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UNIVERSITY OF LONDON

IMPERIAL COLLEGE OF SCIENCE AND TECHNOLOGY

Royal School Of Mines

Department Of Geology

Applied Geochemistry Research Group

A Thesis Entitled

Factors Aff ecting Trace Element Distribution

In Stream Sediments In Selected Areas Of Base

Metal Mineralization In Lancashire And Cornwall.

By

Solomon Kwesi Sey

Submitted for the degree of Master of Philosophy.

March 1981. Dedicated to my daughter,

ADWOA ETSIWAA, whose arrival, courage and innocent cheerfulness in adversity, contributed a great deal to the completion of this thesis• ABSTRACT

The geochemistry of drainage sediments from two areas in Cornwall and Lancashire with similar base metal mineralization, was studied in order to assess sources of variability in trace element distribution. The Cornwall area was of uniform lithology and there was widespread development of secondary Fe-Mn oxides in drainage channels. The geology of the Lancashire area was more diverse and secondary Fe-Mn oxide development was restricted. Analysis of variance models were used to derive estimates of spatial variability (related to geology), procedural error (sampling and analytical errors) and annual variation.

Data obtained by atomic absorption spectrophotometric analysis (AAS) of the Cornwall samples had good precision and indicated that for most elements spatial variability contributed most to total variability, even in this area of uniform geology. Spatial variability for ore-elements reflected the presence of mineralization but for other elements spatial variability was attributed mainly to secondary environmental processes. In general, procedural error accounted for up to 40% of total variability and sampling errors contributed more to this than did laboratory errors •

In contrast, data obtained by analyzing the same sample solutions by inductively coupled plasma emission spectrometry (ICP) showed poor precision, with procedural errors exceeding spatial variability for most elements, and laboratory errors exceeding sampling errors. However, the data structure revealed by Principal Component and factor analysis was similar for AAS and ICP data.

The Lancashire samples were analyzed by ICP only and the data obtained had similar precision to the Cornwall ICP data. The more complex geology was reflected in increased spatial variability for some elements.

Annual variation in sediment trace-element geochemistry was evaluated for the Cornwall area only and found to be unimportant•

The influence of secondary Fe-Mn oxides on trace element distribution was confirmed for Zn, Co, Ni and Ag. The possible utility of oxide coatings on the coarse fraction of sediment as a sample medium in geochemical exploration was investigated. Oxide coatings were found to concentrate hydromorphically dispersed elements, such as Ag, whose concentrations are usually low in stream sediments, and to provide useful dispersion trains. ACKNOWLEDGEMNT

I am greatly indebted to a number of individuals and organizations for the successful completion of this study. To my supervisors, Professor J. S. Webb and Dr Martin Hale, I am grateful for their patience, understanding and continuous interest in the project.

I should like to express my gratitude to Dr R. J. Howarth for his interest throughout the study and for reading through the thesis, to my colleagues for mutually beneficial discussions and use of computer programs, and to the computer centre advisory unit, especially Messrs Roger Hunt and Tony

Davison. This thesis was typed with the Draft/Format facility.

I should also like to thank the British Council for financial assistance during the course of the study and the

Ghana Geological Survey Department for granting me the study leave to pursue it.

Finally my thanks go to Nana Aba, Paakow and Maame, for enduring the hardships and family upheavals in the last three years. LIST OF CONTENTS

Chapter one -• Introduction 1

1.1 The nature of dispersion halos and

trains 3

1.1.1 Background 3

1.1.2 Primary halo 4

1.1.3 Secondary halo 5

1.2 Secondary dispersion and geochemical

surveys 5

1.3 Aims of the research 9

Chapter two - Description of the study areas 13

2.1 The Cornwall study area 13

2.1.1 Location 13

2.1.2 Regional setting and geological

structure 13

2.1.3 Granite emplacement and mineralization 15

2.1.4 Geology of the study area 16

2.1.5 Field observations 18

2.2 The Lancashire study area 19

2.2.1 Regional setting 19

2.2.2 Mineralization 21

2.2.3 Location and geology of the study area 23

2.2.4 Field observations 26

Chapter three - Analytical techniques 28

3.1 Orientation studies 28 3.1.1 Procedures 28

3.1.2 Nitric Acid 29

3.1.3 Nitric-Perchloric attack method I 29

3.1.4 Nitric-Perchloric attack method II 30

3.1.5 Determinations 31

3.2 Results for fine fraction 31

3.2.1 Loss on ignition 31

3.2.2 Relative efficiency of extraction 33

3.2.3 Analytical precision 35

3.2.4 Anomaly contrast 36

3.2.5 Summary 38

3.3 Results for Fe Mn coatings on coarse

fraction 39

3.3.1 Relative efficiency of extraction 40

3.3.2 Analytical precision 41

3.3.3 Anomaly contrast 41

3.3.4 Summary 42

3.4 Partial extraction techniques 43

3.4.1 Sodium dithionite 43

3.4.2 Hydroxyammonium chloride 45

3.4.3 Hydrogen peroxide and hydrochloric acid 47

3.5 Procedures used in this study 50

3.5.1 Experimental 51

3.5.2 Extractibility of selected reagents 53

3.6 Alternative procedures for selected

techniques 57

3.7 Other analytical procedures 62

3.8 Summary 62 Chapter four - Analysis of variance on the data 64

4.1 Errors in geochemical data 64

4.1.1 Bias 65

4.1.2 Precision 66

4.1.3 Distribution models 66

4.2 The analysis of variance 67

4.2.1 Normality 67

4.2.2 Homogeneous variance 68

4.2.3 Error independence 69

4.2.4 Zero means 70

4.3 Sampling design 70

4.4 Models for general sampling and

laboratory errors 71

4.4.1 Comparisons between sample types and

localities 73

4.4.2 Annual variation 75

4.5 Estimated variance components in fine

fraction 76

4.5.1 Cornwall study area. AAS results 76

Analytical error 77

Sampling error 77

Spatial variation 77

4.5.2 Cornwall study area. ICP data 78

4.5.3 Lancashire study area. ICP data 79

4.5.4 Variance distribution over sample types

and localities 80

4.5.5 Sources of variability in the Cornwall

study area 84 4.5.6 Summary 86

4.6 Estimated sampling and laboratory errors

in coarse fraction 88

4.6.1 Cornwall study area 88

4.6.2 Lancashire study area 90

4.6.3 Summary 91

4.7 Annual variation 91

4.7.1 Two-way analysis of variance 91

4.7.2 T-Tests 93

(1) Comparisons with standards 94

(2) Comparisons with survey data 94

4.7.3 summary 95

4.8 Discussion 96

4.8.1 Sampling variability 96

4.8.2 Annual variation 98

Chapter five - Geochemical patterns in fine

fraction 100

5.1 Statistical distribution patterns 101

5.1.1 Cornwall study area 103

5.1.2 Lancashire study area 106

5.2 Spatial distribution patterns 107

5.2.1 Lancashire study area 107

5.2.1.1 Total and partial correlations 109

5.2.1.2 Principal component and factor analyses 118

A. Principal component analysis 119

B. Factor analysis 120

5.2.1.3 Regression of background factors on

ore metals 124 5.2.2 Cornwall study area 125

5.2.2.1 AAS data 125

5.2.2.2 ICP data 129

5.2.2.3 Summary 134

5.2.2.4 Total and partial correlations 135

5.2.2.5 Principal component analysis 140

(1) AAS data 140

(2) ICP data 142

5.2.2.6 Factor analysis 143

5.2.2.7 Regression of backgound factors on

ore metals 146

5.3 Patterns in data normalized on

Fe + Mn 147

5.4 Summary and discussion 151

Chapter six -- Hydrogeochemical trends 160

6.1 Trends in Newquay waters 161

6.2 Trends in Clitheroe waters 164

6.3 Summary 166

Chapter seven - Geocheraical patterns in

oxide coatings 167

7.1 Clitheroe study area 170

7.1.1 Spatial distribution patterns 170

A. Non-normalized data 170

B. Normalized data 171

7.1.2 Total correlations 172

7.1.3 Principal component analysis 173

7.1.4 Factor analysis 174 7.1.5 Summary 175

7.2 Newquay study area 176

7.2.1 Spatial distribution patterns 176

7.2.2 Total correlations 177

7.2.3 Principal component analysis 178

7.2.4 Factor analysis 181

7.3 Silver in oxide coatings 182

7.4 Summary 186

Chapter eight - Nature and formation of

Fe-Mn oxides 188

8.1 Structure 189

8.1.1 Primary crystallographic considerations 189 8.1.2 Microscope and electron microprobe studies 193

8.2 Environments and mechanisms of oxide formation 196

8.2.1 Field studies 198

A. Nature, conditions and

environments of formation 198

B. Role of organic matter 201

8.2.2 Laboratory studies 202

8.3 Discussion 205 Chapter nine - Discussion 211

9.1 Regional environmental factors 212

9.2 Local environmental factors 214

9.3 Influence of the secondary environment on sediment composition 216

9.4 Application to the study areas 220 Chapter ten - Conclusions and recommendations 223

10.1 Sources of error in drainage data 223

Limitations of the analytical system 223

Sampling variability 223

Secondary processes and annual variation 224

10.2 Results of analytical trials 224

10.3 Dispersion patterns in the study areas 225

Population distribution models 225

10.3.1 Patterns in fine fraction 226

Cornwall study area (Newquay area) 226

Lancashire study area (Clitheroe area) 227

10.3.2 Patterns in oxide coatings on coarse fraction 228

Newquay area 228

Clitheroe area 229

Oxide coatings as a sample medium 229

10.3.3 Patterns in the waters 230

10.4 Assessment of statistical aids 231

10.5 Controls on trace element distribution in sediment 233

10.6 Recommendations 235

Appendix - Details of analytical techniques 237

References 243 LIST OF TABLES

Table Page

3.1 Loss on ignition 32

3.2 Results of trial attacks on fine fraction 32

3.3 Relative efficiency of extraction, ER, in fine fraction; N-P attacks 33

3.4 Percentage change in concentration attending ignition of test samples 34

3.5 The relative standard deviation (%) for the metals in fine fraction of test samples

by the three methods 35

3.6A Measures of contrast in fine fraction 36

3.6B Contrast ratios in fine fraction 36 3.7A Summary of results for the Fe-Mn coatings on coarse fraction 39

3.7B Relative standard deviation (%) for the results in table 3.7A 39

3.8 Relative efficiency of extraction, ER, for Fe-Mn coatings on coarse fraction; N-P attacks 40

3.9 Some measures of contrast in coarse fraction 41

3.10 Summary of extraction methods for oxide coatings 53

3.11 Results (%) of sequential attacks on test samples 55

3.12 Variations in extractability with some modifications of the peroxide method 56

4.1 Newquay AAs data. F-ratios and variance estimates from a two-level nested ANOVA on all samples and without the anomalous samples 76

4.2 Newquay ICP data. F-ratios and variance estimates from a two-level ANOVA 78

4.3 Clitheroe ICP data. F-ratios and variance estimates from a two-level ANOVA 78

4.4 Newquay AAS data. Summary statistics on clusters defined by principal component analysis 81

4.5 Newquay AAs data. Distribution of variance among the clusters in table 4.4 81

4.6 F-ratios and variance components from comparisons between the clusters in table 4.4 estimated by a three-level ANOVA 82

4.7 F-ratios and variance estimates for spatial variation and procedural error (parts A, B) and analytical error (part C) 88

4.8 Comparison of variance estimates (%) by two-way (Type A) and two-level nested (Types B, C) ANOVA for selected Newquay data 88

4.9 Summary statistics for AAS and ICP results for two A. G. R. G. soil standards on seven elements 90

4.10 Results of T-tests for the soil stards S^

and S^ in the two sampling seasons 94

4.11 Oneway ANOVA results for the standards 94

4.12 ICP results for soil standards: Part A. Results for standard S^ 94 Part B. Results for standard S^ 94

4.13 Attained significance levels for T-tests for equality of AAS and ICP data on 95% confidence intervals 94

4.14 Oneway ANOVA results for the survey data 95

4.15 Correlation between AAS-ICP1-ICP2 data 95

5.1 Changes in moments about the mean for some metals in the study areas 103

5.2 Total correlation matrix for the Clitheroe data 111

5.3 Clitheroe fine fraction data. Partial correlations from selected ternary models derived from product-moment correlations 112

5.4 Partial correlation matrix for the Clitheroe fine fraction data 113

5.5 Loadings on the first six eigen-vectors for the Clitheroe fine fraction data 120

5.6 Regression of background factors on ore elements in the Clitheroe data 124

5.7 Total product-moment correlations for the Newquay ICP data 135

5.8 Partial correlation matrix for the Newquay ICP data 135

5.9 Regression analysis of individual elements 137

5.10 Loadings on the first three principal components for the Newquay ICP data 141

5.11 Detailed structure of the 6-factor model for the Newquay ICP data 143

5.12 Regression of Newquay ICP data on background factors 146

7.1 Total correlation matrix for the Clitheroe oxide data (Fe + Mn fraction) 172

7.2 Loadings on the first three principal components for the Clitheroe oxide data (Fe + Mn fraction) 173

7.3 Loadings in the 6-factor model for the Clitheroe oxide data (Fe + Mn fraction) 174

7.4 Total correlation matrix for the Newquay oxide data (Fe + Mn fraction) 177

7.5 Loadings on the first principal components for the Newquay oxide data (Mn fraction) 178

7.6 Loadings on the first three principal components for the Newquay oxide data (Fe + Mn fraction) 178

7.7 Loadings in the 6-factor model for the Newquay oxide data (Fe + Mn fraction) 181

8.1 Comparison of some X-ray diffraction data on test samples with published data 192

8.2 Random spot analysis for oxide coatings on sample 122, from a background locality in the Newquay area. % metal content per total oxide per spot 195 LIST OF FIGURES

Figure Page

2.1 Location of the study areas 13

2.2 Simplified regional geology of S-W England 14

2.3 Regional structure of S-W England 14

2.4 General paragenesis of ore and gangue minerals in Cornwall 16

2.5 Sample sites and localities

in the Cornwall study area 17

2.6 Geology of the Newquay study area 17

2.7 Geology of the Pennines and adjacent areas 19

2.8 Structure of the Northern Pennines

and the Clitheroe area 21

2.9 Geology of the Clitheroe study area 23

2.10 Sample sites and localities in the Clitheroe area 26 3.1 Variation in trace element extractability by various reagents 53

3.2 Proportion of metals extracted into various fractions in sequential extraction 55

3.3 Metal concentrations after 4, 8 and 16 hrs reaction time with 2M HC1 + for selected samples from the Newquay area 57

4.1 Design of sampling heirarchy in the study areas 70

4.2 Distribution of variance in sample types in the Newquay AAS data 84

4.3 Comparison of 95% confidence intervals for AAS and ICP data Cu, Zn, Mn, Pb, Ca, Mg and Fe in two soil standards 94

4.4 Comparison of 95% confidence intervals for AAS and ICP data for Cu, Zn, Mn, Pb, Ca, Mg and Fe in two sampling seasons 94

4.5 Scatterplot of AAS and ICP data for individual elements in the Newquay area 95

5.1 Histograms and probability plots for Ba, V and Al in the Newquay area 103

5.2 Histograms and probability plots for Mg, Li and Ca in the Newquay area 104

5.3 Histograms and probability plots for Co, La and Ni in the Newquay area 104

5.4 Histograms and probability plots for Pb, Zn and Cu in the Newquay area 105

5.5 Histograms and probability plots for Mn, Fe and Ti in the Newquay area 105

5.6 Histograms and probability plots for Ni and Co in the Clitheroe area 106

5.7 Histograms and probability plots for Fe, Mn and Cd in the Clitheroe area 106

5.8 Histograms and probability plots for Ca, Mg and Al in the Clitheroe area 107

5.9 Histograms and probability plots for Cu, V and Ti in the Clitheroe area 107

5.10 Histograms and probability plots for Zn, Pb and Ba in the Clitheroe area 108

5.11 Dispersion patterns in fine fraction for Fe, Cu and Cd in the Clitheroe area 108

5.12 Dispersion patterns in fine fraction for Mn, Co and Ni in the Clitheroe area 109

5.13 Dispersion patterns in fine fraction for Al, V and Ti in the Clitheroe area 109

5.14 Dispersion patterns in fine fraction for Pb, Zn and Ba in the Clitheroe area 110

5.15 Dispersion patterns in fine fraction for Ca, Mg and CV in the Clitheroe area 110

5.16 Location of sample sites and localities in the Clitheroe area 114

5.17 Scattergrams for Ca-Mg, Mg-Al, Pb-Zn, Co-Ni, Cd-Zn and Mn-Fe in fine fraction Clitheroe study area 114

5.18 Scattergrams for Al-V, Al-Cu, Fe-Al, Fe-V, Fe-Cu and Al-Ti in fine fraction, Clitheroe study area 115

5.19 Scattergrams for Ti-V, Zn-Cu, V-Cu, Mn-Zn, Co-Mn and Cu-Mn in fine fraction, Clitheroe study area 115

5.20 correlallogram for the partial correlations in table 5.4 116

5.21 Scatter biplot for 1st and 2nd eigen-vectors of Q-mode principal component analysis for the Clitheroe data 119

5.22 Scatter biplot for 1st and 2nd eigen-vectors of R-mode principal component analysis for the Clitheroe data 121

5.23 Comparison of 4-, 5- and 6-factor models for the Clitheroe fine fraction data 121

5.24 Dispersion patterns of loadings on the first three factors from the 6-factor model of the Clitheroe data 122

5.25 Dispersion patterns of loadings on the last three factors from the 6-factor model of the Clitheroe data 122

5.26 Scatterplot of Ba regression residuals on background factors (factors 1, 3, 4 and 6) with Ba, Zn and Pb 124

5.27 Dispersion patterns in fine fraction for Mn, Zn and Pb in the Newquay area 127

5.28 Dispersion patterns in fine fraction for Fe, Ca and Mg in the Newquay area 127

5.29 Dispersion patterns in fine fraction for Cu, Co and Cd in the Newquay area 128

5.30 Dispersion patterns in fine fraction for Ti, Ni and li in the Newquay area 130

5.31 Dispersion patterns in fine fraction for Cr, Ag and V in the Newquay area 132

5.32 Dispersion patterns in fine fraction for Al, Ba and La in the Newquay area 132

5.33 Location of sample sites and localities in the Newquay area 133

5.34 Scattergrams of ICP data for Co-Ni, Ti-ca, Ca-Mn, Mn-Co, Mn-Zn and Mn-Fe in fine fraction, Newquay study area 136 5.35 Scattergrams of ICP data for Pb-Zn, Cu-Zn, Zn-Cd, Co-Cd, Cu-Cd and Mg-Zn in fine fraction, Newquay study area 136

5.36 Scattergrams of ICP data for Li-Mg, Mg-Fe, Mg-V, Ba-V, V-Al and Ca-Ba in fine fraction, Newquay study area 137

5.37 correlallogram for the partial correlations in the Newquay ICP data (table 5.8) 138

5.38 Scatter biplot of 1st and 2nd eigen-vectors of R-mode principal component analysis for the Newquay data (AAS) 140

5.39 As for fig. 5.38 but without the

anomalous samples 142

5.40 As for fig. 5.38 for ICP data 142

5.41 Comparison of 4-, 5- and 6-factor models for the Newquay fine fraction data 143 5.42 Dispersion patterns of loadings on the first three factors from the 6-factor model of the Newquay ICP data 144

5.43 Dispersion patterns of loadings on the last three factors from the 6-factor model of the Newquay ICP data 144

5.44 Scatterplot of regression residuals of Pb on background factors (1-3) with Pb, Zn, and Cu, of Zn with Zn and Cu and of Cu with Zn, Newquay fine fraction data 147

5.45 As for fig. 5.44, of Cu with Cu, Cd with Cu, Zn and Cd, and Co with Cu and Cd 147

5.46 Dispersion patterns for Newquay fine fraction data ratiod on Fe and/or Mn: Cu/Mn, Cu/Fe and Cu/Fe+Mn 148

5.47 As in fig. 5.46 for Mn/Fe+Mn and Fe/Fe+Mn 148

5.48 As for fig. 5.46 for Pb/Mn+Fe, Zn/Mn and Zn/Fe+Mn 149

5.49 As for fig. 5.46 for Cd/Fe+Mn, Co/Fe+Mn and Ni/Fe+Mn 149

5.50 As in fig. 5.46 for Ba/Fe+Mn, V/Fe+Mn and Ca/Fe+Mn 150

6.1 pH of Newquay waters 161 6.2 Concentration ranges of metals in

waters from the Newquay area 162

6.3 pH of Clitheroe waters 164

6.4 Concentration ranges of metals in waters from the Clitheroe area 165 7.1 Dispersion patterns for Mn, Fe and Ca in the Clitheroe oxide data 169

7.2 Dispersion' patterns for Zn, Pb and Pb/Zn in the Clitheroe oxide data 169

7.3 Dispersion patterns for Co, Mg and Cu in the Clitheroe oxide data 170

7.4 Dispersion patterns for Pb/Fe+Mn, Zn/Fe+Mn and Co/Fe+Mn in the Clitheroe oxide data 170

7.5 Dispersion patterns for Fe/Fe+Mn, Ca/Fe+Mn and Cu/Fe+Mn in the Clitheroe oxide data 171

7.6 Dispersion patterns for Mn/Fe+Mn, Mg/Fe+Mn and Al/Fe+Mn in the Clitheroe oxide data 171

7.7 Diagramatic representation of the correlations in the Clitheroe oxide data 172

7.8 Scatter biplot of the 1st and 3rd principal components in the Clitheroe oxide data 173

7.9 Dispersion patterns for the loadings on the first three factors from the 6-factor model of the Clitheroe oxide data 174

7.10 Dispersion patterns for the loadings on the last three factors from the 6-factor model of the Clitheroe oxide data 174

7.11 Dispersion patterns in Mn fraction for Pb, Zn and Pb/Zn in the Newquay oxide data 176

7.12 Dispersion patterns in Mn fraction for Cu, Cu/Fe+Mn and Co/Fe+Mn in the Newquay oxide data 176

7.13 Dispersion patterns in Mn fraction for Pb/Fe+Mn, Zn/Fe+Mn and Mn in the Newquay oxide data 176

7.14 Dispersion patterns in Mn fraction for Fe, Mn/Fe+Mn and Fe/Fe+Mn in the Newquay oxide data 176 7.15 Dispersion patterns in Fe+Mn fraction for Mn, Pb and Mg in the Newquay oxide data 177

7.16 Dispersion patterns in Fe+Mn fraction for Zn, Cu and Co in the Newquay oxide data 177

7.17 Dispersion patterns in Fe+Mn fraction for Zn/Fe+Mn, Cu/Fe+Mn and Co/Fe+Mn in the Newquay oxide data 178

7.18 Dispersion patterns in Fe+Mn fraction for Pb/Fe+Mn, Mn/Fe+Mn and Fe/Fe+Mn in the Newquay oxide data 178

7.19 Dispersion patterns of the solution concentrations ratioed on Fe+Mn, for Pb, Mn and Fe in the Newquay oxide data (Fe+Mn fraction) 179

7.20 Dispersion patterns of the solution concentrations ratioed on Fe+Mn, for Zn, Cu and Co in the Newquay oxide data (Fe+Mn fraction) 179

7.21 Symbolic representation of the partial correlations in the Newquay oxide data (table 7.4) 180 «r\ Til 7.22 Scatterplot of the 2 and 3 eigen-vectors from the Principal Component analysis of the Newquay oxide data (Fe+Mn fraction) 180

7.23 Dispersion patterns for the first three factors from the 6-factor model of the Newquay oxide data (Fe+Mn fraction 181

7.24 Dispersion patterns for the last three factors from the 6-factor model of the Newquay oxide data (Fe+Mn fraction) 181

7.25 Silver dispersion in fine fraction and oxide coating below some old mines in the Newquay area 183

7.26 Refinement of the Ag dispersion shown in Fig 7.25 183

8.1 Illustration of the open structure of Mn-oxides 190

8.2 Covariation of some trace metals with Fe and Mn from random spot microprobe analysis of Fe-Mn oxide coatings on background sample from the Newquay area 195 8.3 Covariation of the same trace metals in Fig 8.2 196

Contents in folder at the back of thesis

(A) Overlays for the study areas

(B) Legend for the overlays

(c) Table of percentiles as class limits used in plotting the dispersion patterns - 1 -

CHAPTER ONE - INTRODUCTION

Low density stream sediment surveys are now widely regarded as

the cheapest, most convenient and fastest means for detecting

areas with possible mineralization. The utility of the method

has been amply demonstrated with many major successes (e.g.

Armour-Brown and Nichol, 1970; Nichol et al, 1967; Hawkes et

al, 1960; Lee-Moreno and Caire, 1975; Stephens, 1976; Hawkes

and Webb, 1962; Viewing, 1963, Baldock, 1977). Inevitably

there have been failures as well although they are not

expected to be as frequently reported. Such failures are

partly the result of our lack of complete understanding of all

the natural processes which give rise to geochemical

anomalies. Terminologies such as "significant" versus

"insignificant" or "true" versus "false" anomalies stem,

directly from such uncertainties. As understanding improved

the earlier practice of simply obtaining high concentrations

of some sought metal has given way to the recognition of

trends or patterns of concentrations of several metals.

Correct interpretation of such information undoubtedly

requires great skill and a thorough knowledge of the terrain

under study. However this cannot be successful until the

fundamental processes giving rise to metal distributions are

better understood. This calls for more case studies of

specific basins under a wide range of environmental

conditions, especially now that stream sediment data have - 2 -

found application in several other disciplines. For example

in assessing man's continual global interference of the

environment it is necessary to build steady-state and

time-dependentt models for individual element or pollutant

cycles. This requires assessment of the natural sources,

transport paths, fluxes, sinks and reaction rates of elements

to provide the base-line for such evaluations (U. S. Natl.

Acad. Sci., 1973). Since the ultimate destination of stream

loads is the sea soils, waters and sediments of coastal

environments have been designated areas of immediate concern

because of the threat to fisheries. However there are wider

applications concerning soil fertility in regard to livestock

and food crops as well as medical geography (e.g. Thornton,

1975; Elderfield et al, 1971; Thomson et al, 1972; Thornton

and Webb, 1974; Webb, 1971, 1973; Webb and Atkinson, 1965).

Thus transport routes and the partitioning of elements among

the organic and inorganic dissolved load of streams as well as

the suspended and bottom materials in these, are important

areas of geochemical research. Apart from providing the

necessary utility data such information are of value in their

own right (Webb, 1970).

Excellent accounts on the historical development and

primary considerations of trace element distribution or

dispersion are available in several texts (Rankama and Sahama,

1950; Goldschmidt, 1954; Hawkes and Webb, 1962; Rose et al,

1979; Mason, 1966; Hawkes, 1957; Ginzburg, 1960; Levinson,

1974). Although geochemical practice is said to date from antiquity it is only recently that its systematic development has emerged and this has been attributed to, among other - 3 -

things, (Levinson, 1974) :

(i) the recognition of the nature of primary and

secondary dispersion halos and trains,

(ii) the development of rapid and accurate analytical

methods, especially (in the exploration for metallic

deposits) atomic absorption spectrometry, and

(iii) the use of rigorous statistical and computer

methods as aids in the interpretation of atrslytical data.

These major developmental efforts are closely intertwinned and

are always involved in any geochemical work. For example

where it is possible to choose, or if so desired develop the

analytical technique, the main consideration is the purpose of

the survey. Unbiased and vigorous computer-based statistical

techniques are now widely available for data interpretation

although not easily accessible to developing nations.

Nevertheless interpretation of computer data is still largely

subjective. This is because the detailed nature of dispersion

halos is yet to be fully established.

1.1 THE NATURE OF DISPERSION HALOS AND TRAINS

1.1.1 BACKGROUND

In igneous rock formation there is a differentiation of major

elements governed by the physico-chemical conditions of

formation. The minerals formed from the co—existing major - 4 -

elements under the various conditions constitute the basis for

the genetic classification of the igneous rocks. Some trace

and minor elements can enter the lattices of these minerals

while others tend to be enriched in later-stage fluids which

may give rise to ore bodies. Metamorphism of igneous rocks,

or of sedimentary or metamorphic rocks obtained from

pre-existing ones, may lead to further differentiation or

association of elements in the new rocks formed. It is thus

possible to perceive of a rock body or unit which possesses

some particular chemical attributes or characteristics, based

on its minerals and their content of minor and trace elements.

Such a characteristic may be quite distinct from any imparted

to it as a result of mineralization within it. It is the

expression of this chemistry which is usually re-furred to as

the "background" in geochemical samples.

1.1.2 PRIMARY HALO

An ore body may be emplaced at the time of rock formation or

later. In either case the chemistry of the host rock adjacent

to the ore is altered as new, more stable minerals are formed.

This is the primary halo, caused by the primary dispersion of

ore and ore-related elements from the conduits along which the

ore-solutions travelled. Differences in the chemistries and

mobilities of the elements give rise to different associations

on a local scale. Thus some elements may travel further from

the ore body, form a close association which may be

characteristic to the mineralization and be classified as

pathfinders or indicator elements. - 5 -

1.1.3 SECONDARY HALO

In the secondary environment of low temperatures and pressures

with free oxygen circulation and organic activity, the rocks

again respond by weathering to form more stable minerals.

This reconstitution results in new associations of the

minerals governed mainly by their different mobilities in the

new environment. Elements associated with the ore body will

occur in concentrations far greater than is found in barren

bedrock. This is the secondary dispersion halo.

1.2 SECONDARY DISPERSION AND GEOCHEMICAL SURVEYS

In general weathering is mainly controlled by the climate and

topography, the two are the main factors controlling

vegetation development, and all three give rise to the

hydrodynamic processes whose main activity is the removal of

weathering products in solution or by erosion. Organic

processes tend to aid both physical and chemical weathering

and in the latter case organo-metallic complexes may serve to

mobilize otherwise immobile elements. The final residual

products of weathering constitute the soil. The soluble ones

join the groundwater circulation and can remain within it for

as long as the chemistry and structural characteristics along

the flow path permits it. However field studies indicate that

they soon join the surface circulation where they may be

partly deposited as encrustations, coatings or nodules. The

major constituents of such deposits are Fe-Mn oxides. - 6 -

Drainage surveys are conducted on the premise that a properly

collected sample of active sediment is a representative

composite of the rocks and soils upstream. Partly as a result

of differential erosion of the source materials and sorting

under turbulence within the stream, it is highly unlikely that

in any one sample the rocks and soils would be represented in

the exact proportions exposed in, or indeed eroded from the

catchment. This is not a disadvantage since the intention is usually to Identify different populations and infer their

significance. More important, however, are changes in

sediment composition caused by processes classified under the

general weathering cycle or secondary environment. Such

processes have been shown to alter the character of sediments

to the extent where they no longer are representative of their provenance, but rather give some indication of the nature of

the weathering processes (Horsnail, 1968; Young, 1971). One agency by which this is brought about has been shown to be the

exceptional sorptive capacity of secondary hydrated oxides of

Fe and Mn, even when present in amounts that are quite small

compared to the total mass of sediment (Jenne, 1968).

The influence of Fe-Mn oxides begins early in the

soil-forming process. Many organic acids and solutions that carry plant nutrients in soil are known complexes involving

Fe, while encrustations and nodules of Fe-Mn oxides have been considered to retain vital nutrients in soil. Where an organic litter accumulates in the uppermost soil level, organic acids form, leach the A horizon immediately below and redeposit the less mobile leachate constituents in a lower B - 7 - horizon. This zone is normally enriched in Fe-Mn oxides which retain many trace metals, thus making it generally the most useful sampling medium in soil geochemistry.

In tropical regions the major dispersion mechanism for most metals is hydromorphic, whereby they are leached from their source rocks in true solution or as stable fine colloids. Where leaching is especially intensive laterite, an extremely ferruginous soil horizon, is widely developed. The ability to distinguish true gossans from barren lateritic hardpan pavements is an important asset in geochemical exploration. A critical basis for adequate interpretation is the appreciation of the role of Fe-Mn oxides in the concentration processes for the minor and trace elements. For example it has been shown in Australia that locally high concentrations of Cu and Ni within goethite indicated buried mineralization whereas a uniform content did not (Clema and

Stevens-Hoare, 1973; Moeskops, 1976; Andrew, 1978) and

Mazzucchelli and James (1966) reported similar results for As.

However Mn oxide within the gossan or laterite can give rise to false anomalies because of its high eidsorptive capacity for these metals.

In most temperate regions laterites do not form but Fe-Mn oxides are widely reported from most climatic regimes, especially in stream and lake sediments (e.g., Jenne, 1968;

Horsnail, 1968; Calvert and Price, 1970; Schoettle and

Friedman, 1971; Nowlan, 1969; Canney, 1966; Horsnail et al,

1969). In such environments the scavenging ability of Fe-Mn oxides has mainly led to problems in geochemical data interpretation (Horsnail, 1968; Horsnail et al, 1969; Bolviken - 8 - and Sinding-Larsen, 1973; Young, 1971; Butt, 1971). On the positive side they have been regarded as responsible for maintaining soil fertility in some cases by releasing vital

trace elements to the roots of crops, or alternatively rendering inavailable metals that occur in concentrations that would make them poisonous if freely available to soil moisture. There is also the attractive prospect that this scavenging property could be exploited in mineral exploration.

Such a technique might be especially suited to detecting deep-seated or blind ore bodies with leakage halos in which

Fe-Mn oxides are actively forming. An indication of such potential is given by Maurice (1973) who found that base metals were concentrated in Fe-Mn oxide coatings formed in the crevices and sink-holes within thick glacial overburden overlying mineralization in Ireland.

In principle there appears to be no serious objection to the idea of using Fe-Mn oxides as a sample medium in mineral exploration. Metals can only be precipitated if they are available in solution, and metal ratios may determine whether they are derived from a true ore body or merely represent the continuous leaching of some rock or soil containing high levels of the metals (Carpenter et al, 1975, 1978). However there are practical difficulties, mainly connected with obtaining sufficient sample for analysis and to ensure a uniform, systematic coverage during field sampling. - 9 -

1.3 AIMS OF THE RESEARCH

It is evident that the secondary environment is very complex,

a good illustration being the formation of different soil

types over similar rock units under similar environmental

conditions. In fact so many possibilities exist that it is

practicable to treat any given locality as if it were quite

distinct from any other.

The above considerations formed the substantive basis of

the present work which involved attempts to :

(1) gain some insight into the sources of error that most

adversely affect drainage data,

(2) derive characteristic chemical patterns for the

base-metal mineralizations in the selected areas,

(3) evaluate the extent of the influence on trace element

patterns by Fe-Mn oxides, and

(4) investigate the possible use of Fe-Mn oxide coatings

on coarse sediment as a sample medium in mineral

exploration.

The main theme was to identify and collate factors that affect

the distribution of trace elements in the stream sediments,

with particular regard to mineral exploration. The problem in

a typical geochemical survey may be considered to be of two

general types : - 10 -

(i) choosing the appropriate parameters and ensuring

that estimates or measurements of these parameters are

reliable enough to bring out the geochemical

characteristics sought, and

(ii) interpreting the results of the estimates.

The first type of problem requires information on :

(a) geology (all aspects)

(b) the environment (climate, soil types, Eh-pH

conditions, etc),

(c) cultural activities (mining, engineering and

agricultural practices).

The prob|em may be easier in areas with known mineralization for it may be possible to choose, after a preliminary orientation survey the most efficient or reliable parameters with which to work. In a regional reconnaisance survey in. unfamiliar terrain almost all the elements that can be determined by the available analytical system may have to be analyzed. In both cases the geological information, if available, is the first evaluative of major importance since it may enable favourable districts to be chosen without preliminary field work. Information on the environment is needed in consideration of the behaviour of the elements, the paths they are likely to follow in the surface geochemical cycle and hence the methods that are to be used to trace them back to their source. The environmental processes are - 11 - complicated by changes in Eh-pH conditions for which the geochemical data may have to be corrected. Sporadic or erratic concentrations caused by contamination from cultural sources may have to be treated in similar fashion.

The second type of problem involves reducing or transforming the data into a form that can be interpreted in terms of the objectives of the survey. With regard to mineral exploration, if all interferences can be minimized or actually eliminated, then anomalous values should logically indicate some mineralization (which may or may not be economic) • This would make interpretation relatively unambiguous, provided anomalies can be that easily identified. Both qualitative visual examination and the more rigorous basic as well as advanced statistical treatments can be used. With the ever-increasing laboratory productivity there is a clear need for investigating further the usefulness of the multivariate statistical methods to reduce the vast amounts of data so that it can be more readily interpreted.

The thesis has been arranged as far as possible to reflect this approach to problem-solving. In the next chapter are given given brief descriptions of the geology, secondary environment and mineralization in each of the study areas.

Chapter three contains the preliminary results of studies made to evaluate the analytical techniques used. It is essential to ensure that data is of the required quality before any interpretation or judgement is made. Quality control in the laboratory can be effectively managed and this leaves most of the effort to be directed at errors connected with sample - 12 - collection, where the main problem is with ensuring a. representative sample. Such problems are treated in chapter four. Chapter five contains detailed descriptions of the populations and spatial distributions of the elements analyzed in fine fraction and a similar treatment for those determined in Fe-Mn oxide coatings on the coarse fraction is presented in chapter seven. Principal component and factor analyses are employed to obtain metal associations whose probable significance are described. Chapter six describes the metal distribution trends in the waters from the study areas as an indication of the probable importance of hydromorphic processes. In chapter eight the nature, formation and role of

Fe-Mn oxides is discussed and some X-ray diffraction data is presented. Chapter nine contains a brief discussion of factors affecting metal distribution in drainage in the light of the preliminary results obtained from this study. The final chapter is a summary of the findings and conclusions in the preceding chapters with recommendations on the aspects of the problem that require further research. - 13 -

CHAPTER TWO - DESCRIPTION OF THE STUDY AREAS

Two areas in England with similar mineralization but set

in terrains of contrasting geological complexity were selected

for study. It was hoped that in the area of simple geology

information might be obtained on the influence of

mineralization and the secondary environment on metal

distribution uncomplicated by trends across geological

boundaries, to be contrasted with results using the same

techniques on data from the area of complex geology.

2.1 THE CORNWALL STUDY AREA

2.1.1 LOCATION

The Cornwall study area lies just south-east of Newquay

(fig 2.1) and forms part of the district demarcated as sheet

SW85 (O.S. 1:25,000 map series) and bounded by the British

national grid co-ordinates E 8000-9000 and N 5000-6000. The

district is covered by sheet 346 (Geological survey 1-inch

series). The drainage system surveyed lies north of northing

coordinate 5300 and the A30, which is the main trunk road to

south-western England running along the divide separating the

adjacent drainage system from the survey area.

2.1.2 REGIONAL SETTING AND GEOLOGIC STRUCTURE

The general structure of south-west England involves an E-W

synclinorium comprising Carboniferous strata in central and miles

Fig 2-1 LOCATION OF STUDY AREAS - 14 - western Devon and part of western Somerset, flanked to the north and south by Devonian strata. The E-W trend is dominant over most of the region (Edmonds and McKeown, 1975). This fairly continuous series of meta-sediments (known locally as the killas) have a complex history involving the development of a Palaeozoic miogeosyncline, late tectonism, low-grade regional metamorphism and mineralization and subsequent partial denudation. Figures 2.2 and 2.3 show the solid geology and major strutural features over the region.

The Devonian consists of conglomerates, sandstones, slates, shales, and mudstones with marls in Cornwall and western Somerset. Carboniferous limestones and shales constitute the Millstone Grit and Culm Measures in Devon.

Contemporaneous with the meta-sediments are numerous

"greenstones" (sills, dykes and lavas of dolerite, diabase and basalt) and felsite. Shales, however, are the most widespread lithologic unit, contain few fossils and usually possess a slaty cleavage.

Approximately east of longitude 3 deg. 35 min. W the

Palaeozoic rocks are overlain unconformably by another fairly- continuous series covering the Permian to Lower Jurassic, and which constitutes an anticlinorium in eastern Somerset. The

Permian extends in a relatively narrow band as far west as

Highampton in central Devon, where it terminates in a fault outlier. The ensuing account concerns only the major portion of the region west of longitude 3 deg. 35 min. W. N A

K EY

Permo- Lias

Carboniferous

Middle- Upper Devonian Lower Devonian

IGNEOUS & METAMORPHIC ROCKS

Dolerite. Basalt and Greenstone'

Lizard Complex

Fig- 2 2 SIMPLIFIED REGIONAL GEOLOGY OF S-W ENGLAND (after Edmonds et.al.i 975) KEY Mune fmHt i TtiWf FOLDS FANNING Nm RH RMVI OVER NORTHWARDS TO AXIAL PLANE CiimM fftirtW fnm DIPS OF JO'S AT LVNUCUTH Ul|« ban IM

Mjftf |M

• 0 KIIiwIih

FIG Regional structure of 3- W England (r*p>oowc«

2.1.3 GRANITE EMPLACEMENT AND MINERALIZATION

In late Carboniferous and early Permian times granite

bosses were emplaced into the predominantly argillaceous,

intensely folded and faulted rocks of Cornwall and Devon.

Large bosses (cupolas) of granite, each with its aureole of

contact-matamorphosed killas, lie in a zone trending in a

general NE-SW direction from the Scilly islands to Dartmoor.

In the root of the granite masses intense assimilation and

granitization of the country rocks took place; there is said

to be only one place on Dartmoor where the roof rocks were

preserved (Edmonds and McKeown, 1975). Otherwise the grade of

contact metamorphism is usually low, and falls off rapidly

with distance from the contact.

The numerous ore-fields which have made this metallogenic

province so famous are closely related to the granites. After

granite consolidation, fissures opened up along zones of

weakness in which the lodes were emplaced. Initially Sn and

Cu minerals were deposited in E-W fissures, followed by Pb,

Zn, Fe and Ba minerals, in places in E-W fractures, but

chiefly in N-S cross-courses.(Vipan, 1959). There is clear

evidence that the minerals are zoned spatially, both laterally

and with depth (Millman, 1953). Lead, Ag and Sb minerals

occur at more remote distances from granite, mostly in

unaltered killas, while the Sn and Cu lodes are located close

to or within the granite intrusives. The lodes dip steeply-

north or south and zones often overlap, so that two or more

metals oocur together. This is often true for Pb-Zn ores.

The gangue minerals are also zoned (Vipan, 1959); those - 16 -

generally occuring in Pb-Zn lodes include quartz, fluorspar,

calcite and barytes.

The zoning has been described as emanations from centres

of mineralization (Dines, 1956) around which the lodes are

clustered, the minerals being deposited in a series of zones

which are often inclined at a low angle to the neighbouring

granite-killas contact-zone (Hosking and Trouson, 1959).

Hosking (1966) notes that this zoning is particularly evident

for both Sn-Cu and Pb-Zn mineralization in the St. Agnes-

Perranporth-Lambriggan district, of which the area chosen for

the present study forms part. A vertical zoning scheme has

been compiled (Edmonds and McKeown, 1975) which suggests

possible associations in samples derived from lodes. Hosking

(1966) also states that although the region is better known

for its tin and copper output (and to a lesser extent Pb and

Zn), substantial amounts of other metals, notably Co, Fe, Mn.

Ni, Ag, W, U, As, Bi and Sb, have been recovered as well. Fig

2.4 illustrates the general paragenesis of ore and gangue

minerals which may be used to recognize metal associations

indicative of mineralization. Hosking (1949) postulated the

shape of a batholith from which all the exposed granite bodies

would be off-shoots. This was subsequently confirmed with

gravity and magnetic measurements by Bott et al (1958), thus

narrowing down the search for hypothermal ores since these

occured close to or within granite.

2.1.4 GEOLOGY OF THE STUDY AREA

The Newquay-Newlyn-East area is an undulating tableland about FIG. 2A General paragenesis of ore and gangue minerals in the Cornish region, (after Edmonds et al)

Type Approx. Range & MaJor ore G angue of formation distribution mineral of mi nerals deposit temp- important assemblages economic °C. metals « Haematite

, ! Sider ite Stibnit* — • 50 -200 a jz Fe Sb Tetrahedrite ui — Buornon ite Argentite Ag Pb Zn Ga Sph

200 -3oo Pitchblende Nicolite o U Ni Co Cobalti te «to 2

Cpy Asp Sph Py

Wolframite Scheeli te

Cu Cpy Asp

Wolframite 300- 5 oo Scheelite Cassi terite As W re E Cassiterite km 0 JZ Wolframite Sn Scheelite Asp

Cassiterj te Speculiarite Molybdenite 1° ?1T 1a - 17 -

300 ft O.D. which ends seaward in abrupt cliffs. It is cut by numerous valleys, the majority of which follow the dominant strike of rocks and run from ESE to WNW. The main stream in this small basin is the Gannel, running approximately E-W with

NW-SE and N-S tributaries (fig 2.5).

The area occupies the western end of a major anticlinal axis, displaced locally by faults, that runs from Trenance, north of Newquay, to Dartmouth in Devon (fig 2.3). The lithology is quite uniform (fig 2.6). The drainage basin lies almost entirely in Lower Devonian shales affected by recumbent folding and overthrusts and cut by fault-controlled basic intrusives and felsite. The widespread development of Fe-Mn oxides is evident in stream sediments and bank material in several localities.

Several mines operated around Newlyn-East, the most important of which were Penhallow moor, East Wheal Rose,

Shepherds and Deerpark. Deerpark is an extension of the Great

Perran iron lode which was worked open-cast for a short period and yielded some few hundred tons of iron ore and several tons of Pb and Zn in 1875-1879. The others were the important base metal producers in the district and East Wheal Rose in particular was famous for the large amounts of Ag recovered from the ore. A small mine existed near Higher Penscawn in locality 10. Near locality 4 in the northeastern corner of the study area a Sn-Cu lode was exploited on the fringes of the metamorphic aureole of the St Austell granite. Small trials are mentioned by Reid et al (1906) at Trerew and

Trerice mill. At the latter site the author found no evidence of past mining activity, but at Trerew farm heaps of crushed NORTH ilO1 J20.0Q 530.00 5«0 00 5.50 OQ 560-00 570:QQ 580-00 590-00 600-00 2} o ro (ji

al r-> o?\r Q a Z Z=D a

—« _ or na • o ca

>> -Z 5- o o a r— O o 2r > o f— ab ^ m acr c/i. o a a oto" oa

aj aO

-- c. r ^ po* n> N N

} km

KEY metamorph i c uiulji aureole

mid-upper devonian

lower devonian

f f felsite on fault

fels ite

do l er it e Fig 2 6 GEOLOGY OF THE NEWQUAY STUDY AREA © mines 1 sn z mtgke-v peivscq\w) 3 £qst Wh*»( Rose 4 Deev-p^i 5 PE-NMLOW MOOR 6 -SKE^HE. - 18 -

shale with quartz float were found.

2.1.5 FIELD OBSERVATIONS

The banks of most first and second order streams are under

some form of vegetation, wherein thick swamps and marshes are

common. Municipal trash (old cars and machinery, tyres etc.)

may be found dumped in some of the bigger woods or forests.

Samples were not collected close to any of these.

In localities with swamps and marshes Fe-Mn oxides form

profusely, coating rock exposures, boulders and finer sediment

and bank soils a uniform black or grey colour. Often this is

a superficial coating affecting only the first few inches of

sediment. This is strikingly revealed by boulders embedded to

a depth of some six inches in sediment; the immersed portion

may be bleached colourless but the top exposed portion is

coated black with Mn oxide. Sometimes bright-red Fe oxides

occur as independent patches on boulders residing in the

middle of the stream bed. In most cases, however, they occur

beneath the veneer of Mn oxides. This is more evident on

pebbles sticking out of the bank. Iron oxides are more

visible on coarse fraction near the headwaters and divides of

the streams.

Visible contamination from old mines is most evident on

Penhallow moor, where many of the mine heaps are devoid of any

vegetation. The visible coating in the stream sediment here

is Fe oxide and is in sharp contrast to the thick Mn oxide

coatings found in the tributary just below the main mass of

mine wastes (locality 2). The section of stream from - 19 -

Penhallow moor down through East Wheal Rose for a distance of

more than about 2 km is coated bright-red with Fe oxides.

Elsewhere old mine wastes are characterized by visibly high Fe

oxide content in the adjacent stream, with visible Mn oxides

only a short distance further downstream.

Quarries sited on stream courses locally dilute the

sediment with loose rubble. Many of the marshy grounds have

been reclaimed with mine waste or quarry products. As these

are usually devoid of any grass cover, they are easily eroded

and dilute sediment composition with a flood of loose, barren

waste.

It is evident from above that Fe-Mn oxides play an

important role in the geochemistry of the sediments from this

study area. This might complicate geochemical data

interpretation despite the essentially simple lithology of the

area. However despite these and several other problems

encountered in the region (Hosking, 1971), stream sediment

geochemistry may be successfully applied to fundamental

studies of the type undertaken here.

2.2 THE LANCASHIRE STUDY AREA

2.2.1 REGIONAL SETTING

The Clitheroe district lies in the central part of the

Pennine range, an upland chain extending from the Derbyshire

Dome to the Tyne valley in Northumberland (fig 2.7). The

northern, central and southern portions of this range form

distinct structural and physiographic entities (Wray, 1936). N /I

CLACJAL DEPOSITS RCAMIAN MOT SHOWN UMCSTONC. MARL. nr.

ACCENT CAR SONIFEROUS ALLUVIUM. BLOWN SANO. ETC. B COAL MEASURES- MILLSTONE CALTS. MESOZOIC ROCKS NEWER THAN TRIA33IC. CARBONIFEROUS UMESTONE SEMES. UMCSTONC. SANOSTQNE. SMAIX ETC' ROCKS OLDER THAN CARBONIFEROUS

TRIASSIC SANOSTONCS. CRITS SHALES. UMCSRONCS. ETC. KEUPEFT MAAU KEURCR FT BUNTER SANOSTONCS IGNEOUS ROCKS CAANITES. LAVAS TUFFS. OVKES ETC.

SCALE 9 5 10 20

• Clitheroe study area

Fig 2-7 Geology of the Pennines end adjacent areas (Reproduced from Wray, 1936) - 20 -

The northern Pennines consist mostly of Carboniferous

limestone rocks dipping gently to the east. These are

normally massive, highly jointed and and have a fissured

surface that gives rise to a karst topography. Features such

as bare rock pavements, dry valleys, swallow holes, caves,

gorges and cliffs, are common. The Carboniferous rocks rest

on a highly distorted basement of pre-Cambrian and Palaeozoic

rocks with concealed granite bodies of middle to lower

Devonian age (Schnellmann and Scott, 1969). Carboniferous

limestones also form the main mass of the southern Pennines

and the surface features described above for the northern

Pennines are well represented on the Derbyshire Dome.

Between the two sections of the range lies the mid-Pennines, a high moorland tract in which alternating

sandstones and shales belonging to the Millstone Grit are widely developed. The vegetation on these windswept ridges is thin, the soil poor and acid. Wide stretches of peat occur

interspersed with patches of heather in the drier and better-drained slopes. To the west are the lowland plains of

Lancashire and Cheshire floored by red rocks of Triassic age.

The continuity of the plains topography is somewhat arrested by two broad belts of moorland known as the Rossendale

"Forest" and Bowland "Forest"; the two coincide with uplifts of Carboniferous rocks trending in a general NE-SW direction.

Bowland Fells consists mainly of Lower Carboniferous sediments while the Rossendale unit is composed largely of Millstone

Grit and Coal Measures. Along the eastern edge of the

Pennines the Carboniferous rocks are overlain unconformably by-

Permian and Triassic strata. - 21 -

Structurally the region is complex. The northern Pennines

consists of two relatively rigid blocks, the Alston and

Askrigg blocks, defined on the north by the Stublick faults,

on the west by the Pennine and Dent fault systems, and in the

south by the Craven faults (fig 2.8). This structure is

replaced to the west beyond the Dent faults by a complex fold

pattern cut by extensive faulting. South of the Craven faults

the rocks are strongly folded about an irregular dome

elongated in a NNW-SSE direction and flanked on the east by

the east Pennine coal basin. This folding is most intensive

in the Lancashire coal basin with a dominant NE-SW trend

involving several major anticlines, including the Clitheroe

anticline. They are arranged en echelon between the coal

basin and the Craven faults, where they bend sharply from the

rigid north Pennine block. Between the two coal basins the

Millstone Grits are folded into a large monocline which has

been named the Pennine Anticline.

Recent glaciation has severly affected the Pennine region

and left a wide cover of till over most places. On the ridges

the till provenance is mostly the local bedrock but in the

Lancashire plain extensive till cover has been derived from

red beds on the Irish Sea floor.

2.2.2 MINERALIZATION

Of the 8.7 million tons of Pb concentrate estimated to have

been produced in the United Kingdom, mostly in the eighteenth

and nineteenth centuries, over 75% is thought to have come A B

FIG 2.8 Structure of the Pennine region and the Clitheroe area. (reproduced from Bott and Masson-Smith, 1957a (A) and Earp et al, 1961 (B)) - 22 - from the north Pennine fields alone, and some Q% from

Derbyshire (Schnellmann and Scott, 1969). The north Pennine fields are also thought to have accounted for some 52% of total Zn concentrate produced. Most of this output came from small operations and probably only some six major mines yielded 200,000 tons or more of concentrate. The chief ore was galena with much subordinate amounts of sphalerite.

Gangue minerals include calcite, fluorite and barium minerals

(barytes and witherite).

The productive horizon in the Pennine area has been the

Lower Carboniferous Limestone series. The strength of this suite of rocks enabled fractures to remain open to receive ore solutions while their reactivity enhanced ore deposition.

Most of the production came from vein ore-shoots developed along fissures localized by normal movements on faults with variable dips. Thus the ore-shoots coincide with the presence of hard beds in the wall rock (i.e., zones of steeper dip) and are limited above and below by shales in which the fissures may be virtually closed. In Derbyshire some ore bodies occur as elongated vertical shoots or "pipes", formed by widening in joints or at the intersection of joints.

Of the two blocks in the northern Pennines, the Askrigg and Alston blocks, the latter was the more productive and zoning of the mineralization is also well illustrated in it.

In the central part of the block, in upper Weardale and the southern part of the Alston moor, fluorite is the principal gangue mineral; in the surrounding fringe barium minerals occur to the exclusion of fluorspar (Dunham and Dines, 1945).

In the inner parts of the barium fringe the mineral may be - 23 -

rightly regarded as the gangue to the ore which is richest in

this zone, but in the outermost parts the sulphide content of

veins are so small that barytes and witherite become the

valuable minerals. The veins form a set of conjugate ENE and

NNW fractures but it is the former direction in which

virtually all the ores are found; NNW veins are seldom

mineralized.

Dunham (1967) discusses possible facies control on

mineralization in regard to pre-Carboniferous basement . rock

topography. He relates favourable mineralization to a "block

facies" associated with reef belts and a brittleness

associated with location in topographic highs in the basement.

Lack of mineralization is related to a "basin facies" and the

suggestion is made that this might account for the lack of

mineralization in the Ribblesdale fold belt, which in spite of

extensive minor faulting is poorly mineralized even where the

Lower Visean limestones outcrop.

2.2.3 GEOLOGY OF THE STUDY AREA

The study area lies in the district covered by geological

survey sheet 68 (1-inch series) and described in a special

memoir by Earp et al (1961) who give an excellent resume of

its geological history. The area is bounded by the British

national grid co-ordinates E 7700-8500 and N 4200-5000 (O.S.

maps SD74 and SD84, 1:25,000 series). The drainage system

surveyed is that of the Ings Beck which is one of the

important tributaries of the river Ribble.

Of the succession of rocks in the district only the KEY

o ; ® . Q. •o Pendle Grit re ( MILLSTON E G_R lTj_ _j :§_ o

o Pendleside z UJ D < O Sst < tr £ x o o (o Ravensholme Lst GO

^^ Pendleside Lst < X w c Boliandoceras © beds z o o h- re v) O cc o J

=«CHATBURN LST GROUP

MINE FAULTS Ba Barnoldswick Fault Mi Middop Fault Tw Twiston Fault Ho Horrocksford Hall Thrust

Fig 2.9 Geology of the Clitheroe study area - 24 -

Carboniferous Limestone series and the base of the Millstone

Grit are represented (fig 2.9). The oldest exposed

Carboniferous rocks are the Chatburn Limestone Series (Chi) which consists of calcareous shales with thin dark-grey limestones in the lower part, but with the upper part made up mainly of well-bedded dark-grey crystalline limestones with shale partings. The Worston Shale Series which follow consist mainly of calcareous mudstones and shales with numerous thin limestone bands; a variable lenticular limestone, the

Pendleside Limestone, occurs at the top. A marker horizon of tough porcellaneous cementstones containing the fossil

Bollandoceras hodderense occurs in the middle section. At its lower part are the almost pure banks or mounds of lime-sediment, the knoll limestones, which are believed to pass laterally into beds of shale or locally into well-bedded light-grey limestone. The Bowland Shale Series (BWS) which completes the Lower Carboniferous is one x mainly marine mudstones and shales with impersistent argillaceous limestones; a locally developed shelly limestone, the

Ravensholme Limestone, occurs near the base but the lower part is made up of a highly variable group of Sandstones called the

Pendleside Sandstone. The Pendle Grit which consists of sandstones and sandy shales are locally designated as the base of the Millstone Grit.

The important structural elements within the study area include the Clitheroe anticline. It is associated with, and runs parallel for most of its length in a ENE direction to the

Horrocksford Hall thrust. The latter dies out in the Middop fault which is considered a tear fault and an accomodation - 25 - structure to the Horrocksford Hall thrust (Earp et al, 1961 ),

The Barnoldswick fault is a prominent tear fault running unhindered in a NW direction until it dies out in the Middop fault in Chi. This occurs on the crest of the Sawley-Gisburn-

Swinden anticline, running en echelon with the Clitheroe anticline. Some minor faults run in a NNW direction and die out in the Horrocksford Hall thrust and Middop fault. It is one of these, the Twiston fault, which hosts mineralization at the Skeleron mine. In contrast to the ENE direction of the northern Pennine lodes, all the veins found in the Clitheroe area run roughly NW-SE. The Skeleron mine produced mainly Pb but was better known for the high Ag content of the ore.

As mentioned earlier the area is extensively covered with glacial debris, collectively termed the Pennine Drift, occuring mostly in the lowlands and restricted in the high hills. Around Pendle hill, the highest level it has beer found is the 1200 ft contour. Most of this cover is boulder clay, a heterogeneous blend of rounded and sub-angular scratched stones enclosed in a variable matrix of impermeable stiff grey, brown or occasionally reddish clayey gravel.

Various types of soil associations have been recognised in the study area, based on till provenance (Hall and Folland, 1970;

Curtis et al, 1976) but the common feature is that of gleying which results from impeded drainage. A "salop" association of poorly drained soils is found mainly in the flat or gentle slopes below 250 ft 0.D and is derived from slightly calcareous reddish-brown till whose finely textured subsoil impedes the drainage. The "wilcox" association is found on the uplands and comprises peaty gley organic soils developed - 26 -

over till derived from underlying Carboniferous rocks, in

response to the high rainfall and low temperatures experienced

over such heights.

2.2.4 FIELD OBSERVATIONS

In contrast to the Cornwall study area swamps and marshes are

not as widespread. At low contour levels Fe-Mn oxides are

most evident near weirs, bridges, and wooded areas with wide

free-flowing courses strewn with boulders. Patchy, restricted

occurences are found in isolated localities, even within

limestone. The most striking developments, however, occur on

highland moor and at the break in slope from the moor to lower

topographic levels. The predominant visible phases are Fe

oxides which colour the sediments red, but Mn oxides become

visible near the break in slope; in locality 5 (fig 2.10) only

Mn oxides were observed on the moor. In tranquil flow the

streams on the moor carry solitary, large suspensions of

Fe-oxide rich particles (probably attached to clay or

amorphous silica), but after a heavy downpour huge quantities

of mud and sediment enriched with these phases are transported

downstream.

Sediments in the upper reaches of some streams within

limestone were found cemented into hard pavements that yield

little fine sediment; in locality 5 strong flow over exposed

hard, flat-lying slates and shales yields much gravel and

little fine material. In several localities thick glacial

deposits on the banks contribute large quantities of sediment

to the drainage through erosion and slumping. KEY

^ locality g 9

(ID Worstron TOIA/

o i 7700.00 7780.00 7860.00 7940.00 B020.CO 8100.00 8180.008260.OO8340.00 8420.00 8500.00 EAST

FIG 2-10 CLITHEROE SAMPLE sues and L0CALU IES - 27 -

At the Skeleron mine site only a few mounds of mine waste remain and form the bank of the stream here. Fragments of mainly vein-quartz and shale are stained with Mn oxide.

The main problem in this study area thus involves separating the secondary environmental effects which originate largely on the moor and are inevitably superimposed on sediments further downstream, from the geochemical differences arising from varying lithology. - 28 -

CHAPTER THREE - ANALYTICAL TECHNIQUES

3.1 ORIENTATION STUDIES

As a preliminary study of analytical techniques two

extractants were tried on five samples to determine which

appeared to offer the best advantage in terms of analytical

precision, ease, speed and convenience of analytical

procedure, and maximization of anomaly contrast. The reagents

used were conc. nitric acid and a 4 : 1 mixture of conc.

nitric and perchloric acids. Two closely related procedures

were tried on the nitric-perchloric acid mixture. These acids

are normally used for near-total attacks but were employed in

this instance to also evaluate the nature of the results

obtainable with Fe-Mn coatings on coarse sediment. The samples

used were obtained from a drainage system adjacent to the

Cornwall field area. They comprised two soils (one anomalous,

one background), two stream sediments (one anomalous, one

background) and an alluvium sample.

