The effect of filtration on analyses of surface water samples. A study from the Salars of Coipasa...

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Journal of Geochemical Explo

The effect of filtration on analyses of surface water samples.

A study from the Salars of Coipasa and Uyuni, Bolivian Altiplano

David Banks a,*, Howard Markland b, Paul V. Smith c, Carlos Mendez b,

Javier Rodriguez b, Alonso Huerta b, Ola M. Sæther d

aHolymoor Consultancy, 8 Heaton Street, Brampton, Chesterfield, Derbyshire, S40 3AQ, UKbURS Dames and Moore, Edificio Creyland, Avenida Santa Cruz #1344, Piso 1, Cochabamba, Bolivia

cURS Dames and Moore, 5 Industrial Way, Salem, NH, 03079, USAdNorges Geologiske Undersøkelse, N-7491 Trondheim, Norway

Received 23 July 2004; accepted 4 April 2005

Available online 28 June 2005

Abstract

Analyses by inductively coupled plasma atomic emission spectrometry (ICP-AES), inductively coupled plasma mass

spectrometry (ICP-MS) and cold vapour atomic absorption (for Hg, CVAA) of a wide range of elements in some 300 surface

water samples from the Salar de Uyuni and Salar de Coipasa catchments of the Bolivian Altiplano have been undertaken.

Comparison of analyses of acidified aliquots of unfiltered sample water with water filtered at 0.45 Am reveals that the following

elements are not affected significantly by filtration in this high-pH environment: B, Ca, Li, Mg, K, Si, Na, Sr, S. The following

elements appear to experience significantly elevated concentrations in unfiltered samples, relative to filtered: Al, (As to a minor

extent), Ba, Be, Cd, Cr, Co, Cu, Fe, Pb, Mn, Hg, Ni, P, Ag, Tl, Ti, V. The effect appears to be related to the presence, and

subsequent dissolution in acid preservative, of Fe-, Al-or Mn-oxyhydroxide flocs (or coatings on silicate particles) in unfiltered

samples, and their retention or precipitation on filters.

D 2005 Elsevier B.V. All rights reserved.

Keywords: Analysis; Arsenic; Filtration; Particulate matter; Surface waters; Bolivia

1. Introduction and objective

Many water sampling protocols recommend the

filtration of water samples prior to analysis, often at

0375-6742/$ - see front matter D 2005 Elsevier B.V. All rights reserved.

doi:10.1016/j.gexplo.2005.04.003

* Corresponding author. Tel./fax: +44 1246 230068.

E-mail address: [email protected] (D. Banks).

a filter of nominal pore size 0.45 Am (USGS, 1984,

2004; Hauge and Breedveld, 1991; UNESCO et al.,

1992; Appelo and Postma, 1996; Mather, 1997; Yes-

kis and Zavala, 2002). The purpose of this is to

remove any particulate fraction from the water, such

that only the bdissolvedQ fraction of an element is

analysed. In practice, this recommendation is not

always adhered to in environmental surveys, either

ration 86 (2005) 104–118

Fig. 1. Outline map of Bolivia, showing location of study area

(in grey) and salars (black).

D. Banks et al. / Journal of Geochemical Exploration 86 (2005) 104–118 105

due to ignorance of the sampling protocols, or because

the investigators regard the particulate content of ele-

ments (e.g. being transported by a river) as being of

interest to the study.

It is important that the decision as to whether or not

to filter water samples is made in an informed way. In

particular, one should realise that:

(a) The presence of particles in a water sample can

seriously disturb an analytical result, especially

in cases where nitric acid is subsequently added

to preserve the water sample for metals analyses

(a common practice). The acid may partially or

totally dissolve particulate matter, releasing

solutes in quantities that are significant com-

pared with the original dissolved phase.

(b) Differing analytical methods may behave dif-

ferently in the presence of particulate matter.

