A Geochemical Modeling Study of the Effects of Urea-degrading

Bacteria on Groundwater Contaminated with Acid Mine Drainage

Tracy L. Fleury

A thesis submitted in conformity with the requirements for the degree of Master of Science Graduate Department of Geology

University of Toronto

@ Copyright by Tracy L. Fleury 1999

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A Geochemical Modeling Study of the Effects of Urea-degrading Bacteria on

Groundwater Contaminated with Acid Mine Drainage

Tracy L. Fleury, Degree of Master of Science, 1999, Craduate Department of Geology,

University of Toronto

Abstract

Aqueous geochemical computer modeling was camed out for an aquifer contaminated

with acid mine drainage at the South Bay mine site to test the feasibility of a proposed

bioremediation treatment plan. The treatment consists of adding urea to the aquifer to

stimulate growth of indigenous urea-degrading bacteria that produce carbonate and

ammonium. These products have the potential for neutralizing the groundwater and

precipitating iron oxides, which may remove heavy metals from solution through sorption

processes.

With aqueous geochemical data for piezometen situated along a hydraulic gradient

from the iailings area to the aquifer's discharge zone, a model of the aquifer's

geochemistry was constmcted with the program MXNEQL'. Once the model was

calibrated to ensure that calculated pH values matched measured field pH values,

titrations with the urea-degradation products were undertaken to simulate introduction of

the urea-degradation products into the groundwater over time. The arnount of carbonate

and ammonium required to raise the pH in the piezometers to 8.0-8.1 ranged from

3.5. 10-5 M - 0.24 4 and 7.0*10-' M - 0.48 M, respectively. As is evident with observed

pH increases and oxide supersaturation as a result of the titnitions, the metabolic products

of microbial urea degradation have been proven to have considerable potential for

treating contaminated groundwater at South Bay.

Acknowledgements

Special thanks to Dr. Grant Fems and the memben of my supervisory comrnittee,

Dn. Jan Gents and Grant Henderson. Grant Fems' guidance was greatly appreciated, as

was his belief in my ability to get the job done. Also appreciated were the data

contributions fiom Margarete Kalin of Boojum Research Limited which made this study

possible. The staff at Boojum was overwhelmingly helpful and generous with their time

and patience.

Recognition is due to Andrew Wolf, who helped me to iron out some of the initial

problems encountered with the modeling program while Prof. Fems was away on

sabbatical. Without Andrew's assistance I'm sure that 1 would have endured a very

fnistrating and discouraging initial few months. 1 also extend my thanks to Dr. Leslie

Warren for helping me to fit in at U of T and for always k i n g available to answer my

questions.

Finally, I would like to thank my fiancé Greg and my farnily for their unwavering love

and support. Without Greg's encouragement I would never have found the courage to

move to Toronto to obtain this degree.

T.L.F.

iii

Table of Contents

Abstract

Acknowledgements

Table of Contents

List of Tables

List of Figures

List of Appendices

1. Introduction

1.1 South Bay Geology and Site Description

1.2 Bioremediation Option for AMD Groundwater Contamination at South Bay

1.3 Study Objectives

2. Mine Drainage 2.1 Sources of Acid Mine Drainage 2.2 Groundwater Contaminated with AMD

3. In Situ Bioremediation 3.1 In Situ Bioremediation of Meta1 Contamination

3.1.1 Biosorption 3.1.2 Biomineralization 3.1.3 Microbial Metal Transformations 3.1.4 REDOX Processes 3.1.5 Metabolic Products W c h Influence pH and Eh

4. Cornputer Modeling 4.1 Modeling of Aqueous Chemistry

4.1.1 Modeling with MINEQL+ 4.1.2 Applying MINEQL+ to AMD Contaminated Groundwater

5. Methodology 5.1 Field Hydrology and Geochemical Data 5.2 Speciation of REDOX Sensitive Components 5.3 Proton Balance Caiculations 5.4 Mode1 Calibration: Matching of field pH with calculated pH 5.5 Urea Metabolite Titrations 5.6 Mud Lake Oxidation Simulations

. . I l

... i11

iv

vi

vii

ix

6. Results

6.1 REDOX Speciation 6.2 Proton Balance 6.3 Mode1 Calibrations 6.4 Titrations with Urea Hydrolysis Products - Ammonium and Carbonate 6.5 Groundwater Oxidation Simulation 6.6 Fate of Zn and Cu fiom the South Bay Zn-Cu Mine

7. Discussion

Appendix A: Field Piezometer Map Appendix B: Titration Results - tables of computed pH and log

saturation index (SI) values Appendix C: Titration Results - graphs of amounts of urea equivalents

added to each well vs. pH and log SI values vs. pH Appendix D: Original Aqueous Geochemistry Data

References

List of Tables

Table 1 :

Table 2:

Table 3:

Tabie 4:

Table 5:

Table 6:

Table 7:

Table 8:

Table 9:

Table 10:

Chernical Complexes in the ~e) ' - H20 System

Organized in Tableaux Format

Kalin Canyon Aquifer Computed Iron Speciation

Kalin Canyon Aquifer Computed Copper Speciation

Aqueous C hemistry Data including Computed

Concentrations of REDOX Active Species

Proton Balance Calculations

Mode1 Calibration Adjustmenis - Changes Made to

H' or DIC Concentrations to Ensure Matching of Field

And Calculated pH Values

Groundwater Oxidation Simulation Results - pe

Titrations and Resultant Changes in pH

Post-Oxidation Titration Results - Total Carbonate and

Ammonium Added to Restore Neutral pH Values

Percent of Zinc Species Before and AAer Titrations

Percent of Copper Species Before and Afler Titrations

Figure 1 :

Figure 2:

Figure 3:

Figure 4:

Figure 5:

Figure 6:

Figure 7:

Figure 8:

Figure 9:

Figure 10:

Figure 1 1 :

Figure 12:

Figure 1 3 :

Figure 14:

Figure 15:

Figure 16:

Figure 17:

Figure 18:

List of Figures

Title

South Bay Mine Site: Located in Northwestem Ontario,

Canada

South Bay Mine Site: detail survey

Kalin Canyon Aquifer Computed lron Speciation

Kalin Canyon Aquifer Computed Copper Speciation

Mode1 Calibration Results - Measured Field pH vs. Calculated

PH

Well M28 - pH vs. Concentration of Urea Equivalents Added

Well M83a -pH vs. Concentration of Urea Equivalents Added

Well M83b - pH vs. Concentration of Urea Equivalents Added

Well M28 - Log SI of Iron Oxides

Well M28 - Log SI of Carbonates

Well M28 - Log SI of lron Sulfides

Well M83a- Log SI of Iron Oxides

Well M83a - Log SI of Carbonates

Well M83a - Log SI of Iron Sulfides

Well M83b - Log SI of Iron Oxides

Well M83b - Log SI of Iron Sulfides

lron Species vs. Distance Along Hydrauiic Gradient Between

Tailings Area and Mud Lake Before Oxidation Simulation

Urea Equivalents Added After Oxidation to Xnc~ase pH to

8.0-8.1 vs. Distance Along Hydraulic Gradient Between Tailings

Area and Mud Lake

vii

Figure 19: Total Urea Equivalents Added Before and After Oxidation to

Increase pH to 8.0-8.1 vs. Distance along Hydraulic Gradient

Between Tailings Area and Mud Lake 47

Figure 20: Adsorption of CU?+ and ~ n " on Hydrous Femc Oxide as a

Function o f pH 50

List of Appendices

Title

Appendix A. Piezometer Location Map 66

Appendix B. Titration Results - tables of computed pH and log

saturation index (SI) values 68

Appendix C. Titration Results - graphs of amounts of urea equivalents

Added to each well vs. pH and log SI values vs. pH 94

Appendix D. Original Aqueous Geochernistry Data 125

1. Introduction

Environmental impacts from abandoned and decommissioned mine sites are serious

concems (Drabkowski, 1993). Acid mine drainage (AMD) is a cornrnon problem of the

mining industry and one that can continue to p lage downstrearn surface and

groundwaters long after mining operations cease. Wind and water erosion work away at

waste dumps, iailings piles and leach heaps that pollute nearby waters with contaminated

sediments, toxic residue and heavy metals, such as iron, copper, zinc and cadmium, al1 of

which tend to become soluble under the acidic conditions (Herbert, 1996; Drabkowski,

1993). These metals and sulfate find their source in the oxidation of metal sulfide

minerals, like pyrite, pyrrhotite, chalcopyrite and sphalerite, that can comprise a

significant portion of the wastes lefi behind at abandoned and decommissioned mine sites

(Alpers and Nordstrom, 1990).

Most Canadian mines contain sulfide minerals either in the ore or in the surrounding

waste rock (Filion and Ferguson, 1990). These minerals are relatively stable in their

natural environment, but through mining activities and their resultant exposure to air and

water, they are subject to oxidation. This produces acid and releases heavy metals into

solution (Drabkowski, 1993; Filion and Ferguson, 1990; Kelley and Touvinen, 1988).

Because of the remote location of many abandoned mine sites, pollution problems

caused by runoff may not be noticed until they are discovered in the watershed and

aquifers kilometers downstream. Left untreated, acid mine waters c m damage plants,

fish and wildlife, as well as adversely effecting human health, through contamination of

water resources (Ledin and Pedersen, 1996; Herbert, 1 994; Drabkowski, 1993). These

pollution problems may persist for centuries @rabkowski, 1993; Kalin et ai., 1989).

Canadian acid-producing waste sites currently total in area over 15,000 hectares. In

Ontario alone there are over 2000 decommissioned or abandoned mines, many of them

containing reactive sulfide minerals (Walter et al., 1994). In the case of abandoned

mines, it is often diflïcult to identify a responsible operator, so the responsibility of

remediation falls on the govenunent. Typically, remediation efforts are extremely

expensive and time consuming. This bas led to an urgent need for inexpensive and tirne-

effective clean -up techniques, opening the door for the possible use of bactena as

remediating agents (Shevah and Waldrnan, 1995; Drabkowski, 1993).

2.2 South Bay Geolom and Site Description

The focus of this thesis is the South Bay mine site approximately 400-km northwest of

Thunder Bay, Ontario on the eastem shore of Confederation Lake, West of Lost Bay in

the Red Lake District (Figure 1).

Figure 1. South Bay Mine Site: Located in Northwestern Ontario, Canada.

(Source: Boojurn Research)

The 75 ha mine site, including a town site and mill, has 760,000 metric tonnes of

tailings from a zinc-copper concentrator, which was operated by BP-Selco from 1971 to

198 1. The tailings basin covers an area of 20 ha and contains tailings with 4 1.1 % pyrite,

4.1 % pynhotite, 0.63% sphalerite and 0.14% chalcopyrite (Kalin, et al., 1990). The test

site consists of a series of twenty-five piesorneters, which span the distance between the

tailhgs area and Mud Lake to the north (Figure 2 and Appendix A).

Figure 2. South Bay Mine Site, detail suwey. Northwestern Ontario, Canada

(Source: Boojum Researc h)

SOUTH BAY MINE SITE w-

MUD iAKE RUNOFF WEST

The bedrock below the test site is in the shape of a buried nonh-south trending valley,

called Kalin Canyon (Figure 2), with steep sides and a flat bottom. The valley follows

the western side of Mud Lake and runs under the gravel pit, which lies between Mud

Lake and the tailings basin. Here the buried valley splits into two amis with one .

underlying the West side of the tailings basin, where it follows to the south and exits the

tailings basin at its south-west corner and continues, apparently, towards Confederation

Lake (Boojum Tech. Ltd., 1996). The buried valley represents the main pathway of the

contaminated groundwater from the tailings basin to Mud Lake. The extent of the valley

beneath Mud Lake and southwest of the tailings basin has not yet been defined (Boojum

Tech. Ltd., 1996).

The basic stratigraphy of the area shows that the coarsest sand and gravel deposits

underlie the gravel pit with the sediment becoming finer and more interbedded with silts

to the south. Southwest of the tailings basin clay lenses are cornmon within the sand.

(Boojum Tech. Ltd., 1996).

1.2 BioremediPlion Option for AMD Groctndwater Contamination ut South Bay

An in situ biological treatrnent to improve the negative impacts of groundwater

contaminated by AMD has been proposed for the South Bay mine site. This proposal

consists of using bacteria that degrade urea and produce ammonium and carbonate ions,

to increase the groundwater pH by virtue of the following reactions (Khakural and Alva,

1 995; Tisdale et al., 1985):

Urea-degradation is accomplished with the enzyme urease, which hydrolyzes urea, an

organic amide compound that is ofien incorporated into agicultural fertilizen (Leszko,

M. et al., 1997; Tisdale et al., 1985).

Bacteria that degrade urea are naturally present in the aquifer as show fiom

microbiological culturing of groundwater from South Bay (Fems, 1999). Thus, the

object of the plan is to introduce urea into the aquifer to increase microbial activity. If

the increased activity of the indigenous bacteria is insuficient to bring about an increase

in pH, a second approach would be implemented. This second approach consists of

inoculating the aquifer with more urea-degrading bactena and additional nutrients.

The rate of production of ammonium and carbonate ions fiom the hydrolysis of urea

by bactena has been deterrnined in a variety of situations. In sanirated soi1 the expected

rate of urea hydrolysis may range from 8 ppm/hr - 12 ppmhr (5.7* 1 o4 M/hr - 8.6. 104

M/hr) with the addition of 400 ppm urea-N (2 .P 10'~ M). These rates begin to level off

after approximately 24 hours with half of the urea k ing hydrolyzed between days 2 and

3, and almost al1 of it k ing hydrolyzed by &y 20 (Hongprayoon, et al., 1991; Delaune

and Patrick, 1970). The result of urea hydrolysis in soi1 closest to the urea is a rise in pH

in excess of 8 or 9 (Tisdale, et al.. 1985).

