University of Nevada, Reno
Limnology, Arsenic Sorption, and Geochemical Modeling of Mine Pit Lakes
A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Hydrogeology
by
Ronald Lee Hershey
Glenn C. Miller /Dissertation Advisor
May, 2010
THE GRADUATE SCHOOL
We recommend that the dissertation prepared under our supervision by
RONALD LEE HERSHEY
entitled
Limnology, Arsenic Sorption, And Geochemical Modeling Of Mine Pit Lakes
be accepted in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
Glenn C. Miller, Advisor
James M. Thomas, Committee Member
Roger L. Jacobson, Committee Member
Charalambos Papelis, Committee Member
V. Dean Adams, Graduate School Representative
Marsha H. Read, Ph. D., Associate Dean, Graduate School
May, 2010
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ABSTRACT
Mining of disseminated low-grade ore requires the removal of hundreds of millions
of kg of rock resulting in large open pits. This type of large-scale mining has increased
substantially in the past several decades in the western U.S. with the development of very
large open pits in Arizona, Montana, California, and Nevada. Of over 30 large mines in
Nevada, eight will each excavate more than 600 billion kg of ore and waste rock.
Studies of a large pit lake in Yerington, Nevada (Anaconda Mine), where the lake
has been filling with groundwater for over 30 y, shows that the pit-lake limnology is
similar to two nearby natural terminal lakes. The pit lake, even though it has a much
smaller surface area-to-mean depth ratio than the natural lakes, is also monomictic and
oligotrophic. The pit-lake’s water chemistry has slightly alkaline pH at approximately 8.1
and total dissolved solids of approximately 600 mg L-1. Pit-lake ions are predominantly
2+ + 2- Ca , Na , and SO4 , and concentrations for these ions are similar at different depths. In toxicity tests, the zooplankton Daphnia magna exposed to pit-lake water diluted in
Walker River water had a 48 hr LC50 of 44.4 % with none of the animals surviving for
48 hr in 100 % pit-lake water. Elevated Cu and Se concentrations in the pit lake are well
above aquatic life water-quality standards and may be adversely affecting phyto- and
zooplankton populations.
From laboratory batch sorption experiments, pit-lake sediments from a Carlin-type
gold deposit pit lake had the greatest capacity to adsorb As(V) and As(III) under both
oxidizing and slightly reducing conditions. Sediments from a porphyry copper pit lake
ii had the least adsorption capacity while sediments from a quartz-adularia precious metal deposit pit lake had adsorption capacity between the other two sediments. Surface complication modeling suggests that the adsorption capacity of the sediments is controlled by the presence, and amount of, amorphous Fe hydroxides in the sediments.
Modeling of the geochemical evolution of the Yerington pit-lake chemistry over a six-year period from 1994 to 2001 successfully reproduced the observed pit-lake chemistry and most of the observed changes in water chemistry except for Cu. Modeling demonstrated that the pit-lake water chemistry is dominated by the current pit-lake chemistry and that other water inputs to the lake including precipitation, spring flow, and deep groundwater are a small percentage (< 10 %) of the water in storage in the pit lake.
Evaporation, although a small component of the overall water balance for the pit lake
(~ 3 % of the annual water input), is slowly increasing the TDS of the lake.
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ACKNOWLEDGEMENTS
I would like to thank my advisor, Glenn C. Miller, for his scientific guidance and interest in mining and pit-lake issues. I am also indebted to my committee: Glenn Miller,
Jack Hess, Jim Thomas, Lambis Papelis, and Dean Adams. I would like to also thank the
Desert Research Institute, Roger Jacobson, John Warwick, and Michael Young for their
financial support, and Debi Noack for her assistance with preparing the final version of
the dissertation.
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CONTENTS Abstract...... i Acknowledgements...... iii List of Figures...... vi List of Tables ...... vii CHAPTER 1. Introduction...... 1 Background...... 3 Existing Pit-Lake Water Quality...... 3 Conceptual Model for Predicting Pit-Lake Water Quality ...... 6 Limnology...... 8 Wall-Rock Oxidation...... 9 Metal Removal by Sorption...... 10 Evaporative Concentration ...... 12 Pit Lake Models...... 13 Objectives ...... 15 Approach...... 16 Characterization of Limnological Processes in an Existing Pit Lake...... 16 Quantification of As Sorption on Different Pit-Lake Sediments...... 18 Development of an Approach to Modeling Pit-Lake Water-Quality ...... 19 References...... 19 CHAPTER 2. Limnology and Water Quality of A Porphyry-Copper Pit Lake and Comparison to Two Nearby Terminal Desert Lakes in Northern Nevada, USA ...... 25 Introduction...... 26 Study Site...... 28 Morphology ...... 28 Climate...... 30 Geology...... 30 Hydrology ...... 31 Methodology...... 31 Results...... 35 Water Temperature ...... 35 Dissolved Oxygen...... 36 Nutrients...... 37 Biological Activity...... 38 Hypolimnetic Oxygen Deficit...... 38 Particulate Organic Matter...... 42 Plankton ...... 43 Water Quality...... 47 Acute Toxicity Tests...... 47 Discussion...... 51 Limnological Description of Yerington Pit Lake ...... 51 Comparison of Yerington Pit Lake to Desert Lakes...... 54 Cu and Se...... 60 Conclusions...... 64 Acknowledgements...... 65
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References...... 65 CHAPTER 3. Laboratory Experiments of As(V) and As(III) Sorption Onto Pit-Lake Sediments from Three Different Ore-Body Types ...... 71 Introduction...... 72 Methodology...... 74 Pit-Lake Sediment Characterization ...... 74 Experimental Procedures ...... 76 Results...... 78 Pit-Lake Sediment Characterization ...... 78 As(V) Adsorption ...... 85 Effect of Ionic Strength Effect...... 85 Effect of pH...... 87 Adsorption Isotherms...... 88 As(III) Adsorption ...... 91 Effect of Ionic Strength...... 91 pH Effect...... 91 Adsorption Isotherms...... 94 Surface Complexation Modeling...... 96 Discussion...... 101 As(V) Adsorption ...... 101 As(III) Adsorption ...... 103 Conclusion ...... 105 References...... 107 CHAPTER 4. Modeling of Physical and Chemical Processes in the Yeringinton Pit Lake, Yerington, Nevada, USA ...... 110 Introduction...... 111 Background...... 113 Study Site...... 113 Morphology...... 114 Climate...... 114 Geology...... 114 Hydrology ...... 116 Methodology...... 118 Pit-Lake Data ...... 118 Historical Data ...... 118 Data Collection ...... 119 Geochemical Modeling...... 126 Pit Filling ...... 126 Geochemical Modeling...... 131 Results and Discussion ...... 136 Preliminary Modeling ...... 137 Final Modeling...... 143 Conclusions...... 151 References...... 152 CHAPTER 5. Conclusions...... 155
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APPENDIX A. Vertical Profile Data for the Yerington Pit Lake...... 157 APPENDIX B. SEM-EDX Analysis of Pit-Lake Sediments...... 163 APPENDIX C. SEM-EDX Analysis of Wall Rocks ...... 178 APPENDIX D. Water-Level Data for the Yerington Pit Lake...... 201 APPENDIX E. Saturation indices for Yerington pit lake...... 203
LIST OF FIGURES 1.1. Chemical and physical processes in pit lakes...... 2 2.1. Location of the Yerington pit lake, Pyramid Lake, and Walker Lake Nevada, USA...... 29 2.2. Contours of temperature of the Yerington pit lake from July 2000 to September 2001...... 36 2.3. Contours of dissolved oxygen of the Yerington pit lake from July 2000 to September 2001...... 37 2.4. Volume weighted average dissolved oxygen concentrations in the Yerington pit lake from July 2000 to September 2001...... 41 2.5. Chlorophyll a and dissolved oxygen concentrations vs. depth in the Yerington pit lake...... 42 2.6. Change in TDS (A) and Cl- (B) concentrations from 1995 through 2001...... 49 2.7. Change in Cu (A) and Se (B) concentrations from 1995 through 2001...... 50 2.8. Normal distribution probability plot and 95 % confidence interval of 48 h toxicity test probit data for Daphnia magna...... 51 3.1. pe-pH diagram for predominant aqueous species of arsenic at equilibrium and 298.15 K and 1 atmosphere pressure (from Nordstrom and Archer, 2003)...... 73 3.2. SEM micrograph of Yerington sediment showing characteristic flaky nature of smectite/montmorillonite clay...... 80 3.3. Adsorption of 6.67 μM As(V) on 1 g L-1 Yerington (A), Tuscarora (B), and Big Springs (C) pit-lake sediments as a function of ionic strength (I)...... 86 3.4. Adsorption of 1.33 μM As(V) on 1 g L-1 Yerington, Tuscarora, and Big Springs pit-lake sediments as a function of pH. Maximum adsorption occurs at pH ≤ 6 and decreases with increasing pH for Tuscarora and Big Springs...... 87 3.5. Adsorption of As(V) on 1 g L-1 Yerington (A), Tuscarora (B), and Big Springs (C) pit-lake sediments as a function of concentration at pH 5 (circles), 7 (squares), and 9 (diamonds)...... 89 3.6. Adsorption of 6.67 μM As(III) on 1 g L-1 Yerington (A), Tuscarora (B), and Big Springs (C) pit-lake sediments as a function of ionic strength (I)...... 92 3.7. Adsorption of 6.67 μM As(III) on 1 g L-1 Yerington, Tuscarora, and Big Springs pit-lake sediments as a function of pH...... 93 3.8. Adsorption of As(III) on 1 g L-1 Yerington (A), Tuscarora (B), and Big Springs (C) pit-lake sediments as a function of concentration at pH 5 (circles), 7 (squares), and 9 (diamonds)...... 95
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3.9. Adsorption of As(V) on pit-lake sediments with diffuse double-layer outputs using mononuclear surface reactions, Yerington (A), Tuscarora (B), and Big Springs (C)...... 99 3.10. Adsorption of As(III) on pit-lake sediments with diffuse double-layer outputs using mononuclear surface reactions, Yerington (A), Tuscarora (B), and Big Springs (C)...... 100 4.1. Chemical and physical processes in pit lakes...... 112 4.2. Location of Yerington pit lake...... 115 4.3. Location of wells at the Yerington pit lake...... 118 4.4. Water-level measurements and modeled water-level changes with time for the Yerington pit lake starting in June 1978 at the end of mining and through June 2002...... 128 4.5. Correlation between pan evaporation at the Yerington pit lake and pan evaporation at the Fallon, NV long-term station from February 2001 to June 2002...... 129 4.6. Changes in deep groundwater inflow relative to pit-lake volume with time...... 137 4.7. Measured and mixing model results for TDS and Cl concentrations in the Yerington pit lake...... 138 4.8. Measured and mixing model results for Cu and Se in the Yerington pit lake...... 139 4.9. Measured and final model results for TDS and Na in the Yerington pit lake...... 146 4.10. Measured and final model results for pH and Cl in the Yerington pit lake...... 147 4.11. Measured and final model results for SO4 and Se in the Yerington pit lake...... 148 4.12. Measured and final model results with chrysocolla and Cu adsorption (top) and final model results without chrysocolla, but with Cu adsorption (bottom) in the Yerington pit lake...... 150
LIST OF TABLES
1.1. Geochemistry of existing pit lakes (modified from Miller et al., 1996)...... 4 2.1. Nutrients for the Yerington pit lake pit-wall springs and Walker River...... 39 2.2. Particulate organic carbon and nitrogen in the Yerington pit lake and stoichiometric ratios as approximate indicators of relative nutrient limitations...... 43 2.3. Phytoplankton cell densities in the Yerington pit lake...... 44 2.4. Water chemistry for the Yerington pit lake pit-wall springs and pit dewatering wells...... 48 2.5. Comparison of limnological parameters of the Yerington pit lake to Walker and Pyramid lakes ...... 55 2.6. Cu and total organic carbon in sediments in the Yerington pit lake...... 62 3.1. Quantitative X-ray diffraction results from three pit-lake sediments used for arsenic adsorption experiments...... 79 3.2. Chemical composition of pit-lake sediments by x-ray fluorescence (XRF) and inductively coupled plasma–mass spectrometry (ICP-MS)...... 81 3.3. Sediment characteristics for three pit-lake sediments used in arsenic adsorption experiments...... 83
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3.4. Linear (Kd) and Freundlich (KF) isotherm parameters for As(III) and As (V) adsorption onto three pit-lake sediments...... 90 3.5. Reactions used to model As(V) and As(III) sorption onto pit-lake sediments...... 97 3.6. Model input values and resulting intrinsic surface complexation constants for mononuclear As(V) and As(III) adsorption on pit-lake sediments using the diffuse double-layer model in PHREEQC...... 98 4.1. Water chemistry data for the Yerington pit lake...... 120 4.2. Water chemistry data for groundwater and Walker River water at the Yerington pit lake...... 122 4.3. Quantitative X-ray diffraction results and total organic carbon (TOC) for Yerington pit-lake sediments...... 124 4.4. Chemical composition of Yerington pit-lake sediments by x-ray fluorescence (XRF) and inductively coupled plasma–mass spectrometry (ICP-MS)...... 124 4.5. Example values for the calculation of GWi, deep groundwater input, into the Yerington pit lake...... 130 4.6. Gas or mineral phase considered in preliminary geochemical modeling of the Yerington pit-lake...... 133 4.7. Model input for adsorption of Cu onto iron hydroxides (from Dzombak and Morel, 1990)...... 135 4.8. Example of interactive modeling process conducted to select mineral assemblage for final pit-lake water chemistry modeling...... 142 4.9. Mineral assemblage used and final Yerington pit-lake water chemistry simulation results...... 145
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CHAPTER 1 INTRODUCTION
Mining of disseminated low-grade ore requires the removal of hundreds of millions
of kg of rock resulting in large open pits. This type of large-scale mining has increased
substantially in the past several decades in the western U.S. with the development of very
large open pits in Arizona, Montana, California, and Nevada (NV). Of over 40 large
mines in NV, eight will each excavate more than 600 billion kg of ore and waste rock
(Miller et al., 1996).
Often, dewatering of groundwater is required to access ore below the water table.
Several of the open pit mines in NV presently are operating at levels between 100 and
500 m below the water table. When mining is completed and dewatering discontinued,
groundwater will flow into the pits creating standing bodies of water called pit lakes.
More than 30 open pit mines in NV will eventually create pit lakes containing an
estimated one trillion liters of water (Miller et al., 1996; Shevenell, 2000a, b).
Because the larger pits are now being mined and few pit lakes presently exits, the
eventual water quality of these lakes is largely unknown. Pit lakes that become
contaminated may affect scarce water resources, particularly in the western U.S., and
may also provide a source of groundwater contamination. Because many pits are of
immense size, remediation of poor water-quality pit lakes will be very expensive and, in
many cases, impractical. Therefore, it is critical to identify the important processes that
affect pit-lake water quality and to be able to model these processes so that long-term pit-
lake water quality may be predicted.
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Several factors that may control pit-lake water quality include their unusual shape,
limnology, and geochemistry. These factors are characteristically different than found in
naturally occurring water bodies. Pit lakes will have a lower surface-to-volume ratio than
most other lakes and a correspondingly small littoral zone. Limnological factors such as
vertical stratification, turnover, and biological processes may be substantially different.
Geochemical reactions such as dissolution of wall-rock minerals, including those
containing metals, will affect the eventual water quality in the pit lakes as will sorption of
aqueous metals onto precipitating phases. Some of the physical and chemical processes
that are important in pit lakes are illustrated in Fig. 1.1 (Bowell, 2002; Castendyk and
Webster-Brown, 2007a; Davis, 2003; Davis et al., 2006; McCullough and Lund, 2006;
Tempel et al., 2000).
Figure 1.1. Chemical and physical processes in pit lakes.
Arsenic, often associated with precious metals ore deposits, is an important contaminant released during large-scale gold mining. Like most metals, As is highly
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mobile in acidic environments, which are common at precious metal mines. However, many precious metals mines in NV are carbonate hosted and non-acid generating. This does not necessarily limit the mobility of As since negatively charged oxyanions of As are present in these well-buffered higher pH environments. Because of the complex oxidation/reduction and sorption behavior of As, prediction of the long-term mobility of
As in pit lakes is uncertain.
The research presented in this dissertation examines several aspects of predicting pit-lake water-quality: identifying limnological processes, delineating the mobility and fate of As in pit lakes, and developing geochemical modeling techniques encompassing these processes. The results of this dissertation provide a framework for prediction of pit- lake water quality and for assessing the long-term impacts of pit lakes on the environment.
BACKGROUND
Existing Pit-Lake Water Quality
Existing pit lakes provide examples of a variety of possible geochemical outcomes for pit lakes (Table 1.1 modified from [Miller et al., 1996]). The best example of severely degraded pit water quality is at the Berkeley pit in Butte, Montana where Cu was mined from a porphyry deposit. As part of the largest complex of Superfund sites in the nation, this pit is acidic, containing substantial metal concentrations, and other constituents that exceed Safe Drinking Water Act maximum contaminant levels. The pH of the Berkeley pit lake ranges from 2.7 at the surface to 3.2 at depth. Dissolved oxygen decreases in the upper three m and becomes anoxic at depth. Over the top five m, most of the Fe(III) is
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Table 1.1. Geochemistry of existing pit lakes (modified from Miller et al., 1996). Constituent EPA Berkeley Liberty pit, Kimbley pit, Yerington South pit, Center pit, North pit, Cortez (pH in pH Drinking pit, Butte, Robinson Robinson pit, NV, 1991 Getchell Getchell Getchell pit, NV, units, others in Water MT, 1987 District, NV, Disritct, NV, Mine, NV, Mine, NV, Mine, NV, 1992 mg L-1) Standard 1993 1993 1982 1982 1982 pH 6.5 to 8.5 2.8 3.21 7.61 8.45 5.96 5.27 7.67 8.07 TDS 500 6,240 3,580 631 2110 2140 2420 432 Cl 250 9 48.9 286 36 34.4 30.2 25.7 24.4
SO4 250 5,740 3,700 1,00 270 1,380 1,410 1,570 90.2
NO3 as N 10 <0.04 <0.02 0.67 0.01 0.01 0.01 0.207 F 1.4-2.4 18.5 3.01 1.4 2.4 2.4 1.6 2.4 Cr 0.05 0.107 0.059 <0.005 <0.02 <0.02 <0.02 Mn 0.05 95 116 0.17 0.32 1.8 4.3 0.13 0.0017 Fe 0.3 386 62.2 <0.05 0.01 0.8 2.1 0.16 0.134 Cu 1 156 37.1 0.06 0.16 0.04 0.04 <0.005 Zn 5 280 52.1 1.81 0.01 0.33 0.4 0.02 0.002 As 0.05 0.05 <0.005 <0.005 0.003 0.009 0.008 0.38 0.038 Se 0.01 <0.005 <0.005 0.13 <0.002 <0.002 0.003 Ag 0.05 0.022 0.021 <0.01 <0.005 <0.005 <0.005 Cd 0.01 1.3 0.647 <0.005 <0.001 <0.005 <0.005 <0.005 Ba 1 <0.002 <0.002 0.034 0.06 Hg 0.002 <0.0002 <0.0002 <0.0005 <0.2 <0.2 <0.2 0.00046 Pb 0.05 <0.005 <0.005 <0.005 <0.05 <0.05 <0.05 0.0043
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reduced to Fe(II). Arsenic concentrations are low in the upper 15 m and increase with
depth. Trace metals, including Pb, Cd, Cu, Mn, and Zn are present in high concentrations
that increased with depth (Davis and Ashenberg, 1989; Gammons and Duaime, 2006;
Gammons et al., 2003; Newbrough and Gammons, 2002).
At the Robinson District porphyry copper mine Ely, NV, the Liberty pit had a pH of
3.2 while the Kimbley pit was slightly alkaline (pH 7.6). Both the Kimbley and the
Liberty pits have calcareous wall rocks, but historical acid leaching of perimeter dumps at the Liberty pit resulted in acidification of the pit lake (PTI, 1994b). The variation in
Robinson District pit water quality illustrates the differences that can occur even among pits in close proximity at a single mine site and demonstrates the difficulty encountered understanding the geochemical evolution of a pit lake.
Similar to the Berkeley pit, the Anaconda pit in Yerington, NV is also within a porphyry copper ore deposit. However, unlike the Berkeley pit, the Yerington pit lake is alkaline (pH 8.4) because the ore body was predominantly oxidized and ore minerals were mostly copper silicates and oxides. The water quality is substantially better than the
Berkeley pit with only Se and Mn exceeding drinking water and aquatic wildlife standards.
The Getchell Mine in outside Winnemucca, NV contains arsenopyrite (AsFeS2) as one of the main mineral phases associated with the gold deposit (Miller et al., 1996).
Three pits (North, Central, and South pits) filled with water from 1969 through 1985 and were later dewatered again. In 1982, filtered composite samples were collected from each of the pit lakes. Constituents above drinking water standards included pH, Fe, As, TDS,
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SO4, and Mn. More recent groundwater samples, close to the ore body, had As
concentrations as high as 0.8 mg/L (Bowell and Parshley, 2005; Davis et al., 2006;
Tempel et al., 2000).
The pit lake at the oxide-ore limestone-hosted Cortez Mine at Cortez, NV started
filling with water in the early 1970’s. Because of the benign water quality in this pit, fish were introduced in the early 1980’s and a population has been sustained without feeding,
indicating that there is enough primary productivity in the pit lake to support a food web.
Unlike many other pits, the Cortez pit lake is relatively shallow and contains a sizeable littoral zone that supports plant life. In 1996, this pit lake drained rapidly, mostly likely because of dewatering activities at the nearby Pipeline Mine.
From these examples, it is clear that pit-lake water quality is highly variable. Pit lakes that occur in sulfide-rich ore bodies generally will have poor water quality with low pH and high metals concentrations. Pit lakes in oxidized ore bodies that contain appreciable carbonate are likely to have better quality water with neutral or alkaline pH
and much lower metal concentrations. More examples of water quality in existing pit-
lakes in NV are found in (Shevenell et al., 1999) and (Davis and Eary, 1997).
Geochemical trends in pit lakes from the U.S. and Canada were examined by (Eary,
1999).
Conceptual Model for Predicting Pit-Lake Water Quality
A general conceptual model for predicting pit-lake water quality assumes that the final water composition of a pit lake will result from a combination of processes. These processes include oxidation of pit wall rocks, transport of leachate from pit wall rocks by
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groundwater flowing into the pit, equilibration of pit-lake water with atmospheric oxygen
and carbon dioxide, precipitation of saturated labile minerals within the lake water column, sorption of metals and metalloids onto the precipitated minerals, and evaporative concentration of pit-lake waters in arid areas. Ideally, these processes can be incorporated into a model that can be used to predict long-term pit-lake water quality.
Dewatering and excavation of an ore body exposes wall rock to oxygen, allowing
oxygen to diffuse into rock through fractures and pores, and to react with sulfide
minerals. Oxidation of sulfide minerals in wall rocks produces sulfuric acid (Moses et al.,
1987) and the rate of sulfide mineral oxidation is limited by oxygen diffusion into the
rock matrix (Davis and Ritchie, 1987; Pantelis and Ritchie, 1991). In sulfide bearing rock
lacking buffering capacity, the interstitial water becomes acidic, thereby accelerating
growth of iron-oxidizing bacteria and increasing the sulfide mineral oxidation reaction
rate by several orders of magnitude (Hiskey and Schlitt, 1982; Lowson, 1982; McKibben
and Barnes, 1986; Moses et al., 1987; Nordstrom, 1976, 1977, 1982). Acidic conditions
will release metals and metalloids, such as As, from the sulfide minerals present in the
wall rocks. Conversely, the presence of carbonate minerals may maintain alkaline pH in
interstitial waters, inhibiting iron-oxidizing bacteria growth, and precluding excessive
solubilization of metals and metalloids (Nicholson et al., 1988, 1990).
Oxidation continues in wall rock until the pit is inundated by groundwater at which
time oxidation essentially stops because of the slow diffusion rate of oxygen in water.
Groundwater flowing into the pit leaches sulfide mineral oxidation products from the pit wall rocks and transfers them to the pit lake. Chemical reactions occur in the pit lake as
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groundwater and wall-rock leachate equilibrate with the atmosphere and various labile
minerals precipitate. As these minerals precipitate, metals and metalloids, such as As, adsorb onto the mineral surfaces. The precipitated minerals then settle out of the water
column forming sediments at the bottom of the lake sequestering the sorbed metals and
metalloids. Because of the arid climate in the western U.S., solutes introduced into a pit
lake are also concentrated by evaporation enhancing mineral precipitation.
Limnology
The physical characteristics of pit lakes will be different than most natural lakes and
man-made reservoirs. Pit lakes will have much smaller surface areas and much greater
depths than most other lakes (Doyle and Runnells, 1997; Lyons et al., 1994; Naugle and
Atkinson, 1993). In addition, most pit lakes will have virtually no shoreline or shallow water area, limiting the development of biological communities in the littoral zone.
Because of these unique physical characteristics, pit-lake limnology may be very different. (Lyons et al., 1994) indicated that Pyramid and Walker lakes currently undergo seasonal stratification and that they are the best climatological analogies to the pit lakes in northern NV. (Lebo et al., 1993) investigated the water quality of Pyramid Lake and reported that seasonal variations in physiochemical parameters demonstrated typical patterns for warm monomictic lakes in the northern hemisphere. The water column was isothermal (fully mixed) during January through March and then thermally stratified throughout the spring, summer, and fall. Dissolved oxygen concentrations in the water column were highest during winter isothermal conditions and then decreased in the hypolimnion during the stratified period.
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Understanding the development and destruction of physical stratification in a pit
lake is vital to understanding and predicting the principal chemical reactions that control pit-lake water quality (Atkins et al., 1997; Balistrieri et al., 2006; Bird, 1993; Castendyk
and Webster-Brown, 2007a; Causius et al., 2003; Denimal et al., 2005; Dowling et al.,
2004; España et al., 2009; Gammons and Duaime, 2006; Hamblin et al., 1999; Parshley
and Bowell, 2003; Saunders-MacDonald, 1992; Stevens and Lawrence, 1998; von
Rohden and Ilmberger, 2001). Turnover of pit lakes is important because of the central
role of oxygen in many chemical reactions. If stratification occurs and there is no mixing,
deep waters may become anoxic. With reducing conditions, iron hydroxides, which form
on exposed surfaces during mining, would re-dissolve and As sorbed onto these mineral
phases would be mobilized. In addition, As precipitated or adsorbed under oxidizing
conditions and removed from pit lakes by sedimentation may be affected. Aggett and
O'Brien (1985) observed both the release of As from lake sediments during stratification
and an increase in the ratio of As(III) to As(V). Conversely, the lack of oxygen in the
hypolimnion under anoxic conditions would limit the oxidation of sulfide minerals and
minimize the continued release of As from the pit wall rocks. If pit lakes are well mixed,
sulfide oxidation and the resulting increased acidification may be important.
Wall-Rock Oxidation
Dewatering and excavation activities expose sulfide minerals in pit wall rock to
oxygen in the atmosphere. During dewatering, residual pore and fracture water will be
retained in pit wall rock under now oxidizing conditions and will facilitate weathering
and generation of acid leachate. When dewatering ceases, remnant products of oxidation
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reactions in the pit walls, including As, are available for leaching into the pit lake by
inflowing groundwater. Pit-water chemistry is affected by the rock exposed in the pit surface where the majority of acid and metals will leach from acid generating rock. When potential wall-rock acid generation is evaluated, different rock types expected to be present in the pit wall are selected for static net carbonate value and kinetic humidity cell tests (Connors et al., 1997; Davis et al., 2006; Dowling et al., 2004; Fennemore et al.,
1998; Lapakko and Wessels, 1995; Morin and Hutt, 2001).
Net carbonate value tests determine the difference between acid generating potential and acid neutralizing potential. To simulate reactivity and leachability of metals from post-mining wall rock, humidity cell tests are run using core materials representative of the expected pit surface. Cumulative mass release of acid and metals are then used in conjunction with the wall-rock oxidation rind thickness model to calculate pit-lake bulk chemistry. Sensitivity analysis of several current models suggests five predominant parameters in determining bulk chemical composition (PTI, 1992, 1994a, b, 1995). These parameters are metals released from wall rock, acid released from wall rock, net carbonate value distribution, wall rock porosity, and available moisture in wall rock. Pit- lake metal concentrations are dependent upon pH and abundance of mineral surfaces
(including iron oxides and oxyhydroxides) that can be very effective metal sorbents.
Metal Removal by Sorption
The pit-lake water-quality prediction assumes that large concentrations of As will be removed from pit-lake water by adsorption on iron hydroxides (PTI, 1992, 1994a, b,
1995). As alkaline groundwater is mixed with acidic leachate from wall rock, the iron
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released from the wall rocks, under oxidizing conditions, will precipitate as iron
hydroxides in the pit lake. Dissolved As is sorbed on iron hydroxide particles that are
subsequently removed by sedimentation. Anion adsorption is typically highly pH-
dependent, increasing with decreasing pH (Davis and Leckie, 1980; Dzombak and Morel,
1990; Hayes et al., 1988; Smith, 1991; Swallow et al., 1980). Ion adsorption is typically
more complex in natural systems receiving acidic rock leachate where adsorption may be
limited by the availability of sorbing phases, such as iron hydroxides and oxyhydroxides.
For example, (Smith et al., 1992) found that adsorption of As anions increased with
increasing pH similar to the behavior of metal cations, but that adsorption of several
metals peaked at pH near 7 then decreased at higher pH. Such apparently inconsistent behavior may result when adsorption is affected by both aqueous ion interactions and competition for available adsorption sites, as well as from sorption of ternary complexes.
Ion sorption on surface hydroxyl groups of oxide and hydroxide minerals can also
be highly ionic strength dependent. Ionic strength dependent sorption is frequently associated with formation of weak, outer-sphere, ion pair complexes, whereas ionic strength independent sorption is typically considered an indication for the formation of strong, inner-sphere, surface coordination complexes (Hayes and Leckie, 1987; Hayes et
al., 1988; Hayes et al., 1987). The correspondence between binding affinity of ions for
mineral surfaces and ionic strength dependence is consistent with spectroscopic studies
that are capable of distinguishing between possible coordination environments of sorbed
complexes (Hayes et al., 1987).
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Evaporative Concentration
Concentration of solutes in pit lakes by evaporation of water from the lake surface is
potentially an important component for predicting long-term pit lake water quality in arid environments (PTI, 1992, 1994a, b, 1995). Pit lakes, once they have reached their maximum level, are often considered to be terminal lakes in these settings since loss of water by evaporation is assumed to be in equilibrium with groundwater inflow. This process would increase concentrations of toxic elements such as As over time and could potentially produce pit lakes that are hazardous to birds, animals, and humans (Davis et
al., 2006; Eary, 1998; Shevenell, 2000b).
To predict long-term pit-lake water quality, a water balance of the system should be
constructed and used to estimate the rate of pit filling and final lake elevation (Fontaine et
al., 2003; Hancock et al., 2005; Marinelli and Niccoli, 2000; Shevenell, 2000a; Tempel et
al., 2000). From these calculations, the balance between evaporation loss and
groundwater inflow can be determined and then the amount of evaporative concentration
can be evaluated. Using this methodology and comparing existing pit lake water quality,
(Shevenell, 2000b), determined that pit lakes are likely to experience significantly less
water-quality degradation from evapoconcentration than natural lakes because the surface
area-to-depth ratios in pit lakes are usually much less than natural lakes. (Eary, 1998) also
examined existing pit-lake data and determined that pit lakes can be categorized
according to the ratio of Ca to alkalinity. Lakes with 2mCa < malkalinity can be expected to
evolve chemically into alkaline pH lakes because of evapoconcentration while lakes with
2mCa > malkalinity would evolve to near neutral pH. Trace elements such as As and Se,
13
occurring as aqueous oxyanions, can accumulate in the pit-lake water over time as pH
increases from neutral to alkaline conditions.
Pit Lake Models
Predictive pit-lake water-quality modeling methodologies have been developed by
the mining industry to secure regulatory permits for open pit operations. These
methodologies are generally used to demonstrate that resultant long-term (hundreds of
years) pit-lake water quality will be below regulatory maximum concentrations limits,
and in the state of NV, will not contaminate groundwater (PTI, 1992, 1994a, b, 1995).
However, because most pits that have been modeled using these methodologies are still
being mined and pit lakes have not yet formed, the accuracy of these models are presently
unknown. More recently, pit-lake modeling has received attention in the academic
literature (Castendyk and Webster-Brown, 2007b; Davis et al., 2006; Eary, 1999;
Hamblin et al., 1999; Hancock et al., 2005; Morin and Hutt, 2001; Tempel et al., 2000).
(Fontaine et al., 2003; Marinelli and Niccoli, 2000; Shevenell, 2000a) presented analytical solutions for estimating pit-lake water balances and filling rates; important information needed to support pit-lake water-quality modeling. (Lewis, 1999) presented
an analytical method for estimating the equilibrium groundwater inflow rate into a pit
lake. (Fennemore et al., 1998) developed a pyrite oxidation model for predicting the amount of pyrite oxidation in pit wall rocks in arid regions of the southwestern U.S.
Modeling results were compared to humidity cell tests results designed to oxidize pyrite under favorable moisture and oxygen conditions to field experiment results where wall- rock samples were allowed to oxidize under ambient weather conditions at the mine site.
14
Limnology is an important component controlling pit-lake water quality. Several studies have characterized limnological processes in pit lakes (Atkins et al., 1997;
Balistrieri et al., 2006; Causius et al., 2003; Parshley and Bowell, 2003; von Rohden and
Ilmberger, 2001; von Rohden et al., 2009). (Hamblin et al., 1999) simulated vertical transport in a pit lake in British Columbia, Canada. In this northern location, lake turnover was restricted to the near surface because of ice cover and relatively high salt content at depth. (Tempel et al., 2000) modeled the North pit at the Getchell Mine, NV.
Since a pit lake presently does not exist there, analogs were used to estimate physical limnological conditions. From these analogs, it was assumed that the North pit was dimictic (turned over twice yearly) and holomictic (the entire water column mixes during turnover). The epilimnion was estimated to comprise approximately 10 % of the total pit- lake depth with the hypolimnion comprising 90 %. (Balistrieri et al., 2006) examined the seasonal cycling of temperature and salinity in the Dexter pit lake in northern NV and described an approach for modeling the physical processes.
(Kempton et al., 2000) presented an overview of the uncertainties associated with long-term predictions of pit-lake water quality. They suggested that efforts to improve model accuracy should be focused on establishing accurate probability distributions for key model parameters. The key parameters discussed were groundwater quality, overall water balance, solute loading from wall rock, and chemical reactions in the lake.