3.1.1 PROCEDURES

The samples were oven-dried at 105 deg. Celsius and sieved.

The -170 micron and +2 to -5 mm portions were retained for

analysis and the rest discarded. The fine size fraction chosen

is the approximate metric equivalent of the -80 mesh commonly

used, while the coarse size fraction was selected for its - 29 -

availability and easy handling. Replicate analyses w«>ro

performed on these, the number of analyses depending on Lho

availability of material. Part of the -170 micron material was

ignited to eliminate volatiles and organic matter and to find

their influence on the properties sought. This involved

weighing 250 mg of sample into a fused silica crucible and

burning it in a muffle furnace at 450 deg. Celsius for four

and a half hours. The loss on ignition (LOI) is an approximate

estimate of the content of organic matter (table 3.1). A

synopsis of the digestion procedures are set out below.

3.1.2 NITRIC ACID

One ml of the conc. acid was added to 250 mg of fine fraction

or 2 ml to 5 gm of coarse fraction in a 18 x 180 mm test tube.

This was left on an aluminium block at 105 + 5 deg. Celsius

with periodic checks to ensure that the acid was being

refluxed. The tubes were removed after one hour, made up to 10

ml with deionized water, shaken and left overnight to settle.

This was a very fast, safe and simple procedure which required

little attention.

3.1.3 NITRIC-PERCHLORIC ATTACK METHOD I (N-P I)

In this procedure 5 ml of the acid mixture was added to the

sample in 18 x 150 mm rimmed test tubes which were suspended

in a hot air bath. The contents were then boiled to dryness or

near-dryness when the fine material would have acheived a

white or yellowish-white colour. The residue was leached with - 30 -

2 ml of 5M HCL on a sand bath at about 60 dog. Celsius for

3-10 mins and then diluted to 10 ml with deionized water. The

resulting solution was thus 1M in HC1. The samples were shaken

thoroughly to mix and left overnight to settle.

This method has the advantage over the nitric acid method

of being more intensive. However it requires much attention

and is very slow. It takes a long time for the reagent to dry

up, and this appeared to depend on the nature of the sample

and how efficiently the air-bath operated. It was therefore

decided to ascertain whether this last phase of the reaction

could be eliminated without any loss in information. The

following procedure was adopted for this purpose.

3.1.4 NITRIC-PERCHLORIC ATTACK METHOD II (N-P II)

The procedure was identical to the one above except that

boiling did not proceed to dryness. The test tubes were

removed when about 1 ml of reagent was left. A rough estimate

of this was made by adding 1 ml of cold reagent to a sample

chosen at random and kept on the bench for comparison. The

reactants were diluted to 10 ml with deionized water, shaken

and left overnight to settle.

This proved to be a much faster procedure than N-P I. The

main problem was with estimating how much reagent was left and

removing the tubes on time to ensure equivalent or equal

reaction time for all samples. Obviously the dimensions of the

test-tubes and the volumes of sample vary (especially with

coarse fraction) but it was necessary to assume that this was - 31 -

within tolerable limits.

3.1.5 DETERMINATIONS

Estimates of metal concentrations were made on a Pelkin-Elmer

model 403 Atomic Absorption Spectrophotometer (AAS) for eleven

elements. Lead, Zn, Ba and possibly Cd and Cu were sought for

their direct relationship to mineralization; Mn and Fe for

their possible control on a number of elements; Ca for its

interference on the Ba determination; and Al and Mg as an

estimate of the intensity of the acid attack. Barium

determinations were made in a 1% Sr solution. Subsequent to

the orientation survey, multielement data obtained by

Inductively Coupled Plasma emmision Spectroscopy (ICP) were

compared to AAS data. The ICP system comprised a Radyne

plasma generator and an ARL 29000 B quantometer assembled in

the department.

3.2 RESULTS FOR FINE FRACTION

The results are set out in tables 3.1 to 3.9, 3.11 and 3.12

and are discussed on the basis of the relative efficiency of

extraction, analytical precision and anomaly contrast. The

analytical precisions for AAS analyses using these digestion

methods are often quoted as better than 20%. In comparing the

various properties discussed below arbitrary 20% limits were

used. Differences between different methods within + 20% of

the means were considered unimportant.

3.2.1 LOSS ON IGNITION

None of the samples contained more than about 15% by weight of

volatiles (table 3.1). - 32 -

Table 3.1 Loss On Ignition

Sample Replicates % LOI

T1 * 16 13

T2 12 5

T3 20 5

T4 20 15

T5 20 10

* T1 = alluvium; T2 = background sediment; T3 = anom.

sedt; T4 = background soil; T5 = anom. soil.

It is difficult to tell whether the weight loss is due solely

to moisture and fine organic matter as oxidation of sulphides

and the loss of formation water in clays could conceivably

contribute to it. In the anomalous samples where sulphide

oxidation might be expected to be important the LOI were not much different from those for the background samples. The

stream sediment samples consisted mostly of discrete free grains with very little visible clay size material or organics

and their low weight losses may be due solely to moisture

content• Table 3.2 Results of trial attacks on fine fraction (Fe, Al in %, others in p.p.m)

SAMPLE N REAGENT IGN.* Pb Zn Ba Mn Fe Cu Co Cd Ca Mg Al

T1 4 NITRIC NO 260 188 72 250 2. 03 31 12 1. 2 1015 2390 1. 20 4 YES 290 216 117 313 2. 33 29 15 1. 5 1100 2900 1. 81 4 N-P I NO 271 193 396 250 2. 20 33 16 1. 4 1065 3260 5. 33 4 YES 306 220 543 303 2. 58 36 16 1. 3 1275 3675 6. 13 4 N-P II NO 292 204 176 260 2. 33 36 16 1. 3 1140 3140 2. 98 4 YES 316 221 413 313 2. 63 36 20 1. 9 1313 3613 5. 18

T2 3 NITRIC NO 240 376 44 1613 3. 83 50 28 1. 6 1660 5187 1. 51 3 YES 229 363 92 1700 3. 72 50 28 2. 2 1583 5467 1. 75 3 N-P I NO 253 365 315 1453 3. 73 55 27 2. 4 1540 5973 4. 44 3 YES 233 357 458 1567 4. 17 48 27 2. 0 1667 6167 5. 40 3 N-P II NO 252 373 147 1667 3. 85 47 28 2. 4 1787 6027 2. 40 3 YES 273 367 275 1667 4. 08 46 32 2. 3 1683 6067 3. 82

T3 5 NITRIC NO 1048 406 75 1616 3. 46 55 22 1. 8 1508 4744 1. 33 5 YES 1029 397 105 1750 3. 45 54 21 1. 9 1610 5020 1. 55 5 N-P I NO 1024 412 332 1576 3. 46 49 25 2. 5 1704 5472 4. 53 5 YES 1050 406 462 1650 3. 77 51 25 1. 8 1700 5550 5. 01 5 N-P II NO 1072 412 194 1712 3. 58 48 25 2. 5 1724 5408 2. 44 5 YES 1230 403 352 1820 3. 82 48 29 2. 6 1830 5580 4. 13

T4 5 NITRIC NO 89 97 57 1528 4. 05 26 18 1. 2 4016 2464 1. 89 5 YES 103 119 110 1830 4. 73 28 16 1. 7 4650 3040 2. 77 5 N-P I NO 96 108 383 1376 4. 02 27 27 2. 0 3648 3624 7. 04 5 YES 112 129 561 1760 5. 03 32 24 1. 8 4460 4090 8. 40 5 N-P II NO 102 107 172 1528 4. 24 29 24 1. 8 3976 3232 4. 72 5 YES 123 130 385 1900 5. 28 32 30 1. 9 4680 3880 6. 53

T5 5 NITRIC NO 249 114 70 872 3. 74 28 18 1. 3 1972 1548 1. 75 5 YES 279 136 116 1020 4. 29 29 20 1. 3 2390 1630 2. 68 5 N-P I NO 250 119 392 824 3. 80 31 18 1. 8 2848 1436 9. 32 5 YES 264 129 506 970 4. 36 31 26 1. 2 3150 1620 7. 60 5 N-P II NO 261 120 198 864 3. 96 31 22 1. 7 2536 1552 4. 11 5 YES 301 140 413 1050 4. 62 33 29 2. 2 3050 1770 6. 50

* IGN. = ignition; N = number of replicates. - 33

3.2.2 RELATIVE EFFICIENCY OF EXTRACTION

The Al concentration reflects the extent of dissolution of

clays and other aluminosilicates. Over the given range of

sample types the NP-I attack gave 2.5 to 3 times and the N-P

II 2 to 2.5 times as much Al as nitric acid alone (table 3.2).

Thus the acid mixture gives a more intensive attack than

nitric acid alone does. This agrees with the findings of

Foster (1973). Table 3.3 gives the ratios of the mean

concentrations of each element in each sample, both ignited

and unignited, for the two N-P methods. For ratios within +

20% of unity the two methods are considered more or less

identical. On this basis it may be concluded that :

(i) There is overall, very little to choose between the two

methods. The Er ratios at the right end of the table represent

the overall efficiency for extraction of all the elements in

each sample. It can be seen that the range of values are well

within the arbitrary limits set above.

(ii) Considering the individual elements separately only Ba

and Al consistently show a definite advantage with N-P I. Thus

boiling to dryness merely ensures a prolonged attack on

silicate minerals.

Table 3.4 shows the effects of ignition on Er. In most

cases ignition increased the concentration of most elements,

up to as much as 100% (fevc^htsge Cha^s^ Uss the relative

standard deviation described below). Exceptions occur when

there is either little or no effect or a decrease in

concentration. The following points are worth noting from Table 3.3 Relative efficiency of extraction, *ER, in fine fraction; N-P attacks.

UNIGNITED SAMPLES

Sample Pb Zn Ba Mn Fe Cu Co Cd Ca Mg A1 ER

T1 .93 .95 2.25 .96 . .94 .89 1.14 1.08 .93 1.04 1.79 1-17 T2 1.01 .98 2.15 .87 .97 1.18 .95 1.00 .86 .99 1.85 117 T3 .96 1.00 1.72 .92 .97 1.01 1.00 1.00 .99 1.01 1.86 1-13 T4 .95 1.01 .90 2.23 .95 .94 .97 1.14 .92 1.12 1.49 1-15" T5 .96 .99 1.98 .95 .96 1.00 .82 1.05 .93 1.12 2.27 MS> ER .96 .99 2.07 .92 .96 1.00 .98 1.05 .93 1.06 1.85 1 -1 L 6ER .03 .02 .22 .04 .01 11 .12 .06 .05 .06 .28 C.V. (%) 3 2 11 4 1 11 12 5 5 6 15

IGNITED SAMPLES

Sample Pb Zn Ba Mn Fe Cu Co Cd Ca Mg A1 £R

T1 .97 1.00 1.32 .97 .98 1 03 .82 .67 .97 1.02 1. 18 T2 .85 .97 1.67 .94 1.02 1.04 .84 .86 .99 1.02 1.42 l-ofc T3 .85 1.01 1.31 .91 .99 1 06 .86 .63 .93 1.00 1.21 •98 T4 .91 .99 1.46 .93 .95 1.00 .80 .95 .95 1.05 1.29 ' ho3 T5 .88 .92 1.23 .92 .94 .97 .90 .55 .92 1.03 1.17 -35

ER .89 .98 1.40 .93 .98 1.02 .84 .74 .95 1.02 1.25 t-oo tfER .05 .03 .17 .02 .03 .04 .04 .16 .03 .02 .10 C.V. (%) 5 4 12 3 3 4 5 22 3 2 8

metal concentration in solution by N-P I * ER = metal concentration in solution by N-P II. - 34 -

table 3.4 :

(i) Ba was the most affected by ignition with increases in

concentration from about 15% to over 100%. The N-P II always

gave results over 70% higher. Nitric acid gave increases of 30

to 90% while N-P I only returned increases of up to 30%.

Aluminium gave results similar to Ba. N-P II gave increases of

10 to 60%, nitric acid 10 to 50% while N-P I gave low

increases of 10 to 15%, except in the anomalous soil sample where there was a decrease of some 7%.

(ii) In contrast relatively small increases were recorded for

the other metal concentrations. In both background and

anomalous stream sediments all the methods gave practically

the same results for Pb and Zn as when the sampled were unignited. In the alluvial sample N-P II gave the same

results, nitric acid <10% and N-P I about 10% increase. In the

soil samples all three methods gave increases of about 10%.

For Mn and Fe there were essentially no changes in the stream

sediments, 10 to 15% increases in the alluvium and 10 to 25%

increases in the soils with all three methods. With Cu practically no changes were observed in all samples with all

three methods, except a probably dubious 15% increase in the background soil with N-P I. Cobalt and Cd behaved similarly to

Cu but more erractically. In alluvium N-P II gave increases of

some 25% while in the soils both N-P methods gave increases of

10 to 20%.

It may also be observed from table 3.4 that the advantage with N-P I (c.f. table 3.2) is reduced for Ba (ca. 10%) and Al

(ca. 25%). Thus on the whole ignition tends to give higher Table 3.4 Percentage change in concentration attending ignition of test samples.

SAMPLE N REAGENT Pb Zn Ba Mn Fe Cu Co Cd Ca Mg Al

T1 4 NITRIC 12 15 63 25 15 -6 25 25 8 21 51 4 N-P I 13 14 37 21 17 9 0 -11 20 13 15 4 N-P II 8 8 135 20 13 0 43 46 15 15 74

T2 3 NITRIC -5 -3 109 5 3 0 17 38 -5 5 16 3 N-P I -8 -2 45 8 12 13 0 •-17 8 3 22 3 N-P II 8 -2 87 0 6 -2 14 -4 -6 1 59

T3 5 NITRIC -2 -2 40 8 0 -2 -5 3 7 6 17 5 N-P I 3 -1 39 5 9 4 0 -27 0 1 11 5 N-P II 18 -2 81 6 7 0 16 5 6 3 69

T4 5 NITRIC 16 23 93 20 17 8 -11 42 16 23 47 5 N-P I 17 19 46 28 25 19 -11 -10 22 13 19 5 N-P II 21 21 124 24 25 10 25 8 18 20 38

T5 5 NITRIC 12 19 66 17 15 4 11 2 5 21 53 5 N-P I 6 8 29 18 15 0 44 32 13 11 -18 5 N-P II 15 17 109 22 17 6 32 31 14 20 58 - 35 -

results and this must be due to the physical breakdown of

minerals which renders them more susceptible to acid attack.

The consistently higher Ba and Al results may represent the

breakdown of feldspar grains while increased Mg in alluvium

and soil samples may imply the presence of magnesian

aluminosilicates. However the increases in Al concentration,

are well within the much poorer precision attending ignition

(see below).

3.2.3 ANALYTICAL PRECISION

For such comparisons it is preferable to use the relative

standard deviation, Sr, defined as

Sr = (Si/Xibar) * 100%,

where Si = the standard deviation and Xibar = the mean value.

The precision was poorest for Ba (23-46%) in nitric acid or

N-P II. It was better with N-P I although poor still in the

anomalous soil (table 3.5). Cadmium, Co and Cu, in that

order, follow Ba in poor precision. On the other hand the

precision was very good for Pb, Zn, Mn and Fe with all three

methods•

Calcium, Mg and Al generally showed intermediate

behaviour, but for Al the precision was particularly poor in

the anomalous soil sample. The relative preference for the

three methods are :

N-P I : Pb Zn Ba Cu

N-P II : Co Cd Ca Mg Al; for Co, Cd and Ca, N-P I also gave

good precision but the N-P II results were better.

Even : Fe Mn. Table 3.5 The relative standard deviation ( % ) for the metals in fine fraction of the test samples by the three methods.

METAL REAGENT T1 T2 T3 T4 T5 *IGN. NIGN. IGN. NIGN. IGN. NIGN. IGN. NIGN. IGN. NIGN.

Pb NITRIC 3 2 3 7 8 5 6 4 5 4 N-P I 7 3 8 6 9 4 5 3 9 2 N-P II 2 5 5 3 17 9 5 2 2 2

Zn NITRIC 3 6 3 4 4 5 8 5 7 4 N-P I 5 1 6 3 4 5 10 0 7 3 N-P II 6 6 8 3 3 5 9 2 6 3

Ba NITRIC 12 20 17 0 12 26 25 21 20 26 N-P I 6 5 9 11 10 5 10 7 15 18 N-P II 20 0 17 23 9 46 31 39 11 8

Mn NITRIC 5 8 6 1 8 3 4 2 3 5 N-P I 2 8 4 3 2 2 4 2 10 3 N-p II 8 9 6 4 2 3 3 1 3 3

Fe NITRIC 2 5 2 1 4 2 5 1 3 3 N-P I 2 2 2 2 3 1 7 2 7 4 N-P II 2 4 5 1 4 2 2 1 3 4

Cu NITRIC 4 4 17 17 35 35 6 6 2 2 N-P I 2 4 20 10 18 12 5 2 8 8 N-P II 5 5 6 3 17 7 6 13 2 11

Co NITRIC 27 0 10 0 26 10 14 13 0 12 N-P I 39 20 11 9 20 7 9 8 16 20 N-P II 20 17 18 14 9 7 0 0 8 10

Cd NITRIC 27 15 27 0 29 12 34 0 21 14 N-P I 23 29 0 0 15 8 15 0 23 20 N-P II 13 15 12 0 16 11 22 2 13 3

Ca NITRIC 4 8 7 6 9 17 5 2 4 2 N-P I 5 5 4 3 5 5 7 13 8 5 N-P II 2 2 7 7 10 2 5 1 3 2

Mg NITRIC 5 3 2 5 4 14 5 5 3 4 N-P I 2 23 1 10 2 3 7 10 10 6 N-P II 4 1 4 9 4 9 8 4 6 10

Al NITRIC 2 6 10 3 6 5 14 6 11 5 N-P I 4 3 8 7 12 10 7 9 11 25 N-P II 15 4 13 8 15 4 25 4 15 4

* IGN. = Samples ignited; NIGN. = Samples not ignited. - 36 -

For each test sample table 3.5 compares the relative

standard deviation, Sr, for each element and extractant before

and after ignition. In general ignition lowers the precision

for most elements in most sample types and for any or all

three methods. For Al this is most evident with N-P II.

Similar poorer precision was obtained for Cu, Co and Cd, but

the trends are not consistent over all the test samples.

Precision for Pb, Zn and Fe also appear to deteriorate

slightly after ignition. For Mn precision deteriorates

slightly with nitric acid but is unaffected with both N-P

methods. On the other hand while Mg precision is essentially

unchanged with N-P II it is slightly improved with nitric acid

and definitely so with N-P I which now yields a better

precision for the metal than N-P II. With Ba precision

improves after ignition with all three digestion methods but

is still best with N-P I.

3.2.4 ANOMALY CONTRAST

Deriving contrast measures in this exercise is simplified

because the identities of the anomalous and background samples

are known. Table 3.6 lists some metal ratios that yield some

relatively high numbers which could be used as measures of

contrast. The first part (A) shows the types of ratios that

could be used to make out by inspection, areas or clusters of

relatively high values in any given data set, thus deriving

anomalies. The second part (B) compares some of these ratios

for the background and anomalous samples. The various

combinations of metal ratios were chosen by inspection of the Table 3.6A Measures of contrast in fine fraction.

Sample Reagent Ign. Pb/Zn Ba/Zn Pb+Ba/Zn Pb. Ba/Zn (1) (2) (3) (4)

T1 Nitric No 1.38 .38 1.77 .53 Yes 1.34 .54 1.88 .73 N-P I No 1.40 2.05 3.46 2.88 Yes 1.39 2.47 3.86 3.43 N-P II No 1.43 .86 2.29 1.23 Yes 1.43 1.87 3.30 2.67

T2 Nitric No .64 .12 .76 .07 Yes .63 .25 .88 .16 N-P I No .69 .86 1.56 .80 Yes .65 1.28 1.94 .84 N-P II No .68 .39 1.07 .27 Yes .74 .75 1.49 .56

T3 Nitric No 2.58 .19 2.77 .48 Yes 2.59 .27 2.86 .69 N-P I No 2.49 .81 3.29 2.00 Yes 2.59 1.14 3.72 2.94 N-P II No 2.60 .47 3.07 1.23 Yes 3.05 .87 3.93 2.6 7

T4 Nitric No .92 .59 1.51 .54 Yes .87 .92 1.79 .80 N-P I No .89 3.55 4.44 3.15 Yes .87 4.35 5.22 3.78 N-P II No .95 1.61 2.56 1.53 Yes .95 2.96 3.91 2.80

T5 Nitric No 2.18 .61 2.80 1.34 Yes 2.05 .85 2.90 1.75 N-P I No 2.10 3.29 5.40 6.92 Yes 2.05 3.92 5.97 8.03 N-P II No 2.18 1.65 3.83 3.59 Yes 2.15 2.95 5.10 6.34 Table 3.6B Contrast ratios in fine fraction.

Sample Reagent Ign. 1A/B * 2A/B 3A/B 4A/B PbA-B BaA-B PbA/B BaA/B

T3 : T2 Nitric No 4 2 4 7 808 31 49 2 Yes 4 1 3 4 800 13 5 1

N-P I No 4 1 2 3 771 17 4 1 Yes 4 1 2 4 817 4 5 1

N-P II No 4 1 3 5 820 47 4 1 Yes 4 1 3 5 957 77 5 1

T5 : T4 Nitric No 2 1 2 3 160 13 3 1 Yes 2 1 2 2 176 6 3 1

N-P I No 2 1 1 2 154 9 3 Yes 2 1 1 2 152 -55 3 1

N-P II No 2 1 2 2 159 26 3 1 Yes 2 1 2 178 28 3 1

A = anomalous, B = background sample. The number refers to the appropriate metal ratio in table 3.6A. The figures are rounded off for clarity. - 37 -

results, and from a consideration of the type of mineralization. Lead, Zn and Ba were chosen because of their relationship to mineralization (Ba especially because it occurs in minor amounts and small differences in its concentration might be more easily made out). Barium always appears in the numerator (as does Pb) because it varies systematically, while the relatively invariant Zn concentration appears in the denominator. It is clear from this table as constructed, that

(i) N-P II often gives a slightly better contrast than

N-P I.

(ii) there can be a remarkable consistency in some of

these ratios (e.g Pb/Zn) which implies that the three

methods yield data containing the same information; It

may also be a reflection of the nature of the sample or

deposit.

The effects of ignition on anomaly contrast may be Infered from table 3.6 as follows:

(1) the contrast is improved upon ignition, and

(ii) N-P I always provides the best contrast, N-P II the

next best and nitric acid the least.

The above conclusions depend solely on the method of computing the ratios. For example exchanging Ba positions for Zn results in exactly the opposite conclusions, but this would be - 38

unacceptable. The Pb/Zn ratio for instance implies that in the

background sample the high mobility of Zn renders its

concentration db L^d-St, equal to that of Pb but that in the

anomalous sample the concentration of Pb should be greater

since it is less mobile. It is worth recalling the consistency

in the Pb/Zn ratios in table 3.6 and noting its possible

usefulness in exploration studies. The Pb.Ba/(Zn)2 ratio gave

similar results but its relative instability (which may be an

indication of the sensitive response of Ba to ignition and

acid attack) makes it a less useful proposition. The sometimes

ambiguous results obtained with the other two ratios (Ba/Zn

and Pb+Ba/Zn) may be due to the same reason.

3.2.5 SUMMARY

(i) The nitric-perchloric acid digestion is confirmed to be

much stronger than nitric acid alone. With the acid mixture it

is necessary to proceed to dryness to ensure a more complete

breakdown of silicate minerals although this is apparently

only advantageous to Ba and Al. Boiling to dryness also gives

better precision for Pb, Zn, Ba and Cu although by not so

doing much better precision may be obtained for Co, Cd, Ca, Mg

and Al. Analytical precision for Mn and Fe are similar with

both N-P I and N-P II. The latter method is much faster but

requires a more critical control of the air-bath which may not

be worth the extra effort to save time.

(ii) Anomaly contrasts provided by the two nitric-perchloric

methods are similar and equivalent to those obtained with

nitric acid alone. - 39 -

(iii) Ignition generally serves to physically break down

individual minerals and renders them more susceptible to acid

attack. This is shown by increased extraction for Ba and Al

(feldspars) and Mg (Mg-aluminosilicates). There may be slight

losses through volatilization especially in the case of Pb, Ba

and Fe but this is probably more than compensated by gains

through increased access to the mineral lattice.

(iv) Ignition introduces changes in precision. Barium

precision improves with all three methods although it is still

best with N-P I; that for Ca improves only with nitric acid

and N-P I. For all other metals precision deteriorates.

(v) Contrast between background and anomalous samples is

improved upon ignition. The improvement is greatest with N-P I

and least with nitric acid.

(vi) N-P I was used in subsequent work and ignition was

considered unnecessary.

3.3 RESULTS ON FE-MN COATINGS ON COARSE FRACTION

Table 3.7A lists the analytical results for Fe-Mn coatings on

gravel using the above reagents. Those in part A of the table

were calculated on the assumption that in all cases the

surface area available for reaction and the amount of coating

taken into solution is the same. Thus the factor for

converting the element concentrations in solution into p.p.m

in the sample coatings was a constant, 4 (with a further 10%

correction for Ba because of the addition of sufficient

solution to obtain a matrix in which the concentration of Sr

is 1%). In part B of the table reaction surface is still Table 3.7A Summary of results for Fe-Mn coatings on coarse fraction.

p.p.m conversion factor constant.

Sample N Reagent Pb Zn Ba Mn Fe Cu Co Cd Ca Mg Al

* T7 5 Nitric 102 155 19 1220 11952 9 11 1.05 338 1677 4648 5 N-P I 150 192 55 1328 14960 9 12 1.15 228 2328 10160 5 N-P II 354 112 32 1148 19600 18 13 1.40 225 2664 10080

T8 2 Nitric 11 89 14 900 6340 6 12 .42 140 2200 2340 * 2 N-P I 16 112 30 704 19600 5 13 .62 133 3200 10400 2 N-P II 14 128 14 684 15600 15 15 .60 124 1940 6800

* p.p.m conversion factor variable •

Sample N Reagent Pb Zn Ba Mn Fe Cu Co Cd Ca Mg Al

T7 5 Nitric 1938 2977 368 2.48 24.97 158 141 21 4139 3.16 8.71 5 N-P I 2353 3085 846 2.27 23.67 141 201 19 3628 3.59 16.02 5 N-P II 5025 3447 337 1.66 27.37 235 200 19 3435 3.73 13.48

T8 2 Nitric 317 2672 360 2.64 19.00 182 349 13 4215 6.69 7.00 2 N-P I 233 1624 427 1.06 29.05 75 184 9 1881 5.08 15.59 2 N-P II 269 2484 282 1.34 29.82 103 296 11 2427 3.84 13.32

* Results for Mn, Fe, Mg and Al in this section in %, all other results in p.p./n. T7 - coarse fraction of anom. sedt. (T3); T8 = equivalent of tne bgrd. sedt. (T2). Table 3.7B Relative standard deviation (.%) for the results in table 3.7A

p.p.m conversion factor constant

Sample N Reagent Pb Zn Ba Mn Fe Cu Co Cd Ca Mg Al

T7 5 Nitric 60 35 58 42 45 78 40 30 67 48 49 5 N-P I 38 28 27 16 28 40 27 18 35 36 29 5 N-P II 52 56 73 4 23 60 50 46 13 37 55

T8 2 Nitric 45 24 11 35 36 57 5 20 4 64 47 2 N-P I 44 40 5 15 32 55 44 32 50 4 27 2 N-P II 42 18 2 14 33 57 15 9 9 2 8 p.p.m conversion factor variable

Sample N Reagent Pb Zn Ba Mn Fe Cu Co Cd Ca Mg Al

T7 5 Nitric 52 14 62 44 28 51 67 23 68 25 25 5 N-P I 21 18 41 41 8 24 28 17 21 12 13 5 N-P II 32 27 45 26 13 47 9 18 34 28 30

T8 2 Nitric 43 21 21 36 33 59 2 17 1 67 45 2 N-P I 3 7 42 34 16 11 3 16 4 44 21 2 N-P II 23 2 39 31 13 40 5 39 11 21 12 - 40 -

assumed to be the same for all samples but that different

amounts of coating were available for reaction. The conversion

factor in this case was variable and was computed separately

for each sample. This was done by saving the sample after

reaction, washing thoroughly with deionized water, drying to

constant weight in an oven at 110 deg. Celsius and weighing to

find the difference. The conversion factors were then

calculated using the actual weights dissolved in the reaction.

It can be seen from table 3.7A that :

(i) the level of concentration is more truly represented by a

variable conversion factor (part B).

(ii) comparing the results for stream sediment coatings with

those for the fine fraction ,

(a) there is considerable enrichment in Zn, Mn, Fe,

Cu, Co and Cd.

(b) Ba and, more clearly Pb in anomalous samples, show a

slight but appreciable enrichment.

(c) Ca shows a three-fold increase in concentration but

as before there is no contrast between anomalous and

background samples

(iii) there is much more Fe in the coatings than Mn although

they may possess the grey or black colour typical of Mn

oxides.

3.3.1 RELATIVE EFFICIENCY OF EXTRACTION

Because of the rather high relative standard deviations (table

3.7B) it is difficult to make any generalizations on this. The

following tentative conclusions may be drawn from table 3.8 : Table 3.8 Relative efficiency of extraction, ER, for Fe-Mn coatings on coarse fraction; N-P attacks.

A. p.p.m conversion factor constant

SAMPLE Pb Zn Ba Mn Fe Cu Co Cd Ca Mg Al

T7 .42 .77 1.72 1.16 .76 .50 .92 .79 1.01 .87 1.01 T8 1.23 .88 21.4 1.03 1.26 .83 .87 1.03 1.07 1.65 1.53

ER .83 .83 1.93 1.09 1.01 .67 .89 . 91 1.04 1.26 1.27

B. p.p.m conversion factor variable

SAMPLE Pb Zn Ba Mn Fe Cu Co Cd Ca Mg Al

T7 .47 .90 2.51 1.37 .87 .60 1.01 1.00 1.06 .96 1.19 T8 .87 .65 1.51 .79 .97 .73 .62 .82 .78 1.32 1.17

ER .67 .77 2.01 1.08 .92 .66 .81 .91 .92 1.14 1.18 - 41 -

(i) N-P II appears the most effective in extracting Fe from

all samples, and Pb, Zn and Cu from anomalous samples.

(ii) N-P I appears more suitable for Mg, Ba, Co and Al; it

also appears to extract Co from anomalous samples as

efficiently as N-P II.

(iii) Nitric acid appears best suited for Ca and background

contents only, of Zn, Mn and Co. The probable significance of

this apparent trend was not investigated.

Considering only the nitric-perchloric methods it appears

N-P II releases more Pb, Zn, Cu and probably Co while N-P I

releases more Ba, Mg and Al, but that the two release

identical amounts of Mn, Fe, Ca and Cd. Again this reflects

the more intensive attack on silicate minerals by N-P I.

3.3.2 ANALYTICAL PRECISION

The precision is generally poor for all elements with all

three methods (table 3.7B) but it is better for background

samples. N-P I offers the best method in terms of overall

precision, followed closely by N-P II. Poor precision is

especially noticeable for Pb, Cu, Ba, Co and surprisingly, Mn

and Fe. Making the conversion factor variable results in

better precision.

3.3.3 ANOMALY CONTRAST

Table 3.9 lists the contrast measures for the coarse fraction

constructed in the same manner as for fine fraction. The - 42 -

heading A refers to cases where the conversion factor is held

constant and B for varying conversion factor. It seen from

this table that,

(i) The Fe-Mn coatings on coarse fraction give contrast

measures higher than those given by the finer stream sediments

associated with them.

(ii) N-P II provides the best contrasts. This is especially so

for element ratios; it yields the least contrast measures in

background samples and the highest ones in anomalous samples,

thus inherently improving contrast.

(iii) Making the conversion factor constant results in higher

contrast for N-P II if Ba is present and occurs in the

numerator; on the other hand a variable conversion factor with

Ba in the denominator results in very sharp contrasts for N-P

II.

(iv) N-P I and nitric acid give similar contrast measures

which are independent of the choice of conversion factor.

3.3.4 SUMMARY

The relative merits or demerits of the three acid attacks as a

means of studying oxide coatings is not of interest here since

they all are too severe and attack the lithic fragments as

well. However two important results are still valid and worth

noting :

(1) Trace elements are enriched in oxide coatings of stream

sediments•

(2) Anomaly contrasts are much higher in these oxide coatings

than the accompanying fine fraction. - 43 -

These are the advantages to be exploited for exploration

purposes. To ensure that trace elements so utilized are

derived solely from oxide coatings more selective extraction

techniques are required. These are evaluated in the next

section.

3.4 PARTIAL EXTRACTION TECHNIQUES

Several reagents have "been successfully employed to

selectively remove Fe-Mn oxides from several types of samples.

These range from fast, simple procedures using inorganic acids

on marine Mn-nodules (Cronan, 1975) to slow complex

applications involving buffers and organic-inorganic acid

mixtures on soil (Le Richie and Weir, 1963). Three main

reagents, namely sodium dithionite, hydroxyammonium chloride

and hydrogen peroxide + hydrochloric acid, have found wide

application and are briefly described.

3.4.1 SODIUM DITHIONITE

This reagent is mainly used for the total removal of Fe-Mn

oxides. According to Chao and Theobald (1976), Deb (1949) was

the first to use it for the removal of "free Fe-oxides" from

soils. He is reported to have found it superior to several

others in terms of the effective removal of the oxides and

less destructive effect on clay minerals. Since then several

modifications of his method have been developed but those of

Mehra and Jackson (1960) and Coffin (1963) are the most

popular. - 44 -

Mehra and Jackson (1960) used a citrate- dithionite-bicarbonate mixture in which dithionite reduces the

Fe which is chelated by citrate anions, while the bicarbonate buffers the system. Their findings included :

(i) an optimum pH of 7.3 for the dissolution of Fe-oxides.

(ii) Sulphur and FeS are not precipitated

(iii) the reaction is very rapid and is mostly over in 15 mins, although some samples may take as much as an hour. Three fifteen minute extractions and two washings at 80 deg. Celsius were required for complete removal.

(iv) there is very little destructive effect on clay minerals.

In contrast the method by Coffin (1963) requires a single extraction at 50 deg. Celsius for 30 mins. using dithionite in a citrate buffer at a pH of 4.75. Some of his findings were :

(i) The precision is very high.

(ii) Pyrite (FeS) is totally unaffected and no more than 5% of the Fe is removed from siderite, ilmenite and magnetite by the

30 min. extraction. In the same period 60% of the Fe was removed from lepidochrosite, 10% from goethite, and 5-20% from a number of haematites.

(iii) No appreciable destruction of clay minerals Was observed other than an especially reactive nontronite on which

4 hrs reaction resulted in a 23% loss of its Fe and a 28% reduction in its exchange capacity.

McKeague and Day (1966) reported that the extent of dissolution of haematite or goethite by dithionite depends largely on the crystallinity and fineness of grinding of the

Fe oxides; finer grinding results in greater extraction. By - 45 -

sequentially extracting, first with acid oxalate and then with

dithionite, they were also able to differentiate between

amorphous and more or less crystalline phases.

3.4.2 HYDROXYAMMONIUM CHLORIDE

This reagent is credited with the total removal of Mn oxides

and partial removal of Fe oxides, the proportion of Fe oxides

removed depending mainly on the strength used. Chester and

Hughes (1967) were the first to report its use for the removal

of Fe-Mn oxides from pelagic sediments and gave the following

procedures and results :

(i) Used alone at molar concentration the reagent removes all

the Mn oxides and 50% of the Fe oxides (based on 1M HC1

extraction = 100%).

(ii) 0.02M of the reagent in 25% (v/v) acetic acid at 100 deg.

Celsius for 3 hrs with regular shaking; add conc. nitric acid

to destroy excess reagent. All the Mn oxides and almost all

the Fe oxides are extracted from marine Mn nodules. Later

experiments showed other Fe-oxides to be soluble (e.g.

amorphous pelagic Fe-oxides and hydroxides, adsorbed Fe-Mn

phases as well as Pb, Zn, Co, Ca and U). Clay minerals and

heavy minerals in sediment were practically unaffected.

Chao (1972) chose nitric acid as the solvent for the

hydroxyammonium chloride. For optimum results he recommended

the following :

(i) pH 2, this to prevent hydrolysis and the consequent

formation of insoluble hydroxides of the heavy trace elements - 46 -

released with Mn.

(ii) the reagent concentration should be .1M in .01M nitric acid.

(iii) thirty minutes shaking.

On the average 85% of Mn oxides and only 5% of the Fe oxides are said to be released. However it is not mentioned whether clay mineral lattices are left intact, but at such pH it is to be expected that they would be attacked.

Chao and Sanzalone (1973) later reported that hydroxyammonium chloride interfered in the AAS determination, of Co, Ni, Cu and Pb by suppressing absorption at the resonance lines for these metals. To overcome this they advocated the -use of the APDC-MIBK chelating-extraction. system. The removal into the organic phase was not only said to enhance the sensitivity of AAS analysis for these metals, their concentration levels were also said to be raised. At the moment it appears the two procedures of Chao (1972) and

Chao and Sanzalone (1973) are the dominant ones used (e.g,

Carpenter et al, 1975; Suarez and Langmuir, 1976).

Whitney (1974) is probably the first to report the use of a reagent containing hydroxyammonium chloride to dissolve

Fe-Mn encrustations on stream pebbles. He used 50 ml of a solution of .3M ammonium citrate with 2% (w/v) hydroxy ammonium chloride adjusted to pH 7 with ammonium hydroxide, on 100 gm of pebbles at room temperature for ten minutes. The solution was then filtered, diluted to 100 ml and analyzed directly for

Fe, Mn, Pb, Zn, Cd, Cu, Co and Ni by AAS. He reported severe interferences, especially Mn on Fe and Fe on Co and Ni. The _ 47 -

nature of the interferences were not made clear.

3.4.3 HYDROGEN PEROXIDE AND HYDROCHLORIC ACID

Hydrogen peroxide is a strong oxidizing agent but in the

presence of much stronger oxidants such as chlorine, Ce(+4)

and the permanganate anion, it behaves as a reducing agent

(Cotton and Wilkinson, 1960). The reagent can thus play a dual

role in extractions, either as a major reactant or as a

catalyst. For example Taylor and McKenzie (1966) used hydrogen

peroxide acidified to pH 3 with nitric acid to extract Mn and

associated trace elements _ from some Australian soils. The

reaction took three days to completion and the authors

reported results comparable to AAS results using HF attack.

Hydrochloric acid gradually reduces oxymanganese ores,

liberating chlorine in the process (Dolezal et al, 1966). The

dissolution is speeded in the presence of a reducing agent,

the choice of which depends on the nature of the analysis •

Hydrogen peroxide is frequently used because its oxidation

products do not Interfere in the analysis and any excess can

be simply decomposed by boiling. Iron oxides and hydroxides

also dissolve in HC1 at varying rates. Generally hydrated

oxides are more easily decomposed in HC1 than anhydrous ones.

The solubility of silica and cassiterite are said to be

negligible. The acid is generally unsuitable for the

dissolution of high-aluminium minerals because of the low

solublity of aliminium trichloride. However trace elements

released from the lattices of such minerals are usually - 48 -

soluble and therefore it is essential to minimize HC1 attack on aluminosilicates in partial extractions.

Among recent uses of the two reagents may be cited those by Cronan (1975) and Plant (1971). Cronan (1975) used hot 50%

HC1 to dissolve discrete marine manganese nodules and reported that the silicate core-matrix was left intact. Plant (1971) used a 1% HC1 solution containing five drops of 100 vols hydrogen peroxide for 12 hrs to obtain "extractable" Mn, Fe, trace elements and uranium. She found only trace amounts of silica and alumina in the leachate, implying minimal attack on aluminosilicates.

Rose (1974) has recently discussed extraction techniques from basic chemical principles in which he uses the stability relations for Mn(2+)/Mn(n+) and Fe(2+)/Fe(3+) phases in an

Eh-pH diagram. In presenting a case for a more deductive approach to the development of extraction techniques he places major emphasis on mineral-solution equilibria citing Fe-Mn oxides as examples. He points out that their stability diagrams indicate that the oxides will dissolve under reducing and/or strong acid conditions, conditions which are met by all the extraction techniques described above. After choosing the appropriate reducing agent the next problem involves the maintenance of the dissolved species in solution and this depends on pH. Sometimes it may be necessary to complex them to acheive this, and a buffer may have to be employed to acheive quantitative results. It is important also to protect other matrices in the sample from the specific attack being used. As an example the solubilty of clay minerals largely - 49 - depends on that of Al which is minimal in the pH range 5 to 8; maintaining the reaction pH in this range will therefore ensure minimal effect on clay minerals. Thus it is possible to predict optimum reaction conditions for testing; kinetic variables (e.g. temperature, physical state of samples, effect

of electro-magnetic radiations etc.) which are less susceptible to generalizations can then be conveniently assessed under the chosen set of conditions.

In principle such a scheme should render easy the modification of techniques and interpretation of results. It should also enable the design of a sequential extraction scheme during an orientation survey to obtain such vitally important information as :

(a) the proper selection of sample medium and extraction technique to optimize contrast of ore-related anomalies to background, and

(b) the geochemical behaviour and mobilty of indicator elements.

Chao and Theobald (1976) have compiled one such comprehensive scheme that offers maximum information on a single sample. The various fractions they proposed are :

(i) Fraction 1 : .1M hydroxyammonium chloride in .01M nitric acid (after Chao and Sanzalone, 1973); this is to remove Mn oxides and includes easily removed "soluble" and

"exchangeable" Cu.

(ii) Fraction 2 : .25M hydroxyammonium chloride in .25M HC1 at

70 deg. Celsius for 30 mins; this is to remove amorphous

Fe-oxides. - 50 -

(iii) Fraction 3 : sodium dithionite at 50 deg. Celsius (after

Coffin, 1963); this is to remove pseudo-crystalline Fe-oxides.

Organic chelated Cu, if present, is partially dissolved in

fraction 2 or 3. If this is present in appreciable amounts an

additional step is necessary after the removal of the Mn

oxides (e.g. the extraction of organic Cu with potassium

pyrophosphate, after McLaren and Crawford, 1973a).

(iv) Fraction 4 : potassium chlorate and HC1 in 4N nitric

acid, 20 mins boil (after Olade and Fletcher, 1974); this is

to dissolve sulphide minerals. Alternatively the method by

Lynch (1971) may be used.

(v) Fraction 5 : A mixture of HF and nitric acid to completely

break down silicate matrices.

3.5 PROCEDURES USED IN THIS WORK

The procedures adopted for this work and described below are

modifications of some of the methods described previously.

Apart from following the general deductive lines mentioned

above some considerations were special to this work :

(1) The material attacked is the Fe-Mn coatings on coarse

fraction of sediments. Procedures were thus required for

removing Mn oxides without virtually affecting Fe oxides, and

for removing both active oxides without attacking the coarse

lithic fragment itself.

(2) A routine method was required to enable the coarse

fraction sampled together with the usual fine fraction to be

used systematically for exploration, just as the fine fraction - 51 -

is so utilized at present. Thus a simple procedure involving

few reagents and requiring no more care or attention than is

demanded in routine methods using the fine fraction would be

preferable.

In addition there were practical problems concerned with

obtaining representative samples in sufficient quantity for

analysis. By retaining fragments whose shortest dimensions

were greater than 2 mm and less than 5 mm a fairly uniform

size fraction was obtained. In part this made for sample

representativity since a 5 gm subsample usually contained

several hundred grains, but there was the additional advantage

of regularly maintaining a high surface area for reaction.

Organic debris was avoided but coating free fragments were

included as in a normal sample. The coatings were attacked on

the fragments and the amount dissolved obtained by weight

loss. Attempts at alternative methods were found either

unsatisfactory or unsuited to a routine procedure. For example

few grains in any one sample had coatings thick enough to be

manually scraped free and such a method could easily result in

biased samples both in the field and the laboratory. Samples

were also packed loosely in polythene bottles and securely

fixed on a mechanical shaker but self-abrasion in this manner

yielded inadequate sample material; moreover the fragments

tended to break up and dilute the samples with rock flour.

3.5.1 EXPERIMENTAL

Twenty-five millilitres of reagent is added to 5 gm of sample - 52 - in 25 ml or 50 ml beakers. After reaction the beakers are stirred and the leachate and several washings of the samples with deionized water are filtered into 100 ml or 150 ml beakers. The latter are transfered to a hot plate and boiled to dryness or near-dryness• The residues are leached in molar

HC1 while warm. The beakers with the remains and filter papers are dried to constant weight and the amount of coating leached found by weight loss.

The 25 ml provides a liberal excess of reagent on the 5 gm of sample to enable continuous reaction overnight. Initial trials with 10 gm of sample gave results similar to those given by 5 gm. Whatman filter paper number 540 (11 cm diameter) was found fairly fast and held all visible particulates. Tests on weight loss after drying ten of these filter papers (two randomly selected from each of five different boxes) until they were almost charred, showed no more than a maximum 2.3% in lost weight; the actual weight loss was well within the error incurred in weighing on the balance used. The influence of the final matrix on AAS analyses was ascertained by sending the final solution for analysis in three forms, namely :

(i) directly in the reagent used

(ii) an aliquot of the reagent taken and made up to be 1M In

HC1

(iii) the solution boiled to dryness and the residue leached in molar HC1.

The last approach was chosen as it allowed the same matrix in the final solution and made it easier to destroy - 53 -

excess hydroxyammonium chloride. Results by this method were

of good precision and high, those in the matrix of the reagent

used were of good precision but low, while those made up to be

1M in HC1 were of poor precision and low. For the total

removal of Mn oxides without practically affecting Fe oxides

2M hydroxyammonium chloride was used overnight at room

temperature. For the removal of both Mn oxides and amorphous

Fe oxides 5% HC1 containing 12 drops of 20 vols hydrogen

peroxide was applied overnight at room temperature. The

details of all the individual extractions as well as a

sequential scheme adopted for confirmatory purposes are given

in the appendix. Some preliminary results on their performance

acheived with test samples now follow, from which the reasons

for choosing the adopted procedures may become apparent.

3.5.2 EXTRACTABILITY OF SELECTED REAGENTS

Earlier trials with 1%, 2%, 4% and 5% HC1 (after Plant, 1971)

indicated that the 5% solution gave results very similar to 4%

sulphuric acid, without corroding the lithic fragments. The

results of trials using the individual reagents contained in

table 3.10 are portrayed in fig 3.1. For this purpose the

highest results obtained with 4% sulphuric acid or 5% HC1 with

peroxide were arbitrarily regarded as 100% extraction. The use

of sulphuric acid as a reagent for total extraction was

suggested by its application as the 10% solution in 2% "Decon"

for the cavitation cleaning of oxide stains. The 4% acid

strength used was that found to just bleach the gravels

without extensive corrosion of the fragments themselves (as Table 3.10 Summary of extraction methods for oxide coatings.

TOTAL PARTIAL-INORGANIC ACIDS PARTIAL-ORGANIC ACIDS + ACID MIXTURES

1. 4% HgSO^ 1. 4% HC1 + 10 drops 1. NHpOH.HCl alone (HXL) at 1M; 2.2M of 20 vols HpOp for 4 hrs at room temperature, 2. conc. HNO. (after Plant; T97D (after Chester and Hughes, 1967)

3. NapS2°4 in 2. M HXL + 1016 Acetic acid for 1.5 hrs citrate buffer at 100 deg. Celsius. at pH 7.3 for (after Chester and Hughes, 1967) 30 minutes at 50 deg. Celsius, 3. HXL (.5M) + Ammonium Citrate (.25M) (after Coffin, 1963). (after Whitney, 1975)

4. M HXL + ,8M HNO for 4 hrs (after Chao, 1972)

5. .25M HXL + .25M HC1 at 80 deg. Celsius for 30 mins. (after Chao and Theobald, 1976) fig. 3*1 Variation in trace eleaent extractability by various reagents.

100-2

k £ 80-

X hno^

0 nh2oh..HG l + O 60- nh oh,.HG L + o o 2 w. •V- • nh2oh,.HG L + k • nh2oh,.HG l • HGl + H2°2 a UUO-

2 0-

Mn - 54 -

inffered from the degree of polish imparted to them).

Concentrated nitric acid was tried as a total extraction

reagent for comparison.

It is seen from fig 3.1 that with the exception of Mn, Zn and Co, none of the reagents containing hydroxyammonium chloride extracts more than 30% of any of the other metals analyzed in the coatings. Hydroxyammonium chloride alone extracts 48% Mn, 15-25% Zn, Co and Ni, and less than 10% of the other metals, with as little as 1% Fe. Mixed with acetic acid a lot more Pb (30%), Fe (19%), Ca (15%), Cu (22%), Co

(46%) and Ni (29%) is released. This may be taken as an indication of the greater stability of complexes formed with organic acids by the heavy metals and members of the first transition metal series. The addition of nitric acid to hydroxyammonium chloride gave results comparable to using the reagent alone, except in the case of Co where as much as 37% is extracted; this is accompanied by minor increases in Ni,

Fe, Pb and Zn, and a minor decrease in the amount of Cu extracted. On the other hand mixture with ammonium citrate results in slightly lower extraction of Zn, Pb and Cu, but slightly higher Co, Ni and Fe. Thus methods using hydroxyammonium chloride with or without simple inorganic acids mainly extract Mn oxides and very little of the Fe oxide phases.

The extremely low solubility of Pb and Ca and the somewhat limited solubility of Zn and Cu sulphates, cast some doubt on the relative extractability bounds derived with respect to sulphuric acid and the HCl-peroxide methods alone. - 55 -

Since the latter reagent did not remove all the coating the

relative extractabilities shown in fig 3.1 were probably only partially true. In particular the hydroxyammonium methods were expected to extract at least as much Mn as the HCl-peroxide one, but the latter yielded nearly twice as much. It is also worth noting that although the conc nitric acid bleached the fragments the metal levels were quite low for a total extraction. Furthermore the high Mg contents in both nitric and sulphuric acid extracts suggested that a lot of the metal may have been derived from the lithic fragments themselves rather than released from the coatings. To clear these uncertainties it was necessary to perform a sequential extraction using reagents that were unlikely to corrode the fragments to any appreciable extent while removing all oxides from them. The choice of reagents and methods were :

(i) hydroxyammonium chloride alone, for the removal of Mn oxides (after Chester and Hughes, 1967)

(ii) hydroxyammonium chloride containing HC1, to remove amorphous Fe oxides (after Chao and Theobald, 1976)

(ill) sodium dithionite in a buffer of sodium citrate and citric acid, to remove pseudo-crystalline Fe oxides (after

Coffin, 1963).

Fig 3.2 is a bar diagram showing the proportion of total metal extracted at each stage in the sequence for each of the metals analyzed; it is arranged in the order of decreasing extraction into the first fraction. In table 3.11 are the actual results in percent. The percentage of total metal extracted in the sequential extraction refers to the total Table 3.11 Results {%) of sequential attacks on test samples.

HXC1 * HXC1+HC1 DITHI0NITE

S1 S2 S3 S4 S1 S2 S3 S4 S1 S2 S3 S4

Mn 4.82 9.70 6.40 5.90 .31 .47 30 .35 .08 .13 .07 . 10

Zn .49 1.00 .69 .59 .18 .74 .30 .39 .17 .27 .15 .14

Pb .77 2.40 .69 .93 .39 5.53 .44 1.46 .05 .29 .05 .10

Fe 1.95 3.10 2.10 1.98 11.28 19.71 11.71 14.08 12.56 18.53 12.18 14.08

Ca 1.49 2.40 1.33 1.41 .67 3.71 .41 1.30 .10 .30 .10 .12

Mg .30 .44 .30 .30 .31 .52 .32 .32 .07 .11 .06 .06

Cu .01 1.80 .01 .01 .02 .04 .02 .02 .01 .01 .01 .01

Co .02 .06 .04 .04 .01 .02 .01 .01 .02 .03 .02 .02

Ni .02 .04 .03 .02 .01 .02 .02 .02 .02 .03 .02 .02

* HXC1 = Hydroxyammonium chloride. S1-S4 are sample identifiers. FIG- 3.2 Proportions of metals extracted into various fractions in sequential extraction.

100-1

80-

3 Na2S204 fraction O •O „ 0) 6 0 o (0

X 0) 40.

o\o NH2OH-HCI + HCI fraction

20-1

MH2OH-HCI fraction 0-J

MN CA ZN CO PB MG NI Cu FE - 56 -

amount of coating removed. It is evident from table 3.11 that

these never sum up to 100% and this immediately implies the

presence in the general oxide structure, of metals or

components other than those determined. One likely species is

amorphous silica. Although silica was not determined in the

analyses, it was often observed while on the hot plate that

some samples contained clouded particulates upon which some

probably colloidal Fe oxide phases adhered. The information

from fig 3.2 and table 3.11 may be summarized as follows :

(i) Hydroxyammonium chloride alone does extract over 90% of

the total Mn present in the coating but only as little as 7%

of the Fe. Some 30% of Cu and 40-55% of the other metals are

extracted as well. Thus the reagent is very useful for the

partial extraction of Mn and associated trace elements while

largely preserving Fe oxides.

(ii) Half of the remaining Fe and most of the Cu, Zn, Pb and

Ca is contained in the second fraction. Chao and Theobald

(1976) describe this as the "amorphous Fe oxides" fraction.

(iii) The remaining more or less crystalline Fe oxides contain

some 15% of the Zn, 32% of the Co and 38% of the Ni.

Considering that the second stage of the extraction also removes Fe oxides, this result means that the Zn, Co and Ni contents are more or less evenly distributed between the two metal-oxide phases. However over 50% of the Zn and Co, 40% of the Ni and Pb and 30% of the Cu are contained in the very reactive Mn oxide phases.

The above results highlight some of the uncertainties involved with the interpretation of total analyses where most Table 3.12 Variations in extractability with some modifications of the peroxide method.

Sample Method Mn Zn Pb Fe Ca Mg Cu Co Ni Al

69A 1 * .83 .83 .42 28.34 8.67 2.33 .27 .15 .05 13.84 2 .77 1.85 .34 30.78 7.70 2.77 .38 .35 .26 12.93 3 1.18 .61 .17 49.65 7.41 3.19 .15 .14 .09 11.86 4 .65 .13 .13 15.00 3.85 1.50 .05 .08 .05 6.25

38A 1 3.11 .54 5.54 49.95 .57 .14 .10 .16 .03 1.62 2 3.10 .55 6.19 52.78 .33 .11 .10 .16 .03 1.69 3 3.70 .72 6.10 73.02 1.04 .30 .11 .18 .04 2.37 4 2.95 .48 4.90 32.14 .51 .10 .07 .15 .02 1.15

81A 1 8.84 .36 .13 32.55 9.7 .93 .06 .09 .03 3.44 2 4.94 .27 .09 29.38 9.10 .88 .03 .10 .04 4.94 3 7.45 .40 .12 47.04 7.45 2.20 .05 .09 .05 7.06 4 4.55 .19 .07 23.60 3.79 .55 .02 .05 .02 2.38

87A 1 20.16 .21 .05 15.46 8.96 .53 .02 .13 .06 1.46 2 27.70 .29 .05 17.11 7.34 .75 .02 .20 .09 1.96 3 29.00 .28 .05 18.72 6.03 1.35 .02 .17 .09 3.54 4 9.23 .29 .09 16.58 9.10 3.68 .02 .10 .04 2.68

* key : 1 = 2M HC1 for 4 hrs; 2 = 2M HC1 for 8 hrs; 3 = 2M HC1 overnight (16 hrs); 4=5% Hcl overnight. Metal concentrations are in percent. - 57

of the metals may be located in the Fe-Mn oxide fraction of

sediment. In the case of Co and Ni for example, it may be

rightly expected that the fraction in the mobile or reactive

Mn oxide phases will be largely controlled by changes in Eh

and pH. It is quite probable, however, that the portion lodged

in the largely inactive Fe--oxide fraction may be simply

controlled by clastic dispersion processes affecting eroded

soil and bank material. In this case it is quite impossible to

isolate the effects caused by changes in secondary

environmental parameters. Partial extractions that

sufficiently isolate the different phases as has been

demonstrated above, serve to remove such uncertainties, and

makes it possible to monitor metal variability in relation to

Mn oxide stability in the environment. This has been used for

the fine fraction (e.g., Ellis et al, 1967; Horsnail, 1968)

but its applicability remains to be demonstrated for the

coarse fraction.

3.6 ALTERNATIVE PROCEDURES FOR SELECTED TECHNIQUES.

The HCl-peroxide method is attractive in its simplicity.

However the overnight reaction time might be inconvenient in

some cases and so higher concentrations and various reaction

times were tried to ascertain possible savings with time. The

5% HC1 solution is about .6M; 2M concentrations were tried for

periods of 4, 8 and 16 (overnight) hrs respectively. The

results for two anomalous and five background samples from the

Cornwall study area are presented in table 3.12 and fig 3.3.

The anomalous samples were collected just below East Wheal FIG 3.3 Metal concentrations after 4V 8 & 16 hrs reaction with 2M HC1+H202 for selected samples from the Newquay area. (Al, Mg, Ca, Fe, Mn in X; - = results for 5% HCl+H^ overnight (16 hrs))

I \ I \

104-J

2 CL a. -Fe Ca£ppm) 5 *1c£j - Fe

-Fe - Fe -Zn

.Ca -Zr. -Mn Co_ 103J Mg -CO Ca Pb Nj -PbCo PbC^S... Mn Mn CO Pb" :CiiUn M -Al Ni MO " 9 Mg — -Cu Nj- .Ca Cu -Ai u; 6 9' er 3 8A - 58 -

Rose (sample 38A, locality 9 of fig 2.5) and 2 km further

downstream (43A). A coating-poor sample (69A) was collected

some 2 km below Higher Penscawn mine (locality 10) in drainage

where unusual dilution of the sediment has rendered metal

contents very low and comaparable to background. The other

background samples come from locality 1 where Fe-Mn oxides are

abundant•

Table 3.12 contains the metal concentration levels in percent obtained for each sample over the given reaction time but the trends are better visualized from fig 3.3, from which

the following observations may be self-evident :

(i) the 5% HC1 solution extracts much less than the 2M solution

(ii) the concentration of Ca falls off with time on all types of fragments

(iii) on the relatively coating-poor background sample with low Mn and very high Fe (sample 69A) ,

(a) A1 and Pb concentrations fall off with time i

(b) Fe and Mg concentrations rise continuously with time;

for Fe this is initially at a slow rate but increases

after 8 hrs

(c) Mn concentration initially falls, but rises rapidly

after 8 hrs

(d) Zn, Co, Ni and Cu concentrations rise rapidly after

4hrs and then fall sharply after 8 hrs.

(e) The 5% solution extracts nearly all the Pb, about 50%

of the Mn and Al, and much smaller quantities of the - 59 -

other metals.

(iv) On the background sample with high Mn and Fe where Fe exceeds Mn (sample 8IA),

(a) Aluminium concentration rises uniformly with time.

(b) Manganese, Zn, Fe, Pb, Cu and Mg concentrations fall

initially then rise overnight; the rate of fall and rise

decreases roughly in the order given.

(c) Cobalt and Ni levels increase slowly and tend to

level off or fall slightly overnight.

(d) The 5% solution extracts about 50% of the Fe, Zn, Co,

Cu and Pb, and about a third or less of the other metals.

(v) On the background sample containing very high Mn and high

Fe where Mn exceeds Fe (sample 87A),

(a) Magnesium and Al increase continuously with time.

(b) Manganese and Fe increase and appear to level off

with time, but the concentrations of Zn, Cu, Co, Ni and

Pb increase initially and tend to fall at varying rates

overnight. The 5% solution extracts all the Zn, Pb, Cu

and Mg; nearly all the Fe, Al and Ca, and up to 50% of

the Mn, Co and Ni released by the 2M solution overnight.

(vi) On the anomalous sample with low Mn and very high Fe

(sample 38A), - 60 -

(a) Fe concentration rises slowly at first, then rapidly

after 8 hrs

(b) Co and Mg fall initially but rise after 8 hrs, the

rate for the latter being very rapid

(c) The concentrations of Zn, Mn, Ni and A1 remain more

or less constant for the first 8 hrs of reaction, after

which they rise, tha rate of rise for Zn being very high.

(d) the concentrations of Pb and Cu rise to some extent

after the first 8 hrs of reaction but appear to level off

later.

(e) The 5% solution extracts nearly all the Mn, Zn, Ni,

Co, Pb and Cu, about half of the Ca and A1 and less than

half the Mg and Fe.

These trends are difficult to interpret and although further studies are required before any firm generalizations can be made, it is thought that they are related to mechanisms in the dissolution reaction as well as the nature and thickness of coating. For example the decrease in Ca concentration with prolonged reaction may be due to some precipitation reaction

(e.g as the oxide, sulphate or phosphate) or preferentially sorbed onto freshly precipitated oxides of Fe and A1

(Kinniburgh et al, 1976).

On coating-poor fragments the acid directly attacks the lithic fragments (in this case shales and slates) and because aluminium chloride is of low solubility Al concentration falls continuously while that of Mg rises (presumably by forming - 61 -

soluble complexes with the chloride ion). Not surprisingly

the Mg and Al concentrations are highest in such samples. The

concurrent rise and fall in the concentrations of Zn, Cu, Co

and Ni may represent their release from and re-precipitation

or re-adsorption onto fragment surfaces as the pH rises.

