Dunn Colin E. Biogeochemistry in Mineral Exploration

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Sam 10 Annona glabra Annonaceae Pond apple Leaves contain acetogenins

Sam 8 Bauhinia thonningii Caesalpiniaceae Camel’s foot Antiseptic properties used for treating

wounds

Sam 13 Cecropia spp. Cecropiaceae Trumpet tree Caustic latex. Invariably infested with

stinging ants

Sam 7 Cola nitida Malvaceae Bitter cola Cola nut can mimic malaria-like

symptoms; contains caffeine and

cyanide

Sam 3 Costus afer Zingiberaceae Very acidic. Edible ﬂowers. Anti-

inﬂammatory

Sam 12 ‘Fern’ Polypodiaceae Fern

Sam 1 Inga edulis Fabaceae Monkey tail

Sam 6 Inga spp. Fabaceae Monkey tail

Sam 2 Malvaviscus arboreus Malvaceae Wax mallow

Sam 4 Manihot esculenta Euphorbiaceae Cassava Roots used to make the intoxicant

‘cassiri’ and tapioca. Rich in cyanide

compounds, making it resistant to most

insect predators

Sam 5 Pachira aquatica Bombacaceae Water chestnut; Money

tree

Sam 14 Persea americana Lauraceae Avocado

Sam 16 Psychotria punctata Rubiaceae Wild coffee

Sam 11 Tamarindus indica Caesalpiniaceae Tamarind

TABLE 8-III Continued

Sample # Botanical name Family Common name Notes on some characteristics

222 The Eden Project – Source of a Biogeochemical Database

TABLE 8-IV

Eden Project – analyses of dry leaves from 71 species. Relative concentrations of each of the 51 elements that were determined. Sorted in

order of concentration factor (last column)