(Tempel et al., 2000) presented a detailed geochemical modeling approach for predicting As concentrations at the Getchell Mine, NV. In this effort, historical pit-lake water-quality data were used to validate the forward model predictions. Limnological
15 information from several other pit lakes was included in the modeling approach. Model calculations were conducted assuming two 6-month intervals per year representing summer and winter stratification events.
2- From initial forward modeling simulations, estimated SO4 concentrations were an order of magnitude lower than observed values. To adjust modeling simulations, a reactivity factor of 103 was incorporated. The reactivity factor accounted for additional conditions such as increased surface area caused by fracturing of wall rock and microbial mediation of sulfide oxidation rates. Modeled As concentrations for the hypolimnion were in good agreement with observed values since hypolimnion simulations were controlled by equilibrium with realgar (AsS). But modeled epilimnion concentrations were much higher than observed since As was not controlled by equilibria with As- bearing minerals. The authors hypothesized that the observed lower As concentrations result from adsorption onto hydrous oxides and clay mineral phases.
OBJECTIVES
The principal objective of this research was to improve understanding of the chemical controls on pit-lake water quality. Particular focus was placed on examining the chemical behavior of As and its fate in pit lakes. Specific objectives include:
1. Characterization of limnological processes in an existing pit lake. 2. Quantification of As sorption on different pit-lake sediments. 3. Development of an approach to modeling pit-lake water-quality.
16
APPROACH
Characterization of Limnological Processes in an Existing Pit Lake
This portion of the research investigated the physical limnology of a pit lake to gain
an understanding of the limnology’s effects on pit-lake water quality. The Anaconda pit
in Yerington, NV was excavated to exploit a porphyry-copper deposit and since cessation
of mining, groundwater has filled the pit creating a pit lake. The pit has been filling for
30 years and lake levels continue to rise. This pit lake was an ideal local to investigate the
physical limnology of a pit lake. The objectives of this portion of the research included evaluating whether seasonal vertical density stratification develops, evaluating any
vertical water-quality changes associated with seasonal vertical density stratification, and
developing a conceptual model showing how the physical limnology affects the observed
lake water quality.
The approach to address the objectives included several different techniques such as
monitoring vertical temperature and water-quality parameters at different times during
the year, collecting water samples for dissolved chemical constituents at different depths
at different times during the year, and comparing observed limnological and geochemical
data to other natural lakes and man-made reservoirs in northern NV.
Historical data are important in constructing the existing physical and chemical
framework of the Yerington pit lake. The mineralogy of the extracted ore, gangue,
alternation, and host rock are important to understanding the sources of dissolved
constituents in pit-lake water. All available historical data on pit dimensions, mine
17
mineralogy, and pit-mine lake water quality were collected from mine operators and
regulatory agency files.
Vertical profiles of temperature and water-quality parameters were collected at several different locations within the lake using a Hydrolab Environmental Data System.
Profiles were conducted at the deepest part of the lake and at the west and east ends of the
lake. Vertical profiles consisted of temperature, DO, EC, and pH. Water samples, based
on vertical profile results, were collected at specific depths for laboratory analysis.
Laboratory analyses of pH, EC, major dissolved ions, selected trace ions, and nutrients
were conducted.
(Lyons et al., 1994) indicated that Pyramid and Walker lakes currently undergo
seasonal stratification and that they are the best climatological analogies to the pit lakes
of NV. They suggested that reviewing the physical limnology of these lakes would be
very useful in investigating pit lakes. (Lebo et al., 1993) investigated the water quality of
Pyramid Lake and reported that seasonal variations in physiochemical parameters
demonstrated typical patterns for warm monomictic lakes in the northern hemisphere.
Limnological studies of lakes in NV including Lahontan, Pyramid, and Walker lakes
were reviewed to gain an understanding of how daily and seasonal temperature
fluctuations control the development and timing of lake stratification and turnover and
results from these studies were compared to the limnological data collected at the
Yerington pit lake.
18
Quantification of As Sorption on Different Pit-Lake Sediments
The conceptual model for pit-lake water-quality prediction assumes that large
concentrations of As will be removed from pit-lake water by adsorption onto iron
hydroxides (PTI, 1992, 1994a, b, 1995). Dissolved As is sorbed onto iron hydroxide
particles and is subsequently removed by sedimentation. This assumption was examined
by collecting and analyzing pit-lake sediment samples for iron-hydroxide precipitates and
other sorbing phases and conducting As sorption experiments on these sediments.
Sediment samples were collected from three different pit lakes, the Yerington pit
lake, the Dexter pit lake in Tuscarora, NV, formed in a quartz-adularia gold deposit, and
the 303 pit lake at the Big Springs Mine, north of Elko, NV formed in a Carlin-Trend
type gold deposit. Sediments were characterized for mineralogical composition by
quantitative x-ray diffraction (QXRD) and for mineral morphology and elemental
composition by scanning electron microscopy and energy dispersive x-rays (SEM-EDX).
Specific surface areas of the sediment were measured using N2 adsorption and the BET
method.
The effect of geochemical parameters of pH, Eh, ionic strength, and sorbent type on the observed fractional uptake of As by suspended sediments was systematically investigated in the laboratory. Experiments were conducted with As(III) and As(V); pH of 5, 7, and 9; ionic strength of 0.006, 0.01, and 0.1M; and with sediments from the pit lakes in Yerington, Tuscarora, and Big Springs. Surface complexation modeling of the experimental results was conducted.
19
Development of an Approach to Modeling Pit-Lake Water-Quality
To investigate the relationship of pit-lake chemical and limnological processes, a modeling strategy using preexisting geochemical codes was developed. The modeling strategy incorporated results form the limnological study at the Yerington pit lake and As
sorption behavior evaluated in the laboratory sorption experiments. The modeling
strategy was applied to the Yerington pit lake to test the ability of the strategy to
reproduce the observed water chemistry, particularly pH and TDS. Since aqueous As
concentrations in the Yerington pit lake are at or below detection, the modeling strategy
used surface complexation modeling of dissolved Cu, similar to that developed for
interpretation of As sorption experiment results, to estimate aqueous Cu concentrations.
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21
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22
Hiskey, J.B., and Schlitt, W.J., 1982, Aqueous oxidation of pyrite, in Schlitt, W.J., and Hiskey, J.B., eds., Interfacing Technologies in Solution Mining: Proceedings 2nd SIME-SPE International Solution Mining Symposium, AIME, p. 55-74. Kempton, J.H., Locke, W., Atkins, D., and Nicholson, A., 2000, Probabilistic quantification of uncertainty in predicting mine pit-lake water quality: Mining Engineering, v. 52, p. 59-64. Lapakko, K.A., and Wessels, J.N., 1995, Release of acid from hydrothermal quartz- carbonate hosted gold-mine tailings., in Hynes, T.P., and Blanchette, M.C., eds., Mining and the Environment, Volume I, p. 139-148. Lebo, M.E., Rhodes, C.L., and Goldman, C.R., 1993, Pyramid Lake, Nevada water quality study, 1989-1993. Volume II, limnological description, Division of Environmental Studies, University of California, Davis, CA. Lewis, R.L., 1999, Predicting the steady-state water quality of pit lakes: Mining Engineering, v. 51, p. 54-58. Lowson, R.T., 1982, Aqueous oxidation of pyrite by molecular oxygen: Chemical Reviews, v. 82, p. 461-497. Lyons, W.B., Doyle, G.A., Petersen, R.C., and Swanson, E.E., 1994, The limnology of future pit lakes in Nevada: The importance of shape: Tailings & Mine Waste '94, v. ISBN 95 5410 3647, p. 245-248. Marinelli, F., and Niccoli, W.L., 2000, Simple analytical equations for estimating ground water inflow to a mine pit: Ground Water, v. 38, p. 311-314. McCullough, C., and Lund, M., 2006, Opportunities for Sustainable Mining Pit Lakes in Australia: Mine Water and the Environment, v. 25, p. 220-226. McKibben, M.A., and Barnes, H.L., 1986, OXIDATION OF PYRITE IN LOW- TEMPERATURE ACIDIC SOLUTIONS - RATE LAWS AND SURFACE TEXTURES: Geochimica Et Cosmochimica Acta, v. 50, p. 1509-1520. Miller, G.C., B., L., and A., D., 1996, Understanding the water quality of pit lakes: Environmental Science & Technology, v. 3, p. 118A-123A. Morin, K.A., and Hutt, N.M., 2001, Prediction of water chemistry in mine lakes: The minewall technique: Ecological Engineering, v. 17, p. 125-132. Moses, C.O., Nordstrom, D.K., Herman, J.S., and Mills, A.L., 1987, AQUEOUS PYRITE OXIDATION BY DISSOLVED-OXYGEN AND BY FERRIC IRON: Geochimica Et Cosmochimica Acta, v. 51, p. 1561-1571. Naugle, G.D., and Atkinson, L.C., 1993, Estimating the rate of post-mining filing of pit lakes: Mining Engineering, v. 45, p. 402-404.
23
Newbrough, P., and Gammons, C.H., 2002, An experimental study of water-rock interaction and acid rock drainage in the Butte mining district, Montana: Environmental Geology, v. 41, p. 705-719. Nicholson, R.V., Gillham, R.W., and Reardon, E.J., 1988, PYRITE OXIDATION IN CARBONATE-BUFFERED SOLUTION .1. EXPERIMENTAL KINETICS: Geochimica Et Cosmochimica Acta, v. 52, p. 1077-1085. —, 1990, PYRITE OXIDATION IN CARBONATE-BUFFERED SOLUTION .2. RATE CONTROL BY OXIDE COATINGS: Geochimica Et Cosmochimica Acta, v. 54, p. 395-402. Nordstrom, D.K., 1976, Kinetic and equilibrium aspects of ferrous iron oxidation in acid mine waters, Geological Society of America, Volume Abstracts with Programs: Boulder, CO. —, 1977, Hydrogeochemical and microbiological factors affecting heavy metal chemistry of an acid mine drainage system [Dissertation thesis]: New Haven, CT, Yale University. —, 1982, Aqueous pyrite oxidation and the consequent formation of secondary iron minerals, Acid sulfate weathering: Madison, WI, Soil Society of America, p. 37-54. Pantelis, G., and Ritchie, A.I.M., 1991, Macroscopic transport mechanisms as a rate- limiting factor in dump leaching of pyritic ores: Applied Mathematical Modelling, v. 15, p. 136-143. Parshley, J.V., and Bowell, R.J., 2003, The Limnology of Summer Camp Pit Lake: A Case Study: Mine Water and the Environment, v. 22, p. 170-186. PTI, 1992, Chemogenesis of the Gold Quarry pit lake. Prepared for Newmont Gold Company, PTI Environmental Services, Boulder, CO. —, 1994a, Assessment of pit-lake chemogenesis and waste-rock characterization at the Lone Tree Mine. Prepared for Santa Fe Pacific Gold Corporation, PTI Environmental Services, Boulder CO. —, 1994b, Hydrogeology of the Robinson mining district, White Pine County, Nevada. Prepared for Magma Nevada Mining Company., PTI Environmental Services, Boulder, CO. —, 1995, Predicted water quality in the Round Mountain Gold Company pit lake. Prepared for Round Mountain Gold Corporation., PTI Environmental Services, Boulder CO. Saunders-MacDonald, M., 1992, Water quality in open pit precious metal mines: Reno, NV, University of Nevada, Reno.
24
Shevenell, L., 2000a, Analytical method for predicting filling rates of mining pit lakes: example from the Getchell Mine, Nevada: Mining Engineering, v. 52, p. 53-60. Shevenell, L., Connors, K.A., and Henry, C.D., 1999, Controls on pit lake water quality at sixteen open-pit mines in Nevada: Applied Geochemistry, v. 14, p. 669-687. Shevenell, L.A., 2000b, Water quality in pit lakes in disseminated gold deposits compared to two natural, terminal lakes in Nevada: Environmental Geology, v. 39, p. 807-815. Smith, K.S., 1991, Factors influencing metal sorption onto iron-rich sediment in acid mine drainage: Golden, CO, Colorado School of Mines. Smith, K.S., Ficklin, W.H., Plumlee, G.S., and Meir, A.L., 1992, Metal and aresenic partitioning between water and sediment at mine-drainage sites in diverse geologic settings, in Kharaka, Y.K., and Maest, A.S., eds., 7th International Symposium on Water-Rock Interaction: Park City, UT, A.A. Balkema. Stevens, C.L., and Lawrence, G.A., 1998, Stability and meromixis in a water-filled mine pit: Limnology and Oceanography, v. 43, p. 946-954. Swallow, K.C., Hume, D.N., and Morel, F.M.M., 1980, SORPTION OF COPPER AND LEAD BY HYDROUS FERRIC-OXIDE: Environmental Science & Technology, v. 14, p. 1326-1331. Tempel, R.N., Shevenell, L.A., Lechler, P., and Price, J., 2000, Geochemical modeling approach to predicting arsenic concentrations in a mine pit lake: Applied Geochemistry, v. 15, p. 475-492. von Rohden, C., and Ilmberger, J., 2001, Tracer experiment with sulfur hexafluoride to quantify the vertical transport in a meromictic pit lake: Aquatic Sciences, v. 63, p. 417-431. von Rohden, C., Ilmberger, J., and Boehrer, B., 2009, Assessing groundwater coupling and vertical exchange in a meromictic mining lake with an SF6-tracer experiment: Journal of Hydrology, v. 372, p. 102-108.
25
CHAPTER 2
LIMNOLOGY AND WATER QUALITY OF A PORPHYRY-COPPER PIT LAKE AND COMPARISON TO TWO NEARBY TERMINAL DESERT LAKES IN NORTHERN NEVADA, USA
Ronald L. Hershey, Raymond Kepner, and Glenn C. Miller
26
INTRODUCTION
A substantial number of large mine pit lakes will form throughout the world in the near future because of large-scale mining of low-grade, metal ores. Many of these future pit lakes are located in arid climates, notably in Nevada (Shevenell et al., 1999) and the western USA (Bolen, 2002), and may impact water resources (Miller, 2002). Because presently there are very few large pit lakes in arid climates, the eventual water quality of these lakes is mostly unknown. Pit lakes, if contaminated, will impact wildlife and could be long-term sources of groundwater contamination. Therefore, it is critical to understand the processes that control pit-lake water quality so that long-term water quality can be predicted.
Lyons et al. (1994) observed that pit-lake morphology is different from most natural water bodies and man-made reservoirs. In general, pit lakes have lower surface area-to- mean depth ratios than other lakes and reservoirs (Shevenell, 2000); they also have small littoral zones. These authors hypothesized that limnological processes like vertical stratification, turnover, and biology may be substantially different in pit lakes. For example, lakes with smaller surface area would have less evaporation, and therefore, a greater chance that the water column would not overturn and mix seasonally (Balistrieri et al., 2006). Also, changes in zooplankton community composition can have a direct impact on water clarity (Balvert et al., 2009).
The most studied arid pit lake is the Berkeley pit lake in Butte, MT (e.g. Davis and
Asenberg, 1989; Gammons et al., 2003; Pellicori et al., 2005). But, the limnology of the
Berkeley pit lake has been complicated by other activities, including diversion of surface
27 water into the lake and removal of water to recover Cu that may have altered the physical limnology of the lake (Gammons and Duaime, 2006). There have also been studies of metal pit lakes in Australia, New Zealand, and Canada (e.g. Castendyk and Webster-
Brown, 2007; McCullough and Lund, 2006; Salmon et al., 2008; Stevens et al., 2005), and coal, gravel, and metal pit lakes in Europe (e.g. Ercilla et al., 2008; España et al.,
2009; Herzsprung et al., 2005; Koschorreck et al., 2007; Migaszewksi et al., 2009;
Ramstedt et al., 2003). However, most of these studies, and others of pit lakes in the
USA (e.g. Davis et al., 2006; Dowling et al., 2004; Parshley and Bowell, 2003; Tempel et al., 2000), have focused only on geochemical processes and physical limnology.
Concentrations of toxic elements can be high in pit lakes (e.g. Blodau, 2006; Castro and Moore, 2000; Davis et al., 2006), particularly in acidic conditions. Attempts have been made to modify or remediate pit-lake water quality by a variety of methods. For example, Fisher and Lawrence (2006) describe a meromictic lake where the epilimnion was fertilized with nitrate and phosphate to increase primary production to remove dissolved metals by adsorption onto plankton that eventually die and sink. Others have applied various media including lime, straw, organic and inorganic carbon, phosphorous, ethanol, and coal ash to change water quality by modifying the bacterial and/or algal communities (Bozau et al., 2007; Dessouki et al., 2005; Frömmichen et al., 2004;
Frömmichen et al., 2003; Fyson et al., 2006; Loop et al., 2003; Tittel and Kamjunke,
2004; Totsche et al., 2006).
Shevenell (2000) compared water-quality parameters of pit lakes to two natural, terminal lakes in NV, the same two lakes in this study, but focused on water volumes and
28
fluxes, including evaporation and its effect on dissolved constituent concentrations.
Shevenell did not consider the limnology of pit lakes or natural lakes. She determined
that evaporative concentration at pit lakes was less than at natural lakes because of
smaller pit lake surface area-to-depth ratios.
Few have examined the physical and biological limnology of pit lakes (e.g. Balvert
et al., 2009; Kalin et al., 2001; Packroff, 2000; Żurek, 2006). However, in this paper, the
physical and biological limnology, and water quality, at a porphyry copper pit lake in an
arid climate is described and compared to two nearby natural, terminal lakes.
STUDY SITE
The pit in Yerington, NV (Fig. 2.1) exploited a porphyry-copper deposit, and since
cessation of mining, groundwater has filled the pit creating a pit lake. The pit has been
filling for 30 y and pit-lake levels continue to rise. Because of its easy access and
relatively large size, it is an ideal locale to investigate the limnology of an existing large
pit lake.
Morphology
The pit is oblong; 2,000 m long and 800 m wide. The southeast edge of the pit, close
to the Walker River, is at 1,341 m elevation while the northwest edge is at 1,414 m. The
pit’s total depth is approximately 210 m. Waste-rock piles surround the pit on the north
and south. In April 2002, the lake was 118 m deep with a total volume of 36.5 million m3.
The lake’s surface area-to-mean depth ratio is 13, within the range of other pit lakes
(Lyons et al., 1994).
29
Figure 2.1. Location of the Yerington pit lake, Pyramid Lake, and Walker Lake Nevada, USA.
30
Climate
Yerington is 85 km southeast of Reno, NV and lies in the rain shadow of the Sierra
Nevada. Yerington’s climate is typical of western NV with warm summers, mild winters, and little rainfall. The average maximum temperature in July is 33.3 °C and 9.0 °C in
January. Average annual precipitation is 13 cm. Because of the large number of sunny days, warm temperatures, and low humidity, evaporation is high. In Fallon, NV, 63 km northeast of Yerington, annual pan evaporation averages 94 cm.
Geology
The mine is on an alluvial fan 3 km east of the Singatse Range, which lies west of
Mason Valley and rises 580 m above the valley floor. The pit is located within the
Yerington batholith, a composite granitic body that intruded volcanic and sedimentary rocks. The batholith is composed of quartz monzodiorite intruded by a lesser volume of quartz monzonite. Late-stage granite porphyry dike swarms associated with the porphyritic-granitic stocks intruded the central portions of the batholith and the mine is centered over one of these dike swarms (Cartin, 1986; Moore, 1969).
The ore body was an economic concentration of oxide ore with a minimum secondary enrichment. The principal oxidation product was chrysocolla. cuprite, tenorite, and melaconite had wide distribution within the oxide zone while malachite and azurite occurred, but were not abundant (Wilson, 1963). Lying between the primary sulfide and the chrysocolla horizon was a transition zone where chalcocite, cuprite, melaconite, native copper and chrysocolla occurred superimposed upon primary mineralization.
31
Underlying the transition zone, pyrite and chalcopyrite occurred as minute grains in the
groundmass and narrow seams.
Hydrology
The eastern edge of the pit is about 360 m from the Walker River, which supplies
water to the alluvial aquifer. Groundwater in the alluvial aquifer flows northward, the
same direction as the river. However, prior to the flood of January 1, 1997, only minute
amounts of water (measured by authors) from the alluvial aquifer reached the pit because
of a range front fault on the east edge of the pit. The fault juxtaposes relatively
impermeable granite against the saturated alluvial aquifer, creating a hydraulic barrier
between the pit and the alluvium. This barrier was breached during the flood because a
channel was purposefully cut between the river and the pit to drain flood water. Since
then, groundwater from the alluvial aquifer has been flowing into the pit at 6 to 7.5 L s-1
(measured by authors). On the western side of the pit, the thickness of the overlying alluvium increases substantially. Several small springs on the western pit wall issue from the alluvium/bedrock contact. Flow from these springs varies seasonally at 3 to 4 L s-1
(measured by authors). The pit continues to fill with groundwater. From 1991 until the
January 1997 flood, the lake increased in volume about 0.99 million m3 y-1. The average
rate of change in pit-lake volume after the flood from 1997 to 2001 was about 1.29
million m3 y-1.
METHODOLOGY
Sampling was conducted over several years. Vertical profiles of temperature, electrical conductivity (EC), dissolved O2 (DO), and pH were obtained using a Hydrolab
32
DataSonde™4. Profiles were conducted at one station at the deepest part of the lake
(Fig. 2.1) and two stations at opposite ends of the pit lake near the spring flow into the lake. The sonde was calibrated daily; the pH probe was calibrated with pH 7 and 10
buffers; EC was calibrated with a 500 mS cm-1 standard; DO was calibrated at 100 %
saturation at ambient temperatures and the elevation of the pit-lake surface. Profile points
were collected at various depths; the data sonde was held stationary until temperature and
DO stabilized.
Water-quality samples were either collected with a Van Dorn bottle or a peristaltic pump and polypropylene tubing. Major-ion, trace-element, and nutrient samples were filtered in the field with a 0.45 μm polysulfone cartridge filter. Cation and trace-element samples were acidified in the field with either reagent grade (cations) or Seastar Baseline nitric acid (trace elements). Trace-element samples were collected in polypropylene bottles pre-washed with nitric acid. Nutrient samples were not acidified; an unfiltered nutrient sample was also collected. pH, EC, total dissolved solids (TDS), major-ion, and
- nutrient analyses (total inorganic carbon [TIC], total organic carbon [TOC], nitrate [NO3
- + -N], nitrite [NO2 -N], ammonia [NH4 -N], total kjeldahl nitrogen [TKN], total phosphorous [TP]) were conducted at the Desert Research Institute’s Analytical
Chemistry Laboratory in Reno, NV (Desert Research Institute, 2009) following standard
U.S. EPA procedures. Trace-element analyses were conducted by ICP/MS at the Desert
Research Institute’s Ultra-Trace Chemical Laboratory.
Particulate organic carbon (POC) and particulate organic nitrogen (PON) samples were filtered onto pre-combusted glass-fiber filters. The filters were dried at 105 oC for
33
24 hr, fumed with HCl for 24 hr, and placed in a dessicator until rolling. The filters were
rolled into tin disks before analysis with a PerkinElmer 2400 series II CHNS/O analyzer.
Results were corrected by subtracting filter and tin disk blanks signals. After analysis, the
net signal count and a standard curve was used to calculate the POC and PON of the
samples (Clesceri et al., 1998; Karl et al., 1991).
For chlorophyll-a (chl-a) analysis, 500 mL samples were filtered (0.45 μm pore-
size, cellulose-acetate filters) at < 380 mm Hg vacuum. Filters were folded into larger
qualitative filters, wrapped in aluminum foil to exclude light, and frozen for one week.
Filters were extracted in the dark for 24 hr in 10 mL of 90 % MgCO3-buffered acetone at
5 °C. Measurements were made with a Turner Designs (Sunnyvale, CA) model 111
fluorometer and corrected for phaeophytin by addition of 5 % HCl (Holm-Hansen et al.,
1965).
For phytoplankton densities, 1.0 L samples were preserved in 1 % acid Lugol’s solution. Samples were concentrated by settling 600 mL of sample for one week in glass cylinders and aspirating off the top 550 mL. The remaining 50 mL of samples were concentrated to 3 mL by low-speed centrifugation (~ 3000 × g for 20 min). Total phytoplankton densities were determined by triplicate hemacytometer cell counts under the light microscope (Olympus BX60) ×200. Minimum detection limit for cells or cyanobacterial filaments was 6 mL-1. Phytoplankton were classified into major taxonomic
groups including unicellular chlorophyta (green algae), filamentous cyanobacteria,
diatoms, phytoflagellates (cryptomonads, chlamydomonads and chrysomonads), and
others.
34
Samples were scanned for metazooplankton with the Lugol’s-preserved, sample
concentrates; 1.0 mL of concentrate was placed in a Sedgewick-Rafter cell and examined
under the light microscope ×100. Minimum detection limit for individual zooplankton
was 5 L-1.
To test the toxicity of the pit-lake, acute toxicity tests were conducted using pit-lake water and Daphnia magna and Ceriodaphnia dubia; these organisms are filter feeders and important in freshwater metazooplankton communities (Hickman et al., 1998). Pit-
lake water was diluted in either Walker River water or moderately hard, reconstituted
water (U.S. EPA, 1989). Survival experiments were conducted with 10 neonate animals
in 50 mL of different amounts of pit-lake water and either river or reconstituted water.
Test concentrations were 0 (control), 10, 25, 50, 75, and 100 % pit-lake water. Water was
changed daily to avoid build up of metabolic byproducts. Animals were fed 10 drops of a
1:1 mixture of yeast–cereal leaves–trout chow and the algal species Selenastrum
capricornutum in deionized water each time the water was changed. Experiments were
maintained at room temperature; living and dead animals were counted daily and
experiments were run for four days. Mortality in some controls after 72 h prohibited use
of results longer than 48 h. Probit analysis (Finney, 1971) was used to estimate acute
concentration–mortality regressions (SAS Institute, 1985). There was no mortality in
controls within 48 h.
35
RESULTS
Water Temperature
Maximum surface-water temperatures, at the sampling station at the deepest part of the lake (Fig. 2.1, Appendix A) occurred during the summer at > 20 °C from May through September (Fig. 2.2). Conversely, minimum water temperatures occurred during
January and February at approximately 6 °C. The seasonal pattern in surface-water
temperatures indicates the heating of surface waters during spring/summer and cooling
during fall, typical for thermally stratified lakes in the Northern Hemisphere (Lebo et al.,
1993).
Thermal stratification of the water column in the Yerington pit lake begins in early spring and intensifies during the spring/summer heating of the water column (Fig. 2.2).
During late January and throughout February, the water column was uniformly cold at
5.9 to 6.5 °C indicating minimal impedance to vertical mixing. Following the isothermal conditions of winter, surface-water temperatures increase until mid-summer. The warming of surface waters was observed by the end of March when the temperature rose above 12 °C. As surface waters warmed, a strong temperature gradient (0.67 °C m-1) was established between 10 and 30 m and persisted from June through September.
36
Figure 2.2. Contours of temperature of the Yerington pit lake from July 2000 to September 2001.
During the autumn, thermal stratification weakened, degrading the thermocline
(Fig. 2.2, Appendix A). In September, the surface mixed layer was shallow (0 to17 m) and warm (20 °C). The surface, isothermal layer progressively cooled and became deeper during the late autumn and winter. By December, surface temperatures were about 8 °C with a deeper mixed layer; the thermocline was located between 40 and 60 m. By
January, the water column was again isothermal. The temperature time series show that the Yerington pit lake is monomictic, turning over only once per year.
Dissolved Oxygen
DO was consistently high (> 8.5 mg L-1) throughout the water column after winter
turnover (Fig. 2.3). After turnover, there was a progressive decrease in DO in the hypolimnion as O2 was consumed. Minimum bottom water DO was approximately
5 mg L-1 from the end of October until full mixing of the water column in February. DO
37
at the end of January 2001 showed that full mixing of the water column had not occurred
even though isothermal conditions were achieved by this time.
Figure 2.3. Contours of dissolved oxygen of the Yerington pit lake from July 2000 to September 2001.
DO increased in the epilimnion from February through early September. Maximum
DO occurred at the surface in February (9.7 mg L-1) and moved deeper below the surface
(15 m) by mid September (9.8 mg L-1). During autumn, maximum DO decreased and
moved deeper as the epilimnion cooled and wind-induced mixing occurred. By the end of
January, maximum DO had decreased to (8.8 mg L-1) and had mixed to 60 m below the
lake surface.
Nutrients
- N was moderately low in the Yerington pit lake. NO3 -N was lower in surface
waters (n = 8, x = 113 mg L-1, calculated from the values in Table 2.1) and at 20 m
(n = 2, x = 90 mg L-1) than in deeper waters below the photic zone at 30 m (n = 3,
38
x = 140 mg L-1), 50 m (n =5, x = 126 mg L-1), and 100 m (n = 8, x = 161 mg L-1).
+ -1 NH4 -N was consistently lower than NO3-N (< 5 to 13 μg L ). TKN varied little over
- - time and depth (n = 36, x = 110). Total nitrogen ([TN], sum of NO3 -N, NO2 -N, and
TKN), when averaged over all depths for each sampling date, also varied little over time
ranging from 197 to 360 μg L-1. TP was also consistently low (< 5 to 20 μg L-1).
A general trophic classification of lakes and reservoirs in relation to P and N
(Wetzel, 2001) shows that the Yerington pit lake would be classified as oligotrophic. TP
concentrations at Yerington averaged 5 μg L-1 (low oligotrophic range) and TN averaged
254 μg L-1 (well below the oligotrophic average and range). TN/TP for the sampling
times ranged from 43 to 100. These ratios for the Yerington pit lake indicate a substantial
P shortage as a ratio of 10 shows a balance of nutrients and 16 shows a P shortage
(Wetzel 2001).
Biological Activity
Hypolimnetic Oxygen Deficit
The volumetric rate of O2 consumption in the hypolimnion during summer
stratification (O2 deficit) provides an estimate of the productivity of a lake (Wetzel,
2001). Organic matter, synthesized in the trophogenic zone, sinks into the hypolimnion and decomposes. Changes in O2 in the hypolimnion reflect the rates of loading of organic
matter and of decomposition.
39
Table 2.1. Nutrients for the Yerington pit lake pit-wall springs and Walker River. - - + a a b b Date Depth TIC TOC NO3 NO2 NH4 TIN TKN TN TIP TP TN/TP TIN/TIP (m) (mg L-1) (mg L-1) (as N) (as N) (as N) (μg L-1) (μg L-1) (μg L-1) (μg L-1) (μg L-1) (μg L-1) (μg L-1) (μg L-1) Pit Lake 04/07/95 0 90 50 80 100 120 08/04/95 0 1.3 100 7 100 200 300 15 50 0.8 120 <5 120 100 220 <5 100 0.6 140 <5 140 <100 190 <5 61 10/17/95 0 1.1 100 6 100 200 300 <5 50 0.7 100 <5 100 200 300 <5 100 0.6 100 <5 100 200 300 5 100 02/05/96 0 1.0 120 <5 120 <100 170 <5 50 0.7 140 <5 140 100 240 <5 100 0.7 150 <5 150 <100 200 <5 55 04/16/98 0 0.7 130 <5 130 <100 180 6 30 0.7 150 <5 150 <100 200 <5 100 0.6 160 6 160 <100 210 <5 65 09/15/98 0 1.5 20 5 20 200 220 <5 30 0.8 160 <5 160 100 260 <5 100 0.7 170 <5 170 <100 220 <5 93 08/23/00 0 3.5 140 9 140 200 340 <10 20 1.4 100 7 100 200 300 <10 100 3.9 240 8 240 200 440 20 50
40
Table 2.1. Nutrients for the Yerington pit lake pit-wall springs and Walker River (continued). - - + a a b b Date Depth TIC TOC NO3 NO2 NH4 TIN TKN TN TIP TP TN/TP TIN/TIP (m) (mg L-1) (mg L-1) (as N) (as N) (as N) (μg L-1) (μg L-1) (μg L-1) (μg L-1) (μg L-1) (μg L-1) (μg L-1) (μg L-1) 08/30/01 0 4.6 110 <10 13 115 80 195 1 <5 5 1.5 110 <10 <5 115 170 285 1 10 10 2.1 110 <10 <5 115 80 195 1 6 15 3.0 120 <10 <5 125 80 205 1 5 20 4.3 80 <10 <5 85 120 205 1 6 25 2.9 80 <10 <5 85 130 215 1 5 30 1.9 110 <10 <5 115 80 195 1 9 35 2.6 150 <10 <5 155 60 215 <1 5 40 2.8 170 <10 12 175 50 225 1 <5 09/13/01 45 125 1.8 190 <10 8 195 80 275 2 5 50 1.0 190 <10 4 195 80 275 5 10 55 1.3 190 <10 6 195 80 275 7 12 60 1.5 190 <10 5 195 110 305 9 12 65 1.0 200 <10 6 205 80 285 11 14 70 0.9 200 <10 3 205 80 285 10 14 80 0.9 200 <10 6 205 80 285 9 15 90 0.9 210 <10 4 215 90 305 9 15 100 124 0.6 210 <10 4 215 80 295 12 18 43c 34.4c East Spring 02/05/96 400 8/23/00 1.2 90 10 200 60 West Spring 10/17/95 2550 04/16/98 0.9 <5 100 10 08/23/00 3.5 2670 8 300 <10 Walker River (at Mason gauge) Average 140 <10 <10 530 670 50 120 1990-1995 a Calculated using 1/2 detection limit b Calculated using 1/2 detection limit and average of all depths for date c For samples collected 08/30/01 and 09/13/01
41
The DO decreased in the hypolimnion during both summer stratification periods
(Fig. 2.4). Hypolimnetic O2 deficits were calculated as described in Wetzel and Likens
(1991) for both time periods using 25 m as the top of the hypolimnion, which was the
depth of the pycnocline (Fig. 2.2). The July to November 2000 O2 deficit was 0.026 mg
-2 -1 -2 -1 O2 cm d and the April to September 2001 O2 deficit was 0.019 mg O2 cm d . These
-2 -1 rates are close to the upper limit for oligotrophic lakes (< 0.025 mg O2 cm d , Wetzel,
2001).
10
9
8
) 7 -1
6
5
4
Dissolved Oxygen (mg L (mg Dissolved Oxygen 3
2 40 - 70m 70 - 90m 1 > 90m
0 JASONDJFMAMJJAS Month Figure 2.4. Volume weighted average dissolved oxygen concentrations in the Yerington pit lake from July 2000 to September 2001. Dissolved oxygen profiles collected from one station at the deepest part of the pit lake.
42
0 A 20
40
60 Depth (m) Depth 80
100 Chyl a DO 120 01234567891011 Concentration (μg L-1)
0 B 20
40
60 Depth (m) 80
100 Chyl a DO 120 01234567891011 Concentration (μg L-1)
Figure 2.5. Chlorophyll a and dissolved oxygen concentrations vs. depth in the Yerington pit lake. A = August 2000. B = September 2001.
