interactions between salinity, acidity, and geochemistry ... · interactions between salinity,...

_______________________________________

Interactions between salinity, acidity, and

geochemistry of acidic lakes in the Wheatbelt

and Goldfields regions.

Geoff Abbott

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Environmental Engineering Project Dissertation

October 2007

Supervisor: Assoc. Prof. Carolyn Oldham

iii

Abstract

In this project three natural acid-saline lakes in the Goldfields and Wheatbelt regions of Western

Australia were analysed to determine the similarities and differences in chemical processes

occurring in the water bodies. Extensive research has been performed on both natural and

anthropogenic acid-saline lakes throughout the world, however research into the inland lakes of

Western Australia has been limited, focusing only on a few large playa lakes. A preliminary

investigation was made into the chemical composition and interactions in Lake Gilmore – a large

acid-saline playa lake – and two smaller lakes of similar pH and salinity, Kondinin Lake and

Green Lake. This investigation was conducted by performing a desktop assessment of previous

literature, and the collation and analysis of field data collected in September 2006.

Due to being a preliminary investigation, a limited number of samples were collected which

prevented detailed statistical testing. Instead analysis was performed on parameters such as pH,

dissolved ion concentrations and solid phase composition using graphical analysis. The

proportions of major ions in solution were investigated using Piper Plots and also by determining

the order of ionic dominance. For each parameter and method of analysis, comparisons were

made between the three lakes to detect whether significant similarities or differences were

observed.

Macroscopic influences such as origin of solutes, acidity generated by groundwater iron levels,

and the evolution of the lake water by evaporation and precipitation processes, were found to be

the same across the three lakes. However, differences were found in the smaller scale chemical

properties and interactions in the lakes such as the order of ionic dominance, saturation indices,

and pH influence on ion concentrations. Further testing is required in order to confirm these

differences, due to the extremely limited number of samples collected at Kondinin Lake and

Green Lake. It was also recommended that research into the soil types and bedrock material of

the region is conducted in order to understand their effect on the lakes. Overall it was concluded

from this preliminary investigation that the chemical processes and interactions are not identical

in the three lakes investigated.

v

Acknowledgements

I would like to thank a number of people for their support and contributions, for without them

this dissertation would not have been possible.

Firstly, many thanks must go to my supervisor, Associate Professor Carolyn Oldham, for her

guidance and assistance throughout the course of this project, and also her efforts in the

collection of field data.

I would also like to thank Ursula Salmon and Stefan Peiffer for their contributions in the field

and the subsequent sample testing and analysis.

Finally, I would like to acknowledge my family and friends for their help.

- Mum for your support, advice and proofreading

- Claire for proofreading, and also your patient support and understanding

- Dad for dispensing advice

- Chris for your technical support with computer issues

- Dan and Mike for proofreading

Without all of your assistance this paper would not have eventuated, and I hope to somehow

return the favour one day. Thank you.

vi

Contents

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

List of Tables ................................................................................................................................... ix

List of Equations .............................................................................................................................. x

Glossary ............................................................................................................................................ 1

1 Introduction .............................................................................................................................. 2

1.1 Chemical interactions and controls in Lake Gilmore, Kondinin Lake, and Green Lake .. 2

1.2 Aim .................................................................................................................................... 3

1.3 Objectives .......................................................................................................................... 3

2 Literature review ...................................................................................................................... 5

2.1 Background region ............................................................................................................ 5

2.2 Salinity in lakes ................................................................................................................. 7

2.3 Acid generation processes ............................................................................................... 12

2.4 Influence of pH on water chemistry ................................................................................ 14

2.5 Missing information ........................................................................................................ 15

2.6 Study Sites ....................................................................................................................... 17

2.6.1 Lake Gilmore ............................................................................................................ 17

2.6.2 Green Lake ............................................................................................................... 19

2.6.3 Kondinin Lake .......................................................................................................... 19

3 Methodology .......................................................................................................................... 20

3.1 Fieldwork ......................................................................................................................... 20

3.2 Data organisation and analysis ........................................................................................ 20

3.2.1 Saturation Indices ..................................................................................................... 20

3.2.2 Graphical Analysis ................................................................................................... 21

vii

3.2.3 Ionic Dominance ...................................................................................................... 21

3.2.4 Piper Plots ................................................................................................................ 21

4 Results .................................................................................................................................... 25

4.1 Solid phase ....................................................................................................................... 25

4.2 Saturation Index ............................................................................................................... 26

4.3 Ionic dominance .............................................................................................................. 28

4.4 Piper Plots ........................................................................................................................ 28

4.5 Graphical Analysis .......................................................................................................... 34

5 Discussion .............................................................................................................................. 39

5.1 Solid phase and dissolved mineral levels ........................................................................ 39

5.2 Ionic dominance .............................................................................................................. 41

5.3 Piper Plots ........................................................................................................................ 43

5.4 Limitations and errors ...................................................................................................... 46

6 Conclusions ............................................................................................................................ 47

7 Recommendations for future investigation ............................................................................ 49

8 References .............................................................................................................................. 50

9 Appendices ............................................................................................................................. 54

9.1 Appendix A - Field notes ................................................................................................. 54

9.2 Appendix B – Sampling data ........................................................................................... 75

9.2.1 Water sample test data .............................................................................................. 75

9.2.2 Laboratory report ...................................................................................................... 86

viii

List of Figures

Figure 1. Cross-sectional view of a typical Australian playa lake. (Johanesson 1994) .................. 5

Figure 2. Paleodrainage channels on the Yilgarn Block (Clarke 1994). ........................................ 6

Figure 3. Map demonstrating the abundance of salt lakes in Western Australia (Geddes 1981). .. 9

Figure 4. A simple three end-member EJH model (Radke 2002). ................................................. 10

Figure 5. A more complex chart of E-J-H end-member pathways (Long 1992). ........................... 11

Figure 6. Path of an ion released from the weathering front of bedrock, showing vertical

diffusion and lateral groundwater flow movements through the soil profile (Mann 1983). .......... 13

Figure 8. Map of the region surrounding Lake Gilmore and Green Lake. Green Lake is located

20km to the South of the map (Johanesson 1994). ......................................................................... 17

Figure 7. Rainfall and evaporation data for the Yilgarn Block region of Western Australia (Mann

1983). .............................................................................................................................................. 18

Figure 9. Piper plot diagram for all lakes. .................................................................................... 30

Figure 10. Piper plot diagram for Lake Gilmore. .......................................................................... 31

Figure 11. Piper plot diagram for Green Lake. ............................................................................. 32

Figure 12. Piper plot diagram for Kondinin Lake. ........................................................................ 33

Figure 13. pH and redox potential in lake water and seepage samples. ....................................... 34

Figure 14. Measured concentrations of Al and Si relative to pH. ................................................. 35

Figure 15. Levels of solid phase Al relative to pH. ........................................................................ 36

Figure 16. Levels of solid phase SiO2 relative to pH. .................................................................... 37

Figure 17. Fraction of total Fe which reacted with sodium dithionite. ......................................... 38

Figure 18. Piper Plot chart from a study on Lake Tyrell, Victoria (Herczeg 1991). ..................... 44

Figure 19. Proposed path for water evolution in Lake Tyrell and the three lakes of this project. 45

ix

List of Tables

Table 1. Constants used in calculating CO32- and HCO3

- concentrations in aqueous solution.

(Nazaroff 2001) .............................................................................................................................. 22

Table 2. Dominant mineral composition guesses made by Stefan Peiffer based on percentage

mineral composition of soil/sediment samples at the three lakes. ................................................. 25

Table 3. Saturation indices closest to zero in each water sample. ................................................. 26

Table 4. Minerals with lowest saturation index values. ................................................................. 27

Table 5. Minerals with highest saturation index values. ................................................................ 27

Table 6. Order of ionic dominance in water samples taken from the three lakes. ......................... 28

Table 7. Calculated concentrations of HCO3- and CO32- in solutions sampled. ......................... 28

Table 8. Converted concentrations (milliequivalents) of ions in solutions sampled.. ................... 29

Table 9. pH, Eh, and Iron testing data on water samples. ............................................................. 76

Table 10. Raw data for ion concentrations in water samples – millimolar units. ......................... 77

Table 11. Ion concentration data converted to milliequivalents. ................................................... 77

Table 12. Milliequivalent ion concentration data reformatted for GW Chart input. ..................... 77

Table 13. Calculated saturation index values for a range of sparingly soluble salts in the water

samples taken. The values which are closest to zero are shown in bold. ....................................... 78

Table 14. Description, location and type of each solid sample collected at the three lakes. ........ 80

Table 15. Details of sample depths, some testing results, and details of which tests were

performed on each sample. ............................................................................................................ 81

Table 16. Data obtained from water content testing of solid samples. .......................................... 82

Table 17. Percentage composition testing results of each solid sample collected, and estimated

dominant minerals. ......................................................................................................................... 84

Table 18. Data and results from various tests performed to determine free iron levels. ............... 85

Table 19. Report sheet for the laboratory test results. ................................................................... 86

Table 20. Report sheet for the laboratory test results (cont’d). ..................................................... 89

Table 21. Report sheet for the laboratory test results (cont’d). ..................................................... 90

x

List of Equations

Equation 1. Overall ferrolysis reaction, proposed by Mann to be the cause of groundwater

acidity in the Yilgarn Block (Mann 1983). ..................................................................................... 13

Equation 2. Formula for calculation of carbonic acid concentration in an open system at

equilibrium (Nazaroff 2001). .......................................................................................................... 23

Equation 3. Relationship between pH and concentration of H+ ions in solution. ......................... 23

Equation 4. Dissociation of carbonic acid. .................................................................................... 23

Equation 5. Dissociation of bicarbonate. ....................................................................................... 23

Equation 6. Relationship of equivalents to moles. ......................................................................... 24

Equation 7. Precipitation/dissolution of alunite (Long 1992) ....................................................... 42

Equation 8. Precipitation/dissolution of jarosite (Long 1992) ...................................................... 42

1

Glossary

Adsorption The accumulation of gases, liquids, or solutes on the surface of a

solid or liquid, bound by weak electrostatic forces.

Anthropogenic Caused by human influence.

Diagenetic Originating from the conversion (by compaction or chemical

reaction) of sediment to rock.

DOC Dissolved Organic Carbon. The amount of organic carbon (such as

decomposed plant matter) present in a solution.

Ephemeral Existing for a short period of time.

Milliequivalents A unit of concentration which accounts for the stoichiometric

reactivity of multi-valent ions.

Paleodrainage Subsurface drainage features where water flows in unconsolidated sediments that have filled in ancient valleys and watercourses.

Playa A flat, dry lake bed situated at the bottom of an internally drained

desert basin, occasionally flooded after recent rainfall.

Stoichiometric A ratio of elements which fits exactly with the chemistry occurring.

2

1 Introduction

1.1 Chemical interactions and controls in Lake Gilmore, Kondinin

Lake, and Green Lake

Over hundreds of years, anthropogenic influence on the natural landscape has led to the increase

of salinity and acidity in natural water bodies across Australia. However, many lakes are

naturally acidic and/or saline due to natural processes rather than artificial influences. Lake

Gilmore is an acid-saline lake formed by natural processes and situated in the Goldfields region

of Western Australia. Green Lake and Kondinin Lake are smaller lakes located in the Goldfields

and Wheatbelt respectively, and have pH and salt levels similar to Lake Gilmore. Although these

three lakes exhibit high levels of acidity and salinity, it is not known whether the controlling

processes of the chemistry within these water bodies are alike or different.

In order to understand the interactions between the acidity, salinity, and geochemistry of a lake, it

is necessary to know what ions, compounds and minerals are present at the site. Australian salt

lakes are typically dominated by Na+ and Cl- ions; however the fact that the lakes are highly

acidic suggests that Fe and Al will have a significant presence in the solution. The level of acidity

in a body of water is dependent on both internal processes and external inputs. Common causes

of acidity include high iron levels in groundwater inflow, oxidation of pyrite, acid rain and acid-

mine drainage. Extensive research has been performed on the acidification processes and

chemical interactions of artificial lakes, particularly in the Lusatia region of Germany where the

flooding of open-cut mines has led to the formation of lakes which are acidified by the oxidation

of pyrite. Considerably less investigation has been made into the chemical processes of natural

Australian acid-saline lakes such as Lake Gilmore, Green Lake, and Kondinin Lake. As a result

of this, little is known about the differences between individual lakes.

It has been hypothesized that natural acid-saline lakes such as Lake Gilmore provide an insight

into the conditions present in these ancient lakes, and how they developed into carbonate

buffered, neutral pH water bodies. The information and understanding gained from this project

3

will contribute to a larger-scale project investigating the evolution of primordial lakes into life-

supporting water bodies.

1.2 Aim

The aim of this project was to conduct a preliminary investigation into the chemical interactions

occurring within the three lakes and to determine the similarities and differences between them,

by conducting a desktop assessment of field data and previous literature.

1.3 Objectives

The objectives of this project were:

• To collate and manipulate field data collected from the three lakes in September 2006

• To conduct an extensive review of related literature

• To investigate trends and patterns of a wide range of parameters across the three lakes, as

well as within each lake

• To compare observations to established theories about geochemical processes in acid-

saline lakes

• To gain a deeper understanding of the processes controlling water quality in each of the

three lakes

• To determine whether the lakes are chemically similar to each other

• To identify subject areas requiring further investigation

The methodology required to achieve these objectives had two separate modules. Firstly, a

desktop assessment of previous studies and literature related to the topic was conducted.

