HYDROGEOCHEMISTRY AND HYDROLOGY OF A BASALT AQUIFER SYSTEM, THE ATHERTON TABLELANDS, NORTH QUEENSLAND

Katrina Louise Locsey

Bachelor of Applied Science

(Queensland University of Technology)

Master of Applied Science

(Queensland University of Technology)

School of Natural Resource Sciences

A thesis submitted for the Degree of Doctor of Philosophy of the

Queensland University of Technology

2004 STATEMENT OF ORIGINAL AUTHORSHIP

The work contained in this thesis has not been previously submitted for a degree or diploma at any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made.

Signed ………………………………

Katrina Locsey

Date ……………………………… ABSTRACT

The Atherton Tablelands basalt aquifer is a major source of groundwater supply for irrigation and other agricultural use. The Tertiary to Quaternary age basaltic aquifer can be regarded as a generally unconfined, layered system, comprising numerous basalt flows separated by palaeo-weathering surfaces and minor alluvial gravels of palaeo-drainage channels. Layers of massive basalt and clay-rich weathered zones act as local aquitards, with some local perched aquifers also present. The aquifer is regarded as a system in which several factors interact to produce the overall characteristics of the hydrogeochemistry of the groundwaters. They include the mineralogical composition of both the basalt aquifer and the thick overlying weathered zone, the porosity and permeability of the basalt aquifer, its thickness, bedrock composition, and climate and topography.

The hydrogeochemical processes operating in this aquifer system have been investigated though the analysis of 90 groundwater samples collected from October 1998 to October 1999, groundwater chemistry data provided by the Queensland Department of Natural Resources & Mines for more than 800 groundwater samples, rain water samples collected during 1999 by CSIRO, stream chemistry data provided by CSIRO and James Cook University, and mineralogical and whole rock geochemistry data of drill chip samples.

The methods used in this research study include the assessment of groundwater major ion chemistry data and field physico-chemical parameters using hydrochemical facies and statistical approaches, investigation of the mineralogical composition of the aquifer, assessment of concentrations and activities of the ions in solution, the degree of saturation with respect to both primary and secondary minerals, and hydrogeochemical modelling to determine the likely controls on the chemical evolution of these groundwaters.

The basaltic groundwaters are mostly Mg-Ca-Na, HCO3 type waters, with electrical conductivities generally less than 250 µS/cm and pH values from 6.5 to 8.5. Dissolved silica (H4SiO4) comprises a large proportion of the total dissolved load, with average concentrations of around 140 mg/L. Concentrations of potassium, chloride and sulphate are low, that is, generally less than 3 mg/L, 15 mg/L and 10 mg/L, respectively. Despite the very low salinity of the Atherton Tablelands basalt groundwaters, the relative concentrations of the major ions are comparable to groundwaters from other basaltic regions, and are consistent with expected water- rock interactions.

A variety of multivariate statistical techniques may be used to aid in the analysis of hydrochemical data, including for example, principal component analysis, factor analysis and cluster analysis. Principal component factor analyses undertaken using the hydrochemical data for the Atherton groundwaters has enabled the differentiation of groundwaters from various lithological formations, the underlying geochemical processes controlling groundwater composition in the basalt aquifer to be inferred, relative groundwater residence and flow directions to be inferred and mapping of the estimated thickness of the basalt aquifer.

The limitations of multivariate statistical methods have been examined, with emphasis on the issues pertinent to hydrochemical data, that is, data that are

i compositional and typically, non-normally distributed. The need to validate, normalize and standardize hydrochemical data prior to the application of multivariate statistical methods is demonstrated.

Assessment of the saturation states of the Atherton basalt groundwaters with respect to some of the primary minerals present indicate that the groundwaters are mostly at equilibrium or saturated with respect to K-feldspar, and approach equilibrium with respect to the plagioclase feldspars (albite and anorthite) with increasing pH. These groundwaters are at equilibrium or saturated with respect to the major secondary minerals, kaolinite, smectite (Ca-montmorillonite) and gibbsite. They also tend to be saturated with respect to the oxidation products, goethite and hematite, common accessory minerals in the Atherton Tablelands basalt sequence.

Silicate mineral weathering processes are the predominant influence on the composition of these basalt groundwaters. These weathering processes include the weathering of pyroxenes, feldspars and other primary minerals to clays, aluminium and iron oxides, amorphous or crystalline silica, carbonates and zeolites, releasing ions to solution. The contribution of substantial organic carbon dioxide to the groundwater is an important factor in the extent to which silicate mineral weathering occurs in this aquifer system. Evaporative enrichment of recharging waters, oxidation and ion-exchange reactions and the uptake of ions from, and decomposition of, organic matter, are processes that have a minor influence on the composition of the basalt groundwaters.

The relationships observed between mineralogical compositions, basalt character and groundwater occurrence in the Atherton Tablelands region improved the understanding how groundwater is stored and transmitted in this basalt aquifer system. Groundwater is mostly stored in vesicular basalt that may be fresh to highly weathered, and movement of this water is facilitated by pathways through both vesicular and fractured basalt.

Related work undertaken as part of this research project showed that the groundwater flow patterns defined by the hydrogeochemical interpretations correspond well with the spatial trends in water level fluctuations, and response to recharge events in particular. Groundwater baseflow to streams and discharge to topographic lows in the Atherton Tablelands region is indicated by the relationships between the major cations and anions in the stream waters. Fracture zones are likely to be preferred pathways of groundwater movement. Recharge estimates, based on a chloride mass balance, range from 310 mm/yr in the north-western part of the study area (north of Atherton) to 600 mm/yr in the wetter southern and eastern parts of the study area. These recharge estimates should be treated with caution however, due to the low groundwater chloride concentrations and the high variability in rainfall chloride concentrations.

The findings of this research project have improved the understanding of the hydrogeochemical processes controlling the composition of the low salinity basalt groundwaters in the Atherton Tablelands region, and are applicable to other basalt groundwater systems, particularly those in high rainfall environments.

Key words: Hydrogeochemistry · Basalt aquifer · Mineralogy · Multivariate statistical analysis

ii ACKNOWLEDGEMENTS

I would like to acknowledge the support and encouragement of my principal supervisor Dr Malcolm Cox, who has been particularly helpful over many years, both during and prior to this PhD research. His establishment of the QUT research projects in the Atherton Tablelands region, his guidance in directing this research, provision of the equipment necessary to undertake the research, and maintenance of scholarship and travel funds, is gratefully acknowledged. My associate supervisor Dr Micaela Preda has also contributed considerable time in reviewing manuscripts and assisting with X-ray diffraction analyses, and has held many helpful discussions with me during the course of this research.

This research project was funded by the Land and Water Resources Research and Development Corporation and the Queensland Department of Natural Resources and Mines (QDNR&M). Without the assistance of these organisations, this research would not have been possible. Mr Bruce Pearce, Mr Graham Herbert, Mr Andrew Durick and Mr John Bean (QDNR&M) have provided much advice, data and assistance, particularly in the early stages of this research program. I would particularly like to thank Bruce Pearce, who organised and co-ordinated much of the hydrogeological work undertaken in the Atherton Tablelands region in recent years.

I am also grateful for the data and samples provided by Dr Peter Cook, Dr Andrew Herczeg and Ms Kerryn McEwan from CSIRO Land and Water, and staff at James Cook University, who have also undertaken work on the Atherton Tablelands. Helpful information and data has also been provided by the QUT Honours students, Ms Lindsey Buck, Ms Michelle Sheldrick and Mr Martin Moloney. Laboratory assistance was provided by Ms Sharyn Price and Mr Tony Raftery (QUT). The assistance of Dr Deidre Stuart (QUT) with calculations presented in Appendix VI is gratefully acknowledged.

Travel grants from the QUT Office of Research and the School of Natural Resource Sciences enabled the attendance of conferences in South Africa, Townsville and Darwin, which has been very beneficial to my research and professional development. I would also like to thank staff of the School of Natural Resource Sciences, particularly the administrative and computing staff who helped in many ways over the past few years. I also appreciate the encouragement of my fellow postgraduate students, in particular Mr John Harbison, who reviewed many documents for me and shared in many interesting and helpful discussions about our work over these past few years.

I would like to thank my friends and family in Brisbane and Melbourne, who have given me much encouragement during the course of this research. My thanks to my dear friend Leisl, my sister Michelle and brother-in-law Stewart for their help and encouragement, and to their angels Madeleine, Lauren and Benjamin who always make my time away from study so enjoyable. Finally and most importantly, I would like to thank my parents Valerie and Robert Locsey for their support over the past few years, and for their work and the many sacrifices they have made to provide their daughters with the best possible education.

iii PUBLICATIONS BY CANDIDATE FROM PhD STUDY Refereed papers (International journals) Published PAPER 2: LOCSEY K.L. & COX M.E. 2002. Statistical and hydrochemical methods to compare basalt and basement rock hosted groundwaters: Atherton Tablelands, north- eastern Australia. Environmental Geology, 43 (6), pp. 698-713. Paper and supplementary material published online 10th October 2002 – Environmental Geology, © Springer-Verlag, DOI 10.1007/s00254-002-0667-z. Manuscript prepared for Hydrogeology Journal PAPER 3: LOCSEY K.L., PREDA M. & COX M.E. 2002. Water – rock interactions: an investigation of the relationships between mineralogy and groundwater composition and flow in a subtropical basalt aquifer.

International conference publications Reviewed and published papers PAPER 1A: LOCSEY K.L. & COX M.E. 2000. Chemical character of groundwater in a basalt aquifer, North Queensland, Australia. In Sililo O. et al. eds. Groundwater: Past achievements and future challenges. Proceedings of the XXXth International Congress of the International Association of Hydrogeologists, Cape Town, pp. 555-560. A.A.Balkema, Rotterdam. PAPER 1B: LOCSEY K.L. & COX M.E. 2001. A hydrochemical classification scheme for a basaltic aquifer as an indicator of groundwater flow position. In Seiler K.P. and Wohnlich S. eds. New approaches to characterising groundwater flow. Proceedings of the XXX1st International Congress of the International Association of Hydrogeologists, Munich, pp. 1217-1221. A.A.Balkema, Swets & Zeitlinger B.V., Lisse. APPENDIX IV: LOCSEY K.L. & COX M.E. 2002. Hydrochemical variability as a tool for defining groundwater movement in a basalt aquifer: The Atherton Tablelands, North Queensland. In Proceedings of the International Association of Hydrogeologists International Groundwater Conference: Balancing the Groundwater Budget, Darwin, 12-17 May 2002. Abstract and Poster APPENDIX I: LOCSEY K.L. & COX M.E. 2001. Climatic, mineralogical and weathering controls on the geochemical character of groundwater – the Atherton Tablelands basalt aquifer, North Queensland. In Proceedings of the 4th International Conference on Environmental Chemistry and Geochemistry in the Tropics (GEOTROP 2001), Townsville, 7-11 May 2001.

iv TABLE OF CONTENTS Page Abstract…………………………………………………...…………………………………...………..i Acknowledgements……………………………………..…………………………………………….iii Publications by candidate from PhD study………...…………………….………………….……...iv INTRODUCTION……………………………………………………………………...……1 LITERATURE REVIEW………………………………………………………….……..…6 Introduction……………………………………………………………………………………...….…6 Previous work in the Atherton Tablelands region……………………………………...…………...6 Basalt aquifer systems………………………………………………...…...…………………………..9 Background…………...………….…………………………………………….……………...9 Columbia River Plateau………….………………………………….……………………….11 Deccan Basalts…………………………….………………………………………….…...…12 Other basalt aquifers...…..……………………………………………….…………………..12 Basaltic oceanic islands……….……………………………...………………………….…..13 Australian basalt aquifer systems...………………………………………….……………….14 Processes controlling groundwater composition………………………..…………………...……..15 Background…………...………….…………………………………….…………………….15 Carbon dioxide in water………….………………………………………………….……….16 Silicate mineral dissolution and weathering………………………………………….…...…17 Oxidation and reduction……………………………………………………….……………..24 Ion-exchange…………….…….…………………………….………………...……………..25 Evaporation…………………….…………………………………….………………………26 Organic matter……………………………………………………………………….………27 Other factors influencing groundwater composition…...……………………….……...……28 Mineralogical and hydrochemical methods for modelling hydrogeochemical processes……………………………………………….…………………..………………………….30 Rock chemistry and mineralogy....…………………………………….…………………….30 Mineral dissolution and solubility.…………………………………………………….…….30 Mass balance approach……………………………………………….…………………...…33 Hydrogeochemical modelling……...………………………………………………….……..34 Related hydrogeochemical studies and scope for further research.……………………….....…...36 Statistical methods for hydrochemical data assessment………..……………………...……..……41 Background…………...………….………………………………………….……………….41 Data distribution and validation…..………………………………….………………………41 Transformation and standardization methods………………………………………….…….43 Multivariate data assessment…………………………………………………….……….….45 Graphical methods...…………………………………….………………….…………...…46 Principal component analysis…………………………………….………………….…...47 Factor analysis………………………………………………………………….………..…48 Principal component factor analysis…………………………….…………………....…50 Limitations of factor analysis………………………………………………….…..…...…53 Cluster analysis…………………………………………………………….…………….…54 Alternative multivariate statistical methods…….………………….………………...…55 Application of some multivariate statistical methods to hydrological and hydrochemical studies and scope for further work………….……………………………………………………….....……58 Background…………...………….………………………………………………….……….58 Hydrochemical processes………....…………………………………………….……………58 Differentiation by source………………………...………………………….……………….61 Other studies and scope for further work……..………………………….…………………..62 Conclusions…………………………………………….………………………………………..……64 References…………………………………………….……….……………..…………………...…..65

v PAPER 1A CHEMICAL CHARACTER OF GROUNDWATER IN A BASALT AQUIFER, NORTH QUEENSLAND, AUSTRALIA………………………………………………………………………...….……..96 PAPER 1B A HYDROCHEMICAL CLASSIFICATION SCHEME OF A BASALTIC AQUIFER AS AN INDICATOR OF GROUNDWATER FLOW POSITION……………………………………..……………………..110 PAPER 2 STATISTICAL AND HYDROCHEMICAL METHODS TO COMPARE BASALT- AND BASEMENT ROCK-HOSTED GROUNDWATERS: ATHERTON TABLELANDS, NORTH-EASTERN AUSTRALIA…………………………………………………………………………………123 PAPER 3 WATER – ROCK INTERACTIONS: AN INVESTIGATION OF THE RELATIONSHIPS BETWEEN MINERALOGY AND GROUNDWATER COMPOSITION AND FLOW IN A SUBTROPICAL BASALT AQUIFER…………………………………………………………………………….………157 GENERAL CONCLUSIONS…………..……………………….………………………..197 APPENDICES

APPENDIX I Climatic, mineralogical and weathering controls on the geochemical character of groundwater – the Atherton Tablelands basalt aquifer, North Queensland…….………………………………………..200

APPENDIX II Extracts from Locsey and Cox (unpubl.), a report submitted to QDNR&M and LWRRDC, and additional related work Hydrogeochemical cross-sections: inferring relationships between hydrochemistry and groundwater movement.……………………….……………………………………………………………...……202

APPENDIX III Extracts from Locsey and Cox (unpubl.), a report submitted to QDNR&M and LWRRDC, and additional related work Groundwater–stream interaction: the Atherton Tablelands, North Queensland…………………………………………………………………………………...………212

APPENDIX IV Hydrochemical variability as a tool for defining groundwater movement in a basalt aquifer: the Atherton Tablelands………………………………………………………………………...……..…218

APPENDIX V Supportive approaches and applications of work presented in PAPER 2 Multivariate data analysis of the Atherton Tablelands groundwaters: Approaches Thickness of the basalt aquifer Groundwater flow directions inferred from the principal component factor analysis...……………..240

APPENDIX VI Extracts from Locsey and Cox (unpubl.), a report submitted to QDNR&M and LWRRDC, and additional related work Groundwater recharge: a chloride mass balance approach Reliability of the chloride mass balance approach: consideration of additional input sources of chloride……………………………………………………………………………………………….245

APPENDIX VII

vi Chemical analysis of natural waters……………………………………………………………..…...254

APPENDIX VIII Mineralogical analysis - X-ray diffraction…………………………………………………………...262

APPENDIX IX Field and laboratory data: groundwater and rain water samples……………………………..………265

APPENDIX X Mineralogical data……………………………………………………………………………..……..276

APPENDIX XI Groundwater data sourced from the Queensland Department of Natural Resources and Mines...…..279

vii INTRODUCTION INTRODUCTION

Hydrogeochemical processes are important in defining groundwater hydrology in complex, layered basalt aquifers, particularly where groundwaters are of very low salinity. An assessment of fresh groundwaters, that may show subtle variations in chemical character, requires an understanding of the hydrogeochemical processes controlling groundwater composition. The relationships between groundwater composition, hydrogeochemical processes and groundwater hydrology have particular relevance to basalt aquifer systems, as they commonly contain relatively fresh groundwaters. They also tend to have complex groundwater flow patterns, due to their geological structure, that may be defined using hydrogeochemical interpretations.

This research project tests the hypothesis that hydrogeochemical processes, and silicate mineral weathering processes in particular, are the most significant influence on the chemical character of very low salinity groundwaters in a basalt aquifer system in a subtropical environment, and that an understanding of such processes can be used to define groundwater hydrology. This research explores the relationships between groundwater composition, hydrogeochemical processes and groundwater hydrology; it aims to determine the controls on groundwater composition and the nature of groundwater storage and movement in a subtropical basalt aquifer system. The groundwater resources contained within the basalt and basement rock aquifers of the Atherton Tablelands, North Queensland, Australia (Figure 1), provide an ideal setting for such an investigation.

145º 22' 145º 46' º ' º ' -17 8 % -17 8 Walkamin Lake

# Cairns Tinaroo Atherton Kairi Tablelands %Tolga % eek Cr zlin Ma %Atherton Queensland r e v i R n o r r a B 05001000Kilometers % Malanda Upper % Barron N o r th J o h n s to n e R iv e r Millaa Millaa % Queensland 145º 22' 145º 46' º ' º ' Australia -17 31 Rivers -17 31 Roads (Major and Minor) N Basalt 0 5 10 15 20 Kilometers

Figure 1. Location map of the study area: the Atherton Tablelands, North Queensland, Australia.

1 The research described in this thesis forms a component of a major program investigating the aquifer systems of the Atherton Tablelands. This program was funded by the Queensland Department of Natural Resources and Mines (QDNR&M) and the Land and Water Resources Research and Development Corporation (LWRRDC). The project has involved collaboration between QDNR&M, CSIRO Land and Water and the Queensland University of Technology (QUT). QDNR&M have undertaken extensive assessment and monitoring of the groundwater resource, developed conceptual and numerical groundwater flow models of the north-eastern part of the region, and developed a groundwater management plan for this area. The contribution by CSIRO focused on determining groundwater ages and recharge rates using isotope hydrology, and identifying groundwater flow zones and base flows to streams using radium-tracing techniques.

The QUT component of the research program comprised three Honours projects undertaken in the School of Natural Resource Sciences, as well as this PhD research project. The Honours projects involved an assessment of the physical features of volcanism in the Atherton Tablelands region (Buck 1999), stratigraphic interpretation of the lava field using petrogenetic modelling (Sheldrick 1999) and a geophysical study assessing the use of magnetic surveys in the identification of physical features of the basalt pile (Maloney 1999). A concurrent Masters degree study by Bean (1999) addressed the hydrogeology and groundwater resource potential of the northern part of the region.

The hydrochemistry of groundwaters from these aquifers has been assessed in this current investigation using several approaches, including descriptive statistical approaches, multivariate data assessment using principal component factor analysis, and a classification scheme approach. The hydrogeochemistry has also been assessed using a hydrochemical facies approach, and by examining the processes that influence groundwater composition. These processes include enrichment by evaporation and transpiration, interaction with primary and secondary minerals in the soils and the weathered and fresh rock layers, ion-exchange effects, and leaching of ions from organic matter. The mixing of waters sourced from different aquifers is also a factor that is addressed.

The associations between aquifer rock mineral phases and their relative quantities, and groundwater occurrence and composition, have enabled an understanding of the nature of groundwater migration and the controls on groundwater composition.

The methods utilized here have more commonly been applied to regional aquifer systems in arid, semi-arid and temperate climates, where groundwaters have substantially higher concentrations of ions and are considerably older. The application of these methods to a study of a very low salinity groundwater system in a subtropical basalt environment, however, is unique and has shown the usefulness of these approaches in such a setting. The importance of aquifer systems in tropical and subtropical environments is being increasingly recognized, particularly in developing countries in southern Asia and sub-Saharan Africa, where communities are often strongly reliant on locally recharged, non-sedimentary aquifer systems.

2 In addition, the methods used in this research project rely predominantly on major ion chemistry and field measurements, and demonstrate the depth of information that can be gained from basic hydrochemical data. This aspect of the research project is particularly relevant to hydrogeological studies undertaken in remote environments or in locations where the use of more expensive analytical techniques, such as isotope chemistry, are either impractical or prohibitive due to cost. Facilities for the geochemical analysis of waters in many countries in Africa for example, are limited to major and minor ions (Edmunds 1996). Methods used in this study are therefore anticipated to be widely applicable.

The research aims are accomplished through a literature review, and a series of research papers and associated work; each paper demonstrates the applicability of particular methods to improving the understanding of a subtropical basalt aquifer system.

PAPER 1 is comprised of two related papers (1A and 1B). PAPER 1A entitled “Chemical character of groundwater in a basalt aquifer, North Queensland, Australia” examines the variations in hydrochemical composition in the aquifer and proposes that the aquifer may be divided into hydrochemical zones, depending on the groundwater composition. These zones range from areas where the groundwater composition is predominantly controlled by rainfall composition, to a mixed water type, to more evolved water that shows the effects of silicate mineral weathering processes.

The hydrochemical relationships between the major ions in the groundwaters of the Atherton Tablelands are further examined in PAPER 1B “A hydrochemical classification scheme for a basaltic aquifer as an indicator of groundwater flow position”. Five key indicators of the chemical evolution of these groundwaters (i.e. 2+ - - 2- - - Mg /Cl , HCO3 + CO3 /Sum Cations, HCO3 /Cl , H4SiO4 concentration and the - 2- percentage of HCO3 + CO3 of major anions) were used in a rating-based classification scheme to identify the relative positions of groundwaters along inferred flow paths, and enabled the identification of areas of preferred recharge. The concept of classification schemes is used in other fields in hydrogeology, such as in vulnerability and risk mapping studies, and has been applied here to classify groundwaters based on their chemical compositions. This work is also presented as a conference abstract and poster in APPENDIX I.

The cross-sections and accompanying data presented in APPENDIX II demonstrate how such a classification scheme can be applied to infer relationships between hydrochemistry and groundwater movement. One of the key indicators of hydrochemical evolution (i.e. Mg2+/Cl-) has also been useful in determining the nature of groundwater and stream-water interaction on the Atherton Tablelands. This work is presented in APPENDIX III.

The classification scheme presented in PAPER 1B was refined and modified to examine only those groundwaters of a ‘basaltic’ hydrochemical composition in APPENDIX IV “Hydrochemical variability as a tool for defining groundwater movement in a basalt aquifer: The Atherton Tablelands, North Queensland”.

3 APPENDIX IV also further demonstrates the effects of evaporative enrichment and silicate mineral weathering processes on the compositions of these groundwaters.

Multivariate data analysis, specifically principal component factor analysis, as well as standard descriptive statistical and hydrochemical methods are demonstrated in PAPER 2 “Statistical and hydrochemical methods to compare basalt- and basement rock-hosted groundwaters: Atherton Tablelands, north-eastern Australia”. This study makes use of a large set of hydrochemical data from QDNR&M as well as samples collected and analysed by the author during the course of study. An early application of principal component factor analysis to these data provided a qualitative assessment of the likely host rocks of groundwater from unidentified lithological units and is presented in APPENDIX V.

The methods used in PAPER 2, particularly the principal component factor analysis, show that the hydrochemical variability can be used to distinguish basalt- and basement rock-hosted groundwaters, and thus enable the definition of the likely host rocks of groundwaters from unidentified lithological units. This approach resulted in an improved understanding of the thickness of the basalt aquifer and, thereby, the extent of the resource (APPENDIX V). The principal component factor analysis of the basaltic hydrochemistry also enabled the interpretation of the likely hydrogeochemical processes controlling the composition of these waters. Where a particular process controlling groundwater composition can be related to groundwater residence, flow directions may be inferred (APPENDIX V).

Consideration needs to be given to the use of multivariate statistical methods in analysing compositional and non-normally distributed data. Transformation and standardization methods are discussed in PAPER 2, as are the numerous options involved in applying a principal component factor analysis and the need to assess the robustness and interpretability of the results.

A combination of hydrochemical and mineralogical approaches to characterizing groundwater evolution and movement in a subtropical basalt aquifer are presented in PAPER 3 “Water – rock interactions: an investigation of the relationships between mineralogy and groundwater composition and flow in a subtropical basalt aquifer”. Mineral phases present at various depths in the basalt aquifer were identified and quantified. The relationships between the basalt mineralogy, and groundwater occurrence and composition, due to water – rock interaction, have improved the understanding of groundwater storage and transport in this aquifer system. Mass balance calculations and hydrochemical modelling, when used in conjunction with an understanding of the aquifer mineralogy, enabled a more accurate identification of the hydrochemical processes influencing this aquifer system.

Related hydrogeochemical work within this study, that is, groundwater recharge calculations based on a chloride mass balance, is presented in APPENDIX VI. This work has not previously been prepared for publication. The recharge estimates are, however, quoted by Pearce and Durick (2002), who support the estimates by independent recharge calculations, based on a soil moisture model and groundwater usage estimates. Methods used to analyse water and rock samples are presented in APPENDICES VII and VIII, respectively. Results of the groundwater analyses for

4 samples collected by the author during the course of study, as well as results of rain sample analyses (samples obtained by CSIRO and analysed by the author), are presented in APPENDIX IX. Mineralogical data for rock samples analysed are presented in APPENDIX X and reference to QDNR&M groundwater data used in this study is made in APPENDIX XI.

The research findings demonstrate that if the hydrochemical variables examined include those expected to differentiate groundwaters by source and by process, then multivariate statistical analysis, mineralogical and hydrochemical assessments, and interpretation of hydrogeochemical processes, can be useful methods for the examination of a subtropical basalt aquifer system. Limitations of these methods and the need for verification of the processes inferred are particularly relevant when dealing with groundwaters with low concentrations of ions, because interpretations are often based on subtle variations in groundwater composition.

References

BEAN J.A. 1999. A conceptual model of groundwater behaviour in the Atherton Basalt Province, Atherton Tablelands, Far North Queensland. MAppSc thesis, Queensland University of Technology, Brisbane (unpubl.), pp. 159. BUCK L.J. 1999. Physical features of volcanism and their relationship to groundwater, Atherton Basalt Province, North Queensland. BAppSc(Hons) thesis, Queensland University of Technology, Brisbane (unpubl.), pp. 178. EDMUNDS W.M. 1996. Geochemical framework for water quality studies in sub- Saharan Africa. Journal of African Earth Sciences 22 (4), 385-389. MOLONEY M. 1999. The magnetic method applied within a Quaternary volcanic plateau – Atherton, North Queensland. BAppSc(Hons) thesis, Queensland University of Technology, Brisbane (unpubl.), pp. 81. PEARCE B.R. & DURICK A.M. 2002. Assessment and management of basalt aquifers on the Atherton Tablelands, North Queensland, Australia. In Proceedings of the International Association of Hydrogeologists International Groundwater Conference: Balancing the Groundwater Budget, Darwin, 12-17 May 2002. SHELDRICK M.K.M. 1999. Stratigraphic interpretation of a lava field using petrogenetic modelling. BAppSc(Hons) thesis, Queensland University of Technology, Brisbane (unpubl.), pp. 57.

5 LITERATURE REVIEW LITERATURE REVIEW

Introduction

This research is based on the basalt aquifers of the Atherton Tablelands, North Queensland. The approach to this study is to consider the basalt pile as an aquifer system in which several natural processes operate to define the chemical characteristics of the groundwater.

The literature review outlines:

ƒ previous work undertaken in the Atherton Tablelands region,

ƒ the nature of groundwater occurrence in various types of basalt aquifers,

ƒ processes controlling groundwater composition, particularly in basalt aquifers,

ƒ mineralogical and hydrochemical methods for assessing the influences of these processes,

ƒ related hydrogeochemical studies and scope for further research,

ƒ statistical methods for hydrochemical data assessment, and

ƒ the application of multivariate statistical methods to hydrological and hydrochemical studies, and discusses the scope for further application of some of these methods to the assessment of very low salinity groundwaters.

This review considers both publised literature on relevant subjects, as well as technical reports and unpublished material specific to the Atherton Tablelands region.

Previous work in the Atherton Tablelands region

The study area is located within the basaltic lava field of the Atherton Basalt Province, a high rainfall, subtropical elevated plateau with a substantial depth of weathering. The Atherton Basalt Province forms part of the Eastern Australian Volcanic Zone, and is an example of an intraplate continental basalt field (Stephenson 1989). The province is characterized by a variety of volcanic features that include shield volcanoes, composite cones, maars, cinder cones and one diatreme (Stephenson et al. 1980). The basalts overlie Devonian metamorphic rocks of the Hodgkinson Formation, various Permo-Carboniferous granites and felsic Carboniferous volcanics (Donchak & Bultitude 1994).

Detailed cross-sectional mapping by Pearce (2002), which is based on drill logs, shows evidence of up to 22 possible phases of volcanism and indicates that the basalt aquifers comprise a complex series of multi-layered flow events, interspersed with

6 highly weathered soil profiles. Pearce (2002) also identified major fault zones within the profile, and has proposed that the basalt pile behaves as an unconfined aquifer. Hydraulic conductivity values for the basalt aquifer system vary from 0.1 – 104 m/day and specific yields from 1 – 20 %, with an average specific yield of 6.4 % (Pearce & Durick 2002).

The first detailed examination of the groundwater resources in the region was undertaken by Leach (1986), who examined the Atherton basalt aquifer system within the Atherton Shire. Leach (1986) observed that the basalt is up to 120 m thick, and characterized the aquifer system into two aquifer zones, a highly weathered, vesicular upper aquifer, and an older, denser, unweathered to slightly weathered underlying basalt aquifer. Transmissivity and storativity values were calculated by Leach (1986), as well as an average storage volume for the basalts within the Atherton Shire.

An assessment of the physical features of volcanism in the Atherton Basalt Province in relation to groundwater occurrence was undertaken by Buck (1999). This included an examination of the general geology and volcanology of the area, examination of the geomorphological structures within the region and mapping of the base of the volcanic pile. Based on a lineation analysis, surface and basement geomorphological investigations, geophysical observations, borehole investigations and field mapping, Buck (1999) proposed that volcanism in the area is structurally controlled and, based on the tectonic setting and the type and style of volcanic activity, that a comparable basalt field is the South Auckland Volcanic Field, New Zealand (Briggs et al. 1994).

Based on laboratory geochemical work and petrographic microscope analyses, Sheldrick (1999) observed that the volcanic rocks of the Atherton region are predominantly olivine tholeiites, alkali olivine basalts and basanites. The textures of the basaltic rocks range from highly porphyritic to aphyric; they generally show an intergranular texture characterized by small pyroxene and opaque minerals, and associated olivine coexisting with plagioclase in the groundmass. Pyroclastic deposits are found throughout the study area, and are classified as crystal-rich ashes, and ultramafic xenoliths found in scoria deposits are harzburgites (Sheldrick 1999).

Using aeromagnetic, ground magnetic and magnetic susceptibility methods, Moloney (1999) inferred that the volcanic activity in the area appears to have been localized, and influenced by pre-existing basement faults. Ground magnetic data was used to define the aerial extent of the volcanic formations in the region, and magnetic susceptibility measurements used to identify paleo-weathering surfaces within the volcanic pile (Moloney 1999).

The hydrogeology of the northern part of the Atherton Basalt Province was examined by Bean (1999), with the aim of developing a conceptual hydrogeological model for the basalt aquifers in the main irrigation area. The study was based on water levels, pump tests, borelogs and physico-chemical data. Bean (1999) proposes that there were at least three phases of volcanism, with the end of each phase being defined by a weathering surface, and that re-activation of pre-existing basement structures has resulted in hydraulic connection throughout the basalt pile. Bean (1999) observed

7 that the basalt thickness in the northern irrigation area is generally between 70 and 90 m (with a maximum of 130 m), that the basalt aquifers are heterogeneous and anisotropic in character and recharged locally, and that the piezometric surface reflects the surface topography. Bean (1999) also proposes that field pH and EC can be used to distinguish between basalt- and basement-derived groundwaters, and that - - ratios of aqueous HCO3 / Cl can be used to confirm hydraulic flow paths where minimal water level data is available.

