Applying Chemometrics to Determine Dispersion of ...

Water Air Soil Pollut (2018) 229:206 https://doi.org/10.1007/s11270-018-3868-0

Applying Chemometrics to Determine Dispersion of Mine Tailing-Affected Sediments from Submarine Tailing Disposal in Bøkfjorden, Northern Norway Anne Mette T. Simonsen & Kristine B. Pedersen & Lis Bach & Beata Sternal & Juho Junttila & Bo Elberling

Received: 4 April 2018 / Accepted: 1 June 2018 # Springer International Publishing AG, part of Springer Nature 2018

Abstract Mine tailing management is one of the largest environmental issues related to mining operation. This study uses chemometrics to assess the dispersion of iron mine tailing-affected sediments in Bøkfjorden, Northern Norway. Metal concentrations (Al, As, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Zn) and physico-chemical sediment Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11270-018-3868-0) contains supplementary material, which is available to authorized users. A. M. T. Simonsen : B. Elberling Center for Permafrost (CENPERM), Department of Geoscience and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, 1350 Copenhagen K, Denmark A. M. T. Simonsen Arctic Technology Centre, Department of Civil Engineering, Technical University of Denmark, Building 118, 2800 Lyngby, Denmark K. B. Pedersen (*) Akvaplan-niva AS, Framsenteret, Post Box 6606, Langnes, 9296 Tromsø, Norway e-mail: [email protected] L. Bach Department of Bioscience, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark B. Sternal Institute of Geology, Adam Mickiewicz University in Poznań, Bogumiła Krygowskiego 12, 61-680 Poznań, Poland B. Sternal : J. Junttila Department of Geosciences, UiT—The Arctic University of Norway in Tromsø, Post Box 6050, Langnes, 9037 Tromsø, Norway

characteristics (conductivity, organic matter, sulphate, chloride, grain size, CaCO3, pH) were analysed in seven sediment cores collected in a transect out of the fjord along with two reference cores. Results of hierarchical cluster analysis and principal component analysis allowed to distinguish between mine tailing-affected and non-affected sediments. Non-affected sediments were especially characterised by high levels of organic matter whilst mine tailing-affected sediments varied significantly in sediment characteristics depending on location in the fjord. Crucial parameters to reveal mine tailing-affected sediments varied between the target metal Fe along with metals of Cd and Mn, albeit less significant. Variations in mine tailingaffected sediment characteristics could be attributed to other anthropogenic activities in the fjord. Despite potential disturbances, chemometrics made it possible to identify dispersion of mine tailing-affected sediments to cover the inner and middle parts of the fjord. The study demonstrates the advantage of applying chemometrics on complex fjord systems, which in this case was used to distinguish mine tailing-affected sediments from areas with elevated levels of metals not necessarily related to the mine. Keywords Submarine mine tailing disposal . Metal dispersion . Principal component analysis . Hierarchical cluster analysis . Fjord sediments

1 Introduction Mine tailings are a by-product from the mineral separation process of metals associated with base metal mining

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operations. After processing of the ore, firstly involving crushing and grinding followed by physical and chemical separation methods, the waste material is characterised as a fine-grained slurry, potentially containing chemicals from the mineral processing along with residual metals. The ore processing generates large quantities of mine tailings since the target metal mostly constitutes a small fraction of the ore (Dudka and Adriano 1997). Management of mine tailings is a major environmental challenge for the mining industry since the waste handling and disposal can cause an extensive and long-lasting disturbance to the environment (Cooke and Johnson 2002). The impact is related to the large volumes as well as the potential toxicity of metals, which due to their nonbiodegradable nature, may cause severe ecological effects. The possibilities of tailing disposal techniques are site-specific and strongly influenced by geological, topographical and socio-economic factors. Consequently, tailings are often deposited on land, i.e. behind cross valley or hillside dams, or it may be back-filled into closed open-pit or underground mines (Edraki et al. 2014). Mine tailing storage on land has caused negative impacts on the surrounding environment, mainly due to failure of dam constructions (Vogt 2013) or generation of acid mine drainage (AMD) (Dold 2014a; Sima et al. 2011). AMD originates from the oxidation of sulphidic mine waste deposited on land and poses an environmental risk due to the mobilisation of contaminants. An alternative method is submarine tailing disposal (STD), where mine tailings are discharged into marine environments, differentiated according to disposal depths (Dold 2014b; Ellis and Ellis 1994). The method has historically been practised worldwide (Dold 2014b; Vogt 2013) but is now limited in use due to several reported cases of severe STD-induced environmental effects including smothering of the seabed (Burd 2002; Kutti et al. 2015), change of benthic fauna composition (Josefson et al. 2008) and toxicity of mobilised metals (Bryan and Langston 1992; Pedersen et al. 2017) along with bioaccumulation of metals (Larsen et al. 2001; Riget et al. 1997). Previous cases from Greenland (Elberling et al. 2002; Søndergaard et al. 2011) and Canada (Wilson et al. 2005) have emphasized another consequence of STD, namely the uncontrolled dispersion of mine tailing-related metals that can have a severe and long-term impact on the environment. Due to several environmental consequences reported from the use of STD, countries joined forces to establish legal frameworks (e.g. IMO 1972; OSPAR Commission

