by Raymond E. Healy · coherent geochemical associations from the data set, and allowed spatial modelling of the expression of the underlying geochemical processes. The superior spatial

Evaluation of Shallow Soil Geochemical Data from

Boliden Tara Mines’ Prospecting Licence

Areas 3545 & 3488 using an

Integrated Factor Analysis & GIS Method

by

Raymond E. Healy

Consulting Geologist

E-mail: [email protected]

May 22nd 2014

R. E. Healy, 2014 Evaluation of Soil Geochemical Data from Boliden’s PL Areas 3545 & 3488

1 Contains Ordnance Survey Ireland data © OSi 2012.

DECLARATION

This project was undertaken by Raymond E. Healy, who alone discharged all aspects

of the research, including preparation of this report, which is his sole responsibility. Mr. Healy

formerly operated as the consulting firm Minoretek in Winnipeg, Manitoba, Canada, where

he held the professional designation of P.Geo. Mr. Healy has over twenty years experience in

mining and exploration geology, principally in the field of applied mineralogy.

This report was prepared using data under licence from the Geological Survey of

Ireland (GSI), the Central Statistics Office (CSO), the Environmental Protection Authority

(EPA), and the Ordinance Survey of Ireland (OSI)1.

The findings in this report reflect Mr. Healy’s best judgement based on the analysis of

the data and other information available at the time of writing, and he reserves the right to

revise these findings if further information germane to the subject should subsequently come

to light. Use of this report is predicated on the reader’s understanding and acceptance of the

foregoing, and the copyright statement below.

Signed:

Copyright (c) 2014 by Raymond E. Healy.

This report is made available under the terms of the Creative Commons

Attribution-ShareAlike 3.0 License http://creativecommons.org/licenses/by-sa/3.0/


TABLE OF CONTENTS

1. ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

3. METHOD - INTEGRATED USE OF FACTOR ANALYSIS AND GIS . . . . . . . 6

4. LOCAL GEOLOGY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

5. COOLTOMIN GEOCHEMICAL DATA SET . . . . . . . . . . . . . . . . . . . . . . . . 15

5.1. STATISTICAL ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

5.1.1 Correlation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 21

5.1.2. Factor Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

5.2. SPATIAL ANALYSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

5.2.1. Soil Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5.2.2. Effect of Undetermined Components in Soil . . . . . 35

5.2.3. Metal Dispersion and Sampling . . . . . . . . . . . . . . . . 37

5.2.4. Spatial Modelling of Factor Scores . . . . . . . . . . . . . . 38

6. DISCUSSION AND CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

7. REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

8. APPENDIX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61


1

1. ABSTRACT

A geochemical data set from a study area straddling PL Areas 3545 and 3488, from the

southwestern end of the Irish orefield, was interrogated using an integrated Factor Analysis and GIS method.

Ten factors were extracted from the data set of 42 inorganic elements in 1,421 soil samples. The factors

constitute geochemical associations that reflect underlying geochemical processes, including ore-forming and

secondary dispersion processes, related to Irish Type carbonate-hosted Zn-Pb mineralization. The spatial

distribution of the factors was mapped by interpolating the factor scores using Ordinary Kriging.

Five pedogenic factors were identified, including the overwhelmingly dominant F1, which is

attributed to variable clay-oxide contents due to podzolisation. F3 has significant loadings for Ca, Mg, Sr, Ca

and La, and describes variation in the leaching of elements associated with carbonates, and retention of

elements associated with resistate minerals, due to gleisation. F4 has significant loadings for Fe, Co, Mn and

Ni, and reflects the variation in the content of Fe-Mn oxides-hydroxides, which co-precipitate and scavenge

Co and Ni. Two other pedogenic factors describe variation in Y (F7), and in Ge and Tl (F9), whilst F10 is the

only anthropogenic factor identified, and likely describes the application of P in fertiliser and sludge.

Two ore-related factors are identified. F2 has significant loadings for Bi, Cu, Pb, Sb, Sn, Te and Zn,

and reflects the signature of Irish Type Zn-Pb mineralisation due to mechanical dispersion in till of ore-related

elements with low mobility in an alkaline secondary environment. F6 is also an ore-related factor, with

significant loadings for As and Sb, and shows numerous minor peaks clustered in a arc west and south of the

major F2 anomaly at Cooltomin. The major F6 anomaly coincides with the northern end of the Cooltomin F2

anomaly. Differences in the distributions of F2 and F6 are not explicable by secondary mechanical or

hydromorphic dispersion, and likely reflect the greater primary dispersion of As and Sb as highly

differentiated vein mineralisation. Two ‘possibly’ ore-related factors are identified. F5 has significant loadings

for S, Se and U, with minor loadings for Cd, Mo and V. This is an association of elements that are very mobile

in oxidising alkaline environment, but for which reducing conditions, such as in gleys and peaty soils, act as a

geochemical barrier, giving rise as ‘false' anomalies. All F5 anomalies are potentially explicable in terms of

‘false' anomalies. F8 has a single significant loading for Cd. F8 peaks coincide largely with areas which form

geochemical barriers to Cd (e.g., gleys) generating ‘false' anomalies, characteristically without associated Zn.

The F2 scores show a strong anomaly centred on Irish Grid Ref. 131700,144550 at Cooltomin. The

anomaly extends 900m in a N-S direction, and most probably overlies subcroping mineralisation, or is

displaced laterally by glacial movement and/or soil creep, and presents a highly prospective target. Other

prospective targets include: (1) minor anomaly overlying Rathkeale Beds at Gortroe (131100,143100); (2)

cluster of satellite anomalies on the Waulsortian-Rathkeale contact west of Cooltomin (centred at 131100,

144250), especially given the association with a major NE fault, and the latter's association with volcanics;

and (3) minor anomaly overlying Rathkeale Beds at Ranahan (132150,143250). The principal F5 peak at

132850,144075 is probably a ‘false’ anomaly, but cannot be rejected given the association with a major fault

which likely acted as conduits for mineralising fluids.

The soil samples were taken from the A horizon, and are thus shallow and vulnerable to several

deleterious effects, which can obscure the signature of mineralisation. Some anomalous features in the data

are attributable to soil type effects and possibly anthropogenic effects associated with shallow samples. The

geochemical method employed is a partial extraction, and does not include silicate minerals that are

insoluble during acid digestion (e.g., quartz) or organic matter. These unanalysed and gravimetrically

significant soil components are a determinant in the chemical and factor analysis. Calculation of Total

Normative Mineralogy allowed identification of soils with high contents of unanalysed components, such as

at Ardlaman, and which cannot be explained on the basis of the indicated soil types. Imprecise sampling of

the A horizon can generate variable organic content, highlighting the sensitivity of shallow soil sampling.

The study has shown that Factor Analysis integrated with GIS is a powerful technique for

interrogating geochemical data, and has the potential to be extremely useful in geochemical surveying

applied to mineral exploration. The superior spatial definition and pattern recognition afforded by the

technique, can discriminate the signatures due to ore-forming processes or secondary dispersion of

mineralisation, and thereby potentially enhance anomaly detection and target generation.


2

2. INTRODUCTION

Geochemistry is widely used in geology, soil science, and in the wider environmental

sciences. Conventional treatment of geochemical data focusses predominantly on descriptive,

univariate and bivariate statistical techniques that evaluate the characteristics of individual

elements or element pairs in isolation. Using the geo-coordinates of the samples, the spatial

distribution of individual elemental concentrations or ratios of elemental concentrations are

also routinely rendered as maps and sections.

Geochemical data sets are intrinsically multivariate, and the full potential of these

data sets, which is typically obscured by complex inter-element relationships and

associations, can only be identified by multivariate statistical techniques. Multivariate

statistical techniques such as Factor Analysis (FA) offer a more integrated approach to

multi-element analysis, in which the inter-relationships of all the elements in a data set are

modelled simultaneously. FA attempts to resolve the structure within the correlation

coefficient matrix by clustering a large number of elements into a small number of

uncorrelated, generalised factors, each of which describes significant variation in the data

(Davis 1986). The extracted factors constitute geochemical associations that describe the

variation in the raw data, and are interpreted to reflect underlying geochemical processes.

Factor scores describe the degree to which the factors are expressed in the

composition of the samples (Davis 1986). The scores are thus estimates of the contribution of

the factors to each original variable and can be calculated for each sample. In practise it may

be possible to classify the samples into coherent groups on the basis of the factor scores of

each sample using appropriate scatter plots (Healy & Petruk 1994). Importantly, rather than

mapping the distribution of individual elements or element pairs, the expression of

geochemical processes as described by the factors can be mapped on the basis of the spatial

distribution of factor scores.

Using an integrated Factor Analysis & GIS method, Healy (2013) investigated the

Dublin Surge data set, which consists of elemental concentrations for 31 inorganic elements

in 1,058 topsoil samples from across the Greater Dublin Area (Glennon et al. 2012). The

Dublin Surge project is a baseline study of heavy metals and persistent organic pollutants in

the topsoils in the Greater Dublin Area. The Factor Analysis & GIS method discriminated six

coherent geochemical associations from the data set, and allowed spatial modelling of the

expression of the underlying geochemical processes.

The superior spatial definition and pattern recognition provided by the method is

potentially exploitable for target identification in mineral exploration applications. The

capability of the method to extract the signature of individual geochemical processes offers

the potential to isolate the signature of ore-forming processes from the masking effects of

other geochemical processes, and thus allow improved target identification through the more

precise modelling of its spatial distribution.


3

Geochemistry is one of the four principal tools used in exploration for buried mineral

deposits, a multi-disciplinary exercise that also uses geophysics, remote sensing and

geological mapping. Geochemical anomalies are often expressed by more than one element,

reflecting suites of associated elements specific to the ore-forming process of each type of

ore deposit (McQueen 2008). However, differences in geological, geomorphological and

environmental settings can impart unique surface geochemical signatures to individual

deposits. Consequently, geochemical prospecting demands rigorous analysis, not least to

extract the signatures due to regolith and pedogenic processes, soil type effects or

anthropogenic effects. The utility of exploration geochemistry has been demonstrated in

terrain with thick, transported glacial cover, such as Canada and Ireland (Levinson 1974,

McClenaghan 2007, DCMNR 2006). Indeed, geochemistry has played a critical role in the

discovery of each of the major Irish base metal deposits (i.e., Silvermines in 1963, Gortdrum

in 1966, Tynagh in 1967, Navan in 1970, Galmoy in 1986, and Lisheen in 1990), and often

using shallow soil geochemistry (DCMNR 2006, Ashton 2006).

Boliden Tara Mines, hereinafter referred to as Boliden, agreed to trial the integrated

Factor Analysis and GIS method on a geochemical data set from Prospecting Licence Areas

3545 and 3488 at the southwestern terminus of the Irish Orefield, immediately northwest of

Rathkeale, Co. Limerick (See Fig. 1). Boliden holds these Licence Areas in order to explore for

Irish Type carbonate-hosted Zn-Pb mineralisation, specifically targeting “base-of-Waulsortain

hosted zinc-lead mineralisation related to early Visean faulting” (Tara Mines 2004). The two

Prospecting Licence areas are contiguous, and lie within the Navan-Silvermines mineral trend,

approximately 9km south of the Courtbrown Zn-Pb deposit. Weakly disseminated Ag, As, Cd,

Cu, Fe, Pb, Sb and Zn sulfide mineralisation has been intersected by drilling, and is hosted

principally in Waulsortian Reef Limestone, and secondarily in Rathkeale Beds and Ballysteen

Limestone, with associated enrichments of Hg, Mo, Te, Tl, W, Th and U (Blakeman pers.

comm. 2014). Blakeman also notes that multi-element soil anomalies exhibit strong structural

control, and are dominated by Pb, Zn and minor Cu values, with attendant As, Cd and Sb.

The study area measures roughly 2 x 4 kms, and comprises 41 N-S oriented sampling

traverses spaced 100m apart with sampling approximately every 50m (See Fig. 2). The

traverses transect the roughly east-west trending contact between Waulsortian limestones

and the Rathkeale Formation. The sampling is not uniform throughout the study area, with

several un-sampled sections, particularly in the southeast of the study area. Although these

represent significant deviations from systematic sampling, Kriging is effective in interpolating

the results from non-random and clustered samples. The soil samples were collected by hand

auger, consistently from the shallow A horizon of the soil.

The data set as received from Boliden consists of elemental concentrations for 46

inorganic elements in 1,421 topsoil samples, and for convenience is hereafter referred to as

the Cooltomin geochemical data set.


4

Figure 1. General geology map of the southwestern end of the Irish orefield (See inset for approximate location), showing the boundaries of the

prospecting licence areas. Note that the study area (i.e., hatched area outlined in red) is located in prospecting licence areas 3545 and 3488 at the centre of

the map, northwest of Rathkeale. Data from Geological Survey of Ireland, Ordnance Survey of Ireland, and the Central Statistics Office.


5

Figure 2. Map of the study area straddling the boundary between Prospecting Licence Areas 3545 and 3488, and showing the 1,421 sampling points along 41 N-S

oriented traverses. The boundary of the study area is outlined in red. Contacts of the major geological units, namely Durnish Fm. (DU), Rathkeale Fm. (RK), Visean

Limestones (VIS), and Waulsortian Limestones (WA) are shown in red-brown. Boundaries of the Rathkeale urban area is just visible in dark grey (bottom right

corner). Data from Boliden Tara Mines, Geological Survey of Ireland, Ordnance Survey of Ireland, Central Statistics Office, Open Street Map, and Google Earth.


6

3. METHOD - INTEGRATED USE OF FACTOR ANALYSIS AND GIS

Factor analysis is a statistical method for discerning the regularity and order of

phenomena, and as such uncovers the underlying or latent structure in observed data

(Rummel 2012, and Garson 2012). Because factor analysis reduces attribute space from a

large number of observed variables to a smaller number of factors, it is widely used in

disciplines where large quantities of data are analysed, such as the social, behavioural and

physical sciences, including geology and geochemistry.

Factor analysis is a broad term for a set of allied statistical procedures that describe

observed variables in terms of a smaller number of variables or factors (Yang 2009). The term

has come to include principal component analysis (PCA) and common or principal factor

analysis (FA), and these two methods are commonly confused. Although both methods use

extraction and rotation procedures, and both explain observed variables using fewer derived

variables, the two methods are substantively different in mathematical expression and

purpose. Demsar et al. (2012) compare the two methods by stating that FA creates a model

of the lower dimensional space, whilst PCA produces a data-driven, linear projection. Yang

(2009) states that PCA is not a true factor analysis, whilst Davies (1986) states that PCA is not

a statistical procedure, but rather a mathematical manipulation.

Common variance is that fraction of variance in an observed variable that is shared

with other observed variables, whilst unique variance is specific to that observed variable, and

error variance comprises the residual (Yang 2009). FA differentiates the fractions of common

variance, specifically excluding unique and error variance, as it is a correlation-focussed

method. Consequently, FA resolves the structure within the correlation coefficient matrix by

clustering a large number of elements into a small number of uncorrelated, generalised

factors, each of which describes significant variance in the data (Davis 1986). The objective of

FA is to reveal the latent structure expressed in the observed variables, and so FA treats

observed variables as a function of the unobserved underlying factors (Yang 2009).

In Exploratory Factor Analysis (EFA) the analyst refrains from à priori constructs, and

undertakes the analysis to intuit the factor structure, rather than impose a preconceived

structure (Garson 2012). The exploration is an iterative process of applying alternative

analytical parameters, such as extraction methods, numbers of factors, rotation methods,

etc., until convergence on a viable solution is achieved. The analyst applies multiple criteria,

such as Kaiser criterion, scree tests, proportion of explained variance, communalities, degree

of cross-loading and comprehensibility, where the latter includes heuristic, domain-specific

criteria (Garson 2012 and Yang 2009). Thus, the intermediate results drive the outcome of the

analysis, revealing the underlying structure of common factors that is manifest in the

interrelationships among the observed variables (Yang 2009). This multi-faceted decision-

making process is guided by the over-arching purpose of achieving interpretable parsimony.

There are two principal products of a factor analysis, namely the factor matrix and

factor scores. The principal output from factor analysis is the factor matrix, where each factor


7

bears a factor loading for each observed variable. A factor loading is the correlation between

the observed variable and the factor, and is analogous to the Pearson’s r correlation

coefficient (Garson 2012). The square of the loading is the proportion of the variance in the

variable explained by the factor (Davis 1986). Thus, the sum of the squares of the loadings for

all variables on a factor indicates the proportion of variance explained by that factor.

Similarly, the sum of the squares of the loadings for a given variable, called the communality,

indicates the proportion of variance in that variable explained by all of the factors.

Maximising factor simplicity with the fewest number of factors, each with high factor

loadings for highly correlated variables, and low factor loadings for the remaining variables, is

expected to deliver optimum factor comprehensibility. The attainment of simple structure is

measurable using various metrics, including simplicity indices (Lorenzo-Seva 2003). Garson

(2012) observes that the inferential process of interpreting and naming factors, based on the

principal variables that load heavily onto the factors, can be fraught with subjectivity, and is

dependent on the expertise of the domain expert.

The second significant output of an analysis is the factor scores. These are scores for

each case or observation (e.g., soil sample) on each factor, or more precisely, estimates of the

contribution of each factor to each observation. Hoffman (2011) cautions against the use of

factor scores because of the issue of factor score indeterminacy. Indeterminancy flows from

the common factor model, in which parameters are not uniquely defined, such that no unique

factor solution exists (Grice 2002, DiStefano et al. 2009). By corollary, factor scores based on

factors are also not uniquely defined, and are thus indeterminate. DiStefano et al. (2009)

conclude that the common ‘refined’ methods of calculating factor scores, including that of

Ten Berge et al. (1999), which is used by the program Factor 8.10 (Lorenzo-Seva & Ferrando

2012), are affected by indeterminacy. Grice (2002) and DiStefano et al. (2009) stress the

importance of investigating the stability of the factor structure and degree of factor score

indeterminacy. In this study, it was found that the factor models gave relatively stable

solutions, and that the factor scores are rational and reproducible (See Subsection 4.1.2.