Particulate Organic Matter
POC and PON concentrations were generally low (< 170 μgL-1 POC, < 16 μgL-1
PON; Table 2.2); C:N was above 10 for all depths sampled with an average of 14.5.
These values are above the Redfield ratio of 6.6 (Wetzel, 2001) indicating moderate to severe N limitation in the Yerington pit lake.
43
Table 2.2. Particulate organic carbon and nitrogen in the Yerington pit lake and stoichiometric ratios as approximate indicators of relative nutrient limitations. Date Depth POC PON C:N Chl-a C:Chl-a (m) (μg L-1) (μg L-1) (μg L-1) 08/30/01 0 168.1 12.1 16.21 0.12 116.64 5 137.8 15.7 10.24 0.20 57.37 10 102.2 8.8 13.55 0.23 37.00 15 102.2 8.8 13.55 0.34 25.03 20 106.4 10.5 11.82 1.06 8.36 25 106.0 12.4 9.97 1.27 6.95 30 96.7 8.6 13.12 1.99 4.05 35 96.3 9.7 11.58 3.85 2.08
09/13/01 40 95.1 8.9 12.46 3.90 2.03 45 75.9 6.3 14.05 0.61 10.36 50 79.1 7.0 13.18 0.63 10.45 55 72.0 5.9 14.24 0.84 7.14 60 59.1 3.8 18.14 0.47 10.47 65 68.5 5.3 15.08 0.41 13.91 70 76.9 6.5 13.80 0.36 17.79 80 71.0 4.7 17.62 0.31 19.07 90 69.7 4.1 19.83 0.26 22.32 100 85.8 4.5 22.24 0.29 24.63
Average 14.48 21.98
Stoichiometric Ratios as Approximate Indicators of Relative Nutrient Limitationsa Degree of Nutrient Limitation Ratio Deficiency None Moderate Severe C:N N < 8.3 8.3 - 14.6 > 14.6 C:Chl-a General < 4.2 4.2 - 8.3 > 8.3 a from Wetzel (2001), Table 13-17, Page 278
Plankton
In April 1998, the phytoplankton community was dominated by two taxa: a unicellular chlorophyta of genus Oocystis, and a filamentous cyanobacterium (Table 2.3).
The dominant phytoplankton species was identified as either Oocystis solitaria or O. parva. Maximum Oocystis sp. densities were observed at 10 m (Table 2.3).
44
Table 2.3. Phytoplankton cell densities in the Yerington pit lake. Date Depth Non-flagellated Chlorophyta Cyanobacteria Bacillariophyta Euglenoids Phytoflagellates Other Total (m) (Cells mL-1) (Filaments mL-1) (Diatoms) (Cells mL-1) (Cells mL-1) (Cells mL-1) (Cells mL-1) (Cells mL-1) 04/16/98 0 2,635 1,302 8 <2 <2 15 3,960 10 3,023 1,810 6 <2 <2 79 4,919 15 2,729 504 10 <2 333 102 3,679 21 1,652 594 8 <2 506 163 2,923 25 923 517 17 <2 542 169 2,167 09/14/98 1 3,183 1,296 17 35 10 146 4,788 10 3,525 1,254 8 48 2 1,590 6,427 15 967 502 <2 15 85 2,823 4,392 20 935 108 10 10 92 1,996 3,152 30 313 38 6 8 38 827 1,229
Date Depth Non-flagellated Cyanobacteria Bacillariophyta Phytoflagellates Heterotrophic Totala (m) Chlorophyta (Filaments mL-1) (Diatoms) (Cells mL-1) Bacteria (Cells mL-1) (Cells mL-1) (Cells mL-1) (Cells mL-1) 08/23/00 19 3,308 487 <2 403 2.31e5 3,795 40 11,554 655 118 1,377 5.04e5 13,704 aexcluding heterotrophic bacteria
45
Numerous, short (100 to 1,000 μm) filaments (trichomes) of cyanobacterial cells were observed in the lake in April 1998 (Table 2.3). These were particularly abundant in the upper water column (< 10 m) in the photic zone where they were observed at densities of 1,000 to 2,000 mL-1. Cyanobacterial numbers declined substantially between
10 and 15 m, although they continued to be observed down to 30 m. No unicellular cyanobacteria were observed in these samples and individual cyanobacterial cells were not counted.
Specialized cyanobacterial cells (e.g. heterocysts or akinetes) were not observed and filaments appeared to be composed of uniform vegetative cells. The filamentous cyanobacterium was tentatively identified as either Oscillatoria limnetica or O. tenuis.
Short trichomes were abundant throughout the water column and appeared to contain cells with pseudo-vacuoles, which allow cyanobacteria to regulate their position in the water column by gas accumulation or release.
In April 1998, the only other taxa that were found in large numbers were two species of phytoflagellate. These were not found in the upper water column, but were found at significant densities in samples collected at 15, 21, and 25 m. Both these organisms were biflagellate, pigmented cryptophytes. One flagellate had distinctly green plastids, likely containing both chlorophylls a and b. This organism was tentatively identified as a smaller member of the genus Cryptomonas. The other dominant phytoflagellate is possibly a smaller, elongate chlamydomonad. Few additional photosynthetic taxa were observed. Diatoms were rare throughout the water column in
April 1998 and the only species observed was a small pennate diatom.
46
In September 1998, the phytoplankton community was more diverse. Although
Oocystis and Oscillatoria continued to dominate in surface waters, large euglenids were
also present throughout the water column. At depths of 10 m and greater, smaller
chlorophytes became abundant, reaching densities > 2,000 mL-1 at 15 m. These
chlorophytes appeared to be of at least two types. Small, spherical cells believed to be a
type of Chlorella sp. (Chlorophyceae) were present at all depths > 1 m.
In August 2000, additional samples were collected from 19 and 40 m. These
samples corresponded to the DO maximum at 19 m and the chl-a maximum at 40 m.
Chlorophyta, cyanobacteria, and phytoflagellates were present at both depths; diatoms
were only present at 40 m (Table 2.3). At 40 m, Chlorella sp., Oocystis sp., an identified
euglenoid species, the chrysopyte Hyalobryon sp., and a few types of small pennate
diatoms dominated the community. At 19 m, cyanobacteria were a more conspicuous
component of the community since other members were less abundant than at 40 m.
At 19 m in August 2000, the chlorophyta were twice as abundant (3,300 cells mL-1)
as they were at 21 m in April 1998 (1,650 cells mL-1), and three times as abundant as at
20 m in September 1998 (900 cells mL-1). In April 1998 at 21 m and 19 m in August
2000, cyanobacteria were comparable in density (500 to 600 cell mL-1), but at 20 m in
September 1998, cell density was much lower at approximately 100 cells mL-1. In April
1998 and August 2000 at 21 and 19 m, respectively, phytoflagellates had comparable
densities (400 to 500 cells mL-1), while in September 1998, phytoflagellates density was
much lower at <100 cells mL-1. Diatom densities were consistently low for the similar
47
depths for three sampling dates (< 2 to 17 cells mL-1), the one exception being the 40 m sample collected in August 2000 (Table 2.3).
No metazooplankton were found in any of the samples at any of the depths.
However, they may have been present at a density lower than 5 individuals L-1.
Water Quality
The Yerington pit lake has relatively good water quality (Table 2.4). The pH is
alkaline at approximately 8.1 and the TDS are approximately 600 mg L-1. Pit lake ions
2+ + 2- are predominantly Ca , Na , and SO4 , and the ion chemistry is very similar at different
depths (Table 2.4). Concentrations of Cu and Se are elevated relative to most lakes.
The TDS from 1995 to 2001 have remained constant (Fig. 2.6A); however, Cl- has
increased about 15 % (Fig. 2.6B). Cu concentrations were initially high in the pit lake,
but decreased with time (Fig. 2.7A). In 1995, Cu concentrations were >150 μg L-1, but had decreased to below 50 μg L-1 in 2001. Se concentrations were about 100 μg L-1 and remained relatively constant from 1995 to 2001 (Fig. 2.7B).
Acute Toxicity Tests
Daphnia magna exposed to pit-lake water diluted in Walker River water had a 48 hr
LC50 of 44.4 % (30.6 to 63.1 95 % C.I.) with none of the animals surviving for 48 hr in
100 % pit-lake water (Fig. 2.8). Under the same test conditions, Ceriodaphnia dubia were more sensitive to pit-lake water with a 48 h LC50 of 27.5 % (0.0 to 45.1 % C.I.) and 41.7 %
(30.2 to 51.0 95 % C.I.) in two separate tests. Using pit-lake water diluted in reconstituted
water, C. dubia were similarly sensitive with a 48 hr LC50 of 28.5 % (10.9 to 40.7 95 % C.I.).
48
Table 2.4. Water chemistry for the Yerington pit lake pit-wall springs and pit dewatering wells. 2+ 2+ + + - 2- - 2- - Date Depth pH Ca Mg Na K HCO3 CO3 Cl SO4 SiO2 F Cu Se TDS (m) (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) (μg L-1) (μg L-1) (mg L-1) Pit Lake 0 8.25 84.5 15.2 71.3 5.30 151 33.3 270 30.5 1.4 114 110 587 04/07/95 50 7.98 87.5 14.9 72.0 5.36 152 33.3 269 30.6 1.4 135 110 590 100 7.84 88.7 14.9 72.0 5.30 151 32.9 277 30.2 1.4 158 110 598 0 8.54 85.6 15.0 74.3 5.53 138 6.8 34.6 264 33.2 1.4 52 130 586 08/04/95 50 7.98 87.1 14.5 71.6 5.53 150 33.6 267 31.1 1.4 158 130 587 100 7.94 90.1 14.8 71.5 5.40 152 33.5 277 30.9 1.4 151 130 601 0 8.22 87.2 16.3 78.8 5.38 135 37.9 266 34.4 1.4 12 120 595 10/17/95 50 7.90 95.4 16.0 76.1 5.40 148 37.3 276 31.4 1.4 151 120 613 100 7.95 96.5 15.9 75.1 5.20 150 36.3 277 31.6 1.4 157 120 614 0 8.12 90.1 16.1 77.8 5.12 143 36.5 279 33.3 1.3 75 130 611 02/05/96 50 7.75 94.6 16.1 76.2 5.06 149 36.1 282 31.7 1.4 129 130 618 100 7.79 95.7 16.0 77.0 5.01 150 36.1 277 32.1 1.4 123 120 615 0 8.36 79.2 14.7 72.5 4.95 146 37.7 272 34.8 1.28 51 130 590 04/16/98 30 8.18 78.7 14.6 71.9 4.83 143 37.8 276 34.8 1.38 55 100 592 100 8.13 79.2 14.6 71.6 4.88 145 37.3 277 34.8 1.32 57 100 593 0 8.39 73.4 15.3 81.8 5.49 124 2.2 38.9 262 37.2 1.28 11 118 578 09/14/98 30 8.13 83.0 15.4 78.8 5.35 146 37.5 267 34.8 1.31 61 122 596 100 8.10 84.1 15.2 79.0 5.35 146 37.9 267 34.7 1.32 51 125 598 0 8.34 81.0 16.0 81.3 5.50 144 1.3 38.6 265 39.4 1.29 8 98 601 08/23/00 20 8.25 83.3 15.8 77.5 5.30 144 2.2 37.7 269 37.2 1.29 19 95 600 100 8.06 87.9 16.1 78.6 5.40 148 37.5 277 36.7 1.38 36 100 615 0 8.29 75.8 15.5 77.0 5.43 143 39.0 262 38.0 1.46 8.1 100 602 08/30/01 20 8.25 78.7 15.0 74.3 4.92 145 38.6 263 35.7 1.18 24 104 610 09/13/01 100 8.04 81.1 30.7 75.8 5.28 144 41.0 291 36.2 1.23 34 102 597 East Spring 02/05/96 23.8 18 <2 08/23/00 25.9 8.7 <0.2 West Spring 10/17/95 41.9 <5 16 08/23/00 46.4 0.44 12 Dewatering Wells 01/09/01 Well 2 46.5 1.6 0.35 01/18/01 Well 4 37.8 0.88 1.2
49
700
A
650 ) -1
600 TDS (mg L
550 Surface 100 m 500 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02
44
42 B
40 ) -1 38
36 Cl (mg L Cl (mg
34
32 Surface 100 m 30 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 Date
Figure 2.6. Change in TDS (A) and Cl- (B) concentrations from 1995 through 2001.
50
180
160 A
140
120 ) -1 100 Surface g L μ 80 100 m
Cu ( Average 60
40
20
0 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02
140
B 120
100 )
-1 80 g L μ 60 Se (
40 Surface 100 m 20 Average
0 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 Date
Figure 2.7. Change in Cu (A) and Se (B) concentrations from 1995 through 2001.
51
Figure 2.8. Normal distribution probability plot and 95 % confidence interval of 48 h toxicity test probit data for Daphnia magna
DISCUSSION
The surface area-to-mean depth ratio of the Yerington pit lake is typical of other pit
lakes. Lyons et al. (1994) suggested that pit lakes with low ratios might have
substantially different limnological processes than natural lakes or man-made reservoirs.
However, in the case of the Yerington pit lake, the physical limnology appears to be
similar to two terminal lakes close by, Walker and Pyramid lakes.
Limnological Description of Yerington Pit Lake
Water temperature and DO concentrations show that the Yerington pit lake is
monomictic. From 2000 to 2001, turnover occurred in February. Because there is a lack
52
of solute-induced stratification, mixing is wind and thermally driven. As the epilimnion starts to cool in the autumn, wind driven mixing increases the depth of the metalimnion,
gradually mixing more of the water column as the autumn progresses. In winter, the entire water column becomes isothermal and there is minimal inhibition to full mixing.
As shown by DO concentrations, complete turnover occurs sometime after the onset of isothermal conditions.
Pit lakes, partially because of their immaturity as a biological habitat, and partially
because of their lack of nutrients, would be expected to have limited biological activity during filling. When a pit lake starts filling after mining, it is essentially a sterile environment. Biological activity would develop slowly as birds, air deposition, groundwater flow, or other mechanisms introduce bacteria, algae, and other plankton.
Even after introduction, biomass increase would be limited by nutrient availability.
Biological activity at the Yerington pit lake, which has been filling for 30 y, is limited.
Nutrient concentrations, hypolimnetic O2 consumption rates, and chl-a concentrations all
show that the Yerington pit lake is oligotrophic.
Since water input to the pit is limited to groundwater, nutrient addition to the lake will likely continue to be limited. Groundwater is generally low in nutrients (Table 2.1) and the only other inputs such as surface runoff (minimal), atmospheric deposition, and inputs from animal contact will not introduce significant nutrient loads.
Biological sampling showed low photic zone phytoplankton densities in April
(12,600 mL-1) and September (15,600 mL-1) 1998. The phytoplankton densities in the
photic zone were dominated by chlorophyta during these two sampling events.
53
Cyanobacteria densities were about one-third of the chlorophyta (Table 2.3). Low
phytoplankton densities in the Yerington pit lake were also observed by Atkins et al.
(1997) in September 1995 and March and June 1996, but their sampling procedures were different so direct comparisons of the cell densities to this study cannot be made. In contrast to this study, Atkins et al. (1997) observed substantially greater cyanobacteria densities relative to chlorophyta in March and June, but similar chlorophyta densities
relative to cyanobacteria in September. The cyanobacteria dominance observed by Atkins
et al. (1997) may be the result of sampling during cyanobacteria blooms, or perhaps,
differences in counting procedures between the two studies (counting filaments vs. cells).
During the flood of January 1997, a substantial amount of Walker River water entered the pit lake. The exact amount of water added during this event is unknown since lake water levels were not measured between December 1996 and July 1998. However, lake volume changes measured after the flood can be used to estimate the volume of water that entered the pit during the flood. From July 1998 through April 2002, the average annual volume change was 1.25 million m3. The total volume change from
December 1996 through July 1998 was 2.78 million m3. Using the average annual volume change, the amount of water added to the pit for the 19 months from December
1996 through July 1998 was 1.98 million m3 leaving 0.8 million m3, or < 3 % of the total
lake volume, added during the flood.
The floodwaters were potentially a source of nutrients to the pit lake that would not
occur otherwise. The nutrient concentrations from 1990 through 1995 at the Mason gauge
on the Walker River, upstream of the Yerington pit lake, indicate that nutrient
54
concentrations in the Walker River are comparable to, and somewhat higher than, pre- and post flood concentrations in the pit lake (Table 2.1). It is possible that floodwater nutrient concentrations were much higher, but pit-lake nutrient concentrations do not
show evidence of a large nutrient input.
Evaporation appears to be an important process at the Yerington pit lake. The TDS
from 1995 to 2001 remained constant; however, Cl- increased about 15 %. The Cl-
increase indicates that evaporation from the lake surface is an important physical process at the pit lake while the constant TDS indicates that other geochemical processes including mineral precipitation are controlling pit-lake water chemistry. This amount of evaporation from the pit lake is more than twice that estimated by Shevenell (2000).
Comparison of Yerington Pit Lake to Desert Lakes
Limnological parameters compared to the Yerington pit lake for Walker Lake are from Beutel and Horne (1997), Beutel et al. (2001), Cooper and Koch (1984), and Horne et al. (1994). Data for Pyramid Lake are from Lebo et al. (1993), Lebo et al. (1994),
Lebo et al. (1992), Galat et al. (1981), Galat and Verdin (1988), and Horne and Galat
(1985). A summary of limnological parameters for the different lakes are presented in
Table 2.5.
Both Walker and Pyramid lakes are saline terminal lakes that are within 100 km of the Yerington pit lake (Fig. 2.1). These lakes have been greatly affected by water diversions over the last 100 y, and because of the loss of water with time, have much higher TDS (13,100 and 5,300 mg L-1, respectively) than the Yerington pit lake
(600 mg L-1). Shevenell (2000) suggested that evaporation from pit lakes will have much
55 less effect than seen at these arid terminal lakes partially because the surface-to-mean depth ratio of pit lakes will be more than 1,000 times smaller. The surface-to-mean depth ratio for both Walker and Pyramid lakes is 7,500 while the Yerington pit lake is only 13.
Table 2.5. Comparison of limnological parameters of the Yerington pit lake to Walker and Pyramid lakes Parameter Yeringtona Walkerb Pyramidc Total Dissolved Solids (mg L-1) 600 13,100 5,300 Surface Area (km2) 0.68 140 450 Maximum Depth (m) 118 30 102 Mean Depth (m) 52 18.6 60 Surface Area/Mean Depth (km) 1.3E+01 7.5E+03 7.5E+03 Epilimnion Temperature (°C)d 20 - 23 20 - 24 20 - 24 Hypolimnion Temperature (°C)d 6.0 - 6.7 10 - 12 5 - 6 Winter Dissolved Oxygen (mg L-1)e 8.7 - 9.7 10 - 12 > 9 Epilimnion Dissolved Oxygen (mg L-1)d 7.2 - 7.4 6 - 8 7 - 8 Hypolimnetic Oxygen Consumption 0.019 - 0.026 0.12f 0.018 - 0.026 -2 -1 (mg O2 cm d ) Total Inorganic Nitrogen (mg L-1)g 25 - 248 40 - 1915h 4 - 155 Total Phosphorous (mg L-1)i < 5 - 20 62 - 88 56 - 120 Chlorophyll a (mg L-1) 0.1 - 3.8 < 1 - > 15 0.3 - 3.1 Phytoplankton Density 0 to 30 m 1.8 - 2.0 E+04 NA 0.4 - 6.6 E+04 (Cells mL-1) Zooplankton (Number L-1) < 5 j 48 - 582k 20 - 240l a = This study b = Beutel et al. (2001); Beutel and Horne (1997) c = Lebo et al. (1993) d = June - September, Yerington 2000-01, Walker 1995-96, Pyramid 1989-91 e = February-March Yerington 2000-01, Walker 1995-96, Pyramid 1989-91 f = Beutel (2001) - + g = Range for all depths and all times measured (NO3 -N + NH4 -N) + h = Mostly NH4 -N released from sediments during anoxia i = Range for all depths and all times measured j = microscopic scan per sample per depth, net hauls not conducted k = 25 m to surface net hauls l = 10 m to surface net hauls NA = Not Available
Similar to Yerington, Walker and Pyramid lakes are monomictic. Because of
Yerington’s greater depth (> 118 m) and much smaller surface area, hypolimnetic waters are cooler (6.0 to 6.7 °C) than Walker Lake (10 to 12 °C), but similar to the deeper
56
Pyramid Lake (5 to 6 °C). Because of seasonal, lower TDS surface-water flow into
Walker and Pyramid lakes from rivers, the strength of stratification and the timing of
mixing are more variable in these lakes than those seen at Yerington. High freshwater
inflow into Pyramid Lake during wet years can sufficiently dilute the salinity of surface
waters so that winter mixing is physically impossible and temporary meromixis occurs
(Lebo et al. 1993). At both Walker and Pyramid lakes, summertime evaporation, and
limited mixing between surface and bottom waters, causes surface TDS to increase. A
noticeable increase in surface water TDS during the summer is not seen at Yerington
because of its much smaller surface area.
Each of the three lakes has similar variations in DO for part of the year. Winter
mixing transports O2-rich surface waters into the deeper parts of all three lakes. During
this time of the year, DO concentrations in all lakes range from 9 to 12 mg L-1. During
stratification, epilimnetic water remains well oxygenated by atmospheric diffusion and
phytoplankton photosynthesis in all lakes. Surface-water DO concentrations ranged between 6 and 8 mg L-1 in all lakes. However, after the onset of stratification,
hypolimnetic water in Walker Lake undergoes O2 depletion and becomes anoxic
(< 5 mg L-1). Anoxia has also been observed in the past in Pyramid Lake (Galat et al.
1981; Galat and Verdin 1988); however, from 1987-1992, the time period examined by
Lebo et al. (1993), the hypolimnion did not become anoxic.
Hypolimnetic anoxia usually is caused by O2 consumption by bacteria degrading
dead phytoplankton and other detritus that sinks. During stratification, O2 in the
hypolimnion cannot be replenished so it continues to be consumed until gone.
57
-2 -1 Hypolimnetic O2 consumption rates for Walker Lake were 0.12 mg O2 cm d (average of 1994 to 1996 and 1998, Beutel 2001). DO consumption rates for Pyramid Lake
-2 -1 historically have been 0.041 to 0.064 mg O2 cm d (Lebo et al. 1993). These rates have
produced anoxia in both Walker and Pyramid hypolimnia. Between 1987 and 1992,
-2 -1 consumption rates for Pyramid Lake decreased (0.018 to 0.026 mg O2 cm d ). These
-2 -1 rates are close to those calculated for Yerington, 0.019 and 0.026 mg O2 cm d for 2000 and 2001, respectively. Both Yerington and Pyramid hypolimnetic water did not become anoxic at these rates. Lebo et al. (1994) noted that the decreased O2 consumption rates at
Pyramid Lake were associated with drought conditions that decreased turbidity and increased water clarity.
Another consideration for O2 consumption in pit lakes is the presence of sulfidic
minerals. In addition to bacterial degradation of phytoplankton, oxidation of sulfide
minerals in pit wall rocks and bottom sediments will consume O2 in an isolated
hypolimnion. This process has been observed in the Berkeley pit lake (Davis and
Ashenberg 1989). The Berkeley pit lake is always stratified and has an anoxic
hypolimnion. Sulfide oxidation not only consumes O2, but also produces acidity. The
Berkeley pit has pH values < 3.
At Yerington, the ore body consisted of an upper oxide zone and a lower sulfide
zone. At the present lake level, the sulfide ore is submerged under the pit lake. Unlike the
acidic Berkeley pit lake, the pH of the Yerington pit lake is approximately 8.1. At
Yerington, there is sufficient acid neutralizing capacity in the lake because the well-
buffered lake water receives carbonate from inflowing groundwater (East Spring
58
-1 - -1 -1 135 mg L HCO3 , West Spring 134 to 238 mg L , Well 2B 161 mg L , Well 4
177 mg L-1), there is annual mixing of surface waters that are in equilibrium with atmospheric CO2, and low phytoplankton densities limit the consumption of CO2 during photosynthesis.
Both Walker and Pyramid lakes are N-limited (Beutel and Horne, 1997; Beutel et
+ - al., 2001; Lebo et al., 1994). Phytoplankton use NH4 -N and NO3 -N. In Walker and
Pyramid lakes, when phytoplankton use all of the available inorganic N, species of
+ cyanobacteria (e.g. Nodularia sp. and Anabaena sp.) convert atmospheric N2 to NH4 -N by N2 fixation. Although cyanobacteria are present in the Yerington pit lake, they do not appear to be species that fix N2. Phytoplankton productivity in the Yerington pit lake does not appear to be N limited. P for phytoplankton is plentiful in both Walker and
Pyramid lakes (Beutel and Horne, 1997; Beutel et al., 2001; Lebo et al., 1994). This is in contrast to Yerington where P was near or below the detection limit of 5 μg L-1. The availability of N and P in the Yerington pit lake is typical of other oligotrophic lakes
(Wetzel, 2001).
In 1995 and 1996, chl-a in Walker Lake ranged from < 1 to > 15 μg L-1 in surface waters (0 to 25 m) and showed a bimodal trend in time with a spring peak, summer minimum, and late summer peak (Beutel and Horne, 1987). The chl-a peaks corresponded with large populations of Nodularia and Anabaena, the dominant phytoplankton in Walker Lake. Chl-a in Pyramid Lake ranged from 0.3 to 3.1 μg L-1 for surface waters (0 to 25 m). Similar to Walker Lake, a spring peak in chl-a was observed and attributed to a diatom bloom. A Nodularia bloom that increased chl-a concentrations
59
in late summer was also observed. During the summer, chlorophyta, particularly
Sphaereocystis schroeteri, dominated the Walker Lake phytoplankton. Careful study of phytoplankton at Pyramid Lake shows a general seasonal succession from diatom dominance in winter and spring to chlorophyta and cyanobacteria dominating the rest of the time with occasional Nodularia blooms. Chl-a concentrations at the Yerington pit lake are comparable to those at Pyramid Lake (Table 2.5).
Walker Lake was reported to have four dominant species of zooplankton. Increased salinity in Walker Lake has negatively impacted zooplankton populations and species diversity (Beutel and Horne, 1997; Beutel et al., 2001). Maximum abundance for two of the four species was 100 individuals L-1. Zooplankton abundance and biomass in surface waters of Pyramid Lake exhibited large seasonal variations corresponding with variations in phytoplankton densities, their food source (Lebo et al., 1993). Maximum zooplankton biomass during population peaks was 170 to 240 individuals L-1. Yerington pit lake
zooplankton were scarce. Atkins et al. (1997) found the maximum zooplankton population dominated almost exclusively by bdelloid rotifers at a maximum population
density of 0.7 individuals L-1 in the summer of 1995. Perhaps one reason for the small
metzooplankton population in the Yerington pit lake is the related unpalatability of
dominant phytoplankton taxa (i.e. Oocystis sp. and filamentous cyanobacteria). More
importantly, however, zooplankton populations in Yerington are likely impacted by
elevated trace element concentrations (particularly Cu and Se), and Yerington pit lake
water is toxic to zooplankton.
60
Cu and Se
Cu and Se concentrations are elevated in the Yerington pit lake. Cu is both a trace
nutrient needed for phytoplankton and a toxic metal at elevated concentrations (Stokes,
1979). In April 1995, the depth-averaged Cu concentration was 136 μg L-1 (Fig. 2.7A), almost 15 times aquatic life standard (9.17 μg L-1, 96 h average, Nevada Administrative
Code 2008). However, dissolved Cu concentrations have been decreasing with time. By
2001, the depth averaged Cu concentration had dropped to approximately 20 μg L-1
(Fig. 2.7A), twice the aquatic life standard.
Dissolved Cu concentrations in the Yerington pit lake may be controlled by several processes. Since the pit lake is still filling, and groundwater Cu concentrations are much lower than lake concentrations (Table 2.4), inflowing waters may be diluting Cu.
However, average annual flow into the pit lake is only 3.5 % of the total lake volume; therefore, dilution over a 5 y period could account for no more than a 15 % reduction in dissolved Cu concentration in the lake as a whole. Also, this calculation does not consider the additional Cu mass added to the lake from the groundwater.
Dissolved Cu concentrations in the pit lake may also be reduced by secondary Cu mineral precipitation. Thermodynamic equilibrium calculations show that ore deposit Cu minerals are below saturation. This suggests that Cu minerals in the wall rock are dissolving and that precipitation of secondary Cu minerals is minimal. Cu mineral dissolution may offset any lowering of Cu concentrations in the lake by dilution from groundwater.
61
Dissolved Cu in the lake may be removed by algal uptake. Cu is a trace nutrient for
algae and the decrease in dissolved Cu may be the result of it being consumed. Wetzel
(2001) listed the primary physiological functions of Cu as a trace nutrient for aquatic
organisms, which included redox reactions of respiration and photosynthetic electron
transport. Stokes (1979) showed several different species of algae efficiently removed
dissolved Cu from water including Chlorella pyrenoidosa, which removed 54 to 80 % at
pH 8.0 after 2 h. Chang and Sibley (1993) showed increased Cu uptake by the
chlorophyta Oocystis pusilla with increased Cu concentrations and the most algal growth
at 200 μg L-1 Cu. This Cu-tolerant genus was the dominant phytoplankter in the
Yerington pit lake.
Also, organic matter has a strong affinity for adsorbing Cu. Cu and TOC
concentrations in sediments from the Yerington pit lake are highly correlated (Table 2.6).
Dead algae, as well as other organic matter, settle out of the water column and
accumulate in sediments, sequestering Cu from the lake. Gibson (1972) showed the
cyanobacterium Anabaena flos-aquae to be sensitive to the application of CuSO4 and that it bioaccumulates Cu. He suggested that application of increased concentrations of
CuSO4 to control other less Cu-sensitive algae would result in greater amounts of Cu
accumulation in A. flos-aquae. The dead cells with the Cu would settle to the bottom of
the reservoir resulting in a build-up of Cu in the sediments with potential adverse affects
on bottom fauna. Cyanobacteria were abundant in the Yerington pit lake and it has also
been shown that living cyanobacteria can take up and sequester Cu. For example, Singh
62
et al. (1992) found that Nostoc calcicola could take up Cu, but that uptake was maximal at pH’s lower than that occurring in the Yerington pit lake.
Table 2.6. Cu and total organic carbon in sediments in the Yerington pit lake. Depth TOC Cu (m) (mg Kg-1) (mg Kg-1) 15 11,000 52,000 37 147,000 119,000 100 80 25,000
As suggested by both water-quality standards and the toxicity test results, Cu
concentrations in the Yerington pit lake could be adversely affecting aquatic life. Cu is
toxic to algae because it interferes with the activity of enzymes on cell membranes
(Horne and Goldman, 1994). The interference prevents cell division and leads to an
eventual cessation of photosynthesis by product inhibition. As little as 0.1 μg L-1 of Cu can kill some algae in water with low chelation potential; at 5 to 10 μg L-1, many
cyanobacteria are affected (Horne and Goldman, 1994).
Zhang et al. (2001) showed the toxic effect of Cu on the growth of unicellular
-1 chlorophyta with 96 h EC50 (effective concentration) of 67, 51, and 202 μg L for
Chlorella pyrenoidosa, Scenedesmus obliquus, and Closterium lunula, respectively, while
Chang and Sibley (1993) saw Oocystis pusilla growth depressed above 400 μg L-1 Cu.
Application of CuSO4 to control nuisance algae has been used for over 60 y because it is
not toxic to fish at levels typically applied. However, algae can develop resistance to Cu.
Dakel and Mitman (2001) and Mitman (2001) have isolated multiple species of algae at
the Berkeley pit lake that can tolerate very high concentrations of dissolved Cu in low pH
environments.
63
Dissolved Cu not only affects phytoplankton, but has also been show to impact
zooplankton. Winner (1984) showed antennal damage to Daphnia pulex in 10 μg L-1 Cu on day 28 of his experiments with all animals exhibiting antennal damage by day 32.
Luna-Andrade et al. (2002) studied Cu effects on population growth of the saltwater rotifer Brachionus plicatilis. In their experiments, the presence of Cu reduced peak
-1 population densities. Khangarot et al. (1987) reported a 48 h LC50 of 93 μg L Cu for
Daphnia magna.
Whether Cu in the Yerington pit lake is limiting phytoplankton and zooplankton
population growth and diversity, or is actually being accumulated and removed by
phytoplankton, is unclear. Toxicity tests using Yerington pit-lake water showed the lethal
effect of the pit-lake water on D. magna and C. dubia. However, pit-lake water not only contains high levels of Cu, it also has high concentrations of Se.
Se concentrations at Yerington are about 100 μg L-1 and remained relatively
constant between 1995 and 2001. These concentrations are 20 times the chronic (5.0
μg L-1, 96 h average, Nevada Administrative Code 2008), and 5 times the acute aquatic
life standards (20 μg L-1, 1 h average, Nevada Administrative Code 2008). However, very
little is known about the biochemical significance of Se to aquatic organisms (Wetzel,
2001).
Winner (1984) exposed Daphnia pulex to low concentrations of Cu (10 μg L-1), and
Cu together with Se (5 μg L-1). He observed antennal damage to daphnids in the Cu-only
water, but not in the water containing both Cu and Se. He concluded that the addition of
64
Se as a micronutrient eliminated antennal damage and reduced the chronic toxicity of Cu
since survival rate was increased. Se concentrations in these experiments were
substantially lower than those in the Yerington pit lake (Table 2.4). Bennett (1988) exposed Chlorella pyrenoidosa, a chlorophyta, to four different Se concentrations. He
observed a near-linear decrease in growth rate in response to increased Se concentrations.
Se concentrations in these experiments were much higher (530-1,370 μg L-1) than those
in the Yerington pit lake. Besser et al. (1993) exposed an alga (Chlamydomonas
reinhardtii), D. magna, and a fish to different Se species, selenate, selenite, and seleno-L-
methionine. All three organisms concentrated the organic species more strongly than the
inorganic species. Bioconcentration factors for the organic species were ×16,000 for the
alga and ×200,000 for the daphnids.
Although the aquatic life standards are quite low, and the pit lake concentrations are
high, the role of Se toxicity in the pit lake is uncertain. From the toxicity tests conducted
for this study, two species of zooplankton were adversely affected by dilutions of pit-lake
water. Whether these toxic effects are the result of Cu and/or Se, or some other element
not currently identified, is uncertain. To determine whether Se is limiting phyto- and/or
zooplankton growth and diversity in the pit lake, laboratory experiments measuring pit- lake plankton Se uptake and pit-lake plankton sensitivity to different Se concentrations are needed.