With coating-rich samples the increasing rise in the

concentrations of Mg and Al either implies the two metals are

incorporated in the coatings, or increasing access to the

lithic fragments with prolonged reaction or both. Where Fe

exceeds Mn the concentration of the latter metal and

associated trace elements tend to fall before rising again, while Fe may follow suit or rise gradually at first and then more rapidly later. With Mn in excess all the trace elements

closely follow Mn in an initial rise and a levelling off or gradual fall in concentration, whereas that of Fe rises slowly

throughout. Higher concentrations of ore-related metals tend

to produce a similar effect in the anomalous samples although

the Mn concentration in these is much lower.

All these trends are probably indications of the drive to achieve equilibrium between species in solution and the remaining coatings on the fragments or freshly precipitated oxides. Whatever their full significance it would appear that a prolonged reaction time may prove beneficial to ensure data reproducibility. The 2M HC1 solution releases much more metals than the 5% solution used in the main work and it may be useful to explore the possibility of its reducing analytical variance since differences at higher concentration levels may be less important. However the two solutions give - 62 -

similar performance in the discrimination of background and

anomalous samples. Hence if the HCl-peroxide method is to be

used as a general reconnaisance tool the 2M solution should

not be employed unless coating-free fragments are to be

manually removed from the samples. This is to ensure that

lithic fragments are not attacked as well.

3.7 ANALYSIS FOR SILVER

As a further enquiry into the nature and possible use of Fe-Mn.

oxides in mineral exploration the Ag content in oxide coatings

in the dispersion below East Wheal Rose were studied. A

simple leach with nitric acid and dilution to a small volume

was found adequate to yield reproducible results. The details

are given in the appendix at the end of the thesis and the

results are presented in chapter seven.

3.8 SUMMARY

1. Fe-Mn oxide coatings on the coarse fraction of stream

sediments provide geochemical information similar to that

obtained routinely from the fine fraction. However the oxide

data tend to give higher metal concentrations and

anomaly/background ratios. A simple analytical procedure for

oxide coatings has been demonstrated and its performance in

the study areas is described in later chapters.

2. Trace metals can be enriched in oxide coatings on coarse

fraction, mostly in the more mobile or active Mn oxide

fraction. - 63 -

3. The HCl-peroxide method is suitable as a general technique by virtue of its partial dissolution of all the soluble and amorphous oxide fraction of the coatings, a fraction which probably exerts the critical control on trace metal dispersion.

4. The reaction mechanisms involved in the release of trace metals from oxide coatings are probably quite complex, but reproducible results are obtained if reaction is allowed to proceed over a long period (overnight), when equilibrium may have been established. - 64 -

CHAPTER FOUR - ANALYSIS OF VARIANCE ON THE DATA

The usual procedure in exploration geochemistry is to derive

trends of trace element distribution that could be attributed

to mineralization, as distinct from those due to the bedrock

geology and effects of the secondary environment. The

patterns derived can naturally be only as reliable as the data

used. Reliability here implies firstly, that the samples

actually relate directly or indirectly to the target, and that

the analytical procedures used to obtain the data are accurate

and precise enough for the purpose.

Factors bearing on the reliability of routine drainage

samples are investigated in this chapter using analysis of

variance models. A short account is first presented on errors

in geochemical data and on the principles of the analysis of

variance. The analysis of variance results are then presented

and discussed as neccessary.

4.1 ERRORS IN GEOCHEMICAL DATA

Miesch (1967a) has discussed the basic concepts of the theory

of error in geochemical data and summarized the formal rules

for the analysis of variance. The following account is taken

largely from his presentation.

For any geochemical problem the total error can be

decomposed into field and laboratory components whose sum must

not be so large as to obscure the natural trends sought. Both - 65 -

are affected by two fundamental types of error, namely bias

and imprecision.

4.1.1 BIAS

Overall bias is present when each measurement differs from its

true value by a similar amount. It may be the result of a

procedural bias on the part of the sampler or analyst, or it

may be due to the very nature of the samples. Personal bias

can be easily overcome if the experiment is designed to

include a randomisation procedure at some stage, but bias due

to the nature of the samples is difficult to control.

Laboratory bias results when the analytical procedure is

inherently incorrect. Overall bias, however, will not affect

any trends in variation although it may seriously affect

accuracy. Because only the relative magnitude of some

property is sought in exploration geochemistry, overall bias

is not normally a serious problem.

Bias can vary from locality to locality or even among

sampling stations within a locality, and is usually present to

some extent in geochemical data. This is known as variable

bias and is the most serious type of error in data, for if

present to a great extent it could render data interpretation

impossible. Variable bias is the result of changing sampling

conditions which may be caused by personal inefficiency, or in

the stream sediment regime, any combination of several natural

processes such as those in the secondary environment, flow

characteristics of the channel and the very nature of the - 66 -

samples. Personal bias can be controlled or largely

eliminated with a well-designed and executed sampling plan.

On the other hand if it is due to natural causes not even the

most objective sampling system can overcome it completely.

4.1.2 PRECISION

The collection of each individual sample or chemical analysis

of it can be regarded as a statistical trial, an estimate of a

true (unknown) quantity from which the obtained result differs

by some amount of error. As the number of trials increase the

precision or variation of the total error, the variance, tends

to be the same value for each locality in a survey. Objective

sampling at all levels and stages in a survey eliminates the

subjective personal element in sampling imprecision. The

nature of the samples and their inherent variability then

control the variance among the samples, and the precision thus

tends to vary.

Variable precision commonly occurs with trace element

data because the variance tends to be proportional to the

level of concentration. Its main effect is to distort the

significance of deviations from the mean. Appropriate data

transformation procedures are available to reduce this effect.

4.1.3 DISTRIBUTION MODELS

The fundamental theory of error and error treatment involves

the Gaussian or normal distribution. Thus it is useful if - 67 -

sampling errors are normally distributed. In practice

markedly non-gaussian analytical errors are rarely encountered

(Thompson and Howarth, 1976a) and it may be assumed that this

is the case with sampling error as well (Gy, 1954, quoted by

Adams, 1960). Moreover data transformation procedures used to

correct variable precision often give rise to frequency

distributions close to Gaussian, while many statistical

techniques tolerate large departures from nomalcy.

4.2 THE ANALYSIS OF VARIANCE

The analysis of variance involves the separation of a total

variance into separate conceivable sources contributing to the

whole, and by comparing any two separate variance estimates in

the F-test, to test also for differences between their means.

Numerous examples of its application to geological problems

are cited in the literature (e.g Griffiths, 1967; Krumbein and

Graybill, 1965). The formal requirement for a valid analysis

of variance are that the errors must have zero means, be

independent, have a common or homogeneous variance, and be

normally distributed. These are requirements for most

statistical methods of data analysis but none of them are ever

fully met by real data. The effect of each on the F-test will

now be considered.

4.2.1 NORMALITY

The most important departures from normality are likely to be

either non-zero skewness or non-zero kurtosis (Lindman, 1974) - 68

and it is the latter which is likely to have any appreciable

effect on the F-ratio. This is because although the mean

square within category , Msw, is an unbiased estimate of the

error no matter what form the population distribution takes,

the variance of Msw depends on the kurtosis of the

distribution. A high kurtosis will give rise to low F-ratios

and the comparison will seem less significant than is really

the case, while a low or negative kurtosis will yield high

F-ratios and the rejection of null hypotheses at levels of

significance higher than is warranted by the data.

Nevertheless the effects of non-zero kurtosis can be ignored

because corrected F-ratios are not practically different.

4.2.2 homogeneous variance

By homogeneous variance it is meant that the variance should

be the same throughout the sites sampled and at all levels of

concentration. The exact effects of the lack of homogeneous

variance are difficult to evaluate, but the F-ratio tends to

be bigger and the validity of variance estimates become

questionable as the true component of error is not a single

value but a range of values (Lindman, 1974). Moreover some

bias always exists and this introduces variable bias

implicitly. An unusually high F-ratio could thus be a clue to

the presence of variable precision, inasmuch as the the

analysis of variance is employed usually in those instances

when it is necessary to compare groups that are not obviously

different. However the F-test tends to be unaffected by

heterogeneous variance so long as each group contains the same - 69 -•

number of observations. A test for variance homogeneity has

been devised by Bartlett but it is less robust than the test

it sets out to justify. In any case it is informative whether

or not heterogeneous variance is suspected, to examine the

groups separately.

Variable or uncommon variance occurs under conditions

similar to those that might lead to variable analytical bias

but the latter is more commonly found in practice. Suitable

data transformations are available to correct it, as pointed

out earlier.

4.2.3 ERROR INDEPENDENCE

Errors lack independence if they relate to locality. Variable

(unknown) bias is then present in the data; mean square

estimates are in error, the additivity of variance, so

fundamental to the analysis, is destroyed, and the analysis of

variance may be in vain. The F-test compares two independent

estimates of the same error and it is imperative that samples

are randomly obtained and each observation made on it is

independent of all other observations; otherwise the F-ratio

becomes strongly affected. In most cases the F-ratio is

larger because error dependencies tend to underestimate the

analytical error, Msw, again leading to the rejection of a

null hypothesis at a much higher level of significance than is

really the case. Randomizing the collection, treatment and

analyses of the samples will largely eliminate this problem,

but not completely, because it may well be caused by the - 70 -•

nature of the samples. Caution is thus necessary in drawing

conclusions from geochemical data (Miesch et al, 1976).

4.2.4 ZERO MEANS

If there is an overall bias in the data the errors do not sum

to zero, but as mentioned earlier, if all the other

assumptions are fairly met, this requirement is not critical

to the analysis of variance.

Various precautions, mentioned at the appropriate stage,

have been taken to ensure as close an agreement with the above

requirements as possible in this work.

4.3 SAMPLING DESIGN

Active stream sediments were collected from the two study

areas and a fine fraction (-170um) and coarse fraction

(+2mm-5mm) were retained for analyses. Sampling was conducted

in a heirarchy comprising sample sites at the top and

duplicate analyses at the base (fig. 4.1A). Sample sites

were chosen at approximately 200m intervals. At each site

duplicate samples were collected 5-10m apart, duplicate A

always being collected downstream of duplicate B (fig 4.IB).

Sample preparation and analyses were organised in random

sequence. The sample collection may be conceptually

considered random as well (Miesch et al, 1976). FIG 4.1 DESIGN OF THE SAMPLING HEIRARCHY

•10m 1

B • A stream flow i

200m

B - 71

4.4 MODELS FOR GENERAL SAMPLING AND LABORATORY ERRORS

The data from each study area was initially examined in

its entirety to ascertain the general success of the approach.

For this purpose a two-level nested analysis of variance is

appropriate and the model can be set up as follows:

Yijk = U + Ai + Bj(i) + Ek(ij).

i.e, each measurement on a sample is an estimate of the

population mean U of the distribution, from which it differs

by an amount equal to its deviation from the mean of the site

from which it was collected, Ai, plus the deviation from its

own mean value, Bj(i), and the error in the analysis itself,

Ek(ij). If several analyses are performed on a specimen the

laboratory error can be estimated. The model may then be

changed slightly to:

Yijk = U + Ai + Bj(i) + Eji

where Eji is the difference between the true concentration in

the specimen and the mean of the analyses on it (or the error

in the mean analysis of a specimen). This is the model used

here •

The usual assumptions are made:

Ai " N(0, <5

A), Ai is normally distributed with zero mean

and variance /v Bj(i) ~ N (0, (S ), Bj(i) is normally distributed with

zero mean and variance cL , - 72 -

'V Eji ~ N(0, 6 ), Eji is normally distributed with zero

mean and variance .

In this study,

1=1,2, ,n = the number of sites sampled in any one

study area

j=l,2 = the number of samples collected at a site

k=l,2= the number of analyses performed on a sample

U = the overall mean of the constituent measured on all

samples considered as one population

U + Ai = the true mean concentration of a constituent at

the ith site

U + Ai + Bj(i) = the true mean concentration in the jth

sample at the ith site and

Eji = the error in the mean measurement on the jth sample

at the ith site.

The Null hypotheses tested were :

Ho(l) = 0 for all i. i.e., it is proposed that the true mean concentration at each site is a true estimate of the population mean U, within experimental (laboratory) errors.

H0(2) :ZBjci» 0 for all j and all i.

Here the contention is that there is only one subpopulation at any one sampling station or site (i.e. within the domain defined by the duplicate sampling interval) and that the true - 73 -•

metal content in each sample collected therefrom is a true

estimate of the mean for the station.

Ho(2) are usually the more interesting hypotheses to

consider because it is the underlying assumption in any

geochemical survey; at any level in a survey one either

assumes or attempts to define a uniformity of conditions for a

certain domain size in order to design an optimum sampling

plan.

4.4.1 COMPARISONS BETWEEN SAMPLE TYPES OR LOCALITIES

Apart from geology only mineralization (with its concomitant

contamination) and secondary oxides were expected to be the

important controls on metal distribution in the study areas.

Some combination of such controls resulted in an efficient

differentiation between sample stations in the two-level

nested design described above. By parcelling out sub-areas or

localities of uniform geochemical character it was thought

possible to obtain more information on the natural sources of

sampling error. To verify this the clusters resulting from a

scatter plot of the first two principal components were

compared in a three-level nested analysis of variance. This

was done for the Cornwall study area only as the geology is

more uniform and any such information may be enhanced. The

model for this comparison is as follows :

Yijkl = U + Ai + Bj(i) + Ck(ij) + El(ijk)

where in this instance, 74 -

i=l,2,...,4 = the number of clusters (sample types),

j=l,2,....,n = the number of sample stations within

each cluster (sub-group),

k=l,2 = the number of samples per station, and

1=1,2 = the number of analyses per sample.

U is an overall mean; Yijkl is the metal content obtained from the 1th analysis of the kth sample from the jth station of the ith sub-group. Ai is a random contribution from the ith sub-group, Bj(i) a random contribution from the jth station of the ith sub-group, Ck(ij) a random contribution from the kth sample in the jth station of the ith sub-group, and El(ijk) the random error associated with the 1th analysis of the kth sample from the jth station of the ith sub-group (Chork,

1976). Again it is assumed that each of the factors in the model is normally and independently distributed with a mean of zero and an associated variance :

Bj(i) ~ N(0,

Ck(ij) ~ N(0, (£)

El(ijk) ~ N(0, (f")

The Null hypothese involved are :

Ho(l) : 2AL = 0 (the mean contents in the sub-groups

are equal)

H0(2 ) (the mean contents of the sites are

equal)

Ho(3) 0 (the mean contents in a sample are

equal) 4.4.2 ANNUAL VARIATION

A simple design to estimate annual variation was tested. The

Cornwall study area was sampled in the winters of 1976 and

1977. All the duplicate samples from the first survey were

analyzed in duplicate. In the latter survey the A subsamples

from 116 sites were randomly selected and analyzed, twelve of

them (about 10%) being analyzed in duplicate. The appropriate

model for estimating annual variation is given by Lindman

(1974) :

Yij = U + Ai + Bj + ABij + Eijk

where i=l,2 = the number of groups to be compared,

and j=l,2, ,n = the number of samples per group.

Yij is the metal content in the jth station during the ith

sampling period. U is an overall mean; Ai represents the row

effect (the randomly chosen sample station), Bj is the column

effect (sampling period, fixed). Eij is an error term. The

above model includes an interaction term ABij. An interaction

could result, for example, if the seasonal changes in weather

critically affected Fe-Mn oxide formation or stability.

Although the reasonable assumption was made that such an

interaction was unlikely to be important in this case,

estimates of the interaction were made to ascertain its

significance under such conditions. The necessary assumptions

and Null hypotheses to test are : o. Ai ~ N(0, SA) o- Eij ~ N(0, 5 )

Ho(l) ^A-t, = 0 (mean metal contents are equal in each - 76 -•

sample station)

Ho(2) : ^L&j^) = 0 (the metal contents are equal at each

sampling period).

4.5 ESTIMATED VARIANCE COMPONENTS IN FINE FRACTION

4.5.1 CORNWALL STUDY AREA. AAS RESULTS

Table 4.1 contains the F-ratios and estimated variance

components from a two-level nested analysis of variance on the

AAS results using all dupliicate samples from the first

survey. Results are tabulated for all data and with anomalous

samples removed, the latter being arbitrarily chosen as those

within 2 km downstream from old mines. Also given are the

ratios of various variance estimates as measures of the

ability to recognize variation between and within sites, over

and above analytical error. The last column is intended to

show the influence or contribution of the anomalous samples to

the variance distribution. For reasons which will be

explained later, all data used (unless otherwise stated) are

logtransformed data.

Considering all samples it is clear that for all elements

the between-station variance exceeds within-station variance,

the ratio of the two varying from about 3 for Ca to 10 for Mg.

With the exception of Fe and Ca for which the within-station

variance equals analytical error, the latter is smaller in all

other elements by about two times for Mn and Cu, seven times

forMg and over ten times for Zn and Pb. Thus the variability

between stations (spatial variation) exceeds Table 4.1 Newquay AAS data. F-Ratios and variance estimates from a two-level nested analysis of variance on all data and without anomalous samples.

F-RATIOS VARIANCE COMPONENTS (%)

Metal N betw. with. betw. (A) with. (B) error (C) A/B B/C A/B+C

Mg 274 * 17.3 4.6 87 8 5 10.4 1.8 6.6 .88 338 19.0 4.8 88 8 4 11.3 2.0 7.5

Pb 274 6.3 30.3 72 26 2 2.7 14.6 2.6 .33 338 17.1 33.0 88 11 1 8.3 15.3 7.8

Cu 274 6.2 5.7 69 22 9 3.1 2.3 2.2 .52 338 10.9 5.6 80 14 6 6.0 2.3 4.2

Zn 274 4.8 39.0 65 33 2 1.9 18.6 1.8 .39 338 10.5 26.8 82 17 1 4.9 12.9 4.6

Fe 274 5.6 2.6 62 17 21 3.7 .8 1.6 .47 338 10.1 3.1 77 12 11 6.7 1.1 3.4

Mn 274 3.1 3.8 45 32 23 1.4 1.4 .8 .40 338 5.8 4.5 66 22 12 3.1 1.8 2.0

Ca 274 2.7 3.7 41 34 25 1.2 1.4 .7 .47 338 4.9 3.0 60 20 20 2.9 1.0 1.5

* Anomalous samples are those within 2 km of old mines (removed). All F-ratios are significant at .01 probability. Q = (A'/B'+O/CA/B+C), where the prime refers to results excluding anomalous sampl - 77 -• all experimental errors and enables the discrimination of sample types, which is one main objective of a geochemical survey.

ANALYTICAL ERROR

The relatively high analytical error for Ca is due to its low concentrations in the area. The other elements gave highly precise results.

SAMPLING ERROR

Low concentration levels also account for the high sampling error for Ca. Manganese has high sampling error but this is considered to be due to local controls on Fe-Mn oxide stability. With the exception of the ore-elements Zn, Pb and

Cu, sampling errors were, on average only moderate. High sampling variability for ore-elements near ore-bodies is a well-known feature which is regarded as significant indication for mineralization when coincident with high ore-metal concentrations.

SPATIAL VARIATION

As mentioned above spatial variation (or variance among sites) is significantly greater than the experimental errors involved in this scheme. The relatively small component of variance for Ca is due to its similar, low concentrations at most sites. The moderate to high components for Mn and Fe reflect the widespread development of Fe-Mn oxides in most streams.

Magnesium, Zn, Pb, and Cu gave very high components of between-site variance. This is to be expected of the last - 78 -•

three because of the mineralization and the long dispersion

trains from it. For Mg, however, this is at first puzzling,

considering that the lithology is more or less uniform and

comprises shales and slates rich in chlorites. The condition

has resulted from large fluxes into some streams of talus from

old quarries, and even more important, quarry products

currently used to reclaim marshland. They give rise to local

highs in Mg up to 1.5 km long and are associated with

relatively high Fe values. A detailed description is given in

chapter five.

4.5.2 CORNWALL STUDY AREA. ICP DATA

F-ratios and variance estimates for the ICP results are given

in table 4.2. The highest precision acheived was with Zn

which gave only 7% analytical error. Copper gave a reasonable

17% analytical error but for the remaining sixteen elements

analytical errors were very high. The elements worst affected

were La, Ba, Cr, Ag, V and Al, with analytical errors

exceeding 80%. Errors below 80% but greater than 45% were

estimated for Fe, Li, Ni, Ti, Cd, Mg and Mn. In all these

elements the procedural error equalled or more usually,

greatly exceeded spatial variation. Poor to fair analytical

precisionswere obtained for Pb (37%), Ca (31%) and Co (26%)

but with these elements spatial variation was nearly twice the

procedural error.

Low concentrations are likely to be the cause of the poor

analytical precision for Ca and Ag; for Ag and Al too the

final HC1 solution should lead to a quantitative precipitation Table 4.2 Newquay ICP data. F-ratios and variance components from a two-level nested ANOVA.

A. F-RATIOS

Li Mg Ca Ba La Ti V Cr Mn Fe Co Ni Cu Zn Cd Al Pb A*

F2 2.5 5.3 7.0 1.4 1.1 4.7 1.5 1.6 4.7 2.3 6.7 2.4 12.7 20.4 4.7 1.9 9.6 1 Fl 1. 1 .9 1.4 1.0 1.1 1.0 1.1 1.0 1.2 1.0 1.7 1.3 1.6 2.6 1.1 1.0 .8

B. VARIANCE COMPONENTS (%)

Level * Li Mg Ca Ba La Ti V Cr Mn Fe Co Ni Cu Zn Cd Al Pb Ag

2 28 50 64 9 4 49 12 12 50 25 64 29 78 87 48 18 63 12 1 1 0 6 0 4 1 4 0 5 1 10 10 5 6 1 0 0 0 0 71 50 31 91 92 51 84 88 45 74 26 61 17 7 51 82 37 88

C. comparison with AAS variance estimates

AAS/I C P Mg Ca Mn Fe Cu Zn Pb

Level 2 1.76 .93 1.32 3.08 .98 .94 1.40 % diff. 76 7 32 208 2 6 40

* Levels : 2 = between sites = spatial variation (F2); 1 = within site = sampling error (Fl); 0 « laboratory errors. * Table 4.3 Clitheroe ICP data. F-ratios and variance components from a two-level nested ANOVA.

A. F-RATIOS

Mg Ca Ba Ti V Mn Fe Co Ni Cu Zn Cd Al PI

F2 3.6 23.7 7.8 9.2 13.0 15.9 10.9 2.3 1.5 2.5 52.0 5.2 20.7 2, F1 1.0 1.5 2.2 1.1 .9 1.6 1.2 .9 1.1 1.0 1.4 1.4 1.4 1,

B. VARIANCE COMPONENTS(Jt)

Level Mg Ca Ba Ti V Mn Fe Co Ni Cu Zn Cd Al Pb

2 38 87 70 68 72 82 73 21 12 26 94 44 85 32 1 0 3 11 1 0 4 2 0 5 0 1 0 3 2 0 62 10 19 31 28 14 25 79 84 74 5 56 12 66 - 79 -•

of the chlorides out of solution, thus reducing further the

already low levels and probably introducing chance

fluctuations in Ag concentration. It is difficult, however,

to postulate similar reasons for the poor analytical precision

obtained for Fe, Mn, Pb, Cd, Mg and Co, especially as the same

sample solutions were used in the AAS analyses. Inspection of

tables 4.1 and 4.2 show that the AAS data are consistently the

more precise. High analytical errors in the ICP data tend to

grossly underestimate sampling error and spatial variation.

Table 4.2C shows the ratios of spatial variation with ICP to

those given by AAS and the percentage differences

corresponding to these ratios. It is seen that estimates of

spatial variation by ICP are comparable to AAS for Cu, Zn and

Ca but much lower for Pb, Fe, Mn and Mg, with Fe yielding the

worst result (three times lower). Thus very similar

geochemical patterns should be obtained with the two

analytical systems only for Cu, Zn and Ca.

4.5.3 LANCASHIRE STUDY AREA. ICP DATA

Table 4.3 contains the analysis of variance results for

fourteen elements for the Clitheroe area. Again the

analytical errors in the ICP system are frequently so high

that the sampling error estimates are evidently unrealistic

and the spatial variation is often lost. Excellent analytical

precision was obtained for Zn (5%), Ca (10%), and Al (12%).

Precision was good to fair for Mn (14%), Ba (19%), Fe (25%), V

(28%) and Ti (31%), but poor (>50%) for Cd, Mg, Pb, Cu, Co and

Ni. - 80 -•

High Ca concentrations in this limestone country led to

high analytical precision and low sampling error for the

metal, but it may also have increased Ca-interference in the

analysis. Higher concentrations and probably more soluble

forms in moor samples may account for the improved precision

for Ba, Ti, V, Fe, Mn and Al compared with those achieved with

the Newquay samples. However Cd precision remains unchanged

while poorer precision is returned for Pb, Mg, Ni, Co and Cu.

From tables 4.2 and 4.3 it may be deduced that the ICP system

is not always reliable for the analysis of Pb, Mg, Co, Fe, Cd,

V, Ti, Fe, Cu, Ba and Al. The ICP system may be acceptable

for Mn but it is only with Zn and Ca that the analytical

precision of ICP data are comparable to those of AAS.

4.5.4 VARIANCE DISTRIBUTION OVER SAMPLE TYPES AND LOCALITIES

The results in tables 4.1, 4.2 and 4.3 are estimates for the

entire populations in the study areas and are necessarily

composite in nature. Moreover the choosing of the boundaries

of the study areas was in a sense arbitrary and therefore the

overall estimates they present divulges little information on

the contribution of the individual sub-populations to the

total variance estimates. The Newquay AAS data is used to

illustrate how such information may be obtained and thus

reveal some natural sources of sampling variability.

It is often helpful to omit obviously anomalous or

contaminated samples in examining data. This was done with

the AAS data in table 4.1 . The Q-values in this table show

that upon removing the anomalous samples which constitute some - 8

20% of the population, the ratio of spatial variation to

procedural error is reduced in all cases but Mg. This implies that Mg has little to do directly with mineralization. Of the

remainder the order of decreasing influence on spatial variability is: Pb»Zn> Mn>Ca=Fe>Cu. This trend confirms the expectation that the variability of the ore-metals, and in this area the Fe-Mn oxides as well, is strongly influenced by contamination. The meaning of the Q-values may be gauged from changes in the variance distribution for each element. In all cases both the analytical and sampling errors are increased because the extreme differences in concentration due to the inclusion of contaminated samples is removed. In absolute terms the increase in analytical error is unimportant for Mg,

Zn and Pb as it is still below 5%. Analytical error for Cu is nearly doubled, but only to about 10%. It is doubled to over

20% for Mn and Fe and worsened from 20% to 25% for Ca.

Serious increases in sampling error occur for all elements except Mg, for which it remains unchanged. Thus in the absence of contamination or anomalous samples, the effects of variables such as the form of dispersion and local environmental controls emphasize the errors inherent in sampling both in the field and the laboratory. The sheer quantity of ore-metal grains and their potent influence on sample composition overshadows such phenomena while emphasizing the obvious differences with the largely background samples •

The above trend may be further illustrated by examining

the individual sub-groups alone and comparing them. To do Table 4.4 Newquay AAS data. Summary statistics on groups of data defined by principal component analysis.

Group N * Statistic Mn Zn Pb Fe CaMgCu DESCRIPTION

1 22 Mean * 5253 4795 6515 7.04 .12 .41 493 Anomalous samples, being three Min. 121 1292 1620 2.16 .02 .29 118 subsets of high, intermediate Max. 34070 11570 32500 17.79 .73 .80 2269 and low content with respect S.D. 7708 2988 7279 4.47 .17 .11 479 to the levels of Fe-Mn oxides. C.V (*) 147 62 112 64 134 26 97

2 35 Mean 2014 1573 1417 4.88 .15 .62 213 "Threshold samples", being Min. 620 320 73 3.23 .04 .41 213 the dispersion train from Max. 8850 6340 3710 7.29 .26 .78 1133 anomalous samples 2 km or S.D. 1587 1219 1077 1.11 .05 .10 202 more downstream from old mines C.V (%) 79 78 76 23 35 17 95 Includes some "false" ones.

3 70 Mean 1037 317 99 4.56 .15 .87 35 Background samples relatively Min. 287 122 37 3.06 .05 .45 11 enriched in Fe, Mn, Ca and Mg. Max. 7900 773 1355 6.01 .29 1.25 177 S.D. 1126 177 166 .61 .06 .18 20 C.V (%) 109 56 168 13 39 21 57

4 43 Mean 535 233 73 3.33 .17 .60 56 Background samples which Min. 158 110 26 2.67 .09 .40 16 are low in all elements. Max. 1180 472 607 4.58 .27 .94 129 S.D. 172 54 89 .38 .04 .09 26 C.V(%) 32 23 122 11 24 15 47

N = number of sites, Mean = arithmetic mean. Results for Fe, Ca and Mg In %, the rest in p.p.m. Table 4.5 Newquay AAs data. The Distribution of variance among the groups in table 4.4.

M n Z n P b F e C a M g C u Level 2 1 0 2 1 0 2 1 0 2 1 0 2 1 0 2 1 0 2 1 0

Total « 66 22 12 82 17 1 88 11 1 77 12 11 60 20 20 88 8 4 80 14 6

Group 1 76 19 5 71 29 <1 92 7 1 85 9 6 39 4 57 58 27 15 82 10 9

Group 2 44 46 10 38 60 2 59 40 1 62 35 3 59 33 9 66 31 4 35 62 4

Group 3 53 16 31 80 18 2 79 19 2 54 6 40 57 28 15 92 3 5 71 13 16

Group 4 19 52 29 35 58 7 68 28 4 77 19 4 51 28 21 86 5 9 83 11 6

* The total for the entire suite of samples. - 82 this the data were divided into groups (clusters) by principal component analysis (chapter 5). Fig 5.38 shows a scatter-plot of the component scores on the first two principal components generated from all the samples using the seven elements. Four broad clusters were recognized and their summary statistics and variance components within them are given in tables 4.4 and 4.5 while table 4.6 contains the F-ratios and variance components of comparisons between them. From fig 5.38 and table 4.4 the following group characteristics were inferred :

GROUP 1 : Comprises the anomalous samples, very rich in Mn,

Fe, Zn, Pb and Cu, but low in Mg and depleted in Ca. They are divisible into three sub-groups of low, intermediate to high and very high, based on the Fe and Mn levels. Analytical error is very high for Ca (57%), moderate for Mg (15%) but very low for the other elements (<10%). Sampling errors are high for Mn, Zn and Mg. The high sampling error for Zn is interesting and is thought to be due partly to its high mobility and sensitivity to AAS and its control by Mn oxides.

The high spatial variability for Zn and Pb is attributed mainly to dilution with distance from the old mines but may also reflect inhomogeneity arising from variable drainage characteristics controlling dispersion.

GROUP 2 : Consists of the continuation of group one type samples further downstream to the edge of the study area. It merges slowly with a subset of group three samples which were collected from a locality where secondary oxides are extensively developed and in which Fe and Cu are enriched. Table 4.6 F-ratios and variance components from comparisons between the groups in table 4.2 estimated by a three-level nested ANOVA.

Test 1 vs 2 1 vs 3 1 vs 4 2 vs 4 2 vs 3 3 vs 4

Metal Level F *Var F ?Var F $Var F %Var F %Var F %Var

Mn 3 * 1.3 1 24.0 40 12.0 22 55.3 47 12.4 15 12.8 12 2 5.5 67 5.8 41 6.2 52 23 20 3.6 43 3.4 41 1 9.5 26 6.7 14 3.4 14 7 2 25 3.3 23 2.5 20 0 6 5 12 8 20 27

Zn 3 28.6 45 281.2 89 138.4 80 87 2 60 38.1 40 5 8 7 2 3.1 28 4.2 7 7.8 16 2 2 15 4.1 36 6.6 68 1 32.4 26 33.1 4 36.1 4 25.0 23 26.4 22 21.1 23 0 2 <1 <1 2 2 2

Pb 3 24.8 44 296.9 90 162.4 83 78.5 61 44.2 45 5.8 7 2 5.4 38 10.4 8 10.9 14 4.2 24 5.5 38 7.8 71 1 159.3 17 15.9 2 22.9 3 68.4 15 61.1 17 19.9 20 0 <1 <1 <1 <1 <1 2

Fe 3 4.2 10 21.1 39 13.4 25 21. 4 30 3 3 4 14.7 16 2 10.5 72 12.8 51 10.1 56 5.9 49 4.8 55 6.2 53 1 6.2 13 4.9 7 2.0 6 14.5 18 2.3 16 1.9 10 0 5 3 12 3 26 22 Table 4.6 continued.

Ca 3 13.3 22 31.0 37 35.0 39 5 1 7 .8 0 4.5 4 2 3.4 32 3.2 24 3.5 25 3.9 51 4.2 58 4.0 52 1 1.4 7 1.3 5 1.3 5 5.3 28 6.2 31 4.1 27 0 39 34 31 13 12 17

Mg 3 43.3 57 79.8 70 95.8 74 1.2 <1 16.8 24 14.2 18 2 4.8 27 8.1 22 15.0 22 7 8 75 13.2 64 28.3 74 1 8.2 13 3.6 4 3.5 2 7.3 19 6.2 9 2.3 3 0 4 3 2 6 3 5

Cu 3 20.3 37 70.5 69 106.0 74 21 3 29 43.8 42 3.5 4 2 3.8 36 12.6 26 9-1 19 4 0 42 4.0 33 9.3 73 1 13.6 24 3.8 3 2.8 3 16.1 26 6.9 19 3.0 12 0 4 2 4 4 6 12

* 3 = between group; 2 = between sites within group ; 1 = between stations within site, or in this case between samples within site; 0 = laboratory error - 83 -•

Analytical error is low for all elements and sampling error assumes greater importance. The highest sampling errors for all but Mn occur in this group; for Mn it is the second highest sampling error. As in group one the high sampling error for Mg may be due to uneven contribution from mine dumps and quarry wastes while in the case of Ca this is essentially due to the low concentrations. Stream flow and channel characteristics may exert tremendous influence here. For Zn,

Pb and Cu further dilution and inhomogeneous mixing or partial fractionation during transport in the bottom sediment are evidently important causes of high sampling error. On the other hand local but extensive development of Fe-Mn oxides abound. Moreover sediment richly coated with such oxides is carried downstream from group one sources. Thus the influence of Fe-Mn oxides on sampling variability is likely to be quite important but their contribution cannot be isolated.

GROUP 3 : Comprises background samples most of which contain moderate concentrations of Fe and Mn, some enriched in these elements to some extent, with a small subset extremely rich in

Mn and another enriched in Fe and Cu but not Mn. With these samples spatial variation accounts for most of the metal variability. Magnesium has a very high spatial variance for the reasons mentioned above. Sampling error is comparatively low (6-20%) for all elements. Analytical errors are low except for Mn (31%) and Fe (40%).

GROUP 4 : These are normal background samples with low contents of the ore-elements and Fe and Mn but high contents of Ca and Mg. Manganese oxides form at several sites but are - 84 -•

not as extensive as those in the other groups. Sampling

errors for Mn and Zn exceed 50% of total variability. High

analytical errors were found for Mn and Ca only.

From Table 4.6 the general relationships between any two

of the above groups may be deduced and seen to affirm the

efficiency of the principal components analysis. Thus

although in groups one and two the detailed distribution for

Mn and Fe are different, on the average they have similar

contents of these metals and to some extent also Ca, but there

are very significant differences in their content of ore

metals. The high analytical error for Ca in group one stands

out. On the other hand groups three and four, the background

samples, are quite similar except for small but significant

differences in their content of Mn and Fe. Differences in Mg

concentrations are not valid because of the contribution from

unnatural sources mentioned above.

4.5.5 SOURCES OF VARIABILITY IN THE CORNWALL STUDY AREA

The components of variance in table 4.5 have been plotted in

fig 4.2 to facilitate the recognition of trends in metal

variability. Fig 4.2A shows the general trend of behaviour

with distance. It is easily seen that variance between sites

(200m apart) greatly exceeds sampling variance (10m) which is

greater than or equal to analytical error. This is an

indication of the general success of the experiment• Figs

4.2B-E are bar diagrams of the procedural error for the

elements and groups in table 4.5 arranged in increasing order

and show the trends with systematic changes in group mean. - 85 -•

Two main factors may be seen to affect procedural error, namely the level of concentration and secondary Fe-Mn oxides.

In group one are found the highest concentrations of Mn,

Zn, Pb, Fe and Cu, but the lowest contents of Ca and Mg. The order of increasing analytical error is that of decreasing concentration : Zn=Pb

The effect of contamination is only partially reduced in group two samples. All the elements show high sampling errors which may be ascribed largely to increasing influence on distribution by drainage processes such as sorting, sifting, mixing and dilution. Of the ore elements Cu and Zn are the most seriously affected. In the case of Cu this may be because where it occurs it is an accesory mineral and thus more easily affected by the sedimentary processes. With Zn, however, high mobility and control by Mn oxides may be equally

important. Calcium shows in this and the remaining groups a

consistently high sampling error. This points directly to its

relatively flat geochemical landscape but it may also indicate

its low concentration levels or a response to the same

environmental controls on Mn oxide stability. 1 2 2-3 iog distance cm}

6o* B

o z 2 4oJ N =35 N s 22

6v» 2° ^.SAMPLING {MOR

01 ltd ^ANAiTTical €**01t pb f. Cu Mn mBC« «|g f« pb C« MnZft 4u 0o*

Z 60. z

4o< m = 70 N « 43

2 o.

Mg in Pb Cu Co Mn Mg Cu Fc Pb Co ZfcMto

FIG 4,2 Distribution of variance in sample types in the Newquay AAS data. - 86 -•

With the exception of the inexplicably high analytical

errors for Mn and Fe no important trend is evident in group

three.

In group four the high sampling errors for Pb, Mn and Zn

are quite distinct. While it is difficult to estimate the

role of Fe-Mn oxides in influencing Zn and Pb variability in

group two there can be no doubt that it is of singular

importance in group four. Iron has a relatively small

sampling error in group four probably because the greater part

of the metal is derived from lattice sites in silicate

minerals than is contributed by secondary oxides.

Manganese and Zn show such high sampling errors in group

four that their spatial variation is lost. The evidence from

the other elements implies, however, that such spatial

variation must exist. This raises an interesting question

about the validity of point-source geochemical maps in

background areas for the hydromorphically mobile elements such

as Mn and Zn. Such problems should not normally arise in

exploration since sampling is so designed as to include

background and suspected anomalous samples for comparison. In

studies of agricultural aplication involving defeciencies in

such elements, however, some smoothing procedure may be

beneficial.

4.5.6 SUMMARY

It has been demonstrated for the fine fraction, that :

(a) Atomic absorption spectrophotometry is a much more

reliable analytical technique than the inductively coupled - 87 -• plasma emission spectrography used in this work. The precision of ICP analyses are comparable to those by AAS only for Zn and Ca.

(b) For most elements sampling errors are not severe in this type of terrain unless concentrations are rather low, as was found to be the case with Ca, when the analytical error is also high.

(c) The variability of Zn, Pb and to some extent Ca, are sympathetic to those of Mn and Fe, but Cu appears to behave independently of the secondary oxides.

Sampling error is important for Mn, Zn and Ca at all times but not for Fe and Pb. Iron shows high sampling error only when the development of Fe oxides is not uniform or

restricted, or when sedimentological processes take over. The

influence of Mn oxides is most felt in background localities

over a uniform terrain such as that south-east of Newquay when

its development is restricted. Both Fe and Mn show high

analytical errors in samples from background localities where

Fe-Mn oxides form freely. This suggests a sensitive response

to localized changes in Eh-pH conditions.

(d) The AAS analytical system is very adequate for the

purposes of this survey. The experimental design adopted here

is powerful enough to distinguish anomalous samples from

threshold and the latter effectively from background, despite

interferences caused by raised concentration levels attributed

to secondary environmental processes. - 88 -•

4.6 ESTIMATED SAMPLING AND LABORATORY ERRORS IN COARSE FRACTION

4.6.1 CORNWALL STUDY AREA

Table 4.7 contains the F-ratios and variance estimates for

spatial variation and procedural error obtained for the

stronger peroxide attack (part A) and the milder

hydroxyammonium attack (part B). Part C of the table contains

estimates of sampling and analytical errors based on

twenty-three replicate analyses from sixteen randomly selected

sites using the peroxide attack. The most significant result

is that the variance estimates are highly comparable to those

determined for the fine fraction and is actually better in

some cases (compare with tables 4.1 and 4.2).

The hydroxyammonium method gave excellent results with

procedural error less than 10% for Pb, Zn, Mn and Fe.

Reasonable results were obtained for Al, Co and Cu (19-30%

procedural error), but poor results were produced for Mg, Ca

and Ni with errors in excess of 55%.

The peroxide method returned procedural errors ranging

from 15% for Pb to 66% for Ca. Lead, Mn, Zn, Cu, Fe, Al and

Ca showed increased errors compared with the milder attack,

while Ni and Mg showed decreases. The slight decrease for Co

may not be important.

The poorer result for Fe obtained with the peroxide

method may be partly due to the fact that the reagent

dissolves mostly the amorphous fraction of the coatings, a Table 4.7 F-ratios and variance estimates for spatial variation and procedural error for the peroxide method (part A) and hydroxyaumonium method (part B) with' estimates of analytical errors for the peroxide method )part C)

PART N LEVEL Mn Zn p % Var F % Var F Pb? i Var F Fe % Var FCa #Var F*1® %Var FCu %Var FCo %Var F^1 ^Var F^ %Var

A 324 2 8.0 78.3 5.1 67.7 11.7 84.7 2.3 40.0 2.0 33.7 3.8 58.7 4.0 60.4 6.3 73.1 7.0 75.5 2.5 43.0 1 21.7 32.3 15.3 60.0 66.3 41.3 39.6 26.9 24.5 57.0 0 0 0 0 0 0 0 0 0 0 0

B 84 2 21.9 91.5 23.8 92.1 55.5 96.5 20.6 90.9 2.6 45.6 2.6 44.7 5.8 71.0 5.7 70.4 2.1 36.3 9.3 81.0

1 8.5 7.9 3.5 9.1 54.4 55.3 29.0 29.6 63.7 19.0

0 0 0 0 0 0 0 0 0 0 0

C 46 2 12.3 71.6 24.3 62.9 6.3 65.6 1.7 13.1 3.8 51.7 7.5 29.5 16.3 66.7 26.6 50.0 15.6 49.8 5.9 24*7

1 .64 0 .2 0 1.1 1.2 .6 0 3.7 0 .4 0 .1 0 .2 0 .2 0

0 28.4 37.1 33.2 86.9 44.6 70.5 33.3 50.0 50.2 75.3

N = no replicates (duplicates for part C) Table 4.8 Comparison of variance estimates (%) by two-way (type A) and two-level nested (types B, C) ANOVA for selected Newquay data.

Type Source Mg Ca Ba Ti V Mn Fe Co Ni Cu Zn Cd Al Pb

A betw. grp. 10 10 10 14 0 0 49 7 0 0 2 7 15 5 with. grp. 81 70 63 78 51 80 35 70 58 79 88 81 65 78 interaction 2 6 0 2 6 13 5 10 7 17 8 0 1 10 error 9 14 27 6 42 7 11 13 35 5 2 11 19 7

B betw. site 57 96 0 90 0 24 0 11 0 49 94 92 0 93 with. site 40 2 37 6 27 58 49 44 27 42 6 6 56 5 error 4 2 63 4 73 18 52 45 73 10 0 2 44 2

C betw. site 60 89 35 96 0 40 0 0 70 28 96 73 0 96 with. site 20 4 17 3 14 41 37 54 6 54 3 11 31 2 error 20 7 49 1 86 19 64 46 23 18 1 16 69 2 - 89 -• fraction whose proportion on gravel may vary widely in the same site. Moreover the amorphous coatings are generally friable and therefore differential abrasion in situ and handling abrasion in the laboratory could lead to losses serious enough to cause such high differences in replicate analyses. Note that the stronger attack which releases more

Fe from the more crystalline oxides gave rise to a higher precision for Ni.

For Mg and Al the high procedural errors with both methods is due to the fact that the reagents were not designed to attack silicate minerals. While the presence of the two metals may depend to some extent on fine clay particles adhering to gravel, the composition of the host gravel may be more important. A sub-sample that contained a lot of lithic fragments as host would give higher contents of Mg and Al than one in which the host gravel consisted predominantly of quartz float. Further the softer, or more fractured, fragile, fissile or weathered the lithic fragments the more clay would be taken into solution.

With Ca high procedural error is probably the result of low concentrations as obtains in fine fraction.

It was possible to obtain only a few duplicate analyses in the coarse fraction and the only clue to the nature of the procedural errors was obtained from these. Although small samples tend to underestimate error statistics the results in table 4.7C were expected and is probably true generally. Note that the estimates in table 4.7C compare quite favourably to - 90 -•

those in part A of the table, for Mn, Zn and Cu. It is clear

that the source of the procedural error lies solely In the

laboratory. They are described in detail in chapter three.

4.6.2 LANCASHIRE STUDY AREA

The high carbonate content of the sediments in this area

tended to overwhelm the samples and thus defeated the main aim

of obtaining enhanced concentrations. In this respect the

hydroxyammonium method was unsuccessful. In addition the pH

of the reaction system may have been raised too high to enable

adequate solution of the oxide coatings, a possibility which

requires further study. Table 4.8 therefore contains only the

results for the HCl-peroxide method.

Very precise data were obtained for Mn, Zn, Pb, Fe, Cu,

Co and Ni, but precision was poor for Al, Ca and Mg. While

the high procedural error for Al remained unchanged those for

Ca and Mg were worse than was acheived in the Newquay area.

The difference lies with the occurence of limestone in this

area. In localities with limestone bedrock most of the coarse

fraction consist of limestone fragments that dissolve

spontaneously in the reagent used. These fragments were

retained with the oxide-coated fragments in order to avoid any

possible bias as a result of handpicking gravel with

readily-visible coatings for analysis. Differences in the

content of Ca and Mg in subsamples „taken for analysis were

thus a direct reflection on the fragmental composition and

inherent variability in the gravel at the site. Table 4.9 Summary statistics for AAS and ICP results for two A.G.R.G. soil standards on seven elements.

Soil Mn Zn Pb Fe Ca Mg Cu AAS ICP1 ICP2 AAS ICP1 ICP2 AAS ICP1 ICP2 AAS ICP1 ICP2 AAS ICP1 ICP2 AAS ICP1 ICP2 AAS ICP1 ICP2

N * 25 13 6 25 13 6 25 13 6 25 13 6 25 13 6 25 13 6 25 13 6

S3 MIN. 300 371 551 72 66 79 48 40 88 3.2 3 6 4.4 .42 .31 .61 .37 .19 .40 14 15 14 MAX. 560 950 772 120 121 111 64 161 197 4.0 9.1 5.0 .49 .95 .71 .44 .64 .47 30 22. 20 MEAN 487 613 643 81 92 85 54 92 129 3.5 5.3 4.7 .45 .58 .65 .39 .41 .43 18 18 17 S.D. 83 162 95 11 16 40 4 39 45 . 18 17 .19 .02 21 .04 .02 .13 .02 3 2 2 %C. V 17 26 15 14 17 47 7 42 35 51 32 40 4 36 6 5 32 5 17 11 12

N 26 13 6 26 13 6 26 13 6 26 13 6 26 13 6 26 13 6 26 13 6 S4 MIN. 132 113 212 420 436 573 260 233 325 2.7 3.1 3.6 .58 .43 .77 .24 .13 .30 76 85 86 MAX. 240 338 308 576 629 731 328 407 481 3.1 6 5 4. 1 .64 1.1 .90 .29 .39 .34 108 104 1 08 MEAN 204 231 260 511 533 651 296 311 404 2.9 3. 9 3.8 .60 .73 .83 .27 .28 .32 91 95 97 S.D. 32 64 39 32 62 74 19 63 69 .13 1.5 .19 .03 . 20 .05 .01 .08 .02 9 6 11 16 28 15 6 12 11 6 20 17 45 39 50 5 27 6 4 29 6 10 6 11

* ICP1 = results for first survey, ICP2 those for the second survey. N = number of replicate analyses. - 91 -•

On the other hand the high performance for Pb attained

with Newquay samples was reproduced and there was a lot of

improvement with Mn, Zn, Cu, Co and Ni. This improvement may

be mainly due to the levelling effect of the large amounts of

material dissolved, mostly limestone, which effectively

dampened the data noise associated with the small sample

weights used in the Newquay area.

4.6.3 SUMMARY

To summarise, it appears that the present techniques developed

for the coarse fraction yield useful results to enable stable

geochemical patterns to be derived for some terminal stages in

the hydromorphic mobility of Mn, Zn, Pb, Cu, and Co. Such

patterns may be sometimes uncertain for Ni, Fe and Ca, and

probably dubious for Mg and Al. In the case of Fe this can be

a serious drawback since the oxide is sometimes reported to

control the distribution of certain trace elements in soils

and sediments.

4.7 ANNUAL VARIATION

4.7.1 TWO-WAY ANALYSIS OF VARIANCE

As stated above only some 10% of the samples in the second

survey weVe analyzed in duplicate and a two-way analysis of

variance is valid only for these. The results are presented

in table 4.8 (part A) which also contains the results of a

two-level nested analysis of variance (part B) for these

samples as well as estimates for the same population of sample - 92 sites from the first survey (part C).

As only a small subset of samples is involved, table

4.8(A) is interpreted as suggesting that only those elements with a between-group (i.e, between sampling periods) variance of 5% or less may be assumed to have yielded identical results in the two surveys. These are V, Mn, Ni, Cu, Zn and Pb. The remainder showed appreciable between-group variance ranging from 7% for Cd to 15% for Al, but Fe showed as much as 49% difference. Most of the elements also showed measurable interaction terms which were lower than the analytical error, but Mn and the ore-elements gave interaction terms that far exceeded analytical error. Inspection of parts B and C of table 4.8 shows that for these elements, sampling error appreciably exceeded analytical error in either or both of the surveys. Thus the interaction term may simply be an expression of sampling variation in relation to sample sites or levels of concentration.

Comparing table 4.8 B and C the results for V, Ti, Fe,

Zn, Pb and probably Ca and Co may be said to be similar in the

two surveys. The spatial variation for Mg and Al are

comparable but significant increases in their sampling errors

occured during the second survey. Sampling and analytical

errors were decreased for Cu and increased for Ba, while Mn

showed ari* increase in sampling error only. As a first

approximation, therefore, data from the two surveys may be

said to possess equal variance for V," Ti, Fe, Zn, Pb, Ca and

Co, but unequal for Ba, Cu and Mn. - 93 -•

4.7.2 T-TESTS

To enable the use of all the data from the second survey to

estimate annual variation, T-tests offer a good alternative to

analysis of variance as no duplicate samples are required and

the number of samples need not be the same. The test assumes,

however, that the populations are normally distributed.

Significant tests are available for cases when the variance in

the groups are equal or unequal (Cooper, 1969). A form of the

latter approach used here is due to Ryan et al (1978) and is

incorporated in an interactive statistical package called

Minitab-version II (Ryan et al, 1978). For each element two

tests were run to compare the results from the two surveys

with each other, and where available with the AAS data as

well. The tests involved were :

(a) A one-way analysis of variance to give some

indication about the equality of variances and a visual

plot of the 95% confidence interval bands about the

population means to show their degree of overlap.

(b) A two-sample T-test for differences between the

population means.

In all cases Minitab obtains the 95% confidence interval

for the statistic and tests against the chosen Null

hypothesis. The detailed computations are described in Ryan

et al (1978). - 94 -•

(1) COMPARISONS WITH STANDARDS

Two soil standards were randomly interspersed in several replicates with the batches sent for AAS and ICP analyses.

Table 4.9 contains the summary statistics for the data.

Tables 4.10 and 4.11 give the results of T-tests and one-way analysis of variance respectively, performed to compare the

AAS results with those by ICP in the two surveys.

Of the seven elements analyzed by AAS only Cu results are in complete agreement with both ICP data in both standards.

AAS-Mg is comparable to Mg-1 (for explanation see fig 4.3) in both standards while Zn-1 and Pb-1 are comparable to the AAS equivalents in standard S-4. In all other cases the ICP data are higher in both standards. Evidently there is some bias in the ICP data which merits further examination. Table 4.12 contains the ICP results for the standards as received from the laboratory. It is obvious that while precision within batches may be acceptable, the between-batch differences are quite serious for most elements. In the results for standard

S-4 in the second survey for example, there is a distinct change halfway through the analysis. Thus some pattern exists in the ICP data which is difficult to interpret. It is refreshing on hindsight to note the worth of randomizing the laboratory stage of data collection. Without it not much weight could have been attached to the geochemical patterns discussed later.

(2) COMPARISONS WITH SURVEY DATA

With the survey data the detailed results are somewaht complex Table 4.10 Results of F-Tests on AAS and IGP data for two Soil Standards

STANDARD Mn Zn Pb Fe Qa Mg Cu DF S* D* DF S D DF s D DF S D DF S D DF S D DF S D AAS-ICP1 19 .020 R 18 .045 R 12 .006 R 12 .001 R 12 .070 A 12 .984 A 31 .75 A AAS-ICP2 9 .003 R 6 .026 R 5 .002 R 6 .152 A 7 .000 R 7 .000 R 10 .54 A ICP1-ICP2 16 .461 A 12 .391 A 13 .073 A 6 .502 A 13 .113 A 12 .362 A 10 .40 A

AAS-ICP1 16 .288 A 16 .216 A 13 .52 A 12 .000 R 12 .046 R 12 .880 R 35 .084 • A AAS-ICP2 8 .009 R 5 .004 R 5 .008 R 7 .000 R 6 .000 R 6 .000 R 6 .26? A ICP1-ICP2 16 .176 A 10 .006 R 11 .012 R 14 .339 A 13 .065 A 13 .078 A .6 .766 A

= * S level of significance achieved by the test of Mean^ = Mean^ vrs Mean^ f Mean2

D - Decision; R = reject, A = cannot reject Null hypothesis, HQ T Mean^ = Mean,

at the 9% confidence interval. Table 4.11 Oneway ANOVA results on AAS and ICP data for two soil standards

Mn Zn Pb Fe Ca Mg Cu S S S S S S S S S S s S S S 4 4 4 3 4 3 4 3 4 3 4 3 3 3 AAS-ICPi P 7.80 1.69 5.76 2.30 21.73 .77 37.77 58.38 7.63 9.71 .00 .05 .09 2.33 38 DF 37 38 37 38 37 38 37 38 37 38 37 38 37 .094 .088 .069 .038 PsD .097 .098 .062 .039 .120 .054 .07 5 .060 .091 .070 D R A R A R A R R R R A A A R

AAS-ICP2 P 10.92 9.83 10.27 45.15 126.16 53.10 10.46 181.5 272.18 204.4 26.3 64.13i .27 1.77 DP 30 31 30 31 30 31 31 31 30 31 30 31 30 31 PsD .083 .076 .055 .035 .069 .040 .205 .021 .021 .022 .019 .021 .071 .043 D R R R R R R R R R R R R A A

ICP1-ICP2 P .37 1.26 .67 11.61 3.05 8.22 .90 .47 1.34 1.96 .41 1.71 .76 .15 DP 18 18 18 18 18 18 18 18 18 18 18 18 18 18 PsD . 104 ,116 .072 .052 . 188 .082 .283 .086 .13 .10 .135 .128 .055 .034 D A A A R R R A A A A A A A A

BASED ON Ca Mg Cu Mn Zn Pb Pe X SD X SD X SD X SD N X SD X SD X SD AAS 25 2.68 .09 1.91 .05 1.73 .03 .54 .03 -.35 .02 -.41 .02 1.25 .08 J 1.26 .06 ICP1 13 2 *?7V .12 1.96 .07 1.93 .20 .70 .13 -.26 .15 -.41 .16 ICP2 6 2.80 .06 1.99 .06 1.98 .20 .67 .02 -.19 .02 -.36 .02 1.23 .05

Sj. AAS 26 2.30 .08 2.70 .03 2.47 .03 m .02 -.23 .02 -.5? .02 1.96 .04 ICP1 13 1.35 .13 2.72 .05 2.49 .09 .61 .10 -.15 .12 -.58 .15 1.98 .03 ICP2 6 2.41 .07 2.81 .05 2.52 .10 .58 .02 -.08 .03 -.50 .03 1.99 .05

* PSD = pooled standard deviation! SD = standard deviation \ x = mean for N replicates. Table 4.12 ICP Results for soil standards. Part A - standard S

0 Batch Repl. Mg Ca Ba Ti V Mn Fe Co Ni Cu Zn Cd Al Pb

42011 .33 .85 474 363 209 233 3.93 20 38 87 573 11.70 3.78 349 42012 .34 .88 455 351 199 230 4.05 18 44 89 599 13.20 3.76 355 42013 .34 .87 477 350 190 212 4.00 18 39 86 580 13.26 3.24 325 -i 42014 .30 .77 577 388 249 284 3.63 22 59 107 713 17.02 3.50 445 42015 .30 .79 526 383 253 290 3.62 24 67 105 707 17.46 3.46 481 42016 .31 .80 519 378 263 308 3.71 22 61 108 731 16.94 3.48 471

41011 .28 .66 466 633 205 226 3.13 11 54 95 544 11.08 3.60 343 41012 .30 .75 375 544 193 221 3.37 14 48 89 575 11.45 3.61 350 41013 .32 .82 444 422 216 261 3.63 44 110 90 575 12.84 3.60 406 41014 .30 .64 359 584 205 223 3.14 32 82 99 557 12.19 3.90 407 41015 .29 .64 426 443 199 233 3.17 20 66 96 550 11.21 3.78 378

41021 .33 .83 472 931 227 242 5.41 18 58 100 516 10.64 4.23 257 41022 .25 .57 338 645 159 165 4.37 6 7 95 436 8.16 4.20 261 41023 .39 1.07 544 1179 287 325 6.53 19 64 104 592 12.08 4.21 260 41024 .37 1.05 694 674 284 338 4.48 21 46 85 629 13.71 3.93 283 41025 .33 .91 645 680 269 296 4.10 24 140 95 585 12.46 3.66 331

41031 .13 .43 332 439 122 113 3.52 5 38 89 440 4.00 2.63 233 41032 .16 .54 549 786 172 186 4.57 4 31 99 462 5.82 2.95 246 41033 .16 .56 444 697 170 170 5.06 9 41 102 468 9.08 3.17 293 Table 4.12 Part B - Results for standard S

Batch Re pi. Mg Ca Ba Ti V Mn Fe Co Ni Cu Zn Cd Al Pb

32011 .43 .64 243 301 83 551 4.44 9 14 14 79 5.38 4.94 95 32012 .45 .65 239 303 82 559 4.73 15 17 16 87 5.40 4.91 89 32013 .45 .71 237 307 82 569 4.96 15 16 17 91 4.88 4.92 88 32014 .42 .64 289 338 104 702 4.60 14 29 20 105 5.97 4.68 162 32015 .40 .61 269 307 100 705 4.55 7 25 18 111 6.05 4.46 141 32016 .43 .68 295 354 112 772 4.79 13 31 18 111 7.20 4.75 197

31011 .40 .46 266 470 90 578 3.58 8 21 17 82 4.70 5.20 92 31012 .42 .51 284 508 92 573 4.00 16 7 18 92 4.63 5.28 101 31013 .43 .52 278 601 90 589 4.02 28 69 17 92 5.59 5.38 146 31014 .40 .49 260 417 79 542 3.84 24 34 15 84 4.56 5.04 109 31015 .42 .54 252 426 85 571 3.99 49 68 20 99 6.09 5.01 161

31021 .51 .83 431 657 129 779 5.62 18 44 19 119 9.23 5.62 115 31022 .50 .70 354 778 112 679 7.21 13 14 20 93 6.80 5.82 72 31023 .64 .95 475 1331 162 950 9.05 23 67 22 121 8.96 6.13 82 31024 .49 .61 401 990 118 629 6.83 15 29 19 90 6.84 6.09 66 31025 .48 .91 403 632 130 802 5.78 40 166 21 102 9.35 5.11 129

31031 .19 .32 249 700 70 376 4.32 1 11 15 73 2.01 3.66 42 31032 .19 .31 241 655 67 371 4.33 1 17 15 66 2.01 3.65 40 31033 .28 .44 347 1088 94 524 5.98 1 8 18 77 1.21 4.63 46

st Key : batch/replicate number 32011 reads soil standard S^ (1 digit) sent with samples from second survey (2n digit) in first batch (next two digits) first replicate (last digit). Table 4.13 Attained significance levels for T-tests for equality of AAS and ICP data on 95? confidence intervals.