For example, it might be suspected that bhotQanalytical methods (e.g. inductively coupled

plasma) might volatilise particulate or colloidal

matter and incorporate it into the analysed

plasma, whereas bcoolQ methods (colorimetry,

ion detectors) may be less sensitive to its

presence.

(c) Filtration is an arbitrary fractionation process.

Filtration at 0.45 Am will generally not remove

particles less than that nominal pore size (al-

though some smaller particles may be retained

as pore throats become clogged as filtration

progresses), nor will it remove most of any

colloidal fraction.

(d) The effect of filtration will affect different

elements differently. One would expect highly

soluble species (e.g. chloride) to occur mainly

in the dissolved phase and to be relatively

unaffected by filtration, whereas other elements

(e.g. rare earths, thorium) may be more

blithophileQ and have a tendency to be associ-

ated with the particulate phase. Still other ele-

ments may have a tendency to form colloidal

flocs (e.g. iron, manganese at high pH—see

Banks et al., 1997).

(e) Filtration, in itself, may have a potential adverse

effect on sample quality, as the filter material

may contain impurities which can be leached

(e.g. ammonium in some filter papers or tin

from some sealed filter units, BSI, 1998; Reim-

ann et al., 1999; see also Horowitz et al., 1996).

The filter membrane may also act as a nucle-

ation/precipitation surface for some dissolved

elements.

In order to make an informed assessment

concerning the effects of filtration, it is necessary to

have access to documented case studies. Although the

impact of filtration on analyses is well-recognised,

relatively few studies have identified which specific

elements are especially affected. Banks et al. (1999)

and Reimann et al. (1999) studied the impact of

filtration at 0.45 Am (and 0.10 Am, in the latter

study) on groundwater samples from wells in Norwe-

gian crystalline bedrock and found that filtration had

rather little effect. They warn, however, in their con-

clusions, that it is difficult to generalise this result, as

such groundwaters by their very nature have rather

low particulate contents.

This paper seeks to provide documentation of the

impact of filtration on water samples from a very

different medium: surface water (river/stream) sam-

ples from the Bolivian Altiplano (Fig. 1), which are

generally of high pH, often have a high particulate

content and which can be rather saline (Fig. 2). In

particular, the paper seeks to identify those elements

Fig. 2. Boxplots (see text for explanation) showing distributions of field-determined pH and electrical conductivity, and laboratory-determined

chloride and Total Suspended Solids. For each parameter, the bTotalQ data set for all surface water samples is shown, together with four subsets

containing analyses from the (i) bUyuniQ and (ii) bCoipasaQ catchment areas only and analyses for bRainyQ and bDryQ season sampling rounds

only. Sample numbers in each data set are shown above the boxplots.

D. Banks et al. / Journal of Geochemical Exploration 86 (2005) 104–118106

which are most and least impacted by filtration in this

particular hydrochemical environment.

2. The study area

The data are derived from a regional geochemical

study of the catchments of the Salars (salt flats) of

Uyuni and Coipasa on the Bolivian Altiplano (Fig. 1).

The study was performed by the environmental engi-

neering consultancy URS Dames and Moore Norge, in

cooperation with the National Geological and Mining

Service (Servicio Nacional de Geologıa y Minerıa

[SERGEOMIN]), for the Vice Ministry for Mining

and Metallurgy (VMMM) of Bolivia. The project ob-

jective was to assess the impact of mining activities on

the catchments and to provide a baseline against which

to monitor future activities. The study (including sam-

pling and analytical protocols, analytical results, etc.)

is fully reported by URS Dames and Moore Norge

(2000) and summarized by Banks et al. (2004).

The Altiplano is a high-altitude intermontane pla-

teau located between the dominantly metasedimentary

Cordillera Oriental and the volcanic Cordillera Occi-

dental of the Andes (USGS and GEOBOL, 1992). The

study area extends roughly from latitude 188 to 22830VSouth, and from longitude 66815V to 69815V West,

encompassing the hydrographic drainage basins

(catchment areas) of the Salar de Uyuni (salar area

10,085 km2) and the Salar de Coipasa (2239 km2).