Water Air Soil Pollut (2018) 229:206

2007) that restrict the use of STD. The common agreements have proved as an important tool to promote effective control of STD and have limited the practise worldwide. According to the regulations of the OSPAR Commission (2007), the dumping of all wastes is prohibited with the exception of inert material that is unlikely to release chemical constituents. On the basis of this exception, Norway is one of few countries still practising STD (Kvassness and Iversen 2013; Ramirez-Llodra et al. 2015). Unsuitable conditions for on-land deposition, including lack of space due to a mountainous topography (Kvassness and Iversen 2013; Klima- og Forurensningsdirektoratet 2010), have resulted in a long record of STD practises in Norway. One of these practises was the iron ore mine of Sydvaranger Gruve, near Kirkenes in Northern Norway, which was active between 1906–1997 and 2009–2015. Mine tailings from processed ore were discharged through a pipeline into the adjacent fjord, Bøkfjorden (Fig. 1). From 1971 to 1997, more than 56 million tons of mine tailings were discharged into the fjord (Klima- og Forurensningsdirektoratet 2010) whilst the exact amount of discharge released during the second part of mine operation is unknown. Several environmental investigations commissioned by the mining company since 1988 assessed the effects of mine tailings on Bøkfjorden (Berge et al. 2012, 2014; Skei and Rygg 1989; Skei et al. 1995). The latest investigations were mainly focused on evaluating the potential biological effects with the use of uni- and bivariate analyses and bottom fauna analyses. Besides from high iron concentrations related to mine tailing discharge, the investigations showed generally low heavy metal concentrations in fjord sediments to have been dispersed to large parts of the fjord. The previous investigations showed mine tailing dispersal to be influenced by several hydrographical factors, such as freshwater river inputs, changes in salinity as well as influencing bottom and tidal currents (Berge et al. 2012). The behaviour of particles related to mine tailing discharge in aquatic environments is hence controlled by several interacting factors (Eggleton and Thomas 2004) that are essential for understanding mine tailing dispersion. This complex system of multiple factors needs proper handling, in which statistical tools of chemometrics have served useful. These methods are a collection of multivariate statistical approaches, involving extraction of maximum information based on projections that enable interpretation of complex data (Eriksson et al. 2014). Recently, chemometrics have gained use in


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Fig. 1 Map of Bøkfjorden with the location of sediment sampling stations: St 1–St 7, transect out of the fjord; R1 and R2, two reference stations. The mine tailing disposal site is marked by a red dot

environmental studies (Filgueiras et al. 2004; Guillén et al. 2012; Loska and Wiechuła 2003; Pedersen et al. 2015, 2016; Sternal et al. 2017). The chemometric techniques of hierarchical cluster analysis (HCA) enable merging of (dis)similar variations in complex data set whilst those of principal component analysis (PCA) visualise relationships among variables. The methods have previously been used to identify pollution sources, dispersion patterns as well as differentiation between pollution-affected and non-affected sediments (Facchinelli et al. 2001; Filgueiras et al. 2004; Loska and Wiechuła 2003; Pedersen et al. 2017). The aim of this study is to investigate the dispersion of mine tailingaffected sediments (MT) in Bøkfjorden, by the application of chemometrics to metal concentrations and physico-chemical sediment characteristics. For this purpose, concentrations of Al, As, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn together with physico-chemical sediment characteristics of conductivity, loss on ignition, sulphate,

chloride, grain size, CaCO3 and pH were analysed in sediment cores from nine locations in Bøkfjorden. The study focuses on assessing the potential of applying chemometrics as tools for marine tailing dispersion.

