Examining Model Predictions of Zinc and Copper Aqueous Speciation and Freshwater Ecotoxicity: Case Study of

Ross Lake, Flin Flon, Manitoba, Canada

by

Sumera Yacoob

A thesis submitted in conformity with the requirements for the degree of Master of Applied Science

Department of Chemical Engineering and Applied Chemistry University of Toronto

© Copyright by Sumera Yacoob 2013

ii

Examining Model Predictions of Zinc and Copper Aqueous Speciation and Freshwater Ecotoxicity: Case Study at

Ross Lake, Flin Flon, Manitoba, Canada

Sumera Yacoob

Master of Applied Science

Department of Chemical Engineering and Applied Chemistry

University of Toronto

2013

Abstract

Models of aqueous metal speciation and ecotoxicity have become commonplace due to their

ability to estimate metal behaviour. This study evaluated commonly used aqueous geochemical

speciation and ecotoxicity models with application to a mine impacted lake in northern

Manitoba.

The geochemical speciation model Winderemere Humic Aqueous Model (WHAM) was

compared with Diffusive Gradients in Thinfilm (DGT) measurements of zinc and copper. DGT

measurements in the water column corresponded well with WHAM-estimated Zn2+

, Cu2+

was off

by up to 100x. Additional metal, either from small organic bound species or dissolution of metal

sulphides from resuspended sediment, served to improve model estimates.

The single metal Biotic Ligand Model (BLM) predicted acute toxicity to Daphnia magna

attributable to copper but not zinc, at low pH (3.55 – 5.5). Comparison of results did not show a

significant difference between the single and mixture BLMs, suggesting a non-interactive effect

on metal toxicity for measured water chemistry.

iii

Acknowledgements

I would like to express my heartfelt thanks to Professor Miriam Diamond for her support,

patience and hard work throughout this process, without which this project may never have been

completed. This project presented many challenges along the way, and her understanding and

positivity were essential to this coming together.

I would like to thank HBMS for funding this project. In particular I would like to thank Stephen

West, Joel Nielson and Ray Tardiff for their kindness and help during my trips to Flin Flon.

Thank you to Robert Santore and Dr. Helga Sonnenberg for your expertise and helpful

discussions which always reinvigorated my interest in this topic.

Thank you to Dr. Celine Gueguen for all her helpful contributions on the project and

participation on my defense committee.

I thank Professors Charles Jia and Susan Andrews for participating in my defense committee.

Appreciation and thanks go out to the entire Diamond Lab group. Your smiling faces and

suggestions and advice were always helpful. In particular, thank you to Dr. Emma Goosey for all

your help in the field and lab.

Thank you to all of my family and friends, who supported my work and never let me be too hard

on myself. Thanks especially go out to my parents and sister whose pride in my work always

touched my heart and for supporting my decision to leave consulting and focus on academia.

And lastly, but certainly never least, thank you to my husband Imran whose love, patience,

generosity, advice, listening ear and computer programming skills helped more than you realize.

iv

Table of Contents

Acknowledgements ........................................................................................................................ iii

List of Tables ................................................................................................................................ vii

List of Figures .............................................................................................................................. viii

List of Appendices ......................................................................................................................... ix

Chapter 1 Introduction .....................................................................................................................1

1.1 Study Metals and Study Site ................................................................................................2

1.1.1 Zinc ..........................................................................................................................2

1.1.2 Copper ......................................................................................................................3

1.2 Geochemical and Ecotoxicity Modelling .............................................................................3

1.3 Diffusive Gradient Thinfilms ...............................................................................................5

1.4 Project Summary ..................................................................................................................6

1.5 Research Objectives .............................................................................................................6

1.6 Thesis Outline (Outline all papers contained) .....................................................................6

1.7 References ............................................................................................................................8

Chapter 2 - Comparing DGT Measurements and WHAM Estimates of Zinc and Copper: A

Case Study at Ross Lake in Northern Canada ..........................................................................10

2 Abstract .....................................................................................................................................10

2.1 Introduction ........................................................................................................................11

2.2 Case Study .........................................................................................................................14

2.3 Field Measurements ...........................................................................................................15

2.3.1 DGT Deployment...................................................................................................15

2.3.2 DGT Analysis ........................................................................................................16

2.4 Water Chemistry Sampling ................................................................................................17

2.5 Geochemical Modeling ......................................................................................................17

v

2.6 Comparison of DGT to WHAM Predicted Free Ion Concentration ..................................18

2.7 Comparing DGT Values to Metal Bound to DOC.............................................................20

2.8 Dissolution Kinetics ...........................................................................................................21

2.9 Implications........................................................................................................................23

2.10 Tables .................................................................................................................................25

2.11 Figures................................................................................................................................28

2.12 References ..........................................................................................................................32

Chapter 3 - Investigating Toxicity Using Single and Metal Mixture BLM Models: A Case

Study at Ross Lake ....................................................................................................................35

3 Abstract .....................................................................................................................................35

3.1 Introduction ........................................................................................................................36

3.2 Case Study – Ross Lake .....................................................................................................37

3.3 Methods..............................................................................................................................39

3.3.1 Field Measurements ...............................................................................................39

3.3.2 Speciation and Toxicity Modelling Using WHAM and BLM ...............................40

3.4 Results ................................................................................................................................41

3.4.1 Measured Water Chemistry ...................................................................................41

3.4.2 Metal Speciation and Toxicity ...............................................................................42

3.4.3 Single Versus Metal Mixture BLM Toxicity Analysis ..........................................43

3.5 Implications........................................................................................................................45

3.6 Tables .................................................................................................................................47

3.7 References ..........................................................................................................................51

Chapter 4 - Conclusions .................................................................................................................54

4 Research Summary....................................................................................................................54

4.1 Scientific Contributions .....................................................................................................55

vi

4.2 Research Outlook ...............................................................................................................56

4.3 References ..........................................................................................................................57

vii

List of Tables

Table 2.1: Comparison between DGT-measured metal concentrations with those obtained from

geochemical modelling and analytical measurements. ……………………………………..…25

Table 3.1: Toxic Units calculated for Fathead Minnow, Rainbow Trout and D. magna using the

single metal BLM. Results are presented as Toxic Units (TU) which is the ratio of the amount of

metal measured and the amount of metal estimated to be toxic to the respective organism listed.

A value of TU >1 indicates toxicity……………………………………………………………47

Table 3.2: Toxic Units calculated for Cutthroat Trout, Rainbow Trout, D. magna and H. azteca

using a mixture analysis BLM. Results are presented in Toxic Units…………………………47

viii

List of Figures

Figure 1.1: Schematic of BLM presenting Chemistry, physiology and toxicology (Paquin, et al.,

2002)……………………………………………………………………………………….……4

Figure 1.2 Cross section of DGT device in solution. Diffusive gels layer used in this study

consisted of gel layer and filter. Thickness of diffusive boundary layer (DBL) depends on flow

rate of water. (Zhang and Davison 1995)…………………………………………………….……….5

Figure 2.1: Map of Ross Lake in Flin Flon, Manitoba, Canada. (Bhavsar et al 2004)……..…28

Figure 2.2: Concentrations of measured and modelled Zn at five locations in Ross Lake (FFC is

Flin Flon Creek, NB is north basin, 3rd

Ave is Third Avenue, SB is south basin, and RC is Ross

Creek). The first bar is Zn measured by means of DGT, the second bar is Zn2+

, inorganic Zn

species and Zn-DOC estimated using WHAM VI, and the third bar is Zn2+

estimated using

WHAM plus Zn2+

dissolved from resuspended sediment. Values reflect conditions in (a) fall

2010, and (b) summer 2011………………………………………………………….…………29

Figure 2.3: Concentrations of measured and modelled Cu at five locations in Ross Lake (FFC is

Flin Flon Creek, NB is north basin, 3rd

Ave is Third Avenue, SB is south basin, and RC is Ross

Creek). The first bar is Cu measured by means of DGT, the second bar is Cu2+

, truly dissolved

inorganic Cu species and Cu-DOC estimated using WHAM VI, and the third bar is Cu2+

estimated using WHAM plus Cu2+

dissolved from resuspended sediment. Values reflect

conditions in (a) fall 2010, and (b) summer 2011……………………………………….……..30

Figure 3.1: Map of Ross Lake in Flin Flon, Manitoba, CA (Bhavsar et al. 2004). Site of case

study downstream of Zn and Cu tailings facility……………………………………………….48

Figure 3.2: Measured and WHAM-modelled total dissolved metal ,WHAM-derived free metal

ionand BLM modelled LC50 concentrations at five locations in Ross Lake determined for fall

2010 and summer 2011, (a) Zn and (b) Cu)………………………………………….…………49

Figure 3.3: WHAM calculated speciation over pH (3-9) for Zn (a) and Cu (b) to show the

species distribution as a function of pH. Water chemistry taken from North Basin in July 2011.

Shaded regions indicate pH ranges during fall 2010 (6.5 -7) and summer 2011 (3.5 – 5.5). …….50

ix

List of Appendices

Appendix 1: Supporting Information for Chapter 2…………………………………………58

Appendix 2: Supporting Information for Chapter 3…………………………………………63

Appendix 3: Detailed Report on Analytical Methods, Results and Quality Control ……......67

1

Chapter 1 Introduction

1. Introduction

Metals are used for numerous purposes in our daily lives. As such, mining and mineral

processing is a large industry, particularly in Canada which is rich in mineral resources.

Unfortunately, as a result of increased metal production, discharges from some mining sites have

led to environmental contamination at concentrations toxic to biota (Taylor et al, 2010).

The study of metals in the environment is complicated due to their ability to exist as different

chemical species. Total and dissolved metal concentrations are often the criteria through which

metal contamination is assessed. These concentrations, however, only give a limited idea of the

nature and toxicity of metals in the environment. Metals speciate differently dependant on

ambient water chemistry, rendering similar dissolved concentrations of metals toxic in some

lakes and non-toxic in others.

Measurement techniques for metal speciation and toxicity can be costly, timely and impractical;

therefore the use of mathematical models is becoming increasingly commonplace. Mathematical

models allow for the prediction of metal species distribution and toxicity based on water

chemistry parameters which are easily obtained.

Mathematical models are constantly evolving with the state of the science and must be evaluated

in order to ensure we have an accurate understanding of environmental conditions. All models

are based on simplifying assumptions that may not always be appropriate. As our understanding

of metal chemistry evolves, so to must the models.

With the improvement of geochemical speciation and toxicity models, we may at some point be

able to set site-specific water quality criteria with the intention of improving water quality

standards. By doing this we can consider ambient water quality and its impact on metal

speciation in setting regulatory limits on metal concentrations.

Data collected from Ross Lake in Flin Flon, Manitoba has been used as a case study for this

thesis research. Ross Lake has a history of high metal concentrations as it is directly

2

downstream of a mine tailings facility that has been operated by Hudson Bay Mining and

Smelting (HBMS) in the town of Flin Flon for over 80 years.

1.1 Study Metals and Study Site

Metal speciation and toxicity measurements and modelling were performed on zinc (Zn) and

copper (Cu). Zn and Cu have been mined by HBMS at their Flin Flon site since the 1930s and

Ross Lake has been subjected to extremely large inputs of metals as a result. As such Ross Lake

has high contamination of these metals. In particular, high Zn concentrations have become a

concern, as Ross Lake has been converted from a net sink for Zn to a net source (Bhavsar et al.

2004a,b). Cu concentrations in the lake have generally been below levels specified by

government regulations, and have not been cause for concern.

1.1.1 Zinc

Over 11 million tons of Zn is produced on an annual basis globally, most of it for galvanizing to

protect steel from corrosion (International Zinc Association, 2011). Other uses include

production of brass, bronze and other Zn alloys (USGS, 2013). Zn is used by a variety of

industries such as transportation, construction, consumer goods and general engineering

(International Zinc Association, 2011)

Zn is an essential mineral for all organisms, necessary for functions such as cellular metabolism,

protein synthesis, wound healing and DNA synthesis. Zn also promotes healthy growth in

pregnancy, childhood and adolescence in humans (National Institutes of Health, 2011).

Zn occurs naturally in air, water and soil (International Zinc Association, 2011), however due to

anthropogenic inputs, background concentrations are rising. Industrial discharges from mining,

coal and waste combustion and steel processing all contribute to higher Zn concentrations in the

environment (Agency for Toxic Substances and Disease Registry , 2013). These industrial

discharges into air, soil and water then result in environmental exposures.

At elevated levels Zn can cause toxicity to animals and is phytotoxic (Manahan, 2001). The free

ion form of Zn is toxic to aquatic life in the micromolar range (Muyssen et al. 2006). However

3

the toxicity of Zn is highly dependent on water chemistry parameters such as pH and hardness

(Heijerick et al. 2002)

1.1.2 Copper

Cu is one of the oldest metals in use because of its properties of ductility, malleability, thermal

and electrical conductivity and resistance to corrosion (USGS 2013). Cu is a commonly found

trace element in the earths crust (Flemming & Trevors, 1989). Cu is used in building

construction, electronics, transportation, industrial machinery and general products (USGS,

2013). Electrical uses account for 75% of Cu use. Cu can enter the environment through

smelting, mining, industrial and domestic waste emission, application of fertilizers, sewage

sludge, algicides, fungicides and molluscicides (Flemming & Trevors, 1989).Cu is an essential

element for humans and animals however high concentrations are toxic resulting in damage to

liver and sometimes kidneys (Stern 2010). Due to the high toxicity that Cu poses to algea,

copper sulphate has commonly been used as an algicide in eutrophic lakes (Flemming &

Trevors, 1989).

Cu has a strong affinity for binding with organic ligands in solution and for that reason is

typically not bioavailable (Arai, Harino et al. 2009). As with Zn, Cu in its free ion form is most

toxic, as well as CuOH+ (de Schamphelaere and Janssen 2001). Again, its toxicity is very

sensitive to pH and water chemistry.