1.3 Study Objectives

The object of this study is to use the geochemical modeling program MINEQL* to test

the feasibility of the proposed bioremediation treatment plan. This will be done by

calculating mineral speciation, mineral saturation states and the extent of pH changes

resulting from the additions of ammonium and carbonate produced through the microbial

degradation of urea. Also of interest to this project is the question of what may happen to

newly neutralized groundwater once it emerges into Mud Lake and undergoes complete

oxidation upon equilibration with atmospheric oxygen. This question will be addressed

through the oxidation simulation portion of the study along with any questions

conceming the potential need for additional treatment of this water should oxidation

result in acidification of the groundwater.

With the information from this geochemical modeling study, such as expected

changes to pH and minera1 log SI values, insight will be obtained as to whether the

bioremediation plan for the South Bay site has the potential to successfully reduce AMD

contamination of the groundwater.

2. Mine Drainage

2.2 Sources of Acid Mine Drainage

Sulfide mineral oxidation cm occur either directly or indirectly. Direct oxidation is

achieved through reaction with oxygen in air and water. Indirect oxidation results fiom

the reduction of ferric iron. Using pyrite as an example, since it is the main sulfide

minerai involved in acid-generation, the reactions leading to AMD are (Evangelou and

Zhang, 1995):

FeS2 + '/?O? + H?O 3 ~ e ' + + 2 ~ 0 ~ ~ - + 2 ~ + (3

~ e ~ ' + 5 / 2 ~ z ~ + '140~ 3 Fe(OH)3 + 2H+ (4)

Fe" + '1~0~ + H+ a Fe3' + '1211z0 ( 5 )

FeS2 + 14~e" + 8H20 15~e'+ + 2 ~ 0 ~ ~ - + 16H+ (6)

with reactions 3 to 5 representing direct oxidation and reaction 6 representing indirect

oxidation.

As the reactions proceed, temperature increases because the oxidation of pyrite is an

exothermic reaction (DuM, 1997; Rosenblum and Spira, 1995; Kelly and Touvinen,

1988), and this leads to an increase in reaction rates (Clark and Noms, 1996; Chapelle,

1993). Within a pH range of 2 to 4, the problem is compounded with the activity of

bacteria, principally Thiobacillus ferrooxidans, an acidophilic chernolithotrophic

microorganism that is "ubiquitous" in environments containing pyrite. Presence of the

bacteria can catalyze the reactions and increase their rates fkom 10 to 100 times faster

than they were onginally (Evangelou and Zhang, 1995; Okereke and Stevens, 199 1).

With rainfall and snowmelt added to the situation, the result is a flow of acid water away

fiom the waste site and into streams, lakes and groundwater systems (Herbert, 1994;

Filion and Ferguson, 1990).

There are a number of factors that can infiuence these oxidative reactions. Some of

them include pH, alkalinity, pyrite surface area and abundance, and temperature

(Bomissel-Gissinger, et al., 1998; Evangelou and Zhang, 1995; Hutchison and Ellison

eds., 1992). As the pH rises there is a reduction in the rates of reaction as the activity of

the acidophilic T. ferrooxidam decreases (Evangelou and Zhang, 1 995; Hutchison and

Ellison eds., 1992).

Changes in pH affect microbial populations by influencing the availability of electron

donon such as ferrous iron, the oxidation of which is extremely sensitive to pH (King,

1998; Kelley and Touvinen, 1988). At neutml to alkaline pH values, the abiotic rate of

ferrous iron oxidation increases rapidly while biotic iron oxidation decreases so as to be

almost non-existent (Evangelou and Zhang, 1995).

Temperature, another important geochemical factor, serves to influence the reaction

rates in that, as a general rule, every 10 OC increase results in a doubling of the reaction

rates. This stems from the relationship between bactenal activity and the free-energy of

reaction, which is itself related to temperature (Hutchison and Ellison eds., 1992). The

optimum temperature for ferrous iron oxidation by T.firrooxidam is 30-45 O C , while

above these temperatures groups of themophilic chernolithotrophic bacteria contribute to

the oxidation of the tailings. Sulfide mineral-oxidizing, acidophilic bacteria have an

optimum temperature of about 50 OC (Clark and Noms, 1996; Comell and Schwertmann,

1996; Ledin and Pedersen, 1996).

The above factors may play a role in regulating pyrite oxidation rates, but their

influences stop at actually controlling wbether or not acid generation will occur. The total

surface area of reactive pyrite available for oxidation is directly proportional to the

arnount of acid generated, and this direct dependency can be used to predict the

magnitude of acid production (Evangelou and Zhang, 1995).

Besides the above conditions, reactions with carbonates, hydroxides, aluminosilicates

and other minerals that may be present, can greatly effect the acid-generating potential of

a site (Sherlock, et al., 1995). In the initial stages of acid generation, dissolution of

carbonates is favored with calcite being the first to be consurned (Sherlock, et of., 1995;

Herbert, 1994; Morin and Cherry, 1988). Carbonates have the ability to consume acid

and buffer the pH at near neutral values by virtue of the following reactions (Drever,

1997; Ritchie, 1994; Hutchison and Ellison eds., 1992):

2H' + CaC03 * ca2+ + COz + H20 (for calcite) (7)

2 ~ + + [CaMg(C03)z] = ~ a " + M ~ ~ + + CO2 + H20 (for dolomite) (8)

The combined reaction of pyrite with calcite is:

4FeSz + 8CaC03 + 1 5 0 2 +6H20 * 4Fe(OH)3tsi + 8 ~ 0 4 ~ - + 8ca2+ + 8C02 (9)

During the dissolution of the carbonates, metal hydroxides precipitate as cements or

grain coatings, as in equation 9 (Herbert, 1994; Ritchie, 1994; Hutchison and Ellison eds.,

1992). As acid generation continues following the depletion of the carbonate minerals,

the pH declines abruptly until dissolution of the next pH buffers in the series, the

hydroxides (Herbert, 1994). Acid consurnption by the hydroxides leads to a buffenng of

pH between the values of 3 and 4 (Herbert, 1994).

Under very low pH conditions, after al1 carbonates and simple hydroxides are

depleted, the dissolution of aluminosilicates, a late stage weathering process, becomes an

important acid neutralizing mechanism (Stumm and Morgan, 1996; Sherlock, et al.,

1995; Blowes and Ptacek, 1994). The ability to neutralize results fiom the tendency of

the aluminosilicates to degrade when in contact with acid. This leads to the consumption

of protons through the formation of clay minerais, which in mm, are capable of removing

protons through ion exchange reactions (More1 and Hering, 1993).

Other acid-consuming processes include the removal of sulfate as gypsum and the

removal of sulfate and ferrous iron through the formation of jarosites (Lin and Qvarfort,

1 996; Herbert, 1994).

2.2 Groundwater Contaminated with AlMD

The nature and magnitude of the effects of acid mine drainage on groundwater are

currently not well understood (Herbert, 1994) but when groundwater is contaminated, the

problems that arise becorne apparent. In addition to use by municipalities and industry,

groundwater works to transport solutes fiom one surface site to another. Even in times of

drought, contaminated groundwater c m continue to donate acid, sulfate and heavy metals

to surface waters (Herbert, 1994; Hennigar and Gibb, 1987). This makes understanding

the transport of solutes in groundwater of the utmost importance. The remainder of this

section is concemed with descnbing the main transport processes acting on contaminant

solutes in groundwater systems.

There are a number of processes fùnctioning within the underground water system,

that influence or dictate the rnovement of contaminants as solutes in groundwater. The

physical transport of solutes occurs under the influence of three basic processes.

Advection is the process by which moving groundwater carries with it dissolved solutes

(Batu, 1996; L m and Mackay, 1995; Fetter, 1994), and difision is the process by

which solutes move from areas of high concentration to areas of low concentration

orever, 1997; Batu, 1996; LUM and Mackay). Lastly, dispersion is the process by

which the solute mixes with others in solution to become dilute. In modeling the physical

transport of solutes, diffusion and dispersion effects are often indistinguishable fiom one

another. In these situations they are often considered a single process (Fetter, 1994).

Besides physical processes, there are a number of chemical interactions, which effect

the transport of solutes. These processes can cause enhancement or retardation of solute

movement through complexation or through modification of the solute's properties. Of

these chemical processes, adsorption is the most important in that it can set the stage for

other chemical processes to occur. Adsorption reactions occur at the solid-water

interface where solutes accumulate at an interface through a variety of chemical

interactions (Honeyrnan and Santschi, 1988). These reactions are critical for processes

such as precipitation, dissolution, and ion exchange, as discussed belûw (Honeyman and

Santschi, 1988).

Adsorption of a metal by surface Ligands is stmngly pH dependent. Complex

formation is cornpetitive, with metal cations competing with protons for the amphotenc

surface sites of solids. At low pH, when proton concentrations are high, metal adsorption

is minimal. Conversely, an increase in pH, coinciding with a decrease in proton

concentration, will lead to an increase in metal adsorption. This behavior has been well

documented with iron and manganese oxides, and clays (Jain and Ram, 1997; Anderson

and Benjamin, 1990; Chapman et al., 1983).

Precipitation, the chemical process that transfers solutes from solution into a solid

phase, occurs under sahuating conditions. This can occur homogenously in solution, or

heterogeneously in response to adsorption of mineral constituents to the surfaces of solids

(Ferris, 1993). The pH of a system is a critical factor in the heterogeneous minerai

precipitation process since adsorption is so highly pH dependent, and also since many

precipitation reactions themselves are pH dependent (Chen, et al., 1997; Dario and Ledin,

1997; Fujikawa and Fukui, 1997). Also, it has been s h o w that relatively minor changes

in pH c m have profound impacts on mineral speciation in acid sulfate systems (Allen and

Hansen, 1997; Bigham et al., 1996). This information is important since the

bioavailability of a metal, and consequently its toxicity, is dependent on its physical and

chemical form (Allen and Hansen, 1996).

Solutes that become adsorbed ont0 or incorporated during precipitation into minerals

can be released again into the dissolved state, through desorption or dissolution,

respectivety. Desorption is the process by which adsorbed matenal is released from the

solid on which it is adsorbed, while dissolution is the chemical process by which a

mineral dissolves through chemical weathering (Chen, et ai., 1997; Dario and Ledin,

1997; Drever, 1997). Under acidic conditions, protons become bound to surface oxide

ions and, thus, the bonds beiween the metal ions and the surface ligands are weakened.

This results in a loss to solution of metal ions, which leaves the protons to maintain the

surface charge balance (Walther, 1996).

Some of the processes discussed above can proceed as a result of bacterial processes.

Through adsorption, organisms are capable of extracting trace elements from solution.

Stmctural polymers in the ceIl membranes and the ce11 walls of bacteria have fùnctional

groups that act as sites with which dissolved metals may interact (Fein, et al., 1997).

Through heterogeneous nucleation, bacteria can serve as sites for the precipitation of

minerals such as iron oxides or calcium carbonates which c m , in tum, incorporate trace

metals through sorption and solid solution reactions (Fems, et al., 1995). Bactena can

also selectively produce localized supersaturation through metabolic activity, which leads

to precipitation through homogeneous nucleation (Fortin and Beveridge, 1997). For

example, below the surface of sulfidic tailings deposits where reducing, anoxic

conditions exist along with more neutral pH values, sulfate reducing bacteria thrive and

produce H2S-rich micro-environrnents, which lead to the precipitation of iron sulfides

(Fortin and Beveridge, 1997).

3. In Siru Bioremediation

One of the fastest growing fields in the treatment of hazardous waste is

bioremediation. In situ bioremediation refers to techniques that are used to clean up

contaminated groundwater aquifers and surface soils in their place of origin. Typically

this works through stimulation of microorganisms that are deliberately added, or by

stimulating those that are already present in the aquifer or soil. Stimulation of the

bacteria is done through the controlled addition of nutrients that are nonnally lacking in a

natural environment. When added, the nutrients promote growth and activity of the

desired microorganisms (Ledin and Pedersen, 1996; Ritimann, et al., 1 994). These

mate riz!^ r.amally consist of "an electron-acceptor substrate (like oxygen), an energy-

yielding electron-donor substrate (like a sugar or natural gas), inorganic nutrients (like

nitrogen or phosphorus) and materials to help dissolve/desorb immobile substrates (like

surfactants)" (Ledin and Pedersen, 1996).

3.1 I n Siru Bioremediation of M d Contamination

Bioremediation, until recently, bas been focused pnmarily on the microbial

degradation of organic contaminants. This is accomplished with microorganisrns that are

capable of degrading the organics to environrnentally benign cornpounds like carbon

dioxide, water and inorganic forms of Cl, N and S. The microbes accomplish this as they

work to gain energy fiom the reduced carbon in the organic molecules by oxidizing the

carbon to carbon dioxide (Lovley and Coates, 1997).

Recent studies have shown, however, that microorganisms can also be effective in

remediating metal contamination, although by processes that differ from biodegradation.

These processes c m include the following. The removal of metals fiom contaminated

water through adsorption ont0 microbial biomass (biosorption) and the complexation of

the metals onto bacterial surfaces acting as nucleation templates for precipitation

(biomineralization) (White, et al., 1997). Also important is the process conceming the

conversion of the contaminant to forms that are easily precipitated, such as the microbial

oxidation of dissolved ~ e * * and to insoluble oxides, which are effective sinks for

additional metals that adsorb to these oxides (indirect biomineralization). Finally, these

processes also include the microbial alteration of the contarninants' REDOX state to one

that is less soluble, as well as the microbial production of substances that effect the pH or

Eh of the sumunding solution to that more favorable for metal accumulation (He and

Tebo, 1998; Lovley and Coates, 1997; White, et al., 1997; Ledin and Pedersen, 1996;

Lovley, 1995).

3.1. I Biosorption

Microorganisms have a strong affinity for a wide variety of aqueous metal cations.