Secondly, collation, manipulation and analysis of field data collected from the three lakes were

performed.

4

This dissertation presents the findings of the background research, as well as the methodology,

raw figures, results, and analysis of the field data collected. It also provides recommendations for

further investigations into such natural acidic lakes.

5

2 Literature review

2.1 Background region

The three lakes investigated in this project are situated on the Yilgarn Block, an extremely large, weathered

granite mass covering much of Western Australia. Drainage in the eastern half of the Yilgarn Block is mostly

internal groundwater flow, with surface water rarely being of sufficient quantity to flow overland (Mann

1982). The combination of high annual pan evaporation rates (between 2000-2400mm), low annual rainfall

(300-400mm), and internal drainage causes the playa lakes to act as drainage sumps for the area (Mann 1982;

Luke 1987). These flow paths are shown in Figure 1. Incoming groundwater contains an imprint of the

mineral weathering processes occurring below the surface as it flows towards the playas, and subsequent

evaporation of the lake water causes these minerals and ions to become concentrated (Mann 1982). Much of

the internal drainage stems from remnant paleodrainage channels, as shown in

Figure 2 (Mann 1982; Clarke 1994).

Figure 1. Cross-sectional view of a typical Australian playa lake. (Johanesson 1994)

6

Figure 2. Paleodrainage channels on the Yilgarn Block (Clarke 1994).

7

2.2 Salinity in lakes

Salt lakes are numerous in the inland of Western Australia, as shown in Figure 3. A 1978 study of

over 100 water bodies determined that over half of them had a salinity of 3% or greater (Geddes

1981). Most of these lakes are dominated by Na and Cl ions, and have solute composition quite

similar to seawater (Herczeg 1991; Long 1992). Australian lakes can be classified into 4

categories (Lyons 1990):

• Large playa lakes

• Small closed lakes or pans

• Crater lakes

• Coastal lakes

Large playa lakes are typically greater than 10km in length, usually dry for the majority of the

year, and occur in ancient paleodrainage channels, whereas small closed lakes or pans often also

contain ephemeral standing water, but are smaller and younger (Lyons 1990). Crater lakes and

coastal lakes will not be dealt with in the scope of this project.

Possible sources of solutes in salt lakes include atmospherically transported marine aerosols

(known as cyclic sea salt), ancient seawater, evaporated river water, and water-rock interface

processes (Long 1992). Due to the location of the study sites, the lack of flowing surface water

nearby, and the dominance of Cl ions, the source of evaporated river water can be ruled out in

this case (Long 1992). Given that the primary source of water input to the three lakes being

investigated is from groundwater inflow, the origins of the solutes could be from being dissolved

into the groundwater as it moves laterally through the soil profile towards the lakes. This would

suggest that ancient seawater, or water-rock interface processes could be the source. However, a

common theory states that the origins of solutes in Australian inland lakes is from cyclic sea salt

(Long 1992). By performing detailed chemical analysis and comparing the ionic composition to

that of seawater it can be determined whether this is the likely source of solutes. Previous

research into the ionic composition of salt lakes in Western Australia has revealed that the most

common order of ionic dominance is similar to many lakes in Eastern Australia:

Cations: Na>Mg>Ca>K

Anions: Cl>SO4>HCO3 (Geddes 1981; Long 1992; Johanesson 1994)

8

Mass balances combined with the major dominance of Na and Cl confirms that the most likely

source of ions is from atmospheric (cyclic) sea salt (Long 1992; Johanesson 1994). The

dissolution of ions from weathering processes at the groundwater-bedrock interface is also likely

to have some influence on the ionic makeup of the inflowing groundwater, however the extent to

which this occurs is not known. The ratios between concentrations of various Rare Earth

Elements (REE) and Cl in solution, for example neodymium/chloride, samarium/chloride, and

dysprosium/chloride, may be used to compare solute origins to seawater, due to their

conservative behaviour (Johanesson 1994). A previous investigation into the ratios of REE

present in the waters of Lake Gilmore, a large acid-saline playa situated on the Yilgarn Block,

found that ratios between REE and other ions such as Cl were not entirely consistent with those

found in evolved seawater (Johanesson 1994). This suggests that another source, such as

interactions between bedrock and acidic groundwaters, is contributing REE to the groundwater

entering the lake, reinforcing the theory that the solutes present in such lakes are not purely

derived from cyclic sea salt (Johanesson 1994).

Eugster-Jones-Hardie (EJH) models detail the various chemical pathways different water types

can follow as they are evaporated, according to the source of the water and its initial ionic

makeup. As the water evaporates and various chemicals are precipitated, the ratios of ions in

solution are changed, and this determines what will next precipitate. Figure 4 shows a simple

three end-member EJH model, with seawater evaporating along path 1B (Radke 2002). After

initial evaporation, CaCO3 is precipitated, and then the subsequent ratios of different ions

determine the path which will occur as further evaporation continues.

9

Figure 3. Map demonstrating the abundance of salt lakes in Western Australia (Geddes 1981).

10

Figure 4. A simple three end-member EJH model (Radke 2002).

The precipitation of different minerals both controls and is controlled by the ratios of ions in

solution. If ratios are in stoichiometric proportions they will be removed at equal rates as

precipitation occurs, and their relative proportions to each other in solution will remain constant

(Long 1992). However if ratios are not in stoichiometric proportions, the less abundant ion will

become depleted more quickly and change the character of the chemical solution. Figure 5 shows

the complex variety of paths which can occur as a solution undergoes evaporation, dependent on

the initial ionic composition of the solution before evaporation commences. The process of

chemical change that occurs in a body of water as it evaporates is commonly referred to as the

‘evolution’ of the water, and includes processes such as changes in pH and the precipitation of

salts as the ions are gradually concentrated due to the removal of water through evaporation.

11

Figure 5. A more complex chart of E-J-H end-member pathways (Long 1992).

The influence of pH and other factors such as ion exchange/adsorption, cyclic wetting/drying,

sulphate reduction, and magnesium carbonate formation can also affect the evolution of water as

it undergoes evaporation (Long 1992). Inversely, evaporation can alter the pH of a lake as the

solution evolves. If a solution contains sufficient Mg and Ca to remove HCO3 by precipitation as

evaporation occurs, the pH of the solution will decrease slightly. Conversely, if there is

insufficient Ca and Mg in the solution, the excess HCO3 will become concentrated and cause

alkalinity in the system, raising the pH (Long 1992). The high level of acidity in the three study

lakes suggests that HCO3 will have a minimal presence. This is further reinforced by the

abundance of calcite in the region, suggesting that Ca is not scarce. It is hence expected that

anion dominance will follow the general order of Cl>SO4>HCO3.

12

2.3 Acid generation processes

Acidity in water bodies can arise from either natural or artificial causes. These include pyrite

oxidation, sulphide oxidation, acid mine drainage, acid rain, and ferrolysis (McArthur 1991;

Long 1992; Friese 1998; Jeffries 2000). Acid mine drainage and acid rain do not affect the

regions being investigated in this project. Soils containing pyrite (FeS2) are commonly referred to

as acid sulphate soils, and the oxidation of pyrite is the cause of acidification in many lakes such

as Lake Tyrell, a major acid-saline lake in Eastern Australia (Long 1992). However, acid sulphate

soils are highly unlikely to be present due to the dry, leached, weathered landscapes of the study

sites, rather than waterlogged coastal lowlands which typically produce acid sulphate soils. The

Yilgarn Block has been exposed for approximately 60 million years, creating weathering profiles

up to 40m deep (Johanesson 1994), effectively ruling out acid sulphate soils. The oxidation of

diagenetic pyrite in the sediments of these lakes has been deemed highly unlikely by previous

research due to a lack of density-driven reflux in the lake systems (McArthur 1991). H2S has not

been noted in the groundwaters of the Yilgarn Block, and previous papers have eliminated the

oxidation of H2S as a potential mechanism of acidification (McArthur 1991). The oxidation of

basement sulphides is unlikely as a source of acidification due to the fact that acidity is

widespread across Western Australia, regardless of the sulphide lithology of the bedrock in each

region. The historical intensive weathering previously mentioned has also preferentially removed

sulphides during the formation of the kaolinite pallid zone (McArthur 1991).

The oxidation and hydrolysis of iron, also referred to as “ferrolysis”, is a common cause of

acidity in Western Australian groundwater, and has been previously investigated by Mann et. al.

1992, and McArthur et. al. 1991. The two papers both investigate the potential for acidity

generation in significant detail, however each paper proposes a different theory on the exact

process occurring.

13

Oxidation and Hydrolysis of Iron from weathered bedrock (Mann 1983)

Figure 6. Path of an ion released from the weathering front of bedrock, showing vertical diffusion and lateral

groundwater flow movements through the soil profile (Mann 1983).

Mann (1983) proposes that acidity on the Yilgarn Block stems from the oxidation and hydrolysis

of ferrous iron in groundwater while the soil is waterlogged. The Yilgarn Block receives most of

its rainfall during winter (Mann 1983; McArthur 1991), increasing the chance of the soil profile

being saturated for a period of time each year. Mann hypothesised that groundwater at depth has

a near-neutral pH, low Eh, high iron and low aluminium content, with iron levels caused by the

weathering and dissolution of minerals from the bedrock surface. As the groundwater moves

towards the surface under vertical diffusion and lateral groundwater flow, as shown in Figure 6,

Fe2+ is oxidised, causing a decrease in pH and an increase in Eh. As pH decreases below 4, this in

turn causes an increase in aluminium and silica levels due to the dissolution of the abundant

kaolinite in the soil profile. As pH decreases even further iron levels increase slightly, potentially

caused by the redissolution of Fe2O3 due to the solubility of Fe3+ in acid conditions. Mann also

proposes a simplified overall equation for the process of ferrolysis, shown in Equation 1 below.

4 2 6 2 2 4 8

Equation 1. Overall ferrolysis reaction, proposed by Mann to be the cause of groundwater acidity in the

Yilgarn Block (Mann 1983).

14

Microbial Reduction of Iron Oxyhydroxides (McArthur 1991)

While McArthur (1991) accepts the hypothesis of ferrolysis as the primary source of acidity in

the Yilgarn Block, the source of groundwater-borne Fe2+ is disputed. Rather than originating

from the weathering of iron in basement rock material, McArthur hypothesised that iron

oxyhydroxide surface films on particles of aquifer material undergo microbial reduction,

releasing Fe2+ into the groundwater. Ferrolysis occurs during the movement of this saline, anoxic,

high-iron groundwater, and also during the mixing of this groundwater with oxygenated recharge

water as it infiltrates the dunes of the Yilgarn Block. It is proposed that the alkalinity produced by

the reduction of iron oxyhydroxides is counteracted by the large amounts of calcrete in the region

studied (McArthur 1991).

The conditions required for both potential sources of groundwater iron, and consequent

ferrolysis, are present in the region being investigated. Climate conditions are appropriate in

several aspects; winter rainfall levels cause the soil profile to be saturated for a period of the year,

allowing the movement of Fe2+ through the soil profile. This periodic recharge, combined with

high evaporation levels at each of the three lakes, provide optimal conditions for the formation of

calcrete deposits which are a sink for the alkalinity produced by the initial reduction of Fe2+

(Mann 1979; Luke 1987). Iron is also evident in soils of the area, shown by the red/brown

colouration of the dune systems and the abundance of kaolinite (Mann 1983; McArthur 1991).

2.4 Influence of pH on water chemistry

The chemical interactions within a lake or body of water are complex, and pH is often a major

driving factor. Due to the focus of this project on acidic lakes, the influence on water chemistry

of pH levels greater than 7 will not be discussed.

One effect of acid precipitation is the increase in mineral weathering rates in soil and at the

bedrock-groundwater interface (Kilham 1982). With particular reference to the soils of the

Yilgarn Block study region, this causes the dissolution of kaolinite at sufficiently low pH,

15

mobilising aluminium into the groundwater (Mann 1983). Heavy metal concentrations, such as

nickel, manganese and copper, generally increase as pH decreases (Beamish 1976). Rare earth

elements such as neodymium, samarium and dysprosium, are increasingly soluble at low pH.

This causes them to dissolve from granite-groundwater interfaces on the Yilgarn Block and travel

within solution to the playa lakes, where they are precipitated as pH rises (Johanesson 1994).

Overall, as pH decreases, the metal content of the solution increases due to increased solubility.

Soluble iron and aluminium levels are strongly interlinked with pH, as both iron and aluminium

can cause acidity, and acidity causes an increase in the solubility of each (Mann 1983). As pH

decreases below 6, soluble iron levels begin to decrease. Aluminium solubility also increases as

pH drops (Mann 1983). A subsequent increase in the pH of acidic groundwaters such as those

found on the Yilgarn Block causes the precipitation of iron oxyhydroxides, alunite and jarosite

(Johanesson 1994). This in turn causes the removal of REE from the solution as these elements

are bound up in the precipitated minerals.

A decrease in pH in a lake causes a decrease in DOC levels, due to both the increased

precipitation of DOC and its breakdown to CO2 (Donahue 1998). This is partly due to the

increased levels of aluminium caused by low pH. In lakes acidified by atmospheric SO2,

increased sulphate levels are observed (Beamish 1976), however this is unlikely to occur at the

study sites due to their isolation from industrial sources of SO2. Bicarbonate alkalinity is also

proportional to pH (Beamish 1976).