1.2 Geochemical and Ecotoxicity Modelling

Geochemical speciation models are based on the simplifying assumption of thermodynamic

equilibrium. Although several geochemical models are commercially available, this study makes

use of the Windermere Humic Aqueous Model (WHAM) (Tipping, 1994) to be consistent with

toxicity calculations done using the Biotic Ligand Model (BLM).

WHAM is advanced in its ability to account for organic matter in both the colloidal and

particulate phase (Gandhi et al. 2011).The model makes use of a variety of submodels such as

the Humic Ion Binding Model V, an inorganic speciation code for aqueous solution, precipitation

of aluminum and iron oxyhydroxides, cation-exchange on representative clay and the adsorption-

desorption of fulvic acids (Tipping, 1994). WHAM uses as input, a number of easily obtained

4

water chemistry parameters (e.g., pH, DOC, cation concentrations, anion concentrations) to

calculate speciation. All of our calculations have made use of the WHAM default data base of

stability constants.

BLM was used to predict metal toxicity to aquatic species using Ross Lake as a case study. The

BLM uses site specific water quality to determine LC50 values (Paquin, et al., 2002). BLM is an

extension of geochemical modelling with the addition of the gill as a biotic ligand with a

particular metal binding strength (Playle, 2004). A downloadable version of BLM is made

available through HydroQual®. The model pairs physiology and water chemistry to determine

toxicity (Figure 1).

Figure 1.1 - Schematic of BLM presenting chemistry, physiology and toxicology (Paquin, et al.,

2002)

The BLM takes into consideration competitive effects from cations and complexation of anionic

species which may inhibit toxicity (Playle, 2004). Current versions of BLM consider a single

metal. Santore et al. (in prep) have developed a new version that considers the competitive

effects of metal mixtures at the biotic ligand.

5

1.3 Diffusive Gradient Thinfilms

Diffusive Gradients in Thinfilms (DGT) was selected as a measurement tool of labile metals in

solution. DGT have gained widescale usage as they are able to detect metals insitu and can

decrease contamination and inaccuracies caused by handling and storage.

DGT are passive membrane samplers, used in this study to measure trace metal speciation in the

water column. DGT were introduced in 1995 at Lancaster University by Zhang and Davison. For

the measurement of trace metals, DGTs consist of a hydrogel of known thickness layered with an

ion exchange resin (Figure 1.2), in this case Chelex-100 (Zhang and Davison, 1995).

Figure 1.2 Cross section of DGT device in solution. Diffusive gels layer used in this study consisted of gel layer and

filter to separate particulate matter. Thickness of diffusive boundary layer (DBL) depends on flow rate of water.

(Zhang and Davison 1995).

Ions pass through the diffusive gel, of thickness Δg, where dissociation of labile complexes

occur. Ions then pass into the resin gel in which they accumulate. Solution passes into the

diffusive gel through the diffusive boundary layer (DBL) where transport of ions occur through

molecular diffusion. A linear steady state concentration gradient is created between solution and

resin gel (Zhang, 2003). The resin gel is removed after retrieval for analysis of mass of trace

metals.

Metals able to dissociate within the diffusive gel layer are considered labile, this is governed by

dissociation and formation kinetics (Gaabass et al. 2009).

6

The labile metal concentration (Cdgt) is calculated using the following equation (Zhang and

Davison 1995).

Cdgt= mΔg/DAt (1)

Where m is the mass on the resin gel, Δ g is the thickness of the diffusion gel, D is the diffusivity

of metal ions through the gel, A is the surface area of the gel, and t is the total time deployed

1.4 Project Summary

The goal of my thesis research was to examine speciation and toxicity modeling. In the case of

the geochemical speciation model, model results were compared against field measurements

using several measurement methods. I compared two versions of the Biotic Ligand Model, one

that considers a single metal and the other, a newly developed version that assesses metal

mixtures. Ross Lake was used as a case study for these assessments. As mentioned above, the

lake has elevated levels of Zn and Cu from mining operations. This research has used, as its

basis, previous research conducted on this lake that investigated the speciation, fate and

ecotoxicity of Zn and Cu (Bhavsar et al. 2004 a,b, Gandhi et al. 2011)

1.5 Research Objectives

The following research objectives were addressed in this thesis using Ross Lake, Manitoba, as a

case study:

Conduct field campaign to assess water chemistry,

Model metal speciation and ecotoxicity in water column,

Address discrepancies in speciation estimations through metal dissolution from sediment

and through different interpretations of measured concentrations, and

Determine the metal causing ecotoxicity in Ross Lake

1.6 Thesis Outline (Outline all papers contained)

The chapters of this thesis consist of journal articles in preparation for submission.

Chapter 2 : Comparing DGT Measurements and WHAM Estimates of Zinc and Copper : A Case

Study at Ross Lake in Northern Canada

7

Co-authors: Miriam L. Diamond, Celine Gueguen

Contribution: S. Yacoob performed field work with assistance from Hudson Bay Mining &

Smelting, produced model data, data analysis and wrote manuscript. Reasearch and manuscript

preparation were completed under supervision of M.L. Diamond. C. Gueguen performed

analysis of DGT samples and commented on the manuscript.

This chapter looks at estimations of Zn and Cu speciation using the Windermere Humic

Aqueous Model (WHAM) and evaluates the results against measured concentrations obtained

from Diffusive Gradients in Thinflim (DGT). A summary of similar studies which have

compared model and measurement methods are presented and assessed. The study explores

different reasons for discrepancies between modelled and measured concentrations, both looking

at the shortcomings of measurement technique and model assumptions. This paper also addresses

the vagueness in terminology commonly used in this field of study.

Chapter 3: Investigating Toxicity Using Single and Mixture BLM Models: A Case Study at Ross

Lake

Co-authors: Miriam L. Diamond (University of Toronto), Robert Santore (HydroQual), Helga

Sonnenberg (Stantec Consulting)

Contributions: S. Yacoob performed field work with assistance from Hudson Bay Mining and

Smelting, produced model data for the single metal BLM, analyzed model results and wrote the

manuscript. Research and manuscript preparation were completed under supervision of M.L.

Diamond. Robert Santore provided model results from the metal mixture BLM. Helga

Sonnenberg provided toxicity testing data.

This chapter compares estimates of ecotoxicity from the single metal BLM and the newly

developed metal mixture BLM. Similarities and differences in model results are highlighted and

explained. Efforts are made to explain why acute toxicity is exhibited in specific instances and

not in others.

8

Chapter 4: Conclusions

This chapter contains a summary of findings, scientific contributions and outlook for further

research.

1.7 References

Agency for Toxic Substances and Disease Registry. (2013, March 6). Toxicological Profile for

Zinc. Retrieved March 6, 2013, from Agency for Toxic Substances and Disease Registry Website

: http://www.atsdr.cdc.gov/toxprofiles/tp60-c6.pdf

Arai, T., Harino, H., Ohji, M., & Langston, W. (2009). Ecotoxicology of Antifouling Biocides.

Tokyo: Springer.

Bhavsar, S. P., M. L. Diamond, et al. (2004)a. "Dynamic coupled metal transport-speciation

model: Application to assess a zinc-contaminated lake." Environmental Toxicology and

Chemistry 23(10): 2410-2420.

Bhavsar, S. P., M. L. Diamond, et al. (2004)b. "Development of a coupled metal speciation-fate

model for surface aquatic systems." Environmental Toxicology and Chemistry 23(6): 1376-1385.

de Schamphelaere, K. A. C. and C. R. Janssen (2001). A Biotic Ligand Model Predicting Acute

Copper Toxicity for Daphnia magna:  The Effects of Calcium, Magnesium, Sodium, Potassium,

and pH. Environmental Science & Technology , 48-54.

Flemming, C. A., & Trevors, J. (1989). Copper Toxicity and Chemistry in the Environment: A

Review . Water, air, and soil pollution , 143-158.

Gaabass, I., J. D. Murimboh, et al. (2009). "A Study of Diffusive Gradients in Thin Films for the

Chemical Speciation of Zn(II), Cd(II), Pb(II), and Cu(II): The Role of Kinetics." Water Air and

Soil Pollution 202(1-4): 131-140.

Gandhi, N., S. P. Bhavsar, et al. (2011). "Critical Load Analysis In Hazard Assessment of Metals

Using a Unit World Model." Environmental Toxicology and Chemistry 30(9): 2157-2166.

Heijerick, D. G., De Schamphelaere, K. A., & Janssen, C. R. (2002). Predicting acute zinc

toxicity for Daphnia magna as a function of key water chemistry characteristics: Development

and validation of a biotic ligand model. Environmental Toxicology and Chemistry , 1309-1315.

International Zinc Association. (2011). Zinc Uses. Retrieved March 1, 2013, from International

Zinc Association Website: http://www.zinc.org/basics/zinc_uses

Manahan, S. E. (2001). Fundementals of Environmental Chemistry . Boca Raton: CRC Press .

Muyssen, B. T., De Schamphelaere, K. A., & Janssen, C. R. (2006). Mechanisms of chronic

waterborne Zn toxicity in Daphnia magna. Aquatic Toxicology , 393-401.

9

National Institutes of Health. (2011, September 20). Dietary Supplement Fact Sheet: Zinc.

Retrieved March 1, 2013, from Office of Diet Supplements:

http://ods.od.nih.gov/factsheets/Zinc-HealthProfessional/

Paquin, P. R., Gorsuch, J. W., Apte, S., Batley, G. E., Bowles, K. C., Campbell, P. G., et al.

(2002). The biotic ligand model: a historical overview. Comparative Biochemistry and

Physiology , 3 - 35.

Playle, R. C. (2004). Using multiple metal-gill binding models and the toxic unit concept to help

reconcile multiple-metal toxicity results. Aquatic Toxicology , 359-370.

Stern, B. R. (2010). "Essentiality and Toxicity in Copper Health Risk Assessment: Overview,

Update and Regulatory Considerations." Journal of Toxicology and Environmental Health-Part

a-Current Issues 73(2-3): 114-127.

Taylor, L. N., L. A. Van der Vliet, et al. (2010). "Sublethal Toxicity Testing of Canadian Metal

Mining Effluents: National Trends and Site-Specific Uses." Human and Ecological Risk

Assessment 16(2): 264-281.

Tipping, E. (1994). WHAM—A chemical equilibrium model and computer code for waters,

sediments, and soils incorporating a discrete site/electrostatic model of ion-binding by humic

substances. Computers & Geosciences , 973-1023.

USGS. (2013, February 14). Copper Statistics and Information. Retrieved March 1, 2013

USGS (2013, March 11, 2013 ). "Zinc Statistics and Information." Retrieved March, 12 2013,

from http://minerals.usgs.gov/minerals/pubs/commodity/zinc/#pubs.

Zhang, H. and W. Davison (1995). "Performance Characteristics of Diffusion Gradients in Thin

Films for the in Situ Measurement of Trace Metals in Aqueous Solution." Analytical Chemistry

67(19): 3391-3400.

Zhang, H. (2003). DGT –for measurements in waters, soils and sediments. Lancaster, UK, DGT

Research Ltd.

10

Chapter 2 - Comparing DGT Measurements and WHAM Estimates of Zinc and Copper: A Case Study at Ross Lake in Northern Canada

Co-authors: Miriam L. Diamond (University of Toronto) and Celine Gueguen (Trent University)

2 Abstract Metal toxicity in freshwater systems is exerted by the free metal ion and potentially some

inorganic species in the truly dissolved phase, all of which are difficult to measure. Alternatives

to measurement include estimating the free metal ion and other species’ concentrations by means

of geochemical modelling and/or using Diffusive Gradient Thin films or DGTs. DGTs measure

a functionally defined fraction of “DGT-labile” metal. A literature review of studies comparing

measurements of DGTs with modelling and/or measurements indicated that DGTs sample a

range of species.

This study sought to compare measurements of DGT-labile Zn and Cu with those obtained from

geochemical modelling in a mine-contaminated lake. DGTs were deployed for 48 hours in Ross

Lake, Manitoba, Canada, in fall 2010 (pH 6.5-7) and summer 2011 (pH 3.5-5.5). At this time

water samples were taken and analyzed for total and total dissolved metals, major cations and

anions, alkalinity and other water quality parameters that were used as input to the geochemical

speciation model WHAM.

DGT-Zn concentrations approximated Zn2+

which comprised >70% of the truly dissolved phase,

plus some contributions from inorganic species, notably ZnSO4 which was the other major

11

species in the truly dissolved phase, or Zn2+

supplied by the oxidative dissolution of resuspended

ZnS. Contributions from these other sources were more evident at low pH.

DGT measurements of Cu exceeded that of Cu2+

which comprised <10% of total dissolved Cu at

pH 6.5-7 but rose to 30-60% at pH 3.5-5.5. Contributions to the DGT-labile fraction from other

inorganic species were not expected since these species contributed minimally to the totally

dissolved species. Rather, the analysis suggested either contributions from Cu-DOC complexes

which comprised >80% of total dissolved Cu, or Cu2+

dissolved from resuspended sediment.

2.1 Introduction

There is general consensus that the free metal ion concentration is primarily related to freshwater

ecotoxicity (Campbell 1995) with possible contributions from other truly dissolved inorganic

metal species (de Schamphelaere and Janssen 2001). For clarity, we define these and other terms

in Table S1. Several measurement and modelling methods can be used to approximate free metal

ion concentrations such as Diffusive Gradients in Thin films (DGT), Voltammetry, Ion Selective

Electrodes, Competitive Ligand Exchange, etc. DGT have grown in popularity because of their

ease of use under a wide range of field and laboratory conditions and relatively low expense.

Geochemical models can be used to estimate the free metal ion concentration when measurement

is not feasible due to cost, logistical challenges, etc. WHAM VI (Tipping 1994), which similarly

to other geochemical models (i.e. CHESS, MINEQL+, MINTAQ2) assumes thermodynamic

equilibrium, has been found to adequately represent metal speciation over a wide range of water

chemistries (Søndergaard, Elberling et al. 2008; Gandhi, Bhavsar et al. 2011).