Element Units Genus Common name Biome Area Maximum

concentration

Average

concentration of

all samples

Conc. factor

relative to all

71 Eden

samples

Ge ppm Dendrocalamus Bamboo HTB Seychelles 10.2 0.23 43.6

Co ppm Pentas Star Cluster HTB W.Africa 47.2 1.17 40.2

La ppm Ficus Fig WTB Med. 2.86 0.08 34.2

Nb ppm Leucadendron Silver tree WTB S.Africa 0.53 0.02 31.6

Ce ppm Ficus Fig WTB Med. 3.4 0.13 26.1

W ppm Pentas Star Cluster WTB W.Africa 7.1 0.28 25.3

Ag ppb Calamus Rattan HTB Malaysia 9670 429 22.6

Bi ppm Cryptostegia Rubber vine HTB Seychelles 0.52 0.03 20.4

Tl ppm Ficus Fig WTB Med. 0.82 0.06 14.7

Sb ppm Myrsine Cape Myrtle WTB S.Africa 7.1 0.49 14.4

As ppm Cecropia Trumpet tree HTB S.America 28.3 2.0 14.0

Cs ppm Rhumora Fern WTB S.Africa 16.7 1.23 13.6

Cd ppm Asphodelus Onionweed WTB Med. 2.2 0.17 12.8

Pb ppm Ballota False dittany WTB Med. 5.8 0.48 12.2

Mn ppm Tabernanthe Leaf of God HTB W.Africa 4196 485 8.7

Mo ppm Acacia Acacia HTB S.America 10.8 1.34 8.1

Y ppm Ficus Fig WTB Med. 1 0.13 7.8

Re ppb Rothmania Gardenia HTB Seychelles 149 19 7.8

Na % Impatiens Busy-lizzie HTB Seychelles 1.6 0.21 7.7

Zn ppm Impatiens Busy-lizzie HTB Seychelles 455 62 7.4

Ta ppm Tetraclinis Arar WTB Med. 0.006 0.0008 7.3

Ba ppm Blechnum Fern WTB S. Africa 115 16 7.1

Au ppb Eriocephalus Kapok bush WTB S.Africa 33.5 4.8 7.0

Al % Pentas Star Cluster HTB W.Africa 0.04 0.006 6.5

Th ppm Pentas Star Cluster HTB Malaysia 0.05 0.008 6.1

Rb ppm Etlingera Torch ginger HTB Malaysia 216 38 5.7

Continued

223Biogeochemistry in Mineral Exploration

TABLE 8-IV Continued

Element Units Genus Common name Biome Area Maximum

concentration

Average

concentration of

all samples

Conc. factor

relative to all

71 Eden

samples

U ppm Tetraclinis Arar WTB Med. 0.98 0.18 5.5

Be ppm Myrsine Cape Myrtle WTB S. Africa 0.5 0.09 5.5

Ni ppm Dendrocalamus Bamboo HTB Seychelles 5.2 1.3 3.9

Cu ppm Cryptostegia Rubber vine HTB Seychelles 37 9.7 3.8

Te ppm Calamus Rattan HTB Malaysia 0.04 0.011 3.8

Sn ppm Casuarina She-oak HTB Seychelles 0.52 0.14 3.6

Zr ppm Pogostemon Patchouli HTB Malaysia 0.7 0.20 3.6

Hf ppm Pogostemon Patchouli HTB Malaysia 0.018 0.005 3.5

Li ppm Musa Banana HTB W.Africa 26 7.4 3.5

Hg ppb Syzygium Java plum HTB Malaysia 125 36 3.4

B ppm Ficus Fig HTB Malaysia 277 83 3.3

Cr ppm Dendrocalamus Bamboo HTB Seychelles 7.2 2.21 3.3

Sr ppm Ficus Fig HTB Malaysia 115 38 3.0

Ca % Bougainvillea Bougainvillea WTB Med. 4.71 1.597 2.9

Se ppm Ballota False dittany WTB Med. 0.6 0.22 2.8

S%Impatiens Busy-lizzie HTB Seychelles 1.03 0.37 2.8

K%Psychotria Coffee HTB S.America 5.09 2.10 2.4

P%Ficus Fig HTB Malaysia 0.577 0.245 2.4

Fe % Musa Banana HTB Malaysia 0.029 0.013 2.3

Mg % Tabernanthe Leaf of God HTB W.Africa 0.953 0.412 2.3

Ti ppm Fern Fern HTB S.America 19 8.6 2.2

Sc ppm Calamus Rattan HTB Malaysia 0.4 0.22 1.8

Ga ppm Musa Banana HTB Malaysia 0.1 0.06 1.7

In ppm o0.01 o0.01

V ppm o1 o1

224 The Eden Project – Source of a Biogeochemical Database

TABLE 8-V

Eden Project – analysis of dry leaves from 71 species. Average concentrations by biome and geographic area, compared to the ‘Reference

Plant’ of Markert (1994)

Warm temperate biome Hot tropical biome Ref. plant

Mediterranean

(n ¼ 12)

South Africa

(n ¼ 14)

All WTB

(n ¼ 26)

Seychelles

(n ¼ 12)

Malaysia

(n ¼ 8)

West Africa

(n ¼ 7)

South America

(n ¼ 16)

All HTB

(n ¼ 43)

Markert (1994)