Comparison M n Z n P b F e C a M g C u

AAS-ICP1 .83 .81 .59 .25 .09 .55 .56 DF * 502 508 503 508 505 509 509 Decision * A A A A A A A

AAS-ICP2 .02 .26 .001 .000 .000 .03 .72 DF 505 509 509 495 509 498 509 Decision R A R R R R A

ICP1-ICP2 .02 .16 .007 .000 .000 .007 .84 DF 502 507 504 503 422 496 509 Decision R A R R R R A

* DF = computed approximate degrees of freedom. A = accept * R = reject null hypothesis of equality at alpha = .05 CURS « 1 I••»»•••»»•I«»»*4***4• •—' 11 CU14 •l*«*«t**t*lt*tmtt**t* • l » CUR 11 ttlllllllllMllllllllimttmi CU14I IttMtttMttttlMlittttat****! CUU1 |l>MIWWIMIMHIMIIHmilW»«t>HM«mt CUS42 lllllllltllUIMIIItlinuHIHIIIIIIIIItlll ».in (•»« 1.223 i>nt I.an i.Mt 1.223 i.»« i.ns t.rro 1.733 2.000 3.012 "Iom

« , 1 , „, 4 4 » 4 1 • 1 'jy""l IMMtlMlttlMIIlltmillllllHItMittiI l ZMZM44M1 IIIMIIMM[UlUHlMUtUI I i*wa imi>iu»imtiiu»w«iuinwn ZM*2 .ttttimttHMMMitm 1.843 1.373 1.72* 1.734 1.744 zToi® 2.044 2.474 aTot 2.724 2.744 2.774 2.424 2.434 •

Sri. 144444441444444.1 ' MM 1444444414444441 MU] llttllttwitwmiutl MMI 1444444444144444444441 7 t t .....444441444444444444441 NIUl 14444444444444441444444444444444I 2.430 2.700 2.733 1.I04 i.au . au * u. i.tu •> 1AA t. iUl f..

7332 14443143331- • . 144441444441 74321 13333331333331 1.44444144443331 1 77*22 - ' 134334433313444334331 + _ - 144444444331434443444331 1.03 1.7® 1.33 1.73 2.40 2.10 •• 2?23 2,449 3,*m >"«» l.M«" V.'tU 2 '.XT

C432 14*444144431 •. . CA34 1****1**4*1 14444444144444*41 C444I I444444414444441 Mln ( ' 144444443331444444<3444f- CM41 144*444*4414**4*44*441 -.423 -.20* -.200 -.243- —.130 -.124" -.743 -.250 -.200 -.130 -.100 -.080

7142 144*****1**4*441 * ' K " * 7144 144*41444*1 • ' ' . ' 1 71431 !***•*•••* (V*********! 'J"J 144444414444441 7CS22 144*******4*4*1***4*4*44**441 ' . 1*44444434414*4***4*41 ( | | )•••••, | | -——•—»-. ... — — — i | | .43 .33 - .43 . 73 .33 .73 1.03 ,42* ,479 •*»• .433 . 743

"332 • 14*444444414443444432 — 70421 133344444444*414**44444444331 -4,733 4 14444444144444441 H3422- W441 1444444*4441****4***4*41 8Ma -•423 - -.273 —.] - > | ? _ " t ( 14**44*4*44444*43144344444444444431 —»•••- -—»«33 --.133 -.043 -.403 -Isoo l^m it*®® "li®

INDIVIDUAL 95 PERCENT C, I* FOR LEVEL MEANS (BASED ON POOLED STANDARD DEVIATION)

FIG 4®3 Comparison of 95% confidence intervals for AAS and ICP data for Cu, Zn, Mn, Pb, Ca, Mg and Fe in two soil standards®

KEY : MGS3 » AAS data for Mg in standard Sg MGS42= ICP data for Mg in standard S in 2nd sampling season LGCU1 1****************1*****************1 LGCU2 1*****************1****************1 AASCU 1****************1*****************1 + 4 4 + + 4 1.710 1.740 1.770 1.800 1.830 1.84

4 4 + + 4 4 L8ZN1 1*************1**************1 LGZN2 1**************1**************1 AASZN 1**************1**************1

2.600 2.640 2.680 2.720 2.760 2.80

LGMN1 1***********1************1 LGMN2 1************1***********1 AASMN 1************1***********1 2.840 2.880 2.920 2.960 3.000 3.0* + + + 4 + 4 LGPB1 1*********1********1 LGPB2 1*********1*********1 AASPB 1*********1*********1 4 + + + + 4 2.10 2.20 2.30 2.40 2.50 2.4

+ 4 4 4 + 4- LGCA1 1********1********1 LGCA2 1********1********1 AASCA 1********1********1 + 4 + 4 4 4- -.960 -.920 -.880 -.840 -.800 -.740

T

. . + + + + + LGMG1 1********1********1 LGMG2 1*********1******* AASMG 1********1********1 4 4 4 4 4 4——— -.220 -.200 -.180 -.160 -.140 -.120-

4 4 4 4 4 —4 LGFE1 1****1****1 LGFE2 I****I#**#I AASFE 1****1****1 4 4 4 4 4 4 .595 .630 .665 .700 .735 *77

INDIVIDUAL 95 PERCENT C. I. FOR LEVEL MEANS (BASED ON POOLED STANDARD DEVIATION)

FIG. 4.4 Comparison of 9% confidence intervals for AAS and ICP data in two sampling seasons. KEY: AASMG = AAS data for Mg st LGMG1 = ICP data for Mg in 1 , sampling season IGMG2 = ICP data for Mg in 2 sampling season - 95 -•

but generally they are quite similar to what obtains in the

standards. The relationships are listed in tables 4.13 and

4.14 while fig 4.4 shows the 95% confidence interval bands and

their degrees of overlap.

Results for the seven elements analyzed by AAS were

highly comparable to the ICP equivalents. Copper showed

complete overlap or identical contents in AAS and both ICP

results. Cobalt and Ni results by ICP were comparable but Al

and Ti results in the latter survey were lower. For the

remainder the results of the latter survey were higher, often

much higher than in the first survey. This implies an overall

bias in the ICP data for such elements in the latter survey.

Table 4.15 contains correlation coefficients between the

three results and fig 4.5 shows typical scatter-plots

corresponding to some of these. In a good number of cases

there were strong correlations between the results and

confirms the overall bias in the ICP data. This reassuring

result implies that while estimates of the concentration may

not coincide exactly there is a strong correspondence between

the results that retains the essential geochemical trends in

the population.

4.7.3 SUMMARY

»

Annual variation in this context applies mainly to elements of

high hydromorphic mobility. Six such elements are Mn, Fe, Zn,

Cu, Co and Ni. The last four did not show any significant

differences between the two sampling periods. Manganese and Table 4.14 Oneway ANQVA results for AAS and IGP data in two sampling seasons

Mn Zn Pb Pe Ca Mg Cu AAS-IGP 1 P 1.49 .80 .60 .98 2.86 .31 .20 DP 511 511 511 511 511 511 511 *PsD .50 .47 .80 .17 .35 .16 .42 * D A A A A A A A

AAS-ICP2 P 12.85 1.2 4.3 59.31 26,83 4.67 .30 DP 511 511 511 511 511 511 5U PsD .52 .9 .16 .29 .18 .43 D R A R R R R A

ICP1-ICP2 P 6.80 1.99 8.17 90.65 48.24 48.24 ,04 DP 511 511 511 511 511 511 511 PsD .47 .48 .8 .14 .30 .18 .43 D R A R R R R A

BASED ON „ _ Mn Zn Pb Fe Ca Mg - Cu X SD X SD X SD X SD x SD x SD X SD AAS 2.98 .54 2.70 .49 2.3 .80 .66 .15 -.97 .34 -.21 .17 1.8 .43 IGPl 3.04 .46 2.70 .46 2.3 .90 .66 .15 -.103 .36 -.21 .16 1.8 .42 IGP2 3.15 .50 2.70 .49 2.5 .80 .78 .13 -.84 .22 -.17 .19 1.8 .43

PSD = pooled standard deviation; D decision, i A = accept, R = reject, Null hypothesis for equality at confidence interval Table 4.15 Correlations between AAS-ICP1-ICP2 data.

R Z n T i C u M n M g A 1 F e P b B a C d Co C a N i V

AAS-ICP1 .98 .95 .93 .90 .89 .84 .76 -

AAS-ICP2 .93 .88 .81 .87 .74 .82 .48 -

ICP1-ICP2 .93 .91 .88 .83 .82 .74 .72 .68 .57 .53 .50 .50 .40 .37

Key : - not available. 3.5- 4*• ••• & 2.8- * • ts55j 2.1. 13 o var « CM 1.4- ii mgBtS •

0.7- t 1 1 1 r 1 1 1 r~ i r t r 1.8 2.4 3.0 3.6 4.2 4.8 1.8 2.4 3.0 3.6 4.2 4.8 0.6 1.2 1.8 2.4 3.0 3.6 LGGU1 LGZN1 LGZN1

l.o A

o.6 -

0.2

FIG. 4.5 Scatterplot of individual elements for AAS and ICP data in the Newquay area, from two sampling seasons. - 96 -•

Fe did but this is most likely the result of analytical bias.

It is thus concluded that annual variation in trace-element

content of sediments is unimportant and probably non-existent

in the Newquay area.

4.8 DISCUSSION

4.8.1 SAMPLING VARIABILITY

Sampling errors are of practical importance in choosing

threshold values, contour intervals and cell sizes for

smoothing procedures in exploration geochemistry and with

continuing improvements in analytical estimation techniques

increasing emphasis has been directed at studies on sampling

variability. Miesch et al (1976) emphasized that even with

the most careful planning and execution of a sampling design

some bias and sampling errors should be expected as they might

well be inherent in geological materials. Howarth and

Lowenstein (1971) suggested that in regional surveys a final

decision on a sampling plan and analytical procedures should

await an initial orientation survey in which a heirarchical,

nested design of analysis of variance was used to determine

the main variance components. This approach has been shown to

be of immense benefit (e.g Plant et al, 1975).

Sampling variability naturally depends on sample

inhomogeneity but apparently, mere differences in the physical

sizes of minerals is not the major cause, for while grinding

to homogenize has been shown to be beneficial the gains are

minimal (Harden, 1962; Garrett, 1966; Howarth and - 97 -•

Lowenstein, 1971; Plant, 1971; Chork, 1977). The results from this work suggest that the presence and variability of Mn and

Fe oxides directly affect sampling variability for Zn and Pb but not Cu. This is in complete agreement with the findings of Bolviken and Sinding-Larsen (1973) who conducted a similar survey on a regional scale in Norway and reported that :

(a) Metal variability between sites was most often significant and much greater than within-site variability and/or analytical error. They pointed out that this was inevitable in view of the large numbers of degrees of freedom (large number of samples) usually involved in such surveys and undoubtedly, the collection of samples across different bedrock types.

(b) Metals with poor analytical sensitivity such as V showed high sampling error in lower concentrations, but Fe showed high sampling error at high concentrations. In some 20% of the cases studied Mn showed higher sampling errors than any other metal, especially in the middle range of the Mn distribution. They ascribed this behaviour by Mn to slight changes in local Eh/pH conditions that controlled the dissolution or precipitation of Mn oxides.

(c) At high concentrations metal contents were similar in 1st and 2nd order streams and lower in 3rd order streams. This was not attributed to dilution alone as metal content did not always decrease with increasing stream order. Manganese showed this errant behaviour best and its variability was used to demonstrate that secondary processes tended to act against

dilution and so influence variability. This is as should be

expected for it is usually the host mineral or phase (in this - 98 -•

case Mn-oxide is the more important phase) which controls

metal content that also controls metal variability.

4.8.2 ANNUAL VARIATION

Seasonal variation in sediment metal content has been

monitored in diverse climatic regimes and related to local

changes in the rainfall pattern. Govett (1960) found the

cx-Cu in Zambian streams to be directly affected by the

discharge rates of the streams with periods of high rainfall

coinciding with low proportions of the finer fractions of

sediment on which the mobile Cu is held. In North Wales,

Fanta (1972) showed that rainfall maxima and minima coincided

with the highest and lowest contents of Mn, Co and Ni in

drainage sediments, especially near poorly drained peaty soils

where secondary environmental changes would be most evident.

In Appalachian North America, Chork (1977) found only slight

and unimprotant seasonal changes in sediment of some streams

for Cu, Pb, Zn, Co, Ni, Mn and Fe. In agreement with Chork

(1977), no important annual changes were found in the Cornwall

study area. It is probable however that seasonal variations

can be important if very subtle geochemical trends are sought

(Horsnail et al, 1969). As mentioned earlier Mn contents in

sediments from the Cornwall study area were found to be highly

variable and impart similar variability to Zn and Pb. Thus

should Mn content be found to vary seasonally and base metals

in sediment varied sympathetically to it, exploration data

would require a stricter screening than is normally done. For

example if sampling is conducted throughout the year it may be - 99 -• advisable to test if seasonal variation should be compensated before compiling a general map since spurious patterns might result. However in a similar study conducted elsewhere in the region, Hosking (1971) found no seasonal variation in the Cu content of stream sediments. 100 -

CHAPTER FIVE - GEO CHEMICAL PATTERNS IN FINE FRACTION

In this chapter the distribution of elements in the fine

fraction of stream sediments is described. Histograms and

probability plots are used to assess the underlying structure

in individual element data and their covariation with each

other. Diagrams to illustrate the spatial distribution of the

data are computer generated using four class limits (in most

lognormal data)

to allocate each sample to one of five classes and circles of

increasing diameter drawn to represent them. The 95

percentile corresponds approximately to the mean plus two

standard deviations, a criterion widely employed as the

anomalous class in exploration geochemistry. Also presented

are spatial plots of hydromorphically dispersed elements

normalized on Fe and/or Mn to evaluate the extent of the

control on their composition in thsfine fraction by secondary

environmental processes.

Total product-moment correlations and partial

correlations derived from them are used in an initial,

subjective interpretation of the statistical and spatial

distributions. Simple scattergrams are used to illustrate the

nature of the relationships suggested by such correlations.

The results are compared with those from the more ri^oro^s

multivariate statistical techniques of principal components

and factor analyses, in an attempt to derive unbiased

* table of VViese is m the folder qt the bqcx of the thesis. - 101 -

association of elements.

5.1 STATISTICAL DISTRIBUTIONS

The requirement that data conform to the normal distribution

as closely as possible was mentioned in connection with the

analysis of variance (chapter 4). It is brought up again in

connection with the forms of distributions that trace element

data assume or are supposed to assume. The matter has been

extensively discussed (Ahrens, 1954a, b; Tennant and White,

1959; Vistelius, 1960; Shaw, 1961; Krumbein and Graybill

1965; Clark and Garret, 1974; Link and Koch 1975; Govett et

al, 1975; Chapman, 1978; Sinclair, 1974; Miesch, 1977; Howarth

and Earle, 1979). It was Ahrens (1954a) who first drew

attention to the fact that positively skewed geochemical data

were rendered normal upon logtransformation and proposed that

"The concentration of an element is lognormally

distributed in a specific igneous rock".

It was soon argued that positive skewness was not peculiar to

lognormal data and that not only was it difficult to define a

specific or uniform population in geochemical terms, but that

most populations encountered in practice were heteregeneous.

Shaw (1961) pointed out that most sedimentary rocks were mixed

on varying scales and element distributions within them would

be complex. He cautioned the interpretation of frequency - 102 - distributions without regard to the procedural errors inherent in data acquisition and was particularly concerned that the nature and limits of a given population be defined geochemically before trying to find a model to fit it.

It is yet to be resolved whether the lognormal or other probability models should be used on a predictive or stochastic basis, but its use as a descriptive device is well established (e.g Tennant and White, 1959; Vistelius, 1960;

Sinclair, 1974) although indiscriminate or whole-sale transformations of data sets can be detrimental (Govett et al,

1975; Link and Koch, 1975; Chapman, 1978; Miesch, 1977;

Howarth and Earle, 1979).

Lognormal distributions are by their very nature positively skewed, although the converse is not always true.

Positive skewness may merely reflect inhomogeneity at the scale of sampling involved in the collection of the data

(Govett et al, 1975) but frequently it also implies that the population is polymodal, in which a fair percentage of the data are high (often extremely high) and impart a long tail to the right (positive skewness) of the distribution. The majority of the population consists of background concentrations in say, rocks or soils, and the proportion with the unusually high values reflects a special feature (e.g. ore-body, Fe-Mn oxide precipitates etc.). Logtransformation reduces the large differences and renders the data more compact and amenable to simple statistical manipulations, although this tends to defeat the main purpose in geochemical exploration where one seeks to maximize such differences.

When the distribution is rendered negatively skewed as a - 103 -

result of logtransformation it means there is a large

proportion of very high values in the data.

Govett et al (1975) stressed the spatial significance of

exploration geochemical data and showed that most stream

sediment data consists of several mixed smaller populations

which, given the appropriate analytical tools to separate

them, can be shown to tend to normality and thus making

logtransformation unnecessary. Nevertheless its simplicity

and practical utility attracts frequent use of the lognormal

distribution.

5.1.1 CORNWALL STUDY AREA

Histograms and probability plots for each element are

presented in figs 5.1 to 5.5. Barium, V and probably Al are

normally distributed; this may not be strictly true of the Al

data since it is not truly continuous, the data having been

rounded off to whole numbers as part of the analytical

procedure. Magnesium, Li, Ca, Ni, Co, and to a lesser extent

Ti, Fe and La, may be considered as mixed normal populations.

However the lognormal model fits these data equally well and

is statistically superior since the modulus of the skewness is

reduced in all cases when the data are logtransformed (table

5.1). On the other hand V, Cu, Cd, Mn, Zn and Pb are mixed

lognormal populations. The Cd distribution was so similar to

that of Zn that it was not included in the diagrams •

Logtransformation also reduces the kurtosis and coefficient of

variation for most elements. Thus although a negative skew Table 5. 1 Changes in moments about the mean

for some metals in the study areas

NEWQUAY AREA

M g C a P b M n C o T i

DATA STAT RAW LOG RAW LOG RAW LOG RAW LOG RAW LOG RAW LOG r o SKEW .5 -.3 2.7 -1.3 6.8 -1.0 1.0 - - -

AAS KURT. 2.6 2.7 22.3 6.8 60 2.6 71 4.4 - - -

... C.V. 31 -75 49 -27 290 33 .194 13 - -

SKEW .5 .2 .5 -1.4 3.4 -.4 4.9 .9 6.6 1.0 1.3 .6

ICP KURT. 2.9 2.9 2.9 5.9 15 8 35 3.7 60 5.3 3 4 2.1

C.V. 30 -71 48 -29 149 33 150 13 145 20 72 11

CLITHEROE AREA

SKEW . 9 .01 -.1 -1.4 6.9 2.2 2.8 .4 10-1.1 .7 1

ICP KURT. 3.3 2.8 2.0 4.0 56 10 13 2.8 4.6 6.5 3.0 2.2

C.V. 38 -31 56 82 276 21 87 10 63 22 43 7 141 el.* s"pmi,a«» • ih new a 1.71 «td oev • o.oflt no. of l0010 tmnvomo . ? * ^ it • ii h n l« m a a h _ CUMULATIVE PERCENT

•' —«—t—1 1 1 1 1 1 1 • 1 •

' /

^ /•' . t/ / • ^^ i.n •-•a rl e«* g»5w"'iomi . ill rl ww • 0.71 970 ocv - o.ios ki * * '•l 11»i i—j 1 1 l cumulative percent

FIG. 5.1 Histograms and probability plots for Ba, V and Al in the Newquay area. - 104 - was imparted to the distributions for Ca, Co and Cd, the

entire data matrix was logtransformed prior to further

statistical analysis.

An interactive computer graphics program was used to

partition the probability curves by inspection.

Sub-population boundaries are selected near inflection points

and the program then calculates a hypothetical population

consisting of normal subpopulations with parameters identical

to those of the real data and in the proportions chosen, and

tests it against the original probability curve. A

partitioning was accepted if the test curve fitted the

probability curve as closely as possible, confirmed by a

Kolmogorov statistic stating so at the acheived level of

significance. The subpopulations identified for each element

here were acceptable at the 90% level.

Three data types were recognized on the basis of the

number of subpopulations within each element distribution :

(a) Unimodal : Ba-V-Al.

The slopes of the linear probability plots for the three

metals are very similar and their spatial distribution may

well be similar, probably governed by a common mineral phase

or distributive process (fig 5.1). «

(b) Bimodal : Zn-Cd-Cu-Pb-Ti-Mn-Fe-Ca-Co-Ni-La

Two distinct discontinuous subpopulations can be made out

for Zn, Cd, Pb, and probably Ti. The form of the histograms

for Pb, Zn, Cu and Cd suggests a close relationship between MG . or OKtftrNMONs • no HCMno t * -o-n 910 oev « o-no no. or iooio TRAnaromts a t

LI no. or 003erfrtions • 160 ream , 90 sto 0c» « 0-140

cfl. or » no renom « -0- cosea»aw t ionstos oev * 0-236 no. of look) taanstoars r 1

FIG, 5-2 Histograms and probability plots for Mg, Li and Ga in the Newquay area. CO NO. or OKCHMTIONS • 1(0 MM . |.It STO OEV . 0.3IB

Lfi NO' OF OMCRVRTIONB • IN MM • t-oo »TO on • 0'in NO. or LOOIO TRMUROMI .1 • i ii n m ««!• « _ _ CUMULATIVE PERCENT

FIG. 5.3 Histograjns and probability plots for Co, La and Ni in the Newquay area. - 105 -

these metals (fig 5.4). In this respect the higher

subpopulation for Cu is more degenerate than it is for the

others but the probability curve confirms that a bi-modal model is quite an efficient description. For the other

elements in this group the subpopulations are continuous and

identifiable only with the help of the probability plots.

Lanthanum presents an interesting example: a one-population model would seem efficient but this was tried and found to be

significantly different at the 97.5% level, although not so at

the 90% level, while the two-population model was accepted at

the 90% level.

(c) Polymodal : Li-Mg

Three distinct subpopulations are recognizable for Li and

Mg and their forms are very similar (fig 5.2).

Examination of the statistical distributions alone

readily suggests interesting associations which may be

assigned tentative interpretations borne out by the spatial

distribution patterns described below :

(a) a bedrock association comprising Li, Mg, and perhaps Ba, V

and Al •

(b) mineralization and Fe-Mn oxides comprising Pb, Zn, Cd and

Cu, and

(c) a secondary environment effect comprising mainly Fe and

Mn. PB

- J.27 STO Of* • 0.786 no. or iooto n«isfMns .- i • i* n m

l-7» »•« jm j.«t «j

ZN NO. r 08SENVN1 IONS . 170 ME6N . 2.6® 5to Of* . 0.407 NO. r LOOIO TRfltisrom t i l» » it m n.un ii _ CUMULATIVE PERCENT

CU NO- OF 0BSENMTI0N3 • 170 MEN* . 1.62 STO OEV • 0-442 NO. or LOOIO RFTFTNVOWIS ; I t I II M N MMH » M m CUMULATIVE PERCENT

FIG. Histograms and probability plots for Fb, Zn and Cu in the Newquay area. MN NO. or OOSCRFNTI0N3 • 170 NE«N « 2.97 STO OEV « 0.391 1 nN NO. OF LOOTO RRANSROFWS A I ' ' ' ' • N M M H M * M a CUHUIRT(VE PERCENT

FE NO. or OKENVRTIONS « 170 NEW • O.M STO OEV • 0-I30 NO. or LOOtO TRRK8F0IW8 = t .

TI no. or onommoM • in hem • t.40 9t0 ofv • 0-171 no. or Looio tmmtorm • i

FIG. 5*5 Histograms and probability piots for Mn, Fe and Ti in the Newquay area. - 106 -

5.1.2 LANCASHIRE STUDY AREA

Figs 5.6 to 5.10 contain the histograms and probability plots

for the Clitheroe data. For most elements the population

distribution is degenerate :

(a) Unimodal : Co-Ni

The Co and Ni data (fig 5.6) are the closest

approximation to unimodal distributions, with less than 10% of

the data in each close to the detection limit and forming a

very small detached tail.

(b) Bimodal : Cd-Ca- Cu

The Cd, Ca and Cu data are the only ones that clearly fit

a two population model although three-population models are

equally acceptable (figs 5.7-5.9). Their intermediate

subpopulations are considered unimportant.

(c) Polymodal : Ba-Al-Mg-V-Ti-Zn-Pb-Mn-Fe

A good case for the three-population model can be made

for Ba, Al, Mg, V, and Ti in all of which adequate numbers of

samples are found in the three subpopulations; for these

metals too the distribution is uniformly continuous. The

model also fits the data for Zn, Pb, Mn and Fe, but this is

only evident in the probability curves.

The possible significance of these distributions is that

(a) Co and Ni are controlled by one uniform process over 30

20 y.

0 -54 0.98 1.42 1-86 2.30 2.74 3.18 CLLGNI NO. OF OBSERVATIONS : 104 "ERN = ' '86 STO OEV : 0.441

62 03 •°- °' U33 TT 3.28 CLLGCO NO. OF OBSERVATIONS = 104 MEAN = 1.33 STO OEV = 0.650

FIG. 5.6 Histograms and probability plots for Ni and Go in the Glitheroe area. »•'» 0.34oTij o.7» oT«r CLLGFE or 2BSEM*«T10N3 .104 HERS!kN . 0-63 3T0 OEV • 0.109

CUMULATIVE PERCENT

S.ZT • •II

I-H

t.y >.u t.47 2-41 M] CLLOMN j.a« s.44 ).M ITii t.ai ' • ' ' ' ' i Jl ' ' I I I I i • • •• CLLGMN CUMULATIVE PERCENT NO. OF OBSERVRTIONS . 104 HEM . 2.93 STO OEV . 0-306

-3.09 1.98 -0787 JJJ ^35

CLLGCD NO. OF OBSERVATIONS : 104

MEAN = 0.24 STD OEV . |.ioa 5 10 20 30 40 50 60 70 80 90 95 9H 99 CUMULATIVE PERCENT

FIG. 5-7 Histograms and probability plots for Fe, Mn and Cd in the Clitheroe area. - 107 -

non-calcareous bedrock but inhibited in calcareous localities

(suggested by inspection of the data).

(b) Cd and Cu are enriched in moor samples only, while Ca is

obviously very high in limestone localities and low in others.

(c) The linearity and striking similarity of the probability

plots for Ba, Cu, V, Ti, Al, Mg, Fe and Mn strongly suggest a

common provenance and dispersion. Lead and Zn obviously

portray the contrast between mineralized and barren localities

but the flattening of the Zn probability plot suggests some

vague similarity to the other metals in this group.

5.2 SPATIAL DISTRIBUTIONS

5.2.1 CLITHEROE AREA

The spatial distribution patterns for Mn, Fe, Ti, Co, Ni, V,

Cu and Al are all similar (figs 5.11-5.13). The Fe pattern

(fig 5.11A) is typical with the highest levels of the metal

(>6%) almost exclusively confined to the highland moor

environment (localities 4, 5, 8, 9, fig 5.16). Moderate to

high contents of Mn (950-1600 ppm) occur also in locality 7.

Thin coatings (locally profusely developed) of Mn oxides

visible on coarse lithic fragments in sediment near Skeleron

in locality 1 are reflected in moderate to high contents of Fe

(3.5-5.5%), Mn (950-1600 ppm), Co (17 ppm), Ni (61-140 ppm)

and Cu (21-270 ppm). In all other localities apart from the

moor the concentrations of the above elements in sediment are

low. r-r—i— 1 r

•8.70 .0.11 s.oo oTTi oTr? iTF T

CLLGCR no. or obsermtions • io« 8ebn . 0.4b 510 oev « 0.394 '» • 14 M M <0 CUMULATIVE PERCENT

-°-Bt °-68 -0-5?-0.36 -0.20 -0.04 CLLGMG NO. OF OBSERVATIONS = 104 ^IfflN = -0.52 STD OEV : 0-161

-o-1c o i: •04 0-88 CLLGRL NO. OF OBSFRvftllONS -- 104 MEAN . 0 •'J S10 OEv : 0.244

FIG. 5«0 Histograms and probability plots for Ca, Mg and Al in the Clitheroe area. • •i7 i ••« TT»o Hi* j!5t CLLGV NO- or OBSERVRTIONS « 104 • * « «o * w 10 « M MM HERN . 1-66 STO OEV « 0-244 CUMULATIVE PERCENT

J-J4 2. ** »••» »m TST—Ti CLLGTI 2°-OF OBSERVATIONS . 104 HERN . 2.63 S70 OEV. 0-193 • I* tt 10 <0 to (0 TO M 10 M M CUMULATIVE PERCENT

FIG. 5'9 Histograms and probability plots for Cu, V and Ti in the Clitheroe area. - 108 -

The Mg pattern is quite similar to that of Fe, but the distribution of Mg is largely controlled by the lithology (fig

5.15B). The highest Mg concentrations (6-9%) coincide with the more argillaceous beds in the Worston shale series.

The highest, solitary Cd value (682 ppm) occurs below

Skeleron and while the metal may well be associated with base-metal mineralization, there is no continuous trend of high Cd values (fig 5.11C). Slightly raised values of Cd

(13-27 ppm) occur in localities five, eight and nine where its distribution pattern closely follows that for Zn (fig 5.14B).

Mineralization is uniquely defined by the Pb distribution pattern. The highest values of the metal (.11-1.52%) are maintained in samples as far as 1 km below Skeleron mine (fig

5.14A). Dilution causes a drop in Pb values after the confluence with Twiston Beck but the dispersion train is maintained at moderate to high levels (145-1100 ppm) for a further 2 km. Moderate Pb concentrations occur at isolated sites on Pendleside moor (in localities 5, 8 and 9), but all other localities are low in the metal.

The highest Zn concentrations (.16-.81%) coincide with those of Pb but Zn levels fall off more rapidly below the confluence with Twiston Beck (fig 5.14B). Moderate (460-500 ppm) and high (553-1560 ppm) levels of Zn occur on the moor, where its pattern of distribution closely follows that for Mn, and to a lesser• extent Fe. Zinc is thus an important member of the moorland association comprising Fe, Ti, Mn, Co, Ni, V,

Cu, Cd and Al. CLLGZN CLLGZN NO. OF OBSERVATIONS : 104 1.65 Li -I 1 1 1—l I ' i • MEAN : 2.46 STO DEV : 0.371 5 10 20 JO 43 sc 60 70 8C 50 95 S< 99 CUMULATIVE PERCENT

CLLGPB M. OF 0BSERV-r 7 IONS : 104 N*«N s 2.o- .- 0-4 38 I ? 5 10 20 30 40 SO 60 7'j eo 90 96 49 94 CUMULATIVE PERCENT

1 tm j.91 2.u ^07

CLLGBfl HO. OF OBSERVAT IONS • 104 HERN . 2.J8 STO OEV • 0-230 J ICLLCB I AI L, I • t II n » •« H H 9 M «B M t« CUMULATIVE PERCENT

FIG. 5.10 Histograms and probability plots for Zn, Pb and Ba in the Clit'neroe area. f i

ox 8 5.

8 i % 8 | 3 8 Otoo.M 7*180.00 TtSoToQ 7940.00 3020.00 8100.00 1180.00 8280-00 3>40.00 8420.00 ROO.OO EAST

CLITHEROE FINE FRACTION ICP LOO-FE

in O 07 A

in o> 3 o r in li 9.... CO

in *Q>„ .< co

in •A 00 in "T700.00 7*780.00 7880.00 7940.00 8020.00 8*100.00 3*180.00 8*280.00 3340.00 8420.00 &00.00 p EAST o • H CLITHEROE FINE FRACTION ICP LOG-CU a rH in iH • in rt Ml U o > rH to •H P 0 rH C 8 •p O 0) jo O i >> E U •a >» 0) to a0)

7*700.00 7880.00 7&40.00 •IttO.OO 3100.00 3180.80 8280.80 1*40.80 8420.00 Aoo. **7700.00 EAST

CLITHEROE FINE FRACTION ICP LOG-CO

FIG. 5.11 Dispersion patterns in fine fraction for Fe, Cu and Cd in the Clitheroe area. - 109 -

Intermediate (357 ppm), high (439 ppm) and anomalous (>

530 ppm) contents of Ba occur in sediments below Skeleron (fig

5.14C). High Ba levels occur also in Twiston beck (locality

2) where the stream runs parallel to a fault of the same age

and strike as that hosting the Pb-Zn-Ag orebody in the

Skeleron mine. However these relatively high Ba values do not

coincide with high values of Zn (270-368 ppm) and Pb (87-144

ppm), although the latter are in the high regions of their

respective background ranges. This fault could host

mineralization.

High Ca concentrations are confined to localities where

the streams drain the Chatburn Limestone (fig 5.15A). A close

similarity exists between the Ca pattern and that for Cr which

is difficult to interpret (fig 15C), but with this exception

only, the Ca pattern contrasts with those for all other

metals. There is no apparent pattern related to bedrock

geology, mineralization or secondary environmental processes

in the spatial distribution for La.

5.2.1.1 TOTAL AND PARTIAL CORRELATIONS

Table 5.2 contains the product-moment correlation coefficients

for the Clitheroe data, from which the following observations

were made :

(a) Lanthanum is either uncorrelated or has a small negative

correlation with the other elements•

(b) Calcium has a significant positive correlation only with o ©

"VniO.lO 7*780.00 7880.00 7840.00 ibM.aO 1100.00 0180.00 >2 SO. DO 1340.00 0470.00 ftoo.o EAST

CLITHEROE FINE FRACTION ICP LOG-NN

H s* '8 SJ o - * • a • * _O o O a O O

\ o o B

"T700.00 7*780.00 7880-M 7*40.00 1020.00 t'lOO.OO 1180.00 SsoToO 0340.00 0420.00 &00.0 EAST

CLITHEROE FINE FRACTION ICP LOG-CO

?ii "O.. . . . * 4 -o

o o b o

5 riWao 77*0.80 7k80.W 7840.00 otao.oo i'iqp.00 (180.80 tkao.ao tHo-io 8420*00 ioo.i EAST

CLITHEROE FINE FRACTION ICP LOG-NI

FIG. 5.12 Dispersion patterns in fine fraction for Mn, Co and Mi in the Clitheroe area. 9 I f I

o

6

Sm.ii yim.m 7hH.M TCOTm abn.oo 1100.00 iiao.aa IHO.OO u*o.oo MIO.OO EAST

CLITHEROE FINE FRACTION ICP LOG-AL

s* zs I

Q

Tto yTH.OO 7000.QQ 7*0.5 25575 i'tOO.M llH.M 1H0.M (*0.M M20.M EAST

CLITHEROE FINE FRACTION ICP LOG-V i § o o

* s i 6 s m ft

trta.m 7*O.M 7*0.00 otoo.ao ^ ITOP.ao O'IM.M O*M.M «*O.M MZOM itoo.

CLITHEROE FINE FRACTION ICP LOO-TI

FIG 5,13 Dispersion patterns in fine fraction for Al, V and Ti in the Clitheroe area. i?

8 9 .... I o" '••o..o0o0 .O .o.

"5o7oo frao.oo 7E0T00 7940.0a SOJO.OO «'I 00.0a o'tea.oo oieo.oo 1340.00 0420.00 A 00.00 ERST

CLITHEROE FINE FRACTION ICP LOG-PB

S* X 8 5J s o i o o o % o

SWoo 7*700-00 7960-00 7'340.00 0020.00 0100.00 4180.00 8280.00 1340.00 0420.00 Aoo. ERST

CLITHEROE FINE FRACTION ICP LOG-ZN

8 *

8 SH *8 u •o ^b "VO

8 s *¥wo.oo 7*700.00 7teo.oo 7^0.00 sSioTooERS oioo.oT o 0100.00 oirao.oo 55T5 0420.00 A00.00

CLITHEROE FINE FRRCTION ICP LOG-Bft

FIG. 5,14 Dispersion patterns in fine fraction for Pb, Zn and Ba in the Clitheroe area. - 110 -

Cr. With the other elements the correlation is either insignificant or significant but negative.

(c) Barium is positively correlated with Pb, Zn, Cd and Fe, but negatively correlated with La. There is no significant correlation with the other elements.

(d) Cadmium is uncorrelated with Co, Ni and Pb.

All other correlations among the elements are positive at various levels of significance. Relationships between Pb or

Zn on one hand and the other elements on the other, are discussed to illustrate the complexity of the associations involved •

Lead has its highest positive correlation with Zn. With

Ni, Co, Cu and Ba the correlation coefficients are lower but still significant at .001 probability. Its correlations with

Mg, Mn, Fe, Ti, V and Al are significant at .01 probability.

A heirarchy of associations may thus be postulated for the Pb data which may be imagined to be associated with the distribution of the metal in various types of sediments and/or fractions within them.

Zinc has high correlations with Mn, Pb, Fe, Ti, V, Al,

Cu, Cd, Mg and Ba, with low but significant correlations with

Co and Ni. Thus Zn could be said to be more or less widely distributed in several types and phases of sediment, with some minor but important occurrence in a Co-Ni association. The full significance of these correlations and the distribution patterns of the elements are discussed in connection with principal component and factor analyses. I I il s £IJ r 8 O " 5J O, » ' o..o0o0 O

8 ii 8 ii 8 8 'TOO.00 77m.ao 7880. oa 7940.00 8020.00 8 too. 00 OTSO-OO 8280.00 8340.00 8420.00 ROO.OO EAST

CLITHEROE FINE FRACTION ICP LOO-PB

? I 8 i £ 8 s|*J X i8i j O O o O 8 O O

"Vroo.oo 77m.OO 7880.00 7940.00 8020.00ERS 8100.0T 0 l't80.00 8280.00 8340.00 8420.00 ftoo.

CLITHEROE FINE FRACTION ICP LOG-ZN

P *8 5J •oOo.'QD.^ % *vo O

8 ' , . Stw.OO 77m.OO 7880. OO 7*40.00 0*20.0ERS0 T 8100.00 8180.00 8*80. OO0*40.m 0420.00 WOO-00

CLITHEROE FINE FRACTION ICP LOG-BA

FIG. 5.14 Dispersion patterns in fine fraction for Pb, Zn and Ba in the Clitheroe area. I

I . o° a ; 0/° * • o* * ' '. s* - X cr " 8 51 ) • • .o 8 ii a | a 8 STQO.OO 7*780.00 7880.00 7840.00 <1)20.00 O'LOO.OO <280.00 <340.00 <420.00 00.1)0 EAST i'tao.oo A

CLITHEROE FINE FRACTION ICP LOG-CA

. O

Sooo.oo 7*700.00 7880. W 7340.00 <020.00 <100.00 1180.00 <280.00 <340.00 0420.00 &00.00 EAST

CLITHEROE FINE FRACTION ICP LOG-HG

0-bo. .o-0°.. . • o 8 .O sj om s i • "O 8 fj 8 u 8 g| "3700.00 7*780*00 7300.00 7340.00 Ob20.0EAS0T 80.00 <340.00 <320.00 ftfiO.OO

CLITHEROE FINE FRACTION ICP LOG-CR

FIG. 5.15 Dispersion patterns in fine fraction for Ca, Mg and Cr in the Clitheroe area. - Ill -

One conspicuous feature of table 5.2 is the high positive correlations amongst most elements. This is probabaly a reflection of the fact that several processes combine to control the distribution of the elements in space and time.

For example the chemistry of a particular rock type, mineralization and secondary environmental processes may concurrently contribute to the chemistry of the sediments in several localities, thus giving rise to high correlations between the elements involved. It suggests that there may be spurious correlations in the data, in the sense that such associations are not fundamental as perhaps would occur in the same mineral or rock fragment, but are rather the result of chance processes that brought them together in the same sediment. Such situations may require auxiliary examination by partial correlation coefficients in order to account for associations that do not make geological sense (Vistelius,

1957). Vistelius gives a simple example in which he uses data published by Adams (1955) to demonstrate that U mineralization in the Volcanic rocks of Lassen Park, California, was controlled by the content of K alone and not by both K and Na.

Adams (1955) had been led to conclude otherwise because of strong correlations between U, K and Na. Vistelius showed however that the high K-Na correlation had given rise to a false correlation between Na and U.

Table 5.3 contains the relationships from table 5.2 stratified arbitrarily into levels of decreasing correlation from top to bottom (columns 1 and 2). Column three of the table gives the correlation diagram for each level. The rest Table 5-2 Total correlation matrix for the Clitheroe data.

M g C a B a L a T i V C r M n F e C o N i C u Z n C d A 1 Mg 1 Ca - 1 Ba - - 1 La - - -.21 1 Ti .61 -.53 - - 1 V .69 -.52 -.18 .93 1 Cr - .58 - -.18 - 52 -.48 1 Mn .56 -.55 - - .82 .86 -.40 1 Fe .63 -.51 .20 - .90 .94 -.47 .82 1 Co .30 -.30 - -.20 .49 .51 - .48 .47 1 Ni .33 -.19 - - .36 .43 - .37 .36 77 1 Cu • 71 -.36 - - .83 .91 -.37 .76 .87 53 .52 1 Zn .53 -.23 .50 -.20 .63 .63 - .68 .66 30 .25 .59 1 Cd .41 -.48 .22 -.19 .63 .69 - 46 .61 64 .58 .57 1 Al .71 -.53 - - .93 .97 -.48 .85 .92 .48 .40 .88 .63 .72 1 Pb .35 - .45 -.22 .29 .29 37 .31 .30 .48 .52 .41 .67 .27

* Values of R at 100 D.F. (D.F = N-2 = 103 in this case) at various significant levels : Level : .001 .01 .02 .05 .10 R : 321 .254 .230 .195 .164 not significant. - 112 -

of the table gives some examples of partial correlations computed from their total correlations, for any pair of elements in a group of three, using the relationship given by

Vistelius (1957)

R'ij.k = Rij - Rik . Rjk

f((1 - Rik2)(1 - Rjk2)) where R'ij.k is the partial correlation coefficient between i and j holding k constant, and

Rij, Rik and Rjk are the total correlation coefficients.

A correlation between two variables may be enhanced to a spuriously high value if both vary in the same manner with each of a common set of of exxtraneous conditions (Stirling and Pollack, 1968). Conversely a spuriously low correlation may result if one variable behaves in a manner opposite to the other variable. Eliminating the effects of spurious variables

is called partialling out (the effects of such variables) and

the resultant correlation coefficients are called partial

correlations. The general model for the partial correlation

between X and Y eliminating variables 1 through k is (Stirling

and Pollack, 1968) :

(Rxy) 2/1.2 - - .k = (Ax/y.1.2 - - k) (Ay/X.1.2 - - .k)

where Ax/y.1.2 - .k is the regression weight in the equation

predicting X from k variables including Y, and

Ay/x.1.2 - .k is the regression weight in the equation

predicting Y from k variables including X. The two approaches

will now be examined in turn in relation to the data.

The above example by Vistelius suggests that the ternary Table 5.3 Clitheroe fine fraction. Partial correlations from selected ternary models derived from product-moment correlations.

Total Corr. (r) Level Associations Correllalogram Partial Correlations (r')

Irl>.9 (1) Al-V-Ti-Fe Cu-Fe-Mn (1,2) Ni-Mn-Fe (2,3,5) ** V-Fe-Ti-Cu Cu-Fe Cu-Mn Mn-Fe Ni-Mn NI-Fe hn-Fe Fe-Ti .87 .76 .82 .37 .36 .82 r .43 .08 .50 .14 .11 .64 r

•8

Kiy : positive correlation; negative correlation. Major reductions in correlation underlined; ** figures in brackets refer to arbitrary "levels" of correlation in the table. - 113 - model approach may be helpful in deciding how much partitioning there is between two major competing phases in the control of a particular variable, a possibility which is investigated with two examples from this sstudy :

EXAMPLE 1A : Although the greater proportion of Al is expected from the chlorites and other micaceous minerals in the shales

(Mg-Al silicates), a significant proportion of the metal is probably authigenic to the moorland environment as clay minerals and amorphous aluminium hydroxides developed from the former in response to the environment. Aluminium may thus be expected to form meaningful correlations with those elements mobilized in the environment and enriched in the sediments.

This is confirmed by partial correlations obtained from ternary models of Mg and Al with Fe, Ti, Mn, V and Cu; the model with Cu suggests a significasnt source of the metal from lattice sites within Mg-rich minerals in the rocks.

EXAMPLE 2A : The relative importance of Fe and Mn in the control of some metals is highlighted. Manganese has a slightly greater effect than Fe in the control of Ni and Zn but the control of Co and Ti is evenly shared. On the other hand Fe seems to be the main control on V and Cu; its

influence on Ni and Co is slight.

Such deductions as in the the above two examples, while obviously useful, may prove misleading or unjustified since

they may not be fully substantiated when other or several

variables are considered together.

Table 5.4 contains the significant partial correlations

computed with due regard to all the variables in the data set. Table 5.4 Partial corelation matrix for the Clitheroe fine fraction data.

M g C a B a T i M n Fe Co Ni Cu Zn Cd Al Pb

Mg 1

Ca .64 1

Ba -.19 .18

Ti -.18 1

V .15 32 1

Mn 27 1

Fe 35 -.15

Co 19 .15 .24 1

Ni .21 .15 65

Cu .15 14 ,40 ,20

Zn -.15 21 .47 25 43 23 1

Cd 22 25 19 -.19 23 15 1 33 Al 50 -.32 39 22 19 .41

Pb .19 .31 16 50 -.25 14 -.21 87 - 114 -

The above examples are re-examined to illustrate :

EXAMPLE IB : The bedrocks are confirmed as the greater source of Al but authigenic products are very important too (Ti and V are enriched in the secondary environment and may be regarded as environmental vectors). However there is no important contribution to Cu variance from aluminosilicates.

EXAMPLE 2B : Manganese is confirmed to be more important than

Fe in the control of Zn but has no effect on Ni, while Fe surprisingly varies inversely with Ni. Manganese has some control on Co but most of the Co variance is explained with Ni while Fe has no effect. Iron explains a large part of the variance of V and a small proportion of Cu variance but has no

effect on Ti although Ti also explains a large proportion of

the variance of V. As predicted by the ternary models Mn has no effect on Cu, V and Ti.

It is clear from the above examples that some caution is needed in the use of the ternary model as it does not take all

the facts into consideration. From tables 5.2 and 5.4 the

obvious example of a spuriously low correlation is that

between Ca and Mg. While the total correlation coefficient is

not significant (spuriously low) the partial correlation

coefficient is high and significant at .001 probability. Fig

5.17 contains the Ca-Mg scattergram from which it may be

observed that on the whole there is little, if any linear

trend in the Ca-Mg relationship. However when the samples are

broadly classified on the basis of bedrock geology very strong

correlations are found in samples from localities with

predominantly shale bedrock containing limestone horizons Kl

XD o

KEY

c hatjbu 3l F^vjT] locality g

^

, 0 00 , 1 0 "° ' " °' ' ,'„D.oo »

fic 5-16 CLITHEROE SRMPLE SITES fiflo LOCALITIES FIG. 5ol7 Scattergrams for Ga-Mg, Mg-Al, Pb-Zn, Go-Ni, Gd-Zn and Mn-Fe in fine fraction, Glitheroe study area.

-•20. • • e •81 • • • . • ° .i- 4-2 • A «J A 71 • O<* p?*o O« A A o - '4 0 J o A ® r=81 4 oA 9*A o A 3-4 J • «o O ° AO o° L •4- 4 GQ oA" A ^ -60- O o A Ao aj~ CL A •2- 2-6 •St V A A —80- a 0- *a A 1-8-f r = 07 -•95- -24 4 -•47 -15 •81 -•90 -74 « r- P- —r— '17C A'49 -1,2 --26 M G 2-0 2-4 2-9 3-3 3-7 Z N 2-4- • 4 1-oH 22- k * 3-5 J r=8 5 •8 r — 2*0- - 58 LU A si9'* • 2 U, .30 H •61 1-8- V0Taa« ®b o o # o N 4 4 O A ^ o« * A A A. A • 2'5h A o .oV AA AA * A O 1-6- A A » A A A A A^ A A A •2 A 4 20 •41 •82 2-1 1 2 16 -2-0-ri - -61 -08 ^8 1^5 T5 2¥ ' c o D 3'1M N 3'4 3-7

KEY: t Samples collected over ° Samples collected ever $ Samples collected over A Anomalous dispersion ' Bowland Shale (BWS) Worston Shale (WS) Chatburn Lst (GHL) below Skeleron Mine - 115 -

(Bowland and Worston shale series, chapter 2). In this case two processes may have given rise to the spuriously low correlation :

(i) The limestone-rich and shale-rich bedrock units are distinct and spatially exclusive, hence high Ca does not coincide with high Mg in most of the samples; any linear trend in the shales should be cancelled out or depressed when

Ca-rich samples are added.

(ii) Secondary processes in the environment are enhanced over shale and restricted over carbonate-rich rocks. Hence Ca and

Mg behave in opposite manner with respect to a common set of variables, namely the association with those elements which typifies the moorland localities. In this instance both correlation measures are valid; the total correlation gives the overall picture iri the population, while the partial

corelation reveals a more subtle relationship in a subset of

the population of samples •

As an example of a spuriously high correlation the Cu-Mg

association may be cited. Magnesium and Al are highly

correlated because they play a dominant role in the chemistry

of the phyllosilicates in the rocks and the clays formed in

sediments by the secondary environment• Copper is mobilized

and enriched in the sediments by these same processes that

probably co-precipitate the metal with amorphous or colloidal

clay minerals. Thus a high correlation with Al results in an

enhanced or induced high correlation with Mg which is proved

to be false by the partial correlation coefficient. From

table 5.4 certain metal associations are suggested which may FIG. 5.18 Scattergrams for Al-V, Al-Gu, Fe-Al, Fe-V, Fe-Gu and Al-Ti in fine fraction, Clitheroe study area.

2-2- •00 • •• 1-8 H • o-r a* • 20- •60 O % o . Ao 1-6 A0' 1-8- 3 , 40- O A°o »A ° 1-4 H 1-6- •20H A*

Ti AA . 1-2 A A 1-4- A AA ^ 0 A M A A A A 1.0 J . A 1 AM -20-I -ao •10 •31 52 J3 -10 •10 •31 -52 —I— —T" ~r —i— A L •73 •19 •38 •57 •76 95 A L FE

22- 1-8-] 30 J 2-0- : o =>1-6 t. • 1-8- 2-8 • • • o.

O A .4?°

1-6- 2-6- *>A A o o > HJ aS • A •L A. 1-4- . .A.* $ * AA A 2-4 . 1-2J AAA A AA A* A A A A AAA A A A A ri— —r T „A A A •19 —i 1— •38 •5 7 •7 6 •95 •2 8 •47 1-0 -T— FE •6 6 p ^ 8 5 •10 To 73 FIG. 5.19 Scattergrams for Ti-V, Zn-Cu, V-Cu, Mn-Zn, Co-Mn and Gu-Mn in fine fraction, Glitheroe study area.

2-2 1-8 1-flH . 8 3 2-0 O • 1-6 °A° APo i®; 1-6 0 m 1-8- pgoiP * 3 a Vo ^ ••• » A S® o® - 0 a." o \ • O 1-4h AO. 1-4-1 A A o o 0 * A A o o \ Ao 1-6- 0 OA LA A A °a«®W»A A a A A Ao AA V® A aa 1-2 A AA AG A A ° 1"2-J ifA • aaama^ 1*4- AA AA A A* A AA AA * 10 A A —l- I &T-A |\A 2-4 2-6 2-7 2-9 3-0 —1 1— —T^ —I 1— 2-2 20 2-4 2-9 33 3 7 1-2 1-4 16 y 18 20 2 2 T I ZN

3-9. 3-8-1 3*7 H 3*5- 3-4H 3*3 J Z Aoo..# 3-0- • • N A/4AoA 3-OH A A # A A*®®**® 2-9 A o0 2*5-1 ^ A A 8 X - o o A *Ao V. O O O O Ao A A aA

AA A M^A AA A 2-6H A 2-0 J aa£aaa^ A A» A A 2-5 A AA a a A ^ A —I— —1— —r* —r- 2-5 IF 3*1 ~l— 3*5 3*8 •41 •82 1*2 1*6 2*1 -•47 -15 •17 49 •81 M N C 0 C A - 116 -

be better observed in the corresponding correllogram in fig

5.20. These are :

(i) Mn-Zn-Co-V

The very high partial correlations (RT) between Mn and

Zn, Co and V reflects control on the latter by Mn oxides, especially in the moor environment. High negative Rf with Pb and Cd probably indicates that the mobilities of these metals are low. Manganese has a small but significant negative RT with Fe (alpha=.097) which probably reflects a partial geochemical separation of the two metals in the moor environment. Cadmium has negative R1 with all elements except

Zn, Al and V. Thus the negative Mn-Cd and Fe-Cd R' are

puzzling in view of the similar spatial variation of Zn and Cd

and the high positive correlation in their scattergram (fig

5.17). It could denote a difference in mobility. The Cd-V R'

may be related to the high Rf between V and Fe. This would

suggest that if it is not occupying interstitial or lattice

sites in Zn sulphides, Cd constitutes an important lattice

metal in clay minerals or is simply adsorbed onto them.

(ii) Fe-V-Cu-Zn-Al

The Fe-V-Cu association suggests that V is strongly

associated with Fe, and that Cu probably substitues for V

since the Cu-Fe R' is small. Also Fe partly controls the Zn

dipersion but its R' with Al may be due to Fe oxide coatings

on clay minerals.

(iii) Ni-Co-Pb

The high negative Rf between Ni and Pb may be explained

as follows. Cobalt varies directly with Pb only in a certain KEY

Level of significance, .019 Z ^ .001 ~

.05 Z ^ 1. .02

solid lines = positive correlation broken lines = negative correlation

FIG. 5.20 Correllogram the partial correlations in table 5»4 - 117 - fraction of the samples, perhaps in an organic phase of sediment. Cobalt then varies directly with Ni in another fraction of the samples and accounting for a greater proportion of the Co variance than Pb does. Manganese may play a part in the Co-Ni association, but mostly the relationship seems to be peculiar to the two metals.

(iv) Mg-Al

This obviously represents clay minerals and micas.

(v) V-Ti-Al; Cd-Al

These relate to the moor environment and probably represent co-precipitation phenomena.

(vi) Zn-Pb

This represents mineralization, mostly involving samples below Skeleron Mine.

(vii) Cd-Zn

Also associated with moor samples and probably indicates

Cd substitution for Zn in oxide phases.

(viii) Ba-Ca

An association that may indicate gangue mineralogy accompanying sulphide mineralization; the R' is probably rendered small because the host rock is limestone in which Ba is not enriched.

(ix) Ca-Mg

As explained previously, this represents mixed geology.

Figs 5.17-5.19 show some scatterplots for the data, from which the detailed nature of the inter-element relationships may be - 118 -

further deduced.

Partial correlations can thus reveal fundamental binary

relationships that may be masked by multiple relationships

with other variables in a data set. To facilitate recognition

of mutual element associations for the data in table 5.4 its

correlallogram is given in fig 5.20 for coefficients

significant at .05 probability. Where these relationships are

complex it may be advisable to further examine the data by a

multiple regression in which all the variables enter the

regression equation independently. This way the more direct

associations may be made clearer.

5.2.1.2 PRINCIPAL COMPONENT AND FACTOR ANALYSES

It was evident that the above observations were those most

obvious to the eye, and that important joint trends could not

be recognized outright. Further treatment of the data was

thus warranted, and principal component and factor analyses

were chosen. The importance of such multivariate statistical

techniques in the analyses of multi-element data can be judged

from the rapid rise in their application and the extensive

discussion they have generated (e.g Chapman, 1978; Temple,

1978; Govett et al, 1975).

Fourteen elements (Mg, Ca, Ba, Ti, V, Mn, Fe, Co, Ni, Cu,

Zn, Cd, Al, Pb) were selected with regard to the procedural

errors inherent in them and the possible significance of their

spatial distribution. The rejected elements, La, Cr and Ag

tended to distort groupings and misclassify anomalous samples - 119 - with background and vice versa. Sample number 21 was deleted as a low outlier which tended to compress plots of the principal components and so obscure the natural clusters sought; it did not, however, misclassify samples.

A. PRINCIPAL COMPONENT ANALYSIS

A Q-mode analysis based on the similarities amongst sample types was examined first (fig 5.21). The first four eigen-vectors account for 86.4% of the total variability, denoting that at most four underlying factors explain the geochemistry of the sediments in the area. Fig 5.21 shows that five subpopulations or sample types may be described based on the effect of the first two eigen-vectors which account for nearly 70% of total variance. Remarkably, no samples were misclassif ied and the five sample types identified are :

(i) dispersion from the old mine including suspected mineralized samples from locality two,

(ii) background samples from localities underlain by Worston

shale, some enriched in elements typical of the association

found in the moorland environment,

(iii) background samples from limestone localities (mostly

Chatburn Limestone),

(iv) background samples from localities outside the moor

environment and underlain by Worston shale and Bowland shale

and slightly enriched in Ni and Co, and

(v) background samples from the moor environment (bedrock

mostly Bowland shale, some Worston shale). 6-9

4-1-

K E 2-0- A Dispersion from Skeleron Mine

• Dispersion from suspected H mineralization o -p • 2-Background samples derived o from WS, some enriched in (1) moor-association elements > 3-Background samples derived -2-0- from CHL a> bq 4-Background samples derived •H from WS and BWS and slightly- enriched in Ni and Go -4-0- 5-Moor samples, derived mainly from BWS, some from WS •a CM

-5-9 -5-9 -l-i 1-3 st 1 Eigenvector

FIG. 5.21 Sca-tter "biplot of first two eigenvectors from Q-mode PGA of the Clitheroe data.. - 120 -

A better appraisal of the eigen vectors giving rise to such subpopulations is obtained from the R-mode analysis (fig

5.22). Table 5.5 lists the first six of the fourteen possible principal components (eigen vectors x (eigen values)1/2) in the data. The R-mode analysis based on the relationships between the measured variables results essentially in the

subpopulations obtained by Q-mode analysis, but this time there are some misclassifications (fig 5.22) which may be caused by the natural variability in the samples or the errors

incurred in obtaining the data. It may be deduced from the component loadings on the elements in table 5.5 that the first

principal component which alone accounts for some 56% of total

variability is associated with the geology (limestones as

against other rock types) and processes in the secondary

environment. The second and fifth components have high

loadings on Zn, Pb and Ba and are interpreted as an indication

of mineralization, while the third and fourth are associated

with complex aspects of the secondary environment and geology

respectively. It is not clear from these components what

feature (geology or secondary environmental processes) is the

dominant control on sediment composition. To resolve this a

varimax rotation was performed on the eigen vectors to yield

factors that would hopefully have only one dominant process on

each.

B. FACTOR ANALYSIS

Table 5.5 lists the factors obtained which are compared with

equivalent 4- and 5-factor models in fig 5.23. A six-factor

model was adjudged the best on the basis of the returned Table 5.5 Loadings on first six eigen vectors. Clitheroe fine fraction.

VARIABLE EIGEN VECTORS

1 2 3 4 5 6

Mg .25 .15 -.17 -.58 -.08 .06

Ca -.18 .39 -.29 -.52 -.15 0

Ba .08 .52 .13 .35 -.65 -.36

Ti .33 -.09 .09 0 .07 -.25

V .35 -.10 .05 -.10 -.05 -.11

Mn .32 -.06 .07 .08 .19 -.05

Fe .34 -.05 .08 -.05 0 -.25

Co .20 -.13 -.56 .34 .09 -.21

Ni .18 -.14 -.60 .18 -.34 .42

Cu .33 -.05 0 -.22 -.09 -.12

Zn .27 .39 .09 .15 .31 .22

Cd .26 0 .36 .05 -.28 .66

Al .34 0 .07 -.11 -.07 -.05

Pb .15 .58 -.16 .15 .44 .13

EVAL * 7.86 1.91 1.33 .99 .53 .44

Var (%) 56 70 79 86 90 93

EVAL = eigen value; Var = cumulative variance principal component = eigen vector.(eigen value)**.5 - 121 - communalities of the variables, the percent of total variance explained and the fact that reasonable explanation could be given to the factors obtained. Figs 5.24 and 5.25 are the

spatial plots of the sample scores of the retained factors.

Based on the factor loadings, the geology, plots of the factor

scores and the discussions above the following explanations

are offered for the factors :

Factor one is the combined effect of the diverse geology

and the secondary environment. The three main geological

units (Chatburn Limestone, Worston Shale and Bowland Shale)

are mutually exclusive spatially and chemically. No

repetitions of formations occur within the domain of the study

area chosen and thus distinct sections of the area are

distinct geochemically. This is the first reason for the

efficient classification given by the Q-mode analysis.

Moreover the maintenance of secondary processes is not

possible in carbonate-rich environments with high pH; even in

the shale units, secondary processes are most extensive in the

moor environments where the bedrock is mainly Bowland shale.

Factor two is the next most important (13.4%) and

describes mineralization. The suspected mineralization in

locality two Is not clearly marked out (fig 5.24B). Factor three

(12.9%) contributes as much to the total variance as factor

two but there is no obvious explanation for it. It is mainly

associated with the depletion of Ni and Co and to a lesser

extent V, Cu, Mn and Al. It coincides with Chatburn Limestone

boundaries with Worston Shale (fig 5.24C) and may signify the 6-5"

Pb Cr

AN Ba Ca / 3-2- Zn / K E u o / A -p A / / A/ Ag Mg A Samples within 1 km from Skeleron Mine o • ' / A Co A Anomalous dispersion further from i A Ni • A • A ; - , Skeleron Mine ^ A • Dispersion from suspected a> / - --r mineralization bo - A _ • Samples within or close to the moor •H MnC u M ft Fe Al W ( - A - v _ Ca-rich samples Cd . V Ti aC M / •y

La -2-5 x T T -3-8 -1-2 1-5 4-1 6-7 st eigenvector FIG. 5.22 Scatter biplot of first two eigenvectors from R-mode PCA of the Clitheroe data. FIG 5-23 Factor Loadings In Fine Fraction, Clitheroe Study Area-

4- factor model 5- f actor model 6 - factor model

1CO-, - + ,-100

Cd- Al -r (Co) r Ba-(Cd-Zn-Pb) '-Ca-Mg.(pb)* (PU-Zn) - - Ba Mr-Cd - 80. - Mg - Ca (Mn-Cd-Ti) _ Ni-CO+Cu-- - Ca - Mg Mn-V-AI-Fe) Ni -CO- ( Cu-V- - Co- Ni- (Cu- - Al -Mn- Fe-Ti) 0\0 V - Al - Mn] -Pb-Ba-Zn-lCd) UJ 60 _ Pb-Zn-Ba- u z (Mn-Mg-Co) - Pb - Zn • (Ba- Mn • Mg) ac. < Ti - V* Mn - Fe - -C- a Mg-Ti-V-Mn-- > ' Ca V - AI • Fe - T i •- _ Ca Cu-Zn-Cd-Al- Fe-Cu-Zn-Cd* £4Q Cu-Mn-Mg-Cd- ( Ni - Co) Al-l CO- Nil

3 Zn -(Co - Ni] 5 20

* • 2 ± Load jng <4 - 122 - absence of Mn-oxides or organic matter in the sediment.

