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ACTA

UNIVERSITATIS

UPSALIENSIS

UPPSALA

2006

Digital Comprehensive Summaries of Uppsala Dissertationsfrom the Faculty of Science and Technology 200

Geochemical Landscape Analysisfor the Risk Assessment of AcidMine Drainage in a WetlandEnvironment

ANDREA SZUCS

ISSN 1651-6214ISBN 91-554-6601-Xurn:nbn:se:uu:diva-6992

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To my sons.

List of Publications

This thesis is based on the following papers that are referred to by their Ro-

man numerals. The publishers kindly gave their permission for reproduction

of papers.

I Szucs, A. Jordan, G. and Qvarfort, U., 2002. Geochemical model-

ling of acid mine drainage impact on a wetland stream using landscape

geochemistry, GIS and statistical methods. In: A. G. Fabbri, G. Gaal

and R. B. McCammon (eds), Deposit and Geoenvironmental Models

for Resource Exploitation and Environmental Security, NATO Science

Series, 2. Environmental Security, 80, pp. 425-460. Kluwer Academic

Publishers, Dordrecht. II Jordan, G., Szucs, A., Qvarfort, U. and Szekely, B., 1997. Evalua-

tion of metal retention in a wetland receiving acid mine drainage. In:

X. Xuejin (editor), Geochemistry, Proceedings of the 30th IGC, Vol.

19, pp. 189-206. VSP Publisher, Utrecht. III Szucs, A. and Jordan, G., 2005. Geochemical landscape analysis:

development and application to the risk assessment of acid mine

drainage. A case study in Central Sweden. Science of the Total Envi-

ronment (submitted).

IV Jordan, G. and Szucs, A., 2002. Environmental Mapping of Geo-

chemical Systems. In: P. T. Bobrowsky (editor), Geo-environmental

Mapping: Theory, Methods and Applications, pp. 57-74. A. A.

Balkema, Lisse.

APPENDIX

V Jordan, G. and Szucs, A., 1997. The role and future of geology in

modern environmental research and decision support. In: H. Wang, D.

F. Branagan, Z. Ouyang, X. Wang (eds), Theory of Geology, Proceed-

ings of the 30th IGC, Vol. 26, pp. 237-249. VSP Publisher, Utrecht.

Other publications published as result of PhD research.

1. Szucs, A., Jordan, G. and Qvarfort, U., 2006. Geochemical landscape

analysis: development and application to the risk assessment of acid mine

drainage. Case study in Central Sweden and Hungary. Abstracts, 2nd Na-

tional Conference on Landscape Ecology, Debrecen, Hungary.

2. Jordan, G. and Szucs, A., 1999. Environmental Mapping of Geochemical

Systems. Abstracts, International Conference and Annual Meeting of

FOREGS on European Geochemical Mapping, Vilnius, Lithuania (oral

presentation).

3. Jordan, G. and Szucs, A., 1998. Environmental Mapping of Geochemical

Systems. Abstracts, NATO Advanced Studies Institute Meeting, Matra-

haza, Hungary (oral presentation).

4. Szucs, A. and Jordan, G., 1998. Integrated modelling of acid mine drainage

impact on a wetland using landscape geochemistry, GIS technology and

statistical methods. Abstracts, NATO Advanced Studies Institute Meeting,

Matrahaza, Hungary (oral presentation).

5. Szucs, A. and Jordan, G., 1997. Modelling of acid mine drainage impact on

a wetland using landscape geochemistry, GIS technology and statistical

methods. Abstracts, IAMG „97. Int‟l. Symp. on Geomathematics, Barce-

lona, (oral presentation).

6. Jordan, G. and Szucs, A., 1997. Appliacation of landscape geochemical

methods and GIS technology for geoenvironmental mapping. Abstracts,

GAC Special Symposium on Geoenvironmental Mapping, Ottawa (poster).

7. Jordan, G., Szucs, A., Qvarfort, U. and Szekely, B., 1996. Evaluation of

metal retention in a wetland receiving acid mine drainage. Abstracts, 30th

International Geological Congress, Beijing, (oral presentation).

8. Jordan, G and Szucs, A. 1996. The role and future of geology in modern

environmental research and decision support. Abstracts, 30th Int‟l. Geol.

Congr., Beijing, (oral presentation).

9. Szucs A., 1995. An integrated systems approach to biogeochemical proc-

esses: evaluation of the control mechanisms of trace metal speciation in a

wetland stream environment affected by acid mine drainage. Licenciate

Thesis, Uppsala University.

Acknowledgements

I would like to thank Ulf Qvarfort for giving continuous support during the

years of my thesis work at the Uppsala University. He provided research

facilities and a good working environment for thesis work and fruitful dis-

cussions. His outstanding ability of solving emerging problems immediately

made my research work much efficient. I would like to express my gratitude

to Keith Bennett, head of Earth Sciences Department for his patience and

kind understanding during my PhD work at his department.

My sincere thanks to my friends at the Uppsala University. Bertram

Schott, Manhal Sirat, Barna Zsigmond, and many others. Without the per-

sonal support of friends outside of the university I would not have been able

to complete my thesis. Special thanks to Anikó and Edward Ledwaba and

the Heszler family for providing true friendship during my stay in Sweden.

This thesis would not exist without the support of the Geological Institute

of Hungary (MAFI). First of all, I would like to say thank you to Gábor

Gaál, former MAFI director, and Károly Brezsnyánszky, Director of MAFI

for their continuous interest in my work and their understanding support. I

thank István Horváth, György Tóth and Emőke Jocháné Edelényi of the

Hydrogeology Division for providing facilities and giving friendly support

for the preparation of this thesis.

The three months of stay as a visiting scientist at the International Insti-

tute for Geo-Information Science and Earth Observation (ITC) in Enschede,

the Netherlands was important for the success of my thesis. Professor An-

drea Fabbri, Head of Geological Survey Division, provided a welcoming

environment and important professional support. I thank the Vekerdy family

for being so friendly to me during my stay in the Netherlands.

The period of my PhD work has brought the greatest success and happi-

ness in my life: the birth of my three sons, Marcell, Milan and Nicholas.

Thanks for being with me.

Research has been carried out with the help of scholarships from the

Swedish Institute, the Soros Foundation and the Hungarian National Science

Fund ( OTKA T022133). Their assistance is gratefully acknowledged.

Contents

1 Introduction .............................................................................................. 11

2 Aim of the study ....................................................................................... 13

3 Study Area ................................................................................................ 14

4 Methods ..................................................................................................... 16 4.1 Geochemical landscape analysis for AMD contamination risk

assessment ............................................................................................... 16 4.2 Sampling and analytical procedures ............................................... 19 4.3 Data analysis and modelling ............................................................ 21

5 Sampling design based on geochemical landscape modelling .............. 22

6. Results ...................................................................................................... 25 6.1 Geochemical abundances ................................................................. 25

6.1.1 Stream ........................................................................................ 25 6.1.2 Mire ............................................................................................ 27

6.2 Geochemical gradients ..................................................................... 30 6.2.1 Stream ........................................................................................ 30 6.2.2 Mire ............................................................................................ 30

6.3 Geochemical processes ..................................................................... 32 6.3.1 Stream ........................................................................................ 33 6.3.2 Mire ............................................................................................ 35

7 Geochemical landscape analysis for AMD impact assessment ............ 37 7.1 Stream environment ......................................................................... 37 7.2 Mire environment ............................................................................. 40

8 Conclusions ............................................................................................... 43

Summary in Swedish .................................................................................. 45

References .................................................................................................... 47

11

1 Introduction

Since most of the elements used by the society come from mineral extrac-

tion, mining of mineral resources provide essential raw material for sustain-

able development. Mining however leads to severe impact on the environ-

ment, including contamination by toxic metals. A recent Europe-wide survey

identified wide-spread pollution problems caused by mining, abandoned

mines in particular (COM, 2003). Younger et al. (2002) estimated that about

1,000 to 1,500 km of watercourses are polluted by metal mine discharges in

the EU. Through mineral extraction and subsequent mineral processing huge

amount of wastes are produced. Metals and metal compounds in wastes tend

to become chemically more available, which can result in the generation of

acid mine drainage (AMD). Due to great volumes and slow chemical proc-

esses, mine waste can release toxic compound for a very long time on the

scale of centuries and thousands of years (BAT, 2003). Thus, remediation of

mine sites, including abandoned mines, has to consider long-term solutions

and remediation technologies have to be sustainable for a long time (Sind-

ing, 1999). As an alternative, passive in situ remediation methods for AMD

treatment using wetlands that provide means of cheap and sustainable reme-

diation has been proposed (PIRAMID, 2003). Around the mine site, soils

and surface water in the receiving environment are often contaminated with

harmful elements or compounds of AMD. These contaminated sites act as

secondary sources for pollution, especially for historic sites (Jordan and

D‟Alessandro, 2004).

AMD flowing from abandoned ore mines is a major environmental pollu-

tion problem in historic mining areas in central Sweden. If left untreated,

AMD can have adverse effects on natural ecosystems and human health.

