Dunn Colin E. Biogeochemistry in Mineral Exploration

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In the following commentaries on each element, unless indicated otherwise, the

analytical procedure has involved digestion of 1 g of milled dry tissue in nitric acid,

followed by aqua regia, with a quadrupole ICP-MS ﬁnish. This is a very economical

multi-element analytical procedure that is provided by a number of commer cial

analytical laboratories. The reader should appreciate that a total analysis (e.g., by

INAA) or by other digestions can be requested for speciﬁc elements that may present

problems with respect to analytical interferences from this digestion (e.g., As, Cr, Pd,

S, Se, V). Furthermore, data can be obtained from high-resolution ICP- MS instru-

mentation (HR-ICP-MS) that provide lower detection limits than those presented

here without inter-element interferences. At the time of writing, HR-ICP-MS is not

widely available at commercial laboratories and is more costly, but holds excellent

potential for future analysis of dry vegetation samples.

Aluminium (Al) (Fig. 9-2)

Aluminium is a common constituent of plants, and in dry tissue it is usually

present at concentrations quite close to the detection limit by ICP-MS of 0.01%. At

this level, precision is typically +/–100%, but improves considerably at slightly

higher levels, such as found in V6 for which RSD is 11%. If an isolated anomalous

value occurs in the ﬁeld data, contamination should ﬁrst be suspected.

Aluminium is an essential element for many plants, and helps to control colloidal

cell properties. However, in soils with a high level of soluble Al compounds, most

plant species have developed resistance mechanisms to avoid or tolerate the poten-

tially toxic effects of Al (Foy et al., 1978; Jansen et al., 2003). Tea is somewhat

enriched in Al (Owen et al., 1992; Dong et al., 2001), and about 32% of Al-

accumulating species belong to the large, mainly tropical, Rubiaceae Family. This

family comprises about 450 genera and more than 6500 species of trees and shrubs,

including such plants as wild coffee. In the data listings from the Eden Project

V6 NIST 1575a

Target

Mean

Std. Dev.

RSD %

0.05

0.047

0.1

0.06

0.01

Al %

0.01

0.02

0.03

0.04

0.05

0.06

0.07

V6 - 30 controls among 600 samples

Concentration

Detection

Fig. 9-2. Aluminium – Precision and accuracy on dry tissues – V6 and NIST 1575a (see

Fig. 9-1).

232 Biogeochemical Behaviour of the Elements

samples (CD in back pocket), the two plants containing the most Al were from the

Rubiaceae Family. Aquatic plants have the capability of accumulating high levels of

Al – e.g., Platyhypnidium ripariodes (an aquatic moss comprising the SRM BCR-

CRM-061) contains in excess of 1% Al in dry tissue. In general, bark of conifers is

far more enriched in Al than twigs or foliage of the same species.

In biogeochemical exploration, if plots of Al data indicate trends across a survey

area, they may be delineating zones of alteration, because the breakdown of feldspars

to clay minerals enhances the capability of roots to absorb Al from the significantly

greater surface area offered by ﬁne-grained clay minerals compared to well crystal-

lized feldspars.

Antimony (Sb) (Fig. 9-3)

Precision is fair to poor at the levels of Sb commonl y present in plants. Whereas

the control data rarely exhibit extreme ‘spikes’ in the data, values are sometimes

erratic. It is usual for precision to be considerably improved when Sb values in survey

samples are above 0.2 ppm Sb.

Although Sb can be readily taken up by plants in soluble forms it is considered a

non-essential element (Kabata-Pendias, 2001) and it is usually present at low-ppm

levels. In plants from the Eden Project, those from the Mediterranean regions have

higher concentrations than those from the tropics, reaching a maximum of 7 ppm Sb

in leaves from the South African Cape Myrtle and 5 ppm Sb in heather. This is in

spite of soils from the two biomes having similar Sb contents.

Mushrooms analysed by INAA contain mostlyo100 ppb Sb in background ar-

eas, but up to 0.14% Sb in Chalciporus piperatus (Peppery Bolete) is reported in

samples from the Pr

ibram mining district (Pb–Zn–Ag–Cu–U) in the Czech Republic

(Borovic

ka et al., 2006b).

During the process of reducing plant tissues to ash by controlled ignition at 4751C

there is some loss of Sb from many plant species. Typically, this loss is about 20%,

Sb ppm

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

V6 - 30 controls among 600 samples

Concentration

Detection

V6 NIST 1575a

Target

Mean

Std. Dev.