CONCLUSIONS
Limnological studies of a large mine pit lake in NV that has been filling for over
30 y shows that, limnologically, the pit lake is similar to two nearby natural, terminal
65
lakes. The pit lake, even though it has a much different surface area-to-mean depth ratio
than the natural lakes, is also monomictic and oligotrophic. Additionally, the pit lake, like one of the terminal lakes, does not develop an anoxic hypolimnion during stratification.
Elevated Cu and Se concentrations in the pit lake are much greater than aquatic life water-quality standards and may be adversely affecting phyto- and zooplankton.
ACKNOWLEDGEMENTS
The authors acknowledge U.S. Environmental Protection Agency for partially funding this research. The authors acknowledge Chris Fritsen, Jeramie Memmott, and
Don Sada for chl-a analysis, phytoplankton identification, and review of the draft manuscript.
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Horne AJ, Goldman CR (1994) Limnology, 2nd edn. McGraw-Hill, New York. Horne AJ, Roth JC, Barrat NJ (1994) Walker Lake-Nevada, State of the Lake, 1992-94. Report to the Nevada Division of Environmental Protection from Dep Civ Eng Environ Eng, Univ Calif Berkeley. Kalin M, Cao Y, Smith M, Olaveson MM (2001) Development of the phytoplankton community in a pit-lake in relation to water quality changes. Water Res 35:3215- 3225. Karl DM, Dore JE, Hebel DV, Winn C (1991) Procedures for particulate carbon, nitrogen, phosphorus, and total mass analyses used in the US-JGOFS Hawaii Ocean time series program. In: Marine particles: Analysis and characterization, Am Geophys Union, pp 71-77. Khangarot BS, Ray PK, Chandra H (1987) Preventive effects of amino acids on the toxicity of copper to Daphnia magna. Water Air Soil Pollut 32:379-387. Koscorrekc M, Bozau E, Frömmichen R, Geller W, Herzsprung P, Wendt-Potthoff K (2007) Processes at the sediment water interface after addition of organic matter and lime to an acid mine pit lake mesocosm. Environ Sci Technol 41:1608-1641. Lebo ME, Reuter JE, Goldman CR, Rhodes CL (1994) Interannual variability of nitrogen limitation in a desert lake: influence of regional climate. Can J Fish Aquat Sci 51:862-872. Lebo ME, Reuter JE, Rhodes CL, Goldman CR (1993) Pyramid Lake, Nevada water quality study, 1989-1993. Vol II, Limnological Description. Div Environ Stud, Univ Calif Davis. Lebo ME, Reuter JE, Rhodes CL, Goldman CR (1992) Nutrient cycling and productivity in a desert saline lake: observations from a dry, low-productivity year. Hydrobiol 246:213-229. Loop CM, Scheetz BE, White WB (2003) Geochemical evolution of a surface mine lake with alkaline ash addition: Field observations vs. laboratory predictions. Mine Water Environ 22:206-213. Luna-Andrade A, Aguilar-Duran R, Nandini S, Sarma SS (2002) Combined effects of copper and microalgal (Tetraselmis suecica) concentrations on the population growth of Brachionus plicatilis Mueller (Rotifera). Water Air Soil Pollut 141:143-153. Lyons WB, Doyle GA, Petersen RC, Swanson EE (1994) The limnology of future pit lakes in Nevada: The importance of shape. In: Tailings & Mine Waste '94, ISBN 95 5410 3647, pp 245-248. McCullough CD, Lund MA (2006) Opportunities for sustainable mining pit lakes in Australia. Mine Water Environ 25:220-226. Migaszewski ZM, Galuszka A, Halas S, Dąbek J, Dołęgowska S, Budzyk I, Starnawka E, Michalik A (2009) Chemical and isotopic variations in the Wiśnówka Mała mine pit water, Holy Cross Mountains (south-central Poland). Environ Geol 57:29-40.
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Miller GC (2002) Precious metals in pit lakes: controls on eventual water quality. Southwest Hydrol 1:16-17. Mitman GG (2001) Bacterial effects and algal bioremediation by Chlorella ellipsoidea Gerneck of the Berkeley Pit Lake system. J Phycol 37:36. Moore JG (1969) Geology and Minerals Deposits of Lyon, Douglas, and Ormsby Counties, Nevada. Nevada Bureau of Mines Bull 75. Mackay School of Mines, Univ Nevada Reno. Nevada Administrative Code (2008) NAC 445A.144 Standards for toxic materials applicable to designated waters. http://www.leg.state.nv.us/nac/NAC- 445A.html#NAC445ASec144. Accessed 3 Oct 2009. Packroff G (2000) Protozooplanktion in acidic mining lakes with special respect to ciliates. Hydrobiol 433:157-166. Parshley JV, Bowell RJ (2003) The limnology of Summer Camp pit lake: A case study. Mine Water Environ 22:170-186. Pellicori DA, Gammons CH, Poulson SR (2005) Geochemistry and stable isotopic composition of the Berkeley pit lake and surrounding mine waters, Butte, Montana. Appl Geochem 20:2166-2137. Ramstedt M, Carlsson E, Lövgren L (2003) Aqueous geochemistry in the Udden pit lake, northern Sweden. Appl Geochem 18:97-108. SAS Institute (1985) SAS procedures guide for personal computers, ver 6. SAS Institute, Incorp. Salmon SU, Oldham CE, Ivey GN (2008) Assessing internal and external controls on lake water quality: Limitations on organic carbon-driven alkalinity generation in acidic pit lakes. Water Resour Res 44:W10414. doi:10.1029/2007WR005959. Shevenell LA (2000) Water quality in pit lakes: Disseminated gold deposits compared to two natural, terminal lakes in Nevada. Environ Geol 39:807-815. Shevenell LA, Conners KA, Henry CD (1999.) Controls on pit lake water quality at sixteen open-pit mines in Nevada. Appl Geochem 14:669-687. Singh SP, Singh RK. Pandey PK, Pant A (1992) Factors regulating copper uptake in free and immobilized cyanobacterium. Folia Microbiol 37:315-320. Stevens CL, Fisher TSR, Lawrence GA (2005) Turbulent layering beneath the pycnocline in a strongly stratified pit lake. Limnol Oceanogr 50:197-206. Stokes PM (1979) Copper accumulations in freshwater biota. In: Nriagu JO (ed) Copper in the environment, Wiley, New York, pp 357-381. Tempel RN, Shevenell LA, Lechler PJ, Price JG (2000) Geochemical modeling approach to predicting arsenic concentrations in a mine pit lake. Appl Geochem 15:475-492.
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CHAPTER 3
LABORATORY EXPERIMENTS OF AS(V) AND AS(III) SORPTION ONTO PIT-LAKE SEDIMENTS FROM THREE DIFFERENT ORE-BODY TYPES
Ronald L. Hershey, Charalambos Papelis, Glenn C. Miller
72
INTRODUCTION
Arsenic (As) is often associated with precious metals ore deposits and is a major contaminant released during large-scale gold mining. Arsenic can be present at higher concentrations in acidic environments (e.g., Davis and Ashenbery, 1989), which are common at numerous precious metal mines. However, many precious metals mines in
Nevada (NV) are sediment hosted and non-acid generating. This does not necessarily limit the mobility of As since negatively charged oxyanions of As exist in these well- buffered, higher pH, aqueous environments (pH 6.5 to 8.5; Shevenell et al., 1999;
Shevenell, 2000). Because of the complex oxidation/reduction and adsorption behavior of
As, prediction of the long-term mobility of As in pit lakes is uncertain.
Oxidation and dissolution of As-bearing minerals form dissolved inorganic As(III) and As(V) that can be transported by fluids. Arsenic transport is often controlled by adsorption of As onto particles of soil and sediment and the distribution of dissolved
As(III) and As(V) is dependent on oxidation/reduction potential of the transporting fluid.
Arsenic (V) species are dominant under oxidizing conditions and include deprotonated
- 2- 3- oxyanions of arsenic acid (H2AsO4 , HAsO4 , AsO4 ) while As(III) species exist under
0 - mildly reducing conditions as arsenious acid (H3AsO3 , H2AsO3 ) (Fig. 3.1). Previous
studies have shown that As(V) forms high affinity, inner-sphere Fe-As(V) surface
complexes on Fe(III) oxides (Waychunas et al., 1993; Lumsdon et al., 1984) and As(III)
forms inner-sphere surface complexes on goethite surfaces (Sun and Doner, 1996).
73
Figure 3.1. pe-pH diagram for predominant aqueous species of arsenic at equilibrium and 298.15 K and 1 atmosphere pressure (from Nordstrom and Archer, 2003).
The behavior of As in pit lakes was investigated by conducting a series of laboratory experiments where As is allowed to adsorb onto pit-lake sediments. Experiments were conducted over a range of pH with both As (III) and As (V) allowed to adsorb onto sediments collected from existing pit lakes found in the three main ore body types of NV, porphyry copper, quartz-adularia precious metal, and Carlin-type gold deposits
(Shevenell et al., 1999). Desorption was not considered in this study.
74
METHODOLOGY
Pit-Lake Sediment Characterization
Sediments were collected from the Anaconda pit, a porphyry copper deposit in
Yerington, NV (hereafter referred to as Yerington), the Dexter pit, a quartz-adularia precious metal deposit in Tuscarora, NV (Tuscarora), and the 303 pit, a Carlin-type gold deposit at the Big Springs Mine, north of Elko, NV (Big Springs). Grab samples were collected with an Ekman type dredge from the deepest part of each lake: 17 m at
Tuscarora and 27 m at Big Springs. Because of the large size of the Yerington pit, sediment samples were collected from five different depths, 2.4 m, 15.2 m, 37 m, 76 m,
and 100 m. Samples were transferred from the dredge to pre-cleaned amber glass bottles
and were topped-off with pit-lake water. Samples were transported and stored cool.
Samples were then dried under a nitrogen atmosphere at room temperature, crushed, and
sieved. All particles that passed through a 150-μm sieve were retained for the adsorption
isotherm experiments. For the Yerington pit, a composite sediment sample was prepared
from the five depths sampled. An amount of sediment from each depth, proportional to
the total area of that depth, was combined to produce the sample used in the adsorption
isotherm experiments.
The minerals making up the samples were identified by quantitative phase analysis
using the Rietveld method and x-ray powder diffraction (QXRD) at the University of
British Columbia, Vancouver, BC. Step-scan QXRD data were collected over a range 3-
70°2θ with CuKα radiation on a standard Siemens (Bruker) D5000 Bragg-Brentano
diffractometer equipped with a diffracted-beam graphite monochromator crystal, 2 mm
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(1°) divergence and anti-scatter slits, 0.6 mm receiving slit, and incident-beam Soller slit.
The long fine-focus Cu X-ray tube was operated at 40 kV and 40 mA, using a take-off angle of 6°. Bulk elemental composition was determined by X-ray fluorescence (XRF) and trace elemental composition was determined by dissolving sediments with aqua regia and analyzing the resultant solution by inductively coupled plasma-mass spectrometry
(ICP-MS). XRF and ICP-MS analyses were conducted by the NV Bureau of Mines and
Geology at the University of NV, Reno. Specific surface area was determined by nitrogen adsorption with a Micromeritics ASAP 2010 Analyzer and the BET model (Brunauer et
al., 1938). The morphology and composition of the sediments were examined by
scanning electron microscopy (SEM) combined with energy dispersive X-ray
spectroscopy (EDX). A JEOL JSM-840A SEM/EDX was used to examine several areas
of the sample under different magnification. Total organic carbon (TOC) of the sediment
was measured by the Desert Research Institute’s Water Chemistry Laboratory, Reno, NV.
The sediments were also treated with two different selective extraction methods to
quantify amorphous Fe and Al oxides and citrate-dithionite extractable Fe and Al
(Loeppert and Inskeep,1996; Bertsch and Bloom, 1996; Manning and Goldberg, 1997).
500 mg of sediments were treated for amorphous Fe and Al oxides by the acid
ammonium oxalate in darkness-Tamm’s reagent method. In this method, sediments were
first treated with ammonium acetate to pH 5.5 to remove CaCO3 and then treated with pH
3.0 ammonium oxalate. For citrate-dithionite extractable Fe and Al, Na citrate, Na
dithionite, and de-ionized water were added to 500 mg of sediments and allowed to react
for 16 h. The supernatant from both procedures were analyzed for Fe and Al by flame
76 atomic adsorption by the Desert Research Institute’s Water Chemistry Laboratory, Reno,
NV.
Experimental Procedures
Arsenic adsorption experiments were conducted in 15 mL polypropylene centrifuge tubes with 1g L-1 sediment, either As(III) or As(V), 1M NaCl to adjust ionic strength, either 0.1M HCl or NaOH to adjust pH, and distilled de-ionized water (de-aired for
As(III) experiments) to a total volume of 10 mL. Reagent-grade As compounds were purchased to prepare spike solutions for the experiments. The adsorbate added was either
NaAsO2 to make As(III) spike solutions or Na2HAsO4·7H2O to make As(V) spike solutions. Stock spike solutions were made to 1.33 M As. Arsenic (III) spike solutions were made fresh daily while As(V) spike solutions were made fresh about every three weeks. After the initial acid or base addition, the solution was allowed to equilibrate without further pH adjustments. The samples were equilibrated for 24 h and were continuously mixed by end-over-end rotation at approximately eight revolutions per minute.
Following equilibration, the pH of the suspension was measured; this is the final pH reported for the experiments. The pH meter was calibrated daily with pH 4.00, 7.00, and
10.00 buffers. Solid-solution separation was achieved by centrifugation at 1580 rcf for 20 minutes and a 2-mL aliquot of the supernatant was removed for As analysis.
The samples were analyzed by hydride-generation atomic absorbance (AA) spectrometry and speciated according to the method described by Manning and Martens
(1997). A high-performance liquid chromatography pump pushed the sample through a
77
column that separated the As(III) from the As(V). The separated species were then
pumped into a continuous hydride generation apparatus producing arsine gas that was
then transferred into an electric furnace in a Varian 8050 spectrophotometer. The
reported detection limits of this method are 1 μg L-1 for As(III) and 2.5 μg L-1 for As(V).
Occasionally, during this study, the method was unable to accurately delimit As(V) concentrations below 5 μg L-1.
Preliminary experiments were conducted prior to the adsorption isotherm
experiments. Initially, experiments were conducted to examine the effect of ionic strength
and pH on As adsorption. Ionic strength dependence experiments were conducted at ionic
strengths of 0.006, 0.03, and 0.1 M NaCl and pH values of approximately 5, 7, and 9. pH dependence experiments were conducted at pH values of approximately 5, 6, 7, 8, and 9 at an ionic strength of 0.01M NaCl.
As(III) and As(V) adsorption isotherm experiments were conducted at pH of
approximately 5, 7, and 9, and at an ionic strength of 0.01M NaCl. Arsenic (III)
experiments were prepared using de-aired distilled de-ionized water. Adsorption
experiments were conducted sequentially with increasing spike concentration until the
adsorbed concentrations did not change with increased spike concentration. Individual
reactors were prepared in triplicate for all experiments; results are the average of
triplicate experiments. The relative standard deviation for the triplicate experiments was,
in most cases, less than 10 %.
78
RESULTS
Pit-Lake Sediment Characterization
The pit-lake sediments were collected from three important ore-body types
(porphyry copper, quartz-adularia, sediment-hosted Carlin-type) found in NV that are
often mined below the water table. Porphyry copper deposits are very large,
mineralogically and geochemically complex deposits hosted by felsic intrusive rocks
(Shevenell et al., 1999). Pyrite, various copper sulfides, and other base-metal sulfides are
abundant. Calcite abundance is generally low except in outer halos of propylitic alteration and in surrounding rocks. Quartz-adularia precious metal deposits contain variably low to moderate concentrations of pyrite and other sulfides. These deposits are generally hosted by volcanic rocks that contain little calcite. Alteration suites involve potassic alteration,
argillic alteration, and silicification. These rocks have low to moderate buffering capacity
(Shevenell et al., 1999). Carlin-type deposits, also called sediment-hosted disseminated
precious metal deposits, are generally large-tonnage, low-grade epigenetic gold deposits
hosted predominantly in sedimentary sequences. Host rocks are often carbonaceous and
contain abundant calcite; however, within the ore body, hot acidic hydrothermal fluids
dissolve away much of the carbonate and introduce silica (Muntean, 2006). These
deposits typically contain abundant pyrite and lesser amounts of arsenopyrite, realgar,
orpiment, stinite, and cinnabar (Shevenell et al., 1999).
QXRD spectra showed the presence of the following minerals for Yerington
sediments: major minerals of muscovite, calcite, plagioclase, and quartz with minor
minerals of clinochlore, epidote, goethite, and ankerite (Table 3.1). Smectite group clay
79 was also abundant, but could not be quantified because of its disordered crystal structure and was not included in the total % of the analysis. The characteristic flaky nature of smectite/montmorillonite clay can be seen in Yerington sediment in SEM micrographs
(Fig. 3.2, Appendix B). Tuscarora sediment is composed of muscovite, K-feldspar, kaolinite, plagioclase, and quartz. A minor amount of smectite clay is also present, but not quantified. Big Springs is composed mostly of muscovite and quartz with minor amounts of kaolinite, plagioclase, K-feldspar, pyrite, ankerite, and calcite. Smectite clay is not present.
Table 3.1. Quantitative X-ray diffraction results from three pit-lake sediments used for arsenic adsorption experiments. Mineral Chemical Formula Yerington Tuscarora Big Springs (wt. %) (wt. %) (wt. %)
Muscovite KAl2AlSi3O10(OH)2 39 39 47 Quartz SiO2 13 9 35 Calcite CaCO3 19 1 Plagioclase NaAlSi3O8 – CaAl2Si2O8 17 14 4 K-Feldspar KAlSi3O8 19 3 Kaolinite Al2Si2O5(OH)4 19 7 2+ Clinochlore (Mg,Fe )5Al(Si3Al)O10(OH)8 7 3+ Epidote Ca2(Fe ,Al)3(SiO4)3(OH) 2 2+ Ankerite Ca(Fe ,Mg,Mn)(CO3)2 <1 2 Pyrite FeS2 2 Goethite α –Fe3+O(OH) 1 Chabazite? (Ca0.5,K,Na)4[Al4Si8O24]·12H2O 1 Total 100 100 100 Smectite Group abundant minor none (not included in total)
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Figure 3.2. SEM micrograph of Yerington sediment showing characteristic flaky nature of smectite/montmorillonite clay.
From XRF analysis, all three sediments have significant Si content (Table 3.2), in good agreement with QXRD results of dominant silicate minerals and quartz. This is not surprising since the pit lakes are fairly young (< 25 years) and much of the sediment collected appears, from SEM analysis, to be fine-grained wall-rock material that has sloughed-off the pit walls. Big Springs, a sediment hosted deposit, has the most Si
(59.4 %) from the predominant ore-body rocks such as siliceous argillite, carbonaceous siltstone, and chert (Youngerman 1992). The second largest component for all three sediments is Al, similarly to Si, the Al is present in aluminosilicate minerals found in the wall rocks. Tuscarora has the most Al (20.9 %) while Yerington and Big Springs has somewhat less (14.5 and 14.6 %, respectively). Other major elements include Fe, Ca, K,
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Table 3.2. Chemical composition of pit-lake sediments by x-ray fluorescence (XRF) and inductively coupled plasma–mass spectrometry (ICP-MS). XRF Oxide Yerington Tuscarora Big Springs (%) (%) (%)
SiO2 47.0 52.4 59.4 TiO2 0.42 0.51 1.38 Al2O3 14.6 20.9 14.5 Fe2O3 4.08 5.94 7.36 MnO 0.05 0.07 0.07 MgO 2.35 1.34 1.26 CaO 11.47 1.06 1.88 Na2O 1.35 0.63 0.08 K2O 1.85 3.96 2.99 P2O5 0.28 0.16 0.34 LOIa 15.1 12.0 9.63 Total 98.6 99.0 98.9
ICP-MS Element Yerington Tuscarora Big Springs (ppm) (ppm) (ppm) -2 SO4 2100 500 3700 Ag 0.139 3.48 0.814 As 12.4 27.6 811.0 Au 0.072 0.469 0.047 Ba 457 546 1560 Be 3.20 2.50 2.32 Cd 0.41 0.34 0.42 Co 24.1 16.3 38.8 Cr 17.0 26.8 216 Cu 6120 259 145 Mo 8.05 2.54 7.59 Ni 23.5 14.1 215 Pb 30.0 48.9 34.8 Sb 7.96 7.78 137 Se 13.0 0.68 15.9 Sr 439 74.2 84.9 Th 16.6 4.54 9.69 U 14.7 4.9 8.02 V 50.6 55.9 113 Zn 45.8 117 213 aLOI = Lost On Ignition
82
Na, and M. Yerington has substantially more Ca (11.47 %) than the other sediments (1.34
and 1.26 %), most of which is in the form of calcite. Fe, varies between the sediments with Big Springs having the most Fe (7.36 %), Tuscarora has 5.94 %, and Yerington has
the least (4.08 %). Fe oxides are very important As adsorbents. Mn, another important
adsorbent was present at about equal, but very low, concentrations in the sediments (0.05-
0.07 %).
Trace element concentrations from acid digestion and ICP-MS showed that Big
Springs has substantial amounts of As (811 ppm) while Yerington and Tuscarora
sediments have much less As, 12.4 and 27.6 ppm, respectively (Table 3.2). Yerington has
other considerable trace element concentrations of Ba (457 ppm), Cu (6,120 ppm), Sr
(439 ppm), and U (14.7 ppm). Tuscarora has considerable concentrations of Ba (546 ppm), Cu (259 ppm), and Zn (117). Big Springs has large concentrations of Ba (1,560
ppm), Cr (216 ppm), Cu (145 ppm), Ni (215 ppm), Sb (137 ppm), and Zn (213 ppm).
The measured surface area, by BET, for the sediments varied from a low of
13.9 m2 g-1 for Yerington to a high of 38.1 m2 g-1 for Tuscarora (Table 3.3). Big Springs
sediment had a BET surface area of 17.5 m2 g-1. All sediments had substantial meso-
porosity but little micro-porosity.
For Yerington, TOC was measured for each depth discrete sample, but not on the
composite. A calculated TOC for the composite Yerington sample, based on total surface
area of the depth discrete samples, is 7,200 mg of carbon per kg of sample (mg kg-1).
Tuscarora had 10,500 mg kg-1 TOC and Big Springs had 13,000 mg kg-1 (Table 3.3).
83
Table 3.3. Sediment characteristics for three pit-lake sediments used in arsenic adsorption experiments. Sediment solution pH and As measured from a 1 g L-1 sediment-water mixture. Sediment Yerington Tuscarora Big Springs Surface Area (m2 g-1) 13.9 38.1 17.5 Sediment Solution pH 9.59 7.24 9.03 Total Organic Carbon (mg kg-1) 7,200 10,500 13,000 Sediment Solution As(V) (μM) 0.04 0.04 0.35 Sediment Solution As(III) (μM) <0.01 0.04 0.15 Ammonium Oxalate Extract Al (mg g-1) 0.9 1.4 1.0 Fe (mg g-1) 2.4 6.5 18 Citrate-dithionite Extract Al (mg g-1) 0.1 0.5 0.3 Fe (mg g-1) 5.1 8.4 19
Aluminum and Fe results from the selective extractions are presented in Table 3.3.
The Big Springs sediment had significantly more extractable Fe than the other two sediments while Yerington had the least extractable Fe. Regardless of the extraction method, Big Springs released similar amounts of Fe (18-19 mg Fe per g of sediment).
Yerington on the other hand had twice the Fe released with the citrate-dithionite extract as compared to the ammonium oxalate method. Tuscarora released less Fe with the ammonium oxalate method than the citrate-dithionite method (6.5 and 8.4 mg Fe per gram of sediment). Much less Al was released by both methods as compared to Fe for all sediments. More Al was extracted with ammonium oxalate than with citrate-dithionite for all sediments. Tuscarora released more Al than Big Springs or Yerington.
The soil solution pH of each sediment was obtained by adding sediment to high purity water at the same solid/liquid ratio used in the experiments (1 g L-1), and letting the suspension mix by end-over-end rotation for 24 h. Soil solution pH experiments were
84 conducted in triplicate and the mean result reported. The mean 24 h pH values were 9.59 for Yerington, 7.24 for Tuscarora, and 9.03 for Big Springs (Table 3.3).
Since at least some As is present in the sediments, experiments were conducted to quantify the amount of As in the sediment samples that is easily released either by desorption or mineral dissolution. Sediment samples were combined with high purity water with at least 18 MΩ resistivity and the As content measured after equilibration.
One g L-1 of sediment was combined with high purity water and the suspension was then mixed by end-over-end rotation for 24 h, the same solid/liquid ratio and procedure used in the experiments. The easily released As after 24 hr of mixing, at the natural soil solution pH of each sediment, was 0.04 μM As(V) and < 0.01 μM As(III) for Yerington,
0.04 μM As(V) and 0.04 μM As(III) for Tuscarora, and 0.35 μM As(V) and 0.15 μM
As(III) for Big Springs (Table 3.3). For calculation of experimental adsorption results, these amounts were added to the overall initial spike concentrations and no other adjustments were made. Possibly more As(V) and As(III) is present on the sediment surfaces and could be released at pH values other than the natural soil solution pH.
However, As release experiments for each sediment at the pH values of the isotherm experiments (5, 7, and 9) were not conducted to quantify As release at other pH values.
For As(V), maximum adsorption on Fe and Al oxides occurs from at least pH 5 to at least pH 7 (e.g., Goldberg, 2002; Goldberg, 1986; Manning and Goldberg, 1996) suggesting that the easily released As(V) amounts from the Yerington and Big Springs sediments at their natural soil solution pH are a maximum. Because As(V) adsorption is less on Fe and Al oxides at about pH 9 and greater (e.g., Goldberg, 2002; Goldberg,
85
1986; Manning and Goldberg, 1996), the easily released As(V) for Tuscarora at its soil
solution pH of 7.24 likely does not represent the maximum amount that could be released
from the sediment surface at pH 9 of the experiments. For amorphous Fe oxides, adsorption of As(III) is relatively constant over the pH range 5 to 9 (Golderg, 2002); for
amorphous Al oxides in Goldberg (2002), As(III) adsorption is at a maximum at around
pH 8 and then decreasing below, and above, pH 8. These results suggest that the As(III)
released from the pit-lake sediments at their soil solution pH is likely close to a minimum
amount that could be released from the surfaces.
As(V) Adsorption
Effect of Ionic Strength Effect
Adsorption of 6.67 μM As(V) on 1 g L-1 of Yerington, Tuscarora, and Big Springs sediments as a function of ionic strength is shown in Fig. 3.3. For the most part, adsorption appears to be independent of ionic strength. Variation in ionic strength, at a given pH, is at most 17 % (Tuscarora pH 9) and as little as 2 % (Big Springs pH 9). There does not appear to be any consistent trend in the small variations in ionic strength between the different pH values of a sediment or between sediments at a given pH. At this concentration, Big Springs and Tuscarora show similar fractional uptake with almost
100 % As(V) adsorbed at the lower pH (approximately pH 5); Yerington shows about
40 % less fractional uptake.
86
100 90 A I = 0.006 80 I = 0.03 70 I = 0.1 60 50 40
Adsorbed (%) 30 20 10 0 45678910
100 90 B 80 70 60 50 40
Adsorbed (%) Adsorbed 30 I = 0.006 20 I = 0.03 10 I = 0.1 0 45678910 100 90 C 80 70 60 50 40
Adsorbed (%) 30 I = 0.006 20 I = 0.03 10 I = 0.1 0 45678910 pH
Figure 3.3. Adsorption of 6.67 μM As(V) on 1 g L-1 Yerington (A), Tuscarora (B), and Big Springs (C) pit-lake sediments as a function of ionic strength (I). Adsorption is independent of ionic strength.
87
Effect of pH
At the lowest spike concentration of 1.33 μM As(V) on 1 g L-1 of sediment
(Fig. 3.4), all three sediments show maximum adsorption of As(V) at approximately pH ≤ 6 and then decreasing adsorption as pH increases. This behavior is typical of As(V) adsorption onto Fe oxides at low As concentrations (e.g., Pierce and Moore, 1982;
Dzombak and Morel, 1990). Yerington, however, also has less adsorption at decreasing pH. Decreasing As(V) adsorption with decreasing pH has been observed on montmorillonite (Goldberg 2002; Manning and Goldberg 1996), muscovite (Chakraborty et al. 2007), and synthetic zeolites (Chutia et al. 2009).
100
90
80
70
60
50
40 As(V) Sorbed (%) Sorbed As(V) 30 Yerington 20 Tuscarora Big Springs 10
0 2345678910 pH
Figure 3.4. Adsorption of 1.33 μM As(V) on 1 g L-1 Yerington, Tuscarora, and Big Springs pit-lake sediments as a function of pH. Maximum adsorption occurs at pH ≤ 6 and decreases with increasing pH for Tuscarora and Big Springs. Yerington has maximum adsorption at approximately pH = 6 and decreasing adsorption with increasing pH, but also has decreasing adsorption with decreasing pH.
88
Adsorption Isotherms
The results of As(V) adsorption under different spike concentrations on 1 g L-1 of
Big Springs and Tuscarora sediments (Fig. 3.5) are consistent with As(V) adsorption observed for other geologic materials (e.g., Manning and Goldberg, 1997; Goldberg and
Glaubig, 1998; Decker et al., 2006); adsorption increases with decreasing pH. However,
Yerington is distinctly different in that As(V) adsorption is approximately the same at pH
5 and 7 at initial spike concentrations greater than approximately 30 μM (Fig. 3.5). At lower initial spike concentrations, As(V) adsorption on the Yerington sediment is less at pH 5 as compared to pH 7. Big Springs sediments show the greatest capacity to adsorb
As(V) (at pH 5) while Yerington shows the lowest adsorption capacity, consistent with results previously shown in Fig. 3.3 and 3.4. Maximum adsorption of As(V) was
approximately 2.0 g kg-1 for Big Springs, 1.5 g kg-1 for Tuscarora, and 1.0 g kg-1 for
Yerington.
Parameters describing equilibrium partitioning of As(V) at the sediment-water interface were determined for both linear and Freundlich isotherms (Fig. 3.5, Table 3.4).
Fitting sorption data with a linear isotherm produces the distribution coefficient, Kd, a
-1 ratio of the mass of sorbate adsorbed per mass of sorbent, qe (g kg ), to the concentration
-3 of sorbate in solution, Ce (g m ):
Kd = qe / Ce
For a typical inorganic species, Kd is frequently a strong function of pH,
temperature, and other geochemical conditions (e.g., speciation, redox potential) (Stumm
89
1992). However, adsorption of inorganic species on mineral surfaces is often non-linear.
3.0 A 2.5 ) -1 2.0
1.5 pH 5 1.0 pH 7 Adsorbed (gkg 0.5 pH 9 0.0 02468101214 3.0 B 2.5 ) -1 2.0 pH 5
1.5 pH 7 1.0 Adsorbed (g kg (g Adsorbed 0.5 pH 9 0.0 02468101214 3.0 C 2.5 )
-1 pH 5 2.0
1.5 pH 7 1.0 Adsorbed (g kg (g Adsorbed 0.5 pH 9 0.0 02468101214 Aqueous (g m-3)
Figure 3.5. Adsorption of As(V) on 1 g L-1 Yerington (A), Tuscarora (B), and Big Springs (C) pit-lake sediments as a function of concentration at pH 5 (circles), 7 (squares), and 9 (diamonds). Best fit Freundlich isotherms are also shown.
90
Table 3.4. Linear (Kd) and Freundlich (KF) isotherm parameters for As(III) and As (V) adsorption onto three pit-lake sediments. 2 2 Sediment pH Kd r KF 1/n r (m3 kg-1) ((g kg-1)/(g m-3)1/n) As(III) Yerington 5 0.09 0.92 0.17 0.73 0.95 7 0.06 0.95 0.15 0.58 0.98 9 0.17 0.99 0.24 0.85 1.00
Tuscarora 5 0.07 0.95 0.14 0.71 0.96 7 0.27 0.96 0.42 0.50 0.96 9 0.24 0.89 0.79 0.44 0.98
Big Springs 5 0.16 0.92 0.51 0.47 0.96 7 0.34 0.98 0.56 0.62 0.98 9 0.32 0.90 0.85 0.49 0.96
As(V) Yerington 5 0.17 0.94 0.35 0.54 0.99 7 0.12 0.88 0.44 0.36 0.96 9 0.07 0.64 0.07 0.23 0.92
Tuscarora 5 0.16 0.81 0.83 0.30 0.98 7 0.14 0.92 0.61 0.40 1.00 9 0.08 0.96 0.19 0.53 1.00
Big Springs 5 0.49 0.87 1.26 0.29 0.95 7 0.16 0.60 0.75 0.21 0.94 9 0.13 0.89 0.24 0.42 0.97
Therefore, as an alternative to the linear isotherm, the Freundlich isotherm was used:
1/n qe= KF Ce
-1 -3 1/n where KF, (g kg )/(g m ) , would be equivalent to a Kd when n = 1. From Table 3.4, most of the As(V) isotherms are very non-linear since the Freundlich exponents are not close to one. The non-linearity of the adsorption data can also easily be observed in
Fig. 3.5. Thus, the Kd values for As sorption on pit-lake sediments have limited usefulness.
91
As(III) Adsorption
Effect of Ionic Strength
Adsorption of 6.67 μM As(III) on 1 g L-1 of Yerington, Tuscarora, and Big Springs
sediments as a function of ionic strength is shown in Fig. 3.6. Big Springs shows the most
As(III) adsorption at about 70% at pH 9 while Tuscarora (50 %) and Yerington (25 %)
have less As(III) adsorption. Adsorption of As(III) appears to be somewhat dependent on
ionic strength with generally increased adsorption at higher ionic strengths. The variation
in As (III) adsorption because of ionic strength is greatest at pH 7 with adsorption at an
ionic strength of 0.1 M being as much as 30 % higher than at an ionic strength of 0.006
M. The effect of ionic strength is minimal at pH 5 and pH 9 for all three sediments except
Yerington, which has noticeable variation in As (III) adsorption because of ionic strength
at pH 9 of about 20 %.
pH Effect
Adsorption of 6.67 μM As(III) on 1 g L-1 of Yerington, Tuscarora, and Big Springs
sediments as a function of pH is shown in Fig. 3.7. At this spike concentration, Big
Springs takes up roughly 40 % more As(III) than Yerington and about 20 % more than
Tuscarora. Maximum adsorption occurs around pH 8 for all sediments and adsorption
decreases with decreasing pH. The decrease in As(III) adsorption with decreasing pH is characteristic adsorption behavior for As(III) on Fe oxides (e.g., Pierce and Moore, 1982;
Dzombak and Morel, 1990).