2 Study Area Bøkfjorden is located in Northern Norway in the region of Finnmark (Fig. 1). It is North–South orientated, approximately 21 km-long fjord with water depths increasing up to 300 m towards the fjord mouth. It is a part of a bigger fjord system connected via its mouth with Varangerfjorden, at its western side with Korsfjorden and at the SSW part of its head with Langfjorden. Water exchange to Varangerfjorden is restricted by a sill, which forms a natural barrier at the mouth, enhancing water column layering of a brackish surface layer (Berge et al. 2012). The layer is made by minor fresh water

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inputs from Pasvikelva at the SSE part of the fjord head and Ropelv in the eastern part besides from Korsfjorden and Langfjorden. The island of Reinøya is located in the middle of the fjord outside the area of Ropelv. The fjord hosts the town of Kirkenes along with the iron ore mine 10 km south of Kirkenes (Fig. 1). The iron ore mine south of Kirkenes is based on a banded iron formation with alternating quartzdominated and iron oxide layers. The iron occurring mainly as magnetite constitutes between 21 and 32% of the ore deposits (Boyd et al. 2012). The ore was extracted firstly from open pit and later underground mines, where it was crushed and subsequently transported by train for further processing in Kirkenes. Here, metal separation generated a waste product, mine tailings, that was discharged into Bøkfjorden through a submarine pipeline at 28 m depth 450 m northwest from land (marked with a red dot on Fig. 1) (Berge et al. 2012). In the period between 1971 and 1997, the first mining operation discharged 1.7–3.5 million tons/year of mine tailings into Bøkfjorden and hence released more than 56 million tons in total into the fjord (Klimaog Forurensningsdirektoratet 2010). The second mining operation was permitted to discharge up to 4 million tons/year of mine tailings but discharged only 2.6 million tons in 2012 (Kvassness and Iversen 2013).

3 Methods 3.1 Sediment Sampling Sediment sampling was conducted in November 2016 aboard the R/V Helmer Hanssen of UiT—The Arctic University of Norway (Table 1). Sediment cores were collected from seven stations representing a transect out of the fjord (St 1–7) along with two reference stations (St R1 and R2) (Fig. 1) by the use of a multi-corer operating six core liners (11 cm diameter, 70 cm length). Three replicate cores were collected from each station, where one was retrieved for geochemical and grain size analysis. The upper 20 cm of the sediment cores was sliced into 1cm sections and subsequently frozen until used. Sediment samples were freeze-dried prior to the analyses. 3.2 Analyses Total metal concentrations of Al, As, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn were measured after total digestion


(Danish standard DS259). According to the procedure, HNO3 (20 mL) was added to the sediment sample (1 g) and autoclaved (200 kPa, 120 °C, 30 min). Solid particles were removed by vacuum filtration through 0.45 μm filters, and the liquid was diluted to 100 mL. Inductively coupled plasma-optical emission spectrometer (ICP-OES) was used to measure metal concentrations in the liquid phase. Carbonate content was determined by adding HCl (20 mL) to sediment (1 g) in a Scheibler apparatus. The production of CO2 was measured volumetrically and was calibrated with CaCO3. Organic matter content was measured by loss on ignition (LOI). Sediment (2.5 g) was heated to 550 °C for 1 h, and the percentage loss was calculated. Grain size analysis was carried out with a laser diffraction particle size analyzer with polarisation intensity differential scattering (PIDS) analysing the range from 0.017 to 2000 μm following the procedures described in Sternal et al. (2017). Chloride and sulphate were measured by adding micropore water (25 mL) to sediment (10 g). The sample was shaken overnight. Solid particles were removed by filtration through 0.45 μm filters. The concentrations of chloride and sulphate in the liquid phase were measured by IC (ion chromatography). Conductivity was measured by adding distilled water (25 mL) to sediment (5 g). The sample was shaken for 1 h and measured for conductivity by the use of a radiometric analytical electrode. pH was measured by adding KCL (1 M, 12.5 mL) to sediment (5 g). The sample was shaken for 1 h before the sample was measured for pH by the use of a radiometric analytical electrode. 3.3 Statistical Analysis Multivariate statistical analyses were applied to the obtained data set by the use of the open-source software R (R Core Team 2018) and the SIMCA software version 14.1.0.2047 from MKS Umetrics AB. Prior to statistical analyses, the data set was transformed since variables varied in magnitude and units. In order to achieve equal contribution from each variable, data was logarithmically transformed, centred and scaled to unit variance to make variables comparable and dimensionless. A hierarchical cluster analysis (HCA) was performed in order to cluster sediment samples in the fjord according to the sediment characteristics. The HCA