RSD %

0.06

0.01

(0.01)

0.02

Fig. 9-3. Antimony – Precision and accuracy on dry tissues – V6 and NIST 1575a (see Fig. 9-1).

233Biogeochemistry in Mineral Exploration

but it can be higher. In Fig. 9-4, losses during ashing of Acacia from Australia are

shown for different tissues. Except for minimal loss from the bark, other tissues

exhibit fairly consistent losses.

Over and near Au–As–Sb mineralization, there is commonly a clear but subtle

relationship of Sb in plant tissues to the zone of mineralization. Studies in Bolivia

using ‘Thola’ vegetation (Baccharis spp.) have demonstrated a positive response in Sb

to mineralization (Viladevall and Queralt, see ‘Case History’ in Chapter 11).

Arsenic (As) (Fig. 9-5)

Many survey samples yield values close to the detection limit of 0.1 ppm As with

the attendant poor precision. V6 has an average of 0.58 ppm, at which level precision

is good but values below 1 ppm As should still be treated with caution, because there

are a number of analytical interferences that make accurate measurement by ICP-MS

difﬁcult. It should be noted that some laboratories declare that low levels of As

cannot be accurately measured by ICP-MS, whereas others claim to have circum-

vented this problem and provide reasonably precise data. At low levels of As, data

for ﬁeld and analytical duplicates should be carefully checked to verify reproduc-

ibility of the data.

For As concentrations at higher levels, such as those found typically in Douglas-

ﬁr, the analytical reproducibility from two splits of the same samples can be re-

markably good. Sixty-four Douglas-ﬁr twigs were re-analysed by ICP-MS, a few

weeks after the ﬁrst set of analytical data were received. The re-analysis was of new

splits of the original milled sampl e, not re-analysis of solutions. Figure 9-6 shows that

the precision obtained on repeats of 64 samples was almost perfect throughout the

Dry v Ash

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

Conc.

Sb_dry ppm

Sb_Ash_dry

equiv. ppm

Bark

Wood

Litter

Phyllodes

Roots

Average

Fig. 9-4. Antimony in Acacia tissues and underlying forest litter. Comparisons of losses

during ashing. ‘Dry equivalent’ is the concentration in ash, normalized to a dry-weight basis.

234 Biogeochemical Behaviour of the Elements

range of concentrations (1–94 ppm As), although the repeat analysis provided data

that wer e consistently more than 10% lower than the original so there was a problem

with the accuracy of the data. This emphasizes the importance of inserting controls

that will assist in identifying batch-by-batch drift in the analytical data (Fig. 9-6).

Warren et al. (1968) were the ﬁrst to note the extraordinary ability that Douglas-

ﬁr (Pseudotsuga menziesii) has to accumulate As, even in areas where there is little As

in the ground. The reason for the scavenging of As is unknown, although it is

speculated here that, since in the spring As enrichment is the greatest in the growing

tips, the tree may be using As as a defence against insects attacking the succulent new

growth. Subsequently, it has been found that the coniferous trees western hemlock

(Tsuga heterophylla) and mountain hemlock (Tsuga mertensiana) from British

Columbia are also capable of concentrating high levels of As, thus establishing their

suitability as sample media for mineral exploration (Dunn, 1995a,b). There is a

As ppm

0.2

0.4

0.6

0.8

V6 - 30 controls among 600 samples

Concentration

Detection

V6 NIST 1575a

Target

Mean

Std. Dev.

RSD %

0.6

0.2

0.04

0.02

0.14

Fig. 9-5. Arsenic – Precision and accuracy on dry tissues – V6 and NIST 1575a (see Fig. 9-1).

Arsenic (ppm) in Dry Douglas-fir twigs

= 0.9957

100

0 20 40 60 80 100

Original

Repeat

Fig. 9-6. Reproducibility of arsenic data on separate splits after several weeks (dry twigs of

Douglas-ﬁr) (n ¼ 64).

235Biogeochemistry in Mineral Exploration

wealth of literature on the transfer of As from soils into plants, and these are suc-

cinctly summarized by Lombi and Nolan (2005) with additional observations by

Anke (2005). It seems that phospholipids can play a role in the metabolism of

primitive plants (algae and fungi), partly because As is chemically similar to phos-

phorus. Since seaweeds are macroalgae, this would account for the relative enrich-

ments of As in brown seaweeds – notably by the common wrack-weed Fucus that

predominates in many intertidal zones around the world, and by the gulf weed

Sargassum (Dunn, 1990, 1998a,b,c). Consequently, coastal biogeochemical exp lora-

tion surveys, especially fjorded coastlines, can take advantage of this phenomenon by

collecting brown seaweeds from strategic locations (e.g., drainage into the sea) in

inlets in order to identify and prioritize exploration targets. Fucus is particularly

useful in that it can grow in brackish water. Rugged, highly indented fjord coastlines

receive a substantial amount of fresh water from rainfall and melting winter snows,

and as a result the dominant brown seaweed is common ly Fucus.