92
100 90 I = 0.006 M A 80 I = 0.03 M 70 I = 0.1 M 60 50 40
Adsorbed (%) Adsorbed 30 20 10 0 45678910
100 90 I = 0.006 M B 80 I = 0.03 M 70 I = 0.1 M 60 50 40
Adsorbed (%) Adsorbed 30 20 10 0 45678910
100 C 90 80 70 60 50 40
Adsorbed (%) Adsorbed 30 I = 0.006 M 20 I = 0.03 M 10 I = 0.1 M 0 45678910 pH
Figure 3.6. Adsorption of 6.67 μM As(III) on 1 g L-1 Yerington (A), Tuscarora (B), and Big Springs (C) pit-lake sediments as a function of ionic strength (I). Adsorption varies with ionic strength.
93
100 Yerington 90 Tuscarora Big Springs 80
70
60
50
Adsorbed (%) 40
30
20
10
0 45678910 pH Figure 3.7. Adsorption of 6.67 μM As(III) on 1 g L-1 Yerington, Tuscarora, and Big Springs pit-lake sediments as a function of pH. Maximum adsorption occurs at approximately pH 8 and decreases with decreasing pH. Yerington and Big Springs also adsorb less As(III) at pH 9.
Some As(V) was observed in some, but not all, of the As(III) batch experiments and the occurrence, or lack thereof, was highly variable. Tallman and Shaikh (1980) showed that conversion from As(III) to As(V) is relatively slow, and in the absence of added redox agents, the As(III)/As(V) ratio remained unchanged for at least three weeks. Scott
and Morgan (1995) observed oxidation of As(III) to As(V) over short time periods (less
than two hours) during batch experiments with synthetic birnessite (δ-MnO2). They
concluded that Mn oxidizes aqueous As(III) at the particle surface and then As(V) and
Mn(II) are released to the aqueous medium.
94
In this study, from XRF results, Mn is present at about the same concentration in all
three pit-lake sediments. Arsenic (V) AA spectrophotometer peaks in As(III) spiked
batch experiments, at low As(III) spike concentrations, were sometimes seen at or below
the detection limit. Since Mn is a fairly small component of the total elemental
composition of each sediment, and the amount of Mn in each sediment is about the same,
it is difficult to ascertain whether Mn catalyzed oxidation of As(III) was producing the
small amount of As(V) periodically observed in the As(III) experiments. On rare
occasions, at the lowest spike concentrations, the As(V) present was as much as 50 % of
the measured aqueous concentrations. In most cases, when As(V) was present at low
spike concentrations, it was less than 20 % of the aqueous concentrations. At the highest
spike concentrations, when As(V) was present, it was generally less than 10 % of the aqueous concentrations. The plots of % adsorbed As(III) include any observed aqueous
As(V) as As(III) (i.e. plots actually show % adsorbed total As).
Adsorption Isotherms
Adsorption of As(III) under different spike concentrations on 1 g L-1 Yerington,
Tuscarora, and Big Springs sediments is shown on Fig. 3.8. The results for Tuscarora and
Big Springs are consistent with As(III) adsorption observed for other geologic materials
(e.g., Manning and Goldberg, 1997; Bowell, 1994; Decker et al., 2006); adsorption increases with increasing pH. However, Yerington sediment behavior is variable, which may be the result of its low adsorption capacity. Maximum adsorption of As(III) was
2.3 g kg-1 for Big Springs, 2.1 g kg-1 for Tuscarora, and 1.7 g kg-1 for Yerington.
95
3.0 A 2.5 ) -1 2.0 pH 9 1.5
1.0 pH 5 Adsorbed (g kg (g Adsorbed 0.5 pH 7
0.0 02468101214
3.0 B 2.5
) pH 9 -1 2.0
1.5 pH 7
1.0 pH 5 Adsorbed (g kg (g Adsorbed 0.5
0.0 0 2 4 6 8 10 12 14
3.0 C 2.5 pH 9 ) -1 2.0 pH 7 1.5 pH 5
1.0 Adsorbed (g kg (g Adsorbed 0.5
0.0 02468101214 Aqueous (g m-3)
Figure 3.8. Adsorption of As(III) on 1 g L-1 Yerington (A), Tuscarora (B), and Big Springs (C) pit-lake sediments as a function of concentration at pH 5 (circles), 7 (squares), and 9 (diamonds). Best fit Freundlich isotherms are also shown.
96
Parameters describing equilibrium partitioning of As(III) at the sediment-water interface were determined for linear and Freundlich isotherms (Fig. 3.8 and Table 3.4).
As seen with As(V), most of the As(III) isotherms are non-linear so the Freundlich exponents are far from one.
Surface Complexation Modeling
Surface complexation modeling (SCM) of the As sorption reactions on pit-lake sediments was conducted to gain insight into which surfaces are controlling As adsorption. Arsenic adsorption is a complex function of the interrelationship between the properties of the solid surfaces, pH, the concentration of As and competing ions, and As speciation
(Stollenwerk, 2003). The SCM of Dzombak Morel (1990), the diffuse double-layer model (DDL) as implemented in the aqueous speciation model PHREEQC (PHREEQC v.2.15.06; Parkhurst and Appelo 1999), was used to model As adsorption. Modeling attempted to predict As adsorption on pit-lake sediments by using mineralogical and chemical analyses of the sediments with pure phase adsorption data from the literature.
Aqueous and surface complexation reactions were added to the PHREEQC database as needed to conduct the modeling. Surface complexation reactions, in forms consistent with PHREEQC formulations, were derived from the WATEQ4F database
(http://wwwbrr.cr.usgs.gov/projects/GWC_coupled/phreeqc/). Thermodynamic data for aqueous As reactions were modified from Nordstrom and Archer (2003) and included only those species shown in Fig.3.1 also from Nordstrom and Archer (2003). As discussed by Foster (2003), hydrous metal oxides, and specifically, hydrous ferric oxides
(HFO), are the most important source/sink for As in aquifer sediments. Also, through a
97
wealth of research, the affinity for HFO to adsorb As has been well documented (e.g.,
Dzombak and Morel 1990; Bowell 1994; Goldberg 2002). Therefore, it was assumed that
As uptake by the pit-lake sediments resulted from adsorption onto HFO and that
adsorption on other surfaces, although likely, was insignificant relative to HFO (e.g.,
Goldberg and Glaubig, 1998; Manning and Goldberg, 1997; Lin and Puls, 2000;
Goldberg, 2002). It was also assumed that the ammonium oxalate extract Fe was a reasonable representation of the amount of Fe in the form of HFO. All As adsorption reactions were modeled as mononuclear reactions (Table 3.5). Modeled surface reaction
As(V) int As(V) int As(III) int intrinsic equilibrium constants ( K2 , K3 , K1 ) were optimized manually
until the sum of squared error between experimental and modeled data was minimized
(log K ± 0.25 for As(V) and log K ± 0.025 for As(III). Input parameters and modeling
results for each sediment surface are presented in Table 3.6.
Table 3.5. Reactions used to model As(V) and As(III) sorption onto pit-lake sediments. Model Reactions log K
+ + * ≡SOH + H ↔ ≡SOH2 7.24
≡SOH ↔ ≡SO- + H+ -8.88*
-3 + - As(V) int ≡SOH + AsO4 + 2H ↔ ≡SHAsO4 + H2O K2
-3 + - As(V) int ≡SOH + AsO4 + H ↔ ≡SAsO4 + H2O K3
As(III) int ≡SOH + H3AsO3 ↔ ≡SH2AsO3 + H2O K1 * Dzombak and Morel 1990, Table 8.7, Page 250, I = 0.01M
98
Table 3.6. Model input values and resulting intrinsic surface complexation constants for mononuclear As(V) and As(III) adsorption on pit-lake sediments using the diffuse double-layer model in PHREEQC. Parameter Yerington Tuscarora Big Springs BET surface area (m2 g-1) 13.9 38.1 17.5 Suspension density (g L-1) 1.0 1.0 1.0 Ammonium Oxalate Fe (mg g-1) 2.4 6.5 18 Site densitya (mol) 8.81e-6 2.63e-5 6.62e-5b 2.50e-5 As(V) int log K2 24.25 23.00 21.50 As(V) int log K3 18.50 17.25 18.50 As(V) SSEc 456 547 202 As(III) int log K1 4.425 4.700 4.825 As(III) SSEc 116 480 64 aAmmonium oxalate extract Fe x mol of sites per mol Fe (0.02; Dzombak and Morel 1990) bResults not tabulated, see text for discussion cSSE = sum of squared errors
Surface complexation modeling results using HFO as the only reactive surface and
the ammonium oxalate extract Fe content of the sediments to estimate the amount of
HFO are in generally good agreement with experimental data (Fig. 3.9 for As(V) and Fig.
3.10 for As(III)). For Yerington, the model reasonably predicted As(V) sorption at pH 5
and 9, but underestimated the amount of sorption at pH 7. For Tuscarora, the model is in
good agreement with As(V) experimental data. For Big Springs, the ammonium oxalate
extract Fe content overestimated the amount of adsorption of As(V) (not shown) and
predicted 100% adsorption at both pH 5 and pH 7. It was assumed that the ammonium
oxalate extraction may have also dissolved some FeS2 (Table 3.1) present in the Big
As(V) int Springs sediment. A series of simulations were conducted using the K2 and
As(V) int K3 of Tuscarora and varying the surface site density until there was a reasonable match of the model to the Big Springs experimental data. The resulting surface site density was 2.50e-5 mol. This surface site density was then used to remodel the Big
99
Springs As(V) experimental data to produce the intrinsic equilibrium constants in Table
3.6 and was also used to model Big Springs As(III) data.
100 A 90 80 70 60 50 40
Adsorbed (%) Adsorbed 30 20 10 0 45678910
100 B 90 80 70 60 50 40
Adsorbed (%) Adsorbed 30 20 10 0 45678910
100 C 90 80 70 60 50 40
Adsorbed (%) Adsorbed 30 20 10 0 45678910 pH
Figure 3.9. Adsorption of As(V) on pit-lake sediments with diffuse double-layer outputs using mononuclear surface reactions, Yerington (A), Tuscarora (B), and Big Springs (C). Conditions: 1 g L-1, 6.67 μM As(V), reaction time = 24 h.
100
100 A 90 80 70 60 50 40
Adsorbed (%) Adsorbed 30 20 10 0 45678910
100 B 90 80 70 60 50 40
Adsorbed (%) Adsorbed 30 20 10 0 45678910
100 C 90 80 70 60 50 40
Adsorbed (%) Adsorbed 30 20 10 0 45678910 pH
Figure 3.10. Adsorption of As(III) on pit-lake sediments with diffuse double-layer outputs using mononuclear surface reactions, Yerington (A), Tuscarora (B), and Big Springs (C). Conditions: 1 g L-1, 6.67 μM As(III), reaction time = 24 h.
101
Surface complexation modeling of As(III) on the Yerington sediment using HFO
produced very good agreement with experimental data. Modeling of As(III) adsorption
on Tuscarora sediments produced the general shape of the experimental data, but
overestimated adsorption at pH 6 and 7 and underestimated adsorption at pH 8. Using the
reduced surface site density for Big Springs the SCM model using HFO produced
excellent agreement with experimental data.
DISCUSSION
As(V) Adsorption
Adsorption of As(V) on the pit-lake sediments exhibits typical strongly sorbing
oxyanion behavior seen on Fe oxides and other natural materials. Maximum adsorption
occurs at pH ≤ 6 and then adsorption decreases rapidly as pH increases. One of the
important controls on adsorption of As(V) is the effect of pH on the composition of surface functional groups by protonation and deprotonation reactions (Stollenwerk,
2003). Greater As(V) adsorption at low pH occurs because of more favorable adsorption
- energies between the more positively charged surface and negatively charged H2AsO4 , the predominant As(V) species between pH 2.2 and 6.9 (Fig. 3.1). As pH increases from
2- 6.9, the predominant As(V) species is HAsO4 , net surface charge becomes less positive
and adsorption is less favorable.
Upon exposure to water, metal ions on the adsorbent surface (e.g., Fe, Al, and Mn) complete their coordination shells with OH groups (Stollenwerk 2003). Depending on pH, these OH groups can bind or release H+, resulting in the development of surface
+ charge. Arsenic adsorbs by ligand exchange with OH and OH2 surface functional
102 groups, forming an inner-sphere complex (e.g., Fendorf et al., 1997; Waychunas et al.,
1993). Changes in ionic strength affect the electrostatic forces near mineral surfaces; however, anions that form inner-sphere complexes, such as As, coordinate directly with the oxide surface, independent of ionic strength. Arsenic (V) adsorption on the pit-lake sediments is independent of ionic strength indicating formation of inner-sphere complexes.
Adsorption isotherms of As(V) exhibit non-linear behavior at higher As concentrations as available surface sites are filled. A Freundlich isotherm best describes this behavior. Isotherm parameters were a strong function of pH. The Freundlich isotherm parameter, 1/n (0.2-0.61), shows the highly non-linear behavior under the conditions studied. Over the range of pH examined in this study, 5, 7, and 9, maximum adsorption occurs at pH 5 and minimum adsorption occurs at pH 9.
Of the three sediments, Big Springs, a Carlin-type gold deposit, adsorbs more As(V) than Tuscarora, a quartz adularia precious metal deposit, and Yerington, a porphyry copper deposit, adsorbs the least. Based on QXRD analysis, all sediments are composed predominantly of silicate minerals, mostly feldspars with some clay minerals. Yerington has significant calcite present although it is not readily apparent with SEM. The Fe content is highest in Big Springs and lowest in Yerington and from both XRF and extract data, is abundant in all three sediments. The Fe content of the sediments correlates well with the amount of As adsorption seen in the laboratory experiments and surface complexation modeling also indicates that most As uptake results from adsorption onto
Fe minerals. Aluminum and Mn oxides, feldspars, and clay minerals provide additional
103
surface sites for As adsorption. However, others have shown that the amount of As adsorption by these oxides and minerals is much less than HFO (e.g., Lin and Puls, 2000;
Goldberg, 2002; Chakraborty et al., 2007: Chutia et al., 2009). Also, because these elements and minerals are similar in content in each sediment (Tables 3.1, 3.2, and 3.3), their effect on the observed As adsorption variations between sediments are probably minimal.
As(III) Adsorption
Adsorption of As(III) on pit-lake sediments exhibited different adsorption behavior from As(V). Arsenic (III) adsorption behaves more like cation adsorption than anion adsorption. For As(III), adsorption is less at lower pH and increases as pH increases.
Under the pH conditions of this study’s experiments, 5, 7, and 9, the predominant As(III)
0 species under slightly reducing conditions is H3AsO3 (at pH less than 9.2, see Fig. 3.1).
Typical adsorption behavior of As(III) on HFO shows maximum adsorption from
about pH 7 to 8 where the surface charge is close to the IEP (pristine point of zero charge
0 for HFO). Because of the neutral charge of H3AsO3 , adsorption energy is most favorable
at the hydrous Fe(III) oxide IEP. For the pit-lake sediments, maximum adsorption also
occurs around pH 8. Surface complexation modeling using HFO shows good agreement
with As(III) adsorption experimental data.
As(III) adsorption onto the pit-lake sediments is weakly dependent on ionic strength
suggesting that possibly some of the adsorption might be forming of outer-sphere
complexes in addition two inner-sphere complexes. In the case of outer-sphere
complexes, at higher ionic strength, more counter ions (positively charged ions, in this
104
case Na+ from the NaCl solution used to adjust ionic strength) would be attracted to the
0 surface, effectively screening the negative surface charge and allowing H3AsO3 to adsorb as outer-sphere complexes (Papelis, 2001). Goldberg and Johnson (2001) suggested that at pH 5, a component of surface associated As(III) was physisorbed (outer- sphere complexation) on amorphous HFO (Foster, 2003)
Adsorption isotherms of As(III) exhibit non-linear behavior at higher As concentrations as available surface sites are filled. A Freundlich isotherm best describes this behavior. Isotherm parameters were a strong function of pH. The Freundlich isotherm parameter, 1/n (0.3-0.67; and one at 1.10), shows the highly non-linear behavior under the conditions studied. Over the range of pH examined in this study, 5, 7, and 9, maximum adsorption occurs at pH 8 and minimum adsorption occurs at pH 5.
Similar to As(V), Big Springs adsorbed more As(III) than Tuscarora and Yerington.
As with As (V), there is a good correlation with Fe content and amount of As(III) adsorbed; Big Springs has the most Fe and adsorbs the most As(III) while Yerington has the least Fe and adsorbs the least As(III). The sediments adsorb less As(III) than As(V).
This may be a function of different dissolved species. Arsenic (V) occurs as negatively
- 2- charged anions, H2AsO4 and HAsO4 , over the range of pH examined, while As(III)
0 occurs as the neutrally charged species H3AsO3 . Maximum adsorption of As(V) is
greater because of the lower energy requirements for the As(V) dissolved species to
0 complex with surface sites relative to neutral H3AsO3 .
Surface complexation modeling supports the hypothesis that the presence and amount of amorphous HFO in the sediments controls As adsorption. Because of HFOs
105 very large surface area relative to other amorphous materials and relative to minerals, it has significant adsorption capacity (Stollenwerk, 2003). Surface complexation models using HFO as the only reactive surface provided good fits to the experimental data indicating that the dominant surface controlling As adsorption was HFO and that the oxalate extract Fe was a reasonable estimate of the amount of HFO available for uptake of As except for Big Springs. In the case of Big Springs, the oxalate extract Fe overestimated the amount of HFO because of the presence of pyrite in the sediment, which likely also dissolved during the extraction. Yerington had the lowest amount of oxalate extracted Fe and the lowest amount of As uptake of the three sediments.
CONCLUSION
Laboratory data showed that As adsorption onto pit-lake sediments is dependent upon pH, redox condition, As speciation, and Fe content. Sediments from a Carlin-type gold deposit pit lake had the greatest capacity to adsorb As(V) and As(III) under both oxidizing and reducing conditions. This adsorption capacity appears to be controlled by amorphous HFO, which the amount of HFO can be reasonably approximated with an ammonium oxalate extraction provided pyrite is not present in the sediment. Sediments from a porphyry copper pit lake had the least adsorption capacity while sediments from a quartz-adularia precious metal deposit pit lake had adsorption capacity between the other two sediments.
Adsorption of As(V) showed typical strongly sorbing anion behavior. One hundred percent fractional uptake occurred at pH ≤ 6 and decreased rapidly with increasing pH.
Adsorption was independent of ionic strength indicating formation of inner-sphere
106
complexes. Adsorption isotherms for As(V) were strongly non-linear at higher spike
concentrations with a Freundlich isotherm best describing adsorption behavior.
Adsorption of As(III) exhibited adsorption behavior that looked more like cation adsorption than anion adsorption. Fractional uptake was lower at lower pH and increased with increasing pH. Maximum adsorption occurred around pH 8. Adsorption of As(III) was weakly ionic strength dependent suggesting formation of some outer-sphere complexes in addition to inner-sphere complexes. Adsorption isotherms were strongly non-linear with a Freundlich isotherm best describing adsorption behavior. Experimental results and surface complexation modeling suggest that pit-lake sediments rich in Fe from the oxidation of Fe sulfide minerals in the ore bodies provide a medium to sequester As in pit-lake systems.
107
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Bertsch, P.M, and Bloom, P.R., 1996. Aluminum, Chapter 18, Methods of Soil Analysis, Part 3, Chemical Methods, Bigham, J.M. (ed.). Soil Science Society of America, Inc., Madison, WI, USA. Bowell, R.J., 1994. Sorption of arsenic by iron oxides and oxyhydroxides in soils. Applied Geochemistry, Vol. 9, p. 279-286. Brunauer, S., Emmett, P.H., and Teller, E., 1938. Adsorption of gases in multimolecular layers. Journal of the American Chemical Society, Vol. 60, p. 309-319. Chakraborty, S., Wolthers, M., Chatterjee, D., and Charlet, L., 2007. Adsorption of arsenite and arsenate on muscovite and biotite mica. J of Colloid and Interface Science 309:392-401. Chutia, P., Kato, S., Kojima, T., and Satokawa, S., 2009. Arsenic adsorption from aqueous solution on synthetic zeolites. J of Hazardous Materials 162:440-447. Davis, A., and Ashenbery, D., 1989. The aqueous geochemistry of the Berkeley Pit, Butte Montana, U.S.A. Applied Geochemistry, Vol. 4, p. 23-36. Decker, D.L., Papelis, C., Tyler, S.W., Logsdon, M.J., Šimůnek, J., 2006. Arsenate and arsenite sorption on carbonate hosted precious metals ore. Vadose Zone Journal 5:419-429. Dzombak, D.A. and Morel, F.M.M., 1990. Surface Complexation Modeling. John Wiley & Sons, New York, 393 p. Fendorf, S., Eick, M.J., Grossl, P., and Sparks, D.L., 1997. Arsenate and chromate retention mechanisms on goethite. 1. Surface Structure. Environmental Science and Technology, Vol. 31, No. 2, p. 315-320. Foster, A.L., 2003. Spectroscopic investigations of arsenic species in solid phases. In: Welch A.H., Stollenwerk, K.G. (Eds.), Arsenic in Ground Water, Kluwer Academic Publishers, Boston, p. 27-65. Goldberg, S., 2002. Competitive adsorption of arsenate and arsenite on oxide and clay minerals. Soil Sci Soc Am J 66:413-421. Goldberg, S., 1986. Chemical modeling of arsenate adsorption on aluminum and iron oxide minerals. Soil Science Society of America Journal, 50:1154-1157. Goldberg, S., and Johnson, C.T., 2001. Mechanisms of arsenic adsorption on amorphous oxides evaluated using macroscopic measurements, vibrational spectroscopy, and surface complexation modeling. Journal of Colloid and Interface Science, 234:204- 216.
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Goldberg, S., and Glaubig, R.A., 1998. Anion sorption on a calcareous montmorillonitic soil – arsenic. Soil Science Society of America Journal, Vol. 52, p. 1297-1300. Lin, Z., and Puls, R.W., 2000. Adsorption, desorption and oxidation of arsenic affected by clay minerals and aging process. Environmental Geology 39:753-759. Loeppert, R.H., and Inskeep, W.P., 1996. Iron, Chapter 23, Methods of Soil Analysis, Part 3, Chemical Methods, Bigham, J.M. (ed.). Soil Science Society of America, Inc., Madison, WI, USA. Lumsdon, D.G., Fraser, A.R., Russell, J.D., and Livesey, N.T., 1984. New infrared band assignments of the arsenate ion adsorbed on synthetic goethite (α-FeOOH). Journal of Soil Science, Vol. 35, p. 381-386. Manning, B.A., and Goldberg, S., 1996. Modeling arsenate competitive adsorption on kaolinite, montmorillonite, and illite. Clays and Clay Minerals, 44:609-623. Manning, B.A., and Goldberg, S., 1997. Arsenic (III) and arsenic (V) adsorption on three California soils. Soil Science Society of America Journal, Vol. 162, p. 121-131. Manning, B.A., and Martins, D.A., 1997. Speciation of arsenic(III) and arsenic(V) in sediment extracts by high-performance liquid chromatography-hydride generation atomic adsorption spectrophotometry. Environmental Science and Technology, Vol. 31, p. 171-177. Muntean, J., 2006. The rush to uncover gold’s origin. Geotimes, April 2006. http://www.agiweb.org/geotimes/apr06/feature_GoldOrigins.html. Retrieved November 23, 2009. Nordstrom, D.K., and Archer, D.G., 2003. Arsenic thermodynamic data and environmental geochemistry. In: Welch A.H., Stollenwerk, K.G. (Eds.), Arsenic in Ground Water, Kluwer Academic Publishers, Boston, p. 1-25. Papelis, C., 2001. Cation and anion sorption on granite from the Project Shoal Test Area, near Fallon, Nevada, USA. Advances in Environmental Research 5:151-166. Parkhurst, D.L., and Appelo, C.A.J., 1999. User’s guide to PHREEQC (Version 2) – A computer program for speciation, batch-reaction, one-dimensional transport, and inverse geochemical calculations. U.S. Geological Survey Water-Resources Investigations Report 99-4259. Pierce, M.L., and Moore, C.B., 1982. Adsorption of arsenite and arsenate on amorphous iron hydroxide. Water Research, Vol. 16, p. 1247-1253. Scott, M.J., and Morgan, J.J., 1995. Reactions at oxide surfaces. 1. Oxidation of As(III) by synthetic birnessite. Environmental Science and Technology, Vol. 29, p. 1898- 1905.
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Shevenell, L., Conners, K. A., and Henry, C. D., 1999. Controls on pit lake water quality at sixteen open-pit mines in Nevada. Applied Geochemistry, Vol. 14, p.669-687. Shevenell, L.A., 2000. Water quality in pit lakes: Disseminated gold deposits compared to two natural, terminal lakes in Nevada. Environ Geol 39:807-815 Stollenwerk, K.G., 2003. Geochemical processes controlling transport of arsenic in groundwater: a review of adsorption. In: Welch A.H., Stollenwerk, K.G. (Eds.), Arsenic in Ground Water, Kluwer Academic Publishers, Boston, p. 68-100. Stumm, W., 1992. Chemistry of the Solid-Water Interface. John Wiley and Sons, New York. Sun, X., and Doner, H.E., 1996. An investigation of arsenate and arsenite bonding structures on goethite by FTIR. Soil Science, Vol. 161, No. 12, p. 865-872. Tallman, D.E., and Shaikh, A.U., 1980. Redox stability of inorganic arsenic (III) and arsenic (V) in aqueous solution. Analytical Chemistry, Vol. 52, p. 196-199. Waychunas, G.A., Rea, B.A., Fuller, C.C., and Davis, J.A., 1993. Surface chemistry of ferrihydrite: Part 1. EXAFS studies of the geometry of coprecipitated and adsorbed arsenate. Geochimica et Cosmochimica Acta, Vol. 57, p. 2251-2269. Youngerman, A.G., 1992. Structural control, alteration, and primary mineralization at the Big Springs Gold Mine, Elko County, Nevada. Master’s Thesis, University of Nevada, Reno.
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CHAPTER 4
MODELING OF PHYSICAL AND CHEMICAL PROCESSES IN THE YERINGINTON PIT LAKE, YERINGTON, NEVADA, USA
Ronald L. Hershey, James M. Thomas, Glenn C. Miller
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INTRODUCTION
Mining of disseminated low-grade ore requires the removal of tens of thousands of metric tons of rock resulting in large open pits. This type of large-scale mining has increased substantially in the past several decades in the western U.S. with the development of very large open pits in the states of Arizona, Montana, California, and
Nevada (NV). Often, dewatering of groundwater is required to allow mining below the water table. When mining is completed and dewatering discontinued, groundwater will flow into the pits creating standing bodies of water called pit lakes. More than 30 open pit mines in NV will eventually create pit lakes containing an estimated one trillion liters of water (Miller et al., 1996).
Pit lakes that become contaminated may affect scarce water resources, particularly in the western U.S., and may also provide a source of groundwater contamination.
Because many pits are of immense size, remediation of poor water-quality pit lakes will be very expensive and impractical. Therefore, it is critical to identify the important processes that affect pit-lake water quality and to be able to model these processes so that long-term pit-lake water quality may be predicted. Ideally, knowing the important water- quality processes of pit lakes, prior to, and during mining can provide valuable information that can be used to design mine operations to minimize long-term environmental impacts.
A general conceptual model for predicting pit-lake water quality originally developed by the mining industry to secure regulatory permits for open pit operations
(Davis, 2003) assumes that the final water composition of a pit lake will result from a
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combination of processes. These processes include oxidation of pit wall rocks, transport
of leachate from pit wall rocks by groundwater flowing into the pit, equilibration of pit-
lake water with atmospheric oxygen and carbon dioxide, precipitation of saturated labile
minerals within the lake water column, sorption of metals and metalloids onto the
precipitated minerals, and evaporative concentration of pit-lake waters in arid areas (e.g.,
Tempel et al., 2000; Davis, 2003; Davis et al., 2006). Some of the physical and chemical processes that are important in pit lakes are illustrated in Fig. 4.1.
Figure 4.1. Chemical and physical processes in pit lakes.
This conceptual model is used to construct computer models that usually demonstrate that resultant long-term (hundreds of years) pit-lake water quality will be below regulatory maximum concentrations limits, and will not contaminate groundwater
(e.g., Kempton et al., 1997; Castendyk and Webster-Brown, 2007a). However, because most pits are still being mined and pit lakes have not yet formed, the accuracy of these
113 models is mostly unknown. Miller and Hershey (2000) contend that current predictive pit-lake models are far from accurate based upon large differences in water quality between a few present day pit lakes and predictions for several mine permit models.
Recently, pit-lake modeling has received more attention in the academic literature (e.g.,
Hamblin et al., 1999; Eary, 1999; Tempel et al., 2000; Morin and Hutt, 2001; Fontaine et al., 2003; Hancock et al., 2005; Balistrieri et al., 2006; Castendyk and Webster-Brown,
2007a; Castendyk and Webster-Brown, 2007b).
Study of existing pit-lakes should provide some insight into the relevant geochemical processes and define bounds on their control on water quality in the pit-lake environment. The water-chemistry data from an existing pit lake in NV and comparison to results of modeling simulations that attempt to recreate the observed gross water chemistry are presented in this paper. From this modeling exercise, important chemical reactions are identified and their closeness to equilibrium is compared to observed pit- lake water chemistry. Ideally, the important chemical reactions and their degree of non- equilibrium provide information that can be used to construct other pit-lake models and to assess the reliability of existing predictions.
BACKGROUND
Study Site
The Anaconda pit in Yerington, NV (hereafter referred to as Yerington) was excavated to exploit a porphyry-copper deposit and since cessation of mining, groundwater has filled the pit creating a pit lake (Fig. 4.2). The pit has been filling for
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over 30 y and lake levels continue to rise. This pit lake is an ideal local to investigate the geochemical reactions producing the observed water chemistry.
Morphology
The pit is oblong in shape; 2000 m long from northwest to southeast and 800 m
wide. The southeast edge of the pit is at an elevation of 1,341 m while the northwest
edge is 1,414 m. In April 2002, the pit lake was 118 m deep with a total volume of
36.5 million m3 and a surface area of 682,000 m2.
Climate
Yerington is 85 km southeast of Reno, NV and lies in the rain shadow of the Sierra
NV. Yerington’s climate is typical of west central NV with warm summers, mild winters,
and little average annual rainfall. The average maximum temperature in July is 33.3 °C
and 9.0 °C in January. Average annual rainfall is only 13 cm. Because of the large
number of sunny days, warm temperatures, and low humidity, evaporation is high.
Geology
The mine is within the Yerington batholith, a composite granitic body of Middle
Jurassic age that intruded volcanic and sedimentary rocks of Triassic-Jurassic age. The
Yerington batholith is composed of equigranular quartz monzodiorite intruded by a lesser volume of equigranular quartz monzonite. Late-stage granite porphyry dike swarms associated with the porphyritic-granitic stocks intruded the central portions of the batholith and the mine is centered over one of these dike swarms (Cartin, 1986).
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Figure 4.2. Location of Yerington pit lake.
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The porphyry copper ore deposit was an important economic concentration of oxide ore with a minimum economic secondary enrichment. The principal oxidation product was chrysocolla, which occurred irregularly dispersed throughout the rock and as narrow seams along fractures. Cuprite, tenorite, and melaconite had wide distribution within the oxide zone while malachite and azurite occurred but was not abundant (Wilson, 1963).
Lying between the primary sulfide and the chrysocolla horizon was a thin transition zone where chalcocite, cuprite, melaconite, native copper and chrysocolla occurred superimposed upon primary mineralization. Immediately underlying the transition zone, the primary sulfide minerals, pyrite and chalcopyrite occurred as minute grains in the groundmass of the porphyry, in feldspar and quartz phenocrysts, and as narrow seams.
Sodium-calcium metasomatism affected more than one-third of the altered granitic rock associated with the ore deposit. This type of alternation is characterized by the conversion of magmatic minerals to more sodium- and/or calcium-rich minerals including k-feldspar to oligoclase and biotite to actinolite. Except for the presence of this alteration type, alternation and mineralization assemblages in the ore deposit are similar to those observed at other copper deposits. Potassic alternation dominated the main levels of the deposit and was structurally overlain and crosscut by sericitic alteration (Cartin,
1986).
Hydrology
The pit is found on an alluvial fan three km east of the Singatse Range, which flanks the Mason Valley on the west and rises 580 m above the valley floor. The eastern edge of the pit is about 360 m from the Walker River, which supplies water to the alluvial
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aquifer. Groundwater in the alluvial aquifer near the mine flows northward, the same
direction as the river. Prior to the flood of January 1, 1997, only a small amount of water
from the alluvial aquifer reached the pit because of an intervening range front fault on the
east edge of the pit. The fault juxtaposes relatively impermeable ore body granite against
the saturated alluvial aquifer on the Walker River side of the fault, affectively creating a hydraulic barrier between the pit and the river’s saturated alluvium This barrier, however, was breached during the 1997 flood as a channel between the river and the pit was cut to drain water flooding the town of Yerington. The flood cut was subsequently filled in, but significantly more water now flows into the pit.
On the western side of the pit, the thickness of the overlying alluvium increases substantially from both an increase in elevation of the alluvial fan surface and a decrease in elevation of the alluvium/bedrock contact. Several small springs on the western pit wall issue from the alluvium/bedrock contact.
Large diameter wells, varying between 60 and 90 m deep, were drilled in 1952 along the eastern edge of the pit to de-water the ore body. These wells were drilled on the western side of the range-front fault separating the ore body from the Walker River and alluvial aquifer. Depth to groundwater in these wells at the time of drilling ranged from
24 to 27 m. As mining deepened the pit, two additional de-watering wells were drilled inside the pit and the other de-watering wells outside the pit were reamed and deepened.
Information on the well completion and pumping rates during mining are limited for de- watering wells both inside and outside the pit. The location of the de-watering wells is shown in Fig. 4.3.
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N 0 m 300 m
Figure 4.3. Location of wells at the Yerington pit lake.
METHODOLOGY
Pit-Lake Data
Historical Data
Historical water-chemistry data were obtained from the NV Division of
Environmental Protection files (NDEP) and Arimetco Inc., the most recent owner of the
Yerington mine and pit lake. Only those water-chemistry analyses with a cation/anion
difference of less than 10 % were retained. Historical and pit-lake water chemistry data
collected for this study are listed in Table 4.1. Water chemistry data for other waters used
in model are listed in Table 4.2. Ore-body geology was obtained from Cartin (1986) and
Wilson (1963). Pit dimensions and pit-lake water-level changes were obtained from
Arimetco Inc. Precipitation data for Yerington, NV and pan evaporation data from the
closest long-term station in Fallon, NV were obtained from the Western Regional
Climate Center (Western Regional Climate Center, 2009).