Abundant natural wetlands may offer inexpensive treatment alternatives to

chemical neutralisation and precipitation processes in this region. However,

the effectiveness of wetland treatment depends on many chemical and bio-

logical factors, and the long-term capacity of wetlands to retain metals has to

be assessed by the modelling of metal-sediment interactions and by evaluat-

ing the stability of the wetland system as a whole. In this study the attenua-

tion of six metals Pb, Ni, Cu, Zn, Fe and Mn originating from abandoned

mines and waste rock dumps is investigated in the receiving stream (Paper

I) and mire (Paper II) sediments at Slättberg in central Sweden, where acid

mine leachate has been discharging for over 70 years into the wetland stream

12

and mire. Stream and mire sediments, as potential geochemical barriers for

AMD metals, are analysed using a sequential chemical extraction procedure

in order to investigate the control mechanisms of metal retention in these

sediments.

Among the various sampling media, stream sediments have been widely

used to investigate element distributions in surface environments (e. g., Car-

penter et al., 1975; Davenport, 1990; Mantei et al., 1996; Salminen et al,

2003). Stream sediments are a dynamic sampling medium that can represent

the geochemistry of nearby landscapes in terms of both the natural geo-

chemical heterogeneity, and the pollution caused by mining in the study area

(Plumlee and Logsdon, 1999). The abundant clays, Al, Mn, and Fe oxides,

and humic material in stream sediments collectively provide a large reactive

surface area for the effective control of dissolved metal content of aquatic

systems, therefore these sediments act as a geochemical barrier for metals.

The organic-rich reducing mire sediments studied in this research provide

abundant reactive surface for AMD toxic metal retardation by organic com-

plexation and metal sulphide formation. The chemistry of the contaminated

groundwater discharging into the studied stream and mire is well docu-

mented (Herbert, 1994) thus the Slättberg study area offers a good opportu-

nity for the geochemical modelling of AMD metal fate in the receiving wet-

land environment.

Assessment of long-term stability and contamination risk of AMD metals

requires contaminant fate modelling along the source-pathway-receptor

chain (van Leuwen and Hermens 1996; CARACAS 1999). In other words,

AMD contamination risk assessment requires not only the modelling of geo-

chemical processes but the analysis of the receiving landscapes (SENES

2000). In a review of the problem, Jordan (2004) points out that the complex

problem of AMD risk assessment requires methods that can address both (1)

geochemical contaminant fate modelling and (2) landscape analysis. In order

to link geochemical contaminant fate modelling and landscape analysis for

the risk assessment of AMD along the source-pathway-receptor chain, a

simple geochemical landscape analysis tool is developed using geochemical

modelling and landscape ecology spatial analysis methods (Paper III). Spa-

tial analysis of geochemical processes uses geochemical landscape analysis

implemented with GIS technology (Paper IV). Methods of geochemical

landscape analysis and spatial modelling are demonstrated through a case

study in the Slättberg study area in central Sweden.

13

2 Aim of the study

- The main objective of the thesis is to determine the relative partitioning

of six metals, Pb, Ni, Cu, Zn, Fe and Mn, into five sedimentary fractions

likely to be affected by various environmental conditions (Paper I and

II).

- The second objective is to evaluate the stability of various metal forms

within the stream and mire sediments by correlating the spatial distribu-

tion of chemical parameters and to determine the relation between these

parameters and samples using statistical techniques (Paper I and II).

- A third objective is to evaluate the stability of the wetland system, in an

effort to determine if wetland environment can act as permanent metal

sinks (Paper II).

- The final objective is to link geochemical contaminant fate modelling

and landscape analysis for the contamination risk assessment of AMD

(Paper III and IV).

- Specific objectives of investigation are

(1) the geochemical modelling of metal behaviour in the studied oxi-

dising wetland stream sediments (Paper I), and

(2) the geochemical modelling of metal behaviour in the studied re-

ducing mire sediments (Paper II).

The hypothesis is that, based on the partitioning of metals determined by

a sequential extraction procedure, metal retention by stream and mire sedi-

ments can be assessed, and results of geochemical process modelling can be

linked to spatial landscape analysis for the identification of geochemical

barriers and for the efficient risk assessment of AMD contamination. The

organic-rich reducing mire and oxidising stream sediments along the path-

way of flowing AMD is hypothetised to serve as double reducing-oxidising

barrier for metals in the mine effluent.

In this thesis geochemical modelling uses univariate and multivariate sta-

tistical and data analysis methods. The emphasis is on geochemical model-

ling and on linking geochemical process modelling to spatial analysis.

14

3 Study Area

The studied wetland and stream in the Slättberg nickel mine area is located

in central Sweden (Fig. 1). Vegetation on hill slopes surrounding the wetland

and stream consists of pine and spruce forest with mosses and heather. The

studied wetland is an ombrotrophic bog slightly raised with swampy parts

along wetland streams and with a 3-5 m wide shallow marginal open water

zone. In the marginal waters iron-oxihydroxide precipitates (bog iron) are

observable. The wetland is in a groundwater discharge area (Herbert, 1994).

According to field observations the groundwater table is about 50 cm below

surface in the dry season and 20 cm when highest in the autumn, giving a 30

cm wide zone of fluctuating reducing-oxidising conditions. The thickness of

the wetland sediments is 2-2.5 m on average, which is characteristic of bogs

in central Sweden (Franzen, 1985). The vegetation in the bog is typically

hummocky bog vegetation characterised by Sphagnum species, primarily

Sphagnum fuscum, with sparse pines. The bog is fed and intersected by a

braiding river and a smaller stream, both crossing the mineralization and

accompanying mining area upgradient to the mire. Down gradient to the

mire the stream widens into a fen, a shallow stagnant water. The stream is

1.5 deep on average and it runs on thick (1-2 m) organic rich sediment bed.

Both in the excavated ditches and in the fen ochreous precipitates are locally

observable on the sediment surface.

The geology of the area is characterised by island-arc igneous rocks of the

Early Proterozoic Svecofennian orogeny (Gaal, 1990) (Fig. 1). In the central

valley, the bedrock is covered by Quaternary gravely-sandy glaciofluvial

deposits of 6 to 15 metres in thickness and the surrounding area is dominated

by bouldery-sandy moraine covered by podzol soils. The main geological

feature of the area is an approximately 1.6 km long and 6 m wide mineral-

ised amphibolite dyke in a WSW-ENE orientation, which is intruded into a

hornblende bearing granitic gneiss host rock (Tegengren, 1924) (Fig. 1). The

ore association is characterised by pyrrhotite (Fe1-x

S), pyrite (FeS2), pent-

landite ((Fe,Ni)9S

8) with bravoite ((Fe,Ni)S

2), millerite (NiS) and linneite

((Co,Ni)3S

4) and chalcopyrite (CuFeS

2) (Magnusson, 1953; Wickman,

1994). As a result of metasomatic processes the dyke has been strongly al-

tered and feldspar is replaced to a great extent by sericite, chlorite and epi-

dote.

15

Six abandoned waste rock dumps and mine shafts are situated along the

dyke upstream to the studied stream segment (Fig. 1). Wastes derived from

the excavated mine shafts and tunnels have been dumped directly adjacent to

the mines, covering a surface area of about 1300 m2 with a thickness be-

tween one to two meters each. Mining of copper, iron and nickel ores com-

menced in 1805 and ceased during the 1940s. However, the bulk of the

waste was produced in the course of several years from 1917. Metals are

leached from waste rock dumps into groundwater in high concentrations

(pH: <3, Fe: 1110 mg/L, Mn: 4.4 mg/L, Cu: 40 mg/L, Zn: 18.5 mg/L, Ni:

32.1 mg/L, Pb: 0.29 mg/L, SO4: 3750 mg/L; Herbert, 1994) and are dis-

charged into excavated ditches and the studied stream segment. These con-

centrations are considerably attenuated by the point where AMD discharges

into the mire (pH: 3.96, Fe: 0.03 mg/L, Mn 1.98 mg/L, Cu: 4.4 mg/L, Zn:

4.6 mg/L, Ni: 2.5 mg/L, Pb: <0.05 mg/L, SO4: 460 mg/L; Herbert, 1994).

Figure 1. Location of the study area. Inset: Solid box indicates the location of the

area in Sweden (60 48'N, 15 15'E). Shading shows physiographic zones. The study

area is situated in the southern boreal zone of Scandinavia (medium grey shading)

(figures show average annual precipitation). The study area falls into the region of

the Svecofennian island arc lithologies of Early Proterozoic age (dotted line). Large

map: Map is the result of GIS overlay operations of topographical, hydrological,

geological and quaternary geological layers. The studied mire and stream section are

marked with thick lines.