Factor four (9.3%) has large loadings on Ca and Mg with probably unimportant negative loadings on Ti, Mn and Cd. Fig

5.25A suggests that it is a feature common to the boundaries between Worston shale and Chatburn limestone or Bowland shale.

It is some complex geological factor that is revealed by the partial correlation between Ca and Mg. At the base of the

Bowland shale series on Pendleside scarp is the Ravensholme

Limestone horizon. It is therefore probable that it is the equally high contributions of Ca from this limestone and Mg from the shale bedrock which result in this factor. The scatter-plot, however, does not seem to support this contention.

Factor five is a Ba-Pb-Zn factor which contributes some

7% to the total variance. It has a continuous trend in locality two which is attributed to unknown mineralization.

It is less conspicuous than the Skeleron dispersion train probably because of the absence of former mining activity, the contamination from which could have raised the ore-metal concentrations to the levels found below Skeleron. A trend of intermediate to high factor scores below locality 9 (fig

5.25B) presents no immediate explanation.

Normally no effort is spent on reification beyond about

the fourth factor, because higher factors (and principal

components) are likely to be chance combinations resulting

from the inherent errors in the data. One popular criterion

is to drop any factor whose variance contribution is less than 3

o Q Q O'0° o

o

"CoToo"soToo7SBO.OO 7340.0a 8023.00 ^ko.ca a'isa.ca siso.ao T^oToo 8420.0a esoo.00

CLITHEROE FINE FRRCTION 6-FACTOR MODEL!14 VRRIRBLES) FRCIOR1

°O°O°0OO

'7700.00 7*730.00 7*830.00 7*340.00 8*020.00 8100.00 8*180.00 8230.CO 8340.00 8420.00 ftflO. 00 EAST

CLITHEROE FINE FRRCTION 6-FRCTOR MODEL! 14 VARIABLES) FACT0R2

PC o

• o o"

.o •

7700.00 7*780.00 7390.00 7940.00 8020.00 8*100.00 3*180.00 8*280.30 8*340.00 8420.00 8500.00 EAST

CLITHEROE FINE FRACTION 6-FRCTOR MODEL!14 VARIABLES 1 FRCT0R3

FIG 5.24 Dispersion patterns of Scores on the first three factors from the 6-factor model for the Clitheroe area. o

I*

; -o.

lU« >U4i RMxi^Ay.M ikMX 4MJI CL1THCR0C f INC FRICTION R-ffCTON MOOCH 14 VNRIMlISl FNCT0R4

f* " • •<>••- I o° \ o —•O®.

I B • i i i ^SMB mix •<».• CUTHEROC FRC FRNCTION f-FNCTON MOKlt 14 FMtffCCSi FflCTQffS

O o pp*O. ..®o.. . — o • \

•: •• • •• •

RmX. O^Af., CLIflCROC FINC FRICTION I-FNCTON ICOCLI14 VIMINtLEOI FACTOR*

on t] •FIG 5-25 Dispersion patterns of Scores last three factors from the 6-factor mo

the mean error in a variable (Marriot, 1974), although it is not specified whether this should be the smallest error variance. As factor five has shown, however, important trends may be discarded.

Factor six contributes only 5.8% to total variance and is associated with mainly the depletion of Cd, Zn and Al, but with a small enrichment of Co and Ca. A depletion in the former three elements could denote the absence of clay in fine fraction (which would have Zn and Cd on exchange sites) and thus denote a high energy of the stream in localities with high scores on this factor, or alternatively, distance from argillaceous rocks. The small Ca contribution may be reflected in high scores in Chatburn Limestone samples, but no ready explanation can be offered for the Co contribution. A comparison with factor three (fig 5.24C) reveals a paradoxical

situation in which the same sites are poor in Co (factor

three) but relatively rich in Co (factor six). A tentative

explanation for this is that factor three represents the lack

of secondary processes in Chatburn Limestone, while factor six

represents such processes in isolated sites in Worston Shale

in contact with the limestone. However, it is possible that

factors three and six may well be mathematical artefacts

(Temple, 1978). - 124 -

5.2.1.3 REGRESSION OF BACKGROUND FACTORS ON ORE METALS

Due to the high contents of ore metals at several sites on the

moor it was decided to ascertain how much of the variance of

the ore metals was associated with secondary processes.

Factors 1, 3, 4 and 6 which represent normal background

processes were regressed on Ba, Zn, Cd and Pb. The results

(table 5.6) are summarized as follows :

(i) Ba dispersion is unaffected by secondary processes on the

moor since it is depleted in that environment. Nor is it

associated with bedrock geology. The residuals are highly

correlated with the original data (fig 5.26A) and to a lesser

extent, with Zn and Pb. As shown above, Ba constitutes a

factor in which Zn and Pb play a subordinate role.

(ii) Factor 6 alone accounts for over 50% of the Cd variance.

This confirms the high inherent errors in the Cd data, since

this factor is largely inexplicable and may well be due to

noise. Factor 6 also accounts for some 5% of Zn variance.

(iii) Factor 1 accounts for 25% of the Zn variance, 33% of

that for Cd and only 2% for Pb. Thus both Zn and Cd are

affected to a great extent by secondary processes; for Cd this

may be the sole factor controlling dispersion. In contrast Pb

is little affected and thus any apparent trend of high Pb

values on the moor is probably held in an organic phase, or

possibly indicates minor mineralization.

It is reasonably clear then that Ba and Pb variance in

this area are due solely to clastic dispersion from

mineralization. But while Zn is mainly associated with Table 5.6 Regression of background factors on ore elements in the Clitheroe area.

Dependent % of variance explained Variables correlated variable with residuals F1 F3 F4 F6 Total

Ba <1 <1 <1 <1 1 Ba

Zn 25 0 <1 5 30 Ba Zn Pb Cd

Cd 33 0 5 52 90 Ba Pb

Pb 2 1 3 0 6 Zn Pb Cd FIG 5.26 Scatterplots of metal regression residuals on background factors (l,3,4&6), M , with metal values, Clitheroe area. h - 125 -

mineralization, a third of its total variance is associated

with secondary processes, mainly controlled by Mn Cadmium

dispersion is probably mostly controlled by secondary

dispersion phenomena.

5.2.2 NEWQUAY AREA

5.2.2.1 AAS DATA

High concentrations of Mn were found in samples close to somaKnotoh

base-metal mineralization (fig 5.27A). In several localities

along most stream courses Mn was enriched to some extent and

very high values unrelated to mineralization occured in some

of these. The highest of such Mn concentrations was found in

locality one (see overlay 1 or fig 5.33), where a small wood

protects the stream and the ground is marshy with black, peaty

soils forming the bank. The whole section of stream,

including the bank, has been stained black with Mn oxides.

Similar conditions obtain in localities two to six. In the

very acid mine wastes on Penhallow moor low Mn concentrations

were found.

The formation of extensive Mn oxide coatings such as

those just mentioned is described in some detail in chapter

eight, where it is shown to be the result of processes in the

secondary environment unrelated to mineralization. In

localities two and six, however, some contribution is

suspected from acid mine waters originating in localities

seven and ten respectively. - 126 -

The Zn distribution pattern is generally similar to that

of Mn but is not so in detail (fig 5.27B). High

concentrations, somewhat displaced from mine dumps, depict

mineralization and contamination. However in locality one,

which is unmineralized but yielded the highest Mn values, Zn

levels were very low. The relatively low levels of Zn

downstream of the mineralized locality six are due to

excessive dilution caused by easily eroded earth used to

reclaim some marshy farmland. A low but sustained enrichment

of Zn was found in sediments from locality four in the

north-eastern edge of the area. The source is probably some

accessory sulphides accompanying Sn mineralization within the

metamorphic aureole of the St. Austell granite but the

sustained dispersion train may be due to the adsorption of Zn

by Mn oxides•

Lead concentrations are moderate to high downstream of

SpireKoowfi mineralization (fig 5.27C). On Penhallow moor the Pb

levels were only moderate compared to those found just below

East Wheal Rose (see overlay 1 for location of place names).

It is probable that the higher Pb contents in the latter

locality has resulted from the addition of anomalous samples

washed downstream from Penhallow moor.

There was a slight enrichment of Pb in some localities

without known mineralization (e.g localities 1 and 5) where Mn

oxide coatings were observed and Mn levels are slightly higher

than normal background levels. It is probable that Pb is

sorbed onto Mn-oxides in such localities but the possibility

of Pb being partly bound in less obvious organic debris cannot - 127 - be discounted, although no direct assessment of the organic

fraction was made. Lead concentrations at some sites in

locality 1 were too high to be explained by either process.

Lead could be derived from some old trash nearby but this is

probably unlikely and barring the possibility of the nearby

farm containing earth from some old mine dumps, the short

dispersion must denote some mineralization. Again sediments

from locality four were slightly enriched in Pb.

Like Mn, high Fe levels were found to be directly

associated with dipersion from the old mines in most cases

(fig 5.28A), except with Penballow moor samples which

contained very low levels. The Fe distribution pattern is

very similar to that for Mn except in localities with high Mg,

where Fe levels were also raised. A more significant

difference between the Fe and Mn patterns was that Fe levels

fall off more rapidly with distance from mineralization than

was found with Mn. This is probably due to Fe oxides and

hydroxides being relatively more stable in the secondary

environment and thus precipitating closer to their source than

Mn oxides.

Calcium appeared to be mostly concentrated in Mn oxides

unrelated to mineralization (fig 5.28B). Background levels

were uniformly low. In acid environments or old mine sites Ca

appeared to be mostly leached away, and the few high values

below Shepherds and East Wheal Rose mines could have resulted

from sparsely distributed calcite gangue fragments.

Calcareous horizons are said to occur in the bedrock sequence

underlying the area but a detailed lithologic map was not si * o« & SB b •OOO "l| > Sj s si

)3i oto.io ikg.N oto.oo oto.oo _ oto.oa SaoToo JnToi tta.sa STi 9». EAST >10*

CORNWALL FINE FRACTION AAS LOG-UN

28 "aj X iX s fe 2J o o_ o o* %

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CORNWALL FINE FRACTION AAS LOG-ZN

h

28 "S x o© is 1 - 2 S. •'O

00 110.80 8to.i8 130.80 840.88 ObO.OO 880.88 8*70.81 8*0.08 HoTm 3)0.80 EAST «1Q CORNWALL FINE FRACTION AAS LOG-PB

FIG 5.27 Dispersion patterns in fine fraction for Ti, Ni and Li in the Newquay area. • ° •.

t • , '' • o ooo o o 8 O o 28 q 9 » o o°<5• o. "Si J? •. 0 z is 'h 8 j •

8 i "HJO.BI a'lo.ai 5o75 now ta.aa _ a&o.oa 3o75 3o75 3O7B5 SSoToi ibo.M EAST •10' CORNWALL FINE FRACTION AAS LOC-FE

o*°'.. . ..'O'On

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28 o "si z

8 T "c>

"VM.oa 51575 SoTaa 5575 a+o.sa . ato.aa soo.oa 3o7S 5575 ato.aa 3M.ea EAST »io CORNWALL FINE FRACTION AAS LOC-CA

s, a Si o

8 TT - Si .o\ $ 3

3 •Si X Ss t 8 SJ

WN AiJa 3575 5575 5575 55751 35755 55755 5T55 55755 4aj cEAS NET T "JO.INL CORNWALL FINE FRACTION AAS LOG-NO

FIG 5.28 Dispersion patterns in fine fraction for Ti, Ni and Li in the Newquay area. - 128 -

available to aid in evaluating the Ca pattern.

Sediments collected near old mines were among those with the lowest concentrations of Mg (fig 5.28C), but with a few exceptions the Mg pattern was generally uniform. The exceptions were localities with old quarries on the stream banks, or marshland reclaimed with rubble, presumably from nearby slate and shale quarries. The quarry products are easily removed by sheetwash after rain and give rise to locally high Mg concentrations associated with slightly enriched Fe values as much as 2 km downstream of the source.

Such flooding of the sediment with barren rock waste has led to a high dilution rate which has obscured the dispersion from the Zn-Pb mineralization in locality six. In some localities, however, slight Mg enrichment was natural; here the streams were more or less reduced to a spring flow in soft, fractured bedrock.

Two explanations were thought plausible for the slight Fe enrichment. The first was that Fe and Mn were easily leached

from the disagregated rock fragments and the more soluble Mn was carried away in solution but Fe precipitated out and was enriched in the sediments. The second possibility might be that the loose micaceous minerals in the rubble were more efficient collectors for Fe oxides precipitating from marshy waters than solid rock in situ. Both possibilities probably operate together.

Copper, like Pb and Zn, faithfully depicted

mineralization (fig 5.29A). Together these three ore-metals 3 t\ 8 ij ooo 3 51 38 •a X is o o o

irei aYa-M mm STw M.H STM SUM 4mm "XTSS Ami ii« ERST aio1 CORNWALL FINE FRRCTION RRS LOG-CU

8 .

28 "5] o

o

"too.oo I'IO.OO 020.00 e'so.oo o.o.oo iso.oa soo.oo tio.oo wo.oa ibo.oo "2)0. oo EAST .10 CORNWALL FINE FRACTION ICP L0D-C0

8 Sj 8 i * 'ooo 5 28 "§l X © is o •a

Toi I'IO.IO o^o.ti ik.N i4oERS. tTo SoToaltro Ao.ii fn.oi ato.oo rto.utbo.a o

CORNWALL FINE FRRCTION ICP LOG-CO

FIG 5.29 Dispersion patterns in fine fraction for Ti, Ni and Li in the Newquay area. - 129 -

outline all known mineralization, including the Sn-lode just

outside the study area beyond locality four. Again Cu was

depleted in Penhallow moor and Shepherds. A close association

with Fe is suggested upon comparison of the two dispersion

patterns, especially in sediments from locality four.

SUMMARY

High concentrations of Pb, Zn and Cu in sediments of streams

draining the Lower Devonian south-east of Newquay indicate

mineralization, especially when they are all coincident.

There may be a Fe-Cu association that is significant

indidcation of mineralization. High Mn levels may be an

indication of base-metal mineralization when supported by

similar levels of Cu, Pb, Zn and Fe.

All seven elements determined (Mn, Zn, Pb, Fe, Ca, Mg,

Cu) are taken into solution in the acid, mineralized

environments, and some may be deposited a short distance from

their sources • Some Ca is precipitated with Mn oxides

(possibly as phosphates or easily soluble carbonates), but the

concentration of Mg is generally the same in unmineralized

terrain without exposed quarries or not yet disturbed by marsh

reclamation projects.

5.2.2.2 ICP DATA

The distribution patterns for the seven elements described for

the AAS data were almost identical to those given by the ICP

data despite the relatively poorer precision of the latter

technique. Despite the instability of patterns derived for - 130 -

data with high procedural errors general statements have been made for the ICP data because the F-Tests for between-site

variance were significant at .01 probability for all but La

(.05 probability). This meant that at least general differences between sites and localities existed which could be recognized from a properly constructed map. Choosing the

percentiles as class limits effectively dampened data noise while permitting direct comparisons of spatial plots for the

elements. It is remarkable that the patterns for such

elements permit plausible reification.

The Co distribution pattern was very similar to those of

both Fe and Mn, but there appeared to be a closer relationship

with the former (fig 5.29B). Cobalt depicted mineralization

very well and could be a useful indicator element in this area

and environment.

Cadmium uniquely defined all types of mineralization, for

unlike Co there was no enrichment at all in unmineralized

terrain with or without Fe-Mn oxide enrichment (fig 5.29C).

It may be a valuable indicator element in this terrain.

High Ti concentrations were found in locality four where

the sediments are partlty derived from the metamorphic aureole

of the St. Austell granite. A long dispersion train was

found in the main trunk of the Gannel river (fig 5.30A). The

source could be rutile disseminated in the aureole and/or

associated with Sn mineralization. The pattern in this

locality is strongly coincident with high Cu and especially

Ca, and the metal is probably in a secondary, more soluble 1 SI •••oo* °om 8 QOO ij 2 P.

28 "S|

TOO. oo 010.00 820.00 030.00 140.00 8*0.00 880.00 0*0.00 0*0.00 8*0.00 9)0.00 ERST «10

CORNWALL FINE FRACTION ICP LOG-TI

•v o

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CORNWALL FINE FRACTION ICP LOG-NI

o- % • o .o4.

O o(5)*0 23 •il

"3)0. Too. 08 s'to.oo 820.00 880.00 840.00 8*0.00 0*0.00 8*0.00 8*0.00 8*0.00 ERST "10 CORNWALL FINE FRACTION ICP LOG-LI

FIG 5.30 Dispersion patterns in fine fraction for Ti, Ni and Li in the Newquay area. - 131 - form than it would be in primary rutile. The possibility that this could be part of effluent from a paint or clay works was not investigated.

Anomalous Ni concentrations occured near former base-metal mines. High Ni was found near the Sn lode near locality four, but equally high values occured in background localities with profuse coatings of Fe-Mn oxides (fig 5.30B).

There did not appear to be any prefered systematic relationship with either Fe or Mn; the Ni pattern may be sympathetic to that for Mn in one locality (locality 5, samples 55-62), Fe in another (locality 4), or both in others

(parts of locality 1). This may merely be a reflection of the predominant oxide in the locality, for Fe far exceeds Mn in locality four, while the relative amount of Mn in parts of localities 5 and 1 are higher. This suggested that Ni was easily incorporated in either oxide. With a 72% procedural error for the determination of Ni, however, this must be a tentative interpretation.

The Li distribution pattern was identical with that for

Mg and was explained as lattice Li in the Mg-phyllosilicates ubiquitous in the area (fig 5.30C). Slightly raised levels of

Li were found in locality four and coincide with similarly

raised levels of Al. This was taken as an indication of the

presence of K-feldspars (and/or clays derived from them)

contributed by material from the granite aureole. Lithium

could also have been derived from the tourmaline always

associated with Sn mineralization in Cornwall, but the method

of attack used was not considered strong enough to release Li - 132 - from such a source.

Not surprisingly Cr dispersion is very obscure. There is a notable coexistence with Ti in locality four (Fig 5.31A) and a less clear association with Fe in localities four, seven and nine. These may indicate the dispersion of primary oxides

such as rutile, ilmenite, chromite and cassiterite, even

though these minerals would only be slightly attacked by the

reagents used in the analysis. Some high Cr values also tend

to occur near mineralization, but such relationships are very

uncertain.

The method of sample digestion used to obtain these data

was not suitable for Ag and the Ag data was only examined to

ascertain the possibilities it offered for further work. The

distribution pattern obtained was rather featureless (fig

31B). However, the more or less random, high Ag

concentrations would seem to indicate two possible important

associations, namely with mineralization and Fe-Mn oxides.

The dispersion below East Wheal Rose is noteworthy for this

mine was known for its high recovery of Ag. This apparent

trend suggested that the content of Ag in Fe-Mn oxides could

be used to trace mineralization, a possibility which was

Successfully tested later.

Figures 5.31C and 5.32A show the very similar dispersion

patterns for V and Al. Both elements were closely associated

with Mg and there was also an association with high Fe and Mn

levels unrelated to mineralization. Thus two sources of V and

Al were indicated. One was the lattice-held metal, mostly in ••o • . . o•

V 0 •o O- -? ° o • • . . . • O. . 28 "SI o . s i» "SI °o

"*00.00010.00820.00880.80 840.00 8*0.00 800.00 8*0.00 8*0.00 190.80 9)0.04 ERST "10 CORNWALL FINE FRACTION ICP LOG-CR

• * " • O . • O- • O • O o •o. . . • .. s>- o o 9 o . . .•o- ao «.

Q. • o ... P o o

"MO.00 110.80 0*0.80 080.80 140.00 8*0.00 8*0.80 8*0.08 1*0.08 8*8-83 9)0.00 ERST «1

« i .o. P o

28 ti « "si z is -o. S "si

e8i %Too 110.00 COJI 080.00 040ERS.MT. o*a.o"10o Sum oSToi Sjo Sum 2mo CORNWALL FINE FRACTION ICP LOG-V

FIG 5.31 Dispersion patterns in fine fraction for Cr, Ag and V in the Newquay area. ° o O O®CJO0 28 o ti * X •I o° «00 o o-

•'">•«• MflToi i3o. oo S^-So^ KoToo MOTOO Ko^a tco.oo

CORNWALL FINE FRACTION ICP LOO-AL

• 4 « % o • . * . • o O. • • *

28 OO . "4 O • ^ X Ss •.o^'-O.. f • • • . 8 O. i. 5.

5>

"Vo.oo i"io. ot tfra.it o'ao. oo i'«o.w aio.oo ooo.oo 0*10.01 mo.00 too.00 Sm.m EAST "101 CORNWALL FINE FRACTION ICP LOG-BA

3l s 8J . * o' -O

•• • . >

o . o 0 '•••...-o.. 23 "4 o. o 6 -o o {Ooo» "'O o

tooToo 110.00 szo. 00 a'x.ao a'

FIG 5.32 Dispersion patterns in fine fraction for Al, Ba and La in the Newquay area. - 133 - chlorites and other phyllosilicates containing Mg. The other source was attributed to Fe-Mn oxides, in which the behaviour of V should closely follow that of Fe. The Al was thought to occur either as a major constituent of clays or micas in the lithic fragments on which the oxides were coated, or as

"colloidal" aluminium hydroxide. Both forms were probably represented.

Four samples collected near Shepherds were unique in containing low concentrations of all elements except Al and

Ba. As these were the only background samples collected from the Middle Carboniferous this Ba-Al association could well be peculiar to the bedrock in this locality.

Comparison of the Ba pattern with those for Mn and Fe shows a close correspondence between the three which accounts for most of the high Ba values (fig 5.32B). Low to moderate levels were found close to base-metal mineralization and no definitive association was apparent. It would appear that there was little or no Ba associated with ore-formation, or mechanical dispersion for any barytes gangue was very poor.

Lanthanum, with the worst procedural error, surprisingly gave a coherent distribution pattern indicative of base-metal mineralization (fig 5.32C). Although U mineralization occurs in the region no La or other rare-earths have been previously reported in connection with base-meatl mineralization. No other information could be sensibly derived from the pattern. I

ffiHD Newquay

113 Sonnet _

130 131]32 142 I4 nip8 % l40 155 47 167 158 153 U V 125> TO • St tnocler Key

6i nnnni New 8w 1 347 / i6. 0 <^uay I o\Ain 11)35943 St. Hno4ev- VI n TO5- 23

330.30 310.00 320.00 830.00 840. QO 850.00 850.00 870.00 880.00 8SO.QO SOO.OO ERST •10'

FIG 5'33 CORNWALL SAMPLE SITES AND LOCALITIES - 134 -

5.2.2.3 SUMMARY

(i) Although Atomic Absorption Spectrophotometry is a much

more precise analytical tool than Inductively Coupled Plasma

emmision spectrometry, identical geochemical patterns can be

obtained from them. In the Newquay area very similar

statistical and spatial distribution patterns were obtained

from the same samples for Mn, Zn, Pb, Fe, Ca, Mg and Cu.

(ii) High levels of Pb, Zn, Cu, Cd and Co, either singly or

coincidentally, outlined all known mineralization. Iron, Mn

and Ni patterns were also associated with mineralization but

did not yield unambiguous results; there were localities in

which the contents of these three metals were as high or

higher than those in the mineralized localities. Tin

mineralization in the north-eastern edge of the area was

distinctly marked by Ti, and less so by Cr. Lanthanum

results, although the least precise, reflect base-metal

mineralization.

(iii) Nickel, Co, V, Al and Ba were enriched in those

unmineralized localities where Fe-Mn oxides abound.

(iv) Because of the uniform geology, no natural, distinctive

patterns related to bedrock could be forecast and none was

found. As explained above, however, marsh reclamation

projects locally raised the levels of those elements which

make up the bulk of the acid-soluble constituents of the

quarry rubble used. High Mg, Li, Al and V were found to be

important pointers to such localities and occured

coincidentally. Iron was moderately enriched in such

localities partly because it is an important constituent of - 135 -

the chlorites which constitute an important modal component of

the rocks, and partly because it is easily removed by

weathering to form the secondary oxides. Oxide formation was

probably enhanced by the porous nature of the reclaimed land

(to permit efficient draining) which gave rise to increased

supply of free oxygen to the metal-rich marsh waters,

(v) A strong Ba-Al association in four background samples from

Mid-Upper Carboniferous rocks was found, but it is not certain

whether this was a significant indication of a different

lithology.

5.2.2.4 TOTAL AND PARTIAL CORRELATIONS

Table 5.7 contains the total product-moment correlations (R)

for the Newquay ICP data. Considering coefficients below .5

but higher than .3 (arbitrarily chosen) it is seen that La is

vaguely associated with Mn, Zn, Cd and Pb. This accounts for

the ability of La data to depict base metal mineralization in

spite of their high inherent errors. It is probably unlikely

that the La association with all four metals is due to pure

chance, and La is possibly fixed in Mn oxides precipitating

near the old mines. A Ca—Ni association can be identified in

samples low in Zn, Cd and Pb. This may reflect the presence

of the two in secondary carbonate precipitates, or as

substituents in some silicate lattice. A V-Fe-Ni association

may similarly reflect the importance of silicate minerals on

sediment composition. As observed earlier Fe-Mn oxides abound

near the old mines but the R indicate an imperfect

relationship of Mn with Cu, and of Fe with Zn, Cd and Pb. cu

oT3 rH i n.

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ro• o•o •oo • < r i• ro • ro• i ro

O I ro | | | in | | | |

r-n I i <•r i ro• i I i iron• -• I

•H co• I in••• -d- ro• ro | |• m |• ro• ro H I I I I I I

rt ro ro• I I ro• | | | | I <•r • | •< f

rt —Il^oi— I I l I I I I | 1001 PQ

rt »~h i i<±i i i i i o o i <± o i

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L i Mg Ca Ba La Ti Mn Fe Co Ni Cu Zn Cd Al Pb

Li 1

Mg .75 1

Ca .28 15 1

Ba 14 28 1

La -.24 17 .16 1

Ti 36 -.24 .12 1

V 29 24 .33 15

Mn -.11 37 .11 22 1

Fe 17 11 -.27 .12 - 24 12 . 12

Co .17 13 .44 1

Ni 12 16 .18 49 Cu 12 - -. 14 -.11 39 1

Zn 19 .38 -.26 26 18 .30 26 1 18 Cd -.37 .13 .10 11 23 -.14 .19 .33 .47 1 49 Al 12 .51 20 41 -.21 14 .16 17 1

Pb 08 -.19 .09 - 26 .13 - -. 14 28 - 136 -

Aluminium forms similar weak associations with Fe and Ni which mostly originate in clay minerals. Titanium forms no strong correlation with any element other than Ca, and this merely reflects the unique high trend of Ti values below locality 4 where high Ca values were also found. It may represent a common detrital dipersion of titaniferous minerals and feldspars derived from the St. Austell granitic complex.

Total correlations higher than .5 exist between elements occuring mainly in bedrock minerals and their weathered products (Li-Mg-Ba-V), and in Fe-Mn oxides and ore-minerals

(Fe-Mn-Co-Ni-Zn-Cd-Pb). Between the two groups the R is high and negative, in effect confirming the low concentrations of background elements in anomalous samples. All the Ag R, except that with Cr, are negative and insignificant. This quite possibly signifies the high random errors common to the data for both metals.

It is evident that some of the R contradict. For example the Ba-V R is .7 but Ba is not significantly correlated with

Li or Mg, although the latter have high positive R with V.

Both Ba and V, however, have high R with Al. This was taken as indicating a multiple source for V although it forms a single lognormal population; similar apparent inconsistencies may imply similar provenance features. Figs 5.34-5.36 contain some scattergrams for the data and the details of some of the correlations may be derived from them.

Table 5.8 contains the partial correlation matrix for the

same data, and its corresponding correllogram (fig 5.37) 3*0 Mn 3^7

2-8 3-0 H 21 £ r = 10 r = -81 B 2-2H A to B AA 3 U „ • * • 6 1-5 A •

8 J* 1-0-1 3 0 M n 3*7 1-0 1-7 Co 2-3 —i— 30 TT 24 Ti 2-8 3-1 FIG. 5.35 Scattergrams of IGP data on fine fraction, Newquay study area.

A anomalous dispersion undifferenirated D dispersion "below locality h- - 137 - indicates more complex relationships in the Newquay data.

Some prominent ones stand out, however, and are also listed in fig 5.37. These are :

(i) Mn-Ni-Co-Ca-Cd-Ba : This signifies sorption by Mn oxides.

(ii) Ba-V-Al : An expression of the composition of micas in bedrock or clay minerals.

(iii) Mg-Li-V-Fe : This represents unusual enrichment in sediment of barren rock waste, and mostly marks out reclamation sites.

(iv) Zn-Cd-Pb : Mineralization

(v) Ti-Cu-Ca : This represents dispersion below locality 4.

(vi) Cd-Cu-Zn; Mg-Ca-Li : The significance of these were not immediately clear.

The remarkable thing about these associations is that they were exactly those obtained by the factor analysis. Some of the R' are also self-contradictory. For example the high positive R' between Mn and Co and Co and Cd are contrasted by a high negative R' between Mn and Cd. Similar contradictions are evident for the following : Mn-Zn-Cd; Zn-Cd-Pb; Ba-Ti-Ca;

Mg-V-Al; Ca-V-Ba; Ca-V-Ti; Mg-Zn-Cd-Pb. Some surprising positive R' are also noticeable (e.g Mg-Zn). The latter is particularly puzzling since the ' Mg-Cd and Mg-Pb R' are understandably high and negative.

To better evaluate the partial correlations in the

Newquay data, multiple regressions were run with all the

elements as free variables. The results (table 5.9) contain

the essential information in the partial correlation matrix.

Of the Li variance 80% is due to Mg and 5% to Ca. The 4-0 A A a a 1-9 J a a 3 6 r r • 80 a a a a a a a 1-5 A A AA4k a aa TJ • • 3-1 q1-H c ISI 2-6 H -7 - . xvsr^r. r = • 88 ««tl>

~ezt a a 1-9" 1-9 a 40 a a a a " a 363 1-5- r = -43 a a A A 1-5-4 A A* a a 'a a- a a a a a a a A A a£ aa a & A A A "O « •• a ^ "O O a O V Aa c • • a N •• • •» •7 r =-67 r - - 91

•3J 1 t&tx&h i. •3 2-14

Co 1-2 17 Cu 2-5 30 1-8 2'0 L a 2-2 2-4 FIG. 5.34- Scattergrams of ICP data on fine fraction, Newquay study area. A anomalous dispersion e undifferentiated • dispersion below locality 4 Table 5.9 Regression analysis of individual elements.

Dependent '% of variance accounted for by independent variables variable

Li Mg Ca Ba La Ti V Mn Fe Co Ni Cu Zn Cd Al Pb Total

Li 80 5 85 Mg 80 3 1 1 2 2 1 90 Ca 20 3 6 10 7 10 11 67 Ba 2 2 6 2 1 66 79 La 4 2 2 1 19 28 Ti 1 1 34 1 2 2 5 9 2 20 77 V 3 2 3 72 80 Mn 3 6 71 3 83 Fe 7 3 1 44 5 3 1 66 Co 71 7 7 85 Ni 8 3 65 76 Cu 3 2 82 87 Zn 1 8 1 1 2 77 1 91 Cd 2 2 82 6 92 Al 8 72 2 82 Pb 3 3 67 73

NOTE : With the exception of La , Ca and Fe none of the elements correlate with their residuals.. The metal-residual scatterplots for Ba, Ca and Pb are mostly suppressed by outliers, but those which are not show no linear trends. 1-9.

1-8-

1-7- >

r = —-32 1-5 —r- —i 1— —i— -2-0^ 2-1 23 T I 2-8 3-0

FIG. 5.36 Scattergrams of ICP data on fine fraction, Newquay study area.

a anomalous dispersion undifferentiated # d dispersion below locality b - 138 -

occurrence of Li is thus confirmed as mainly a substitute for

Mg in silicate lattices. By corollary, Mg resides solely in silicate lattices. Thirty percent of the Ca variance is unique to the metal and is probably due to data noise. The remaining 70% is spread evenly over several metals : Li (20%),

Zn (11%), V (10%), Ni (10%), Mn (7%), Ti (6%) and Ba (3%), to mention only the more important. Thus Ca may be associated with clays, feldspars and secondary Mn oxides, and also probably occurs as discrete or mixed carbonate precipitates in sediment. For Ba the variance is mostly explained by Al

(66%), Ti (6%) and 2% each by V, Mg and Ca, and 1% by Zn.

Thus the metal resides mainly in feldspars and clays. Some

20% of the total variance is unique to the metal and may well represent the error in the data.

Over 70% of La variance is unique to the metal and may truly reflect the inadequacy of the analytical system for this metal. Lanthanum is the only metal to show a perfect linear trend in a scatterplot of its residuals. The Ti variance is due largely to Ba (34%), Pb (20%), Cu (5%), Zn (9%), and 2% each by Mn, Fe and Al, an 1% by Li and V. The covariation with Ba, Li, Ca, V and Cu arises from the latter metals occuring in high levels with Ti below locality 4. Lead, Zn and Ba levels are simultaneously low in this locality. About

20% of the Ti variance seems to be due to random errors.

For V, Al is proved to be the main control accounting for

72% of total variance, with Cd (3%), Mg (3%) and Ba (2%) as the other noteworthy elements which are all partly or mainly N

K E

Level of significance, o^

.019 Z ^ /. .001

.0 5 - c< 6. .02

solid lines = positive correlation broken lines = negative correlation

37 Correllog ram for the partial correlations in the Newquay IGP data (table 5.8). - 139 - incorporated in silicate minerals.

Most of the Mn variance is accounted for by Co (71%), Ti

(6%), Zn (3%) and Ca (3%). For Fe a third of the total variance is unique and confirms the high analytical errors inherent in the data. It may partly indicate, however, that some of the metal occurs in an inert, crystalline state. Of the remainder 44% is accounted for by Ni, 7% by Mg, 5% by Zn,

3% each by Cd and Ti, and a little over 1% each by Mn and Al.

For Al, V (72%), Ba (8%) and Cd (2%) are the most important elements accounting for the variance. This indicates a lattice association in clays and feldspars.

Nickel variance was mostly accounted for by Co (65%), Li

(8%) and Mn (3%). For Co, Mn accounts for most of the variance (71%), with Ni (7%) and Cd (7%) as the next in importance. A probable explanation for this discrepancy is that while Mn controls Co dispersion, Ni only substitutes for

Co positions in the oxide structure, thus resulting in a closer relationship between Co and Ni than between Ni and Mn.

Seventy-seven percent of Zn variance is due to Cd, 8% to

Ti, 2% to Cu and 1% each to Ca, Mn, Co and Pb. Possibly the intimate substitution of Cd for Zn in sphalerite is more regular than the association of sphalerite with galena in the ore. For Cd itself Cu (82%), Zn (6%) an Co and Mg (2% each) are the important associates of the metal. Cadmium accounts for most of the Cu variance (82%), with only 3% by Ti and 2% by Zn. These reversed and reciprocal relationships probably represent the intimate mineral assemblages accessory to the - 140 -

main ore minerals. The Pb variance is as expected largely due

to Zn (67%). Titanium (3%) and Mg (3%) both enter the

regression equation on account of their mutually exclusive

occurrence of high values with low values of Pb. Some 25% of

the Pb variance is unique to the metal and may partly denote

some random factor associated with the clastic dispersion

agents controlling Pb distribution in the drainage.

It may be noted that all the partial correlations in

table 5.8 are accounted for. More important, the relative

contribution to the total variance by each metal is shown in

its proper perspective.

5.2.2.5 PRINCIPAL COMPONENT ANALYSIS

(1) AAS DATA

Fig 5.38 shows the clusters obtained in an R-mode analysis of

the AAS data, based on the seven elements analyzed. The

inter-cluster relationships were presented in chapter four

under analysis of variance, and only a brief description is

necessary here. Basically four sample types or Subpopulations

can be recognized as follows :

(i) Anomalous dispersion close to old mines; this may be

subdivided into three :

(a) those with very high contents of Fe-Mn oxides,

(b) those with moderate contents of Fe-Mn oxides, and

(c) those with low contents of Fe-Mn oxides. 4-4 7a~4T

2-8-

I K- E u I • A o l 4 -p / ^ V \ / % • x x a\ o 1-1- / • * \ • Anomalous samples from old mine sites ^ A V GuPb A I A Zn Dispersion further from old mine sites « \ • l A A 1 d) vr.it •v Do« Background samples enriched in Fe-Mn oxides t>0 with high contents of ore elements •H --8 # i* ' i •••• *i A I Background samples enriched in Fe-Mn oxides only 1. • u ^ V. • • / ^ II Ordinary background samples (low in most elements) ti Vw *cm v / -2-6- Mg Ca \ Fe !

-4-4 —i— -J— -4-4 -2-6 —i— -•8 1-0 2-8 4-6 st Eigenvector

FIG. 5*38 R-mode Principal component analysis of Newquay AAS data. Scatterplot of first two eigenvectors.

i - 141 -

(ii) samples containing anomalous or threshold levels of the ore elements but further removed from the mines; includes some falsely anomalous types grouped with these on account of their high content of Fe—Mn oxides*

(iii) Samples from background localities in which Fe-Mn oxides form profusely, and

(iv) Samples from background localities with little or no Fe-Mn oxide precipitation.

Thus a clear distinction can bei made between anomalous, threshold and background samples. The misclassification of part of subpopulation 3 with 2 stems from the enrichment of Cu in Fe oxides in locality 4.

The first principal component accounts for 53% of total variance and has positive loadings on all elements except Ca and Mg; the Ca loading is probably unimportant. This association represents mineralization and Fe-Mn oxide precipitation accompanied by a depletion of Ca and Mg. The latter metals may have been leached into lower soil horizons by acid waters near the mine sites. The second component has negative loadings on Mn, Fe, Ca and Mg. The localities with the highest scores on this component are Penhallow moor and several others where Fe-Mn oxide precipitation is low. It accounts for some 23% of total variance. The third component accounts for only 12% of the variance and has a high positive loading on Ca and a negative loading on Mg, with a small loading on Fe. This component highlights those localities where high Ca values were recorded but its significance is Table 5.10 Loadings on the first three principal components for the Newquay ICP data.

P.C. Li Mg Ca Ba La Ti V Mn Fe Co Ni Cu Zn Cd Al Pb Var. *

1 .5 .5 .3 -.2 -.5 .5 .1 -.8 -.6 -.8 -.5 -.8 -.9 -.9 .1 -.9 37

2 .7 .7 .2 .7 0 -.4 .9 .3 .6 .3 .6 -.3 -.1 -.2 .8 -.1 26

3 -.3 -.1 -.8 .5 .3 -.5 .3 -.4 -.3 -.5 -.5 -.2 .1 -.1 .4 .2 14

Total = 77%

* Variance of metal accounted for by principal component (P.C.). - 142 - obscure (table 5.10).

Fig 5.39 shows the scatter-plot of the first two

components after removing the anomalous samples (subpopulation

1). It is seen that the distinction between threshold and background samples is still sharp. The loadings on the first

component remains unchanged but now accounts for only 39% of

total variance. The second component has a positive loading

on Ca and high negative loadings on Fe and Mg. It thus

appears to be a fusion of the second and third components for

the whole data set, and accounts for 27% of the variance. The

third component accounting for 16% of total variance is

associated with the enrichment of Mn and Ca. The localities

with the highest scores on this component are those in which

Fe-Mn oxides are readily visible. This Mn-Ca association may

have been overshadowed by the strong correlations between Mn

and Fe with the ore-metals in the presence of the anomalous

samples•

(2) ICP DATA

The clusters given by the ICP data using sixteen elements were

identical to those obtained with only seven elements by AAS

(fig 5.40). The inversion of the figure is due to the changed

signs of the loadings but it does not affect the result. The

ICP clusters appear more compact and although the fusion of

part of subpopulation 3 with 2 still occurs, the former is not

clearly distinguishable as a detached subset. It could not be

decided whether this compactness was due to the increased

number of variables effecting a more precise subdivision, or

whether the higher inherent errors in the ICP data served to 5-5

K E Y 3-2- Ca A Continuation of dispersion from n old mine sites • i i Cu 1-3- i # / Background samples enriched in < • Pb Fe, Mn and Mg Ml nr. ^ & A A ^ •• . A AAA ii Background samples with low Zn contents of most elements Mn / inr.r-* A : • ' -1-1 r

-3-2 -

Fe Mg -5-5 —I— T —r~ —T~~ -5-5 3*2 -1-0 1-2 3-5 5*7 st Eigenvector

FIG. 5.39 R-mode FCA of the Newquay AAS (}ata less anomalous samples, Scatterplot of first two eigenvectors, 4-0 V Al Ba Li Mg 2-6" Ni Fe K E

• • • ^Mn ••i; • s A Anomalous dispersion from old ( Co mine sites 1-01 • H o A Dispersion further from old mine sites -P / a \ A A o Pb •J* Zn \ A (D Cd V A A^AA > • « • I-Background samples, some enriched - -8- / v a/ in Fe-Mn oxides Cu fit \ ' • • • | a) \ faO \ / A\ Ti II-Background samples low in most • , •H V V > elements except Ti (largely the \ \ • i dispersion below locality 4) -2-4- \

-4-0. -4-0 i i 1 2-4 --8 -9 2-5 4-2 st Eigenvector

FIG. R-mode PGA of the Newquay IGP data. Scatterplot of first two eigenvectors. - 143 -

suppress local variations in the clusters.

The first component accounts for 37% of total variance

and is associated with the absence of ore metals and secondary

oxides, and the presence of Li, Mg and Ti. Implicit in this

component is the fact that all the old mine sites are

associated with high Fe-Mn oxide activity. Group 4 samples

have high scores on this component. The second principal

component (26% of the variance) has high positive loadings on

Li, Mg, Ba, V, Fe, Ni and Al. It highlihgts those localities

with artificially raised levels of these metals originating

from cultural sources. The third component accounts for only

14% of the variance and has low positive loadings on Al and

Ba, with high negative loadings on Ca, Ni and Ti. Some of the

samples having high scores on the second component also have

high scores on this one. The highest scores, however, occur

on Penhallow moor and this component may well represent active

leaching of bedrock.

It is apparent that the first principal component still

combines mineralization and secondary proccesses. To isolate

these a varimax rotation of the eigen vectors was run and the

results are described next.

5.2.2.6 FACTOR ANALYSIS

Again a six-factor model was considered the most appropriate.

Table 5.11 contains the detailed structure of the model, while

fig 5.41 compares it with the 4- and 5-factor models. Table 5.11 Detailed structure of the 6 factor model for the Newquay ICP data.

Factor L i M g C a B a L a T i V M n F e C o N i C u Z n C d A 1 P b Var. *

1 .13 .02 .50 .14 .10 -.30 .10 .89 .69 .86 .88 .26 .34 .40 .05 .34 22.6

2 -.29 -.28 .08 -.93 -.08 .43 - 85 - .06 -.15 0 -.22 .17 0 .07 -.90 -.04 40.5

3 -.74 -.82 0 .09 .13 -.17 -.42 .14 -.42 0 - 13 16 .21 .28 -.27 .34 53.0

4 -.27 0 -.63 .16 .13 -.80 0 .20 .19 0 0 0 .39 .07 .13 .49 63.3

5 .20 .08 .15 -.10 -.95 .10 0 -.07 11 0 0 -.07 -.18 -.14 0 -.15 70.1

6 .38 .40 .48 .06 - 20 0 0 -.21 --30 -.39 .11 -.91 -.75 -.83 .13 -.55 90.6 comm. * .91 .93 .90 .93 .99 .95 .91 .90 .81 .90 .85 .95 .91 .96 .92 .80

* Var = cumulative variance; comm. = communality. FIG 5-41 Factor Score Loadings In Fine Fraction, Newquay Study Area.

4 - Factor Model 5 - Factor Model 6- Fa c to r Model

100-, •-loo

Cu-Cd-Zn-Pb-p Ca - Mg - Li L a 80. Ca-Li-Mg.-- Cu-Zn-Cd-Pb-La Pb. L8o o\° La

£ 6o. Co-Mn-Ni-Fe- < Mg-L I-A I - Pb Ti -Ca _L Pb- Zn 4- Ca-Cd-Zn L60 Mg-Li-V-Fe _

Ba -Al. V- 4ol 4-Ti Ba -A I -V Ti Ba-AI-V - - Ti L40 z> (-Co- N i-Mn-Fe - o 20 J 4- Mn-Ni-Co-Fe Ca-Mg-L I Cu-Zn-Cd-Pb ,20 Ga-Cd-Zn-Gu Ca. Cd - 144 -

Factor 1 has high positive loadings on Mn, Ni and Co, with moderate loadings on Ca, Zn, Cd and Pb. Thus it represents the secondary environmental processes. Fig 5.42A is the spatial plot for this factor which may be compared with similar plots of the individual elements highly loaded on this factor. It accounts for 23% of total variance.

Factor 2 (18% of total variance) has high negative loadings on Ba, V and Al, with a high positive loading on Ti.

It is an effective discriminant between samples from the main

Gannel channel, and the remaining suite of samples from the study area.

Factor 3 shows low levels and high variance for Li, Mg, V and Fe, associated with high levels and low variance for Zn,

Cd and Pb. The highest scores occur in some of the known mineralized localities (fig 5.42C). It accounts for 13% of total variance. Factor 4 reflects low levels of Ca and Ti coincident with high Pb and Zn and accounts for 10% of the variance. It is obviously associated with mineralization but it also stresses the absence of base metals at and below locality 4, where high Ca and Ti levels occur (fig 5.43A).

Factor 5 accounts for only 7% of total variance and is associated with the absence of La (fig 5.43C). Factor 6 is associated with , tow levels and high variance for Pb, Cd, Zn,

Cu, and to some extent Co and Fe, with high levels and low- variance for Ca, Mg and Li. Clearly this factor is associated with the absence of mineralization (fig 5.43B). It accounts for v • - 6

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FIG 5.42 Dispersion patterns of -Scares on the first three factors from the 6-factor model of the Newquay ICP data. "too.as aio.au azo.aa aso.oo a

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FIG 5,43 Dispersion patterns of "5coxeS on the last three factors from the 6-factor model for the Newquay ICP data. - 145 -

21% of total variance.

Thus it has been possible to isolate certain processes operating to control sediment composition in the area and to estimate their relative contribution. This was not possible with principal components due to the fusion of the eigen vectors. From the estimates of the percentage of total

variance accounted for it is evident that :

(1) Secondary environmental processes are the most important

single factor controlling sediment composition in this small

basin.

(2) Extensive mining activity in the past has quite likely

enhanced ore metal levels by several orders of magnitude, with

the result that contamination from old mine wastes is almost

equally important in determining sediment composition.

(3) Quite important and working against the first two factors

are dilution effects arising from marsh reclamation practices

and allied cultural activity. These tend to lower the content

of ore metals while increasing those of background elements

such as Li, Mg and V, with a slight enrichment in Fe.

(4) The virtual confinement of high Ti to the main trunk of

the Gannel acts as an effective discriminant between the

Gannel and the rest of the drainage system. There is

insufficient information on the significance of the high Ti.

The remaining factors are probably too noisy to be of any

importance. - 146 -

5.2.2.7 REGRESSION OF BACKGROUND FACTORS ON ORE METALS

To" confirm the interpretation given to the above factors,

those associated solely with phenomena operating within

background localities (factors 1-3) were regressed on the ore

metals as well as Mn, Fe, Co and Ni (table 5.12) :

(i) Factor 1 accounts for most of the variance for Mn (79%),

Co (74%) and Ni (77%), with only 47% for Fe and < 17% for any

of the ore metals.

(ii) Factor three is of some importance in the dispersion of

Fe (17%) and Cd (8%), but much less so for Zn (4%), Ni (2%)

and Mn (2%), and unimportant to the others.

(iii) Factor 2 is as important as factor 1 in Pb dispersion

(11%), but with the exception of Ni (5%) and Fe (2-3%) this

factor has little influence on ore-metal dispersion or Fe-Mn

oxide activity.

The net contribution of the three factors to the variance

of the ore metals (Pb, Zn, Cu and Cd) is thus small. Most of

the variance of these metals is directly associated with

dispersion from in situ mineralization or mine dumps. On

the other hand most of the variance (65-85%) in the data for

Mn, Fe and the possible pathfinders Co and Ni, are associated

mainly with processes in the secondary environment. For Fe

nearly 20% of the total variance has resulted indirectly from

marsh reclamation projects.

Factor 2 is shown to be unimportant as a direct influence

on the variance of all but Pb. This is hardly surprising Table 5.12 Regression of Newquay ICP data on background factors.

Dependent Percent of variance accounted for Variables correlated variables with residuals F1 * F2 F3 Total

Mn 79 0 2 81 Mn Co Cu, Zn, Cd

Fe 47 2 17 66 Fe, Mn. Co, * Ni, Cu, Zn,

Co 74 0 0 74 x * X X X X X X X X X X X

Ni 77 5 2 84 Fe, Co Cu, Zn, Cd, Pb

Cu 7 3 3 13 Fe, Co Cu, Zn, Cd, Pb

Zn 12 0 4 16 Mn, Fe Co Cu, Zn, Cd, Pb

Cd 16 0 8 24 Mn, Fe Co, Cu, Zn, Cd, Pb

Pb 12 11 0 23 Mn, Fe Co Cu, Zn, Cd, Pb

* See table 5-11 for a description of the factors. All residual plots with Ni are suppressed by outliers. - 147 -

since the highest Ti levels occur away from the localities

with base-metal mineralization. The relatively high

contribution to Pb variance has been explained above. The

high positive loading on Ti in this factor is rather

misleading because the metal also occurs in sediment depleted

in Ba, V and Al.

The regression residuals for the above elements are not

correlated with any of the background elements (Li, Mg, Ca,

Ba, V and Al), but strong linear trends occur with any of the

ore metals, and sometimes Mn, Fe, Co and probably Ni. For the

latter metals only those samples collected near the old mines

show the linear trends in the residual plots (figs 5.44 and

5.45). Thus the significance of the factors deduced above are

confirmed as substantially correct.

5.3 PATTERNS IN DATA NORMALIZED ON FE + MN

In an attempt to compensate for increased concentrations

resulting from Fe-Mn oxide sorption, data, especially partial

extraction data, are often normalized or ratioed on Fe and/or

Mn, the idea being to obtain concentrations per unit oxide.

While the trace metal-oxide relationship is not likely to be

that simple, the technique has been shown to reduce data

scatter (Nowlan, 1976) and to improve downstream detectability

of anomalous trends (Carpenter et al, 1978). The Newquay fine

fraction data, was similarly treated to ascertain any changes

in dispersion patterns, or similarities with the ratioed oxide -2-0-J

4*1 • • 3 • 2-J • • • • » • • 3-3 J 2- 3 H V • ^ #. • •!• /•sN*• r• . .••A• ini z> UJ 2 7 1-7

JJ^-n r 10 1 1— 1-1 - • 5 -«2 2 ~l Nr 1-1

FIG. Newquay fine fraction IGP data. Scatterplot of metal regression residuals, MR, on background factors (1-3) with the metals shown. *. . •

• a! .•'« • •

•••• • ,—«•— -•1 CD R

•••

• u* •

- .5

FIG. 5.^5 Newquay fine fraction IGP data. Scatterplot of metal regression residuals, MR, on "background factors (1-3) with the metals shown. - 148 - data. Such visual examination may provide a graphic estimate of the importance of Fe-Mn oxides in dispersion.

Normalizing fine fraction data in this manner may not be very satisfactory. Firstly the data represent a near-total extraction and therefore the total amount of metal present may not necessarily depend to any significant degree on the content of Fe-Mn oxides, unless the metals mostly reside in the oxide phases. Moreover the trends obtained may reflect more the relative amounts of Mn and Fe rather than any control by them. As an example the normalized (or ratioed) data for

Cu would seem to indicate that the metal is mainly controlled by Mn oxides below localities 8 and 9 where the trend of high

Cu values is subdued (fig 5.46A, compare with fig 5.29A). In localities 4 and 7, however, Fe appears to be the main control on Cu in sediment. But as is shown shortly these are merely those localities in which the relative amounts of Mn to Fe are high and low respectively. Table 5.8 also shows that there is no significant partial correlation between Cu and Fe or Mn.

When ratioed on the total contents of Fe and Mn, however, such dispcrepancies are largely removed, and with the exception of the enhanced trend in locality 7 the dispersion pattern is not very different from that of the unratioed data (fig 5.46C).

The enhancement in locality 7 arises merely from the fact that only minimal amounts of secondary oxides, predominantly Fe oxides, are stable in the acid environment.

The changes in the dispersion patterns for Fe and Mn (as compared with unratioed data) illustrate those for the elements. Although Fe exceeds Mn at most sites the relative 8 "oo o oi* ""

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FIG 5,46 Dispersion patterns for Newquay fine fraction data ratioed on Fe and/or Mn

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FIG 5.47 Dispersion patterns for Newquay fine fraction data ratioed on Fe and/or Mn Mn/Fe+Mn and Fe/Fe+Mn - 149 - amounts of the two oxides vary from site to site. The Mn/Fe and Mn/Fe+Mn patterns are quite similar to those of the unratioed data. For Mn/Fe+Mn the prominent trends of high values indicate those sites in which the relative amounts of

Mn to Fe is higher than average (e.g locality 2, fig 47A).

For Fe/Fe+Mn however the pattern is quite different. The high trends indicate those localities in which Mn oxides are hardly visible; such trends are amplified in localities where Mg and

Fe are artificially enriched, as explained previously. The Cu patterns (fig 5.46) may now be compared to those of the ratioed data for Fe and Mn and the above comments seen to be self-evident. The effects hold for the patterns of other metals as well :

(i) Mn is the principal "controlling" oxide phase below East

Wheal Rose (locality 8) although the highest Fe levels occur in this locality, and also below Shepherds (locality 9). Iron oxides are relatively more important in locality 4.

(ii) On Penhallow moor (locality 7) where both oxides occur in minimal amounts the ore metal patterns are enhanced.

Notice that the high Pb content at the head of the tributary in locality 1 is not suppressed in the ratioed data.

Unless this has resulted from human interaction with the environment as mentioned above (i.e from nearby trash or marsh reclaimed with contaminated mine waste) this locality merits further investigation. With Zn the ratioed data amplify the continuous trend below locality 6 to high-threshold levels and thus reveals more clearly the old mine near Higher Penscawn.

The continuous trend below locality 9, however, is diminished. '•O3 oJ o o

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FIG 5.48 Dispersion patterns for Newquay fine fraction data ratioed on Fe and/or Mn Pb/Mn+Fe, Zn/Mn and Zn/Fe+Mn *1

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FIG 5.49 Dispersion patterns for Newquay fine fraction data ratioed on Fe and/or Mn: Cd/Fe+Mnf Co/Fe+Mn and Ni/Fe+Mn. - 150 -

Ratioed on Fe only these changes are reversed with trends suppressed in locality 6 but enhanced in localities 3 and 9.

The Zn/Fe+Mn pattern is similar to the Zn/Fe pattern and is shown in fig 5.48C.

The Cd pattern (fig 5.49A) is largely unchanged and the trend below locality 9 is still uncertain, perhaps indicating that the Shepherds lode mostly contained galena while the

Penhallow and East Wheal Rose lodes had considerable quantities of sphalerite. The Co pattern is similarly unchanged much, but the still low values in locality 7 casts some doubt on this metal being a pathfinder for the Penhallow lodes (fig 5.49B). It is possible, though, that its high mobility has caused its depletion in the acid environment.

Even more doubtful is Ni as a pathfinder; its anomalous trend in locality 8 is completely lost and those in localities 3 and

9 are somewhat subdued (fig 5.49C). Moreover high values in background localities are increased. These changes suggest that Ni anomalies are more likely to be caused by enrichment in secondary oxides.

The high Ba trend in locality 1 (fig 5.32B) is reversed in the Ba/Fe+Mn data (fig 5.50A) and although levels are enhanced in locality 7 a new NE-SW trend of high values becomes more sharply defined. It runs about parallel to the southern ridge and its significance is obscure; a similar pattern was obtained for the V/Fe+Mn data (fig 5.50B). The Ca pattern (fig 5.50C) is very similar to the unratioed Ti pattern (fig 5.30A). o o O'- •

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FIG 5.50 Dispersion patterns for Newquay ^Ine fraction data ratioed on Fe piid/or Mn: Ba/Fe+Mn, V/Fe+Mn and Ca/Fe+Mn - 151 -

5.4 SUMMARY AND DISCUSSION

As pointed out by Hawkes and Webb (1962), an effective

interpretation of geochemical data involves the consideration

of multiple populations. To be able to identify such

subpopulations and assign meanings to them is the ultimate

objective in all statistical applications. A host of

univariate and multivariate techniques are available for such

purposes and graphical representation is the most useful way

of presenting the results.

Of the univariate methods simple histograms and

probability plots serve to identify multiple populations in

the data for any one variable and are usually the first to be

employed. But as discussed earlier there is no agreement

about the fundamental nature of statistical distributions of

trace elements in nature, although the lognormal distribution

has been shown to be a very useful descriptive device. In the

Newquay study area metals such as Ba, V and Al occuring in

bedrock minerals and not seriously affected by mineralization,

secondary processes or cultural activity, were found to be

approximately normally distributed single populations. This

is probably due to the uniform lithology. The data for all

other metals consisted of at least two subpopulations although

the degree of clarity varied. This was attributed to the

different contribution to the total metal population from

sources such as bedrock, mineralization, sorption by Fe-Mn

oxides and the effects of the reclamation of agricultural

land. In the Clitheroe study area the effects of varied - 152 - bedrock, lithology and a distinctive geographic distribution of both the lithology and the secondary environment has led to a similar data structure with virtually no metal existing as a single population.

Histograms and probability plots are also used directly to obtain threshold limits (e.g Sinclair, 1974; Chaffee,

1977). It was mentioned earlier that the 95th percentile of a normal or normalized population, includes all those values higher than the mean plus two standard deviations, and that it is a criterion often used to define the anomalous class in exploration. However since most exploration data consist of mixed background and anomalous sets, a more realistic approach to estimating threshold values, especially in terrain of varying lithology, would be to separate the subpopulations and fix the threshold at the lower end of the subset considered to be associated with mineralization (Govett et al, 1975;

Chapman, 1978). The importance of logtransformation is critical here. The interpretation of logtransformed data may be fairly simple (Marriot, 1974) : the mean of the log data is the log of the geometric mean concentration of the metal, and

the usual assumptions of additive variances and covariances of

the raw data become multiplicative. As already discussed

above, however, anomalous trends may be suppressed while

trends in lithologic units become enhanced. Moreover in some multivariate applications the meanings of some computed

results are difficult to interpret (e.g Malmqvist, 1978).

Whether such disadvantages are considered more serious than

the risk of obtaining statistical data without satisfying - 153 -

prerequisite conditions for such analysis is at the moment a matter of personal opinion. With most exploration data the histograms are positively skewed and by making inter-element relationships clearer, logtransformation is a positive advantage.

Similarities in the histograms and probability plots and examination of scatterplots and correlation coefficients suggested plausible, although imperfect associations in the data. These were more or less verified by spatial plots of

the individual metal concentrations and new or larger associations were suggested. A mere visual comparison of the

spatial plots, however, can be complicated and actually their

interpretation can only he tentative if the mineralogy and

petrology of their provenance rocks are unknown. A simple,

approximate way of resolving this may be the use of partial

correlations. Spurious results are often unavoidable with

total correlations because several processes operate on the material that finally finds its way into the stream channel.

Partial correlations remove some of the unimportant

associations but its interpretation can still be difficult,

especially if the number of variables is large. Correllograms

were used to portray the partial correlations in the data.

Due to the extensive coverage of secondary processes in the

Newquay area the correllogram failed to aid data

interpretation further. Rather scatterplots were found

effective in portraying the detailed nature of the

associations.

In view of the recognition that several genetic factors - 154 - give rise to sediment composition only limited information can be obtained with univariate statistics. Instead multivariate methods are employed to better understand the underlying structure of the measurements, which are selected to include, as much as possible, the products of all the genetic processes envisaged. Some of the more advanced statistical procedures used in this study were multiple regression, principal component and factor analyses.

Multiple regression is really a problem in univariate

statistics since it is only the distribution of the dependent variable (around a mean determined by the independent variables) that is of concern (Marriot, 1974). Multiple

regression of the entire Newquay data was used to evaluate the

important bilateral relationships which were difficult to

extract even from the partial correlation matrix. This was

considered ideal since the analysis is based on the same

partial correlations. The results confirmed most expected

associations, but the details were somewhat surprising and

possibly indicated intimate associations in specific mineral

phases. To obtain the overall relationships between the

different phases and hence the processes that give rise to

their being present in the sediments required the application

of principal component or factor analysis.