2.5 Missing information

While previous research has acknowledged that groundwater-bedrock interaction processes

contribute solutes to the lake inflow, the extent to which this occurs has not been quantified, or

investigated in depth. Other factors that affect the composition of lake inflow have also been

acknowledged as occurring but not quantified, such as the precipitation of calcite during

groundwater flow and the removal of ions by cation exchange processes.

16

The source of acidity in lakes on the Yilgarn Block has been discussed in several previous

studies; however it is still uncertain whether the iron that is causing acidity is from the microbial

reduction of iron oxyhydroxides in aquifers, or from the weathering of bedrock below the soil

profile. Due to the abundance of salt lakes and playas on the Yilgarn Block of Western Australia,

many have remained unnamed and no research has been performed on the chemical processes

occurring in the water bodies and the groundwater inflow. Many of these lakes can be classified

as Type 2 Australian lakes (small closed lakes or pans) which are younger than the large playas

(Type 1) of the region. It is not known whether the sizes and ages of these two different types of

lakes influences the chemical composition of the water bodies, potentially causing dissimilar

processes to occur in the lakes. Three lakes situated on the Yilgarn Block have been sampled and

investigated in order to gain a better understanding of the differences between large acid-saline

playas and smaller lakes of similar acidity and salinity.

17

2.6 Study Sites

2.6.1 Lake Gilmore

Lake Gilmore is a large acid-saline playa located 140km North of Esperance, in the Goldfields

region of Western Australia, as shown in Figure 7 (Johanesson 1994). The region contains many

such playas and other similar groundwater formations. A semi-arid climate prevails in the area

with approximately 300mm of annual rainfall, as shown in Figure 8, predominantly occurring

during the winter months (Mann 1982; McArthur 1991).

Figure 7. Map of the region surrounding Lake Gilmore and Green Lake. Green Lake is located 20km to the

South of the map (Johanesson 1994).

18

The lake consists of highly saline, dense, evaporated brine at its centre, with lower salinity

groundwater seeping in along the margins of the lake after rainfall events, as shown in Figure 1

(Johanesson 1994). Except for a short duration following heavy rainfall events, most playas in

this region do not exhibit standing water, instead having a shallow water table within 4m of the

surface (McArthur 1991). This is true for Lake Gilmore, which does not have standing water for

much of the year. Several previous studies have investigated various aspects of the geochemistry

and acidity of this lake, and relevant portions of their findings are discussed later in this literature

review.

Figure 8. Rainfall and evaporation data for the Yilgarn Block region of Western Australia (Mann 1983).

19

2.6.2 Green Lake

This small lake has not been officially named, due to the abundance of much larger salt lakes

throughout the region. However for the purpose of this project it will be referred to as “Green

Lake”, due to the light-green colour of the standing water at the time of sampling. It is located

20km South of Salmon Gums and 90km North of Esperance, and bisected by Highway 1 which is

shown in Figure 7. Due to their close proximity to each other, the major climatic conditions of

Lake Gilmore and Green Lake are essentially identical.

2.6.3 Kondinin Lake

Referred to as “Kondinin Lake” for this project, this small salt lake is located approximately 8km

to the North-West of Kondinin, off Highway 4. The Wheatbelt region has a higher rainfall than

the Goldfields, receiving approximately 350-375mm annually (Dolling 1994). Although there are

other larger lakes situated in the area, the very low pH of this lake makes it suitable for

comparison with Lake Gilmore and Green Lake.

20

3 Methodology

3.1 Fieldwork

This paper focuses purely on the data analysis and comparison between the three lakes, as the

fieldwork and data collection had already been conducted prior to the commencement of this

study. As a result, details of the fieldwork methodology will not be entered into in this

dissertation.

3.2 Data organisation and analysis

Data from the fieldwork required extensive rearranging and sorting in order to achieve a useful

format for analysis. Handwritten notes from fieldwork were converted to a digital format due to

the advantage of being able to search for particular comments more quickly. This required

substantial formatting and rearranging to convert the notes from a simple line-by-line format into

a table of easy to understand data and notes. This information is displayed in Section 9.1.

Extensive sampling data was collected during the fieldwork, requiring collation, editing and

manipulation to obtain a useful format for calculations. The majority of the data manipulation

work simply consisted of rearranging information into a more accessible and logical format. Due

to the quantity of this data, it is displayed in Section 9.2. However much of the data was used to

obtain graphs shown in the Results section.

3.2.1 Saturation Indices

Prior to the project, calculations were performed on the water sample data to determine the

saturation indices of various minerals. These calculations were performed using the program

PHREEQC. The minerals which exhibited the saturation index value closest to zero were

21

tabulated for ease of display, along with the minerals with the highest and lowest saturation

indices.

3.2.2 Graphical Analysis

Due to the relatively small number of samples taken, detailed statistical analysis of the data could

not be performed. However, with graphical analysis an overall impression of the similarities and

differences between the three lakes could be obtained. Various parameters were plotted against

each other in order to determine the relationships between such aspects as pH, redox potential,

proportions of minerals, and ion concentrations in solution.

3.2.3 Ionic Dominance

A simple comparison was made between the orders of ionic dominance in water samples from

each of the three lakes. The values used were the concentrations of each ion in units of

milliequivalents.

3.2.4 Piper Plots

Piper plots are a form of trilinear diagram, which simultaneously show the relative proportions of

major cations or anions in solution. This gives an indication of which ions are most dominant in

solution, and is useful for comparing ion ratios between samples and/or lakes. The program GW

Chart 1.13.0.0 was used to generate these plots; however some data manipulation and

calculations were first required in order to obtain the necessary input parameters.

The data used was taken from the laboratory testing performed on lake water and seepage water

samples W1 through to W8 inclusive. W1-W5 were seepage samples taken from Lake Gilmore,

W6 was a standing water sample from Kondinin Lake, and the Green Lake samples W7 and W8

were seepage and standing water respectively.

22

The program required the following parameters in order to generate piper plots of water samples:

• Ca2+

• Mg2+

• Na+

• K+

• CO32-

• HCO3-

• Cl-

• SO42-

• TDS (Total Dissolved Solids)

Values for CO32- and HCO3

- required calculations in order to be determined, since they were not

measured. Several assumptions were made in calculating the concentrations of these ions in

solution. Firstly, it was assumed that the water could be treated as a natural body of water open to

the atmosphere, giving the partial pressure of CO2 shown in

Table 1. Secondly, it was assumed that the samples were taken under conditions close to 25°C

and 1atm pressure. This allowed the dissociation constants Ka1 and Ka2 shown in

Table 1 to be used for the calculations.

Constant/parameter Description Value used Reference

p(CO2) CO2 partial pressure 3.5 10-4atm (Nazaroff 2001)

K1 H2CO3 dissociation constant 4.47 10-7 (Nazaroff 2001)

K2 HCO3- dissociation constant 4.68 10-11 (Nazaroff 2001)

Km H2CO3-CO2 equilibrium

constant

1.58 10-3 (Nazaroff 2001)

KH(CO2) Henry’s constant for CO2 0.034M atm-1 (Nazaroff 2001)

Table 1. Constants used in calculating CO32- and HCO3

- concentrations in aqueous solution. (Nazaroff 2001)

23

Since CO2(aq) and H2CO3 are difficult to differentiate experimentally, many calculation

processes combine them into one variable, denoted as H2CO3*. In an open-water system at

equilibrium, exposed to a natural CO2 partial pressure of 3.5 10-4atm, the concentration of

H2CO3* can be calculated using the formula shown in Equation 2.

1

Equation 2. Formula for calculation of carbonic acid concentration in an open system at equilibrium

(Nazaroff 2001).

A value of 1.19 10-5M was calculated. Note that this value is independent of the pH in a

solution.

Next, the measured pH of the solution and =

Equation 3 was used to calculate the concentration of H+ ions in the solution.

Equation 3. Relationship between pH and concentration of H+ ions in solution.

The concentration of H+ ions derived from the measured pH, and the calculated concentration of

H2CO3*, combined with dissociation constants K1 and K2, allowed

Equation 4 and Equation 5 to be used to solve for the two unknown values: HCO3- and CO3

2-

concentrations in each water sample.

Equation 4. Dissociation of carbonic acid.

Equation 5. Dissociation of bicarbonate.

24

The results of these calculations are displayed in Table 7, situated in Section 4.4.

GW Chart requires the input of these values as either proportions, percentages, milliequivalents

per litre (meq/L), or mg/L. However, the laboratory test results and calculations were in

millimolar units, requiring a conversion calculation. This was done using the relationship shown

in Equation 6 below. The results of these calculations are displayed in

Table 8, shown in Section 4.4. These values were used as the final input into the GW Chart

program to obtain the Piper Plots.

Monovalent ions: 1 equivalent = 1 mole

Divalent ions: 1 equivalent = 0.5 moles

Trivalent ions: 1 equivalent = ⅓ mole

Equation 6. Relationship of equivalents to moles.

25

4 Results

4.1 Solid phase

Section 9.2 contains the data obtained by analysis of the solid samples collected at each lake

during the fieldwork of the project. Due to the size of the data spreadsheet it will not be displayed

in this section. The percentage composition of each sample is shown for a range of salts typically

found in acid-saline playas. Included with this data is a guess at the dominant mineral

compositions of each sample, which are displayed below in Table 2.

Sample location Solid phase mineral composition guess

Gilmore 1 Quartz

Gilmore 1 Quartz, Iron oxide

Gilmore 1 Quartz, Al oxide




Gilmore 4 (soil profile) Quartz, Al oxide, Iron oxide

Gilmore 4 (soil profile) Quartz, Al oxide, NaCl

Gilmore 4 (soil profile) Quartz, Al oxide, Iron oxide

Gilmore 5 Alunite, Gypsum, NaCl, Quartz

Gilmore 5 Al oxide, Kaolinite, Fe oxide, NaCl, Quartz

Gilmore 6 Al oxide, Gypsum, Fe oxide, NaCl, Quartz

Gilmore 6 Quartz, Kaolinite, Alunite

Gilmore 6 Quartz, Kaolinite, Alunite, Al oxide, Iron oxide

Gilmore 6 Quartz, Kaolinite, Alunite, Iron oxide


Green Lake Quartz, Al oxide, Alunite, Iron oxide

Kondinin Quartz, Al oxide, Iron oxide Table 2. Dominant mineral composition speculations made by Stefan Peiffer based on percentage mineral

composition of soil/sediment samples at the three lakes.

26

4.2 Saturation Index

Table 3 illustrates the various minerals with saturation indices closest to zero. There was no

obvious pattern or correlation between lakes and minerals. Each lake with multiple samples

exhibits different minerals with saturation indices close to zero, rendering it impossible to extract

any patterns, however SiO2 and gypsum (CaSO4.2H2O) were dominant.

Location

Mineral with

Saturation Index

closest to 0

Saturation Index

of Mineral

Gilmore 1 SiO2 -0.0047

Gilmore 2 SiO2 0.023


Gilmore 5 Gypsum -0.0565

Gilmore 6 Gypsum/SiO2 0.0357/-0.0559

Kondinin Gypsum 0.0302

Green Lake 1 SiO2 -0.0465

Green Lake 2 Alunite -0.0088

Table 3. Saturation indices closest to zero in each water sample.

Upon examination of the minerals with the lowest saturation index, no distinct patterns were

found, however Schwertmannite (Fe16O16(OH)12(SO4)2 was common, and exhibited strongly

negative values.

27

Location

Mineral with

lowest

Saturation Index

Saturation Index

of Mineral

Gilmore 1 Al(OH)3 -5.527

Gilmore 2 Schwertmannite -23.7063

Gilmore 3 Schwertmannite -12.0929


Gilmore 6 Al(OH)3 -5.0487

Kondinin Schwertmannite -10.3617

Green Lake 1 Schwertmannite -11.4249

Green Lake 2 Schwertmannite -11.9649

Table 4. Minerals with lowest saturation index values.

Examination of the minerals with the highest saturation indices revealed Chalcedony (Quartz,

SiO2) and Goethite (FeO(OH)) to be dominant, however the highest value overall was Jarosite

(KFe3(OH)6(SO4)2), observed at Green Lake 1.

Location

Mineral with highest

Saturation Index

Saturation Index of

Mineral

Gilmore 1 Chalcedony 0.8348

Gilmore 2 Goethite 1.3806

Gilmore 3 Goethite 2.8142



Kondinin Goethite 3.0009

Green Lake 1 Jarosite 6.3294

Green Lake 2 Goethite 2.7122

Table 5. Minerals with highest saturation index values.

28

4.3 Ionic dominance

Table 6 illustrates the order of dominance for cations and anions in each of the three lakes.

Although the specific proportions were different in the cases where multiple samples were tested

(Lake Gilmore and Green Lake, five and two samples respectively), the overall orders of ionic

dominance were identical. In both of the samples from Green Lake, a different order of base

cation dominance to Lake Gilmore and Kondinin Lake was exhibited.

Location Cation dominance Anion dominance

Lake Gilmore Na>Mg>Ca>K Cl>SO4>HCO3

Kondinin Lake Na>Mg>Ca>K Cl>SO4>HCO3

Green Lake Na>Mg>K>Ca Cl>SO4>HCO3

Table 6. Order of ionic dominance in water samples taken from the three lakes.