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Table 1 Coordinates and measurements of collected sediment cores in Bøkfjorden Station

Core ID

Sampling date

Latitude (N)

Longitude (E)

St 1

IG16-1801

St 2

IG16-1797

St 3 St 4

6/11–16

69° 43.954′

30° 01.975′

57.8

27

Inner

6/11–16

69° 44.525′

30° 04.289′

100.9

31

Inner

IG16-1802

6/11–16

69° 45.798′

30° 05.614′

141.8

37

Middle

IG16-1810

6/11–16

69° 48.444′

30° 06.391′

231.5

36

Middle

St 5

IG16-1813

6/11–16

69° 49.658′

30° 06.665′

230.8

40.5

Outer

St 6

IG16-1814

6/11–16

69° 50.538′

30° 05.084′

225.6

42

Outer

St 7

IG16-1815

6/11–16

69° 51.524′

30° 07.058′

269.3

40

Outer

St R1

IG16-1809

6/11–16

69° 47.025′

29° 59.614′

157.5

46

Korsfjorden

St R2

IG16-1804

6/11–16

69° 46.591′

30° 09.834′

58.2

20

Ropelv

clustered sediments into groups of similar analysed variables. Euclidean distances were applied to measure the (dis)similarity, and Ward’s linkage criterion was used to merge the data. The dissimilarity matrix achieved was visualised in a dendrogram, representing the levels of similarities between sediment samples. The optimal number of clusters was determined by the Elbow method based on the percentage of variance explained as a function of the number of clusters. A principal component analysis (PCA) was used to identify predominating sediment characteristics further allowing to distinguish MT-affected from non-affected sediments. The purpose of PCA was to summarize multivariate data variance into a low-dimensional space. The Elbow method was applied to find the lowest number of principal components capable of explaining the most variability in the data set. The method was based on the percentage of variance explained as a function of the number of components.

4 Results 4.1 Metal Levels and Physico-Chemical Sediment Characteristics Average values of metal concentrations and physicochemical characteristics (mean ± SD) of the sediment cores are shown in Table 2. These average values present a compressed overview of vertical sediment characteristics for each sediment core whilst the complete data set can be found in appendix A in the Supplementary Material. The average metal concentrations mainly show three different spatial trends. The concentrations

Water depth (m)

Core length (cm)

Location in fjord

of heavy metals As, Cr, Ni and Zn increase towards the outer fjord mouth (St 6 and St 7) and in the reference cores (R1 and R2) whereas the metal trends decrease closer to the pipeline outlet (St 1). Opposite to this trend, Mn shows a decrease in concentration with distance from the pipeline outlet (St 1 513 mg/kg; St 7 322 kg/ mg), albeit sediments from Ropelv (St R2) consist of the lowest Mn concentrations in the fjord (233 mg/kg). Cd, Cu, Fe and Pb show concentrations independent on distance to the outlet. The highest concentrations of these metals occur in cores retrieved from the inner part of the fjord; Cd (1.74 mg/kg), Fe (45,941 mg/kg) and Pb (119 mg/kg) in sediments of St 1, located closest to the pipeline outlet, and Cu in St 2 (49.0 mg/kg). Sediment from the reference station in Korsfjorden, St R1, consists of the second highest concentrations of Cd (1.56 mg/kg) and Fe (35,650 mg/kg) in the fjord whilst sediment from the station in the fjord mouth, St 7, consists of the lowest concentrations (0.26 mg/kg Cd and 14581 mg/kg Fe). The sediments of Bøkfjorden are mostly fine-grained with dominating silt. St 1 is the most fine-grained with silt fractions of 80.3%, whilst the most coarse-grained in the fjord are St 2 (sand fraction 28.0%) and St 3 (sand fraction 51.5%). The sediments of the reference stations (St R1 and St R2) and the middle fjord (St 4 and St 5) tend to be more fine-grained, due to higher silt fraction contents, compared to the outer fjord. Generally, LOI, conductivity, chloride and sulphate show increasing levels with distance to the pipeline outlet, except from St 2 and 3, where the measured parameters deviate from the trend by decreasing markedly. Here, the organic matter content (LOI) ranges from 0.28% (St 1) to 0.96% (St 6), whilst sediments in St 2