Since DGTs were introduced by Zhang and Davison in 1995, these authors and a growing

number of other researchers have worked towards characterizing and clarifying what DGTs

12

measure under which conditions. Briefly, DGTs strictly measure the flux of trace metal through

a defined thin layer of diffusive gel to a binding agent, usually Chelex 100 (Zhang and Davison

1995, Davison and Zhang 2012 inter alia). All metal species that can dissociate as they pass

through the layers of the exterior membrane (0.45µm), gel (which ranges from 0.4-2mm) and

resin (0.4 mm) will be captured by the Chelex resin binding agent. These species include,

depending on ambient freshwater chemistry, the free metal ion, truly dissolved inorganic species

(e.g., metal hydroxides, sulphate and carbonate species), and readily disassociative, small

molecular weight metal-DOC complexes (e.g., Zhang and Davison 2000, Veeken and Leeuwen

2010). For DGTs, “labile” is defined in the dynamic sense as that fraction of metal that can

dissociate sufficiently fast when transported from the bulk solution through the filter, DGT gel

and resin so that it can bind at the chelating resin (Uribe, Mongin et al. 2011). DGT has been

successfully used to measure Zn concentrations at pH as low as 3.5 and Cu concentrations in

solutions with pH as low as 2 (Gimpel, Zhang et al. 2001).

Clearly, the species captured by DGTs, and hence that are considered “DGT-labile”, depend on

the specifications of the DGT (i.e., thickness of gel and resin layers), ambient aqueous chemistry

(i.e., metal speciation), and conditions of deployment such as temperature and deployment time

(Zhang, Davison et al. 1998). Most metal species in the total dissolved phase (i.e., truly

dissolved and DOC-bound species) can bind with the DGT resin given sufficiently thick gel and

resin layers and deployment time, provided that the supply of a species is not limited. Zhang and

Buffle (2009) modelled metal species fluxes at a planar consuming interface, such as a DGT

resin, as a function of diffusion path length, diffusion coefficient and dissociation constant. They

concluded that with path lengths greater than 5-10 µm, which is much shorter than that of DGTs,

complexes of truly dissolved inorganic species of metals could be captured by a reacting surface

13

and would thus be considered labile, e.g., metal complexes with carbonate, hydroxide. In

addition, metal-fulvic acid complexes could be labile for quickly reacting metals such as Cu and

Pb at large metal to fulvic acid ratios, but not slowly reacting metals such as Zn and Ni at low

metal to fulvic acid ratios. Metal aggregate/particulate complexes would not be considered

labile.

With the goal of clarifying the lability of metal species with respect to DGTs, Uribe et al. (2011)

and Puy et al. (2012) derived mathematical expressions for lability as a function of gel and resin

thickness (diffusive path length), a complex’s dissociation rate constant, and a diffusion

coefficient. The model indicated that for most complexes, the diffusion time through the resin

layer, not the gel layer, controlled metal-complex dissociation followed by metal binding to the

resin. Simply, a complex can be considered labile if r > (DML/kd)1/2

where r (m) is resin-layer

thickness, DML (m2 s

-1) is the diffusion coefficient of the metal complex, and kd (s

-1) is its

dissociation rate constant. Puy et al. evaluated the results of their model against data from a

simple system containing various Cd-nitrilotriacetic acid solutions. They concluded that this

approach shows promise for interpreting results of metal lability from DGTs, although further

model evaluation is needed using heterogeneous ligands.

Other studies have compared results from DGT deployments with measurements and/or

geochemical model estimates (Table 2.1). Zn in particular showed good agreement between

DGT measured and modelled values (Meylan, Odzak et al. 2004; Guthrie, Hassan et al. 2005),

Cu was more difficult to model with regard to its free ion complex (Gimpel, Zhang et al. 2003;

Guthrie, Hassan et al. 2005). This has often been attributed to kinetic limitations attributed to

strong Cu-fulvic acid binding, especially at low concentrations of Cu (Warnken, Lawlor et al.

2009). The best correspondence between metals measured from DGT deployments and the free

14

metal ion estimated using a geochemical model was found in acid oligotrophic systems since

most trace metals in the truly dissolved phase are in their free ion form and not bound to ligands

(Gimpel, Zhang et al. 2003). A review of the studies listed in Table 1 did not reveal consistent

bias in the comparison between metals captured by DGTs and those modelled in WHAM.

However, where some studies compared DGT to free ion metal concentrations (Meylan, Odzak

et al. 2004; Yapici, Fasfous et al. 2008), others compared DGT estimates to the truly dissolved

total inorganic fraction modelled using WHAM (Gimpel, Zhang et al. 2003; Søndergaard,

Elberling et al. 2008; Gueguen, Clarisse et al. 2011). Therefore it is often unclear what fraction

of metal DGT is in fact measuring.

The goal of this study was to compare Cu and Zn concentrations measured using DGTs versus

concentrations generated using WHAM VI, using Ross Lake in Flin Flon, Manitoba, Canada, as

a case study. Cu has fast reaction kinetics whereas those for Zn are intermediate to slow (Zhang

and Buffle 2009). We explored several explanations to account for differences between the

metals captured by DGTs and those complexes, estimated using WHAM, which are considered

to be DGT-labile. In addition, we evaluated the hypothesis that DGTs measured metal

contributions from kinetically controlled metal dissolution from the particle-phase not captured

by WHAM VI.

2.2 Case Study

Ross Lake is located in northern Manitoba in the Town of Flin Flon (Figure 2.1). Flin Flon sits at

a longitude and latitude of (54o 46’ N, 101

o 52’W). The lake consists of two basins, hereto

referred to as the north and south basin. The lake has an area of 550,000 m2 in the north and

150,000 m2 in the south and maximum depths of 7 and 2.3m respectively (Bhavsar, Diamond et

15

al. 2004). Since 1930, Hudson Bay Mining and Smelting (HBMS) mines, and until recently,

smelted Zn and Cu-bearing sulphidic ore. Mine tailings effluents are discharged into Flin Flon

Creek which then flows into the north basin of Ross Lake. The north and south basins are

connected through a culvert which passes underneath Third Avenue. The south basin of Ross

Lake then discharges into Ross Creek. As a result of 80 years of inputs from the tailings pond

overflow, the average concentrations of Zn and Cu in the surficial sediments of Ross Lake are

26,900 and 11,000 mg kg-1

. Over time as metal loadings have decreased due to much improved

practices at the mine, the lake has shifted from a net sink to source of Zn, with higher

concentrations of Zn leaving than entering the lake (Bhavsar et al. 2004a).

Bhavsar et al. (2004 a,b) developed a coupled metal transport and speciation model known as

TRANSPEC which combines a speciation/complexation module with the fugacity/aquivalence

approach to determine metal fate. Application of the model to Ross Lake showed elevated Zn

contributions to be the result of sediment resuspension.

2.3 Field Measurements

2.3.1 DGT Deployment

Two field monitoring campaigns were conducted, one in fall 2010 and the other in summer 2011.

Open pore DGTs (DGT Research Ltd) were deployed in triplicate, midway between the water

surface and sediment using an anchor buoy system for 48 hr deployment periods. DGT units

were preloaded with Chelex-100 resin gel and open pore diffusive gel discs. Units (2.5 cm

diameter) consisted of a 0.4 mm thick resin gel layer, 0.8 mm thick diffusive gel layer, and a

0.135 mm thick filter paper. DGTs were placed in clear plastic plates which allowed for

membrane exposure on both sides. Upon deployment and retrieval, pH, temperature,

conductivity, Oxidation Reduction Potential (ORP), and dissolved oxygen (DO) were measured

16

using a HYDROLAB Datasonde 4 Multiprobe and Datasurveyor 4 Data Display. pH was

calibrated daily using 3 buffer solutions (pH values of 4, 7, and 10); DO and conductivity meters

were checked daily to ensure outputs were accurate.

Once retrieved, DGTs were rinsed with Milli – Q water to remove debris and stored in plastic

bags. In the lab at HBM&S, DGTs were disassembled within one hour of retrieval using Teflon

coated tweezers, and the resin gel layer of each membrane was transferred to a 2.5 ml centrifuge

tube. All instruments including water containers, tweezers, plastic bags, and centrifuge tubes

were acid washed to ensure minimized trace metal contamination. Samples were then

refrigerated until they were sent to Trent University for analysis.

2.3.2 DGT Analysis

Analytical results were reported as Ce (µg/L), the concentration of metals in 1M HNO3 (µg/L).

The mass of metal in the resin gel (M) was estimated as

M = Ce (VHNO3 + Vgel)/fe (1)

Where VHNO3 (ml) is the volume of HNO3 added to the resin gel, Vgel (mL) is the volume of the

resin gel, and fe is the elution factor for each metal set to 0.8. The concentration of Cu and Zn in

the resin gel, CDGT (µg/L) was obtained as

CDGT = MΔg/(DtA) (2)

Where Δg (mm) is the thickness of diffusive gel, D (cm2 sec

-1) is the diffusion coefficient of

metal in the gel, t (sec) is the deployment time and A (cm2) is the exposure area (Zhang 2003).

Temperature based diffusion coefficients for each metal were provided by DGT Reasearch Ltd.

and can be found in Table S2 of supplementary information (SI).

17

2.4 Water Chemistry Sampling

Water samples were also collected to determine concentrations of total metals, dissolved metals,

sulphide, DOC, TOC, nutrients and alkalinity, etc., as required as inputs for WHAM. Water

samples were collected in acid washed, high density polyethylene containers and amber glass

bottles. Samples for total and dissolved metal analysis were preserved with a 1:3 nitric acid and

water solution, sulphide samples were preserved with 2 ml of 2 Normal zinc acetate and 1 ml of

6 Normal sodium hydroxide. TOC and DOC samples were preserved with a 1:1 solution of

hydrochloric acid and water. Samples for analysis of functionally dissolved metals were filtered

on-site using a PhenexTM

0.45 µm filter. Field samples were immediately stored in coolers,

transferred to refrigerators and sent for analysis within 24 hours to ALS Labs in Winnipeg,

Manitoba. Details on analytical methods used by ALS can be found in SI.

Quality control was completed through ALS Labs in Winnipeg. Method blanks were reported to

be below limits of detection. Laboratory control samples and duplicates were run and met all

quality control parameters. Detailed quality control information is contained in SI.

2.5 Geochemical Modeling

WHAM VI was used to calculate Zn and Cu speciation. WHAM assumes thermodynamic and

chemical equilibrium and, unlike many other geochemical models, it includes a sophisticated

treatment of metal binding to humic and fulvic acids (Tipping 1998). This is particularly

important when considering Cu which has a high tendency to bond with organic matter.

Water chemistry data measured during the two field campaigns were used as model inputs (Table

S3). DOC and TOC concentrations from summer 2011 were used for fall 2010 for lack of data.

Humic substances were assumed to be 60% of measured DOC values with a fulvic to humic ratio

18

of 9:1 (Gueguen, Clarisse et al. 2011). Default stability constants from the WHAM VI database

were used for model calculations.

2.6 Comparison of DGT to WHAM Predicted Free Ion Concentration

Concentrations of Zn and Cu in the DGTs varied from 1.9 × 10-6

to 1.11 ×10-5

(M) (126 - 733

µg/L) and 5.19 × 10-9

-1.17 × 10-7

(M) (1.3 – 43 µg/L), respectively (Figures 2.2a,b and 2.3 a,b).

Metal concentrations in the DGTs did not differ consistently between sampling campaigns in

2010 and 2011. Concentrations in triplicate DGT at each site were within a factor of 3. Median

concentrations are used for comparison purposes in this study. Zn concentrations increased

moving from upstream to downstream, confirming previous results that the lake is a net source of

Zn (Bhavsar, Diamond et al. 2004). Cu concentrations were consistently higher in the lake than

in either creek. The results suggest that either the lake is not a net source of Cu or that the low

Cu concentrations measurements at Ross Creek reflect uptake by aquatic macrophytes that were

abundant at this sampling location (Jain, Vasudevan et al. 1989).

Zn concentrations measured by the DGTs were within a factor of 1 to Zn2+

concentrations

estimated by WHAM VI only in Flin Flon Creek, particularly in fall 2010 when the pH varied

between 6.4 and 7.1. Downstream of Flin Flon Creek, DGT-measured Zn was about double the

WHAM-estimated concentration of Zn2+

. The “reasonable” agreement between Zn measured

using DGTs and Zn2+

modelled with WHAM is consistent with the other studies (Meylan, Odzak

et al. 2004; Guthrie, Hassan et al. 2005). It is also consistent with Zn speciation in the lake

whereby >70% of Zn was estimated to exist as Zn2+

using WHAM VI (see Section 3.4.2). This

could either be attributed to Zn2+

added by dissolution from resuspended sediment (see below).

19

For Cu, DGT measurements were 1-2 orders of magnitude higher than WHAM-calculated Cu2+

concentrations, particularly in summer 2011. Measurements of Cu-DGT above that of Cu2+

was

consistent with the results of Gunthrie et al. (2004). The additional Cu in the DGTs could be

inorganic Cu species such as CuOH+, Cu(OH)2, CuSO4, CuHCO3, CuCO3, Cu(CO3)2

2-, CuCl

+, as

found by Gueguen et al. (2011) and Sondergaard and Elberling et al. (2008), or the result of Cu2+

added by dissolution from sediment resuspension.

Speciation between the seasons changed considerably with respect to Cu whereby Cu2+

comprised from 30-60% of the total dissolved concentration during the summer 2011. This high

contribution from Cu2+

is attributed to low pH of 3.5-5.5 measured during the summer 2011

campaign as metals do not bind as strongly with DOC at low pH due to competition with protons

for binding sites (Benedetti, Milne et al. 1995). During the fall campaign, when pH was

circumneutral, Cu2+

comprised <5% of total dissolved concentrations and Cu-DOC comprised

most of the remainder. Similar speciation was estimated using WHAM VI in Ross Lake several

years earlier when pH in the lake was 7.8 (Gandhi, Bhavsar et al. 2011).