Ag (ppb) 44 110 79 932 1625 200 145 649 20

Al (%) 0.006 0.005 0.006 0.006 0.005 0.010 0.006 0.007 0.008

As (ppm) 1.0 1.3 1.2 1.8 1.7 3.9 3.0 2.6 0.1

Au (ppb) 4.0 5.9 5.0 5.3 6.9 2.4 3.5 4.5 0.2

B (ppm) 69 49 58 71 121 95 103 96 40

Ba (ppm) 20 21 21 17 13 9 13 13 40

Be (ppm) 0.09 0.12 0.11 0.08 0.08 0.07 0.08 0.08 0.001

Bi (ppm) 0.02 0.02 0.02 0.06 0.02 0.02 0.01 0.03 0.01

Ca (%) 1.80 1.42 1.60 1.86 1.55 0.94 1.68 1.59 1

Cd (ppm) 0.29 0.23 0.26 0.21 0.11 0.18 0.05 0.13 0.05

Ce (ppm) 0.37 0.13 0.24 0.08 0.09 0.07 0.04 0.07 0.5

Co (ppm) 0.12 0.13 0.12 0.24 0.22 7.98 1.20 1.86 0.2

Cr (ppm) 2.09 1.71 1.88 2.72 2.39 2.56 2.14 2.42 1.5

Cs (ppm) 0.59 2.50 1.62 0.50 2.25 0.73 0.97 1.04 0.2

Cu (ppm) 9.4 8.0 8.7 10.3 9.5 11.7 10.0 10.3 10

Fe (%) 0.012 0.015 0.014 0.013 0.014 0.013 0.011 0.012 0.015

Ga (ppm) 0.06 0.05 0.06 0.06 0.06 0.06 0.05 0.06 0.1

Ge (ppm) 0.07 0.01 0.04 0.86 0.24 0.27 0.07 0.36 0.01

Hf (ppm) 0.006 0.006 0.006 0.006 0.007 0.004 0.003 0.005 0.05

Hg (ppb) 21 19 20 53 56 40 40 46 20

In (ppm) o0.01 o0.01 o0.01 o0.01 o0.01 o0.01 o0.01 o0.01 0.001

K (%) 1.71 1.90 1.81 2.11 2.47 2.98 2.11 2.32 1.9

La (ppm) 0.29 0.07 0.17 0.04 0.04 0.04 0.02 0.03 0.2

Li (ppm) 5.68 9.24 7.59 8.30 7.03 7.80 5.25 6.85 0.2

Mg (%) 0.37 0.32 0.35 0.50 0.46 0.46 0.41 0.45 0.2

Mn (ppm) 379 352 364 632 492 1037 263 535 200

Mo (ppm) 0.79 2.24 1.57 1.58 0.43 1.08 1.43 1.23 0.5

Continued

225Biogeochemistry in Mineral Exploration

TABLE 8-V Continued

Warm temperate biome Hot tropical biome Ref. plant

Mediterranean

(n ¼ 12)

South Africa

(n ¼ 14)

All WTB

(n ¼ 26)

Seychelles

(n ¼ 12)

Malaysia

(n ¼ 8)

West Africa

(n ¼ 7)

South America

(n ¼ 16)

All HTB

(n ¼ 43)

Markert (1994)

Na (%) 0.127 0.507 0.331 0.352 0.091 0.033 0.058 0.142 0.15

Nb (ppm) 0.013 0.048 0.032 0.008 0.009 0.010 0.007 0.008 0.05

Ni (ppm) 0.8 1.1 1.0 1.5 1.2 2.3 1.5 1.6 1.5

P (%) 0.232 0.214 0.222 0.252 0.284 0.246 0.261 0.260 0.2

Pb (ppm) 1.0 0.4 0.7 0.4 0.5 0.5 0.3 0.4 1

Rb (ppm) 24 42 34 28 61 43 41 41 50

Re (ppb) 9 3 6 37 8 39 24 27 0.1

S (%) 0.36 0.44 0.40 0.51 0.21 0.36 0.31 0.35 0.3

Sb (ppm) 0.53 1.72 1.17 0.11 0.07 0.06 0.13 0.10 0.1

Sc (ppm) 0.19 0.19 0.19 0.23 0.24 0.24 0.23 0.23 0.02

Se (ppm) 0.23 0.16 0.20 0.28 0.18 0.26 0.20 0.23 0.02

Sn (ppm) 0.18 0.08 0.13 0.21 0.14 0.14 0.13 0.16 0.2

Sr (ppm) 47 34 40 44 38 20 38 37 50

Ta (ppm) 0.002 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001

Te (ppm) o0.01 o0.01 o0.01 o0.01 o0.01 o0.01 o0.01 o0.01 0.02

Th (ppm) o0.01 o0.01 o0.01 o0.01 o0.01 o0.01 o0.01 o0.01 0.005

Ti(ppm)8889109995

Tl (ppm) 0.10 0.04 0.07 0.04 0.11 0.04 0.03 0.05 0.02

U (ppm) 0.3 0.2 0.2 0.1 0.2 0.1 0.1 0.1 0.01

V (ppm) o1 o1 o1 o1 o1 o1 o1 o1 0.5

W (ppm) o0.1 0.29 0.18 o0.1 0.06 1.25 0.30 0.35 0.2

Y (ppm) 0.20 0.16 0.18 0.11 0.13 0.07 0.07 0.09 0.2

Zn (ppm) 69 74 71 80 49 43 50 57 50

Zr (ppm) 0.22 0.23 0.22 0.23 0.27 0.14 0.13 0.18 0.1

Denotes modiﬁcations to Markert’s compilation.

226 The Eden Project – Source of a Biogeochemical Database

Another summary of the data shows the average concentrations of each element

by geographic region (Table 8-V).

