)4 Hydromagnesite -22.62 -31.38 -8.77 Mg5(CO3)4(OH)2:4H2O Hydroxylapatite 10.99 -33.34 -44.33 Ca5(PO4)3OH K2MoO4 -13.92 -10.65 3.26 K2MoO4 Lime -21.52 11.18 32.70 CaO Magnesite -3.15 -10.61 -7.46 MgCO3 Mg(OH)2(active) -7.75 11.04 18.79 Mg(OH)2 Mg3(PO4)2 -3.08 -26.36 -23.28 Mg3(PO4)2 MgHPO4:3H2O -0.53 -18.70 -18.18 MgHPO4:3H2O MgMoO4 -4.20 -6.05 -1.85 MgMoO4 Mirabilite -9.22 -10.33 -1.11 Na2SO4:10H2O MoO3 -9.10 -17.10 -8.00 MoO3 MoS2 -105.97 -176.23 -70.26 MoS2 Na2Mo2O7 -10.92 -27.52 -16.60 Na2Mo2O7 Na2MoO4 -11.91 -10.42 1.49 Na2MoO4 Na2MoO4:2H2O -11.65 -10.42 1.22 Na2MoO4:2H2O Natron -13.67 -14.98 -1.31 Na2CO3:10H2O Nesquehonite -5.94 -10.61 -4.67 MgCO3:3H2O O2(g) -40.32 42.77 83.09 O2 Periclase -10.54 11.04 21.58 MgO Portlandite -11.63 11.18 22.80 Ca(OH)2 Sulfur -45.35 -47.49 -2.14 S Thenardite -10.65 -10.33 0.32 Na2SO4 Thermonatrite -15.61 -14.97 0.64 Na2CO3:H2O  212  -----------------End of simulation.  8.3 References Parkhurst, D. L., Appelo, C.A.J., 1999. Users Guide to PHREEQC—A Computer Program for Speciation, Reaction-path, 1D-transport and Inverse Geochemical Calculations. US Geol. Surv. Water Resour. Invest.  213  

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