To investigate the likelihood of dissolved inorganic Zn and Cu species contributing to the metals

measured by the DGTs, we calculated DGT-based lability using the expression developed by

Uribe et al. 2011, as discussed in Section 2.1. The calculation assumed the same diffusivity

coefficient (DML) of 1.5 × 10-10

(m2 s

-1) as that presented in Uribe et al. (2011) for lack of

species-specific diffusion coefficients. The calculation were relatively insensitive to the value of

DML compared to the dissociation rate constant kd which varied by up to 13 orders of magnitude

across metal species. kd values were calculated from the formation constants (kf) provided in the

WHAM database. Details of the species distribution and lability calculations are listed in Table

S4.

20

The results of these calculations showed that most inorganic species of Zn and Cu were not

considered labile with the exception of MeCl+ for both Zn and Cu, which had relatively high

dissociation constants in comparison to other complexes in solution. However, the contributions

of MeCl+ to labile concentrations measured by DGTs would be negligible since these species

were estimated to contribute <1% of total dissolved metal concentrations (see Section 3.4.2).

2.7 Comparing DGT Values to Metal Bound to DOC

Another explanation for the elevated DGT-measured metals beyond the free metal ion

concentrations modelled using WHAM was that metal-DOC complexes were captured by the

DGT, particularly for Cu. DOC plays a dominant role in the binding of Cu natural waters

(Guthrie, Hassan et al. 2005), with Ross Lake being no exception. Gueguen et al. (2011)

surmised that open pore gel DGTs, such as were used in this study, captured inorganic metal ions

and small organic metal species.

Zn does not have a high affinity for binding with DOC. WHAM VI estimated that Zn-DOC

complexes accounted for an average of 3% of total dissolved Zn during both sampling

campaigns. Furthermore, as noted above, Zhang and Buffle (2009) estimated that Zn-fulvic acid

complexes were unlikely to dissociate along the diffusion path length comparable to that of a

DGT. Thus, we concluded that Zn-DOC was unlikely to contribute significantly to the Zn

captured by the DGT.

Conversely WHAM VI estimated that 70% of total dissolved Cu consisted of a Cu-DOC

complex. Thus, it is possible that a fraction of Cu complexed with small organic complexes

could be captured by the DGT, as expected according to the calculations of Zhang and Buffle

(2009). However, if inorganic dissolved metal species have insufficient time to dissociate and be

21

captured by the chelating resin (as calculated in Section 2.6), then it’s unlikely that Cu-DOC

complexes will do so (Zhang and Davison 2001).

2.8 Dissolution Kinetics

The final possibility considered to account for the DGT-measured metal species was the

dissolution of particulate metal from resuspended sediment in this shallow lake, as hypothesized

by Bhavsar et al. (2004). Specifically, we hypothesize that the oxidation of metal sulphides could

provide a source of free metal ion to the water column not considered in the WHAM VI

equilibrium calculations. Others have hypothesized that the oxidation of metal sulphides from

anoxic sediment could be a source of metals to the water column either due to natural

resuspension or dredging (e.g., Forstner and Salamons 1991; Caille et al. 2003; Sigg and Behra

2005; Naylor, Davison et al. 2012 inter alia).

In this study we quantified metal dissolution from anoxic, sulphidic sediment using the method

of Gandhi et al. (2007) whereby the equation estimating the change in free metal ion

concentration, CMe2+ (mg L-1

d-1

), due to metal sulphide dissolution was

dCMe2+/dt = Kdissoluton[MeS] (1)

Where Kdissolution (s-1

) is a measured first order rate constant, and MeS is the metal sulphide

concentration. The concentration of ZnS was assumed to be 98% (Evans 2000) of the total metal

concentration in the sediments that was nearly 3% (Yacoob unpublished data). Cu

concentrations in Ross Lake sediment are 1% or 11,000 mg kg-1

. However, since there were no

data for the composition of Cu in the sediments, CuS was assumed to comprise 45% of the total

concentration (Farley, Carbonaro et al. 2011). Farley et al. found in a typical Shield lake that

22

most Cu binds with particulate organic matter (POM) and then deposits to the sediment. As the

POM decomposes it releases Cu into the pore water where most would precipitate as CuS.

Equation 1 was solved using a linear integration method. The amount of metal sulphide

resuspended into the water column was calculated using the TRANSPEC model (Bhavsar et al.

2004a). Values for kinetic rates of reoxidation of MeS were taken from Boon (1996 inter alia)

who listed them for 30oC. In the absence of data for the Arrhenius equation for temperature

correction, the rate was decreased by 10x to reflect colder temperatures in Ross Lake. All

values are listed in Table S5.

Zn2+

contributed by dissolution of ZnS was approximately double that of the concentration

measured by the DGTs. In contrast to Zn, the inclusion of Cu2+

due to dissolution plus Cu2+

calculated using WHAM VI came to within an order of magnitude of DGT-measured Cu during

both sampling campaigns. Two exceptions were Flin Flon Creek and Ross Creek in summer

2011. The exception of Flin Flon Creek lends credence to the dissolution hypothesis because

this site would be unlikely to have accumulated fine grained sediment with metal sulphides that

would be subject to resuspension since the creek is well scowered. Rather, the easily

resuspended fine grained material accumulates in the basins where the agreement with the

kinetic calculations is better. Therefore the possibility of kinetic dissolution in the lake has not

been discounted.

Ross Creek is the other anomaly. DGT concentrations were significantly lower than WHAM

estimates for Cu. This location was downstream of the discharge from the town’s waste water

treatment plant and had luxurious growth of the aquatic plant Salvinia. Low Cu concentrations at

this location may be explained though uptake by the abundant aquatic macrophytes. DOC at this

23

location was slightly, but not significantly higher than upstream locations (7.1, in comparison to

6.8 in the South Basin and 6.7 mg DOC/L at Third Avenue).

2.9 Implications

The intention of this work was to further explore what the DGT is measuring using geochemical

modeling along with kinetically controlled dissolution. As seen from Table 2.1, many studies

have compared WHAM estimates and DGT measurements with inconsistent results. Whereas

some studies found that WHAM estimates of the free metal ion overestimates DGT-measured

concentrations (Meylan, Odzak et al. 2004), others found these modelled values underestimated

the free metal ion (Guthrie, Hassan et al. 2005). Yet other studies found very little agreement

between DGT-measured and geochemical modelled free ion estimates (Unsworth, Warnken et al.

2006).

In the case of Ross Lake, DGT-measured Zn was best approximated by WHAM-estimated Zn2+

but not with the addition of inorganic truly dissolved Zn species, as suggested by Gimpel et al

(2003). The calculation of Uribe et al. (2012) also suggested that the inorganic species would

have insufficient time to dissociate during diffusion through the DGT gel and chelating resin.

The minimal contribution of species other than Zn2+

is consistent with the slow reactivity of Zn

(Zhang and Buffle 2009). Thus, the DGTs appear to have measured Zn2+

in this system at

circumneutral pH. However, at low pH the DGTs captured additional Zn, either from the

dissolution of inorganic species or resuspended sediment.

The results for Cu were different from those of Zn. The DGTs in Ross Lake appeared to capture

Cu2+

plus either inorganic truly dissolved species, some Cu-DOC complexes, or Cu2+

added to

the lake, but not creeks, due to dissolution of resuspended sediment. This is consistent with the

24

faster reactivity of Cu than Zn (Zhang and Buffle 2009) but in the case of inorganic species is not

supported by the calculation presented by Uribe et al. (2011).

pH played an important role in Cu speciation whereby WHAM calculations estimated

significantly more Cu in the free ion form at low pH, which was seen in higher DGT Cu

concentrations at this time. In the range of pH encountered from 3.5 to 7, Zn speciation was not

as greatly affected as Cu.

Overall, the results suggest that the DGTs measurements differ according to metal and ambient

conditions, which supports the findings of others. Further work field testing DGTs would be

required to further understand what DGTs measure. It would be helpful to use DGT to measure

metal sulphide content in the sediment and to measure metal sulphide dissolution kinetics.

25

2.10 Tables

Table 2.1. Comparison between DGT-measured metal concentrations with those obtained from geochemical modelling and analytical

measurements.

Reference Metals DGT Geochemical

Model Analytical

Details

Comment

(Yapici, Fasfous et

al. 2008)

Cd, Co, Cu, Ni,

Pb, and Zn

Diffusive & resin gels 0.4 &

0.4mm thicknesses. Deployed in

lab 24 hrs,

WHAM VI - Good agreement between DGT and WHAM-

VI predicted Me2+ in municipal waste and

aqueous mine effluent samples. Assumed

90:10 FA:HA. Used different diffusive gel

binding layer thicknesses

(Meylan, Odzak et

al. 2004)

Zn and Cu Diffusive gel 0.8 mm, resin

0.4mm. Deployed 48 hrs

WHAM VI,

NICA-Donnan,

SHM

Competitive Ligand

Exchange and Voltammetry

Good agreement between exp Cu 2+and DGT

Cu, but WHAM-over predicted Cu2+ by ~102

(river) to 103 (microcosm). Good agreement

between exp Zn2+, DGT Zn & WHAM Zn2+.

(Guthrie, Hassan

et al. 2005)

Cu, Cd, Ni, Zn - WHAM V, VI Cathodic and Annodic

Stripping Voltametry

Saw good agreement for Cd, Ni,Zn. WHAM

VI was better for prediction than WHAM V.

Cu was under-predicted by WHAM

(Lofts and Tipping

2011)

Cd, Al, Cu, Ni,

Zn - WHAM /Model

VII

Voltammetry Agreement best for Al, Cd, Co, Ni, and Zn.

Agreement was different based on analytical

method. Incorporating uncertainty in

modelling results helped with assessment

(Unsworth,

Warnken et al.

2006)

Cd, Cu, Ni, Pb DGT used in softwater river,

hardwater stream and hardwater

lake.

WHAM VI,

MINTEQ

Voltammetry at a gel

integrated microelectrode

(GIME), Donnan membrane

technique and hollow fiber

permeation liquid membrane

GIME concentrations generally lower than

those measured with DGT. DGT

concentrations of Cu generally matched well

with maximum dynamic concentration

calculated by WHAM.

(Balistrieri and

Blank 2008)

Cd, Cu, Pb, Zn Three preloaded open pore

DGTs deployed at each site.

WHAM VI,

NICA-Donnan

and Stockholm

Humic Model

- Good agreement when labile Cu

measurements were compared with dynamic

metal concs in WHAMVI. All models

showed good agreement for Cd and Zn.

26

Reference Metals DGT Geochemical

Model Analytical

Details

Comment

(Gimpel, Zhang et

al. 2003)

Mn, Fe, Cu, Zn Diffusive gel 0.92 & 1.16 mm.

Deployed in lakes for 48 hrs.

WHAM Dialysis also used for in situ

measurements.

WHAM able to predict that DGT would

contain mostly dissolved inorganic species of

Mn & Zn in acidic to neutral oligotrophic

waters. Less agreement Fe & Cu in

circumneutral lakes. WHAM I over-

predicted Cu-DOC binding.

(Søndergaard,

Elberling et al.

2008)

Al, Mn, Cu 48 DGT measurements at four

different sites for a 1.5 month

period. One DGT measurement

at a time. DGT deployed for 4 –

6 hours.

WHAM VI - WHAM VI predictions of dissolved

inorganic metal fraction agreed well with in

situ measurements of ‘labile’ Al, Mn, and Cu

(Gueguen, Clarisse

et al. 2011)

Cd, Co, Cu, Ni,

and Pb

Deployed in triplicate, used open

pore and restricted pore gels to

distinguish importance of metal

organic complexes and their

capacity to dissociate in the

diffusive layer.

WHAM VI - Cu, Cd and Pb DGT concentrations

comparable to inorganic truly dissolved

species estimated using WHAM VI.

(Zhang 2004) Ni, Zn Used hydrogels of varying pore

sizes

WHAM ,

ECOSTAT

Anodic Stripping

Voltammetry (ASV)

Found good species distribution with WHAM

when competitive binding with Fe(III) was

considered. Free ion activities were modelled

with both models, found good agreement for

humic binding but exposed some problems

with default database with regard to inorganic

species complexation. ASV results showed

concentrations between DGT inorganic and

total dissolved concentrations.

(Zhang and

Davison 2000)

Cu, Cd Three sets of three DGT devices

were deployed with differing

WHAM Anodic Stripping

Voltammetry (ASV)

Found good agreement for Cu.

27

diffusive gels.

(Zhang and

Davison 2001)

Cu Used open and closed pore gels

to determine organic and

inorganic concentrations of Cu.

WHAM Anodic Stripping

Voltametry (ASV)

Found good agreement between

measurements of inorganic concentrations of

Cu. Good agreement for high concentrations

of Cu between DGT and WHAM. DGT

concentration of inorganic Cu was higher

than WHAM at low Cu concentrations.

(Warnken, Lawlor

et al. 2009)

Al, Fe, Mn, Ni,

Cu, Cd, Pb, and

Zn

34 clusters of DGT deployed at

headwaters of streams. Used data

from triplicates of .8 mm gel

open pore DGT

WHAM - Good agreement for Zn, Cd, and Cu. Lower

concentrations of Cu showed kinetic

limitations likely due to strong Cu-Fulvic

binding.

28

2.11 Figures

Figure 2.1: Map of Ross Lake in Flin Flon, Manitoba, Canada. (Bhavsar et al 2004)

29

30

Figure 2.2. Concentrations of measured and modelled Zn at five locations in Ross Lake (FFC is Flin Flon

Creek, NB is north basin, 3rd

Ave is Third Avenue, SB is south basin, and RC is Ross Creek). The first

bar is Zn measured by means of DGT, the second bar is Zn2+

, inorganic Zn species and Zn-DOC

estimated using WHAM VI, and the third bar is Zn2+

estimated using WHAM plus Zn2+

dissolved from

resuspended sediment. Values reflect conditions in (a) fall 2010, and (b) summer 2011.