This results fiom the presence of surface organic functional groups such as amino,

carboxylic, hydroxyl and phosphate sites on ce11 walls and extracellular polyrners (Fein,

et al., 1997). The adsorption of metals ont0 the ce11 walls is an abiotic process controlled

by the acidhase properties of the functional groups and by the affinity of each of the

groups for certain aqueous metals (Fein, et al., 1996; Ledin and Pedersen, 1996; Volesky

and Holan, 1995). The net charge of the cells is genedly negative in the near-neutral pH

range and so metal cations are more greatly adsorbed at these pH values than at more

acidic values (Ledin and Pedersen, 1996).

Precipitation relating to microbial activity is achieved thmugh heterogeneous

nucleation, which involves the formation of essential nuclei on the surfaces of bacteria,

which catalyze nucleation by reducing the activation energy bmier (Warren and Fems,

1998). The activation energy barrier inhibits the spontaneous formation of a solid phase

fiom a supersaturated solution. This ban-ier c m be reduced by increasing the degree of

solution supersaturation through metabolic activity andior by lowenng the interfacial

energy of the solid phase through the promotion, by organic surfaces, of chemical

bonding at nucleation sites (Fems, 1993).

3.1.3 Microbia il Metal Tmns fonnations

Some microorganisms are capable of deriving energy fiom the oxidation or reduction

of metals, which in tum results in the precipitation of minerais. In the case of sulfate-

reducing bacteria, sulfate is used as a terminal electron acceptor by the bacteria and

consequently is reduced to sulfide (Hamilton, 1998). This generation of sulfide results in

several advantages to bioremediation processes. These are the creation of reducing

conditions, removal of acidity and the precipitation of metals as insoluble sulfides

(White, et al., 1997; Ledin and Pedersen, 1996; Fems, 1993). Similarly, microbial

oxidation of ~ e " and hAn3+ to insoluble oxides can be effective in treating high

concentrations of ~ e " and ~ n ~ ' in groundwater, as well as providing a sink for other

metals that sorb to the oxides (Lovley and Coates, 1997; Corne11 and Schwertmm,

1 996; Ledin and Pedersen, 1 996).

3.1.4 REDOX Processes

Microbial metabolic activity cm remove some metals, such as cr6+, u6+, TC'+, CO)+

and se6', from contarninated growdwater by reducing them to a lower REDOX state.

These organisms use the metals as terminal electron acceptors in anaerobic respiration

and therefore reduce them to an insoluble reduced form fiom a highly soluble oxidized

state (Lovley and Coates, 1997; White, et al., 1 997).

3.1.5 Metabok Producis That Influence pH and Eh

A number of organisms specialize in the degradation of organic compounds, the by-

products of which cm include both acids and bases. Essential to this thesis is the

example of urea-degrading bacteria, which produce both carbonate and ammonium as by-

products of the degradation reactions (Equation 1). These products can be effective in

raising the pH of the surrounding solution to values favorable for the

adsorption/precipitation of dissolved metals to the surfaces of the bacteria themselves or

to the surfaces of precipitating oxides (Comell and Schwertrnann, 1996; Ledin and

Pedersen, 1996).

4. Computer Modeling

4.1 Modeling of Aqueous Cihemistry

Geochemistry often involves descnbing the chemical States of natural waters, which

can be very dificult when the systems are compositionally cornplex. Quantitative

geochemical modeis have proven to be very usefùl in understanding such systems. The

bulk of the geochemical modeling process consists of conceptualizing and defining the

system of interest. This is done by descnbing the type of system to be rnodeled, its

composition and its charactenstics (Bethke, 1996; Madé, Clement and Fritz, 1994).

The simplest type of system is the "closed" equilibrium system; bat is, the

composition is fwed with no mass transfer and the temperature is known. The types of

models that deal with this type of system are used to predict the distribution of mass

among the species and minerals, the species' activities, the saturation States of the

minerals and the fbgacities of gases that rnay exist in the system. In these models, the

initial equilibrium system constitutes the entire geochemical model, and as such, are not

true reaction models since they really descnbe only state and not process (Bethke, 1996;

Madé, Clement and Fritz, 1994).

Other more cornplicated types of models are called "open" systems and they allow for

the transfer of mass or heat into or out of the system. This leads to a variation in its

composition and/or temperature over the course of the calculation. In these reaction patb

models, the initial equilibnum system is the model's starting point and fiom there, the

model can calculate how the changes will affect the system's equilibnurn state (Bethke,

1996; Madé, Clement and Fritz, 1994).

The composition of a system is defmed with the selection of components, which are

the independent variables, or ions, From which every species in a system may be defined

(Chapman et al., 1982). Once the components are chosen, their concentrations must be

accurately hown for input into the cornputer program, since these values are used

directly in mass balance equations and mass action (equilibrium constant) equations

(Bethke, 1996; Chapman, et al., 1982).

The charactenstics of a system include temperature, pressure, pH, pe (a measure of the

REDOX potential which is related to Eh via Equation 1 O), etc. In order to calculate the

system's equilibrium state, the temperature and pressure must be known. The

equilibrium state calculation is important because it allows for the further calculation of

the species distribution, the mineral saturation States and the gas fugacities. Any other

information that the user can apply to the system can serve only to make the results of the

modeling more accurate (Bethke, 1996; Madé, Clement and Fritz, 1994). By realizing

that the fate of every component in the system is dependent on the behavior of every

other component present, chemical equilibrium models can effectively simulate the

behavior of chemically reactive contaminants in an aqueous system (Fnnd and Molson,

1994).

4.1. 1 Modehg wifh MINEQL+

MINEQL' is an "interactive &ta management system for chemical equilibnum

modeling" chosen for this project because of its easy-to-use and easy-to-read tableau

format as well as for its applicability to this study (Schecher and McAvoy, 1994). The

chemical equilibrium approach is beneficial in that it provides the user with a collection

of potential chemical reactions by solving mass balance equations using equilibrium

constants (Schecher and McAvoy, 1994).

2 1

The tableau format uses matrices to present data and relationships relevant to the

system as in Table 1, which shows how the tableau would appear for a simple ~ e ) * - H20

system. Abave the stoichiometric matrix and forming the top row of the tableau, is a list

of al1 of the components chosen for the model. To the lefi of the matrix, forming the first

column of the tableau, is a list of al1 of the possible species that may form fiom the

seiected components. Contained in the actual matrix of the tableau is the stoichiometric

information for these components and species. The last two coiumns of the tableau, to

the right of the matrix, are the lists of equilibrium constants (Log K) and enthalpy values

(AH) for the species fonned from the components. Analytical concentrations for each of

the components are laid out in the bottom row of the tableau; these values must be

manually inputted from the user's analytical field data (Stumm and Morgan, 1996).

Table 1. Chernical Complexes in the F~~*-H*o System Organized in Tableaux Format

Name OH -

Each row of the stoichiometric matrix gives the stoichiometric coefficients for the

formation of each species. These coefficients are the exponents of the components in the

FeOH 2+

Fe(OH)2 ' Fe2(0H)2 4+

Fe(W2aq F ~ & o H ) ~ - 5+

Fe (OHk Total Conc.(M)

H20 H (+) Fe (3+) 1 - 1 O 1 -1 1 2 -2 1 2 -2 2 3 -3 1 4 -4 1 4 -4 3 O O O

Log K -1 4.00

Delta H 13.345

-2.19 -5.67 -2.95

-13.60 -21.60 -6.30

10.399 0.000 13.500 0.000 0.000 14.300

mass laws. The colurnns list the stoichiometric coefficients of the mole consentation

equations of the components (Stumm and Morgan, 1996).

MINEQL* is capable of performing a single run, through the Run option, or a series of

multiple rus , through the Multiple Run option. Runs cm consist of caiculations for the

distribution of chemical species in a system, as well as surface adsorption and the

precipitation/dissoluiion of solids. Output Manager displays the results of these

calculations (Schecher and McAvoy, 1994).

Multiple run titrations are also within the extensive capabilities of the MMEQL+

program. This includes titrations of components and of geochemical parameters such as

pH and pe.

4.1.2 Appiying MINEQL* to AMD Contuminared Grouradwuter

One of the most important computations that the program MINEQL+ can be used for

is to determine the aqueous speciation of dissolved components present in groundwater.

This information is cntical because it allows for the assessrnent of the degree of toxicity

the system exhibits, since only free ionic forms of metals are considered toxic (Erten-

Unal, et al., 1998; Boruvka, et al., 1997; Allen and Hansen, 1996). The aqueous

speciation can also be used to evaluate the state of the system with regards to its deviation

fiom chemical equilibrium, as well as to determine which minerals are supersaturated

and, therefore, likely to precipitate. Mineral precipitation is particularly important in that

it may result in a reduction in the hydraulic conductivity of the aquifer, and thus affect

the flow of the groundwater and contaminant transport away fiom the South Bay site

(Herbert, 1 996).

5. Methodology

S. 2 Field Hydrotogy and Geochemical Data

Twenty-five piezometers were chosen at the South Bay mine site for inclusion in this

modeling study. These twenty-five piezometers are situated along a hydraulic gradient

from West of the iailing basin to the north-north-west corner of Mud Lake (Appendix A).

Sampling and geochemical analyses of groundwater were performed by personnel from

Boojum Research according to the following protocol. Afier bailing the piezorneters,

samples were collected, filtered through 0.45 pm filten, and acidified on the site with

HN03 (1 .O% v/v final concentration). Multi-element analyses were performed using

inductively coupled plasma - atomic emission spectrornetry (ICP-AES) and liquid

c hromatography (LC).

During sarnple collection field measurements were made of pH, Em (platinum vs.

AgO/AgCl), electrical conductivity and temperature within thirty minutes of collection.

After shipment of the sarnples in coolen to the laboratory, these rneasurements were

repeated. Em was converted to the hydrogen electrode standard (Eh) while conductivity

was corrected to 25°C. For the purposes of the simulations, only the measwments taken

in the field were used for input to the MINEQL' information files with Eh being

converted to pe by the following equation (Walter et al., 1994):

where R is the gas constant, T is absolute temperature and F is Faraday's constant.

The data provided by Boojum Research (Appendix D) had complete cation and

anion data for only five of the twenty-five piezometers; these were M28, M39a, MSO,

M60a and M63. Of the remaining twenty piezometers, data was obtained for al1 cations

except ammonium, with no anion concentration data. The missing data was accomodated

through a number of assumptions and approximations. These assumptions and

approximations are as follows: 1) the concentrations for ammonium in the twenty wells

with incomplete data sets were set to 3.60* 10" M, the concentration of ammonium in

piezometer M28. This well is upgradient of the AMD contamination, and is assumed to

be indicative of normal background ammonium concentrations; 2) the missing chloride

concentration data in the twenty wells were set to 1.7* 1 o4 M, a value that represents an

average of the chloride concentrations within the five piezometers with complete data

sets. This was done because chlonde nomally behaves conservatively, and can be

expected to be present at fairly uniforni concentrations; 3) al1 wells were analyzed for

elemental sulfûr and since REDOX speciation calculations (Section 5.2) showed that, in

the five wells with complete &ta sets, sulfate comprised 100% of the total sulfur present,

the sulfate concentrations for the remaining twenty piezometers were calculated directly

fiom their respective elemental sulfur concentrations and 4) the missing carbonate and

nitrate concentrations within those twenty piezometers with incomplete data sets were set

to zero since for each anion, only one of the five wells with complete data sets contained

detectable amounts.

5.2 Speciation of REDOX Sensitive Components

The geochemical data for each piezometer, obtained fiom field sample analysis, was

entered into a MMEQL' tableau and saved as a MINEQL' information file. This was

done by first selecting the components present in each sample fiom the Components list.

After scanning the thennodynamic database, those minerals and inorganic compounds

most cornmonly formed in acid mine drainage systems were moved to the Type VI

species list to permit calculation of saturation States (Bigham, et al., 1996; Herbert, 1996;

Blowes and Ptacek, 1994). The remaining minerals and inorganic compounds were

completely deleted fiom the information file. With this done the molar concentrations of

each component were entered into the program. The C02(g, log K value was then

changed to 2 1.66 to fix the pC02 with the atrnosphere (w5 atm), and the field pH and

pe values were entered into the Log K column of the Type III Fixed Solids species list.

Under the Run option the field temperature was entered and then the entire file was

saved.

At this point, speciation of the REDOX sensitive components, iron, copper and sulfur,

became possible. By leaving pe, ~e)+/Fe~+, HzO and pH in the Fixed Solids species list

and moving al1 other REDOX couples to the Type VI list (not considered in the mass

balance computation), the iron speciation problem was defmed. Each REDOX couple

had to be computed separately to avoid phase rule violations. The problem was run to

calculate the ~ e ~ + and the ~ e ~ ' concentrations for each piezometer sample. A similar

strategy was employed in speciating the copper REDOX couple, except in this case the

~e)+/Fe?+ was removed fiom the Type III Fixed Solids species list and the cu'/cu2+ was

allowed to remain. Again, besides pe, H20 and pH, al1 other REDOX couples were

moved to the Type VI list. Sulfur was speciated in the same manner.

With the iron, copper and sulfur speciated for each of the piezometer sarnples, the new

~ e " , ~e- '+, CU' and CU'+, HS- and ~ 0 ~ ~ ' concentrations were then entered into the

MINEQL' tableaux, and these updated information files were saved.

5.3 Proton Balance Calculation

In an Excel spreadsheet, the concentrations of each component were entered. The

concentration values were multiplied by the ionic charge of the component and the

resultant equivalent anion and cation values were surnmed. Ideally the difference

between the sums should equal zero; that is the sum of the anions should equal the sum of

the cations. The degree to which these sums vary fiom zero is an indication of the proton

condition of the sample, or the accumulation of error in the ionic analyses. By entering a

concentration value for total H' into MINEQL* , and removing pH as a fixed boundary

condition, changes in pH can be computed. To fbrther examine this, a mode1 calibration

test was devised.