4.4 Piper Plots

The results for the calculations of HCO3- and CO3

2- concentrations are displayed in Table 7. A

strong correlation exists between pH and ionic carbonate/bicarbonate concentrations, due to the

direct impact of pH on the dissociation relationship. For samples with lower pH, the

concentrations of carbonate and bicarbonate are lower.

Sample pH [HCO3-] (mM) [CO3

2-] (mM)

Gilmore 1 3.0 4.90678 10-6 2.11509 10-13

Gilmore 2 3.1 6.02674 10-6 3.1908 10-13

Gilmore 3 3.0 5.11274 10-6 2.29637 10-13

Gilmore 5 5.2 0.000771303 5.22619 10-9

Gilmore 6 3.9 4.16263 10-5 1.5222 10-11

Kondinin 2.9 4.51942 10-6 1.79432 10-13

Green Lake 1 3.4 1.37159 10-5 1.65267 10-12

Green Lake 2 2.7 2.64813 10-6 6.16047 10-14 Table 7. Calculated concentrations of HCO3- and CO32- in solutions sampled.

29

The data contained in

Table 8 shows the converted input values for the GW Chart program. Of particular note is the

dominance of Na and Cl. In all eight samples, Na has concentrations that are at least 5 times

higher than the next most dominant ion, Mg. Similarly, Cl exhibited concentrations in excess of

11 times higher than the next most dominant anion, SO4.

Table 8. Converted concentrations (milliequivalents) of ions in solutions sampled. All values were rounded to

2 decimal places for ease of display in this table, however non-rounded values were used for GW Chart inputs.

Figure 9 through to

Figure 12 show the visual output obtained from the GW Chart program used to create Piper Plots.

The values for Lake Gilmore are closely clustered together, indicating that the ionic proportions

in solution are similar across all of the 5 samples collected. The two samples from Green Lake

are also closely clustered together and are similar to those of Lake Gilmore, although they exhibit

higher proportions of Mg. The values from Kondinin Lake are significantly separated from those

of the other two lakes, indicating that the ionic ratios are different. Cation ratios for Kondinin

Lake are the most significantly different to the other two lakes, with a higher proportion of Na

and K compared to Ca and Mg in solution.

Location Ca(meq) Mg Na K CO3 HCO3 Cl SO4 TDS

Gilmore 1 6.00 156.38 1000.00 5.13 4.23 10-13 4.91 10-6 1342.86 72.92 2473.58

Gilmore 2 6.50 156.38 956.52 6.41 6.38 10-13 6.03 10-6 1285.71 68.75 2371.04

Gilmore 3 6.50 156.38 956.52 6.15 4.59 10-13 5.11 10-6 1314.29 68.75 2398.88

Gilmore 5 33.00 740.74 4000.00 25.64 1.05 10-8 7.71 10-4 6000.00 291.67 10558.44

Gilmore 6 46.00 600.82 3826.09 21.54 3.04 10-11 4.16 10-5 5428.57 250.00 9725.02

Kondinin 50.00 362.14 4347.83 14.87 3.59 10-13 4.52 10-6 6285.71 175.00 10947.68

Green

Lake 1 6.50 197.53 913.04 12.31 3.31 10-12 1.37 10-5 1314.29 102.08 2398.22

Green

Lake 2 19.00 987.65 4782.61 64.10 1.23 10-13 2.65 10-6 5428.57 479.17 11027.18

Figgure 9. Piper p

30

plot diagram ffor all lakes.

Figuree 10. Piper plo

31

ot diagram forr Lake Gilmorre.

Figurre 11. Piper pl

32

lot diagram foor Green Lakee.

Figure

12. Piper plot

33

t diagram for Kondinin Lakke.

34

4.5 Graphical Analysis

The information shown in

Figure 13 indicates that the relationships between pH and redox potential are quite similar across

the three lakes. A slight increase in redox potential was observed in samples from Lake Gilmore

with higher pH.

Figure 13. pH and redox potential in lake water and seepage samples.

0

100

200

300

400

500

600

0.0 1.0 2.0 3.0 4.0 5.0 6.0

Eh

pH

pH vs. Redox potential

Lake Gilmore

Green Lake

Kondinin Lake

35

The dissolution of kaolinite clays can be observed in Figure 14, which indicates that as pH

decreases, higher levels of Si and Al become mobilized into solution. No marked difference was

observed between the three lakes with regard to these parameters.

Figure 14. Measured concentrations of Al and Si relative to pH.

0

1

2

3

4

5

6

7

8

0

1

2

3

4

5

6

7

0.0 1.0 2.0 3.0 4.0 5.0 6.0

Si con

centration

(mM)

Al con

centration

(mM)

pH

pH vs. dissolved Al & Si

Gilmore Al

Gilmore Si

Kondinin AlKondinin SiGreen Lake Al

36

A significant difference was observed when comparing the relationship between Al2O3 and pH in

Kondinin Lake with the relationships in Lake Gilmore and Green Lake. While samples from

Green Lake and Lake Gilmore demonstrated low levels of Al2O3 at low pH, Kondinin Lake

exhibits much higher levels of Al2O3. At the low pH of 2.9, Kondinin Lake showed Al2O3 levels

similar to those in Lake Gilmore at a pH reading of 5.2. This is a significant difference and

indicates that the chemical processes in the lakes are not identical.

Figure 15. Levels of solid phase Al relative to pH.

0

5

10

15

20

25

0.0 1.0 2.0 3.0 4.0 5.0 6.0

Al2O3 % com

position

of solid pha

se

pH

Al2O3 (solid phase) vs. pH

Gilmore

Kondinin

Green

37

Similarly, in Figure 16 a low level of SiO2 is observed at low pH in Kondinin Lake. At a pH of

approximately 3, Green Lake and Lake Gilmore are closely clustered together whereas the value

for Kondinin Lake was much lower.

Figure 16. Levels of solid phase SiO2 relative to pH.

0

10

20

30

40

50

60

70

80

90

100

0.0 1.0 2.0 3.0 4.0 5.0 6.0

SiO2 % com

position

pH

SiO2 levels

Gilmore

Kondinin

Green

38

Solid samples were also tested with sodium dithionite. The results for the fraction of total Fe

which reacted with sodium dithionite in each sample are shown below in Figure 17. No

significant patterns can be found in the data, as the singular values from Green Lake and

Kondinin Lake both fall within the range of values observed at Lake Gilmore.

Figure 17. Fraction of total Fe which reacted with sodium dithionite.

0

0.2

0.4

0.6

0.8

1

1.2

G1 G3 G8 G9 G13 G20 G27 Green Lake Kondinin

Fraction of total iron reactive with dithionite

39

5 Discussion

The three lakes showed similarities and differences across the range of parameters which were

sampled, tested, and compared. Through the analysis of these results it was possible to see if the

lakes contain similar chemical processes, and if not, where the differences lie. During this process

a better understanding was gained of the chemical processes occurring in these three lakes.

5.1 Solid phase and dissolved mineral levels

As shown in Figure 15, Kondinin Lake exhibits high levels of Al2O3 at low pH, compared to

Green Lake and Lake Gilmore which show decreasing levels of solid phase aluminium as pH

decreases. The levels of Al2O3 present at pH 2.9 in Kondinin Lake are similar to those observed

at a pH of 5.2 in Lake Gilmore, indicating a substantial difference between the two lakes in this

regard. Higher levels of Al present in Kondinin Lake could be a potential cause for this.

However, upon cross-comparison with soluble Al levels shown in Figure 14, it can be seen that

Kondinin Lake exhibits slightly lower concentrations of soluble Al at low pH than comparable

samples in Lake Gilmore – 4.44mM at pH 2.9, compared to 4.81mM at pH 3.0. This suggests

that rather than higher overall levels of Al in both solid phase and in solution, there is instead a

higher proportion of Al bound in solid phase at low pH when compared to the other lakes.

The limited number of samples prevents definite conclusions being drawn from such differences,

however it is possible that the area surrounding Kondinin Lake contains lower levels of kaolinite,

which dissolves at low pH levels and would cause elevated levels of Al and Si in groundwater

inflow. This theory is further reinforced by the soluble Si levels shown in Figure 14, where

Kondinin Lake contains the lowest Si concentrations measured in samples of comparable pH.

Solid phase Si levels are also noticeably lower in Kondinin Lake, as shown in Figure 16. The

percentage composition of SiO2 in the sample from Kondinin Lake is less than 60% at a pH of

2.9, compared to values higher than 80% in other samples of similar pH. Combined with

40

comparatively low Si levels in solution at the same location, this suggests that Kondinin Lake has

lower overall Si levels than Lake Gilmore and Green Lake.

An insight into the interactions between solid phase and dissolved ions can be gained by

investigating the saturation indices of minerals, to determine whether the solution is

undersaturated, saturated, or oversaturated in regards to each. The saturation index shows the

relationship between the solubility product and the calculated or measured ionic activity of a

sparingly soluble salt (Peiffer 2007). In this case positive values are the most important because it

indicates that the solution is oversaturated with respect to the particular mineral, and will

precipitate if the conditions are conducive. Iron oxyhydroxides and quartz were found to have the

highest saturation index, indicating potential for the precipitation of these salts if conditions such

as pH were to change. Alunite, goethite and chalcedony were found to have predominantly

positive values for saturation index calculations. Jarosite was also found to have a very high

saturation index for the Green Lake 1 sample. It is likely that the low pH of the lake waters is

preventing the precipitation of these minerals despite the solution being oversaturated. Overall,

the saturation index data confirms that the ion levels are suitable to precipitate minerals such as

jarosite, alunite, and iron oxyhydroxides after groundwater enters the playa and a subsequent pH

increase occurs.

The results of iron extraction testing with sodium dithionite give an indication of the amount of

iron which is not bound in complex silicate minerals, and are shown in Figure 17 above. The

fraction of iron shown by this test is commonly referred to as “free iron”, as it does not react with

iron which is bound up in complex mineral formations. The wide range of values found in Lake

Gilmore indicate that iron varies from being mostly bound in primary silicate minerals in some

samples such as G1, to being mostly contained in a “free” state such as in sample G20. Green

Lake and Kondinin Lake show high and low levels of dithionite-reactive iron respectively. It

could be hypothesised from this that Green Lake and Kondinin Lake have different levels of free

iron, however both of these values are well within the range measured in Lake Gilmore samples.

Due to the fact that only one sample from each of Kondinin Lake and Green Lake was tested with

dithionite, and the values are within the range observed in Lake Gilmore, no substantial

difference between the three lakes is proven by the data.

41

5.2 Ionic dominance

The order of ionic dominance in water samples can give an insight into both the origin of the

solutes (e.g. seawater, river water, rock weathering, and cyclic sea salt) and the processes

occurring within the solution as it evolves, such as the precipitation of various minerals.

Table 6 displays the order of ionic dominance in each of the three lakes sampled. Green Lake

shows an unusual difference in that K exhibits higher concentrations than Ca, compared to the

other two lakes which have higher concentrations of Ca instead. This was true for both samples

taken from Green Lake, reducing the possibility of the sample being a statistical outlier.

A potential sink for Ca and K is adsorption onto cation exchange sites (Herczeg 1991). Due to

ionic substitutions, clay minerals have a permanent negative charge on their surface which allows

cations to bind to the mineral (Williams 2001). However due to the weakness of this bond cations

may be exchanged between the mineral and the soil solution. K concentrations are also controlled

by adsorption onto clay minerals (Herczeg 1991; Long 1992). The dominance of Na in the

historical sea salt signature of the soil profile would cause many of the cation exchange sites to be

occupied by Na+ ions. However as waters pass through the soil profile, the higher affinity of

Ca/K for clay mineral exchange sites would lead to the exchange of Na+ and Ca/K, increasing the

Na/Ca or Na/K ratios and reducing the overall concentration of Ca/K (Herczeg 1991; Long

1992).

The exchange of K ions (and to a lesser extent, Ca) with clay mineral surfaces during

groundwater flow is highly likely, and is widely accepted as the cause of early removal of K ions

in the water evolution process (Herczeg 1991; Long 1992). However this cannot be confirmed or

denied in the scope of this project. This is due to the fact that cation exchange occurs in the soil

profile prior to the groundwater entering the lake system, and the sampling regime of this project

was contained within the boundaries of the three playa lakes.

A likely cause of the low Ca levels in all samples is the precipitation of calcite (CaCO3). This has

the effect of removing Ca ions from the solution before the groundwater enters the lake. If Ca

42

levels were originally low this would alter the order of ionic dominance to that which occurs in

Green Lake. Another cause is the precipitation of gypsum, CaSO4.2H2O. The precipitation of

these minerals results in a decrease of the concentrations of the constituent minerals in solution

(Herczeg 1991). As a result, the precipitation of calcite and gypsum would remove Ca ions from

solution, as well as HCO3 and SO4 respectively in each case. This theory is reinforced by

observations in another acid-saline lake by Long et al. in 1992 - Lake Tyrell, Victoria. Calcite

was observed to form quite early during the groundwater evolution process, lowering Ca and

HCO3 levels. Subsequent acidification removed remaining HCO3, leaving a solution enriched in

Mg, Al, and Fe. After the onset of evaporative conditions experienced in the lake, gypsum was

precipitated which further removed Ca. It is highly likely that a similar evolution and evaporation

sequence is causing the order of ionic dominance in Green Lake.