31

Figure 2.3: Concentrations of measured and modelled Cu at five locations in Ross Lake (FFC is Flin Flon

Creek, NB is north basin, 3rd

Ave is Third Avenue, SB is south basin, and RC is Ross Creek). The first

bar is Cu measured by means of DGT, the second bar is Cu2+

, truly dissolved inorganic Cu species and

Cu-DOC estimated using WHAM VI, and the third bar is Cu2+

estimated using WHAM plus Cu2+

dissolved from resuspended sediment. Values reflect conditions in (a) fall 2010, and (b) summer 2011.

32

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33

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35

Chapter 3 - Investigating Toxicity Using Single and Metal Mixture BLM Models: A Case Study at Ross Lake

Co-Authors:Miriam Diamond (University of Toronto), Robert Santore (HydroQual), Helga Sonnenberg (Stantec Consulting)

3 Abstract

Virtually all metal releases to water bodies from mining operations occur as mixtures, but

knowing which metal could exert the greatest toxicity is important when developing pollution

prevention plans. We report on a case study in which we used field measurements, equilibrium

speciation modelling using WHAM and ecotoxicity assessment using the Biotic Ligand Model

(BLM) for single metals and metal mixtures to evaluate the source(s) of toxicity in a mine-

impacted lake virtually devoid of biota.

Ross Lake has received Zn and Cu enriched mine tailings effluents for over 80 years and is now

a net source of Zn to downstream water bodies. Only sparse benthos live in the lake. WHAM

estimated that Zn2+

comprised >70% of total dissolved Zn at pH <7.5 at concentrations very

close to the acute LC50 estimated by the single metal BLM for fathead minnow, rainbow trout

and Daphnia magna. Cu2+

comprised <5% total dissolved Cu at pH 6.5-7, but that rose to 30-

60% at pH 3.5-5.5 which occurred in summer and which was presumed to be caused by thiosalt

oxidation. At low pH, Cu2+

clearly exceeded the acute LC50 for D. magna. The metal mixture

BLM predicted similar results as the single metal BLM. It was concluded that high measured

calcium concentrations from liming in the tailings pond ameliorated Zn toxicity, whereas low

pH's detected in summer 2011 caused Cu toxicity. Further investigation is warranted of the

36

practice of lowering the pH of the tailings pond to ~7 using sulphuric acid because of the

potential for severe pH depression in the lake caused by thiosalt oxidation.

3.1 Introduction

Metal bioavailability and toxicity in aquatic systems are challenging to assess due to the

dependence of metal speciation on ambient chemistry, (Chapman and Wang 2000; Adams and

Chapman 2005 inter alia). As has been stated in the literature numerous times, total metals and

their total insoluble fraction are a poor indicator of toxicity (Allen, Hall et al. 1980; Campbel and

Stokes 1985; Tokalioglu, Kartal et al. 2000). Rather, the free metal ion is considered to exert

toxicity (Campbell 1995). The presence or lack of competing ions in the water column may also

serve to increase or decrease toxicity (Santore, Di Toro et al. 2001).

The Biotic Ligand Model (BLM) was developed to address these complexities (Di Toro, Allen et

al. 2001; De Schamphelaere and Janssen 2004). It is now widely used as a tool for estimating

metal toxicity, as seen by the numerous studies and agencies which make use of it (Paquin,

Gorsuch et al. 2002; Niyogi and Wood 2004). The BLM considers the competitive effect that

ions have on toxicity and treats the biological membrane as a ligand with a specific metal

binding strength (Playle 2004).

Most studies looking a metal toxicity do so on a single metal basis, however metals typically

occur as mixtures in natural waters (Enserink, Maas-Diepeveen et al. 1991; Utgikar, Chaudhary

et al. 2004). Ecotoxicity testing with metals has shown synergistic (greater than additive),

antagonistic (less than additive) and non interactive (strictly additive) effects of mixtures on

metal toxicity (Wang 1987; Preston, Coad et al. 2000). Examples include experimentation with

fresh water algae which showed increased toxicity when algae were exposed to mixtures of Cu +

37

Cd, but decreased toxicity when algae were exposed to mixtures of Cu + Zn, and Cu + Cd + Zn

(Franklin, Stauber et al. 2002).

These experimental results point to the necessity of accounting for the antagonistic/synergistic

interactions which may serve to decrease/increase toxicity of metal mixtures. To address this

need, Santore et al. (in prep) have developed a version of BLM that considers metal mixtures.

The goal of this study was to compare the results of several methods to assess freshwater

ecotoxicity, using the case study of Ross Lake in Flin Flon, Manitoba, Canada. Three modelling

approaches were used for the assessment – first estimating the free metal ion concentration by

means of the geochemical model WHAM, second using the single metal BLM and third, the

newly developed metal mixture BLM. The modelling efforts relied on field measurements taken

during two sampling campaigns.

3.2 Case Study – Ross Lake

Ross Lake (Figure 3.1) is located in northern Manitoba in the Town of Flin Flon at longitude 54o

46’ N and latitude 101o 52’W. Ross Lake has been receiving mine tailing effluents for over 80

years from an upstream Cu and Zn mine operated by Hudson Bay Mining and Smelting

(HBM&S). Mine tailing effluents are discharged into Flin Flon Creek which then flows into the

north basin of Ross Lake. The north and south basins are connected through a culvert which

passes underneath Third Avenue. The south basin of Ross Lake then discharges into Ross

Creek. The lake is devoid of biota except for some highly pollution tolerant chironamids in the

sediment.

In recent years, Ross Lake has experienced annual drops in pH believed to be attributed to

annual thiosalt breakdown during the spring melt. Thiosalts release sulphuric acid when broken

38

down by bacteria (Environment Canada 2012). This acidification is further compounded by

sulphuric acid addition at the discharge point of the tailings facility. The sulphuric acid addition

is done to reduce the pH of the tailings overflow to ~7 from 11 (due to liming which precipitates

Zn) in compliance with Metal Mining Effluent Regulations (MMER) in Canada.

Average concentrations of total dissolved Zn and Cu in the water of Ross Lake are .34 and 0.025

mg/L respectively. Concentrations of Zn and Cu in surface sediment range from 8,600-50,000,

and 7,640-13,100 mg kg-1

. These concentrations are substantially less than maximum

concentrations of 218,000 and 32,200 mg kg-1

found 18-20 cm below the sediment-water

interface in cores taken from the lake in fall 2010 (Yacoob, unpubl. data).

Numerous studies have been conducted on the lake of which a majority have focused on elevated

Zn levels (Evans 2000; Rudnitski 2001; Bhavsar, Diamond et al. 2004). Bhavsar et al. (2004a,b)

have documented the transformation of the lake from a net sink to a net source of Zn, i.e., higher

Zn concentrations are now measured at the outlet than inlet of Ross Lake.

Ecotoxicity testing was performed by Aqua Tox in Guelph, Ontario, on Flin Flon Creek water

during July 2011 as part of the mine’s efforts to address the effects on the surrounding area

caused by mining operations. The tests involved 14 day contaminated sediment only and

contaminated water only exposure testing on the invertebrate Hyallela azteca. The Chronic test

results showed 24% mortality in the test using Flin Flon Creek water and 100% mortality based

on chronic exposure to Flin Flon Creek sediment overlain with clean water. In these tests, Flin

Flon Creek water samples that had a pH of 4 when collected and were adjusted to pH 7. High

mortality due to sediment exposure is suspected to be the result of metal feedback into the water

column from the contaminated sediment.

39

3.3 Methods

The investigation was carried out through field measurements and mathematical modelling.

Field data were used to characterize water chemistry, and to parameterize and evaluate model

results. Total dissolved and bioavailable fractions of metals were modelled using the

geochemical model Windermere Humic Aqueous Model 6 (WHAM VI) (Tipping 1994).

Toxicity was modelled using the single and metal mixture BLMs (Di Toro, Allen et al. 2001)

(Santore, 2013 in prep).

3.3.1 Field Measurements

Sampling campaigns were conducted in fall 2010 and summer 2011. Water column samples

were collected at five locations at Ross Lake: Flin Flon Creek (FFC, outflow from the tailing

pond and inflow to Ross Lake), the centre of the North Basin (NB), Third Avenue (3rd

Ave),

centre of South Basin (SB), and Ross Creek (RC, outflow of Ross Lake).

Water samples were collected to determine concentrations of total metals, dissolved metals,

sulphide, DOC, TOC, nutrients, alkalinity, etc. as required as inputs for WHAM and BLM.

Surface water samples were taken using a Kemmerer water sampling bottle deployed midway

into the water column. All sample bottles were pre-cleaned; plastic bottles were acid washed, and

glass bottles were baked (450oC). Samples for total and dissolved metal analysis were preserved

with 3ml of a 1:3 nitric acid and water solution and collected in 250 ml high density

polyethylene (HDPE) bottles. Water samples for sulphide analysis were preserved with 2 ml of 2

N zinc acetate and 1 ml of 6 N sodium hydroxide, and collected in 125 ml HDPE bottles.

Samples for TOC/DOC analysis were preserved with 2 ml of 1:1 hydrochloric acid and water

solution, and were collected in 100 ml amber glass bottles. Samples for analysis of alkalinity

were collected in 125 ml HDPE bottles (no preservative), and for nutrients were collected in 500

40

ml low density polyethylene containers (no preservative). Dissolved metals were filtered on site

using a PhenexTM

0.45 um filter. Water samples were immediately stored in coolers, transferred

to refrigerators and sent for analysis within 24 hours to ALS Labs in Winnipeg, Manitoba.

Details of methods of analysis can be found in Supplementary Information (SI).

At the time of sampling pH, temperature, dissolved oxygen (DO) and conductivity were

measured using a HYDROLAB Datasonde 4 Multiprobe and Datasurveyor 4 Data Display. The

pH was calibrated daily using 3 buffer solutions (pH values of 4, 7, and 10) and DO and

conductivity meters were checked daily against standards to ensure outputs were accurate.

Quality control was completed through ALS Labs in Winnipeg. Method blanks were used and

were reported below limits of detection. Laboratory control samples and duplicates were run and

met all quality control parameters. Details of quality control can be found in SI.

3.3.2 Speciation and Toxicity Modelling Using WHAM and BLM

WHAM VI was used to calculate Zn and Cu speciation. WHAM assumes thermodynamic

equilibrium. It is comprised an inorganic speciation code for aqueous speciation

and the Humic Ion-Binding Model VI, both of which assume thermodynamic equilibrium

(Tipping 1998). The more sophisticated treatment of metal binding to humic and fulvic acids is

is particularly important when considering Cu which has a high tendency to bond with organic

matter.

Water chemistry data measured during the two field campaigns were used as model inputs

(summarized in Table S1). DOC and TOC concentrations from summer 2011 were used for fall

2010 in the absence of measured data. Humic substances were assumed to be 60% of measured

DOC values with a fulvic to humic ratio of 9:1 (Gueguen et al. 2011). Default stability

constants from the WHAM VI database were used for the modeling calculations.

41

The Biotic Ligand Model (BLM) was used to estimate values of acute LC50 for Cu and Zn for

fathead minnow, rainbow trout, and D. magna. Site specific water chemistry (Tables S2) was

used to parameterize conditions at five locations for both the fall 2010 and summer 2011

sampling campaigns. Concentrations of sulphate and Ca were outside of the BLM calibration

range however, the model has low sensitivity to these parameters and thus, the results are

expected to be reasonable (Personal communication R. Santore). As with the WHAM

calculations, DOC concentrations measured in summer 2011 were used for the spring calculation

in the absence of measured values.

3.4 Results

3.4.1 Measured Water Chemistry

Water chemistry results are summarized in SI. The pH in Ross Lake during fall 2010 ranged

between 6.5 - 7, however pH was 3.5 - 5.7 in summer 2011. The low pH during summer 2011

was attributed to the beginning of thiosalt breakdown brought on by warm weather. This

phenomenon of very low pH in Ross Lake has been recorded by the mine for several years

since the mine began adding sulphuric acid to reduce the pH of its tailings overflow in

compliance with the MMER. Elevated temperatures are favourable for the breakdown of thiols to

S2O3, and sulphite. Bacteria such as Thiobacillus ferrooxidans and Thiobacillus thiooxidans aid

in oxidizing these compounds into sulphuric acid (Kuyucak 2007) which causes pH to decrease.

Prior to the addition of sulphuric acid, the pH in the lake during summer was ~8, elevated from

that of nearby lakes because of the liming of the upstream tailings pond (Bhavsar et al. 2004a).

DO measurements were uniform throughout the water column (typically in the range of 9 mg/L)

indicating the system was well mixed and well oxygenated which was expected due to the

shallow depth of the lake. The lake had high Ca concentrations between 150 – 200 mg/L in the

42

fall and 300 – 400 mg/L in summer. As mentioned above, these high Ca concentrations were due

to the addition of CaCO3 (lime) to the tailings pond to raise pH to an optimal level for Zn

precipitation. Sulphate concentrations in Ross Lake were also high (700-1300 mg/L) as expected

from the mining of sulphidic ore and addition of sulphuric acid to reduce the pH of the limed

tailings water. Total Zn and Cu concentrations were also high. Total Zn concentrations ranged

between 0.1 – 0.5 mg/L and were higher at the outflow than inflow of the Lake. Total Cu

concentrations were relatively consistent throughout the basins, ranging between 0.02 – 0.05

mg/L. Total dissolved concentrations of Zn and Cu were indistinguishable from those of total

metals, at 0.1- 0.5 mg/L and 0.02 - 0.035 mg/L, respectively. These results indicate that nearly

all metals were in their dissolved form. Alkalinity varied between <1.0 – 7 mg/L as CaCO3

equivalents and calculated hardness was between 836 – 1160 mg/L CaCO3 .

3.4.2 Metal Speciation and Toxicity

Total dissolved concentrations of Cu and Zn modeled in WHAM were within 10-fold of

measured values for both sampling times (Figure 3.2 and 3.3). Zn2+

accounted for ~70% of the

total dissolved concentration on average (Figure 3.3a). This percentage was relatively

insensitive to pH until pH 8 at which point it declined dramatically while ZnCO3

correspondingly increased. Inorganic species such as ZnOH2, ZnCl+ and ZnOH contributed

negligibly to the species distribution of Zn. Organically bound Zn comprised <5% of total

dissolved Zn. Thus, at acidic and circumneutral pH, Zn2+

and ZnSO4, dominate in contrast to

ZnCO3 at pH >8 which prevailed before sulphuric acid addition due to MMER compliance.