Except for Ag, the average concentrations of elements by region are generally

quite similar and provide, therefore, a broad indication of what might be considered

the norm for each geographic area. This adds another dimension to Markert ’s (1994)

compilation of data to deﬁne a world ‘Reference Plant’ (see Chapter 1), for which the

data are repeated in the last column of Table 8-V and which generally show a close

correspondence with the data from the Eden Project biomes. There are some dif-

ferences, such as relatively high Co and Re in samples from West Africa (which for

these purposes includes the Democratic Republic of Congo, DRC). This raises the

possibility that plants from West Africa might have developed a tolerance for Co,

because of the significant world-class Co deposits in the Katanga region of the DRC.

With respect to higher Au and As levels than in Markert’s refer ence plant, these

values from the Eden Project may reﬂect the ‘green waste’ that has been added to the

soil mix. Elevated levels of Li, B and U probably reﬂect the composition of the

somewhat radioactive and alkali-rich Hercynian granites that make up part of the

soil mix.

The Eden Project database in its present prototype form gives a glimpse of its

potential for providing comparative baseline information for the many thousands of

species currently growing under its domes. However, even in its limited form, it can

be used as a ﬁrst pass for selecting a suitable biogeochemical sample medium. If a

particular species that is shown in these tables to accumulate an element of interest

proves not to be present in a proposed survey area, given no other baseline infor-

mation, other species and genera of the same family would be the preferred sample

media. Databases for all plants are not available, and so the data provided here can

be used just as a general guide. From these tables, a short list of plants can be

generated to take to the ﬁeld or, better still, take to a botanist with the knowledge of

the proposed survey area for an opinion on the likelihood of which of the preferred

species/genera might be present.

227

Biogeochemistry in Mineral Exploration

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Chapter 9

BIOGEOCHEMICAL BEHAVIOUR OF THE ELEMENTS

INTRODUCTION

As a general guide, the achievable levels of precision that are currently available

from commercial laboratories for analysis by 1CP- MS of 1 g samples of dry veg-

etation are as follows:



up to 2 times detection limit +/– 100%,



2–5 times detection limit +/– 50%,



5–10 times detection limit +/– 25%, and



>10 times detection limit from about +/– 10 to 15%.

These broad generalizations can be improved upon by analysing larger samples

(at greater cost) and these levels of precision vary from one element to another and

from one laboratory to another. For example, Au reproducibility at sub-ppb levels is

poor to fair. As noted in Chapter 6, the ofﬁcial listings for NIST 1575a (pine needles)

show that ﬁve laboratories provided data for gold, all by INAA, yet returned average

values of 0.56–2.6 ppb Au. Conversely, the precision for Bi close to detection is

commonly good. Analyses for Rb, invariably present at levels well above detection,

generate precision better than +/– 5% RSD.

Perfect precision for all elements is an unreasonable expectation for a typical

analytical dataset generated by currently available low-cost ICP-MS from commer-

cial laboratories. The cost of providing ‘assay qualit y’ data for more than 60 elements

by ICP-MS would be several hundred dollars per sample instead of the few tens of

dollars that are usually charged for a multi-element package. Given these constraints,

a certain amount of tolerance should be given to the overall quality of the data that

are generated, bearing in mind that for biogeochemical explora tion purposes it is

pattern recognition of element distributions that is of greater importance than perfect

precision and accuracy. It is better, therefore, to plot data distributions as percentile

ranges rather than rely upon absolute values.

THE IMPORTANCE OF DATA QUALITY

From a purely statistical analysis of data that are close to detection, on occasion

an impression might be gained that the overall data quality is poor. Usually, the

‘poor’ precision is conﬁned to levels very close to detection – and therefore to be

expected. At higher levels precision invariably improves substantially. If it does not,

then there is a problem that needs to be discussed with the analytical laboratory.

The research chemist can, with meticulous and commonly time-consuming care,

obtain highly reproducible analytical data from the analysis of dry vegetation sam-

ples. In the ‘real world’ of commercial analytical laboratories where technicians

systematically produce a vast amount of data for a wide range of elements at ex-

traordinarily low cost the question posed by Bettenay and Stanle y (2001) needs to be

reiterated: ‘Are the data ﬁt for the purpose on hand?’ That is to say, can meaningful

interpretations be extracted from a dataset that is not perfect?