Factor Analysis). However, factor models for different numbers of extracted factors logically

gave variable factor solutions. It is considered that minimising the negative affects of factor

score indeterminacy is predicated on the validity of the interpretations of the domain expert

during the iterative process of converging on the final factor model.

As factors are underlying constructs that influence the expression of observed

variables (Suhr 2005), factors extracted from geochemical data constitute geochemical

associations that reflect the underlying geochemical processes. Consequently, the factor

scores describe the degree to which the geochemical processes are expressed in the

composition of the samples (Davis 1986). DiStefano et al. (2009), Garson (2012) and Rummell

(2012) note that factor scores are often used as new variables in subsequent analysis or

modelling, which is the primary purpose of this study. Crucially, the spatial distribution of the

factor scores allows the expression of geochemical processes, as described by the factors, to

be modelled, mapped and analysed using GIS technology.


8

A geographic object or entity can be defined in terms of its spatial location, attribute

(including dimension or geometry) and time. Spatial data represents locational information or

“the where”. Attribute data is non-spatial information that is a characteristic of the entity,

and represents “the what”, such as a name, label, description, classification, measure, etc.

Thus, geographic data contain attribute, spatial, and possibly temporal components, based on

the specifications of the relevant spatial data model, and are typically stored as x,y

coordinates, possibly dates/times, and one or more attributes. The data space can thus be

deconstructed into n-dimensional attribute space (i.e., n = no. of variables), 3-dimensional

geographic space (i.e., x,y,z coordinates), and 1-dimensional temporal space, where the latter

space-time components form the reference framework for the attribute space (Demsar et al.

2012). Unlike relational databases, GIS can relate otherwise disparate data using spatial, and

increasingly spatio-temporal, location as the primary index variable for referencing the

attribute data, such that any observed property that has an associated location can be

interrogated by GIS.

In their overview of the multivariate statistical techniques used across disciplines in

soil studies, Mostert et al. (2010) report that PCA is the ‘workhorse’ of multivariate analysis of

soils, and is often combined with other techniques, particularly Cluster Analysis (CA). As

stated earlier, factor analysis is a broad term for a set of allied statistical procedures which

typically include common factor analysis (FA) and principal component analysis (PCA). By

extension, the work of Demsar et al. (2012) on the use of PCA on spatial data is instructive

here, and provides a framework for categorising the possible application of FA on spatial data

into: (1) Non-Spatial Approach, using standard non-spatial FA on spatial data; and (2) Spatial

Approach using FA adapted for spatial effects. An examination of the latter Spatial Approach

to FA of spatial data is beyond the scope of this study, and is not considered further here.

The non-spatial approach to FA avoids issues related to the non-stochastic character

of spatial data (e.g., spatial autocorrelation), by using standard FA on attribute space only,

commonly as a precursor to spatial modelling and analysis. The entire data set is processed by

FA, and gives global results without consideration of spatial effects (i.e., analysed with a

statistical program, not GIS). Extending the framework of Demsar et al. (2012), the Integrated

FA & GIS method used here falls into the Spatial Objects FA subcategory of the non-spatial

approach. Spatial Objects FA pertains to factor analysis on data related to spatial objects, such

as sampling points analogous to the ‘Discrete Objects’ conceptual model of geographic

variation. A Spatial Objects FA can be spatially modelled and analysed using factor scores,

which are the transformed attribute data values corresponding to each spatial location. This

subcategory is the predominant form of FA used on spatial data in the geosciences, including

the application in this study. The use of the non-spatial approach to factor analysis integrated

with GIS exploits the power and resilience of a well established, statistical method to analyse

data globally (i.e., non-spatially), coupled with the capabilities for subsequent spatial

modelling and analysis by GIS. This approach has been employed in numerous applications,

including mineral exploration (De Vivo et al. 1998, Singh et al. 2002, Harraz et al. 2012, and

Yousefi et al. 2012), and geology and mining geology (Lado et al. 2008, Perrotta et al. 2008,

and Healy 2013).


2The early Carboniferous is taken to correspond to the former Lower Carboniferous or Dinantian

Series, which are now obsolete terms (Heckel & Clayton 2006). The period corresponds to the

Tournaisian (359-345 Ma) and Visean (345-326 Ma) Stages of the Mississippian Subsystem. The

Tournaisian is subdivided into the Hasarian, Ivorian and the lower part of the Chadian Substages, as

the Chadian straddles the boundary with the Visean. The Visean is subdivided into the upper part of

the Chadian, and the Arundian, Holkerian, Asbian and Brigantian Substages. The Courceyan

corresponds to the two lower Substages of the Tournaisian (i.e., Hasarian and Ivorian).

9

4. LOCAL GEOLOGY

Ireland is the largest zinc producer in Europe, and since the mid-1960's has witnessed

the development of five major Zn-Pb mines, namely Tynagh, Silvermines, Lisheen, Galmoy

and the giant Navan deposit (i.e., . 105Mt at 8.1% Zn and 2.0% Pb; Ashton et al. 2010). This

world class Zn-Pb province, known as the Irish Orefield, hosts deposits that are referred to as

Irish-Type Carbonate-Hosted Zn-Pb deposits, and which have characteristics common to both

Mississippi Valley-Type (MVT) and sedimentary exhalative (SEDEX) deposits. There is a

developing consensus that the deposits formed, not by exhalation onto the palaeo-seafloor

(i.e., a syn-sedimentary model), but rather by replacement of carbonate hostrocks that were

previously subjected to regional dolotomisation (Wilkinson et al. 2010, Hitzman et al. 2002).

The sulfide mineralisation is commonly associated with the development of black matrix

breccia (BMB), supporting a post-lithification, epigenetic ore model.

The deposits form typically stratabound lenses within shallow marine carbonates of

Courceyan to Chadian age (i.e., early Carboniferous2), and their distribution is both

stratigraphically and structurally controlled. The deposits occur in the Waulsortian mudbank

complexes of south and central Ireland or in the Navan Group of north central Ireland (See

Fig. 3). Blaney & Redmond (2010) note that the Waulsortian and Navan Group tend to be the

stratigraphically lowest, non-argillaceous, carbonate units occurring locally at each deposit.

The deposits are also located adjacent to major normal faults bounding the margins of

sedimentary basins. These NE-trending listric faults are Caledonian structures reactivated

during early Carboniferous extensional tectonics, and provided conduits for the hydrothermal

ore-forming fluids (Wilkinson et al. 2010). The latter authors report that precipitation of ore

occurred when upwelling, high temperature (i.e., 130-240OC), moderate salinity (i.e., 8-19%

NaCl equiv.), metal-bearing fluids mixed with low temperature, high salinity brines containing

reduced sulphur, preferentially within more permeable Carboniferous horizons.

Irish-Type mineralisation has been known to occur in the Limerick area since the

discovery of sub-economic deposits at Courtbrown and Carrickittle in the 1960s. However,

with the recent discovery of major Zn-Pb deposits at Pallas Green and Stonepark, the Limerick

Basin has emerged as an important sub-district of the Irish Orefield, and the focus of

considerable exploration activity (Wilkinson et al. 2010, Blaney & Redmond 2010).


10

Figure 3. Idealised section showing stratigraphic extent of carbonate-hosted Zn-Pb

deposits in Irish Orefield. Redrawn from EMD (2006).

The early Carboniferous sequence of carbonates that are host to the Irish-Type

deposits were laid down by a northward-advancing, marine transgression of a shallow tropical

sea across the Old Red Sandstone continent. The Limerick Basin (also known as the Shannon

Trough or Basin) and the Dublin Basin are intra-cratonic, sedimentary basins that developed in

response to crustal extension during the early Carboniferous. The Limerick Basin has an axial

Caledonide trend, but is closed to the northeast, separating it from the largely coeval Dublin

Basin. A thick succession of sediments and volcanics (i.e., .3km) were deposited during the

Tournaisian, Visean and Namurian on the southern side of the Limerick Basin (Redmond 2010,

Holdstock 2004). Significant volcanic activity occurred within the Limerick Basin during the

early Carboniferous, and there is an atypically close association between igneous rocks and

Irish-Type deposits within the basin (Redmond 2010).

The study area lies on the southern margin of the Limerick Basin, and at the western

end of the ENE-trending Rathkeale syncline (See Fig. 1). In the late Carboniferous, broad open

folding developed on a regional scale during the Hercynian orogeny, resulting in ENE-trending

synclines (e.g., Rathkeale) and anticlines (e.g., Ballingarry) in much of Munster. The simplified

early Carboniferous stratigraphy occurring in the general locality is shown in Figure 4, and

summarised below, based largely on Sleeman & Prachet (1999) and Tara Mines (2004), but

without explicit individual references.

The Devonian to earliest Courceyan Old Red Sandstone (ORS), which forms the base

of the stratigraphy, occurs as an inlier south of Rathkeale, due to the anticline formed by ENE

trending folding of Hercynian age. As the Carboniferous marine transgression advanced

northward, the ORS was overlain by a thin sequence of shallow water sandstones and


11

Figure 4. Early Carboniferous stratigraphy of

the Rathkeale area. Modified from Tara

Mines (2004). Colours scheme same as for

Fig. 1.

mudstones of the Lower Limestone Shale (early

Courceyan). These are succeeded by the Ballysteen

Fm. (mid-Courceyan), a thick (i.e., approx. 250m)

sequence of argillaceous bioclastic limestone, which

becomes increasingly argillaceous upwards.

The Ballysteen Fm. is succeeded by a very

thick (i.e., approx. 1km) sequence of Waulsortian

Limestones (late Courceyan to Chadian), reflecting

rapid deepening of the Limerick Basin. The main

lithology of the Waulsortian are pale-grey, massive,

unbedded, biomicrite wackestone, with abundant

crinoids and bryozoa, and often with the distinctive

stromatactis structure. The Waulsortian formed

steep carbonate mudmounds or banks in the

increasingly less energetic environment presented by

the northward retreating shoreline. The banks were

commonly separated by dark-grey, argillaceous,

shelve limestones, although the banks did coalesce

into continuous sheets covering much of the area.

The banks represent carbonate accumulations that

developed on regional scale carbonate ramps with

depths range from approximately 300m to 100m,

below the wave base and largely in the aphotic zone

of a tropical sea. Although the banks lacked a

framework building organism, unlike modern day

coral reefs, the initial gel-like cohesion of the muds

allowed steep depositional slopes (Lees 2006).

The Rathkeale Fm. (Arundian) succeeds the

Waulsortian, and consists of a thick (i.e., #460m)

succession of dark, non-fossiliferous, argillaceous

limestones and shaly mudstones. These basinal

sediments were deposited during a period of ramp

sedimentation, with the Rathkeale Fm. representing the outer ramp facies to the west. The

Rathkeale Fm. locally exhibits tight folding and cleavage development, but this deformation

does not penetrate into the underlying Waulsortian, suggesting that the top of the

Waulsortian may have acted as decollement. In contrast, Blakeman (pers. comm. 2014)

reports that the contact between the Waulsortian and Rathkeale Fm. is an inverted faulted

contact within the study area, but elsewhere is a gradational contact.


12

The Rathkeale Fm. shoals upwards into the overlying Durnish Fm. (Holkerian to

Asbian), which consists of a thick sequence (i.e., 300m) of dark, cherty blue-black bioclastic

limestones, representing a mid ramp facies. The Durnish Fm. is succeeded by the Shanagolden

Fm. (late Asbian), a thin (i.e., 75m) sequence of well-bedded, micritic limestones, representing

carbonate deposition in gradually deepening water, corresponding to a mid to outer ramp

facies. This is overlain by the Parsonage Beds, a thin unit (i.e., 24m) of shallow-water micrites,

and the Corgrig Lodge Beds, a thin unit (i.e., 6m) of limestones and shales, which represent

the top of the Visean. A band of rocks identified as Visean Limestones (Undifferentiated)

occur immediately to the southwest of the study area and extend south beyond Newcastle.

This early Carboniferous stratigraphy is overlain by a thick succession of Namurian

sediments (i.e., including the Clare Shale Fm., the Shannon Group and the Central Clare

Group, the Gull Island Fm., and the Tullig Sandstone), which are shown lying off to the west in

Figure 1. Also shown west of the study area at Shanagolden East and Carron’s House are

volcanics, which are Visean age volcanoclastics, and reflect significant volcanic activity on the

margins of the Limerick Basin. It is noteworthy that there is a close spatial association of the

volcanics with the major ENE-trending fault that extends into the study area.

The gross stratigraphy within the immediate study area is simple, with Waulsortian

Limestone overlain by Rathkeale Fm. and minor Durnish Fm. in the axis of the Rathkeale

syncline. Blakeman (pers. comm. 2014) reports that the roughly E-W trending, contact

between the Waulsortian and the Rathkeale Fm. is a fault contact, which is offset by NW

faults, and with mineralisation apparently concentrated at these structural intersections.

Overburden thicknesses in the study area vary from 3 to 9m, and underlie well-

drained arable and dairy farmland with few marshy areas (Blakeman pers. comm. 2014). The

overburden consists dominantly of glacial till derived from Carboniferous limestone, with

lesser amounts of alluvium and rock subcrop, including karstified rock, and minute pockets of

cutaway peat, fen and undifferentiated lake sediments (See Fig. 5). The till was deposited in

the Pleistocene, most probably during last great advance of the British-Irish Ice Sheet around

25,000 BP (i.e., the late Midlandian stage), prior to initiation of deglaciation around 20,000 BP

(Coxon & McCarron 2009). These author note that ice sheet dispersal centres formed in the

northern half of the country, with the direction of ice movement radiating outwards, which

gave rise to ice movement in an approximately southwestern direction within the study area.

The topsoil in the study area is typical of dry mineral soils found in the flat and

undulating lowlands (Gardiner & Radford 1980). The topsoil consists dominantly of grey

brown podzolics and brown earths (derived from calcareous parent material), with lesser

rendzinas and lithosols, mineral alluvium, and surface water and groundwater gleys, and

minute pockets of cutaway peat and undifferentiated lake sediments (See Fig. 6).


13

Figure 5. Subsoil map of study area within Prospecting Licence Areas 3545 and 3488. The boundary of the study area is outlined in red. Relevant subsoil

map codes (e.g., Tls denotes Till derived from Carboniferous Limestone) are from Fealy & Green (2009), are discussed in the text and listed in Table 1 of

the Appendix. Data from Environmental Protection Agency, Ordnance Survey of Ireland, and the Central Statistics Office.


14

Figure 6. Soil map of study are within Prospecting Licence block. The boundary of the study area is outlined in red. Relevant soil map codes (e.g.,

BminDW denotes Grey Brown Podzolics and Brown Earths derived from mainly calcareous parent material) are from Fealy & Green (2009), are

discussed in the text, and are listed in Table 1 of the Appendix. Data from Environmental Protection Agency, Ordnance Survey of Ireland, and the Central

Statistics Office.


3 This report contains Ordnance Survey Ireland data © OSi 2012.

4http://www.openstreetmap.org/

5http://gis.dcenr.gov.ie/imf/imf.jsp?site=ExplorationCompanyReports

15

5. COOLTOMIN GEOCHEMICAL DATA SET

The soil samples were collected from the A horizon by hand auguring, and each

sample was analysed by ALS Laboratories. The concentration of 45 inorganic elements in each

of the 1,421 samples were determined using an in-house multi-acid digestion technique,

followed by analysis using inductively coupled plasma-atomic emission spectrometry

(ICP-AES). The acid digestion is only a partial extraction technique that does not dissolve all

silicates, and is thus indeterminate for Si. Au was determined by fire assay. Data pertaining to

analysis of standards, duplicates, detection limits, etc. were also provided.

Two types of data are also appended to the row of each sample in the Cooltomin data

set. These are: (1) spatial data in the form of x,y coordinates in metres (i.e., Irish National Grid

coordinates); and (2) other attribute data (e.g., sample name, location description, sample

type, analytical batch, etc.). Spatial data were also downloaded from: (1) CSO - boundary map

of electoral divisions, boundary map of city and towns, and map of primary national roads

(Central Statistics Office 2013); (2) EPA - soil, subsoil and Corine maps of Ireland

(Environmental Protection Agency 2013); and (3) GSI - 1:100,000 geological bedrock map of

Ireland, and boundary map of prospecting licence areas (Geological Survey of Ireland 2013);

and include data from the Ordinance Survey of Ireland (OSI)3.

In addition, the trace of the Deel River within the study area was digitised using

Google Earth (approximate accuracy of ± 25m), while the road network within the study area

was exported from Open Street Map4, as discrete points with x,y coordinates, which were

subsequently edited into line segments to represent the road network. Also, the approximate

x,y coordinates of several drillholes and points of interest (e.g., Courtbrown Zn-Pb deposit,

drillhole CT1, 3488/15 and Ovoca A & B anomalies) were estimated from maps associated

with various progress, renewal and moratorium reports for the two Prospecting Licence Areas

at the Geological Survey Ireland website5. These reference data were principally used to give

geographic context to the various renderings of the factor score data.

The Open Source executable program called Factor 8.10 (Lorenzo-Seva & Ferrando

2006, 2012), which runs in the Windows NT and 7 environment, was downloaded and

installed. Factor is an exploratory factor analysis program, and performs correlation and

factor analysis. The program lacks a null data function, so values of half the limit of detection

(i.e., HLD) of each element were retained for 'not detected' values.


16

5.1. STATISTICAL ANALYSIS

Univariate statistical analysis is typically the first procedure used to quantitatively

explore raw data, including the process of data verification and data scrubbing or cleansing.