Cu speciation is highly sensitivity to pH (Figure 3.3b). At the lowest pH, Cu was ~70% Cu2+

but

contributed <1% of the total dissolved Cu at circumneutral pH. At neutral pH, Cu-DOC

43

complexes accounted for approximately 90% of total dissolved Cu with all inorganic forms

contributing <10%. The dominance of Cu-DOC is typical of freshwater systems (Cabaniss and

Shuman 1987, Sciera et al. 2004). At low pH, Cu does not appreciably bind to DOC as protons

will compete with metals for sites on DOC (Benedetti, Milne et al. 1995). The other main

inorganic truly dissolved species predicted by WHAM was CuSO4 accounted for up to 20% at

pH 3.5 but <5% at pH 6.5-7.

Percentages of free ion Me2+

of total dissolved at circumneutral pH were consistent with Gandhi

et al. (2011) work on Ross Lake.

Modelled BLM LC50 values expressed in terms of the free metal ion are presented for rainbow

trout for Zn and Daphnia magna for Cu due to their high sensitivity to the respective metals

(Blaylock, Frank et al. 1985; Hansen, Welsh et al. 2002)(Figure 3.2). Free metal ion

concentrations of both metals were below the calculated BLM free ion LC50 during fall 2010

when the pH was 6.5-7 indicating that the single metals were not causing toxicity to these

species. However, with the low pH in summer 2011, Cu2+

exceeded the BLM free ion LC50 by

10x and Zn2+

concentrations were very close to the LC50. These results unambiguously indicate

acute toxicity due to Cu at low pH, if any biota would have been able to withstand the acidity of

the lake. This result of toxicity due to Cu at low pH was not anticipated because previously, high

Zn concentrations were considered be of greatest concern, but was based on conditions prior to

the implementation of MMER which requires the lowering of pH from the tailings overflow.

3.4.3 Single Versus Metal Mixture BLM Toxicity Analysis

Toxicity was investigated in greater detail using the BLM, including a comparison of results

from the single versus mixture metal BLMs (Santore et al. 2013 in prep) (Table 3.1 and 3.2).

44

Again, the mixture BLM was developed for acute toxicity. The same water chemistry data were

used in the mixture BLM, but in addition the mixture BLM used data from a suite of metals (Al,

Cu, Zn, Co, Cd, Ag, As, Fe) (Table S2).

For the comparison of the two BLMs, the results are presented in Toxic Units (TU) which is the

ratio of the amount of metal measured and the amount of metal estimated to be toxic to an

organism. A value of TU >1 indicates acute toxicity.

Considering the single metal BLM, the toxic units of Zn were between 0.1 and 1, with rainbow

trout being most sensitive. Values within an order of magnitude of 1 for an acute toxicity

assessment are of concern since the concentrations are well within the range of causing chronic

toxicity. That Zn was not estimated to have greater toxicity due to its high concentrations is

likely due to Ca concentrations ameliorating toxicity (Santore, Mathew et al. 2002). Zn and Ca

compete for similar sites on the gill, and since concentrations of Ca at > 150 mg/L far exceeded

that of Zn of 0.1 - 0.5 mg/L, Ca likely had an inhibitive effect on Zn toxicity. This explanation is

consistent with Zn TU values for summer (measured Ca concentrations 310 – 440 mg/L) being

about 10 times less than TU values in fall (measured Ca concentrations 158 – 179 mg/L).

In comparison to Zn, the toxic units for Cu (single metal BLM) exceeded 1 for D. magna during

summer 2011 and were within an order of magnitude of 1 for rainbow trout and fathead minnow

(with the exception of Ross Creek). Since measured Cu concentrations were similar during both

campaigns, predicted toxicity to D. magna was likely the result of the combination of low pH

and high Cu2+

concentrations. Several reasons account for this result. First, as discussed above,

the proportion of Cu in the free ion form is 30-69% at low pH. D. magna is highly sensitive to

free ion copper, at pH < 7 (Meador 1991). Secondly, low pH depresses the respiration rate of D.

45

magna by reducing CO2 diffusion across the gill and thereby inhibiting O2 uptake (Alibone and

Fair 1981).

The TUs estimated by the single and mixture BLMs were similar for Zn and Cu (Table 3.2). As

mentioned previously, metal mixtures can have an additive toxicity, less than additive, or more

than additive effect (Norwood, Borgmann et al. 2003). In this specific instance we have

concluded that mixtures appear to be strictly additive for the measured water chemistry at Ross

Lake. In addition, the mixture BLM results indicated that Cu was acutely toxic to H. azteca.

In both the single metal and mixture analysis for Zn at Flin Flon Creek, TU was calculated as

being extremely low. pH at this location (3.55) was the lowest recorded across both sampling

campaigns. The theoretical explanation (because organisms would not survive such low pH) is

likely related to proton binding which provide competition for ligand attachment (Cusimano,

Brakke et al. 1986).

Lower TU for Cu in Ross Creek are related to lower dissolved concentrations of Cu at this

location. Total dissolved Cu are an order of magnitude lower than all upstream locations, despite

total concentration of Cu being consistent with the rest of Ross Lake. This may be due to the

uptake of copper by aquatic macrophytes which were abundant at this location (Jain, Vasudevan

et al. 1989).

3.5 Implications

It is clear to anyone living in Flin Flon that Ross Lake is a toxic environment to biota. This was

scientifically confirmed by toxicity testing in which exposure to the sediments of Flin Flon Creek

caused 100% mortality to H. Azteca in bioassays. However, the bioassays do not indicate the

cause of toxicity and hence the need to investigate metal chemistry and toxicity through

46

mechanistic modelling. Results from the single metal and metal mixture BLMs provided similar

results – acute Cu toxicity at low pH and likely chronic toxicity from Zn and Cu at low and

circumneutral pH. Cu toxicity is unambiguous at low pH. Metal speciation modelling provides

the explanation for toxicity of the prevalence of Zn2+

at all values of pH <8.5 and Cu2+

at pH <5.

This application of the metal mixture versus single metal BLM does not provide significantly

different results that would point to a different management strategy. However, the metal

mixture BLM provides additional confirmation of toxicity caused by a combination of elevated

free metal concentrations plus periodically low pH in the lake.

What do the results suggest in terms of environmental management? It is clear that lowering

concentrations of metals from the tailings pond overflow is a necessary step towards reducing the

impact of the mining operation. This must be a long term strategy since the large repository of

Zn and Cu in the sediments due to historical discharges contribute to increasing water column

metal concentrations (Bhavsar et al. 2004a,b, Gandhi et al. 2011).

A second step would be examining the efficacy of lowering the pH of the tailings pond overflow

from >8 to ~7 by the addition of sulphuric acid, in compliance with MMER. Thiosalt formation

occurs during grinding, aeration and floatation process of refining sulphidic ores. Decomposition

of thiosalts occurs through bacterial mediation and produces sulphuric acid, thus decreasing pH.

Prior to the mine reducing the pH of the tailings overflow to 7, thiosalt breakdown reduced the

lake pH from >8 to an alkaline pH of 7.8. However, now the decrease of pH at the tailings

outflow results in a lake pH low enough to be directly toxic to biota and to increase metal

toxicity. Thus, one could ask if the MMER are achieving their desired goal of environmental

protection at sites of mining activity

47

3.6 Tables

Table 3.1 Toxic Units calculated for Fathead Minnow, Rainbow Trout and D. magna using the single metal BLM . Results are presented as Toxic Units (TU) which

is the ratio of the amount of metal measured and the amount of metal estimated to be toxic to the respective organism listed. A value of TU >1 indicates toxicity.

Copper TU Zinc TU

Location Season Fathead

Minnow

Rainbow

Trout D. magna

Fathead

Minnow

Rainbow

Trout D. magna

Flin Flon Creek Fall 2010 0.11 0.13 0.90 0.11 0.37 0.05 North Basin Fall 2010 0.08 0.09 0.52 0.26 0.81 0.11

Third Avenue Fall 2010 0.09 0.10 0.63 0.24 0.77 0.10 South Basin Fall 2010 0.06 0.06 0.26 0.28 0.80 0.12 Ross Creek Fall 2010 0.06 0.07 0.49 0.31 0.99 0.13

Flin Flon Creek Summer 2011 0.10 0.16 5.79 7.00E-4 2.46 E-3 2.79E-4 North Basin Summer 2011 0.28 0.41 9.96 0.07 0.24 0.03

Third Avenue Summer 2011 0.16 0.21 2.89 0.11 0.39 0.04 South Basin Summer 2011 0.23 0.32 6.87 0.08 0.29 0.03 Ross Creek Summer 2011 0.03 0.04 0.93 0.07 0.24 0.03

Table3.2 Toxic Units calculated for Cutthroat Trout, Rainbow Trout, D. magna and H. azteca using a mixture analysis BLM. Results are presented in Toxic Units

(TU)

Copper TU Zinc TU

Location Season D. magna H. azteca Cutthroat

Trout

Rainbow

Trout D. magna H. Azteca

Flin Flon Creek Fall 2010 0.94 0.94 0.13 0.27 0.18 0.24

North Basin Fall 2010 0.62 0.62 0.28 0.59 0.41 0.54

Third Avenue Fall 2010 0.74 0.74 0.27 0.56 0.39 0.51

South Basin Fall 2010 0.33 0.33 0.28 0.57 0.39 0.52

Ross Creek Fall 2010 0.57 0.57 0.35 0.73 0.50 0.66

Flin Flon Creek Summer 2011 5.22 5.22 9.4 E-4 1.94E-4 1.34E-3 1.77E-3

North Basin Summer 2011 10.17 10.17 0.09 0.18 0.12 0.16

Third Avenue Summer 2011 3.33 3.33 0.14 0.29 0.20 0.26

South Basin Summer 2011 7.32 7.32 0.11 0.22 0.15 0.20

Ross Creek Summer 2011 0.84 0.84 0.09 0.18 0.13 0.17

48

Figures

Figure 3.1: Map of Ross Lake in Flin Flon, Manitoba, CA (Bhavsar et al. 2004). Site of case

study downstream of Zn and Cu tailings facility.

49

Figure3. 2 – Measured and WHAM-modelled total dissolved metal ,WHAM-derived free metal ionand

BLM modelled LC50 concentrations at five locations in Ross Lake determined for fall 2010 and summer

2011, (a) Zn and (b) Cu).

1.0E-06

1.0E-05

1.0E-04

FFC NB 3rd

Ave

SB RC FFC NB 3rd

Ave

SB RC

Co

nce

ntr

ati

on

(M

)

Measured Total Dissolved Zn WHAM Total Dissolved Zn

WHAM Zn2+ BLM LC50 Zn2+ Rainbow Trout

1.0E-10

1.0E-09

1.0E-08

1.0E-07

1.0E-06

FFC NB 3rd

Ave

SB RC FFC NB 3rd

Ave

SB RC

Co

nce

ntr

ati

on

(M

)

Measured Total Dissolved Cu WHAM Total Dissolved Cu

WHAM Cu2+ BLM LC50 Cu2+ D. Magna

Fall 2010 Summer 2011

Fall 2010

Summer 2011

Fall 2010

Summer 2011

Fall 2010

50

Figure 3.3 – WHAM calculated speciation over pH (3-9) for Zn (a) and Cu (b) to show the

species distribution as a function of pH. Water chemistry taken from North Basin in July 2011.

Shaded regions indicate pH ranges during fall 2010 (6.5 -7) and summer 2011 (3.5 – 5.5).

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

3 4 5 6 7 8 9

Con

cen

trati

on

Fra

ctio

n

pH

Zn2+

ZnCO3

Zn- DOC

ZnCO3

Zn(OH)2

ZnCl+

Fall 2010

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

3 4 5 6 7 8 9

Con

cen

trati

on

Fra

ctio

n

pH

Cu2+

CuSO4

CuHCO3

CuCl+

Cu-DOC

Summer 2011 Fall 2010

ZnSO42-

Summer 2011

51

3.7 References

Adams, W. and P. Chapman (2005). Assessing the Hazard of Metals and Inorganic Metal

Substances in Aquatic and Terrestrial Systems. Pensacola, FL, USA, SETAC.

Alibone, M. R. and P. Fair (1981). "The effects of low pH on the respiration of Daphnia magna

Straus." Hydrobiologia 85(2): 185-188.

Allen, H. E., R. H. Hall, et al. (1980). "Metal speciation. Effects on aquatic toxicity."

Environmental Science & Technology 14(4): 441-443.

Benedetti, M. F., C. J. Milne, et al. (1995). "Metal Ion Binding to Humic Substances:

Application of the Non-Ideal Competitive Adsorption Model." Environmental Science &

Technology 29(2): 446-457.

Bhavsar, S. P., M. L. Diamond, et al. (2004). "Dynamic coupled metal transport-speciation

model: Application to assess a zinc-contaminated lake." Environmental Toxicology and

Chemistry 23(10): 2410-2420.

Blaylock, B. G., M. L. Frank, et al. (1985). "COMPARATIVE TOXICITY OF COPPER AND

ACRIDINE TO FISH, DAPHNIA AND ALGAE." Environmental Toxicology and

Chemistry 4(1): 63-71.

Campbel, P. G. C. and P. M. Stokes (1985). "Acidification and Toxicity of Metals to Aquatic

Biota." Canadian Journal of Fisheries and Aquatic Sciences 42(12): 2034-2049.

Campbell, P. G. C. (1995). Campbell, P. (1995). Interactions between trace metals and aquatic

organisms: a critique of the Free-ion Activity Model. Metal Speciation and

Bioavailability in Aquatic Systems. D. R. T. A. Tessier. New York, John Wiley.

Canada, E. (2012). Metal Mining Technical Guidance for Environmental Effects Monitoring.

Ottawa, Environment Canada

Chapman, P. M. and F. Y. Wang (2000). "Issues in ecological risk assessment of inorganic

metals and metalloids." Human and Ecological Risk Assessment 6(6): 965-988.