Similarly, the precipitation of jarosite (KFe3(SO4)2(OH)6) or alunite (KAl3(SO4)2(OH)6) would

have the effect of altering the ion dominance order by lowering K concentrations in the solution,

as shown by the reactions in Equation 7 and Equation 8 below (Herczeg 1991; Long 1992).

3 2 6 6

Equation 7. Precipitation/dissolution of alunite (Long 1992)

3 2 6 6

Equation 8. Precipitation/dissolution of jarosite (Long 1992)

The conditions on much of the Yilgarn Block are ideal for alunite precipitation, due to a constant

source of acidity to generate low pH; a supply of K and SO4 due to marine-origin solutes; and

abundant Al, supplied by kaolinite clays present in the soil profile and lake sediments (McArthur

1991). Jarosite also precipitates under similar conditions, however Fe is substituted for Al in the

mineral structure. The likelihood of these two minerals precipitating has been discussed in

Section 5.1 above.

43

5.3 Piper Plots

The Piper Plots generated using the program GW Chart revealed that the ionic proportions in the

three lakes are significantly different. While all three of the lakes were clearly dominated by Na

and K in the case of cations, and Cl in the case of anions, the data for Kondinin Lake was

completely separated from the values for the other lakes, which were closely clustered together.

This difference in ionic proportions in the lakes indicates that the evolution and evaporation of

the water is being affected by unknown processes, causing different ionic proportions to arise at

the end result. This could potentially be caused by differences in the initial ion content of

inflowing groundwater to Kondinin Lake, which would mean that as the water evolves and

evaporates, salts would precipitate in different amounts compared to Lake Gilmore and Green

Lake. The initial ion content of the groundwater could potentially be affected by many factors.

The Kondinin region receives higher rainfall than Lake Gilmore and Green Lake, which could

cause intensified leaching of the soil, altering the ionic makeup of the soil solution (Dolling

1994). Lake Gilmore and Green Lake have a closer proximity to the coast which could aid in the

deposition of cyclic sea salt into the soil profile. The most likely cause however is a combination

of subtle differences between climate, bedrock, and soil types of each region; however it is not

possible to determine this within the scope of the project due to the fact that no samples of this

type of data were taken.

44

Figure 18. Piper Plot chart from a study on Lake Tyrell, Victoria (Herczeg 1991).

The anion charts for the Piper Plot analysis on the three lakes of this project return data which is

quite similar to the anion plot obtained by an investigation into Lake Tyrell in Victoria, and Lake

Cowan in Western Australia. The data from a previous study by Herczeg et. al. is shown below in

Figure 18, with Lake Tyrell and Lake Cowan situated in the lower right-hand corner. This is an

important similarity because both lakes are acid-saline playas. Lake Cowan is situated

approximately 100km North of Lake Gilmore, suggesting that climatic and geochemical

influences are likely to be similar. The source of solutes in Lake Tyrell has been attributed to

cyclic sea salt (Long 1992). The combination of an identical source of original solutes (cyclic sea

salt) and highly acidic groundwater inflow means that the major processes in the water evolution

pathways of Lake Tyrell and the three lakes of this project are most likely identical.

The path of brine evolution proposed by Herczeg et. al. in 1991 is shown in Figure 5. In Figure

19 the specific path of brine evolution which is occurring in Lake Tyrell is shown. From the

45

analysis of the data in this project, and the similarities with Lake Tyrell, it is theorised that Lake

Gilmore, Green Lake and Kondinin Lake follow the same brine evolution pathway. Observations

of quantities of gypsum and calcite at each of the lakes reinforce this theory. This is further

evidenced by the fact that the saturation indices of gypsum, alunite, jarosite, and iron

oxyhydroxides are all close to, or higher than zero, indicating that the solution is close to

saturation or oversaturated with regards to each of these minerals.

Figure 19. Proposed path for water evolution in Lake Tyrell and the three lakes of this project.

46

5.4 Limitations and errors

Due to the fact that this project was a preliminary investigation, a relatively small number of

samples were collected from the field, particularly from Kondinin Lake and Green Lake.

Although an insight into the general similarities and differences between the three lakes could be

gained, the limited sample space prevents detailed statistical analysis from being performed. In

order to create statistical proof that these similarities and differences are a true representation of

the lakes, a more detailed sampling regime is required.

Another aspect of the project which is severely limited is the lack of temporal variation in the

sample space. Samples were collected over the course of 5 consecutive days, giving an insight to

the conditions present at the lakes during spring. However, as discussed in Section 0, the water

levels in salt lakes of this region are highly ephemeral and strongly dependent on rainfall events.

Two sources contribute to lake recharge in this region – infiltration of freshwater rainfall through

the shallow dune systems surrounding the playas, and the inflow of acidic, saline groundwater

due to internal drainage. Because of this, the sampling results may be dependent on the amount of

time since the last rainfall event. A recent rainfall event would have the effect of diluting the lake

waters, which would alter the evaporation processes which cause the evolution of the lake water.

Conversely, if the lakes had experienced a period of drought and hence minimal inflows in the

lead up to sampling, more highly evolved seepage waters would be expected. The data was

sampled during spring which would suggest that the lakes had received rainfall during recent

months. However, Lake Gilmore contained no standing water which indicates that most of the

inflow from recent rainfall events had been evaporated leaving only seepage water. Kondinin

Lake and Green Lake contained standing water, however due to the lack of previous research

conducted on these two lakes it is not known whether standing water is ephemeral or permanent.

The lack of a temporal aspect to the sampling means that analysis cannot determine whether the

similarities and differences between the chemical processes in the three lakes are influenced by

seasonality and recent rainfall.

47

6 Conclusions

Prior to this preliminary investigation into Lake Gilmore, Green Lake, and Kondinin Lake,

understanding of the similarities and differences between large saline playa lakes and smaller

closed lakes was limited. The chemical processes occurring in Lake Gilmore and other large

saline lakes in the region had been investigated to an extent by previous studies. However, these

focussed on larger playa lakes. Due to the small size of Kondinin Lake and Green Lake, these

two water bodies had not been investigated in any detail, nor compared to other playas such as

Lake Gilmore.

The results of this project have allowed an insight into the chemical composition of the three

subject lakes and the precipitation processes occurring inside them, thus clarifying the pathway of

water evolution which is occurring. Calcite and gypsum are precipitated early in the process of

evaporation. This is followed by acidification due to groundwater iron levels, after which the

water enters the playas and undergoes further evolution. Gypsum is again precipitated, followed

by minerals such as alunite, jarosite, and iron oxyhydroxides as the pH increases. This pathway

of evolution fits a model proposed in a previous investigation on Lake Tyrell, a large acid-saline

playa located in Eastern Australia. Due to the limited number of samples collected from Green

Lake and Kondinin Lake, and the lack of background information and previous studies at these

two sites, it can only be hypothesised that this is the evolution pathway of the lakes, rather than

determined as the definite process that is occurring. Overall, the superficial analysis of this

preliminary study indicates that the macroscopic processes influencing lake chemistry are the

same in the three lakes.

Conversely, the analysis of sample data indicated that differences between the three lakes exist in

the more subtle aspects of lake chemistry. These include ionic proportions and the order of ion

dominance, which indicated that either the waters are at different stages of evolution, or the

solute sources are slightly different. The relationships between pH and solid phase minerals

showed significant differences between the three lakes, particularly Kondinin Lake. Overall

levels of elements such as Si and Al were also observed to be different across the three lakes.

48

From the preliminary investigation of these parameters it can be hypothesised that the smaller

scale interactions within the three lakes are different.

In conclusion, the results of this study indicate that while the macroscopic factors and processes

(such as acidic groundwater inflow, origin of solutes, and lake water evolution by evaporation

and concentration) influencing the water chemistry of these three lakes are the same, the smaller

scale processes and chemical factors (such as ionic dominance, saturation indices, and pH

influence on ion concentrations) are different. The results also gave an insight into the processes

and interactions occurring within the lakes, however, considerable further investigation is

required to understand these in detail, and to confirm the existence of differences between the

three lakes through the use of statistical testing.

49

7 Recommendations for future investigation

As this project was a preliminary investigation into the three sites, the number of samples

collected during fieldwork was minimal, and restricted to lake sediments, soil samples, and water

samples. This gave a general overview of the differences and similarities in the lakes and allowed

the interactions and processes to be tentatively defined. It is recommended that further research

into these three lakes be conducted, requiring an extensive sampling regime. In order to obtain a

more complete understanding of the three lakes, aspects such as temporal variation and spatial

variation both within and outside the lakes require further investigation. Statistical testing is also

required to prove the theories of chemical processes occurring within the lakes, however without

sufficient sample data these tests cannot be performed. Hence it is recommended that any further

investigations collect sufficient samples that statistical tests can be conducted.

Investigation into the bedrock and soil types of the groundwater catchment surrounding each lake

would also be beneficial in future studies on these lakes. A focus on the weathering processes

occurring at the surface of the bedrock would give insight into the amount that this dissolution

contributes ions to the groundwater solution, and eventually the ionic composition of the lakes.

An investigation into the extent of cation exchange in the soil profile would be useful in

understanding the processes affecting lake chemistry such as ionic composition. The creation of a

database of soil types on the Yilgarn Block, or at least in the areas surrounding these lakes could

provide insight into the properties of the soil, and the subsequent influences this may have on the

lakes. Due to the size of the area, this could be performed using remote sensing techniques.

A comparison of the soil types, bedrock and lake water at several lakes could potentially give an

understanding of the interactions between these three aspects. If such a study were conducted to a

sufficient extent, the findings could be used to create a model for the interactions between a wide

range of soil and bedrock types and the final outcome of lake chemistry. This would allow the

lake chemistry to be predicted by investigating the soil and bedrock types of the surrounding

area, and vice versa, which would reduce the amount of sampling required at other lakes in future

investigations.

50

8 References Battarbee, R. W., Howells, G., Skeffington, R.A., Bradshaw, A.D. (1990). "The Causes of Lake Acidification, with Special Reference to the Role of Acid Deposition (and Discussion)." Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 327(1240): 339-347. Beamish, R. J. (1976). "Acidification of Lakes in Canada by Acid Precipitation and the Resulting Effects on Fishes." Water, Air, and Soil Pollution 6: 501-514. Blodau, C., Hoffmann, S., Peine, A., Peiffer, S. (1998). "Iron and sulfate reduction in the sediments of acidic mine lake 116 (Brandenburg, Germany): Rates and geochemical evaluation." Water, Air, and Soil Pollution 108: 249-270. Buttner, O., Becker, A., Kellner, S. Kuehn, B., Wendt-Potthoff, K., Zachmann, D.W., and Friese, K. (1998). "Geostatistical Analysis of Surface Sediments in an Acidic Mining Lake." Water, Air, and Soil Pollution 108: 298-316. Clark, D., Fritz, P. (1997). Environmental Isotopes in Hydrogeology, CRC Press. Clarke, J. D. A. (1994). "Evolution of the Lefroy and Cowan palaeodrainage channels. Western Australia." Australian Journal of Earth Sciences 41(1): 55-68. Dickson, B. L., Herczeg, A.L. (1992). "Naturally-occurring radionuclides in acid-saline groundwaters around Lake Tyrell, Victoria, Australia." Chemical Geology 96: 95-114. Dolling, P. J., Porter, W.M. (1994). "Acidification rates in the central wheatbelt of Western Australia. 1. On a deep yellow sand." Australian Journal of Experimental Agriculture 34: 1156-1164. Donahue, W. F., Schindler, D.W., Page, S.J., Stainton, M.P. (1998). "Acid-Induced Changes in DOC Quality in and Experimental Whole-Lake Manipulation." Environmental Science and Technology 32: 2954-2960. Faulkenham, S. E., Hall, R.I., Dillon, P.J., Karst-Riddoch, T. (2003). "Effects of Drought-Induced Acidification on Diatom Communities in Acid-Sensitive Ontario Lakes." Limnology and Oceanography 48(4): 1662-1673. Friese, K., Wendt-Potthoff, K., Zachmann, D.W., Fauville, A., Mayer, B., Veizer, J., (1998). "Biogeochemistry of Iron and Sulfur in Sediments of an Acidic Mining Lake in Lusatia, Germany." Water, Air, and Soil Pollution 108: 231-247. Geddes, M. C., De Deckker, P., Williams, W.D., Morton, D.W., Topping, M. (1981). "On the chemistry and biota of some saline lakes in Western Australia." Hydrobiologia 82: 201-222.