5.4 Model Calibrarion

Calculation of pH was used to test the accuracy of each of the 25 piezometer models.

This was done by accessing the updated information files and leaving pe, H20, COzc,, and

~ e ~ + / F e ~ ' (the dominant REDOX couple) in the Fixed Solids species list, and moving al1

other REDOX couples to the Type VI list. If the results from the running of these

problem sets indicated that changes needed to be made in order to have the calculated pH

values match the measured field pH values, then the following modifications were done.

When the calculated pH was lower than its respective measured field pH, the total

dissolved inorganic carbon (DIC) concentration was added to the system until both pH

values matched. Conversely, when the calculated pH was higher than its respective

measured field pH, the total H+ concentration was increased. This was done rather than

manipulating pC02 values for the following reasons. Since the groundwater is meteoric

in ongin, it is therefore in equilibrium with atmospheric pCOz. As such, DIC

concentration values should be expected to range fiom 1 0 ' ~ to 1 O*' M over a pH range of

4-6 (Fems, 1999), which is consistent with the range of pH values observed at the South

Bay site (Appendix D). Also, pC02 of the groundwater is not likely to be significantly

changed by addition of CO2 from decomposing organic matter in the soi1 since the soi1

and tailings are not organic nch. Lastly, with the exception of well M28, analytical field

data did not include DIC concentration data.

The new DIC and proton concentrations were entered and saved into the MINEQL*

tableaux to update the information files.

5.5 Urea Mdabolite Titrations

The titrations were run fiom the updated idonnation files with H20, pe, and

~e'+&' maintained in the Fixed Solids species list only. Under the Multiple Runs

option, beginning and ending concentrations were chosen for the addition of ~ 0 3 ~ - and

NH~', the hydrolysis products arising from bacterial urea degradation. The beginning

carbonate concentrations were set to the amount already present in each of the simples,

while the ending concentrations, detemined through trial and error, were set to that

required to raise the pH of each sample to 8.0-8.1. The beginning and ending ammonium

concentrations were set to twice those of the carbonate since in the microbial breakdown

of urea there are two ammonium ions produced for every carbonate ion (Equation 1 ).

Exiting from Multiple Runs and entering the Run manager, the problem sets were

allowed to run. The resulting changes in the saturation indices (Log SI) of mineral

species were observed by accessing the Output Manager and selecting the Species option

in the titration files. Those minerals most commonly fonned in AMD systerns, and so of

interest here, include the carbonate minerals malachite (Cu2C03(0H)2), calcite (CaC03),

dolomite (CaMg(C03)2), siderite (FeC03), smithsonite (ZnC03) and rhodochrosite

(MnC03), the sulfide minerals pyrite (FeS?), chalcopyrite (CuFeSz), mackinawite

((Fe,Ni)&), millerite (NiS) and sphalerite ((Zn,Fe)S), the sulfate minerals jarosites K

(KFe3(S04)2(OH)6) and ((H3O)Fe3(SO4)2(OH)6), alum K Wl(S04)2'6H20), gypsum

(CaSO44Hz0), calcanthite (CuS045H20), melanterite (FeS04'7H20), bianchite

(Zn,Fe)S04'6HzO), chalcocyanite (CuS04), celestite (SrS04) and zincosite (ihS04), and

finally the oxides nordstrandite Al(OH), (A), gibbsite Al(O& atacamite (CU~(OH)~CI),

goethite (a-FeO(OH)), hematite (a-Fe203) and femhydrite (5FezO3*9H20). Also

included were the inorganic compounds 4(OH)ioSOd, Al(OH)S04, Ni(OH)6S04,

ZII~(OH)~SO~, Zn2(OH)2S04, ZnS04-1 &O, Cu2S04, MnS04 and Fe3(OH)s. Log SI

values for these minerals and compounds were tabulated, and those that were or became

supenaturated were graphed.

5.6 Mud Lake Oxidption SimuIation

Upon completion of the titrations, the study was extended to investigate what may

happen to the pH when the groundwater undergoes complete oxidation, simulating

emergence from the aquifer into Mud Lake. This was done through a pe titration of the

information files with the following computational changes. First, carbonate and

ammonium concentrations were set to the final values in the titration calculations (the

total concentrations that were required to raise the initial field pH values to 8.0-8.1).

Within the Fixed Solids option only pe, ~e~+/Fe*', COz (,, and H20 were allowed to

remain and the redox titration was implemented by choosing Log K of pe in the Multiple

Run option. For each well, the beginning pe was the originally measured field pe, while

the ending pe values in each were set to 13, a pe value typical of an aerobic lake impacted

by AMD (More1 and Hering, 1993).

in those cases where the final pH after complete oxidation fell below 7.00, a

subsequent carbonate and ammonium titration was performed to see how much additional

urea degradation would be required to raise the newly acidic pH back up to 8.0-8.1.

These titrations were defmed by changing the pe value in the Fixed Solids species list to

13 in the information files. The beginning carbonate concentrations were set to the final

values in the initial titrations while the ending values, determined through trial and error,

were set to those required to mise the pH to 8.0-8.1. The beginning and ending

ammonium concentrations were set to twice those of the carbonate, for reasons described

in the above Titrations section, and the problems were run.

6. Results

6. l REDOX Speciution

Computed concentrations of both ~ e ' + and Fe2+ in each of the wells are listed in Table

2. The fraction of the total iron concentration that is ~e' ' and the fraction of the total iron

that is ~ e ' + in each sarnple are also sumrnarized in Table 2 as well as Figure 3 (these

fractions were obtained via the equations listed below Table 2). It should be noted that

the piezometers are listed in order of their decreasing proximity to the tailings basin.

Although there was no apparent correlation between the amount of total iron present

and the piezometers' proximity to the tailings area (Table 2), there did appear to be a

rough correlation between the ~ e ' + and ~ e " fractions and the piezometers' location.

Figure 3 shows a plot of the fraction of ~ e ' + venus the fraction of Fe3+ in each

piezometer. M28 and M83A are closest to the tailings area but are considered

uncontaminated since the underground groundwater flow paths originating fiom the

tailing area do not reach them. In these piezometers, most of the total iron is ~ e ?

Conversely, al1 other piezometers are effected by the underground flow of contaminants

and show a trend of decreasing oxidation with increasing distance fiom the tailings area.

Piezometer M83B is the first to be effected by contaminant flow fiom the tailings and

is the most oxidized of al1 the piezometers with 45.6% of its total iron king ~ e ~ + . This

begins a dom-gradient trend of decreasing amounts of ~ e ) ' at M89, M87 and M88.

M34, at 470 m along the gradient, follows M88 with decreased ~ e " concentrations of

approximately 5% of the total iron. This is followed by M39A at 1.2% ~ e " , a level

comparable with the background at M28.

Table 3 and Figure 4 summarize MINEQL' results for the copper speciation

calculations. Similarly, the first w o colurnns of Table 3 list the calculated concentrations

of CU' and Cu2* present in each sample and the last two columns list their fractions of the

total copper present (the fractions were calculated via the equations listed below Table 3).

Of the twelve piezometers that exhibited detectable concentrations of copper (>0.03

ppm), half of them contained no CU+; that is 100% of their total copper was CU? Within

these twelve samples there appears to be a correlation between the fractions of the total

copper that were Cu+ and CU" and the piezometers' proximity to the tailings basin.

Those of the twelve piezometers that were firthest from the tailings area appear roughly

to be those least oxidized, which supports the results from the iron speciation calculations

where a similar pattern was observed. In each of the wells, the entire sulfur content was

in the form of the oxidized sulfate.

Table 2. Kalin Canyon Aquifer Computed iron Speciation

Table 3. Kalin Canyon Aquifer Computed Copper Speciation

Well M28

M83A M83B M89 M88

M72A M t 2 8 MMC M87 M69 Mas M86 M39

M39A Mm M34 M79 M n M80

M6ûA M60B Y63 M62 M36 M37 O I

Note: f(cuLf)t = [CuL*]/([~u'+] + [CU' and f(cu+)t = [cu+]/([cu~'] + [CU+]).

6.2 Proton Balance

Computed concentrations of iron and copper REDOX species are shown in Table 4

along with a complete chernical profile for each of the wells. These values were used to

determine the charge balance and proton condition of the groundwater (Table 5) via the

following equation (Schecher and McAvoy, 1994):

where H+ represents protons, n is the number of cationic species, m is the number of

anionic species, Zi is the charge on the positive species, zj is the charge on the negative

species, Mi is the concentration of cationic species i and Lj is the concentration of the

anionic species j.

The percent difference or the charge imbalance for each well is also given. These

values represent the degree to which the samples deviate from expected ionic

equilibriurn. It is important to note here again that twenty of the piezometers were not

analyzed for anions and so the assumptions that were made to obtain numbers for the

missing data may be a significant cause of the obsemed charge imbalances within the

groundwater.

Table 4. Aqueous Chemistry Data Indudlng Computed Concentrations of REDOX Active Species

M28 M83A M83E M89 M88 M72A MME M72C M87 M69 Mû5 M86 M39 Concentrations (M)

O 7.30E-06 9.90E-06 0.00088 4.30E.05 5.30E-06 0.04026 0.00151 3.20E-O4 2.WE-05 7.1M-05 ?.BO€-05 9.60E-07

0.00334 0.01195 0.00165 O.ml8 1.20E-05 0.00349 0.01233 0.01016 3.20E96 0.00093 0.00019 0.00046 0.00224

O 5.60E-06 3.70E-07 290E.06 O 1.30506 4.70E-05 9.40E-05 O O 6.10E-07 8.SOE-08 1.NE-06

O O l.lOE-12 O O O 2.lM-07 O 5.14E-08 7.64E-08 1.39E-09 3.24E-08 O

O O 1.58E-07 9.80E-& 1.70E.07 O 6.79506 8.OûE-06 276E.08 1.24E-06 1.48E.06 3.28E-07 O

2.lOE.06 0.010376 9.BOE-OS 1.23E.09 5.40E-05 0.00215 0.0476 0.1428 2.49E-05 7.39E.05 6.WE.05 209E-O4 4.19E-a

5.00609 4.07E-07 6.21E-05 4.68EM 9.00E.06 1.M-07 293E-05 6.17E-06 6.34E-06 7.59€-08 6.22E-07 3.02E-07 6.55E-07

0.00015 0.00022 O 5.60E.M O 0.00011 0.00047 0.0005 O 5.40E-05 0.00013 6.6ûE-05 0.00012

M39A M81 M34 M79 M73 MW MWA M60B Mô3 M62 M36 M37 Concentrations (M)

O O 8.ME-06 O O O O 7.70E-06 O 5.6OE-06 O 1.60E-05

Table 5 . Proton Balance Calculations

A1 3+ Ca 2+ Co 2+ Cu +

Cu 2+ Fe 24 Fe 3+ K +

Mg 24 Mn 2+ NH4 + Na* Ni 2+ Sr 2+ Zn 2+ CI - s04 2- CO3 2- NO3 - -

mion iun

m h r u m

%W.

Concentrations (eqrL) O 2.19E45 2.97E-05 2WE-03 1.29E-W 1.59E-05 7 . W - W 4.53E-03 9.60E-04 6.00E-05 2.13E-04 2.54E-04 2.WE-06

Concentrations (eqk) O O 2.46EQS O O O O 231E45 O 1.68E-05 O 4.80E.05

6.3 Model Calibrarion

Preliminary pH computational runs showed that only four piezometers had calculated

pH values above their respective measured field pH. In these four cases, the

concentration of H' was increased by trial and error to levels where the calculated pH

equaled the measured field pH within a '0.05 difference. These four piezometers, M28,

M89, M72C and M37, required proton additions ranghg from 1 * lo4 M to 8* 105 M H+

(Table 6).

In al1 of the remaining 21 wells, the initial calculated pH values were below their

respective measured field pH values. To overcome these inconsistencies, carbonate was

added to each sample to ensure matching of the calculated pH values with the measured

field pH values within a ?0.05 difference. These carbonate additions ranged from 2* 1 om6

M to 1.5* 1 oJ M (Table 6). Figure 5 shows the results of the calibration with calculated

pH plotted versus the measured field pH for each well.

Fi y r e 5. Model Calibration Results - Measured Field pH vs. Calculated pH

O 1 2 3 4 5 6 7 8

- - - Measured pH

-

38

Table 6. Mode1 Calibration Adjustments - Changes Made to H' or DIC Concentrations to Ensure ~ a t c h i n ~ of Field and Calculated pH Values -

Actual field pH

6.84 6.02 5.72 3.05 5.21 6.33 5.67 4.37 6.54 6.81 5.74 6.39 6.09 6.12 6.08 6.74 6.06 6.97 6.1 5.89 6.62 5.98 5.94 6.53 3.22 -

- Initial calc.

PH 8.98 5.35 4.94 4.07 4.69 5.27 4.99 4.72 4.75 4.87 4.69 4.65 5.51 5.68 5.66 5.06 5.66 5.63 5.72 5-69 5.1 5.69 5.35 5.61 3.99 -

- Final calc.

PH 6.85 6.03 5.75 3.06 5.23 6.37 5.67 4.43 6.54 6.79 5.7 6.38 6.04 6.17 6.12 6.72 6.03

7 6.1 5.91 6.64 5.98 5.93 6.51 3.17 -

Diff. Between âctual pH and Final Calc. pH

Changes to Conditions

Set [~+]t=7.79'10~' Set [C03)=1.25'1 Set [C03]=104 Set [H+ 1 Set [C03]=4'1 Set [C03]=1 .SV (Y5 Set [CO314 .SI O4 Set [H+]?= 1 o4 Set [~03]=5.5'1 (Y5 Set [C03]=5'1 Set [~03]=8'l Set [C03]=l.l5*l O4 Set [C03]=4*1 o6 Set [co~]=SI 0; Set [C03]=5'1 O* Set [~03]=3*1 O" Set [c03]=4*1 o6 Set [C031=3'10.~ Set [co~]=s.s'~ O" Set [C03]=2*10.' Set ( ~ 0 3 ~ 2 . 3 ' 1 0 ' ~ Set [~03]=3*l O" Set [C03]=9*10" Set (C03]=1*1 o6 set pi+]=9'1 O"

* denotes those wells with complete data sets Note: [CO3] is equivalent to toi1 dissolved inorganic carbon (DIC).