16

4 Methods

4.1 Geochemical landscape analysis for AMD

contamination risk assessment

Landscapes result from the interaction of the geospheres, biosphere and

socio-economic systems (Forman, 1995). The physico-chemical properties

of landscape components (rock, soil, ground and surface water, vegetation

and atmosphere) and their spatial relationship relative to matter transport

determine the geochemical character of the landscape. Landscape structure

(pattern) is described in terms of geochemical abundances and geochemical

gradients. Landscape functioning is described in terms of element migration

(chemical reactions), geochemical flows (transport processes) and landscape

geochemical barriers. Landscape dynamics is described by landscape classi-

fication and historical geochemistry. The basic geochemical system, elemen-

tary landscape, is defined as a landscape volume which is homogeneous with

respect to matter transport pattern and forms a part of the land surface in

which the interactions between rocks, surface- and ground water, organisms,

atmospheric constituents and solar energy are maintained constant at any

given time (Fortescue, 1980). In practice, landscape units are mapped which

are areas homogeneous with respect to at least one landscape component.

Soil, the product of these interactions, serves as the criterion for determining

the lateral boundaries of the elementary landscapes (Glazovskaya 1963).

Elementary landscapes extend vertically from the top of unweathered rock to

the uppermost level of vegetation cover. Geochemical input and output is

called positive and negative extra landscape flow (+/- ELF), respectively.

Matter flow within the landscape can form the main migration cycle (MMC)

if element migration is cyclic in the landscape, and the landscape geochemi-

cal flow (LGF) when element migration is characterised by flow through the

landscape (Kozlovskiy 1972). In order to simplify mapping of soil types and

soil variants, soil and land cover types are clustered into three groups on the

basis of the migrational environment of chemical elements (Polynov, 1956).

Where the water table is always below the surface, an eluvial landscape

characterised by downward salt movement in well-drained soils is formed. If

the water table and the daylight surface coincide a super-aqual landscape

(bog or marsh) appears, where the dominant pattern of salt movement is

upward and horizontal. A lake or stream environment, where the water table

is above the land surface, is called an aqual landscape characterised by circu-

17

lar salt movement. Classification of elementary landscapes have been ex-

tended with the consideration of their spatial relationships through surface

and subsurface matter flow in a way analogous to the soil catena concept of

soil science (Glazovskaya 1963; Gerrard 1992). Landscape classes can be

extended with the „trans-„ prefix emphasising that the given landscape is

characterised by throughflow. For example, on the basis of transport intensi-

ties in surface waters active stream segments are classified as trans-aqual

landscapes while lakes and stagnant waters are called aqual landscapes.

Similarly, eluvial landscapes are subdivided as eluvial, trans-eluvial and

eluvial-accumulative landscapes on hill tops where erosion and downward

movement of elements dominates, on hill slopes where down-gradient

throughflow dominates and in valleys which are characterised by deposition

(Glazovskaya, 1963). In this study the delineation and classification of ele-

mentary landscapes were implemented with GIS by the overlay of the hy-

drology layer and the slope break model derived from the digital elevation

model (DEM) (Fig. 2) (Papers I, II and IV). Then, relationships between

landscapes were analysed by matter transport models, such as run-off and

watershed models derived also from the DEM (Fig. 2).

Elementary landscapes that are geochemically related by material trans-

port (landscapes in a watershed, or areas at recharge and discharge regions of

the same groundwater system, for example) form a geochemical landscape.

Geochemical landscape analysis studies geochemically „dependent‟ land-

scapes where the geochemistry of the area such as stream segments is influ-

enced by another landscape such as an area with mineralisation up-slope. In

this study a run-off model derived from the DEM was used for calculation of

surface matter transport vectors (Fig. 2).

Elementary landscapes were used to define geochemical barriers. These

were classified based on matter transport directions at elementary landscape

boundaries, and the geochemical character of landscapes determined by their

acid neutralising capacity and redox conditions on either side of the barrier

(Perelman, 1986) (see Fig. 3). For example, in the eluvial landscapes, char-

acterised by podzol soils underlain by granitic glacial till, groundwater is

likely to be oxidising, and in the super-aqual mire landscape it is reducing,

while active stream segments are also oxidising environments in the study

area (Figs 1, 2 and 3). This information was added to the attributes of ele-

mentary landscape polygons in the GIS database (Fig. 2). Then, the elemen-

tary landscape map was overlaid with the run-off model using GIS technol-

ogy in order to define flow direction at landscape boundaries (Fig. 2). Ac-

cordingly, the oxidising active stream segment receives weakly acidic (pH 3-

6.5) reducing gley water from the neighbouring mire and an oxidising barrier

is likely to form in the receiving stream sediments with an expected element

association of Fe, Mn and Co, for example (an A6-type barrier; see Perelman

18

1986) (Fig. 3). Where the water flow direction is just the reverse, that is

weakly acidic oxidising water from glacial eluvial landscapes discharges into

the reducing gley mire environment a reducing gley barrier forms (C2-type;

Perelman 1986) (Fig. 3).

Figure 2. A. Grey shading shows elementary landscapes derived from the hydrology layer (Fig. 2C) and the slope-break model (Fig. 2B). Geochemical landscape flow is also shown derived from the watershed (Fig. 2D) and run-off (Fig. 2E) models. The map shows elementary landscapes and their relationships by matter flow lines. Solid box: area shown in Fig. 3A. B. and C. Slope-break model derived from DEM and hydrology layer, respectively. D. and E. Watershed and run-off models derived from DEM, respectively.

19

In addition, adsorption is a major process for metal retardation in organic-

rich peat sediments, hence complex reducing gley-adsorption barriers form.

The landscape geochemical base map obtained in this way shows areas ho-

mogeneous with respect to geochemical flow patterns, their relationship by

surface and subsurface transport processes within and between geochemical

landscapes and matter flow control elements, such as geochemical barriers

(Fig. 3). The landscape geochemical base map was used for the selection of

sampling sites in this study.

4.2 Sampling and analytical procedures

Twenty-three stream sediment samples were collected with a Plexiglas

sediment-corer of 5 cm diameter. All samples were collected during Spring.

Each sample represented a composite of the loose, upper 10-15 cm of the

sediment column. At locations where the sediment was more compact and

SO4

2-

reduction was evidenced by dark colour and H2S odour, the core was

dissected into 5 cm sections and the uppermost (0-5 cm) and the lowermost

sections (25-30 cm) were reserved for separate analysis. Fifteen peat cores 5

cm in diameter were also taken using a hand-held steel tube corer in the wet-

land. Altogether fifty 10 cm long samples were selected 30 cm apart from

each other in the cores for chemical analysis. The uppermost 10 cm sections

of the cores were not selected for analysis in order to eliminate the effect of

atmospheric metal contamination. All samples were extruded into polyethyl-

ene bags in the field and transported to the laboratory on ice. Water pH was

measured in the field with a Beckman 21 pH meter. Loss-on-ignition (LOI)

was determined on sediment samples dried at 105 C for 48 hours and subse-

quently ignited at 550 C for 6 hours.

Samples were stored at +4 C until laboratory analysis. All samples for

chemical analysis were sieved with a stainless steel sieve of 2 mm mesh size.

The samples were thoroughly homogenised and 6 g test portions of each

sample were weighed into centrifuge tubes. The selective chemical extrac-

tion procedure based on the method of Tessier et al. (1979) was applied as

described in Table 1.

All extractions were made with chemicals of analytical grade or better.

Between each extracting steps the separation of solution and particulate mat-

ter was obtained by centrifugation at 3000 rpm for 15 minutes. The super-

natant was pipetted into glass tubes and acidified to pH<2 with concentrated

HNO3. The residue was rinsed with a small amount of deionised water (10

ml) and centrifuged for another 15 minutes. This second supernatant was

discarded. Analysis of test solutions for Pb, Ni, Cu, Zn, Fe and Mn was con-

20

ducted by flame atomic absorption spectrophotometry (Varian 1275). Com-

plementary to chemical extractions, XRD analysis was also performed on

selected sediment samples.

Table 1. Description of the applied sequential extraction method.

Pore water Sediment pore water was obtained by centrifugation of sediment

samples at 3000 rpm for 15 minutes.

Fraction 1 exchangeable metals and metals bound to carbonates The solid resi-

due of the pore water extraction was leached with 1 M ammonium-

acetate (NH4OAc) adjusted to pH 5 with acetic-acid (HOAc) at room

temperature for 6 hours under continuous agitation.

Fraction 2 metals bound to Fe and Mn hydroxides (reducible phase) The residue

from (1) was leached with 0.3 M sodium-dithionite (Na2S

2O

6) added

to 0.175 M sodium-citrate (NaC6H

5O

7) at pH adjusted to 4.8 with

HOAc. Continuous agitation was maintained for 6 hours at room

temperature.

Fraction 3 metals bound to organic matter and amorphous sulphides (oxidisable

phase) The residue from (2) was extracted with 0.02 M nitric-acid

(HNO3) mixed with 30% hydrogen-peroxide (H

2O

2) in 3:5 proportion

at pH 2 and heated gradually to 85°C with occasional agitation for 5

hours. After cooling 3.2 M NH4OAc was added in 20% HNO

3 and

double volume deionised water, and then kept under continuous agi-

tation for 30 minutes in order to prevent adsorption of extracted met-

als on oxidised sediments.

Fraction 4 residual metals The residue from (3) was digested with hot aqua regia

(HCl:HNO3 3:1) at boiling temperature for 1 hour, then the samples

were left to cool overnight.