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Gell, P. A. (1997). "The Development of a Diatom Database for Inferring lake Salinity, Western Victoria, Australia: Towards a Quantitative Approach for Reconstructing Past Climates." Australian Journal of Botany 45: 389-423. Goldstein, R. A., Gherini, S.A., Driscoll, C.T., April, R., Schofield, C.L., Chen, C.W. (1987). "Lake-Watershed Acidification in the North Branch of the Moose River: Introduction." Biogeochemistry 3(1): 5-20. Herczeg, A. L., Lyons, W.B. (1991). "A chemical model for the evolution of Australian sodium chloride lake brines." Palaeogeography, Palaeoclimatology, Palaeoecology 84: 43-53. Holmer, M., Storkholm, P. (2001). "Sulphate reduction and sulphur cycling in lake sediments: a review." Freshwater Biology 46: 431-451. Irfanullah, H. M. D., Moss, B. (2005). "Effects of pH and predation by Chaoborus larvae on the plankton of a shallow and acidic forest lake." Freshwater Biology 50: 1913-1926. Jeffries, D. S., Lam, D.C.L., Wong, I., Moran, M.D. (2000). "Assessment of changes in lake pH in southeastern Canada arising from present levels and expected reductions in acidic deposition." Canadian Journal of Fisheries and Aquatic Sciences 57(2): 40-49. Johanesson, K. H., Lyons, W.B., Fee, J. H., Gaudette, H.E., McArthur, J.M. (1994). "Geochemical processes affecting the acidic groundwaters of Lake Gilmore, Yilgarn Block, Western Australia: a preliminary study using neodymium, samarium, and dysprosium." Journal of Hydrology 154: 271-289. Jones, B. G. (1990). "Cretaceous and Tertiary sedimentation on the western margin of the Eucla Basin." Australian Journal of Earth Sciences 37(3): 317-329. Kilham, P. (1982). "Acid Precipitation: Its Role in the Alkalization of a Lake in Michigan." Limnology and Oceanography 27(5): 856-867. Lessmann, D., Deneke, R., Ender, R., Hemm, M., Kapfer, M., Krumbeck, H., Wollman, K., Nixdorf, B. (1999). "Lake Plessa 107 (Lusatia, Germany) - an extremely acidic shallow mining lake." Hydrobiologia 408/409: 293-299. Letolle, R., Chesterikoff, A. (1999). "Salinity of Surface Waters in the Aral Sea region." International Journal of Salt Lake Research 8: 293-306. Long, D. T., Fegan, N.E., Lyons, W.B., Hines, M.E., Macumber, P.G., Giblin, A.M. (1992). "Geochemistry of acid brines: Lake Tyrell, Victoria, Australia." Chemical Geology 96: 33-52. Luke, G. J., Burke, K.L., O'Brien, T.M. (1987). Evaporation Data for Western Australia, Department of Agriculture Western Australia.

52

Lyons, W. B., Chivas, A.R., Lent, R.M., Welch, S., Kiss, E., Mayewskim P.A., Long, D.T., Carey, A.E. (1990). "Metal concentrations in surficial sediments from hypersaline lakes, Australia." Hydrobiologia 197: 13-22. Mann, A. W. (1982). "Hydrogeochemistry and weathering on the Yilgarn Block, Western Australia - ferrolysis and heavy metals in continental brines." Geochimica et Cosmochimica Acta 47: 181-190. Mann, A. W. (1983). "Hydrogeochemistry and weathering on the Yilgarn Block, Western Australia - ferrolysis and heavy metals in continental brines." Geochimica et Cosmochimica Acta 47: 181-190. Mann, A. W., Horwitz, R.C. (1979). "Groundwater calcrete deposits in Australia some observations from Western Australia." Australian Journal of Earth Sciences 26(5): 293-303. McArthur, J. M., Turner, J.V., Lyons, W.B., Osborn, A.O., Thirlwall, M.F. (1991). "Hydrochemistry on the Yilgarn Block, Western Australia: Ferrolysis and mineralisation in acid brines." Geochimica et Cosmochimica Acta 55: 1273-1288. National Research Priorities (2007). National Research Priorities - Overview. Nazaroff, W. W., Alvarez-Cohen, L. (2001). Environmental Engineering Science. New York, John Wiley and Sons, Inc. Nelson, J. A. (1989). "Critical Swimming Speeds of Yellow Perch Perca Flavescens: Comparison of Populations from a Naturally Acidic Lake and a Circumneutral Lake in Acid and Neutral Water." Journal of Experimental Biology 145: 238-254. Nixdorf, B., Mischke, U., Labmann, D. (1998). "Chrysophytes and Chlamydomonads: Pioneer Colonists in Extremely Acidic Mining Lakes (pH <3) in Lusatia (Germany)." Hydrobiologia 370: 315-327. Peiffer, S. (2007). G. Abbott. Bayreuth. Peters, N. E., Murdoch, P.S. (1985). "Hydrogeologic Comparison of an Acidic-Lake basin with a Neutral-Lake Basin in the West Central Adirondack Mountains, New York." Water, Air, and Soil Pollution 26: 387-402. Radke, L. C. (2002). Water Allocation and Critical Flows: Potential Ionic Impacts on Estuarine Organisms. Coast to Coast: 367-370`. Schafran, G. C., Driscoll, C.T. (1987). "Spatial and temporal variations in aluminum chemistry of a dilute, acidic lake." Biogeochemistry 3: 105-119. Van Bodegom, P. M., Van Reeven, J., Van Der Gon, A.C.D. (2003). "Prediction of Reducible Soil Iron Content From Iron Extraction Data." Biogeochemistry 64: 231-245.

53

Verboom, W. H., Pate, J.S. (2006). "Bioengineering of Soil Profiles In Semiarid Ecosystems: the 'phytotarium' concept. A review." Plant Soil 289: 71-102. Verboom, W. H., Pate, J.S. (2006). "Evidence of active biotic influences in pedogenetic processes. Case studies from semiarid ecosystems of south-west Western Australia." Plant Soil 289: 103-121. Williams, I. (2001). Environmental Chemistry. New York, John Wiley and Sons, Ltd.

54

9 Appendices

9.1 Appendix A - Field notes

Thursday 21st Sept 2006

Friday 22nd Sept 2006 Fine, cloudless day, wind 10m/s

Location General observations pH Observations at

depth

Samples taken Photos Notes

Lake

Gilmore

No standing water. Crossed

railway line south of lake and

followed rough track back north to

where the first tongue of the lake

almost reaches railway line

DSC00574-

DSC00588

Location General observations pH Observations at depth Photo

Merredin,

Lake

Chander

Evidence of gas evolution under surface of

microbial mat. Many bubble structures.

When cut surface mat off bubble, many have thin

(2mm) black layer (sulphidic?)

Slight smell of H2S.

Seepage, 6.3 Seepage at 10cm depth DSC00573

55

(northern most tongue).

Dunes

behind lake

Calcrete and ferricrete knobs –

calcrete poorly crystalline

Western

shore

grey-green deposits

Northern

shore

Red deposits – older than above.

Very black deposits – hard

and old

500m into

centre

Gypsum crystals on surface.

Evidence of bubbles, black

sulfidic layer underneath. Smell of

H2S.

600m into

centre

Sediment softer.

Seepage

4.7

1. Dig holes, rapidly

fill with water, looks

like gas ebullition.

Smells H2S.

700m into

centre

Seepage

10cm

deep, 4.8.

4.7 at layer

5.

Some very intense

black patches under

surface of gypsum

crystals.

1. Top layer crystals,

darker brown layer

56

underneath. 1cm.

2. Thin scraping of

surface, reveals

grey-green. 1cm.

3. Orange layer.

1cm.

4. Black layer (2cm

thick)– very slimey,

gelatinous

5. Red, brown gritty

layers, more

porewater, and gas

ebullition.

800m into

centre

Dug hole, filled with water Seepage,

4.9

Tested pH

electrode in

drinking water, pH

increased to >6 so

electrode ok.

Gilmore 1

Closer to

Thin layer of grey, consolidate

material ~1cm thick.

5g of

“jarosite”

added to

Underneath, mottled

orange (jarosite?)

and brown/red, rust-

57

shores near

black

rocks.

100ml DI

water,

swirled,

pH = 3.85.

Yellow

ppt.

coloured

5g of “red

ppt” added

to 100ml

DI water

pH ~ 3.95

G17

5g of

“surface

grey layer”

added to

~25ml DI

water, pH

~4

G16

5g of grey

material

underlying

iron layers.

~2cms

G19

58

depth +

100ml DI

water, pH

3.8

Yellow material,

~2cm

G18

Small scrapings of

grey

G23

Gilmore 1.

2m north of

previous

patch

Moisture increasing

Grey overlying layer

~ 2cm

Slurry experiments

conducted on

looser part of red

material and

underlying grey

material (from

which porewater

oozed

Red layer 25cm

thick

Seepage –

2.8

Grey layer – slow

porewater seepage.

pH 3.8 Deep red slurry.

Collected aq.

(slurry) and solid

sample

G20

5g +

100ml

water, pH

= 4.6,

Deep grey slurry G22

59

repeated

4.2

Confirmed

pH 2.8

Rechecked slow

seepage water (in

situ)

Slurry

tested, pH

3.9

Yellow material,

near the deep red,

G21

Deep yellow

sediment collected.

60

Saturday 23rd September Mainly clear with some clouds

Slurry samples have settled since yesterday, now testing TFe and pH in supernatant

Method – HACH 255

Sample Type Notes pH Fe(II) µM

Surface Underlying grey 3.93 0.36 7

Surface Yellow 3.55 0.37 7

Surface Red 3.64 0.74 Only ~17ml in vial

Blank = 0.01

Fe(II)

0.33 With dilution to 25ml

DI water

Deep Red 3.62 0.35

Deep Yellow 3.75 0.19 3

Deep Grey 4.00 0.13

Pore seepage Old (yesterdays)

Also developed white flocs

2.75 1.45 30

Pore seepage Fresh

ppt formed in vial after addition of chemicals

White flocs similar to se flocs

2.8 1.41

Iron strips. TFe 0.05mg/L

Now doing a dilution expt on deep red material - ~5g diluted in DI water:

61

DI Water added pH

10ml * 3.25

100 3.88

250 4.05

* After 10ml added, decanted into larger beaker, including sediment slurry.

DI Water added * pH

100 3.75

100 4.00


depth

Samples taken Photo Notes

South of L2 Solid grey green material,

fresher ppt outer surface of

lake, at edges

4.17

~100ml DI,

pH=5.6, 70mV,

20°C

12pm: 27°C, pH

5.75

Slurry of “calcrete”

– broken up with

hammer. 390µS/cm

– cond in “calcrete”

sample.

G26 Acid expt – a few

drops conc. HNO3,

see if gas evolved

Fresh grey green

- no gas

observed.

Hard nodals, white

grey - no gas

observed.

62

Test in seepage

hole: pH 2.78,

227 mV

Check of

calibration pH

meter (WP-80D

TPS) (not

calibrated since in

lab) : pH 7 buff

gives 6.75, 3.6mV

pH 4 buffer:

162mV, pH 3.92

L2 grey, sticky down to 50cm Porewater

seepage @ 40cm,

pH 3.2

204mV, temp 16.4 Porewater seepage

@ 40cm = W2

10cm deep - G24


depth


L3 Collected seepage water

and solid ~11am

3.34pH Seepage: 196 mV G27 DSC00590-

DSC00615

L2 Stefan collected solid G25 From saturated

63

sample region

L2 Very surface material –

hard, pulverised with

hammer.

+100ml DI, pH

4.9

Conductivity

(HACH): 415µS/cm.

26.4°C

L2 seepage water

salinity out of

range! {Measure in

lab, dilute?}

L3 Seepage salinity

out of range.

L1

Max conductivity ranges:

0-199.9µS/cm 0-199.9g/L

0-1.999mS/cm 0-1.999

0-19.99mS/cm 0-19.99

Seepage salinity

out of range.

L4 10cm root zone Theory: erosion of

this profile - lead

to patchiness in

location 1 (L1)?

20-50cm? fine

orange material

(dark orange). “red

layer just below root

zone”

G1

20cm brown/red

material between

nod, white nodules.

G2

64

“calcrete”

15cm? yellow/tan

layer. “layer below

calcrete”

G3

Further below –

white nodules?

L5 Holes dug yesterday - water

in holes

4.7

4.8 (same as in

hole dug

yesterday)

Salt crystallized on

edges overnight

W4 Dug fresh hole –

collected black

material in brown

amber glass bottle

for Jason Plumb.

Less degassing

than yesterday?

Too close to

disturbed area?

Surface layer

gypsum (crystals)

G4

Orange layer G6

Black layer G7

Gypsum – lower

layer (just beneath

surface)

G5

Deeper brown

material, from which

water seeped

G8

Lower red/orange

layer.

L6 Gypsum (no FeS) Seepage water 70cm brown @ G9

65

3.7 (W5) sandy “top layer

underneath salt

crust”

2cm grey, clayey

70cm white, clayey G12

“Dark grey layer on

top of white layer”

G10

“Chunky grey in

white material, ie.

mixture

G11

4cm – coarse

material (calcretes?)

Sandy material

(pink)

G13

Below white layer W5

L7 ~10-15cm brown 20cm+ of cream

sandy material,

with patches of pale

yellow + large

crystals – 10cm

(Gypsum?)

G15 No water in ~40cm

Solid samples of G14

66

crystals +

yellow/cream

67

Slurry experiments

Sample Test details pH Notes

G7 0.5-1tsp in 20ml with

black material from

Location 5

6.1

G5 (L5) Surface layer including

gypsum, 1tsp in 100ml

DI

6.08

G8 1tsp in 100ml 6.3 Much higher than

seepage water at same

point!! Even though

sample was from where

seepage water comes

out.

“red layer below black”

Degassing CO2?

Sunday 24/9/06

pH in aqueous samples collected yesterday

1st: check pH meter. 12°C slow response!

68

pH 4 buffer reads:

165mV, pH 3.95 (pH slowly decreasing, mV increasing)

(Channel 1) pH 7 reads: 12°C, pH 7.06, mV 2.9 (channel 2 reads 6.74 – what was yesterday??)