The formal theory, details of the matrix algebra and

critical reviews of the use of principal component and factor

analyses over a wide range of geological problems have been

covered by several authors (e.g Davis, 1973; Howarth, 1973a;

Klovan and Imbrie, 1971; Joreskos et al, 1976; Marriot, - 155 -

1974; Ehrenberg, 1975; Closs and Nichol, 1975; Burwash and

Culbert, 1975; Burwash, 1978; Mackin and Owen, 1978; Chapman,

1978; Temple, 1978; Trochimczyk and Chayes, 1978). The basic idea is to reduce dimensionality in the data matrix, to define new variables that retain and if possible, reveal the structure inherent in the data. In principal component analysis this is done by finding a linear combination of the original variables without any assumptions as to how many this can be. In factor analysis the complex mathematical formulation is such that any number of such combinations lower than the original number of variables is possible and therefore the number of factors must be specified for any run.

In other words the solutions are not unique.

The other important assumptions in factor analysis are that the relationships are linear and that the residuals are uncorrelated and normally distributed. The requirement of linearlity, basic to most other statistical applications, is very important in the geological context. In mineral exploration for example, the wide range of data and the presence of extreme values may adversely affect the analysis.

In practice such problems are often overcome by discarding outliers and transforming the data by appropriate procedures.

Methods for computing the components or factors vary

(Davis, 1973; Joreskos et al, 1976) as well as rules for rotating the factors. It is important that data are normalized or standardized and scaled by the square roots of their eigen vectors • This is to ensure that the component

scores are independent of measurement scale in the original - 156 - variables as well as uncorrelated. Standardization complicates the theory of the analysis but Marriot (1974) regards this unimportant since the theory is at any rate approximate when applied to real data. It means, however, that two surveys of the same terrain may not be strictly comparable since the standard deviations used for standardizing the data may be quite different.

Sometimes a consideration of two or more components may be more easily interpreted in terms of natural phenomena than the individual components themselves. Such vectors may be

"rotated" for this purpose, a procedure that is obligatory in factor analysis where the original vectors are often difficult to interpret. If the method of factoring is the same as that for deriving the components the new factors may not be very different from the unrotated components of the same order or rank. Principal component analysis can actually be considered as a special case of factor analysis but historically and conceptually they are usually considered apart• The differences, however, are difficult to define. In the programs used for the data in this work principal components were obtained by the maximax method (Davis, 1973). The factor

program was merely an extension of the component analysis in which the varimax rotation was used. Interpretation of the

results of the two procedures were therefore quite similar,

and are briefly summarized below :

(1) Factor analysis produced more specific new variables whose

interpretation confirmed the preliminary analysis based on

spatial plots of the original variables. In contrast the - 157 - results of principal component analysis were fused into the first two or three vectors.

(2) A spatial plot of the factors was necessary to gain the geographic and full geochemical significance of any implied process. This was because only poor scattergrams could be obtained with the factors. In contrast a simple scattergram of the first two principal components was usually sufficient to classify the samples and permit interpretation based on the clusters of samples and the associated metals. A geographic plot of component scores in the Clitheroe study area gave broadly similar information as the factor scores but trends in the latter were clearer.

(3) The additional information derived with these dimension-reducing procedures is the quantitative definition of geochemical processes in terms of those elements which best estimate them, and their relative importance in controlling sediment composition.

(4) Scattergrams of Q-mode principal components gave a more efficient classification of sample types than the R-mode analysis, but the latter gave the more important results concerning geochemical processes. Q-mode factor analysis was not used in this work.

Most of the criticism levelled against factor analysis concerns its complex theoretical basis, the subjective choice of factors and rotation procedures, and reification of the derived factors (Temple, 1978; Ehrenberg, 1975; Marriot,

1974). There appears to he little gain in complaining about the theory or the arbitrary choice of factor rank since this - 158 -

is a direct consequence of the former. Reification, however, is largely affected by personal prejudices (or choice). If it is admitted that geological processes are very complex and do not operate in isolation, then the arbitrary method of rotation may be considered desirable, and in a sense objective. On the other hand it is easy to see that this could lead to false or misleading factors, for the correlations used to derive them may have resulted purely by chance, due for example to large random errors in the data.

Perhaps the main criticism should be directed at the methods of obtaining the data in the first place, whether they are of the right quality and sufficiently represent the range of processes operating in the area. Indeed factor analysis, in

spite of all its heuristic shortcomings, is said to yield more meaningful results in geological applications than principal

component analysis which is free from such criticism (Temple,

1978). As a final word Marriot (1974) cautions that no mathematical formulation is, or could be, designed to give

physically meaningful results; any such meaningful result must

be attributed to chance, unless the data possesses a strongly

marked structure that shows up in the analysis.

The preceding criticisms, especially on factor analysis,

would be unjustified in the two study areas. In the Newquay

area all the processes, both natural and artificial, were

efficiently sorted into separate factors, in spite of the low

analytical quality of some of the data. In the Clitheroe area

factor analysis was successful in emphasizing the role of the

secondary environment even in this terrain with such

contrasting lithology. - 159 -

The interpretation of stream sediment data is often much more involved than would appear to be the case. To begin with, there is often a complex distribution of the elements

(especially trace elements) in the parent rocks. Then depending on the nature of weathering, the soil-forming processes and the pattern of groundwater movements, the elements may be dispersed in a variety of ways into the stream, all the time being susceptible to secondary processes in the environment which further modify the pattern of distribution. The resulting unknown distribution of trace metals in sediment is then estimated by sampling. Samples are

imperfect representations of the population sought. Although

statistics helps to establish relationships between samples

and the population, thereby permitting judgement on the

reliability of a specimen and how closely it resembles the

population, practically all such relationships assume a

condition of randomness in the selection of the samples. In

practice this is difficult to realize although there are

formal procedures for doing so.

A further handicap is that it may be impracticable or

costly, on a routine basis, to chemically extract and

accurately measure the amounts of all the elements in the

sample. Thus one is often faced with the situation where the

information is largely incomplete, upon which attempts must be

made to arrive at useful conclusions. The need for rigorous

statistical techniques to aid in such evaluation then becomes

obvious• - 160 -

CHAPTER SIX - HYDROGEOCHEMICAL TRENDS

During passage through bedrock dynamic equilibrium reactions

occur between carbon dioxide charged meteoric water and the

minerals in its flow path. As a result there is a net loss of

metals into the groundwater. The presence of carbon dioxide

lowers the pH and so directly aids metal solution. Metals

rendered soluble in soil interstitial water, usually under

euxinic conditions, are flushed out by the main mass of

groundwater. At points where groundwater rejoins the surface

circulation, Eh and pH conditions may be quite different from

those under which the solute constituents were dissolved. The

oxyhydroxides of Fe and Mn may precipitate out and adsorb some

of the metal ions in solution. The importance of secondary

processes such as these may be directly estimated by

measurements of Eh and pH. While the latter property is quite

easily monitored, numerous problems are encountered with the

measurement and interpretation of Eh (e.g Nowlan, 1969;

Horsnail, 1968). Morris and Stumm (1967) suggest that careful

analysis of the ionic species (that form a redox couple) in

water will give an excellent pointer to the redox state of the

system. Such an approach was not feasible within the scope of

this study. Rather it was decided to ascertain metal

concentration levels in relation to pH and the formation of

Fe-Mn oxides in the localities. Due to kinetic factors and

the dynamic nature of adsorption in the surface enviroment,

not all the soluble metal load will be precipitated. That - 161 -

this is so is attested to by the fact that most of the soluble

load of rivers ends up in the sea. The pH and total content

of dissolved heavy metals may therefore provide useful

information on the location and extent of hydromorphic

processes in drainage.

The water samples were collected at the same time as the

stream sediments. Samples were taken at the rate of one every

fourth to sixth sediment sample, in order to acheive some

uniform coverage at a low density. Samples for pH

measurements were collected in 100 ml polythene bottles with

air-tight caps, while those for analyses were stored in 1

litre bottles. In either case the bottles were well rinsed

with the stream water, and as much as was obvious to the

unaided eye, only clear samples were collected. The litre

bottles were acidified to keep all metals in solution

indefinitely; pH readings were taken within 12 hrs of

collection.

6.1 TRENDS IN NEWQUAY WATERS

Waters in the Newquay area are neutral to acid and near the

mine dumps pH can be very low (fig 6.1). The pH measurements

made in two separate sampling seasons were in very close

agreement. The pH is a good indication of the type of

secondary oxides observed. On Penhallow moor, no Mn oxides

and very little Fe oxides are found because of the very acid

waters which maintain Mn and Fe in solution. Further

downstream at East Wheal Rose where the pH is raised closer to 60

7-1 6*9 9 Sn

key 54 ©SP spring • HP 9 disused mines Dp Deerpark SH Shepherds PM penhallow moor EWR East Wheal Rose HP unamed mine near QC Higher Penscawn O >6-2 Z Sn tin lode

52 —r- l— 80 85 90 EASTING S

Fig 6 7 pH OF NEWQUAY WATERS - 162 - neutral, Fe oxides predominate in the extensive coating of secondary oxides which renders the stream bed a reddish-brown for at least 1 km before the typically black Mn oxides become noticeable. At Shepherds a similar situation obtains but the distances are reduced and the Mn oxides are deposited almost immediately with Fe oxides. In localities with higher pH Mn oxides are the more obvious either because they occur in excess as shown by analytical data on samples from parts of locality 1 (chapter 3), or more usually simply because they form the outer coating on the fragments. This pattern is a direct reflection of the relative stabilities or mobilities of

Fe oxides and Mn oxides.

The water samples have been grouped into four categories as follows :

(i) those collected at or very close to old mines,

(ii) those further downstream from old mines but still

distinguishable from background,

(iii) those from background localities where Fe-Mn oxides

are abundant, and

(iv) those from background areas without Fe-Mn oxides.

Fig 6.2 shows the concentration ranges for various metals in the categories just defined. The metal concentrations depend on pH, the availability of source material and secondary processes. Concentrations of Ca, Mg and to a lesser extent

Fe, are lowest in category 1 but Ca and Mg values are somewhat enriched in category 2 waters, possibly concentrated into •8.

.8

.7

pH

.6

.5

1" r

3° - 3

i 2." 2."

1 M

1 1 2 PH Mn Zn Pb CoxlO" NixlO'i CuxlO* Fe*10 CaxlO MgxlO Al

Fig 6*1 Concentration ranges of metals in Newquay waters (meantisd)

keyipbgrd: 3=bgrd with Fe-Mn ox ides; 1" dispersion from old mines; 1= at or closa to mfnes- - 163 -

colloidal complexes or suspensions. For the three metals there appears to be little difference in concentration in categories 3 and 4. In contrast Mn, Zn, Pb, Co and Ni are enriched in category 1 and 2 waters with values falling off regularly into categories 3 and 4 for Pb and Ni. Cobalt levels remain unchanged in categories 1 and 2 although higher than in categories 3 and 4. Manganese, Zn and Co show significant drops in levels in category 3 waters compared to those in category 4. This is a clear demonstration of the capacity of Fe-Mn oxides to co-precipitate with or scavenge trace metals in drainage. This is obviously a very important process in the study area and the surface water chemistry may be considerably affected by the presence of the oxides in the drainage. The Al values in category 2 are much lower than in the others in which the levels are invariant. For Cu there appears to be no significant trend in metal concentrations over the categories defined.

It is not clear why the concentrations of Mg and Ca are relatively lower in category 1 waters, especially since they are also low in the associated fine fraction. If they are being dissolved away in the fine fraction it ought to be reflected in the overlying water but this is apparently not the case. This inconsistency in the Mg and Ca trend could be resolved through a more subtle differentiation and analysis of particle size fractions than has been done here (e.g., Perhac,

1972), the proper conduction of which must await the development of adequate filters (Mill, 1976). - 164 -

6.2 TRENDS IN CLITHEROE WATERS

As expected of an area where limestones constitute the

dominant lithology, waters in the Clitheroe area are alkaline

(fig 6.3). The lowest pH values were found on the moor, where

they are closer to 7. Again the pH serves as a rough guide to

the type of visible oxide found. Coatings of Fe oxide are

visible only on or close to the moor while Mn oxides occur

also in isolated localities at much further distances from the

moor.

The distribution of pH and the virtual restriction of

visible oxides to the moor permits a general enviromental

distinction between the moor on one hand and the the remaining

localities on the other. In view of the importance attached

to secondary enviromental processes it may have been more

helpful, perhaps, to compare the two enviromental entities

than to explain the hydrogeochemistry in terms of the

underlying bedrock. Fortunately, however, the moor is

developed almost entirely in one lithologic unit• This

permits the grouping of the water samples on the basis of

geology, mineralization and the secondary enviroment as

follows :

(i) waters collected from background localities in BWS,

within which the moor enviroraent is located,

(ii) waters from background localities in WS,

(iii) those collected at or below the Skeleron mine (in

Chi), and 50]

Fig 6'3 PH OF CLITHEROE WATERS - 165 -

(iv) waters from background localities in Chi.

This is the general sequence of lithologic formations in the direction of stream flow. Trends in trace element contents in waters from this area are more difficult to deduce because effects due to one lithologic unit are likely to be superimposed on those further downstream. However two dominant, mutually exclusive trends are easily made out (fig

6.4) :

(a) Ca concentration is highest in category 4 waters and

lowest in category 1 waters.

(b) Most other metal concentrations generally decrease

from category 1 through to category 4 waters.

This arises because of the restricted hydromorphic mobility in alkaline media for most trace and heavy metals. As a result the hydrogeochemical trends in this area merely represent the effects of dilution on moor waters. The relatively high Cu and Pb contents in category 4 waters cannot be explained in this manner. They could represent colloidal suspensions of the metal hydroxides, or perhaps chelated with insoluble fine organic colloids (Hem, 1976, 1975; Loring, 1976; Goleva et al,

1970). But even this may be doubtful since carbonate reactions restrict organic complexation in carbonate-rich waters (Jackson and Skippen, 1978). M pK

•5- v i 4 w I c o .3. 1 '14 •c o -2 c o o

1 4-..4 <| H' 0 J Mn Zn Pb X10"1 Cu X10'1 Fax 10 Ca(ppm)x10 Mg(ppm) PH

Fig64 Concetration ranges of metals in water from the Clitheroe area(meantia-d) bgrd-CHL;bgrd-BWS(moor)=l.i=bgrd-WS;3=at & dow^tream of mine-in CHL- - 166 -

6.3 SUMMARY

(1) pH is an important parameter of the secondary enviroment.

In waters draining limestone localities in the Clitheroe area,

the pH and Ca levels are high while trace element contents are

low. Hydromorphic mobility is severely restricted because

most metal hydroxides and carbonates are insoluble in the

alkaline waters and precipitate out. By contrast neutral to

acid waters near Newquay contain relatively higher

concentrations of trace metals.

(2) Acid waters emerging from old mine dumps have high

concentrations of trace metals. Also neutral to slightly

alkaline waters emerging from peaty or moorland soils contain

high levels of trace metals. The mode of occurence of the

metals in solution, however, may be quite different. In both

enviroments Fe oxides form in the adjacent stream channel.

(3) The concentrations of Zn, Co, Pb, Ni, Cu and to a lesser

extent Fe and Mg in localities near Newquay, are reduced where

Fe-Mn oxides are abundant • The mechanism by which this is

effected is not well known. Hem (1975) has shown that most

trace elements carried in streams do not occur in true

solution. Perhac (1972) suggests that by far the greater

proportion of "soluble" metals occur in the colloidal

fraction. Thus it may be probable that it is mainly the

particulates and the colloidal species that are sorbed onto

the Mn oxides. « - 167 -

CHAPTER SEVEN - GEOCHEMICAL PATTERNS IN OXIDE COATINGS

The ability of Fe-Mn oxides to influence trace element levels

/ in soil and sediment is well known. In exploration

geochemistry attention has been drawn to the false anomalies

associated with environments in which these oxides form freely

(Jenne, 1968; Horsnail, 1968; Bolviken and Sinding-Larsen,

1973). On the other hand it has always been known that

secondary hydromorphic dispersion of heavy metals, especially

in tropical environments, is frequently associated with these

same oxides. Partial extraction techniques using the fine

fraction of samples are based on this fact and have been

successfully applied to several problems (e.g Ellis et al,

1967; Coffin, 1963; Taylor and McKenzie, 1966; Le Riche and

Weir, 1963; Chester and Hughes, 1967; Chao, 1972; Loring,

1976; Gatehouse et al, 1977). Similar studies on coarse

fractions have only recently received attention in the field

of mineral exploration (e.g Nowlan, 1976; Whitney, 1974;

Carpenter et al, 1975, 1978; Chao and Theobald, 1976; Rose,

1975b). However a routine or systematic procedure is yet to

be published. Nowlan (1969), for example, collected

conventional stream sediments systematically and separately,

from nodules of oxides which were sought and collected where

available. It is difficult to see how the two results could

be comparable, for while the stream sediment is in active

transport the nodules tend to form and remain in situ beneath

the abraded surface of the channel. Carpenter et al (1978) - 168 - suggest an elaborate surface area estimation technique for boulders coated with the oxides, but this is hardly suited to a routine procedure. This is equally true of samples comprising 100 g of pebbles, used by Whitney (1974).

The main difficulties with any such routine procedure involves two requirements :

(i) ensuring adequate or measurable quantities of the

usually thin coatings in each sample, at an even density

over the field area, and

(ii) estimating the amount of coating extracted.

The practical problems in the laboratory include :

(i) A method for obtaining or removing the coatings.

Coatings may be directly scraped from the fragments,

ground to homogenize and weighed. But this is tedious

and unproductive. Alternatively the coating may be

directly dissolved on the fragments. This has its own

problems, mainly concerned with minimizing attack on the

silicate core fragments and estimating the amount of

coating dissolved (chapter 3).

(ii) Obtaining representative subsamples for analysis.

This requires ensuring that fairly similar surface areas

are available for reaction in each case, so as to

maintain a uniform variance and reproducible or

comparable results. - 169 -

The main problem, however, lies with field sampling, where the practical interest is concerned with the manner of sample collection. Many possible sources of high variability need investigating, such as the depth of burial and spatial location with respect to points where groundwater surfaces.

In this project the interest was only to ascertain the possible use of the coatings as a sample medium for mineral exploration. The approach used was to retain the coarse fraction collected with the fine silts. Although the two fractions are not strictly in the same hydraulic size range results should be more comparable than say, between the fine fraction and nodules of oxide or oxide-coated boulders.

Moreover the sizes retained allow for easier handling and the procedure can be easily adapted for field use. The details are described in chapter three and the appendix.

This chapter contains the results obtained and compares them with their equivalents in fine fraction. Most of them are based on weight-loss estimates of the amount of coating

removed (non-normalized, data). For the Newquay area the patterns obtained from Weight-loss data are also compared with

those obtained by using the concentrations of the trace

elements taken into solution by the sample attack and normalized on the content of Fe and Mn in solution. This modification was designed to avoid the tedious weight-loss

determination. All metal concentrations, including normalized

data, were logtransformed.

Because of the high pH of the solutions the

hydroxyammonium method was unsuccessful in the Clitheroe area.

Even in the Newquay area the generally minute sample weights oo 3 o

7*780. •• ?W).aQ 7840.00 0)120.00 I'lOO.OQ iTsO.OO (780.00 • 340.00 0420.00 ftoo.oo EAST CLITHEROE OXIDE DflTfl LOG-MN n i

¥

6 o o o o

^oo.ao 7^0.ao TUO.OO TOM.OO otao-aartqo.ao oToo.i o Aoo.oo oSoo-oo oteo.oo ERST CLITHEROE OXIDE DflTfl LOG-FE

q |d- -0 . .-0-.Q

% *

3 1L- W.0 7840.00 ,'.00.00 ,Woo 0^.00 .Woo ,20., ER81 CLITHEROE OXIDE DflTfl LOG-Cfl

FIG 7*1 Dispersion patterns for Mn, Fe and Ca in the Clitheroe oxide data. STOO.OO 7*700.00 7MO.M 7*40.00 OBZO.oo 1100.00 I'loo.oo uao.oo «?HO.OO 04».00 ftoo.OO EAST CLITHEROE OXIDE DATA LOG-ZN

§j

• * * * o © o o o o o

"TWO.00 7*700.00 TbOO.OO 7^40.00 OittO.OO II00.00 II80.00 0*00.00 IVa.OO 84 20.00 ftoo.oo EAST CLITHEROE OXIDE DRTfl LOG-PB

'CP. •0*0o o

1L- +M.OO *40.00 ^.00 00.00 ,180.00 ,200.00 ^.0 EAST clitheroe oxide drtr ife • mn frrct10n) pb/zn

FIG 7.2 Dispersion patterns for Zn, Pb and Pb/Zn in the Clitheroe oxide data. - 170 -

obtained with this reagent made it rather inconvenient to use.

For purposes of comparison, however, 43 samples were selected

at a low, even density for testing with this reagent. Their

data are dealt with wherever appropriate and compared with the

main set of data using HCl-peroxide (chapter three). The "Mn

fraction" refers to extracts using hydroxyammonium chloride

alone and the "Fe + Mn fraction" to those using HCl-peroxide.

7.1 CLITHEROE STUDY AREA

7.1.1 SPATIAL DISTRIBUTION PATTERNS

A. NON-NORMALIZED DATA

For all ten elements (Mn, Zn, Pb, Fe, Ca, Mg, Cu, Co, Ni and

Al) the spatial distribution patterns are strikingly similar

to those in the fine fraction (figs 7.1-7.3). The notable

difference may be the rate at which most dispersion trains

fall to lower values. This is well exemplified by the Zn and

Pb patterns (fig 7.2) which show sharp cut-offs in the

anomalous trend below Skeleron Mine. Manganese and Fe show a

similar feature, with the high values confined to the moor.

The detailed distribution of the latter is somewhat altered,

with Fe now showing up as enriched in locality 7, while

locality 4 shows similar anomalous values for Mn as locality

7. The Ca trend depicts localities underlain by limestone

while Mg, Al, Cu, Co and Ni are all confined to the moor, as

obtains in fine fraction data. Notice that the high I) •• a °t> §; ©

L ERST CLITHEROE OXIDE DATA LOG-CO

o o o * •. o o q 9 8

"VToo.DO 7*80.00 7*80.00 7*40.00 BO70.00 a'lOO.OO I'lSO.OO S750. M 1*40. OO 8470.00 %00. ERST CLITHEROE OXIDE DATA LOG-MG

o °o o §

7*80.80 *80. M *40.00 0*20.00 0100.00 8180.80 (*80.M 0*40.00 1420.0 0*00.00 7 7 ERST CLITHEROE OXIDE DATA LOG-CU

FIG 7.3 Dispersio patterns for Co, Mg and Cu in the Clitheroe oxide data. oo II

3 i 8 e. 8 8 -7700.00 7*80.00 7BOO.OO 7*40.90 8070.90 easV0t9 -S O 1180 90 8*80.00 8**0.00 84 70.00 88C0 -O CLITHERQE OXIDE OflTA (MN • FE FRACTION) L0G-P9/FE-MN

°oo0c£093d.

-7*00. M 7*00.00 7880.00 7*40.00 8070.00 t'lOO-OO0180-00 1760.OO 1*40.00 8420.03 7*f j.OQ EAST CLITHEROE OXIOE OflTfl (HN • FE FRACTION) LOG-ZN/FE-MN

8

>iU t8 sj

^ Y" o: Qo

"+00-00 7*W0O 7880.80 7*40.00 8070-00 ^ *n».00 8180.00 8280.10 8*40.00 8420.00 (fcoa.w

CLITHEROE OXIOE OflTfl (MN • FE FRACTION ) ^Q-CO/FE^flN

FIG 7.4 Dispersion patterns for Pb/Fe+Mn, Zn/F*»rMn and Co/Fe+Mn in the Clitheroe oxide data. - 171 -

Mg trend is shifted to the moor proper, implying that apart from shales, co-precipitation with oxides on the moor is an important source of Mg in sediment.

Fig 7.2C shows the pattern of Pb/Zn values in the coatings which was expected to enhance the anomalous dispersion trains. The Pb/Zn ratio was found to be a consistent measure of anomaly contrast (chapter 3). While

Skeleron does show up, many other sites have threshold or even anomalous values of this ratio which cannot be explained.

B. NORMALIZED DATA

The metal/Mn+Fe patterns are very similar to those of the unratioed data only for Pb, Zn, Cu, Ca, Ni, Co and Fe (figs

7.4 and 7.5). For Fe the relatively high Mn contents in

localities 5 and 4 is reflected in subdued values. The

anomalous trend for Pb below Skeleron is extended while high

values on the moor are subdued (fig 7.4A); one threshold value

shows up in locality 2 where some mineralization is suspected.

The extension of the anomalous dispersion pattern is even more

striking for Zn (fig 7.4B).

In contrast the patterns for the normalized data for Mn,

Mg and Al are completely reversed with the highest values now

occuring mainly below locality 6 (fig 7.6). High levels occur

also at a few sites near locality 2 for Mn and Mg, and at

several other sites for Al. The significance of these

reversals are not clear, but are assumed to merely reflect the

lower contents of Fe and Mn in these localities, perhaps

coincident with high fractional contents of Mn. While the • oo o

o o o o

Trail-to 7780.00 7ABA.SO 7»«o.ao 9020.00iioo.ao aiao.oo BTSO.OO KMO.OO MTO.OO Roo.oo EAST CLITHEROE OXIDE DATA I UN • FE FRACTION) L0G-FE/FE*F1N

°0°* o o do oo^ .o •o , ' ®' o o"

"v700.w 7*780.00 7s0t0o 7340.00 9020.00 9100.00 9180.00 92so.oo 8940.00 9420-00 roo.oo EAST CLITHEROE OXIDE DATA (MN • FE FRACTION) LOG-CA/FE+-MN

o o

o o o o si ______Sooo.oo 7780.00 7880.00 7940.00 8020.30EAS 8100.0T 0 8180.00 8280.00 8940. M 8420.00 ftoo.

CLITHEROE OXIDE DATA (MN • FE FRACTION) LOG-CU/FE-»MN

FIG 7,5 Dispersion patterns for Fe/Fe+Mn, Ca/Fe+Mn and Cu/Fe+Mn in the Clitheroe oxide data. 2 o z 3 .o si

' •. o a q,

"rrao.oo 7*700.00 7'ooo.co 7*0.00 eboa.oo aioo.ao oiaa.oo aooo.oo ss«a.oo 9420.00 ft00.ao ERST

CLITHEROE OXIDE OflTA (MN • FE FRACTION) LOG-MN/FE+MN

o o o 00/ 1*4 ro 'o • o. z oo 2 a .O 9\ OO

B

+700.00 7*780.00 7080-90 7*0.00 9020.00 9100.00 1180.00 9280.00 9340.00 9420. X 9300.00 EAST

CLITHEROE OXIDE 0ATA ( MN «• FE FRACTION) LOG-MG/FE + MN

°°"o . o-

o o • o •

"T700.00 +700.00 7060.00 7*0.00 1020.00 1100-00 1100.00 0260-00 0*0.00 1420.00 &00.00 ERST

CLITHEROE OXIDE OflTA'(MN • FE FRACTION) LOG-AL/FE+ttN

FIG 7.6 Dispersion patterns for Mn/Fe+Mnf Mg/Fe+Mn and Al/Fe+Mn in the Clitheroe oxide data. - 172 -

hydromorphic mobilities of Mn and Fe are severely restricted

in these more alkaline localities, the same cannot be said of

Mg and Al, so leading to the high ratios.

The similarities of the patterns in the ratioed data to

those of the unratioed data and the fine fraction, confirm

that the dispersion of trace elements in this area is largely

controlled by Fe-Mn oxides in the sediment.

7.1.2 TOTAL CORRELATIONS

It follows from the similarities in the spatial patterns just

described, that similar associations are obtained from the

correlation coefficients as were found in the fine fraction

data (table 7.1). Inevitably all the correlations are highly

significant. Fig 7.7 is a simplification of table 7.1 (after

the fashion of Closs and Nichol, 1975) and three associations

may be readily recognized from it as follows :

(i) Ca - depicts the absence of Fe-Mn oxide precipitation in

localities underlain by limestone. The correlation

coefficients with all other elements are negative.

(ii) Zn-Pb-Fe-Co-Ni-Cu - this is thought to represent both

mineralization as well as secondary processes on the moor,

(iib) Mn-Zn-Cu-Co-Ni-Al-Fe - this is solely concerned with

secondary processes on the moor.

(iii) Mg-Mn-Al - probably this indicates some Fe-Mn oxide

precipitation in localities underlain by shale, but it is more

likely a reflection of the effects of the secondary

environment on the trace metals below locality 6, as described Table 7.1 Total correlation half-matrix for the Clitheroe study area (Fe + Mn fraction)

M n Z n P b F e C a Mg Cu Co Ni Al

Mn 1

Zn .80 1

Pb .59 .67 1 Fe .88 .68 .61 1 Ca -.69 -.54 -.43 -.69 1 Mg .63 .48 .50 .61 .37 1

Cu .84 .66 .63 .90 .67 .59 1 Co .83 .72 .39 .76 .55 .42 .71 1 Ni .83 .73 .62 .81 .63 .52 .73 .74 1 Al .82 .61 .70 .62 .87 .57 .81 .67 .77 1 FIG 7-7 Symbolic representation of the correlation matrix in table 7»1 (Clitheroe oxide data)

mn zn PB FE CA MG CU CO NI

ZN ® PB O FE O o - o CA -o MG o CU o -O CO - o (?) (?) NI (?) o o o (?) O o AL o o o (?)

NOTABLE ASSOCIATIONS: (i) Mn-Zn-Cu-Co-Ni-Al-Fe (ii) Zn-Pb-Fe-Co-Ni-Cu

KEY:- negative correlation £ r .80 (•) .71 / r / .80 Q .61 / r / .70

O .51^ r / .60 . .40 <. r ^ .50 - 173 -

above.

7.1.3 PRINCIPAL COMPONENT ANALYSIS

The environmental constraint caused by the high pH in most

localities give rise to fusion of the components. The first

principal component alone accounts for nearly 71 % of variance

(table 7.2) and has a high negative loading on Ca and high

positive loadings on all other metals. It merely expresses

the absence of Ca where other metals abound. This is a

faithful reflection of both the restriction of secondary

processes and the coarse fraction lithology - and therefore

the geology.

The second principal component accounts for only 8% of

the variance and represents mineralization in limestone

bedrock. However it is a poor descriptor of mineralization

since some anomalous samples are described as background and

vice versa. The negative loading on Co probably reflects the

relative paucity of hydromorphic mobility near Skeleron. The

third component accounts for 6% of total variance and probably

represents moor localities where Pb and/or Zn levels are lower

than average.

Fig 7.8 shows a scatterplot of the first and third

components which give the best classification of the samples.

Comparison with fig 5.38 shows that the two are generally

similar but that the oxide data is less efficient. Perhaps

this is to be expected as the coatings from the area are

probably similar in many respects, except for those Table 7.2 Loadings on the first three principal components for the Clitheroe study area (Fe + Mn fraction).

P.C.* M n Z n P b F e C a M g C u C o N i A 1 Var.

1 .95 .83 .72 .94 -.74 .67 .91 .82 .89 .89 71

2 -.09 .04 .49 -.06 .32 .51 0 -.35 -.08 .03 6

3 .06 -.37 -.41 .14 -.09 .46 .13 -.02 -.12 .04 5

Total = 82%

P.C. = principal component = eigenvector x eigenvalue * * .5 Var. = % of total contribution per eigenvalue. 3-6

qc o o 2-2- MG k o o 2 • o o • • • • • # oa • • • ui •8" A0A CU FE •t MN > °9Po da AL K E 0 ^ d" % • CO CA °8°°0 ° ao a ° NI ul a a o dB • - -7- A Dispersion below Skeleron Mine CO • ZN • PB A Dispersion from suspected mineralization u] • -2-2- • • Samples from the moor proper (mainly over BWS) 00 • Samples over WS

-3-6 i O Samples over GHL -•7 -3 6 2-2 e 2 3 3'8 st El 6 E N V E C T OR

FIG. 7.8 Scatter biplot of 1st and 3rd eigenvectors in the PGA of the Clitheroe oxide data. - 174 -

exceptionally enriched in the ore elements (especially Pb)

near Skeleron.

.4 FACTOR ANALYSIS

Again a six-factor model was used. Factor 1 accounts for 22%

of the total variance. It has high negative loadings on Co,

Zn, Mn, (low on Ni, Fe, Cu and Al), and a low positive loading

on Ca (table 7.3). The spatial plot (fig 7.9A) is identical

with the Ca pattern, except that the trend near Skeleron is

missing. It is also indistinguishable from the corresponding

factor in the fine fraction (fig 5.24A) and is thus a

reflection of the coarse fraction lithology.

Factor 2 has a very high negative loading on Mg, with low

loadings on Mn, Zn and Cu. The spatial plot makes little

sense but accounts for some 13% of total variance.

Factor 3 accounts for 15% of the variance, has high

negative loadings on Pb and Zn and low loadings on Cu, Ni, Al,

Mn and Fe, with a very low positive loading on Ca. The

spatial plot (fig 7.9C) shows a continuous trend of anomalous

scores in locality 2. In fact it shows this locality as one

of low Fe-Mn oxide activity impoverished in all elements

except Ca, although this is not evident in the Ca pattern for

both fractions. This factor may also be considered to

represent the absence of hydromorphic dispersion from

mineralization.

Factor 4 accounts for 14% of total variance and like Table 7.3 Loadings in the 6-factor model for the Clitheroe study area (Fe + Mn fraction).

Factor M n Z n P b F e C a M g C u C o N i A 1 Var

1 -.59 - 73 -.15 -.37 .24 -.17 35 -.81 _.45 -.31 22

2 -.34 -.19 -.20 -.28 . 11 -.91 -.27 -.12 -.20 -.24 13

3 -.24 -.53 -.88 -.24 . 16 21 -.30 -.03 -.30 -.32 15

4 -.35 -.21 -.14 -.33 .89 . 11 -.32 -.19 -.28 -.21 14

5 .49 .17 .29 .71 -30 26 .74 .44 .37 .69 24

6 .23 .16 .14 .26 -.15 .11 .04 .19 .65 .33 8

Total = 96% i 8 I 8 n i t si ns i t FACTOR 1

7*100.00 tfcoo.oq 7*40.00 0*20.00 0100.00 0100.90 0*00-00 (>40.00 0*4 20.00 ioo.oo ERST

• • ••'•o..

o* o >

• • . .o • o o o •b o o

FACT0R2

o 700.00 7*700.00 7060.00 7940.00 ERS(020.0T 0 o'loo.oo 0180.00 >200.00 0040.00 0420.00 &oq.oo

. o o " gH* x .O* " 8 o o' ii

FACT0R3 - ' c

4 wo.to 7*700.00 7*00.00 7*40.00 obTS.x 0100.00' 0100.00 0*00.00 0*40.00 0020.00 ioo.oo ERST

FIG 7.9 Dispersion patterns for the 5qm^le Scores oh trie first three factors from the 6-factor model for the Clitheroe oxide data. • oo . imn s o 5 o 2 °q> !

8 ii

FACT0R4 8 g _ _ "TWO.io rno.io 7*00.00 T*4O.OO oboo.ooiiooToo o'too.oo1200.00I540.00ITTOT— ftoo-oo ERST

f8 j

8

oz 8 s •^q o <0 o o.

FACT0R5 o 10 -7(00.00 7*00.00 7060.00 7*40.00 8020.0ERS0T t'loo.oo 'loo.oo 0200.00 t'j'o-oo 0420. ftod.00

•o.. a o o

q.o o FACT0R6

"7700.00 7*80.00 7860.00 7*40.00 0020.00 O'lOO.OO I'lSO.OO 1260.00 1*40.00 0420.00 ft 00-00 ERST

FIG 7.10 Dispersion patterns for the ScoreS on the last three factors from the 6-factor model for the Clitheroe oxide data. - 175 -

factor 3 probably reflects lithologic changes in shale bedrock

towards more calcareous compositions (fig 7.10). It has ^a

very high positive loading on Ca with low negative loadings on

Mn, Fe, Cu and Ni. The highest scores occur near Skeleron and

below locality 9. Factor 5, with very high positive loadings

on Fe, Cu, Al, Mn and Co and low loadings on Ni and Pb,

accounts for some 24% of total variance. It is a special

feature of the secondary environment which is best shown at

and below locality 9. It possibly coincides with the exposed

black shales here.

Factor 6 accounts for only 8% of the total variance and

is associated with the presence of high levels of Ni and low

levels of Al, Fe and Mn. It may represent secondary Ni

enrichment outside the moor, possibly associated with organic

processes•

7.1.5 SUMMARY

(1) The dispersion patterns for individual metals are nearly

identical to those of the fine fraction, but cut-off points

are more sharply defined. The ratioed data extend the

dispersion anomalies for ore metals near mineralization while

suppressing high background values.

(2) Most of the secondary processes are confirmed as

restricted to the moor environment proper.

(3) Principal component analysis of the oxide data was not

very successful, perhaps due to the generally similar nature

of the oxide compositions • The first eigen vector virtually - 176 -

accounted for all the true variance in the data. However the

scatter biplot gave a good classification of the samples on

the basis of geology.

(4) Factor analysis results were similar to those of the fine

fraction, except that a plot of the loadings did not directly

reveal the dispersion below Skeleron Mine. However the oxide

data indicated many more factors probably associated with

subtle changes in lithology, which, although difficult to

interpret, were absent or overshadowed in the fine fraction

data.

7.2 NEWQUAY STUDY AREA

7.2.1 SPATIAL DISTRIBUTION PATTERNS

A. NON-NORMALIZED DATA

The Mn fraction, although of low sample density, shows the

essential trends seen in fine fraction as well as in the Fe+Mn

fraction. The following descriptions are made with reference

to the Fe+Mn fraction but apply equally well to the Mn

fraction; some of the spatial plots for the latter fraction

are shown in figs 7.11-7.14.

The patterns for Mn, Pb, Mg, Fe, and to a lesser extent

Al,s were those that most closely resembled the fine fraction

equivalents (e.g fig 7.15). In contrast the high trends of

Zn, Co, Ni and Cu in locality 8 were missing in the coarse

fraction data (fig 7.16), suggesting that here these metals

probably occur mainly in the clastic material brought

downstream rather than by hydromorphic dispersion. Below

locality 10 however new high trends forZ.n, Cu and Co indicate o o

o o

8 **oo.oa no. no >20.00 ooo.oo I'.o.oo obo.oq o'eo.oo 0*0.00 uo.oo 8*0.00 3)o.«a EAST .KT

NEWQUAY OXIOE OATA (MN FRACTION) LOG-PB

o • o oo

o o

"Voo.oo 810.00 820.00 880.00 8*0.30 850.00 880.00 8*0-00 880.00890.005)0.00 east .10' NEWQUAY OXIOE OATA (MN FRACTION ) LOG-ZN

23 o mh\ o o o

8.

•w.01 t'10.08 820.08 8*0.88 v*0.08 8*0.00 880.00 8*0.00 8*0.08 8*0.8 0 3)0.08 EAST NEWQUAY OXIDE DATA (MN FRACTION) PB/ZN

FIG 7C11 Dispersion patterns in Mn fraction for Pbf Zn and Pb/Zn in the Newquay oxide data. o

o

"800.00 810-00 8JO.OO 830.00 840.00 830.00 880.00 870.00 880.00 890.00 700.08 EAST -1 0' NEWQUAY OXIDE DATA IMN FRACTION) LOG-CU

o

2s o mH

"800.00 810.00 820.00 830.00 840.00 850.00 860.50 8*70.00 880. CO 890.0 3 900.00 EAST •10^ NEWQUAY OXIDE DATA (MN FRACTION) L0G-CU/F£*MN

o • o "j E is x2j

"800.00 8'io.oa 820.88 830.00 140.00 880.0a 880.00 8*70.00 HToToi 830.00 800.00 east olo1 NEWQUAY OXIDE DATA (MN FRACTION) LOG-CO/FE+MN

FIG 7.12 Dispersion patterns in Mn fraction for Cu, Cu/Fe+Mn and Co/Fe+Mn in the Newquay oxide data. o 0 . . o o o

910.00 020.00 830.00 s«7OOR~l5ojoo I'eo.oo ?7oToo eao.o'o moToo too. 00

NEWQUAY OXIDE DATA I MN FRACTION) LOG-PB/FE^MN

o o o 28 o •il

"Voo.oo S'IO.OO 820. oo a 30.30 940.00 oso.oq 990.00 oYo.oo O'so.oo990.00 wo.EO EAST .10 NEWQUAY OXIDE DATA (MN FRACTION) LOG-ZN/FE+MN

8 4 8 4 s 5 3 "4 x o is o *4 8 o 4

*vm.m 810.09 8>0.88 930.00 940.00 sotoq 800.00 0>0.00 850.00 890.00 9».m EAST -10* NEHQUAY OXIDE DATA IMN FRACTION) LOG-MN

FIG 7.13 Dispersion patterns in Mn fraction for Pb/Fe+Mn, Zn/Fe+Mn and Mn in the Newquay oxide data. o « O o

o o

"Voa.oo e'10.00 120.00 >30.09 a'«o .00 930.00 9150.00 e>o.oo oso.oo 990.00 Sao.ro ERST .10' NEWQUAY OXIDE DATA (MN FRACTION) LOG-FE

o

o o 23 ©

"TOO.OO >10.00 >20.08 >30.00 >40.00 >30.00 860.00>>0.00 MO.00 >90-00 >00.00 ERST -10'

NEWQUAY OXIDE DATA (MN FRACTION) LOG-MN/FE+MN

o o

6

"too. 00 >10-00 920-00 >30.00 >40.00 >50.00 8*60.00 6>0.00 980.00 >90.00 900.00 ERST -10"

NEWUQY OXIDE DATA.(MN FRACTION) LOD-FE/FE+MN

FIG 7,14 Dispersion patterns in Mn fraction for Fe, Mn/Fe+Mn and Fe/Fe+Mn in the Newquay oxide data. - 177 -

the active precipitation of ore metals from underground water

which reveals the base metal mineralization further upstream

near Higher Penscawn (fig 7.16). Aluminium shows high levels

in locality 7 but this must be due to the reagent attacking

the coating-free fragments.

B. NORMALIZED DATA

The ratioed data are very much identical to the unratioed

ones. For Zn and Cu a new trend of threshold values in

locality 7 reveals its identity as an old mine site, but Co is

not so affected (fig 7.17). The prominent Pb anomaly is

better defined and decays progressively to the edge of the

study area (fig 7.18). The ratioed data obtained by

weight-loss are also indistinguishable from those obtained by

using the solution concentrations normalized on Fe and Mn

(figs 7.19 and 7.20); only Ni shows a slight change by having

anomalous values in locality 7 • Thus it is not really

neccessary to obtain the actual amounts of coating extracted.

7.2.2 TOTAL CORRELATIONS

Table 7.4 shows that in most cases the correlations are not

very high although they still are quite significant. This

contrasts with the Clitheroe area where the correlation

coefficients are uniformly high. A logical explanation for

this might be that Fe-Mn oxide activity in this area is more

widespread and therefore the overall correlation spans

different types of local environment. This contrasts with the

sharper environmental distinctions in the Clitheroe area,

where secondary processes are practically restricted to the Table 7.4 Total correlation matrix for the Newquay study area (Fe + Mn fraction)

M n Z n P b F e C a Mg Cu Co Ni Al

M n 1

Z n .43 1

P b .05 .30 1

F e .46 .11 .08 1

C a .43 .06 -.34 18 1

M g -.04 -.21 - 43 -.10 .37 1

C u -.10 -.49 .17 .06 -.10 01 1

C o .71 .30 .13 .45 • 30 -.02 04 1

N i .62 .27 -.18 .38 74 .07 -07 .57 1

A 1 -.18 - 23 -.15 -.09 .14 75 14 -.13 -.16 1 * • •. .. * * • • • •w * • . • 3 a« Q * •, • # « 8 • , o• • . ... . ' i , • * . . s * * • . •• •r \ w a •i * 9 > "o e Ea o Za Oo 0. / I • s V. •••

: A Mn i mm « kja' mm mm mm m.j a Ki SUM 9M 9 ^^ CM T »ir * • a • > . V 1 0 *• * 8 » * • ... . * . • * i «* • o &* \ O Ps • t • • # 3 B •» <>9• • • <5 X « • - b- •••o * 9 £x8i o • 8 <1) 3 o \ •M • a i Pb

i* Lm CMT

• o * * •«# • *o. 3 • "Cb . 8 b d . • 0. -0 i 0" r-

38 *• • b * • o sj oK8 - • z3 : ' o 8 • . 3 • m c S Mg 1 i liHU M Aiji 9am AIM imji Aim 9M tmm mm 9a m 9a. ami •ir FIG. 7.15 Dispersion patterns in Fe+Mn fraction. Newquay oxide data. Zn

mm afei^da^ Jkm 4mm Am Am

1 o oo

o

at 00* "q©, •il c o K 9 Q3D"'

Co

"wit"mp

a *i 8 *j \ •o b '. o

5j a • opO-O •sr:-.. :* * ...

Cu

' ^ ^ ^ FIG. 7.1o Dispersion patterns in Fe+Mn fraction, Newquay oxide data. i sj s i\ o o ? o p. o

28 o* . o •a cm •. © z i* mi\

Too. oo aio.ao 020.00 ex. 00 oio.oooso.oo soo.oo0*0.00 8*0.000*0.00 ooo.o" ERST »I0 NEWQUAY OXIDE DATA (MN • FE FRACTION) LOG-PB/FE*MN

' o. o <3

• . o o* & ' "o- %o

Too. 00 s'10.00 820.00 830.00 040.00 050.00 8SO. 00 8*0.00 OSO.OO 890.00 900.CO ERST .10' NEWQUAY OXIDE DATA (UN • FE FRACTION) LOG-MN/FE+MN

3 „ O . *

••'•••. <3 . • o- . b

o

Too.00 810.00 820.00030.00840.00OSO.OO 860.00 8*0.00 880.00 8*0.00 900.00 ERST »10* NEWQUAY OXIDE DATA (MN + FE FRACTION) LOG-FE/FE+fIN KLg 7-18 Dispersion patterns in Fe+Mn :;.. • fraction for Pb/Fe+Mh, Mn/Fe+Mn and Fe/.Fe+Mn in the Newquay oxide data. - 178 -

moor.

Some obvious associations are suggested if table 7.4 is

examined in the form shown in fig 7.21 :

(i) Mn-Ni-Co-(Fe-Ca-Zn) - this is evidently associated with

Fe-Mn oxides and actually corresponds to the first principal

component for the data (see below).

(ii) Mg-Al - probably expresses the absence of oxide coatings

since unusually high contents in these metals implies direct

attack on lithic fragments.

(iii) Pb-Zn - expresses mineralization.

The last two metal associations combine to give the

second principal component. Thus the essential geochemical

processes are shown by the correlation coefficients alone.

The results appear much simpler than those of the fine

fraction, although the same conclusions are reached.

7.2.3 PRINCIPAL COMPONENT ANALYSIS

A. Mn Fraction

The results are difficult to interpret. The first component

indicates high variance for all but Pb and accounts for 54% of

total variance (table 7.5). It would appear that this

expresses relatively uniform amounts of Pb extracted into this

fraction. However samples with very high scores on this

factor also contain high Pb. If this component is regarded as

an expression of ionic substitution in the oxide, then Pb is

suggested to be present in a form other than ionic, perhaps Table 7.5 Loadings on the first principal components for the Newquay study area (Mn fraction).

P.C. M n Z n P b F e C a M g C u C o N i A 1 Var.

1 .75 .78 -.06 .87 .83 .82 .59 .81 .82 .64 54

2 -.41 -.54 -.46 .19 .46 .53 -.22 -.40 -.24 .59 18

3 -.33 .03 .79 .36 -.21 -.01 .50 -.32 -.17 .38 14

Total = 86%

Table 7.6 Loadings on the first three principal components for the Newquay study area (Fe + Mn fraction).

P.C. M n Z n P b F e C a M g C u C o N i A 1 Var.

1 .86 .49 -.11 .57 .64 -.02 -.20 .79 .86 -.22 32

2 .09 .44 .64 .10 -.53 -.89 -.11 .01 -.18 -.74 23

3 .09 -.52 .23 .42 -.09 -.12 .89 .23 .03 .03 14

Total = 69% . . o- ov 28 "2 x *aj o °Q

"too. 04 t'lo.oo 4*0.00 i so. oo oto.oo ito.oo loo.oo oto.oo oto.oo oto.oo 3».oo EAST «10 NEWQUAY OXIDE OATH (MN • FE FRACTION) L0G-ZN/FE»f1N

sj o o *o e> . 8 P . . 0*>•.>* i\ o s p. • '-qpc&q^ 28 "s z gx • •. o '21 o &

*

8

m».00 o'to.oo 020.00 0*0.00 140.00 0*0.00 460.00 1*0.00too. 00tSwTJ o 3)0.00 EAST •10* NEWOUHY OXIDE DATA I UN • FE FRACTION) LOG-CU/FE*HN

oo o

• *• c "" o o° ,cq 28 o x•s i o is tw'' 'H O 8 x

51 8 110.00 010.0 3 0*0.00 0*0.00 040.00 1*0. oo too.00 0*0.00 oto.oo oto.oo O&O.tl east • 10* NEWQUAY OXIDE DATA (MN • FE FRACTION) L0G-CC/FE*f1N

FIG 7.17 Dispersion patterns in Fe+Mn fraction for Zn/Fe+Mn, Cu/Fe+Mn and Co/Fe+Mn in the Newquay oxide data. i.

3

\NO.OO I'IQ.QO eio.ao a'so.oo 140.00 ROTOO B'OO.OOeYo.ooMO.DO rao.oo MO.O* east »io' NEWQUAY OXIDE DATA (MN • FE FRACTION) LOG-PB/FE*MN

si

TOO. 00 910.00 920.00 930.00 940.00 950.00 960-00 9*70.00 880.00 890.00 MO.00 EAST .10'

NEWQUAY OXIDE DATA (MN • FE FRACTION) lOG-MN/FE+MN

• o ^ /•., <3

o

o

"500.00 910.00 820.00 930.00 940.00 8SO.OQ 860.00 870.00 880.00 890.00 900.00 EAST • 10'

NEWQUAY OXIDE DATA (MN • FE FRACTION) LOG-FE/FE+MN FUg 7-18 Dispersion patterns in Fe+Mn ;:.. , ;L fraction for Pb/Fe+Mh, Mn/Fe+Mn and Fe/.Fe+Mn in the Newquay oxide data. - 179 - held in discrete mineral grains embeded in a cement of the oxide.

The second component accounts for 18% of total variance and has negative loadings on Mn, Zn, Pb and Co, with positive

loadings on Ca, Mg and Al. It distinguishes coating-poor

samples from rich ones. The third component is associated with the presence of Pb and Cu with subordinate amounts of Fe,

Co and Al, and an absence of Mn. This was interpreted as mineralization coincident with Fe oxides. Since very little

Fe oxides are extracted into this fraction, this assertion

needs confirmation in the results for the Fe+Mn fraction. It

accounts for 14% of total variance.

On the whole the results were not very clear and this may

be due to the small size of the data. Not enough members of

each class are available to enable a clear trend to emerge.

B. FE+MN FRACTION

The results for this fraction are clearer (table 7.6). The

first component accounts for 32% of total variance and has

high positive loadings on Mn, Ni, Co, Ca, Fe and Zn.

Localities with high scores of this component are those in

which Fe-Mn oxide precipitation is pronounced. It thus

confirms that Ca, Co, Ni and Zn are enriched in these oxides.

Notice that Pb is not necessarily enriched in such localities.

The second component has high positive loadings on Pb and

Zn with high negative loadings on Mg, Al and Ca. This is

associated with mineralization and accounts for 23% of the

variance. Sites with high scores on this component occur in o o o

o qo o

Too. 00 IIO.OO 120.00 1*0.00 840.00 880.00 880.00 8*0.00 800.00 8*0.00 MO. OO ERST -to1

NEWQUAY OXIDE DATA (FE • MN FRACTION: UG.ML-1) L0GP8/FE*MN

o * ° o„ b* b q oQ .

*o.

Too.00 810.00 820.00 080.00 840.00 850.008*0.008*0.008*0.00890-00900.00 ERST "10 NEWQUAY OXIDE DATA (FE • MN FRACTION: U G. M L-1 ) LOG-MN/FE + MN

• o

o . 'c>

g^ ERST .101 Too.01 110.00 020.00 0*0.00 890.00 0*0.00 0*0.00 1*0.00 0*0.00 0*0.00 5m.OS rocr -in« NEWQUAY OXIDE DATA (FE • MN FRACTION: U 0. M L-l) LOG-FE/FE*MN

FIG 7.19 Dispersion patterns of the solution concentrations ratioed on Fe-»-Mnf for Pb, Mn and Fe in the Newquay oxide data (Fe+Mn fraction). O' Or "3

"800.0 a 110.00 020.00 too. 00 a'40.00 a so. 00 too. 00 a*7o.oo e'ao.oo a'90.00 iao.oo 1 NEWQUAY OXIDEers DATt A .i0(FE • MN FRACTION: U G- M L-I) LOG-ZN/FE»MN

- 'e>ow .... o »

oo^o "3.

o

-,00.00 t'10.00 020.00 830.00 840.00 BSO.OO880.0 0 8*70.8 0 880.0 0 890.0 0 9CO.OO EAST "10 NEWQUAY OXIDE DATA (FE • MN FRACTION: U G. M L-l) LOG-CU/FE-MN

o ao. b

°coq o d.

"tbotOo t'lO.OO820.00 830.00 840.00 080.00 880.00 0*70.00 mm.00 890.00 5*1.00 EAST "101 NEWQUAY OXIDE DATA (FE • MN FRACTION: U G. M L-l) LOG-CO/FE+MN

FIG 7.20 Dispersion patterns of the solution concentrations ratioed on Fe+Mn, for Zn, Cu and Co in the Newquay oxide data (Fe+Mn fraction). - 180 - localities with known past mining activity, and are sharply marked. Some false anomalies also occur but these are easily made out by the lack of any continuous trend. The loadings on

Mg, Al and Ca confirm the observation in fine fraction that these metals are indeed depleted in mineralized localities.

The third component has positive loadings on Cu and Fe and a negative loading on Zn, accounting for 14% of the variance.

This probably signifies localities with high proportions of amorphous Fe oxides. As shown in chapter three this fraction

contains the highest proportion of Cu.

It is interesting to note that component 3 of the Fe+Mn

fraction was fused with component 2 to form the third

component for the Mn fraction. Fig 7.22 shows the scatterplot

of the first and second components of the Fe+Mn fraction which

cluster the samples into the following subpopulations :

(i) Coating-rich samples.

(ii) Anomalous samples, some of which occur as coating-poor

samples as a result of intensive bleaching by acid waters.

Anomalies are sharply defined.

(iii) Samples in which Fe oxides greatly exceed Mn oxides,

with the former probably occuring mostly in the amorphous form

and hosting most of the Cu.

Comparison of fig 5.88 for the fine fraction with fig

7.22 shows that quite similar results are obtained with the

oxide data. The anomalous groups are now more compact, but a

finer distinction of the nature of the oxide phases results in

a further fussion of the background subpopulations enriched in

Fe-Mn oxides. FIG. 7.21 Symbolic representation of the correlations in table 7.4- (Newquay oxide data, Fe+Mn fraction)

m n Zn P b Fe C a M g C u C o

Zn

Pb Fe o Ca o o

Mg o Cu -o Co o o o N i o A I

NOTABLE ASSOCIATIONS: (i) Mn-Zn-Ni-CO-Fe-Ca (ii) Pb-Zn (iii) Mg-Al (iv) Ca-Mg

KEY:- negative correlation Q .70 < r'^.75 (J)-61 ^ L .70 O'51 - r' -

O .40 4 r'£.50 o.30^ r' / .39 <..30 blank 59 Cu

u-q-

r i K E ••V• • * 2 0 o ' - ji Pb Dispersion from old mine sites -p 1 Co •f' •- _•." a .A o I-Subpopulation relatively enriched / 0- of dispersion further from known Ca a> a Mg mineralization. A a> III A A/ t»o isip II-Background samples with relatively •h high oxide coatings. -2-0- III-Mainly coating-poor samples * Zn

-1*0-

-5-9. l t -5 -9 -3-5 -7-7 7 j 5-7 6-1 ,nd Eigenvector

FIG. 7.22 Scatterplot of 2nd and 3rd eigenvectors from FCA of the Newquay oxide data (Fe+Mn fraction).

j - 181 -

7.2.4 FACTOR ANALYSIS

The detailed structure of the six-factor model for the Fe+Mn

fraction is given in table 7.7. The six factors are almost

equally important as they account for about the same

proportion of total variance. Factor 1 shows a high variance

for Ca, Ni and to a lesser extent Mn and Co. It reflects the

high geographic variation associated with these metals in the

acid environments of Penhallow moor and East Wheal Rose.

Factor 2 with high negative loadings on Mg and Al may reflect

the extent of the attack on lithic fragments, as well as the

distribution of coating-free samples. Factor 3 is associated

with low to moderate levels of Zn and high variance for Zn and

to some extent Mn, with high levels and variance for Cu,

mostly in localities" 6 and 10. High Mn and Zn levels coincide

with low Cu levels, indicating control of Zn dispersion by Mn

oxides and the absence of such control on Cu.

Factor 4 with high negative loadings on Pb and Zn

obviously reflects base metal mineralization, a feature absent

in locality 4. Factor 5 is associated with very high Fe and

low Mn and denotes those localities in which Fe oxides are far

in excess of Mn oxide phases. It accounts for only 10% of

total variance. Factor 6 has high positive loadings on Mn,

Zn, Co and Ni,with a very low loading on Fe. It is taken to

imply a more effective control on Co and Ni by Mn oxide phases

in those localities with the highest scores in this factor.

Figs 7.23 and 7.24 show the spatial plots for these

factors, which, except for factors 1 and 3, compare favourably Table 7.7 Loadings in the 6-factor model for the Newquay study area (Fe + Mn fraction).

Factor M n Z n P b F e C a M g C u Co N i A 1 Var.

1 -.38 -.08 .16 -.14 -.92 .18 .08 -.16 -.83 .05 18

2 .06 .11 .18 .05 -.21 .89 -.06 .05 .10 -.95 18

3 .14 .71 -.05 -.02 .05 .01 -.95 -.03 .05 -.11 14

4 -.13 -.43 -.94 -.05 .16 .28 -.18 .13 .05 -.02 13

5 .21 -.07 .07 .94 .04 .07 -.01 .19 .15 0 10

6 .76 .42 -.05 .29 .12 .01 .08 .92 .42 -.10 19

Total = 92% 8 . o o o ° °o o o . . o* 'o 28 "si x is *§j

+00.00 8*10.00 120.00 030.00 140.08 030.00 000.00 »>0.00 080.00 090.00 5)0. nil ERST -10'

NEWQUAY OXIDE DATA (FE * MN FRACTION) FACTOR 1

*a . •••••• . .—.

o o • .

28 •a]

°Ooo o. o o. *o_

nboToo o'io-oo oio.oo 130.00 a'40.00 K0T00 BoToo iYo.oo uo.oo 090.00 mo. 00 ERST .1CT NEWQUAY OXIOE DATA (FE • MN FRACTION) FACTOR 2

9o . >"

. .0 28 o. 0 i •ooo ' 0 . p

+00.00 t'lO-OO 020.00 130-00 140.00 050.00 060.00 lYo.OI 600. oo SoToa 5m.00 ERST Bio1

NEWQUAY OXIDE OATA (FE • MN FRACTION) FACTOR 3

of scores FIG 7.23 Dispersion patterns ^for the first three factors from the 6-factor model for the Newquay oxide data (Fe+Mn fraction). • • o

o. 28

si *?j

"8ooTo« (To.at tio.oo 180.00 i'«o.oo sSo.oo iso.oo 080.oo MO.OO 180.00 Sio.oi EAST .lO1 NEWQUAY OXIDE DATA (FE • MN FRACTION) FACTOR 4

* • . . • o. • • • • 9 \ » \ o •o. o-. 0 28 ' a zn 58 •o. *sj o.

s. 8

"800.00 8*10.00 020.00 (80.00 840.00 (SO.OO 880.00 ('10.00 880.00 890.00 5)0.GC EAST .JO1 NEWQUAY OXIOE DATA (FE • MN FRACTION) FACTOR 5

0 o o o

8 .0

% o 0° ' 28 •al

o.

8.

8 SooTae tio.ot 5o7oi 180.00 080.00 Solos oho.00 180.00 180.00 080.00 5».oo east •lo1 NEWQUAY OXIOE OATA (FE • MN FRACTION) FACTOR 6

of Score?

FIG 7.24 Dispersion patternsAfor the last three factors from the 6-factor model for the Newquay oxide data (Fe+Mn fraction). - 182 -

with similar factors in the fine fraction. Factors 1 and 3 in

the oxide fraction probably represent more subtle indications

of the secondary environment that may have been overshadowed

by diluents of the fine fraction.

7.3 SILVER IN OXIDE COATINGS

Silver is a common trace element in practically all tellurides

and selenides, in some of which it occurs in considerable

quantities (Boyle, 1968). It is particularly abundant in

Au-tellurides in which it replaces Au. Silver also appears to

replace Pb, Bi and Cu in their tellurides and selenides. The

common sulphides (arsenopyrite>pyrite>pyrrhotite) often

contain considerable amounts of Ag. The ZnS polymorphs

sphalerite, wurtzite and schalenblende nearly always contain

Ag as a microconstituent, especially those rich in the metal.