6.4 Tirrations with Urea Hydrolysis Produas - Ammonium ami Carbonate

The results of the titmtions are summarized in Figures 6-16, as well as in Appendices

B and C (Figures 6- 16 show the graphs of the first three wells along the gradient oniy;

graphs for the rernaining wells can be found in Appendix C). The concentrations of

carbonate required to raise the pH of those piezometers with original pH values of 3-4.99

up to 8.0-8.1 ranged fiom 3 * 1 o4 M to 0.24 M carbonate. In those piezometers with

original pH values of 5-5.99,4.6* 1 o - ~ M to 6* 105 M of carbonate were required, while in

those rernaining piezometers with starting pH values of 6 to 7,3.45* 10" M to 1.5* 10;' M

of carbonate were needed to raise the pH values to 8.0 to 8.1.

Figure 6. Well IIR2BpH vs Concentration of Urea

8.2 T Equivalents Added

0.0038 0.0039 0.004 0.0041 0.0042 0 . w 0.0()44

Lkea Concentration (M)

------- - - - -- --

Figure 7. M I l -pH vs Concentration of U m Equivalents Added

8.4 1 I

Figure 8. Well -pH vs Chcentration of U m Equivalents Mded

1 O 0.0001 0.0002 0.0003 0.0004 0.0005 0.0006 0.0007

Uma Concentration (M) - - - - -

Of the minerals and compounds considered (these are listed in Section 5.9 , MINEQL*

detennined tliat groundwater at every piezometer was supersaturated with respect to

goethite and hematite before the start of the titrations. Seventeen were also found to be

initially supersaturated with respect to femhydrite, fifieen with jarosite K, eight with

Fe3(OH)*, five with chalcopyrite, three with jarosite H and two with pyrite (Figures 9-1 6

and Appendix C). There were no correlations between initial mineral supersaturation and

the piezometers' proximity to the tailing area.

Throughout the course of the tiûations, piezometer groundwaters that were not

initially supenaturated with respect to Fe3(OH)8 and femhydrite, becarne so. Nineteen

also becme supersaturated with respect to pyrite, eighteen with siderite, sixteen with

calcite, sixteen with rhodochrosite, fourteen with Zn@H)6SOs, thirteen with jarosite H,

thirteen with smithsonite, seven with dolomite, six with jarosite K, four with chalcopyrite

and four with Z~I~(OH)~SO~, again al1 in piezometer groundwaten that were not already

originally supenarurated with respect to these minerals. Important to note is that these

minerals becarne supersaturated at some t h e during the course of the titrations but in

some cases did not necessail y remain so (Figures 9- 16 and Appendix C).

6.5 Groundwuter Oxidation Simulation

As shown in Table 7, which summarizes the results of oxidation simulations, only

eight of the twenty-five wells (32%) had their new pH values fa11 below 7.00. The new

pH values of these eight wells fell to values ranging fiom 3.4 to 4.6. There did appear to

be a relationship between the location of these wells and decreases to their pH as a result

of oxiàation. Seven of these eight wells were among the ten furthest fiom the tailings

area. In each of these piezometers, complete oxidation occurred at a pe of 13 (equivalent

to an Eh of 735 mV).

Of the sixteen that exhibited little or no decrease in pH as a result of oxidation, eight

showed no change what so ever, and expenenced complete conversion of ~ e " to ~ e ~ '

within the pe range of 7 to 13. Six of these eight were among the eleven closest to the

tailings area. The remaining nine piezometers had their pH values drop slightly, but none

below 7.00. These nine experienced complete conversion of ~ e " to ~ e ~ + within a pe

range of 6 CO 8. For companson, iron species versus the location of the wells along the

hydraulic gradient before the oxidation simulation are surnmarized in Figure 17.

There also appeared to be a correlation between the original field pe values and any

decreases to the pH associated with oxidation. The mean value of the original field pe

values of the eight wells with extreme decreases in pH was a low 2.46 (equivalent to an

Eh of 140 mV). In the eight wells where no change in pH occurred, an original field pe

range of 4.2-10.6 was observed, with the mean value being 6.98 (Eh = 395 mV). The

final nine pierometers that experienced slight decreases in pH had a mean field pe of 2.74

(Eh = 155 mV).

Table 7. Groundwater Oxidaüon Simulation Results - pe Titrations And Resultant Changes in pH -

Weil - M20

M83A Ma36 M89 MW

M72A Mi25 M72C M87 M69 Ma5 M86 M39

M39A M81 M34 M79 M73 Mao

M60A M608 M63 M62 Y36 M37 -

Changes to pH none

decmase to 4.6 none none none

decrease !O 7.3 decrease lo 7.57

none decrease t o 8.0 decrease to 8.38

none decrease t o 7.96 decrease 10 8.0 decrease to 4.2 decrease to 4.0

none decrease to 3.4 decrease to 7.77 decrease to 3.8 decfease 10 4.2 decrease t a 8.05 decrease to 3.6 decmase to 3.5 decrease to 8.01

none

A kther carbonate and ammonium titration was performed on the eight wells, which

undement a pH drop of below 7.00. The amount of urea (carbonate and ammonium

equivalents) needed to restore pH values to 8.0 to 8.1 are given in Table 8 and in Figure

18 vernis the location of the wells dong the gradient. Figure 19 summarizes the amounts

of urea added before oxidation and the sum of urea added before and afier oxidation

versus the location of the wells dong the gradient.

Table 8. Post-Orridation Titration Results - Total Carbonate and Ammonium Added to Restore Neutra1 pH Values

. Weil 1 [CO31 (Wrdded 1 INW1 ( M ) M 1 Ending pH M83A 1 7.00'1 O-' i 1 .4OW1 0" I 8.04

Figure 18. Urea Equivaients Added Mer Oxidatkn to kicrease pH to 8.Gû.1 vs DManœ A k q Hydraulk Gradient Between Tailings h a and Allud Lake

M39A M81 M79 Y80 M W A M63 M62

Oistance Along Gradient (m)

8.&Y104 2.50'1 O= 6.20'10~ 4.50'10~ 1 .20'1 O" 4.00'10.~ 1 .80'1 o3

l.6&10' 1 0~01 5.00'10" 1.24'1 9.00'10~ 2.40'10' 8.00'1 0' 3.60'1 0'

8.03 8.03 8.04 8.09 8.12 8.07

0 Sun of Um Addedealaeard A f t a Qtagm- - --

6.6 Fate of Zn and Cu from the South Bay Zn-Cu Mine

The South Bay mining operation focused on recovery of zinc and copper. It is the

speciation of these metals, which now comprise part of the AMD contamination problem,

and not their total concentrations in an aqueous system that primanly determines their

level of toxicity and chernical reactivity. Those species that are dissolved, aqueous free-

ion complexes are most toxic to living biomass because they exist in forms that are easily

assimilated (Allen and Hansen, 1996; Bourg, 1995; Reddy, Wang and G~OSS, 1995;

Sager, 1992).

The speciation of zinc and copper in the piezometer samples was detennined in

MXNEQL' calculations. Aqueous zinc species anticipated fiom the computations

included primarily the following: zn2+, Zn (0H)z (,,, Zn CO3 cas,, Zn (SO& and Zn SOa

(Table 9). Aqueous copper species included primarily CU', Cu2*, Cu(OH)? (a,>, Cu(C03)

a d CuS04 (,, (Table 10).

Table 9. Percent of Zinc Species Before and After Titrations -

i

Species

[ni tial

pH 6.85 6.03 5.75 3.06 5.23 6.37 5.67 4.43 6.54 6.79 5.7 6.38 6.04 6.1 7 6.1 2 6.72 6.03

7 6.1 5.91 6.64 5.98 5.93 6.51 3.17

Final

pH 8.09 8.01 8.02 8.09 8.02 8.1

8.05 8.1 8.04 8.09 8.09

8 8.05 8.05 8.03 8.1

8.08 8.06 8.02

8 8.09 8.01

8 8.04

% Zinc Species: Initial (Final)

ZnS04 14.9 (8.3) 59.7 (50)

69.8 (1 9.9) 34.9 (25.6)

0 (0) 39.1 (36.8) 68.5 (59.9) 70.7 (63)

0 (0) 2.7 (1.4) 12.1 (6.6)

7.9 (5) 27.8 (1 7.7) 61 (49.4)

65.3 (56.7) 6.2 (3.1)

67.3 (55.3) 15.4 (9)

70.4 (62.7) 65.2 (56.2) 12.8 (6.8)

68.6 (60.2) 55.2 (45.1)

0 (0) 47.8 (34.3) es included: Zi

Table 10. Percent of Copper Species Before and After Titrations

Initial

pH 6.85 6.03 5.75 3.06 5.23 6.37 5.67 4.43 6.54 6.79 5.7 6.38 6.04 6.1 7 6.1 2 6.72 6.03

7 6.1 5.91 6.64 5.98 5.93 6.51

Final

pH 8.09 8.01 8.02 8.09 8.02 8.1 8.05 8.1 8.04 8.09 8.09

8 8.05 8.05 8.03 8.1 8.08 8.06 8.02

8 8.09 8.01

8 8.04

% Coppper Species: Initial (Final)

Cu" 47.4 (0) 39.8 (0) 72.0 (0) 67.6 (O) 99.7 (O) 57.9 (0) 26.5 (0) 15.3 (O) 76.6 (O) 51.9 (O) 88.2 (O) 81.3 (O) 72.4 (O) 38.2 (O) 33.1 (O) 58.0 (O) 29.5 (O) 28.4 (0) 22.2 (0) 33.3 (O) 62.1 (O) 26.5 (O) 45.3 (O) 78.6 (O) 54.7 (O)

ratues includc

cuso. t,,

7.4 (0) 58.6 (0) 27 (0)

32.4 (0) 0 (0)

33.5 (O) 73.2 (1 . l) 84.7 (1.4)

0 (0) 1.3 (0)

1 0.8 (0) 6.2 (O)

24.7 (0) 59.2 (O) 66.9 (0) 3.4 (0)

69.4 (O) 4.6 (0)

76.7 (1.4) 65.9 (0) 8.1 (O)

72.6 (1.2) 53.5 (0)

0 (0) 45.3 (O)

: CuC12 and i

As the pH increased in the titrations, concentrations of 2n2' and ZnSO4 (,) decreased,

while increases were seen in the concentrations of ZII(OH)~ (aql and Zn(C03) (,). At low

pH values, the major zinc species were 2n2+ and ZnSQ whereas zn2' and ZnCOa (,,

were the major species at pH 8. Decreases in the concentration of CU" were also

observed in those piezorneters with detectable copper levels, with simultaneous increases

in the concentration of the CU(OH)~ species. Cu+ exhibited neither an increase nor a

decrease. At low pH values, the major copper species was CU'+, while CU(OH)~ was

the major species at pH 8.

Al1 noted increases and decreases in species' concentrations balanced out so that the

total zinc and copper in solution did not change, although several inorganic compounds

and minerals containing either Zn or Cu, such as Zn4(0H)&04, Zn2(OH)2S04,

smithsonite and chalcopyrite, became slightly supersaturated during the course of the

titrations (Appendix B). Nevertheless, it is expected that adsorption processes are most

likely to control the solubility of these metals. In groundwater impacted by AMD, the

fate of heavy metals is determined largely by the formation of iron oxide minerals

(Herbert, 1 996; Herbert, 1 994).

As pH rises, iron oxides typically form owing to the hydrolysis and precipitation of

~ e ) ' (Stumm and Morgan, 1996). Simultaneously, the negative surface charge of the iron

oxides increases because of deprotonation of surface hydroxyl groups, which generally

makes sorption of heavy metals extremely favorable (Dario and Ledin, 1997; Jain and

Ram, 1 997; Herbert, 1996).

Figure 20. Adsorption of cu2+ and 2n2+ on hydrous ferric oxide as a function of pH

- - - - -A - .-

(adapted fiom ~reve r , 1997).

As seen in Figure 20, the 50% sorption edge for cu2* and 2n2+ are 5.2 and 6.2,

respectively (Drever, 1997). Thus, one would expect that at a pH of 8, which is the final

pH value reached at the end of the urea titrations, both metals should sorb strongly to iron

oxides (Herbert, 1996). Since considerable iron oxide precipitation is anticipated from

the titration log SI data, removal of zinc and copper from solution as absorbates on the

oxides is likely. The amount of copper and zinc removed would depend strongly on how

much iron oxide was fonned.

7. Discussion

The contaminated groundwater at the now closed South Bay mine site has been the

focus of this study. The groundwater is acidic and contains heavy metals generated

through oxidation of the sulfide-rich mine waste that exist at the site. A biological

treatment plan has been proposed for the site, which consists of introducing urea fertilizer

into the aquifer to encourage the growth of indigenous urea-degrading bacteria, and thus

the production of urea hydrolysis products. It is these products that have the potential to

treat the groundwater by neutralizing low pH. This change is favorable to the

precipitation of iron oxides, which have the potential to remove ~ e ~ + and scavenge heavy

metals through adsorption processes, especially at high pH values. But along with these

processes, is the potential for changing the hydraulic characteristics of the aquifer. A

decrease in hydraulic conductivity by precipitating minerals and growing bactena rnight

result in a slowing or redirection of groundwater flow. Futther computer modeling

andor field monitoring may tell if these processes are apt to occur and to what extent.