From Carolyn: Yesterday we were using Channel 2 and reading from Channel 2

pH testing data

Wrong

pH

Sample Type pH Temp µ Fe(II)

3.7 Location 6 Seepage

water

3.64 13 182.7 0.17 *


water

4.79

(increasing

slowly?)

12.8 122.7 0.09 ^


water

2.85 13.3 225.1 1.64 #

3.2 Location 2 2.90 12.5 221.0 1.01 $$

4.9? Location 2 Surface

crust

slurry

5.2 13.2 100.3

69

“Calcrete”

clurry,

near

Location 1

Slurry 6.05 13.9 53.3

* suspicious – almost no colour, but 0.22

- re-zeroed, tested again – read 0.16

- without re-zeroing 0.14

- ditto 0.18

- re-zeroed, re-read 0.19

Battery low…

Turned off, restart, re-zero – 0.17

Replaced batteries, error message “offset”. Reset, went away.

Retested same sample – still 0.17mg/L

Very slight pink + turbidity (absorbing?)

^ also almost colourless! Repeat – 0.08.

# very pink and slightly turbid

Repeat without moving: 1.77, 1.62, 1.81, 1.76, 1.70

$$ Almost no pink, very turbid! Re-read – 0.93, 0.94, 0.93

Read blank – 0.01mg/L

70

G7

pH in black material slurry expt from yesterday: 7.04!

71

Sunday 24/9/06 cont’d


depth


L8

S 32°36.501’

E 121° 33.741’

Alt 239m

Soil profile

Drainage flood plain

Sand layer over white

material

Red/dark brown

10cm

G30 DSC00616-

618

Light/pale brown

3cm

G31

Slurry expt, 1tsp

in 100ml DI

pH 7.05, 19.6°C

White, clayey top

and hardened 40cm

G32

White clayey, with

purple/dark red clay

material + leaves

G33

Also took seepage

sample (missing)

72

Monday 25/9/06


depth


Green Lake

20km south of

Salmon Gums

S 33°03.376’

E 121°40.578’

Alt 230m

Light green in colour,

white crystals – thick

border

Red soil around edge

Dug hole, light

brown soil to

~15cm, then bright

red

GL1 DSC00624-

DSC00628

Seepage water,

pH 3.6

Water and sediment

samples collected.

W7

Lake water, 2.6 W6

Crystals, GL2

73

Tuesday 26/9/06


depth


Magic Lake @

Hyden/Wave

Rock

Roadside drain, leaving

Hyden

Salt-affected area, red

colour around/in drain

>8 High productivity –

green algae

DSC00629-

DSC00632

Red material from

road construction?

>6 Adjacent puddle,

less green

“Salt Lake”,

outside

Kondinin on

Hwy 4 to

Corrigin

S 32°27.259’

E 118°12.160’

Alt 260m

pH 2.8 in lake, 2.6 in

fluoro green puddles

Seepage water

pH 2.9, 225mV

Pit dug on North

shores of lake

Sediment dark

brown/grey brown

No obvious signs of

iron oxides (red)

DSC00633-

DSC00640

West shore, loamy

material, but with

more iron (or at least

more red) (recently

ppted?)

K1

West shore, very

fine orange material

K2

74

in shallows and

extending into lake

Right near water’s

edge: 1st pit

~5cm red loamy

sediment

~7cm dark grey

Then hit white, hard

layer

2nd pit, right at

water’s edge.

Layers between

0.5cm – 5cm of

black sulphidic

material

W8

75

9.2 Appendix B – Sampling data

9.2.1 Water sample test data

Sample #

Location Type of water

pH (in situ) Fe(II) (uM) EMF pH (after

filtration) pH EMF (Eh)

Corrected for pH 7 and reference cell (~220

mV)

W1 "Gilmore

1" seepage 2.8 26 286 3.0 416 397.8928571

W2 "Gilmore

2" seepage 3.2 15 281 3.1 454 441.1607143

W3 "Gilmore

3" seepage 3.4 0 285 3.0 438 420.9464286

W 4 "Gilmore

5" seepage 4.9 2 163 5.2 387 498.4821429

W 5 "Gilmore

6" seepage 3.8 3 234 3.9 403 439.6785714

W 6 "Kondinin" lake 2.8 288 2.9 439 418.7857143

W 7 "green

lake" seepage 3.7 261 3.4 347 355.2321429

W 8 "green

lake" lake 2.6 301 2.7 445 411.0892857

Sample #

Abs Fe(II) c(Fe(II) mM c(Fe(II) nicht-

linear

W1 0.376 0.035 0.044

76

W2 0 0.000 0.001

W3 0.063 0.006 0.005

W 4 0.035 0.004 0.003

W 5 0.015 0.002 0.002

W 6 0.3 0.028 0.032

W 7 0.852 out of range 0.155

W 8 0.427 out of range 0.052

Table 9. pH, Eh, and Iron testing data on water samples.

Sample # Al

(mM) Ca Fe K Mg Na Si CHLORIDE SO4 Fe(III) HCO3 CO3 TDS

W1 6.667 3.000 0.018 5.128 78.189 1000.000 1.286 1342.857 36.458 -

0.026

4.907E-

06

2.115E-

13 2473.577

W2 5.185 3.250 0.001 6.410 78.189 956.522 1.393 1285.714 34.375 0.0006.027E-

06

3.191E-

13 2371.040

W3 4.815 3.250 0.021 6.154 78.189 956.522 1.250 1314.286 34.375 0.0165.113E-

06

2.296E-

13 2398.878

W 4 0.000 16.500 0.000 25.641 370.370 4000.000 0.096 6000.000 145.833 -

0.003

7.713E-

04

5.226E-

09 10558.439

W 5 0.052 23.000 0.000 21.538 300.412 3826.087 0.357 5428.571 125.000 -

0.002

4.163E-

05

1.522E-

11 9725.016

W 6 4.444 25.000 0.214 14.872 181.070 4347.826 0.857 6285.714 87.500 0.1824.519E-

06

1.794E-

13 10947.680

W 7 2.037 3.250 1.232 12.308 98.765 913.043 1.179 1314.286 51.042 1.0781.372E-

05

1.653E-

12 2398.219

W 8 7.037 9.500 0.375 64.103 493.827 4782.609 1.250 5428.571 239.583 0.3232.648E-

06

6.160E-

14 11027.178

77

Table 10. Raw data for ion concentrations in water samples – millimolar units.

Sample # Al

(meq) Ca Fe K Mg Na Si CHLORIDE SO4 Fe(III) HCO3 CO3

W1 20.000 6.000 0.036 5.128 156.379 1000.000 2.571 1342.857 72.917 -0.077 4.907E-06 4.230E-13

W2 15.556 6.500 0.003 6.410 156.379 956.522 2.786 1285.714 68.750 0.001 6.027E-06 6.382E-13

W3 14.444 6.500 0.043 6.154 156.379 956.522 2.500 1314.286 68.750 0.048 5.113E-06 4.593E-13

W 4 0.000 33.000 0.000 25.641 740.741 4000.000 0.193 6000.000 291.667 -0.010 7.713E-04 1.045E-08

W 5 0.156 46.000 0.001 21.538 600.823 3826.087 0.714 5428.571 250.000 -0.005 4.163E-05 3.044E-11

W 6 13.333 50.000 0.429 14.872 362.140 4347.826 1.714 6285.714 175.000 0.547 4.519E-06 3.589E-13

W 7 6.111 6.500 2.464 12.308 197.531 913.043 2.357 1314.286 102.083 3.233 1.372E-05 3.305E-12

W 8 21.111 19.000 0.750 64.103 987.654 4782.609 2.500 5428.571 479.167 0.968 2.648E-06 1.232E-13

Table 11. Ion concentration data converted to milliequivalents.

Sample # Ca Mg Na K CO3 HCO3 CHLORIDE SO4 TDS

W1 6.00 156.38 1000.00 5.13 4.23E-13 4.91E-06 1342.857 72.917 2473.577

W2 6.50 156.38 956.52 6.41 6.38E-13 6.03E-06 1285.714 68.750 2371.040

W3 6.50 156.38 956.52 6.15 4.59E-13 5.11E-06 1314.286 68.750 2398.878

W 4 33.00 740.74 4000.00 25.64 1.05E-08 7.71E-04 6000.000 291.667 10558.439

W 5 46.00 600.82 3826.09 21.54 3.04E-11 4.16E-05 5428.571 250.000 9725.016

W 6 50.00 362.14 4347.83 14.87 3.59E-13 4.52E-06 6285.714 175.000 10947.680

W 7 6.50 197.53 913.04 12.31 3.31E-12 1.37E-05 1314.286 102.083 2398.219

W 8 19.00 987.65 4782.61 64.10 1.23E-13 2.65E-06 5428.571 479.167 11027.178

Table 12. Milliequivalent ion concentration data reformatted for GW Chart input.

78

Sample #

Alunite Al(OH)3(a) Gibbsite Gypsum Chalcedony SiO2(a) Schwertmannite Goethite Fe(OH)3(a) Jarosite-K Kaolinite

W1 0.52 -5.53 -2.84 -1.27 0.83 0.00 - - - - -2.30

W2 0.82 -5.36 -2.67 -1.25 0.86 0.02 -23.71 1.38 -4.53 -6.60 -1.91

W3 0.28 -5.61 -2.92 -1.25 0.82 -0.02 -12.09 2.81 -3.09 -2.10 -2.50

W 4 - - - -0.06 0.28 -0.56 - - - - -

W 5 0.12 -5.05 -2.36 0.04 0.78 -0.06 - - - - -1.39

W 6 -0.26 -6.03 -3.34 0.03 1.24 0.40 -10.36 3.00 -2.98 -1.02 -2.43

W 7 2.15 -4.77 -2.08 -1.09 0.79 -0.05 -11.42 5.84 -0.07 6.33 -0.87

W 8 -0.01 -6.55 -3.86 -0.14 1.43 0.59 -11.96 2.71 -3.27 -0.07 -3.10

Table 13. Calculated saturation index values for a range of sparingly soluble salts in the water samples taken. The values which are closest to zero are

shown in bold.

79

9.2.2 Solid sample test data Sample # Location GPS Description

G17 gilmore 1 S 32°36.539, E 121° 33.680, Alt 240 m red ppt

G 16 gilmore 1 grey layer

G 19 gilmore 1 grey material underlying iron layers

G 18 gilmore 1 yellow ppt

G 23 gilmore 1 small scrapings of grey

G 20 gilmore 1 red layer, with seepage water

G 22 gilmore 1 grey material

G 21 gilmore 1 yellow material near red material

G 26 gilmore 2 S 32°36.572, E 121° 33.578, Alt 237 m solid grey material ppt onto surface of lake, at edges

G 24 gilmore 2 grey, sticky

G 25 gilmore 2 from saturated region

G 27 gilmore 3 S 32°36.551, E 121° 33.612, Alt 238 m solid from zone of seepage water

G 1 gilmore 4 (=soil profile) S 32°36.493, E 121° 33.726, Alt 236 m red material from soil profile just below root zone

G 2 gilmore 4 (=soil profile) calcrete I

G 3 gilmore 4 (=soil profile) yellow below calcrete I

G 4 gilmore 5 S 32°36.614, E 121° 33.972, Alt 234 m surface gypsum crystals

G6 gilmore 5 orange layer below gypsum

G7 gilmore 5 black layer

G 5 gilmore 5 gypsum layer (just beneath surface)

G 8 gilmore 5 brown material from which water seeped

g 9 gilmore 6 S 32°36.600, E 121° 33.837, Alt 234 m brown sandy top layer underneath salt crust

G 10 gilmore 6 dark grey layer on top of white layer

G 11 gilmore 6 chunky grey white layer (mixture)

G 12 gilmore 6 white layer

without number gilmore 6 coarse material, calcretes ? (no sample)

G 13 gilmore 6 sandy materials (pink) with seepage

80

G 14 gilmore 7 S 32°36.592, E 121° 33.782, Alt 233 m creamy sandy material with patches of pale yellow + gypsum crystals

G 15 gilmore 7 large gypsum crystals

G 30 gilmore 8 S 32°36.501, E 121° 33.741, Alt 239 m red dark brown

G 31 gilmore 8 light pale brown

G 32 gilmore 8 white clayey, top hardened

G 33 gilmore 8 white clayey, top hardened with purple red clay material

Green lake green lake S 33° 03.376, E 121° 40.578, Alt 230 m red seepage containing layer

K1 Kondinin S 32°27.259, E 118° 12.160, Alt 260 m loamy material with some iron

K2 Kondinin fine yellow/orange material on sediment surface

Table 14. Description, location and type of each solid sample collected at the three lakes.

Sample # Depth (25) Slurry pH Slurry Fe(II) (mg/L) Fe-Extraktion XRD IR

G17 2 3.64 0.74 + + +

G 16 2 +

G 19 2 3.93 0.36

G 18 2 3.55 0.37 + + +

G 23 2 (?)