Because univariate statistics treats variables individually using descriptive measures, such as

mean, standard deviation, variance, student’s t, etc., these techniques are most useful in the

analysis of simple systems. However, univariate statistics fall short in the analysis of complex

systems, as they treat variables as being independent, and thus cannot detect or analyse the

relationship between variables, known as covariance. Thus, univariate statistics are commonly

used as a prelude to multivariate statistical analysis.

The Cooltomin dat set contains concentrations for 46 inorganic elements in 1,421

samples. A cursory examination of the data set showed that Au and B concentrations are

available for only 160 samples, whereas very high proportions (i.e., >50%) of the samples

yielded Ag and Hg concentrations that are below the respective limits of detection. Ten other

elements show concentrations in one or more samples with values less than the limit of

detection (See Table 1). These ‘left-censored’ data are most commonly handled by imputing

a value by substitution, such as LLD/2 (i.e., half the limit of detection or HLD) or LLD/%2.

Hewett & Ganser (2007) state that the US EPA recommends using substitution methods when

the percent censored data is <15%, and other methods such as maximum likelihood

estimation (MLE) when the percent censored data is >15%. In this study, elements with

greater than 20% censored data were rejected from further analysis (i.e., Au, Ag, B and Hg).

Although retained, Se (19.6%), W (9.5%), Ta (8.0%) and Te (7.1%) exhibit significant

percentages of censored data, which is considered in the context of the interpretation of the

results for these elements. The respective HLD was used for all retained censored data.

Many statistical procedures assume that the variables are distributed normally, such

that significant deviations from normality can increase the likelihood of Type I or II errors, in

which the acceptance of false or rejection of true outcomes occurs. Deviations from

normality can be due to data entry errors and missing data values (e.g., -999), as well as the

valid reasons of outliers and the nature of the variable itself (Osborne 2002). Outliers

represent extreme values relative to the rest of the sample, and may be artifacts generated

during sampling, analysis or may be real. In the case of mineral exploration, outliers may

represent real values corresponding to elemental enrichment related to mineralisation, which

manifests as a bimodal distribution, and which constitutes a mixture of two unimodal

distributions. Removal of such outliers is implicitly contraindicated, as identifying anomalous

values indicative of mineralisation is a primary objective in exploration geochemistry (or

contamination in environmental geochemistry), and can potentially be mitigated by

transformation, if necessary. As the current multivariate analysis was intended to be

exploratory, outliers were not removed from the data set, in order to avert the induction of

bias and the potential deletion of real variance. Because standardisation is built into the


17

factor analysis technique, the data was not standardised as this has no affect on the factor

results.

Although Reimann et al. (2011) state that data normality is not essential for principal

factor analysis, they argue that transformation may be indicated in order to ensure all

variables approximate to normal distributions. Filzmoser et al. (2009) state that using raw

data or improperly transformed data leads to biased results from factor analysis. In contrast,

Stanley (2006) argues that as most geochemical distributions are multi-modal, transformation

for the purpose of achieving normality is “neither generally achievable nor justified”. Garson

(2012) states that the assumption of normality pertains to significance testing of coefficients,

whereas communality is the inherent measure of ‘goodness of fit’ and indicates those

variables to reject in factor analysis. As the construction of confidence intervals around the

model parameters, or computation of significance tests are not a requirement here, the

assumption of normality is obviated. Two common types of transformation (i.e., square root

and log transformation) were trialed on the current data set, and gave Factor Analysis results

with largely comparable factors to the analysis of the untreated data set, but with some loss

of resolution. Osborne (2002) notes that as transformation alters the nature of variables, it

should not be used unless there is an overriding reason. Consequently, the current data set

was not transformed. As will be seen below (See Section 5.1.2 Factor Analysis), those

elements with distributions exhibiting high skewness and kurtosis (e.g., Na, Ba, Hg, Sn and U),

gave low communalities during Factor Analysis, confirming the above assertion of Garson.

Summary statistics for the Cooltomin data are given in Table 1. The coefficient of

variation [CoV = (std dev / mean) * 100] is a measure of the relative variance exhibited by an

element’s distribution. Interestingly, with the exception of Ca, only the ore-related elements

Cu, Mo, Se, Te and Zn all exhibit CoV values above 100, whilst Cd, Sn and U exhibit values

above 90. Average Enrichment Factors (EF) were also calculated for each element using Al as

the reference element, according to the method of Tasic et al. (2008). These authors state

that EF values close to unity indicate crustal sources, whereas values in excess of 1 indicate

significant fractions from non-crustal sources (i.e., anthropogenic). Healy (2013) found that

EF values in excess of 5 strongly indicated elements of anthropogenic origin, such as Hg, Cd,

Pb, Mo, As and Zn in the Dublin Surge data set. Only Cd, As and Mo exhibit EF’s with values in

excess of 5 (See Table 1), and this enrichment is interpreted to be indicative of contributions

from ore-forming and hypergene processes, rather than anthropogenic processes. By

definition, ore-forming processes are those that sufficiently concentrate useful elements into

accessible parts of the Earth’s crust so as to be profitably extracted, and are thus processes

that give rise to substantial enrichment of ore-related elements.


18

Table 2 presents the median concentrations of the 42 elements in the Cooltomin soil

samples, the global reference soil of De Caritat et al. (2012), as well as in the Dublin Surge soil

samples (Healy 2013). The most notable feature amongst the major elements is the high Ca

contents relative to the reference soil, reflecting the predominance of limestone as the

parent material of the Cooltomin soils, whether derived directly from bedrock or indirectly

from till. The median Ca content of the Cooltomin soils is 1.55% Ca, which is higher than the

median Ca value of 0.57% Ca for the reference soil by approximately a factor of three. The

maximum Ca concentration of 35.98% Ca approximates to 90 wt% calcite, the most likely Ca

host mineral (may also include some dolomite, gypsum or apatite), and is considered

anomalously calcareous.

The median Al content of the Cooltomin soils is 4.56% Al, which is of a comparable

order as the median Al value of 4.92% Al for the reference soil, and translates to median clay

contents of less than approximately 40% in the Cooltomin soils. The median Mn contents of

the Cooltomin soils (i.e., 1,516 ppm Mn) is significantly higher than that of the reference soil

(i.e., 465 ppm Mn), and is consistent with the high pH and oxidising conditions of well

drained, carbonate-rich soils. Interestingly, despite the known association of Ba with the Irish

Type carbonate-hosted Zn-Pb deposits, the median Ba contents of Cooltomin soils is

moderately depleted relative to the reference soil (i.e., 233 and 353 ppm Ba, respectively).

The depleted Ba contents reflect the high proportion of carbonate source rocks, relative to

siliciclastic or argillaceous source rocks, where the latter tend to be enriched in Ba.

The ore-related elements As, Cd, Mo, Pb and Zn have median contents in Cooltomin

soils that are elevated by a factor of 3 or more relative to the reference soil. The median

contents of Bi and Cu are only moderately elevated (i.e., #2) relative to the reference soil,

whilst those of Sb, Se, Sn, Te, U and W are indeterminate. Nonetheless, it is apparent that the

data set has captured the geochemical signature of mineralisation for a significant number of

elements with affinity to Irish Type carbonate hosted Zn-Pb ore.


19

Table 1. Summary Statistics for 42 Inorganic Elements in the Cooltomin Data Set

Element Units Max. Min. Mean Median Std.

Dev.

CoV Enrich.

Factor

LLD No.

HLD

Al % 7.77 0.14 4.20 4.56 1.22 29.16 1.00 0.01 0

Fe % 6.15 0.10 2.58 2.69 0.61 23.61 1.22 0.01 0

Mg % 4.10 0.12 0.60 0.52 0.32 53.09 1.34 0.01 0

Ca % 35.98 0.11 3.44 1.55 5.45 158.51 2.93 0.01 0

K % 2.77 0.03 1.13 1.25 0.39 34.49 1.01 0.01 0

Na % 0.42 0.01 0.22 0.24 0.09 39.07 0.63 0.01 0

P % 0.54 0.01 0.12 0.11 0.05 39.13 2.28 0.00 0

S % 0.69 0.02 0.09 0.08 0.06 65.77 0.01 0

As ppm 426.10 0.90 43.75 39.70 27.72 63.35 8.57 0.20 0

Ba ppm 435.00 38.00 218.22 233.00 64.45 29.54 0.71 2.00 0

Bi ppm 2.50 0.02 0.20 0.20 0.12 58.77 1.44 0.05 29

Cd ppm 38.27 0.16 1.93 1.69 1.87 96.59 18.23 0.02 0

Ce ppm 70.77 1.40 49.13 51.90 11.43 23.26 1.10 0.10 0

Co ppm 46.20 0.40 14.95 15.10 4.48 29.93 1.81 0.10 0

Cr ppm 108.33 2.00 55.48 60.00 15.36 27.69 1.16 2.00 0

Cu ppm 881.90 1.90 28.25 24.60 34.01 120.40 2.04 0.20 0

Ga ppm 18.54 0.05 10.18 11.10 3.01 29.56 0.21 0.10 1

Ge ppm 4.80 0.05 1.61 1.20 1.08 66.89 0.10 38

La ppm 42.80 0.90 30.40 32.20 6.97 22.92 2.48 0.50 0

Li ppm 514.00 1.00 30.43 31.15 19.08 62.69 3.73 2.00 11

Mn ppm 4310.86 39.00 1381.14 1516.00 661.44 47.89 3.52 5.00 0

Mo ppm 41.06 0.06 1.66 1.45 1.69 101.36 5.21 0.05 0

Nb ppm 15.24 0.12 7.08 7.61 3.21 45.36 0.75 0.05 0

Ni ppm 152.30 4.10 53.38 54.50 12.76 23.90 3.27 0.20 0

Pb ppm 1151.80 1.60 68.07 66.20 43.34 63.67 4.20 0.20 0

Rb ppm 169.90 0.05 87.77 97.30 30.02 34.20 1.67 0.10 0

Sb ppm 22.88 0.07 1.65 1.56 1.11 67.27 0.05 0

Sc ppm 16.80 0.20 7.82 8.30 2.29 29.32 0.10 0

Se ppm 27.32 0.25 1.78 1.01 2.75 154.61 0.50 279

Sn ppm 31.50 0.10 1.49 1.50 1.38 92.67 0.20 10

Sr ppm 600.00 13.97 58.37 46.00 48.18 82.55 0.58 2.00 0

Ta ppm 1.86 0.01 0.53 0.54 0.29 54.23 0.01 113

Te ppm 2.21 0.01 0.09 0.08 0.11 116.38 0.05 101

Th ppm 11.60 0.20 5.80 6.30 1.82 31.47 0.85 0.01 0

Ti ppm 3706.00 42.10 2177.52 2469.68 850.94 39.08 0.74 10.00 0

Tl ppm 8.23 0.11 1.89 1.91 0.95 49.95 0.02 0

U ppm 40.72 0.30 2.34 2.10 2.19 93.81 0.10 0

V ppm 181.00 1.00 60.73 63.00 19.36 31.87 1.08 2.00 2

W ppm 4.60 0.05 0.87 0.90 0.40 45.34 0.10 135

Y ppm 56.00 1.20 32.23 33.80 8.77 27.21 1.46 0.10 0

Zn ppm 7222.00 7.60 172.99 167.90 195.75 113.16 3.85 0.20 0

Zr ppm 113.00 2.00 72.09 82.00 27.34 37.93 0.31 1.00 0

Notes: 1. Ag, Au, Be and Hg are rejected for high proportions (i.e., >20%) of censored values.

2. Coefficient of variation (CV) calculated as: CV = (std. dev. / mean) * 100.

3. Enrichment Factor calculated from EF = (Esample/Rsample)/(Ecrust/Rcrust), using Al as the

reference element, after method of Tasic et al. (2008).

4. ‘LLD’ denotes lower limit of detection.

5. ‘No. HLD’ denotes number of values undetected, and given value of half limit of detection.


20

Table 2. Median Abundances of Inorganic Elements in the Cooltomin Soils,

Preliminary Empirical Global Reference Soil, and Dublin Surge Soils.

Element Units Cooltomin Global Soil Dublin Surge

Al % 4.56 4.92 2.88

Fe % 2.69 2.38 2.21

Mg % 0.52 0.42 0.43

Ca % 1.55 0.57 3.73

K % 1.25 1.33 0.74

Na % 0.24 0.41 0.07

P % 0.11 0.05 0.1

S % 0.08 na. na.

As ppm 39.7 5.0 13.4

Ba ppm 233 353 175

Bi ppm 0.20 0.15 na.

Cd ppm 1.69 0.10 1.70

Ce ppm 51.9 51.0 31.9

Co ppm 15.1 9.0 9.6

Cr ppm 60.0 56.0 44.3

Cu ppm 24.6 13.0 35.0

Ga ppm 11.1 11.0 na.

Ge ppm 1.2 na. na.

La ppm 32.2 14 17.8

Li ppm 31.1 9.0 28.4

Mn ppm 1516 465 946

Mo ppm 1.5 0.3 1.5

Nb ppm 7.61 na. na.

Ni ppm 54.5 18.0 41.0

Pb ppm 66.2 17.0 73.7

Rb ppm 97.3 63 na.

Sb ppm 1.56 na. na.

Sc ppm 8.30 na. 6.10

Se ppm 1.01 na. na.

Sn ppm 1.50 na. na.

Sr ppm 46.0 85.0 127.0

Ta ppm 0.54 na. na.

Te ppm 0.08 na. na.

Th ppm 6.3 8.0 na.

Ti ppm 2470 3597 201

Tl ppm 1.91 na. na.

U ppm 2.10 na. na.

V ppm 63.0 63.0 72.1

W ppm 0.9 na. na.

Y ppm 33.8 25.0 14.9

Zn ppm 167.9 47.0 172.0

Zr ppm 82.0 284.0 12.9

Notes: 1. “na” denotes not available.

2. Data for Preliminary Empirical Global Soil from De Caritat et al. (2012), and for Dublin

Surge from Healy (2013).


21

5.1.1 Correlation Analysis

A correlation coefficient matrix was constructed by Factor 8.10 as an initial step in the

factor analysis. The correlation coefficient matrix allows the inter-relationships of the

elements to be simplified by revealing the strength of the linear relationship between pairs of

elements. For a sample size of 1,421 observations, correlation coefficients or r-values less

than approximately 0.10 are not significantly different from zero at the 95% confidence limits.

However, even an r-value of 0.450 indicates that only 20% of the total variance in X and Y can

be explained by their linear relationship.

Over 250 r-values greater than 0.450 were obtained, whether negative of positive,

and indicate significant linear relationship for the respective element pairs (See Table 3). Of

these, 94 r-values are greater than 0.750, and thus indicate very strong linear relationships

between the respective element pairs. Most of these element pairs are identified as

belonging to a dominant Al association of 21 elements (i.e., Al, Fe, K, Na, Ba, Ce, Co, Cr, Ga,

Ge, Nb, Ni, Rb, Sc, Ta, Th, Tl, V, W, Y and Zr). Numerous element pairs exhibiting significant

negative r-values are revealed, most of which are element pairs involving either Ca or Sr with

elements of the Al association, except for two element pairs involving S with Ce and La.

The multitude of significant r-values is bewildering, and unhelpful in extracting

coherence from the complexity of the data set. Nonetheless, several observations can be

made. As stated above, the bulk of the r-values >0.750 refer to strongly and positively

covarying inter-relationships between 21 elements of an Al association (i.e., Al, Fe, K, Na, Ba,

Ce, Co, Cr, Ga, Ge, Nb, Ni, Rb, Sc, Ta, Th, Tl, V, W, Y and Zr), and reflecting a clay-oxide-

hydroxide association, probably derived from siliciclastics. The remaining r-values >0.750

refer to multi-element associations dominated by: (1) Zn with Bi, Cu, Mo, Pb, Sb, Sn and Te,

probably reflecting an ore association; (2) Ca with Sr, probably reflecting a limestone

association; (3) S with Se and U; (4) As with Sb; (5) Mn with Co; and (6) Cd with Se. Several

elements occur in more than one association, suggesting significant fractionation of the

variance in these elements generated by the influence of multiple geochemical processes. The

distinct negative covariance between elements of the Al and Ca associations suggests that the

soils are strongly differentiated between those principally derived from siliciclastic sediments

and limestone, respectively. These associations of positively covarying elements are

interpreted to reflect significant underlying geochemical processes, and form the core of

some of the factors extracted during factor analysis (See 5.1.2. Factor Analysis).

P and Li exhibit no significant r-values, indicating that these two elements do not

covary significantly, whether positively or negatively, with any of the other 40 elements. This

lack of significant covariance suggests the occurrence of these elements is controlled by

highly differentiated geochemical processes, possibly even of anthropogenic origin. These two

elements exhibit moderately elevated CoV values (i.e., 39 and 63, respectively) and EF values

(i.e., 2.28 and 3.73, respectively), which are not indicative of anthropogenic origin (See Table

1). Healy (2013) observed a strong association of Li and Be in the Dublin Surge data set, but is

indeterminate here, as Be was not analysed and relationships outside the data set are not

testable.