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Data Collection
Water-chemistry samples were collected periodically from 1995 through 2001
(eight times plus discrete depth samples) with either a Van Dorn bottle or a peristaltic pump and polypropylene tubing. Major-ion and trace-element samples were filtered in the field with a 0.45 μm polysulfone cartridge filter. Cation and trace-element samples were acidified in the field with either reagent grade (cations) or Seastar Baseline nitric acid (trace elements). Trace-element samples were collected in polypropylene bottles pre- washed with nitric acid. pH, electrical conductivity (EC), total dissolved solids (TDS), and major-ion analyses were conducted at the Desert Research Institute’s Analytical
Chemistry Laboratory in Reno, NV (Desert Research Institute, 2009) following standard
U.S. Environmental Protection Agency procedures. Trace-element analyses were conducted by ICP-MS at the Desert Research Institute’s Ultra-Trace Chemical
Laboratory.
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Table 4.1. Water chemistry data for the Yerington pit lake. 1991a 1992 a 1993 a 1994 a 1995 b 1995 b 1995 b Date 11/21/91 7/10/92 6/8/93 4/29/94 4/7/95 4/7/95 4/7/95 Depth UN UN UN 100 m 0 m 50 m 100 m (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) pH 7.67 8.61 8.25 8.42 8.25 7.98 7.84 Ca 77.8 106 94.1 93 84.5 87.5 88.7 Mg 12.9 13.2 14.6 15 15.2 14.9 14.9 Na 65.4 68.7 81.8 74 71.3 72.0 72.0 K 0.12 5.02 7.2 NA 5.30 5.36 5.30 HCO3 111 120 125 134 151 152 151 CO3 10 Cl 42.5 39.0 35.5 35 33.3 33.3 32.9 SO4 280 280 273 270 270 269 277 N 0.1 0.12 <0.01 0.17 0.09 0.08 0.12 F 1.4 0.85 1.0 NA 1.4 1.4 1.4 SiO2 NA NA NA NA 30.5 30.6 30.2 TDSc 566 603 600 589 587 590 598 Cu 0.038 0.130 0.143 0.18 0.114 0.135 0.158 Se 0.139 <0.002 <0.002 0.140 0.11 0.11 0.11 As 0.004 <0.02 <0.025 0.003 0.004 0.003 0.003 Fe <0.150 <0.005 <0.005 0.05 <0.01 <0.01 <0.01
1995 b 1995 b 1995 b 1995 b 1995 b 1995 b 1996 b Date 8/4/95 8/4/95 8/4/95 10/17/95 10/17/95 10/17/95 2/5/96 Depth 0 m 50 m 100 m 0 m 50 m 100 m 0 m (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) pH 8.54 7.98 7.94 8.22 7.90 7.95 8.12 Ca 85.6 87.1 90.1 87.2 95.4 96.5 90.1 Mg 15.0 14.5 14.8 16.3 16.0 15.9 16.1 Na 74.3 71.6 71.5 78.8 76.1 75.1 77.8 K 5.53 5.53 5.40 5.38 5.40 5.20 5.12 HCO3 138 150 152 135 148 150 143 Cl 34.6 33.6 33.5 37.9 37.3 36.3 36.5 CO3 6.8 7 SO4 264 267 277 266 276 277 279 N 0.10 0.12 0.14 0.10 0.10 0.10 0.12 F 1.4 1.4 1.4 NA 1.4 1.4 1.3 SiO2 33.2 31.1 30.9 34.4 31.4 31.6 33.3 TDSc 572 570 591 575 594 596 593 Cu 0.052 0.158 0.151 0.012 0.151 0.157 0.075 Se 0.13 0.13 0.13 0.12 0.12 0.12 0.13 As 0.003 0.003 0.003 0.004 0.002 0.003 0.007 Fe <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01
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Table 4.1. Water chemistry data for the Yerington pit lake (continued). 1996 b 1996 b 1998 b 1998 b 1998 b 1998 b 1998 b Date 2/5/96 2/5/96 4/16/98 4/16/98 4/16/98 9/15/98 9/15/98 Depth 50 m 100 m 0 m 30 m 100 m 0 m 30 m (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) pH 7.75 7.79 8.36 8.18 8.13 8.39 8.13 Ca 94.6 95.7 79.2 78.7 79.2 73.4 83.0 Mg 16.1 16.0 14.7 14.6 14.6 15.3 15.5 Na 76.2 77.0 72.5 71.9 71.6 81.8 78.8 K 5.06 5.01 4.95 4.83 4.88 5.49 5.35 HCO3 149 150 146 143 145 144 146 CO3 2.2 Cl 36.1 36.1 37.7 37.8 37.3 38.9 37.5 SO4 282 277 272 276 277 262 267 N 0.14 0.15 0.13 0.15 0.16 0.10 0.13 F 1.4 1.4 1.28 1.38 1.32 1.4 NA SiO2 31.7 32.1 34.8 34.8 34.8 37.2 34.8 TDSc 618 615 590 592 593 589 595 Cu 0.129 0.123 0.051 0.055 0.057 0.157 0.075 Se 0.13 0.12 0.13 0.10 0.10 0.12 0.13 As 0.004 0.003 0.004 0.004 0.004 0.003 0.007 Fe <0.01 <0.01 NA NA NA NA NA
1998 b 2000 b 2000 b 2000 b 2001 b 2001 b 2001 b Date 9/15/98 8/22/00 8/22/00 8/22/00 8/30/01 8/30/01 9/13/01 Depth 100 m 0 m 20 m 100 m 0 m 20 m 100 m (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) pH 8.10 8.34 8.25 8.06 8.29 8.25 8.04 Ca 84.1 81.0 82.3 87.9 75.8 78.7 81.1 Mg 15.2 16.0 15.8 16.1 15.5 15.0 30.7 Na 79.0 81.3 77.5 78.6 77.0 74.3 75.8 K 5.35 5.5 5.3 5.4 5.43 4.92 5.28 HCO3 146 144 144 148 143 145 144 CO3 1.3 Cl 37.9 38.6 37.7 37.5 39.0 38.6 41.0 SO4 267 265 269 277 262 263 291 N 0.17 0.14 0.10 0.24 0.11 0.08 0.21 F 1.32 1.29 1.29 1.38 1.46 1.18 1.23 SiO2 34.7 39.4 37.2 36.7 38.0 35.7 36.2 TDSc 598 601 598 615 586 584 635 Cu 0.051 0.0075 0.019 0.036 0.0081 0.0236 0.0337 Se 0.120 0.098 0.095 0.100 0.100 0.104 0.102 As <0.005 0.0064 0.0047 0.0043 0.0070 0.0063 0.0057 Fe NA 0.0003 0.0004 0.0004 0.0005 0.0005 0.0005 aHistorical data from Arimetco Inc. bData collected for this study c Calculated: TDS=Ca+Mg+Na+K+Cl+SO4+N+F+SiO2+½HCO3+½CO3 TDS = Total dissolved solids UN = Unknown NA = Not analyzed
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Table 4.2. Water chemistry data for groundwater and Walker River water at the Yerington pit lake. Well 49a Well 41a Southeast Springb Southeast Springb Walker Riverb Date 2/27/78 2/27/78 2/5/96 8/23/00 8/23/00 (mg L-1) (mg L-1) (mg L-1) (mg L-1) (mg L-1) pH 7.40 7.90 8.28 8.25 8.20 Ca 98.2 46.1 29.9 24.5 21.2 Mg 9.7 6.1 6.59 5.8 5.2 Na 124.0 53.0 31.7 28.8 22.4 K 3.23 1.66 4.04 3.9 3.6 HCO3 234 189 135 134 118 CO3 Cl 37.0 17.0 13.5 10.0 6.2 SO4 304 74.1 40.3 25.5 19.4 N NA NA 0.4 0.09 0.06 F 1.30 1.78 0.51 0.50 0.36 SiO2 NA NA 23.8 25.9 16.7 TDSc NC NC 218 192 154 Cu 0.004 0.011 0.018 0.0087 0.0009 Se NA NA <0.002 <0.0002 <0.0002 As NA NA 0.009 0.0069 0.0096 Fe 0.008 0.012 <0.01 0.0001 0.0001
Northwest Springb Northwest Springb Well 2b Well 4b Date 10/17/95 8/23/00 1/9/01 1/18/01 (mg L-1) (mg L-1) (mg L-1) (mg L-1) pH 8.47 8.42 7.70 7.79 Ca 87.9 83.3 38.1 34.3 Mg 16.5 15.6 7.23 6.75 Na 144 142 40.7 39.5 K 4.71 4.8 4.60 3.88 HCO3 183 238 161 177 CO3 7 6.7 Cl 86.3 76.6 27.6 15.4 SO4 278 258 45.8 40.1 N 2.55 2.67 1.16 0.51 F 0.82 0.85 0.49 0.54 SiO2 41.9 46.4 46.5 37.8 TDSc 758 753 293 267 Cu <0.005 0.0004 0.0016 0.0009 Se 0.016 0.012 0.0004 0.0001 As 0.011 0.011 0.0097 0.0067 Fe <0.01 0.0004 0.0002 0.0002 aHistorical data from Arimetco Inc. bData collected for this study TDS = Total dissolved solids c Calculated: TDS=Ca+Mg+Na+K+Cl+SO4+N+F+SiO2+½HCO3+½CO3 UN = Unknown NA = Not analyzed NC = Not calculated, no SiO2
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Sediment samples were collected with an Ekman type dredge in 1995 and 1997.
Samples were transferred from the dredge to pre-cleaned amber glass bottles and were
topped-off with pit-lake water. Samples were transported and stored cool. Samples were
then dried under a nitrogen atmosphere at room temperature, crushed, and sieved. All
particles that passed through a 150-μm sieve were retained. The minerals making up the
sediment samples were identified by quantitative phase analysis using the Rietveld
method and x-ray powder diffraction (QXRD) at the University of British Columbia,
Vancouver, BC. Step-scan QXRD data were collected over a range 3-70°2θ with CuKα
radiation on a standard Siemens (Bruker) D5000 Bragg-Brentano diffractometer
equipped with a diffracted-beam graphite monochromator crystal, 2 mm (1°) divergence
and anti-scatter slits, 0.6 mm receiving slit, and incident-beam Soller slit. The long fine-
focus Cu X-ray tube was operated at 40 kV and 40 mA, using a take-off angle of 6°. Bulk elemental composition was determined by X-ray fluorescence (XRF) and trace elemental composition was determined by dissolving sediments with aqua regia and analyzing the resultant solution by inductively coupled plasma-mass spectrometry (ICP-MS). XRF and
ICP-MS analyses were conducted by the NV Bureau of Mines and Geology at the
University of NV, Reno. The morphology and composition of the sediments were examined by scanning electron microscopy (SEM) combined with energy dispersive X- ray spectroscopy (EDX). A JEOL JSM-840A SEM/EDX was used to examine several areas of the sample under different magnification. Total organic carbon (TOC) of the sediment was measured by the Desert Research Institute’s Water Chemistry Laboratory,
Reno, NV. QXRD, XRF, ICP-MS, and TOC for pit-lake sediments are listed in Tables
4.3 and 4.4.
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Table 4.3. Quantitative X-ray diffraction results and total organic carbon (TOC) for Yerington pit-lake sediments. Mineral Chemical Formula Yerington (wt. %) Muscovite KAl2AlSi3O10(OH)2 39 Quartz SiO2 13 Calcite CaCO3 19 Plagioclase NaAlSi3O8 – CaAl2Si2O8 17 2+ Clinochlore (Mg,Fe )5Al(Si3Al)O10(OH)8 7 3+ Epidote Ca2(Fe ,Al)3(SiO4)3(OH) 2 2+ Ankerite Ca(Fe ,Mg,Mn)(CO3)2 <1 Goethite α –Fe3+O(OH) 1 Chabazite? (Ca0.5,K,Na)4[Al4Si8O24]·12H2O 1 Total 100 Smectite Group abundant (not included in total)
Depth (m) TOC (mg Kg-1) 15 11,000 37 147,000 100 80
Table 4.4. Chemical composition of Yerington pit-lake sediments by x-ray fluorescence (XRF) and inductively coupled plasma–mass spectrometry (ICP-MS). XRF ICP-MS Oxide Yerington (%) Element Yerington (ppm) -2 SiO2 47.0 SO4 2100 TiO2 0.42 Ag 0.139 Al2O3 14.6 As 12.4 Fe2O3 4.08 Au 0.072 MnO 0.05 Ba 457 MgO 2.35 Be 3.20 CaO 11.47 Cd 0.41 Na2O 1.35 Co 24.1 K2O 1.85 Cr 17.0 P2O5 0.28 Cu 6120 LOI 15.1 Mo 8.05 Total 98.6 Ni 23.5 Pb 30.0 Sb 7.96 Se 13.0 Sr 439 Th 16.6 U 14.7 V 50.6 Zn 45.8
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Several pit-wall rock samples were collected in April 1998 above and below the pit-
lake level to identify changes in mineralogy and Cu concentration as the rocks were submerged. Samples were collected at 1.5 m above the lake surface and 4.6, 6.1, 8.2,
12.2, and 15.2 m below the lake surface by knocking loose samples with a hammer.
Samples collected below the lake surface were collected using scuba gear. These samples were examined by SEM-EDX for wall-rock morphology and composition. Results are listed in the Appendix C.
In February 2001, a staff gauge was installed to monitor water-level rise in the pit lake. Periodically, the water level was read from the staff gauge and converted to elevation above mean sea level. Water level measurements were recorded until June
2002.
In February 2001, a class A evaporation pan was installed at the east end of the pit lake on an alluvial fan created when Walker River flood waters were diverted into the pit in January 1997. Water loss from the pan was monitored continuously with a pressure transducer and datalogger. Pan water was replenished every three-to-five days.
Evaporation was monitored until June 2002.
Two areas of springs issuing from the pit wall supply water to the pit lake. Spring flow at one spring seep on the eastern edge of the pit was estimated visually once in
February 1996 to flow less than 1 L s-1. After the January 1997 flood breached the range-
front fault between the Walker River and the pit, spring flow increased substantially. In
June 2000, one spring flow measurement was conducted with a 6-inch parshell flume at
8.2 L s-1 and in then once again in December 2000 at 5.1 L s-1. Two springs issuing from
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the northwest pit were measured once in June 2000 with a 3-inch modified parshell flume
with a total flow of 3.4 L s-1 and then once again in December 2000 at 3.8 L s-1.
Geochemical Modeling
Pit Filling
To model the geochemical changes in a pit lake with time, knowledge of the physical pit-lake filling process is required. For example, the change in lake volume with time is needed to quantify the relative proportions of the different sources of water flow into the pit lake, each with its own unique geochemical characteristics. The process of pit-lake infilling can be thought of as water-level recovery in a pumped well. For future pit lakes, analytical or numerical solutions using the equations of Theis (1935) or
Papadopulos and Cooper (1967) can be used to estimate pit-lake infilling (e.g., Naugle
and Atkinson, 1993). However, in the case of Yerington, data exist that can be used to
model pit-lake infilling similar to the method of Shevenell (2000).
To quantify the volume of the pit, a pit bench map was secured from Arimetco Inc.
The pit was mined in 7.6 m benches; each bench with its associated elevation is recorded
as a distinct contour line on the pit bench map. The bench contours were digitized and
tied to several survey points on the map. The digitized bench contours were then entered
into the geographic information system software ARCMAP, which was used to calculate
the lake surface area at each bench elevation.
To quantify the change in volume with time as the pit fills, an analytical equation was used to model pit filling. The pit lake began filling in June 1978 when the pumps in the de-watering wells were turned off. From, December 1990 to December 1996,
127
Arimetco Inc. recorded water-level changes about quarterly. Thereafter, Arimetco Inc.
collected a water level elevation in July 1998 and NDEP collected an elevation in
September 2000. The author collected water-level changes periodically from February
2001 to June 2002. There are no other historical water-level data available from the
initiation of pit filling until December 1990 as the mine was abandoned during this time
period. Water-level data are listed in Appendix D.Available data were used in a curve
fitting routine to describe water-level changes with time:
Water-Level Elevation = A * Days 1/B
where A and B are constants. The curve fit is shown on Fig. 4.4. From the water-level
change with time model, the water level for any given time period since filling began can
be calculated. Elevations were then used to calculate the pit-lake volume change for that
time period from the digitized bench map.
Pit-volume change is a result of:
V2 – V1 = GWi + SpringSE + SpringNW + P + R – E – GWo
where (V2 – V1) is the pit-volume change, GWi is the groundwater flowing into the pit,
GWo is the groundwater flowing out of the pit, SpringSE and SpringNW are the spring
flows into the pit, P is precipitation that falls onto the pit-lake surface, R is the precipitation that runs off the exposed pit walls, and E is evaporation from the pit-lake
surface. GWo is assumed to be zero since the pit lake is still filling. R was also assumed
to be zero for simplification of the geochemical model, because it is assumed to be a
minor contributor to total pit-lake volume changes (Davis et al., 2006), and because the
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geochemistry of this water is unknown. All the other variables are known except for
GWi.
Figure 4.4. Water-level measurements and modeled water-level changes with time for the Yerington pit lake starting in June 1978 at the end of mining and through June 2002.
-1 For modeling purposes, SpringSE was assumed to be 1 L s before the January 1997
-1 -1 flood and 7.3 L s after the 1997 flood. SpringNW was assumed to be constant at 3.5 L s and P came from annual precipitation records for Yerington, NV. The closest long-term pan evaporation station to Yerington is the station in Fallon, NV, 60 km northeast of
Yerington. Pan evaporation at the edge of the pit lake was also measured from February
2001 to June of 2002. Pan evaporation data from the pit lake were compared to the recorded Fallon pan evaporation data for the same time period and a linear correlation between the two was calculated (Fig. 4.5). This correlation was used to convert Fallon
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evaporation data from other time periods to pit-lake evaporation data. Pit-lake
evaporation measurements were then adjusted by 0.7 to account for differences in pan
evaporation and actual lake evaporation (Hounam, 1973). From these data and annual pit-
volume changes, GWi was calculated.
y = –20.48 + 1.0559 x r = 0.911 4/13/01 to 6/02/02
Figure 4.5. Correlation between pan evaporation at the Yerington pit lake and pan evaporation at the Fallon, NV long-term station from February 2001 to June 2002.
To illustrate typical values for the calculation of GWi, results for year 2001 are presented in Table 4.5. 2001 was chosen because of large number of measured data points for evaporation and pit-lake water-level rise. The net volume change in the pit lake for 2001 using measured pit-lake water-levels and the pit bench map was 1.2 million m3, evaporation at the pit-lake pan was 640,000 m3 (corrected per Hounam, 1973),
precipitation falling directly on the pit-lake surface was 50,000 m3, flow into the pit from
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the southeast spring (using an average discharge of 7.3 L s-1) was 230,000 m3, flow into
the pit from the northwest springs (average discharge of 3.5 L s-1) was 110,000 m3,
resulting in a net groundwater influx to the pit lake, by difference, was 1.5 million m3 or
65 % of the volume change (GWi/(V2 – V1 - SpringSE - SpringNW - P + E)). For
comparison, estimated values for 1991, the first year where water-level measurements are
available, are also presented in Table 4.5. In 1991, groundwater inflow into the pit was estimated to be 79 % of the volume change.
Table 4.5. Example values for the calculation of GWi, deep groundwater input, into the Yerington pit lake. GWi determined by difference. See text for equation. V = volume, E = evaporation, P = precipitation, SES = southeast spring, NWS = northwest spring.
Year V2-V1 E P SES NWS GWi GWi (m3) (m3) (m3) (m3) (m3) (m3) (%) 2001 1,233,650 642,433 50,056 229,687 109,850 1,486,490 65 1991 1,363,106 538,274 76,676 31,956 109,850 1,682,897 79
During the flood of January 1997, Walker River water entered the pit lake. The
exact amount of water added during this event is unknown since lake water levels were
not measured between December 1996 and July 1998. However, lake volume changes
measured after the flood can be used to estimate the volume of water that entered the pit
during the flood. From July 1998 through April 2002, the average annual volume change
was 1.25 million m3. The total volume change from December 1996 through July 1998
was 2.78 million m3. Using the average annual volume change, the amount of water
added to the pit for the 19 months from December 1996 through July 1998 was 1.98 million m3 leaving 0.8 million m3, or < 3 % of the total lake volume, added during the flood. Another approach to estimate the amount of flood water added to the pit lake is to
131
take the total lake-level rise from December 1996 to July 1998 (4.51 m) and subtract the
average lake-level rise per year from 1992 to 1996 (1.79 m) and then subtract the average
lake level-rise for the first nine months of each year from 1992 to 1996 using the quarterly measured water levels (1.44 m) (assuming lake-level rise is minimal from July through September because of maximum evaporation). This amount is then multiplied by the lake surface area for 1997 (617,000 m2) to give an estimated amount of water added
to the pit lake from the 1997 flood of 0.79 million m3, in good agreement with the latter
method.
Geochemical Modeling
The geochemical software PHREEQC (Parkhurst and Appelo, 1999) was used to
model processes in the Yerington pit lake. PHREEQC uses a hypothetical 1 kg of water
for modeling geochemical reactions so all inputs and processes were scaled accordingly.
Water chemistry inputs to the model consisted of precipitation, spring flow, deep groundwater, and pit-lake water. The relative contributions of each, plus evaporation, in
% of the total volume change, were calculated using the pit-filling model. These percentages were then used to mix the geochemical constituents of the different inputs, allow the mixture to react with pit minerals and atmospheric gases, and then concentrate
the mixture by evaporation.
Precipitation chemistry was constructed from a combination of data from Drever
(1982, p. 169), and from Parkhurst and Appelo (1999, p. 209, Example 4). The spring-
flow chemistry used was the southeast pit-wall spring from February 1996 for
simulations before the flood of 1997 and from August 2000 for simulations after the
132
flood; the northwest spring-flow chemistry used was from October 1996 for pre-flood
simulations and from August 2000 for post-flood simulations was from (Table 4.2). The
historical water-chemistry data for Well 49, which was a dewatering well installed in the
bottom of the pit during mining, was used to represent the deep groundwater chemistry
entering the pit lake (Table 4.2). This water chemistry analysis did not include
concentrations for SiO2, Se, and As. Since deep groundwater is the principle component
-1 of the pit lake from the pit-filling model, a value of 30 mg L for SiO2, similar to the pit
lake, was assigned for modeling purposes; no values were entered for Se and As. Pit-lake chemistry evolution simulations focused on chemistry data collected for this study (April
1995, February 1996, April 1998, August 2000, and September 2001), but also used historical data from April 1994 (Table 4.1) as a starting point for modeling. Simulation time steps were conducted at roughly annual intervals so the pit-lake chemistry data used were from 100 m since the Yerington pit lake is monomictic and turns over during the winter months, thoroughly mixing the water chemistry of the lake annually (Hershey et
al., in revision [see Chapter 2]). Because the water chemistry of 1997 flood waters is
unknown, modeling simulations did not consider any pit-lake water quality changes from
addition of flood water. Simulations were run from April 1994 to April 1995, April 1995
to February 1996, April 1998 to August 2000, and from August 2000 to September 2001.
The minerals and gases selected for reaction with pit-lake mixtures were derived
from the geologic descriptions of the ore deposit, wall-rock and sediment sample
analysis, saturation state (SI) calculations by PHREEQC, and preliminary PHREEQC
simulations (Table 4.6). Because of the limited thermodynamic data and mineral
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Table 4.6. Gas or mineral phase considered in preliminary geochemical modeling of the Yerington pit-lake Gas or Mineral Chemical Formula Saturation State1 2 CO2(g) CO2 -3.0 2 O2(g) O2 -0.7 Adularia KAlSi3O8 U Albite NaAlSi3O8 U Annite KFe3AlSi3O10(OH)2 U Anorthite CaAl2Si2O8 U Quartz SiO2 S - O SiO2(a) SiO2 S - O Chrysocolla CuSiH4O5 U Cuprite Cu2O U Chalcopyrite CuFeS2 U Pyrite FeS2 U Calcite CaCO3 S - O Fe(OH)3(a) Fe(OH)3 S - O Goethite FeOOH S - O Montmorillonite-Ca Ca0.165Al2.33Si3.67O10(OH)2 S - O Montmorillonite- (HNaK) Mg Fe Al Si O (OH) S - O Aberdeen 0.14 0.45 0.33 1.47 3.82 10 2 1U = undersaturated, S = saturated, O = oversaturated 2log partial pressure
inventory in the PHREEQC database file, the more inclusive accompanying WATEQ4F database file was used. To represent the primary rock forming minerals of the host intrusive of the ore body, albite (Na-plagioclase), anorthite (Ca-plagioclase), and quartz were selected. Additionally, QXRD analysis of the sediments indicated that muscovite is a major component of the sediment, likely primary residual mineral from sloughing of wall rocks into the pit lake, so both adularia (KAlSi3O8) and Annite
(KFe3AlSi3O10(OH)2) were considered to represent a K silicate mineral. Annite also provided another source for Fe. The oxide ore minerals, chrysocolla (CuSiH4O5) and cuprite, and the sulfide ore minerals, pyrite and chalcopyrite, were chosen to represent the ore available for dissolution in the model to provide Cu, Fe, and in the case of chrysocolla, additional Si. The chrysocolla dissociation reaction and equilibrium constant
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were taken from the LLNL database accompanying the PHREEQC program as it is not a
mineral included in the WATEQ4F database. From QXRD analysis of the sediments,
calcite, goethite, and two different chemical composition montmorillonites were chose as
labile minerals available in the model to precipitate. Additionally, the amorphous phases of SiO2 and Fe(OH)3 were considered.
In addition to reaction with mineral phases, model simulations also included
equilibration of pit-lake water with atmospheric gases. CO2 and O2 were initially set at
10-3.5 and 10-0.7 atm, respectively; the atmospheric partial pressures for these gases at sea
level. At the elevation of the Yerington pit lake, the partial pressure for these gases is
slightly less that at sea level; however, the differences in partial pressure were assumed to
have a minimal effect on modeling results in this study. After preliminary simulations,
-3.0 - the partial pressure of CO2 was increased to 10 atm to bring the HCO3 content of the
modeled pit-lake water closer to observed concentrations. This change is consistent with
calculated partial pressures for Yerington pit-lake waters (Appendix E) and observed elevated CO2 concentrations in lakes (Cole et al., 1994; Davis et al., 2006).
Because Cu concentrations in pit-lake waters were decreasing during the sample collection period, adsorption of Cu onto iron hydroxides was added to the model. Input
data and reactions for this process were taken from Dzombak and Morel (1990) and listed
in Table 4.7.
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Table 4.7. Model input for adsorption of Cu onto iron hydroxides (from Dzombak and Morel, 1990). SURFACE SPECIES 0 + + Hfo_sOH + H = Hfo_sOH2 Log K = 7.24 Hfo_sOH0 = Hfo_sO- + H+ Log K = -8.88 Hfo_sOH0 + Cu+2 = Hfo_sOCu+ + H+ Log K = 2.85
SURFACE Hfo_sOH Moles of surface sites = 8.809e-6 Sediment surface area = 13.9 m2 g-1 Total mass of surface = 1 g
Evaporation of pit-lake water was also taken into account in modeling simulations.
After input waters were mixed and reacted with minerals and atmospheric gases, the resulting solution was then evaporated by the amount calculated with the pit-filling model for each time period simulated with PHREEQC. In the PHREEQC model, the 1 kg of pit- lake water is reduced in mass by a specified % determined from the pit-filling model.
This results in an increase in the number of moles of dissolved constituents in a mass of
water that is less than 1 kg. Before the next modeling simulation can be conducted, the
mass is returned to 1 kg of water, but the same number of moles of dissolved constituents
is retained, reflecting the increased concentration of the evaporated water.
Several different types of modeling techniques can be employed to model pit-lake
water chemistry (Castendyk and Webster-Brown, 2007a). A pre-lake predictive model of
the Yerington pit lake was never undertaken so comparison of predictive model results to
actual pit-lake data cannot be conducted. Tempel et al. (2000) used a forward technique
to model a pit lake using reactant minerals proportioned according to their occurrence in
wall rocks and applying published reaction rates in a series of time steps. This technique
is similar to those employed in many pre-lake predictive models. Results from this model
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were then compared to historical water-chemistry data in a temporary pre-existing pit
lake. Without prior information on the types and degree of chemical and ore oxidation reactions within the pit walls during mining, their model substantially underestimated the previously observed water chemistry. As a result, a “reactivity factor” of 1,000 was
needed to modify the model to make predictions about future water. In this study of the
Yerington pit lake, a different modeling approach was taken since wall-rock reactions during mining, and prior to creation of the pit lake, are also unknown. Here, pit-lake interaction with wall rocks is modeled with an inverse approach whereby the amounts of minerals (and gases) dissolving or precipitating affect the modeled pit-lake water chemistry. In this approach, the modeler evaluates the reactions occurring for geochemical consistency, compares the model results to observed data, and gains an understanding of the main reactions controlling the chemical evolution of the pit lake over time. The different reaction rates of the minerals are not specifically considered.
RESULTS AND DISCUSSION
Initially, pit filling is dominated by deep groundwater inflow (GWi), its chemical
signature, and the flushing of wall-rock oxidation and chemical reaction products into the
lake. Unfortunately, there are no pit-lake chemistry data available during this time period.
As the pit lake fills, the lake chemistry then becomes dominated by the large volume of water in storage in the pit lake (Fig. 4.6). From this point, water chemistry changes in the pit lake tend to be very gradual.
137
100
90
80
70
60
50 Percent 40
30
20
10
0 1975 1980 1985 1990 1995 2000 2005 Year
Pit-Lake Volume Deep Groundwater Inflow
Figure 4.6. Changes in deep groundwater inflow relative to pit-lake volume with time. Percent deep groundwater inflow is calculated from the total pit lake volume excluding annual precipitation, spring flow, and evaporation (therefore, total % deep groundwater inflow plus pit-lake volume does not equal 100 % in this figure). There is presently no outflow from the pit lake.
Preliminary Modeling
Preliminary modeling was conducted where no chemical reactions were considered,
that is, only the different input waters were mixed together in the proportions from the
pit-filling model. Results show a relatively good agreement between mixed and observed
TDS concentrations with measured concentrations being slightly higher; however, the
trends with time are different (Fig. 4.7). Observed TDS is increasing slightly with time
while mixed TDS is relatively constant. The constant-mixed TDS results from the
dominant influence of the large volume of pit-lake water relative to the other inputs. The gradual increase in measured TDS is likely the result of ongoing evaporation from the lake surface. Measured Cl (Fig. 4.7), like TDS, is gradually increasing also showing
138 influence of evaporation. Observed Cu concentrations are high (relative to the alkaline pH) since the pit lake is located in a porphyry copper deposit; however, Cu concentrations are decreasing with time (Fig. 4.8). Mixed Cu concentration also show
800
700 ) -1 Measured 600 Modeled Flood TDS (mg L (mg TDS
500
400 Jan-93 Jun-94 Oct-95 Mar-97 Jul-98 Dec-99 Apr-01 Sep-02
50
40 ) -1 Measured Modeled Flood Cl (mg L 30
20 Jan-93 Jun-94 Oct-95 Mar-97 Jul-98 Dec-99 Apr-01 Sep-02
Figure 4.7. Measured and mixing model results for TDS and Cl concentrations in the Yerington pit lake.
139
0.200
0.150 ) -1 Measured 0.100 Modeled
Cu (mg L Flood
0.050
0.000 Jan-93 Jun-94 Oct-95 Mar-97 Jul-98 Dec-99 Apr-01 Sep-02
0.200
0.150 ) -1 Measured 0.100 Modeled Flood Se (mg L (mg Se
0.050
0.000 Jan-93 Jun-94 Oct-95 Mar-97 Jul-98 Dec-99 Apr-01 Sep-02
Figure 4.8. Measured and mixing model results for Cu and Se in the Yerington pit lake.
decreased concentrations with time as they are influenced by the large change in Cu concentrations from the previous time steps. Se concentrations at Yerington are about 0.1 mg L-1 (Fig. 4.8); these concentrations are 20 times the chronic, and five times the acute,
NV aquatic life standards. Measured Se concentrations show a slight decreasing trend
140
from 1994 to 1998 and then relatively constant concentrations. Mixing model results
show a continued decrease in concentration as the input waters are all very low in Se.
Other preliminary modeling simulations were conducted to identify and constrain the important lake-wall rock reactions that influence pit-lake water chemistry. This
included identifying minerals that were the major contributors to changes in water
chemistry and how much of each mineral would dissolve or precipitate. An important consideration, in addition to the observed occurrence of minerals in ore-body descriptions
or by analytical method, was the calculated saturation state (SI) of the minerals with
respect to pit-lake water (Appendix E). For a given mineral dissolution reaction:
a A + b B = c C + d D (1)
at equilibrium c d a b activity C · activity D / activity A · activity B = Keq (2)
where Keq is the equilibrium constant for the chemical reaction. Away from equilibrium,
the ion activity product (IAP), the left side of equation 2, will not equal Keq. If log IAP/
Keq is > 0, the solution is oversaturated with respect to the mineral (C and/or D) and the
mineral will tend to precipitate. If log IAP/ Keq is <0, the solution is undersaturated with
respect to the mineral and it will tend to dissolve. For example, the SI of calcite for the
100 m pit-lake sample collected in April 1995 is 0.38 (Appendix E), therefore, the pit
lake is oversaturated with respect to calcite and calcite will likely precipitate in the pit
lake. Generally, SI is a good indicator of mineral dissolution or precipitation provided the
kinetics of the reaction are also favorable.
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The actual SI for each mineral for each pit-lake sample (Appendix E) was
considered when selecting the SI for model simulations. However, to keep the
simulations general so that the modeling technique can be applied to other pit lakes, the
SI assigned to each mineral was limited to positive 0.5, 1, or 2 for oversaturated minerals
and from -1 to -5 for undersaturated minerals.
Different combinations of minerals in Table 4.6 were used in model simulations to
evaluate their effect on final model pit-lake chemistry. Also considered was the SI of the
mineral in the pit lake and the relative amount of the mineral that was available for
dissolution if present in pit wall rocks. The resulting model chemistry from each
simulation was compared with actual pit-lake water chemistry and the contribution of
each mineral to the model results was evaluated. A total of 38 different simulations were
considered.