The study area occupies a total area of approximately

71,183 km2.

Elevation in the study area ranges from approxi-

mately 3650 m above mean sea level (asl) in the salars

to over 5000 m asl in the surrounding mountains,

reaching 6542 m asl at Mount Sajama volcano. The


region is characterised by an arid to semi-arid climate,

sparse vegetation and sparse population. The majority

of the precipitation in the region is received within a

relatively short rainy season, extending roughly from

December to March. The region generally receives

very little rainfall during the rest of the year, with

total annual average rainfall ranging from b100 to 500

mm (increasing northwards). Water loss from the

basins occurs largely through evaporation, with

Class A open water tank evaporation data indicating

a mean annual evaporation rate of about 1200–2200

mm (Banks et al., 2004). The two salars (salt flats)

represent the evaporative sinks of the internal drainage

basins considered in this study and have accumulated

vast quantities of evaporite salts.

The catchments (which are often hilly or moun-

tainous) draining towards the salars contain a wide

variety of historically and currently worked metalifer-

ous and sulphur mineral deposits (USGS and GEO-

BOL, 1992).

Two rounds of river/stream water sampling were

carried out at a network of sampling stations within

the catchments draining towards the salars, during the

rainy (January/February 2000) and the dry (March/

April 2000) seasons, resulting in some 300 samples.

Particulate contents in the waters can be seen to be

considerably higher during the rainy season (Fig. 2)

due to increased surface water flows and sediment

transport capacity. For each sampling visit, both un-

filtered and filtered (0.45 Am) aliquots of the water

samples were collected.

For the purposes of this paper, a detailed consid-

eration of the area’s hydrochemistry is unnecessary,

although a summary has been published by Quinta-

nilla et al. (1995). The study (Banks et al., 2004) did,

however, result in the following salient findings:

(i) With the exception of a very small number of

mines, mining had little impact on the regional

hydrochemistry of this rather well-buffered

(high pH) system. The hydrochemical data pre-

sented in this paper are thus largely regarded as

representing natural water quality.

(ii) Although the surface water samples were taken

from the rivers draining towards the salars, rath-

er than in the salars themselves, some of the

waters were very saline. There is some tendency

for surface water salinity to increase down-

stream (towards the salars) due to evaporative

concentration.

(iii) Individual catchments had very different charac-

teristic water chemistries depending on the to-

pography, geology and prevalent mineralization

of the catchment. In particular, extremely high

(presumed natural) arsenic concentrations were

found in catchments draining from areas of na-

tive fumarolic sulphur mineralization and ther-

mal/saline springs in the Cordillera Occidental.

3. Sampling and analytical protocols

Field measurements were made of the following

parameters (meters calibrated on a daily basis):

– pH and water temperature using a Fisher Scientific

Accumet AP61 or AP63 combined pH/temperature

meter;

– electrical conductivity (EC) using a Hanna HI 9033

conductivimeter;

– dissolved oxygen (DO) using a YSI 55 immersion

probe.

Surface water samples were generally collected as

grab samples from the watercourse using a deconta-

minated container, and decanted into individual sam-

ple containers. Where there were multiple channels,

such as in braided river systems, composite samples

were collected, comprised of quanta from each chan-

nel in proportion to the relative magnitude of flow.

Sample flasks were provided by the analytical

laboratory and, where necessary, were pre-filled with

the appropriate preservative agent (ultrapure concen-

trated nitric acid in the case of unfiltered and filtered

samples destined for ICP-AES/ICP-MS analyses). In

the case of samples destined for ICP-MS/ICP-AES/

CVAA analyses, both unfiltered and filtered aliquots

were taken.