22

Table 3. Correlation Coefficient Matrix for 42 Inorganic Elements in Cooltomin Data Set

Al Fe Mg Ca K Na P S As Ba Bi Cd Ce Co Cr Cu Ga Ge La Li Mn Mo Nb Ni Pb Rb Sb Sc Se Sn Sr Ta Te Th Ti Tl U V W Y Zn Zr

Al 1.000

Fe 0.635 1.000

Mg -0.047 -0.334 1.000

Ca -0.597 -0.727 0.425 1.000

K 0.917 0.487 0.041 -0.460 1.000

Na 0.820 0.358 -0.035 -0.449 0.866 1.000

P 0.005 0.176 0.006 -0.215 0.018 -0.115 1.000

S -0.402 -0.305 0.115 0.294 -0.424 -0.396 0.291 1.000

As 0.117 0.288 -0.018 -0.156 0.098 -0.002 0.122 0.054 1.000

Ba 0.964 0.642 -0.067 -0.543 0.902 0.793 0.015 -0.380 0.110 1.000

Bi 0.360 0.317 -0.053 -0.275 0.324 0.233 0.180 -0.078 0.182 0.361 1.000

Cd -0.029 0.051 -0.058 -0.024 -0.113 -0.079 0.131 0.428 0.103 -0.021 0.001 1.000

Ce 0.710 0.804 -0.367 -0.844 0.567 0.444 0.123 -0.516 0.144 0.665 0.334 -0.059 1.000

Co 0.571 0.811 -0.268 -0.589 0.437 0.307 0.191 -0.230 0.234 0.591 0.334 0.179 0.721 1.000

Cr 0.948 0.603 -0.065 -0.585 0.851 0.794 0.015 -0.329 0.132 0.910 0.349 0.060 0.672 0.546 1.000

Cu 0.075 0.075 0.018 -0.046 0.062 0.031 0.103 0.220 0.120 0.119 0.379 0.251 0.024 0.062 0.122 1.000

Ga 0.973 0.630 -0.084 -0.615 0.902 0.817 -0.016 -0.420 0.117 0.946 0.370 -0.013 0.718 0.592 0.920 0.079 1.000

Ge 0.532 0.310 -0.076 -0.323 0.543 0.488 -0.061 -0.251 0.141 0.517 0.258 -0.024 0.280 0.269 0.482 0.053 0.552 1.000

La 0.814 0.775 -0.271 -0.798 0.678 0.556 0.093 -0.484 0.179 0.754 0.356 -0.009 0.913 0.704 0.792 0.045 0.820 0.499 1.000

Li 0.426 0.269 0.005 -0.260 0.365 0.342 0.015 -0.151 0.047 0.404 0.154 -0.005 0.308 0.263 0.414 0.034 0.425 0.189 0.344 1.000

Mn 0.263 0.626 -0.311 -0.453 0.252 0.167 0.215 -0.365 0.082 0.287 0.153 -0.056 0.593 0.607 0.196 -0.065 0.268 0.080 0.497 0.056 1.000

Mo 0.012 0.240 -0.146 -0.082 -0.046 -0.015 0.102 0.239 0.091 0.062 0.368 0.344 0.019 0.260 0.048 0.477 0.033 0.022 0.034 0.026 0.026 1.000

Nb 0.767 0.350 -0.078 -0.431 0.778 0.862 -0.081 -0.354 -0.010 0.741 0.212 -0.028 0.477 0.380 0.806 0.030 0.758 0.368 0.564 0.315 0.182 -0.007 1.000

Ni 0.686 0.730 -0.197 -0.594 0.569 0.391 0.203 -0.111 0.346 0.661 0.316 0.280 0.704 0.800 0.688 0.148 0.689 0.363 0.770 0.304 0.419 0.164 0.434 1.000

Pb 0.240 0.289 -0.046 -0.244 0.216 0.122 0.204 -0.029 0.364 0.244 0.568 -0.002 0.295 0.274 0.230 0.492 0.231 0.160 0.296 0.086 0.209 0.424 0.101 0.308 1.000

Rb 0.901 0.576 -0.065 -0.543 0.932 0.795 0.049 -0.478 0.111 0.871 0.365 -0.126 0.664 0.524 0.848 0.011 0.895 0.567 0.781 0.353 0.389 -0.016 0.748 0.614 0.254 1.000

Sb 0.227 0.228 0.073 -0.107 0.204 0.125 0.098 0.135 0.498 0.246 0.456 0.217 0.130 0.267 0.289 0.596 0.230 0.160 0.213 0.093 0.043 0.405 0.154 0.366 0.653 0.181 1.000

Sc 0.936 0.655 -0.084 -0.597 0.830 0.719 0.016 -0.381 0.151 0.901 0.401 0.035 0.720 0.632 0.927 0.092 0.941 0.614 0.864 0.405 0.272 0.070 0.722 0.734 0.263 0.878 0.282 1.000

Se -0.259 -0.123 0.045 0.186 -0.357 -0.293 0.063 0.803 0.041 -0.225 0.017 0.557 -0.369 -0.055 -0.168 0.332 -0.265 -0.082 -0.306 -0.081 -0.358 0.453 -0.257 0.023 0.064 -0.398 0.249 -0.187 1.000

Sn 0.253 0.186 -0.026 -0.151 0.256 0.220 0.054 -0.025 0.045 0.281 0.427 0.000 0.163 0.155 0.252 0.493 0.257 0.208 0.203 0.095 0.078 0.355 0.219 0.172 0.459 0.260 0.378 0.273 0.075 1.000

Sr -0.450 -0.626 0.283 0.867 -0.329 -0.256 -0.262 0.215 -0.177 -0.387 -0.231 -0.016 -0.737 -0.515 -0.427 -0.041 -0.461 -0.208 -0.667 -0.199 -0.413 -0.039 -0.267 -0.513 -0.230 -0.414 -0.113 -0.453 0.171 -0.109 1.000

Ta 0.662 0.293 -0.054 -0.358 0.704 0.816 -0.087 -0.310 -0.053 0.627 0.165 -0.039 0.398 0.294 0.669 0.041 0.641 0.411 0.429 0.271 0.173 -0.001 0.808 0.330 0.072 0.639 0.081 0.578 -0.183 0.158 -0.197 1.000

Te -0.019 -0.159 0.265 0.277 0.036 0.064 -0.088 0.038 -0.074 -0.006 0.200 -0.011 -0.189 -0.114 -0.025 0.287 -0.032 0.002 -0.127 -0.038 -0.091 0.269 0.016 -0.111 0.273 0.011 0.239 -0.018 0.144 0.255 0.336 0.114 1.000

Th 0.924 0.570 -0.098 -0.582 0.876 0.845 -0.035 -0.441 0.092 0.890 0.375 -0.031 0.670 0.563 0.895 0.063 0.932 0.602 0.791 0.388 0.277 0.032 0.843 0.617 0.216 0.891 0.223 0.918 -0.275 0.267 -0.422 0.747 0.005 1.000

Ti 0.936 0.456 0.013 -0.455 0.905 0.884 -0.056 -0.351 0.089 0.915 0.324 -0.020 0.515 0.451 0.893 0.076 0.919 0.576 0.683 0.389 0.126 0.029 0.820 0.564 0.186 0.864 0.224 0.877 -0.217 0.259 -0.300 0.680 0.045 0.906 1.000

Tl 0.435 0.122 0.212 -0.012 0.376 0.259 0.012 0.051 0.271 0.392 0.264 0.167 0.072 0.199 0.431 0.143 0.414 0.515 0.286 0.201 -0.169 0.053 0.149 0.353 0.164 0.388 0.296 0.501 0.130 0.149 -0.020 0.193 0.160 0.403 0.480 1.000

U 0.101 0.147 -0.119 -0.132 -0.026 0.029 0.081 0.495 0.032 0.114 0.057 0.379 0.003 0.166 0.152 0.163 0.111 0.052 0.058 0.061 -0.147 0.330 0.082 0.242 0.010 -0.047 0.124 0.147 0.667 0.035 -0.072 0.034 -0.043 0.100 0.110 0.087 1.000

V 0.849 0.613 -0.129 -0.564 0.693 0.679 0.060 -0.215 0.120 0.837 0.304 0.195 0.600 0.605 0.869 0.150 0.840 0.438 0.707 0.392 0.168 0.208 0.679 0.682 0.198 0.676 0.265 0.836 0.011 0.209 -0.398 0.576 -0.034 0.792 0.814 0.379 0.329 1.000

W 0.728 0.361 0.033 -0.315 0.709 0.679 -0.021 -0.230 0.102 0.709 0.364 0.030 0.376 0.406 0.718 0.143 0.734 0.582 0.547 0.291 0.079 0.120 0.642 0.477 0.226 0.701 0.296 0.738 -0.089 0.292 -0.197 0.584 0.150 0.771 0.791 0.502 0.109 0.663 1.000

Y 0.709 0.639 -0.125 -0.580 0.555 0.380 0.169 -0.235 0.287 0.635 0.393 0.133 0.715 0.638 0.724 0.116 0.700 0.432 0.865 0.318 0.322 0.070 0.397 0.785 0.321 0.646 0.358 0.795 -0.113 0.172 -0.510 0.215 -0.082 0.636 0.612 0.467 0.146 0.636 0.532 1.000

Zn 0.159 0.201 -0.059 -0.147 0.145 0.125 0.159 0.020 0.107 0.184 0.591 0.018 0.176 0.195 0.162 0.584 0.153 0.042 0.164 0.068 0.156 0.606 0.119 0.171 0.727 0.170 0.551 0.166 0.148 0.558 -0.120 0.104 0.465 0.154 0.138 0.069 0.004 0.142 0.203 0.168 1.000

Zr 0.923 0.454 -0.003 -0.466 0.921 0.903 -0.040 -0.360 0.096 0.898 0.313 -0.026 0.527 0.447 0.869 0.085 0.911 0.575 0.683 0.376 0.180 0.007 0.832 0.574 0.195 0.868 0.229 0.854 -0.251 0.253 -0.313 0.714 0.043 0.907 0.979 0.437 0.083 0.778 0.768 0.604 0.142 1.000

Notes: Significant positive r-values (> +0.450) for geochemical associations dominated by Al, Zn, Ca, S, As, Mn and Cd are indicated by colour.


23

5.1.2. Factor Analysis

As stated earlier, the factor solution was converged upon using iterative analyses and

applying multiple criteria. Between 6 and 14 factors were extracted for two extraction

methods (i.e., MRFA and PCA), and multiple iterations using different rotations methods were

also tested. For the purpose of interpretation an arbitrary cut-off is adopted (i.e., ±0.450;

same as for the analogous r-values), below which the factor loadings are considered much

less significant (Harbaugh & Merriam 1968).

A solution based on ten factors extracted using Minimum Rank Factor Analysis

(MRFA) and Varimax orthogonal rotation was adopted (See Table 4). This model explained

80.3% of the total variance and 91.1% of the common variance, with good reliability

estimates (i.e., mean = 0.892), on 10 latent variables (i.e., factors) versus the original 42

observed variables. A plot of some of the metrics used in this decision-making process is

shown in Figure 7. All the factor models (i.e., from 6 to 14 factors) extracted significant

negative factor loadings for Ca and Sr, whilst the factors models with 6 to 10 factors extracted

additional significant negative factor loadings on Mg and Te. The attainment of simple

structure is defined as the extraction of a small number of factors with high loadings for

certain elements and low loadings for other elements. Negative factor loadings can be an

indication of an inferior factor solution, although factor loadings are correlation coefficients

analogous to Pearson’s r, between the original variable (i.e., element) and the new variable

(i.e., factor), and thus can have a negative sign indicative of inverse relationships.

Although the 8 factor model showed superior performance on several metrics,

particularly root mean square residual (RMSR) and Bentler’s simplicity index, the 10 factor

model was adopted as it reflected reasonable attainment of simple structure and geological

interpretability. Although there were only minor differences in the significant factor loadings

on the Zn factor (i.e., for Bi, Cu, Mo, Pb, Sb, Sn, Te and Zn) in all 9 models, the 10 factor model

was adopted as it also explained a greater proportion of the total variance with higher

reliability, and yielded superior communalities for elements across all factors. Thus, the

adopted factor model is a compromise solution between the potentially competing objectives

of attaining simplicity and parsimony versus achieving geological interpretability.

From Table 5 it is apparent that Li and Sn are both poorly resolved by the factor

analysis with very low explained variance (i.e., low communalities of 0.195 and 0.441,

respectively). This reflects the lack of common variance in these elements, as is apparent in

the correlation coefficient matrix with no significant r-values for Li, and only one minor

significant r-value for Sn (i.e., 0.558). Furthermore, Mg, Bi and Te each exhibit only

moderately significant communalities (i.e., >0.450 and <0.600), indicating that the variance in

these elements is only moderately resolved by the factor analysis. As factor analysis only

resolves the structure in common variance, and rejects unique and error variance, it cannot

resolve the bulk of the variance in these elements.


24

F-6 F-7 F-8 F-9 F-10 F-11 F-12 F-13 F-14

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Main Parameters Used in Selection of Factor Analysis Model

Variance (100s) Prop. Common Variance Mean Reliability

Bentler's Simplicity Root Mean Square Residual No. Low Communalities (10s)

No. Sig. -ve Loadings No. Sig. +ve Loadings Zn Factor Reliability

Figure 7. Plot of metrics used in decision-making process to select factor

analysis model solution. Note inflexion points in multiple curves, particularly at

8, 10 and 12 factors.

The adopted factor analysis model reveals that the fundamental geochemical

character of the soils is defined by ten multi-element factors, and which are interpreted to

constitute geochemical associations that reflect significant underlying geochemical processes

in the soil formation (See Tables 5 and 6). The ten factors account for 91.1% of the common

variance and 80.3% of the total variance in the data set. Three associations dominate,

accounting for 66.5% of the common variance and 53.4% of the total variance in the data set,

and initially could be identified as clay-oxide-hydroxide, lime and ore associations.

Table 4. Principal Metrics Used in Selection of Factor Analysis Model

Factor

Model

Variance Proportion

of

Common

Variance

Mean

Reliability

Zn Factor

Reliability

Bentler's

Simplicity

RMSR No. of Low

Comm.

No. of

Significant

-ve

Loadings

No. of

Significant

+ve

Loadings

F-6 30.801 0.834 0.918 0.938 0.692 0.030 4 4 42

F-7 31.657 0.857 0.903 0.938 0.663 0.026 4 4 42

F-8 32.443 0.878 0.891 0.943 0.632 0.022 2 4 44

F-9 33.141 0.896 0.888 0.944 0.554 0.019 2 4 45

F-10 33.743 0.911 0.892 0.944 0.533 0.017 2 4 44

F-11 34.157 0.921 0.877 0.944 0.542 0.015 1 3 44

F-12 34.556 0.931 0.879 0.946 0.500 0.013 1 2 47

F-13 34.924 0.943 0.867 0.956 0.296 0.012 1 2 46

F-14 35.222 0.950 0.864 0.962 0.231 0.010 1 2 48

Note: 1. “RMSR” denotes root mean square residual.

2. “Comm.”denotes communalities.


25

Table 5. Principal Factor Matrix for the Cooltomin Data Set

Element F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 Comm.

Al 0.943 0.057 0.184 0.145 -0.040 0.034 0.155 -0.036 0.013 0.004 0.976

Fe 0.442 0.109 0.437 0.674 0.055 0.132 0.084 -0.054 0.001 -0.037 0.884

Mg 0.045 -0.003 -0.493 -0.301 -0.063 0.071 0.290 -0.026 -0.123 0.284 0.525

Ca -0.440 -0.061 -0.826 -0.236 0.011 -0.009 0.054 -0.001 -0.017 -0.048 0.941

K 0.926 0.060 0.036 0.071 -0.171 0.053 -0.015 -0.065 0.052 0.123 0.922

Na 0.910 0.033 0.032 -0.057 -0.105 -0.032 -0.266 -0.019 0.007 -0.033 0.918

P -0.063 0.121 0.214 0.168 0.124 0.034 0.034 0.040 -0.016 0.729 0.644

S -0.347 0.050 -0.132 -0.179 0.738 0.095 -0.019 0.166 -0.017 0.344 0.873

As 0.038 0.107 0.093 0.138 0.032 0.824 0.045 0.012 0.098 0.033 0.733

Ba 0.915 0.092 0.123 0.203 -0.004 0.028 0.106 -0.054 0.000 -0.007 0.917

Bi 0.281 0.589 0.126 0.109 0.016 0.069 0.123 -0.071 0.145 0.094 0.509

Cd -0.019 0.039 0.016 0.077 0.388 0.046 0.027 0.893 0.011 0.028 0.960

Ce 0.522 0.089 0.621 0.449 -0.216 -0.004 0.149 -0.010 -0.081 -0.053 0.946

Co 0.434 0.100 0.263 0.739 0.080 0.109 0.122 0.113 0.005 0.015 0.860

Cr 0.925 0.071 0.202 0.091 0.027 0.069 0.150 0.061 -0.046 -0.026 0.944

Cu 0.053 0.707 0.042 -0.135 0.186 0.096 0.035 0.214 -0.033 -0.012 0.615

Ga 0.926 0.061 0.212 0.156 -0.043 0.028 0.137 -0.018 0.047 -0.039 0.956

Ge 0.532 0.028 0.092 0.010 -0.008 0.074 -0.004 -0.024 0.690 -0.064 0.779

La 0.664 0.078 0.513 0.367 -0.155 0.037 0.251 0.034 0.111 -0.054 0.950

Li 0.403 0.015 0.097 0.038 0.026 0.010 0.133 -0.020 -0.053 -0.005 0.195

Mn 0.122 0.075 0.244 0.756 -0.323 -0.024 -0.175 0.020 -0.022 0.160 0.814

Mo -0.017 0.619 0.004 0.217 0.406 -0.051 -0.076 0.146 0.035 -0.122 0.641

Nb 0.878 0.023 0.061 -0.004 -0.066 -0.020 -0.281 0.048 -0.151 -0.051 0.887

Ni 0.558 0.074 0.295 0.492 0.142 0.255 0.244 0.215 0.023 0.074 0.843

Pb 0.130 0.748 0.135 0.122 -0.030 0.299 0.052 -0.069 0.046 0.093 0.718

Rb 0.871 0.076 0.129 0.208 -0.221 0.016 0.032 -0.071 0.152 0.114 0.916

Sb 0.190 0.632 -0.015 0.016 0.119 0.569 0.099 0.162 -0.024 -0.011 0.811

Sc 0.879 0.084 0.213 0.209 -0.001 0.046 0.244 0.022 0.159 -0.046 0.958

Se -0.215 0.184 -0.110 -0.069 0.880 0.029 0.030 0.243 0.053 -0.023 0.935

Sn 0.226 0.611 0.048 -0.026 0.025 -0.051 -0.013 -0.028 0.097 0.002 0.441

Sr -0.277 -0.047 -0.823 -0.171 0.051 -0.071 -0.046 -0.009 0.010 -0.174 0.826

Ta 0.780 0.023 -0.024 0.007 -0.062 -0.071 -0.432 0.058 0.007 -0.012 0.809

Te 0.052 0.492 -0.466 -0.029 -0.023 -0.139 0.032 0.007 0.031 -0.024 0.485

Th 0.929 0.070 0.170 0.136 -0.071 0.009 -0.019 -0.002 0.132 -0.047 0.940

Ti 0.967 0.049 0.031 0.007 -0.004 0.033 0.056 -0.018 0.093 -0.016 0.952

Tl 0.425 0.066 -0.194 -0.086 0.122 0.228 0.414 0.133 0.507 0.088 0.751

U 0.122 -0.007 0.106 0.060 0.796 -0.018 0.009 0.072 -0.033 -0.061 0.674

V 0.821 0.061 0.200 0.190 0.247 0.023 0.127 0.102 -0.047 -0.088 0.852

W 0.775 0.159 -0.055 0.035 0.040 0.037 0.050 0.027 0.264 0.000 0.706

Y 0.563 0.103 0.352 0.299 -0.012 0.183 0.518 0.128 0.138 0.045 0.880

Zn 0.091 0.943 0.025 0.102 0.016 -0.042 -0.031 -0.047 -0.025 0.051 0.917

Zr 0.960 0.051 0.041 0.021 -0.050 0.051 -0.014 0.002 0.087 0.019 0.940

Variance 15.057 3.905 3.467 2.867 2.735 1.368 1.283 1.132 1.025 0.904 33.743

%CommonVariance

44.62 11.57 10.28 8.50 8.11 4.06 3.80 3.36 3.04 2.68

Cumulative%Variance

44.62 56.19 66.46 74.97 83.07 87.13 90.92 94.28 97.32 100.00

ReliabilityEstimate

0.991 0.944 0.930 0.891 0.935 0.818 0.877 0.932 0.815 0.786

Notes: 1. ‘Comm.’ denotes communality, the fraction of explained variance in an element.