The age of the Atherton Tablelands groundwaters have been estimated by Cook et al. (2001) to range from 5 to 30 years based on a CFC-11 (chloroflurocarbon) dating method, and recharge across the Tablelands has been estimated using a chloride mass balance approach. Radon concentrations in the groundwaters indicate that flow rates decrease with depth, and that there is an active upper flow zone of approximately 30 m thickness (Cook et al. 2001). Stable isotope and chloride concentrations of stream waters in the area are similar to concentrations in the groundwaters, indicating that most of the river flow is from groundwater inflows, rather than surface runoff; thus, groundwater extraction, particularly during the dry season, has the potential to impact on ecosystems dependant on these stream waters (Cook et al. 2001). Groundwater ages for the Atherton Tablelands have also been estimated by Herczeg - (2001) based on HCO3 concentrations using a model based on weathering rates, porosity and mean grain size; residence times were calculated to be between 5 to 120 years.

Earlier studies of the Atherton Tablelands include assessments of Cainozoic volcanism in the region, geological and soil mapping and some investigations of groundwater resources.

A review of Cainozoic volcanism in north-eastern Australia (Stephenson et al. 1980) covered 12 volcanic provinces in the region, including the Atherton Tablelands area. The review includes some useful information on basaltic petrology and rock geochemistry, age determination, vent distribution and structural relationships for the area. Petrological and geochemical descriptions of some basaltic rocks from the Atherton Tablelands were also provided by Morgan (1968). The regional geology was summarised by Best (1960, 1962), and a review of available information on the Atherton basalts completed by Bedford (1983). A summary account of the Atherton Volcanic Province and possible courses for some lava flows in the region were sketched by De Keyser and Lucas (1968), Blake (1972) and Stephenson and Griffin (1976). The development of maars and the morphology of Lakes Eacham and Barrine are discussed by Timms (1976).

The general nature of soils developed in the Atherton Tablelands region has been outlined by Isbell et al. (1968), and the red and brown basaltic soils that occur in the region described in some detail by Isbell et al. (1976, 1977). Soil mapping, including references to source rock, has been conducted in the region (Malcolm & Nagel 1997) as well as a soils and agricultural land suitability assessment (Malcolm et al. 1999). An assessment of the mineralogy of the soils on basalt in North Queensland by Simonett and Bauleke (1963) found that weathering intensity increased with increasing rainfall. Poorly crystalline kaolinite is the dominant clay mineral in these soils; halloysite may be present in high rainfall areas; gibbsite

8 content increases with increasing rainfall; the content of iron oxides such as hematite and goethite also increase modestly with increasing rainfall; magnetite, ilmenite and titanomagnetite may also be present (Simonett & Bauleke 1963).

Some assessments of groundwater resources in the Atherton Tablelands area have been undertaken by the Queensland Water Resources Commission (formerly the Irrigation and Water Supply Commission, and now QDNR&M). A description of the geology of the area and summary of the history and other details of existing wells were provided by Gloe (1949), following an apparent decline in well water levels during 1947 and 1948. Results of a groundwater drilling program in the early 1950’s are outlined by O’Shea (1954). A broad outline of the groundwater resources in North Queensland, including a brief mention of the basalts in the Atherton region, is provided by McEniery (1980). A groundwater investigation in the adjoining Mulgrave River region (Muller 1978) provides some information on groundwater yields for other rock units also present in the study area. An investigation of the groundwater occurrence and chemistry of the Hodgkinson Formation (metamorphics) in the Cattle Creek catchment, north-west of the Atherton study area was completed by Lait (1998).

Basalt aquifer systems

BACKGROUND

Basalt is an igneous rock formed by the solidification of molten material (magma) at the earth’s surface. The minerals comprising basalt are predominantly the silicate (Al-Si) group of minerals, composed of combinations of silica tetrahedra in, for example, linear forms (e.g. pyroxene) and individual tetrahedra (e.g. olivine) (Deutsch 1997). The feldspars, such as albite (NaAlSi3O8) and anorthite (CaAl2Si2O8) plagioclase feldspars, which form a solid solution subdivided into six mineral species, are tektosilicates composed of a three-dimensional network of silica tetrahedra (Faure 1998). Non-crystalline solids such as glass may also be present in basalt. A compilation of the chemical compositions of igneous rocks by Turekian and Wedepohl (1961) and Vinogradov (1962), presented by Faure (1998), shows basalt composition (including both volcanic and plutonic rocks of basaltic composition) as 23.5 % Si, 8.6 % Fe, 8.28 % Al, 7.2 % Ca, 4.55 % Mg, 1.87 % Na and 0.83 % K. Average concentrations of some other elements include 11 400 ppm Ti, 1750 ppm Mn, 1130 ppm P, 452 ppm Sr, 385 ppm F, 315 ppm Ba and 300 ppm S (Faure 1998).

A common mode of occurrence of basalt aquifer systems is as lava flows interbedded with weathered and pyroclastic material and sediments. The main features of such aquifer systems are vesicles, fractures and interflow sediments (Singhal & Gupta 1999). Basalt aquifers are, as a result, commonly described as having dual porosity. The porosity, permeability and groundwater flow characteristics of fractured rocks is poorly understood (Singhal & Gupta 1999). The porosity of unfractured volcanic rock varies from less than 1 % in dense basalt to more than 85 % in pumice (Schoeller 1962). Typically, dense basalt will have 1 – 10 % porosity and vesicular basalt 10 – 50 % porosity (Davis & DeWiest 1991). The main flow paths in basalt,

9 however, are largely a function of other primary and secondary features, such as joints, fractures, shear zones, faults and other discontinuities (Ecker 1976; Davis & DeWiest 1991; Barnes & Worden 1998; Singhal & Gupta 1999). Auto-brecciation of top and bottom surfaces of lava flows, for example, produces blocky tops and bases to lava flows, providing permeable pathways for groundwater flow (Davis 1969; Uhl 1979; Mazor 1997; Singhal & Gupta 1999).

Volcanic rocks are generally highly susceptible to weathering, particularly in tropical and subtropical environments (Singhal & Gupta 1999). Weathering develops secondary porosity, opening pre-existing fractures and producing pathways that are more pervious to fluid flow (Davis 1969; Domenico & Schwartz 1998). For example, the Columbia River Basalts of the U.S.A. consist of numerous basalt flows, with occasional interbedded sand and gravel units (Domenico & Schwartz 1998). Each basalt layer was affected by weathering processes, which were terminated by the emplacement of subsequent lava flows. Weathered profiles at he top of lava flows constitute major pathways for groundwater movement (e.g. Druecker & Fan 1976; Freeze & Cherry 1979 Jalludin & Razack 1994) in addition to cooling joints, fractures and vesicles (Hearn et al. 1985). Weathering features, such as highly weathered basalt, palaeosols, as well as silty to gravelly sediments, have also been observed between lava flows in the Tertiary intraplate continental basaltic field of the Atherton Basalt Province (Bean 1999; Buck 1999), within which the study area is located.

Groundwater in basalt aquifer systems can occur under perched, unconfined and confined conditions. Localized perched aquifers, for example, are present in the Atherton Tablelands region, due to the occurrence of impervious formations, such as ash beds and dense basalt flows above the regional aquifer. On a regional scale, however, hydraulic connection throughout the basalt pile, which may have developed due to re-activation of pre-existing basement structures, and localized recharge across the area, has resulted in regionally unconfined aquifer conditions (Bean 1999). Confined conditions can also be created where vesicular or fractured basalt is located between massive basalt units (e.g. Singhal 1973).

The features that impart porosity and permeability to basaltic rocks are outlined by Stearns (1942), Davis (1969), Davis and DeWiest (1991) and Singhal and Gupta (1999). They include scoriae, breccia zones between flows as discussed above, cavities between pahoehoe lava flows, shrinkage cracks, gas vesicles, lava tubes, tree moulds, and fractures and lineaments. Several of these features have been observed in the basaltic rocks of the Atherton Tablelands region, as discussed by Buck (1999). Dykes and sills can be barriers to groundwater flow, particularly if they are more than several metres thick (Davis & DeWiest 1991; Bromley et al. 1994; Singhal & Gupta 1999); however, sills and dykes are not significant features of the Atherton Tablelands. On a large scale the permeability of basalt is very anisotropic (Freeze & Cherry 1979). Horizontal hydraulic conductivity is generally several times greater than vertical hydraulic conductivity, due to the presence of interflow spaces and horizontal fractures (Peterson 1972; Davis & DeWiest 1991; Singhal & Gupta 1999). Groundwater flow in the Atherton Tablelands region, for example, is strongly anisotropic in the direction of groundwater flow, with the vertical component of flow considerably less than the horizontal component (Buck 1999).

10 Infilling of vesicles and fractures by secondary minerals, such as zeolites, calcite, secondary silica and clays, can lead to decreasing porosity and hydraulic conductivity of basaltic rocks with geological age or degree of weathering (Davis 1974; Ecker 1976; Singhal & Gupta 1999). A lower permeability of weathered volcanic rocks compared to unweathered rocks on the island of Oahu in Hawaii, for example, was reported by Oki et al. (1998), although weathered basalts can also form efficient aquifers (e.g. Deolankar 1980). Infilling of vesicles with zeolites and clays has been observed in basalt chips taken from bores in the Atherton – Tolga – Kairi area, and in basalt boulders across the Atherton Tablelands, by the author and by Bean (1999) and Buck (1999). Singhal and Gupta (1999) also note that recharge to volcanic aquifers is generally higher in younger volcanics, as the weathering effects and lower permeability of older volcanics results in high runoff.

The landforms and drainage patterns typical of volcanic terrain are discussed by Singhal and Gupta (1999), and by Buck (1999) specifically in relation to the study area. Some examples of basalt aquifer systems are discussed below.

COLUMBIA RIVER PLATEAU

Plateau basalts, also known as continental flood basalts, usually consist of a number of flows of varying thickness superimposed on each other; the thickness of individual layers ranges from less than 1 m to 30 m, most being 10 to 30 m (Singhal & Gupta 1999). The Columbia River and Snake River basalts in the north-western part of the United States are examples of plateau basalts; they occupy an area of more than 200 000 km2 covering the states of Washington, Oregon and Idaho. The generally flat-lying and dense basalt sequence (composed of between 120 to 150 individual flows), is of Miocene to Quaternary age (17.5 – 6 Ma), reaches a maximum thickness of 1500 m in the central part of the basin and has an average total thickness of about 550 m, with extensive river – deposited sediments between many of the basalt flows (Freeze & Cherry 1979; Shelton 1983; Hearn et al. 1990; Singhal & Gupta 1999). A summary of the formation of the Columbia River basalts is given by Hooper (1982). The basalt aquifers of the Columbia Plateau are an important source of water for agricultural, domestic and municipal uses (Hearn et al. 1985).

The Columbia River Basalt Group lavas are classified as tholeiites and consist predominantly of plagioclase feldspar, pyroxene and opaque metal oxides, with accessory minerals such as apatite, olivine and Fe- and Ti-oxides (Deutsch et al. 1982). Secondary minerals formed at relatively low temperatures (< 100 °C), such as smectite (primarily nontronite), the zeolite clinoptilolite, iron oxide and various forms of silica, are found in the fractured and vesicular zones of the basalt flows, which are the major paths of groundwater flow (Hearn et al. 1985). Groundwater flow through the basalt tends to parallel the flow units because the most permeable parts of the lava flows are generally along the fractured contact zones between the lava flows (Deutsch et al. 1982). The composition of groundwaters from the Columbia Plateau basalts is discussed by Newcomb (1972), and by Deutsch et al. (1982) for the eastern Washington area, with an emphasis on the solubility controls on the water composition.

11 DECCAN BASALTS

The Deccan Traps of India, of Upper Cretaceous to Eocene age (65 – 60 Ma), which have a maximum thickness of about 1500 m, are also an example of a major plateau basalt province (Singhal & Gupta 1999). The Deccan Volcanic Province comprises generally flat-lying basaltic flows (from a few metres to 50 m thick) in a multiaquifer system separated by thin impervious tuffaceous layers referred to as “red beds” (Pawar & Shaikh 1995; Singhal & Gupta 1999). The Deccan basalts of western and central India cover an area of over 500 000 km2 and form an important source of water supply (Kulkarni & Deolankar 1993). The Deccan basalts tend to be either vesicular – amygdaloidal type basalts or finer grained dense basalts (Athavale et al. 1983; Kulkarni & Deolankar 1993). The occurrence of groundwater in the basalt aquifers is controlled by the degree of weathering and jointing, the presence of vesicles, interconnection between vesicles by fissures and cracks, and intertrappeans (interflow sedimentary deposits) (Pawar 1993; Singhal & Gupta 1999). The hydrogeology of the Deccan basalts is described by Lunkad and Raymahashay (1978), Athavale et al. (1983), Uhl and Joshi (1986), Kulkarni and Deolankar (1993) and Narayanpethkar et al. (1994).

The basaltic rocks of the Deccan Plateau consist of predominantly augite and calcic plagioclase feldspars, with minor olivine. The weathering products are predominantly kaolinite, bauxite or laterite under extreme leaching conditions, and montmorillonite and minor kaolinite and illite under more moderate leaching conditions (Ratha & Sahu 1993). Groundwater in parts of the Deccan basalts, which is heavily exploited, is affected by agricultural pollution. The impact of irrigation and fertilizers on the quality of groundwater in a small watershed of the Deccan Trap Hydrologic Province is discussed by Pawar and Shaikh (1995).

Other examples of plateau basalts include the extensive Early Jurassic (190 Ma) basalts of the Karoo Province, South Africa, which cover an area of around 3 × 106 km2, the Middle Paleozoic to Mesozoic basalts of the Siberian Traps, Russia, which cover an area of around 1.5 × 106 km2, and the Lower Cretaceous (140 – 120 Ma) Parana Volcanics of Brazil (900 000 km2) (Singhal & Gupta 1999).

OTHER BASALT AQUIFERS.

Other basalt aquifer systems include the Middle Tertiary basalts, which form part of an extensively exploited aquifer system within the Basin of Mexico, and belong to the Mexican Trans-volcanic Belt (Edmunds et al. 2002). Groundwater from the granular and fractured volcanic units supplies about 70 % of the total water supplies of Mexico City (Edmunds et al. 2002). Due to the declining water levels and subsequent land subsidence, as well as the potential for contamination, the hydrology and hydrochemistry of the aquifers have been investigated by several workers (e.g. Mazari & Mackay 1993; Birkle et al. 1998).

Some other examples of basalt aquifer systems include the Gedaref basin in eastern Sudan, which is in part filled with Tertiary basalt lava flows and supplies water to an agricultural centre of grain production (Hussein & Adam 1995), basalts of the

12 Ethiopian Rift Valley (McKenzie et al. 2001), plateau basalts in north-eastern Jordan (Lloyd 1965; Abu-Jaber 20001) which store predominantly Na-HCO3 type waters that are probably transmitted through fracture zones, highly vesicular zones and inter-flow alluvium (Lloyd 1965), the lavas and pyroclastic deposits of the Kumamota Plains, located on the west side of Mount Aso Volcano, southern Japan (Mahara & Igarashi 1993), and the basalts of the Blackburn Hills volcanic field of western Alaska (Moll-Stalcup & Arth 1991).

BASALTIC OCEANIC ISLANDS

The basaltic oceanic islands are associated with both intraplate and plate margin volcanism and are generally comprised of large, gently-dipping shield volcanoes. Basaltic oceanic islands include both high islands (e.g. Hawaiian Islands) and low islands (e.g. Cook Islands). Basalt flows are usually thin (6 m or less), and their high permeability is mostly due to clinker zones in the aa type flows, lava tubes and gas vesicles in the pahoehoe flows, columnar joints and irregular openings (Peterson 1984). Ash beds can form confining layers (Singhal & Gupta 1999).

Young basaltic lavas, such as those found typically in the Hawaiian Islands, French Polynesia, and parts of Samoa, are extremely permeable; older lavas that contain more pyroclastic material, such as on the islands of Yap, Truk and Pohnpei in Pacific Micronesia are poorly permeable and the most productive aquifers are sedimentary alluvial deposits and weathered lavas (Peterson 1993). Examples of oceanic island basalt aquifer systems also include, for example, the basaltic rocks of the volcanic massifs of the island of Réunion in the western Indian Ocean, east of Madagascar (Join et al. 1997; Louvat & Allègre 1997) and the fractured volcanic aquifers on Tenerife, the largest of the Canary Islands in the Atlantic Ocean (Ecker 1976). Development of groundwater resources on these islands is commonly hampered by salt-water intrusion. The hydrogeology of volcanic ocean islands is described by Peterson (1972, 1993).

The intraplate Hawaiian Islands consist of six major populated islands and numerous other small islands, formed by extrusion of basaltic lavas as the Pacific Plate moved over the Hawaiian hotspot (Peterson 1993). The largest of the islands, Hawaii, comprises five major shield volcanoes, Kohala, Mauna Kea, Hualalai, Mauna Loa and Kilauea. The basaltic lavas are very permeable due to their young age and the thinness of individual flows (Peterson 1993). The chemical quality of groundwater on five of the Hawaiian islands is described by Swain (1973). The hydrogeology and hydrochemistry of the Puna District, located on the east and south-east slopes of Kilauea Volcano and on the east slope of Mauna Loa Volcano, are described by Druecker and Fan (1976). The groundwater in the Puna District occurs as perched, dyke or basal water and is of Na,Ca-HCO3 or Na,Mg-HCO3 type, and in coastal areas, influenced by seawater intrusion (Druecker & Fan 1976). The hydrogeology of the basalt aquifer systems on the island of Oahu is described by Visher and Mink (1964), Takasaki and Valenciano (1969), Rosenau et al. (1971) and Oki et al. (1998).

13 AUSTRALIAN BASALT AQUIFER SYSTEMS

During the Cainozoic there was widespread basaltic igneous activity in eastern Australia along and adjacent to the Eastern Highlands, that resulted in the formation of more than fifty recognized igneous provinces (Wellman & McDougall 1974). Price et al. (1997) notes that since the time of continental break-up and extending over an interval of at least 60 Ma, the south-eastern Australian margin has been the site of intermittent intraplate volcanism (the Atherton Basalt Province being an example of this as discussed above). The magmas that formed the igneous provinces of eastern Australia are thought to originate from a magma source or sources, with a limited latitudinal extent, within the asthenosphere; migration is considered to be related to the movement of the Indian (Australian) lithospheric plate relative to the underlying asthenosphere (Wellman & McDougall 1974). Various models for the generation of the eastern Australian basalts are also discussed by McDonough et al. (1985), Ashley et al. (1995), Price et al. (1997), Stephenson et al. (1998) and Sutherland (1998).

The Newer Volcanics Province of western Victoria and south-eastern South Australia comprises Late Tertiary to Quaternary basalts (dominated by tholeiitic and transitional basalts with alkalic rock types and basaltic icelandites (SiO2 > 52 %) being less common), which were emplaced as extensive lava flows, scoria cones, small shield volcanoes and maar deposits over an area of 15 000 km2 (Price et al. 1997). Fractures in the basalt constitute the primary pathways for groundwater flow in the Newer Volcanics basalts. Groundwater from these basalts is used for stock watering, irrigation and domestic purposes, and contributes to water supplies for the towns of Penshurst, Dunkeld, Caramut, Mortlake, Streatham and Skipton (Kiernan et al. 2002). The hydrogeology and the hydrochemistry of these basalts are described, for example, by Riha and Kenley (1978) and Finegan (1994). The chemical evolution of the groundwaters in the Tertiary basalts of Victoria has also been studied by Komarower and Wall (1981). The groundwaters in this temperate climate (annual rainfall ~ 800 mm) are more saline than those of the Atherton Tablelands region, with total dissolved solids in the range 1000 – 10 000 mg/L; their compositions are attributed to the weathering of feldspars to beidellite clays (Komarower and Wall 1981).

Tertiary olivine basalt at one time covered large areas in east-central Queensland and is extensively preserved near Clermont, Emerald, Springsure and Rolleston in a region known as the Central Highlands (Gunn 1974). The prolonged volcanic activity during the middle Tertiary also resulted in several basaltic occurrences in south-eastern Queensland. The greatest area and thickness of material accumulated in the McPherson Ranges, eastern Darling Downs and Bunya Mountains; smaller areas of basalt are found at Tamborine Mountain, Bundamba, Cooper’s Plain, Archerfield, Redland Bay, Burleigh, Buderim, Black Mountain, Cooran and Pinbarren (Ferguson 1954a). The composition of groundwaters contained within the Tertiary basalts at Ormiston, south-east of Brisbane, is described by Barclay (1997) in relation to weathering and recharge processes and surface and salt-water mixing.

14 Processes controlling groundwater composition

BACKGROUND

The primary controls on the dissolved constituents in groundwater are the original chemical character and temperature of the water as it enters the zone of saturation, the distribution, solubility and exchange capacity of the rock minerals, the porosity and permeability of the aquifer, and the flow path of the water (Back & Hanshaw 1965; Freeze & Cherry 1979; Appelo & Postma 1996; Mazor 1997). These processes by which groundwater attains its chemical character have been investigated for the basalt aquifer system of the Atherton Tablelands, North Queensland.

The concentration of dissolved and undersaturated ions in groundwater is generally considered proportional to the length of the flow path and the residence time of the groundwater. Rapidly moving groundwater is likely to have lower concentrations of ions than slowly flowing groundwater over an equivalent distance through an equivalent aquifer matrix (e.g. Chebotarev 1955; Back 1966; Back & Hanshaw 1970; Palmer & Cherry 1984; Tóth 1984; Herczeg et al. 1991).

The chemical character of groundwater is related to the time of contact between groundwater and the type and solubility of aquifer minerals as well as other factors, such as the amount of dissolved carbon dioxide present, the amount of aquifer surface area in contact with the hydraulically effective pore volume, the temperate and the reaction rate (Claassen & White 1979). Groundwater chemically evolves systematically along flow paths in the subsurface given sufficient water residence time (Paces 1976; Wallick & Tóth 1976; Tóth 1984; Veeger 1996; Jankowski & Acworth 1997). The spatial distribution of chemical species may therefore be used to infer the direction of groundwater movement (e.g. Love et al. 1993; Schreiber et al. 1999; Stuyfzand 1999).

A hydrochemical classification scheme was developed by Szczukariew and Priklonski in 1955, in which groundwater types are classified based on the concentrations of the major ions constituting greater than 20 % of the total anions and cations (Alekin 1970). The concept of ‘hydrochemical facies’ was later developed by Back (1960, 1961, 1966), Morgan and Winner (1962) and Seaber (1962) to describe cation and anion concentrations within defined composition categories (Freeze & Cherry 1979). The trilinear or Piper diagram (Piper 1944) can be used to define hydrochemical facies, as proposed by Back (1961) and Back and Hanshaw (1965). Hydrochemical facies reflect the effects of chemical processes in the lithological environment and the prevailing groundwater flow patterns (Back & Hanshaw 1965). It should be noted, however, that the classical hydrochemical evolution proposed by Chebotarev (1955) has limited relevance to silicate rock aquifer systems, as chloride and sulfate ions are not significant constituents in silicate rocks, and therefore, there is no development towards chloride and sulfate hydrochemical facies in these rocks (Freeze & Cherry 1979).

15 Among rocks of volcanic origin, basalts are particularly sensitive to chemical weathering (Berner & Berner 1996). Chemical processes controlling the composition of groundwater, such as dissolution, hydration, hydrolysis, oxidation – reduction reactions, direct attack by acids on the rocks, chemical precipitation of minerals, ion-exchange reactions and concentration by evaporation and transpiration, are discussed by Hem (1985) and Tóth (1984, 1999).

The natural processes that influence the composition of groundwaters in basalt aquifers include production of carbonic acid, mineral dissolution and weathering, oxidation and reduction reactions, ion-exchange and sorption, and evaporation. Decay of organic matter is also an important process particularly with respect to the production of carbon dioxide. The selective uptake of ions and / or leaching of ions from vegetation may also influence groundwater composition. These aspects are discussed with respect to the general concept, and in relation to the study area.

CARBON DIOXIDE IN WATER

The chemical evolution of groundwater begins when rain water infiltrates the soil. Carbon dioxide present in the atmosphere dissolves in rain water and forms aqueous CO2, which associates with water molecules to form carbonic acid, H2CO3, as shown in Equation 1. Carbonic acid, a weak acid, tends to dissociate into hydrogen, bicarbonate and carbonate ions (Johnson et al. 1977; García et al. 2001) in two steps (Equations 2 and 3), releasing one proton in each step (Appelo & Postma 1996; Deutsch 1997; Domenico & Schwartz 1998).

+ → CO 2(aq) H 2O H 2CO3 (1)

+ − H CO → H +HCO 2 3 3 (2) = −6.3 K1 10

− + − HCO → H +CO 2 3 3 (3) = −10.3 K 2 10

In the absence of other acids or bases, equilibration with atmospheric CO2 results in slightly acidic conditions in open systems (García et al. 2001). Based on the present- -3.5 day atmosphere CO2 pressure of 10 atm, unpolluted rain water is slightly acidic with a pH value of 5.6 (Appelo & Postma 1996). The distribution of CO2 species in water as a function of pH is described by Appelo and Postma (1996). At different pH values, different species of CO2 are dominant according to the dissociation 2- constants, K, noted above. Carbonic acid is dominant at pH < 6.3, CO3 becomes - dominant at pH > 10.3, and at intermediate pH values HCO3 is the major CO2 species in water (Appelo & Postma 1996).

The decay of organic matter, an oxidation reaction that may occur in soil and also within aquifers where fossil organic matter may be present, and the respiration of plant roots, also produce CO2 (Witkamp & Frank 1969; Mazor 1976; Freeze &

16 Cherry 1979; Buyanovsky & Wagner 1983; Solomon & Cerling 1987; Herczeg & Payne 1992; Appelo & Postma 1996; Mazor 1997; Andrésdóttir & Arnórsson 1999; Berner 1999), as shown in Equation 4:

+ → + CH 2O O2 H 2O CO 2(g) ,(4)

where a carbohydrate, CH2O, is used as a simplification for organic matter.

In general, the waters that enter igneous rocks from the soil zone have a CO2 content 10 to 100 times higher than that expected from equilibrium with the earth’s -1.5 atmosphere (Feth et al. 1964), that is, the pressure of CO2 in soils is commonly 10 -2.5 to 10 atm (Appelo & Postma 1996). Temperature increases CO2 production in soil (Harmon et al. 1975; Drake & Wigley 1975; Brook et al. 1977; Drake 1983). Moisture conditions, microbial activity, availability of organic matter and soil structure are also important factors controlling biological activity, and therefore the production of CO2 (Freeze & Cherry 1979). A map of world CO2 pressure in soil (Brook et al. 1983) indicates that some of the highest soil CO2 pressures are found in North Queensland.

Production of CO2 can also occur in subsoil vadose zones and shallow saturated zones (e.g. Thorstensen et al. 1983; Wood & Petraitis 1984; Keller 1991). The generation of CO2 in the subsurface has been attributed to organic substrates (e.g. Chapelle & Knobel 1985; McMahon et al. 1990) and microbiological communities (e.g. Chapelle et al. 1987; Ghiorse & Wilson 1988; Bennett & Rogers 2000).

+ The production of CO2 and H2CO3, and the release of H ions to solution are important initial processes for water – rock interaction (Paces 1972; Berner 1999). The rain water that infiltrates the soil and bedrock is low in dissolved solids, slightly acidic and is undersaturated with most, if not all, common minerals (Arnórsson 1999). Weathering processes then release ions to solution. Weathering processes are commonly grouped into three broad categories: physical (or mechanical), chemical and biological. While these processes are active in all climatic environments, physical weathering tends to be dominant in cold and arid climates, and chemical weathering is dominant in warm and humid climates (Larsson 1984). Chemical weathering processes are considered to be the most significant natural processes controlling the composition of the Atherton Tablelands groundwaters; they are discussed below.

SILICATE MINERAL DISSOLUTION AND WEATHERING

The effect of silicate mineral weathering on groundwater chemistry tends to be less apparent than the dissolution of carbonate minerals, for example, due to the generally slow weathering rate of silicate minerals (Appelo & Postma 1996; Tóth 1999). The weathering of silicate minerals, however, is estimated to contribute about 45 % of the total dissolved load of the world’s rivers (Stumm & Wieland 1990; Stumm & Wollast 1990), and Appelo and Postma (1996) note that silicate mineral weathering is an important buffer mechanism against acidification of soil and groundwater. On

17 a geological time scale, silicate mineral weathering is one of the most important sinks for atmospheric CO2 (Berner et al. 1983; Stumm & Wieland 1990; Taylor et al. 1999). The amount of CO2 consumption during weathering also varies depending on the type of siliceous rock. Taylor et al. (1999), for example, estimate that basalt weathers twice as fast as granite, and that CO2 consumption resulting from basalt weathering is almost three times greater than during the weathering of granite, due to the higher Ca2+ and Mg2+ concentrations in basalt.

Igneous rocks such as basalt contain appreciable amounts of aluminosilicate minerals, which form at temperatures far above those near the land surface. These minerals are therefore thermodynamically unstable, and dissolve, or weather to clays and oxides when in contact with water (Freeze & Cherry 1979; Deutsch 1997). The distribution of primary silicate minerals during weathering was observed by Goldich (1938), who showed that the susceptibilities of silicate minerals to weathering could be related to their position in Bowen’s (1928) reaction series shown in Figure 1. Olivine is considered the most easily weathered silicate mineral, and quartz as the mineral most resistant to weathering (Goldich 1938). In a chemical weathering study of basalts and andesites, Colman (1982) similarly found the susceptibility of various minerals to weathering in the sequence: glass > olivine > pyroxene > amphibole > plagioclase > K-feldspar, but with some variability, which is consistent with the dissolution susceptibility calculated by Aiuppa et al. (2000) for the Mount Etna basalts. In a study of the weathering of some eastern Australian basalts, Eggleton et al. (1987) observed the susceptibility series: glass ~ olivine > plagioclase > pyroxene > opaque minerals. The order of plagioclase and pyroxene susceptibility found in the work of Eggleton et al. (1987) is the reverse of that commonly described (e.g. Loughnan 1969). Eggleton et al. (1987) note that although plagioclase began to alter earlier than pyroxene, once started, pyroxene weathered relatively quickly, and was completely altered before plagioclase; consequently, plagioclase started to weather earlier than pyroxene, but persisted longer.

Figure 1. The Goldich (1938) weathering sequence.

18 Secondary minerals, such as clays (e.g. kaolinite and montmorillonite) and Al- and Fe-oxides (e.g. gibbsite, goethite and hematite), are formed during silicate mineral weathering processes (e.g. Carr et al. 1980). These clays and oxides form by incongruent dissolution of aluminosilicate minerals, whereby the ratio of the elements that appears in solution is different to that in the dissolving mineral (Loughnan 1969; Freeze & Cherry 1979; White & Claassen 1979; Holdren & Speyer 1986; Appelo & Postma 1996; Aiuppa et al. 2000; Négrel & Lachassagne 2000). The removal of dissolved constituents by the precipitation of secondary minerals ensures that the water remains undersaturated with respect to the primary minerals; the primary minerals, therefore, continue to dissolve and secondary minerals precipitate (Arnórsson 1999). A general reaction (Garrels & Mackenzie 1967; Bricker et al. 1968; Sarin et al. 1989; Singh & Hasnain 1999; Négrel & Lachassagne 2000; Das & Kaur 2001) for the weathering of silicate rocks with carbonic acid is shown in Equation 5:

+ → + + + + + + (Na, Ca, Mg, K) silicate H 2CO3 H 4SiO 4 HCO3 Na Ca Mg K solid products (5)

In silicate weathering reactions, sodium is mainly derived from Na-feldspars such as albite (NaAlSi3O8), or any member of the plagioclase solid solution series between albite and anorthite (i.e. the Ca-feldspar, CaAl2Si2O8) (Appelo & Postma 1996; Négrel & Lachassagne 2000). Clay minerals may also release exchangeable sodium (Renick 1924). Calcium is released during plagioclase weathering, and calcium and magnesium from the weathering of pyroxenes such as augite ([Ca1.5MgAl0.3Si1.7]O6). Magnesium is also released to solution by the dissolution of olivine, as shown in Equation 6, and can also be derived from the weathering of biotite and amphiboles. Potassium concentrations in basalt groundwaters are typically low due to the very small percentage of potassium in the composition of basaltic rock (Wood & Low 1986).

− + + → + + + Mg 2SiO 4 4CO 2 4H 2O 2Mg 2 H 4SiO 4 4HCO3 (6)

Weathering reactions for some primary minerals commonly found in basalt are presented in Equations 7 – 9, with the clay mineral kaolinite as the weathering product. Kaolinite is commonly the dominant alteration product of aggressive water attack on silicate minerals (Garrels 1967).