The filtration procedure utilised Nalgene integrated

filtration flasks. The initial sample was collected from

the stream in a 2-l sample container (liquinox-washed

and double rinsed in sample stream). That portion to

be filtered was transferred to the upper compartment

of the filtration unit. Pressure from a hand pump was

applied to the filtration flask, driving water through

the 50-mm diameter, 0.45 Am pore-size cellulose filter


membrane into the final (lower) compartment of the

flask, which was pre-filled with nitric acid preserva-

tive. The nominal filtration capacity for each sample

was 500 ml.

Water samples were stored in the field in cool-

boxes, and were subsequently shipped in the cool-

boxes to Philip Analytical Services Corp, Vancouver,

British Columbia, Canada, for analysis. The analytical

suite included the following:

(i) The following elements (on both filtered and

unfiltered water samples, in accordance with

USEPA SW846 Method 6020A) by a combina-

tion of inductively coupled plasma atomic emis-

sion spectrometry (ICP-AES): Al, Sb, Ba, Be,

Bi, B, Ca, Cr, Co, Cu, Au, Fe, Li, Mg, Mn, Mo,

Ni, P, K, Si, Na, Sr, S, Sn, Ti, V, Zn, Zr and

inductively coupled plasma mass spectrometry

(ICP-MS) techniques: As, Cd, Pb, Se, Ag, Tl.

Analytical limits of detection are shown in

Table 1.

(ii) Mercury (Hg) on filtered and unfiltered water

samples by cold vapour atomic absorption

(CVAA), in accordance with USEPA method

245.1.

(iii) Total suspended solids (TSS) by vacuum filtra-

tion (method SM18 2540 D).

Table 1

Limits of analytical detection for each element in filtered and

unfiltered water samples (mg/l)

Parameter Unfiltered Filtered Parameter Unfiltered Filtered

Al 0.06 0.02 Hg 0.00005 0.00005

Sb 0.02 0.015 Mo 0.005 0.004

As 0.001 0.001 Ni 0.01 0.008

Ba 0.001 0.001 P 0.1 0.04

Be 0.0002 0.001 K 0.5 0.4

Bi 0.02 0.02 Se 0.001 0.001

B 0.04 0.008 Si 0.8 0.03

Cd 0.0001 0.0001 Ag 0.0001 0.0001

Ca 0.05 0.01 Na 0.5 0.01

Cr 0.002 0.002 Sr 0.001 0.001

Co 0.004 0.003 S 0.1 0.03

Cu 0.003 0.001 Te 0.02 0.02

Au 0.0001 0.0001 Tl 0.0001 0.0001

Fe 0.05 0.003 Sn 0.02 0.02

Pb 0.001 0.001 Ti 0.003 0.003

Li 0.002 0.002 V 0.003 0.003

Mg 0.05 0.02 Zn 0.01 0.002

Mn 0.002 0.002 Zr 0.003 0.003

(iv) Total dissolved solids (TDS) by vacuum filtra-

tion and desiccation (method SM18 2540 C).

(v) pH and EC by immersion electrode (methods

SM18 4500-H B and SM18 2510 B).

(vi) Chloride and sulphate by ion chromatography

(method SM19 4110C).

(vii) Alkalinity and acidity by titration against strong

acid or base (respectively) to end points of 4.5

and 8.3 (method SM18 2320 B). The t-alkalinity

referred to in this paper represents alkalinity

titrated to pH 4.5.

(viii) Nitrogen species by colorimetric methods (EPA

600-351.2 and SM18 4500-NO3�F). Samples for

Kjeldahl-N analysis were field-preserved with

sulphuric acid to pH=2.

Quality control of analysis (by means of laborato-

ry-field comparison, blank and duplicate samples and

calculation of ion balance error) is documented by

Banks et al. (2004). In summary, the data were found

to be of acceptable quality, although Banks et al.