2. Significant loadings (>0.450) coloured using scheme applied to correlation coefficients in

order to identify geochemical associations (See Table 3).

3. Red colour applied to communalities indicates <0.450 values.


26

The factors are named on the basis of interpreted geochemical association or soil-

forming process, or after the dominant constituent element.

Factor 1 is the dominant factor accounting for 44.6% of the common variance and

35.9% of the total variance in the data set, and includes significant loadings for Al, K, Na, Ba,

Ce, Cr, Ga, Ge, La, Nb, Ni, Rb, Sc, Ta, Th, Ti, V, W, Y and Zr. Of the 21 elements identified using

correlation coefficients as occurring in the Al association, only Fe has been reclassified into

another association. Notably, Fe gave a loading of 0.444, and thus has a minor, yet

gravimetrically significant fraction of variance incorporated into Factor 1. Most of these

elements in Factor 1 are defined as lithophile elements (i.e., silicate affinity: Al, K, Na, Ba, Ce,

Cr, La, Nb, Rb, Sc, Ta, Th, Ti, V, W, Y and Zr.), with one siderophile element (i.e., oxide affinity:

Ni), and two chalcophile elements (i.e., sulfide affinity: Ga and Ge).

From the elemental abundances in Table 2, it is apparent that the gravimetrically

predominant elements are Al, K and Fe, even though the latter is not classified sensu stricto as

an association element. These elements indicate that the host mineralogy of the association

is non-refractory during acid digestion, such as clays and oxide/hydroxides, and which is

probably derived from the siliciclastics component in the soils. Notably, Mg does not load

onto Factor 1 (i.e., 0.045), whilst Ca has a near-significant negative loading (i.e., -0.440),

suggesting this association lacks any significant carbonate component. Although Ba is typically

associated with Irish Type ores, Ba loads very strongly on to Factor 1 (i.e., 0.915). This

indicates that the Ba signature is not ore-related, but more probably reflects an argillaceous

detrital component, probably represented in the weathered environment by Ba-rich clays.

McLean & Bledsoe (1992) report that the relative affinity of montmorillonite for alkaline

earths is Ba > Sr > Ca > Mg, confirming the affinity of Ba for the clay fraction.

Factor 1 is an association of elements hosted in clays and oxides-hydroxides that are

variably retained in the A horizon of the soils. The factor is interpreted to represent the

principal effect of the podzolisation process on the A horizon, namely the elluviation of clay

and oxides. In soils generated overwhelmingly from limestone till, the factor thus describes

the range in clay and oxide content of the soil A horizon from, for example, Gleys to Brown

Earths to Grey Brown Podzolics. High factor scores correlate with high clay and oxide

retention in the A horizon, and hence increased podzolisation. Although not the product of

podzolisation, Lithosols lie on the extreme low end of the range in Factor 1 scores. The parent

material of Lithosols is limestone bedrock, not till, and thus lacks significant clay or oxide-

hydroxide content.

Factor 2 accounts for 11.6% of the common variance and 9.30% of the total variance

in the data set, and includes significant loadings for Bi, Cu, Mo, Pb, Sb, Sn, Te and Zn. Seven of

these elements are defined as chalcophile elements (i.e., Bi, Cu, Pb, Sb, Sn, Te and Zn), with

one siderophile element (i.e., Mo). The elements strongly loaded onto Factor 2 include 6 of

the elements that Blakeman (pers. comm. 2014) states occur in, or are associated with, the


27

mineralisation (i.e., Ag, As, Cd, Cu, Fe, Pb, Sb, Zn, Hg, Mo, Te, Th, Tl, W and U), albeit Ag and

Hg were not included in the factor analysis. Factor 2 is interpreted to reflect the dominant

signature in the secondary environment of carbonate hosted Zn-Pb mineralisation, and is

identified as an ore association.

The dominant soil types in the study area are Grey Brown Podzolics and Brown

Earths, which are well drained and derived from calcareous parent material. Most elements

of the association are characterised by low mobility in a secondary environment with

oxidising, alkaline conditions, and in the presence of carbonate (Levinson 1974). Although Cu

and Mo have very similar mobilities in the primary environment, unlike Cu, Mo is mobile in

oxidising alkaline secondary environment, except critically in the presence of carbonate (i.e.,

forms insoluble carbonate). Sn is essentially immobile in the secondary environment due to

the refractory character of the principal Sn-bearing ore minerals (e.g., cassiterite) and rock-

forming minerals (e.g., sphene or magnetite) and their strong tendency to become resistate

minerals (Salminen 2005). Thus, the factor encompasses ore-related elements with low

mobility in the prevailing secondary environment, and describes the variation in

concentrations of these elements in the A horizon due to localised (i.e., strongly spatially

controlled) primary and secondary dispersal. The lack of Cd covariance with Zn and other

elements of Factor 2 reflects the differential mobility of Cd in the alkaline secondary

environment, and negates its use here in the validation of Zn anomalies as suggested by

Salminen (2005).


in the data set, and includes significant negative loadings for Ca, Mg, Sr and Te, and significant

positive loadings for Ce and La. Five of these elements are defined as lithophile elements (i.e.,

Ca, Mg, Sr, Ce and La), with one chalcophile element (i.e., Te). The highest loadings are for the

two alkaline earths Ca and Sr (i.e., -0.826 and -0.823, respectively), which indicate a limestone

signature, given the strong tendency of Sr for diadochic substitution with Ca in carbonates.

The weak significant loading on Mg (i.e., -0.493) is consistent with Mg in calcite, or more

probably dolomite. The weak significant loading for Te (i.e., -0.466) is considered potentially

spurious, particularly in the context of the significant percentage of censored Te data, and the

fact that Te exhibits only a single weak significant correlation coefficient (i.e., 0.465 with Zn).

Ce and La exhibit significant positive loadings, which thus have an inverse relationship to Ca,

Sr and Mg. Ce and La tend to concentrate in resistate minerals such as monazite (Salminen

2005). Ce and La are also significantly loaded on to Factor 1, and their inverse relationship to

Ca, Sr and Mg further suggests a limestone signature. Because Varimax factor rotation is

orthogonal, and generates factors that are uncorrelated, oblique factor rotation may have

been preferable to describe the relationship between Factors 1 and 2.

Factor 3 is an association of elements characteristic of limestone that are variably

leached or retained in the A horizon of the soils. The factor is interpreted to represent the

effects of the major soil-forming process gleisation on the A horizon. Gleisation is caused by


28

waterlogging, which reduces water movement and leaching, and is associated with the

development of anaerobic, reducing conditions. The factor describes variation in the leaching

of elements associated with carbonates (i.e., Ca, Mg and Sr), and retention of elements

associated with resistate minerals (e.g., Ce and La) in the A horizon due to the process of

gleisation. Ca and Mg tend to be the first elements to be leached during soil formation, and

their retention in the A horizon is indicative of the limited leaching regime in gley soils.

Given that the soils in the study area are generated overwhelmingly from limestone

till, Factor 3 is not a limestone signature, but rather a lime or gley signature, where high

Factor 3 scores are indicative of high Ca retention due to gleisation. Factors 1 and 3, which by

definition are uncorrelated, are interpreted to represent the effects of two major soil-forming

processes on the A horizon, and their non-antithetic relationship is consistent with being two

of many competing soil-forming processes acting simultaneously upon the soils.


in the data set, and includes significant loadings for Fe, Co, Mn and Ni. Three of these

elements are defined as siderophile elements (i.e., Fe, Co and Ni), with one lithophile element

(i.e., Mn). Fe and Mn are well known to occur together in the secondary environment as

oxides and hydroxides. These oxides-hydroxides are insoluble in all but the most acidic and

reducing conditions found in the weathering environment, and tend to co-precipitate Co and

Ni (Levinson 1974, Salminen 2005). In addition, Co and Ni have a strong affinity for these

oxides-hydroxides, which through adsorption, scavenge Co and Ni that might otherwise

remain in solution, if conditions are acidic or oxidising. Interestingly, Ce, La and Y, which tend

to occur in resistate minerals and are essentially immobile in the secondary environment, give

minor but non-significant loadings on Factor 4.

Fe-Mn oxides-hydroxides are essentially ubiquitous in soils, commonly occurring as

coatings with high surface areas, and are a major soil surface controlling metal mobility in

soils, and the principal control on the fixation of Co, Ni, Cu and Zn in soils (Jenne 1968, Young

2010). Numerous adsorption sequences have been established from experimental work,

indicating relative affinity amongst heavy metals, such as Hg, Pb, Cu, Zn, Co, Ni and Cd for the

many specific Fe-Mn oxides-hydroxide species, and tend to confirm the similarity of

behaviour of Co and Ni (Levinson 1974, Young 2010). There are numerous factors influencing

the adsorption of the heavy metals, including concentration of the specific metal, the amount

and strength of organic chelates and complex-forming ions in solution, pH, and presence of

clays and carbonates (Jenne 1968).

Factor 4 is an association of elements characteristic of Fe-Mn oxides-hydroxides that

are variably concentrated in the A horizon of the soils. The factor is interpreted to represent

the variable concentration of Fe-Mn oxides-hydroxides in the A horizon. Mn and particularly

Fe are very insoluble in the secondary environment and tend to be concentrated in the A

horizon, along with resistate minerals, by elluviation of other soil components. Thus, Factor 4


29

seems to represent the fraction of Fe, Mn, Co and Ni bound in Fe-Mn oxides-hydroxides, and

which is largely unaffected by podzolisation, although intense podzolisation would also leach

Fe and Mn.


in the data set, and includes significant loadings for S, Se and U. Two of these elements are

defined as chalcophile elements (i.e., S and Se), with one lithophile element (i.e., U). Mo, Cd

and V also show minor but non-significant loadings on the factor (i.e., 0.388, 0.406, 0.247

respectively). S, Se, U, Mo and V (but not Cd) are characterised by very high mobility in the

secondary environment, particularly when neutral to alkaline, but exhibit very low mobility in

reducing environments (Levinson 1974, Salminen 2005). Cd exhibits medium mobility under

most conditions, except reducing conditions, where it also becomes immobile.

Despite their high mobility, reducing conditions are a geochemical barrier to S, Se and

U, and can cause precipitation in significant concentrations as ‘false’ or non-significant

anomalies, which are also typically laterally displaced from their source (Levinson 1974).

Although U can be precipitated by adsorption onto organics, clays and Fe-Mn oxides or by

formation of insoluble compounds in certain oxidising conditions, the association of U with S,

Se, Mo and V is typical of “roll-front” U deposits, formed at the interface of oxidising and

reducing groundwaters (EPA 1995). Thus, Factor 5 seems to encompass elements that are

very mobile in the oxidising alkaline secondary environment, but which rapidly precipitate

due to contact with reducing conditions. Such conditions may occur in organic-rich or peaty

soils and peat in fens or bogs, or where oxidised alkaline conditions encounter seepage of

reduced groundwater, possibly along faults or at a break of slope. The factor is interpreted as

an association of very mobile elements precipitated under reducing conditions probably

associated with abundant organics. Unfortunately, as the organic content of the soils was not

determined, the relationship of the elements loading on the Factor 5 to the organic content is

outside the data set, and is untestable. Nonetheless, this factor is referred to as a reduction

barrier association, and importantly has the potential to generate false anomalies.


in the data set, and includes significant loadings for As and Sb. Both of these elements are

metalloids, are defined as chalcophile elements, and exhibit most similar geochemical

behaviour. However, As exhibits medium mobility, whereas Sb exhibits low mobility in the

secondary environment, except under reducing conditions where both elements become

immobile (Levinson 1974). The lower mobility of Sb relative to As in oxidising alkaline

conditions probably explains the additional association of Sb with the less mobile ore-related

elements of Factor 2. Notably, As exhibits a strong significant loading on the factor (i.e.,

0.824), whereas Sb exhibits a moderate significant loading (i.e., 0.569), due to the masking

effect of an even greater fraction of the variance in Sb being assigned to Factor 2. According

to Levinson (1974) As and Sb are both highly mobile in the primary environment and tend to

concentrate in the late differentiates and laterally distal from the source (i.e., large primary


30

haloes around ore). Thus, Factor 6 may reflect both primary differentiation and differential

secondary mobility of As and Sb relative to the other ore-related elements of Factor 2. An

intersection of vein sulfides grading 14% As (Blakeman, pers. comm. 2014) suggests that

Factor 6 may reflect primary dispersion as highly differentiated vein mineralisation.


in the data set, and includes a single significant loading for Y (i.e., 0.518). Y is a lithophile

element with geochemical behaviour similar to the heavier REEs, exhibiting low mobility in all

secondary environments, and tending to concentrate in resistate minerals such as xenotime

and zircon (Salminen 2005). There are also minor but non-significant positive loadings for Mg

and Tl and negative loadings for Ta and Nb. The mechanism of Factor 7 seems indeterminate,

although the abundance of resistate minerals containing Y in the A horizon are expected to

increase with increasing siliciclastic component in the soil, or by elluviation due to

podzolisation.


in the data set, and includes a single strong significant loading for Cd (i.e., 0.893). Cd is a

chalcophile element with geochemical behaviour similar to Zn, but has medium mobility in

the secondary environment (Levinson 1974). Although the solubility of Cd is largely

unaffected by Eh, Cd has a strong affinity for organic matter, and thus becomes immobile in

reducing environments associated with organics (Salminen 2005). Although Cd has a strong

affinity for Fe-Mn oxides-hydroxides (Young 2010), the dominant fraction of Cd, which is

associated with Factor 8, is unrelated to Fe and Mn, but rather is probably related to organics

or another unidentified factor in the soil.

Because of its strong chalcophile affinities, Cd generally occurs in sulfides, particularly

sphalerite, and the presence of Cd may be used to validate uncertain Zn anomalies (Salminen

(2005). Sphalerite formed at lower temperatures can accommodate higher Cd contents,

resulting in Zn/Cd ratios typically in the range of 200 to 400 for SEDEX and VHMS deposits.

Furthermore, Cd is more chalcophile than Zn, resulting in high Zn/Cd ratios for magmatic

rocks (i.e., 500). The Zn/Cd ratios of approximately 100 at Cooltomin may thus suggest

anomalous enrichment in Cd. Importantly, phosphate fertilisers and sewage sludge both

contain elevated levels of Cd, which when spread on land, act as significant sources of Cd in

soil (Van Kauwenbergh 2002, Salminen 2005). Such an anthropogenic component to Cd might

mask the natural geochemical association of Cd, including any association with ore, and

probably negates its use in validating Zn anomalies.


in the data set, and includes significant loadings for Ge and Tl (i.e., 0.690 and 0.507,

respectively). Ge and Tl are chalcophile elements, but also exhibit strong organophile

properties. Both elements occur at high levels in sphalerite and galena, particularly Ge in low

temperature varieties (Salminen 2005). Ge and Tl enrichment is associated with hydrothermal


31

fluids in the Red Dog Zn-Pb-Ag deposits, Alaska (Slack et al. 2004), whilst Tl enrichment is

associated with mineralisation at Cooltomin (Blakeman, pers. comm. 2014). These elements

exhibit variable mobility in the secondary environment, but are both readily fixed by Fe-Mn

oxides-hydroxides and organic matter, particularly in reducing conditions (Levinson 1974).