Ma et al. (2001) reported that the brake fern (Pteris vittata) is a hyperaccumulator

of As, concentrating up to 7526 ppm As in dry fronds from samples growing on a site

in Florida contaminated with chromated copper arsenate. Further laboratory tests

over a six-week period established concentrations of more than 2.2% As in the same

species grown in soils spiked with 1500 ppm As. Subsequently, other species of this

genus and of other fern genera have exhibited sim ilar ability to concentrate As, but

out of the 20,000 or so species of ferns that have been identiﬁed this appears to be the

exception rather than the rule, an d not characteristic of all ferns species. A regional

biogeochemical survey of the western Ama zon, involving more than 350 samples of

tree fern (Cyathea spp.), yielded a maximum of 2.9 ppm As against a median value

for the dataset of 0.2 ppm As. However, as is repeatedly noted in this book, the

absolute As concentrations are not of particular importance, because the regional

patterns of As enrichment assisted in deline ating zones of Au mineralization.

In the exploration for minerals, whether the sample medium is rock, soil, water or

vegetation, As can be of great value as a pathﬁnder element for various types of

deposit, including gold, base metals and platinum-group metals. Hence, the data for

As should be carefully evaluated and dist ribution patterns assessed with respect to

the geological context of a survey area.

Barium (Ba) (Fig. 9-7)

The detection limit for Ba (0.1 ppm) is well below the typical concentrations in

survey samples (several to tens of ppm in dry tissue). V6 has 9 ppm Ba and precis ion

is excellent.

Barium is a bi-valent element that, along with Mg, Ca and Sr, is one of the

principal alkal ine earths. The remaining elements in this group are the rare Be and

Ra. Barium can be toxic to plants, but it can also be highly concentrated in some

plants such as the brazil nut (Bertholletia excelsa) and Douglas-ﬁr (Pseudotsuga

236

Biogeochemical Behaviour of the Elements

menziesii). At the Hoidas Lake REE-rich allanite deposits in northern Saskatchewan,

mineralization has associated with it Ba-rich feldspar and some barite. An orien-

tation biogeochemical study (Dunn and Hoffman, 1986) showed that birch, Lab-

rador tea, alder, black spruce and jack pine were all enriched in Ba, with highest

concentrations in twigs and bark; cones contained relatively little Ba (Table 9-I). A

maximum of 535 ppm Ba was recorded in dry twigs of alder (three years growth).

The Ba values shown in Table 9-I are unusually high for vegetation in general,

because of the presence of Ba-rich rocks. However, they serve to show the relative

uptake of Ba by a typical range of common plants present in the boreal forests of

North America, Europe and Asia. Furthermore, they demonstrate the relat ive up-

take by the different plant tissues.

Patterns of Ba distribution in biogeochemical datasets can assist in delineating

carbonate-rich zones, because of the chemical afﬁnities of Ba with, in particular, Sr

and Ca both of which are present in carbonates. Kovalevsky (1987) noted that

intense biogeochemical anomalies of Pb, Zn and Ba were identiﬁed in various species

of wormwood (Artemisia spp.) over sulphide bodies buried beneath aeolian sand

some 10–30 m thick. In light of this observation, Ba anomalies in plant tissues should

be sought in areas of suspected sulphide and Irish-type Pb–Zn carbonate mineral-

ization.

At a number of localities Ba enrichment in vegetation has been recorded in a zone

peripheral to that of Au enrichment. At one occurrence, Au enrichment in alder twigs

and black spruce bark had coincident As and Sb anomalies, with up to 300 ppm Ba in

dry bark from an adjacent zone reaching 400 m in width (e.g., Dunn, 1984). This

emphasizes that pattern recognition is of paramount importance in successfully in-

terpreting the significance of biogeochemical data.

Beryllium (Be)

Beryllium is rarely con centrated above the detection limit of 0.1 ppm Be in dry

plant tissue, including the controls considered in this chapter, and so there is no

Ba ppm

V6 -30 controls among 600 samples

Concentration

Detection

V6 NIST 1575a

Target

Mean

Std. Dev.