Albite → Kaolinite

− + + → + + + + 2NaAlSi3O8 2CO 2 11H 2O Al2Si 2O5 (OH) 4 2Na 4H 4SiO 4 2HCO3 (7)

Anorthite → Kaolinite

− + + → + 2+ + CaAl2Si 2O8 2CO 2 3H 2O Al2Si 2O5 (OH)4 Ca 2HCO3 (8)

19 Augite → Kaolinite

[]+ + → CaMg 0.7 Al0.6Si1.7 O6 3.4CO 2 4.5H 2O + 2+ + 2+ + + − 0.3Al2Si 2O5 (OH) 4 Ca 0.7Mg 1.1H 4SiO 4 3.4HCO 3 (9)

Other minerals, such as montmorillonite and gibbsite, can also form as silicate weathering products, as shown in Equations 10 and 11 for the weathering of the Na- feldspar albite:

Albite → Na–montmorillonite

+ 2+ + → + + + 3NaAlSi3O8 Mg 4H 2O 2Na 0.5Al1.5Mg 0.5Si 4O10 (OH)2 2Na H 4SiO 4 (10)

Albite → Gibbsite

+ + → + + + + − NaAlSi3O8 CO 2 8H 2O Al(OH)3 Na 3H 4SiO 4 HCO 3 (11)

In the case of montmorillonite, magnesium is assumed to be derived from the weathering of pyroxenes such as augite, as shown in Equation 9. As can be seen in Equations 7 – 11, aluminium remains conserved in the solid phase, and the main effects of silicate mineral weathering on groundwater composition are an increase in cations, silica and bicarbonate, and a decrease in hydrogen ion concentrations. As all silicate mineral weathering reactions consume acid, there is a pH buffering effect (Appelo & Postma 1996). The increase in cation concentration, accompanied by increases in pH and bicarbonate concentration, can lead to the precipitation of carbonate minerals, such as calcite and dolomite, in igneous rocks (Garrels 1967; Gunn 1974; Uhl & Joshi 1986; Wood & Low 1986; Appelo & Postma 1996; Houston & Smith 1997; Arnórsson 1999) as discussed in PAPER 3. Folk and Land (1975) and Faure (1998) note, however, that although an aqueous solution may be supersaturated with respect to dolomite, precipitation of this mineral requires particular conditions in terms of the salinity and the Mg2+ / Ca2+ ratio of the solution.

The highest concentrations of silica in groundwater tend to be found in volcanic rocks, such as basalt, which contain more reactive minerals than rocks such as granite or schists (Garrels 1976; Appelo & Postma 1996; Langmuir 1997). This characteristic was noted in a comparison of the groundwater compositions from basalt and basement rock aquifers in the study area (PAPER 2). In a study of adjacent basalt and granite drainage systems in Idaho, U.S.A., Taylor et al. (1999) similarly found that silicon fluxes from the basalt basins were more than double those from the granite. A high silica content is also found in the groundwaters of the Karoo sandstone aquifer and overlying basalt in western Zimbabwe, and is attributed to the weathering of mafic minerals in the basalt (Larsen et al. 2002).

Silica is most likely present as monomeric silicic acid (or monosilicic acid), H4SiO4, in the normal temperature and pH ranges of natural water (Krauskopf 1956; Davis & DeWiest 1991; Faure 1998), as shown in Equations 6 and 7 and Equations 9 – 13. If

20 the solution becomes saturated, silica tends to polymerise as a colloid (Krauskopf 1956; Klein 1971). This amorphous silica may settle as a gelatinous precipitate (Krauskopf 1956; Iler 1979), which may later form opal A and opal CT (naturally occurring amorphous silica) as intermediate phases (Kastner et al. 1977) before crystallizing as chalcedony, a cryptocrystalline variety of quartz (Faure 1998). Microcrystalline silica is observed to have precipitated in vesicles of basalt rocks of the eastern Snake River Plain, for example, and is attributed to the addition of H4SiO4 from the weathering of volcanic glass and silicate minerals (Wood & Low 1986).

The source of bicarbonate ions in basalt aquifer systems is from CO2, as discussed above, from silicate weathering reactions as shown for example in Equations 6 – 9, and low concentrations from carbonate rocks that may be present as weathering products (Deutsch 1997). As sulfur is only a minor constituent of igneous rocks, sulfate concentrations in basalt are generally low, and are largely recycled from the atmosphere (Junge 1963; Wood & Low 1986; Davis & DeWiest 1991).

The total dissolved concentrations in groundwaters in silicate rocks are generally low. Mazor (1997), for example, notes that basalt groundwaters are generally of low salinity with concentrations of total dissolved solids less than 400 mg/L. This may be attributed to the slow dissolution kinetics of most silicate minerals. In addition, in the case of massive igneous rocks, groundwater flow along fractures restricts effective flushing of the rock (Appelo & Postma 1996), which therefore limits the degree of water – rock interaction.

The weathering products found in a particular setting (e.g. kaolinite, montmorillonite or gibbsite) are generally interpreted as a reflection of the leaching intensity, which depends on the hydrological conditions as well as the rate of mineral weathering (Ferguson 1954a; Barshad 1966; Yaalon et al. 1966; Garrels 1967; Appelo & Postma 1996; Drever 1997). Many authors (e.g. Simonett & Bauleke 1963; Berner 1971; Appelo & Postma 1996) note that montmorillonite tends to form preferentially in low rainfall, dry climates, and gibbsite and other Al-hydroxides, such as bauxite, in intense rainfall, tropical climates. Reactions of clay species, such as the weathering of Na-, Ca- or Mg–montmorillonite to kaolinite, and the weathering of kaolinite to the aluminium oxide gibbsite, require substantial amounts of water, as shown in Equations 12 and 13:

Ca–montmorillonite → Kaolinite

+ + + → + 2+ + 3Ca 0.33Al4.67Si 7.33O 20 (OH)4 23H 2O 2H 7Al2Si 2O5 (OH)4 Ca 8H 4SiO 4 (12)

Kaolinite → Gibbsite

+ → + Al2Si 2O5 (OH)4 5H 2O 2Al(OH)3 2H 4SiO 4 (13)

It has been proposed, however, that the type of weathering products formed is related to the degree of chemical weathering relative to physical weathering (Nesbitt &

21 Wilson 1992). This is supported by the work of Bluth and Kump (1994), who found in a comparison of rivers draining basaltic terrain that, while a tropical climate and the presence of abundant vegetation favour chemical dissolution, an excess of soil formation (e.g. low rates of physical weathering) will greatly reduce chemical weathering; that is, the balance between chemical weathering and physical removal is a factor controlling the rate of weathering and the type of products formed.

The differing rates of silicate mineral weathering were recognized by Goldich (1938) as discussed above. Later studies (e.g. Wollast 1967; Helgeson 1971; Luce et al. 1972; Paces 1973; Busenberg & Clemency 1976) aimed to gain a quantitative understanding of the dissolution kinetics of primary silicate minerals. Processes that commonly control the rates of dissolution or growth of solid phases in inorganic geochemistry are discussed, for example, by Petrovic et al. (1976), White and Claassen (1979) and Drever (1997). These processes include reaction at the surface of a mineral grain, transport of ions or molecules in solution to or from the grain surface, and diffusion of ions or molecules through a layer of solid reaction products or partially altered primary mineral (Drever 1997).

While some studies (e.g. Wollast 1967; Helgeson 1971; Luce et al. 1972; Stumm & Wollast 1990) indicated that diffusion of solutes through a leached or precipitated layer on the surface of mineral grains was the rate-controlling step in silicate dissolution, with the reaction rates being parabolic, studies by Berner and Holdren (1979), Holdren and Berner (1979), Schott and Berner (1985) and Grandstaff (1986), for example, have shown that dissolution rates are linear (when crystals have been correctly pre-treated) at constant temperature and pH, which is compatible with a mechanism whereby surface reaction controls the reaction rate, rather than diffusion, or the transport of weathering products to and from the mineral surface (Appelo & Postma 1996). Holdren and Berner (1979) found that non-linear rates of dissolution observed at the initial stage of experiments were due to the dissolution of ultrafine (« 1 μm diameter) particles, which are produced during grinding of the sample. Subsequent work (e.g. Schott et al. 1981; Schott & Berner 1983; Casey et al. 1989a,b; Casey & Bunker 1990) has shown the existence of thin altered layers on the surfaces of dissolving feldspars, although the general view is that while diffusion does occur, it is not the major control on the dissolution rates of most silicate minerals (Grandstaff 1986; Drever 1997). The dissolution of volcanic glass, however, may be controlled by diffusion through a partially leached surface layer (White 1983).

Calculations of mean lifetimes of a 1 mm crystal, based on weathering rates at 25 °C and pH 5 from various studies, were determined by Lasaga (1984) and updated by Lasaga et al. (1994); a selection of these is shown in Table 1. They are comparable to the Goldich weathering sequence shown in Figure 1. Reviews of laboratory dissolution rates are also provided by Schnoor (1990), Stumm and Wieland (1990) and Sverdrup (1990).

22 Table 1. The mean lifetime in years of 1 mm crystals of some silicate minerals at 25 °C and pH 5 (Lasaga et al. 1994).

An important study of dissolution kinetics is that by Chou and Wollast (1985), who showed that the albite dissolution rate is strongly pH dependent, and is also influenced by the dissolved aluminium concentration, which inhibits the dissolution of albite. The effects of aqueous pH on silicate reaction rates have also observed, for example, by Wollast (1967), Luce et al. (1972), White and Claassen (1979) and Holdren and Speyer (1986). The dissolution of feldpars is also affected by the partial pressure of CO2 (Sverdrup 1990; Appelo & Postma 1996). In a recent study of the standard Gibbs free energy of H4SiO4, Gunnarsson and Arnórsson (1999) proposed that new data indicate that all silicate minerals are more soluble at low temperatures than has generally been accepted, due to the higher solubility of quartz. They demonstrated this proposal with respect to albite, and showed higher solubility for albite at temperatures below 100 °C, and in particular, below 30 °C (Gunnarsson & Arnórsson 1999).

Another important consideration with regard to silicate dissolution kinetics is that dissolution rates measured in field studies are generally several orders of magnitude lower than those predicted in laboratory studies (Paces 1983, 1984; Velbel 1985; Schnoor 1990; Brantley 1992; Anbeek 1993). This is considered to be due to the effects of temperature, differences in the ratio of effective surface area to total surface area, the precipitation of secondary minerals on the surfaces of primary minerals, which isolates fresh mineral surfaces from the surrounding solution, and hydraulic effects such as preferential flow, rapid flow of water in the aerated zone and fluctuations in the water table which reduce the active mineral surface area exposed to weathering in field situations (Spears 1976; Paces 1983, 1984; Velbel 1990; Swoboda-Colberg & Drever 1993; Drever & Clow 1995; Drever 1997). In addition, anthropogenic processes, such as agricultural and industrial practices, which acidify the groundwater, may also affect field derived rate constants (Paces 1983). The study of silicate mineral weathering rates is a complex one. It is outside the scope of this research project to determine the weathering rates of minerals found in the study area.

Minor silicate minerals that occur in basalt can also contribute to the concentrations of ions in groundwater. Minerals such as apatite and sodalite can release calcium, sodium, phosphorus and chloride ions during weathering. The weathering of apatite, for example, can occur under mildly acidic conditions, given its decreased stability in such environments (Singer 1999). Chloride may also be found in natural glass and fluid inclusions in basalt (Davis & DeWiest 1991).

23 Zeolites are hydrated aluminosilicates of alkali and alkaline earth metals, and are tektosilicates with a more open lattice than other tecktosilicates such as feldspars. They have a high exchange capacity and may therefore participate in ion-exchange reactions (Lloyd & Heathcote 1985). A range of zeolite minerals may precipitate in the vesicles and along fractures of basaltic rocks (Chalmers 1967; Gottardi & Galli 1985; Tschernich 1992). They include chabazite, clinoptilolite, thomsonite, phillipsite, mesolite, scolecite, mordenite, heulandite, stilbite and laumontite. Analcime is a primary constituent in some basalts where it is typically restricted to the groundmass (Coombs et al. 1959; Deer et al. 1966), although it can also occur as phenocrysts in some volcanic rocks (Liou 1971). Analcime is also a common secondary mineral in basalt at low temperatures (< 50 °C) (White et al. 1980; Arnórsson 1999). The presence of these minerals is influenced by pore fluid composition, the partial pressure of CO2, volcanic glass content, depth below surface, time and temperature (Hay 1966; Iijima 1980). Studies of zeolite facies in basalt include those by Hoover (1968) and Fridriksson et al. (1999). Other secondary minerals that may precipitate in basalt, in addition to clays, amorphous and crystallized silica and zeolites as noted above, include pyrite, calcite, aragonite, dolomite, siderite, sepiolite, goethite, hematite and maghemite (Craig & Loughnan 1964; Carr et al. 1980; Pawar 1993; Arnórsson 1999; Fridriksson et al. 1999; Aiuppa et al. 2000).

The relationships between secondary mineral phases and fluid compositions are discussed by Andrésdóttir and Arnórsson (1999) and Fridriksson et al. (1999). In a study of basalts in eastern Iceland, for example, Fridriksson et al. (1999) note that iron and magnesium necessary for the precipitation of a mixed layer chlorite / smectite clay is provided by the rapid hydrolysis of olivine and glass, alteration of chlorite / smectite to zeolites results from an increase in the concentration of Ca2+ relative to Mg2+ and Fe2+ in the fluids, and albitization of plagioclase releases Ca2+ to solution, leading to saturation with Ca-zeolites such as scolecite, heulandite and stilbite. In a study of both thermal and non-thermal groundwaters from basalt in northern Iceland, Andrésdóttir and Arnórsson (1999) showed that the low calcium and magnesium concentrations are controlled by the solubility of calcite and amorphous magnesium-silicate, respectively.

OXIDATION AND REDUCTION

Oxidation and reduction (or redox) reactions may generally be regarded in solution chemistry as reactions involving the transfer of electrons from one atom to another (Drever 1997; Tóth 1999). Oxidation – reduction processes are described by Freeze and Cherry (1979), Appelo and Postma (1996), Deutsch (1997), Faure (1998) and others.

The solubility of some elements will depend on their oxidation state, which is determined by the redox potential or oxidation – reduction potential (Eh) of the environment (Davis & DeWiest 1991). The Eh is a measure of the energy needed to remove electrons from ions in a given chemical environment. The thermodynamic relationship between Eh and the concentrations of dissolved constituents of the solution, assuming that the species participating in the redox reactions are at equilibrium, is based on the Nernst equation. This can be generalized for any redox

24 reaction at 25 °C (Equation 14) (Back & Hanshaw 1965; Appelo & Postma 1996; Deutsch 1997; Drever 1997):

= Ƞ + 0.059 § activity product of oxidized species · Eh E log ¨ ¸ (14) n © activity product of reduced species ¹ where E Ƞ is the standard or reference electrode potential and n is the number of transferred electrons.

High positive values for Eh indicate oxidizing conditions, and low negative values reducing conditions. A decline in groundwater Eh values from an oxidizing state in recharge areas to a reducing state in discharge areas indicates the occurrence of oxidation – reduction reactions in the aquifer (Champ et al. 1979; Fetter 1994).

The electron activity should be regarded as the tendency for an atom to release or accept electrons. The parameter pe is defined in Equation 15:

= pe - log10Įe− (15)

where αe- is the activity of the electrons.

As for Eh, high positive values of pe indicate oxidizing conditions and low negative values reducing conditions (Appelo & Postma 1996). The activity of electrons in solution (and therefore its redox level) can thus be expressed in units of volts (Eh) or in units of electron activity (αe- or pe) (Drever 1997). Eh and pe are related by Equation 16 (Appelo & Postma 1996; Drever 1997):

2.303RT Eh = pe (16) F where F is Faraday’s constant (96.484 kJ per volt gram equivalent), R is the gas constant (8.314 × 10-3 kJ/K.mol), T is the temperature in kelvins, and 2.303 is the conversion from natural to base 10 logarithms. At 25 °C, Eh = 0.059pe.

Values for pe have been calculated from Eh readings and used in mineral / solutions equilibria calculations for the Atherton groundwaters (PAPER 3). As many redox reactions are influenced by pH, redox diagrams can be used to express the stability of species as a function of pe (or Eh) and pH. A diagram showing the stability of water and the ranges of pe and pH conditions in natural environments presented by Drever (1997) and Faure (1998) after Garrels and Christ (1965), for example, is a useful way of identifying waters that are either in contact with or isolated from the atmosphere.

ION-EXCHANGE

Ion-exchange involves a replacement of one chemical for another at the solid surface. Ion-exchange reactions are controlled by the exchange capacity of the solid (Appelo

25 & Postma 1996) and can be important in determining the composition of natural waters (Fetter 1994).

Ion-exchange sites are found primarily on clays and organic materials (Mitchell 1932; Frizado 1979), zeolites and colloidal oxyhydroxides (Drever 1997), and both cation exchange and anion exchange reactions can occur (Fetter 1994). Ion- exchange capacity is a function of mineralogy, particle size, availability of exchange sites, strength of bonding of the exchangeable ions, temperature, pH, Eh, soil moisture and activity of the ions in solution (Hitchon et al. 1971; Barrow & Shaw 1975; Vijayachandran & Harter 1975; Fetter 1994), and can be measured and expressed as a cation exchange capacity (CEC) or anion exchange capacity of a soil (usually as meq / 100 g). Alternatively, an empirical formula can be used to relate the CEC to the percentage of clay and organic carbon (Breeuwsma et al. 1986; Appelo & Postma 1996). The smectite group of clays, including the montmorillonite species, for example, are well known for having a high ion-exchange capacity due to their interlayer charge (Freeze & Cherry 1979; Kittrick 1979; Appelo & Postma 1996; Deutsch 1997; Drever 1997). The cations in the interlayer space of these clay minerals can move into solution to be exchanged for other ions (Appelo & Postma 1996).

The classic case of cation exchange as an indicator of salinization is described by Magaritz and Luzier (1985), Xue et al. (1993) and Appelo and Postma (1996), where seawater intrudes a fresh water aquifer. However, fresh water dominated by Ca2+ - 2+ and HCO3 ions, can also undergo cation exchange, with Ca taken up by the exchanger and Na+ released to solution (e.g. Back 1966; Schwartz & Gallup 1978; Chapelle 1983). While the anion exchange capacity of clays is usually negligible at the pH of most natural material (Deutsch 1997), for more heavily weathered soils where conditions are relatively acidic, as is the case for the Atherton Tablelands, the anion exchange capacity can be considerably higher (Sposito 1989).

EVAPORATION

Evaporation leads to increases in concentration, which are proportional to the amount of water that evaporates. Concentration by evaporation operates mainly in the soil- moisture zone and between rainfall events (Tóth 1999). One of the earliest studies of the effects of evaporation on the composition of fresh waters was undertaken by Jones (1965) on closed-basin brines. The effect of evaporation on the composition of spring waters from the granitic rocks of the Sierra Nevada Mountains of California was later investigated by Garrels and Mackenzie (1967) and is discussed by Faure (1998). Garrels and Mackenzie (1967) concluded that the compounds most likely to form due to evaporation of the spring waters are calcite, gypsum, sepiolite and 2+ 2+ - 2- amorphous silica, accompanied by depletion of Ca , Mg , HCO3 , SO4 and SiO2, and enrichment of Na+, K+ and Cl-, with a rise in pH (Faure 1998).

The evolution of water, due to progressive evaporation and the precipitation of compounds, depends on the operation of several geochemical divides associated with the precipitation of calcite, gypsum and sepiolite as defined by the Hardie-Eugster model described by Drever (1997), and Faure (1998). The principles of evaporative evolutionary paths in relation to the chemistry of closed-basin brines are described

26 by Hardie and Eugster (1970), Eugster and Jones (1979) and Al-Droubi et al. (1980). This approach can be used most effectively to explain the controls on the composition of saline waters, such as saline lakes (e.g. Jones et al. 1977; Eugster & Maglione 1979; Spencer et al. 1985 a,b). The concepts can also be used, however, to aid in the understanding of evaporative effects on any natural waters.

ORGANIC MATTER

The effect of vegetation or organic matter on soil mineral weathering processes is discussed by Drever (1994). Two types of mechanisms are considered by Drever (1994); the direct influence of plants on the physical and hydrological properties of soils that can modify water residence times within the soil and, thus, the contact time between minerals and aqueous solutions, and the chemical influence induced by the production of organic acids by plant roots, microorganisms, and by microbial decay of plant matter (Oliva et al. 1999).

Natural organic matter in soil is composed of degradable plant debris and roots of vegetation. The dissolution of organic matter is the major source of dissolved organic carbon in soil water and shallow groundwater (Thurman 1985; Domenico & Schwartz 1998). Dissolved organic carbon largely constitutes humic substances, consisting mainly of humic and fulvic acids (Wallis et al. 1981; Domenico & Schwartz 1998), which contain the elements carbon, hydrogen, oxygen, nitrogen and sulfur (Sposito 1989; Finger et al. 1992; Frimmel 1992; Deutsch 1997). The open structures of these polymeric molecules means that humic and fulvic acids are capable of having cation exchange sites, and can be excellent ion exchangers, with exchange capacities similar to that of smectites (Rashid 1971; Schnitzer & Khan 1972; Frizado 1979). Solid organic matter can also affect the composition of groundwater, as it adsorbs dissolved organic carbon as well as metals, and can be an important control on the mobility of trace metals (Broadbent & Lewis 1961; Deutsch 1997). The action of organic acids and compounds on clays and other silicates is examined experimentally and discussed by Huang and Keller (1971).

The uptake of inorganic nutrients by vegetation from the soil solution, can also affect the concentrations of ions remaining in the groundwater. In addition, the synthesis of terrestrial biomass requires more cations than anions, and so protons are produced (in addition to oxygen) to maintain the charge balance (Schnoor & Stumm 1985), supplying acidity to soil solutions (Drever 1997). The effect of biomass uptake on mass-balance budgets is discussed by Likens et al. (1977), Sverdrup and Warfvinge (1988), Davis and DeWiest (1991) and Appelo and Postma (1996). The relative effect of biomass on groundwater composition, however, will be less where the input from weathering is greater, which would be the case where the aquifer material is less resistant to chemical weathering (Drever 1997). The chemical constituents most affected by biomass uptake are phosphorus, nitrogen species, potassium and calcium (Drever 1997).

The production of CO2, however, is probably the most important process involving organic carbon, as it leads to the production of carbonic acid in water, as discussed above and shown in Equations 4 and 1, respectively. The effect of plants on weathering has been subject of several studies over recent years (e.g. Drever &

27 Zobrist 1992; Berner & Rao 1997; Bormann et al. 1998; Berner 1999; Moulton & Berner 1999). These studies have shown that the presence of trees accelerates the rate of calcium and magnesium silicate weathering by a factor of approximately three to ten (Berner 1999).

While the effects of organic matter on the composition of the groundwaters of the Atherton Tablelands region are not addressed in detail in this research project, the effects of organic matter on water composition, as discussed above, support one of the hypotheses proposed in PAPER 2, that is, that the leaching of ions from organic matter may affect the concentrations of K+ and to a lesser extent Ca2+ in these groundwaters. In addition, the rates of the silicate weathering processes are likely to be influenced to some extent by the vegetation present in the study area, and consequently the availability of CO2.

OTHER FACTORS INFLUENCING GROUNDWATER COMPOSITION

Average annual rainfall on the Atherton Tablelands ranges spatially from approximately 1300 to 2700 mm. This area is categorized by Davis and DeWiest (1991) as having ‘moderately high precipitation’, that is, in excess of 50 inches (1270 mm) per year. Aquifer systems in areas with abundant rainfall are often fully saturated, have rapid circulation of groundwater and good quality water for most purposes. The abundance of water should, however, result in rapid chemical leaching of soluble and partly soluble material from these aquifers (Davis & DeWiest 1991). A study of the composition of groundwater in this environment, as a means of understanding hydrogeochemical processes, is therefore likely to be a feasible approach.

In addition, the relatively high temperatures in the Atherton Tablelands region may result in somewhat elevated mineral weathering rates in this area, with subsequent effects on the groundwater composition. While Peters (1984) found no conclusive evidence of the effect of temperature on weathering in studies of river chemistry, Meybeck (1986) found a positive correlation between mean air temperature and dissolved silica concentrations in a study of 232 watersheds throughout France. A positive correlation between temperature and the chemical weathering rate of silicate minerals is experimentally established for silicate minerals such as feldspars (Blum & Stillings 1995), and has been observed for some large rivers by Gaillardet et al. (1999), and for smaller watersheds by Velbel (1993), White and Blum (1995) and White et al. (1999). Millot et al. (1999) also observed that rates of silicate weathering are higher in warmer environments, based on their study of weathering rates for the Mackenzie River basin in western Canada compared to tributaries of the Amazon River basin. This relationship is expected to hold for groundwater systems.

Nesbitt and Wilson (1992) found that while the minerals exposed near the surface in soils are controlled by climatic conditions, the weathering mechanisms and leaching characteristics of basalts are apparently controlled more by the bulk composition and primary phases of the parent basalts than by climate. As such, climatic conditions may be expected to contribute to the chemical weathering mechanisms influencing the composition of the Atherton groundwaters. However, the predominant influence is likely to be the composition of the basalt rocks.

28 Other processes such as mixing of waters of different compositions can also be a factor in some groundwater systems. Anthropogenic activities, such as urbanization, agricultural activities and industry, can also significantly affect groundwater composition. The application of nitrogen-based fertilizers in the Atherton Tablelands region is probably the most significant anthropogenic influence on the composition of both surface and groundwaters in the region. An additional minor source of nitrogeneous compounds is from nitric oxides produced by lightening discharges (Davis & DeWiest 1991). As noted above, nitrogen is generally taken up by plants. However, where the input is so high that the vegetation’s ability to take it up is exceeded, nitrate behaves as a mobile anion; this situation is referred to as nitrogen saturation (Drever 1997). The reduction of nitrate by organic matter (denitrification) in soils and aquifers is an important process in the removal of nitrate from solution, leading to volatilisation to nitrogen gas (Appelo & Postma 1996).

These influences on groundwater composition in the Atherton Tablelands region, that is, non-natural processes, are not considered in detail in this study, and will not be further addressed in this literature review.

29 Mineralogical and hydrochemical methods for modelling hydrogeochemical processes

ROCK CHEMISTRY AND MINERALOGY

The mineral compositions of fresh and weathered rocks and soils can be studied, for example, by means of X-ray powder diffraction or by calculating the abundances of selected minerals (referred to as normative minerals) from chemical analyses of the rocks (Faure 1998). Following the choice of minerals to be included in the norm, the oxides are assigned to the normative minerals, with the primary minerals satisfied first, followed by the secondary weathering products (Faure 1998).

X-ray powder diffraction, as well as other analytical techniques, can be also used to trace mineral alterations and to evaluate these alterations in terms of changes in whole rock geochemistry during the weathering process (Eggleton et al. 1987). Changes in rock chemistry and mineralogy as a result of chemical weathering are described by Loughnan (1969), Langmuir (1997) and Faure (1998). Some of the chemical reactions and mineralogical transformations that take place during basalt weathering are described by Craig and Loughnan (1964), Carr et al. (1980), Chesworth et al. (1981), Colman (1982), Eggleton et al. (1987), Nesbitt and Wilson (1992), Gíslason et al. (1996), Aiuppa et al. (2000) and Hill et al. (2000), in terms of the relative mobilities of elements.

MINERAL DISSOLUTION AND SOLUBILITY

The quantification of relations between minerals and dissolved species is described by Garrels and Christ (1965), Paces (1976), Freeze and Cherry (1979), Fetter (1994), Appelo and Postma (1996), Faure (1998) and others. Some of the basics of assessing these relations in terms of a thermodynamic equilibrium approach are outlined below.

The Law of Mass Action states that the rate of reaction is proportional to the concentrations of the reactants as shown in Equations 17 and 18:

aA + bB ↔ cC+dD (17) where the chemical constituents are denoted by capitals, and a, b, c and d are the number of moles of these constituents, and

[][]C c D d K = (18) [][]A a B b where K is the coefficient known as the equilibrium constant or stability constant, and bracketed quantities denote the activities of the constituents. Derivation of the Law of Mass Action based on the first and second laws of thermodynamics is given by Faure (1998).

30 Activities, or ‘effective concentrations’, reflect the chance that the ions will react and form a precipitate (Appelo & Postma 1996) and are calculated based on the ionic strength of the solution (Deutsch 1997). The chemical activity of an ion is equal to the molal concentration multiplied by a factor known as the activity coefficient. Some equations used to derive activity coefficients, such as the Debije-Hückel and the Davies equations, are presented by Lewis and Randall (1961), Back and Hanshaw (1965), Stumm and Morgan (1981), Appelo and Postma (1996), Drever (1997) and Domenico and Schwartz (1998).

To determine the saturation state of a solution with respect to a mineral, the solubility product (K) is compared to the ion activity product (IAP) of the solution. Where the mineral C is being dissolved according to Equation 19:

cC ↔ xX +yY , Kiap is given by (19)

= ()()x y Kiap ĮX ĮY (20)

where α denotes the chemical activity of the ions.

It is essentially a comparison of the activities in the water sample (represented by the IAP) to the activities at equilibrium (K). The ratio between the IAP and K can be used to express the saturation conditions, as shown in Equation 21:

SI = log(IAP/K) (21)

where SI is the saturation index.

The saturation index reflects the direction of the reaction. A negative SI indicates that the solution is undersaturated and that dissolution is expected, while a positive SI indicates that the solution is supersaturated and that precipitation should occur (Nordstrom & Munoz 1994). Because of inherent uncertainties in the calculation of saturation indices, a range of values near zero, such as SI = 0 ± 0.5, are considered to be within equilibrium (Paces 1976; Deutsch 1997).

Some of the theory behind mass transfer calculations, including the computation of molalities, activity coefficients, activities of species in solution, and equilibrium (or saturation) states is given by Helgeson et al. (1970). The calculation of ion activities and saturation states can be undertaken using computer-based hydrochemical models such as PHREEQC (Parkhurst 1995) and WATEQ4F (Truesdell & Jones 1974; Ball & Nordstrom 1991).

Interpretation of saturation indices should be undertaken in conjunction with mineralogical data (e.g. PAPER 3). It should be noted, however, that possible reactive minerals might not be identified in the solid phase because they are at concentrations below the detection limits of the analytical method, or because they may be weathering products, which may form a very small percentage of the whole rock (Deutsch 1997).

31 One of the drawbacks of the saturation state approach, particularly with respect to silicate minerals, as noted by Appelo and Postma (1996) and Drever (1997), is that the aluminium concentration in groundwater is often below the detection limit. Mineral stability diagrams (a type of activity diagram) may therefore be used to display such data, and are based on the assumption that all the aluminium is preserved in the weathering product. Such diagrams facilitate prediction and interpretation of the chemical environments in which mineral assemblages form (Helgeson et al. 1969). A stability diagram for the Na-silicates, for example, is shown in Figure 2. It contains stability fields for the Na-feldspar albite and its possible weathering products, Na-montmorillonite, kaolinite and gibbsite, expressed + + as a function of log [Na ]/[H ] and log [H4SiO4]. The cation / proton activity ratios are a useful measure of evolution of a water body towards equilibrium in a particular geological environment, since progressive water – rock interaction involves simultaneous increase in aqueous cation concentrations from the minerals and decrease in hydrogen ion concentrations (Arnórsson 1999). The lines on stability diagrams represent the alteration of one mineral to another, based on thermodynamic data. A good discussion of the construction of mineral stability diagrams is provided by Faure (1998).

Tardy (1971), Freeze and Cherry (1979) and Appelo and Postma (1996) note that consideration of pure end-member minerals is a simplification of real situations, where solid solutions are more abundant than pure minerals, and due to the slow reaction kinetics of most silicate minerals, it is uncertain whether true equilibrium is ever attained. Silicate stability diagrams can, however, give an understanding of the likely stability relations between minerals and water (Garrels 1976; Appelo & Postma 1996).

The methods described above to assess groundwater solutions in terms of mineral solubility have been widely adopted. For example, Tardy (1971) examined the composition of surface waters from crystalline massifs in Europe and Africa using silicate stability diagrams, and Rogers (1989) used stability diagrams to present groundwater chemistry data from silicate and carbonate rocks from areas of New England, New York and Pennsylvania, U.S.A. In a hydrogeochemical study of a sandstone and carbonaceous claystone aquifer system in the Lethakend – Bothapatlou area at the fringe of the semi-arid Botswana Kalahari, Beekman and Salaolo (1994) also used methods such as assessment of activity ratios. In that study, WATEQ4F (Ball & Nordstrom 1991) was used to calculate mineral saturation states along a flow path, and compared with the mineral composition (using XRD analysis) of end-of-flow-path claystone samples.

Calcium, sodium and magnesium aluminosilicate stability diagrams and saturation indices for some primary silicate minerals, as well as some secondary minerals, have been used to describe the controls on the composition of the Atherton basalt groundwaters in PAPER 3 and APPENDIX IV.

32 10

8 Albite ] + 6 M on ]/[H tm Na + - ori llo 4 n Gibbsite Quartz ite

Log [Na Kaolinite 2 Amorphous Silica

0 -5.0 -4.5 -4.0 -3.5 -3.0 -2.5

Log [H4SiO4]

Figure 2. Na2O-Al2O3-SiO2-H2O system at 25 °C and 1 atm. [ ] denotes acivity

MASS BALANCE APPROACH

The incongruent dissolution of some silicate minerals (whereby the ratio of the elements which appear in solution differs from that in the dissolving mineral) has made the use of mass balance calculations, which relate changes in water chemistry to the dissolution or precipitation of minerals, particularly useful (e.g. Plummer & Back 1980; Velbel 1986; Wood & Low 1986). The solution of mass balance calculations is based on the general reaction shown in Equation 22. For a rock consisting of a mixture of minerals, the contributions of different weathering reactions to the water composition may be determined (Appelo & Postma 1996). initial solution + reactant minerals → final solution + weathering residue (22)

In an important study relating groundwater chemistry to silicate weathering reactions in a granitic area of Sierra Nevada, U.S.A., Garrels and Mackenzie (1967) used a mass balance calculation approach. This is considered a ‘classic’ study using the mass balance approach to an investigation of mineral – water interactions and is discussed, for example, by Garrels (1976), Freeze and Cherry (1979), Appelo and Postma (1996), Drever (1997) and Faure (1998). Using the compositions of snow and ephemeral spring waters, Garrels and Mackenzie (1967) calculated the ‘rock weathering’ component of the major ion concentrations of the spring waters. Based on the average composition of the minerals found in the area, they accounted for the + 2+ 2+ + - Na , Ca , Mg , K , HCO3 and the bulk of the SiO2 concentrations through the stepwise reaction of plagioclase, biotite and K-feldspar to kaolinite. Other examples of the mass balance approach to investigating natural water systems are presented by Plummer and Back (1980).