The main objective of this study was to test the feasibility of the proposed treatment

plan using a computational approach to modeling the geochemical processes that may

occur at the site following addition of the urea hydrolysis products. The study made use

of a series of twenty-five piezometers positioned along a hydraulic gradient between the

tailhg area and the recipient of the contamination, Mud Lake. The buned valley where

most of the piezometers are located is the main pathway by which contamination

migrates away fiom the tailings and it is here that the focus of this study is situated.

Analytical field data for the twenty-five wells required for the work was generously

provided by Boojum Research Ltd.

The speciation of redox sensitive cornponents was the first step taken towards

constructing the models and in testing the feasibility of the proposed bioremediation

treatrnent plan. The results of the calculations not only allowed us to undertake proton

balance calculations by computing the amounts of iron and copper expected to exist as

reduced or oxidized chemical forms (Tables 2 and 3), but it also allowed us to assess the

condition of the aquifer in ternis of whicb redox process (oxidation or reduction) is

predominantly occwing along the hydraulic gradient. Certainly, there is a reducing

trend in the concentrations of ~ e ~ ' and CU'+ as the metals move away from the tailings

and migrate towards Mud Lake (Figures 3 and 4) but without actual geochemical

analyses of the field sarnples for these species, this apparent trend may be a modeling

artifact. This trend means that the metah tend to be in their oxidized forms in the area

closest to the tailings and in their reduced forms ('FeZ' and Cu3 in the ana closest to Mud

Lake. The trend in iron reduction may likely result fiom two processes, which include

chemical reduction by reaction with sulfide, and metabolic reduction by iron-reducing

bactena. Of these processes, the fonner may most likely be the prevalent of the two since

dissolved sulfide in the gmundwater, derived fiom pyrite dissolution the tailings, will

begin to react witb any oxidized iron it encounters by the following reaction (Evangelou

and Zhang, 1995):

FeS2 + 14~e) ' + 8H20 1 5 ~ e ~ ~ +2s04" + 1 6 ~ ' (1 1)

With the redox sensitive components speciated, proton balance calculations became

possible. This step was done so the total proton concentration would be included in the

mass balance calculations done by MINEQL+. This permits calculation of pH, one of the

most critical parameten in predicting the progression of geochemical reactions.

Moreover, with the proton balance and subsequent pH calculations it was possible to

assess the degree of accuracy obtained with the initial models. What was found was that

some of the calculated pH values did not closely match the measured field pH values,

indicating that uncalibrated models based on incomplete groundwater chemistry data did

not provide a good representation of the groundwater system. As such, the models

needed adjustment to ensure that measured field pH values matched calculated pH

values.

The results of the proton balance calculations (Table 5) showed that piezometea M28,

M89 and M80 yielded positive imbalances (i.e. surplus of positive charge), while al1 of

the others had negative imbalances (i.e. positive charge deficit). Five of those with

negative imbalances had positive charges over 50% more than the negative charges with

M36 exhibiting the greatest imbalance at 93%. These high values are presumed to result

fiom the analytical data that was lacking dissolved anionic or cationic constituents,

respective1 y. Initial assumptions conceming piezometers with incomplete suites of data

are discussed in Section 5.1. Using these initial assumptions, calculated pH values did

not closely match the measured field pH values. In fact, four of the wells (M28, M89,

M72C and M3 7) had calculated pH values 0.3 5 to 2.14 pH units above their measured

field pH values, while the remaining wells' calculated pH values fel10.38 to 1.94 pH

units below their measured field pH values (Table 6).

Mode1 calibration was achieved by using the initial computational runs as a guide to

adjust total proton or DIC concentrations. As mentioned in Section 5.4 adjustment of

DIC and total H' concentrations rather than pCOz values was done pnncipally because

the water in the aquifer is meteoric in ongin and, therefore, is in equilibrium with

atmospheric &O2. Aquifer waters in equilibrium with atmosphenc pC02 have DIC

concentration values typically in the range of lu5 to lu7 M over a pH range of 4 to 6,

which is consistent wi th the range of pH values for the South Bay groundwater

ascertained fiom field measurements. Secondly, the pC02 of the aquifer water is not

likely to be significantly changed by addition of CO2 fiom decomposition of organic

matter in the soi1 because the tailings and the soils are not organic nch. Lastly, with the

exception of well M28, analytical field data did not include DIC concentration data.

Once the models' DIC and H' concentrations were adjusted so that al1 rneasured field pH

values matched their respective calculated pH values, urea titrations were undertaken

with confidence that computational results would actually mirnic bactenal activity.

The titrations of the piezometers witb the urea-degradation products carbonate and

ammonium were done to simulate the introduction of carbonate and ammonium by urea-

degrading bacteria. The titrations carried out lead to increases in groundwater pH values

up to pH 8.0-8.1, with sirnultaneous supersaturation of iron oxides, carbonates and

sulfides. The major concem at this point is whether these minerals have the potential to

precipitate and clog the pore spaces of the aquifer resulting in acid generation (Equation

14) and a decrease in the hydraulic conductivity, thus causing a redirection of

groundwater flow to somewhere other than Mud Lake. The potential for this to happen

rnay not be very high along the gradient since reducing conditions appear to dominate,

but where the groundwater emerges into Mud Lake, oxidation occurs immediately

causing iron oxide fornation. This rnay not be a large problem in this case, however,

since the solid formation will be occurring within Mud Lake and not within the aquifer.

So although the titrations result in supersaturation of iron oxides, sulfides and carbonates,

precipitation of these minerals rnay not occur as there are other factors that corne into

play, which rnay prevent precipitation processes fiom occumng. Such factors include

groundwater flow rates, pH and the presence of solid surfaces on which sorption

processes rnay instigate crystal formation. The magnitude of the potential for

precipitation to occur rnay be evaiuated through expenmental work and /or additional

modeling.

Simulations of groundwater oxidation were perfonned in an attempt to mimic the

oxidation of the groundwater as it emerges into Mud Lake. These computations were

undertaken to gain some insight conceming the success of the treatment in maintaining

neutral pH values under renewed oxidizing conditions. The results showed drops in pH

in sixteen of the wells, with very small decreases (4 pH unit) in half of these with none

falling below pH 7 (Table 7). In the remaining wells, oxidation dropped pH to values

below 4.6. These results are explained by the conversion of ~ e " to ~ e ) + through

cornplete oxidation. Subsequent hydrolysis of the femc iron then becomes the major

source of acid generation by virtue of the following reactions (Stumm and Morgan, 1996;

Evangelou and Zhang, 1995):

~ e ~ ' + H20 = F~(oH)~' + H+ (12)

Fe3' + 2H20 = F~(oH)~+ + 2 ~ ' (13)

Fe3+ + 3H20 3 Fe(OH)3 o, ,, (,, + 3 ~ ' ( 14)

The addition of the urea hydrolysis products ~ 0 ~ ~ ' and N H ~ + was an effective means

of buffering the pH of the groundwater fiom these acid generating reactions in two-thirds

of the wells as s h o w in Table 7. As already mentioned, seven wells experienced no

change in pH at all, while eight experienced decreases but none below pH 7. Further

titration with additional carbonate and ammonium served to reestablish more neutral pH

values in the wells that experienced significant drops in pH in response to oxidation. The

correlation between the wells that expenenced drops in pH and the wells' proximity to

Mud Lake (seven were among the ten closest to Mud Lake), may stem from the fact that

the iron in this m a , as suggested by MINEQL+caiculations, is most likely ferrous iron

since conditions here are thought to be reducing. Thus, when all of this ferrous iron is

oxidized to femc iron and the femc iron undergoes hydrolysis (via the Equations 12- 14),

the pH in these wells drops. So while acidity is being generated near the tailings through

the reduction of femc iron by iron sulfide, the urea hydrolysis products are suficient to

buffer the pH. Acidity generated through re-oxidation of fenous iron and femc iron

hydrolysis cm, however, be neutralized upon further addition of carbonate and

ammonium.

Total arnounts of urea needed to raise the pH to 8.0-8.1 in the wells in both the initial

titrations and in the titrations done after the oxidation simulation (Figure 19) ranged from

3.5* 1 M in M87, to 2.4' 1 M in M72C, with the average urea concentration being

2.5* lu3 M. Results fkom previous experîmental studies involving urea-degrading

bactena show that the addition of 2 . P lo5 M urea to saturated soi1 yields initial rates of

urea hydrolysis of 5.7* 1 M/hr to 8.6* 1 O*' M/hr (Hongprayoon et al., 1 99 1 ; Deluane

and Patrick, 1970). These rates level off afier approximately 24 hours with al1 of the urea

being hydrolyzed and consurned within 20 days (Hongprayoon et a/., 1991 ; Deluane and

Patrick, 1970). Considering this information, the average concentration of urea needed

for pH neutralization (2.5' 10') M) at South Bay could be hydrolyzed within 20 days, a

very reasonable time frame for groundwater treatment. Of course a number of factors

corne into to play that may serve to increase or decrease urea hydrolysis rates. These

include geological and geochemical parameters, as well as constraints on microbial

growth.

Geological parameters that may influence the rate of urea hydrolysis by urea-

degrading bacteria include most importantly adequate hydraulic conductivity, which is

important in allowing the bactena access to the urea substrate, as well as allowing

degradation products to circulate within the aquifer.

Geochemical factors that may impinge on rates of urea hydrolysis encompass such

parameters as temperature, which results in increased rates of hydrolysis as it increases

up to values near 45'C, after which rates begin to fa11 (Khakural and Alva, 1995; Xu,

Heeraman and Wang, 1993). Increased rates are also seen as pH increases up to a value

of 8, again after which rates begin to decrease (Tisdale, et al., 1985; Deluane and Patrick,

1970). The presence of oxygen, or rather the lack of it, has been s h o w to retard

h ydrol ysis impl ying that aerobic or facul tativel y anaerobic bactena (bacteria that grow

under ei ther aerobic or anaerobic conditions) contribute principally to urea degradation

(Kbakural and Alva, 1995; Savant, James and McClellan, 1985). Environments with high

electncal conductivity also tend to retard urea hydrolysis (Sankhayan and Shukla, 1976).

On the other hand, increased rates are typically seen in conjunction with increased

concentrations of substrate (Singh, and Nye, 1984) and organic matter (Khakural and

Alva, 1995).

Microbiological factors that may effect urea hydrolysis include the presence of

protozoa that may prey on bacteria and result in low rates of hydrolysis. As well, the

presence of competitors such as other bacteria may out-compete the urea-degraders for

substrate space, electron-donors, electron-acceptors or other necessities for growth and

survival (Ledin and Pedersen, 1996). Further study in the field and in the lab to yield

some potential rates specific to the South Bay site would be valuable.

Geochemical rnodeling offers a quantitative way to investigate the geochemical

impacts of bactena in natural and contaminated systems. Moreover, when models are

sufficiently accurate, they can be powerfùl tools for assessing the success of

bioremediation treatrnents (Fein, et al., 1997; Hudak and White, 1 997).

Geochemical models primarily describe how chemical speciation is controlled by the

equilibrium thennodynamics of reactions occumng in aqueous environments. Limited

use of geochemical modeling in the past to evaluate bioremediation of inorganic

contaminants in the subsurface has been due to the fact that the models assume chemical

reactions proceed until the system is at equilibrium. However, the equilibrium

assumption is typically not valid for bioremediation because the key reactions are usually

controlled by kinetics where concentrations of reactants and products Vary with respect to

time (NRC, 1993). To a certain extent, this problem has been sidestepped in this study

by the incremental addition of carbonate and ammonium during the titraiion modeling

which cm be viewed as time-steps (e-g. dC/dt) that, in reality, correlate to rates of urea

hydrolysis. Also, by not including miaerals in the mass balance calculations done by

MINEQL', the extent to which South Bay groundwater departed from true chemical

equilibnum was revealed by variations in mineral saturation States. As such, modeling

becomes a valuable tool for linking conceptual understanding of the bioremediation

process to actual field conditions.

The rnost critical parameter influencing success of the bioremediation treatment is

how well the subsudace materials transmit fluids. Hydraulic conductivity is a measurr: of

this and, optimally for treatrnent to succeed, the hydraulic conductivity of the aquifer

must be 1 O-' cm/s or greater (King, et al, 1998; Alexander, 1994) to allow for adequate

circulation of the urea-substrate and the products of urea-hydrolysis, which promote

neutralization of the groundwater pH.

The sediments along the hydraulic gradient range from silty sand to gravel, so the

hydraulic conductivity of the site can be expected to range fiom 10" to 1 c d s (Fetter,

1994). In fact measurements made by SCIMUS Inc. for Boojum Research Ltd. have

yielded a range for hydraulic conductivity of the sand and gravel deposits in the buried

valley of between 0.01 to 0.093 cm/s (SCIMUS, 1 W8), which is extremely favorable for

the bioremediation treatment proposed for this site. This leads to the second most

important site characteristic favoring in situ bioremediation, which is the presence of a

relatively uniform subsurface medium, which is again important for uniform circulation

of supplied nutrients and hydrolysis products (King et al, 1998; Alexander, 1994; NRC,

1993). The buried valley through which the contaminants are travelling on their way to

Mud Lake is principally filled with sands and gravels which are good media for fluid

flow, and are relatively unifonn throughout (SCIMüS, 1998).

Also important for treatrnent success is consistent groundwater flow in speed and

direction throughout the changing seasons, so that the bacteria cm continue to break

down the nutrients without a period of stagnant growth where no usehl products are

generated (King et al., 1998; Alexander, 1994; NRC, 1993). Currently, the main

direction of groundwater flow is to the north towards Mud Lake (Kalin, et al. 1990). It

flows in this direction fairly consistently, but the seasonal flow rate is thought to be

highly variable. Measurements by Boojum Research Ltd. have put the rate of

contaminant fiow (Q) towards Mud Lake between the range of Z.S* 104 to 5.3*104

m3/sec across a cross-section through the buned valley between piezometers M8 1, M79

and M80.