Partitioning of metals between the various fractions should be considered as

operationally defined by the extraction procedure rather than a reflection of

scavenging by a specific mineral phase (Kheboian and Bauer, 1987; Rauret

et al., 1989; Nirel and Morel, 1990). Also, regarding the selectivity of the

extractants (Tessier et al., 1979; Bodek et al., 1988; Karlsson et al., 1988)

much care should be taken in the interpretation of the results. Low selectivity

has been reported for NH4OAc in fraction 1 (Gibbs, 1977) and in this study

no effort was made to extract the carbonate-bound fraction separately. No

attempt was made to separate metals adsorbed by different forms of organic

matter (Karlsson et al., 1988). Uncertainty in the metal content of the oxidis-

able phase (fraction 4) arises from the fact that some humic and fulvic acids

are extremely stable and the oxidation of all forms of organic matter may not

be complete. It is noted also that only 80-90% of residual metals are ex-

tracted by hot aqua regia (Alloway, 1990).

21

4.3 Data analysis and modelling

In this study, geochemical processes were studied as stochastic processes

and statistical tools were used for data analysis. Univariate exploratory data

analysis (EDA) techniques (Tukey, 1977; Hoaglin et al., 1983) followed by

multivariate statistical analyses, such as cluster and Q-mode principal com-

ponent analysis, were used to investigate element distribution patterns, geo-

chemical abundances and gradients in the sediments and to identify sample

populations. Correlation analysis and canonical correlations were applied to

investigate significant relationships among the variables measured. All cor-

relations were visually controlled and robustness of regression analysis was

assured by interactive outlier rejection. Throughout geochemical modelling

the use of robust and non-parametric techniques were preferred to classical

statistical methods because geochemical datasets are often characterised by

small sample sizes, multi-modal populations, outliers and non-normality

(Kurzl, 1988).

22

5 Sampling design based on geochemical landscape modelling

Sampling had a double-fold objective. In the mire, the objective was to re-

construct the spatial extent of AMD contamination plume and analyse the

controlling processes of metal transportation and retention. In the stream, the

goal was to investigate trace metal attenuation in stream sediments. Samples

representative of processes at the stream-mire oxidising barrier were col-

lected regarding the heterogeneity of this environment. For the design of

sample locations the local geochemical flow patterns were analysed by the

classification of elementary landscapes. The permanently water-saturated

wetland was classified as a reducing gley super-aqual landscape, and the

neighbouring areas as eluvial-accumulative landscapes (Glazovskaya, 1963;

Perelman, 1972) (Fig. 2). The active stream channel was classified as a

trans-aqual landscape and the stagnant fen water as an aqual landscape.

Using the DEM constructed by triangulated irregular network interpola-

tion the watershed, slope- and slope-break, and the run-off models were de-

rived, as shown in Figure 2. By the overlay of the classified elementary land-

scape map geochemical relationships between adjacent landscapes could be

analysed and geochemical barriers were classified as shown in Figure 3.

Landscape geochemical barriers were classified as complex reducing gley-

adsorption barriers where slightly acidic groundwater is discharging into the

water-logged mire sediments (Perelman, 1986). The wetland stream sedi-

ment was classified as a complex oxidising-adsorption barrier (A6-G6) ac-

cording to Perelman‟s classification scheme (Perelman 1986). This forms

when weakly acidic (pH 3-6.5) reducing gley water encounters relatively

oxidising geochemical conditions, and is characterised by the retention of

trace metals by adsorption on, or co-precipitation with, oxides and/or by

organic matter chelation. However, considering the vertical redox profile

likely to build up in the stream sediment column, the landscape geochemical

model of trace metal retention in the upper surface sediments can be refined,

as shown in Figure 3 (Paper I).

Based on the objectives of sampling and prior landscape geochemical model,

sediment-sampling locations were identified in the active stream channel

downstream to the waste rock dumps, in the effluent ditches and in the fen.

23

Figure 3. Sampling design with landscape geochemical methods and GIS process-ing. A. Landscape geochemical base map showing elementary landscapes of the study area, surface matter flow directions and classified landscape geochemical barriers. Locations of mine shafts and waste rock dumps are shown by the “■” sym-bol, stream sample locations by the “▲” symbol and locations of peat sediment cores in the mire by “●”. Drainage ditches in the mire are indicated by dotted lines. B. Landscape geochemical cross section indicating matter transport directions and patterns within and between landscapes (MMC: main migration cycle; LGF: land-scape geochemical flow; ELF: extra landscape flow). C. Conceptual model of land-scape geochemical barriers in the aqual landscape. Locations of the landscape sec-tion are shown in A. Sections 1 and 2 are located in the active stream channel and section 3 in the stagnant mire surface water (fen).

24

By the superimposition of potential metal contamination sources, such as ore

mines and waste rock dumps, the path of AMD from sources to end at com-

plex reducing hydrogen sulphide-adsorption barriers was traced (Fig. 3).

Although the water flow model is based on surface transport directions, rela-

tive homogeneity of the hydrological properties of the aquifer over the study

area was assumed and the surface run-off directions could be regarded as

reasonable approximations of groundwater flow as well.

Horizontal sampling in the mire density was higher (5 m separation in the

groundwater flow direction) close to the edge of the mire in order to detect

abrupt changes in concentration gradients due to spatially separated proc-

esses. Samples further along the flow direction were placed 10 m apart. All

samples were located 15 m apart from each other parallel to the edge of the

mire (Fig. 3). Vertical sampling density was designed to characterise geo-

chemical conditions in the peat profiles above, at and below the groundwater

fluctuation zone representing oxidising, fluctuating oxidising-reducing and

permanent reducing conditions (Fig. 3) (Paper II).

25

6. Results

6.1 Geochemical abundances

6.1.1 Stream

Robust summary statistics of metal concentrations in each fraction in the

stream sediments are listed in Table 2. According to the enrichment factors,

calculated as ratios of metal concentration in each fraction to the sums of

fractions 1 to 4 in the sediment, metals are mostly abundant in the oxide-

bound fraction (Fe 70-80%, Ni 50-60%, Pb ~40%, Zn ~30%), except for Cu

and Mn. This indicates the importance of the reducible fraction in the sedi-

ment studied. The higher proportion of Mn in fraction 1 (between 50-60%)

and the variability of Fe in the oxide-bound phase suggest that redox condi-

tions are heterogeneous and Mn is probably in its reduced form.

Organic matter, beside oxides, is a major sink for trace metals in the wet-

land stream, which is reflected by the high relative concentrations of metals

in the oxidisable phase, particularly for Cu (50-60%) and Pb (30%);

whereas, the residual fraction accounts in all cases for less than 20% of the

total metal content.

Stem-and-leaf displays, frequency histograms and box-plots of the data

sets showed polymodal behaviour with positively skewed populations and

were characterised by the presence of less than 20% outlying values, as de-

fined by the inner-fence criteria (Tukey, 1977), as shown in Figure 4A. Dis-

tribution shapes were similar for Fe and Mn in all fractions. For both ele-

ments, high frequency of values occurred at the lower limit of the range,

representing sediment samples from the stagnant surface water, and the dis-

tributions approximated the shape of a lognormal distribution. In order to

stabilise variability, the original variables were re-expressed by log-

transformation for further analysis (Velleman and Hoaglin, 1981). Cluster

analysis (CA) and Q-mode principal component analysis (PCA) of the log-

transformed data helped to further explore data structures in the multidimen-

sional space. Using the average linkage method and the Euclidean-distance

as a measure of similarity in the cluster analysis, Fe and Mn samples sepa-

rated into two groups representing sediments in the active stream channel

and sediments in the stagnant water areas.

26

Table 2. 5-number summaries of the measured variables in the stream sediments.

Metal concentrations in the fractions are in ppm. Loss-on-ignition (LOI) is in % of

dry (105 ºC) sample. Sample size: 23. Refer to Table 1 for a description of the ex-

traction method used to leach each fraction.