33 The mass balance approach to the investigation of groundwater has a number of limitations that should be considered, as outlined by Appelo and Postma (1996). They are summarised below:

ƒ the solution of mass balance equations is not necessarily unique, and therefore, a mass balance calculation does not prove that particular reactions take place, although through a series of elimination processes, there often remains a single reaction model that is consistent with the observed data (Plummer & Back 1980; Plummer et al. 1983; Wood & Low 1986),

ƒ there are no thermodynamic constraints on the calculations, and the calculations do not consider what is kinetically consistent,

ƒ mass balance calculations assume steady state conditions. Variations in groundwater composition along a flow path are assumed to be due to reactions with minerals and not, for example, due to temporal variations in recharge water composition, and

ƒ a homogeneous reaction between the points of analysis is assumed.

Careful interpretation of the mass balance calculations and consideration of alternative reaction schemes is therefore required. In addition, for a thorough examination of the controls on groundwater composition, factors other than mineral weathering should be taken into consideration. These include evaporation, ion- exchange and redox reactions, input from dry deposition and possible biomass input and uptake.

The mass balance approach to describing the controls on groundwater composition is an inverse method of modelling aquifer geochemical interactions (Deutsch 1997); both inverse and forward modelling methods are discussed in the following section. A mass balance approach has been applied to this study to define the likely mineral weathering processes controlling the composition of the basalt groundwaters (PAPER 3). Hydrochemical models such as BALANCE (Parkhurst et al. 1982), NETPATH (Plummer et al. 1994) and PHREEQC (Parkhurst 1995) can be used for mass balance calculations. PHREEQC (version 2) has been utilised in this capacity in the study of basaltic water – rock interaction processes (PAPER 3).

HYDROGEOCHEMICAL MODELLING

In addition to the calculations described above, which can be computed with hydrogeochemical models, computer programs such as PHREEQC (an improvement on the PHREEQE hydrogeochemical model developed by Parkhurst et al. (1980)) can be used to calculate how a water composition changes in response to reactions and to test hypotheses. Hydrogeochemical models can also be used to compare field or laboratory data with geochemical model calculations, and to validate numerical procedures and reaction schemes (Appelo & Postma 1996).

The types of chemical reactions that can be modelled include gas exchange, ion speciation, ion complexation, oxidation and reduction, mineral dissolution and

34 precipitation, and adsorption and desorption reactions (Deutsch 1997). Data (or components) used to model a particular aquifer setting may include pH, Eh (or pe), temperature, solution composition and aquifer mineralogy.

The modelling of aquifer geochemical interaction can be considered in terms of inverse or forward approaches. Where there is sufficient data to define a groundwater flow path and changes in groundwater composition along the flow path, then an inverse method (mass balance approach) can be used to account for the change in solution composition based on the dissolution or precipitation of reactive phases (Deutsch 1997). With the inverse approach the phases and solute constraints necessary to produce the observed changes are specified (e.g. Fryar et al. 2001). Mass balance computer codes that can be used for inverse modelling include BALANCE (Parkhurst et al. 1982), its successor NETPATH (Plummer et al. 1994), and PHREEQC (Parkhurst 1995).

The forward method of geochemical modelling can be used to predict the composition at a down gradient location based on natural geochemical processes along a flow path, or to determine how an aquifer system will respond to the addition of a reactant or some change in conditions (Deutsch 1997). In the forward method, reactants are added to a starting solution, which may be open to gas exchange (e.g. in the vadose zone) or closed to gas exchange (in the saturated zone) (e.g. Fryar et al. 2001). The most commonly used computer codes for forward-reaction modelling are PHREEQC and MINTEQ (Felmy et al. 1984).

Descriptions and comparisons of forward and inverse methods of geochemical modelling are given by Plummer (1984, 1992), Parkhurst and Plummer (1993) and Nordstrom and Munoz (1994). The capabilities of the modelling codes noted above, as well as other programs, are described by Nordstrom and Munoz (1994), Appelo and Postma (1996) and Deutsch (1997). Reviews of aqueous geochemical models, including transport models, include those by Nordstrom et al. (1979), Grove and Stollenwerk (1987), Engesgaard and Christensen (1988), Mangold and Tsang (1991), Plummer (1992) and Parkhurst and Plummer (1993). The program used for this research investigation, that is, PHREEQC (version 2), is a computer program written in the C programming language, and is designed for low – temperature aqueous geochemical calculations of speciation, phase distribution, and reaction paths (Nordstrom & Munoz 1994; Parkhurst & Appelo 1999).

Recent examples of hydrogeochemical modelling approaches to the assessment of the controls on groundwater composition in various lithological settings include those by Frengstad and Banks (2000), Pereira and Almeida (2000) and Martínez and Bocanegra (2001).

35 Related hydrogeochemical studies and scope for further research

One of the earliest examinations of hydrogeochemical processes was undertaken by LeGrand (1958), in an assessment of the chemical character of water in the igneous and metamorphic rocks of North Carolina. In a study of two suites of rocks approximating granite and diorite in composition, LeGrand (1958) showed that lithological determinations based on the chemical character of groundwater are generally reliable in regions of similar climate and topography. Further, anomalies in dissolved mineral constituents that are not due to differences in rock type, climate, or topography may indicate either abnormal structural conditions, or the presence of concentrated mineral deposits. With respect to geological structure, it has been proposed that groundwater is not chemically affected by the type of openings, but rather by the chemical character of the rock and by the length of time it is in contact with the rock (LeGrand 1958). Accordingly, only those structures that cause the circulation of water to be greatly accelerated or retarded might be detected by an appraisal of the chemical character of the water.

A correlation was similarly shown to exist between the physical and chemical characteristics of groundwater and lithology in a study in São Paulo, Brazil (Szikszay et al. 1981). Differences in hydrochemical type were attributed to lithological variations in the four main rock types, comprising granitic rocks, sediments, volcanics, and sandstone interbedded with basalt. This study may have been significantly enhanced through a more comprehensive examination of water – rock interaction processes. Szikszay et al. (1981) were able, however, to show that hydrochemical anomalies in the shallow aquifer in São Paulo can be used as an indicator of the presence of ascending deep water through fractures of fault zones.

Water – rock interaction processes have been widely investigated through hydrochemical studies in a range of lithological settings, including for example, sedimentary rocks and metabasalts in the Catoctin Mountains, Maryland (Katz et al. 1985; Katz 1989), schist in the Piedmont Province of Maryland (Bricker et al. 1968), granite and other felsic rocks in the Bohemian massif of the former Czechoslovakia (Paces 1972), and granitic rocks in Stripa, Sweden (Nordstrom et al. 1989; Grimaud et al. 1990; Waber & Nordstrom 1992), Vedée, France (Beaucaire et al. 1995), Cornwall, England (Edmunds et al. 1984; Smedley 1989), the Taejon area, Korea (Jeong 2001a) and the Loch Vale Watershed of Colorado (Mast & Drever 1990). Such investigations in basaltic provinces, however, are limited (e.g. Deutsch et al. 1982; Wood & Low 1986; Davis et al. 1989; Benedetti et al. 1992, 1994, 1999) and there is considerable scope for further research into the role of basaltic water – rock interaction on the control of groundwater composition, and the use of this knowledge in identifying the nature of groundwater occurrence, flow directions, and recharge and discharge areas.

The interaction of meteoric waters with basalts has received far less attention than in granitic, rhyolitic, metamorphic and mixed terrains (Gíslason & Eugster 1987b). Geothermal waters of meteoric origin associated with basaltic rocks have been studied by Ellis and Mahon (1964, 1977), Arnason (1977), Ármannsson et al. (1982), Arnórsson et al. (1983a,b), Olafsson and Kristmannsdóttir (1989), Cox and Browne (1998), and Andrésdóttir and Arnórsson (1999). The alteration of basalt by seawater

36 has been studied by Bischoff and Dickson (1975), Mottl and Holland (1978), Seyfried and Mottl (1982) and Chandrasekharam et al. (1989).

Dilute meteoric waters, however, respond more readily than seawater to dissolution and precipitation reactions and, thus, they can be used as geochemical probes to define the nature of such reactions (Gíslason & Eugster 1987b; Benedetti et al. 1994, 1999). Laboratory studies have been conducted to define the rates of dissolution of basaltic rocks and their dependence on temperature, solution composition and on crystallinity (e.g. Gíslason & Eugster 1987a; Gíslason et al. 1993). These laboratory studies were undertaken in conditions duplicating weathering conditions in north-east Iceland. A field study in north-east Iceland (Gíslason & Eugster 1987b) involved sampling and analysis of rain, snow, melt water, cold springs, hot springs and geothermal fluids, derived from meteoric inflow. Processes of solute acquisition, saturation states of the waters with respect to primary and alteration minerals, reaction paths, reaction progress and mass transfer were defined. Equations were derived which relate residence time to active surface area, characteristic rock particle radii, characteristic crack widths and hydraulic conductivities of aquifers (Gíslason & Eugster 1987b).

A study of mineral-solution equilibrium conditions in basaltic groundwater systems in Iceland has also been undertaken by Arnórsson (1999), who notes that the sparse vegetation, and therefore, minimal dissolved CO2 from organic sources, means that limited reaction is required with the reactive basaltic minerals to saturate the water with secondary minerals, such as calcite and epidote at low and high temperatures, respectively, that subsequently precipitate from the water. In those groundwaters, equilibrium is closely approached with the secondary minerals at temperatures as low as 40 °C (Arnórsson et al. 1983a) and in extreme cases at 10 – 20 °C (Arnórsson 1999). Gíslason and Eugster (1987b) also note that in north-east Iceland, after initial saturation with atmospheric CO2, dissolution and precipitation reactions in the waters take place sealed off from the atmosphere and without significant CO2 contributions from soils.

Significant differences in field conditions between north-east Iceland and the study area of the Atherton Tablelands, North Queensland, may contribute to substantial differences in basalt dissolution rates, groundwater saturation states with respect to primary and alteration minerals and reaction paths. These differences in field conditions include climatic variation (particularly temperature) and differences in weathered zone and soil profile development. The weathered zone in the Atherton Tablelands region is commonly up to 30 m deep and soil profiles are well developed. There is also dense vegetation in some areas of the Tablelands, and CO2 contributions from the soils may be substantial.

A conceptual model for the chemical evolution of groundwaters of the Columbia River Plateau is described by Hearn et al. (1990), in terms of the progressive changes in groundwater chemistry with increasing residence time, and the composition of secondary mineral phases. Hearn et al. (1990) propose that the evolution of these groundwaters is determined primarily by the hydrolysis of volcanic glass and pyroxene, and the stoichiometry of secondary alteration products.

37 In humid tropical climates, soil profiles develop rapidly on basalt, due to the higher reactivity of basalt minerals compared to other siliceous rocks. In the Atherton Tablelands region, where deep weathering profiles are common, the groundwater composition may be significantly influenced by the mineralogy of alteration products in the weathered zone.

In an assessment of groundwater quality in the weathered Deccan basalt of the Malwa Plateau, India, a relationship between the thermodynamic stability of the soil minerals and groundwater composition was identified (Lunkad & Raymahashay 1978). This work was based on the general conclusion that basalt weathers to kaolinitic red soils under good drainage conditions and weathers to montmorillonite– rich black soils where drainage is poor. Lunkad and Raymahashay (1978) used methods developed by Garrels (1967) and Garrels and Mackenzie (1967, 1971) who showed that formation of montmorillonite by CO2 leaching of plagioclase results in a - groundwater HCO3 /SiO2 mole ratio of more than three, compared with values less than three for kaolinization. Kaolinization of an ideal augite similarly results in a - HCO3 /SiO2 mole ratio equal to six.

An investigation of the hydrochemistry and weathering products of Vietnam Shelf Islands, including the basalts of Re Island, indicated that groundwater is in equilibrium with weathering products and is not in equilibrium with primary aluminosilicates of the parent rocks (Shvartsev 1978; Korotky et al. 1995). The groundwater compositions reflect rapid water circulation and drainage on steep slopes and the nature of stable minerals in basement rocks.

A general qualitative summary of the genesis of groundwaters from igneous rocks was given by Garrels (1967), which included an assessment of the correlation between silicate mineral compositions, and water compositions and alteration products. Several useful diagrams showing the relationships between water compositions (using mole ratios and concentrations) and theoretical mineral weathering reactions are presented by Garrels (1967); some of these diagrams were applied to this research project (PAPER 3). Garrels (1976) again later emphasized the importance of the determination of the chemical composition of waters and correlation with the compositions of water-bearing rocks. As discussed above, however, such studies of meteoric waters in basaltic terrain are limited, particularly in tropical and subtropical environments.

A study of the aqueous geochemistry and diagenesis of the basalt aquifer system of the eastern Snake River Plain, Idaho, was undertaken by Wood and Low (1986). The low-temperature aqueous geochemical approach applied to this study provided a useful basis for the study of the Atherton Tablelands basalt aquifer system. The aquifer system of the eastern Snake River Plain is comprised of olivine basalt and is in a semi-arid environment (average precipitation of approximately 300 mm / yr) with an average annual temperature of approximately 8 °C, which is markedly different to the climatic setting of the Atherton Tablelands. In addition, the predominant clay mineral in the eastern Snake River Plain basalts is a calcium- smectite (Wood & Low 1986), in comparison to the dominant kaolinite presently forming in the Atherton region. Wood and Low (1986) used a mass-balance approach to determine the amount of solutes generated and precipitated in the

38 aquifer, and thermodynamic arguments to identify specific solute sources and sinks. They were able to identify and quantify the reactions controlling solute concentrations in the groundwaters and showed that the aquifer is not inert, but is undergoing active diagenesis and is both a source and sink for solutes (Wood & Low 1986).

A study of the mobility and fluxes of major, minor and trace metals during basalt weathering and groundwater transport at the Mount Etna volcano, Sicily, was undertaken by Aiuppa et al. (2000). The mineralogy and chemistry of the weathering profiles were studied to assess the geochemical mobility of elements during basalt weathering, in the manner similarly undertaken by Carr et al. (1980) and Nesbitt and Wilson (1992). The low temperature, meteoric groundwaters of the basaltic Etnean aquifer are strongly affected by the input of magma-derived CO2, and the subsequent dissolution of the primary minerals olivine, clinopyroxene and plagioclase (Aiuppa et al. 2000).

The effects of basalt weathering have also been examined by Hill et al. (2000) in a study of the Tertiary basalts overlying the Ulster White Limestone Formation of Northern Ireland; the changes in whole rock chemistry and mineralogy from basalt through to laterite and iron-rich crust were examined. Hill et al. (2000) observed that primary olivine, plagioclase feldspar, and augite were successively weathered and replaced by a mineral assemblage consisting of hematite, gibbsite, goethite, anatase, meta-halloysite and kaolinite. All of the elements for which the mass balance could be calculated were depleted in the iron crust, with enrichment of only Al, LOI, Cr, Cu and V in the laterite horizon (Hill et al. 2000). The groundwater chemistry of the Tertiary olivine basalts in Northern Ireland was examined by Barnes and Worden (1998), who proposed that the chemical breakdown of feldspars, releasing Na+ and adsorbing Mg2+ following the formation of smectite, and cation exchange of Na+ for Mg2+ at exchange sites on clay, are possible influences on the evolution of the basaltic groundwaters.

Benedetti et al. (1994) notes that studies of water – rock interactions under tropical humid conditions are rare. Benedetti et al. (1994) examined solute acquisition by meteoric waters in the basaltic area of Ribeirão Preto (Paraná, Brazil) to quantify the water-basalt interaction, to address the impact of biomass on weathering and to estimate the age of the weathering processes affecting the basalts under humid tropical conditions. Studies of basalt weathering processes have also been undertaken in a humid tropical environment by Benedetti et al. (1999) and Gérard et al. (1999). These studies examined the effects of the early stage of weathering on soils, streams and spring waters around the active volcano, Mount Cameroon, Africa. In an approach somewhat similar to that taken in this research project, Benedetti et al. (1999) observed relationships between alkalinity and altitude, alkalinity and dissolved silica, and the sum of the cations and alkalinity; they used these relationships to infer the time of interaction between water and basalt, and also proposed that the aqueous major element concentrations are controlled by weathering reactions. Benedetti et al. (1999) concluded that the aqueous Si4+ and Al3+ concentrations in that area are controlled by amorphous mineral phases, mostly amorphous Fe-hydroxides and Al-rich allophanes. Gérard et al. (1999) proposed that these secondary phases are related to the weathering of glass, which represents a

39 significant proportion of the tephra materials in the Mount Cameroon area. The weathering of glass, characterized by the release of Si4+, Mg2+, Ca2+, Na+ and K+ to solution, is the major control on the stream and spring water compositions around Mount Cameroon (Benedetti et al. 1999; Gérard et al. 1999).

Climates with ‘moderately high precipitation’, defined by Davis and DeWiest (1991) as in excess of 50 inches (1270 mm) / yr, such as parts of North Queensland, southern India, north-eastern South America, and parts of eastern central, central and western Africa, usually have marked wet and dry seasons. These areas can therefore be affected by shortages of water for domestic and agricultural purposes during severe dry seasons (Davis & DeWiest 1991). Studies of groundwater systems are therefore important for sustainable management of groundwater resources in these environments. There is scope for further research into the controls on groundwater composition as a means of improving the understanding of groundwater occurrence and movement in aquifer systems in subtropical and tropical environments.

40 Statistical methods for hydrochemical data assessment

BACKGROUND

Variations in water chemistry variables are often the result of complex interactions as discussed above. It is therefore useful to examine the relationships between two or more variables to gain an understanding of the processes affecting a groundwater system (Steinhorst & Williams 1985). An understanding of the chemistry of natural waters may be approached through an investigation of statistical associations between dissolved constituents and environmental parameters (such as lithology), enabling a deduction of cause-and-effect relationships (Drever 1997). The most widely used statistical technique in geochemistry is R-mode factor analysis (discussed below), which is recommended, for example, for identifying groundwaters sourced from different lithological formations based on variations in chemical composition (Drever 1997).

Some descriptive statistical approaches for examining water chemistry data, and several methods of multivariate data analysis useful in hydrology and hydrochemistry are discussed below. In addition, methods of data validation and data preparation, such as transformation (to normalise) and standardization procedures are addressed.

DATA DISTRUBUTION AND VALIDATION

Some summary measures of the distribution of data (e.g. the arithmetic mean, median, mode and harmonic mean) and the dispersion of data (e.g. the variance, standard deviation and range) are described by McBean and Rovers (1998). Another useful summary measure is the coefficient of variation, which is used to describe the relative amount of variation in a population. The sample estimate of the coefficient of variation (COV) is defined in Equation 23 as:

S COV = (23) x

where S is the standard deviation and x is the mean.

Most statistical methods of data analysis require some conformity of distribution, usually the normal distribution (Jöreskog et al. 1976; Reimann and Filzmoser 2000), and some methods also assume multivariate as well as univariate normality. Multivariate normality is the assumption that all variables and all combinations of variables are normally distributed. Multivariate normality also implies that the relationships between the variables are linear. The correlation coefficient, which is central to R-mode factor analysis, for example, will be adversely affected if the variables have highly skewed distributions.

The normal (or Gaussian) distribution is based on the Central Limit Theorem, that is, any variable that can be regarded as the sum of a large number of small independent

41 contributions is likely to follow the normal distribution (Rock 1988; McBean & Rovers 1998). The normal distribution is symmetrical about its mean, with tails that extend to both positive and negative infinity, and the bell shaped algorithm can be defined by two parameters, the mean and the standard deviation. There are many other statistical distributions found in water chemistry and environmental studies. A summary of the normal, lognormal, bimodal, Poisson, exponential and Pareto distributions, as well as several other distributions, is provided by McNeil (2002).

Most variables in the earth and natural sciences have an asymmetric distribution (Reyment 1971; Rock 1988; Goovaerts 1997). The lognormal distribution, for example, is common for many types of hydrological and chemical data, that is, the data are positively skewed with no negative values (McBean & Rovers 1998). The lognormal distribution for geochemical data was proposed by Ahrens (1953, 1954a, 1954b, 1957), although this idea was criticized, for example, by Aubrey (1954, 1956), Chayes (1954), Miller and Goldberg (1955) and Vistelius (1960). The normal and lognormal distributions in geochemistry are discussed by Reimann and Filzmoser (2000), who propose that distributions are rarely normal or lognormal. While the logarithmic transformation is commonly applied to hydrological data, as discussed below, a careful examination of the data should always be undertaken to determine the distribution type and appropriate transformation method.

To assess for normality, an examination of skewness and kurtosis as well as the coefficient of variation is usually undertaken (Rummel 1970). Skewness is related to the symmetry of the data distribution and kurtosis to the peakedness of the distribution. McBean and Rovers (1998) state that the normal distribution has a skewness of zero, a kurtosis coefficient value of three and a COV less than 0.5 or 1.0, with a COV exceeding 1.0 strongly indicating that the data are not normally distributed.

Other tests for normality include “goodness-of-fit” tests, such as the chi-square, the Shapiro-Wilk for small sample sizes (< 50) and the Shapiro-Francia and Kolmogorov-Smirnov tests for larger sample sizes (> 50). These tests for normality are described by McBean and Rovers (1998) and in many other texts dealing with the statistical treatment of data. Histograms and probability tests can also be used to confirm normality.

Univariate and multivariate outliers also need to be removed from data, due to their strong influence on the calculation of correlation coefficients, which in turn influences, for example, the calculation of components or factors in principal component analysis or factor analysis. In addition, tests for linearity, such as graphical analyses of pairs of variables should be undertaken prior to most statistical procedures (Jöreskog et al. 1976). Thorough explanations of methods for detecting outliers and procedures for validating data are provided by McNeil (2002).

Another important measure is the correlation coefficient, a measure of the strength of association between two variables measured on a number of individuals (Jöreskog et al. 1976; Rollinson 1993). The parametric Pearson product-moment coefficient of linear correlation makes considerable assumptions about the nature of the data assessed, that is, that the units of measurement are equidistant for both variables,

42 there is a linear relationship between the variables, and both variables are normally distributed (Rollinson 1993). In some circumstances, therefore, a non-parametric approach such as the Spearman rank correlation coefficient is more appropriate.

Most of the statistical methods discussed in this literature review are parametric, assuming that the data are normally distributed. This is generally not the case in the earth and natural sciences, as has been discussed. Rock (1988) notes, however, that parametric tests are robust, that is, not too seriously affected by departures from normality. Alternative non-parametric tests include the Runs test, the Wilcoxon test, the Mann-Whitney test, the Kruskal-Wallis test and the Spearman rank correlation coefficient. A discussion of some non-parametric tests, for use specifically with water quality data, is provided by Lettenmaier (1976). An example of a hydrogeochemical study in which non-parametric methods, such as the Wilcoxon and the Mann-Whitney tests, have been used is that by Korkka-Niemi (2001).

TRANSFORMATION AND STANDARDIZATION METHODS

Transformations are used to normalise data, to reduce the influence of extreme values or outliers (Goovaerts 1997), and to remedy failures of linearity and homoscedasticity (the variability of one variable for a given value of another) (Rummel 1970).

A transformation of data should not be considered as a stratagem to make the data fit some preconceived notion. Logarithms of amounts, for example, are no less reasonable than amounts expressed in mg/L or ppm, and experience indicates that logarithmic values more closely reflect natural distributions of variables (Jöreskog et al. 1976). In addition, the logarithmic (as well as the square root) transformation is monotonic, and so therefore preserves the rank of the original data in the cumulative distribution (Goovaerts 1997).

Two transformations, the logarithmic (natural or Briggsian) and the square root, are commonly applied to skewed distributions, which pull in the tail of the distribution by reducing the arithmetic size of the intervals as the values increase (Jöreskog et al. 1976). Other transformations include the reciprocal, squared, exponential, rank and arcsine. Descriptions and uses of these and other transformation methods, as well as examples from published literature, are provided by McNeil (2002). An analysis of the use of transformation was undertaken by Bartlett (1947) and good discussions of distribution types and transformation methods are provided by Rummel (1970) and Hirsch et al. (1991).

The logarithmic transformation is widely considered as the most appropriate transformation method in studies in hydrology (e.g. Leopold 1962; Wallis 1965; Reeder et al. 1972; Miller & Drever 1977; Symader & Thomas 1978; Williams 1982). Wallis (1965) does point out, however, that each data set should be carefully analysed to determine the most appropriate transformation method. In addition, the effect of the transformation of the data should also be examined and should not be assumed to have the effect of normalizing the data. Reyment (1971), for example, found that a logarithmic transformation of some natural data resulted in improved

43 univariate kurtosis, but in greater skewness of the data. In a factor analysis of some hydrochemical data, Ruiz et al. (1990) found that if the logarithms of concentrations are used instead of the actual values, the communalities calculated are much lower, indicating that the correlation is not improved by the transformation of those data. Rummel (1970) also recommends that the adequacy of a transformation be assessed, a procedure that has been undertaken for this study (PAPER 2).

The transformation method found to be the most appropriate for this study (PAPER 2) was the natural logarithm ln (x + 1). This method has also been used by Hitchon et al. (1971) and Steinhorst and Williams (1985) to normalise hydrochemical data.

The simplest procedure to achieve multivariate normality is to transform each variable using the appropriate univariate technique as discussed above, and although this approach does not guarantee multivariate normality, it does increase the likelihood (Jobson 1992). Another approach for detecting multivariate outliers, that is, the Mahalanobis distance measure, is discussed by Dillon and Goldstein (1984) and Jobson (1992). Important references on multivariate normality include Mardia (1970) and Reyment (1971).

Many types of geological and natural resources data are also problematic in that they are compositional. That is, the data are expressed, for example, as %, ‰ or ppm, which sum to a constant value (Jolliffe 1986; Reyment & Jöreskog 1996). This type of data is termed closed (Rock 1988) as it forms a closed array (Rollinson 1993). The principal consequence of ‘closure’ (or the ‘constant sum effect’) is that correlations can produce misleading results (Rollinson 1993). The ‘constant sum effect’ is addressed by Aitchison (1982, 1983, 1984) in a series of detailed papers. Aitchison (1986) proposes that log-ratios of the variables should be calculated to free closed data from their restricted space (or ‘simplex’), with the preferred transformation being the centred log-ratio, where the divisor is the geometric mean of each variable. This method is discussed and supported by Jolliffe (1986) and Reyment and Jöreskog (1996), although an example of a principal component analysis using both raw and centred log-ratio transformed data provided by Jolliffe (1986), shows that there were little differences between the two sets of results. The applicability of the centred log-ratio transformation to the hydrochemical data used in this study was examined in PAPER 2.

The implications of ignoring the distribution of data prior to using statistical analytical methods requiring normality should not be underestimated. For example, in an assessment of a study by Salman and Abu Ruka’h (1999), Szava-Kovats (2000) proposed that a log-ratio transformation should have been applied to that data, to eliminate the effects of closure and negative correlation between variables. A lack of consideration of issues pertinent to compositional data and of the requirement of factor analysis for univariate and multivariate normality by Salman and Abu Ruka’h (1999) resulted in spurious interpretation of the factor analysis results (Szava-Kovats 2000).

44 Standardization refers to converting original data to standard normal deviates or z scores by centring them about their mean (x) and rescaling them by the reciprocal of their standard deviation ()S (Steele & Matalas 1971; Jöreskog et al. 1976; Steinhorst & Williams 1985; Rock 1988; Auf der Heyde 1990; Briz-Kishore & Murali 1992; Hussein & Adam 1995; Join et al. 1997) as shown in Equation 24, where

x − x z = (24) S

Standardizing combines the effects of column normalization and column centring, and is appropriate in studies where the variables are measured on different scales and in different units (Steinhorst & Williams 1985; Schot & van der Wal 1992; Reyment & Jöreskog 1996), as is the case in this study (PAPER 2).

MULTIVARIATE DATA ASSESSMENT

Multivariate data analysis is concerned with analyzing multiple measurements on one or several samples (Cooley & Lohnes 1971). Graphical methods (such as Piper, Stiff and Schoeller diagrams), principal component analysis and factor analysis are widely used methods of multivariate data analysis in hydrochemical studies. In both principal component analysis and factor analysis, the main aim is to replace the original variables with a smaller number of underlying variables, that is, to reduce the dimensionality of a variable set (Dunteman 1989). Principal component analysis consists in finding an orthogonal transformation of the original variables to a new set of uncorrelated variables, called principal components (Chatfield & Collins 1980). That is, principal component analysis reduces the number of variables to a smaller number of principal components that are linear combinations of the original variables. Factor analysis has similar aims to principal component analysis, but is based on a linear statistical model, which specifies a number of underlying variables called factors (Chatfield & Collins 1980).

Many modern multivariate studies of water chemistry data use a variety of methods. A combination of multivariate methods have been used by van Tonder and Hodgson (1986), including cluster analysis, principal component analysis and discriminant analysis, to define hydrochemical facies in groundwater. It has also been noted by McNeil (2002) that whichever multivariate technique is selected as the primary analytical tool, it is advisable to support it with another method for verification. In this study (PAPER 2 and APPENDIX V) principal component factor analysis has been used to identify the likely hydrochemical processes controlling groundwater composition in the Atherton Tablelands region. Principal component factor analysis has also been used as a form of discrimination, as defined by Kendall and Stuart (1966), to allocate unknown samples to defined groups (PAPER 2 and APPENDIX V), and cluster analysis has been applied to the results to confirm groupings (PAPER 2).

Some graphical methods for multivariate data analysis, principal component analysis, factor analysis, principal component factor analysis and cluster analysis are discussed below and examples of applications of these methods provided.

45 Particular emphasis is placed on the application of these methods to hydrological and hydrochemical studies and avenues for further application of these methods are identified. Some limitations of factor analytical procedures are also discussed, and some additional multivariate data analysis methods, that is, multiple regression analysis, discriminant analysis, canonical correlation analysis, common principal component analysis, correspondence analysis and classification schemes are briefly addressed.

Graphical methods

Hydrochemical data are commonly displayed in graphical form. Many graphical forms have been proposed, including X-Y scatter plots, bar graphs (e.g. Collins 1923), pie diagrams and nomographs (Freeze & Cherry 1979; Hem 1985; Davis & DeWiest 1991; Domenico & Schwartz 1998). The use of graphical methods to interpret water chemistry data is discussed by Zaporozec (1972). Several of the methods described below have been used in this study.

The most widely used graphical method of displaying hydrochemical facies is the Piper diagram (Piper 1944), similar to that developed by Hill (1940), which is an effective tool for segregating analytical data, with respect to sources of dissolved constituents, modifications in the character of water along a flow path, and related geochemical problems. The procedure is based on a multiple trilinear diagram, commonly displaying the percent concentrations of the major dissolved constituents 2+ 2+ + + - 2- 2- - (i.e. Ca , Mg , Na , K , HCO3 , CO3 , SO4 and Cl ). Recent examples of the application of the Piper diagram to hydrochemical studies include those by McKenzie et al. (2001), Naik et al. (2001) and Umar et al. (2001).

The Durov (Durov 1948) and the Expanded Durov (Lloyd 1965; Lloyd & Heathcote 1985) diagrams are also widely used forms of trilinear graphical representation for hydrochemical data (e.g. Lawrence et al. 1976). The Durov diagram can be used to classify water into types (or facies), to determine the concentration of chemical constituents and total dissolved solids, to study the origin of the chemical composition of water and for correlating chemical analyses (Abdulaziz et al. 1997).

The Stiff (1951) pattern is also used to present chemical analyses, and is useful in making a rapid visual comparison between water from different sources. The Stiff pattern is based on a polygonal shape created from four parallel horizontal axes extending from either side of a vertical zero axis. Cations are plotted in milliequivalents per litre on the left of the zero axis and anions are plotted on the right (Fetter 1994).

Other diagrams used to present hydrochemial data include the Langelier-Ludwig (1942) diagram and the Schoeller (1956) diagram. Cross-sections are also used to display hydrochemical data and interpretations in two, or even three dimensions, which are sometimes referred to as ‘fence diagrams’ (e.g. Back 1961; Amadi et al. 1989; Stein & Schwartz 1990; Schreiber et al. 1999; Stuyfzand 1999; Tóth 1999).

46 Principal component analysis

The technique of principal component analysis was first described by Pearson (1901) in his mathematical methodology of fitting planes by orthogonal least squares, and was later independently developed by Hotelling (1933, 1936a) for analyzing correlation structures. These papers can be found among a collection of papers edited by Bryant and Atchley (1975). A precursor to principal component analysis, however, was the reduction of a square matrix into its singular components (Sylvester 1889). The historical development of principal component analysis, albeit with an emphasis on meteorological applications, is provided by Preisendorfer (1988).