(2004) warned that reported values for Cu, Sb, Zn,

Kjeldahl- and total N should be treated with caution,

especially at low levels. Zn, in particular, was found to

yield poorly reproducible results and positives in

blanks, indicating a suspicion of contamination during

sampling or preservation.

4. Statistical and graphical presentation of data

Presentation of data in this report has been per-

formed using non-parametric techniques (techniques

involving the use of median, quartiles etc., rather than

averages and standard deviations), in order to mini-

mise the influence of outlying data points and skewed

data distributions.

(i) Figs. 2, 3, 7, 8 and 9 employ the bboxplotQ, anon-parametric means of displaying the statisti-

cal structure of a data distribution. In such a

diagram the central bboxQ contains the interquar-tile range of data, with a horizontal line marking

the median. The horizontal parentheses on either

side of the median are a robust measure of the

95% confidence interval on the median. Thus, if

the parentheses of two adjacent data distribu-

tions overlap, we can say that the medians are

Fig. 3. Boxplots (see text for explanation), showing distributions of Al, Fe, Mn and Na in unfiltered water samples. See caption to Fig. 2 for

explanation of subsets. Note elevated concentrations of Al, Fe and Mn in rainy season samples relative to dry season.


not significantly different at the 95% confidence

level. The bwhiskersQ show the extraquartile

data, while squares and crosses show near and

far (respectively) boutlyingQ data (defined by a

statistical algorithm).

(ii) Figs. 4–6 and 9 employ x–y plots.

In both forms of graphical presentation, data below

the analytical limit of quantification have been set

equal to that limit. Elements where the large majority

of data fall below the detection limit have been omit-

ted from the graphical presentation.

5. Comparison of wet season and dry season results

Figs. 2 and 3 presents the distributions of a small

selection of parameters within the total surface water

dataset (typically n =297). Furthermore, for each pa-

rameter, distributions are presented for subsets

corresponding to the two catchments of the Salar de

Uyuni (n=166) and the generally slightly less saline

Salar de Coipasa (n =131), to permit a visual assess-

ment of any systematic differences in hydrochemical

distributions between the two catchments (see Banks

et al., 2004, for more information). Data are also

subdivided into subsets corresponding to samples col-

lected during the wet (n =153) and dry (n =144)

seasons.

The most marked difference between rainy and

dry seasons is the significantly higher concentration

of total suspended solids during the rainy season, as

one would expect given the higher flows and higher

sediment transport capacities in the rivers. It was

also found that for the following parameters, signif-

icantly greater (at a 95% significance level) median

concentrations are found during the wet than the dry

season:

Al_U, Ba_U, Cu_U, Fe_U, Pb_U, Mn_U, P_U,

K_U, Tl_U, Ti_U, V_U, nitrogen species, Cr_F, Cu_F

Fig. 4. x–y plots showing concentrations of each element in unfiltered versus filtered (0.45 Am) aliquots of each sample. Rainy and dry season

samples are shown by different symbols. Note that all analyses below the analytical limit of quantification (Table 1) are set equal to that limit.





(where _F indicates filtered samples, and _U indicates

unfiltered samples).

Very few parameters exhibit significantly lower

median values in the rainy than the dry season: dis-

solved oxygen, pH_laboratory (but not field), As_F,

Sb_U.

For all other parameters (including the majority of

parameters in filtered samples) differences cannot be

said to be significant at a 95% level. The most inter-

esting observations to be noted from the above are:

(i) The decline in concentrations of many of the

bunfilteredQ parameters in the dry season. This

suggests that particulate and colloidal matter

have a significant impact on analyses of unfil-

tered samples.

(ii) The fact that metals such as Al, Fe, Mn (which

may form colloidal flocs in waters of high pH)

appear to be associated with suspended solids,

whereas elements such as Si, Ca, Na, Mg are

not. This suggests that the analytical discrepan-

cies between filtered and unfiltered samples in

rainy and dry seasons may be related to the

presence (and subsequent dissolution in acid

preservative) of Fe-, Al-, Mn-oxyhydroxide

flocs, rather than to silicate particles themselves.