An example of the process modeling the water chemistry changes from 4/29/94 to
4/7/95 is shown in Table 4.8. For simulation 17 consisting of the minerals adularia, albite, quartz, chrysocolla, calcite, Fe(OH)3(a), and montmorillonite-aberdeen, a moderately good
fit to pit-lake water was achieved in that modeled TDS is within 70 mg L-1 of the
measured TDS; modeled major-ion concentrations for Mg, Na, K, Cl, and SO4 are within
7 mg L-1 or less; the modeled trace element Se is within 0.03 mg L-1; and, As is the same
for both modeled and measured concentrations. Simulation 17 is a poor fit for Ca
-1 1 (modeled 16 mg L less than measured) HCO3 (½), SiO2 ( /5), and Cu (modeled concentration one order of magnitude higher). Simulation 17 did not produce any Fe. In simulation 18, adularia was replaced with annite to add a different aluminum silicate
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Table 4.8. Example of interactive modeling process conducted to select mineral assemblage for final pit-lake water chemistry modeling. Shown here are the modeled pit-lake water chemistry changes from 4/29/94 to 4/7/95. See text for discussion of process. Simulation SI Available Simulation SI Available Simulation SI Available (mol) (mol) 17 18 19 CO2(g) -3.5 10 CO2(g) -3.5 10 CO2(g) -3.5 10 O2(g) -0.7 10 O2(g) -0.7 10 O2(g) -0.7 10 Adularia -1 1 Annite -4 1 Annite -4 0.1 Albite -2 1 Albite -2 1 Albite -2 1 Quartz 0 1 Anorthite -6 1 Anorthite -6 1 Chrysocolla -1 0 Quartz 0 1 Quartz 0 1 Calcite 0.5 0 Chrysocolla -1 0.01 Chrysocolla -1 0.01 Fe(OH)3(a) 0 0 Pyrite -5 0.001 Pyrite -5 0.001 Montmorillonite-Ab 2 0 Calcite 0.5 0 Calcite 0.5 0 Goethite 5 0 Goethite 5 0 Montmorillonite-Ab 2 0 Montmorillonite-Ab 2 0 indicates change from previous simulation
Simulation pH As HCO3 Ca Cl Cu Fe K Mg Na SO4 Se SiO2 TDS mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 4/7/1995 7.84 0.003 151 88.7 32.9 0.158 <0.01 5.3 14.9 72 277 0.11 30.2 598 17 8.34 0.0029 77.8 73.04 35.9 2.25 0.0000 3.6 14.97 79.35 277 0.1339 6.6 532 18 8.21 0.0031 73.7 0.00 37.7 1.86 0.0002 41,823 0.00 0.00 496 0.1403 4.9 42,40 0 19 7.57 0.0029 14.9 0.00 36.1 2.49 0.0002 4,006 0.00 0.00 475 0.1345 6.3 4534
Simulation Adularia Annite Albite Anorthite Quartz Chrysocolla Pyrite Calcite Fe(OH)3(a) Goethite Montmorillonite- (mol) (mol) (mol) (mol) (mol) (mol) (mol) (mol) (mol) (mol) Ab (mol) 17 3.37E-05 NA -3.7E-05 NA 4.33E-04 -3.21E-05 NA 5.35E-04 0.00E+00 NA 4.99E-08 18 NA -1.00E+00 3.16E-03 2.3E-03 2.98E+00 -2.47E-05 -1.00E-03 0.00E+00 NA 3.00E+00 1.34E-03 19 NA -1.00E-01 3.16E-03 2.3E-03 2.81E-01 -3.56E-05 -1.00E-03 0.00E+00 NA 3.01E-01 1.34E-03 negative value indicates mineral dissolution positive value indicates mineral precipitation
143
mineral that also contained Fe in an attempt to add some Fe to the modeled pit-lake
water. Additionally, anorthite was added to try and increase Ca, pyrite was added to also
increase Fe, and Fe(OH)3(a) was replaced with goethite to provide a different sink for Fe.
Simulation 18 succeeded in increasing the Fe content of the modeled pit-lake water
(0.0002 mg L-1), but also dramatically increased the concentration of K (measured = 5.3
mg L-1, modeled ≈ 42,000 mg L-1) and TDS (measured = 598 mg L-1, modeled ≈ 42,000
mg L-1) with less dramatic changes in
-1 several other constituents including Ca, Mg, Na, (0 mg L for each), and SO4 (modeled
double measured). In simulation 19, the amount of annite available for dissolution was
decreased by one order of magnitude (1 to 0.1 mol), which maintained the Fe
concentration and also produced more modeled concentrations closer to observed (Table
4.8), although still an overall poor match to pit-lake water. Also shown in Table 4.8 are
the moles of each mineral precipitated or dissolved in the simulation. A positive value
means the mineral precipitated and a negative values means the mineral dissolved.
Final Modeling
Based upon the preliminary modeling, the final mineral assemblage, assigned SIs, and moles of minerals available for dissolution are shown in Table 4.9 as are the final
simulation results. Also included in final model simulations was adsorption of Cu onto
the surface of iron hydroxides. These reactions were included to simulate the observed
decrease in Cu concentrations in the Yerington pit lake. Adsorption reactions and input
values were obtained from Dzombak and Morel (1990) and are listed in Table 4.7.
144
Final modeling simulations slightly overestimated the TDS of the Yerington pit lake
(Fig. 4.9, ~30 mg L-1). The higher, modeled TDS concentrations result mostly from
higher modeled-concentrations of Na (Fig. 4.9, ~35 mg L-1), which can be attributed to
excess dissolution of albite (Table 4.7), the only primary rock forming mineral containing
Na in the model (Table 4.5). There was good agreement between simulated and observed
pit-lake pH (Fig. 4.10, difference ~0.4 pH units) indicating that the model accurately
replicated carbonate chemistry and elevated partial pressure of CO2 in the lake. Cl
concentrations were also in good agreement (Fig. 4.10, difference ~1.7 mg L-1) and show
a consistent increase that can be attributed to evaporative concentration of pit-lake water
(Fig. 4.10). Although future pit-lake chemistry is not predicting in this pit-lake model, it is expected that Cl will continue to increase over time as pit-lake filling slows, deep groundwater inflow decreases, and the pit-lake surface expands as higher pit benches are topped.
Final simulations also slightly over predict SO4 concentrations (Fig. 4.11,
-1 ~35 mg L ) as SO4 is added from the small amount of pyrite that dissolved (Table 4.9).
This amount of pyrite dissolution, along with an equally small amount of annite
dissolution (Table 4.9), is required to keep simulated Fe concentrations similar to
observed concentrations (not shown). Inclusion of equilibration of pit-lake waters with
atmospheric O2 is consistent with observed dissolved O2 profiles in the pit lake (see
Hershey et al. in revision, Chapter 2) and provides sufficient oxygen at depth to oxidize
any residual sulfide minerals in submerged pit walls. Simulations are also in good
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Table 4.9. Mineral assemblage used and final Yerington pit-lake water chemistry simulation results. Available Mineral or Gas SI (mol)
CO2(g) -3 10 O2(g) -0.7 10 Annite -4 0.0001 Albite -2 1 Quartz 0.5 1 Chrysocolla -1 0.001 Pyrite -5 0.0001 Calcite 0.5 0 Goethite 5 0 Montmorillonite-Ab 2 0
As HCO3 Ca Cl Cu Fe K Mg Na SO4 Se SiO2 TDS Simulation pH mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 mg L-1 4/7/1995 8.08 0.0029 126 90.1 35.9 0.3386 0.0002 2.9 2.9 110 296 0.1339 20.6 622 2/5/1996 8.03 0.0029 111 92.1 36.8 0.3316 0.0002 6.5 2.3 113 322 0.1291 20.6 649 8/22/2000 8.11 0.0039 140 79.8 39.0 0.3546 0.0002 3.2 2.9 111 310 0.0916 21.3 638 9/13/2001 8.08 0.0038 124 81.8 39.6 0.3360 0.0002 6.8 2.5 114 333 0.0882 20.6 661
Annite Albite Quartz Chrysocolla Pyrite Calcite Goethite Montmorillonite-Ab Simulation (mol) (mol) (mol) (mol) (mol) (mol) (mol) (mol) 4/7/1995 -1.00E-04 -1.49E-03 8.13E-04 -9.74E-06 -1.00E-04 1.19E-04 4.22E-05 1.08E-03 2/5/1996 -1.00E-04 -3.69E-05 7.81E-05 -7.39E-06 -1.00E-04 0.00E+00 3.69E-04 9.31E-05 8/22/2000 -1.00E-04 -1.40E-03 8.44E-04 -1.16E-05 -1.00E-04 1.12E-04 6.38E-05 1.02E-03 9/13/2001 -1.00E-04 -1.59E-05 8.04E-05 -7.13E-06 -1.00E-04 0.00E+00 3.74E-04 7.89E-05 negative value indicates mineral dissolution positive value indicates mineral precipitation
146
800
700 ) -1 Measured 600 Modeled Flood TDS (mg L (mg TDS
500
400 Jan-93 Jun-94 Oct-95 Mar-97 Jul-98 Dec-99 Apr-01 Sep-02
200
150 ) -1 Measured 100 Modeled Flood Na (mg L
50
0 Jan-93 Jun-94 Oct-95 Mar-97 Jul-98 Dec-99 Apr-01 Sep-02
Figure 4.9. Measured and final model results for TDS and Na in the Yerington pit lake.
147
10.0
9.5
9.0
8.5 Measured 8.0
pH Modeled Flood 7.5
7.0
6.5
6.0 Jan-93 Jun-94 Oct-95 Mar-97 Jul-98 Dec-99 Apr-01 Sep-02
60
50
40 )
-1 Measured 30 Modeled Flood Cl (mg L 20
10
0 Jan-93 Jun-94 Oct-95 Mar-97 Jul-98 Dec-99 Apr-01 Sep-02
Figure 4.10. Measured and final model results for pH and Cl in the Yerington pit lake.
148
500
400 )
-1 300 Measured Modeled (mg L 4 200 Flood SO
100
0 Jan-93 Jun-94 Oct-95 Mar-97 Jul-98 Dec-99 Apr-01 Sep-02
0.200
0.150 ) -1 Measured 0.100 Modeled Flood Se (mg L
0.050
0.000 Jan-93 Jun-94 Oct-95 Mar-97 Jul-98 Dec-99 Apr-01 Sep-02
Figure 4.11. Measured and final model results for SO4 and Se in the Yerington pit lake.
agreement with observed Se concentrations (Fig. 4.11, difference ~0.013 mg L-1) although Se is not included in any dissolving or precipitated mineral phases or adsorption reactions. At the slightly alkaline Yerington pit-lake pH (~8), Se (selenate) adsorption onto iron hydroxides should be minimal (Dzombak and Morel 1990). The gradually
149
decreasing Se concentrations are consistent with dilution by spring flow (and
precipitation) that is low in Se (Table 4.2).
Finally, model simulations, even including Cu adsorption onto iron hydroxides,
substantially overestimates Cu concentrations (difference ~0.25 mg L-1) in the pit lake
and fails to replicate the observed decrease in Cu concentrations over the course of pit-
lake sampling (Fig. 4.12). Modeled Cu concentrations are derived from the dissolution of
the primary oxide ore mineral chrysocolla, which is easily seen on the pit walls by casual observation. SEM-EDX analysis of pit-wall rocks clearly shows the presence of
chrysocolla above the pit-lake surface and the loss of Cu in wall rocks with increasing
depth below the pit-lake surface (Appendix C). In modeling simulations excluding
dissolution of chrysocolla, but including Cu sorption onto iron hydroxides, Cu in the pit lake is removed quickly (Fig. 4.12, difference ~0.09 mg L-1). It is likely more likely that
chrysocolla dissolution is continuing to add Cu to the pit lake and that another process
not considered here is controlling the decrease in Cu concentrations. Hershey et al. (in
revision, Chapter 2) discussed the possibility that Cu concentrations are decreasing
because of consumption by algae as a micronutrient and/or adsorption of Cu onto organic
compounds in the pit lake that are ultimately sequestered in pit-lake sediments as the
organics settle out of the lake water column.
150
0.500
0.400
) 0.300 -1 Measured Modeled Flood
Cu (mg L 0.200
0.100
0.000 Jan-93 Jun-94 Oct-95 Mar-97 Jul-98 Dec-99 Apr-01 Sep-02
0.500
0.400
) 0.300 -1 Measured Modeled Flood
Cu (mg L 0.200
0.100
0.000 Jan-93 Jun-94 Oct-95 Mar-97 Jul-98 Dec-99 Apr-01 Sep-02
Figure 4.12. Measured and final model results with chrysocolla and Cu adsorption (top) and final model results without chrysocolla, but with Cu adsorption (bottom) in the Yerington pit lake.
151
CONCLUSIONS
Pit lakes that become contaminated may affect scarce water resources and may also provide a source of groundwater contamination. Because of this, it is critical to identify the important processes that affect pit-lake water chemistry and to be able to model these processes so that long-term pit-lake water chemistry may be predicted. Modeling of the geochemical evolution of the Yerington pit-lake chemistry over a six-year period successfully reproduced the observed pit-lake chemistry and most of the observed trends except for Cu. Modeling demonstrated that the pit-lake water chemistry is dominated by the current pit-lake chemistry and that other water inputs to the lake including precipitation, spring flow, and deep groundwater are a small percentage of the water in storage in the pit lake. Evaporation is an important component of the current water balance and is slowly increasing the TDS of the lake.
Modeling in this study focused understanding the processes that are currently controlling pit-lake water chemistry. A variety of additional modeling scenarios could be undertaken in the future including development of a model that accurately simulates the development of the pit-lake water chemistry from the beginning of pit filling to current conditions, incorporation of annual limnological processes that affect water chemistry, addition of other processes such as Cu adsorption onto organic material in the lake and
Cu sequestration in pit-lake sediments that may be controlling the observed decrease in
Cu concentrations, and a predictive model of the pit lake to estimate the longer-term water chemistry evolution
152
REFERENCES
Balistrieri, L.S., Tempel, R.N., Stillings, L.L., Shevenell, L.A., 2006. Modeling spatial and temporal variations in temperature and salinity during stratification and overturn in Dexter Pit Lake, Tuscarora, NV, USA. Applied Geochemistry 21:1184-1203 Cartin, R.B., 1986. Sodium-calcium metasomatism: chemical, temporal, and spatial relationships at the Yerington, NV, porphyry copper deposit. Economic Geology 81:1495-1519 Castendyk, D.N., Webster-Brown, J.G., 2007a. Sensitivity analyses in pit lake prediction, Martha mine, New Zealand 2: Geochemistry, water-rock reactions, and surface adsorption. Chemical Geology 244:56-73 Castendyk, D.N., Webster-Brown, J.G., 2007b. Sensitivity analyses in pit lake prediction, Martha mine, New Zealand 1: Relationship between turnover and input water density. Chemical Geology 244:42-55 Cole, J.J., Caraco, N.F., Kling, G.W., Kratz, T.K., 1994. Carbon dioxide supersaturation in surface waters of lakes. Science 265:1568-1580 Davis, A., 2003. A screening-level laboratory method to estimate pit lake chemistry. Mine Water and the Environment 22:194-205 Davis, A., Bellehumeur, T., Hunter, P., Hanna, B., Fennemore, G.G., Moomaw, C., Schoen, S., 2006. The nexus between groundwater modeling, pit lake chemogenesis and ecological risk from arsenic in the Getchell Main Pit, Nevada, USA. Chemical Geology 228:175-196 Desert Research Institute, 2009. http://www.dri.edu/dhs-labs-and-capabilities/232-water- analysis-laboratory, Accessed December 23, 2009 Drever, J.I., 1982. The Geochemistry of Natural Waters. Prentice-Hall, Inc., Englewood Cliffs, N.J., 388 p Dzombak, D.A. and Morel, F.M.M., 1990. Surface Complexation Modeling. John Wiley & Sons, New York, 393 p Eary, L.E., 1999. Geochemical and equilibrium trends in mine pit lakes. Applied Geochemistry 14:963-987 Fontaine, R.C., Davis, A., Fennemore, G.G., 2003. The comprehensive realistic yearly pit transient infilling code (CRYPTIC): A novel pit lake analytical solution. Mine Water and the Environment 22:187-193 Hamblin, P.F., Stevens, C.L., Lawrence, G.A., 1999. Simulation of vertical transport in mining pit lakes. Journal of Hydraulic Engineering, October 1029-1038
153
Hancock, G.R., Wright, A., De Silva, H., 2005. Long-term final void salinity prediction for a post-mining landscape in the Hunter Valley, New South Wales, Australia. Hydrological Processes 19:387-401 Hershey, R.L., Kepner, R., Miller, G.C., in revision. Limnology and water quality of a porphyry-copper pit lake and comparison to two nearby terminal desert lakes in northern Nevada, USA. Environmental Earth Sciences. Hounam, C.E., 1973. Comparison between pan and lake evaporation. World Meteorological Organization, Technical Note No. 126, WMO – No. 354, Geneva, Switzerland, 52 p Kempton, J.H., Locke, W.L., Nicholson, A.D., Bennett, M., Bliss, L., Maley, P., 1997. Probabilistic prediction of water quality in the Twin Creeks Mine pit lake, Golconda, NV. In: Fourth International Conference on Acid Rock Drainage Proceedings, Vancouver, B.C., May 31-June 6, 1997, p. 891-904 Miller, G.C., Hershey, R.L., 2000. An alternative physical model for geochemical evolution of pit lakes. Workshop on the Characterization, Modeling, Remediation, and Monitoring of Pit Lakes, Program Agenda, Abstracts and Bios, Presentations, sponsored by U.S. Environmental Protection Agency, April 4-6, 2000, Reno, NV, p. 11 Miller, G.C., Lyons, W.B., Davis, A., 1996. Understanding the water quality of pit lakes. Environmental Science & Technology 30:118-123 Morin, K.A., Hutt, N.M., 2001. Prediction of water chemistry in mine lakes: The minewall technique. Ecological Engineering 17:125-132. Naugle, G.D., and Atkinson, L.C., 1993. Estimating the rate of post-mining filling of pit lakes. Mining Engineering April 402-404 Parkhurst, D.L, Appelo, C.A.J., 1999. User’s guide to PHREEQC (version 2)- a computer program for speciation, batch-reaction, one-dimensional transport, and inverse geochemical calculations. U.S. Geological Survey, Water-Resources Investigations Report 99-4259, 312 p Papadopulos, I.S., Cooper, H.H., 1967. Drawdown in a well of large diameter. Water Resources Research 3:241-244. Shevenell, L., 2000. Analytical method for predicting filling rates of mining pit lakes: example from the Getchell Mine, NV. Mining Engineering March 53-60 Tempel, R.N., Shevenell, L.A., Lechler, P., Price, J., 2000. Geochemical modeling approach to predicting arsenic concentrations in a mine pit lake. Applied Geochemistry 15:475-492
154
Theis, C.V., 1935. The relation between the lowering of the piezometric surface and the rate and duration of discharge of a well using groundwater storage. Transactions of the American Geophysical Union 2:519-524. Western Regional Climate Center, 2009. www.wrcc.dri.edu, Accessed December 23, 2009 Wilson, J.R., 1963. Geology of the Yerington Mine. Mining Geology Congress, June 1963, p. 30-34
155
CHAPTER 5 CONCLUSIONS
Studies of a large pit lake in Yerington, NV, where the lake has been filling with
groundwater for over 30 y, shows that the pit-lake limnology is similar to two nearby
natural terminal lakes. The pit lake, even though it has a much different surface area-to-
mean depth ratio than the natural lakes, is also monomictic and oligotrophic.
Additionally, the pit lake, like one of the terminal lakes, does not develop an anoxic hypolimnion during stratification. Elevated Cu and Se concentrations in the pit lake are much greater than aquatic life water-quality standards and may be adversely affecting phyto- and zooplankton.
From laboratory batch sorption experiments, pit-lake sediments from a Carlin-type
gold deposit pit lake had the greatest capacity to adsorb As(V) and As(III) under both
oxidizing and slightly reducing conditions. Sediments from a porphyry copper pit lake
had the least adsorption capacity while sediments from a quartz-adularia precious metal
deposit pit lake had adsorption capacity between the other two sediments. Surface
complication modeling suggests that the adsorption capacity of the sediments is
controlled by the presence, and amount of, amorphous Fe hydroxides in the sediments.
Adsorption of As(V) showed typical strongly sorbing anion behavior. One hundred
percent fractional uptake occurred at pH ≤ 6 and decreased rapidly with increasing pH.
Adsorption was independent of ionic strength indicating formation of inner-sphere
complexes. Adsorption isotherms for As(V) were strongly non-linear at higher spike
concentrations with a Freundlich isotherm best describing adsorption behavior.
156
Adsorption of As(III) exhibited adsorption behavior that looked more like cation adsorption than anion adsorption. Fractional uptake was lower at lower pH and increased with increasing pH. Maximum adsorption occurred around pH 8. Adsorption of As(III) was weakly ionic strength dependent suggesting perhaps some formation of outer-sphere complexes. Adsorption isotherms were strongly non-linear with a Freundlich isotherm best describing adsorption behavior. The results from this study suggest that pit-lake sediments rich in Fe from the oxidation of Fe sulfide minerals in the ore bodies provide a medium to sequester As in pit-lake systems.
Modeling of the geochemical evolution of the Yerington pit-lake chemistry over a six-year period from 1994 to 2001 successfully reproduced the observed pit-lake chemistry and most of the observed changes in water chemistry except for decreasing Cu concentrations. Modeling demonstrated that the pit-lake water chemistry is dominated by the current pit-lake chemistry and that other water inputs to the lake including precipitation, spring flow, and deep groundwater are a small percentage (<10 %) of the water in storage in the pit lake. Evaporation, although a small component of the overall water balance for the pit lake (~ 3 % of the annual water input), is slowly increasing the
TDS of the lake.
157
APPENDIX A. Vertical Profile Data for the Yerington Pit Lake.
Date Temperature pH EC DO DO Depth (°C) (msiemens) (%) (mg L-1) (m) 7/27/2000 22.65 8.05 810 98.9 7.3 0.5 7/27/2000 22.24 8.17 844 98.5 7.33 5 7/27/2000 22.09 8.23 844 99.4 7.41 10.1 7/27/2000 14.33 8.1 829 112.2 9.82 15.1 7/27/2000 11 8.09 829 108.5 10.23 20 7/27/2000 8.8 8.09 828 103.4 10.27 25 7/27/2000 7.8 8.05 830 96.9 9.87 30 7/27/2000 7.16 7.97 829 86.5 8.95 35.1 7/27/2000 6.85 7.88 833 75.8 7.9 40 7/27/2000 6.6 7.81 834 70.3 7.38 44.9 7/27/2000 6.47 7.77 836 67.7 7.13 49.9 7/27/2000 6.34 7.71 839 62.9 6.65 60.2 7/27/2000 6.3 7.66 841 58 6.13 69.9 7/27/2000 6.3 7.63 842 56 5.92 79.9 7/27/2000 6.3 7.61 841 56.1 5.93 90.5 7/27/2000 6.32 7.6 842 54.9 5.8 99.7
8/23/2000 22.95 8.1 871 95.6 7.02 0.6 8/23/2000 22.04 8.16 873 97.2 7.26 5 8/23/2000 21.94 8.17 875 97.1 7.26 10 8/23/2000 16.76 8.05 862 116.8 9.69 15 8/23/2000 11.93 8.03 865 109.6 10.11 19.8 8/23/2000 9.36 8.04 861 103.4 10.13 25.3 8/23/2000 8.26 8.01 862 97.6 9.83 30.1 8/23/2000 7.44 7.9 867 85.5 8.79 35 8/23/2000 6.95 7.77 868 74.2 7.72 40 8/23/2000 6.7 7.69 870 68.8 7.19 44.9 8/23/2000 6.5 7.64 872 65.3 6.87 50.1 8/23/2000 6.37 7.58 872 59.3 6.26 60 8/23/2000 6.35 7.54 874 56.7 5.98 70 8/23/2000 6.32 7.52 875 54.9 5.8 80.1 8/23/2000 6.34 7.5 875 54 5.7 90.1 8/23/2000 6.37 7.49 876 53.1 5.61 100.4
9/29/2000 19.8 8 873 96 7.44 0.8 9/29/2000 19.21 8 875 96.4 7.56 5 9/29/2000 19.16 8.01 876 95.7 7.51 10 9/29/2000 19.09 8.01 875 96.2 7.56 15.1 9/29/2000 12.9 7.93 871 112.1 10.05 20 9/29/2000 10.33 7.94 869 109.6 10.43 24.9 9/29/2000 8.54 7.88 872 100.6 10 30.1 9/29/2000 7.64 7.75 874 88.3 8.97 35 9/29/2000 7.11 7.58 876 75.4 7.77 40.1 9/29/2000 6.73 7.49 877 68.2 7.09 44.9
158
APPENDIX A. Vertical profile data for the Yerington pit lake (continued). Date Temperature pH EC DO DO Depth (°C) (msiemens) (%) (mg L-1) (m) 9/29/2000 6.55 7.47 877 66.8 6.97 50.2 9/29/2000 6.42 7.4 880 59.5 6.24 60.1 9/29/2000 6.35 7.37 881 57.1 5.99 70 9/29/2000 6.35 7.35 882 55.2 5.79 80 9/29/2000 6.39 7.33 884 52.2 5.47 90 9/29/2000 6.37 7.32 884 52.2 5.47 100
10/30/2000 14.79 8.21 857 92.4 7.95 0.6 10/30/2000 14.72 8.23 857 91.3 7.86 5.1 10/30/2000 14.69 8.24 857 90.6 7.81 9.8 10/30/2000 14.66 8.25 855 91.3 7.88 15.1 10/30/2000 14.64 8.25 857 91.7 7.91 20 10/30/2000 10.63 8.17 856 105.2 9.94 25.1 10/30/2000 8.71 8.09 860 97.2 9.62 30.4 10/30/2000 7.73 7.94 862 83.1 8.42 35 10/30/2000 7.13 7.74 862 68.6 7.06 39.8 10/30/2000 6.85 7.69 863 65.5 6.78 44.9 10/30/2000 6.62 7.65 865 63.1 6.58 50.1 10/30/2000 6.44 7.58 867 57.2 5.99 60.1 10/30/2000 6.39 7.54 869 52.4 5.5 70 10/30/2000 6.39 7.51 871 50.1 5.25 79.9 10/30/2000 6.39 7.51 870 50.3 5.27 90.1 10/30/2000 6.39 7.5 871 49.8 5.22 100.1
11/27/2000 10.52 8.1 837 95.1 9.01 0.9 11/27/2000 10.44 8.15 844 92.7 8.8 5 11/27/2000 10.44 8.17 854 92.8 8.81 9.9 11/27/2000 10.44 8.18 853 107.3 10.19 15.3 11/27/2000 10.44 8.19 852 104.7 9.94 20 11/27/2000 10.42 8.2 852 91.9 8.73 25 11/27/2000 10.31 8.19 851 91.4 8.71 29.8 11/27/2000 7.85 7.9 866 83.9 8.48 35.1 11/27/2000 7.23 7.71 864 71 7.28 40 11/27/2000 6.85 7.62 866 63.5 6.58 45 11/27/2000 6.63 7.58 866 62.1 6.47 50 11/27/2000 6.45 7.52 868 57 5.97 60 11/27/2000 6.42 7.46 871 51.7 5.41 70.2 11/27/2000 6.44 7.43 872 49.1 5.14 79.9 11/27/2000 6.44 7.43 872 48.8 5.1 90 11/27/2000 6.44 7.43 872 47.9 5.02 100.1
12/28/2000 7.97 8.16 860 98.1 9.89 0.8 12/28/2000 7.82 8.15 858 114.1 11.54 5.1 12/28/2000 7.79 8.16 858 92.5 9.36 10
159
APPENDIX A. Vertical profile data for the Yerington pit lake (continued). Date Temperature pH EC DO DO Depth (°C) (msiemens) (%) (mg L-1) (m) 12/28/2000 7.79 8.17 857 90.6 9.17 15 12/28/2000 7.77 8.17 857 90.3 9.14 20 12/28/2000 7.77 8.17 856 90.1 9.13 25 12/28/2000 7.77 8.17 856 91 9.21 30 12/28/2000 7.77 8.17 855 90.5 9.16 35 12/28/2000 7.75 8.16 856 89 9.01 40.1 12/28/2000 6.92 7.8 870 65.7 6.79 45 12/28/2000 6.64 7.68 872 61 6.35 50 12/28/2000 6.47 7.62 873 54.9 5.74 60.1 12/28/2000 6.44 7.58 875 52.4 5.48 70.1 12/28/2000 6.45 7.55 876 49.2 5.15 80 12/28/2000 6.45 7.53 878 48.5 5.08 90.1 12/28/2000 6.45 7.52 878 47.1 4.93 100.1