A thorough explanation of principal component analysis, including examples, can be found in Jolliffe (1986), and Rao (1964) provides ideas for uses, interpretations and extensions of principal component analysis. The following quotation by Rollinson (1993) describes the principal component extraction method:

The method defines a new set of orthogonal axes called eigenvectors or latent roots. For instance, the first eigenvector is the direction of maximum spread of the data in terms of n-dimensional space. It is a ‘best fit’ line in n-dimensional space and the original data can be projected onto this vector using the first set of principal component coordinates. The variance of these coordinates is the first eigenvalue or latent root, and is a measure of the spread in the direction of the first eigenvector. For example, eigenvector 1 may be expressed as

= + + Eigenvector1 x1SiO 2 x 2TiO 2 x 3Al2O3...... (25)

+ + where x1 x 2 x 3 etc., define the principal component coordinates.

The method then defines a second eigenvector, which has the maximum spread at right angles to the first eigenvector, and so on. The eigenvalues are used to measure the proportion of data used in each eigenvector. By definition, the first eigenvector will contain the most information and succeeding eigenvectors will contain progressively less information. Thus it is often the case that the majority of information is contained in the first two or three eigenvectors. Eigenvectors and eigenvalues may be calculated either from a covariance matrix (where the variables are measured in the same units, such as wt% or ppm) or from a correlation matrix where the variables are expressed in different units.

Principal component analysis is a mathematical technique, which does not require an underlying statistical model to explain the error structure (Chatfield & Collins 1980). Principal component analysis makes no assumptions about the underlying structure of the variables, and requires no a priori estimates of the communalities (Seyhan 1985). The percent variance of each variable explained by the components or factors is termed the communality; communalities may be interpreted as indicators of the reliability of the components.

There are numerous computer packages available for principal component analysis. Brief descriptions of some of these packages, that is, the BMDP suite of programs, GENSTAT, MINITAB, SAS and SPSS, are provided by Jolliffe (1986).

47 Factor analysis

Factor analysis is the most commonly used multivariate statistical method in hydrology (Seyhan & Hope 1983). The primary objective of factor analysis is to represent a group of variables (or cases) in terms of several factors (Hotelling 1933, 1936a; Rummel 1970; Harman 1976; Morrison 1990). The specific objectives of factor analysis, as outlined by Seyhan (1985) are:

ƒ to study the intercorrelation of a large number of variables (or cases),

ƒ to interpret each factor according to the intercorrelated variables (or cases) grouped under that factor, and

ƒ to use the factors to omit non-significant variables (or cases).

A variant of factor analysis, that is, principal component factor analysis, has been used to meet such objectives in a hydrogeochemical study of the Atherton Tablelands aquifer systems (PAPER 2).

Like principal component analysis, factor analysis accounts for the variation in a number of variables using a smaller number of factors. However, in factor analysis each variable is expressed as a linear combination of factors, plus a residual term that reflects the independence of the variable (Rummel 1970; Mulaik 1972; Manly 1986). That is, any observed variable is assumed to be influenced by some factors which are common to all the variables and by some unique factors; the unique factors being the residual variance that is not explained by the common factors (Seyhan 1985; Morrison 1990).

In principal component analysis the components account for the maximum variance of all the variables, whereas in factor analysis, the factors are defined to account maximally for the intercorrelations of the variables. That is, principal component analysis is variance-oriented; factor analysis is correlation-oriented. The residual terms are assumed to be small in principal component analysis, and a large part of the total variance of a variable is assumed to be in common with other variables. Factor analysis, however, allows uniqueness to be present in the data and utilizes only that part of a variable that is in correlation with other variables (Reyment & Jöreskog 1996). Communalities are therefore estimated before applying a factor analysis.

The concept of latent factors was first proposed by Galton (1888). The early development of factor analysis may, however, be attributed to Charles Spearman, who observed that correlations between test scores could be accounted for by a simple model (Spearman 1904). Spearman (1927) developed a model for two factors, which was later extended to include multiple factors (Thurstone 1935). The term factor analysis was first introduced by Thurstone (1931). The history of factor analysis has been described by Mulaik (1972, 1986).

Equation 26 is a general factor analysis model reproduced after Manly (1986):

48 = + + + + X i ai1F1 ai 2 F2 ...... aim Fm ei (26)

where X i is the ith test score with mean zero and unit variance; ai1, ai 2 ,...... , aim are the factor loadings for the ith test; F1, F2 ,...... , Fm are m uncorrelated common factors, each with mean zero and unit variance; and ei is a factor specific only to the ith test, which is uncorrelated with any of the common factors and has mean zero. With this model

()= = 2 ()+ 2 ()+ + 2 ()+ () var X i 1 ai1 var F1 ai2 var F2 ...... aim var Fm var ei (27)

= 2 + 2 + + 2 + () ai1 ai2 ...... aim var ei (28)

= 2 + 2 + + 2 where ai1 ai2 ...... aim is called the communality of X i (the part of its variance () that is related to the common factors) while var ei is called the specificity of X i (the part of its variance that is unrelated to the common factors).

Factor analysis may be used to explore the underlying dimensions of the data and as a means of data reduction in what is known as exploratory factor analysis, or as a means of testing specific hypotheses about factor loading patterns in confirmatory factor analysis (Dunteman 1989). The division of these uses, however, may not always be clear (Lewis-Beck 1994). There are many types of factor analysis methods as noted by Seyhan (1985). They differ by the procedural steps in:

ƒ the input data matrix (i.e. R-mode analysis of variables or Q-mode analysis of cases),

ƒ the extraction of the factor loadings (methods of initial factor extraction include principal components, principal factors, centroid, least-squares, maximum-likelihood, minres etc.), and

ƒ the type of rotation of the determined factors, such as orthogonal rotation (e.g. varimax, quartimax or equimax methods) or oblique rotation (e.g. promax, oblimax, oblimin or quartimin methods).

It is outside the scope of this literature review to describe each of these methods. Factor analytical methods are well described by Horst (1965), Cooley and Lohnes (1971), Lawley and Maxwell (1971), Mulaik (1972), Rummel (1970), Harman (1976), Davis (1986), Morrison (1990), Briz-Kishore and Murali (1992), Lewis-Beck (1994), Hair et al. (1995), Reyment and Jöreskog (1996) and Rollinson (1993), the latter specifically in terms of geochemical data. In addition, the main factor analytical procedures, notably the effectiveness of extraction and rotation methods, are discussed by Cattell and Jaspers (1967) based on their investigations using a factor plasmode (a set of measurements which fit a particular form or structure).

More detailed discussion of theoretical methods here is limited to those applied in this study (PAPER 2), that is, R-mode analysis of variables using a principal component factor extraction method, the varimax rotation method, as well as the

49 various criteria for selecting the number of factors to be extracted and methods for calculating factor scores.

Some examples of factor analytical techniques applied to the natural sciences are outlined by Reyment and Jöreskog (1996) and include those in the fields of:

ƒ Sedimentary petrology – Osborne (1967, 1969) used factor analysis to group Ordovician limestones on the basis on characteristics determined in thin section, and inferred the paleoecological significances of the factors.

ƒ Stratigraphy – McElroy and Kaesler (1965) interpreted factors from an analysis of sandstone groundwaters in Kansas in terms of subsidence during deposition of the sandstone, regional distribution patterns, and periods of uplift or non-subsidence.

ƒ Geochemistry – Armands (1972) used principal component and factor analysis of the geochemistry of uranium, molybdenum and vanadium in Swedish alum shale to determine the paragenesis of these elements. The categories established were detrital, authigenic, carbonate, sulfide and organic.

Other examples include those by Auf der Heyde (1990) who applied factor analysis to a hypothetical chemical data set, Ratha and Sahu (1993) who investigated soil geochemical variables in an industrial area of Bombay, India, using R-mode factor analysis and Lin (2002) who used factor analysis to relate industrial waste plants and irrigation systems to heavy metal concentrations in soils in Taiwan.

Principal component factor analysis

Principal component factor analysis is one of several models of factor analysis. Alternative approaches include, for example, maximum likelihood factor analysis (Lawley & Maxwell 1971). Reyment (1991) notes, however, that as practised in the natural sciences, factor analysis is essentially a variant of principal component analysis, with some of the features appertaining to classical factor analysis. This variant was termed principal component factor analysis by Jöreskog et al. (1976).

Following the initial steps in preparing the input data matrix (i.e. selection of R-mode versus Q-mode analysis and transformation and standardization of data if required), a similarity matrix is computed to examine the interrelationships among the variables or cases. This similarity matrix is usually a correlation or a variance-covariance matrix. The use of standardized variables and the correlation matrix ( R ) is advisable if the variables have different units (Morrison 1990) and recommended for hydrological studies (Seyhan 1985). Also, while Q-mode analysis is often used in sedimentology, for example, R-mode analysis is most commonly used in studies of water chemistry (Drever 1997).

50 The principal component factor analysis (with iteration) method in SPSS (v.10.0.5) refers to the estimation of factor loadings and transformation of the correlation matrix in order to determine whether some smaller number of factors will explain most of the variance in the original data (Seyhan 1985). The procedure automatically replaces the main diagonal elements of R with communality estimates, with the iteration procedure improving these estimates (Foster 1998).

Transformation of the factor matrix produces a new pattern of factor loadings that are more easily interpreted. The objective of transformation is to simplify the structure of the factor matrix, as only those factors for which the variables have a simple structure are meaningful (Thurstone 1947). The principal of simple structure proposed by Thurstone (1947) is that a variable should not depend on all common factors but only a few, and that each factor should only be associated with a small portion of the variables (Reyment & Jöreskog 1996). Thurstone (1947) provided a number of rules for the determination of simple structure. These are outlined by Mulaik (1972) and in many texts on factor analysis.

Transformation may be achieved by orthogonal axes rotation, resulting in uncorrelated factors. Seyhan (1985) notes that it is generally considered that principal component factor analysis differs from classical factor analysis mainly because the rotations made with classical factor analysis are not necessarily orthogonal (Blackith & Reyment 1971).

Early analytical solutions for factor rotation, including those by Carroll (1953), Saunders (1953), Ferguson (1954b) and Neuhaus and Wrigley (1954), enabled the reduction of Thurstone’s (1947) simple structure concepts to mathematical functions (Seyhan 1985), based on the quartimax criterion. Other orthogonal rotation methods developed by Kaiser (1956, 1958, 1959) known as the raw varimax criterion and the normal varimax (or merely the varimax) have become the most widely used rotation techniques in multivariate statistical research (Jöreskog et al. 1976; Seyhan 1985; Dunteman 1989). The raw varimax maximises the variance of the squared loadings within each column of the rotated factor matrix. Kaiser later modified this approach with the normal varimax (1958), which takes into account the magnitude of the communalities by normalizing the factor loadings. The factor loadings are divided by their corresponding communality value, and following rotation, each factor loading is multiplied by the square root of the respective communality. An outline and examples of varimax rotation are given by Harman (1976). The varimax rotation was quickly applied to studies in hydrology, by Wong (1963) and Wallis (1965), for example, and remains popular in water chemistry studies (e.g. Suk & Lee 1999; Jeong 2001b, Meng & Maynard 2001).

In a principal component factor analysis with orthogonal rotation, factor loadings are equivalent to correlations between factors and variables (Lewis-Beck 1994). Loadings of the same sign on a factor are positively correlated variables; loadings of opposite signs are negatively correlated variables (Drever 1997).

The number of factors calculated for a particular data set is based on the experience and professional judgment of the hydrogeologist, who must then evaluate the implications of the groupings (Riley et al. 1990). Various criteria may be used to

51 determine the number of factors that should be extracted; reviews of the ‘number of factors’ problem have been made by Browne (1968), Linn (1968), Tucker et al. (1969), Hakstian and Muller (1973), Hakstian et al. (1982), and Zwick and Velicer (1982, 1986).

The Guttman (1954) selection rule, supported by Kaiser (1960), proposes that the least number of factors to be retained is that number for which all eigenvalues greater than or equal to one are kept. An eigenvalue of one means that factor has as much variation as one variable. Other methods include the Cattell (1966) scree test, based on the observation that the factor variance levels off when the factors are largely measuring random error, and the LEV rule (Craddock & Flood 1969; Farmer, 1971), an alternative to the scree test, popular in fields such as meteorology (Jolliffe 1986; Preisendorfer 1988; DeGaetano 1996). Jolliffe (1972) proposes that factors with eigenvalues less than 0.7 should be dropped, while some researchers keep enough factors to account for 90 % of the variance in the data.

It is recommended by Seyhan (1985) that several criteria be applied and results compared, to determine the final number of significant factors. As a general rule, extracting too many factors is preferred to extracting too few factors, as underfactoring can distort the factor structure (Rummel 1970; Reyment & Jöreskog 1996). While the above rules of thumb should be applied to extract the ‘right’ number of factors, it is worth keeping in mind that the extensive practical experience of Cattell (1952) and the empirical studies of Mosier (1939) consistently indicate that overfactoring results in better interpretations than underfactoring. For example, although Reyment and Jöreskog (1996) recommend that the number of factors should be limited to those with at least three significant loadings, the need for interpretability may require more factors than this to be retained. Hitchon et al. (1971), for example, have included several unique factors in their R-mode analysis of subsurface brines, as those factors (such as membrane filtration, solution of halite, chlorite formation and cation exchange) represent important processes controlling the geochemistry of those waters.

In terms of a principal component or factor analysis of chemical data, in addition to the considerations noted above, the number of factors extracted must be less than the number of variables. Steele and Matalas (1974) note that if the ionic species were mutually independent, then the number of principal components m, would equal the number of ionic species (variables) n. However, the ionic species cannot be mutually independent because the cations and anions are in balance, and therefore due to co- linearity among the variables, m ≤ n −1.

In R-mode analysis a factor may be regarded as a function of the original variables. By calculating factor scores, the ‘amount’ of these new factors may be determined and examined (Jöreskog et al. 1976). Factor scores are simply calculated for a principal component factor analysis, as outlined by Lawley and Maxwell (1971), Seyhan (1985), Rock (1988) and Buccianti (1997), and for example by Lee (1969) in a principal component analysis of connate waters in northern Taiwan. Known as exact factor scores, they are linear combinations of the variables (Rummel 1970). Factor scores, therefore, express the degree to which each case represents the property or process that the factor defines. Dekkers et al. (1989) also note that an

52 analysis (such as cluster analysis) of component or factor scores, rather than of the raw data, enables an interpretation of hydrogeochemical processes that are described by more than one variable.

The calculation of factor scores for classical factor analysis is more complex. Methods include regression estimates, estimates based on ideal variables (‘least squares’ criterion), the Bartlett (1937) method of minimizing the error variance, and estimates with orthogonality constraints such as the Anderson-Rubin criterion (Anderson & Rubin 1956). These methods are explained by Rummel (1970), Lawley and Maxwell (1971), Harman (1976), Lewis-Beck (1994) and Reyment and Jöreskog (1996). It is worth noting, however, that some authors (e.g. Schönemann & Wang 1972; Velicer 1976) consider that there is little practical difference between true (or exact) factor scores and factor score estimates.

Limitations of factor analysis

Multivariate methods of data analysis are complex, in both their theoretical structure and in their operational methodology. Care must therefore be taken to adhere to the assumptions in these methods, so that the results may be accurately and practically interpreted (Davis 1986).

Limitations on the use of classical and principal component factor analyses are outlined by Ehrenberg (1962), Matalas and Reiher (1967), Wallis (1968), Seyhan (1985) and others. Some of these limitations include:

ƒ factor analysis was originally developed for psychological data. Hydrological data, however, are different in that they are rarely a large random sample taken from a homogeneous population (Wallis 1968),

ƒ the meaning of factors can be vague, as factors are non-observable (Ehrenberg 1962),

ƒ factor analyses are linear functions; non-linear functions may lead to better results,

ƒ an orthogonal rotation may not be appropriate for all hydrological processes, and

ƒ the variables must be selected carefully, otherwise analyses may be misinterpreted. Also, any new variable added to or dropped from the analysis may result in a different grouping of factors (Steele & Matalas 1974).

Anderson (1957) also recommends prior evaluation of issues such as:

ƒ the types of relationships that exist among the variables (such as, are the relationships linear, and if not, can the variables be transformed to form linear relationships?),

ƒ the number and type of factors to be extracted, and

53 ƒ the variables to be considered for analysis.

In the case of a hydrogeochemical study, the number and type of hydrogeochemical processes expected should be considered. In addition, the variables studied should be consistent with the factors expected (Cattell 1952; Reyment & Jöreskog 1996). For example, in a study of formation waters in Alberta, Canada, Hitchon et al. (1971) chose variables that were relevant to the hypothesis that there is a relationship between particular elements and filtration of groundwater though shale membranes.

Some of the prior considerations and limitations of the principal component factor analysis method as applied to the Atherton Tablelands data have been examined and discussed in PAPER 2, and an assessment of the robustness or sensitivity of the analysis undertaken.

Cluster analysis

Like factor analysis, cluster analysis was first developed by psychologists in the 1930’s and 1940’s, and can also be applied to R-mode (variable oriented) or Q-mode (sample oriented) problems (Seyhan 1985). As an exploratory technique, cluster analysis does not require many of the assumptions that other statistical methods do, except that the data be heterogeneous. Cluster analysis provides an easily understood graphic display (dendrogram) and is a method used frequently in the geological sciences to group samples or variables of a data set (Alther 1979; Pacheco 1998; Meng & Maynard 2001).

Useful texts on cluster analysis include those by Tyron and Bailey (1970), Anderberg (1973), Everitt (1980) and Hartigan (1975), the latter including algorithms and computer programs. Davis (1986) discusses cluster analysis with applications for earth sciences and Gong and Richman (1995) provide a review of cluster analysis procedures.

Cluster analysis groups a set of observations into clusters in such a way that most pairs of observations in the same group are more similar to each other than are pairs of observations in two different groups (Jolliffe 1986). Similarity may be expressed through a set of distances between pairs of objects. One such measure of similarity is the Euclidean distance, which is essentially a straight-line distance between the vectors corresponding to the cases (Hartigan 1975). The Euclidean distance between two points is the length of the distance vector and is found by Pythagoras’s theorem from the square root of the minor product moment of the difference vector (Reyment & Jöreskog 1996). The Euclidean distance function is most frequently used for quantitative variables, which are usually standardized prior to calculation, to ensure they contribute equally to the determination of distance (Manly 1986).

Hierarchical methods of cluster analysis produce a dendrogram in which groups are formed by agglomeration or division. In an agglomerative hierarchical method, individuals begin alone in groups of one, and groups that are close together are merged, whereas a divisive method starts with a complete data set and successively divides it (Manly 1986; Rock 1988). There are numerous linkage methods to define

54 closeness, including single (or nearest neighbour) linkage, furthest neighbour (or complete linkage), group average linkage, centroid sorting, Ward’s and weighted group average methods (Chatfield & Collins 1980; Manly 1986).

Examples of the application of cluster analysis to studies in the natural sciences include those by Campbell et al. (1970) in a classification of some Australian soil profiles, Auf der Heyde (1990) in an analysis of chemical data, Gong and Richman (1995) in an assessment of some North American rainfall data, and DeGaetano (1996) who used both principal component analysis and cluster analysis to delineate climatically similar zones in the north-eastern United States. Cluster analysis was also found to be a useful tool in the detection of temporal and spatial patterns of water chemistry in Lake George, north-eastern New York (Momen et al. 1996) and in the discrimination of groundwaters from different geological units in the Gedaref basin in eastern Sudan, based on their chemical compositions (Hussein & Adam 1995).

Alternative multivariate statistical methods

There are many other types of multivariate statistical methods that may be applied to studies in hydrology and hydrochemistry, although the methods described above, that is, principal component analysis, factor analysis and cluster analysis and their variants, are the methods most commonly used.

Multiple regression analysis is based on the concept that an observed variable can be defined as a function of other variables measured at the same time, although not considered by Davis (1986) as a true method of multivariate analysis, as the variance of only one variable is considered. It has been applied, for example, in a study calculating runoff from catchment physiography in South Africa (Seyhan & Hope 1983). One of the major problems of multiple regression with the usual least squares estimators, however, is the problem of multicollinearity, which occurs when there are near-constant linear functions of two or more of the predictor (regressor) variables, that can lead to unstable and misleading estimates of the regression equation (Jolliffe 1986).

Discriminant analysis may be applied to data in which each observation comes from one of several well-defined groups or populations. Assumptions are made about the structure of the populations, and the main objective is to construct rules for assigning future observations to one of the populations (Jolliffe 1986). Discriminant analysis has been used by Villagra et al. (1990), for example, in a comparison of surface waters and hydrothermal springs. Davis (1986) notes that while discriminant analysis is reasonably robust with departures from normality, it is highly sensitive to outliers in the original data. Because of this, and assumptions in the method, McNeil (2002) proposes that discriminant analysis is not suited to the exploratory analysis of large irregular data collections, and may be more suitable to follow-up analyses in which samples are placed in groups established by classification procedures such as cluster analysis.

55 Discriminant analysis may be regarded as a special type of factor analysis that extracts orthogonal factors to show the differences among several groups (Seyhan & Hope 1983). It has been successfully used by Seyhan and Hope (1983) to determine that two catchments in environmentally different regions of South Africa are statistically different in terms of physiography and hydromorphometry. In a manner similar to this approach, the use of a principal component factor analysis to discriminate between basalt- and basement rock-hosted groundwaters in the Atherton Tablelands region, and the establishment of a rule to allocate unknown samples to one of these two groups (PAPER 2), may in fact be regarded as a form of discriminant analysis.

Canonical correlation analysis involves the division of variables into two groups and an examination of the relationships between these groups (Manly 1986). Originally developed by Hotelling (1935, 1936b), the canonical correlation is the maximum correlation between linear functions of the two vector variables (Cooley & Lohnes 1971). The objective is to successively find pairs of linear functions called canonical variates, such that the correlation between them is maximized, while each new pair is orthogonal to all previously derived linear combinations (Cooley & Lohnes 1971; Jolliffe 1986).

An example of an application of canonical correlation analysis is provided by Jeffers (1978) in a study from the north-west of England. Variables of two types were examined; variables measuring chemical or physical properties of sand or mud samples, and variables measuring the abundance of invertebrate species in the samples. Canonical correlation analysis was used to examine the relationships between the two groups of variables describing environment and species. Although not a method applicable to this particular study, one can envisage where canonical correlation analysis could be applied to a hydrochemical study where, for example, the relationships between variables measuring water quality parameters and variables measuring different types of land use are of interest.

Common principal component analysis is one of the newer methods of multivariate analysis, currently mainly applied in the fields of palaeontology, palaeoecology and evolutionary studies (Reyment 1997). Common principal component analysis is a procedure for the simultaneous principal component analysis of several groups. The method is described by Flury (1984, 1988), Krzanowski (1984) and Jolliffe (1986). The concept of common principal components may be applied where it is suspected that the same components underlie the covariance matrices of each group, but that they have different weights in each group (Jolliffe 1986). Bookstein (1991) notes that this method has an advantage over canonical variate analysis, which has a logical insufficiency if group differences are established a posteriori by some clustering technique (Reyment 1997).

The common principal component analysis may not prove likely to be applicable to a great number of situations, however, mainly because data that meet the strict requirements of this method are relatively rare (Reyment 1997). This method has some attraction for the field of hydrochemistry where, for example, the same processes may effect the composition of waters from different geological formations, but to varying extents. Reyment (1997) states that compositional data can be made to

56 fit the common principal component model by the appropriate log-ratio covariance matrix. Reyment (1997) recommends, however, that currently it would be wise to restrict common principal component analysis to multivariate Gaussian (normal) datasets, as the methods for assessing the stability of common principal component latent vectors still needs to be examined. Common principal component analysis may, however, prove to be useful in some hydrochemical studies in the future.

In a statistical analysis of hydrogeochemical data from the alluvial aquifer of Alto Guadalentín, south-east Spain, Cerón et al. (2000) used both principal component analysis and another multivariate statistical method, correspondence analysis, to assess sources of and processes affecting groundwater. Correspondence analysis proved to be more useful than principal component analysis in that study, as principal component analysis was unable to distinguish the three sources of water, due to the influence of extreme values (Cerón et al. 2000).

Although not strictly a statistical method, another multivariate approach to analyzing data is through the use of classification schemes. Classification schemes are commonly used in hydrogeology such as, for example, matrix and rating systems in groundwater vulnerability and risk mapping studies (e.g. LeGrand 1964; Foster 1987; Adams & Foster 1992). They can also be developed to assess hydrochemical data. An example of a classification scheme approach to assessing hydrochemical data and application of the results are presented for this study (PAPER 1B and APPENDIX IV).

57 Application of some multivariate statistical methods to hydrological and hydrochemical studies and scope for further work

BACKGROUND

Studies in hydrology and hydrochemistry include problems involving complex interactions of many variables and processes. In early hydrological studies (e.g. Anderson 1957; Fritts 1962) these problems were approached using multiple regression analysis. However, multiple regression analysis tends to confound independent effects and to build models that are hard to interpret (Wallis 1965; Eiselstein 1967). Comparative results of multiple regression analysis and multivariate analysis are presented by Snyder (1962) for three applications in hydrology. Snyder (1962) and Rice (1967) propose that multivariate analyses, such as principal component analysis and factor analysis, as well as discriminant and canonical correlation analyses are applicable to a variety of hydrological studies. Statistical methods, such as principal component and factor analyses, were later used in hydrological studies upon the general availability of computing facilities (e.g. Wong 1963; Wallis 1965; Dawdy & Feth 1967; Eiselstein 1967; Diaz et al. 1968; Egleson & Querio 1969; Knisel 1970). A recent evaluation of graphical and multivariate statistical methods for the classification of water chemistry data from part of the arid Basin and Range Province of the south-western U.S.A. is presented by Güler et al. (2002).

In multiple factor hydrological problems, Wallis (1965) recommends a principal component analysis with varimax rotation of the factor matrix, and where many observations are available, followed by an object analysis based on cluster groupings. This approach is essentially an initial principal component factor analysis, followed by cluster analysis of the factor scores. A similar approach has been used in a study of the hydrochemistry of the Atherton Tablelands groundwaters (PAPER 2).

The application of multivariate statistical methods to hydrology and hydrochemistry is generally undertaken with the aim of inferring processes controlling water composition and / or identifying different sources of water. Several examples from the literature grouped into these two broad aims are outlined below, and scope for further work is discussed.

HYDROCHEMICAL PROCESSES

Multivariate statistical methods, and factor analysis in particular, are useful for interpreting routinely-collected groundwater chemistry data and relating those data to specific hydrogeological processes (Lawrence & Upchurch 1982). R-mode factor analysis, for example, has been used to discriminate chemical variables that reflect recharge processes from those strictly related to the dissolution of aquifer materials in the karstic Floridan Aquifer in part of north central Florida (Lawrence & Upchurch 1982). Hitchon et al. (1971) have inferred possible chemical and physical reactions that affect the geochemistry of brines using factor analysis of compositional data in the western Canada sedimentary basin. Several other examples where

58 multivariate statistical methods have been used to in infer hydrogeological or hydrochemical processes are given below.

A useful early application of factor analysis to the study of groundwater chemistry data was undertaken by Dawdy and Feth (1967) who studied the chemical analyses of more than 100 groundwater samples from the Upper and Middle Mojave River Valley, San Bernardino County, California. They inferred that the variance in the data was due to sources of sodium chloride, the carbonate-bicarbonate system and other factors. Dawdy and Feth (1967) note that where there is a lack of negative correlations in the factor matrix, that is, an absence of mutually exclusive components, this is an indication that there is no reaction path by which one set of chemical products replaces another set. For example, none of the components or factors is strictly controlled by equilibrium with minerals in the aquifers. This is relevant to the study of the Atherton Tablelands groundwaters, where the factor analysis results for the basaltic groundwaters show few negative correlations (PAPER 2), and therefore indicate that the Atherton basalt groundwaters are not in equilibrium with the dominant mineral phases. The work presented in APPENDIX IV supports this interpretation, with the calculated saturation indices indicating that some of the groundwaters are in equilibrium with the clays, but that none of the groundwaters have yet reached equilibrium with respect to the primary silicate minerals. Dawdy and Feth (1967) also caution that the sources of the major constituents must be inferred from information other than just the statistical associations in the data.

Factor analysis and cluster analysis have been used by Suk and Lee (1999) to identify the hydrochemical processes controlling groundwater composition in gneiss, granite and alluvium aquifers in a small residential and industrial area of Incheon, Korea. The major controlling factors identified were oceanic rain and wet season, leaked fuel oils and associated degradation products, the partially confining conditions and anthropogenic sources. Factor analysis has also been used by Jeong (2001a) to distinguish between the influence of natural chemical weathering of granitic rocks and anthropogenic inputs in the Taejon area, Korea.

Factor analysis has been used to help infer the main processes influencing groundwater chemistry in the fractured rocks (Permian sandstones and mudstones, and Jurassic dolerite dykes and sills) around Sutherland in the Western Karoo region of South Africa (Adams et al. 2001). These processes are salinization, mineral precipitation and dissolution, cation exchange and human activity.

Processes such as the dissolution of alkali-feldspar minerals, calcite dissolution and inter-aquifer leakage, have been interpreted by Meng and Maynard (2001) as controlling groundwater composition in the Botucatu Sandstone aquifer in part of the Paraná Basin, Brazil, using cluster analysis and factor analysis.

Factor analysis has also been applied to hydrochemical data by Jeong (2001b) to infer mineral-water reaction processes controlling the composition of groundwater stored within Pre-Cambrian granitic gneiss in the abandoned Samkwang mine area in Korea, a research site for radioactive waste disposal. The processes inferred were the

59 dissolution of calcite, chlorite, albite and sulfides, and the precipitation of clay and oxide minerals.

Lawrence and Upchurch (1976) also showed that factor analysis is useful in interpreting water chemistry data and relating those data to hydrogeological processes in an area around Lake City, Florida. Using R-mode factor analysis, Lawrence and Upchurch (1976) showed that particular variables are related to specific processes, those being contact with limestone and dolomite of the Floridan Aquifer, percolation through clastics overlying the Floridan Aquifer, and direct connection with the surface via sinkhole lakes.

Factor analysis and another type of multivariate analysis, that is, correspondence analysis have been used by Usunoff and Guzmán-Guzmán (1989) to define hydrochemical processes controlling groundwater composition in the interbedded sandstones and shales of the Milk River aquifer, Alberta, Canada. These processes are sulfate reduction, mixing of waters of different ages, ion-filtration and ion- exchange on clays (Usunoff & Guzmán-Guzmán 1989).

Factor analysis has been used to relate trace element data to processes such as enrichment related to groundwater acidity, evaporative concentration, and release of ions from clays, organic solids or Fe- or Mn-oxides, in groundwaters from Lake Tyrrell, Victoria (Giblin & Dickson 1992). Principal component analysis has been used by Kundu et al. (2001) in a geochemical appraisal of fluoride contamination of groundwater from high-grade metamorphic rocks in the Nayagarh District of Orissa, India; they showed that the fluorine-rich groundwater was produced due to mixing of hot spring water with the surrounding groundwater.

R-mode factor analysis and Q-mode cluster analysis have been applied to groundwater analyses from fluvial sands and gravels from the Gooi and Vechtstreek area in the Netherlands, to determine the factors controlling groundwater composition (Schot & van der Wal 1992). The processes identified were dissolution of carbonates, decomposition of organic matter, pollution, recharge of surface water affected by Vecht River water, and mixing of fresh and brackish groundwater (Schot & van der Wal 1992).

Principal component analysis and Q-mode cluster analysis have been carried out on groundwater data from two rock types, that is, granite and its contact aureole (schist), in the Nisa region of central Portugal, to determine governing hydrogeochemical processes by Dekkers et al. (1989). The processes identified for the granite groundwaters were evapotranspiration, relation to faults and fracture zones, fertilizer application and oxidation – reduction processes; for the schist groundwaters, evapotranspiration, water – rock interaction and a surface versus depth dipole (Dekkers et al. 1986; Dekkers et al. 1989).

Korkka-Niemi (2001) used principal component analysis to assess groundwater chemistry from igneous and metamorphic rocks (granitoids, gneisses, migmatites and a greenstone belt composed of metavolcanic and metasedimentary rocks) in Finland. Five factors were inferred from the analysis, those being salinity, humus-redox, pH,

60 pollution, and contamination factors. Korkka-Niemi (2001) concluded that the dominant salinity factor for the bedrock groundwaters could be used as an indicator of the residence time of the water.

DIFFERENTIATION BY SOURCE

Examples of the application of multivariate statistical methods to differentiate waters by source are less commonly found in the literature in comparison to ‘process’ studies, although some useful examples are provided below.

A study by Riley et al. (1990) is an example of the use of statistical procedures as a quantitative means for differentiating groundwaters according to hydrochemistry. Riley et al. (1990) used multivariate cluster analysis, MANOVA, canonical analysis and discriminant analysis to investigate the directions of groundwater movement in the Saddle Mountains, Wanapum, and the Grande Ronde basalt formations in the vicinity of the Hanford Reservation. Washington. The statistical analyses indicate that the hydrochemistry of the groundwaters in those basalt formations is distinctly different on each side of the Columbia River where the river passes through the Hanford Reservation, and indicate that the river is a groundwater divide or that the river is roughly coincident with a hydrogeological barrier boundary (Riley et al. 1990). Riley et al. (1990) also found that some of the hydrochemical differences could be related to stratigraphic positions of the basalt flows.