Several of the potentially toxic trace elements

(Pb, Cu, Tl) may be associated (adsorbed or

coQprecipitated) with these oxyhydroxides. Al-

ternatively, it is possible that the postulated

oxyhydroxide phases are present as readily

acid-soluble coatings on detrital particles.

(iii) The elevated concentrations of nitrogen species

during the rainy season are probably due to

leaching of soluble nitrogen compounds from

the soil profile during rainy flushes.

(iv) It is intriguing that one of very few parameters

exhibiting an increase in the dry season is arse-

nic (filtered). This ties in with the regional

findings of Banks et al. (2004) that arsenic (i)

is at least in large part associated with the dis-

solved phase, (ii) is subject to evaporative con-


centration and (iii) occurs as a mobile oxy-anion

under alkaline conditions and exhibits a degree

of correlation with pH.

6. Impact of filtration

Figs. 4–6 illustrate the correlation between filtered

and unfiltered samples for the parameters determined

by ICP and AA techniques where a significant pro-

portion of data fall above the detection limit.

In clean groundwater samples, it has been demon-

strated that filtration only has a minor impact on the

concentrations of most elements (see Reimann et al.,

1999). However, it will immediately be seen from the

figures that filtration has a major effect on concentra-

tions of many of the elements in the surface waters of

the salar catchments. This is probably due to two main

reasons:

(i) in unfiltered samples, particulate matter is pres-

ent in the sample, is dissolved in the preserva-

tive acid and releases significant quantities of

solutes. This results in an berroneouslyQ elevatedconcentration.

(ii) in filtered samples, elements may be retained

on the filter material by sorption or retention

of colloidal flocs, resulting in a depressed

concentration.

Figs. 4–6 may be examined by comparing the

distribution of data points relative to a conceptual

1:1 line across the plot. Points falling below the 1:1

line have elevated concentrations in the unfiltered

samples. The figures indicate that the following ele-

ments are relatively little affected by the filtration

process:

B, Ca, Li, Mg, K, Si, Na, Sr, S

Even with these elements (and especially Ca, Mg,

K and Si), however, there are individual samples,

especially in the rainy season, that are impacted by

filtration and fall below the 1:1 line. The elements

listed above are generally soluble elements, not read-

ily retained in solid phases, with the exception of Ca

and Si. The fact that Ca and Si are not highly impact-

ed by filtration suggests that the filtration effect ob-

served in other elements is typically not related to

retention or passage of lithic silicate or limestone

sediment particles (except for a few rainy season

samples).

The following elements show elevated concentra-

tions in unfiltered aliquots of a substantial number of

samples, especially in the rainy season:

Al, As, Ba, Be (not shown), Cd, Cr, Co, Cu, Fe,

Pb, Mn, Hg, Ni, P, Ag, Tl, Ti, V, (Zn)

Note for Zn, the extremely poor, unsystematic

correlation between filtered and unfiltered data. This

suggests poor reproducibility of the Zn analytical data

(and this was, in fact, confirmed in blank and field

duplicate tests). For the other elements noted above,

the elevated concentrations in unfiltered samples are

probably related to the presence and acid dissolution

of non-silicate phases such as hydroxide flocs, form-

ing under high pH conditions (e.g. for Al, Fe and Mn,

especially), or oxyhydroxide coatings on other parti-

cles. If such colloidal or floc particles form, many

other elements sorb effectively onto their large surface

area and are retained by filtration. Note that, for

arsenic, the majority of samples fall close to the 1:1

line, indicating lack of any effect of filtration and the

association of As with the dissolved phase (see

bComparison of Wet and Dry Season ResultsQ,above). However, some samples plot below the 1:1

line, indicating association of arsenic with a colloidal

or particulate form. Banks et al. (2004) do, in fact,

present evidence from the regional geochemical sur-

vey of the probable partial sorption of arsenic onto

iron oxyhydroxide flocs and precipitates.