G 20 25 3.62 0.35 + + +

G 22 25 4 0.13 +

G 21 25 3.75 0.19 + +

G 26 0 5.2 +

G 24 10 +

G 25 ? (photo)

G 27 11 +

G 1 + + +

G 2 +

G 3 + +

G 4 0

81

G6 0.5

G7 1 6.1

G 5 ? 6.08

G 8 15 6.3 + +

g 9 1-10 +

G 10 10-12

G 11 13

G 12 13-23 +

without number + (calcretes)

G 13 23 + +

G 14 20 +

G 15 20 +

G 30 0-10 + + +

G 31 10-13 + + +

G 32 13-53 7.32 +

G 33 53 - + (purple material)

Green lake + + +

K1 +

K2 + + +

Table 15. Details of sample depths, some testing results, and details of which tests were performed on each sample.

82

Sample # Wet weight [g] Schälchen [g] Dry weight [g] Water content [%]

G 19 40.3553 35.5722 39.8587 10.38238799

G 20 41.1573 35.7157 40.0778 19.83791532

G 22 42.6574 36.5501 41.6002 17.31043178

G 26 44.8171 40.4548 44.5823 5.382481718

G 24 51.6816 44.1035 50.3504 17.56640847

G 27 45.0407 33.7401 42.7751 20.048493

G 1 48.8093 43.3079 48.4186 7.101828625

G 2 41.183 36.0215 40.5257 12.73467015

G 3 48.8673 44.1586 48.6098 5.468600675

G7 53.1283 45.5859 50.1235 39.83877811

G 8 41.115 33.4011 39.8146 16.85787993

g 9 41.2331 33.3507 39.7163 19.24287019

G 12 53.5622 43.6914 52.1067 14.74551202

G12a 39.3682 35.5571 39.2985 1.828868306

G 13 47.6795 39.8151 46.3824 16.49331163

G 32 48.783 41.4611 47.2605 20.79378303

Green lake 39.909 33.9856 38.8583 17.73812338

K1 40.6847 36.0277 40.0461 13.71269057

Table 16. Data obtained from water content testing of solid samples.

83

Sample # SiO2 Al2O3 CaO Fe2O3 K2O MgO Na2O SO3 Cl totc Tot Org

C Mineral guess

Sum of

percentages

G 19 92.2 1.43 0.04 0.7 0.28 0.31 1.83 0.28 2.08 0.08 0.08 quartz 99.31

G 20 82 2.34 0.04 10.5 0.26 0.26 1.03 0.24 0.96 0.21 0.2 quartz, iron oxide 98.043

G 22 92.6 2.14 0.05 1.45 0.32 0.24 0.86 0.1 0.7 0.07 0.07 quartz, Al oxide 98.603

G 26 85.7 4.94 0.06 1.09 0.81 0.37 1.62 0.74 1.33 0.09 0.09 quartz, Al oxide 96.84

G 24 88 4.8 0.06 1.08 0.78 0.32 1 0.53 0.81 0.05 0.05 same material as

G26 97.475

G 27 91.9 2.64 0.03 0.76 0.24 0.17 1.1 0.17 0.98 0.07 0.07 quartz, Al oxide 98.126

G 1 83 4.45 0.22 5.9 1.01 0.78 1.26 0.25 1 0.2 0.17 quartz, Al oxide,

iron oxide 98.236

G 2 85.8 2.34 0.14 0.56 0.36 0.55 3.7 0.63 4.41 0.23 0.23 quartz, Al oxide,

NaCl 98.95

G 3 91.5 2.38 0.07 2.74 0.63 0.25 0.44 0.11 0.28 0.15 0.15 quartz, Al oxide,

iron oxide 98.695

G7 16 6.63 15.4 1.68 0.79 1.65 11 22.4 13.8 0.68 0.57 alunite, gypsum,

NaCl, quartz 90.6

G 8 45.7 19.7 0.14 4.96 1.98 1.78 7.17 2.49 8.35 0.26 0.25

Al oxide, kaolinite ?,

Fe oxide,NaCl,

quartz

92.78

G 9 63.8 10.1 1.82 2.24 1.44 0.85 4.82 4.96 5.43 0.14 0.13

al oxide, gypsum,

Fe oxide,NaCl,

quartz

95.73

G 12 64.3 10 0.18 1 2.58 0.5 3.9 8.8 4.18 0.09 0.09 quartz, Kaolinite,

Alunite 95.62

G12a 82.5 4.93 0.32 3.51 1.47 0.79 0.68 1.14 0.7 0.2 0.15 quartz, Kaolinite, 96.394

84

Alunite, Al oxide,

Iron oxide

G 13 84.4 2.33 0.44 2.01 0.71 0.36 2.91 2.65 3.41 0.05 0.05 quartz, Kaolinite,

Alunite, iron oxide 99.32

G 32 63.4 21.2 0.04 0.82 0.37 0.32 2.44 0.2 2.61 0.03 0.02 quartz, Al oxide 91.45

Green

lake 83.5 4.04 0.1 4.38 1.08 0.22 0.99 2.4 0.87 0.16 0.14

quartz, Al oxide,

alunite, iron oxide 97.881

K1 56.1 18.8 3.62 5.29 1.3 1.2 1.32 0.22 1.04 1.07 0.24quartz, Al oxide,

iron oxide 90.2

Table 17. Percentage composition testing results of each solid sample collected, and estimated dominant minerals.

85

Sample Fetot

(mol/kg) Dithionite-Fe mol/kg

FE HCl mol/kg

FE Fraction Dithionite of total

Fe Fraction HCl of total

Fe Fraction HCl of

dithionite

G1 0.7375 0.0956 0.0112 0.129654743 0.015236081 0.117512718

G3 0.3425 0.0529 0.0065 0.154407857 0.018935904 0.122635622

G8 0.62 0.1583 0.0239 0.255314563 0.038628479 0.151297595

G9 0.28 0.0841 0.0046 0.300287857 0.01632033 0.054348952

G13 0.25125 0.1316 0.0358 0.523844281 0.142572501 0.272165807

G17 1.5234 0.0885 0.058103128

G18 0.2320 0.0091 0.03940891

G20 1.3125 1.3003 0.0140 0.990703084 0.010635253 0.010735056

G21 0.2811 0.0031 0.010923689

G27 0.095 0.0348 0.0017 0.366328116 0.017668337 0.048230907

G30 0.0787 0.0079 0.100347257

G31 0.0476 0.0020 0.041111175

Green Lake 0.5475 0.4244 0.1286 0.775127438 0.234959131 0.303123228

K1 0.66125 0.1248 0.0200 0.188744153 0.030267362 0.16036185

K2 0.0904

Table 18. Data and results from various tests performed to determine free iron levels.

86

9.2.3 Laboratory report

Sample SiO2 Al2O3 CaO Fe2O3 K2O MgO Na2O SO3 Cl totc Tot Org C

UNITS % % % % % % % % % % %

G1 83 4.45 0.22 5.9 1.01 0.78 1.26 0.25 0.996 0.2 0.17

G2 85.8 2.34 0.14 0.56 0.36 0.55 3.7 0.63 4.41 0.23 0.23

G3 91.5 2.38 0.07 2.74 0.63 0.25 0.44 0.11 0.275 0.15 0.15

G7 16 6.63 15.4 1.68 0.79 1.65 11 22.4 13.8 0.68 0.57

G8 45.7 19.7 0.14 4.96 1.98 1.78 7.17 2.49 8.35 0.26 0.25

G9 63.8 10.1 1.82 2.24 1.44 0.85 4.82 4.96 5.43 0.14 0.13

G12 64.3 10 0.18 1 2.58 0.5 3.9 8.8 4.18 0.09 0.09

SARM-3 52.4 13.6 3.22 9.91 5.51 0.28 8.37 0.16 0.12

STD 1.1 52.3 13.5 3.22 9.94 5.54 0.28 8.42 0.15 0.124

0.5% Carbon

CS-2000 0.5

STD 1.2 0.51

1% Org Carbon 0.99

STD 1.3 1

G12a 82.5 4.93 0.32 3.51 1.47 0.79 0.68 1.14 0.704 0.2 0.15

G13 84.4 2.33 0.44 2.01 0.71 0.36 2.91 2.65 3.41 0.05 0.05

G19 92.2 1.43 0.04 0.7 0.28 0.31 1.83 0.28 2.08 0.08 0.08

G20 82 2.34 0.04 10.5 0.26 0.26 1.03 0.24 0.963 0.21 0.2

G22 92.6 2.14 0.05 1.45 0.32 0.24 0.86 0.1 0.703 0.07 0.07

G24 88 4.8 0.06 1.08 0.78 0.32 1 0.53 0.805 0.05 0.05

G26 85.7 4.94 0.06 1.09 0.81 0.37 1.62 0.74 1.33 0.09 0.09

G27 91.9 2.64 0.03 0.76 0.24 0.17 1.1 0.17 0.976 0.07 0.07

G32 63.4 21.2 0.04 0.82 0.37 0.32 2.44 0.2 2.61 0.03 0.02

SARM-2 63.6 17.3 0.68 1.4 15.3 0.46 0.43 <0.01 0.01

STD 2.1 63.5 17.1 0.69 1.39 15.5 0.45 0.42 -0.01 0.01

1.0% Carbon

CS-2000 1

STD 2.2 1.09

2% Org Carbon 1.03

STD 2.3 1.05

Table 19. Report sheet for the laboratory test results.

88

Sample SiO2 Al2O3 CaO Fe2O3 K2O MgO Na2O SO3 Cl totc

Tot Org C

G.L 83.5 4.04 0.1 4.38 1.08 0.22 0.99 2.4 0.871 0.16 0.14

K1 56.1 18.8 3.62 5.29 1.3 1.2 1.32 0.22 1.04 1.07 0.24

CC1 47.1 31.4 0.03 3.24 0.42 0.12 0.09 0.47 0.061 2.65 2.49

CC2 47.5 30.7 0.04 3.23 0.41 0.13 0.12 0.85 0.084 2.62 2.56

CC3 49.4 29.5 0.04 3.14 0.4 0.13 0.1 0.53 0.072 2.72 2.6

CS1 5.99 1.34 44.6 0.62 0.26 2.74 1.08 1.01 0.838 12.2 1.19

CS2 6.37 1.29 44.8 0.6 0.21 2.75 1.1 1.01 0.856 11.9 1

CS3 5.96 1.4 44.7 0.66 0.23 2.74 1.12 1.05 0.786 12 1.25

LK1 60.5 23.9 0.03 1.31 0.82 0.1 0.1 0.15 0.057 2.28 2.06

LK2 56.8 25.5 0.02 2.22 0.85 0.08 0.09 0.27 0.041 2.39 2.25

LK3 60.6 24.1 0.03 1.46 0.81 0.08 0.08 0.23 0.031 2.13 2.08

KEPOA 85.3 7.4 0.05 1.03 0.23 0.06 0.07 0.11 0.012 1.2 1.15

KEPOB 84 7.92 0.06 1.66 0.22 0.06 0.08 0.11 0.016 1.07 1.02

KEPOC 86.1 7.09 0.05 1 0.22 0.05 0.06 0.09 0.015 1.03 1.01

SY-4 49.9 19.5 8.05 6.21 1.66 0.54 7.1

STD 3.1 49.9 19.6 8.07 6.24 1.67 0.55 7.13 0.05 0.52

CaCO3 for CS-2000 12

STD 3.2 12.2

std0093 1.7

STD 3.3 1.72

KEP10A 70.5 14.2 0.05 1.73 1.49 0.1 0.16 0.11 0.034 3.55 3.35

KEP10B 69.4 14.1 0.05 1.7 1.46 0.1 0.17 0.11 0.035 4.15 3.9

KEP10C 70.7 14.2 0.05 1.69 1.49 0.1 0.17 0.11 0.034 3.37 3.25

KEP18A 79.3 10.1 0.05 1.97 0.59 0.08 0.1 0.12 0.031 1.67 1.57

KEP18B 78.6 10.8 0.05 1.63 0.59 0.08 0.1 0.12 0.028 1.76 1.67

KEP18C 80.6 9.66 0.05 1.5 0.62 0.08 0.1 0.12 0.028 1.66 1.61

KEP29A 53 28.6 0.04 2 0.89 0.11 0.11 0.17 0.045 2.45 2.43

KEP29B 53.1 28.2 0.04 2.17 0.86 0.11 0.12 0.17 0.038 2.44 2.27

KEP29C 55.5 27.1 0.04 1.93 0.84 0.11 0.11 0.17 0.038 2.4 2.36

French Iron Ore 609-1

STD 4.1 16.7 4.24 9.64 43.5 0.2 3.27 0.15 2.56 0.005

0.5% Carbon CS-

2000(2) 0.5

89

STD 4.2 0.51

1% Org Carbon(2) 0.99

STD 4.3 1.05

Table 20. Report sheet for the laboratory test results (cont’d).

90

Sample Preparation

The samples have been sorted and dried. The whole sample has been

pulverised in a vibrating pulveriser equipped with a Zirconia bowl.

Analytical Methods

The samples have been cast using a 12:22 flux to form a glass bead which has

been analysed by XRF.

SiO2, Al2O3, CaO, Fe2O3, K2O, MgO, Na2O, SO3, Cl

have been determined by X-Ray Fluorescence Spectrometry

totc

has been determined by Total Combustion Analysis.

The sample has been acidified to remove carbonate and then residual carbon

has

been determined using a total combustion analyser.

Tot Org C

has been determined by Total Combustion Analysis.

Table 21. Report sheet for the laboratory test results (cont’d).

interactions between salinity, acidity, and geochemistry ... · interactions between salinity,...

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