Seyhan et al. (1985) used multivariate statistical analysis (i.e. R-mode and Q-mode factor analysis as well as cluster analysis) of hydrochemical and environmental isotope data to differentiate groundwater of different hydrogeological origins within the Sasso Lungo dolomitic reef aquifer, northern Italy. The groundwaters were identified as sourced from either the Upper Triassic Sciliar dolomite or from the Lower Permian volcanic deposits (tuffs and tuffaceous-calcareous shales), or were identified as a mixed water from these two sources (Seyhan et al. 1985).

Cluster analysis of groundwater chemistry data, has been used by Hussein and Adam (1995) to separate sandstone and basalt groundwaters from the Gedaref Basin in eastern Sudan. The basalt groundwaters, which have total dissolved solids concentrations between 250 and 2730 ppm, could be separated from the sandstone groundwaters using a cluster analysis of major ion concentrations.

Cluster analysis and canonical analysis have been used by Williams (1982) to test the hypothesis that it is possible to utilize a statistical analysis of water quality data to identify pathways of preferential hydraulic connection between groundwater discharge points on the surface of Mount Emmons, Colorado, and pyrite-rich, mineralised zones in the core of the mountain. Cluster analysis and canonical analysis of the water quality data delineated those discharge points that contain groundwater emanating from a mineralised pyrite-rich source; these analyses, along with fault-vein maps were used to identify those springs most likely to be affected by mining in the core of Mount Emmons (Williams 1982).

61 In a related study, Steinhorst and Williams (1985) also used cluster analysis, MANOVA, canonical analysis and discriminant analysis to identify distinct groundwater sources using hydrochemical data. Water samples taken from a diversity of sources from a proposed molydbenum mine site in western Colorado were assessed to determine which sources were affected by the ore body and pyrite associated with it. Steinhorst and Williams (1985) also used these multivariate statistical methods to determine whether water taken from different formations, that is, basalt flows and sedimentary interbeds in south central Washington, are chemically distinguishable.

Q-mode factor analysis has been used to study the groundwaters in the San Pedro River basin in Arizona, and enabled the hydrochemical segregation of waters from confined Miocene – Pliocene sedimentary rocks and unconfined Pleistocene sediments (Usunoff & Guzmán-Guzmán 1989).

Another study, a factor analysis of hydrochemical data from over 2500 analyses of brines by Kramer (1969), showed that the factor groupings are very similar regardless of rock type (limestone, sandstone, dolomite, granite wash, evaporite and shale). This was interpreted by Kramer (1969) as indicating that most of the rock systems studied are open and that a trend towards some equilibrium state occurs.

OTHER STUDIES AND SCOPE FOR FURTHER WORK

Other studies using multivariate statistical methods to assess the chemistry of surface and groundwaters include those by Collins (1967), Lee (1969), Miller and Drever (1977), Ashley and Lloyd (1978), Dalton and Upchurch (1978), Symader and Thomas (1978), Stallard and Edmond (1983), van Tonder and Hodgson (1986), Zielinski et al. (1987), Nölte (1988), Faillat and Blavoux (1989), Ruiz et al. (1990), Christophersen and Hooper (1992), Melloul and Collin (1992), Laaksoharju et al. (1999), Bedbur et al. (2001), López-Chicano et al. (2001) and Reghunath et al. (2002).

Examples in the literature of the specific application of principal component or factor analysis to distinguish processes controlling groundwater composition or differing lithological sources of groundwater for data sets in which there are very low concentrations of ions, are very limited. Join et al. (1997) used principal component analysis to assess the relationship between ionic concentration and geological setting for fresh spring waters on the tropical island of Réunion in the western Indian Ocean. Join et al. (1997) inferred two main factor from the analysis; the first is associated with deep volcanic formations (basal aquifers) and is characterized by the chemical components K+, pH, Ca2+ and Na+, the second is associated with the superficial layers of the volcanoes (superficial and perched aquifers) and is characterized by Cl- - and NO3 .

A recent study using multivariate statistical methods (Q- and R-mode factor and cluster analysis) to assess groundwater composition in a tropical climate was undertaken by Reghunath et al. (2002) in the Nethravathi river basin of southern India. The analyses indicate that exchange between river water and groundwater in

62 these Precambrian crystalline formations plays a dominant role in the hydrochemical evolution of the Nethravathi river basin groundwaters (Reghunath et al. 2002).

An example of the use of a multivariate statistical method for data with low ion concentrations is a principal component and factor analysis by Bridgman (1992), in which the sources of contamination were established for a rainwater chemistry data set containing near-background ion levels. Bridgman (1992) inferred that soil, fertilizer and industrial emission factors influenced rainwater quality in the Hunter region of New South Wales. Reeder et al. (1972) used Q-mode and R-mode factor analyses to assess the chemical and physical properties of over 100 surface waters of the Mackenzie River drainage basin, Canada, and identified the likely factors influencing the composition of those waters.

There is considerable scope for further application of multivariate statistical methods to hydrogeochemical studies, particularly for subtropical aquifer systems with fresh groundwaters, which have received less attention than aquifer systems in temperate, semi-arid and arid climates where the groundwaters are more hydrochemically ‘evolved’. In addition, examples of the application of multivariate statistical methods, particularly principal component factor analysis, to the assessment of basalt aquifer systems are very limited. The study by Join et al. (1997) in a tropical basalt island setting, discussed above, is the only known example in the published literature of the application of a principal component analysis method to assess hydrochemical processes in a basalt aquifer in a tropical climate. The aquifers of the Atherton Tablelands region are, therefore, a useful system for the investigation of hydrogeochemical processes and groundwater sources using statistical methods of data analysis.

63 Conclusions

A primary aim of this study is to demonstrate that hydrogeochemical processes, and silicate mineral weathering processes in particular, are a significant influence on the composition of basaltic groundwaters.

It is evident from this literature review, that there are a wide variety of hydrogeochemical and statistical methods that may be applied to the study of a basaltic aquifer system. They include the assessment of mineral dissolution and weathering processes through the use of aqueous elemental ratios in relation to the stoichiometry of possible reactions, investigation of whole rock geochemistry and mineralogy, including changes in these as a result of weathering, quantification of relations between minerals and dissolved species through the calculation of aqueous ion activities and mineral saturation states, and the use of mineral stability diagrams, as well as hydrogeochemical modelling approaches. Assessment of other natural processes that may influence the composition of basaltic groundwaters, such as the acquisition of carbon dioxide, ion-exchange and sorption mechanisms, oxidation – reduction reactions, evaporation and the effects of organic matter should also be considered.

The review of the literature clearly demonstrates that it is essential to validate, normalise and standardize hydrochemical data sets before they can be subjected to multivariate analytical techniques. Multivariate statistical analyses provide a range of techniques that may be used in hydrogeochemical studies, with the choice of method dependent on the aims of the investigation. Multivariate statistical analyses include, for example, graphical methods, principal component analysis, factor analysis (and variants of these) and cluster analysis.

Few investigations of low-temperature meteoric water – rock interactions in basaltic terrain have been reported, and this is particularly so in tropical and subtropical environments. In addition, the application of principal component or factor analysis methods to distinguish hydrogeochemical processes controlling groundwater composition or differing lithological sources of fresh groundwaters, are very limited. There is considerable scope for further research into the hydrogeochemical processes controlling the composition of fresh basaltic groundwaters in tropical and subtropical environments, and the use of multivariate statistical methods in such an investigation.

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95 PAPER 1A

CHEMICAL CHARACTER OF GROUNDWATER IN A BASALT AQUIFER, NORTH QUEENSLAND, AUSTRALIA

Katrina L. Locsey and Malcolm E. Cox

School of Natural Resource Sciences Queensland University of Technology

In Sililo O. et al. eds. Groundwater: Past achievements and future challenges. Proceedings of the XXXth International Congress of the International Association of Hydrogeologists, Cape Town, 26 November – 1 December 2000, pp. 555-560. A.A.Balkema, Rotterdam.

96 Statement of original authorship

Locsey K.L. (candidate)

Undertook fieldwork and analysed samples, carried out compilation and analysis of data, interpreted and presented results, wrote manuscript.

Cox M.E. (principal supervisor)

Supervised work and reviewed manuscript.

97 Abstract

Due to the increasing demands on the groundwater resources on the Atherton Tablelands, North Queensland over the last 15 years, a number of investigations are being undertaken to determine the extent and sustainability of the resource. The groundwater supplies are predominantly sourced from fresh and weathered basalt, varying from highly vesicular to massive in character, with fracture zones also present. The chemical character of groundwater contained within the basalt can be used to define trends in groundwater movement, based on its chemical evolution. The groundwater chemically evolves from water with no dominant ions to slightly - HCO3 -rich water in areas where direct rainfall infiltration predominates, to a Ca-Mg, HCO3 type water along flow gradient and at discharge zones. Trends in major ion concentrations and relationships between dissolved constituents indicate that reactions with clinopyroxene, plagioclase feldspars and olivine are the principal controls on the chemical character of these groundwaters.

Key words: Groundwater · Basalt · Chemical character

98

PAPER 1B

A HYDROCHEMICAL CLASSIFICATION SCHEME FOR A BASALTIC AQUIFER AS AN INDICATOR OF GROUNDWATER FLOW POSITION

Katrina L. Locsey and Malcolm E. Cox

School of Natural Resource Sciences Queensland University of Technology

In Seiler K.P. and. Wohnlich S. eds. New approaches to characterising groundwater flow. Proceedings of the XXXIst International Congress of the International Association of Hydrogeologists, Munich, 10-14 September 2001, pp 1217-1221. A.A.Balkema, Swets & Zeitlinger B.V., Lisse.

110 Statement of original authorship

Locsey K.L. (candidate)

Undertook fieldwork and analysed samples, carried out compilation and analysis of data, interpreted and presented results, wrote manuscript.

Cox M.E. (principal supervisor)

Supervised work and reviewed manuscript.

111 Abstract

The hydrochemical relationships between the major ions in the groundwaters of the Atherton Tablelands, North Queensland, Australia, have been used to define the relative positions of samples along inferred flow paths. The five key indicators of 2+ - - the chemical evolution of these basalt-hosted groundwaters (i.e. Mg /Cl , HCO3 + 2- - - - CO3 /Sum Cations, HCO3 /Cl , H4SiO4 concentration and the percentage of HCO3 + 2- CO3 of major anions) have been used in a rating-based classification scheme to identify “upper-”, “mid-” and “lower-flow” position groundwaters. The results of this hydrochemical classification correspond well with water level response times to recharge events. The classification scheme is a useful indicator of groundwater flow directions for the Atherton Tablelands basalt aquifer, and may also enable the identification of areas of preferred recharge.

Key words: Hydrochemical relationships · Chemical evolution · Classification scheme · Groundwater flow paths

112

PAPER 2

STATISTICAL AND HYDROCHEMICAL METHODS TO COMPARE BASALT- AND BASEMENT ROCK-HOSTED GROUNDWATERS: ATHERTON TABLELANDS, NORTH-EASTERN AUSTRALIA

Katrina L. Locsey and Malcolm E. Cox

School of Natural Resource Sciences Queensland University of Technology

Environmental Geology 43 (6), pp. 698-713 Published online 10th October 2002 Environmental Geology © Springer-Verlag 2002 DOI 10.1007/s00254-002-0667-z

123 Statement of original authorship

Locsey K.L. (candidate)

Undertook fieldwork and analysed samples, carried out compilation and analysis of data, interpreted and presented results, wrote manuscript.

Cox M.E. (principal supervisor)

Supervised work and reviewed manuscript.

124 Abstract

Multivariate analysis of physico-chemical and chemical data has enabled differentiation among groundwaters sourced from different lithological formations in the Atherton Tablelands region of north-eastern Australia. The main water resource is stored in basalt, although basement rocks such as granite and metamorphics also contain variable amounts of water. Groundwater in the basalt is mostly Mg-Ca-Na, HCO3 type, with electrical conductivities less than 300 μS/cm and pH values from 6.5 to 8.5. Some of the other groundwater is quite similar, making the identification of hydrochemical facies difficult.

Groundwater samples were grouped based on the results of a principal component + 2+ 2+ factor analysis of the major dissolved constituents H4SiO4, Na , Ca , Mg and - HCO3 , as well as pH and electrical conductivity. Based on this differentiation it was possible to identify the likely host rocks of groundwaters from unidentified lithological units, define the basalt thickness and provide a better understanding of the groundwater resource.

Principal component factor analysis has also been useful in identifying the likely hydrochemical processes controlling the composition of these groundwaters, including the production of weak acids in the soil layers, silicate mineral weathering, ion-exchange reactions, evapotranspiration and the leaching of ions from organic matter.

Key words: Atherton Tablelands · Groundwater · Hydrochemistry · Principal component factor analysis · North Queensland

125

PAPER 3

WATER – ROCK INTERACTIONS: AN INVESTIGATION OF THE RELATIONSHIPS BETWEEN MINERALOGY AND GROUNDWATER COMPOSITION AND FLOW IN A SUBTROPICAL BASALT AQUIFER

Katrina L. Locsey, Micaela Preda and Malcolm E. Cox

School of Natural Resource Sciences Queensland University of Technology

Manuscript prepared for Hydrogeology Journal

157 Statement of original authorship

Locsey K.L. (candidate)

Undertook fieldwork and analysed aqueous samples, carried out compilation and analysis of data, interpreted and presented results, wrote manuscript.

Preda M. (associate supervisor)

Undertook XRD analyses, identified and quantified mineral phases, and reviewed manuscript.

Cox M.E. (principal supervisor)

Supervised work and reviewed manuscript.

158 Abstract

The composition of the basalt groundwaters of the Atherton Tablelands region are considered, to elucidate possible mechanisms for the evolution of these very low salinity, silica- and bicarbonate-rich groundwaters. It is proposed that aluminosilicate mineral weathering is the major contributing process to the overall composition of the basalt groundwaters.

The groundwaters approach equilibrium with respect to the primary minerals with increasing pH, and are mostly in equilibrium with the major secondary minerals (kaolinite and smectite), and other secondary phases such as goethite, hematite and gibbsite, which are common accessory minerals in the Atherton basalts.

The mineralogy of the basalt rocks, which has been examined using X-ray diffraction and whole rock geochemistry methods, supports the proposed model for the hydrogeochemical evolution of these groundwaters. The variations in the mineralogical content of these basalts also provide insights into the controls on groundwater storage and movement in this aquifer system. The fresh and weathered vesicular basalts are considered to be important in terms of zones of groundwater occurrence, while the fractures in the massive basalt are important pathways for groundwater movement.

Key words: Basalt aquifer · Aluminosilicate mineral weathering · Mass balance

159

GENERAL CONCLUSIONS GENERAL CONCLUSIONS

The aims of this research project were to examine the effects of hydrogeochemical processes, in particular, silicate mineral weathering processes, on the composition of very low salinity groundwaters in a basalt aquifer system in a subtropical climate. A focus of this study was to use the hydrochemical observations to better define the groundwater hydrology, and thereby provide an additional tool for resource management. Several elements of the system (basalt and basement rocks, groundwaters and steam waters) were investigated using a number of chemical, physical and statistical analytical methods, as well as hydrogeochemical modelling.

The mineralogy of the basaltic rocks of the Atherton Tablelands has been examined using X-ray diffraction and whole rock geochemistry techniques. The results show that there is considerable variation in the relative proportions of the primary and secondary minerals present, and that the sequence of basalt rocks comprises a very heterogeneous package of both fresh and weathered material. Vesicular basalt, that may be fresh to highly weathered, was identified as important in terms of zones of groundwater occurrence, while both fractured and vesicular basalt provide pathways for groundwater movement. Zones between lava flows are also important hydrologically.

The mineralogy of the basalt rocks ultimately determines the occurrence of chemical elements in the groundwaters and the speciation of the weathering products. The concentrations of the major dissolved constituents in the Atherton Tablelands basalt groundwaters are influenced by silicate weathering reactions such as the dissociation of olivine, the weathering of pyroxenes and feldspars to kaolinite and smectites, and the formation of other secondary minerals such as amorphous or crystalline silica, goethite, gibbsite and hematite. Saturation of the groundwaters with respect to carbonate minerals and zeolites leads to the precipitation of these minerals in vesicles and along fracture planes in the basalt. The relationships between the stoichiometry of these weathering reactions and the molar ratios of dissolved constituents in the groundwaters support these proposals. The hypothesis that silicate mineral weathering processes are the predominant influence on the groundwater composition is well supported by the stability of the basalt groundwater with respect to kaolinite and smectite clays, as shown on aluminosilicate mineral stability diagrams, saturation indices with respect to both primary and secondary minerals, and inverse hydrogeochemical modelling.

It is evident that the effects of mineral weathering processes can be observed on the composition of these young (Cook et al. 2001) and very low salinity basalt groundwaters, which receive substantial recharge and undergo extensive flushing annually. These observations were used to relate the compositions of the groundwaters to positions along flow lines (i.e. relative residence times were identified), and to identify likely recharge and discharge areas. The groundwater flow patterns defined by the hydrogeochemical interpretations correspond well with the spatial trends in water level fluctuations, and response to recharge events in particular.

Other processes that influence the groundwater composition include the availability of CO2, oxidation and ion-exchange reactions, and uptake of ions from, and decomposition of, organic matter. Due to extensive soil development and rapid plant 197 growth in the subtropical monsoonal climate of the study area, substantial CO2, and hence hydrogen ions, are available to circulating water; the extent to which silicate weathering occurs is related to the availability of H+ ions. While some evaporation of recharging waters does occur, the effects of evaporative enrichment on the composition of the Atherton basalt groundwaters is limited in comparison to the effects of silicate mineral weathering processes.

Groundwater baseflow to streams and discharge to topographic lows in the Atherton Tablelands region is indicated by the relationships between the major cations and 2+ - - - anions in the stream waters, particularly the ratios of Mg /Cl and HCO3 /Cl , which can be used as indicators of silicate mineral weathering processes. Fracture zones are likely to be preferred pathways of groundwater movement, enabling discharge to streams. Longitudinal radon profiles measured at low flow conditions along the Barron and North Johnstone Rivers (Cook et al. 2001) support this finding. Evidence of a groundwater baseflow contribution to streams, showing that the surface water bodies are an integral part of the groundwater flow system, is an important finding in terms of management of the water resources contained within the groundwater and surface water systems.

Estimates for recharge to the basalt aquifer system of the Atherton Tablelands, based on a chloride mass balance, range from 310 mm/yr in the north-western part of the study area (north of Atherton) to 600 mm/yr in the wetter southern and eastern parts of the study area. Estimating recharge to a groundwater system based on a chloride mass balance approach has limitations, and estimates should be treated with caution, particularly in areas where rainfall chloride concentrations are highly variable (Rosen et al. 1999), and where the groundwaters have very low chloride concentrations (Roe 1995), as is the case for the Atherton Tablelands. The recharge estimate for the north-western part of the study area is supported, however, by an independent recharge estimate (based on a soil moisture model and groundwater usage) by Pearce and Durick (2002). The high recharge estimates for the southern and eastern parts of the Atherton Tablelands are comparable to estimated recharge to aquifer systems in other high rainfall regions (e.g. Pulawski & Øbro 1976; Wright 1984; Uma & Egboka 1988).

The multivariate statistical approaches used in this study to assess the hydrochemical data from the Atherton Tablelands region, and the application of the results are a significant contribution to this field of work. Principal component analysis of the major constituents, pH and electrical conductivities of these very low salinity groundwaters from a subtropical environment, supported by other methods, have shown that:

ƒ groundwaters from different lithological formations, that is, basalt and basement rocks (comprising granite and metamorphosed sediments), are hydrochemically distinguishable,

ƒ the likely sources of groundwater obtained from unidentified lithological units can be defined,

ƒ the underlying geochemical processes controlling groundwater composition, particularly in the basalt aquifer, can be inferred, and

198 ƒ the results can be applied to indicate relative groundwater residence and flow directions, and to map the thickness of the basalt aquifer, which thus improves the understanding of the potential extent of the groundwater resource.

The interpretative methods applied to this research project relied predominantly on major ion chemistry and field measurements, in addition to mineralogical data. Extensive hydrogeochemical information has been gained from these data, and this research demonstrates the potential for basic hydrochemical data to aid in the understanding of the controls on groundwater composition, and the application of this knowledge in terms of groundwater storage and movement.

References

COOK P.G., HERCZEG A.L. & MCEWAN K.L. 2001. Groundwater recharge and stream baseflow, Atherton Tablelands, Queensland. CSIRO Land and Water Technical Report 08/01, pp. 84. PEARCE B.R. & DURICK A.M. 2002. Assessment and management of basalt aquifers on the Atherton Tablelands, North Queensland, Australia. In Proceedings of the International Association of Hydrogeologists International Groundwater Conference: Balancing the Groundwater Budget, Darwin, 12-17 May 2002. PULAWSKI B. & ØBRO H. 1976. Groundwater study of a volcanic area near Bandung, Java, Indonesia. Journal of Hydrology 28, 53-72. ROE R.B. 1995. Release of chloride from basalt: Implications for the chloride mass- balance approach to estimating groundwater recharge. M.S. thesis, Washington State University, Washington (unpubl.), pp. 90. ROSEN M.R., BRIGHT J., CARRAN P., STEWART M.K. & REEVES R. 1999. Estimating rainfall recharge and soil water residence times in Pukekohe, New Zealand, by combining geophysical, chemical, and isotopic methods. Ground Water 37 (6), 836-844. UMA K.O. & EGBOKA B.C.E. 1988. Groundwater recharge from three cheap and independent methods in the small watersheds of the rainforest belt of Nigeria. In Simmers I. ed. Estimation of Natural Groundwater Recharge, pp. 435-447. D.Reidel Publishing Company, Dordrecht, Holland. WRIGHT E.P. 1984. Drilling for groundwater in the Pacific region. In Water Resources of Small Islands, Technical Proceedings (Part 2) on the Regional Workshop on Water Resources of Small Islands, pp. 525-529. Commonwealth Science Council, London.

199 APPENDICES APPENDIX I

4th International Conference on Environmental Chemistry and Geochemistry in the Tropics (GEOTROP 2001) 7 - 11 May 2001, Townsville.

Climatic, mineralogical and weathering controls on the geochemical character of groundwater – the Atherton Tablelands basalt aquifer, North Queensland.

KATRINA LOCSEY School of Natural Resource Sciences Queensland University of Technology Abstract

Due to the increasing demands on the groundwater resources on the Atherton Tablelands, North Queensland over the last 15 years, a number of investigations are being undertaken to determine the extent and sustainability of the resource. The groundwater supplies are predominantly sourced from fresh and weathered basalt, varying from highly vesicular to massive in character, with fracture zones also present. The chemical character of groundwater contained within the basalt can be used to define trends in groundwater movement, based on its chemical evolution.

Recharge by direct rainfall infiltration occurs throughout the Tablelands. Recharge rates may be expected to reflect the rainfall distribution pattern, with rates decreasing from the south-east to the north-west. Recharge has been estimated using a chloride mass balance, assuming that the groundwater chloride concentration is predominantly due to evaporative enrichment, with some contribution from rock weathering.

The groundwater chemically evolves from water with no dominant ions to slightly - HCO3 -rich water in areas where direct rainfall infiltration predominates, to a Ca-Mg, HCO3 type water along flow gradient and at discharge zones. The increasing - concentrations of HCO3 , H4SiO4 and cations along flow gradient, and the 2+ - relationship between cations (e.g. Mg ) and HCO3 , indicate that groundwater chemical composition is influenced by basalt mineralogy. The predominant mineralogy of the Atherton basalts is clinopyroxene, plagioclase feldspars and olivine. Kaolinite and montmorillonite are the dominant clay minerals; minor gibbsite is also present. Where soil development and plant growth are rapid, as is the case in the subtropical, monsoonal climate of the Atherton Tablelands, substantial CO2, and, hence, hydrogen ions are available to circulating water. The extent to which reactions with the minerals occurs, is related to the availability of H+. In the south-east of the Tablelands, where annual rainfall exceeds 2500 mm and intense leaching conditions persist, weathering of albite to gibbsite, influences groundwater chemical composition.

Although these groundwaters have low total dissolved solids, trends in major ion + 2+ 2+ - concentrations and ratios of Na , Ca , Mg , HCO3 and H4SiO4, can be useful indicators of evolution of the chemical character of the groundwater. This aspect can be used to determine the nature of groundwater occurrence and movement.

200 Climatic, mineralogical and weathering controls on the geochemical character of groundwater

Ms Katrina Locsey and Dr Malcolm Cox School of Natural Resource Sciences Queensland University of Technology GEOTROP 2001 4th International Conference on Environmental Chemistry and Geochemistry in the Tropics Townsville, 7 - 11 May, 2001

The hydrochemical relationships between the major ions in the groundwaters of the Atherton Tablelands have been used to define the relative positions of samples along inferred flow paths. Interpretations are based on more than 900 sets of groundwater analyses. The five key indicators of the chemical evolution of these 2+ - - 2- - - basalt-hosted groundwaters (i.e. Mg /Cl , HCO33 + CO /Sum Cations, HCO 3 /Cl , -2- H44 SiO concentration and the percentage of HCO33 + CO of major anions) have been used in a rating-based classification scheme to identify “upper-”, “mid-” and “lower-flow” position groundwaters. The results of this hydrochemical classification correspond well with water level response times to recharge events. The classification scheme is a useful indicator of groundwater flow directions for the Extent of the Basalt Aquifer and Locations of Atherton Tablelands basalt aquifer, and may also enable the identification of Groundwater Bores on the Atherton Tablelands areas of preferred recharge. Groundwater Monitoring Bore

The rate of evaporative enrichment of recharging groundwaters increases from SE to NW, from 4.0 4.0 an evaporation factor of 2.5 around Malanda to 3.6 around Atherton. Augite-Kaolinite (1:3.7) Augite-Kaolinite (1:1.25) 3.5 3.5 (80%) Interactions between groundwater and the basaltic rocks are believed to be the main Albite-Kaolinite 3.0 3.0 Anorthite-Kaolinite processes responsible for the chemical characteristics of the Atherton groundwaters. The 2.5 2.5 Albite-Montmorillonite composition of these groundwaters is primarily attributed to the acidic weathering of primary (20%) (mmol/L)

(mmol/L) 2.0 2.0 - -

aluminosilicate minerals to kaolinite, and other clays and oxides (Locsey and Cox, 2000). The 3 3 predominant mineralogy of the Atherton basalts is clinopyroxene (augite), plagioclase feldspars 1.5 1.5 HCO (probably albite and anorthite) and olivine. Groundwater composition is influenced by the HCO 1.0 1.0 reactions with these minerals. The dissolution of these minerals is shown with kaolinite and 0.5 0.5 montmorillonite as the end products (the weathering of albite to montmorillonite assumes the 0.0 0.0 2+ presence of Mg leached from pyroxenes). 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Augite - Kaolinite Ca2+ (mmol/L) Ca2+ + Mg2+ + Na+ (mmol/L) 2+ 2+ 2+ 2+ + [CaMg0.7 Al 0.6 Si 1.7 ]O 6 + 3.4CO 2 +4.5H 2 O = 0.3Al 2 Si 2 O 5 (OH) 4 + Ca + 0.7Mg + 1.1H 4 SiO 4 + Most samples plot above the augite to kaolinite If the release of the ions Ca , Mg , Na and - 2+ - 3.4HCO3 weathering reaction line (that is, release of Ca HCO3 is considered in terms of the weathering - Albite - Kaolinite and HCO3 on a 1:3.7 moles basis). Excess of reactions above, with the weathering of augite, - +- HCO may be due to olivine dissociation, and albite and anorthite accounting for 80% of the 2NaAlSi O + 2CO + 11H O = Al Si O (OH) + 2Na + 4H SiO + 2HCO 3 38 2 2 225 4 4 4 3 weathering of albite or anorthite to kaolinite, ions released to solution, and the weathering of Anorthite - Kaolinite - which also release HCO3 to solution. albite to montmorillonite accounting for the 2+ - CaAl228 Si O + 2CO 2 + 3H 2 O = Al 225 Si O (OH) 4 + Ca + 2HCO 3 remaining 20% of ions, then the ratio of cations to - Albite - Montmorillonite HCO3 would be 1:1.25. This corresponds well with 2+ + the data for the Atherton groundwaters, indicating 3NaAlSi3 O 8 + Mg + 4H 2 O = 2Na 0.5 Al 1.5 Mg 0.5 Si 4 O 10 (OH) 2 + 2Na + H 4 SiO 4 Hydrochemical that some combination of these reactions is likely Classification to be controlling the groundwater composition. Relationships between the five main chemical indicators 2+ - - 2- - - 2+ - - 2- Mg /Cl Rating HCO3 +CO3 /Sum Cations Rating HCO3 /Cl Rating of groundwater evolution (Mg /Cl , HCO33 + CO /Sum (meq/L) (mmol/L) (meq/L) -- <= 1 0.5 Cations, HCO344 /Cl , H SiO concentration and the <= 0.5 0.25 <= 0.5 0.5 -2- >0.5and<=1 0.75 > 0.5 and <= 1 1 >1and<=3 1 percentage of HCO33 + CO of major anions) > 1 and <= 1.5 1.25 >1 and <= 1.3 2 >3and<=4 2 >1.5and<=2 1.75 > 1.3 3 >4and<=5 2.5 >5 3 2.0 14 > 2 and <= 2.5 2.25 12 >2.5and<=3 2.75 - 2- 1.5 10 >3 3 H4SiO4 Rating HCO3 +CO3 of major anions Rating (mmol/L) (%) 8 “Class” Legend <= 60 0.5 1.0 (meq/L) <= 0.5 0.5 - 6 )/Sum Cations > 60 and <= 80 1 2- 3 /Cl > 0.5 amd <= 1 1 -

3 4 > 80 and <= 87 2 (mmol/L) 0.5 Class < 0.79 > 1 No Rating +CO >87–100 3 - HCO 3 2 Class 0.79 - 1.38 0 (HCO 0.0 Class 1.38 - 2 Classification system defined 0123456 0123456 Mg2+/Cl- (meq/L) Mg2+/Cl- (meq/L) Class 2 - 2.58 by weighted key indicators Class 2.58 - 3 Mg2+/Cl- HCO -+CO 2- HCO -/Cl- H SiO HCO -+CO 2- of 100 10.0 3 3 3 4 4 3 3 90 rating /Sum Cations rating rating anions rating 80 The “Class” equals the sum of the rating 70 Method 0.25 0.25 0.25 0.125 0.125 five weighted ratings within a range 60 A weight 50 1.0

of major anions of 0 - 3, with the lower end of this (mmol/L) 2- Method 0.28 0.28 0.28 - 0.16

3 40 4 range representing an “upper-flow” 30 B weight SiO 4 +CO - H position and the upper end of this

3 20

10 range representing a “lower-flow” Method A for groundwater samples with a H44 SiO concentration <= 1 HCO 0 0.1 position, that is, a longer residence. mmol/L. Method B for groundwater samples with a H44 SiO 0123456 0123456 2+ - Mg2+/Cl- (meq/L) Mg /Cl (meq/L) concentration > 1 mmol/L, or if no H44 SiO data available.

The hydrochemical classification corresponds 800 Feb- May- Aug- Nov- Feb- May- Aug- Nov- Feb- May- well with the groundwater contours and flow Groundwater flow direction lines for the Atherton region Class = 1.71 89 89 89 89 90 90 90 90 91 91 Class = 1.49 0 A = 1.64 Lake 750 A = 1.7 Class = 2.96 X-SectionlocationA-B 45783 Tinaroo N B = 2.9 -10 down groundwater flow B = 2.7 A = 5.94 Surface gradi ent C = 1.1 11000066 700 C = 1.0 B = 10.6 SWL -20 # Groundw at er bor es D = 2.1 showninhydrographs D = 1.7 C = 1.4 Screened (metres) E = 82% -30 E = 78% D = 1.6 Interval 11000064 11000129 650 11000066 E # # = 94% Standing water level-40 (m) # 11000064 11000068 45783 600 -50 # 45431 # 11000068 4543112311000068 11000118 Water level response lags give support to the A # # 11000118B hydrochemical classification system. A r Cross-section A-B, from an upper- to mid- to lower-flow Atherton e recharge water level response lag of three iv position, defined by the hydrochemical classification and R 2+ - - - months between groundwaters classified as Interpolated grid using an inverse distance n groundwater levels. A = Mg /Cl (meq/L), B = HCO3 /Cl o representing an upper-flow position (bores weighted method, with increasing “Class” r -2- 0 2 4 Kilometers r (meq/L), C = HCO + CO /Sum Cations (mmol/L), D = a 33 45783 and 11000066) and groundwaters from 0 - 3 shown in blues to reds. B -2- H44 SiO (mmol/L) and E = HCO 3 + CO 3 of major anions (%). classified as representing a mid-flow position Reference: Locsey, K.L. & M.E. Cox 2000. Chemical character of groundwater in a basalt aquifer, North Queensland, (bores 11000064 and 11000068) is evident. Australia. In O.Sililo et al. (eds), Groundwater: Past achievements and future challenges; Proc. XXXth International This work was conducted as part of a research program funded and supported by the Queensland Department Congress of the International Association of Hydrogeologists, Cape Town, 26 November - 1 December 2000. of Natural Resources and the Land and Water Resources Research and Development Corporation. Rotterdam: Balkema. APPENDIX II

Extracts from Locsey and Cox (unpubl.), a report submitted to QDNR&M and LWRRDC, and additional related work

HYDROGEOCHEMICAL CROSS-SECTIONS: INFERRING RELATIONSHIPS BETWEEN HYDROCHEMISTRY AND GROUNDWATER MOVEMENT .