Nonetheless, as the primary deportment of Ge and Tl is not known, and their behaviour in the

secondary environment is poorly understood, the mechanism of Factor 9 is indeterminate.


in the data set, and includes a single strong significant loading for P (i.e., 0.729). P is a

siderophile element with lithophile and organophile properties depending on prevailing

conditions. P almost exclusively occurs as orthophosphate (i.e., PO43-), and principally occurs

in apatite, monazite and xenotime, or as a trace element in many rock-forming minerals

(Salminen 2005). The latter author reports that P exhibits low mobility in the secondary

environment, as it is readily adsorbed or fixed by clays and hydrous oxides of Fe and Al to

form insoluble Al and Fe phosphates in acid soils. In alkaline soils P is fixed by CaCO3 to form

progressively insoluble Ca phosphates from simple mono- and di-calcium phosphates (e.g.,

monetite or brushite) to apatite.

Because of the process of fixation, P becomes unavailable for plant take-up, and is

regularly reapplied to agricultural land. P is applied to agricultural land by spreading

phosphate fertilisers and sewage sludge (or slurry), which is also P-rich (i.e., #4% of dried

matter; O’Riordan et al. 1986). If this anthropogenic component of P is dominant in the soils

of the study area, then Factor 10 may simply be an expression of the intensity of agriculture,

specifically fertiliser and sludge use.

Table 6. Summary of the Factors and Geochemical Associations

Factor Elements Association

F 1 Al, K, Na, Ba, Ce, Cr, Ga, Ge, La, Nb, Ni, Rb, Sc, Ta, Th, Ti, V, W, Y, Zr Clay/Oxide

F 2 Bi, Cu, Mo, Pb, Sb, Sn, Te, Zn Ore

F 3 Ca, Mg, Sr, Ce, La, Te Lime or Gley

F 4 Fe, Mn, Co, Ni Fe-Mn-Oxides

F 5 S, Se, U Reduction Barrier

F6 As, Sb Metalloid

F7 Y Yttrium

F8 Cd Cadmium

F9 Ge, Tl Germanium

F10 P Phosphorus

Note: Ce, La, Ni, Sb and Te are the only elements to load significantly on more than one factor.


32

5.2. SPATIAL ANALYSIS

In addition to factor loadings extracted for the 42 elements, factor scores were also

calculated for each of the 1,421 soil samples. Factors extracted from geochemical data

constitute geochemical associations that reflect the underlying geochemical processes. Factor

scores are estimates of the contribution of the factors to each original variable (i.e., element

concentration in a sample), and thus describe the degree to which the geochemical processes

are expressed in the composition of the samples (Davis 1986). Thus, the spatial distribution of

the factor scores allows the expression of geochemical processes, as described by the factors,

to be modelled with GIS.

The GIS analysis was done using ArcMap 10.2, a proprietary product from Esri (Esri

2012). The principal feature employed is spatial interpolation to generate prediction surfaces

from the factor scores using the Geostatistical Extension. A companion program of ArcMap

from the ArcGIS suite of programs, called ArcScene, was used to generate 3-D surface

diagrams of the spatial distribution of factor scores.

The factor scores were imported into ArcMap, with each sample represented by a

sample label, a pair of X,Y coordinates for the sample point in the form of Irish Grid (i.e.,

spatial data), and ten factor scores (i.e., attribute data). Attribute data values can be

considered as representing scattered points on hypothetical continuous attribute surfaces.

Spatial interpolation computes a continuous raster surface of predicted values from

geospatially referenced attribute data, such as factor scores, from a smaller number of

sample data points, which may be stratified, clustered or randomly distributed.

There are numerous interpolation methods, each of which is appropriate to particular

data sets because of the built-in assumptions and algorithm design for estimating new values.

Glennon et al. (2012) and Healy (2013) used Ordinary Kriging to interpolate the Surge

inorganic geochemical data, and the same method was employed here. Kriging is an advanced

geostatistical procedure based on statistical models that generates an estimated surface from

a set of points with z values, where the z-values correspond to geochemical data, or in this

case its derivative, factor scores. In addition to generating the predicted surfaces,

geostatistical methods can also measure the accuracy of the predictions. Kriging is useful

when there is a spatially directional bias or anisotropy in the data, and is commonly used for

modelling in soil science and geology. The raster surfaces for the ten sets of factor scores are

each projected in the Irish National Grid TM65, and are given in Figures 1 to 10 of the

Appendix.


33

5.2.1. Soil Maps

A map of the study area showing the location of the townlands, which are used for

geographic reference in the following discussion is given in Figure 11 of the Appendix. The

drainage pattern in the wider area consists of subparallel rivers and streams running north to

the Shannon Estuary. Three rivers/streams transect the study area (See Fig. 12 of the

Appendix). On the east side of the study area, the River Deel runs north from Rathkeale to

Askeaton and ultimately into the Shannon Estuary at Shannon View. A significant unnamed

tributary of the Deel runs from Lisnacullia to join the Deel at Ardgoulbeg, and is named the

Lisnacullia Stream for convenience. On the west side of the study area, the River Ahacronane

runs in an arc from the southwest corner of the study area in the townland of Ballynisky

through the townlands of Lissatotan and Cooltomin to the northwest corner of the study area

at Creeves, ultimately draining into the Shannon Estuary at Aughinish West. In the centre of

the study area, an unnamed stream runs through the centre of the study area from Ranahan

to Ballinloughane, and ultimately drains into the Shannon Estuary at Cahergal. This stream is

named the Ranahan Stream for convenience. Only the River Deel was digitised using Google

Earth, and thus spatial data are not available for the River Ahacronane or the Ranahan and

Lisnacullia streams, the three of which are little more than ‘wet’ drainage ditches. The reader

is encouraged to become familiar with the location of the townlands, rivers and streams in

the study area prior to proceeding further (See Figs. 11 and 12 of the Appendix).

In 1960, the newly constituted An Foras Taluntais (i.e., precursor of Teagasc) began a

National Soil Survey (NSS), and published a county survey series initially with Co. Wexford,

and secondly with Co. Limerick. The soil map of Co. Limerick with accompanying Soil Survey

Bulletin No. 16 was published by Finch & Ryan (1966), and was compiled by surveying and

mapping using direct visual inspection, profile pits and laboratory analysis. The soil map was

developed on a nominal working scale of 1:10,560, condensed down to a publication scale of

1:126,720, and maps the distribution of soil types based on the classification of the Great Soil

Groups of Ireland. This map provides detailed discrimination of Grey Brown Podzolics from

Brown Earths as separate soil groups, each comprised of several individual soil series (See Fig.

13 of Appendix).

The 2nd Edition of the General Soil Map of Ireland with accompanying Soil Survey

Bulletin No. 36 was published by Gardiner & Radford (1980). The map has a publication scale

of 1:575,000, and only discriminates Brown Earths and Rendzinas (33) and Minimal Grey

Brown Podzolics (34) within the study area (See Fig. 14 of Appendix). This small scale results

in generalised features that provide inadequate geographic reference to allow useful spatial

data for the two soil types identified in the study area to be extracted. When the NSS was

wound down in 1988, it had thus produced a national map at general reconnaissance scale of

1:575,000 scale, and county maps at a detailed reconnaissance scale of 1:126,720 for less

than half of the country.


34

Driven by a requirement arising from the EU Water Framework Directive 2000 to

establish nationwide soil and subsoil maps by a consistent, standardised method, Teagasc and

the EPA initiated a Soil and Subsoil Mapping Project, the final report for which was published

by Fealy & Green (2009). The soil map of every county in Ireland were compiled by a remote

sensing and GIS-based methodology. Soil type was predicted using key soil factors (e.g.,

vegetation) and geology (e.g., parent material) and topography (e.g., slope), and using a

qualitative, expert-based classification system. In order to map all the soil variants in a single

national soil map, the classification system of soil types had to be highly simplified relative to

previous soil surveys, but retained a close relationship to the Great Soil Groups in Ireland, and

thus facilitates higher level interpretation. Although the soil map has a maximum online scale

of 1:2,000, the nominal working scale of 1:100,000-150,000 was used during map

preparation.

The Teagasc/EPA Soil Map is cartographically detailed, but is categorically simplified,

and for example, does not discriminate between Grey Brown Podzolics and Brown Earths, the

two dominant soil types in the study area, but instead maps these as a single soil type (See

Fig. 6). Nonetheless, because the Teagasc/EPA National Soil Map is cartographically complex,

offering superior spatial definition and geo-referencing, the spatial distribution of the factor

score data is interpreted with reference to it. Supplementary interpretation relating to the

distribution of certain soil types is based on the Soil Map of Limerick (Finch & Ryan 1966), and

the projection of this onto the study area (See Fig. 15 of the Appendix).

The dominant soil type in the study area is given as BminDW (See Fig. 6), which

consists of Grey Brown Podzolics and Brown Earths (See Table 1 of Appendix). Grey Brown

Podzolics usually form from calcareous parent material, which limits the process of

podzolisation, resulting in less leaching and elluviation of clays from the A horizon. Brown

Earths are acidic soils that tend to occur on lime-deficient parent materials, but can occur on

lime-rich parent materials, and exhibit some leaching of soluble elements, such as Ca and Mg.

There are three other significant soil types in the study area: (1) BminSW (i.e., Rendzinas and

Lithosols) are shallow well drained soils derived mainly from non-calcareous parent material;

(2) BminPD (i.e., Surface Water and Groundwater Gleys) are deep poorly drained soils derived

mainly from calcareous parent material; and (3) AlluvMin (i.e., Mineral Alluvium) is a recent

deposit consisting generally of well sorted and bedded gravel and sand with minor fractions of

clay and silt, and 10-30% organic material (Fealy & Green 2009). The alluvium deposits form

when rivers meander across their valleys and flood over their floodplains.


35

5.2.2. Effect of Undetermined Components in Soil

The soil samples used in this study are considered shallow as they were collected

from the A horizon, which typically is heavily leached, and normally regarded as a poor

sampling horizon (Levinson 1974). The geochemical compositions, and the factor analysis

derived therefrom, apply to the A horizon specifically, and the unique set of soil-forming

processes that are acting upon that horizon. Consideration needs to be given to: (1) the

dilutant or contaminant effect of significant transported components in soils, such as alluvium

(McQueen 2009); (2) the strong effect of organics in marshy and peaty soils, especially fens

and bogs; (3) the affect of anthropogenic processes on shallow soils, including intensive

agriculture and pollution, given the proximity to the urban area of Rathkeale; and (4)

identifying primary dispersion through up to 9m of transported overburden.

The geochemical analysis is a partial extraction, and does not include silicate minerals

that are insoluble (i.e., refractory) during acid digestion (e.g., quartz). Similarly, the organic

content, which is a significant component of the A horizon, is not measured. Thus, significant

gravimetric components of the soil are not included in the geochemical compositions. Whilst

the magnitude of these refractory and organic components may not affect the relationships

between elements in a given sample, it does affect these relationships between samples, and

is thus a determinant in the factor analysis. An increase in the sand or peaty component in the

soil, will depress the abundances of the other soil components, such as clays, carbonates,

oxides and hydroxides (i.e., the analysed aliquot), and the measured chemical signature due

to these minerals.

In order to determine whether there are any significant effects from undetermined

mineral components on the spatial distribution of the factor scores, a total simplified

normative mineralogical composition was calculated for each sample. The normative

mineralogy is based on the concentration of four major elements (i.e., Al, Fe, Mg and Ca) and

simple assumed mineral compositions, and is thus considered indicative only. The four

mineral components are: (1) clay assuming a mean content of 15% Al; (2) Fe-hydroxide

assuming a mean content of 40% Fe; (3) dolomite assuming a mean content of 15% Mg; and

(4) calcite assuming a mean content of 40% Ca. The four normative contents were combined

and the totals were interpolated across the study area and rendered as a map (See Fig. 8).

Areas of the map with low totals (i.e., coloured blue) indicate significant contents of organics

and/or quartz (or other refractory minerals during acid digestion).

The most distinctive feature of Figure 8 is the area in Ardlaman that exhibits

uniformly low Total Normative Mineralogy, and which almost precisely coincides with

correspondingly distinctive areas in the spatial distribution of Factor 1 and 3 scores (See Figs.

1 and 3 of the Appendix). This observation suggests that the samples in the Ardlaman area

contain high quartzose and/or organic contents, which are thus in conflict with the soil maps


36

Figure 8. Map of Total Simple Normative Mineralogy across study area, which was calculated from

concentrations of the major elements Al, Fe, Mg and Ca, and simple assumed mineral compositions.

Data from this study, Geological Survey of Ireland, Central Statistics Office, Ordinance Survey of

Ireland, Open Street Map and Google Earth.

and indicated soil types, and this has a significant bearing on the interpretation of Factors 1

and 3 (See Section 5.2.2. Spatial Modelling of Factor Scores).

High organic contents may relate to excessively shallow sampling, but the magnitude

of the low totals in the Ardlaman area (i.e., 16.9 to 40.8%) argues against such an explanation.

The low totals might also reflect a large fraction of silicate minerals other than quartz that are

refractory during acid digestion. If significant proportions of rock-forming minerals are not

soluble during acid digestion, the value in determining and certifying the Al concentrations in

soil samples is questionable. Irrespective, a large fraction of quartz or refractory silicates in

the Ardlaman area would signify a change in soil type that is not reflected in the soil maps.

The Soil Map of Limerick (Finch & Ryan 1966) indicates a tongue of Lithosol in the Ardlaman

area, but this being derived in situ directly from limestone bedrock, would be expected to

carry minor silicates, and hence give high normative totals. In contrast, the Soil Map of Ireland

(Fealy & Green 2009) indicates the occurrence of undifferentiated Brown Earths and Grey

Brown Podzolics. Thus, the soil maps do not explain the composition of soils observed at

Ardlaman, which may reflect changes in parent material, soil type effects or sampling error.


37

5.2.3. Metal Dispersion and Sampling

The concentration of a specific metal in soils is the result of a complex set of

processes that influenced the dispersion of that metal in the primary and secondary

environment, where the latter is defined here as the zone of oxidation and weathering.

Processes such as sedimentary deposition, burial diagenesis, deformation and faulting, and

magmatic and hydrothermal activity effect control in the primary environment, whilst

glaciation (primarily mechanical) and soil-forming processes (primarily chemical) effect

control in the secondary environment. Secondary dispersion results in the redistribution of

the primary distribution of elements in unweathered rock. Because the physico-chemical

conditions of the primary and secondary environments differ significantly, elements exhibit

differential mobility between these two environments, resulting in disparate elemental

distributions and geochemical associations.

Chemical, biological, mechanical and environmental factors control soil systems, and

thus affect the secondary dispersion of individual elements. Some of the more important

factors are: (1) bulk composition and mineralogical character of material; (2) Eh and pH

conditions; (3) mobility of elements and their compounds in presence of organics; (4)

presence of media capable of limiting dispersion through precipitation (e.g., carbonates and

bacteria); (5) vegetation and micro-organisms (e.g., role in production of humus and

oxidation-reduction); (6) mechanical dispersion (e.g., by gravity movement, surface water,

glaciation and bioturbation); (7) climate; (8) topography; and (9) time (Levinson 1974).

According to Shuman (1991), metals occur in specific forms or ‘pools’ in soil, and these are:

(a) dissolved in solution; (b) exchange sites on inorganic soil constituents; (c) adsorbed on

inorganic soils constituents; (d) associated with insoluble organic matter; precipitated as pure

or mixed solids; (e) in the structure of secondary minerals; and (f) in the structure of primary

minerals. As soil systems are dynamic and highly complex, with multiple components and

processes, perturbations in the system can cause metal redistribution between the ‘pools’.

Differences in geological, geomorphological and environmental settings impart

unique surface geochemical signatures to individual deposits. Anthropogenic effects due to

activities such as agriculture, vehicular traffic or industrial pollution are also superimposed on

the geogenic domain. In glaciated terrain, till sampling at depths of typically >0.75m is often

preferred to mitigate changes in geochemistry due to pedogenic and possibly anthropogenic

processes (McClenaghan 2007). Near-surface soils are more suitable than tills for detailed

geochemical sampling at the property scale, partly for reasons of sampling speed and cost

(Cook & Dunn 2007), but are more removed from the bedrock source and vulnerable to

anthropogenic effects. These shallow soil samples (i.e., <0.5m) are also vulnerable to changes

in geochemistry associated with soils types, and this mixing of sample media introduces

significant noise into the data (Hamilton 2007). Nonetheless, soil sampling is generally

recommended in areas with soil developed over transported overburden with depths of less

than 5m (McQueen 2009). However, the analyst must be cognisant of variation in the data

due to the array of regolith and pedogenic processes, soil type effects and anthropogenic

effects, to which the chosen method of geochemical prospecting is sensitive.


38

Figure 9. 3D surface diagram of Factor 1 scores across the study area. Areas of high

factor scores are in red and brown; intermediate scores in light yellow and blue; and

low scores in dark blues. Viewpoint is from the SSE.

5.2.4. Spatial Modelling of Factor Scores

Factor 1 is the dominant factor accounting for 35.9% of the total variance in the data

set, and includes significant loadings for Al, Na, Ba, Ce, Cr, Ga, Ge, La, Nb, Ni, Rb, Sc, Ta, Th, Ti,

V, W, Y and Zr. Although Fe only has a minor non-significant loading on Factor 1, the

gravimetrically significant elements (i.e., >1wt%) associated with Factor 1 are the major

elements Al, K and Fe. Factor 1 suggests a clay plus oxide/hydroxide association, most

probably derived from the detrital or siliciclastic component in the soil parent material.

Much of the study area exhibits intermediate values for Factor 1, with high values

concentrated in Ardgoulbeg, Cloghanarold in the east, and near Creeves in the northwest (See

Fig. 1 of the Appendix, and Fig. 9). The most conspicuous feature is a rectangular area of

uniformly low Factor 1 scores in Ardlaman, and extending SSW to Ranahan, coincident with

the Ranahan stream and the occurrence of gleys and cutover peat. An area of low factor

scores also occurs near Lisnacullia, and is partly coincident with the Lisnacullia stream, whilst

a ‘boudinage-like’ string of low values is largely coincident with the arc of the River

Ahacronane. Both of these areas also coincide with the occurrence of alluvium and/or gleys.