For the other elements, there are typically too few

samples above the limit of quantification to make an

assessment of the effects of filtration.

Figs. 7 and 8 show the distribution of concentra-

tions for all the elements in filtered and unfiltered

samples in the form of boxplots. These permit one

to readily observe how median and quartile concen-

trations are affected by filtration. For the following

elements, the median concentration of the element is

not significantly affected (at a 95% confidence level)

by filtration:

As, B, Ca, Li, Mg, K, Si, Na, Sr, S

The boxplots in Figs. 7 and 8 do not, of course,

reveal some of the detail in data sets for individual

elements. For example, they appear to demonstrate

that the median concentrations of Mg and K in filtered

and unfiltered samples are not significantly different.

However, Fig. 5 clearly shows that there are some

Fig. 7. Boxplots showing distributions of elements (Al to Mn) in unfiltered (_U) and filtered (_F) aliquots of samples. Note that all analyses

below the analytical limit of quantification (Table 1) are set equal to that limit; hence for some elements, most data plot on a single horizontal

line at the limit of quantification. #=number of samples in each subset.

Fig. 8. Boxplots showing distributions of elements (Hg to Zr) in unfiltered (_U) and filtered (_F) aliquots of samples. Note that all analyses

below the analytical limit of quantification (Table 1) are set equal to that limit; hence for some elements, most data plot on a single horizontal

line at the limit of quantification. #=number of samples in each subset.


Fig. 9. The relationship of the Filtration Ratios for As, Ba, Fe, Mg, Mn and Si to Total Suspended Solids (TSS) and field pH. The Filtration

Ratio is the ratio of concentration in an unfiltered sample to that in the corresponding filtered sample. To demonstrate the Ratio–pH relationship,

the total data set has been ranked according to field pH and subdivided into five 20-percentile subsets. For each subset a boxplot is presented.

#=number of samples in each subset.



samples for these elements from the rainy season

which are impacted by filtration. In the case of K,

these samples are sufficient to render the rainy season

median concentrations higher than dry season median

concentrations in unfiltered samples. They are not

sufficient however, to substantially elevate the median

concentrations for unfiltered samples in Fig. 8, rela-

tive to filtered samples.

The possible factors controlling the impact of fil-

tration on selected element concentrations has further

been investigated by calculating the Filtration Ratio of

the element concentration in the unfiltered sample to

the concentration in the corresponding filtered sample.

This ratio has then been plotted against factors such as

Total Suspended Solids (TSS), pH and electrical con-

ductivity. The Filtration Ratios did not show any sig-

nificant correlation with electrical conductivity (i.e.

salinity) of the water sample. Fig. 9 demonstrates the

relationship of the Filtration Ratio with field pH and

TSS for six elements with rather different geochemical

properties, and where most samples are above the

analytical detection limit (hence giving reliable values

of Ratios). It will be seen that, for Fe and Mn, there is a

clear relationship between the Filtration Ratio and

TSS, even down to relatively small concentrations of

TSS. For Mg, Ba, Ca (not shown) and Si, there is also a

demonstrable relationship with TSS, but only when

TSS exceeds a specific threshold. For As, the relation-

ship with TSS is not clearly demonstrated, although

there is some tendency to an elevated Ratio at high

TSS. Note that, for all elements, the highest ratios are

observed in the rainy season.

Direct examination of the relationship between the

Filtration Ratio and field pH is problematic due to the

clustering of pH values. To overcome this, the data set

has been ranked according to field pH and five subsets

defined on the basis of 20 percentile groupings

according to pH. Boxplots have been constructed in

Fig. 9 for each subset. This technique fails to demon-

strate any relationship between the Filtration Ratio

and pH for most elements (e.g. As, Ba, Mg and Si).