These contaminant flow rates (Q) can be converted into average linear velocities (v)

via the following equation (Fetter, 1994):

v = Q/8A (15)

where 0 is the effective porosity, which for the site is expected to be about 0.4, a value

typical of these types of sediments, (Ferris, 1998), and A, the cross-sectional area

transecting the buned valley, has been determined to be about 250 m' (Boojum, 1996).

Therefore minimum and maximum rates of groundwater veiocity are respectively 0.2 16

and 0.458 miday with the groundwater travelling from the tailings to Mud Lake in 3.38 to

7.1 6 years.

Typically nutrients are delivered by controlling the 80w rate of the water by using

injection wells or infiltration gallenes near the source of contamination, in conjunction

with downstrearn production or recovery wells. Most comrnonly, the water withdrawn

from the production wells dom-gradient from the biostimulation zone is combined with

nutrients and reintroduced to the aquifer up-gradient of the biostimulation zone via the

injection wells or galleries. This allows control of the rate of subsurface flow and

distribution of the nutrients (Lee, et al., 1997; Gersberg, et al., 1995; NRC, 1993).

However, the removal of water from the aquifer for reinjection depends on the

groundwater transit time. This can be attributed to studies by the National Research

Council of Canada that suggest that systems should be designed so that the rate of

groundwater flow fiom injection to discharge or recovery will be about one to three

months. This is important because fluid transport time and system design greatly

influence installation costs (NRC, 1993).

Spacing of injection wells or infiltration gallenes is also very important. A wide

spacing tends to increase the remediation time and residence time in the contaminated

zone, but may still be more cost effective than installing a greater number of close-spaced

wells or galleries. Normally, the injection wells or gallenes are situated at the highest

point of the hydraulic gradient where contamination is detectable. Also, the

concentration of nutrients to be added and the frequency of additions should be

considered carefull y (NRC, 1 993).

Since the distance between the f h t contarninated well, M83A, and Mud Lake is about

565 m, the minimum and maximum arnounts of time that it would take contarninants to

travel this distance are, respectively, 3.38 and 7.16 years, based on the calculated

groundwater velocity values. Therefore products of urea hydrolysis would take the sarne

length of time to travel the distance dong the gradient to Mud Lake, assuming the

aquifer's hydraulic conductivity is not signîficantly decreased by effects of the treatment

(e.g. mineral precipitation). Thus, it may be of interest for the bioremediation plan to

introduce the urea into the aquifer in more than one location aiong the gradient to speed

the spread of the urea. Also, since it has been shown that low pH environrnents retard

urea hydrolysis (Tisdale, et al.. 1985; Deluane and Patrick, 1 WO), best results rnay be

obtained by introducing urea into the aquifer where near neutral pH values are observed

in the down-gradient area closer to Mud Lake.

When nutrients for microbial growth are added to the subsurface, excessive microbial

growth rnay occur around the treatment zone, causing plugging of the effective pore

spaces (pore spaces available for fluid flow) and limiting water flow (Wu, et al., 1997;

Shevah and Waldman, 1995). Adding the nutrients in pulses rnay help alleviate this

problem if it anses. Advective and dispersive processes within the aquifer rnay then

circulate the nutrients downstream causing dispersed ce11 growth throughout the aquifer

and producing a large biostimulation zone (Lee, et al., 1997; NRC, 1993).

Several suggestions for funher study have been briefly mentioned throughout this

discussion. An overview is necessary, however, to pull together these ideas in an effort

to grasp the extent of work that should be done to fully understand what is happening

within the aquifer and to foresee any potential obstacles to the bioremediation process.

Lab tests need to be done to determine the rate of urea hydrolysis by isolated strains of

urea-degrading bactena in samples of South Bay groundwater. This data will be usefùl in

determining how much urea-nutrient solution should be added to the aquifer and how

quickly we can expect to see some positive changes in the contaminated groundwater.

These results rnay also reveal whether or not the changes in pH can be sustained or if

numents need to be injected regularly. They rnay also reveal if the treatment will result

in changes to the hydrogeologic conditions of the aquifer, such as decreasing hydraulic

conductivity through minera1 precipitation a d o r clogging by growth of bacterial

colonies.

Further study on the aquifer itself needs to be done to determine hydraulic

conductivity variations along the gradient and to recalculate flow rates of the

groundwater and contaminants. Besides these hydrogeological parameters, complete

ionic analyses and alkalinity measurements of al1 of the wells along the gradient should

be done. Further experimental work and modeling is needed to evaluate the potentials for

adsorption of Zn and Cu to iron oxides, for dissolution and precipitation of solids, and for

desorption of adsorbed metals on these solids and on aquifer sediments.

Al1 of these suggestions for further study are important for undentanding the

processes that are occumng within the aquifer, and to accurately predict whether or not

the contaminated groundwater will be successfully treated by the bioremediation

treatment plan proposed for this site.

A variety of assumptions were made concerning the chernical composition of South

Bay goundwater in order to permit this computational investigation. These assurnptions

proved helpful in accomplishing the main objective of this thesis, which was to ascertain

whether or not microbial urea degradation cm be used successfully for treating the

contaminated groundwater at the South Bay mine site. In fact, the treatment plan seems

to have considerable potential for success in not only neutralizing the site's acidic pH

values, but also for scavenging harmful heavy metals through adsorption processes

involving precipitating iron oxides. The data that has been generated is very likely a

good representation of what is, and what may occur within the aquifer throughout the

course of the treatment. The ultimate accuracy of these results can now only be truly

evaluated through field testing and subsequent monitoring at the South Bay site.

Appendix A

Piezometer Location Map

MUD LAKE

'r

h m ' s A . - DECAM

Y 7 POND

.s .

LAKE

PREL~MI~RYWDRAUUC~HEA~ D O ~ f R 4 6 ~ N IMMEVTELY ABOVE THE BEDRaCK:SURfACE {JULY Z$ - B C D E

Appendix B

Titration Results - tables of computed pH and log saturation index (SI) values

Appendix B: M28 Titration Results - computational pH and log saturation index (SI) values

Titration î ncrements: i C 0 3 ( 2 - 1 1 M I N H I ( + ) 1 M na Loa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) LOS04 ALOHS04 ATACAM I TE MALACHITE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6SO4 ZN4 (OH) 6SO4 ZN2 IOH)2S04 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNS04, 1H20 BIANCHITE CUS04 CU2S04 MNS04 CELESTI TE Z INCOSITE PYRITE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SI DERITE SMITHSONITE RHODOCHROSIT

Appendix B: M83A Titration Results - computational pH and log saturation index (SI) values

Titration Increments:

Loa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) lOSO4 ALOHS04 ATACAMITE MALACHITE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6SO4 ZN4 (OH ) 6S04 ZN2 (OH) 2SO4 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNS04, 1H2O BIANCHITE CUS04 CU2S04 MNS04 CELESTITE ZINCOSITE PYRITE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SIDERITE SMfTHSONfTE RHODOCHROSIT

Appendix 8: M83B Titration Results - computational pH and log saturation index (SI) values

Titration Increments:

Loa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) lOSO4 ALOHS04 ATACAMITE MALACHITE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSI TE K JAROSITE H NI4 (OH) 6S04 ZN4 (OH) 6304 ZN2 (OH) 2SO4 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNS04, 1H20 BIANCHITE CUS04 CU2S04 MNS04 CELESTITE ZINCOSI TE PYRITE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SIDERITE SMITHSONITE RHODOCHROSIT

Appendix B: M89 Titration Results - computational pH and log saturation index (SI) values

Titration Incrernents:

Loa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) lOSO4 ALOHSO4 ATACAMITE MALACHITE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6SO4 ZN4 ( OH ) 6SO4 ZN2 (OH) 2SO4 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNS04, 1H20 BIANCHITE CUÇ04 CU2S04 MNS04 CELESTITE ZINCOSITE PYRITE CHALCOPYRITE MACKINAWITE MI LLERITE SPHALERITE CALCITE DOLOMITE SIDERITE SMITHSONITE RHODOCHROSIT

Appendix B: M88 Titration Results - computational pH and log saturation index (SI) values

Titration Increments: JC03(2-11 M 0.00004 0.000155 0.00027 0.000385 0.0005 1Na4(+)l M 0.00008 0.00031 0.00054 0.00077 0.001 Ra 5.23 6.92 7.6 7.86 8.02

Loa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) 10S04 ALOHSO4 ATACAMITE MALACHITE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6SO4 ZN4 (OH) 6S04 ZN2 (OH) 2SO4 ALUM Y GYPSUM CHALCANTHITE MELANTERITE ZNS04, 1H20 BIANCHITE CUS04 CU2SO4 MNS04 CELESTITE ZfNCOSITE PYRITE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SIDERITE SMITHSONITE RHODCCHROSIT

Appendix B: M72A Titration Results - cornputational pH and log saturation index (SI) values

Titration Increments: JC03(2-11 M tNHQ(+)I M RB

Loa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) 10S04 ALOHS04 ATACAMITE MALACHITE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6SO4 ZN4 (OH) 6S04 ZN2 (OH) 2SO4 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNSO4, 1H2O BIANCHITE CUS04 CU2S04 MNS04 CELESTITE ZINCOSITE PYRITE CHALCOPYRITE MACKINAWITE MI LLERITE SPHALERITE CALCITE DOLOMITE SI DERITE SMITHSONITE RHODOCHROSZT

Appendix 0: M72B Titration Results - computational pH and log saturation index (SI) values

Titration Incrernents: CCO3(2-II M JNETI(+)I 13

RB

Loa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) 10S04 ALOHS04 ATACAMITE MALACHITE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6SO4 ZN4 (OH) 6SO4 ZN2 (OH) 2SO4 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNS04, 1H2O BIANCHITE CUS04 CU2S04 MNS04 CELESTITE Z INCOS 1 TE PYRITE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SIDERITE SMITHSONITE RHODOCHROSIT

Appendix B: M72C Titration Resuits - computational pH and log saturation index (SI) values

Titration tncrements: lCO3(2-11 M [#H1(+)1 W PH

Loa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) 10504 ALOHS04 ATACAMITE MALACHITE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6SO4 ZN4 (OH) 6SO4 ZN2 (OH) 2SO4 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNSO4, 1H20 BIANCHITE CUS04 CU2SO4 MNS04 CELESTITE ZINCOSITE PYRITE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SIDERITE SMITHSONITE RHODOCHROSXT

Appendix 8: M87 Titration Results - computational pH and log saturation index (SI) values

Titration Increments:

Loa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) lOSO4 ALOHSO4 ATACAMI TE MALACHI TE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6SO4 ZN4 (OH) 6SO4 ZN2 (OH) 2SO4 ALüM K GYPSUM CHALCANTHITE MELANTERITE ZNSO4, 1H20 BIANCHITE CUSO4 CU2 S04 MNSO4 CELESTITE ZINCOSITE PYRITE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SIDERITE SMITHSONITE RHODOCHROSIT

Appendix B: M69 Titration Results - computational pH and log saturation index (SI) values

Titration Increments: [C03(2-11 M 1NR4(+)1 M PB

Loa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) LOS04 ALOHSO4 ATACAMITE MALACHITE FE3 (OH) 8 GOETHI TE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6SO4 ZN4 (OH) 6S04 ZN2 (OH) 2SO4 ALüM K GYPSUM CHALCANTHITE MELANTERITE 2NS04, 1H2O BIANCHITE CUS04 CU2S04 MNS04 CELESTXTE ZINCOSITE PYRITE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SXDERITE SMITHSONITE RHODOCHROSIT

Appendix 8: M85 Titration Results - computational pH and log saturation index (SI) values

Titration Increments: [C03(2- )1 M JWS41+)1 M a?