202 APPENDIX III

Extracts from Locsey and Cox (unpubl.), a report submitted to QDNR&M and LWRRDC, and additional related work

GROUNDWATER – STREAM INTERACTION: THE ATHERTON TABLELANDS, NORTH QUEENSLAND

212 APPENDIX IV

HYDROCHEMICAL VARIABILITY AS A TOOL FOR DEFINING GROUNDWATER MOVEMENT IN A BASALT AQUIFER: THE ATHERTON TABLELANDS, NORTH QUEENSLAND

Katrina L. Locsey and Malcolm E. Cox

School of Natural Resource Sciences

Queensland University of Technology

In Proceedings of the International Association of Hydrogeologists International Groundwater Conference: Balancing the Groundwater Budget, Darwin, 12-17 May 2002.

218 Statement of original authorship

Locsey K.L. (candidate)

Undertook fieldwork and analysed samples, carried out compilation and analysis of data, interpreted and presented results, wrote manuscript.

Cox M.E. (supervisor)

Supervised work and reviewed manuscript.

219 Abstract

The hydrochemcical processes affecting the groundwater composition in the Atherton Tablelands basalt aquifers were examined to assess the potential for using this understanding to identify trends in groundwater movement. The principal objectives of the hydrochemical investigation of the basalt aquifers were to determine whether the effects of silicate weathering and relative residence times could be observed in basaltic groundwaters in a subtropical environment, with substantial recharge and flushing annually, and to use the observed hydrochemical trends to infer groundwater flow patterns, and to identify areas of preferred recharge and discharge.

The groundwater is of very low salinity (generally < 250 mg/L total dissolved ions), typical of groundwaters in tropical environments. Characteristics of the basalt aquifers include a slow rate of weathering of silicate minerals and localized recharge. There is however, an observed evolution of the chemical composition of the groundwaters due to water – rock interaction that can be reconciled with positions of groundwaters along flow paths.

The processes of silicate weathering were determined to be the predominant influence on groundwater composition in the region, due to reactions with primary and secondary minerals releasing ions to solution. It is inferred that the weathering of augite, albite and anorthite to kaolinite, and albite to montmorillonite, are the principal silicate weathering reactions involved in the release of the major ions to the groundwater. Olivine dissociation and the weathering of albite to gibbsite would be expected to have a lesser influence on the groundwater composition. Saturation of the groundwaters with respect to some secondary minerals and subsequent precipitation has a limiting influence on the concentration of the major ions in solution.

The hydrochemical relationships observed are subtle, but can be used to classify the basalt-hosted groundwaters in terms of “residence”, to define trends in groundwater movement and to identify areas of potential preferred recharge and discharge. The groundwater flow patterns defined by the hydrochemical classification correspond well with the spatial trends in water level fluctuations, in particular, response to summer recharge. The identification of areas of preferred recharge (e.g. fracture zones) may be facilitated by the interpretation of hydrochemical variations in the aquifer, as fresher, recharge waters tend to show a distinctive chemical composition. The most chemically evolved groundwaters are found to discharge from springs at the edges of the basalt lava fields, and typically have major ion elemental ratios (e.g. 2+ - - 2- Mg /Cl and HCO3 +CO3 /Sum Cations) eight times those of recharging waters.

Key words: Hydrochemistry · Basalt aquifers · North Queensland, Australia

220

APPENDIX V

Supportive approaches and applications of work presented in PAPER 2

MULTIVARIATE DATA ANALYSIS OF THE ATHERTON TABLELANDS GROUNDWATERS: APPROACHES

Multivariate analysis of the hydrochemical data was initially applied by undertaking four separate principal component factor analyses of groundwaters obtained from basalt, granite and metamorphic rocks, as well as of groundwaters from unknown lithological sources. The variables significantly contributing to the factors for each lithological group are shown in Table 1. The principal component factor analysis results for the unknown group are most similar to those of the basalt-hosted groundwaters. The variables contributing to factor I for these groups are similar; the inclusion of the variables pH and Na+ in factor I of the unknown group should be treated with some caution, as these variables have lower communalities than usual - 2- for the set of factors defined. In addition, the variables Cl , H4SiO4 and SO4 are the single significant variables contributing to three of the factors for both groups. The overall set of factors for the unknown group most closely resembles that of the basalt-hosted groundwaters, indicating that the majority of the groundwater samples comprising the unknown group are likely to the sourced from basalt.

Table 1. A summary of the variable significantly contributing to the factors for each lithological group.

Factor Basalt Granite Metamorphic Unknown - - I HCO3 H4SiO4 pH HCO3 Mg2+ Na+ Ca2+ Ca2+ + - Electrical K HCO3 Electrical Conductivity - Conductivity HCO3 pH Ca2+ pH Mg2+ Electrical Na+ Conductivity + 2- + - II K SO4 Na Cl Na+ Electrical Electrical pH Conductivity Conductivity Mg2+ Cl- 2- SO4 - + III H4SiO4 Cl K H4SiO4 Mg2+ Mg2+ Electrical Conductivity Cl- - 2+ 2- IV Cl Ca H4SiO4 SO4 Mg2+ 2- SO4 2- + V SO4 K

This approach shows that water taken from different formations in the Atherton Tablelands region are chemically distinguishable, and provides a qualitative assessment of the likely predominant host rock of the unknown groundwaters. It does not, however, enable the identification of the unknown samples on an individual

240 basis, a procedure necessary for any further application of the results, such as the definition of the thickness of the basalt aquifer.

Kendall and Stuart (1966) discuss the problems of differentiating between two or more populations on the basis of multivariate measurements. The following quotation from them shows that there are three distinct classes of problem:

Discrimination: We are given the existence of two populations and a sample of individuals from each. The problem is to set up a rule, based on measurements from these individuals, which will enable us to allot some new individual to the correct population when we do not know from which of the two it emanates.

Classification: We are given a sample of individuals, or the whole population, and the problem is to classify them into groups which shall be as distinct as possible.

Dissection: We are given a sample or population and wish to divide it into groups, whether the border lines of subdivision are natural or not.

In PAPER 2, principal component factor analysis has been used to classify the samples into groups in the sense of Kendall and Stuart (1966). The unknown samples were allotted to the groups, addressing the problem of ‘discrimination’ as defined by Kendall and Stuart (1966). PAPER 2 presents the results of a principal component factor analysis, applied in such a way that the unknown groundwater samples could be individually defined, and shows how this information can be applied.

241 THICKNESS OF THE BASALT AQUIFER

Based on the results of the principal component factor analysis used to define the likely host rocks of groundwaters from unidentified lithological units, presented in PAPER 2, the thickness of the basalt aquifer could be better defined. For those samples defined as being basalt-hosted groundwaters, the depths of the screened intervals or open-holes were noted, and added to the known depth of basalt in the area (Buck 1999). A spatial interpolation was undertaken using an inverse distance weighted method (based on 12 nearest neighbours and a power of 2) in the ArcView (v. 3.1) geographic information system. A map of the interpreted basalt thickness with a grid cell size of 500 m is shown in Figure 1, colour coded according to depth ranges. This map of the basalt thickness in the Atherton Tablelands region improves the understanding of the potential extent of the groundwater resource.

Figure 1. Basalt aquifer thickness – an interpolation based on data compiled by Buck (1999) and additional unknown bores defined as ‘basalt’ from a principal component factor analysis of hydrochemical data (PAPER 2).

242 GROUNDWATER FLOW DIRECTIONS INFERRED FROM THE PRINCIPAL COMPONENT FACTOR ANALYSIS

The principal component factor analysis of the basaltic hydrochemistry also enabled the interpretation of the likely hydrogeochemical processes controlling the composition of the groundwaters in the Atherton Tablelands region (PAPER 2). The principal factor (factor I) controlling the groundwater composition of the basaltic + - 2+ 2+ groundwater is dominated by strong loadings of Na , HCO3 , Mg and Ca , as well as electrical conductivity, and accounts for almost 39 % of the variance in the data. As discussed in PAPER 2 factor I is interpreted to be controlled by silicate weathering processes.

Factor scores may be used as an indication of the degree to which interpreted processes control the groundwater composition; they can therefore be used as an indicator of groundwater residence and flow directions. The results of a spatial interpolation of the scores for factor I using an inverse distance weighted method in ArcView (v. 3.1), are shown in Figure 2, coloured from blues (low factor scores) to reds (high factor scores). Inferred groundwater flow directions are also shown.

% Legend Walkamin Factor I Scores Lake -3.47 - -2.39 Tinaroo -2.39 - -1.8 -1.8 - -1.22 -1.22 - -0.64 -0.64 - -0.05 -0.05 - 0.53 0.53 - 1.12 1.12 - 1.7 1.7 - 2.28 2.28 - 2.87 Tolga 2.87 - 3.45 3.45 - 4.04 % % Kairi 4.04 - 4.62 4.62 - 8.96 reek lin C 024Kilometers Maz

Groundwater % flow direction Atherton (inferred)

r e iv R n o rr a B

% N o r Malanda th J R o h iv n e s r to n e Upper % Barron

N

Figure 1. Grid of Factor I scores (PAPER 2), based on an inverse distance weighted interpolation, and inferred groundwater flow directions.

243 References

BUCK L.J. 1999. Physical features of volcanism and their relationship to groundwater, Atherton Basalt Province, North Queensland. BAppSc(Hons) thesis, Queensland University of Technology, Brisbane (unpubl.), pp. 178. KENDALL M.G. & STUART A. 1966. The Advanced Theory of Statistics, Vol. 3, Design and Analysis, and Time Series. Charles Griffin, London, pp. 552.

244 APPENDIX VI

Extracts from Locsey and Cox (unpubl.), a report submitted to QDNR&M and LWRRDC, and additional related work

GROUNDWATER RECHARGE: A CHLORIDE MASS BALANCE APPROACH

245 APPENDIX VII

Chemical Analysis of Natural Waters

The physical parameters of the groundwater samples were measured in the field, and included the parameters electrical conductivity, pH and Eh (referenced to standard hydrogen electrode). Two water samples were then collected in polyethylene or polypropylene, acid washed bottles for analysis of chemical parameters in the ° laboratory. One of these samples was preserved by storage at 4 C for anion analysis. The other was preserved by acidifying to < pH 2 with nitric acid for analysis of cations.

Rain water samples provided by CSIRO Land and Water were analysed for the major cations using ICP-OES, and for the anions, chloride, sulfate, nitrate, phosphate and bromide by ion chromatography (IC) using a Dionex DX300 ion chromatograph. The analyses were checked by calculating the cation-anion balance for each sample.

Cations in Water by Inductively Coupled Plasma – Optical Emission Spectroscopy (ICP-OES)

Cations were analysed by a Varian Liberty 200 inductively coupled plasma optical emission spectrometer (ICP-OES). The instrument was calibrated using synthetic standards. Major cations analysed were Na, K, Ca, and Mg; minor and trace cations were Fe, Al, Zn, Cu, Mn, Sr, Ba, Ti, Li and V. Silica, although not in ionic form in natural waters, was also analysed with this suite of cations. The detection limits used are shown in Table 1.

Table 1. Detection limits.

Element Minimum Detection Limits Na0.015 mg/L K0.20 mg/L Mg 0.009 mg/L Ca 0.06 mg/L Al 0.015 mg/L Si 0.18 mg/L Sr 0.0006 mg/L Mn 0.003 mg/L Fe 0.015 mg/L Zn 0.009 mg/L Cu 0.02 mg/L Ba 0.0007 mg/L Ti 0.009 mg/L Li 0.006 mg/L V0.02 mg/L

254 THEORY OF OPERATION:

The cation concentrations were measured using inductively coupled plasma - optical emission spectroscopy (ICP-OES). This technique involves the water sample being aspirated into a plasma. The intensity of characteristic wavelengths emitted by the excited analyte ions in the plasma are measured by a spectrophotometer. The measured intensity is proportional to concentration, thus concentration of ions in the sample can be determined.

SAMPLE PREPARATION:

Little or no sample preparation is required for analysis of aqueous samples by ICP- OES except for highly turbid samples, which must be filtered (0.45 μm membrane filter) before analysis. As all the water samples analysed in this study have electrical conductivities less than 4000 μS/cm, dilution before analysis was not required. Also, concentration of elements determined must be within the detection limits of the ICP- OES for the results to have analytical meaning.

ANALYTICAL ERROR:

Approximate error (based on repeat analyses) of approximately 5 %.

255 Anions in Water by Ion Chromatography (IC)

The following anions, chloride, sulfate, nitrate, phosphate and bromide, have been determined by ion chromatography (IC) using a Dionex DX300 ion chromatograph with suppressed conductivity detection. The system utilises Dionex AS14 analytical column and AG14 guard column. Conductivity suppression was by a micromembrane suppressor, Dionex CMMS-II.

DETECTION LIMITS:

A working range has been given in Table 2. This range is based on a combination of standard concentration range and instrument working range.

Table 2. Detection Limits.

Element Working Detection Limits F- 0.05 – 12 mg/L Cl- 0.5 – 150 mg/L 2- SO4 0.5 – 100 mg/L Br- 0.05 – 12 mg/L - NO3 0.05 – 12 mg/L 3- PO4 0.05 – 12 mg/L

THEORY OF OPERATION:

ION CHROMATOGRAPHIC PROCESS:

The sample is introduced in the flowing stream and carried into the anion exchange column. Ions interact with the ion exchange sites on the stationary phase in the column. Mobile phase ions (or eluent ions) compete with the sample ions for ion exchange sites on the column. Separation depends upon the different ions having different affinities for both phases. In the case of anion separations the differing affinities for stationary and mobile phases are due to the ionic charge and ion size (ionic radius) of each anion species. Once anions are separated the concentration of each species present in the sample is measured using a conductivity detector. A chromatogram displays peaks in conductivity at various retention times. Each anionic species is identified by its retention time which remains constant throughout successive runs.

STATIONARY PHASE:

The column packing material containing functionalised active sites. For anion determinations the Dionex AS14 anion exchange column is used.

256 MOBILE PHASE (OR ELUENT):

The liquid flowing though the column that contains competing ion for the active sites.

SAMPLE PREPARATION:

Little of no sample preparation is required of analysis of aqueous sample by ion chromatography. However highly turbid samples must be filtered before analysis (0.45 μm membrane filter). As all the water samples collected in this study had electrical conductivities < 700 μS/cm, dilution before analysis was not required.

REAGENTS:

Eluent: 3.5 mM Na2CO3/1.0 mM NaHCO3. Prepare diluting the 100x concentrate 100 fold (i.e. pipette 10 mL of 100x concentrate into a 1000 mL volumetric flask and dilute to the mark with ultra pure water). Fill eluent bottle with this solution and sparge with argon for at least ten minutes before starting eluent pump.

Regenerant solution: Add 2.4 mL of conc H2SO4 to 1000 mL of ultra pure water and dilute further to 2000 mLs. Fill regen bottle with this solution recap and allow to pressurise. After several minutes ensure regen solution is flowing through suppressor.

RESULTS:

Ion chromatography is an excellent method of anion species determination in water samples. It has an extremely good precision with a % RSD of < 2 %. However it is important that results obtained are not taken on face value but are checked to assure data is reasonable. This is particularly important as peaks can be misnamed due to small shifts in retention time. The retention time can change due to a variety of reasons most commonly due to problems with the eluent pump, blockages and inaccurate preparation of eluent. Always check with previous days data to determine if retention times have not changed. Also data should be with the working range of each species listed above.

257 Alkalinity – Acid titration method

Alkalinity or the acid neutralising capacity is determined by acid titration. Alkalinity - - is primarily a function of the carbonate (CO3 ), bicarbonate (HCO3 ) and hydroxide (OH-) ion content. The procedure below is based on that of Greenberg et al. (1992).

DETECTION LIMITS:

0.25 ppm Alkalinity (CaCO3) (mg/L water)

- HCO3 0.305 mg/L

2- CO3 0.15 mg/L

APPARATUS:

250 mL conical flask, calibrated pH meter and 25 mL burette

SAFETY EQUIPMENT:

Laboratory coat, safety glasses.

REAGENTS:

0.1N Standard HCl: SAFETY: This dilution must be carried out in a fume cupboard. Pipette 10 mLs of conc HCl (10 M) into a 1000 mL volumetric flask and dilute to mark.

Standardisation of 0.1 N HCl:

Weigh 0.7 - 0.8 g of pure sodium tetraborate by difference into a 150 mL conical flask, dissolve in about 50 mLs of distilled water and add a few drops of methyl red indicator. Titrate the sodium tetraborate solution with the 0.1N HCl as the titrant until the colour changes to pink. Record the volume of HCl used. Carry out this procedure in triplicate. Use the following equation to calculated the normality of the acid solution.

N HCl = Weight of Na2B4O7 / 190.72 x Vol of Titrant (HCl)

0.02 N Standard HCl: Pipette 200 mLs of standard 0.1N HCl into a 1000 mL volumetric flask and dilute to the mark.

258 PROCEDURE:

The alkalinity of a sample is due to the presence of hydroxide, carbonate or bicarbonate ions. The concentration of each of these ions in a sample can be calculated once the phenolphthalein and total alkalinity have been determined.

1) DETERMINATION OF PHENOLPHTHALEIN ALKALINITY (P)

Pipette 100 mLs of sample into a 250 mL beaker. Measure the pH of the sample.

If pH is less than 8.3 go on to step 2) as P=0.

If pH is greater than 8.3 then titrate the sample with 0.1N HCl to pH 8.3. Use a magnetic stirrer and leave pH probe in sample while titrating. Record volume of HCl used. Calculate alkalinity due to hydroxide, P, by using calculation (a). Go to step 2.

2) DETERMINATION OF TOTAL ALKALINITY (T)

Titrate the sample to the pH 4.7 if the sample alkalinity is unknown. If known choose the appropriate total alkalinity equivalence point from the following table.

The pH values shown in Table 3 are suggested equivalence points for the corresponding alkalinity concentrations.

Table 3. Alkalinity equivalence points.

Alkalinity (mg/L CaCO3) End Point pH: Total 30 4.9 150 4.6 500 4.3 Silicates, phosphates known or suspected 4.5 Industrial waste or complex system 4.5

Record total volume of HCl titrated i.e. include volume of titrant used in step 1

If appropriate, calculate the Total Alkalinity, T, using calculation (b).

If Total Alkalinity, T, is less than 20 mg/L CaCO3 go to step 3.

If Total Alkalinity, T, is greater than 20 mg/L CaCO3 go to step 4.

259 3) Determination of total alkalinity less the 20 mg/L CaCO3 a) Pipette 100 mLs of sample into a 250 mL beaker and titrate using 0.01M HCl to an end point in the range of 4.3 to 4.7. Record the volume and the exact pH. b) Titrate the solution further to reduce the pH exactly 0.30 pH units and record volume. Use calculation (c) to determine Total Alkalinity, T.

4) DETERMINE THE RELATIONSHIP BETWEEN HYDROXIDE, CARBONATE AND BICARBONATE ALKALINITY USING THE TABLE BELOW

NOTE:As the end point is approached make smaller additions of acid and be sure that pH equilibrium is reached before adding more titrant.

CALCULATIONS: a) P (Phenolphthalein Alkalinity)

P mg/L CaCO3 = A x N x 50 000 / volume of sample

where A = mL standard acid used

N = normality of standard acid b) T (Total Alkalinity)

T mg/L CaCO3 = A x N x 50 000 / volume of sample

where A = mL standard acid used

N = normality of standard acid c) Potentiometric titration of low alkalinity (<20mg/L CaCO3):

T (Total alkalinity),

T mg/L CaCO3 = (2B - C) x N 50 000 / volume of sample

where B = mL of titrant to first recorded pH

C = total mL of titrant of reach pH 0.3 unit lower

N = normality of acid

260 The calculation of hydroxide, carbonate and bicarbonate alkalinities is based on the relationship between phenolphthalein and total alkalinity, as shown in Table 4.

Table 4. Formulas for the calculation of hydroxide, carbonate and bicarbonate alkalinities.

Result of Hydroxide alkalinity as Carbonate alkalinity as Bicarbonate alkalinity as titration CaCO3 CaCO3 CaCO3 P = 0 0 0 T P < 1/2T 0 2P T - 2P P >=1/2T 0 2P 0 P > 1/2T 2P-T 2(T-P) 0 P=T T 0 0 Where P = phenolphthalein alkalinity

T = total alkalinity

Report total alkalinity as:

"The alkalinity to pH ____ = ____ mg CaCO3/L"

To convert hydroxide, carbonate and bicarbonate expressed as alkalinity to concentration of their own species to be used in a mass balance multiply by the following factors.

Hydroxide mg/L OH- = mg/l CaCO3 x 0.34

Carbonate mg/L CO32- = mg/L CaCO3 x 0.60

Bicarbonate mg/L HCO3- = mg/L CaCO3 x 1.22

ANALYTICAL ERROR:

Approximate error (based on repeat analyses) of approximately:

Alkalinity 0.6 %

- HCO3 0.6 %

2- CO3 1.7 %

References

GREENBERG A.E., CLESCERI L.S. & EASTON A.D. 1992. Standard Methods for the Examination of Water and Waste Water. APHA-AWWA-WEF, Washington, D.C.

261 APPENDIX VIII

Mineralogical Analysis - X-ray Diffraction

GENERAL

XRD is a widely used technique for mineral identification, particularly for fine- grained materials where the grain size is too small to be usefully studied with the optical microscope. In addition, the XRD analysis can provide information on the degree of structural disorder, particle size, and the nature of isomorphous substitutions.

The method is based on the fact that X-rays are scattered by the electrons around atoms, which form the atomic layers in crystals (lattice spacings). A particular crystalline material has a particular structure or lattice. The scattered X-rays reinforce each other in directions that depend on the lattice repeat distances and the wavelength of the X-rays. The angles of diffraction give an indirect indication of the spacings (d spacings) between atomic layers and therefore can be used for mineral identification.

The advantages of the method include the fact that:

ƒ it is non-destructive,

ƒ the samples are reasonably easy to prepare,

ƒ the material can be processed even in very small quantities, and

ƒ modern computer-linked instruments are quite straightforward to operate and maintain.

The limitations of the XRD analysis include:

ƒ the method is capable of identifying only crystalline materials, and

ƒ the components of the same mineral series (i.e. micas, feldspars, amphiboles) which have very similar crystallographic structures are difficult to separate due to their very similar XRD patterns.

Mineral compositions of the samples were determined using a Philips PW 1050 diffractometer equipped with a cobalt anticathode. The identification and quantification of mineral phases was assisted by computer programs such as Jade (search-match program) and Siroquant (quantification program which expresses the composition of crystalline material within a sample in percentages of dry weight). An internal standard (10 % corundum) was also added to the samples to enable the estimation of amorphous material.

The program Jade searches a large database of mineral XRD intensity patterns and matches those ideal patterns with the experimental ones. The limitation of this

262 search-match process is that similar crystallographic structures result in similar XRD patterns and this can make mineral identification questionable, especially of minerals belonging to the same series such as plagioclase or pyroxenes.

The Siroquant program provides quantitative analyses of mineral phases based on the principle of “pattern synthesis”; an ideal XRD pattern is synthesised from basic crystal structure data for each mineral in the sample. These synthesised patterns are added and fitted by a least squares refinement to the sample pattern. The quantification of the experimental XRD trace is thus indirectly performed by quantifying the synthesised pattern. There is no perfect fit between a calculated (synthesised) pattern and the observed (experimental) one; this imperfect fit results in errors in the quantification of each mineral phase. For the current investigation, there is an error of less than1 % for most mineral phases identified; these errors are mainly based on the goodness of fit between the synthesised XRD pattern and the experimental one.

SAMPLE PREPARATION (after Bish & Post 1989; Jenkins & Snyder 1996)

Micronizing

The micronizing vessel consists of a plastic cylinder filled with 48 stacked small agate or corundum cylinders. The particle size of the sample material to be crushed in this type of mill is to be no larger than 100 microns (i.e. what is obtainable from a swing mill). Approximately 3 g of sample and 10-12 ml of alcohol are placed into the micronization vessel and then into the arm of the mill. The timer on the mill is typically set to 0.2 (hr) (i.e. 12 minutes). Other settings of the timer can be made. The slurry obtained is homogenous and the particle size is ideally in the range of 1 to 5 microns. The mixture of sample/alcohol is placed in a pre-labeled beaker and left to dry overnight in an oven at 50-60 °C. The sample will require remixing prior its use to counteract any segregation of phases during the drying step. The micronized powder is used to identify all the mineral phases of the sample providing that the phases are present in sufficient abundance.

Randomly-orientated powder samples

About 1.5-2 g of powder is lightly packed (to avoid as much as practical pressure orientation), into the back side of a circular cavity of an aluminum plate. The front face of the sample holder rests on a polished metal block. The pressing is done using a small plastic cylinder and a metal ring for guidance. After the powder is packed, the plastic cylinder and metal ring are removed and the second half of the holder is carefully clicked on. The entire holder is then lifted, inverted and placed face upward into the autosample changer carousel. When the entire batch is ready, the carousel is placed into the autosample changer and the data acquisition task begun.

263 References

JENKINS R. & SNYDER R.L. 1996. Introduction to X-ray Powder Diffractometry. Chemical Analysis, Vol. 138, pp. 231-259, Wiley, New York. BISH D.L. & POST J.E. 1989. Sample preparation for x-ray diffraction. Reviews in Mineralogy 20, 72-99. Mineralogical Society of America, Washington D.C.

264 APPENDIX IX

Field and laboratory data: groundwater and rain water samples

Abbreviations bd Below detection limit na Not analysed np Not present

GROUNDWATERS: FIELD DATA RN Sampled SWL Field EC Field pH Field Eh Temp. Screened (m) (μS/cm) (deg. C) Lithology 45431 23/10/98 145.9 6.61 343 22.8 unknown 45675 27/10/98 12.95 99 5.92 355 24.8 unknown 45789 23/10/98 112.6 6.25 372 22 unknown 45807 23/10/98 71 5.46 410 22.4 unknown 45809 23/10/98 143 6.63 370 22.1 unknown 45857 23/10/98 158.9 6.56 339 23 basalt 72094 23/10/98 156.7 6.75 318 23.1 unknown 72145 23/10/98 110.5 6.31 330 22.8 unknown 72153 23/10/98 124.1 6.56 360 23.8 basalt 72918 20/10/98 9.54 205 8.06 252 21.8 basalt 92593 23/10/98 364 6.85 70 23.7 basalt 92678 19/10/98 25.55 64 5.99 452 22.2 unknown 92679 19/10/98 44 5.67 442 22 unknown 92685 19/10/98 16.25 95 5.42 430 20.7 unknown 92689 19/10/98 34.86 56 5.05 454 21.3 unknown 92690 19/10/98 18.76 72 6.14 407 22.2 unknown 92710 19/10/98 55 5.34 461 22.6 unknown 92744 20/10/98 194 7.75 308 22.4 unknown 92753 20/10/98 24.11 251 7.09 300 22.8 basalt 92757 19/10/98 24.3 46 4.44 474 21.7 metamorphics 92760 19/10/98 17.33 84 6.43 367 22.4 unknown 92761 19/10/98 41 5.42 449 22 basalt 109030 20/10/98 15.7 257 8.25 209 22.6 unknown 109031 20/10/98 18.56 58 6.05 415 21.6 metamorphics 109043 20/10/98 47.87 193 6.63 358 21.7 unknown

265 RN Sampled SWL Field EC Field pH Field Eh Temp. Screened (m) (μS/cm) (deg. C) Lithology 109108 19/10/98 15.41 294 7.78 278 23 unknown 109109 20/10/98 108 6.26 335 22.7 granite 11000055 no sample 39 142.8 6.38 350 22.8 basalt 11000056 24/10/98 13.25 164 6.26 289 24.4 basalt 11000061 27/10/98 26.06 167.8 6.65 362 25.2 basalt 11000062 27/10/98 21.61 193.1 6.9 364 24.6 basalt 11000063 27/10/98 24.72 111.2 6.47 390 24.6 basalt 11000064 24/10/98 41.84 210 6.78 347 25.5 basalt 11000066 26/10/98 17.07 124 6 376 24.8 basalt 11000067 28/10/98 17.69 135.5 6.82 374 23.9 basalt 11000068 28/10/98 45.7 148.5 6.75 357 29.5 basalt 11000115 24/10/98 21.11 225 6.83 370 24.9 basalt 11000116 27/10/98 10.96 367 7.36 235 25 basalt/granite 11000117 28/10/98 39.39 161 6.88 344 25.8 basalt 11000118 25/10/98 15.33 338 7.35 319 25.5 basalt 11000125 24/10/98 22.7 244 7.29 318 25.5 basalt 11000126 24/10/98 50.26 203 9.9 70 24.2 basalt 11000127 24/10/98 14.67 122.2 6.8 355 24.7 basalt 11000128 24/10/98 43.1 259 9.5 125 27.6 basalt 11000129 24/10/98 32.17 266 8.42 220 26.7 basalt 11000130 28/10/98 7.61 181 6.91 358 24.8 basalt 11000132 24/10/98 20.88 132 6.9 315 25.8 basalt 11000133 26/10/98 44.96 278 6.86 300 26.5 basalt 11000134 27/10/98 11.92 67.3 5.57 434 24.9 basalt/meta Lake Eacham 19/10/98 na na na na 11000061 19/5/99 17.75 147.8 6.55 na 23.9 basalt 11000064 20/5/99 33.64 189.4 6.97 502 25.1 basalt 11000117 15/5/99 32.10 162.0 6.41 438 25.3 basalt 11000119 15/5/99 8.53 123.0 6.43 456 25.1 basalt 11000128 19/5/99 27.13 253.0 8.91 421 25.7 basalt 11000129 19/5/99 20.73 253.0 7.94 na 27 basalt 11000133 20/5/99 36.86 189.9 7.03 na 25.9 basalt 11000136 19/5/99 14.79 288.0 8.51 432 27.8 basalt 11000137 19/5/99 13.17 150.3 6.80 548 24.8 basalt 11000138 16/5/99 17.46 216.0 7.15 484 25.1 granite 11000139 15/5/99 ~18-19 225.0 6.89 443 22.3 granite 11000140 15/5/99 27.30 279.0 8.10 440 25.6 granite

266 RN Sampled SWL Field EC Field pH Field Eh Temp. Screened (m) (μS/cm) (deg. C) Lithology 11200012 16/5/99 4.06 40.0 5.04 471 25.1 metamorphics 11200018 16/5/99 34.21 264.0 9.25 381 25.9 basalt 11200019 16/5/99 11.21 68.0 6.12 466 23.9 basalt 11000066 16/10/99 13.29 107 6.69 42 24.4 basalt 11000104 15/10/99 25.05 145 5.25 314 25 granite 11000118 16/10/99 13.73 230 7.45 213 24.3 basalt 11000128 15/10/99 33.15 248 8.47 149 23.6 basalt 11000129 15/10/99 23.19 236 8.42 169 22.7 basalt 11000130 16/10/99 3.89 176 7.24 169 23.3 basalt 11000132 16/10/99 14.16 98 7.77 184 24.3 basalt 11000135 16/10/99 39.38 131 7.14 175 24.8 basalt 11000136 16/10/99 15.98 266 8.41 176 25 basalt 11000137 16/10/99 13.74 163 7.71 188 24.1 basalt 11000138 15/10/99 29.04 291 7.9 141 23.7 granite 11000139 15/10/99 20.14 190 7.52 199 20.5 granite 11000140 15/10/99 29.28 272 8.28 146 23.2 granite 11200012 15/10/99 10.16 32 7 131 22.2 metamorphics 11200018 14/10/99 35.03 252 8.24 76 21.7 basalt 11200019 14/10/99 12.18 60 7.4 168 21.8 basalt 11200142 14/10/99 14.33 51 5.36 195 21.5 basalt 11200144 15/10/99 16.77 70 6.52 201 23 metamorphics 11200146 14/10/99 9.47 284 7.72 112 16.5 metamorphics