5-number summaries Fraction 1 Fraction 2 Fraction 3 Fraction 4

Pore water LOI

Pb Minimum 0.03 2.66 0.73 1.73 0 8.61

Lower quartile 0.63 3.66 1.93 1.96 0 44.88

Median 1.47 5 3.3 2.16 0 74.5

Upper quartile 3.13 6.99 4.06 2.53 0 88.98

Maximum 3.73 13.32 7.99 3.93 0.07 97.1

Ni Minimum 0.43 2.96 0.23 0.83 0

Lower quartile 2.03 9.99 1.83 1 0.025

Median 3.83 13.65 3.56 2 0.045

Upper quartile 5.66 20.65 5.43 3.93 0.01

Maximum 17.55 38.63 92.91 6.63 0.58

Cu Minimum 0.1 0.67 1.33 0.63 0

Lower quartile 0.7 1.43 4.66 0.93 0.02

Median 1.17 5 11.32 1.53 0.045

Upper quartile 2.2 10.32 30.3 1.9 0.1

Maximum 219.78 93.24 266.4 30.3 0.45

Zn Minimum 3 4 8.66 5.33 0

Lower quartile 10.99 20.65 21.98 10.99 0.08

Median 23.98 43.29 32.97 17.98 0.11

Upper quartile 54.61 106.56 45.29 34.3 0.53

Maximum 5661 899.1 702.63 40.29 2.9

Fe Minimum 1.33 85.25 3.33 13.99 0.15

Lower quartile 4.93 166.83 43.29 39.63 0.26

Median 104.23 1132.2 131.54 92.24 0.52

Upper quartile 278.06 2763.9 322.68 263.07 1.22

Maximum 1798.2 12187.8 648.35 1964.7 14

Mn Minimum 1.37 0.37 0.17 0.1 0.01

Lower quartile 4.6 1.03 0.37 0.53 0.28

Median 18.65 5.59 1.07 1.9 0.47

Upper quartile 41.29 24.98 1.93 6.99 0.95

Maximum 356.31 76.92 4.36 22.98 1.55

27

The observation that Fe and Mn appear to be bimodally distributed sug-

gests that Fe and Mn can selectively describe the redox conditions of the

system. In the PCA analysis of Fe fractions, shown in Figure 4C, the same

groups were apparent in the plane of the first two principal components

while no similar groups could be separated for the other metals.

Figure 4. Univariate and multivariate exploratory data structure analysis of iron in stream sediments. A. Frequency histogram of log-transformed iron data in the re-ducible phase. Note the separation of populations. B. Box-and-whiskers plot of original iron data in the reducible phase. Outlier and far-outliers are indicated by the symbols “□” and “”, respectively. C. Principal components analysis of log-transformed iron data. Mire- and stream surface water sediment samples are shown as solid boxes and empty circles, respectively, and numbers from 1 to 4 indicate the four metal fractions (see Table 1).

6.1.2 Mire

According to the data in Table 3, most of the Cu (around 50% of the total

concentrations) is found in the oxidisable phase demonstrating its high affin-

ity for organic and/or sulphide complexation. Manganese is probably in its

reduced Mn(II) state which explains its higher proportion in the exchange-

able and water soluble phases (70-80% of total concentrations). Ni is also

abundant in the exchangeable-phase (around 30% of total) indicative of its

mobility in the peat environment. Iron is mostly found in the reducible phase

(70-80% of total) which is somewhat surprising in an acidic reducing gley

environment. This can be explained, however, by the improper selectivity of

28

the extraction procedure 3 (see below) and the existence of resistant crystal-

line iron oxides which is supported by the high concentrations of iron in the

residual phase. All the metals are greatly enriched in the exchangeable or

water soluble phase in samples close to the AMD discharge showing the

location and extent of contamination.

Table 3. 5-number summaries of the measured variables in the peat sediments. Metal concentrations in the fractions are in ppm. Loss-on-ignition (LOI) is in % of dry (105 ºC) sample. Sample size: 50. Refer to Table 1 for a description of the ex-traction method used to leach each fraction.

5-number summaries

Pb Ni Cu Zn Fe Mn

Water soluble Upper eight 0.04 0.9 1.82 3.14 0.86 0.54 Upper quartile 0.02 0.58 0.98 1.38 0.34 0.42 Median 0 0.17 0.38 0.66 0.25 0.1 Lower quartile 0 0.04 0.16 0.28 0.16 0.02 Lower eights 0 0.04 0.06 0.18 0.12 0.02 Fraction 1 Upper eight 2.59 3.61 36.4 62.65 3.4 2.77 Upper quartile 1.16 3.08 9.8 36.75 2.14 2.14 Median 0.335 2.295 0.37 11.375 1.35 1.33 Lower quartile 0.11 0.18 0.18 5.6 0.77 0.95 Lower eights 0.04 0.07 0.14 3.15 0.6 0.6 Fraction 2 Upper eight 4.55 8.4 26.5 37.1 130.9 0.77 Upper quartile 4.55 6.65 10.85 28 71.05 0.39 Median 3.85 4.2 1.385 17.675 45.5 0.32 Lower quartile 3.5 2.45 1.09 7.7 21.7 0.25 Lower eights 3.5 1.82 0.98 5.95 16.1 0.18 Fraction 3 Upper eight 3.05 1.26 110.95 40.95 13.58 0.21 Upper quartile 2.38 0.95 21.7 31.15 7.74 0.21 Median 1.89 0.65 1.805 20.475 5.235 0.18 Lower quartile 1.51 0.46 0.91 5.25 3.82 0.11 Lower eights 1.23 0.39 0.7 1.58 3.01 0.11 Fraction 4 Upper eight 1.52 0.58 7 14 15.6 0.32 Upper quartile 1.36 0.44 1.9 10.8 6.6 0.2 Median 1.12 0.34 0.58 6.7 3.99 0.09 Lower quartile 1.04 0.28 0.44 4 2.38 0.02 Lower eights 0.92 0.26 0.36 3.6 1.8 0.02 pH LOI Upper eight 4.03 98 Upper quartile 3.87 98 Median 3.74 97 Lower quartile 3.64 95 Lower eights 3.54 89

29

If the AMD input were the only major source of heavy metals in the stud-

ied mire then the ratios of median total concentrations to the groundwater

concentrations would be the enrichment (or accumulation) factors due to

retardation which are in this case in the order:

Fe(530)>Pb(320)>>Cu(32)>Zn(18)>Ni(4.8)>Mn(1.4). The enrichment of

iron is hardly feasible since it is mostly mobile in reducing peat sediments

(Henrot and Wieder, 1990). Although Pb has high affinity to organic matter,

it is difficult to explain how it can have a much higher enrichment factor

than the even more strongly-bound Cu (Alloway, 1990) which is more than

100 times more abundant in the discharging AMD. Clearly, Fe and Pb have

an other, probably common, source, too, which is also suggested by their

relatively high concentrations in the residual phase. Manganese seems to

migrate with groundwater almost conservatively in the peat sediments.

In order to study the univariate structure of variables and obtain informa-

tion about their populations, stem-and-leaf displays were constructed for

each variable and outliers and far-outliers were identified using the robust

inner-fence outer-fence criteria (Tukey, 1977). Separation of populations in

the geochemical data was enhanced by the use of Sinclair's graphical method

based on probability graphs (Sinclair, 1974). For copper, as shown in Figure

5, two widely separated populations are apparent: the one with smaller val-

ues contains sample locations further away from the AMD discharge loca-

tion („background population‟) and the other all consists of higher or outlier

values and cover samples close to the AMD discharge location („anomalous

population‟) (Figs. 5A, 5B and 5C). This suggests that there are basically

two spatially separated regions in the course of Cu migration in which dif-

ferent processes are responsible for Cu distribution. (Obviously, if one proc-

ess or group of processes in a laterally homogeneous matrix, such as peat

could be assumed, one population would result.) Water soluble and ex-

changeable phase variables were most skewed probably representing the

effect of contaminant plume while residual phase distributions were most

symmetric for all metals. Symmetric distributions in the residual and oxidis-

able phases for Pb, Mn and Fe indicate that these metals are uniformly dis-

tributed in the sampled mire region because they represent the geochemical

background dominating in the time of mire formation.

Separation of populations in the multidimensional variable space was

studied by CA and PCA methods using log-transformed data (Howarth and

Sinding-Larsen, 1983). Cluster analysis for Cu using Euclidean distance as a

measure of similarity and average linkage method also confirmed the separa-

tion of a background cluster (n=32) and an anomalous one (n=18) with a

sub-cluster for samples at 20 cm depths in the latter which readily merged

into the background cluster at higher similarity levels. This result implies

that these samples are geochemically closer to the background cluster, which

30

supports indirectly the hypothesis that in the background only organic matter

controls Cu retention similarly to the relatively oxidising anomalous samples

at shallow depth. In the anomalous population strong sub-clusters were ap-

parent showing the higher inhomogenity of this region. In the plane of the

first two principal components (93% of the total variance) the same groups

and sub-groups became apparent (Fig. 5D). If water soluble phases represent

the propagating plume, then it is obvious from the figure that separation of

samples is due to differences in the adsorption-retardation processes repre-

sented by the first principal component. Although the presence of sub-

populations was observed for the other metals too, distinct separation such as

for Cu was not so obvious.

6.2 Geochemical gradients

6.2.1 Stream

The spatial heterogeneity of the barrier studied could be analysed using star

symbol plots (Chambers et al., 1983) of the Fe fractions. Star symbol plots

for samples in the active stream channel were similar in shape indicating

similar geochemical conditions (Paper I). The relative concentrations were

higher in the stream which reflect the importance of Fe in this environment

indicated in the plots by the dominance of the oxide-bound, organic-bound

and residual fractions. Deviations from this pattern indicate heterogeneity of

the stream sediments geochemical barrier. Separation of samples represent-

ing sediments in the active stream channel and sediments in the stagnant

water areas are also apparent in map (Paper I).