To assess these questions, this chapter provides notes on each element based on

experience gained from analysis of tens of thousands of dry plant samples by ICP-

MS, and for some elements by INAA, over several years. Interspersed among these

samples, at a density of approximately one in twenty, were several thousand control

samples, including many hundreds of splits of in-house control ‘V6’ described in

Chapters 6 and 7. The accuracy of the data obtained on V6 can be assessed from

comparison of values obtained by the same analytical method on 19 samples of the

international pine needle control material NIST 1575a (discussed in Chapter 6). The

values reported by the National Institute of Standards and Technology for Standard

Reference Material 1575a fall into three categories:



Certiﬁed values: A NIST certiﬁed value is a value for which NIST has the highest

conﬁdence in its accuracy in that all known or suspected sources of bias have been

investigated and accounted for by NIST. However, ‘certiﬁed’ values’ are available

for only twelve elements – Al, Ba, Ca, Cd, Cl, Cu, Fe, Hg, K, P, Rb and Zn.



Reference values: These values are based on results obtained from a single NIST

analytical method. They are

non-certiﬁed values that are the best estimate of the true value; however, the values do

not meet NIST criteria for certiﬁcation and are provided with associated uncertainties

that may not include all sources of uncertainty.

Reference values are available for eleven elements – As, B, Co, Cs, Mg, Mn, Na,

Ni, Pb, Sc and Se.



Information values: Data for another two elements (Ce, Cr) are provided for infor-

mation purposes, only. These are non-certiﬁed values with no uncertainty assessed.

These three categories show that there are reference data available for only 25

elements. In addition, for most elements, the NIST website (www.nist.gov/srm)

shows tables of analytical determinations provided by the laboratories that

participated in testing NIST 1575a. In the table s shown under each element in the

following pages, these categories of data accuracy are combined under the single term

of ‘target value’. The values listed under NIST 1575a that are in parentheses are

those for which data are not certiﬁed, indicating uncertainty as to their true

230

Biogeochemical Behaviour of the Elements

concentrations. For some elements, data are provided in this chapter for which there

are no known published reference results (e.g., Ge, Li, Sn, Ti, Tl, Y and Zr).

ELEMENTS IN PLANTS AND THEIR RELEVANCE TO MINERAL EXPLORATION

Elements are listed in alphabetical order by their element name, rather than by

chemical symbol. Except for a few elements that are grouped together, such as the

platinum group elements (PGE), the rare earth elements (REE) and the halogens (F,

Cl, Br, I), they are discussed individually. Discussion of each element is preceded by a

table alongside a bar chart designed to demonstrate for that element the typical pre-

cision that has been obtained (and can be expected). The ﬁrst (solid) bar of each

histogram shows the detection limit. In most cases, the in-house control V6 has been

used as an example, because of the many hundreds of analyses that are now available.

A secondary control is V17 (mountain hemlock twigs [Tsuga heterophylla]) containing

higher concentrations of those few elements that are consistently below detection in

V6. The bar charts demonstrate the analytical precision (i.e., reproducibility). By way

of assessing the analytical accuracy, data are provided for NIST 1575a. Each control

sample (V6, V17 and NIST 1575a) has statistics computed on determinations of con-

centrations in 30, 22 and 19 samples, respectively. Figure 9-1 is an annotated example.

NIST 1575a

Target*

Mean

Std. Dev.

RSD %

17.6

1.7

Ag ppb

V6 - 30 controls among 600 samples

Concentration

Detection

(12)

1.5

*Target: For NIST 1575a ‘Target’ indicates a certified value, unless it is in parentheses

in which case it is a ‘reference’ or ‘information’ value, or simply the average value

shown in the tables from the NIST website. For V6 the ‘Target Value’ is derived from

the mean of several hundred analyses.

**Mean, Standard Deviation, and Relative Standard Deviation (RSD) values

calculated from:

• The 30 samples of V6 or 22 samples of V17 shown as examples in the bar charts.

• 19 samples of NIST 1575a (not shown as bar charts)

Values for each element are expressed as the same units that are shown in each bar chart

(i.e., silver as ppb in this example). RSD% is widely used in analytical chemistry to

ress the

recision of anal

tical data. This is defined in cha

ter 10.

Fig. 9-1. Annotated explanation of chart shown in the discussion of each element.

231Biogeochemistry in Mineral Exploration