267 GROUNDWATERS: LABORATORY DATA RN Sampled Na K Ca Mg Si Total S Mn Fe Al Sr Ti Ba V Zn Cu Li mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L 45431 23/10/98 7.789 1.919 7.956 7.377 22.13 na bd bd bd bd bd 0.007 na bd bd na 45675 27/10/98 7.519 0.921 2.579 3.945 4.134 na 0.084 0.52 bd bd bd 0.03 na 1.161 bd na 45789 23/10/98 5.988 1.648 4.637 5.609 12.46 na bd 0.08 bd bd bd 0.035 na bd bd na 45807 23/10/98 5.326 0.421 0.944 1.341 3.687 na 0.006 0.608 bd bd bd 0.029 na bd bd na 45809 23/10/98 7.66 1.405 7.586 7.925 20.99 na 0.009 0.076 bd bd bd 0.009 na 0.067 bd na 45857 23/10/98 8.01 2.109 7.112 8.249 18.71 na 0.13 0.187 0.042 0.069 bd bd na bd bd na 72094 23/10/98 8.923 1.866 8.724 8.229 16.48 na 0.187 0.18 bd bd bd 0.007 na 0.021 bd na 72145 23/10/98 5.381 0.958 5.272 5.935 9.931 na bd bd bd bd bd 0.016 na bd bd na 72153 23/10/98 6.211 1.994 6.458 7.63 12.09 na 0.009 1.946 0.373 bd bd 0.015 na 0.109 0.054 na 72918 20/10/98 13.11 2.972 12.73 6.565 12.23 na 0.049 bd bd 0.019 bd 0.002 na bd bd na 92593 23/10/98 49.97 3.079 8.939 6.158 31.51 na 0.205 2.207 bd bd bd 0.058 na bd bd na 92678 19/10/98 4.564 1.543 3.106 2.405 11.01 na 0.016 0.293 bd bd bd 0.007 na 0.131 0.136 na 92679 19/10/98 4.06 0.509 0.314 1.018 5.554 na bd 0.375 bd bd bd 0.011 na 0.025 0.045 na 92685 19/10/98 9.027 0.768 0.465 2.578 5.632 na 0.046 0.017 0.051 bd bd 0.021 na 0.035 bd na 92689 19/10/98 4.497 0.37 0.2 1.252 4.57 na 0.1127 0.2564 0.17 bd bd 0.021 na 0.023 bd na 92690 19/10/98 4.748 0.707 2.4 2.852 8.818 na bd bd bd bd bd 0.015 na 0.02 bd na 92710 19/10/98 3.563 bd 0.433 1.402 6.382 na 0.024 0.026 0.031 bd bd 0.017 na 0.015 bd na 92744 20/10/98 9.462 1.497 11.22 10.32 12.73 na bd 0.024 bd 0.071 bd 0.006 na bd bd na 92753 20/10/98 13.38 1.935 9.16 15.01 22.9 na 0.01 0.08 bd 0.056 bd 0.003 na 0.29 0.023 na

268 RN Sampled Na K Ca Mg Si Total S Mn Fe Al Sr Ti Ba V Zn Cu Li mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L 92757 19/10/98 2.179 bd <0.06 0.86 2.85 na 0.008 0.03 0.29 bd bd 0.015 na bd bd na 92760 19/10/98 4.373 0.395 3.7 3.573 9.128 na bd 0.017 bd bd bd 0.005 na 0.084 0.069 na 92761 19/10/98 3.479 bd 0.288 0.695 3.143 na 0.004 bd bd bd bd 0.013 na bd bd na 109030 20/10/98 13.77 1.804 20.66 11.36 17.29 na 0.003 bd bd bd bd 0.004 na bd bd na 109031 20/10/98 4.005 0.407 2.458 2.002 8.606 na bd 0.397 0.383 bd bd 0.006 na bd bd na 109043 20/10/98 9.537 1.713 10.87 8.44 28.8 na bd bd bd 0.045 bd 0.002 na bd bd na 109108 19/10/98 12.87 2.18 17.57 9.746 15.94 na 0.181 0.036 bd 0.069 bd 0.013 na bd bd na 109109 20/10/98 6.479 0.51 3.775 4.038 15.11 na 0.233 2.575 bd bd bd 0.172 na 0.005 bd na 109108 19/10/98 12.87 2.18 17.57 9.746 15.94 na 0.181 0.036 bd 0.069 bd 0.013 na bd bd na 109109 20/10/98 6.479 0.51 3.775 4.038 15.11 na 0.233 2.575 bd bd bd 0.172 na 0.005 bd na 11000055no samplenananananananananananananananana 11000056 24/10/98 10.78 1.397 7.812 7.184 23.84 na bd 0.037 bd bd bd 0.003 na bd bd na 11000061 27/10/98 8.523 1.874 9.294 9.169 18.9 na bd bd bd bd bd 0.01 na bd bd na 11000062 27/10/98 8.188 3.489 11.6 11.14 17.53 na bd bd bd bd bd bd na bd bd na 11000063 27/10/98 7.223 2.024 4.812 4.142 16.64 na bd 0.019 bd 0.048 bd 0.016 na bd bd na 11000064 24/10/98 9.954 2.141 13.33 11.11 23.51 na bd 0.081 bd bd bd 0.003 na bd bd na 11000066 26/10/98 11.56 1.069 3.448 3.427 11.5 na bd bd bd 0.025 bd 0.007 na bd bd na 11000067 28/10/98 6.189 1.282 5.826 6.698 11.67 na 0.087 0.166 0.016 0.059 bd 0.006 na bd bd na 11000068 28/10/98 7.587 1.935 6.841 6.38 23.23 na bd 0.086 bd 0.065 bd 0.003 na bd bd na 11000115 24/10/98 9.946 2.110 11.58 9.539 19.28 na bd 0.093 0.08 0.108 bd 0.005 na bd bd na 11000116 27/10/98 69.11 1.207 6.167 5.546 17.63 na 0.059 0.122 bd bd bd 0.003 na bd bd na

269 RN Sampled Na K Ca Mg Si Total S Mn Fe Al Sr Ti Ba V Zn Cu Li mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L 11000117 28/10/98 8.427 2.674 8.43 6.881 19.98 na bd bd bd 0.089 bd 0.004 na bd bd na 11000118 25/10/98 15.28 1.349 16.92 21.56 16.82 na bd bd bd 0.022 bd 0.003 na bd bd na 11000125 24/10/98 9.603 2.363 20.02 10.43 21.71 na bd 0.104 bd bd bd 0.048 na bd bd na 11000126 24/10/98 17.66 3.627 85.79 16.28 43.59 na na na na 0.259 bd bd na 0.225 0.035 na 11000127 24/10/98 4.95 1.187 9.051 4.223 13.62 na 0.008 0.274 0.191 bd bd 0.015 na bd bd na 11000128 24/10/98 20.01 2.851 18.82 8.256 13.74 na 0.032 0.121 bd bd bd 0.086 na bd bd na 11000129 24/10/98 12.94 2.217 21.31 10.7 13.46 na 0.003 0.098 bd bd bd 0.01 na bd bd na 11000130 28/10/98 8.775 1.899 10.36 9.28 22.44 na bd bd bd 0.085 bd 0.017 na bd bd na 11000132 24/10/98 5.295 1.150 6.662 5.096 20.68 na bd 0.536 bd bd bd 0.005 na bd bd na 11000133 26/10/98 9.361 2.361 12.52 10.57 24.21 na bd bd bd bd bd 0.002 na bd bd na 11000134 27/10/98 5.662 0.681 1.246 1.753 3.968 na bd bd bd bd bd 0.052 na bd bd na Lake Eacham 19/10/98 2.649 1.003 1.503 2.49 <0.18 na bd bd bd bd bd 0.004 na bd bd na 11000061 19/5/99 7.811 1.759 8.163 8.243 21.08 0.505 bd bd bd 0.089 bd 0.010 bd bd bd 0.012 11000064 20/5/99 9.005 2.110 11.66 9.955 27.63 0.561 bd 0.024 bd 0.135 bd 0.003 bd 0.019 bd 0.017 11000117 15/5/99 8.145 2.557 7.233 5.93 25.33 0.261 bd 0.021 bd 0.104 bd 0.003 0.0210 bd bd 0.017 11000119 15/5/99 28.01 0.352 0.742 0.837 10.77 0.202 bd 0.038 0.042 bd bd bd bd bd bd 0.020 11000128 19/5/99 21.88 2.854 17.18 10.48 15.93 2.729 bd bd 0.051 0.122 bd 0.006 bd 0.011 bd 0.015 11000129 19/5/99 11.54 1.723 20.98 13.17 20.72 2.811 bd bd bd 0.178 bd 0.007 0.0495 0.012 bd 0.019 11000133 20/5/99 8.873 2.234 12.22 10.63 27.64 0.482 bd bd bd 0.132 bd 0.002 bd 0.022 bd 0.017 11000136 19/5/99 42.6 4.548 9.621 4.663 9.786 4.530 0.180 1.685 1.781 0.110 0.013 0.037 0.0146 0.026 bd 0.021 11000137 19/5/99 6.776 1.619 10.05 8.06 23.6 0.227 bd bd bd 0.113 bd 0.006 0.0313 0.014 bd 0.014

270 RN Sampled Na K Ca Mg Si Total S Mn Fe Al Sr Ti Ba V Zn Cu Li mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L 11000138 16/5/99 19.76 1.204 20.29 3.648 23.94 2.133 0.006 0.021 0.078 0.170 bd 0.008 bd 0.011 bd 0.018 11000139 15/5/99 29.12 1.839 9.334 7.313 20.34 2.109 0.079 0.051 0.488 0.041 bd 0.005 0.0277 0.010 bd 0.014 11000140 15/5/99 25.97 2.853 22.09 8.575 18.72 3.789 0.085 bd bd 0.115 bd 0.015 0.0339 bd bd 0.031 11200012 16/5/99 2.670 0.225 0.238 1.092 5.347 0.939 0.121 0.187 0.015 bd bd 0.022 bd 0.062 0.080 0.019 11200018 16/5/99 15.47 3.234 24.46 11.14 18.97 5.578 bd bd 0.364 0.150 bd 0.012 bd 0.009 bd 0.013 11200019 16/5/99 3.265 1.075 3.327 2.268 10.78 0.331 bd 0.038 0.056 0.019 bd 0.008 bd 0.017 bd 0.014 11000066 16/10/99 5.677 0.417 5.003 2.431 14.87 na 0.013 0.468 0.521 bd 0.089 0.011 na 0.477 na na 11000104 15/10/99 1.939 0.732 1.528 1.232 7.129 na 0.066 0.499 0.483 bd 0.038 0.096 na 0.023 na na 11000118 16/10/99 7.065 1.091 18.41 11.68 21.19 na bd 0.284 0.220 bd 0.027 0.003 na bd na na 11000128 15/10/99 15.48 2.115 28.71 8.864 14.77 na bd 5.000 bd bd bd 0.014 na 0.718 na na 11000129 15/10/99 7.649 1.351 41.31 10.96 18.19 na bd 0.061 0.017 0.035 bd 0.015 na bd na na 11000130 16/10/99 4.997 1.442 17.32 7.446 28.24 na bd 5.000 bd bd bd 0.011 na 0.227 na na 11000132 16/10/99 3.35 1.005 7.91 3.541 21.56 na bd 0.193 0.172 bd 0.021 0.007 na 0.038 na na 11000135 16/10/99 4.589 0.740 9.581 4.724 19.91 na bd 0.062 bd bd bd 0.01 na 0.057 na na 11000136 16/10/99 40.9 3.024 7.865 2.576 9.747 na bd 0.987 1.328 bd 0.055 0.014 na 0.030 na na 11000137 16/10/99 4.794 1.494 17.22 6.482 21.81 na bd 0.065 0.016 bd bd 0.017 na 0.093 na na 11000138 15/10/99 6.582 0.965 28.58 13.32 26.58 na bd 0.039 bd bd bd 0.014 na 0.044 na na 11000139 15/10/99 17.73 1.592 12.29 5.75 18.62 na bd 0.089 0.309 bd 0.017 0.01 na 0.065 na na 11000140 15/10/99 19.93 2.006 33.08 8.083 19.56 na bd 0.100 0.062 bd 0.017 0.029 na 0.347 na na 11200012 15/10/99 1.174 0.354 5.41 0.805 5.832 na 0.067 0.116 0.093 bd bd 0.026 na 0.363 na na

271 RN Sampled Na K Ca Mg Si Total S Mn Fe Al Sr Ti Ba V Zn Cu Li mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L 11200018 14/10/99 11.93 2.625 53.84 6.708 14.71 na bd 5.000 bd bd bd 0.019 na 0.026 na na 11200019 14/10/99 1.481 0.537 8.456 2.033 12.59 na bd 0.555 0.301 bd 0.043 0.009 na bd na na 11200142 14/10/99 1.246 0.092 6.053 1.493 5.051 na 0.190 0.100 0.113 bd bd 0.027 na 0.039 na na 11200144 15/10/99 6.716 0.248 3.48 0.19 6.783 na 0.074 7.582 0.387 bd 0.194 0.033 na 0.049 na na 11200146 14/10/99 29.49 1.098 30.7 3.702 9.3 na bd 0.285 0.481 bd 0.07 0.032 na bd na na

272 GROUNDWATERS: LABORATORY DATA (continued)

RN Sampled HCO3 CO3 Cl SO4 FBrNO3 PO4 Ionic Balance mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L % 11000061 19/5/99 64.29 np 11.32 1.33 bd bd 5.46 bd 0.5 11000064 20/5/99 87.13 np 11.39 1.39 bd bd 5.62 bd 0.4 11000117 15/5/99 50.47 np 11.41 0.18 bd bd 5.02 0.21 1.6 11000119 15/5/99 61.63 np 6.22 1.27 bd bd 2.69 bd 3.4 11000128 19/5/99 144.78 0.26 7.69 0.70 bd bd bd bd 2.6 11000129 19/5/99 140.80 np 6.42 0.55 bd bd 3.55 bd 2.5 11000133 20/5/99 92.45 np 13.15 2.55 0.11 0.28 1.98 0.31 1.5 11000136 19/5/99 139.74 np 5.46 3.67 bd bd bd 0.41 10.6 11000137 19/5/99 70.66 np 9.85 1.24 bd bd 2.61 bd 0.2 11000138 16/5/99 118.49 np 8.14 2.37 bd bd bd 0.76 0.6 11000139 15/5/99 126.45 np 6.79 1.31 bd bd 0.15 0.14 3.7 11000140 15/5/99 158.33 np 9.90 3.44 0.41 bd 0.01 bd 0.9 11200012 16/5/99 3.72 np 6.78 1.77 bd bd 0.63 bd 9.5 11200018 16/5/99 126.47 14.63 5.38 4.59 bd bd 0.11 0.30 2.1 11200019 16/5/99 19.65 np 4.89 0.22 bd bd 4.90 bd 0.5 11000066 16/10/99 22.32 np 16.25 2.07 bd 0.08 12.97 0.15 -14.2 11000104 15/10/99 0.80 np 7.69 4.63 bd bd 0.96 bd 3.7 11000118 16/10/99 131.76 np 9.29 0.72 bd 0.1 0.11 0.19 -4 11000128 15/10/99 164.17 np 8.74 1.74 bd bd bd bd 3.3 11000129 15/10/99 176.39 np 7.38 0.88 bd bd 2.09 0.32 2.7 11000130 16/10/99 98.82 np 10.26 1 bd bd 0.97 0.6 1 11000132 16/10/99 53.13 np 4.51 0.13 bd bd 2.91 0.15 -8.4 11000135 16/10/99 53.13 np 8.01 0.42 bd bd 5.57 0.05 -4.7 11000136 16/10/99 159.39 np 5.92 6.6 bd bd bd 0.19 -4.6 11000137 16/10/99 84.48 np 11.41 0.7 bd bd 3.73 0.09 -4 11000138 15/10/99 165.77 np 8.36 2.77 bd bd 0.21 0.43 -3.3 11000139 15/10/99 125.39 np 6.82 2.81 bd bd bd 0.12 -8.7 11000140 15/10/99 175.33 np 9.9 3.13 0.3 0.07 3.38 bd -0.5 11200012 15/10/99 16.47 np 6.36 1.5 bd bd 0.52 bd -7 11200018 14/10/99 188.62 np 9 9.47 0.07 bd 2.01 0.23 6.6 11200019 14/10/99 34.00 np 4.88 1.12 bd bd 2.34 bd -1.5 11200142 14/10/99 0.53 np 4.76 17.55 bd bd 2.82 bd -4.3 11200144 15/10/99 15.94 np 15.29 9.59 bd bd 0.98 bd 2.6 11200146 14/10/99 167.36 np 11.84 7.59 bd 0.08 1.44 bd -0.6

273 RAINWATERS: LABORATORY DATA

Monthly rainfall samples were collected by CSIRO Land and Water from five rainfall stations between October 1998 and November 1999, and analysed at QUT (all units in mg/L).

Malanda Cl Br NO3 PO4 SO4 Mg Na Ca K 1/10/98 3.23 bd 0.84 bd 1.64 0.221 1.573 0.125 bd 9/11/98 2.40 bd 8.90 0.69 1.41 0.352 1.315 0.309 0.513 9/12/98 1.59 bd 1.75 bd 0.68 0.012 0.624 bd bd 20/01/99 1.44 bd 8.92 bd 0.43 0.106 0.637 0.103 0.507 15/02/99 1.20 bd 0.40 bd 0.37 0.052 0.353 bd bd 3/3/99 1.41 bd 0.30 bd 0.35 0.046 0.272 bd bd 4/5/99 3.40 bd bd bd 0.58 0.181 1.655 bd bd 15/6/99 3.26 bd 0.18 bd 0.84 bd bd bd bd 15/7/99 1.67 bd 0.12 bd 0.51 bd bd bd bd 25/8/99 5.69 bd 0.65 bd 1.38 0.018 bd bd bd 13/9/99 2.46 bd 0.44 bd 0.65 bd bd bd bd 5/11/99 4.08 bd 0.19 bd 0.95 bd bd bd bd 29/11/99 1.09 bd 0.51 bd 0.51 bd bd bd bd

Walkamin Cl Br NO3 PO4 SO4 Mg Na Ca K 9/11/98 2.57 bd 0.89 bd 1.01 0.137 0.840 0.170 bd 9/12/98 0.71 bd bd bd 0.69 0.031 0.112 bd bd 20/01/99 2.89 bd bd 1.48 2.44 0.327 1.077 0.114 2.286 15/02/99 0.23 bd bd 0.14 0.33 bd bd bd bd 3/3/99 1.72 bd 0.17 bd 0.82 bd 0.342 0.664 bd 31/03/99 1.74 bd 0.12 bd 0.80 0.054 0.496 0.145 bd 15/6/99 5.90 bd 57.10 12.80 7.10 1.373 6.298 4.371 5.274 15/7/99 0.63 bd 6.66 2.61 0.89 bd bd bd 0.703 25/8/99 5.50 bd 124.30 9.10 5.00 1.860 8.069 3.956 7.750 05/11/99 6.20 bd 87.90 21.70 12.00 2.095 6.189 3.860 11.270

Atherton Cl Br NO3 PO4 SO4 Mg Na Ca K 9/11/98 2.83 bd bd 0.39 1.37 0.212 0.912 1.124 0.379 9/12/98 3.36 bd bd 0.14 1.05 0.246 1.373 0.923 0.561 20/01/99 2.78 bd 8.20 0.17 0.93 0.272 0.845 0.623 0.515 15/02/99 1.21 bd 2.08 0.33 0.50 bd 0.140 0.081 bd 3/3/99 0.99 bd 0.41 0.89 1.60 0.144 0.053 0.134 1.418 31/03/99 1.33 bd 2.83 0.15 0.71 bd 0.216 0.152 bd

274 Atherton Cl Br NO3 PO4 SO4 Mg Na Ca K 30/04/99 4.38 bd 38.91 0.53 3.60 unreliable unreliable unreliable unreliable 31/05/99 1.11 bd 3.50 0.39 1.59 0.106 0.182 unreliable bd 15/6/99 3.20 0.20 26.20 2.00 3.30 0.456 bd 1.828 1.555 15/7/99 0.62 bd 7.34 0.99 0.49 bd bd 0.154 bd 25/8/99 4.06 bd 11.85 1.32 2.00 0.175 bd 1.549 0.471 13/9/99 1.59 bd 6.39 1.20 0.76 bd bd 0.328 0.887 05/11/99 3.16 bd 19.20 4.73 0.13 0.108 bd 0.543 1.000 29/11/99 0.69 bd 1.78 0.51 0.63 bd bd bd 0.200

Upper Barron Cl Br NO3 PO4 SO4 Mg Na Ca K 1/10/98 3.07 bd 7.62 1.98 1.58 0.384 1.361 0.416 1.030 9/11/98 2.15 bd 2.15 0.58 1.15 0.219 0.875 0.726 0.571 9/12/98 1.34 bd 1.01 0.89 0.66 0.026 0.386 0.089 0.376 20/01/99 1.53 bd 26.45 2.50 2.22 0.376 0.462 0.388 1.510 15/02/99 1.94 bd 16.87 0.17 1.58 0.029 0.123 bd 0.282 3/3/99 1.59 bd 12.03 bd 0.87 0.089 0.334 0.064 0.846 31/03/99 0.83 bd 0.11 0.29 0.21 bd 0.045 bd bd 15/6/99 3.20 bd 16.26 2.77 2.36 0.246 bd bd 1.972 25/8/99 5.65 bd 18.77 2.04 4.50 0.366 4.390 1.030 1.041 13/9/99 3.20 bd 0.34 bd 3.15 0.165 2.330 0.956 bd 05/11/99 4.24 bd 3.02 1.07 1.21 0.383 0.145 0.237 2.189 29/11/99 1.16 bd 7.07 0.46 0.75 bd bd bd 0.411

Millaa Millaa Cl Br NO3 PO4 SO4 Mg Na Ca K 1/10/98 3.16 bd 0.09 bd 0.73 0.522 1.362 0.174 bd 9/11/98 3.83 bd 1.68 bd 0.55 0.207 1.876 0.217 0.515 9/12/98 2.34 bd 0.07 0.12 0.46 bd 0.874 bd bd 20/01/99 1.40 bd bd bd 0.09 bd 0.348 bd bd 15/02/99 1.26 bd 0.06 bd 0.21 0.015 0.152 bd bd 31/03/99 1.47 bd 0.07 bd 0.15 bd 0.090 bd 0.737 4/5/99 2.71 bd bd bd 0.45 0.043 1.077 bd bd 15/6/99 1.58 bd 0.03 bd 0.23 bd bd bd bd 15/7/99 1.61 bd 3.67 1.02 0.62 0.033 bd 0.294 0.491 25/8/99 4.21 bd 0.03 bd 0.34 0.044 0.105 bd bd 13/9/99 3.79 bd 0.04 0.25 0.27 bd 0.156 bd bd 5/11/99 4.54 bd bd bd 0.26 0.053 0.551 bd bd 29/11/99 2.82 bd bd bd 0.39 bd bd bd bd

275 APPENDIX X

Mineralogical data

RESULTS OF QUANTITATIVE ANALYSES OF MINERAL PHASES USING SIROQUANT FOR SAMPLES FROM BORE 11000128. (results expressed as wt%) Bore/sample depth Pyroxenes1 Plagioclase K-feldspar Kaolinite Smectite Quartz Muscovite Others2 11000128 / 6m 4.0 4.2 1.7 86.9 1.1 0.9 0.0 1.1(Gd) 11000128 / 19m 2.9 9.4 8.5 73.3 1.8 1.4 0.0 2.8(H,Gd,P) 11000128 / 23m 7.9 36.9 14.2 20.4 17.8 0.6 0.0 2.2 (H,Gd) 11000128 / 32m 13.1 33.9 10.3 17.8 20.0 0.6 0.0 4.2(H,Gd,C,S) 11000128 / 39m 28.0 40.3 11.9 13.3 2.8 0.0 0.0 3.8(Gd,H,C,S) 11000128 / 43m 36.6 44.0 9.9 2.4 3.1 0.6 0.0 3.4(H,Gd,S) 11000128 / 50m 30.6 51.2 11.3 2.7 0.7 0.6 0.0 2.9(H,Gd,S) 11000128 / 52m 30.5 45.4 7.6 2.7 8.7 0.6 0.0 4.6(H,Gd,Gt,S) 11000128 / 58m3 67.3 3.7 5.0 6.9 8.5 0.0 0.0 8.0(Gt,Gb,Gd,H,S,P) 11000128 / 59m3 71.0 2.6 7.6 3.3 4.5 traces 0.0 11.2(Gt,Gb,S,Gd,H) 11000128 / 65m 39.3 36.4 8.2 3.2 8.8 0.6 0.0 3.5(Gd,H,Gt,S) 11000128 / 67m 37.2 44.7 6.8 3.5 4.9 traces 0.0 3.0(Gd,Gt,H,S,Gb) 11000128 / 74m 37.3 39.0 5.8 10.2 4.7 traces 0.0 3.0(Gd,H,Gt,Gb,S) 11000128 / 78m 38.2 39.6 5.5 9.1 3.8 traces 0.0 3.7(Gd,S,H) 11000128 / 82m 39.4 40.2 4.3 10.2 3.2 traces 0.0 2.7(Gd,S,H) 11000128 / 85m 12.2 13.1 2.7 16.8 29.2 26.0 0.0 0.0 11000128 / 89m 0.0 28.8 11.1 2.3 0.4 47.0 10.3 0.0 1 diopside and/or augite 2 hematite (H), siderite (S), goethite (Gt), gibbsite (Gb), pyrite (P) and zeolites such as gottardiite/boggsite (Gd) and chabazite (C), listed in order of decreasing abundance. 3 approximate values as sample also contains small amounts of analcime, which is unquantified.

276 RESULTS OF QUANTITATIVE ANALYSES OF MINERAL PHASES USING SIROQUANT FOR SAMPLES FROM BORE 11000133. (results expressed as wt%) Bore/sample depth Pyroxenes1 Plagioclase K-feldspar Kaolinite Smectite Quartz Muscovite Others2 11000133 / 8m 2.2 1.7 1.2 71.4 0.0 0.0 0.0 23.4(H,Gb) 11000133 / 19m 17.7 27.6 18.5 26.6 1.4 traces 0.0 8.2(C,Gd,H,S,P) 11000133 / 25m 27.9 33.1 10.1 19.8 traces traces 0.0 9.1(C,H,Gd,P) 11000133 / 32m 15.9 52.2 13.6 12.7 2.1 traces 0.0 3.4(H,Gd,P) 11000133 / 36m 29.1 48.8 10.0 7.5 0.8 traces 0.0 3.8(S,Gd) 11000133 / 43m 25.2 52.2 11.1 9.0 0.8 0.0 0.0 1.7(Gd,P) 11000133 / 49m 29.4 44.1 10.2 11.5 traces traces 0.0 4.8(Gd,C,S,H,P) 11000133 / 50m 27.8 42.3 11.8 12.7 traces traces 0.0 5.3(C,Gd,S,H,P) 11000133 / 54m (zeolite) 4.5 22.7 4.9 55.6 5.3 1.3 0.0 5.7(S,C,Gd) 11000133 / 54m 9.4 47.9 18.2 16.9 5.2 1.1 0.0 1.1(Gd) 11000133 / 55m (zeolite) 7.3 3 27.7 11.5 40.0 6.2 1.5 0.0 5.7(C,Gd,H,P) 11000133 / 56m 6.7 3 29.0 14.0 36.7 6.4 1.3 0.0 5.9(H,C,Gd,P) 11000133 / 58m 6.2 29.9 15.0 35.7 10.2 traces 0.0 2.9(H,Gd,C,P) 11000133 / 60m 5.7 32.2 14.9 34.0 10.3 traces 0.0 2.9(H,Gd,C,P) 1 diopside and/or augite 2 hematite (H), siderite (S), gibbsite (Gb), pyrite (P) and zeolites such as gottardiite/boggsite (Gd) and chabazite (C), listed in order of decreasing abundance. 3 includes traces of olivine, most likely fayalite. Note: Samples 11000133 / 54m (zeolite) and 11000133 / 55m (zeolite) were selectively sampled to determine zeolite phases clearly observable in hand specimens.

277 RESULTS OF QUANTITATIVE ANALYSES OF MINERAL PHASES USING SIROQUANT FOR SAMPLES FROM BORE 11000136. (results expressed as wt%) Bore/sample depth Pyroxenes1 Plagioclase K-feldspar Kaolinite Smectite Quartz Muscovite Others2 11000136 / 16m 5.3 25.6 9.4 43.7 12.5 1.3 0.0 2.2(Gd,C,S,P) 11000136 / 19m 10.8 45.2 11.3 27.5 3.1 traces 0.0 2.1(Gd) 11000136 / 29m 10.8 41.4 12.5 23.5 9.6 traces 0.0 2.1(Gd) 11000136 / 33m 0.0 6.1 2.7 61.8 2.6 21.5 0.0 5.4(H,Gd) 11000136 / 42m 2.6 16.1 9.1 30.1 39.5 0.6 0.0 2.0(Gd) 11000136 / 52m 10.5 33.9 11.5 11.8 27.8 0.6 0.0 3.9(Gd,H,P) 11000136 / 54m 7.0 29.1 11.0 11.8 36.7 1.2 0.0 3.2(Gd,H,P) 11000136 / 60m 19.7 33.7 10.2 18.9 14.4 1.5 0.0 1.7(Gd,C) 11000136 / 68m 9.7 9.7 5.8 36.2 33.9 1.5 0.0 3.1(Gd,H,S,C,P) 11000136 / 69m 8.4 10.3 4.9 37.1 34.8 1.2 0.0 3.1(Gd,C,S,H,P) 11000136 / 79m 38.5 40.9 6.9 4.2 2.0 1.7 0.0 5.8(Gd,H,S,P) 11000136 / 86m 22.4 19.1 3.3 23.5 7.1 5.9 18.8 0.0 11000136 / 91m 0.0 31.0 0.0 8.5 0.2 6.5 47.5 6.3 (Ch) 1 diopside and/or augite 2 hematite (H), siderite (S), gibbsite (Gb), pyrite (P), chlorite (Ch) and zeolites such as gottardiite/boggsite (Gd) and chabazite (C), listed in order of decreasing abundance.

278 APPENDIX XI

Groundwater data sourced from the Queensland Department of Natural Resources and Mines

Groundwater data provided by the Queensland Department of Natural Resources and Mines (QDNR&M) has been used in this study, in conjunction with data generated by the author (APPENDIX IX). Data sourced from QDNR&M has been used in work presented in PAPERS 1A, 1B and 2, as well as in APPENDICES I, II, IV, V and VI. Over 800 sets of groundwater chemistry analyses from 1950 to 1999 were provided by QDNR&M for the bores listed below. Most of these analyses are for bores sampled since 1990, with 25 % from bores sampled between 1990 and 1997, and 50 % from bores sampled during 1998 and 1999.

The data is publicly available by contacting the Senior Scientist, Resource Condition and Trend, Resource Sciences and Knowledge, QDNR&M (Tel. (07) 3896 9816).

GROUNDWATER BORE NUMBERS

45004 45412 45542 45769 45835 45928 78185 45155 45417 45544 45781 45837 45930 78186 45156 45418 45547 45782 45840 45969 78187 45159 45420 45549 45783 45841 45970 78188 45170 45425 45612 45785 45846 45971 78190 45201 45427 45629 45789 45847 45980 78191 45365 45428 45645 45790 45852 45981 78195 45366 45429 45647 45791 45853 72010 78203 45367 45431 45655 45794 45854 72011 78281 45368 45433 45656 45795 45855 72020 78303 45371 45438 45659 45797 45857 72094 78425 45375 45477 45660 45804 45858 72145 78445 45380 45509 45661 45807 45859 72153 78755 45381 45520 45675 45808 45861 72155 78760 45396 45524 45678 45809 45863 72156 78779 45397 45525 45679 45818 45865 72393 78794 45399 45526 45680 45819 45866 72628 92379 45400 45527 45692 45820 45869 72770 92549 45403 45529 45693 45821 45870 72918 92593 45404 45530 45703 45822 45875 78001 92593 45405 45531 45715 45823 45888 78141 92668 45406 45532 45717 45825 45889 78156 92670 45407 45533 45720 45826 45891 78181 92671 45410 45534 45725 45829 45910 78182 92672 45411 45535 45726 45831 45916 78183 92673

279 GROUNDWATER BORE NUMBERS (continued)

92675 92739 92790 109053 109108 11000006 11000118 92676 92740 92791 109054 109109 11000007 11000119 92677 92741 92793 109059 109110 11000008 11000120 92678 92742 92794 109061 109112 11000055 11000125 92679 92743 92795 109062 109113 11000056 11000126 92680 92744 92796 109063 109114 11000057 11000127 92681 92746 92797 109064 109115 11000059 11000128 92682 92748 92798 109068 109116 11000060 11000129 92683 92749 92799 109069 109117 11000061 11000130 92684 92750 99076 109073 109119 11000062 11000131 92685 92751 109003 109076 109120 11000063 11000132 92686 92752 109006 109077 109121 11000064 11000133 92687 92753 109008 109078 109122 11000066 11000134 92688 92754 109009 109082 109123 11000067 11000135 92689 92755 109019 109083 109124 11000068 11000136 92690 92757 109022 109085 109125 11000079 11000137 92691 92758 109023 109086 109126 11000080 11000138 92692 92760 109024 109087 109127 11000081 11000139 92710 92761 109030 109088 109128 11000082 11000140 92725 92763 109031 109089 109135 11000083 11200012 92727 92764 109032 109090 109137 11000103 11200013 92728 92765 109033 109093 109141 11000104 11200018 92729 92766 109034 109096 109142 11000105 11200019 92730 92767 109043 109098 11000001 11000114 11200142 92731 92768 109045 109099 11000002 11000115 11200144 92736 92776 109048 109102 11000003 11000116 11200145

280