6.2.2 Mire

In the mire, horizontal metal distributions were studied by the construction

of isoconcentration maps for each fraction of each metal in three horizons

representing peat sediments above, at and below groundwater table fluctua-

tion (10-20, 40-50 and 70-80 cm below mire surface, respectively). As

shown in Figure 5F, it was possible to reconstruct the shape of the plume by

the collected samples, verifying the appropriateness of the sampling design.

It can also be seen that the highest gradient for copper is within about 10 m

from the AMD discharge location. The typical 'plume-shape' pattern similar

to that shown in Figure 5F was found in all phases and depths for Cu and to

a lesser extent for Ni and Zn, but was virtually absent for the other metals

with exceptions of the exchangeable phase for Mn, Fe and Pb and oxidisable

phase for Pb. Uniformly for all metals at all depths, the water soluble phase

and to a lesser extent the exchangeable phase provided the best means to

31

Figure 5. Geochemical modelling of metal behaviour in the studied mire demonstrated

on Cu. „Background‟ and „anomalous‟ samples are shown as empty circles and solid

boxes, respectively, and numbers from 1 to 5 in the figure indicate the five metal frac-

tions. A-C. Univariate exploratory data structure analysis. A. Quantile plot for Cu in

the oxidisable phase. B. Box-and-whiskers plot for Cu in the oxidisable phase. C.

Frequency histogram for Cu in the oxidisable phase after log-transformation. D. Com-

bined Q- and R-mode PCA display for Cu. Samples in the 'anomalous' population at

depth 10-20 cm are marked with 'x'. E-G. Spatial analysis. F. Horizontal distributions

of total Cu (fraction 1+..+5) at different depth as shown by contour lines using kriging

32

study the spatial distribution of the contaminant plume by yielding distribu-

tion patterns similar to that shown in Figure 5F. For copper, the gradients

were far higher in the anomalous population at all depths and phases com-

pared to the background population showing that copper retention is more

efficient in the first 10 m of contaminant propagation. Also, gradients were

higher in the anomalous population for oxidisable phase-bound Cu below

groundwater table indicating that copper is retained more efficiently in this

phase.

Vertical profiles for copper, nickel and ash content in the anomalous

population show a characteristic peak at depth 40-50 cm probably as a result

of remobilisation and deposition of reduced forms at and below the ground-

water fluctuation zone (Pakarinen et al., 1980) (Fig. 5G). The vertical profile

of Cu in peatlands following ash content was observed in other wetlands, too

(Sillanpää, 1972). This peak is absent in the background population (Fig. 5E)

which implies that similar remobilisation processes are insignificant further

away from the AMD input location. Iron has its highest concentration at 10-

20 cm depth and drops with depth because iron is in its mobile reduced

Fe(II) form below the groundwater table and it is likely to move along con-

centration gradients or be uplifted by plants to the oxidising top sediments.

6.3 Geochemical processes

Associations among metals were studied by means of the Pearson‟s correla-

tion analysis of log-transformed data. Where it was geochemically justified,

correlations were subjected to partial correlation analysis in order to correct

for possible induced correlations caused by a common variable. All correla-

tions were checked for significance at the 0.05 significance level and were

graphically examined to avoid spurious correlations. Correlation coefficients

are given in brackets in the discussion below. Potential associations were

first identified by the analysis of Spearman correlation matrices and con-

struction of Draftsman's plots (Chambers, 1983). All the correlations (Pear-

son linear correlation coefficient) were graphically examined to avoid spuri-

ous correlations. Variables were log-transformed to stabilise their variance

and robustness was achieved by the use of interactive outlier rejection. No

more than 10% of the samples were excluded in any case but in the majority

of cases it was below 5%. Pairwise regressions discussed were all significant

at the 0.05 level.

(continued) interpolation. Dark arrow shows AMD discharge into the mire. E and G.

Typical vertical profiles of partial abundances of Cu in the peat sediments in the

„background‟ and „anomalous‟ populations, respectively.

33

6.3.1 Stream

Correlation analysis for metals in the pore water of the stream sediment re-

vealed strong association between Ni and Zn (0.93), and Cu and Zn (0.85).

These elements are associated with the mineralisation and hence this fraction

could most probably reflect contamination caused by mining. In fraction 1

the correlation coefficients between Fe-Mn (0.91) and in the surface sedi-

ments also for Zn-Ni (0.82) suggest common pools for these metals. No cor-

relation with pH is apparent in this fraction, but the strong correlation be-

tween Fe and Mn and their respective strong correlation with reducible Fe

(0.92 and 0.94) suggest that they are specifically adsorbed on Fe oxy-

hydroxides. The lack of any correlation of Cu and Pb, as well as their low

enrichment in this fraction, suggests that these metals are preferably more

strongly bound in the sediment. The strong correlations of Fe with Pb (0.81),

Cu (0.84), Mn (0.89) and a somewhat weaker correlation with Ni (0.59) in

fraction 2 are indicative of Fe oxy-hydroxide scavenging. Reducible Fe and

associated metal concentrations are invariably lower in the more reducing

mire surface water sediments than in the stream sediments. Although, clus-

ters of mire and stream sediment samples can be recognised, they fall on the

same regression line for Pb (see Fig. 6A) and Mn. In the cases of Cu (Fig.

6B) and Ni, however, mire surface water sediment samples are displaced

away from the stream population regression line and do not show correlation

with Fe. A similar pattern was observed for Zn as for Ni and Cu, but was

blurred by the higher scatter of data points, thus did not lead to significant

correlation.

A significant correlation was observed between Pb and Cu (0.72) in frac-

tion 3 in most samples (Fig. 6D). This result suggests that oxidisable Pb and

Cu are extracted from a common pool. Regarding their high affinity for

complexing with organic ligands (Alloway, 1990) these metals are probably

retained by organic matter chelation in this fraction which is in accordance

with the high stability constants of Cu and Pb organic complexes (Stumm

and Morgan, 1981; Bodek et al., 1988). Based on this, one would expect

strong correlation for Pb and Cu with LOI that, however, was not found in

our case. On the other hand, Zn had the second largest concentration in this

fraction and showed positive correlation with LOI (0.72). One explanation of

these results could be that in the investigated size fraction LOI mainly repre-

sents the chemically inert, unprocessed organic debris (Tessier et al., 1980).

Taking into account that Zn has the highest biological accumulation coeffi-

cient (BAC) among the metals studied (Ni(0.03), Cu(0.13), Zn(0.9),

Fe(0.012), Mn(0.4); Brooks, 1973; Perelman, 1975) it is plausible that Zn in

this fraction is extracted from the unprocessed organic debris and, therefore,

represents metal uptake by plants. This would also explain the lack of corre-

34

lation between Pb, Cu and LOI if the amount of decomposed organic matter

is assumed to be subtle compared to the unprocessed organic remains.

Figure 6. Correlation analysis of log-transformed data (A, B, D and E). Mire- and stream surface water sediment samples are indicated by solid boxes and empty cir-cles, respectively, where relevant. Outliers rejected from the regression are marked with crosses. C shows logKa vs. pH for the Fe hydrous oxide system where solid lines refer to the limits compiled by Tessier et al. (1985). Symbols refer to data from the present study. F shows the canonical correlation between metals in fractions 1 and 3.

In the residual fraction (fraction 4) Fe and Pb (0.83) (Fig. 6E) and Fe and Mn

(0.89) were strongly related to each other. Regarding these element associa-

tions and correlations of Fe and Mn with the ash content (0.97 and 0.94,

respectively) it is supposed that the residual fraction represents the natural

geochemical background. Mainly minerals, such as feldspar, quartz and

hornblende, as shown by the XRD analysis, and perhaps amorphous oxides

and clay minerals characterise the composition of the residual fraction.

Moreover, the lack of association of Ni, Cu and Zn in the residual fraction

might further support our earlier hypothesis that these metals originate from

contamination by ore mining in the geochemical landscapes studied in this

research.