RSD %

0.5

Fig. 9-7. Barium – Precision and accuracy on dry tissues – V6 and NIST 1575a (see Fig. 9-1).

237Biogeochemistry in Mineral Exploration

TABLE 9-I

Barium in plant tissues from near an outcrop of massive REE-bearing allanite associated with Ba-rich feldspar (hyalophane) and minor

barite. Modiﬁed after Dunn and Hoffman (1986). Analysis by INA on ashed tissue

Common name Botanical name Tissue

Ba in ash

(ppm)

Ash yield (%) Ba in dry tissue

(ppm)

Paper birch Betula papyrifera Twig

13,000 1.82 237

Paper birch Betula papyrifera Leaf

3000 3.96 119

Paper birch Betula papyrifera Bark

14,000 2.78 389

Labrador tea Ledum groenlandicum Root

18,000 0.8 144

Labrador tea Ledum groenlandicum Stem

1700 1.25 21

Mountain alder Alnus crispa Twig

22,000 2.43 535

Mountain alder Alnus crispa Leaf

8300 3.65 303

Mountain alder Alnus crispa Leaf

8600 3.65 314

Black spruce Picea mariana Twig

16,000 1.87 299

Black spruce Picea mariana Twig

18,000 1.71 308

Black spruce Picea mariana Bark

16,000 2.81 450

Black spruce Picea mariana Trunkwood

12,000 0.32 38

Black spruce Picea mariana Cones

2700 0.5 14

Black spruce Picea mariana Twig

21,000 1.52 319

Jack pine Pinus banksiana Twig

9900 1.68 166

Jack pine Pinus banksiana Needles

7800 2.92 228

Jack pine Pinus banksiana Trunkwood

20,000 0.41 82

Jack pine Pinus banksiana Bark

15,000 2.03 305

Jack pine Pinus banksiana Cones

3100 0.26 8

238 Biogeochemical Behaviour of the Elements

chart for Be. In certain environments (e.g., rare-metal pegmatites, some ultramaﬁc

rocks and oxidized black shales) positive Be concentrations are encountered.

Duplicates of these samples indicate very good precision by ICP-MS for levels above

0.5 ppm Be.

Kovalevsky (1974, 1978) reported a strong Be anomaly (up to 5000 ppm Be in ash

[100 ppm Be dry weight]) in, among other plants, bark of pine in an area with

ﬂuorite–phenakite–bertrandite mineralization. This is exceptional, since by compar-

ison pine bark from the Bernic Lake rare-metal pegmatite mine in Manitoba yielded

a maximum of 190 ppm Be in ash, equal to 18 ppm Be in dry tissue. Black spruce

bark had 6 ppm in dry tissue, whereas twigs of both spruce and bark from the same

trees had 2 ppm Be. Birch, poplar and alder had less than 0.2 ppm Be. The Bernic

Lake study conﬁrmed Kovalevsky’s observation that pine bark concentrates Be to a

greater extent than other tissues and other common plants of the boreal forest.

In foliage samples from the Eden Pr oject, Cape myrtle (Myrsine africana),

containing cyanidin glycosides, concentrated more Be (0.5 ppm) than other species.

Also, the bilberry (Vaccinium myrtillus), containing anthocyanosides, is reported to

concentrate Be up to approximately 5 ppm dry weight (Wedepohl, 1969–1978).

These observations imply that plants containing cyanide complexes may preferen-

tially take up Be.

Bismuth (Bi) (Fig. 9-8)

V6 contains almost exactly 0.02 ppm Bi – i.e., the detection limit, yielding typical

precision at this level of +/– 100%. However, control V17 has 0.05 ppm Be and data

from over 300 analyses inserted among more than 6000 samples during a two-year

period demonstrate very good precision.

This good precision is typical for Bi determined by ICP-MS. If concentrations are

even slightly above detection, Bi data are usually repeatable, and false anomalies are not

observed. Most biogeochemical survey samples yield concentrations below detection,

but any positive values should be closely scrutinized to determine their significance,

V17 NIST 1575a

Target

Mean

Std. Dev.

RSD %

0.07

0.05

0.01

No data

Bi ppm

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

V17 - 22 samples among 440 samples

Concentration

Detection

Fig. 9-8. Bismuth – Precision and accuracy on dry tissues – V17 (see Fig. 9-1).