The River Deel is associated with significant deposits of coincident alluvium, but is not

generally coincident with low Factor 1 scores.

It is clear that the A horizon across most of the study area is relatively clay-oxide-rich,

albeit distinct areas with significantly clay-oxide-depleted A horizons are observed. A

consistent relationship of Factor 1 scores with soil types seems apparent, with reasonable


39

coincidence between areas with low Factor 1 scores and the occurrence of Alluvium and

Gleys. High Factor 1 scores correspond to high clay-oxide contents in the A horizon, which for

most of the soils in the study area, equates with the intensity of podzolisation. As

podzolisation develops in limestone-rich till, carbonates are initially elluviated, preferentially

elevating the clay-oxide content of the A horizon from that of Gleys to Brown Earths to Grey

Brown Podzolics. Interestingly, areas identified as Grey Brown Podzolics in the Soil Map of Co.

Limerick (Finch & Ryan 1966) cannot be consistently discriminated from those of Brown

Earths on the basis of Factor 1 scores (e.g., Compare Factor 1 scores for Ardgoulbeg and

Ballynisky areas with soil types observed in Fig. 15 of the Appendix).

The distinct rectangular area of uniformly low Factor 1 scores in the Ardlaman area

appears not to correspond Alluvium and Gleys, but to an area of Lithosol identified in the Soil

Map of Co. Limerick (Finch & Ryan 1966), but which is not discriminated in more recent Soil

Maps of Ireland (Gardiner & Radford 1980, Fealy & Green 2009). Compare Figure 6, and

Figures 13, 14 and 15 of the Appendix. The low Factor 1 scores of the possible Lithosols

cannot be attributable to impeded podzolisation (as in Gleys), but instead might reflect the

lack of clay-oxide content in the limestone bedrock parent material. However, the spatial

distribution of the Total Normative Mineralogy (See Fig. 8) explains the low Factor 1 scores at

Ardlaman on the basis of undetermined components, namely high quartzose and/or organic

contents. Thus, Factor 1 is an association of elements associated with increasing clay and

oxide contents of the A horizon with increasing podzolisation, although low Factor 1 scores

can also reflect elevated content of undetermined components.

Factor 2 accounts for 9.30% of the total variance in the data set, and has significant

loadings for Bi, Cu, Pb, Sb, Sn, Te and Zn. This factor is interpreted to reflect the signature of

Zn-Pb mineralisation in an alkaline secondary environment due to ore-related elements with

low mobility, and is identified as an ore association. The Factor 2 scores show a strong high

value anomaly centred on Irish Grid Reference 131700,144550 at Cooltomin, with minor

satellite anomalies slightly further to the west, and also to the south at Gortroe (See Fig. 2 of

Appendix and Fig. 10). The Cooltomin anomaly has a N-S orientation, extending for 900m

north from near the Waulsortian-Rathkeale contact at 144200N to 145100N. The anomaly is

interpreted to reflect secondary dispersion in till of low mobility ore-related elements

associated with Irish Type mineralisation. The low mobility of the elements associated with

Factor 2 in alkaline secondary environments suggests that the anomaly is not hydromorphic.

The anomaly most probably overlies subcroping mineralisation, or is displaced laterally by soil

creep and/or glacial movement, and presents a highly prospective target for exploration.


40




It is apparent from Fig. 2 of the Appendix that the distribution of Factor 2 scores also

indicates a N-E trend, which may reflect leakage of mineralising fluids along a set of NE

trending faults, or possibly also the likely direction of Midlandian ice movement, and thus the

direction of mechanical dispersal within the till. The eastern side of the study area shows

uniformly low values, except in the vicinity of the Ovoca B anomaly, and a NE trending linear

feature slightly further to the southwest at Curraghnadeely. These two areas have been

explored with DDHs 3488/3 and 3488/9, respectively (Tara Mines 1997, 2002). Other

prospective targets include: (1) the minor anomaly overlying Rathkeale Beds at Gortroe (i.e.,

131100,143100); (2) cluster of satellite anomalies on the Waulsortian-Rathkeale contact west

of Cooltomin (i.e., centred at 131100,144250), especially given the association with a major

NE fault, and the latter’s association with volcanics; and (3) minor anomaly overlying

Rathkeale Beds at Ranahan (i.e., 132150,143250), which is on a NE trend associated with a

major NE trending fault and DDH 3488/15 (See Fig. 2 of the Appendix).

Factor 3 accounts for 8.25% of the total variance in the data set, and includes

significant negative loadings for Ca, Mg, Sr and Te, and significant positive loadings for Ce and

La. Although the loadings for the Ca, Sr and Mg suggest a limestone signature, Factor 3 is

interpreted to be an association of elements that are variably retained in the A horizon of the

soils as a result of being subjected to gleisation. Gleisation is caused by waterlogging and

reduced water movement and leaching, and is associated with the development of anaerobic,

reducing conditions. The factor describes variation in the leaching of elements associated

with carbonates (i.e., Ca, Mg and Sr), and retention of elements associated with resistate

minerals (e.g., Ce and La) in the A horizon. Because Ca, Sr and Mg are negatively correlated

with Factor 3 (i.e., exhibit significant negative loadings on Factor 3), low factor scores are

indicative of high Ca, Sr and Mg retention in the A horizon due to gleisation. Thus, red colours


41

denote high Factor 3 scores but low values for Ca, Sr and Mg, whilst blue colours denote low

Factor 3 scores but high values for Ca, Sr and Mg (See Fig. 3 of the Appendix). Consequently,

the red symbology is unusually indicative of low concentrations of the significant elements

associated with the Factor (i.e., Ca, Sr and Mg in this case).

The spatial distribution of Factor 3 scores across the study area is irregular and

clustered, with low factor scores concentrated in Ranahan, Lissatotan-Creeves, Lisnacullia and

Curraghnadeely (See Fig. 3 of the Appendix). The area of low scores at Ranahan coincides with

the Ranahan stream and the occurrence of gleys and cutover peat, as well as with the

extension of the Factor 1 Ardlaman low (See Fig. 1 of Appendix, and Fig. 9). In addition, the

area of low Factor 3 scores at Lisnacullia also coincides with an area of low Factor 1 scores,

whilst the areas of low Factor 3 scores in the Lissatotan-Creeves area coincide with the

‘boudinage-like’ string of low Factor 1 scores occurring along the arc of the River Ahacronane.

Although Factors 1 and 3 are by definition uncorrelated, they exhibit sympathetic behaviour

in many areas, reflecting the opposing effects of podzolisation (increases with Factor 1) and

gleisation (decreases with Factor 3).

However, the area of low Factor 3 scores at Curraghnadeely coincides with an area of

high Factor 1 scores. Similarly, the distinctive rectangular area of low Factor 1 scores at

Ardlaman almost precisely coincides with an area of high Factor 3 scores. Thus, this latter

area exhibits low concentrations of elements associated with both Factors 1 and 3, the two

factors associated with opposing soil-forming processes (i.e., podzolisation and gleisation) and

with the dominant major elements Al, Fe, Mg and Ca. However, it has been previously shown

that this area exhibits low Total Normative Mineralogy (See Fig. 8), and thus it is interpreted

that the soils in Ardlaman contain elevated abundances of soil components outside the data

set (i.e., quartz or organics) causing conflicting Factor 1 and 3 scores.


significant loadings for Fe, Co, Mn and Ni. Fe and Mn occur together in the secondary

environment as oxides and hydroxides, and are essentially ubiquitous in soils. These oxides-

hydroxides are insoluble in all but the most acidic and reducing conditions found in the

weathering environment, and tend to co-precipitate or scavenge Co and Ni (Levinson 1974,

Salminen 2005). Factor 4 is interpreted as an association elements related to Fe-Mn oxides-

hydroxides that are concentrated in the A horizon due to the elluviation of other components

in the soil.

The spatial distribution of Factor 4 is relatively dispersed, with a low tendency for

clustering (See Fig. 4 of the Appendix, and Fig. 11). Areas of low Factor 4 scores generally

coincide with areas of Gley (e.g., Ranahan), where the prevailing reducing conditions could

give rise to increased relative mobility of Fe and Mn. Interestingly, the areas of highest Factor

4 scores seem to cluster along the Waulsortian Rathkeale contact (including Ardlaman), and

over the Durnish Fm., perhaps reflecting a higher primary Fe-Mn signature.


42





significant loadings for S, Se and U, and minor non-significant loadings for Cd, Mo and V. With

the exception of Cd, these elements are characterised by very high mobility in the secondary

environment, particularly when neutral to alkaline, but exhibit very low mobility in reducing

environments (Levinson 1974, Salminen 2005). The association of U with S, Se, Mo and V is

typical of “roll-front” U deposits, formed at the interface of oxidising and reducing

groundwaters (EPA 1995). Factor 5 encompasses elements that are very mobile in the

oxidising alkaline environment, but for which reducing conditions act as a geochemical

barrier, causing rapid precipitation, potentially in concentrations as ‘false’ anomalies.

Reducing conditions can occur in organic-rich or peaty soils and peat in fens or bogs, or where

oxidised alkaline conditions encounter seepage of reduced groundwater.

The spatial distribution of Factor 5 is highly clustered, with five distinct peaks on a

relatively uniform background (See Fig. 5 of the Appendix, and Fig. 12). Interestingly, the

rectangular area of low Factor 1 and high Factor 3 scores at Ardlaman recurs with an area of

low Factor 5 scores, although the strongest Factor 5 peak occurs in this area (i.e., at 132850,

144075). This anomaly straddles the Waulsortian Rathkeale contact, and overlies a wedge of

Rathkeale Beds bounded to the north by major NE and NW trending faults (See Tara Mines

2002). However, the anomaly coincides with an area of gleys and cutaway peat, indicating a

strongly reducing secondary environment capable of precipitating the associated S, Se and U.

The string of three peaks arcing from Lissatotan to Creeves is largely coincident the River

Ahacronane flood plain and the occurrence of gleys and alluvium. Similarly, the truncated

anomaly at Lisnacullia coincides with areas of gleys and alluvium associated with the


43




Lisnacullia stream. Finally, the minor anomalies at Cooltomin and Ranahan coincide with

either end of the gleys associated with the Ranahan stream.

All of the Factor 5 anomalies are thus probably explicable in terms of reducing

conditions providing a geochemical barrier to highly mobile elements, and generating ‘false’

anomalies that have no underlying sulfide source. However, the major anomaly at Ardlaman

occurs in the vicinity of intersections of sulfide mineralisation at depth in DDHs 3488/10 and

3488/15, and the major fault that is interpreted to have acted as conduits for mineralising

fluids (Tara Mines 2006). Thus, caution must be exercised in rejecting this anomaly as ‘false’,

as leakage along the fault, or at the intersection of the faults, may have contributed to the

signature.


significant loadings for As and Sb (i.e., 0.824 and 0.569), two elements which exhibit very

similar geochemical behaviour. As exhibits medium mobility, whereas Sb exhibits low mobility

in the secondary environment, except under reducing conditions where both elements

become immobile (Levinson 1974). The lower mobility of Sb in oxidising alkaline conditions

probably explains the lower loading relative to As on Factor 6, and the additional association

of Sb with the less mobile ore-related elements of Factor 2. As and Sb are both highly mobile

in the primary environment, and thus Factor 6 may reflect both primary differentiation and

differential secondary mobility of As and Sb relative to the other ore-related elements of

Factor 2.


44




The spatial distribution of Factor 6 is clustered with numerous minor peaks on a

relatively uniform background (See Fig. 6 of the Appendix, and Fig. 13). The eastern half of the

study area exhibits largely background values, whilst numerous minor peaks cluster in a broad

arc distally west and south of the Cooltomin Factor 2 anomaly. The most prominent Factor 6

anomaly (i.e., centred at 131700, 145000) coincides with the northern end of the Cooltomin

Factor 2 anomaly, whilst there is considerable coincidence of other Factor 2 and 6 anomalies,

such as at Lissatotan and Ranahan, but not at Gortroe or Curraghnadeely. The differences in

the distributions of Factors 2 and 6 cannot be readily explained by secondary mechanical

dispersion from glacial movement or hydromorphic dispersion, but rather by primary

dispersion. The proximity of many Factor 6 anomalies to the Waulsortian Rathkeale contact

and to a major NE trending fault is considered significant. Factor 6 probably reflects primary

dispersion as highly differentiated vein mineralisation, such as the intersection of vein sulfides

grading 14% As (Blakeman, pers. comm. 2014).

Factor 7 accounts for 3.05% of the total variance in the data set, and includes a single

moderate significant loading for Y (i.e., 0.518). Y has geochemical behaviour similar to the

heavier REEs, exhibiting low mobility in all secondary environments, and tending to

concentrate in resistate minerals such as xenotime and zircon (Salminen 2005). The

mechanism of Factor 7 may reflect the abundance of Y-bearing resistate minerals in the A

horizon, which is expected to increase with increasing siliciclastic component in the soil, or by

increasing elluviation.


45

The spatial distribution of Factor 7 is diffusely clustered with low values in the east

grading to high values in the west of the study area (See Fig. 7 of the Appendix). This spatial

pattern is indicative of increased detrital input to the study area from erosion of Namurian

siliciclastics, including sandstones, shales and volcanics, on elevated ground to the west (i.e.,

near Kilcolman). Notably, there is a stepwise reduction in Factor 7 scores across the

Lisnacullia stream at Riddlestown and Kilcool, and again across the River Deel at Ardgoulbeg,

where the factor scores are uniformly low. Because the spatial distribution of Factor 7 is

constrained by the extant drainage patterns, an influx of resistate minerals associated with

erosion of Namurian siliciclastics to the west is indicated as the mechanism underlying Factor

7.


strong significant loading for Cd (i.e., 0.893). Cd has medium mobility in an alkaline secondary

environment, but becomes immobile in reducing environments associated with organics

(Levinson 1974, Salminen 2005). Although Cd has a strong affinity for Fe-Mn oxides-

hydroxides (Young 2010), the dominant fraction of Cd, which is associated with Factor 8, is

unrelated to Fe and Mn. The differential mobility of Cd and Zn specifically in alkaline

secondary environments, appears to have caused a significant divergence in their behaviour,

and the displacement of Cd from the suite of low mobility, ore-related elements associated

with Factor 2. Factor 8 probably reflects precipitation of Cd by organics, or possibly other

factors in the soil, such as Cd-rich phosphate fertilisers and sewage sludge (Van Kauwenbergh

2002, Salminen 2005). Anthropogenic Cd could mask the natural geochemical associations of

Cd, including any association with Irish Type mineralization.

The spatial distribution of Factor 8 is highly clustered with four distinct peaks on a

relatively uniform background (See Fig. 8 of the Appendix, and Fig 14). The most prominent

peak is truncated by the edge of the study area at Lisnacullia, and coincides with the

truncated Factor 5 peak, and distinct lows in Factor 1 and 6 values. This is an area of gleys and

alluvium associated with the Lisnacullia stream, and is interpreted to be a geochemical barrier

to Cd as a consequence of reducing conditions. The minor peak at Ranahan also coincides

with an area of gleys

The minor Factor 8 peak at Cooltomin coincides with the principal Factor 6 (As+Sb)

peak, and the north end of the principal Factor 2 (Ore) peak. Similarly, the minor peak near

Kilcool is coincident with an area of cutaway peat, but also straddles the Waulsortian

Rathkeale contact, and is in line with a major NE trending fault. Although coincident with

areas of gleys or peat, the provenance of these two peaks are uncertain, whether reflecting

primary dispersion, or precipitation as ‘false’ anomalies by organics and reducing conditions in

the secondary environment. Nonetheless, anomalous Cd without associated Zn should be

viewed as questionable hydromorphic anomalies, whilst a contribution from anthropogenic

sources must also be considered.


46





significant loadings for Ge and Tl (i.e., 0.690 and 0.507, respectively). Ge and Tl enrichment is

associated with hydrothermal fluids in the Red Dog Zn-Pb-Ag deposits, Alaska (Slack et al.

2004), and Tl enrichment is associated with mineralisation in the vicinity of the study area

(Blakeman, pers. comm. 2014). These elements exhibit variable mobility in the secondary

environment, but are both readily fixed by Fe-Mn oxides-hydroxides and organic matter

(Levinson 1974).

The spatial distribution of Factor 9 is strongly differentiated with a broad area of high

values in the southwest at Ballynisky, grading to low values in the north and east (See Fig. 9 of

the Appendix, and Fig. 15). These two chalcophile elements are expected to occur in sulfides

in the primary environment, whilst they exhibit variable mobility in the secondary

environment, but are both readily fixed by Fe-Mn oxides-hydroxides and organics (Levinson

1974). The spatial pattern of Factor 9 suggests a source to the southwest of the study area,

whether primary such as Ge and Tl associated with mineralisation, or secondary due to

mechanical dispersion by fluvial processes. The Factor 9 scores appear to be restricted by the

Lisnacullia stream, east of which the factor scores are almost uniformly low. Whilst the

secondary mineralogical deportment of Ge and Tl is unknown, fluvial deposition is indicated

as the process underlying Factor 9, by the control exerted by the extant drainage pattern on

its spatial distribution (i.e., similar to Factor 7). However, given the known association of Ge

and Tl with some SEDEX deposits, an association of Factor 9 with mineralisation cannot be

completely discounted.


47




Factor 10 accounts for 2.15% of the total variance in the data set, and includes a

single strong significant loading for P (i.e., 0.729). P exhibits low mobility in the secondary

environment, as it is readily fixed by clays and hydrous oxides of Fe and Al to form insoluble Al

and Fe phosphates in acid soils, and by CaCO3 in alkaline soils. Because of this process of

fixation, P becomes unavailable for plant take-up, and is therefore regularly reapplied to

agricultural land using phosphate fertilisers and sewage sludge (or slurry).