5.81 ± 2.6

10.1 ± 2.6

13.9 ± 5.4

16.4 ± 3.5

14.3 ± 3.4

21.9 ± 4.1

17.1 ± 4.2

5

6

7

R1

R2

6.90 ± 2.4

2

4

8.62 ± 1.6

1

3.88 ± 0.3

4.36 ± 0.4

5.48 ± 0.4

5.56 ± 0.5

5.14 ± 0.5

5.47 ± 0.6

4.62 ± 0.4

4.69 ± 0.5

5.06 ± 0.6

CaCO3 (%)

0.79 ± 0.4 0.46 ± 0.4 0.70 ± 0.3 2.01 ± 0.6 1.87 ± 0.9 5.82 ± 0.9 4.34 ± 1.4 5.74 ± 1.7 6.60 ± 1.5

As (mg kg−1)

7.60 ± 0.1

7.64 ± 0.1

7.78 ± 0.1

7.64 ± 0.1

7.93 ± 0.2

8.13 ± 0.2

8.76 ± 0.2

8.66 ± 0.1

8.41 ± 0.2

pH

1.74 ± 0.4 0.31 ± 0.1 0.82 ± 0.1 1.34 ± 0.3 0.46 ± 0.0 0.26 ± 0.0 0.26 ± 0.0 1.56 ± 0.1 1.05 ± 0.0

Cd(mg kg−1)

0.61 ± 0.2

0.98 ± 0.3

0.67 ± 0.2

0.96 ± 0.2

0.61 ± 0.2

0.37 ± 0.1

0.15 ± 0.1

0.19 ± 0.1

0.28 ± 0.1

LOI (%)

27.6 ± 8.3 22.2 ± 4.3 14.2 ± 3.3 29.0 ± 5.5 36.1 ± ±8.3 50.9 ± 2.9 44.3 ± 5.0 57.1 ± 3.7 45.1 ± 1.6

Cr(mg kg−1)

53,327 ± 16,537

67,531 ± 14,212

48,811 ± 11,237

50,945 ± 10,332

46,212 ± 18,163

30,373 ± 8604

27,625 ± 13,305

25,424 ± 7523

513 ± 87 417 ± 60 341 ± 59 473 ± 61 391 ± 68 276 ± 22 322 ± 27 300 ± 27 233 ± 9

Mn(mg kg−1)

8946 ± 2619

10,968 ± 2639

7557 ± 2019

8385 ± 2034

7494 ± 2948

4570 ± 1403

3840 ± 1965

3673 ± 1032

5703 ± 2011

Sulphate (mg kg−1)

45,941 ± 8553 31,032 ± 3668 22,100 ± 3749 29,114 ± 4916 25,977 ± 3404 29,806 ± 1431 14,581 ± 1019 35,650 ± 972 27,128 ± 901

Fe(mg kg−1)

36,688 ± 9947

Chloride (mg kg−1)

29.1 ± 8.0 49.0 ± 8.8 35.4 ± 4.5 30.1 ± 7.1 31.1 ± 3.3 25.6 ± 1.7 27.7 ± 1.0 27.2 ± 1.2 21.4 ± 0.6

Cu(mg kg−1)