Cusimano, R. F., D. F. Brakke, et al. (1986). "EFFECTS OF PH ON THE TOXICITIES OF

CADMIUM, COPPER, AND ZINC TO STEELHEAD TROUT (SALMO-

GAIRDNERI)." Canadian Journal of Fisheries and Aquatic Sciences 43(8): 1497-1503.

De Schamphelaere, K. A. C. and C. R. Janssen (2004). "Development and field validation of a

biotic ligand model predicting chronic copper toxicity to Daphnia magna."

Environmental Toxicology and Chemistry 23(6): 1365-1375.

Di Toro, D. M., H. E. Allen, et al. (2001). "Biotic ligand model of the acute toxicity of metals. 1.

Technical basis." Environmental Toxicology and Chemistry 20(10): 2383-2396.

52

Enserink, E. L., J. L. Maas-Diepeveen, et al. (1991). "Combined effects of metals; an

ecotoxicological evaluation." Water Research 25(6): 679-687.

Evans, L. J. (2000). "Fractionation and Aqueous Speciation of Zinc in a Lake Polluted by Mining

Activities, Flin Flong, Canada." Water, Air, & Soil Pollution 122(3): 299-316.

Franklin, N. M., J. L. Stauber, et al. (2002). "Toxicity of metal mixtures to a tropical freshwater

alga (Chlorella sp): The effect of interactions between copper, cadmium, and zinc on

metal cell binding and uptake." Environmental Toxicology and Chemistry 21(11): 2412-

2422.

Hansen, J. A., P. G. Welsh, et al. (2002). "Relative sensitivity of bull trout (Salvelinus

confluentus) and rainbow trout (Oncorhynchus mykiss) to acute exposures of cadmium

and zinc." Environmental Toxicology and Chemistry 21(1): 67-75.

Jain, S. K., P. Vasudevan, et al. (1989). "Removal of some heavy metals from polluted water by

aquatic plants: Studies on duckweed and water velvet." Biological Wastes 28(2): 115-

126.

Meador, J. P. (1991). "THE INTERACTION OF PH, DISSOLVED ORGANIC-CARBON,

AND TOTAL COPPER IN THE DETERMINATION OF IONIC COPPER AND

TOXICITY." Aquatic Toxicology 19(1): 13-31.

Niyogi, S. and C. M. Wood (2004). "Biotic Ligand Model, a Flexible Tool for Developing Site-

Specific Water Quality Guidelines for Metals." Environmental Science & Technology

38(23): 6177-6192.

Norwood, W. P., U. Borgmann, et al. (2003). "Effects of metal mixtures on aquatic biota: A

review of observations and methods." Human and Ecological Risk Assessment 9(4): 795-

811.

Paquin, P. R., J. W. Gorsuch, et al. (2002). "The biotic ligand model: a historical overview."

Comparative Biochemistry and Physiology Part C: Toxicology & Pharmacology 133(1–

2): 3-35.

Playle, R. C. (2004). "Using multiple metal-gill binding models and the toxic unit concept to

help reconcile multiple-metal toxicity results." Aquatic Toxicology 67(4): 359-370.

Preston, S., N. Coad, et al. (2000). "Biosensing the acute toxicity of metal interactions: Are they

additive, synergistic, or antagonistic?" Environmental Toxicology and Chemistry 19(3):

775-780.

Rudnitski, K. D. (2001). The Fate and Speciation of Zinc in Ross Lake: A Mine-Impacted Lake

in Flin Flon, Manitoba. Science Guelph, University of Guelph. Master of Science.

Santore, R. C., D. M. Di Toro, et al. (2001). "Biotic ligand model of the acute toxicity of metals.

2. Application to acute copper toxicity in freshwater fish and Daphnia." Environmental

Toxicology and Chemistry 20(10): 2397-2402.

53

Santore, R. C., R. Mathew, et al. (2002). "Application of the biotic ligand model to predicting

zinc toxicity to rainbow trout, fathead minnow, and Daphnia magna." Comparative

Biochemistry and Physiology Part C: Toxicology &amp; Pharmacology 133(1-2): 271-

285.

Tipping, E. (1994). "WHAM - A CHEMICAL-EQUILIBRIUM MODEL AND COMPUTER

CODE FOR WATERS, SEDIMENTS, AND SOILS INCORPORATING A DISCRETE

SITE ELECTROSTATIC MODEL OF ION-BINDING BY HUMIC SUBSTANCES."

Computers & Geosciences 20(6): 973-1023.

Tokalioglu, S., S. Kartal, et al. (2000). "Determination of heavy metals and their speciation in

lake sediments by flame atomic absorption spectrometry after a four-stage sequential

extraction procedure." Analytica Chimica Acta 413(1-2): 33-40.

Utgikar, V. P., N. Chaudhary, et al. (2004). "Toxicity of metals and metal mixtures: analysis of

concentration and time dependence for zinc and copper." Water Research 38(17): 3651-

3658.

Wang, W. C. (1987). "FACTORS AFFECTING METAL TOXICITY TO (AND

ACCUMULATION BY) AQUATIC ORGANISMS - OVERVIEW." Environment

International 13(6): 437-457.

54

Chapter 4 - Conclusions

4 Research Summary

By examining model predictions of metal speciation vagueness in the literature was identified.

Specifically, there is a need to clarify what is meant by “labile” metal and to better understand

what exactly DGTs measure.

Zn measured by means of DGTs were within an order-of-magnitude of estimated concentrations

of Zn

2+. Other researchers have reported similar correspondence (Meylan, Odzak et al. 2004;

Guthrie, Hassan et al. 2005). However, DGT-measured Cu was 10 – 100x higher than WHAM-

estimated Cu2+

. This finding was also consistent with similar studies (Guthrie, Hassan et al.

2005). This discrepancy for Cu prompted further research into why these discrepancies may be

occurring in Ross Lake.

DGT are intended to measure the labile fraction of metal in solution (Zhang and Davison 1995).

Definitions of labile typically refer to metals that dissociate quickly (Zhang, 2003), lability being

important since it is associated with bioavailability (Jansen et al. 2002). However the open pore

gels used in this study may allow for the diffusion of small organically bound particles

(Gueguen, Clarisse et al. 2011). Thus, the WHAM estimates of the free metal ion concentration

would underestimate the concentration measured by a DGT especially since Cu has a high

affinity for organic matter. Further analysis of DOC composition would be needed to establish

this as a certainty. Determination of particle size distribution would be necessary to conclude if

open pore DGT are allowing for the diffusion of organically bound particles and to what extent

(Zhang, 2000).Through the method outlined in Uribe et al. 2011, we also identified other species

that may be considered labile and thus able to pass into DGT measurements of labile metals.

Additionally, we explored equilibrium assumptions inherent to WHAM and their applicability to

Ross Lake. We proposed that kinetically controlled oxidization of metal sulphides from

resuspended sediment would increase the concentration of free metal ion in the water column.

55

By modeling the oxidation process reasonable agreement was achieved between DGT-measured

Zn and Cu, however more extensive work is needed to test this hypothesis.

When looking at the ecotoxicity model BLM, the single and mixture metal BLMs appear to

provide similar results in terms of metals deemed toxic to specified organisms, however further

comparisons need to be made. The metal mixture BLM indicated less metal ecotoxicity to fish

but similar toxicity to zooplankton from Cu. When acute toxicity was found, it was related to low

pH in the water column which liberated more metal in the free ion form. The finding of levels of

Cu in exceedance of acute toxicity estimates is consistent with the results of Flin Flon Creek

water and particularly sediment causing toxicity to test organisms. Observations during field

work confirmed the lack of biota in the lake except for sparse chironomids in the sediment.

Low pH, the apparent cause of toxicity in the lake, has been directly linked to the addition of

sulphuric acid to the outflow of the finished tailings effluent, as stipulated under Metal Mine

Effluent Regulations. These regulations have set pH at discharge between 6.5 and 9.5 (Canada,

2013). This acidification is further compounded by natural thiosalt break down in Ross Lake

causing low pH (3.5 – 5.5) during warm periods. This has highlighted the need for site specific

regulations which will take water chemistry into account before setting standard regulations

across the board which may, in some instances be detrimental to the health of the lake.

4.1 Scientific Contributions

Development of a kinetic equation to account for metal sulphide oxidation in the water

column in conjunction with traditional equilibrium geochemical model for estimating

metal speciation.

Highlighted the need for clarity in scientific literature with regard to what is measured by

Diffusive Gradients Thinfilms, and

Comparative analysis on single versus mixture BLMs.

56

4.2 Research Outlook

This thesis looked at two model types, geochemical speciation and ecotoxicity, and sought to

evaluate model performance. Further work, which could advance this goal using Ross Lake as a

case study is as follows:

Analyze composition and size of DOC in Ross Lake to determine if colloidal DOC are

being detected by open pore gels of DGT,

Experimentation to measure the kinetics of metal sulphide oxidation in the water column,

and

More case studies to compare single versus mixture analysis BLM using different water

chemistries from other locations. This assessment could include considering Ross Lake

at different times of year and with more frequent water chemistry measurements.

Further modelling efforts could include a temporal model which accounts for the breakdown of

thiosalts in the water column would be a useful addition to this work. This model would help in

investigating whether regulations regarding pH adjustment to tailings effluent followed by

thiosalt breakdown are causing temporary dips in pH in the lake, which this work has highlighted

as being a major cause of toxicity in Ross Lake.

57

4.3 References

Canada, G. o. (2013, February 21). Metal Mining Effluent Regulations. Minister of Justice .

Canada, E. (2012). Metal Mining Technical Guidance for Environmental Effects Monitoring.

Ottawa, Environment Canada

Gueguen, C., O. Clarisse, et al. (2011). "Chemical speciation and partitioning of trace metals

(Cd, Co, Cu, Ni, Pb) in the lower Athabasca river and its tributaries (Alberta, Canada)."

Journal of Environmental Monitoring 13(10): 2865-2872.

Guthrie, J. W., N. M. Hassan, et al. (2005). "Complexation of Ni, Cu, Zn, and Cd by DOC in

some metal-impacted freshwater lakes: a comparison of approaches using

electrochemical determination of free-metal-ion and labile complexes and a computer

speciation model, WHAM V and VI." Analytica Chimica Acta 528(2): 205-218.

Meylan, S., N. Odzak, et al. (2004). "Speciation of copper and zinc in natural freshwater:

comparison of voltammetric measurements, diffusive gradients in thin films (DGT) and

chemical equilibrium models." Analytica Chimica Acta 510(1): 91-100.

Uribe, R., S. Mongin, et al. (2011). "Contribution of Partially Labile Complexes to the DGT

Metal Flux." Environmental Science & Technology 45(12): 5317-5322.

Zhang, H. (2003). DGT –for measurements in waters, soils and sediments. Lancaster, UK, DGT

Research Ltd.

Zhang, H. and W. Davison (1995). "Performance Characteristics of Diffusion Gradients in Thin

Films for the in Situ Measurement of Trace Metals in Aqueous Solution." Analytical

Chemistry 67(19): 3391-3400.

Zhang, H. and W. Davison (2000). "Direct In Situ Measurements of Labile Inorganic and

Organically Bound Metal Species in Synthetic Solutions and Natural Waters Using

Diffusive Gradients in Thin Films." Analytical Chemistry 72(18): 4447-4457.

Zhang, H. and W. Davison (2001). "In situ speciation measurements. Using diffusive gradients

in thin films (DGT) to determine inorganically and organically complexed metals." Pure

and Applied Chemistry 73(1): 9-15.

58

Appendix 1: Supporting Information for Chapter 2

59

Tables for Supplementary Information:

Table S1: Definition of Terms

Term Definition

Truly dissolved Free ion metal concentration + dissolved inorganic species

Total Dissolved Sum of Me-DOC complexes (colloidal fraction) + Truly dissolved

Total Metal Concentration Sum of total dissolved and particulate

Table S2: Dissociation Constants used for DGT Calculations

Location Date Temp (oC)

(Deployment)

Temp (oC)

(Retrival)

Dissociation Constant

(E-6 cm2/sec) (Zn)

Dissociation

Constant (E-6

cm2/sec) (Cu)

Flin Flon Creek Oct-2010 10.3 9.9 3.77 3.99

North Basin Oct-2010 10.15 10.4 3.77 3.99

Third Avenue Oct-2010 10.5 9.4 3.77 3.99

South Basin Oct-2010 8.8 9.3 3.77 3.99

Ross Creek Oct-2010 10.16 10.4 3.77 3.99

Flin Flon Creek July-2011 22.6 21.7 5.6 5.74

North Basin July-2011 21.6 21.4 5.6 5.74

Third Avenue July-2011 21.6 21.7 5.6 5.74

South Basin July-2011 22.8 21.3 5.6 5.74

Ross Creek July-2011 22.6 22.1 5.6 5.74

60

Table S3: Ross Lake water chemistry used as input data for WHAM VI calculations.