However some relatively homogeneous ZnS polymorphs appear to

contain the Ag in lattice sites, either as a substituent for

Zn or merely occupying interstitial vacancies. Most galenas

appear to contain some Ag, but those from polymetallic

deposits tend to be richer in the metal, while those from

Mississippi-type deposits tend to be impoverished in it • Most

of this Ag occurs in admixed Ag minerals, either randomly

distributed or fixed along crystallographic planes. Thus Ag

is a potentially useful pathfinder whose exploitation as such

has been largely neglected, although the metal is frequently

an important by-product in mineral beneficiation. Indeed the

recent surge in precious metal prices has rendered such minor - 183 -

Ag contents in base metal deposits quite economic, thus facilitating its recognition as an ore metal in such occurences. Even so its role in mineral exploration may be largely regarded as that of a pathfinder, and this section reports a preliminary study of its possible role as such.

Figs 7.25 and 7.26 show the dispersion below Penhallow moor and East Wheal Rose, for normalized and non-normalized data in both fine fraction and the oxide coatings. The normalized log-data are similar to the non-normalized log-data. The following points are worth noting :

(i) The oxide fraction contains higher levels of Ag and

the anomaly decays more slowly, thereby extending the

dispersion.

(ii) Both fractions show the two old mines clearly, with

the fine fraction yielding a relatively sharper

definition.

(iii) There is high data scatter near the mine dumps.

The vertical bars (fig 7.25) indicate the standard

deviation for the number of replicates (2-4) where

available. The scatter is much higher in the oxide

pattern.

(iv) Most of the data scatter is removed when the data is

ratioed on Fe and Mn, and the pattern obtained is

strikingly similar to that given by the non-normalized

log-data. It then becomes clear that the oxide fraction

offers a great potential. . .25 'Silver dispersion in fine fraction and oxide coating below some old mines in the Newquay area. FIG 7

| Old mines; PM Penhallow Moor EWR East Wheal Rose

coarse fraction

fine fraction

i EWR FIG, 7.26 Refinement of the Ag dispersion below some old mines in the Newquay area.

| Old mines

1 -8 _ _ _ _ coarse fraction

fine fraction

- + 1-2 - +

e Q. a

CD < - 6 - CD o

^ stream flow

0 J | Penhallow | East Wheal Rose Moor - 184 -

The underlying concept in the use of Fe-Mn oxide coatings is that many trace elements, some of which may be directly

related to mineralization, are concentrated in these coatings by occlusion, co-precipitation or some other means. The unresolved problem here is which of the two, Fe or Mn, plays

the dominant role. One attempt at resolving this is to

selectively leach Mn or Fe and compare the concentrations of

the trace metals with those in the total extractions. The

assumption here is that the occlusion or co-precipitation is

specific, although this may be an oversimplification.

Dyck (1967) showed that fresh laboratory precipitates of

hydrous Fe oxides readily adsorb Ag(+1) ions. Most reports,

however, refer to Mn as the important controlling phase,

either in laboratory preparations or natural precipitates

(Boyle, 1968; Hewett et al, 1963; Anderson et al, 1973; Chao

and Anderson, 1974). The uncertainty is perhaps due to the

fact that trace elements form only a small weight percent of

the oxide phases, and since the latter may not be strictly

stoichiometric this masks the correlations.

The main advantage with using Fe-Mn coatings is that of

amplified concentrations, even in background samples.

Moreover the anomaly cut-off may be sharply defined, while the

dispersion itself can be extended by normalizing the data on

Fe and Mn. The main drawback may be the low precision.

Ultimately, however, it is necessary for the trace metal to be

taken into solution at its source before it can be co-

precipitated with, or adsorbed by Fe-Mn oxides. As described

above Ag is often associated with hypothermal Pb ores, most - 185 -

often as minute inclusions of the sulphide or selenide, or in association with several other pathfinders as in for example, the tetrahedrite-tennanite series of complex minerals

(Khetagurov, 1969; Boyle, 1968). On weathering a small fraction of Ag(+1) ions forms several stable complexes, the most important of which are the thiosulphate (in neutral-alkaline media) and the chloride (Boyle, 1968). The metal may therefore be rendered quite mobile, but not for long. On encountering euxinic conditions native Ag or the sulphide precipitates quantitatively. When spring or groundwater surfaces the higher pH and Eh causes Mn(+2) to undergo hydrolysis and precipitate out as the hydroxide or hydrous oxide. Such pseudo-colloidal complexes are negatively charged and adsorb Ag and other trace metals readily, are neutralized in the process and therefore precipitate out.

Subsequent influx of groundwater leads to adsorption of Ag from solution. Boyle (1968) describes this about

Ag-containing Mn oxides in wad from Keno Hill, Yukon :

(1) Some samples appear to have the Ag uniformly

distributed in the oxide; in others it appears to be

erratically distributed.

(2) Many of the Ag-bearing precipitates contain

considerable quantities of Si, Al, Ti, P, As and Sb.

(3) Samples with an even Ag distribution seemed to

contain discrete Ag-Mn oxide minerals which were

insoluble in acetic acid, potassium cyanide, dilute

nitric acid and sulphuric acid.

(4) Samples with a markedly erratic Ag distribution - 186 -

contain the metal in a variety of ways :

(a) as the native metal in the form of specs, wires, etc.

(b) in the specific manganous oxide complexes just

mentioned, and

(c) as a trace constituent of numerous admixed secondary

minerals such as anglesite, cerrusite, malachite, etc.

This variable mode in which trace elements are held may be one

factor contributing to the high variability in oxide data.

The limited scope of this study prevented any direct

comparisons with the findings by Boyle (1968).

7.4 SUMMARY

(1) A fast and efficient procedure has been demonstrated for

use on Fe-Mn coatings on the coarse fraction routinely

collected with active, fine silty sediment. Results from such

oxide fractions compare favourably to those obtained in the

fine fraction, but more information is potentially available

with oxide data. This include :

(a) extension and amplification of anomalous dispersion

trains for the more mobile ore elements; a sharper

definition of the dispersion cut-off is also possible.

(b) possibly, more subtle information on the nature of

secondary processes operating and their location in the

field. - 187

(2) It is not necessary to determine the amount of coating leached by weight-loss measurements as was done in the main part of this work. It is important, however, that approximately equal quantities of coating are leached to ensure uniform procedural error throughout the analysis. This may be acheived by weighing or scooping equal amounts of gravel of a uniform size range, so chosen as to ensure a large number of grains per sample.

(3) In terrain underlain by limestone the method may work with some difficulty. If feasible coating-free limestone fragments should be manually removed before or during weighing.

(4) Examination of the correlation coefficients alone may provide all the information that is needed, but if possible or available factor analysis will yield more information.

Principal component analysis, perhaps due to the generally similar nature of the samples and the uniformly enhanced metal concentrations, is inefficient in this respect and tends to fuse the components.

(5) As mentioned above the recent rise in precious metal prices has elevated Ag in base metals as an important ore metal. Being quite mobile in the secondary environment it may at the same time be regarded as a pathfinder, especially for

Au-Ag ores. Similar mobile, specific pathfinders may be usefully employed in mineral exploration using the oxide coatings. Enhanced concentrations, in some cases high resolution of the anomaly and extended dispersion trains are some of the advantages to be gained by this method. - 188 -

CHAPTER EIGHT - NATURE AND FORMATION OF FE-MN OXIDES

Freshwater Mn oxides have attracted greater attention only

relatively recently, and most of the earlier work appears to

have been conducted in Australia in connection with soil

fertility problems (e.g Taylor and Mckenzie, 1966; Adams et

al, 1969; Adams and Honeysett, 1964; Ng and Bloomfield, 1962).

However secondary oxides of Mn and Fe occur much more widely

and in several forms • They have been distinguished as fine

suspensions in streams either as the discrete oxides or as

coatings on other mineral particles, as coatings on soil

particles, thin films or encrustations on sediment, rock

exposures in streams and bank soil, and as concretions or

nodules in soil and bottom sediments of streams and lakes

(Carroll, 1958; Gibbs, 1973; Hem, 1976; Jenne, 1968; Horsnail,

1968; Mckyes et al, 1974; Schoettle and Friedman, 1971;

Carpenter et al, 1975; Rose, 1975b; Harris and Troup, 1969).

This wide occurrence in most surficial geological materials

emphasizes the need for a good understanding of their role in

trace element dispersion. In this chapter an attempt-has been

made to examine some of the published field and laboratory

studies in this field, with respect to the mechanisms

considered to be the more important in the genesis of Fe-Mn

oxides in the secondary environment. The emphasis, however,

is on Mn oxides. A few results from this study are also

presented where appropriate. - 189 -

Jenne (1968) compiled a number of research problems

concerned with the control of heavy metals by hydrous Fe and

Mn oxides. They serve as a useful basis for discussing the

empirical observations listed below, and may be grouped under

three main problem areas as follows :

(i) Environmental controls on the formation and

stability of Fe-Mn oxides,

(ii) The • mechanisms of oxide formation and the

sorption-desorption phenomena affecting the associated

heavy metals, and

(iii) The nature (physical, chemical and mineralogical),

mode of occurence and relative importance of Fe oxides as

against Mn oxides in the control of heavy metals.

A consideration of the structural features of the oxides was

regarded necessary to a proper understanding of the literature

account given later, and the nature of the oxides is presented

first.

8.1 STRUCTURE

8.1.1 PRIMARY CRYSTALLOGRAPHIC CONSIDERATIONS

The most common oxide of Mn in surface environments is

probably MnO^ (Burns, 1976). The soft, black, generally

difficult to characterize, mixtures of Mn oxides are called

wads, while the hard, botyroidal equivalents are usually

referred to as psilomelane(s). Both terms, like limonite

(mixtures of hydrous Fe oxides) are generalized names for - 190 - these oxides which are characteristically amorphous to X-rays.

Wads and psilomelanes may age to the more crystalline variety called pyrolusite, to which many artificial preparations approximate in composition. Even for these, X-ray studies show

Mn02 as a complex system and this may explain why different structures are obtained when the oxide is prepared from solutions containing ions of various kinds (Wells, 1962).

Wells (1962) lists MnO, Mn^, Mn^, HnO^ and Mn^ as some of the naturally occuring oxides of Mn. The first three are said to be very similar structurally to similar compounds of Fe. Also there are different structural modifications of

MnO(OH), or manganite, analogous to the Fe and Al compounds, the main difference being that the coordination group around

Mn is a very distorted octahedron. The structural frameworks for these minerals are of two basic types, the three-dimensional arrangement and the layer structure.

The three-dimensional structures are built from single or multiple octahedral chains joined up along their lengths by

sharing corners. If some of the Mn(+4) ions in the framework

are replaced by Mn(+2) or other cations of lower charge the

framework is negatively charged and can accomodate more

cations in the interstices. Fig 8.1A corresponds to a group

of isostructural minerals typified by alpha-MnC^. These

compounds are nonstoichiometric and their open structures also

suggest that large cations are necessary to prevent collapse

of the framework. Accordingly alpha-Mn02 can only be prepared

in the presence of large cations such as K(+l). This would

seem to suggest that this group of compounds will form in

nature only where a supply of such large cations are FIG 8.1 ILLUSTRATION OF THE OPEN STRUCTURE OF MN - OXIDES (after Wells, 1960)

/ Q \ \

A alpha-MnOg and Hollandite, Cryptomelane, Coronadlte. B Psilomelane. - 191 - available. The structure for psilomelanes (fig 8.IB) is even more open and comprises double and triple strings of

octahedral chains with larger tunnels accomodating Ba(+2) and water molecules.

Many ill-defined hydrous Mn oxides resemble clay minerals

and may likewise possess layer structures. For example a well

crystallized lithiophorite has a two-layer structure, both of

the brucite (Mg(OH)z) type with ideal compositions MnO^ and

((Li,A1)(OH)^)• The layers are held together by hydrogen

bonds as in A1(0H)^, though the formula suggests that a net

zero charge would be imparted to a unit formula if all the

anions in the MnO^ layer were oxygens and all were hydroxyls

in the Li-Al layer (Wells, 1962). It is noted that

lithiophorite does not possess vacancies in the Mn oxide

octahedral sheets as is the characteristic with other sheet

structures such as chalcophanite or birnessite, although the

mineral still adsorbs Co and other trace elements (Burns,

1976). This is thought to proceed through an initial

adsorption into the Li-Al layer before these are bonded to the

Mn oxide sheets.

The crystal structures of natural birnessite are unknown

but from electron diffraction studies on synthetic varieties

they are deduced to be similar to those of chalcophanite.

This scheme requires that sheets of water molecules and

hydroxyl groups are located between layers of edge-shared Mn

oxide octahedra; one out of every six octahedral sites is

unoccupied (compared, with one in seven for chalcophanite) and

Mn(+2) and Mn(+3) are considered to lie above and below such

vacancies. The structures of birnessite and chalcophanite may - 192 - also be considered in terms of a hexagonal close packed array of oxygen in which the Mn(+4) ions and vacancies are regularly distributed among the octahedral sites. A similar design may also apply to the hydrated ferric oxyhydroxide polymer

FeOOH.xH^O, but in this case the distribution of Fe(+3) is said to be random. Thus Mn02 and FeOOH polymorphs may easily form intergrowths that would account for the intimate association of Mn and Fe oxides in deep sea Mn nodules reported by some workers (Burns, 1976).

Although most naturally occuring oxides of Mn and Fe are amorphous to X-rays, aged concretions in soil and sediment sometimes yield recognizable lines typical of the more crystalline varieties. Table 8.1 contains d-spacings of the stronger basal reflections for some Mn and Fe oxides, together with some values reported for ill-defined samples and results from this study. It is very difficult to identify the minerals present in the oxide coatings examined in this study, mainly because the characteristic oxide lines overlap with those of more crystalline minerals such as quartz, chlorites and feldspars, while the weaker lines are altogether absent•

The minerals tentatively identified in the study areas are :

(i) Mn oxides : birnessite, lithiophorite.

(ii) Fe oxides : goethite and probably haematite.

It is not known what effect, if any, was induced upon the oxides as a result of bulk drying the samples at 105 deg.

Celsius. According to Francombe and Rooksby (1959), the disordered structure of delta-Fe20^ is still retained after heating at 100 deg. Celsius, and that changes are only Table 8.1 Comparison of some X-ray diffraction data on test samples with published data.

Mangoan Nsutite Nsutite -MnO Haematite Goethite 87A 12A 37 587A (a) (a) (a) (b) (b)

4.10 3.96 4.47 2.69 4.18 4.06 4.00 4.46 4.47 2.66 2.43 4.07 1.51 3.66 2.43 2 . 70 4.16 4.06 2.45 2.34 2.68 1.69 2.69 2.13 2.60 3.88 2.57 2.36 2.13 2.45 1.69 1.38 2.50 2.79 2.46 2.16 1.64 2.36 2.46 2.70 2.13 2.13 2.16 2.39 2.56 1.98 1.67 1.67 2.19 2.46 1.82 139 1.57 2.38 1.67 1.45 2.00 1.54 1.99 1.37 1.67 1.45 1.38

Source : a (Bricker, 1965); b - (Kodama et al 1977). Samples 12A, 37, 87A from Newquay area, 587A from Clitheore area. - 193 -

noticeable at 150 deg. Celsius. The mineralogical types

tentatively identified above are therefore regarded as

essentially those existing in the natural habitat.

8.1.2 MICROSCOPE AND ELECTRON MICR0PR0BE STUDIES

Among the several published works on the morphology and

inter-element relationships in naturally occuring Fe-Mn oxides

may be mentioned those of Cronan and Tooms (1968), Dunham and

Glasby (1973) and McKenzie (1975). Cronan and Tooms (1968)

examined the morphology of some deep sea nodules from the NW

Indian Ocean and observed that :

(i) the nodules consist of segregations of Mn-Fe oxides in a

matrix almost free of Mn but enriched in Fe and silicates •

(ii) Ca and K tend to follow Mn, although the Ca distribution

is slightly irregular.

(iii) both Ni and Cu follow Mn and are concentrated in the

segregations, especially Ni • However both metals are present

in proportionally greater concentrations than Mn in the

interstitial phase. By contrast Co and Ti had a more even

distribution although some enrichment in a central segregation

was apparent in each case •

(iv) in a nodule containing detectable amounts of Zn, the

metal was found to be irregularly distributed, being

concentrated into patches in the interstitial areas but not

associated with any other metals. This was suggested as

signifying the existence of discrete Zn minerals.

Dunham and Glasby (1973) worked with samples representing - 194 - a wide range of surficial enviroments. Deep sea nodules from the Carlsberg ridge in the Indian Ocean showed typical characteristics of oceanic nodules with well developed concentric structure, a low Mn/Fe ratio and high contents of

Cu, Ni, Co and Pb; delta-MnO^ was the principal mineral phase detected. Shallow water nodules from the Jarvis Bay inlet on the Pacific coast of Canada, and from Loch Fyne, western

Scotland, showed imperfect concentric structure and had much higher Mn/Fe ratios. Todorokite was the principal mineral phase in these nodules. Such differences were attributed to differences in nucleation and growth rate resulting from the different concentrations of Mn in the waters from which the nodules formed.

McKenzie (1975) investigated inter-element relationships in soils, soil nodules and ocean floor nodules by electron microprobe. He found that Mn oxides in soil nodules and synthetic Mn oxides had greater affinity for Co than for Cu and Ni. Cobalt was related to Mn in all samples, and a considerable fraction of total Co in the soils used was associated with the Mn oxides. Nickel was related to Mn in all the nodules and some of the soils. In the soils, although the Ni contents were highest in the Mn oxides a large proportion of the total Ni was actually associated with the Fe oxides. Copper, and particularly Zn, accumulated in the Fe oxides in the soils, but in the nodules these elements were associated with either Mn or Fe, and in some cases with with neither. The Co result seems to conflict those of Cronan and A

Tooms (1968), Burns and Fuerstenau (1966), and Stevenson and

Stevenson (1970). Thus it appears that control of trace metal - 195 - dispersion by Fe-Mn oxides is more consistent in the thin films and coatings on soil and stream sediment fragments, than in the accompanying nodules. In soil and freshwater nodules recrystallization with time may segregate, or even remobilize the metals and so complicate the geochemical trends. In this respect oxide coatings present a good prospect as an aid in exploration geochemistry.

The thin, highly friable nature of oxide coatings on-the coarse fraction poses problems that prevent similar detailed studies of this kind in this study. Coatings on fragments from one background sample in the Cornwall study area were scraped, mounted in araldite and subjected to a random spot microprobe analysis (c.f. point analysis of Cronan and Tooms,

1969). Six spots were analyzed for Mg, Al, Si, P, CI, K, Ti,

Na, Fe, Cu and Zn. Cobalt and Ni were not included because of their very low contents in this sample. Table 8.2 contains the percent content of metal per total oxide present at each

spot, together with the detection limits for the metals. The

table shows that in all cases the total percentage is much

less than hundred and must therefore imply the presence of many other unknown phases not included in the analysis. A

similar problem was encountered by Cronan and Tooms (1969).

Most of the Cu values were below the detection limit, which is

in contrast to the regular occurence of relatively high Ca and

P values. The relative amounts of Fe and Mn vary a great

deal, but its significance cannot be deduced directly due to

the random orientation of the coating fragments in this

particular case.

Fig 8.2 shows the covariation between selected trace Table 8.2 Random spot analysis on oxide coatings on sample 122, Newquay area. % metal content per total oxide per spot.

Metal SN DL SN DL SN DL SN DL SN DL SN DL

Mg .37 .042 .21 .036 .26 .046 • 30 048 .23 .047 .33 .047 Al .91 .037 .23 .030 .80 .040 1.22 043 1.37 .043 .86 .041 Si 2.56 .037 2.49 .033 .81 .035 .83 .036 1 94 .043 .57 .035 P 4.54 .056 3.18 .044 1.35 .047 1.13 .048 1.49 .049 3.74 .060 S .32 .038 • 32 .034 00.00 .034 .05 .039 00.00 .035 .12 .036 Cl .13 .034 .06 .031 .21 .038 .16 .039 .47 .041 .18 .038 K .07 .034 00.00 .031 .14 .036 .18 039 .16 .037 .07 .037 Ca 3.02 .060 2.64 .051 1.71 .056 2.00 . 061 1.60 .056 2.36 .062 Ti .13 .042 00.00 .039 .22 .046 .16 .048 .18 .044 .17 .046 Mn 17.09 .155 1.38 .064 20.90 . 192 29.15 .224 18.00 .179 19.92 .187 Fe 25.31 .201 69.26 .270 11.99 .165 9.62 .153 13.09 .170 13 54 . 172 Cu 00.00 .105 00.00 .102 .18 .108 00.00 .113 .20 . 106 00.00 113 Zn .24 .124 00.00 .118 .21 .127 .34 .137 .29 .128 .19 .132 Others 00.00 00.00 00.00 00 00 00.00 00.00

Total 54.67 79-76 38.78 45.12 39.01 42.05

SN = Spot number (random); DL = detection limit. FIG 8.2 Random spot analysis of oxide coatings from the Newquay study area. Covariation of trace metals with Fe and Mn.

60 \ \ \ \ \ © 40 \ u. \ \ \ \ i *Zn ,Ca 20 \ \ O \ I • ^ \A A \ \ 304 c ,M0 A / / / o\o Cu / 20-4* Ao • • • •b A o o / / / / 10 / / / 04 / / §—^ •2 -3 7j 0 1 LOG (Metal (%) + 1) ACu o Zn % Metal 4% Metal"8 1*2 - 196 -

elements with Fe and Mn. Zinc and Al vary directly with Mn

but inversely with Fe, while Mg, Ca and P have no regular

relationships with Fe or Mn. Fig. 8.3 confirms the linear

trends in Fig 8.2, but also shows a strong linear relationship

between Ca and P. There appears to be no regular linear

trends between Mg and Al, Al and Ca, or Ca and Mg. These

results indicate a segregation of Fe and Mn oxides in the

coating, perhaps imparted by an incipient layered structure

similar to that found in nodules. Zinc is probably sorbed to

occupy interstitial vacancies in the Mn oxide but the

association with Al may indicate an intimate mixture of NnO^

and AlCOH)^, the presence of lithiophorite (Burns, 1976), or

simply induced by a covariation of both Mn and Al with a third

species, perhaps the hydoxyl anion. The Ca-P association

suggests the presence of discrete phosphate minerals in the

coating, whose distribution is independent of those of the

oxides.

The above results are strikingly similar to those of

Harris and Troup (1969) on lacustrine nodules from New York.

These workers showed that a chemical banding of Fe and Mn rich

phases existed in the nodules; the nature of the scatterplots

between Fe and Mn and some trace elements were quite similar

to those obtained from this study.

8.2 ENVIRONMENTS AND MECHANISMS OF OXIDE FORMATION

The processes covering the whole sequence of events from the

release of metals into solution through to their adsorption or FIG. 8.3 Random spot analysis of oxide coating from the Newquay study area. Covariation of trace metals.

A L

10 MN % 3 0 1 CA 2 AL '8 V2 - 197 - coprecipitation by Mn or Fe oxides are not entirely understood. Suggested mechanisms are based on field observations and/or laboratory experimentation. The conceptual models for reactions in natural water systems derived from this empirical approach are necessarily simplifications, based on concentrations of cationic species greater than millimolar, and on the assumptions of ideality and equilibrium between the species present• For example

Stumm and Morgan (1970) discuss the solid-solute system for

Fe-Mn oxides on the basis of the ternary system

Metal(n+)-H20-C02, and Barnes and Back (1967), using field measurements of redox potential and pH, concluded that equilibrium had been established between cations in percolating groundwater and ferric oxide minerals in soil.

However natural concentrations of cationic species are often much lower than millimolar, and several species may be present. In particular the influence of organic species may not be quantifiable; usually this is regarded as essentially catalytic and ignored thereafter. Thus the simple equilibrium ternary model is not entirely satisfactory. Morris and Stumm

(1967) earlier advocate dynamic rather than equilibrium models. Tyree (1967) considers that kinetic factors may largely prevent equilibrium being established. Thus PbCO^ will precipitate out if CC>2 is present in a dilute solution of

Pb(+2) ions, although the latter is thermodynamically the more stable. He therefore postulates an approach to equilibrium conditions in natural water at very large half-times, and recommends a qualitative rather than quantitative use of equilibrium models. This is because equilibrium constants in - 198 -

natural water are not known accurately (Tyree, 1967), while

potential measurements of the redox state of natural water

systems may be largely invalid (Morris and Stumm, 1967). For

most practical purposes, however, it seems adequate to regard

natural reactions as proceeding toward some dynamic

equilibrium, under conditions that may be sufficiently

described by stating the Eh and pH of the waters. The

following field and laboratory observations serve to

illustrate this point, and also lay the basis for the

mechanisms of oxide formation described shortly after.

8.2.1 FIELD STUDIES

A. NATURE, CONDITIONS AND ENVIRONMENTS OF FORMATION

(i) The pH of natural water is determined mainly by the

bedrocks (whether carbonate or silicate rocks), but organic

activity and redox proccesses can also have a controlling

effect, at least locally (Bostrom, 1967).

(ii) Areas with low pH and Eh are characterized by active

surface leaching and low contents of Fe-Mn oxides in soil, but

with high contents of these in the adjoining stream sediments

(Horsnail et al, 1969).

(iii) Freely drained oxidizing enviroments do not form

precipitates of Fe-Mn oxides to the same extent as those with

impeded drainage (Horsnail, 1968).

(iv) All the Lake George nodules examined by Schoettle and

Friedman (1971) occured in or within the uppermost 5 cm of a

diagenetically solidified, tough, greasy, varved clay. Where - 199 - nodules were slightly covered with sediment their size increased upward towards the sediment-water interface. The nodules formed around nuclei which usually consist of clay or other available lithic fragments. Harris and Troup (1969) reported similar observations on some Canadian lacustrine nodules, and considered that the concentric structure may imply a cyclic process, possibly a reflection of a fluctuating

Fe:Mn ratio in the overlying water which give rise to alternate bands of Fe-rich and Mn-rich oxides.

(v) A free oxygen supply is necessary for oxide formation, and an adequate mixing process may be important (Jenne, 1968).

(vi) Hem (1976) found that most of the Pb in streams was adsorbed on suspended sediment, either as sorbed ions, incorporated with surface coatings of oxide on discrete suspensoids such as clay particles, in colloidal suspensions of clays and oxyhydroxides of Fe and Mn, or as discrete particles of the carbonate, oxide or hydroxide.

(vii) Perhac (1972) reported that <1% of the total "dissolved" metals in NE Tennessee streams occured in the colloidal fraction and over 95% in true solution, yet the metal concentrations in the colloidal fraction far exceeded those in true solution, being often ten times higher.

(viii) Among the Fe oxides present in soils and sediments haematite and goethite are the most common. Haematite rarely occurs in temperate climates but mixtures of both forms are often found in warmer climates, although conflicting thermodynamic evidence for a difference in stability fields requires that only one form occurs at a time (Fischer and

Schwertmann, 1974). - 200 -

(ix) Manganese oxide precipitates form very rapidly in stream channels. Service (1943), and recently Carpenter and Hayes

(1978) have obtained significant coatings of Mn oxides in a short period, on materials delibrately placed in stream channels for the purpose.

(x) The relative importance of Mn oxide versus Fe oxide and/or organic-held metals is yet to be resolved, but this may well depend on the application. For example a correlation analysis by McLaren and Crawford (1973) indicates that compared with the organic-held Cu, oxide-held Cu has little, if any

influence, on Cu availability in soil moisture and solutions.

It is to be expected, however, that reducing conditions will

release oxide-held Cu, and the authors show that most of this

is held in the Mn fraction.

Although some conflicting results are reported most

studies indicate that Mn oxide plays the more important role

despite its being present in subordinate amount (Childs, 1975;

Jenne, 1968; Chao and Theobald, 1976; Anderson et al, 1973;

Chao and Anderson, 1974; Taylor and McKenzie, 1966, Suarez and

Langmuir, 1976). The greater scavenging capacity of Mn oxides

has been attributed to the large number of non-stoichiometric

structures it can form, and the attendant wide range of zero

point of charge (ZPC) related to their surface and

intersurface properties (Stumm and Morgan, 1970). Chao and

Theobald (1976) and Burns (1976) considered it to be the

increased stability confered on Mn oxides by high crystal

field stabilization energies, especially with the low-spin

Co(3+) ion, a probability which is not feasible for Cu(+2),

Ni(+2) or Zn(+2). The high adsorptive capacity of Mn oxides - 201 - for trace metals may also be infered from the effectively large surface area of the amorphous compounds, the numerous

"broken bonds" in the structure, and the tendency to form colloidal aggregates which flocculate with or bind oppositely charged particles (Burns, 1976; Grassely and Heteny, 1971;

Taylor and McKenzie, 1966; Jenne, 1968).

B. ROLE OF ORGANIC MATTER

(xi) In natural lakes organisms contribute to Mn solution by using up all available oxygen and so developing an oxygen-free environment necessary for maintaining Mn(+2) in solution, even at the typically neutral pH found in most lakes (Ingols and

Enginun, 1968).

(xii) Decomposition of organic matter by microorganisms provides much carbon dioxide in the soil zone (or in organic

sediments) which dissolves and contributes to lowering the pH,

thus increasing metal solution and mobility (White et al,

1963).

(xiii) In oxidizing sulphide deposits, oxidation may be

accelerated by bacteria which oxidize ferrous sulphate to the

ferric salt, a major oxidant of the sulphides. Some of these

bacteria can survive even under very acid conditions, deriving

their energy for growth solely by oxidizing inorganic

compounds and using the energy released to synthesize organic

matter from carbon dioxide and water (Silverman, 1972).

(xiv) Humic acids may directly sorb many inorganic species

(Szalay and Szilagyi, 1967).

(xv) Organic acids provide a ready source of solvents in soil - 202 -

and the anions of some of these are powerful chelates which

further aid metal solution (SchalScha et al, 1967).

(xvi) Some relatively insoluble soil-organic acids dissolve

and complex Pb to an appreciable extent and thereby carry it

in suspension when flushed into the stream. In this state it

may be extracted by some organisms or deposited on sediment

when stream flow is sluggish (Hem, 1976).

8.2.2 LABORATORY STUDIES

(i) If oxygen is passed through a strongly alkaline suspension

of Fe(0H)2, alpha-FeOOH (goethite with hexagonal close packed

structure) is produced. Gamma-FeOOH, or lepidocrocite, is

obtained only in the presence of a strongly oxidizing agent

such as hydrogen peroxide or ammonium persulphate (Bernal et

al, 1958).

(ii) Goethite is formed by dissolution and reprecipitation of

Fe(+3) hydroxide (Feitnecht and Michaelis, 1962) or from

lepidocrocite (Schwertmann and Taylor, 1972). Haematite,

however, is obtained from the aggregated amorphous hydroxide

by internal dehydration (Fischer and Schwertmann, 1974).

(iii) Hem (1963) reported that he could increase the rate at

which Mn was oxidized and precipitated from solution by

increasing the pH, or decrease it by introducing bicarbonate

or sulphate. He also reported that about 40% of Mn was

coprecipitated with Fe at pH 8.0. Handa (1970) could not

reproduce such coprecipitation at pH 7 and attributed this to

the stabiliztion of Mn complexes present in natural waters. - 203 -

In the absence of phosphate the most important of such complexes identified W^re bicarbonate, sulphate, chloride and silicic acid in that order (Handa, 1970).

(iv) Collins and Buol (1970a) rapidly precipitated both Fe and

Mn oxides along Eh-pH gradients in quartz-sand columns and showed that precipitation without catalysis was sluggish.

Hem's experiments (1963b, 1964) were also conducted on a quartzo-feldspathic sand column, where it was shown that the presence of quartz and feldspar accelerates oxide formation.

These results stress the importance of kinetics because oxides may be precipitated even, where the Eh-pH conditions indicate otherwise. A review by Crerar and Barnes (1974) includes

Ferric oxyhydroxides, calcite, silica, freshly precipitated

MnO^ and aged forms, among the surfaces catalyzing Mn oxide precipitation. This was thought to be due to :

(a) Catalyzing surfaces dramatically lowering the

activation energy of the oxidation reaction, by providing

favourable adsorption and reaction sites; in addition the

free energy of adsorption contributes to the overall free

energy of oxidation.

(b) The catalysis effectively raises the local Eh to

levels where the abundant oxygen of marine waters can

precipitate highly oxidized phases.

(v) Many hydrous oxides exhibit exchange properties akin to those of clays (Grim, 1968) which arise from a pH-dependent surface charge. They are cation exchangers at high pH and anion exchangers at low pH (e.g Curtin and Smillie, 1979). At - 204 - neutral pH they have a small capacity for both cation and anion exchange. The rate of coagulation and sedimentation of such colloidal particles is rapidly increased when the surface charge is zero (Parks, 1967). The pH at which this occurs is called the zero point of charge. This property is variable and reflects the composition and defect structure of the oxide, and the identity and concentration of the species in solution. According to Hem (1976) such cation exchange adsorption processes may largely remove Pb (and other cations) from true solution onto the fine particulates carried by a stream, even when the surface is not particularly selective to the cation in question. This is attributed to the large volumes of water that may come into contact with the oxides, and so effectively increasing their cation-exchange capacity.

(vi) Of all the charged species in solution the hydronium ion,

H(+l), is the most easily sorbed (Grassely and Heteny, 1971).

Thus the greater its concentration (the lower the pH) the less ions of any type may be adsorbed. The pH dependence of adsorption, and even the stability of the solid phases present, should be seen in this light.

(vii) The sorption of Ag by Mn oxides appeared to relate more to the amount of occluded large cations (e.g, Na(+1), K(+l)) rather than merely to the surface area of the oxides (Anderson et al, 1973). Thus the important processes involved in sorption were considered to be surface exchange for Mn, K and

Na, as well (subsequently) as exchange for these metals in structural positions.

(viii) Humic acids in cool, temperate regions could be powerful solvents in soil. Baker (1973) obtained humic acids - 205 -

from Tasmanian podzols and showed that even coarse mineral

fragments were easily extracted by these soil organic acids.

Chalcocite, galena, sphalerite, chalcopyrite, haematite,

pyrrhotite, bismuthinite, and many Ni-minerals were reported

as among the most vulnerable, and that pure metals are very

strongly attacked. It was also observed that many insoluble

metal humates were rendered soluble in the presence of the

humic acids•

8.3 DISCUSSION

The formation of secondary Fe-Mn oxides may be considered in

terms of "sources and "sinks", where the sources are the

origins, solubilization and transport paths of Fe, Mn and the

trace metals and the secondary Fe-Mn oxides are the end-result

or sinks. Most common rock-forming minerals contain Fe which

is easily released in the form of various secondary oxides on

weathering. Also sulphides, particularly pyrites, are common

in most sedimentary rocks, especially marine shales deposited

under reducing conditions (Stanton, 1972). A source of Fe is

thus readily available in the secondary environment. Likewise

Mn is common in most minerals and is even more readily

released into solution upon weathering. Micas, pyroxenes and

amphiboles are among the most important minerals with a ready

supply of trace metals to the secondary environment. As

pointed out in chapter one, the driving force behind

weathering is the formation of new mineral phases that are

thermodynamically more stable in the surficial environments,

mostly as oxides (Goldschmidt, 1954). Normally most of the - 206 - end-products of weathering remain as part of the soil which are then physically eroded to form part of the stream sediment. The special significance of waterlogged soils (e.g

Horsnail, 1968) is that acid, reducing conditions are created which renders many metal ions soluble and thus subject to transportation through the groundwater system rather than as part of eroded soil. On encountering higher Eh and pH conditions the Fe and Mn cations are oxidized and precipitate out spontaneously as the hydrous oxides.

The importance of reduction processes is echoed throughout the literature. With deep-sea nodules Chukhrov et al (1976) infer that the rate of deposition of Fe-Mn nodules on the sea-floor is governed mainly by the intensity of reduction processes within the sediment, which in turn depends on the nature and amount of organic matter contained in the

oozes (with Mn - oxidizing bacteria generally thriving better

in the colder environment of the ocean floors), while a high

rate of oxygen supply to the bottom oozes favours Fe and Mn

deposition. The difference in the stream sediment environment

is that the "reworking11 occurs principally in the adjoining

soils and rocks, and not in the sediment itself. However

local temporal variations in Eh and pH do occur in stream

sediments and affect sediment composition accordingly.

The high Eh necessary for the deposition of Fe-Mn oxides

implies aerobic conditions in the surface environment, while

the apparent need for an adequate mixing mechanism or

turbulence (e.g Jenne, 1968, Nowlan, 1969) probably ensures

that the oxygen is regularly replenished, as well as serving

to vigorously mix the oxygen with the colloidal metal - 207 - complexes in solution or suspension. Thus in the study areas weirs, steep channels or rocky stretches of streams tended to form more extensive deposits of Fe-Mn oxides than stretches in the same vicinity where stream flow was more quiescent •

Nowlan (1969) dismisses turbulence as merely incidental as it would not explain the formation of oxide oozes on cliff faces, and postulates an interface of oxidizing and reducing conditions as the necessary and sufficient requirement for oxide formation and deposition. Horsnail (1968) showed that oxide deposition occured well before the groundwater entered the stream and that Fe-oxides generally tended to form first •

Thus the important requirement is the supply of oxygen - or

failing this, perhaps the presence of oxidizing bacteria. As

shown earlier there is abundant evidence on the importance of

organic processes in the solution, transportation and deposition of trace metals in the secondary environment•

However it is only necessary to recall this as an enquiry into

the mechanisms involved would be quite outside the scope of

this study.

The relatively greater stability of Fe-oxides compared to

Mn-oxides is well documented for most surface environments •

Minor decreases in Eh and pH mostly dissolve Mn oxides (Jenne,

1968); for Fe oxides a much greater decrease is required for

dissolution. Conversely minor increases in Eh-pH values

result in spontaneous precipitation of Fe-oxyhydroxide +2

polymorphs out of solution, while Mn (aq) ions may still

remain in solution. Some geochemical separation of secondary

Fe and Mn oxides may thus be evident in redox environments.

This has in fact been observed in the two study areas (chapter - 208 -

5).

Both laboratory and field observations indicate that oxide formation is quite rapid. As described earlier freshly precipitated Mn oxides or broken faces of Mn oxide concretions catalyze the deposition of MnO^ from solution, as does freshly precipitated Fe oxides on Fe oxide precipitation. This may partly account for the rapid precipitation rate of the oxides •

There also appear to be some evidence that Mn oxides catalyze

Fe-oxide precipitation, while Cu may catalyze the deposition of both oxides (Jenne, 1968).

No satisfactory explanation has been offered for the catalytic effect of quartz, feldspars and clays on Fe-Mn oxide precipitation. It is not clear whether such activity is to be regarded as entirely different from the infered coulombic forces that attract colloidal oxide particles to lithic fragments in sediment. Many inorganic structures possess strong surface charges which may be expected to attract ions or polar molecules in solution. Clay minerals are typical examples, and with cation deficiencies in their structures a wide range of cations are adsorbed. Suspended particles and coatings of hydrous Fe-Mn oxides greater than the colloidal-size range are the other adsorbents of importance.

These may carry charges that are reversible in sign, depending on the pH. Ferric hydroxide, for example, forms a suspension of positively charged particles below pH 5.5, but is negatively charged at higher pH. Thus arsenates, phosphates or such anions as will form stable compounds with Fe under acid conditions precipitate out as part of secondary Fe oxides at low pH, while coprecipitation with other cations may be - 209 - expected at neutral or higher pH. Charged colloidal particles of Fe-Mn oxides will thus be attracted to many mineral surfaces. It is interesting to note in this connection, that the (non-stoichiometric) Mn(iv) oxides have isolectric points identical to that of alpha-quartz, while those of alpha-FeOOH

(goethite) and alpha-alumina (corundum) are identical (James and MacNaughton, 1977).

In most laboratory syntheses of Mn oxides the first product is the hydroxide Mn(0H)2 which is then rapidly oxidized, but the most oxidized compound achieved was gamma-MnOOH (Bricker, 1965). Compounds with Mn in higher oxidation states were possible only upon the addition of acid +2 when a disproportionation into Mn (aq) and non-stoichiometric

MnO^ occured. Thus all the laboratory syntheses are possible under supergene conditions, although in surface environments the pH and Eh range in which exposed oxide coatings are found, are such that only MnO^ should be expected.

As summarized by Jenne (1968) the tendency is for the oxides to occur as coatings. Discrete Fe(0H)3 particles appear to form only after a certain quantity has been deposited on kaolinite. An excess of alkali or alkali-earth cations in a clay suspension led to a compact, non-porous coating which resulted in the latter portions of the Fe precipitate forming discrete particles. In contrast hydrogen saturation (low pH?) of the kaolinite led to a definitely amorphous iron oxide precipitate such that precipitation continued on the surface. In addition to precipitation on clay surfaces, colloidal iron hydroxide is readily sorbed onto clay mineral surfaces • Although the amount of Fe oxide - 210 - occuring on kaolinite had ten times the exchange capacity of the clay, it formed no more than eight weight percent iron in the very fine (< .2 micron) kaolinite size fractions. The amount of coating precipitated was thus related to the surface area of the clay rather than its cation exchange capacity. It is this tendency of Fe-Mn oxides to form coatings, their capacity to adsorb or coprecipitate cations, especially Mn oxides, and for Mn oxides the relatively easy redissolution

(mobility), that makes them so important in the control of trace metals. As they coat the mineral fragments it is likely that the principal adsorbents of cations in sediment is in reality the Fe-Mn oxides and not the mineral particles onto which they are coated. It is also to be noted that being very fragile the coatings are easily abraded to form a potent

portion of the finest fraction of sediment traditionally known to contain the highest concentrations of trace metals. The control of trace metals in stream sediments by Fe-Mn oxides

thus appears to be even more important than was previously

thought of• - 211 -

CHAPTER NINE - DISCUSSION

Since secondary dispersion is controlled by the prevailing

environmental processes, a discussion of factors affecting

trace metal distribution in drainage is mainly concerned with

the interaction of bedrock with the environment • The nature

of the secondary environment was briefly described in the

introductory chapter and in chapter eight its controls in the

genesis of Fe-Mn oxides was emphasized. In this chapter the

results obtained from this study are related to the

environmental parameters regarded as the important controls in

trace metal distribution in the study areas. A general

description of environmental factors is presented first.

Environmental factors and processes may be conveniently

classified on the basis of their areal extent as follows :

(1) Those that operate on a regional scale, such as

climate, weathering and vegetation.

(2) Those operating on a restricted or local scale, such

as soil formation, organic activity, hydrology and

relief.

This subdivision is obviously arbitrary. Firstly the

processes are not really independent, as exemplified by the

interdependence between rainfall, relief and vegetation.

Secondly while hydrology and relief may well be considered on

a regional basis, local patterns in vegetation may exert - 212 -

critical controls on physical as well as chemical weathering.

The usefulness of the above classification lies in the implied

emphasis placed on the nature of the parent material locally.

Table 9.1 contains some factors and their measurable

parameters considered to be the main controls on river

geochemistry (Gibbs, 1972).

9.1 REGIONAL ENVIRONMENTAL FACTORS

In general climate determines the type of weathering

encountered in a region as well as its vegetation. The

climatic factors of relevance here are :

(i) rainfall (total, amount, form, intensity, and

temporal and areal distribution),

(ii) temperature (mean, annual, seasonal and diurnal

variations, and their areal patterns), and

(iii) humidity (amount and distribution).

The individual effects of each of these regional factors are

always difficult to isolate precisely, but their apparent

combined effects permit some generalizations (Gibbs, 1967) :

(1) The relative importance of suspended and dissolved

material in a stream depends on the relief, the amount, nature

and distribution of precipitation and the vegetation developed

in response to these. High precipitation, particularly in

areas of low relief, gives rise to dense vegetation which

minimizes soil erosion and thus makes the dissolved load

relatively more important. Conversely low precipitation gives - 213 - rise to sparse vegetation and renders the suspended load more important.

(2) The proportion of stream load carried in suspension depends directly on the runoff. As this increases it firstly dilutes the concentration of dissolved species, and at the same time increases the proportion of the suspended material because of the generally higher velocities and related turbulence. Runoff depends on the relief and the amount, nature and distribution of precipitation. High relief generally produces high runoff but high precipitation does not necessarily have the same effect. A long, slight drizzle will produce less runoff and erosion than a short heavy downpour.

Similarly an even distribution of rainfall throughout the year has less erosive power than a seasonal distribution of wet and dry periods. The temporal distribution of precipitation thus affects the proportion of dissolved and suspended load, and therefore the geochemistry of a river.

(3) The regional importance of temperature lies firstly in the nature of precipitation (ice as against water) and the nature of weathering. Subject to the nature of the bedrock the general effect of climate determines the nature of the overall solute composition of a river. In humid regions the waters are generally of the calcium bicarbonate type, whereas those in arid regions are generally of the chloride and sulphate types• - 214 -

9.2 LOCAL ENVIRONMENTAL FACTORS

The regional environmental factors . control the general

direction of weathering and erosion but the geochemistry of a

small basin within the region may reflect more the influence

of local compositional factors such as the geology, soils and

vegetation, together with geomorphic factors such as the

shape, relief and slope of the basin, and the distribution of

drainage within it • Again some generalizations can be made :

(1) Under a given climate the bedrock determines the nature of

the weathering products, part of which forms the soil and part

dissolved away or physically transported to join the river

load. Loose soils or soft and pervious rocks are more easily

eroded than consolidated or massive impervious ones• The

nature and amount of the dissolved load depends on the rock

type. Generally, argillaceous sediments release more

dissolved salts than granites which release more than

quartzites (Miller, 1961). In this respect although water is

essential in the weathering reactions it plays a more

important role as the solvent for the dissolved ions and in

transporting them (Bricker et al, 1968).

(2) Relief also controls the local rate of erosion and

together with the shape of the basin, determines which bedrock

is likely to contribute most to drainage sediment composition.

The shape of the basin is itself the net result of interaction

between erosion, relief and geology over a long period. At

any stage of development the shape is the general indicator of

the potential energy of the stream and so largely determines - 215 - the compositional variation of the bottom load with distance from their point of entry into the stream. In general sharp relief leads to a smaller percentage of fine material in the bottom load due to the greater turbulence.

(3) The accessibility of soil solutions to the groundwater system and the geometry of the latter determine whether soluble weathering products remain in the soil or end up sooner or much later in the particulate fraction of the stream load. This depends on soil texture, rock porosity and permeability as determined by the fracture systems in surface and suboutcropping bedrock, relief and the general shape of the basin.

(4) Leaching or moisture enrichment in soil is directly affected by organic acids which solubilize several metals because of the lowered pH, or bind relatively insoluble metals in soluble organic complexes. Insoluble organic products and litter are transported together with soil fragments as particulate load.

The direct effects of most of these factors on stream sediment geochemistry are not quantifiable, although their net effect in a basin may be recognized. Their interaction may be considered as being in a state of dynamic equilibrium, so that factors which do not change in our lifetime such as rock type, soils, vegetation, relief, shape and slope of the basin, may be regarded as constant• Thus similar bedrock with similar internal structural features should, given the same climate, relief, vegetation and time, give rise to similar sediment geochemistry. Within a basin, however, there are likely to be - 216 -

local differences in geology, the soil-forming process and

accessibility of soil solutions to groundwater. Further

although the relief does not change, weaker rocks and soils on

higher slopes are preferentially eroded, and metal-rich waters

surfacing from the grounwater circulation release their metal

content in coatings on the stream sides and in bottom

sediment, as does water emerging from ponds, lakes and

marshes. In any one locality or district, some of these local

variations in geology, the secondary environment and the

erosion of weathering products will influence sediment

composition to a greater extent than others. It is important

to recognize and identify these and to be able to assess their

relative importance in order to adequately interpret the

stream sediment geochemistry. It has become increasingly

evident that local changes in the secondary environment

constitute the most important modifying influence on sediment

composition, and accordingly the next section is devoted to

their discussion.

9.3 INFLUENCE OF THE SECONDARY ENVIRONMENT ON SEDIMENT COMPOSITION

Secondary processes in the environment affect stream sediment

composition in two ways, directly and indirectly. Directly

they affect sediment composition by depleting the outlying

soils of metals, thus giving a false impression about the true

composition of the soils eroded into the stream. Thus

sediments low in certain metals may not necessarily imply

soils impoverished in that metal (Butt, 1971). On the other

hand sediment enriched in trace metals may actually have been - 217 - derived from background soils, in this case merely reflecting the enrichment processes in the secondary environment

(Horsnail et al, 1969). This may be regarded as an indirect effect, arising from the fixation of trace metals solubilized with Fe and Mn under low Eh and pH, by the hydrous oxides of the latter which precipitate out spontaneously in the stream where Eh and pH are higher. It is evident that the indirect effect is the more dominant.

The important parameters of the secondary environment to consider are the Eh and pH, which are used to construct stability field diagrams of Mn and Fe oxides at various metal conentrations in solution. Except in special cases, all other effects and processes may be considered secondary, merely leading to changes in Eh and pH (Jenne, 1968). The keyword to the discussion is stability - of the Fe-Mn oxides, for as

Jenne (1968) points out, it appears that sorption by hydrous

Mn and Fe oxides plus complex formation by organics keeps the activity of the other heavy metals so low that precipitation of their hydroxides is unlikely. The pH and Eh are themselves

the result of the processes occuring in the environment and not the cause of such processes (Sato, 1960) and their effect

is to be understood primarily in connection with the stability of the Fe-Mn oxides which control the trace metals, and not in

terms of a direct influence on the latter (Jenne, 1968)

As pointed out in earlier sections, most of the trace metals are associated with Mn oxides. The latter are also the

more sensitive to local changes in Eh and pH, thus accounting

for most of the trace metal variability as well. Small

decreases in Eh and pH need not lead to the actual dissolution - 218 - of Fe- Mn oxides to affect their control on trace metals because their surface charges are pH-dependent Thus the capacity of the oxides to hold on to the adsorbed cations and the nature of the cations (or anions) are affected by changes in Eh and pH. As pH decreases the hydronium ion, H+(aq) is the most strongly adsorbed, thus depleting the local sediment of trace metals. Sometimes the ions are not simply adsorbed to satisfy charge balance on the oxide surface but may constitute part of the oxide structure. For example Grassely and Heteny (1971) infer that Pb(+2) may be actually chemisorbed and Fitzpatrick et al (1978) have reported on pedogenic Ti-Fe oxides formed by co-precipitation and recrystallization from weathering solutions. The metals in this case were derived by the weathering of primary silicates containing Fe and Ti (e.g biotite, hornblende, sphene, etc), rather than by topotactic oxidation of primary titanium oxides such as ilmenite or rutile, which are largely inert to weathering or normal chemical attacks in the laboratory.

An account of trace metal control by the secondary environment is neccesarily descriptive because of the large number of unknowns involved. Although all other effects may be secondary to the central role of Eh and pH. the latter are the direct result of such effects and must be quantified before any formal or rigorous definition of secondary environmental processes can be possible. The use of cation exchange models at equilibrium, for example, are beset with many uncertaintiies (Hem, 1976; James and MacNaughton, 1977).

The findings of MacLaren and Crawford (1973a) illustrate another difficulty that is implicit in attempts at deriving - 219 - quantitative models for the secondary environment:

(i) Correlations did not suggest that oxide-held Cu has an influence as great, if any, as that of organically-held Cu in controlling Cu availability in soil (soluble Cu in soil moisture and solution). This would indicate that the oxide-held Cu is not normally an important source of Cu for plant uptake, but evidently, reducing conditions may well render such Cu available. Of this possible source it was the

Mn fraction that was important since addition of free Fe-oxide did not improve the multiple correlation involving organic carbon and Mn.

(ii) The % of Cu extracted as oxide-held correlated significantly with Fe and not Mn, suggesting that Mn was not an important control. However in the regression equation for the oxide-Cu the Mn constant was 40 times bigger than that for

Fe, thus implying that Mn has the greater effect on the amount of Cu occluded, even allowing for the greater amount of Fe in the soils • Since the amount of Cu in the oxide-held fraction is a function both of initial adsorption and subsequent retention, it may be that Mn dominates the former while Fe, by virtue of its greater abundance is responsible for the actual occlusion.

It is evident that kinetics play a significant role in the genesis of Fe-Mn oxides and the sorption-desorption phenomena associated with them, but few illuminating studies have been made (Jenne, 1968). The effects of kinetics on

trace metal variability, of reactions in the immediate micro-environment such as competing ions and the effect of

chloride ions on increasing heavy metal availability (Jenne, - 220 -

1968), the relative role of concretionary or nodular oxides,

of amorphous alumina and silica, the relative importance of

structural modifications of the Fe-Mn oxides ("active" and

"inactive" forms, whether amorphous or crystalline, as

described in Stumm and Morgan, 1970), all need reappraisal.

It is important for example to determine why precision is poor

in oxide data derived from coatings on coarse fraction or

nodules, but is good in the fine fraction where the oxides are

largely responsible for trace metal contents as well. The

need for such information is paramount; in deriving dispersion

patterns the expectation is that the conentration level of a

metal is practically uniform and that a characteristic

background level can be defined for some localities in the

area. With the currently high variability in oxide data, any

such patterns derived for point-source information would be

highly unstable.

9.4 APPLICATION TO THE STUDY AREAS

To overcome the high oxide-data variability in the study areas

and facilitate comparisons with the fine fraction data,

spatial plots of the percentiles were used (chapters 5 & 7).

As already shown in those chapters the dispersion patterns in

the two fractions are quite similar and imply that Fe and Mn

oxides are the main control on the distribution of mobile

trace metals in the stream sediments from the study areas.

In the Newquay area the lithology is quite uniform,

restricted drainage is widespread and Fe-Mn oxides are common

in the stream channels • The lowest trace metal contents - 221 - occured in background localities remote from past mining activity and showing little or no Fe-Mn oxide precipitation.

Similar low metal contents were found in localities adjacent to recently reclaimed marshland where the loose rock fragments used to cover the land are easily eroded to dilute the sediment for some distance below the locality. That anomalous dispersion from old mines were brought out as efficiently by the oxide data shows that the secondary oxides can accomodate large amounts of trace metals where these are available. Thus in locality 1 where no previous mining activity was evident, trace metal contents were very low although the highest Mn contents, and in fact the most extensive coating of Mn oxides occured here.

In the Clitheroe study area markedly contrasting lithology is manifest in high contents of Ca in localities underlain by limestone, where the high pH severely restricts secondary processes. Secondary processes were almost entirely confined to the moor environment, where the flat landscape with organic-rich soils give rise to restricted drainage and acid, reducing conditions ideal for Fe-Mn oxide precipitation.

Many trace metals were mobilized and concentrated within the coatings which render the upper reaches of the streams bright-red. With the exception of Ca and Ba all the trace metals studied in the area had very high contents in moor

sediments. The ore elements Zn, Cu and even Pb, were highly enriched. It is not known whether the bulk of the trace metals have been derived from organic sources (peaty soils) or

the underlying bedrock (sandstones, black shales and

conglomerates). The dispersion patterns in the remainder of - 222 - the study area merely reflect the dilution of enriched sediments derived from the moor Thus a complex interplay of varying lithology, soil types, secondary processes and drainage pattern has led to the distinctive dispersion patterns obtained in this study area.

Two important inferences may be drawn from the results from the two study areas:

(i) Secondary Fe Mn oxide formation in drainage channels may be quite common, except in limestone-rich terrains. The latter severely restrict their development owing to the high pH which may either prevent leaching, or cause the oxides to precipitate shortly thereafter in the soil profile long before the percolating waters reach the drainage channels.

(ii) When secondary oxides form solely by precipitation of the leachates from inorganic rocks and soils as obtains in the

Newquay area, trace metal enrichment is unlikely unless a rich source of such metals exist in the flow path. In contrast organic soils, by virtue of their high trace element contents, give rise to high trace metal contents in oxide coatings, as was found in the moor sediments near Clitheroe. Thus in the absence of organic-rich soils, unusual enrichment of trace metals in oxide coatings may indicate the presence of some mineralization. - 223 -

CHAPTER TEN - CONCLUSIONS AND RECOMMENDATIONS

10.1 SOURCES OF ERROR IN DRAINAGE DATA

LIMITATIONS OF THE ANALYTICAL SYSTEM

Perhaps the first exercise to undertake in a new project is an

assessment of the capabilities of the analytical system

available, as this largely predetermines the results that can

be obtained, knowledge which is important in planning field

trips to be cost-effective. In the comparative study of the

Cornwall samples (chapters 3 & 4),

(i) AAS results were found to be more precise than those

obtained with ICP.

(ii) In all cases it is essential to randomize sample

collection, preparation and analyses in order to ensure data

quality and/or reliability. Thus although precision between

(and sometimes within) ICP batches was quite low, the data it

yielded gave geochemical information similar to that given by

AAS, thanks to randomization (chapter 5).

(iii) Sampling errors were low for most elements in the

Newquay area, unless concentrations were low, when analytical

errors were also high.

SAMPLING VARIABILITY

(iv) Poor precision was mainly due to low concentrations,

perhaps too close to the detection limits.

(v) Precision is improved by ignition if the metal occurs

largely in some silicate lattice (e.g Ba), and is in addition - 224 -

quite soluble in the final leach solution, otherwise precision

may actually deteriorate upon ignition (e.g Mg, Al). The

ignition essentially serves to physically break down the

silicate minerals, thus making the chemical attack more

penetrating; it may be equivalent to using a more corrosive or

intensive attack.

SECONDARY PROCESSES AND ANNUAL VARIATION

(vi) The presence and variability of Fe-Mn oxides influences

sample variability, especially in background samples where

both Fe and Mn show high analytical errors, interpreted as

responses to localized changes in Eh and pH. Zinc, Pb and to

some extent Ca, vary sympathetically with Mn and Fe in this

respect, but the behaviour of Cu is irregular.

(vii) Annual variation in sediment composition is negligible

in the Newquay study area.

.2 RESULTS OF ANALYTICAL TRIALS

(i) Trace metals can be enriched in oxide coatings of stream

sediments, and measurements of metal contrast ratios are

higher than in fine fraction.

(ii) More often the coatings contain more Fe than Mn although

they appear otherwise, being usually noticeable by the grey

colour of Mn oxides.

(iii) The various oxide phases can be separated by applying a

reducing agent of the appropriate strength in an acid medium.

Hydroxyammonium chloride alone removes most of the Mn and <5?

of the Fe. A mixture of HC1 and Ho0o remove all Mn and - 225 -

perhaps the amorphous Fe oxide phases only. More crystalline

or "aged" Fe oxides are removed with sodium dithionite.

(iv) The HC1 - H^O^ mixture is suitable as a general technique

as it partially dissolves only the easily soluble amorphous

fraction of the coatings, a fraction which probably exerts the

critical control on trace metal dispersion.

(v) The more mobile Mn oxide phases contain most of the Zn and

high proportions of the Co and Ni in the coatings A third

each of the Ni and Co may be held in both the amorphous and

crystalline Fe oxide phases.

(vi) The partial extraction technique demonstrated (chapter 3)

provides meaningful geochemical patterns for Mn, Zn, Pb, Cu

and Co, because the spatial variation for these elements

(chapter 4) exceeds analytical error. The results were

somewhat uncertain for Ni, Fe, Ca, Mg and Al, for which

procedural errors were high.

(vii) The reaction mechanisms involved in the realease of

trace elements from oxide coatings are complex and a long

reaction time (overnight) is necessary to achieve reproducible

results.

10.3 DISPERSION PATTERNS IN THE STUDY AREAS

POPULATION DISTRIBUTION MODELS

Most of the elements conform to some lognormal distribution

model. In addition most had a bimodal or polymodal

distribution, indicating the multiple, more or less distinct

provenance factors in sediment. In the absence of

mineralization or contamination, the other important source of - 226 -

trace elements in sediment was the enrichment process

associated with adsorption by Fe-Mn oxides.

10.3.1 PATTERNS IN FINE FRACTION

CORNWALL STUDY AREA (NEWQUAY AREA)

(i) In spite of the superior data quality of AAS over ICPt

similar statistical and spatial distribution patterns were

obtained for Mn, Zn, Pb, Fe Ca. Mg and Co.

(ii) High levels of Pb, Zn, Cu, Cd and Co, either singly or

coincidentally, outlined all known mineralization. Iron, Mn,

and Ni patterns were also associated with past mining

activity, but the results were not consistent; there were

background localities in which the levels of these metals were

high or higher than those near mineralization. An old tin

mine was distinctly outlined by the Ti pattern, and less

clearly by the Cr pattern. Lanthanum data were the least

precise but still reflected base metal mineralization.

(iii) Nickel, Co, V, Al and Ba appeared to be enriched in

unmineralized localities where Fe-Mn oxides abound.

(iv) No natural patterns related to bedrock geology were

predicted or found in this atudy area because of the uniform

lithology. In localities where barren rock waste have been

used to reclaim marshland the loose earth is easily eroded and

such localities are marked by high levels of Mg, Li, Al and V,

the major and minor'constituents of the phyllosilicates that

make up the bulk of the underlying bedrocks. Secondary

processes leading to the deposition of thin Fe-oxide coatings

also give rise to moderate to high Fe levels in such - 227 - localities. A strong Ba-Al association in a few background samples from the Mid-Upper Carboniferous could not be explained.