12.5 ± 1.5

6.41 ± 1.4

16.2 ± 3.5

18.4 ± 2.0

8.27 ± 3.5

11.5 ± 3.8

51.5 ± 23.3

28.0 ± 15.3

7.97 ± 4.4

Sand (%)

16.4 ± 3.2 21.1 ± 3.7 12.0 ± 2.4 16.8 ± 3.7 20.8 ± 6.6 33.7 ± 1.2 27.3 ± 4.4 36.2 ± 1.1 27.3 ± 0.8

Ni(mg kg−1)

76.4 ± 1.4

77.1 ± 1.1

69.9 ± 3.1

65.5 ± 1.4

77.1 ± 3.7

77.5 ± 2.9

44.2 ± 21.1

64.9 ± 12.9

80.3 ± 2.6

Silt (%)

119 ± 25.2 6.75 ± 3.2 31.0 ± 4.0 24.8 ± 5.2 27.4 ± 5.6 79.0 ± 4.4 79.5 ± 6.3 39.9 ± 2.1 13.1 ± 4.5

Pb(mg kg−1)

11.1 ± 0.8

16.5 ± 1.5

13.9 ± 1.0

16.2 ± 1.2

14.7 ± 2.9

11.0 ± 1.6

4.34 ± 2.3

7.12 ± 2.6

11.8 ± 2.2

Clay (%)

37.8 ± 11.9 34.2 ± 5.3 23.8 ± 3.4 31.1 ± 5.4 47.9 ± 12.7 68.9 ± 3.6 61.4 ± 8.2 61.6 ± 2.1 60.0 ± 4.2

Zn(mg kg−1)

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3

Conductivity (mS/cm)

8946 ± 3050 8731 ± 1392 5929 ± 1250 9480 ± 2502 12,482 ± 3090 19,313 ± 1349 8421 ± 1180 22,039 ± 1437 16,387 ± 919

1 2 3 4 5 6 7 R1 R2

St

Al (mg kg−1)

St

Table 2 Average values of metal concentrations and physico-chemical sediment characteristics (mean ± SD) in sediment cores from Bøkfjorden

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and 3 contain the lowest contents of 0.19 and 0.15%, respectively. In contrast, CaCO3 and pH tend to decrease with distance to the pipeline outlet. 4.2 Grouping of Bøkfjorden Sediments Using Hierarchical Cluster Analysis In order to group sediments with similar characteristics based on the analysed parameters (Table 2), a hierarchical cluster analysis (HCA) was performed. The grouping results of HCA are shown separately for each core sample (Table 3) and as a dendrogram (Fig. 2a). The HCA distinguished two main groups separated into five subgroups in total, which represent five different sediment types. Main group 1 contains the subgroups A, B and C. Subgroup A contains the middle and deeper sediments from St 2 (3–6 cm) and

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St 3 (6–12, 14–20 cm), located in the inner and middle parts of Bøkfjorden. Subgroup B comprises the deeper sediments of St 1 (4–20 cm), located in the immediate vicinity of the pipeline outlet. Subgroup C constitutes the top sediments (0–4 cm) from the stations close to the source of mine tailing deposition (St 1–4) as well as deeper sediments of St 2–5 (see Table 3). As shown in Fig. 2b, the three groups (A, B and C) are hence mainly represented in the inner and middle parts of the fjord. Main group 2 contains subgroups D and E. Subgroup D consists of sediment from St 6 and 7 as well as middle parts of St 4 (10–13 cm) and upper and lower parts of St 5 (1–8, 14–20 cm). Subgroup E represents the two reference cores, St R1 and St R2, taken from Korsfjorden and near Ropelv together with the uppermost layer of St 5 and 7 (0–1 cm).