Loa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) lOSO4 ALOHS04 ATACAMITE YLALACH I TE FE3 (OH) 8 GOETH ITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6S04 ZN4 ( OH 6S04 ZN2 (OH) ZSO4 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNSO4, l H 2 O BIANCHITE CUS04 CU2S04 MNS04 CELESTITE ZINCOSITE PYRITE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCI TE DOLOMITE SIDERITE SMITHSONfTE RHODOCHROSIT

Appendix B: M86 Titration Results - computational pH and log saturation index (SI) values

Titration Increments:

Loo SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) lOS04 ALOHSO4 ATACAMITE MALACHITE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6SO4 ZN4 (OH) 6SO4 ZN2 (OH) 2SO4 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNSO4, 1H20 BIANCHITE CUS04 CU2 SOI MNS04 CELESTITE ZINCOSITE PYRITE CWCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SIDERITE SMITHSONITE RHODOCHROSIT

Appendix B: M39 Titration Results - computational pH and log saturation index (SI) values

Titration Increments: [CO3 (2-1 1 M tNI14(+)1 M RB

Loa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) lOSO4 ALOHSO4 ATACAMITE MALACHITE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6SO4 ZN4 (OH) 6SO4 ZN2 (OH) 2SO4 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNS04, 1H2O BIANCHITE CUSO4 CU2S04 M M 0 4 CELESTITE ZINCOSITE PYRf TE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SIDERITE SMITHSONITE RHODOCHROSIT

Appendix B: M39A Titration Results - computational pH and log saturation index (SI) values

Titration lncrements: [ C 0 3 t 2 - ) 1 M frml(+)l M RB

Loa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) 10504 ALOHSO4 ATACAMITE MALACHITE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6S04 ZN4 (OH) 6S04 ZN2 (OH) 2S04 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNSO4, 1H2O BIANCHITE cuso4 CU2S04 MNSO4 CELESTITE ZfNCOSITE PYRITE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SIDERITE SMITHSONITE RHODOCHROSIT

Appendix B: M81 Titration Results - computational pH and log saturation index (SI) values

Titration Incremsnts: JC03(2-11 M tNHI(+)I M rn Loa SI Data ALOH3 (A) GIBBSITE (Cl AL4 (OH) lOSO4 ALOHS04 ATACAMI TE MALACHITE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6SO4 ZN4 (OH) 6SO4 ZN2 (OH) 2S04 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNS04, 1H2O BIANCHITE cuso4 CU2S04 MNS04 CELESTITE ZINCOSITE PYRf TE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SIDERITE SMITHSONITE RHODOCHROSIT

Appendix 6: M34 Titration Results - computational pH and log saturation index (SI) values

Titration Increments:

Loa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) lOSO4 ALOHS04 ATACAMITE MALACHITE FE3 (OH) 8 GOETH ITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH1 6SO4 ZN4 ( OH ) 6S04 ZN2 (OH1 2SO4 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNS04, lH20 BIANCHITE CUS04 CU2 s04 MNS04 CELESTITE Z INCOÇITE PYRITE CHALCOPYRITE MACKINAWITE MILLER1 TE SPHALERITE CALCITE DOLOMITE SIDERITE SMITHSONITE RHODOCHROSIT

Appendix 6: M79 Titration Results - computational pH and log saturation index (SI) values

Titration I ncrements:

Loa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) lOSO4 ALOHS04 ATACAMITE MALACHITE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6SO4 ZN4 (OH) 6SO4 ZN2 (OH) 2SO4 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNS04, 1H2O BIANCHITE CUS04 CU2 s04 MNS04 CELESTITE ZINCOSITE PYRITE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SIDERITE SMITHSONITE RHODOCHROSIT

Appendix 6: M73 Titration Results - cornputational pH and log saturation index (SI) values

Titration Increments: [CO3(2-11 W 1=4(+)1 M plT

Loa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) lOSO4 ALOHSO4 ATACAM 1 TE MALACHITE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6SO4 ZN4 (OH) 6S04 ZN2 (OH) 2SO4 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNS04, 1H2O BIANCHITE cuso4 CU2S04 MNS04 CELESTITE ZINCOSITE PYRITE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCI TE DOLOMITE SIDERITE SMITHSONITE RHODOCHROSIT

Appendix 8: M80 Titration Results - cornputational pH and log saturation index (SI) values

Titration Increments:

Loa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) los04 ALOHSO4 ATACAMITE MALACHITE FE3 (OH) 8 GOETH ITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6S04 ZN4 (OH) 6S04 ZN2 (OH) SSO4 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNSO4, 1H2O BIANCHITE cuso4 CU2S04 MNS04 CELESTITE Z INCOS ITE PYRf TE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SIDERITE SMITHSONITE RHODOCHROSIT

Appendix B: M6OA Titration Results - computational pH and log saturation index (SI) values

Titration Increments: tC03(2-11 M [ N I I Q ( + ) I M pft

Loci SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) los04 ALOHS04 ATACAMI TE MALACHITE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6S04 ZN4 (OH) 6S04 ZN2 (OH) 2SO4 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNS04, 1H20 BIANCHITE CUS04 CU2S04 MNS04 CELESTITE ZINCOSITE PYRITE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SIDERITE SMITHSONITE RHODOCHROSIT

Appendix B: M60B Titration Results - computational pH and log saturation index (SI) values

Titration Incrernents: fC0312-11 M JNITIt+)I M pfl

Loa SI Data ALOH3 { A ) GIBBSITE (C) AL4 (OH) lOSO4 ALOHS04 ATACAMI TE MALACH 1 TE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6SO4 ZN4 (OH) 6SO4 ZN2 (OH) 2SO4 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNS04, 1H20 BIANCHITE CUSO4 CU2S04 MNS04 CELESTITE ZINCOSITE PYRITE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SIDERITE SMITHSONITE RHODOCHROSIT

Appendix B: M63 Titration Results - computational pH and log saturation index (SI) values

Titration Increments: IC03(2-11 H tNEII (+)I M Pa

Loa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) 10S04 ALOHSO4 ATACAMI TE MALACHITE FE3 (OH) 8 GOETHI TE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6SO4 ZN4 ( OH 1 6SO4 ZN2 (OH) 2SO4 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNSOQ, 1H20 BIANCHITE CUS04 CU2S04 MNS04 CELESTITE ZINCOSITE PYRITE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SIDERITE SMITHSONITE RHODOCHROSIT

Appendix 6: M62 Titration Results - computational pH and log saturation index (SI) values

Titration Increments: [CO3(2-11 W jmu+)l M pff

toa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) los04 ALOHSO4 ATACAMITE MALACH 1 TE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6SO4 ZN4 (OH) 6SO4 ZN2 (OH) 2SO4 ALUM K GYPSUM CHALCANTHITE MELANTERITE ZNS04, lH20 BIANCHITE CUS04 CU2S04 MNS04 CELESTITE ZINCOSITE PYRITE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SIDERITE SMITHSONITE RHODOCHROSIT

Appendix B: M36 Titration Results - computational pH and log saturation index (SI) values

Titration Incrernents: JC03(2-11 W ~ m u + ) i M QG

Loa SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) los04 ALOHSO4 ATACAMITE MALACHITE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6SO4 ZN4 (OH) 6SO4 ZN2 (OH) 2SO4 ALUM K GYPSUM CHALCANTHXTE MELANTERITE ZNS04, 1H2O BIANCHITE cuso4 CU2S04 MNS04 CELESTITE ZINCOSITE PYRITE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SIDERITE SMITHSONITE RHODOCHROSIT

Appendix B: M37 Titration Results - cornputational pH and log saturation index (SI) values

Titration Increments: iC03(2-11 M [ # s r ( + ) ] M iia

Loci SI Data ALOH3 (A) GIBBSITE (C) AL4 (OH) lOSO4 ALOHSO4 ATACAMITE MALACHITE FE3 (OH) 8 GOETHITE HEMATITE FERRIHYDRITE JAROSITE K JAROSITE H NI4 (OH) 6SO4 ZN4 (OH) 6SO4 ZN2 (OH) 2SO4 ALUM K GYPSüM CHALCANTHITE MELANTERITE ZNS04, 1H20 BIANCHITE CUSO4 CU2S04 MNS04 CELESTI TE Z INCOSITE PYRITE CHALCOPYRITE MACKINAWITE MILLERITE SPHALERITE CALCITE DOLOMITE SIDERITE SMITHSONITE RHODOCHROSIT

Appendix C

Titration Results - graphs of amounts of urea equivalents

added to each well vs. pH and log SI values vs. pH

I l -

Well M39a-Log SI of lron Oxides

Well W9a-Log SI of Carbonates

+CALCITE

-SIDERITE !

-X-SMITHSONITE

+ RHODOCH ROSIT --

Weil M39a-Log SI of lron Sultides

2 , 1

Well W7-Log SI of lion Oxides

-- ---- --

+ FE3(0H)8

-C-GOETHiTE

+HEMATITE

-FERRlHYDRiTE

-ICJAROSITE K

+ JAROSITE H

_ . - - _ - - - ..- . .- .-..---_ _

Weil M37-Log SI of Carbonates

- -- -f-CALCITE

FIHODOCHROSIT -----

--- --

Weil M37-Log SI of lron Suffldes

Appendix D

Original Aqueous Geochemistry Data

S80909-1 .XLS SAMPLE DATE 8-Sep-96 8-Sep96 8-Sep96 8-Sep96 8-Sep96 0-Sep96 8-Sep96 SAMPLE VOLUME ASSAYERS CODE 6042.1 6042.2 6003.1 6043.2 6004.1 6044.2 6045.1 SAMPLING LOCATION SOUTH BAY SOUTH BAY SOUTH BAY SOUTH BAY SOUTH BAY SOUTH BAY SOUfH BAY

Mud Lake Mud Lake Mud Lake Mud Lake Town Site Town Site Gravel Pit M63 MW M60A M60A M28 M28 M39A

Pracessing code FA (liltered) WA (whole) FA (filtered) WA (whaie) FA (tiltered) WA (whole) FA (filtered) " F I E L D " Temp. (C) PH Cand. (umhos/cm) Eh (mV) *' L A 8 ** Temp. (C) PH Gond. (umhoslcm) Eh (mV) Acidity (mg) Alkalinity (mg) ELEMEMS (wm) Ag < Al c B c Ba c Be c Bi c Ca Cd < Co Cr C

Cu Fe K Mg Mn Mo c Na Ni c P C

Pb < S Sn < Sr Ti < v < Zn Ammonia.as N c Bromide c Chloriàe Flwride c

Nitrale,as N < Nitriteas N c

ORhophosph.,as P c Sulphate Alkalinity c

Anion Sum Bicarûonate <

Carbonate c Cation Sum Conductivity(25) Hardness Ion Balance Langelier lnd.(20) Langelier Ind.(4) PH Saturation pH(20) Saturation pti(4)

T.D.S. Turûidify

TIC.as C

Agpendix D: Ongnal Geochemistry Data

SBo909-1 .XLS SAMPLE DATE &Sep96 &Sep96 &Sep96 SAMPLE VOLUME ASSAYERS CODE 6045.2 6046.1 6046.2 SAMPLING LOCATIûfU SOUTH BAY S0üII-l BAY SOWH BAY

Gravei Pit Gravol Pit Gmvel Pit M39A Ma0 M80

- FIELD - Temp. (C) PH Cond. (urnhasrcm) Eh (mV) *. L A B " T ~ P . (Cl PH

Cond. (umhoskm) Eh (mV) Audity ( m g ) Alkalinity (mgf) ELEMENTS (ppm) Ag c Al B c Ba c Be c Bi c Ca Cd C

Co Cr c Cu C

Fe K Ma Mn Mo C

Na Ni c P c Pb C

S Sn < Sr Ti c v c Zn Ammoniaas N Btornide Chtonde Fluaride

Mlrate.as N Nitri1e.a~ N

ûIhopbsph.,as P Sutphate Alkalinity Anion Surn

Bicarbonafe Caiûonaie Caiion Sum Conaicliviîy(25) liardness Ion &lance Langelier Ind.(M) -lier lnd.(4) PH Saturation pH(2O) Saturation pH(4)

T.D.S. Tuibidity

nc.as C

Appendix D: Original Geochemistry Data

SB0996-2.XLS SAMPLE DATE SAMPLE VOLUME ASSAYERS CODE SAMPLING LOCATION

Processing code " F I E L D +* Temp. (C) PH Cond. (um hosicrn) Eh (mV) +* L A B " Temp. (C) PH

Cond. (um hoskm) Eh (mV) Acidity (mg/l) Alkalinity (mgil) ELEMENTS (ppm) Ag < Al 6 Ba Be c 6 i < Ca Cd < Co < Cr c

Cu < Fe K Mg Mn Mo < Na Ni P < Pb c S Sn c Sr Ti c v c Zn

9-Sep-96 8-Sep-96

6078 6079 South Bay South Bay GRAVEL MUD L

M34 M36

WA (whole) WA (whole)

8-Sep-96 8-Sep-96 8-Sep-96

6080 6082 6099 South Bay South Bay South Bay

MUD L GRAVEL MUDL M37 M39 M60B

WA(whole) WA(whole) WA(whole)

Appendix D: Original Geochemistry Data

SB0996-2.XLS SAMPLE DATE 8-Sep-96 9-Sep96 9-Sep-96 9-Sep-96 9-Sep-96 SAMPLE VOLUME ASSAYERS CODE 61 01 61 08 6113 61 14 61 15 SAMPLING LOCATION South Bay South Bay South Bay South Bay South Bay

MUD L TAlLlNGS TAlLlNGS TAILINGS TAlLlNGS M62 M69 M72A M72B M72C

Processing code WA (whole) WA (whole) WA (whole) WA (whole) WA (whole) " F I E L D " Temp. (C) PH Cond. (um hoslcm) Eh (mV) ** L A B " Temp. (C) PH Cond. (umhos/crn) Eh (mV) Acidity (mg/l) Alkalinity (mg/l) ELEMENTS (ppm) Ag

Appendix D: Original Geochemistry Data

SB0996-2.XLS SAMPLE DATE SAMPLE VOLUME ASSAYERS CODE SAMPLING LOCATION

Processing code " F I E L D '* Temp. (C) PH Cond. (um hos/cm) Eh (mV) ** L A B " Temp. (C) PH Cond. (um hoskm ) Eh (mV) Acidity (mg/l) Alkalinity (mgIl) ELEMENTS (ppm) Ag c Al B c Ba Be c Bi < Ca Cd < Co Cr Cu Fe K Mg Mn Mo c Na Ni P Pb c S Sn c Sr Ti v < Zn

9-Sep-96 10-Sep96

61 30 61 31 South Bay South Bay TAlLlNG TAlLlNG

M85 M86

WA (whole) WA (whole)

1 0-Sep-96 9-Sep-96 9-Sep-96

61 32 61 33 61 34 South Bay South Bay South Bay TAlLlNG TAlLlNG TAlLlNG

M87 M88 M89

WA (whole) WA (whole) WA (whole)

Appendix C: ûriginal Geochernistry Data

SB-2.WS SAMPLE DATE 9-f3Ws 9--96 IO-Sep96 9-Sep96 1 0-Sep96 SAMPLE VOLUME ASSAYERS CODE 61 16 61 24 61 25 61 27 61 28 SAMPLING LOCATION South Bay South Bay South Bay South Bay South Bay

GRAVEL GRAVEL PIT GRAVEL PIT TAlllNG TAlLlNG

Processing code " F I E L D ** Ternp. (C) PH Cond. (umhodcrn) Eh (mV) ** L A B " Temp. (C) PH Cod. (urn hoslcm) Eh (mW Acidity (mg) Aikalinity (rngl) Ag (ppm) Ai B Ba Be Bi Ca Cd Co Cr Cu Fe K Ms Mn Mo Na Ni P Pb S Sn Sr Ti v Zn

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