1/29/2001 6.29 8.18 883 86.5 9.09 0.7 1/29/2001 6.29 8.17 896 85.8 9.01 5.1 1/29/2001 6.27 8.17 893 85.3 8.96 10 1/29/2001 6.27 8.17 893 84.8 8.91 15.2 1/29/2001 6.27 8.18 892 84.4 8.87 20 1/29/2001 6.27 8.18 891 84.6 8.89 25 1/29/2001 6.26 8.19 891 84.4 8.88 29.9 1/29/2001 6.27 8.19 891 84.2 8.85 35 1/29/2001 6.27 8.19 891 83.9 8.82 40.3 1/29/2001 6.27 8.19 891 83.7 8.8 45 1/29/2001 6.26 8.2 891 83.9 8.82 50 1/29/2001 6.26 8.2 891 83.2 8.75 60 1/29/2001 6.37 7.95 900 66.3 6.95 70 1/29/2001 6.47 7.76 911 50.4 5.27 80.1 1/29/2001 6.49 7.71 911 49.7 5.2 89.8 1/29/2001 6.49 7.7 912 49.2 5.14 99.9
2/22/2001 6.09 8.19 867 91.8 9.7 0.6 2/22/2001 6.08 8.2 862 90.6 9.58 5 2/22/2001 6.06 8.21 859 89.8 9.49 10 2/22/2001 5.98 8.2 854 88.7 9.4 14.7 2/22/2001 5.96 8.2 854 85.8 9.09 20 2/22/2001 5.94 8.19 854 83.9 8.89 25.2 2/22/2001 5.94 8.19 853 84.4 8.95 29.9 2/22/2001 5.94 8.19 854 83.7 8.87 35 2/22/2001 5.94 8.19 854 82.8 8.78 39.9 2/22/2001 5.94 8.19 855 83.3 8.84 45 2/22/2001 5.94 8.19 854 82.5 8.74 49.9 2/22/2001 5.94 8.19 854 83 8.8 60 2/22/2001 5.94 8.18 855 82.8 8.78 70.1
160
APPENDIX A. Vertical profile data for the Yerington pit lake (continued). Date Temperature pH EC DO DO Depth (°C) (msiemens) (%) (mg L-1) (m) 2/22/2001 5.94 8.18 855 82 8.69 80 2/22/2001 5.94 8.18 854 81.3 8.61 90.2 2/22/2001 5.94 8.18 855 82.1 8.71 100
3/31/2001 12.31 8.06 866 100.9 9.17 0.8 3/31/2001 10.05 8.08 862 106.6 10.22 5 3/31/2001 7.75 8.05 860 101.8 10.32 10.1 3/31/2001 6.83 8.01 860 96.8 10.03 15 3/31/2001 6.44 8 861 92.9 9.72 19.9 3/31/2001 6.21 7.98 861 89.4 9.41 25.1 3/31/2001 6.11 7.95 861 85.5 9.02 30.1 3/31/2001 6.09 7.95 861 85.8 9.06 35 3/31/2001 6.04 7.94 861 84.9 8.98 40 3/31/2001 6.01 7.94 861 83.4 8.83 44.9 3/31/2001 5.99 7.93 861 83.6 8.85 49.9 3/31/2001 5.99 7.93 861 81.7 8.65 60.1 3/31/2001 5.99 7.93 862 81.4 8.62 70 3/31/2001 5.97 7.93 862 81.6 8.64 79.9 3/31/2001 5.98 7.93 862 81.1 8.59 90 3/31/2001 5.98 7.93 863 79.7 8.44 100
4/23/2001 12.61 8.08 846 103.1 9.31 0.7 4/23/2001 10.47 8.11 858 105.8 10.04 5 4/23/2001 10.06 8.12 860 107.1 10.26 10 4/23/2001 8.55 8.04 862 105.3 10.46 15 4/23/2001 7.26 7.99 862 99.6 10.22 20 4/23/2001 6.62 7.96 863 94 9.8 25 4/23/2001 6.34 7.93 862 87.3 9.16 30 4/23/2001 6.22 7.92 863 86.6 9.11 35.1 4/23/2001 6.14 7.91 862 83.6 8.82 40 4/23/2001 6.08 7.9 863 83.5 8.83 45.1 4/23/2001 6.04 7.89 863 81.3 8.6 50 4/23/2001 6.03 7.89 863 81.4 8.61 59.8 4/23/2001 6.01 7.89 864 81.2 8.6 70 4/23/2001 6.01 7.89 864 80.7 8.54 80 4/23/2001 6.01 7.88 864 80.5 8.52 89.9 4/23/2001 6.03 7.88 864 78.9 8.35 100.1
5/25/2001 20.4 8.13 898 99.7 7.63 0.7 5/25/2001 18.6 8.15 896 96.1 7.63 5.1 5/25/2001 12.89 8.05 885 105.2 9.44 10 5/25/2001 10.06 7.98 884 102 9.77 15.2 5/25/2001 8.59 7.95 884 97.1 9.64 20.1 5/25/2001 7 7.91 883 92.4 9.54 25.1
161
APPENDIX A. Vertical profile data for the Yerington pit lake (continued). Date Temperature pH EC DO DO Depth (°C) (msiemens) (%) (mg L-1) (m) 5/25/2001 6.57 7.88 883 87.6 9.14 29.9 5/25/2001 6.39 7.85 883 86.8 9.1 35.1 5/25/2001 6.29 7.82 883 84 8.82 40 5/25/2001 6.19 7.8 882 80.1 8.43 45.1 5/25/2001 6.14 7.79 883 81 8.54 50.1 5/25/2001 6.08 7.78 884 80.2 8.48 60 5/25/2001 6.06 7.77 883 79.4 8.39 70 5/25/2001 6.06 7.76 884 78.6 8.31 80 5/25/2001 6.06 7.75 884 77.6 8.2 90 5/25/2001 6.06 7.74 885 77.2 8.16 100.1
6/30/2001 22.47 8.03 880 97.7 7.19 0.7 6/30/2001 20.65 8.06 878 99 7.55 5 6/30/2001 18.97 7.95 863 105.2 8.3 10 6/30/2001 12.31 7.87 864 107 9.73 14.8 6/30/2001 9.26 7.81 864 100.4 9.8 20.3 6/30/2001 7.47 7.78 859 93.8 9.57 25.3 6/30/2001 6.83 7.73 860 88.8 9.21 30 6/30/2001 6.47 7.69 860 84.4 8.83 35.1 6/30/2001 6.34 7.67 859 81 8.5 40 6/30/2001 6.24 7.65 859 79.3 8.35 45.1 6/30/2001 6.19 7.64 860 78.2 8.24 50.2 6/30/2001 6.14 7.63 861 77.3 8.15 60.2 6/30/2001 6.12 7.62 860 75.9 8.01 70.1 6/30/2001 6.12 7.6 862 73.2 7.73 80.5 6/30/2001 6.11 7.59 862 75.3 7.95 89.8 6/30/2001 6.09 7.59 861 74.3 7.85 100.4
8/1/2001 21.75 8.06 849 98.6 7.36 0.7 8/1/2001 21.69 8.09 865 98.8 7.38 5 8/1/2001 21.57 8.08 869 99 7.42 10 8/1/2001 14.44 8.05 856 114.8 9.96 15 8/1/2001 10.23 7.99 855 105.7 10.08 20 8/1/2001 8.07 7.97 862 100.9 10.14 25 8/1/2001 6.83 7.91 854 87.7 9.09 30 8/1/2001 6.58 7.86 854 82.7 8.63 35.2 8/1/2001 6.39 7.82 855 80.4 8.43 40 8/1/2001 6.29 7.8 854 78.2 8.22 45 8/1/2001 6.25 7.78 854 77.6 8.16 50 8/1/2001 6.17 7.75 853 75.5 7.96 60.1 8/1/2001 6.16 7.74 853 74.8 7.89 70 8/1/2001 6.16 7.72 854 73.5 7.75 80 8/1/2001 6.14 7.71 854 72.8 7.68 90.1 8/1/2001 6.14 7.71 855 72 7.59 100.1
162
APPENDIX A. Vertical profile data for the Yerington pit lake (continued). Date Temperature pH EC DO DO Depth (°C) (msiemens) (%) (mg L-1) (m) 9/12/2001 21.6 8.01 867 97.8 7.34 0.8 9/12/2001 21.29 8.08 865 100.8 7.62 5 9/12/2001 21.21 8.12 865 94.7 7.16 10 9/12/2001 16.68 8.01 858 118.5 9.82 14.8 9/12/2001 11.45 8.09 854 115 10.69 20.1 9/12/2001 8.88 8.09 853 106.2 10.5 25.1 9/12/2001 7.27 7.96 852 89.7 9.23 30 9/12/2001 6.8 7.85 855 80.6 8.39 35.1 9/12/2001 6.45 7.8 852 74.3 7.8 40.1 9/12/2001 6.34 7.79 855 73.7 7.76 45 9/12/2001 6.29 7.78 856 73.5 7.74 50 9/12/2001 6.24 7.79 854 73.4 7.74 55 9/12/2001 6.22 7.78 853 72 7.61 60.1 9/12/2001 6.2 7.77 853 71.6 7.56 65 9/12/2001 6.19 7.77 854 71.1 7.51 70 9/12/2001 6.19 7.76 853 70.4 7.44 75 9/12/2001 6.19 7.76 854 70.1 7.4 80.1 9/12/2001 6.19 7.75 854 68.5 7.24 85 9/12/2001 6.19 7.76 854 69 7.3 90.1 9/12/2001 6.17 7.76 854 69.1 7.3 95 9/12/2001 6.2 7.75 854 67.8 7.17 100.1
163
APPENDIX B. SEM-EDX ANALYSIS OF PIT-LAKE SEDIMENTS
Yerington S2_1 2.5 m below lake surface, view of grains, 50x
EDX spectra: Ca, Na, Mg, K, Fe, Cu, Al, Si peaks
Element Line Weight% Cnts/s Atomic% ------O Ka 32.84 285.18 56.27 Na Ka 1.78 27.35 2.12 Al Ka 3.25 90.74 3.30 Si Ka 18.76 613.10 18.32 K Ka 1.94 53.58 1.36 Ca Ka 2.97 76.07 2.03 Cu Ka 38.46 166.73 16.60
164
Yerington S2_2 2.5 m below lake surface, 600x, view of grain
EDX spectra: spectra of small grain: Ca, Mg, K, Fe, Cu, Al, Si peaks
165
Yerington S15_1 15 m below lake surface, 270x, view of grain
EDX spectra: Ca, Na, K, Fe, Al, Si peaks
Element Line Weight% Cnts/s Atomic% ------O Ka 48.74 290.55 62.35 Na Ka 5.52 85.30 4.91 Mg Ka 0.40 8.03 0.34 Al Ka 9.78 230.53 7.42 Si Ka 32.17 766.15 23.44 K Ka 1.20 20.34 0.63 Ca Ka 0.89 13.93 0.45 Fe Ka 0.81 4.61 0.30 Cu Ka 0.49 1.28 0.16
166
Yerington S15_2 15 m below lake surface, 400x, view of feldspar crystal
EDX spectra: Ca, Na, K, Fe, Cu, Al, Si peaks
Element Line Weight% Cnts/s Atomic% ------O Ka 41.55 104.05 58.60 Na Ka 0.70 5.44 0.69 Mg Ka 0.77 8.41 0.71 Al Ka 9.08 117.59 7.60 Si Ka 28.03 374.03 22.52 K Ka 2.57 25.29 1.48 Ca Ka 10.02 89.82 5.64 Fe Ka 3.59 11.61 1.45 Cu Ka 3.68 5.49 1.31
167
Yerington S37_1 37 m below lake surface, 200x, particles, lots of graphite showing
168
Yerington S37_2 37 m below lake surface, 650x, single grain
EDX spectra: Ca, Mg, K, Fe, Cu, Al, Si
169
Yerington S37_3 37 m below lake surface, 5000x, tip of single grain
170
Yerington S37_4 37 m below lake surface, 800x, single grain
EDX spectra: Al, Si peaks
171
Yerington S76_1 76 m below lake surface, 100x, grain
172
Yerington S76_2 76 m below lake surface, close up of grain
EDX spectra: Ca, Mg, K, Fe, Cu, Al, Si peaks
Element Line Weight% Cnts/s Atomic% ------O Ka 37.41 154.80 55.29 Na Ka 0.84 8.87 0.86 Mg Ka 1.84 27.82 1.79 Al Ka 10.37 185.16 9.08 Si Ka 25.89 479.77 21.80 K Ka 3.62 51.14 2.19 Ca Ka 4.33 56.19 2.55 Fe Ka 11.62 54.94 4.92 Cu Ka 4.08 8.82 1.52
173
Yerington S100_1 100 m below lake surface, 250x, grain
EDX spectra: Ca, Mg, K, Fe, Al, Si peaks
174
Yerington S100_2 100 m below lake surface, 1500x, grain surface
175
Yerington Sediment_7 just below lake surface, green pebble
EDX spectra: Ca, Cu, Al, Si
Element Line Weight% Cmpt CmptWT% Cnts/s Atomic% ------O 39.41 61.60 Al Ka 4.58 Al2O3 8.65 90.85 4.24 Si Ka 23.68 SiO2 50.65 531.36 21.08 Ca Ka 1.48 CaO 2.07 24.73 0.92 Cu Ka 30.86 CuO 38.64 87.22 12.15
176
Tuscarora TS1_2
EDX spectra: K, Fe, S, Al, Si
Element Line Weight% Cmpt CmptWT% Cnts/s Atomic% ------O 47.65 62.34 Al Ka 32.42 Al2O3 61.26 1084.66 25.15 Si Ka 11.02 SiO2 23.57 316.95 8.21 S Ka 1.93 S1O4 5.79 46.50 1.26 K Ka 2.64 K2O 3.18 64.55 1.41 Fe Ka 4.34 Fe2O3 6.20 36.36 1.63
177
Tuscarora TS1_3
EDX spectra: Ca, Mg, K, Fe, S, Al, Si
Element Line Weight% Cmpt CmptWT% Cnts/s Atomic% ------O 47.48 63.84 Al Ka 12.20 Al2O3 23.05 448.19 9.73 Si Ka 24.05 SiO2 51.46 890.67 18.43 S Ka 2.16 S1O4 6.47 61.61 1.45 K Ka 4.83 K2O 5.82 134.25 2.66 Ca Ka 2.15 CaO 3.00 53.59 1.15 Fe Ka 7.13 Fe2O3 10.19 66.23 2.75
178
APPENDIX C. SEM-EDX ANALYSIS OF WALL ROCKS
WR1_1, 1.5 m above water surface, green rock coating
Wide angle EDX spectra: Ca, Cu, Si, Al peaks
Element Line Weight% Cnts/s Atomic% ------O Ka 32.84 285.18 56.27 Na Ka 1.78 27.35 2.12 Al Ka 3.25 90.74 3.30 Si Ka 18.76 613.10 18.32 K Ka 1.94 53.58 1.36 Ca Ka 2.97 76.07 2.03 Cu Ka 38.46 166.73 16.60
Primary ore mineral is chrysocolla Cu4H4Si4O10(OH)8
179
WR1_2, 1.5 m above water surface, green rock coating, close up
Botryoidal habit, green to greenish blue
Close in EDX Spectra
Element Line Weight% Cnts/s Atomic% ------O Ka 31.04 291.28 54.48 Na Ka 1.76 29.33 2.15 Al Ka 3.31 100.44 3.44 Si Ka 18.53 658.63 18.52 K Ka 2.07 62.56 1.48 Ca Ka 3.05 85.66 2.14 Cu Ka 40.24 191.78 17.78
180
WR2_1, 1.5 m above water surface, under surface of rock, split open by hammer and crushed in mortar and pistil
EDX Spectra: Ca, Mg, Na, Si, Al peaks
Albite crystal
181
WR4_1, 4.5 m below water surface, surface coating
EDX Spectra: Ca, Cu, Si, Al peaks
182
WR3_1, 1.5 m above water surface, brown surface coating, note Fe
Wide angle EDX Spectra: Ca, Na, Cu, Al, Si, K, Fe
Element Line Weight% Cnts/s Atomic% ------O Ka 35.58 213.55 54.39 Na Ka 0.59 5.22 0.63 Al Ka 10.51 162.75 9.53 Si Ka 27.03 433.56 23.54 K Ka 3.28 40.08 2.05 Ca Ka 1.05 11.89 0.64 Fe Ka 14.56 60.75 6.38 Cu Ka 7.39 14.00 2.84
183
WR5_2, 4.5 m below water surface, fresh interior rock
EDX Spectra: Ca, Na, Cu, Al, Si
184
WR5_3, 4.5 m below water surface, fresh interior rock, mineral crystal
185
WR6_1, 6 m below water surface, fresh interior rock, no image saved, 60x, 1 grain
Wide angle EDX spectra: Ca, Na, Al, Si
Element Line Weight% Cnts/s Atomic% ------O Ka 41.83 338.42 57.26 Na Ka 4.09 62.36 3.90 Al Ka 8.19 196.93 6.65 Si Ka 35.12 862.85 27.39 K Ka 1.78 30.47 0.99 Ca Ka 2.96 47.05 1.62 Fe Ka 2.57 14.94 1.01 Cu Ka 3.45 9.20 1.19
186
WR6_2, 6 m below water surface, fresh interior rock, close up spectra of platy material
EDX Spectra: Ca, Na, Al, Si
Albite grain
187
WR7_1, 6 m below water surface, green surface coating
EDX Spectra: Ca, Cu, Al, Si, K, Fe
Element Line Weight% Cnts/s Atomic% ------O Ka 27.96 195.26 48.15 Na Ka 2.12 20.98 2.54 Al Ka 7.45 130.52 7.60 Si Ka 23.15 448.93 22.70 K Ka 3.21 50.88 2.26 Ca Ka 3.39 49.82 2.33 Fe Ka 3.95 22.50 1.95 Cu Ka 28.78 72.04 12.48
188
WR7_2, 6 m below water surface, close up chrysocolla, dissolution features of chrysocolla
EDX Spectra: Ca, Cu, Al, Si
Element Line Weight% Cnts/s Atomic% ------O Ka 22.86 279.72 43.35 Na Ka 1.67 26.24 2.21 Al Ka 4.25 120.92 4.78 Si Ka 23.12 761.90 24.97 K Ka 1.77 49.12 1.37 Ca Ka 3.64 94.20 2.75 Fe Ka 2.71 27.99 1.47 Cu Ka 39.98 177.53 19.09
189
WR8_1, 8 m below water surface, green surface minerals
EDX Spectra: Ca, Cu, Al, Si
190
WR8_2, 8 m below water surface, close up chrysocolla surface, botryoidal habit, fibrous features
EDX Spectra: Ca, Cu, Fe, Al, Si
191
WR9_1, 8 m below water surface, fresh interior rock
Wide angle EDX spectra: Ca, Cu, Na, Fe, Al, Si
Element Line Weight% Cnts/s Atomic% ------O Ka 31.99 203.47 48.77 Na Ka 4.26 49.66 4.53 Al Ka 11.46 216.99 10.36 Si Ka 29.79 576.24 25.88 K Ka 2.75 40.24 1.72 Ca Ka 3.89 52.67 2.37 Fe Ka 5.43 27.38 2.37 Cu Ka 10.42 23.85 4.00
Plagioclase
192
WR9_2, 8 m below water surface, fresh interior rock, close up of mineral crystal
EDX spectra: Ca, Na, Cu, P, Fe
Apatite?
193
WR10_1, 12 m below water surface, surface of rock samples
Wide angle EDX spectra: Ca, Na, Fe, Al, Si
Element Line Weight% Cmpt CmptWT% Cnts/s Atomic% ------O 47.84 63.08 Na Ka 1.65 Na2O 2.22 26.57 1.51 Mg Ka 1.47 MgO 2.45 33.00 1.28 Al Ka 8.19 Al2O3 15.47 212.96 6.40 Si Ka 31.04 SiO2 66.40 824.98 23.31 K Ka 2.16 K2O 2.60 40.36 1.17 Ca Ka 2.45 CaO 3.43 42.29 1.29 Fe Ka 5.19 Fe2O3 7.42 32.26 1.96
No Cu
194
WR10_2, 12 m below water surface, looking for Cu
EDX spectra: Ca, Mg, Na, K, Fe, Al, Si
Element Line Weight% Cmpt CmptWT% Cnts/s Atomic% ------O 44.13 61.11 Na Ka 2.54 Na2O 3.42 48.87 2.45 Mg Ka 2.03 MgO 3.37 54.80 1.85 Al Ka 8.64 Al2O3 16.33 274.40 7.10 Si Ka 23.99 SiO2 51.32 792.88 18.93 K Ka 4.73 K2O 5.70 116.03 2.68 Ca Ka 2.27 CaO 3.18 51.01 1.26 Fe Ka 11.66 Fe2O3 16.67 94.44 4.62
No Cu
195
WR10_3, 12 m below water surface, close up of fuzzy, botryoidal area
EDX spectra: Ca, Na, Mg, K, Fe, Al, Si
No Cu No Cu on these rock surfaces, 12 m below water
196
WR11_1, 12 m below water surface, fresh interior rock
Wide angle EDX spectra: Ca, Na, Mg, K, Al, Si
Element Line Weight% Cmpt CmptWT% Cnts/s Atomic% ------O 47.90 61.93 Na Ka 3.54 Na2O 4.77 65.22 3.18 Mg Ka 1.83 MgO 3.04 44.89 1.56 Al Ka 9.35 Al2O3 17.66 262.61 7.17 Si Ka 30.91 SiO2 66.13 872.90 22.77 K Ka 3.38 K2O 4.07 66.80 1.79 Ca Ka 3.09 CaO 4.33 56.14 1.60
No Cu, Fe
197
WR12_1, 15 m below water surface, surface of rock
Wide angle EDX spectra: Ca, Na, Mg, K, Fe, Al, Si
Element Line Weight% Cmpt CmptWT% Cnts/s Atomic% ------O 44.35 59.88 Na Ka 4.38 Na2O 5.90 87.28 4.12 Mg Ka 3.58 MgO 5.93 95.76 3.18 Al Ka 9.98 Al2O3 18.86 305.94 7.99 Si Ka 23.38 SiO2 50.01 735.17 17.98 K Ka 4.91 K2O 5.92 115.09 2.71 Ca Ka 3.20 CaO 4.47 68.32 1.72 Fe Ka 6.23 Fe2O3 8.91 48.13 2.41
No Cu
198
WR12_2, 15 m below water surface, close up, looking for Cu
Close up EDX spectra: Ca, Na, K, Cu, Fe, Al, Si
Element Line Weight% Cmpt CmptWT% Cnts/s Atomic% ------O 44.84 59.94 Na Ka 4.97 Na2O 6.69 77.99 4.62 Mg Ka 1.49 MgO 2.46 30.80 1.31 Al Ka 9.89 Al2O3 18.69 239.12 7.84 Si Ka 25.94 SiO2 55.49 635.08 19.75 K Ka 7.46 K2O 8.99 132.02 4.08 Ca Ka 2.53 CaO 3.53 40.48 1.35 Fe Ka 2.90 Fe2O3 4.14 16.91 1.11
No Cu, very little Fe
199
WR13_1, 15 m below water surface, freshly fractured rock
Wide angle EDX spectra: Ca, K, Al, Si, Ti, Fe
Element Line Weight% Cmpt CmptWT% Cnts/s Atomic% ------O 45.18 60.89 Na Ka 3.92 Na2O 5.28 55.75 3.67 Mg Ka 1.62 MgO 2.69 31.18 1.44 Al Ka 9.21 Al2O3 17.40 206.99 7.36 Si Ka 25.65 SiO2 54.88 590.17 19.69 K Ka 5.40 K2O 6.51 90.28 2.98 Ca Ka 2.43 CaO 3.40 37.06 1.31 Ti Ka 1.81 TiO2 3.02 19.89 0.81 Fe Ka 4.77 Fe2O3 6.82 26.28 1.84
No Cu
200
WR13_2, 15 m below water surface, close up of grain
Close up angle EDX spectra: Ca, Na, Mg, Fe, Al, Si
Element Line Weight% Cmpt CmptWT% Cnts/s Atomic% ------O 42.32 61.01 Al Ka 12.13 Al2O3 22.92 256.53 10.37 Si Ka 17.56 SiO2 37.56 375.11 14.42 Ca Ka 16.31 CaO 22.82 246.42 9.38 Fe Ka 11.68 Fe2O3 16.69 61.98 4.82
No Cu
201
APPENDIX D. WATER-LEVEL DATA FOR THE YERINGTON PIT LAKE
Date Elevation (ft amsl) 12/14/1990 4106.2 4/1/1991 4109.0 9/22/1991 4119.7 12/30/1991 4121.6 2/25/1992 4123.3 6/17/1992 4125.4 8/19/1992 4126.0 11/3/1992 4126.3 2/25/1993 4128.6 6/23/1993 4130.4 9/29/1993 4130.6 12/28/1993 4131.8 3/21/1994 4135.0 6/22/1994 4136.8 9/27/1994 4137.4 12/21/1994 4139.7 3/23/1995 4140.9 6/28/1995 4143.6 9/27/1995 4144.3 12/27/1995 4145.1 3/28/1996 4145.7 6/21/1996 4147.4 12/31/1996 4150.4
January 1997 Flood 7/13/1998 4165.2 9/18/2000 4179.2 2/14/2001 4182.2 2/22/2001 4182.4 3/30/2001 4182.5 4/13/2001 4183.4 4/23/2001 4183.7 4/30/2001 4183.8 5/7/2001 4183.9 5/16/2001 4184.1 6/1/2001 4184.3 6/8/2001 4184.3 6/15/2001 4184.4 6/22/2001 4184.5 6/30/2001 4184.5 7/7/2001 4184.6 7/14/2001 4184.7 7/20/2001 4184.7
202
APPENDIX D. Water-Level Data for the Yerington Pit Lake (continued) Date Elevation (ft amsl) 7/28/2001 4184.8 8/4/2001 4184.8 8/11/2001 4184.9 8/16/2001 4184.9 8/25/2001 4185.0 8/30/2001 4185.0 9/8/2001 4185.1 9/16/2001 4185.1 9/25/2001 4185.2 10/4/2001 4185.3 10/14/2001 4185.4 10/25/2001 4185.6 11/4/2001 4185.7 11/16/2001 4185.9 12/1/2001 4186.2 12/7/2001 4186.3 12/30/2001 4186.8 1/19/2002 4187.3 2/4/2002 4187.6 2/25/2002 4188.0 3/20/2002 4188.4 4/3/2002 4188.7 4/20/2002 4189.0 5/4/2002 4189.2 5/18/2002 4189.5 6/2/2002 4189.6
203
APPENDIX E. SATURATION INDICES FOR YERINGTON PIT LAKE.
Sample Depth Date Calcite Adularia Al(OH)3(a) Albite Anorthite Azurite Chlorite14A CO2(g) CuMetal Cuprite Dolomite Fe(OH)3(a) Goethite Gypsum
Pit Lake UN 11/21/1991 0.02 -3.10 -3.99 -2.69 -6.25 -2.85 -5.77 -2.67 -4.57 -1.77 -0.40 -0.50 5.40 -1.20 Pit Lake UN 7/10/1992 1.10 -1.54 -4.93 -2.74 -6.17 -2.65 1.70 -3.59 -5.77 -2.29 1.64 -0.54 5.36 -1.10 Pit Lake UN 6/8/1993 0.73 -1.34 -4.57 -2.62 -6.18 -1.83 -0.89 -3.19 -5.04 -1.55 0.99 -0.47 5.42 -1.15 Pit Lake 100 m 4/29/1994 0.92 -1.51 -4.74 -2.68 -6.20 -1.78 0.51 -3.34 -5.26 -1.66 1.39 -0.49 5.40 -1.16 Pit Lake 0 m 4/7/1995 0.77 -1.45 -4.57 -2.65 -6.21 -1.99 -0.78 -3.11 -5.15 -1.76 1.13 -0.47 5.42 -1.20 Pit Lake 50 m 4/7/1995 0.52 -1.42 -4.30 -2.63 -6.18 -1.37 -2.96 -2.84 -4.58 -1.18 0.61 -0.46 5.43 -1.18 Pit Lake 100 m 4/7/1995 0.38 -1.44 -4.16 -2.64 -6.19 -1.00 -4.10 -2.70 -4.27 -0.83 0.32 -0.47 5.43 -1.17 Pit Lake 0 m 8/4/1995 1.01 -1.35 -4.85 -2.56 -6.16 -3.59 1.58 -3.45 -6.04 -2.96 1.61 -0.52 5.37 -1.21 Pit Lake 50 m 8/4/1995 0.51 -1.39 -4.30 -2.61 -6.17 -1.17 -2.99 -2.84 -4.51 -1.04 0.58 -0.46 5.43 -1.19 Pit Lake 100 m 8/4/1995 0.49 -1.40 -4.26 -2.62 -6.16 -1.17 -3.29 -2.80 -4.46 -1.02 0.53 -0.46 5.43 -1.16 Pit Lake 0 m 10/17/1995 0.70 -1.28 -4.54 -2.45 -6.09 -4.95 -0.70 -3.13 -6.06 -3.65 1.02 -0.47 5.43 -1.19 Pit Lake 50 m 10/17/1995 0.46 -1.38 -4.22 -2.57 -6.13 -1.14 -3.42 -2.77 -4.39 -0.95 0.48 -0.46 5.43 -1.15 Pit Lake 100 m 10/17/1995 0.52 -1.39 -4.27 -2.57 -6.12 -1.14 -3.03 -2.81 -4.46 -1.00 0.60 -0.46 5.43 -1.14 Pit Lake 0 m 2/5/1996 0.64 -1.34 -4.44 -2.49 -6.11 -2.37 -1.59 -3.00 -5.08 -1.90 0.87 -0.46 5.43 -1.17 Pit Lake 50 m 2/5/1996 0.30 -1.39 -4.07 -2.56 -6.12 -1.19 -4.60 -2.62 -4.21 -0.88 0.18 -0.48 5.41 -1.14 Pit Lake 100 m 2/5/1996 0.35 -1.38 -4.11 -2.53 -6.10 -1.28 -4.27 -2.66 -4.29 -0.98 0.27 -0.47 5.42 -1.14 Pit Lake 0 m 4/16/1998 0.83 -1.31 -4.67 -2.49 -6.13 -3.24 0.18 -3.23 -5.70 -2.65 1.27 -0.48 5.41 -1.22 Pit Lake 30 m 4/16/1998 0.64 -1.31 -4.50 -2.47 -6.13 -2.86 -1.26 -3.06 -5.33 -2.27 0.89 -0.46 5.43 -1.22 Pit Lake 100 m 4/16/1998 0.60 -1.30 -4.45 -2.47 -6.12 -2.73 -1.66 -3.00 -5.22 -2.16 0.81 -0.46 5.43 -1.21 Pit Lake 0 m 9/15/1998 0.82 -1.18 -4.70 -2.35 -6.11 -1.84 0.60 -3.27 -5.27 -1.73 1.31 -0.49 5.40 -1.26 Pit Lake 30 m 9/15/1998 0.63 -1.26 -4.45 -2.43 -6.10 -2.37 -1.51 -3.00 -5.10 -1.92 0.87 -0.46 5.43 -1.21 Pit Lake 100 m 9/15/1998 0.60 -1.26 -4.42 -2.43 -6.10 -2.83 -1.80 -2.97 -5.22 -2.21 0.80 -0.46 5.43 -1.20 Pit Lake 0 m 8/22/2000 0.82 -1.10 -4.65 -2.27 -6.01 -2.72 0.38 -3.22 -5.50 -2.28 1.27 -0.70 5.19 -1.23 Pit Lake 20 m 8/22/2000 0.73 -1.19 -4.57 -2.36 -6.05 -4.36 -0.44 -3.13 -5.92 -3.32 1.09 -0.57 5.33 -1.21 Pit Lake 100 m 8/22/2000 0.58 -1.19 -4.38 -2.36 -6.03 -3.22 -1.93 -2.93 -5.30 -2.44 0.77 -0.55 5.34 -1.18 Pit Lake 0 m 8/30/2001 0.74 -1.15 -4.60 -2.34 -6.07 -5.54 -0.12 -3.17 -6.37 -4.13 1.13 -0.47 5.42 -1.25 Pit Lake 20 m 8/30/2001 0.72 -1.27 -4.56 -2.43 -6.10 -4.07 -0.59 -3.13 -5.83 -3.13 1.06 -0.47 5.42 -1.23 Pit Lake 100 m 9/13/2001 0.51 -1.21 -4.36 -2.40 -6.08 -3.29 -0.73 -2.92 -5.29 -2.46 0.94 -0.46 5.43 -1.21
204
APPENDIX E. Saturation indices for Yerington pit lake (continued).
Sample Depth Date Calcite Adularia Al(OH)3(a) Albite Anorthite Azurite Chlorite14A CO2(g) CuMetal Cuprite Dolomite Fe(OH)3(a) Goethite Gypsum Well 49 2/27/1978 0.13 -1.67 -3.73 -2.42 -6.19 -5.47 -8.66 -2.09 -5.29 -3.74 -0.40 0.54 6.43 -1.11 Well 41 2/27/1978 0.35 -1.93 -4.21 -2.76 -6.37 -4.41 -5.08 -2.65 -5.56 -3.29 0.18 0.92 6.81 -1.87 SE Spring 2/5/1996 0.44 -1.85 -4.58 -3.30 -6.73 -4.48 -2.00 -3.16 -6.00 -3.41 0.57 -0.47 5.42 -2.26 SE Spring 8/23/2000 0.34 -1.75 -4.55 -3.22 -6.72 -5.38 -2.33 -3.13 -6.26 -3.99 0.40 -1.16 4.73 -2.51 Walker River 8/23/2000 0.19 -2.35 -4.49 -3.89 -7.14 -8.34 -3.46 -3.14 -7.15 -5.86 0.11 -1.16 4.73 -2.67 NW Spring 10/17/1995 1.06 -1.12 -4.79 -1.97 -5.96 -9.60 1.46 -3.26 -8.03 -7.08 1.73 -1.20 4.69 -1.21 NW Spring 8/23/2000 1.10 -0.97 -4.74 -1.83 -5.89 -9.32 1.09 -3.09 -7.95 -7.02 1.82 -0.59 5.30 -1.26 Well 2 1/9/2001 0.02 -0.90 -4.00 -2.30 -6.04 -6.81 -5.60 -2.52 -6.05 -4.67 -0.33 -0.88 5.01 -2.12 Well 4 1/18/2009 0.12 -1.25 -4.09 -2.58 -6.26 -7.59 -5.28 -2.56 -6.46 -5.31 -0.12 -0.87 5.03 -2.22 UN = Unknown
205
APPENDIX E. Saturation indices for Yerington pit lake (continued). Montmorillonite Montmorillonite Montmorillonite Sample Depth Date Hematite Kmica Magnesite Magnetite Malachite Quartz SiO (a) Tenorite Aberdeen BelleFourche Ca 2 Pit Lake UN 11/21/1991 12.80 -0.09 -1.01 10.80 -1.05 1.52 2.16 -2.00 0.68 -0.59 -0.33 Pit Lake UN 7/10/1992 12.72 -0.40 -0.04 9.74 -0.61 1.38 1.44 -3.94 0.65 -0.62 0.35 Pit Lake UN 6/8/1993 12.85 0.52 -0.32 10.30 -0.20 1.62 1.85 -3.17 0.67 -0.60 0.36 Pit Lake 100 m 4/29/1994 12.81 0.01 -0.11 10.06 -0.12 1.51 1.67 -3.53 0.66 -0.61 0.48 Pit Lake 0 m 4/7/1995 12.85 0.41 -0.22 10.30 -0.33 1.63 1.86 -3.15 0.68 -0.59 0.25 Pit Lake 50 m 4/7/1995 12.88 0.98 -0.49 10.61 -0.01 1.73 2.11 -2.58 0.68 -0.59 0.28 Pit Lake 100 m 4/7/1995 12.86 1.24 -0.64 10.72 0.19 1.75 2.20 -2.32 0.68 -0.59 0.31 Pit Lake 0 m 8/4/1995 12.76 -0.07 0.02 9.86 -1.29 1.62 1.72 -3.64 0.70 -0.57 -0.06 Pit Lake 50 m 8/4/1995 12.88 1.01 -0.51 10.61 0.12 1.76 2.14 -2.56 0.69 -0.58 0.35 Pit Lake 100 m 8/4/1995 12.87 1.07 -0.54 10.64 0.11 1.76 2.16 -2.49 0.69 -0.58 0.32 Pit Lake 0 m 10/17/1995 12.86 0.64 -0.27 10.34 -2.30 1.86 2.11 -2.89 0.73 -0.54 -0.72 Pit Lake 50 m 10/17/1995 12.87 1.17 -0.56 10.68 0.12 1.81 2.23 -2.38 0.69 -0.57 0.31 Pit Lake 100 m 10/17/1995 12.88 1.06 -0.51 10.63 0.13 1.81 2.20 -2.47 0.70 -0.57 0.34 Pit Lake 0 m 2/5/1996 12.87 0.78 -0.35 10.46 -0.62 1.84 2.14 -2.74 0.72 -0.55 0.06 Pit Lake 50 m 2/5/1996 12.83 1.46 -0.71 10.77 0.04 1.87 2.37 -2.06 0.70 -0.57 0.19 Pit Lake 100 m 2/5/1996 12.85 1.39 -0.66 10.75 -0.02 1.88 2.36 -2.12 0.71 -0.56 0.19 Pit Lake 0 m 4/16/1998 12.83 0.33 -0.14 10.15 -1.13 1.78 1.98 -3.18 0.73 -0.54 -0.08 Pit Lake 30 m 4/16/1998 12.87 0.69 -0.33 10.39 -0.93 1.86 2.15 -2.80 0.73 -0.53 -0.07 Pit Lake 100 m 4/16/1998 12.87 0.80 -0.37 10.45 -0.86 1.88 2.19 -2.69 0.74 -0.53 -0.06 Pit Lake 0 m 9/15/1998 12.82 0.40 -0.10 10.10 -0.18 1.90 2.07 -3.14 0.76 -0.51 0.41 Pit Lake 30 m 9/15/1998 12.87 0.84 -0.34 10.45 -0.62 1.90 2.21 -2.69 0.74 -0.53 0.06 Pit Lake 100 m 9/15/1998 12.87 0.89 -0.38 10.48 -0.94 1.91 2.23 -2.63 0.73 -0.53 -0.12 Pit Lake 0 m 8/22/2000 12.39 0.58 -0.13 9.51 -0.78 1.96 2.17 -2.94 0.78 -0.49 0.08 Pit Lake 20 m 8/22/2000 12.66 0.67 -0.22 10.01 -1.90 1.94 2.19 -2.84 0.76 -0.51 -0.52 Pit Lake 100 m 8/22/2000 12.68 1.05 -0.39 10.24 -1.21 1.99 2.34 -2.46 0.76 -0.51 -0.28 Pit Lake 0 m 8/30/2001 12.84 0.63 -0.19 10.25 -2.68 1.98 2.21 -2.89 0.77 -0.50 -0.89 Pit Lake 20 m 8/30/2001 12.85 0.59 -0.24 10.30 -1.71 1.88 2.13 -2.90 0.74 -0.53 -0.43 Pit Lake 100 m 9/13/2001 12.88 1.06 -0.15 10.55 -1.27 2.13 2.43 -2.45 0.75 -0.51 -0.31
206
APPENDIX E. Saturation indices for Yerington pit lake (continued). Montmorillonite Montmorillonite Montmorillonite Sample Depth Date Hematite Kmica Magnesite Magnetite Malachite Quartz SiO (a) Tenorite Aberdeen BelleFourche Ca 2 Well 49 2/27/1978 14.87 1.86 -1.11 14.17 -2.99 2.10 2.73 -1.46 0.68 -0.59 -1.59 Well 41 2/27/1978 15.63 0.65 -0.76 14.82 -2.10 1.98 2.35 -2.46 0.67 -0.59 -0.86 SE Spring 2/5/1996 12.85 -0.02 -0.45 10.27 -1.98 1.06 1.33 -3.63 0.57 -0.70 -0.54 SE Spring 8/23/2000 11.46 0.15 -0.52 8.21 -2.59 0.96 1.32 -3.44 0.60 -0.67 -0.86 Walker River 8/23/2000 11.47 -0.35 -0.65 8.28 -4.56 0.22 0.60 -4.03 0.41 -0.85 -1.85 NW Spring 10/17/1995 11.39 0.30 0.09 7.88 -5.36 1.85 2.04 -3.12 0.81 -0.46 -2.19 NW Spring 8/23/2000 12.61 0.55 0.14 9.77 -5.23 2.24 2.41 -2.86 0.85 -0.42 -2.21 Well 2 1/9/2001 12.03 2.08 -0.93 9.61 -3.74 2.27 2.89 -1.36 0.87 -0.40 -1.75 Well 4 1/18/2009 12.06 1.56 -0.82 9.57 -4.25 1.88 2.46 -1.88 0.78 -0.49 -1.98 UN = Unknown