Table 3 Grouping of sediment samples based and coloured according to the HCA results

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Fig. 2 The HCA with Ward D’s linkage method separating sediments in Bøkfjorden into two main groups (main groups 1 and 2) and five subgroups (subgroups A–D) (a), with the corresponding location in Bøkfjorden and placement in sediment cores (b)

4.3 Identifying Predominating Characteristics of HCA Groups Using Principal Component Analysis An evaluation of predominating sediment characteristics enables an identification of characteristics associated with non- and MT-affected sediments in Bøkfjorden. For this purpose, a principal component analysis (PCA) was applied using metal concentrations and physico-chemical characteristics (Table 2) of the samples on the subgroups defined by the HCA (Table 3). The two first principal components (PCs) explain 65.4% of the variance in the PCA (Fig. 3). The score plot illustrates the distribution of sediment samples in regard to PC1 and PC2 (Fig. 3a). The scores cluster according to aforementioned grouping and hence support the findings of the HCA. The relatively low difference in height in the HCA between subgroups D and E is illustrated by the tight clustering of scores from the subgroups seen in the PCA (Fig. 3a). The tight clustering of these scores indicates more homogeneous sediment characteristics with regard to PC1. In contrast, subgroups A, B and C cluster separately, indicating differences in sediment characteristics. The three subgroups cluster along PC2 in the negative direction of

PC1, and the variation in characteristics is hence less explained due to PC2’s lower percentage of explanation (15.5%). However, according to their placement in the negative direction of PC1, the sediments of subgroups A, B and C differ markedly from subgroups D and E. The loading plot comprises the loadings of the variables where the placing of loadings expresses correlations between variables. This is a measure of the importance of the variable according to the total variance of the data set. The loading plot of PC1 and PC2 (Fig. 3b) shows the variables Al, As, Cr, Ni, Zn, LOI, sulphate, chloride, conductivity and clay content to correlate strongly as illustrated by their close location to one another. The variables’ location far from the origin illustrates their high influence on PC1. The scores of subgroups D and E are positively correlated to these variables and are hence highly influenced by these variables due to the close location (Fig. 3a,b). PC1 carries additionally high loadings on pH, which is negatively correlated with subgroups D and E, indicative of low pH levels in these subgroups. The less explaining PC2 is capable of separating subgroups A, B and C from each other by the variables defining PC2. The variables Mn, Fe, silt and Cd correlate positively on the positive side of


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Fig. 3 PCA score (a) and loading (b) plots of PC1 and PC2 of metal and physico-chemical sediment characteristics on subgroups defined by the HCA

PC2 and are hence highly characteristic for scores of subgroup B. In the negative direction of PC2, subgroup A is defined by a high sand fraction content. The central distribution in the plot of scores of subgroup E is an expression of these samples to be less explained by PC1 and PC2 (Fig. 3a,b). These scores represent samples from the reference stations (St R1 and R2) and the uppermost layers of St 5 and 7 (Table 3). Furthermore, certain variables contribute little to PC1 and PC2, as illustrated by the central location of especially CaCO3. In order to gain further information on sediment characteristics of less explained samples, scores and loadings of PC1 and PC3 are further investigated (Fig. 4a,b). PC3 explains 8.8% of the data set and strongly depends on CaCO3, visualised in the loading plot of Fig. 4b. The location of subgroup E (Fig. 4a) along the positive direction of PC3 shows that scores are characterised by high concentrations of CaCO3 along with low concentrations of Cd and Fe (Fig. 4b). CaCO3 shows low correlation to other variables in all three components, which suggests that the variable could be controlled by other parameters than analysed in this study. As opposed to the score plot of PC1 and PC2 (Fig. 3a), the score plot of PC1 and PC3 (Fig. 4a) shows subgroups D and E to cluster separately along PC3 and hence supports the separation of subgroups in the HCA. Moreover, the absence of PC2 shows that scores of subgroups A, B and C cluster close to each other, which supports the strong influence of PC2 on the separation of these groups, as seen in Fig. 3a.

5 Discussion 5.1 Separating Non-Affected from Mine Tailing-Affected Sediments The HCA (Fig. 2) shows a clear separation between two main groups, which is confirmed in the score plot of the PCA (Fig. 3a). The scores of subgroups D and E cluster among strong correlations of As, Cr, Ni, Zn, Al, clay and LOI in the loading plot (Fig. 3b) which is indicative of strong bindings between metals, clay particles and organic matter. The sediments of these subgroups are hence characterised by high concentrations of organic matter and As, Cr, Ni and Zn, which should not necessarily be mistaken for contaminants. As observed in previous investigations of STD in Bøkfjorden (Skei and Rygg 1989; Skei et al. 1995), mine tailing discharge from Sydvaranger Gruve generally contains low concentrations of organic matter (0.3–0.7%), since the waste product mainly consists of ore material. The opposite case, namely high concentrations of organic matter (3–5%), represented non-affected sediment conditions. Based on these observations, the investigations found non-affected sediments in Korsfjorden (3.8–5%) and in Ropelv (3– 3.6%). Despite the fact that this study uses similar analyses of organic matter content (LOI), the values in this study range generally lower (Table 2). Nevertheless, a markedly difference in organic matter is seen between sediment retrieved from the reference stations (St R1–R2