However, for Fe and Mn, there is a clear increase in

ratio from low to slightly alkaline pH. It is suggested

that that this is related to the progressive formation of

manganese and ferric oxyhydroxide flocs with in-

creasing pH:

4Fe2+ + 10H2O + O2 = 4Fe(OH)3 + 8H+(aq)

Fig. 9 would appear to suggest that, at low

concentrations of TSS, the dominant particulate

forms are flocs of iron, manganese and possibly

aluminium oxyhydroxides, whose concentrations in-

crease both with increasing TSS and increasing pH.

Silicate and carbonate particulate phases appear to

be less important and only become significant at

higher concentrations of TSS. The highest ratios of

unfiltered/filtered concentrations occur in the rainy

season samples. This would appear to confirm that

hypothesis that the differences in element concentra-

tions between filtered and unfiltered aliquots of

individual samples are related to that sample’s par-

ticulate content. The particulate content tends to be

elevated during the rainy season, due to higher

stream-flows having the capacity to mobilise and

retain in suspension higher concentrations of partic-

ulate matter.

7. Conclusions

Samples of surface water from the catchment areas

of the Salars de Uyuni and Coipasa, which are typi-

cally of high pH and can be rather saline, indicate that

analysed concentrations of the following elements in

most samples are not significantly affected by filtra-

tion at 0.45 Am:

B, Ca, Li, Mg, K, Si, Na, Sr, S

x–y plots demonstrate elevated concentrations of

As in unfiltered aliquots of some samples, compared

with filtered samples. The most likely explanation for

this is some degree of sorption of As onto iron oxy-

hydroxide flocs or particle coatings (Sracek et al.,

2004). However, the difference in median concentra-

tions of As in filtered and unfiltered samples is not

statistically significant at the 95% level, confirming

the findings of Banks et al. (2004) that As is typically

a highly soluble and mobile element in high-pH

environments.

The following elements appear to be impacted by

the filtration process, resulting in elevated concentra-

tions in unfiltered samples, relative to filtered:

Al, Ba, Be, Cd, Cr, Co, Cu, Fe, Pb, Mn, Hg, Ni, P,

Ag, Tl, Ti, V

The impact of filtration appears mainly related to

the passage into unfiltered samples (and subsequent

dissolution in preservative acid) of oxyhydroxide


(e.g. Fe, Mn, Al and co-sorbed/precipitated trace

elements) flocs/particle coatings and possibly non-

calcium carbonate particles. These would be retained

or precipitated on the filter and hence not contribute

to the analysis of filtered samples. The effect of

filtration increases with increasing Total Suspended

Solids (TSS) and increasing pH, suggesting that

such oxyhydroxide flocs preferentially form under

neutral or slightly alkaline pH conditions. The effect

of filtration is due only in minor part to the reten-

tion of silicate or limestone particles on filters (fil-

tration has little effect on Si or Ca concentrations).

At high concentrations of TSS, the significance of

carbonate and silicate particles appears to increase,

however.

Samples taken in the rainy season had higher loads

of suspended particles than those in the dry season.

Consequently, median concentrations of the following

elements in unfiltered samples were higher in the

rainy season than the dry:

Al, Ba, Cu, Fe, Pb, Mn, P, K, Tl, Ti, V

In most cases, this can be explained by the associ-

ation of such elements with particulate phases.

Acknowledgements

The study of the baseline environmental chemistry

of the Salar catchments was performed by URS

Dames and Moore Norge in fulfilment of a contract

issued by the Vice Ministry of Mining and Metallurgy

(Viceministerio de Minerıa y Metalurgia [VMMM])

of the Government of Bolivia to perform the scope of

work delineated under Part D of the Project for the

Environment, Industry and Mining (Proyecto de

Medio Ambiente, Industria y Minerıa [PMAIM]),

Subproject No. 2-D3, bGeneration of Environmental

Baseline InformationQ. Support for the project was

provided by the Nordic Development Fund and the

World Bank.

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