239Biogeochemistry in Mineral Exploration

especially since Bi is associated with a many different types of mineralization, including

a variety of Au deposits – high sulphidation epithermal systems (e.g., Summitville,

Colorado), alkalic intrusion-associated Au–Ag (e.g., Cripple Creek, Colorado), many

Au-quartz vein systems and skarns (e.g., Nickel Plate in British Columbia). The control

V17, which exhibits consistently detectable Bi values, was collected from the site of an

undeveloped Au prospect at Mt. Washington on Vancouver Island, where gold-quartz

veins with Ag, Cu and As are associated with Tertiary dacitic tuff, breccia and diorite.

In light of the potential use of Bi data for helping to focus exploration activities,

the additional procedure of reducing samples to ash prior to analysis can be of value,

because ashing significantly concentrates trace elements. Figure 9-9 shows data for

Acacia twigs from Australia that were analysed for Bi as part of a multi-element

package. Note that values in the left scale (concentrations from direct analysis of dry

tissue) are higher than those in the right-hand axis (concentrations in ash that have

been normalized to a dry-weight basis).

The initial analysis of the dry material yielded only one sample with detectable Bi.

Values below detection wer e plotted at half the detection limit of 0.02 ppm Bi. A 15 g

portion of dry material was then reduced to ash, the ash analysed by the same

method, and the result normalized to a dry-weight basis . The solitary positive value

for Bi in the dry tissue was at the detection limit, and it is coincident with the highest

value obtained from the ash (normalized to dry). However, the geochemical ‘relief’ of

the low level Bi data are now apparent, and a second weakly anomalous zone, some

200 m west of the ﬁrst, is evident (Fig. 9-9).

Boron (B)(Fig. 9-10)

Boron is an essential micronutrient for plant metabolism, and recent develop-

ments have contributed to improved understanding of the role of B in plants by

Dry (diamonds) v. ash normalized to dry (squares)

0.005

0.01

0.015

0.02

0.025

1 km traverse

Conc.

0.002

0.004

0.006

0.008

0.01

0.012

0.014

0.016

Bi_dry,

ppm

Bi_ash

norm. to

dry, ppm

Averages

West East

Fig. 9-9. Bismuth in Acacia twigs from a precious metal prospect in western Australia. Con-

centrations in dry tissue versus those in ash, normalized to a dry-weight basis. Analysis by ICP-

MS.

240 Biogeochemical Behaviour of the Elements

demonstrating their role in binding proteins to cell walls and interfering with Mn-

dependent enzymatic reactions (Fig. 9-10)(Blevins and Lukaszewski, 1998).

Concentrations in plants are typically in the tens of ppm B. These levels are well

above the detection of 1 ppm B, so precision is good to excellent, and data can be

plotted with conﬁdence that they reﬂect true variations among the ﬁeld samples. In

general, B data do not provide anomalies or distribution patterns of much signif-

icance for biogeochemical exploration. However, the data sho uld not be ignored,

because there is B enrichment associated with base metal deposits such as the classic

Sedex-type of seaﬂoor sulphides constituting mineralization at the former Sullivan

mine in British Columbia. At the Sullivan mine, a study of the distribution of el-

ements in a lodgepole pine rooted in tourmalinite demonstrated that top stems of the

tree had B concentrations triple those of lower tw igs (Dunn, 1995a,b).

Bromine (Br)

See ‘Halogens’ for detailed discussion of using the labile fraction of Br in mineral

exploration.

Total Br content of plants is best determined by INAA for which analytical

precision is excellent. Bromine is a volatile element, present in most, if not all ter-

restrial plants . Seaweeds concentrate Br more than 100 times the average of 4 ppm Br

in land plants, but it is not known to be an essential element. Bromine can occur in

many forms as complexes within plants. Some Br complexes volatilize during the

ashing process, causing losses of up to 90% of the Br contained within the plant

tissues. However, in spite of the sub stantial loss of Br during ashing it has been found

that on occasion there is a Au/Br association in plant ash from zones of Au min-

eralization (Dunn, 1986a). It appears that the chemical species of Br retained in the

ash may be related to the presence of mineralization, and it has been speculated that

this is because the geochemical halo of a mineral deposit may create physico-chem-

ical conditions that produce differing ratios of Br species within the vegetation

(Dunn and Hoffman, 1986). Concentrations in the hundreds of ppm Br in plant ash

B ppm

V17 - 22 samples among 440 samples

Concentration

Detection

V17 NIST 1575a

Target

Mean

Std. Dev.

RSD %

6.6

0.8

9.6

0.9

Fig. 9-10. Boron – Precision and accuracy on dry tissues – V17 and NIST 1575a (see Fig. 9-1).

241Biogeochemistry in Mineral Exploration