Input Values July 2011

Flin Flon

Creek

July 2011

North Basin

July 2011

Third

Avenue

July 2011

South Basin

July 2011 Ross

Creek

October

2010 Flin

Flon Creek

October 2010

North Basin

October

2010 Third

Avenue

October 2010

South Basin

October

2010

Ross

Lake SPM (g/L) 1.78E-03 1.78E-03 1.78E-03 1.78E-03 1.78E-03 1.78E-03 1.78E-03 1.78E-03 1.78E-03 1.78E-03

Temp (K) 281.66 281.66 281.66 281.66 281.66 281.66 281.66 281.66 281.66 281.66

pCO2 (atm) 3.12E-04 3.12E-04 3.12E-04 3.12E-04 3.12E-04 3.12E-04 3.12E-04 3.12E-04 3.12E-04 3.12E-04

Ph 3.55 5.22 5.725 5.36 5.35 6.4 6.69333 6.605 7.13 6.48

Particulate Humic

Acid (g/l) 6E-06 3.6E-05 0.000018 0.000018 0.000012 6E-06 3.6E-05 0.000018 0.000018 0.000012

Particulate Fulvic Acid

(g/L) 3.24E-10 1.17E-08 2.92E-09 2.92E-09 1.3E-09 3.24E-10 1.17E-08 2.92E-09 2.92E-09 1.3E-09

Particulate Iron Oxide

(g/L) 2.03E-03 7.74E-04 6.61E-04 6.29E-04 6.12E-04 7.57E-05 7.41E-05 8.70E-05 1.05E-04 1.84E-04

Particulate Manganese

oxide (g/l) 6.24E-05 7.87E-05 7.90E-05 7.98E-05 1.08E-04 2.83E-05 3.75E-05 3.75E-05 3.86E-05 4.97E-05

Colloidal Humic Acid

(g/L) 0.00044 0.00041 0.00040 0.00040 0.00043 0.000444 0.000414 0.000402 0.000408 0.000426

Colloidal Fulvic Acid

(g/L) 0.003996 0.003726 0.003618 0.003672 0.003834 0.003996 0.003726 0.003618 0.003672 0.003834

Na(M) 3.18E-03 2.80E-03 2.78E-03 2.64E-03 2.63E-03 1.42E-03 1.02E-03 1.18E-03 9.87E-04 1.04E-03

Mg(M) 6.38E-04 5.02E-04 5.97E-04 5.14E-04 5.88E-04 4.07E-04 3.72E-04 3.85E-04 3.62E-04 3.53E-04

K(M) 2.63E-04 2.28E-04 2.27E-04 2.28E-04 2.16E-04 1.56E-04 1.51E-04 1.52E-04 1.51E-04 1.53E-04

Ca(M) 9.63E-03 8.23E-03 8.68E-03 8.01E-03 8.36E-03 4.67E-03 4.04E-03 4.24E-03 3.94E-03 4.04E-03

Mn(M) 7.17E-07 9.05E-07 9.08E-07 9.17E-07 1.24E-06 3.26E-07 4.31E-07 4.31E-07 4.44E-07 5.72E-07

Cu(M) 4.15E-07 6.94E-07 6.72E-07 6.74E-07 7.00E-07 6.92E-07 7.99E-07 8.48E-07 7.29E-07 7.52E-07

Cl(M) 6.97E-03 5.50E-03 5.39E-03 5.36E-03 5.13E-03 6.97E-03 5.50E-03 5.39E-03 5.36E-03 5.13E-03

SO4(M) 9.17E-03 7.53E-03 7.41E-03 7.35E-03 7.00E-03 3.43E-03 3.43E-03 3.62E-03 3.62E-03 3.62E-03

Zn (M) 1.59E-06 5.78E-06 6.13E-06 6.03E-06 6.00E-06 3.12E-06 6.38E-06 6.09E-06 6.61E-06 7.60E-06

61

Table S4:Species Distribution Calculated by WHAM VI, dissociation constants kd and the ratio of the diffusion coefficient DML to kd.

Species Species Distribution (M) (2010) Species Distribution (M) (2011) kd (s-1

) (DML/kd)1/2

Flin Flon

Creek

North

Basin

Third Avenue South Basin Ross Creek Flin Flon

Creek

North

Basin

Third

Avenue

South

Basin

Ross Creek

CuOH+ 7.01E-10 1.43E-09 1.67E-09 1.90E-09 9.29E-10 4.32E-12 2.16E-10 4.54E-10 2.64E-10 2.72E-10 3.31E-07 6.73E-02

Cu(OH)2 8.12E-13 3.28E-12 3.11E-12 1.19E-11 1.30E-12 6.79E-18 1.60E-14 1.08E-13 2.72E-14 2.73E-14 1.66E-12 3.01E+01

CuSO4 9.35E-09 1.02E-08 1.52E-08 5.27E-09 1.15E-08 8.49E-08 7.94E-08 5.04E-08 6.96E-08 6.85E-08 4.37E-03 5.86E-04

CuHCO3+ 6.56E-09 1.34E-08 1.56E-08 1.78E-08 8.70E-09 4.05E-11 2.02E-09 4.26E-09 2.48E-09 2.54E-09 2.40E-15 7.91E+02

CuCO3 1.94E-10 7.83E-10 7.43E-10 2.85E-09 3.11E-10 1.62E-15 3.83E-12 2.58E-11 6.49E-12 6.52E-12 1.78E-07 9.18E-02

Cu(CO3)22-

5.30E-16 8.08E-15 5.14E-15 2.19E-13 1.20E-15 1.01E-26 5.01E-20 3.47E-18 1.61E-19 1.55E-19 1.20E-10 3.53E+00

CuCl+ 4.54E-10 3.74E-10 5.23E-10 1.78E-10 3.71E-10 1.90E-09 1.62E-09 1.04E-09 1.40E-09 1.41E-09 3.98E-01 6.14E-05

ZnOH+ 1.24E-09 5.00E-09 3.88E-09 1.37E-08 3.65E-09 7.48E-13 1.33E-10 4.53E-10 1.93E-10 1.90E-10 9.12E-06 1.28E-02

Zn(OH)2 8.30E-12 6.60E-11 4.17E-11 4.93E-10 2.95E-11 6.76E-18 5.70E-14 6.19E-13 1.14E-13 1.10E-13 7.94E-12 1.37E+01

ZnSO4 5.08E-07 1.09E-06 1.08E-06 1.16E-06 1.38E-06 4.50E-07 1.50E-06 1.54E-06 1.55E-06 1.47E-06 4.17E-03 6.00E-04

ZnCO3 9.71E-11 7.72E-10 4.88E-10 5.77E-09 3.45E-10 7.91E-17 6.67E-13 7.25E-12 1.33E-12 1.29E-12 1.74E-05 9.29E-03

ZnCl+ 2.29E-08 3.72E-08 3.46E-08 3.62E-08 4.14E-08 9.33E-09 2.84E-08 2.96E-08 2.90E-08 2.81E-08 3.98E-01 6.14E-05

ZnHCO3+ 1.01E-08 4.08E-08 3.16E-08 1.11E-07 2.98E-08 6.10E-12 1.09E-09 3.70E-09 1.57E-09 1.55E-09 7.59E-14 1.41E+02

62

Table S5: Parameters used in the kinetic dissolution model.

Volume of North Basin (m3) 1255680

Volume of South Basin (m3) 345000

Zn Loadings to North Basin (kg/day) 54

Zn Loadings to South Basin (kg/day) 54

Cu Loadings to North Basin (kg/day) 15

Cu Loadings to South Basin (kg/day) 2

Dissolution Rate of ZnS (s-1) 1 * 10-7

Dissolution Rate of CuS (s-1) 1 * 10-8

63

Appendix 2: Supporting Information for Chapter 3

64

Tables for Supplementary Information

Table S1 –Input Parameters for WHAM VI measured during fall 21010 and summer 2011 sampling campaigns.

July 2011

Flin Flon

Creek

July 2011

North

Basin

July 2011

Third

Avenue

July 2011

South

Basin

July 2011

Ross Creek

October 2010

Flin Flon

Creek

October 2010

North Basin

October 2010

Third

Avenue

October 2010

South Basin

October

2010 Ross

Lake

SPM (g/L) 1.78E-03 1.78E-03 1.78E-03 1.78E-03 1.78E-03 1.78E-03 1.78E-03 1.78E-03 1.78E-03 1.78E-03

Temp (K) 281.66 281.66 281.66 281.66 281.66 281.66 281.66 281.66 281.66 281.66

pCO2 (atm) 3.12E-04 3.12E-04 3.12E-04 3.12E-04 3.12E-04 3.12E-04 3.12E-04 3.12E-04 3.12E-04 3.12E-04

pH 3.55 5.22 5.725 5.36 5.35 6.4 6.69 6.61 7.13 6.48

Particulate

Humic Acid

(g/l)

6E-06 3.6E-05 0.000018 0.000018 0.000012 6E-06 3.6E-05 0.000018 0.000018 0.000012

Particulate

Fulvic Acid

(g/L)

3.24E-10 1.1664E-08 2.916E-09 2.916E-09 1.3E-09 3.24E-10 1.1664E-08 2.916E-09 2.916E-09 1.3E-09

Particulate

Iron Oxide

(g/L)

2.03E-03 7.74E-04 6.61E-04 6.29E-04 6.12E-04 7.57E-05 7.41E-05 8.70E-05 1.05E-04 1.84E-04

Particulate

Manganese

oxide (g/l)

6.24E-05 7.87E-05 7.90E-05 7.98E-05 1.08E-04 2.83E-05 3.75E-05 3.75E-05 3.86E-05 4.97E-05

65

Colloidal

Humic Acid

(g/L)

0.000444 0.000414 0.000402 0.000408 0.000426 0.000444 0.000414 0.000402 0.000408 0.000426

Colloidal

Fulvic Acid

(g/L)

0.003996 0.003726 0.003618 0.003672 0.003834 0.003996 0.003726 0.003618 0.003672 0.003834

Total

Na(M) 3.18E-03 2.80E-03 2.78E-03 2.64E-03 2.63E-03 1.42E-03 1.02E-03 1.18E-03 9.87E-04 1.04E-03

Total

Mg(M) 6.38E-04 5.02E-04 5.97E-04 5.14E-04 5.88E-04 4.07E-04 3.72E-04 3.85E-04 3.62E-04 3.53E-04

Total K(M) 2.63E-04 2.28E-04 2.27E-04 2.28E-04 2.16E-04 1.56E-04 1.51E-04 1.52E-04 1.51E-04 1.53E-04

Total Ca

(M) 9.63E-03 8.23E-03 8.68E-03 8.01E-03 8.36E-03 4.67E-03 4.04E-03 4.24E-03 3.94E-03 4.04E-03

Total Mn

(M) 7.17E-07 9.05E-07 9.08E-07 9.17E-07 1.24E-06 3.26E-07 4.31E-07 4.31E-07 4.44E-07 5.72E-07

Total Cu

(M) 4.15E-07 6.94E-07 6.72E-07 6.74E-07 7.00E-07 6.92E-07 7.99E-07 8.48E-07 7.29E-07 7.52E-07

Total Cl

(M)

6.97E-03 5.50E-03 5.39E-03 5.36E-03 5.13E-03 6.97E-03 5.50E-03 5.39E-03 5.36E-03 5.13E-03

Total SO4

(M) 9.17E-03 7.53E-03 7.41E-03 7.35E-03 7.00E-03 3.43E-03 3.43E-03 3.62E-03 3.62E-03 3.62E-03

Total Zn

(M) 1.59E-06 5.78E-06 6.13E-06 6.03E-06 6.00E-06 3.12E-06 6.38E-06 6.09E-06 6.61E-06 7.60E-06

66

Table S2 –Input Parameters used for the single metal and metal mixture BLM, listed per location for fall 2010 and summer 2011 sampling campaigns.

pH Temp DOC HA

Ca -

Dissolved

Mg -

Dissolved

Na -

Dissolved

K -

Dissolved SO4 Cl Alkalinity

Cu -

Dissolved

Zn -

Dissolved

Al -

Dissolved

Ag -

Dissolved

As -

Dissolved

Co -

Dissolved

Cd -

Dissolved

Ni -

Dissolved

Pb -

Dissolved

Fe -

Dissolved

Location Date Media

degrees

C mgC/L (%) mg/L mg/L mg/L mg/L mg/L mg/L

mg/L

CaCO3 mg/L (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L) (mg/L)

Flin Flon

Creek Jul-11

Water

Column 3.55 22.82 7.4 10 439 16.6 74.5 10.4 881 247 <1.0 0.0197 0.0991 0.0436 <0.00010 0.00962 0.00070 0.000581 <0.0010 0.00246 1.20

North Basin Jul-11

Water

Column 5.22 21.68 6.9 10 321 12.2 56.2 8.71 723 195 2.6 0.0358 0.365 0.0389 <0.00010 0.00394 0.00089 0.00263 <0.0010 0.00132 0.24

Third Avenue Jul-11

Water

Column 5.72 22.60 6.7 10 319 14.9 54.2 8.53 712 191 3.4 0.0344 0.391 0.0348 <0.00010 0.00399 0.00091 0.00277 <0.0010 0.000985 0.18

South Basin Jul-11

Water

Column 5.36 21.61 6.8 10 313 12.4 53.4 8.91 706 190 3.3 0.0341 0.383 0.0343 <0.00010 0.00397 0.00090 0.00268 <0.0010 0.000975 0.18

Ross Creek Jul-11

Water

Column 5.35 22.59 7.1 10 310 14.8 53.8 8.14 672 182 6.9 0.00501 0.320 0.0330 <0.00010 0.00424 0.00092 0.000348 <0.0010 0.000256 0.13

Flin Flon

Creek

Oct-

10

Water

Column 6.4 9.32 7.4 10 179 7.76 33.1 6.1 329.6 247 3.2 0.0358 0.205 0.0243 <0.00010 0.0038 0.0005 0.00122 0.0036 0.000482 <0.010

North Basin

Oct-

10

Water

Column 6.69 10.17 6.9 10 166 6.81 27 5.94 335 195 3.2 0.0287 0.436 0.0214 <0.00010 0.00459 0.00083 0.00201 0.0029 0.000617 <0.010

Third Avenue

Oct-

10

Water

Column 6.61 10.11 6.7 10 164 7.05 28.4 6.04 347.4 191 3.2 0.0296 0.403 0.0217 <0.00010 0.0045 0.0008 0.00199 0.0038 0.000607 <0.010

South Basin

Oct-

10

Water

Column 7.13 10.35 6.8 10 161 6.74 26.6 5.9 335 190 3.2 0.0246 0.465 0.0191 <0.00010 0.00455 0.00087 0.00194 0.0032 0.000661 <0.010

Ross Creek

Oct-

10

Water

Column 6.48 10.30 7.1 10 158 7.27 26.7 6.03 335 182 3.2 0.0202 0.51 0.0205 <0.00010 0.00459 0.00084 0.00251 0.004 0.000594 <0.010

67

Appendix 3: Detailed Report on Analytical Methods, Results and Quality Control