(v) Dispersion patterns for most elemets ratioed on Fe and/or

Mn were largely unchanged, except for Zn which revealed an anomalous dispersion train previously unnoticed because of extreme dilution of the local sediments by barren rock waste.

LANCASHIRE STUDY AREA (CLITHEROE AREA)

(i) Apart from the moor the concentrations of Mn, Fe, Ti, Co,

Ni, V, Co and Al were low or only moderate in all other localities. The spatial patterns for the above elements were all similar and reflect the dominance of secondary processes on the moor.

(ii) The Ca distribution pattern contrasts with that of all the other elements except Cr, an association which could not be interpreted. High Ca concentrations are restricted mainly to localities underlain by the Chatburn Limestone.

(iii) Only the Pb pattern unambiguosly marks out the anomalous dispersion below the Skeleron mine. The highest Zn levels coincide with those of Pb but the anomalous Zn dispersion is

shorter. Moderate to high Zn levels occur on the moor, where the pattern closely follows that for Mn, and to a lesser extent Fe. Thus Zn is an important member of the moorland

association of Fe and Mn with Ti, Co, Ni, V, Cu Cd and Al.

The highest Cd values were found below Skeleron, but most of

the Cd pattern is confined to the moor. The Ba dispersion

pattern also outlines the Skeleron mine, but high levels of

the metal in locality 2 did not coincide with similar levels - 228 -

of Pb and Zn, although the levels of the latter are in their

respective "high background" ranges. It is possible that

these high Ba levels coincide with some minor mineralization

as the Twiston Fault which hosts the Skeleron ore-body appears

to pass through the locality.

(iv) No meaningful pattern was found for the La data.

10.3.2 PATTERNS IN OXIDE COATINGS ON COARSE FRACTION

NEWQUAY AREA

(i) The data for the Mn fraction was incomplete but it still

gave spatial patterns similar to those of the Fe + Mn

fraction. Patterns for the ratioed and unratioed data were

similar.

(ii) In comparison with the fine fraction, Mn Pb, Mg, Fe and

perhaps Al, were the elements whose spatial patterns most

closely resembled the fine fraction equivalents.

(iii) Missing high trends for Zn, Co Ni, and Cu in locality 8

probably indicate that here these metals are mainly controlled

by clastic dispersion. In contrast new trends of high levels

for Zn, Cu and Co below locality 10 bring out the old mine

upstream, the clastic dispersion from which was diluted to

background levels by a flood of barren rock waste through

agricultural practices.

(iv) The spatial patterns for the factor score loadings were

similar to those of the fine fraction.

CLITHEROE AREA

(i) The spatial distribution patterns for oxide data, whether

ratioed or unratioed on Fe and/or Mn, were similar to those of - 229 the fine fraction, but cut-off points were sharper for the anomalous trends. Thus the dispersion of trace metals in this area is largely controlled by Fe-Mn oxides.

(ii) The ratioed data extend the anomalous trend for ore metals near mineralization while suppressing high background values.

(iii) Most of the secondary environmental processes occur on the moor and the effects transmitted downstream by clastic dispersion.

(iv) The spatial patterns in the factor score loadings were similar to those of the fine fraction.

OXIDE COATING AS A SAMPLE MEDIUM

(i) The equivalence of the results obtained with fine fraction and oxide data lend credence to the idea of using oxide coatings as a sample medium for geochemical exploration.

(ii) It is not necessary to obtain the actual amounts of coating extracted since patterns obtained with weight-loss data are identical to those given by the solution concentrations ratioed on the Fe and Mn content.

(iii) Using Ag as a test case, the following points are worth noting:

(a) The oxide fraction contains higher levels of Ag

and the anomaly decays more slowly, thereby extending the

dispersion.

(b) Both oxide and fine fractions give a noticeable

anomaly cut-off, but the fine fraction yields a

relatively sharper definition.

(c) There is high data scatter near the old mines, but - 230 -

most of this is removed if the data is ratioed, when the

pattern closely resembles that given by the non-ratioed

log-data.

(iv) The analytical scheme presented in this thesis makes it

possible to obtain oxide data on a routine basis.

10.3.3 PATTERNS IN THE WATERS

(i) In the secondary environment pH is of critical importance.

Waters draining limestones in the Clitheroe area had high pH

levels and as a result only Ca levels were high. By contrast

neutral to acid waters in the Newquay area contained

relatively higher contents of trace metals.

(ii) Acid waters emerging from old mine dumps near Newquay

have high contents of trace metals. Also neutral to slightly

alkaline waters emerging from the peaty moorland soils near

Clitheroe contain high levels of trace metals. The mode of

occurence of the trace metals in solution or suspension,

however, may be quite different in the two cases. In both

environments Fe-Mn oxides form profusely in nearby drainage

channels.

(iii) The concentrations of Zn, Co Pb, Ni, Cu, and perhaps

also Fe and Mg, appear to be lower in water from localities in

the Newquay area where Fe-Mn oxides are abundant. This may

imply that such metals are extracted by the oxides from

solution or suspension. Further more extensive work is

needed to verify this. 231 -

10.4 ASSESSMENT OF STATISTICAL AIDS

(i) Similarities in the histograms and probability plots for

individual elements, and examination of scatterplots and

correlation coefficients suggested metal associations which

were verified by a comparison of the spatial plots of the

individual metal concentrations. The latter suggested even

broader associations which could not be readily interpreted

for the Newquay area.

(ii) Partial correlations were used in an attempt to explain

such associations, but it was necessary to resort to

correlograms to appreciate the significance of the partial

correlation matrix. Due perhaps to the extensive coverage of

secondary processes in the Newquay area, the mass of

criss-crossing lines failed to improve data interpretation.

Rather the scatterplots were more effective in portraying the

detailed nature of the associations, if the partial

correlations are used in conjunction.

(iii) To obtain any fundamental structures in the data,

Principal Component and Factor analyses were used. The R-mode

variant of the techniques was used as they are based on the

correlation matrix. Interpretation of the results by the two

techniques was. similar, but there were practical differences.

Principal component analysis results were fused into the first

two or three vectors. Thus the effect of two or more

geochemical processes were combined in each vector and there

was no way of telling which had the greater effective control

on a particular metal distribution. With oxide data the high - 232 - concentration effect and the resulting high correlations result in even more fused vectors.

In contrast factor analysis produced more specific vectors whose interpretation confirmed the preliminary analysis based on the spatial plots of the original variables

(the elements). However a spatial plot of the factors was necessary to gain a geographic and the full geochemical significance of any implied process. This was due to the poor scattergrams obtained with factor loadings. In contrast principal component analysis gave good scattergrams which were usually sufficient to classify the samples and permit interpretation on the basis of the clusters of samples and the associated metals. A check of the geographic trend of component scores in the Clitheroe area gave similar

information as the factor scores, but it was relatively easier to interpret trends in the latter.

The additional information given by principal component

and factor analyses concerns the relative importance of

infered geochemical processes in the control of the content of

a particular metal in sediment. In this respect too factor

analysis is the more useful as it gives more specific vectors.

To achieve maximum information, however, it is best to use

both techniques together.

(iv) Correlation coefficients were uniformly high in the

Clitheroe oxide data, due to the fact that most of the

dispersion patterns coincide on the moor. In contrast

correlations in the Newquay oxide data were not very high,

although still quite significant. This may be due to the

widespread occurence of secondary processes; thus the overall - 233 -

correlation spans several different types of local

environment.

(v) Principal component analysis of the Clitheroe oxide data

was not very useful, perhaps due to the mutually high

correlations. The first eigen vector alone accounted for all

the true variance in the entire data matrix. Factor analysis

results, however, were clearer and similar to those given by

the fine fraction, but more factors were generated which were

not clearly understood and may well be due to poor data.

The Newquay oxide data yielded results similar to those

given by the fine fraction. The interpretation of the

components or factors were closely similar, but again more

factors probably indicating subtle relationships in the

secondary environment were obtained. With lithology exerting

no strong direct influence, a finer fission of the background

subpopulations related to their content of Fe-Mn oxides was

achieved.

10.5 CONTROLS ON TRACE ELEMENT DISTRIBUTION IN SEDIMENT

/

(i) In the study areas three main factors have been identified

that affect trace element distribution in sediments, namely

the geology (lithology), mineralization (including old mine

wastes) and secondary environmental processes (Fe-Mn oxides).

In the Newquay area also, land reclamation gives rise to local

dilution of trace element levels, but this is artificial and

incidental. In the absence of mineralization lithology and

secondary processes dominate.

(ii) Because the ultimate source of the metals is the minerals - 234 - in the underlying bedrock, lithology is the first factor of importance. The areal distribution of calcareous and argillaceous rocks defines which localities have the greater potential for high trace element content. Thus in the

Clitheroe area, localities underlain by limestone had low trace metal levels.

(iii) Where the lithology is uniform secondary processes exert the most important control on sediment composition, affecting both the level of concentration as well as metal variability.

This control is effected through the genesis of Fe Mn oxides which attract and retain most of the cations and anions identified in natural waters.

(iv) Secondary processes operate when low Eh and pH conditions can be maintained to enable trace metals to be dissolved and held in solution or suspension until the water emerges in adjacent drainage channels where Eh and pH are higher. The

Fe-Mn oxides then precipitate out to coat the sediment, thereby effectively controlling trace metal levels in

sediment. Soils with impeded drainage rich in organics

provide such acid, reducing conditions and are always

associated with the formation of Fe-Mn oxides.

(v) A combination of topography (flatland with slow

groundwater movement), rock type (carbonate rocks prevent a

low pH being achieved), soil type (organic reactions necessary

to provide acidity), rock and soil permeability (free or

impeded drainage) and the drainage pattern itself, determine

whether secondary processes affect sediment in a particular

locality or not.

(vi) The Mn fraction of the oxides exerts greater control by - 235 -

virtue of its lower stability relative to Fe, with respect to

small changes in Eh and pH in the micro-environment.

10.5 RECOMMENDATIONS

(i) It is strongly recommended that even greater attention be

paid to the idea of exploiting Fe-Mn oxides directly as a

sample medium in mineral exploration. This is particularly so

for mobile elements that normally occur in low concentrations

in sediment. The scheme presented as a routine procedure

should be tested further in an effort to ascertain some of the

factors resulting in high data variability. Such work might

include the effects of

(a) mixing of coarse fraction with coatings obtained

from different micro-environments in the sampling

locality (allogenic heterogeneity). This might involve

locating the centres of groundwater emergence.

(b) mixing of coarse fraction in a vertical direction due

to scooping too deep into the channel (authogenic

heterogeneity).

(c)$g^ing or recrystallization of the oxides, and

(d) pH and Eh changes during the extraction of the

coatings in the laboratory.

(ii) The success of the oxide method for Ag suggests that

similar results are possible for elements such as As, Sb, Bi,

Se and Te which are usually associated with base metal

occurences.

(iii) The use of multivariate statistical methods of data - 236 - analysis should be encouraged. This implies access to a major computing facility and the need for extending the availability of such resources.

(iv) Efficient use of the statistical methods requires the production of good quality data. Thus efforts to improve data reliability are called for.

(v) In both study areas indications of unknown mineralization were noted, at locality 2 in the Clitheroe area, and near the head of the tributary in locality 1 in the Newquay area.

These localities need checking. - 237 -

APPENDIX - DETAILS OF ANALYTICAL TECHNIQUES

A. EXTRACTION OF FE-MN OXIDE COATINGS

(1) SOLUTIONS

(1) .5M Ammonium citrate : Dissolve 60.968 gm of the reagent

grade salt (formula weight = 243.23) in 500 ml of deionized

water.

(ii) Hydroxyammonium chloride : To make an approximately 2M

solution disolve 76.439 gm of the salt (formula weight =

69.49) in 500 ml of deionized water; for a IM solution dilute

as required.

(iii) 35% (v/v) Acetic acid : Dilute 35 ml of glacial acetic

acid to 100 ml with deionized water.

(iv) .16M Nitric acid : Dilute 10 ml of the conc acid (16M) to

1000 ml with deionized water.

(v) 5% HC1 : Dilute 25 ml of the conc acid to 500 ml.

(vi) 4% Sulphuric acid : Dilute 20 ml of the conc acid to 500

ml •

(vii) Prepare a citrate buffer which is .15M in sodium citrate

and .05M in citric acid (see Coffin, 1963).

(2) INDIVIDUAL EXTRACTIONS

(i) Conc Nitric acid : This was tried as a total attack but

gave low results.

Add 5 ml of the acid to 5 gm of sample in 18 x 180 mm

test tubes. Transfer to an lauminium block at 100 deg. - 238 -

Celsius for 1 hr, after which time most fragments would have been bleached with only a few ml of acid left. Cool, add DIW, decant and filter into clean test tubes and make up to 20 ml with DIW. Transfer the fragments to clean, weighed 25 ml glass beakers. Thoroughly rinse the test tubes to remove all sandy or clayey particles sticking to the sides into the filter papers• Dry the contents of the beaker to constant weight and determine the amount of oxide leached by difference.

(ii) 4% Sulphuric acid : This was successfully used for the total removal of Fe-Mn oxide coatings, but low results for Pb and Ca are returned because their sulphates are insoluble.

Add 25 ml of the acid to 5 gm of sample in 25 ml glass beakers and leave at room temperature overnight (16 hrs).

Stir and filter solution into 100 ml or 150 ml glass beakers.

Thoroughly wash the remains and rinse filter paper with DXW.

Transfer to hot plate, boil to dryness and leach with 10 ml of

2M HC1 into 18 x 180 mm test tubes. Make up to 20 ml with

DIW, shake to mix and send for AAS analysis. Find the amount of coating leached by weight-loss as above.

(iii) Hydroxyammonium chloride alone : This was used for the removal of Mn oxides and associated elements only. More Mn is released if the reaction proceeds overnight.

Add 25 ml of the reagent to 5 gm of sample in 25 ml glass beakers and leave for 4 hrs or overnight at room temperature.

Stir, decant, wash fragments and filter solutions into 100 ml or 150 ml glass beakers and transfer to a hot plate. Boil to a very low volume, transfer beakers to cooler edges of hot plate and add drops of conc nitric acid until efferverscence - 239 - ceases. Boil to dryness and proceed as in (ii) above.

(iv) Hydroxyammonium chloride + Nitric acid : The results with

this reagent is similar to those given by (iii), but removes

slightly more Zn, Pb and greater quantities of Co and Ni, with a minimal increase in the amount of Fe removed•

Make a 1:1 mixture of 2M hydroxyammonium chloride and

• 16M nitric acid. Leave at room temperature for 4 hrs. The

rest of the procedure is as in (ii) above.

(v) Hydroxyammonium chloride + Acetic acid : This removes most

of the Mn a great deal more of Fe, Zn, Pb, Cu, Co and Ni. It

is probably equivalent to an "amorphous oxide fraction", but

the precision of the data obtained with this method is

relatively much lower than similar ones tried.

Add 130 ml of 2M hydroxyammonium chloride to 85 ml of 35%

acetic acid. Proceed as in (i) above. The recommendation is

for a 4 hr extraction at 100 deg. Celsius (Chester and

Hughes, 1967), but it was observed that most of the reagent

was lost after about 1.5 hrs. If the longer extraction is

required the reagent may be frequently replenished.

(vi) Hydroxyammonium chloride + Ammonium citrate : The results

are comparable to those using (iii), except that slightly

lower Zn, Pb, Cu, and higher Co and Ni are obtained.

Make a 1:1 mixture of 1M hydroxyammonium chloride and .5M

citrate.

Add 25 ml to the sample in beakers and leave at room

temperature for 2 hrs. Proceed as for (iii) and (iv).

(vii) HC1 + hydrogen peroxide : This removes Mn oxides and

probably most of the amorphous Fe oxides.

Add 25 ml of 5% HC1 to each sample in a beaker and 10-12 drops - 240 -

of 20 vols hydrogen peroxide. Leave overnight at room

temperature in a fume cupboard. Stir, decant, rinse

thoroughly and filter as before. Boil down to dryness and

leach in M HC1. Determine the amount leached by weight-loss

as above•

SEQUENTIAL EXTRACTION

FRACTION 1 : 2.2M hydroxy ammonium chloride alone, to remove Mn

oxides and about 7% of Fe oxides.

Add 25 ml of reagent to 5 gm of sample for 4 hrs at room

temperature. Decant, rinse thoroughly with DIW, filter into

larger beakers and boil down to low volume on hot plate.

While still warm, destroy excess of reagent with conc nitric

acid. Boil down to dryness and leach in M HC1.

FRACTION 2 : .25M hydroxyammonium chloride in .25M HC1, to

remove amorphous Fe oxides.

Dilute 100 ml of M hydroxyammonium chloride to 200 ml

with DIW and do the same with 50 ml of 2M HC1. Mix the two

solutions to obtain the required relative concentrations• Add

25 ml of the mixture to the remains of fraction 1 and transfer

to a water bath at 85 deg. Celsius for 30 mins, stirring

occassionally. Decant, rinse, filter, boil to near-dryness

and destroy excess hydroxyammonium chloride. Boil to dryness

and leach as before.

FRACTION 3 : Sodium dithionite in the presence of a buffer of

sodium citrate and citric acid. This method is used on its

own for the total extraction of Fe-Mn oxides but is applied

here to remove the remaining, more or less crystalline Fe - 241 -

oxides.

To the moist remains of fraction 2 add .25 gm of sodium

dithionite and 25 ml of the buffer of pH 7.3. Transfer to a

water-bath and stir constantly at about 55 deg. Celsius.

When fragments are bleached remove beakers and cool. Stir,

decant, rinse and filter extract into larger beakers and

transfer the latter to a hot plate. Boil to dryness and leach

in M HC1. Dry the filter papers with the contents of the

beakers to constant weight (8-10 hrs at 110 deg. Celsius is

sufficient) and determine the total amount of coating removed

by difference.

In the original account for soils Coffin (1963)

reccommends 30 min reaction time, mainly to ensure (since not

all minerals may be bleached white) that reaction is complete.

With Fe-Mn coatings on gravel which are easily visible to the

unaided eye, bleaching may be observed to be completed quite

quickly and the beakers may be removed then. Also the .25 gm

of dithionite was to ensure an excess of the salt for the

quantity of soil used. It was found in these trials that a

small scoopful of the salt was quite adequate for 5 gm and 10

gm samples of coarse fraction.

B. A SIMPLE ANALYTICAL SCHEME FOR OXIDE-HELD SILVER

(1) FINE FRACTION

(i) Weigh .25 gm of sample in 18 x 180 mm test tubes.

(ii) Add 1.0 ml of conc nitric acid and stand on aliminium

block at about 105 deg. Celsius for 1 hr. - 242 -

(iii) Cool to room temperature and add 5 ml of DIW. Shake and leave in a dark cupboard to settle.

(iv) Send for AAS analysis without further treatment.

(2) FE-MN COATINGS ON COARSE FRACTION.

(i) Weigh about 5 gm of coarse fraction into 18 x 180 mm test tubes•

(ii) Add 2.0 ml of conc nitric acid and stand on aliminium block at about 105 deg. Celsius for 1 hr.

(iii) Cool, make up to 10 ml, shake carefully but thoroughly and leave to settle in the dark.

(iv) Send for AAS analysis without further treatment.

(3) STANDARDS

(i) Make a 20% nitric acid solution by pippeting 100ml of conc nitric acid into a 500 ml volumetric flask and topping to the mark with DIW.

(ii) Make a 10 ug/ml solution of Ag(+1) from a 1000 ug/ml AAS standard grade stock solution, using the nitric acid solution just prepared•

(iii) To make standards containing 0.5 ug/ml and 1.0 ug/ml, pippete 5 ml and 10 ml respectively, of the 10 ug/ml solution and make up to 100ml.

(iv) Make a 2.0 ug/ml standard by pippeting 10 ml of the 10 ug/ml solution and making up to the mark in a 50 ml volumetric flask.

(v) Send an aliquot of the 20% nitric acid solution as a blank• - 243 -

REFERENCES

Adams J. A. S., 1955, The uranium geochemistry of Lassen Volcanic National Park, California; Geochim. et Cosmochim Acta 8 74-85.

Adams S. N., Honeysett J. L., Tiller K. G. and Norrish K., 1969, Factors controlling the increase of cobalt in plants following the addition of a cobalt fertilizer: Austr. J. Soil Res. 7 29-42.

and Honeysett J. L., 1964, Some effects of soil waterlogging on the cobalt and copper status of pasture plants grown in pots. Austr. J. Agric. Res. 15 357-367.

Ahrens L. H., 1957, Lognormal distributions. Ill: Geochim. et Cosmochira. Acta 11 205-212.

1954b, The lognormal distributions of the elements. II: Geochim. et Cosmochim. Acta 6 121-131.

1954a, The lognormal distributions of the elements. I: Geochim. et Cosmochim. Acta 5 49-73.

Anderson B. J., Jenne E. A., and Chao T. T., 1973, The sorption of silver by poorly crystallized manganese oxides: Geochim. et Cosmochim. Acta 37 611-622.

Andrew R. L., 1978, Evaluation of copper-zinc gossans in Southern Africa: Trans. IMM sect.B B165-167.

Armour-Brown A., and Nichol I., 1970, Regional geochemical reconnaissance and the location of metallogenic provinces: Econ. Geol• 65(3) 312-330.

Baker W. E., 1973, The role of humic acids from Tasmanian podzolic soils in mineral degradation and metal mobilization: Geochim. et Cosmochim. Acta 37(2) 269-281.

Baldock J. W., 1977, Low-density geochemical reconnaissance in Peru to delineate individual mineral deposits: Trans. IMM 86 sect.B B63-72.

Barnes I., and Back W., 1964, Geochemistry of iron-rich groundwater of Southern Maryland: J. Geol. 72 435-447.

Bernal J. D., Dasgupta D. R., and Mackay A. L., 1959, The oxides and hydroxides of iron and their structural interrelationships: Clay Min. Bull. 4 15-30.

Bolviken B., and Sinding-Larsen R., 1973, Total error and other criteria in the interpretation of stream-sedimant data: in Jones M. J. (ed.), Internl. Geochem. Explor. Symp. London, 285-295. - 244 -

Bostrom K., 1967, Some pH-controlling redox reactions in natural waters: iji Equilibrium concepts in natural water systems, Adv. Chem. Ser. 67 286-311 (Amer. Chem. Soc.).

Bott M. H. P., Day A. A., and Masson-Smith D., 1958, The interpretation of gravity and magnetic surveys in Devon and Cornwall: Phil. Trans. Roy. Soc. 251A 161-191.

and Masson-Smith D., 1957a, The geological interpretation of a gravity survey of the Alston Block and the Durham Coalfield; Quart. J. Geol. Soc. London 113 93-117.

Boyle R. W., 1968b, The geochemistry of silver and its deposits: Geol. Surv. Can. Bull. 160 264 pp.

Bricker 0. P., Godfrey A. E., and Cleaves E. T., 1967, Mineral-water interaction during the chemical weathering of silicates: in Trace inorganics in water, Adv. Chem. Ser. 67 128-142 (Amer. Chem. Soc.).

Bricker 0., 1965, Some stability relations in the system Mn-I^O at 25 deg Celsius and 1 atm; Amer. Min. 50 1296-1354.

Burns R. G., 1976, The uptake of cobalt into ferromanganese nodules, soils and synthetic manganese (IV) oxides: Geochim. et Cosmochim. Acta 40 95-102.

and Fuerstenau D. W., 1966, Electron probe determination of inter-element relationships in manganese nodules: Amer. Mineral. 51 895-902.

Burwash R. A., 1978, Uranium and thorium in the Precambrian basement of Western Canada. II: Petrologic and tectonic controls: Can. j. Earth Sci. 16 472-483.

and Culbert R. R., 1976, Multivariate geochemical and mineralogical patterns in the Precambrian basement of Western Canada: Can. J. Earth Sci. 13 9-18.

Butt C. R. M., 1971, The influence of environment on regional geochemical patterns in Northern Ireland: D.I.C Thesis, Imperial College.

Calvert S. E., and Price N. B., 1970, Composition of manganese nodules and manganese carbonates from Loch Fyne, Scotland: Contr. Mineral, and Petrol. 29 215-233.

Canney F. C., 1966, Hydrous manganese-iron scavenging: its effect on stream surveys: Can. Geol. Surv. Paper 66-54, 11-12 (abstract).

Carpenter R. H., and Hayes W. B., 1978, Precipitation of iron, manganese zinc and copper on clean ceramic surfaces in a stream draining a polymetallic sulfide deposit: J. Geochem. Explor. 9 31-37. - 245 -

Robinson G. D., and Hayes W. B., 1978, Partitioning of manganese, iron, copper, zinc, lead, cobalt and nickel in black coatings on stream boulders in the vicinity of the Magruder mine, Lincoln Co., Georgia (U.S.A): J. Geochem. Explor. 10 75-79.

Pope T. A., and Smith R. L., 1975, Fe-Mn oxide coatings in stream sediment geochemical surveys: J. Geochem. Explor. 4 349-363.

Carroll Dorothy, 1958, Role of clay minerals in the transportation of iron: Geochim. et Cosmochim. Acta 14 1-27.

Chaffee M. A., 1977, Some thoughts on the selection of threshold values as practiced in the branch of exploration research of the U. S. geological survey: Assoc. Explor. Geochem. News1. 21 14-16.

Chao T. T., and Theobald Jnr P. K., 1976, The significance of secondary iron and manganese oxides in geochemical exploration: Econ. Geol. 71 1560-1569.

and Anderson B. J., 1974, The scavenging of silver by manganese and iron oxides in stream sediments collected from two drainage areas of Colorado: Chem. Geol. 14 159-166.

and Sanzalone R. F., 1973, Atomic absorption spectrophotometric determination of microgram levels of Co, Ni, Cu, Pb and Zn in soils and sediment extracts containing large amounts of Mn and Fe: USGS J. Res. 1 681-685

1972, Selective dissolution of manganese oxides from soils and sediments with acidified hydroxylamine hydrochloride: Soil Sci. Soc. Amer. Proc. 36 764-768.

Chapman R. P., 1978, Evaluation of some statistical methods of interpreting geochemical drainage data from New Brunswick: J. Internl.Assoc. Math. Geol. 10 195-224.

Chester R., and Hughes M. J., 1967, A chemical technique for the separation of ferromanganese minerals, carbonate minerals and adsorbed trace elements from some pelagic sediments: Chem. Geol. 2 249-262.

Childs R. W., 1975, Composition of iron-manganese concretions from some New Zealand soils: Geoderma 13 141-152.

Chork C. Y., 1977, Seasonal, Sampling and analytical variations in stream sediment surveys: J. Geochem. Explor. 7 31-47.

Chukhrov F. V., Zryagin B. B., Yermilova L. P and Gorskhov A. I., 1976, The origin of marine Fe-Mn nodules; Mineral Deposita 11(1) 24-32.

Clark I., and Garnett R. H. T., 1974, Identification of multiple mineralization phases by statistical methods: Trans. IMM 83 - 246 -

sect. A A43-52.

Clema J. M., and Stevens-Hoare N. P., 1973, A method of distinguishing nickel gossans from other ironstones on the Yilgarn Shield, Western Australia: J. Geochem. Explor. 2 393-402.

Cloke P. L., 1972, pH-Eh diagrams: in Fairbridge R. W. (ed.), The encyclopedia of geochemistry and environmental sciences IVA, 924-932: Van Nostrand-Reinhold, New York 1321 pp.

Closs L. G., and Nichol I., 1975, The role of factor and regression analysis in the interpretation of geochemical reconnaissance data: Can. J. Earth Sci. 12 1316-1330.

Coffin D. D., 1963, A method for the determination of free Fe in soils and clays: Can. J. Soil Sci. 43 7-17.

Collins J. F., and Buol S. W. , 1970a, Patterns of iron and manganese precipitation under specified Eh-pH conditions: Soil Sci. 110 . 157-162

Cooper B. E., 1969, Statistics for Experimmentalists: Pergamon Press, Oxford. 336 pp.

Cotton F. A., and Wilkinson G., 1966, Advanced inorganic chemistry: Wiley-Interscience, New York. 699 pp.

Crerar D. A., and Barnes H. L., 1974, Deposition of deep-sea manganese nodules: Geochim. et Cosmochim. Acta 38 279-300.

Cronan D. S., 1975a, Zinc in marine manganese nodules: Trans. IMM 84 sect.B B30-32.

and Tooms J. S. , 1969, The geochemistry of manganese nodules and associated pelagic deposits from the Pacific and Indian Oceans: Deep-Sea Research 16 335-359.

1968, A microscopic and electron probe investigation of manganese nodules from the north-west Indian Ocean: Deep-Sea Research 15 215-223.

Curtin D., and Smillie G. W., 1979, Origin of the pH-dependent cation-exchange capacities of Irish soil clays: Geoderma 22 213-224.

Curtis L. F., Courtney I. M and Trudgill S. T., 1976, Soils in the British Isles. Longmans, London. 364 pp.

Davis J. C., 1973, Statistics and data analysis in geology: John Wiley, New York. 550 pp.

Deb B. C., 1950, The estimation of free iron oxides in soils and clays: J. Soil Sci. 1 212-220.

Dines H. G., 1956, The metalliferous mining region of South-West - 247 -

England: Mem. Geol. Surv. Gt. Br. HMSO, London. 2 vols, 795 pp.

Dolezal J., Povondra P., and Sulcek Z., 1960, Decomposition techniques in inorganic analysis: ILIFFE Books Ltd, London.

Dunham A. C., and Glasby G. P., 1973, Petrographic and electron microprobe investigations of some deep- and shallow-water manganese nodules. New Zealand J. Geol. and Geophys. 17 929-953.

Dunham K. C., 1967, Mineralization in relation to the precarboniferous basement rocks, Northern England: Proc. Yorkshire Geol. Soc. 36 195-201.

and Dines H. G., 1945, Barium in England and Wales; Wartime Pamphlet no 46.

Dyck W., 1971a, The adsorption and copprecipitation of silver on hydrous oxides of iron and manganese: Geol. Surv. Can. Paper 70-64 23 pp.

Earp J. R., Poole E. G., Land D. H., Whiteman A. J., Calver M. A., Ramsbottom W. H. C. and Sabine P. A., 1961, Geology of the Country around Clitheroe and Nelson; HMSO, London. 346 pp.

Edmonds E. A., McKeown M. C., 1975, British Regional Geology, S-W England (4th ed.) HMSO, London. 136 pp.

Ehrenberg A. S. C., 1975, Data reduction: John Wiley and Sons, London. 391 pp.

Elderfield E., Thornton I., and Webb J. S., 1971, Heavy metals and oyster culture in Wales: Marine pollution Bull.. 2 44-47.

Ellis A. J., Tooms J. S., Webb J. S., and Bicknell J. V., 1967, Application of solution experiments in geochemical prospecting: Trans IMM 76 sect.B B25-39.

Fanta P., 1972, Effects of seasonal variation on the iron, manganese and associated metal contents of stream sediments and soils: M. Phil. Thesis, University of London.

Feitknecht W., and Michaelis W., 1962, Uber die hydrolyse von eisen(III)-perchlorat-losungen: Helv. Chim. Acta 45 212-224.

Fischer W. R. , and Schwertmann U., 1975, The formation of hematite from amorphous iron(III) hydroxide: Clay and Clay Minerals 23 33-37.

Fitzpatrick R. W., Le Roux J., and Schwertmann U., 1978, Amorphous and crystalline titanium and iron-titanium oxides in synthetic preparations, at near ambient conditions, and in soil clays: Clay and Clay Minerals 26 189-201.

Foster J. R., 1973, The efficiency of various digestion procedures or - 248 -

the extraction of metals from rocks and rock-forming minerals: CIM Bull. 66 85-92.

Francombe M. H., and Rooksby H. P., 1959, Structure transformations effected by the dehydration of diaspore, goethite and delta ferric oxide: Clay Minerals Bulletin 4 1-14.

Garrett R. G., 1969, The determination of sampling and analytical errors in exploration geochemistry: Econ. Geol. 64 568-569.

and Nichol I., 1969, Factor analysis as an aid in the interpretation of regional geochemical stream sediemnt data: Quart. Col. Sch. Min. 64 245-264.

and Webb J. S. , 1969, The role of some statistical and mathematical methods in the interpretation of regional geochemical data: Econ. Geol. 64 204-233.

Gatehouse S., Russell D. W., and van Moort J. C., 1977, Sequential soil analysis in exploration geochemistry: J. Geochem. Explor. 8 483-494.

Gibbs R. J., 1973, Mechanism of trace metal transport in rivers: Science 180 71-73.

1972, River geochemistry; Ln Fairbridge R. W. (ed.), The encyclopedia of geochemistry and environmental sciences, 1042-1050, Van Nostrand-Reinhold, New York. 1321 pp.

1967, The geochemistry of the Amazon River System-I: The factors that control the salinity and the composition and concentration of the suspended solids; Geol. Soc. Amer. Bull. 78 1203-1232.

Ginzburg I. I., 1960, Principles of geochemical prospecting: Pergamon Press, Oxford. 311 pp.

Goldschmidt V. M., 1954, Geochemistry: Clarendon Press, Oxford. 730 pp.

Go leva G. A., Polyakov V. A., and Nechayeva T. P., 1970, Distribution and migration of lead in ground waters: Translated from Geokhimiya 3 344-357.

Govett G. J. S., Goodfellow W. D., Chapman R. P., and Chork C. Y., 1975, Exploration geochemistry - distribution of elements and recognition of anomalies: Math. Geol. 7 415-443.

Grassely Gy., and Heteny M., 1971, The role of manganese minerals in the migration of elements: in IMA-IAG0D joint Symp. Proc., Tokyo-Kyoto; Soc. Mining Geol. Japan Spec. Issue 3, 474-477.

Griffiths J. C., 1967, Scientific methods in analysis of sediments: McGraw-Hill, New York. 508 pp.

Grim R. E., 1968, Clay Mineralogy: McGraw-Hill, New York. 596 pp. - 249 -

Hall B. R., and Folland C. J., 1970, Soils of Lancashire: Rothamstead Experimental Station, England.

Handa B. K., 1970, Chemistry of manganese in natural waters: Chem. Geol. 5 161-165.

Harden G., 1962, Geochemical dispersion patterns and their relation to bedrock geology in the Nyawa area, Northern Rhodesia: Ph.D Thesis, University of London.

Harris R. C., and Troop A. G., 1969, Chemistry and origin of freshwater ferromanganese concretions: Limnol. Oceanogr. 15 702-712.

Hawkes H. E., and Webb J. S., 1962, Geochemistry in mineral exploration: Harper & Row, New York. 415 pp.

Bloom H., Ridell J. E., and Webb J. S., 1960, Geochemical reconnaissance in Eastern Canada: 20th Internl. Geol. Congr., Mexico (1956), 607-621.

1957, Principles of Geochemical prospecting; USGS Bull. 100-F. 355 pp.

Hem J. D., 1978, Redox processes at surfaces of manganese and their effects on aqueous metal ions: Chem. Geol. 21 199-218.

1977, Reactions of metal ions at surfaces of hydrous iron oxide: Geochim. et Cosmochim. Acta 41 527-538.

1976, Inorganic chemistry of lead in water: USGS Prof. Paper 957 5-11.

1976, Geochemical controls on lead concentrations in stream water and sediments: Geochim. et Cosmochim. Acta 40 599-609.

1972, Oxidation and reduction, in Fairbridge R. W. (ed.), The encyclopedia of geochemistry and environmental sciences, 839-842; Van Nostrand-Reinhold, New York. 1321 pp.

1972, Chemical factors that influence the availability of iron and manganese in aqueous systems; Geol. Soc. Amer. Bull. 83 443-450.

1970, Study and interpretation of the chemical characteristics of natural water; USGS Water Supply Paper-1473.

1964, Deposition and solution of manganese oxides: USGS Water Supply Paper 1667-B.

1963, Chemical equilibria and rates of manganese oxidation: USGS Water Supply Paper 1667A 1-64.

Horsnail R. F., Nichol I., and Webb J. S., 1969, Influence of variations in secondary environment on the metal content of - 250 -

drainage sediments: Quart. Col. Sch. Mines 64 307-322.

1968, The significance of some regional geochemical patterns in North Wales and South West England: Ph.D Thesis, University of London.

Hosking K. F. G., 197Problems associated with the application of geochemical methods of exploration in Cornwall, England; CIM Spec. Vol. 11 176-189.

1964, Permo-Carboniferous and later primary mineralization of Cornwall and South-West Devon in Hosking K. F. G. and Shrimpton G. J. (eds.), Present views of some aspects of the geology of Cornwall and Devon; Royal Geol. Soc. Cornwall Commemorative Volume for 1964, pp 201-245.

and Trounson J. H., 1959, The mineral potential of Cornwall: in The Future of non-ferrous mining in Great Britain an Ireland, IMM Symp. Proc. 337-351.

1949, Fissure systems and mineralization in Cornwall; Trans. Roy. Geol. Soc. Cornwall 18 9-49.

Howarth R. J., and Earle S., 1979, The application of a generalized power transformation to geochemical data; J. Internl. Assoc. Math. Geol. 11 45-62.

1973a, The pattern recognition problem in applied geochemistry, in Jones M. J. (ed.), Internl. Geochem. Explor. Symp. Proc. IMM, London.

and Lowenstein P. L., 1971, Sampling variability of stream sediments in broad-scale regional geochemical reconnaissance: Trans. IMM 80 sect.B B363-372.

Ingols R. S., and Enginun M. E., 1968, Biological studies of manganese solution from its dioxide: in Trace inorganics in water, Adv. Chem. Ser. 73 143-148 (Amer. Chem. Soc.).

Jackson K. S., and Skippen G. B., 1978, Geochemical dispersion of heavy metals via organic complexing - a laboratory study of copper, lead, zinc and nickel behaviour at a simulated sediment-water boundary: J. Geochem. Explor. 10 117-138.

James R. 0., and MacNaughton M. G., 1977, The adsorption of aqueous heavy metals on inorganic minerals: Geochem. et Cosmochim. Acta 41 1549-1555.

Jenne E. A., 1968, Controls on Mn, Fe, Co, Ni, Cu and Zn concentrations in soils and water: the dominant role of hydrous Mn and Fe oxides; in Trace inorganics in water, Adv. Chem. Ser. 73, 337-387 (Amer. Chem. Soc).

Joreskos K. G., Klovan J. E, and Reyment R. A., 1976, Geological factor analysis: Elsevier, Amsterdam. 178 pp. - 251 -

Khetagurov G. V., 1969, Distribution of silver and gold in ores, minerals and enrichment products of the lead-zinc deposits of the Greater Caucasus: Translated from Geokhimiya 11 1362-1369.

Kinniburgh D. G., Syers J. K., and Jackson M. L., 1975, Specific adsorption of trace amounts of calcium and strontium by hydrous oxides of iron and aluminium: Soil Sci. Soc. Amer. Proc. 39 464-470.

Klovan J. E. and Imbrie 1971, An algorithm and FORTRAN IV program for large-scale Q-mode factor analysis and calculation of factor scores: J. Internl. Assoc. Math. Geol. 3 61-77.

Koch G. S., and Link R. F., 1970-71, Statistical analysis of geological data: Wiley, New York. 2 vols, (vol 1 375 pp, vol 2 438 pp).

Kodama H., McKeague J. A., Tremblay R. J., Gosselin J. R., and Townsend M. G., 1977, Characterization of iron oxide compounds in soils by Mossbauer and other methods: Can. J. Earth Sci. 14 1-15.

Krumbein W. C., and Graybill F. A., 1965, An introduction to statistical models in geology: McGraw-Hill, New York. 475 pp.

Le Rich H. H., and Weir A. H., 1963, A method of studying trace elements in soil fractions: J. Soil Sci. 14 225-235.

Lee-Moreno J. L., and Caire L. F., 1975, Results of geochemical investigations comparing samples of stream sediment, panned concentrate, and vegetation in the vicinity of the Caridad porphyry copper deposit, Sonora, Mexico: Mining Eng. 27 (12) 68C (abstract).

Levinson A. A., 1974, Introduction to exploration geochemistry: Applied publishing Ltd, Calgary. 612 pp.

Lindman H. R., 1974, Analysis of variance in complex experimental designs: Freeman, San Francisco. 352 pp..

Link R. F and Koch Jnr G. S., 1975, Some consequences of applying lognormal theory to pseudo lognormal distributions; J. Internl. Assoc. Math. Geol. 7 117-1128.

Loganathan P., and Burau R. G., 1973, Sorption of heavy metal ions by a hydrous manganese oxide: Geochim. et Cosmochim. Acta 37 1277-1293.

Loring D. H., 1976, The distribution and partition of zinc, copper and lead in the sediments of the Saguenay fjord: Can. J. Earth Sci. 13 960-971.

Lynch J. J., 1971, The determination of copper, nickel and cobalt in rocks by atomic absorption spectrophotometry using a cold leach: CIM spec, vol 11 313-314. - 252 -

Mackin J. E. and Owen R. M., 1979, The geochemistry of sediments from Little Traverse Bay, Lake Michigan: Influence of physical processes. Can. J. Earth Sci. 16 532-539.

Malmqvist L., An iterative regression analysis procedure for numerical interpretation of regional exploration geochemistry data: Math. Geol. 10 23-41.

Marriot F. H. C., 1974, The interpretation of multiple observations: Academic Press, London-New York. 117 pp.

Mason B., 1966, Principles of Geochemistry: John Wiley and sons, New York-London. 329 pp.

Maurice Y. T., 1973, Geochemical interpretation of base metal enrichment in soils overlying Lower Carboniferous rocks near Dainglan, County Offaly, Eire: Ph.D Thesis, University of London.

Mazzucchelli R. H., and James C. H., 1966, Arsenic as a guide to gold mineralization in laterite-covered areas of Western Australia: Trans. IMM 75 sect.B B286-294.

McKeague J. A., and Day J. H. , 1966, Dithionite- and oxalate- extractable Fe and Al as an aid in differentiating various classes of soils: Can. J. Soil Sci. 46 13-22.

McKenzie R. M., 1975, An electron microprobe study of the relationships between heavy metals and manganese and iron in soils and ocean floor nodules: Austr. J. Soil Res. 13 177-188.

1972, The sorption of some heavy metals by the lower oxides of manganese: Geoderma 8 29-35.

, 1970, The reaction of cobalt with manganese dioxide in minerals: Austr. J. Soil Res. 8 97-106.

McKyes E., Sethi A., and Yong R. N., 1974, Amorphous coatings on particles of sensitive clay soils: Clay and Clay Minerals 22 427-433.

McLaren R. G., and Crawford D. V., 1973a, Soil copper-I; The fractions of copper in soils: J. Soil Sci. 24 172-181.

Mehra 0. P., and Jackson M. L«, 1960, Iron oxide removal from soils and clays by a dithionite-citrate system buffered with sodium carbonate: 7th Natl. Conf. on Clays and Clay Minerals 317-327.

Miesch A. T., 1977, Log transformations in geochemistry: J. Internl. Assoc. Math. Geol. 9 191-194.

(with 12 others), 1976, Geochemical survey of Missouri- methods of sampling analysis and statistical reduction of data: USGS Prof. Paper 954-A 39 pp. - 253 -

, 1967a, Theory of error in geochemical data: USGS ProF. Paper 574-A 1-17.

Mill A. J. P., 1977, Geochemical studies on colloidal and macromolecular constituents in surface waters: Ph.D Thesis, University of London.

Miller J. P., 1961, Solutes in small streams draining single rock types, Sangre de Cristo Range, New Mexico: USGS Water Supply Paper 1535-F 23 pp.

Millman A. P., 1953, Theory and practice of geochemical prospecting — The results of investigations in West Africa and West of England: Ph.D Thesis, University of London

Moeskops P. G., 1977, Yilgarn nickel gossan geochemistry - A review including new data and considerations: in 6th Internl. Geochem. Explor. Symp. Proc., Sydney 449-450.

Morris C. J. and Stumm W., 1967, Redox equilibria and measurement of potentials in the aquatic environment: iri Equilibrium concepts in natural water systems, Adv Chem. Ser. 67 270-285 (Amer. Chem. Soc.).

Neter J., and Wasserman W., 1974, Applied linear statistical models: Irwin Inc, Homewood, Illnois. 842 pp.

Ng Siew Kee and Bloomfield C., 1962, The effects of flooding and aeration on the mobility of certain trace elements in soils; Plant and Soil 16 108.

Nichol I., Horsnail R. F., and Webb J. S., 1966, Regional geochemical reconnaissance in Sierra Leone: Trans. IMM 75 sect.B B147-161.

Nowlan G. A., 1976b, Concretionary manganese-iron oxides in stream and their usefulness as a sample medium for geochemical prospecting: J. Geochem. Explor. 6 193-210.

, 1969, Fe-Mn nodules in stream sediments draining polymetallic sulphide deposits in Maine; USGS Open File Report No. 76-878.

Packham R. F., Rosaman D., and Midgley H. G., 1961, A mineralogical examination of suspended solids from nine English rivers: Clay Minerals Bulletin 4 (25) 239-242.

Parks G. A., 1967, Aqueous surface chemistry of oxides and complex oxide minerals. Isoelectric point and zero point of charge: in Equilibrium concepts in natural water systems, Adv. Chem. Ser. 67 121-160 (Amer. Chem. Soc.).

Parslow G. R., 1974, Determination of background and threshold in exploration geochemistry: J. Geochem. Explor. 3 319-336.

Perhac R. M., 1972, Distribution of Cd, Co, Cu, Fe, Mn, Ni, Pb and Zn in dissolved and particulate solids from two streams in - 254 -

Tennessee: J. Hydrol. 15 177-186.

Plant Jane, Jeffrey K., Gill E., and Fage C., 1975, The systematic determination of accuracy and precision in geochemical exploration data: J. Geochem. Explor. 4 467-486.

, 1971, Orientation studies on stream-sediment sampling For a regional geochemical survey in Northern Scotland: Trans. IMM 80 sect.B B324-345.

Rankama K. and Sahama Th. G., 1950, Geochemistry Chicago Univ. Press, Chicago. 911 pp.

Rankin P. C., and Childs C. W., 1976, Rare-earth elements in iron-manganese concretions from some New Zealand soils: Chem. Geol. 18 58-64.

Reid C., Scrivenor J. B., Flett J. S., Pollard W. and MacAlister D. A., 1906, The geology of the country near Newquay; Mem. Geol. Surv. England an Wales, 131 pp.

Rose A. W., Hawkes H. E. and Webb J. S., 1979, Geochemistry in mineral exploration, Academic Press, London. 657 pp.

1975b, The mode of occurence of trace elements in soils and stream sediments applied to geochemical exploration: Internl. Geochem. Explor. Symp. Vancouver, pp 691-705.

1972, Statistical interpretation techniques in geochemical exploration: Trans. Amer. Inst. Min. Metall. Eng. 252 233-239.

Ryan Jnr T. A., Loiner B. L. and Ryan B. F., 1976, Minitab Student Handbook; Duxbury Press, North Scituate, Mass. 341 pp.

Sato M., 1960, Oxidation of sulphide ore bodies-I: Geochemical environments in terms of pH and Eh: Econ. Geol. 55 928-961

Schalscha E. B., Appelt H. and Schatz A., 1967, Chelation as a weathering mechanism - I; Effect of complexing agents on the solubilization of iron from minerals and granodiorite; Geochim. et Cosmochim. Acta 31 587-596.

Schnellman A. and Scott B., 1969, Pb-Zn mining areas of Great Brittain: IB 9th Comm. Min. Metall. Congr. Proc, Paper 2 (Mining and Petroleum Geology Section).

Schoettle M., and Friedman G. M., 1971, Freshwater iron-manganese nodules in Lake George, New York: Geol. Soc. Amer. Bull. 82 101-110.

Schwertmann U. and Taylor R. M., 1972, The transformation of lepidochrosite to goethite; Clays and Clay Minerals 20 151-158. - 255 -

Service H., 1943* The geology of the Nsuta manganese ore deposits: Gold Coast Geol. Surv. Mem. 55 32 pp.

Shaw D. M.f 1961, Element distribution laws in geochemistry: Geochim. et Cosmochim. Acta 23 116-134.

Silverman M. P., 1972, Sulfide mineral oxidation - microbial: Fairbridge R. W. (ed.), The encyclopedia of geochemistry and environmental sciences: Van Nostrand-Reinhold, New York. 1321 pp.

Sinclair A. J., 1976, Application of probability graphs in mineral exploration: Assoc. Explor. Geochem. Spec, vol no 4 95 pp.

1974, Selection of threshold values in geochemical data using probability graphs: J. Geochem. Explor. 3 129-149.

Singer A., and Navrot J., 1976, Extraction of metals from basalt by humic acids: Nature 262 479-481.

Smith B. H., 1977, Some aspects of the use of geochemistry in the search for nickel sulphides in laterite terrain in Western Australia; J. Geochem. Explor. 8 259-281.

Stanton R. E., 1976, Analytical methods for use in geochemical exploration: Edward Arnold, London. 55 pp.

1966, Rapid methods of trace analysis for use in geochemical exploration: Edward Arnold, London. 96 pp.

Stanton R. L., 1972, Sulfides in sediments: iji Fairbridge . R. W. (ed.), The encyclopedia of geochemistry and environmental sciences: Van Nostrand-Reinhold, New York. 1321 pp.

Stephens Jackie E., 1976, A large new porphyry molybdenum discovery Southern Alaska using geology and geochemistry (abst.): Geol. Soc. Amer. Abstract with program 8(6) 1121.

Stevenson J. S., and Stevenson Louise S., 1970, Manganese nodules from the Challenger Expedition at Redpath Meuseum: Can. Mineral. 10 (4), 599-615.

Sterling T. D., and Pollack S. V., 1968, Introduction to statistical data processing; Prentice-Hall, New Jersey, 663 pp.

Stumm W. and Bilinski H., 1972, Trace metals in natutal waters: Difficulties of interpretation arising from our ignorance on their speciation; Adv. Water Poll. Res. Proc. 6th Internl. Conf. Jerusalem, 1972.

______and Morgan J. J., 1970, Aquatic chemistry: Wiley- Interscience New York. 583 pp.

Suarez D. L., and Langmuir D., 1976, Heavy metal relationships in a Pennyslvannia soil: Geochim. et Cosmochim. Acta 40 589-598. - 256 -

Szalay A. and Szilagyi M., 1967, The association of vanadium with humic acids; Geochim. et Cosmochim. Acta 31 1-6.

Taylor R. M., and McKenzie R. M., 1966, The association of trace elements with managanese minerals in Australian soils: Austr. J. Soil Res. 4 29-39.

Temple T. T., 1978, The use of factor analysis in Geology: Math. Geol. 10 379-390.

1976, Review of "Geological factor analysis" by Joreskos et al: J. Geol. 86 534.

Tennant C. B., and White W. L., 1959, Study of the distribution of some geological data: Econ. Geol. 54 1281-1290.

Theobald Jnr P. K., Lakin H. W., and Hawkins D. B., 1963, The precipitation of aluminium, iron and manganese at the junction of Deer Creek with the Snake River in Summit County, Colorado: Geochim. et Cosmochim. Acta 27 121-132.

Thomson I., Thornton I., and Webb J. S., 1972, Molybdenum in black shales and the incidence of bovine hypocuprosis: J. Sci. Food Agric. 23 879-891.

Thompson M., and Howarth R. J., 1976a, Duplicate analysis in geochemical practice-I: Theoretical approach and estimation of analytical reproducibility: Analyst 101 690-698.

Thornton I., 1975, Applied geochemistry in relation to mining and the environment: Miner. Environ. Internl. Symp. Proc. IMM London, 87-102.

and Webb J. S., 1974, Environmental Geochemistry: Some recent studies in the United Kingdom; in Trace substances in environmental health VII (Hemphill, ed.), Univ of Missourri, Columbia, pp 89-98.

Trochimczyk J., and Chayes F., 1978, Some properties of principal component scores: Math. Geol. 10 43-52.

Tyree S. Y., 1967, The nature of inorganic solute species in water: in Equilibrium concepts in natural water systems, Adv Chem. Ser. 67 183-195 (Amer. Chem. Soc.).

U. S. Natl. Comm. Geochemistry - Panel On Orientations For Geochemistry, 1973, Orientations in geochemistry; Washington Natl. Acad. Sci. 122 pp.

Viewing K. A., 1963, Regional geochemical patterns related to mineralization in central • Sierra Leone: Ph.D Thesis. University of London.

Vipan P. G. L., 1959, Lead and zinc mining in South-west England: in The future of non-ferrous mining in Great Britain and Ireland; IMM Symp. Proc. 337-351. - 257 -

Vistelius A. B., 1960, The skew frequency distribution and the fundamental law of the geochemical processes: J. Geol. 68 1-22.

1958, Paragenesis of sodium, potassium and uranium in volcanic rocks of Lassen Volcanic National Park, California: Geochim. et Cosmochim. Acta 14 29-34.

Webb J. S., 1973» Applied geochemistry and the community (Presidential address): Trans IMM 82 sect.A 23-28.

1971, Regional geochemical reconnaissance in medical geography: iji Environmental geochemistry in health and disease, Mem. Geol. Soc. Amer. 123 31-42.

1970, Some geological applications of regional geochemical reconnaissance: Proc. Geol. Assoc. London 81 585-594.

and Atkinson W. J., 1965, Regional geochemical reconnaissance applied to some agricultural problems in Co. Limerick, Eire: Nature 208 1056-1059.

Wells A. F., 1962, Structural inorganic chemistry (3rd ed.): Clarendon Press, New York. 546 pp.

White D. E., Hem J. D., and Whiting G. A , 1963, Chemical composition of surface waters: USGS Prof. Paper 440F 67 pp.

Whitney P. R., 1975, Relationship of manganse-iron oxides and associated heavy metals to grain size in stream sediments: J. Geochem. Explor. 4 251-263.

1974, Use of oxide-coated stream gravels in geochemical surveys: a test case; Trans. Soc. Min. Eng. 258 294 299.

Wilson A. L., 1976, Concentrations of trace metals in river waters, a review: Water Research Centre, Medmenham (England). Tech. Rep. TR16. 60 pp.

Wray D. A., 1936, British Regional Geology: The Pennines and adjacent areas. IIMSO, London. 87 pp.

Young R. D., 1971, The interpretation of regional geochemical patterns in Northern Ireland: Ph.D Thesis, University of London. table of L) values used as class limits for the dispersion plots

NXIAV PTL Wn Ph Fe Ca MR Cu Co NI Al PTL 64 lA V Mn Ke Co

5f 386 •2k .16 23.33 .56 .07 .13 .05 2.66 55 48 27 14 .019 .85 9 7F .95 1.49 26.30 3-64 .68 .14 .19 .07 3.27 75 58 32 16 .051 12 8? 1.91 2.70 28. 51 4-54 .85 .32 .24 .09 3.87 85 67 37 17 .046 .90 16 95 hi. JI 3-OI 5.27 32.96 6.15 1.24 2.00 .35 .12 5.21 95 88 56 20 .070 .93 27

MX2AV PTL Mn Zn Pb Fe Ca Mg Cu Co NI Al NI Cu Ag Zn Cd Pb

55 11.41 1.38 .28 5.77 5.46 .70 .05 .25 .11 .65 55 15 14 2 78 1.6 33 75 19.82 2.62 2.32 9.21 10.97 1.75 .09 .45 .19 1.08 75 17 24 5 187 2.5 197 85 28.97 5.60 5.70 14.36 17.38 1.95 .12 .59 .23 1.55 85 18 46 7 382 3.6 406 95 JO.48 7.13 9.25 21.63 21.78 3.27 .82 .95 .38 2.85 95 21 71 IS 912 7.3 857 CXAV j. PTL Mn Zn Pb Pa Ca MG Cu Co Ml Al PTL Zn Pb Pa Ca Mg Cu hntu 55 .70 .06 .01 .34 38.37 .53 .002 .01 .01 .17 35 5156 1172 58 2.02 10.61 778 75 2.12 .22 .02 3.20 39.81 .71 .008 .02 .04 .82 75 7957 5543 73 3.13 13.10 1400 85 4.03 .49 .03 9.40 41.12 .96 .02 .06 .08 1.77 83 10660 10260 94 3.74 16.60 1904 95 ' 8.22 .98 .06 20.09 42.36 1.35 .03 .09 .18 2.67 95 34850 36560 143 4.65 24.28 4472

' HX1 . PTL Mn Zn Pb Pa Ca MB Cu Co Ml Al MQAAS PTL m Za Pb Ca Mg Cu C/Jg.nl l) rstsj 55 .15 .02 .006 .89 .11 .02 .003 .005 .002 .10 55 208 79 23 •036 .176 15 75 .24 .03 .057 .93 .16 .03 .007 .007 .0028 .14 75 311 209 220 .049 .197 26 85 .27 .06 .081 .95 .20 .04 .014 .009 .0032 .20 83 443 450 415 .054 .203 53 95 .40 .10 .204 .98 .34 .07 .065 .011 .0047 .30 95 809 1055 962 .067 .222 92

CLICP PTL Ti Cr MQAAS PTL Za Pb Pa Ca MB Ca

55 .317 4.71 257 411 472 48 39 948 55 841 315 86 4.41 .14 .67 49 75 .381 5.57 357 511 585 69 48 1459 75 1431 970 943 5.03 .19 .86 128 85 .426 6.51 439 649 695 85 54 1687 85 2405 2271 1947 3.38 .21 .95 225 95 .573 7.17 530 933 900 109 66 2716 95 5356 5245 5127 7.27 .26 1.10 530

Fa Co . Ml Cu Zn Cd Al Pb MX1AV PTL Pb/Za MX2AV PTL Pb/Za CXAV PTL Pb/Zn

55 3.66 626 33 88 22 277 8 2.79 98 55 .973 53 .932 55 75 4.62 44 75 676 120 28 460 13 3.99 145 75 1.051 75 1.025 85 5.22 85 708 56 133 32 553 27 4.29 213 85 1.193 85 1.183 95 6,76 97 93 769 178 45 1567 100 5.71 1117 95 1.406 93 1.493

sgicp PTL Li Mg Ca Ba La Ti V Cr hn NQFSC PTL PI F2 P3 F4 F5 P6

sxt PTL Mn Zn Pb Pa Ca MI Cu CO Ml Al MX1 PTL PI P2 F3 P4 F5 F6 Fe+Mn 55- .4615 .3872 .3295 .5442 .4505 .3783 .2894 .3180 .2768 ..4463 55 -.087 .032 .641 .457 .208 .163 75 .4753 .4080 .4158 .5577 .4655 .3962 .3342 .3280 .2865 .4641 75 .061 .553 .640 .775 .747 85 .4798 .4209 .4279 .5699 .4868 .4208 .3669 .3340 .2923 .4914 85 .173 1.109 .728 .898 1.037 .900 95 .4906 .4357 .4973 .5925 .5148 .4504 .4217 .3464 .3017 .5319 95 .340 1.737 1.030 1.297 1.363 1.188

mx2 PTL Mn Zn Pb Fa Ca MI Cu Co Ml Al CX PTL m Za Pb Pa Ca Fe+Mn f 55 .5237 .4216 .3767 .4806 .5048 .4142 .2838 .3520 .3076 .3966 35 .6232 .3734 .2283 .4732 1.011 75 .5366 .4510 .4553 .5047 .5210 .4283 .2948 .3760 .3243 .4177 75 .6623 .4050 .2489 .3706 1.065 85 .5421 .4609 .5071 .5268 .5317 .4429 .3335 .3830 .3356 .4470 85 .6868 .4617 .2656 .5236 1.125 95 .5476 .4880 .5954 .5603 .5594 .4645 .3893 .3951 .3436 .5068 95 .7347 .6794 .4142 .3236 1.269

KEY .For non-ratioed fine fraction data, Mg, Ca, Fe and Al are in %, others in ppm. All ratioed data have no units. All oxide data are in %. X = oxide data; NXl=Newquay (Fe+Mn fraction), NX2=Newqpay (Mn fraction), CX=Clitheroe (Fe+Mn f raction).

symbol o Q PTL range Z. 55 55-75 75-85 85-95 > 95 OVERLAY 1

LEGEND

Metamorphic aureole • t St Austell (rsnlt*

Mid-Upper Devonian

it Enoder Lower Devonian

Doterite . PM Summercourt

Felsite on fault

Felsite Lower Penscawn Mines SH Sheperds-Pb Zn PM Penhaltow moor.PbZnCu Ag(?; EWR East Wheal Rote* Zn Pb Ag DP Oeerpark- Fe Zn Pb HPM Higher Penscawn• Zn Pb on . tm n cd Locality 2

NEWQUAY SE GEOLOGY AND LOCALITIES km

"O UimiM a.,,

as — .„• r n-im WS *H(IM IWITAUT CHI C«IIM> LO 1-.III

EINMI«OR HK oioioov *NO LOCSMTICI • LEGEND FOR OVERLAYS 1 & 2

OVERLAY 1 sr*- ^ Metamorphic aureole -pr of St Austell granite TT Mid-Upper Devonian

X Lower Devonian

Geologic boundary 3 Dolerite V>X nD Felsite on fault FF Felsite

^ Mines SH Shepherds - Pb Zn PM Penhallow moor - Pb Zn Cu Ag(?) EWR East Wheal Rose - Zn Pb Ag DP Deerpark - Fe Zn Pb HPM Higher Penscawn - Zn Pb Sn - tin

Locality 2

Newquay Town

• Trevesa Farm

OVERLAY 2

MG Millstone Grit BS Bowland Shale Group WS Worston Shale Group CHL Chatburn Lst Group

^^ Faults

Geologic boundaries

Drainage system

Mines SK = Skeleron - Ba Zn Pb Ag Ba

0 Locality 7 Town

Gisburn