35

6.3.2 Mire

In the mire, the high negative correlations between pH and Ni (-0.76), Mn

(-0.76) and Zn (-0.63) in the water soluble phase suggest that the mobility of

these metals is most dependent on pH in peat sediments which is in accor-

dance with their higher relative concentrations in this (and exchangeable)

phase compared to the other phases. The strong association of Ni, Cu, Zn

and Mn as expressed by their mutually high correlations (between 0.72 and

0.90) in this phase shows that their main common source is the AMD dis-

charge. Direct relationship between exchangeable metals and pH was not

evidenced by correlation analysis. Negative correlations between pH and

oxidisable Pb (-0.75), Cu (-0.56) and Ni (-0.54) were found only for samples

identical to the background population of copper. The correlation can be

explained with the pH dependent 'hydrolysis + adsorption' model (Stumm,

1987) represented in a general form (M: metal):

(H2O)

5M(OH

2)

z+

+ SOH = SOM(H2O)

5

(z-1)

+ H3O

+

(1)

More interesting is the absence of similar correlations in the anomalous

region. This shows that processes regulating metal concentrations in the

oxidisable phase here do not influence or depend on pH directly. Partial cor-

relation on water soluble and oxidisable fractions and pH caused the elimina-

tion of the related correlations which means that these variables are mutually

interrelated in the studied peat system. Correlation between ash content and

metals in the anomalous samples in oxidisable phase was found only for Cu

(0.87) which means that ash content represents primarily oxidisable phase

Cu in this area. The pH dependence of the water soluble phase and the oxi-

disable phase metals and the absence of correlation with exchangeable met-

als suggest that metals are either in the pore solution or form strong inner-

sphere complexes with organic ligands. That is the exchangeable outer-

sphere positions are relatively unimportant in the adsorption process at the

given pH. This may imply that metal adsorption to organic substances is

kinetically fast. In the operationally defined residual phase (fraction 4) sig-

nificant correlation were found between Ni and Cu (0.63), Ni and Fe (0.64)

and Pb and Fe (0.54). The strong negative correlation (-0.71) between pH

and ash content in the background shows that metals in pH dependent phase

are most responsible for the ash content in this region. Considering the re-

sults discussed above, this implies that the ash content for these samples

represents organically bound metals mainly. The absence of similar relation-

ships in the anomalous sample group shows, in accordance with the finding

in fraction 3, that high ash content and metal retention is mainly caused by a

process related to the oxidisable phase in association with Cu. High canoni-

cal correlation was found only for the oxidisable phase (r=0.91) confirming

our hypothesis of previous modelling that retention of metals from solution

36

is primarily due to organic adsorption. The canonical correlation coefficient

is far higher than any individual correlation justifying the use of canonical

variables. It is interesting that samples from both the background and

anomalous region are scattered along the same line indicating that complexa-

tion by organic matter is the main process even in the latter population.

37

7 Geochemical landscape analysis for AMD impact assessment

7.1 Stream environment

Enrichment factors of metals, as well as results of the correlation analysis

confirm the importance of the reducible and the oxidisable phases in control-

ling metal speciation (Fig. 7). Since Mn does not show correlation with any

of the metals except for Fe, it is most probably found occluded in Fe oxy-

hydroxides and does not form a separate phase. Correlations indicate that Pb

and Mn are controlled by co-precipitation in Fe oxy-hydroxides both in the

stream and mire water sediments. Cu and Ni distribution, on the other hand,

can be related to Fe oxy-hydroxides only in the stream sediment samples. As

these metals are primary constituents of the ore mineralization and are also

characterised by associations in the pore water phase, in this fraction they are

regarded as recent contamination originating from ore mine waste rocks.

Therefore, the organic rich wetland stream sediment can be regarded as an

oxidising-adsorption barrier with respect to these metals, as it was hypothe-

sised in our prior model.

Taking into account these constraints and also that rigorous equilibrium

conditions required for thermodynamic calculations of metal adsorption

could not be assumed we applied the adsorption equations proposed by Ben-

jamin and Leckie (1981a,b) to further investigate the sediment samples. Ad-

sorption density ( ) for iron oxy-hydroxides and apparent equilibrium con-

stants (Ka) were calculated and compared to data on trace metal adsorption

on iron oxy-hydroxides compiled by Tessier et al. (1985). The calculations

for Cu, shown in Figure 6C, suggest also that Cu in the reducible fraction in

the stream sediment could only be controlled by adsorption on Fe oxy-

hydroxides. This is coherent with the results of the correlation analysis. Cal-

culations provided similar results for Zn but not for Ni.

Regarding organic matter in the sediments, its chemical activity can be

significantly different within the investigated size fraction due to the pres-

ence of both the decomposed, chemically reactive humic substances and the

chemically inert, unprocessed organic debris. However, fraction 3 could also

represent amorphous sulphide compounds. The formation of metal sulphides

38

is dependent on the availability of reducible sulphur and reactive metals, low

redox potential, pH influencing the solubility of sulphides, and microbial

activity of sulphate reducers for which decomposable organic matter and

circum-neutral pH are required (Rabenhorst and James, 1992).

Figure 7. Global cycle of iron and matter flow control processes. A. Qualitative description of global iron cycle. B. Control mechanisms of iron in landscape systems with emphasis on the fate of iron in AMD, indicated by bold arrow in A. (Compiled after Bigham et al., 1992 and Schwertmann and Fitzpatrick, 1992).

39

It has been found that sulphate reduction is usually restricted to microsites in

sediments where microbes can easily create living conditions favourable for

their activity (Pakarinen et al., 1980; Shotyk, 1988; Rabenhorst and James,

1992). In a tidal marsh, for example, it was found that sedimentary oxides

and sulphate reduction with subsequent formation of pyrite coexisted in the

same macro-environment (Rabenhorst and Haering, 1989). Thus the exis-

tence of oxidised forms extracted in fraction 2 does not exclude the possibil-

ity of sulphate reduction. In the sediments studied, metals and sulphate could

be available from the discharging AMD and pH (pH is around 6 on average

in the stream water) and decomposable organic matter in the sediments

would not limit metal sulphide formation. The characteristic H2S smell and

black colour around organic debris could be observed in some of the sedi-

ment samples in the field, these evidence that sulphate reduction does occur

locally but probably does not dominate organic matter decomposition in the

sediment.

Recent metal contamination caused by mining would appear in the labile,

surface-bound positions of the sediments, and its spatial distribution could be

studied by element associations in the pore water and exchangeable phases.

In the pore water fraction samples representing the stagnant surface water

sediments fell on the same regression line as those of the stream sediment

samples, metal concentrations being systematically higher in the mire pore

water fraction. This indicates that metal input reaches the stream from the

neighbouring landscapes and the spatial distribution pattern of metals in the

sediments does not result from a single upstream source. Potential pools for

the retention of metals in fraction 1 could be outer-sphere positions on min-

erals, such as clays, as well as organic matter surfaces and structural posi-

tions in carbonates. It is known that most of the outer-sphere positions in the

sediments are occupied by alkaline and alkaline earth metals, H and Al,

which exist in concentrations greater than trace metals in the stream water

(Stumm and Morgan, 1981).

Canonical correlation analysis was used to analyse the interaction of sev-

eral mutually related factors to model more realistically adsorption processes

in the sediments studied. High canonical correlation (0.91) was found be-

tween metals in fraction 1 and fraction 3 (Fig. 6F). The canonical correlation

coefficient is higher than any individual correlations justifying the use of the

canonical variables. Assuming that metals in fraction 3 are adsorbed on or-

ganic matter, this indicates that probably organic matter controls metals in

the exchangeable phase.

40

7.2 Mire environment

Results show that precipitation of insoluble Cu sulphides is a major process

close to the location of contaminant discharge while adsorption by organic

matter alone may control metal distribution further away along the migration

path. There are basically three limiting conditions for sulphide formation in

peat sediments: (1) reduction of SO4, for which, in turn, sulphate in suffi-

cient quantities and appropriate biogeochemical conditions are needed; (2)

the quantity and availability of reactive metal forms and (3) the pH depend-

ent solubility of metal sulphides (Boyle, 1977; Brown, 1985; Rudd et al.,

1986; Bodek et al., 1988). In our case it seems that the quantitative require-

ments are satisfied because there is abundant metal and sulphate in the dis-

charging AMD. Reduction of sulphate is conditioned on the presence and

activity of sulphate-reducing bacteria, availability of decomposable organic

matter and low redox potential. While sulphate-reducing bacteria are neces-

sary catalysts for sulphate reduction to occur in peat sediments and require

specific environmental conditions for optimal growth, they can be active in

these acidic peat soils as evidenced by the strong H2S odour of samples be-

low the groundwater table.

However, the high content of Cu in the oxidisable phase, the presence of

two distinct populations in variables of Cu also influencing relationships of

the other metals, the distinction of samples at shallow depth in the anoma-

lous region from samples below the groundwater table for Cu, the pH inde-

pendence of oxidisable Cu in the anomalous population as opposed to its pH

dependence in the background population, all provide indirect evidence that

the most insoluble copper sulphides, probably Cu2S may precipitate closer to

the discharge location. The high ash content of samples in the anomalous

population is in accordance with Lett and Fletcher's (1979) findings that

where microbial SO4 reduction takes place peats are rich in ash. Further sup-

porting evidence is that the H2S gas formation was observable only for cores

in the background region and was absent close to the AMD input.

As Cu is efficiently retained by organic adsorption and sulphide precipita-

tion, the migrating groundwater becomes depleted with respect to copper

within 10-15 meters in its course in the mire (Fig. 5). Beyond this distance

there is little copper sulphide formation, and organic complexation controls

primarily metal retardation. In this region SO4 is also reduced at microenvi-

ronments but the generated H2S remains in gas form in the absence of Cu, as

evidenced by the field observation of H2S only in samples in this area. Here

metals extracted in the oxidisable fraction are bound to organic matter and

give the ash contents which explains the significant correlations between pH

and these variables for samples from this region.

41

Figure 8. Control mechanisms in a mire ecosystem and its relation to the global Cu cycle. Signs „+‟ and „-„ shows positive and negative correlations, respectively. Dashed lines in this part indicate energy flow (R: respiration). The two diagrams are related by their similar organisation as well. Compiled from Cameron et al. (1989), Moor (1989), Butcher et al. (1992) and Schlesinger (1991).