The spatial distribution of Factor 10 is shown in Fig. 10 of the Appendix, and is

diffusely clustered, with no consistent pattern relating to drainage, soil or bedrock geology. A

cursory examination of Google Maps in Satellite View confirms firstly the use of slurry

spreading within the study area, and secondly intensive farming with high quality grass crops,

and the cutting of grass for silage. Maintaining soil P, K and lime levels is considered essential

for such high quality grass crops and silage production, and organic manures are very

effective in balancing soil P. Factor 10 is interpreted as anthropogenic, and constitutes the

expression of the intensity of agriculture, specifically fertiliser and sludge use, which likely

masks any spatial pattern in the natural distribution of P.


48

6. DISCUSSION AND CONCLUSIONS

The Cooltomin geochemical data set is a set of concentrations for 46 inorganic

elements in 1,421 A horizon soil samples from a study area straddling the contiguous

Prospecting License Areas 3488 and 3545 in Co. Limerick. Four of the elements (i.e., Au, Ag,

Hg and B) had excessive proportions of censored data (i.e., >20%) and were rejected from

further analysis. The reduced data set was explored using Factor Analysis integrated with GIS.

A factor model using the MRFA extraction method, coupled with Varimax orthogonal rotation

was adopted and extracted ten generalised factors, and calculated factor scores for each

sample. The factor scores were interpolated using Ordinary Kriging to generate a raster

surface for each of the ten factors. Because each of the raster surfaces is based on

multivariate data, and because factor analysis excludes unique and error variance, the

accuracy and spatial definition of the surfaces are considered superior to that of the

constituent univariate data from which these are derived. The raster surfaces were used to

render a set of thematic maps and selected 3D surface diagrams, which reveal the spatial

expression of geochemical processes acting on the soils of the study area.

The extracted factors constitute geochemical associations that describe the variation

in the raw data, and are interpreted to reflect underlying geochemical processes that have

acted on the composition of the soils. The ten factors, named according to their dominant

elemental associations, are:

Ore Related Factors

F2 (Ore): Bi, Cu, Pb, Sb, Sn, Te and Zn.

Factor 2 accounts for 9.30% of the total variance in the data set, with significant loadings for

Bi, Cu, Pb, Sb, Sn, Te and Zn, which with the exception of Bi and Sn, are all identified as

associated with mineralisation by Blakeman. Thus, Factor 2 reflects the signature of Irish Type

Zn-Pb mineralisation due to non-hydromorphic, secondary dispersion in till of ore-related

elements with low mobility in an alkaline secondary environment. The Factor 2 scores show a

strong high value anomaly centred on Irish Grid Reference 131700, 144550 at Cooltomin, with

minor satellite anomalies slightly further west and south. The Cooltomin anomaly has a N-S

orientation, extending for 900m north from near the Waulsortian-Rathkeale contact at

144200N to 145100N. The Cooltomin anomaly most probably overlies subcroping

mineralisation, or is displaced laterally by glacial movement and/or soil creep, and presents a

highly prospective target for exploration. The distribution of Factor 2 scores also indicates a

N-E trend, which may reflect control of NE trending faults on mineralising fluids, or possibly

direction of mechanical dispersal within the till by Midlandian ice movement. The association

of Ag and Hg with the mineralisation is indeterminate as these two elements were excluded

from the factor analysis due to high proportions of censored data.


49

F6 (Metalloid): As and Sb.

Factor 6 accounts for 3.26% of the total variance in the data set, with significant loadings for

As and Sb. The lower mobility of Sb relative to As in oxidising alkaline conditions probably

explains the greater association of Sb with low mobility ore-related elements of Factor 2, and

of As with Factor 6. Given that As and Sb are both highly mobile in the primary environment,

Factor 6 may reflect both primary differentiation and differential secondary mobility of As and

Sb relative to the other ore-related elements of Factor 2. Factor 6 exhibits numerous minor

peaks clustered in a broad arc distally west and south of the Cooltomin Factor 2 anomaly. The

most prominent Factor 6 anomaly (i.e., centred at 131700, 145000) coincides with the

northern end of the Cooltomin Factor 2 anomaly, whilst there is considerable coincidence of

other Factor 2 and 6 anomalies. Nonetheless, differences in the distributions of Factors 2 and

6 cannot be readily explained by secondary mechanical dispersion from glacial movement or

hydromorphic dispersion. The proximity of many Factor 6 anomalies to the Waulsortian

Rathkeale contact and to a major NE trending fault is considered significant, and suggests that

Factor 6 may reflect greater primary dispersion as highly differentiated vein mineralisation.

Possibly Ore Related Factors

F5 (Reduction Barrier): S, Se and U.

Factor 5 accounts for 6.51% of the total variance in the data set, and includes significant

loadings for S, Se and U, with minor loadings for Cd, Mo and V. Except for Cd, these elements

show very high mobility in the secondary environment, but exhibit very low mobility in

reducing environments. The association of U with S, Se, Mo and V is typical of "roll-front" U

deposits, and suggests an association of elements that are very mobile in oxidising alkaline

environment, but for which reducing conditions act as a geochemical barrier, causing rapid

precipitation, potentially in concentrations as ‘false' anomalies. Reducing conditions can occur

in organic-rich or peaty soils and peat, or where oxidised alkaline conditions encounter

seepage of reduced groundwater. Factor 5 is characterised by five distinct peaks over a

relatively uniform background. The strongest peak straddles the Waulsortian Rathkeale

contact, and overlies a wedge of Rathkeale Beds bounded to the north by major NE and NW

trending faults. However, the anomaly coincides with an area of gleys and cutaway peat,

indicating a strongly reducing secondary environment. Indeed, all of the Factor 5 anomalies

are explicable in terms of reducing conditions providing a geochemical barrier, and generating

‘false' anomalies that have no underlying sulfide source. However, the major anomaly at

132850, 144075 occurs in the vicinity of intersections of sulfide mineralisation at depth in

DDHs 3488/10 and 3488/15, and the major fault that is interpreted to have acted as conduits

for mineralising fluids. Thus, caution must be exercised in rejecting this anomaly as ‘false', as

leakage along the fault, or at the intersection of the faults, may have contributed to the

signature.

F8 (Cd): Cd

Factor 8 accounts for 2.70% of the total variance in the data set, and has a single significant

loading for Cd. Cd has medium mobility in an alkaline secondary environment, but becomes


50

immobile in reducing environments associated with organics. The differential mobility of Cd

and Zn specifically in alkaline secondary environments, appears to have caused a significant

divergence in their behaviour, and the displacement of Cd from the suite of low mobility,

ore-related elements associated with Factor 2. Factor 8 probably reflects precipitation of Cd

by organics, although there may also be a component related to other soil factors, such as

Cd-rich phosphate fertilisers and sewage sludge. The Factor 8 peaks generally coincide with

areas of gleys, peat and alluvium, which are interpreted to be geochemical barriers to Cd.

However, the provenance of the peaks at Cooltomin and Kilcool in particular are uncertain,

whether reflecting primary dispersion or precipitation as ‘false' anomalies. Nonetheless,

anomalous Cd without associated Zn should be viewed as questionable hydromorphic

anomalies.

Pedogenic Factors

F1 (Clay-Oxide): Al, Na, Ba, Ce, Cr, Ga, Ge, La, Nb, Ni, Rb, Sc, Ta, Th, Ti, V, W, Y and Zr. Factor

1 accounts for 35.9% of the variance in the data set, has significant loadings for 19 elements,

and is the overwhelmingly dominant factor. Although Fe does not have a significant loading

on Factor 1, the gravimetrically significant elements associated with the factor are Al, K and

Fe. Factor 1 reflects a clay plus oxide-hydroxide association, most probably derived from the

detrital component in the soil parent material. The A horizon across most of the study area is

relatively clay-oxide-rich, albeit with distinct areas with significantly clay-oxide-depleted A

horizons. A relatively consistent relationship of Factor 1 scores with soil types seems

apparent, with reasonable coincidence between areas with low Factor 1 scores and the

occurrence of Alluvium and Gleys, or elevated contents of undetermined components. Factor

1 is an association of elements associated with increasing clay and oxide contents of the A

horizon with increasing podzolisation from that of Gleys to Brown Earths to Grey Brown

Podzolics.

F3 (Gley): Ca, Mg, Sr, Te, Ce and La.


negative loadings for Ca, Mg, Sr, Te, and positive loadings for Ce and La. The factor describes

variation in the leaching of elements associated with carbonates (i.e., Ca, Mg and Sr), and

retention of elements associated with resistate minerals (e.g., Ce and La) in the A horizon.

Factor 3 is thus interpreted to be an association of elements that are variably retained in the

A horizon of the soils due to gleisation. Because Ca, Sr and Mg are negatively correlated with

Factor 3, low factor scores are indicative of high Ca, Sr and Mg retention in the A horizon due

to gleisation, which is typically associated with the occurrence of gleys, cutover peat and

alluvium. Factors 1 and 3 exhibit sympathetic behaviour in most areas, reflecting the opposing

effects of podzolisation (increases with Factor 1) and gleisation (decrease with Factor 3).

Unusually however, the Ardlaman area exhibits low concentrations of elements associated

with both Factors 1 and 3, the two factors associated with above opposing soil-forming

processes, and with the dominant major elements Al, Fe, Mg and Ca. However, it is

interpreted that the soils in Ardlaman contain elevated abundances of undetermined soil


51

components (i.e., quartz and/or organics), conflating the relationship between Factor 1 and 3

scores.

F4 (Fe-Mn Oxide-Hydroxide): Fe, Mn, Co and Ni.


loadings for Fe, Co, Mn and Ni. Fe and Mn occur together in the secondary environment as

oxides and hydroxides, and are essentially ubiquitous in soils. These oxides-hydroxides are

largely insoluble in the weathering environment, and tend to co-precipitate or scavenge Co

and Ni. Factor 4 is interpreted as an association of elements related to Fe-Mn

oxides-hydroxides that are concentrated in the A horizon due to the elluviation of other

components in the soil. Areas of low Factor 4 scores generally coincide with areas of Gley,

where the prevailing reducing conditions could give rise to decreased elluviation and

increased relative mobility of Fe and Mn.

F7 (Y): Y.


moderate significant loading for Y. Y exhibits low mobility in all secondary environments, and

tends to concentrate in resistate minerals such as xenotime and zircon. Factor 7 seems to

reflect the abundance of Y-bearing resistate minerals in the A horizon, which would increase

with increasing siliciclastic component in the soil, or by increasing elluviation. The spatial

distribution of Factor 7 with low values in the east grading to high values in the west is

suggestive of increased detrital input to the study area from erosion of Namurian siliciclastics

to the west. There is a stepwise reduction in Factor 7 scores across the Lisnacullia stream and

again the River Deel. The apparent control of the extant drainage patterns on the spatial

distribution of Factor 7 indicates that the mechanism underlying Factor 7 is an influx of

resistate minerals associated with erosion of Namurian siliciclastics.

F9 (Ge): Ge and Tl.


loadings for Ge and Tl. These two elements are expected to occur as sulfides in the primary

environment, and exhibit variable mobility in the secondary environment, although both are

readily fixed by Fe-Mn oxides-hydroxides and organic matter. Ge and Tl enrichment is

associated with some SEDEX deposits, and an association of Tl enrichment with mineralisation

in the vicinity of the study area has been indicated previously. The spatial distribution of

Factor 9 shows a broad area of high values in the southwest at Ballynisky, grading to low

values in the north and east. This pattern suggests a source to the southwest of the study

area, whether primary such as Ge and Tl associated with mineralisation, or secondary due to

mechanical dispersion by fluvial processes. The Factor 9 scores appear to be restricted by the

Lisnacullia stream, and this control by the extant drainage pattern on the spatial distribution

of Factor 9, indicates fluvial deposition as the underlying process. Thus, an association of Ge

and Tl with mineralisation is not indicated, but cannot be completely discounted.


52

Anthropogenic Factors

F10 (P): P.

Factor 10 accounts for 2.15% of the total variance in the data set, and includes a single strong

significant loading for P, which exhibits low mobility in the secondary environment. P is

readily fixed by clays and hydrous oxides of Fe and Al to form insoluble Al and Fe phosphates

in acid soils, and by CaCO3 in alkaline soils. Because of this process of fixation, P becomes

unavailable for plant take-up, and is therefore regularly reapplied to agricultural land using

phosphate fertilisers and sewage sludge (or slurry). The spatial distribution of Factor 10 shows

no consistent pattern relating to drainage, soil or bedrock geology. Consequently, Factor 10 is

interpreted as anthropogenic, and constitutes the expression of the intensity of agriculture

practices, specifically fertiliser and sludge use.

The soil samples used in this study are considered shallow as they were collected

from the A horizon, which is ‘normally’ strongly leached and regarded as a poor sampling

horizon. The geochemical compositions, and the factor analysis derived therefrom, apply to

the A horizon specifically, and the unique set of soil-forming processes that are acting upon

that horizon. Such samples are particularly vulnerable to: (1) modifying effects of significant

transported components in the soil (e.g., alluvium); (2) soil type effects (e.g., reducing

conditions of gleys and peaty soils); (3) the affects of anthropogenic processes, including

intensive agriculture and pollution; and (4) increased difficulty discerning primary dispersion

through significant thicknesses of transported overburden. Many of the anomalous features

in the data set are attributable to soil type effects (e.g., Factors 3 and 5) and anthropogenic

effects (e.g., Factor 10) associated with shallow samples, and which tend to conflate and

obscure the geochemical signature of mineralisation. It may be useful to consider sampling a

deeper soil horizon or even the underlying till, particularly where numerous soil types with

widely differing geochemical characteristics are encountered within a study area.

Supplementary

The method of geochemical analysis employed in this study is a partial extraction, and

therefore does not include silicate minerals that are insoluble (i.e., refractory) during acid

digestion (e.g., quartz). Similarly, the organic content, which is a significant component of the

A horizon, is not measured. Thus, significant gravimetric components of the soil are not

included in the geochemical compositions. These refractory and organic components affect

the relationship between samples, and are thus a determinant in the factor analysis. For

example, an increase in the quartzose or peaty component in the soil will depress the

abundances of the analysed soil components, and the measured chemical signature due to

these minerals (e.g., clays, carbonates, etc.).

Unusually, the Ardlaman area exhibited simultaneously low Factor 1 and high Factor 3

scores, indicating low of contents of Al, Fe, Mg and Ca, the dominant major elements in the

soils, and the main associated elements in the two principal and opposing pedogenic

processes (i.e., podzolisation and gleisation). In order to explain this anomaly, the effect of


53

undetermined mineral components on the spatial distribution of the factor scores was

investigated. A Total Normative Mineralogical Composition based on the concentration of the

four major elements (i.e., Al, Fe, Mg and Ca) and simple assumed mineral compositions, was

calculated. The four normative contents were combined and the totals were interpolated and

rendered across the study area. An area of low Total Normative Mineralogy was identified,

which almost precisely corresponds to the distinctive area of low Factor 1 and high Factor 3

scores. This observation indicates that the soils in the Ardlaman area cannot be explained on

the basis of the indicated soil types, including Lithosol, and instead contain high quartzose

and/or organic contents. Although unlikely here, high organic contents could reflect failure to

consistently sample the same A horizon, and again highlights the sensitivity of shallow soil

sampling.

Factor 2, with its associated elements of Bi, Cu, Pb, Sb, Sn, Te and Zn, reflects the

signature of Irish Type Zn-Pb mineralisation dispersed in soils developed on glacial till of

predominantly limestone origin. A strong Factor 2 anomaly is centred on Irish Grid Reference

131700, 144550 at Cooltomin. The Cooltomin anomaly has a N-S orientation, extending for

900m north from near the Waulsortian-Rathkeale contact at 144200N to 145100N, although

the main peak has a N-S length of 325m. The Cooltomin anomaly most probably overlies

subcroping mineralisation, or is displaced laterally by glacial movement and/or soil creep, and

presents a highly prospective target for exploration. It is considered that this anomaly offers

the potential of a more lucrative target than the Factor 5 anomaly in Ardlaman area (i.e., at

132850, 144075), which probably constitutes a ‘false’ anomaly. Other prospective targets

include: (1) the minor anomaly overlying Rathkeale Beds at Gortroe (i.e., 131100,143100); (2)

cluster of satellite anomalies on the Waulsortian-Rathkeale contact west of Cooltomin (i.e.,

centred at 131100,144250), especially given the association with a major NE fault, and the

latter's association with volcanics; and (3) minor anomaly overlying Rathkeale Beds at

Ranahan (i.e., 132150,143250), which is on a NE trend associated with a major NE trending

fault and DDH 3488/15.

This study has demonstrated that Factor Analysis integrated with GIS is a powerful

technique for interrogating geochemical data, and has the potential to be extremely useful in

geochemical surveying applied to mineral exploration. The superior spatial definition and

pattern recognition afforded by the technique, can discriminate the signatures due to ore-

forming processes or secondary dispersion of mineralisation, and thereby enhance anomaly

detection and target generation. The method is recommended for the interrogation of data

derived from soil, till or litho- geochemistry, on a reconnaissance and more particularly on a

detailed, property-scale level.

The method is probably most effective as a geochemical orientation technique to

determine the processes operating on soils and shaping the observed geochemical signatures.

It can be deployed as an exploratory tool to establish the geochemical processes operating

within a new study area, or to reveal hitherto unexplained structure retrospectively on

previously studied areas which had proved problematic using more conventional methods.


54

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61

8. APPENDIX

Documents

by Raymond E. Healy · coherent geochemical associations from the data set, and allowed spatial modelling of the expression of the underlying geochemical processes. The superior spatial