It seems from the analytical results that Fe and Pb were already present in

higher quantities in the peat sediments prior to the beginning of AMD dis-

charge. As shown by the analysis of the reducible and residual phases, they

originate from the background and were transported and deposited in the

42

sediment phase by background surface drainage, probably as Fe/Mn or other

partly crystallised hydrous oxides and in clay minerals. The relatively high

concentrations of Pb in these phases compared to Cu and Ni is consistent

with the fact that Pb is several times more abundant in felsic rocks than in

basic rocks. The high concentrations of these metals in fraction 2 (reducible

phase) may well be the result of the poor selectivity of the extraction proce-

dure but, as we saw the importance of micro-environments in the reducing

gley super-aqual mire landscape, it is not unreasonable to assume the pres-

ence of some oxidised Fe in these sediments, as it was reported to coexist

with SO4 reduction in tidal marshes (Rabenhorst and Haering, 1979).

In order to assess the long-term retention of metals in natural wetlands the

stability of the whole peat sediment system also has to be studied. Although

the naturally high adsorption capacity of wetlands is attractive from a techni-

cal viewpoint for AMD treatment, they are selective towards metals usually

found together in mine waste waters as it was shown by the case study. Most

prominently, mires are extremely vulnerable to dewatering introduced either

by human activity or naturally, such as changes in climate or local drainage

systems (Fig. 8). Drying is not balanced by natural feedback mechanisms

and leads to the irreversible destruction of the mire-peat sediment system

(Moor, 1989; Cameron et al., 1989). Since their metal retention is based

almost exclusively on their reducing waterlogged environments, their long-

term metal retention potential and stability depends only on a single, easily

changeable variable. Upon drying the sediments are eroded and organic mat-

ter and sulphides are fast oxidised and released to the hydrological system.

43

8 Conclusions

- A geochemical landscape analysis method using geochemical modelling,

GIS technology and landscape ecology has been developed for the risk

assessment of heavy metals in the studied sediments (Papers III).

- In accordance with the needs of environmental risk assessment of AMD,

a sequential geochemical mapping procedure has been developed for the

spatial analysis of AMD in stream and wetland sediments in the study

area (Paper IV).

- The analysis of geochemical barriers performed by GIS-based geo-

chemical landscape modelling, in addition to a detailed geochemical

modelling, can contribute to the analysis of environmental geochemical

problems by providing means for the systematic analysis of the relation-

ships between related landscapes (Papers III and IV).

- Application of a sequential chemical extraction procedure could enable

the determination of different metal sinks in surface water and peat

sediments impacted by AMD (Papers I and II).

- Results of data analysis show that heterogeneity of the geochemical bar-

rier in the stream sediment is controlled by redox gradients (Paper I). It

is suggested by the analysis that this landscape geochemical barrier can

be sufficiently characterised by the distribution of Fe fractions, but more

data is needed for the verification of the refined hypothetical models

concerning the occurrence and extent of sulphate reduction in the sedi-

ments. Despite the high overall amount of organic matter in the stream

sediments, Fe oxy-hydroxides are abundant and play major role in the

retention of trace metals by adsorption on their surfaces.

- Mn is probably specifically adsorbed on Fe oxy-hydroxides and, beside

Zn, is least retained in the stream sediment (Paper I). Pb, Cu and Ni are

44

found in considerable quantity in the reducible fraction and are sug-

gested to occur occluded in Fe oxy-hydroxides. On the other hand, or-

ganic matter provides important adsorption sites for Cu and Pb and con-

trols exchangeable metals, too. Ni, Cu and Zn are suggested to represent

the mineralization based on enrichment factor calculation and correlation

analysis in the pore water and the oxide-bound fractions. More specific

investigation of the reducible fraction is required to differentiate be-

tween the natural geochemical background and contamination by min-

ing.

- Where AMD discharges into the studied mire a hydrogen sulphide-

adsorption barrier forms in the peat sediments (Paper II). It was also re-

vealed that under the acidic conditions in the bog only the most stable

copper sulphides may precipitate and control metal adsorption in a rela-

tively short distance of AMD plume propagation (about 10 m). Where

migrating groundwater becomes depleted with respect to Cu, H2S re-

mains in gaseous form and metal retardation is mostly controlled by or-

ganic complexation.

- Results suggest that both the general geochemical characters of the mire

and the metal composition of AMD are important when assessing reten-

tion capacity of wetlands (Paper II). This means that each AMD risk as-

sessment requires individual investigation.

- The evaluation of wetlands for long-term AMD treatment also has to

consider that mires are very sensitive to changes in hydrological condi-

tions and drying of the sediments leads to erosion and hence the release

of adsorbed metals to the environment (Paper II).

45

Summary in Swedish

Surt gruvvatten (AMD) innehållande toxiska element vilka ofta år en huvudor-

sak till mark- och vattenpåverkan inom många gruvområden i Europa. Dessa

kontaminerade områden verkar som sekundära källor för förorening, särskilt

vid platser där gruvbrytningen förekommit under lång tid. Riskbedömning av

försurat gruvavfall och relaterade förorenade landområden kräver ett holistiskt

tillvägagångssätt som gör det möjligt att studera föroreningens omfattning,

emissioner och påverkan på landskapet. Riskbedömningen innefattar följande

steg: (1) en beskrivning av föroreningarnas farlighet, (2) mängd/respons (toxi-

citet) analys, (3) ämnenas transport, (4) exponeringsbedömning, (5) risk karak-

tärisering, och (6) riskbehandling. Föroreningsmodellen i mark och vatten

inkluderar därför studier av steg 1, 3 och 4 längs med källan – väg (transport)

– mottagare (exponering för människor eller ekosystem). Målet med denna

undersökning var att utnyttja en geokemiskt kontamineringsmodell kopplad

till en landskapsanalys för riskbedömning med AMD. Modellering utförs via

källa-väg-receptor kedjan genom utnyttjandet av ett enkelt geokemiskt verktyg

för landskapsanalys som sedan kan användas för att följa planeringen av

markutnyttjandet inom gruvområden. Det specifika målet med den här studien

är att presentera en applikation av geokemisk landskapsanalys med AMD

riskbedömning via en fallstudie i mellersta Sverige. Studien behandlar de

AMD metaller som kan härleds till nedlagda gruvor och deponier och dess

uppträdande i vattendrag och en våtmark vid Slättberg i mellersta Sverige.

Inom detta område, där har försurat gruvvatten påverkat omkringliggande

vattendrag och myrar under ca 70 år.

Inom ramen för undersökningen insamlades tjugotre borrprover får en bäck

som rinner genom området. Varje prov representerade de övre 15-20 cm av

sedimenten i bäcken. Femton borrprov från en närliggande myr har också

samlats in. Totalt har femtio 10 cm långa prover valts belägna 30 cm ifrån

varandra i myren för kemisk analys. Alla prover var insamlade på våren. En

sekventiell extraktionsprocess har använts för att bestämma koncentrationer

av Pb, Ni, Cu, Zn, Fe och Mn som lösligt, utbytbart, reducerbart och oxide-

rande delar. Dessutom bestämdes den organiska andelen (LOI). XRD analys

har också utförts på de valda proverna i syfte att bestämma mineralsamman-

sättningen på sedimenten. Statistiska verktyg har använts för data analys och

modellering. (EDA) tekniker följd av multivariat analys och Q-mode princi-

46

piella komponent analyser, har använts för att bl.a. undersöka fördelnings-

mönster.

Den använda geokemiska modellen och landskapsanalysen för riskbedöm-

ningen av AMD längs med källa-väg-mottagare kedjan har länkats till ett

enkelt geokemiskt landskapsanalysverktyg med användandet av en land-

skapsekologiska rymdanalys.I studie av de geokemiska processer använde

också GIS verktyg. Beskrivningen och klassificerandet av det geokemiska

landskapet har genomfört med GIS med användandet av ett hydrogeologiskt

skikt och ett lutningsskikt (DEM). Den avrinningsmodell som erhölls från

DEM användes sedan för beräkning av ämnestransporten. Denna klassifice-

rades baserat på ämnestransporten och typen på det geokemiska landskapet

bestäms av dess syraneutraliserande kapacitet och redox förhållandet. I un-

dersökningen beskrivs också olika mekanismer som reglerar fördelning och

utflödet av metaller på lång och kort sikt inom ett gruvområde.

Resultaten visar sammanfattningsvis att den använda enkla geokemiska

lanskapsanalysmodellen kan användas effektivt för beskrivning och riskbe-

dömningen av gruvavfall inom ett komplext våtmarksområde. Resultaten

visar också att den generella geokemiska karaktären av både myren och me-

tallsammansättningen av AMD är viktig för uppskattning av kvarhållande

egenskaper för våtmarker.

47

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ACTA

UNIVERSITATIS

UPSALIENSIS

UPPSALA

2006

Geochemical Landscape Analysis for the Risk Assessment of ... › smash › get › diva2:168590 ›...

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Transcript of Geochemical Landscape Analysis for the Risk Assessment of ... › smash › get › diva2:168590 ›...