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Fig. 4 PCA score (a) and loading (b) plots of PC1 and PC3 of metal and physico-chemical sediment characteristics on subgroups defined by the HCA

0.61–0.98%) opposed to the inner fjord (St 1–3 0.15– 0.28%) supported by the clear separation in organic matter content between main groups 1 and 2 in the PCA (Fig. 3) due to the variable’s strong loading on PC1. The investigations of Skei and Rygg (1989) found the metal Ni to be connected to mine tailing discharge, along with Pb and Cd, even though metal concentrations generally corresponded to Norwegian background levels. In contrast, the investigations of Skei et al. (1995) defined Ni to occur naturally. The tight clustering between the metals As, Cr, Ni and Zn in the PCA (Figs. 3 and 4) is an expression of a strong correlation and would presumably represent a natural occurrence of background levels. This is supported by the close clustering of scores representing relatively homogeneous sediment characteristics in sediments of subgroups D and E. Since the entire sediment core of St 6, R1 and R2 consists of one subgroup only, this is an indication of no significant changes in the sedimentation environment and hence no influence of mine tailing discharge. This is further supported by previous investigations of Bøkfjorden (Skei and Rygg 1989; Skei et al. 1995), where no significant vertical changes where found regarding organic matter content, grain size distributions or metal concentrations for sediment cores collected in the same areas of Bøkfjorden. This strengthens the assumption of these areas not to be influenced from mine tailings. In contrast to the tight clustering of subgroups D and E, subgroups A, B and C show a large diversity in

sediment characteristics, as displayed by their large distribution on PC2 in the score plot (Fig. 3a). Here, subgroup A is mainly characterised by a high sand fraction content, subgroup C by elevated Cu concentrations and finally subgroup B is defined by high Fe, Mn, Cd, Pb and silt fraction content. Despite the spread distribution of scores according to PC2, their placement on the negative side of PC1 would suggest that the sediments of subgroups A, B, and C carry highly different characteristics compared to subgroups D and E. The previous investigations of STD in Bøkfjorden (Skei et al. 1995) found elevated concentration of the target metal Fe in sediment to be a strong indicator of mine tailing discharge, which supports the assumption of defining especially sediments of subgroup B as MTaffected. The same investigations (Skei et al. 1995) found coarser-grained sediments of the inner fjord to be associated with the sedimentation of the coarsest particles related to mine tailings. The investigations found the coarsest mine tailing particles to be related to quartz whilst Fe was associated with finer particles, transported further out the fjord. These assumptions are relatable with the coarse-grained sediments found in subgroup A containing sediments from the inner fjord (St 2 and 3), whilst not being associated with high concentrations of Fe as seen in the PCA. This supports results from the HCA and PCA, which categorise MTaffected (subgroups A, B and C) and non-affected (subgroups D and E) sediments in Bøkfjorden.


5.2 Dispersion of Mine Tailing-Affected Sediments in Bøkfjorden Based on the categorisation of sediments, it is possible to identify the dispersion of mine tailings in Bøkfjorden. The HCA distinguishes two highly different groups of sediments (main groups 1 and 2), which combined with their location in the fjord, predominating physicochemical and metal characteristics according to the PCA along with findings of previous investigations that suggested subgroups A, B and C to be dominated by MT-affected sediments. On that basis, this study suggests the surface sediments from the source area (St 1) moving up to the middle part of the fjord (St 4) to be MT-affected (Fig. 2b). At 8–14 cm of depth at St 5, sediments are defined as subgroup C (Table 3), which could indicate historical dispersion of mine tailings. Previous investigations of turbidity measurements in the water column (Berge et al. 2012) indicated that the influence of mine tailing discharge from Sydvaranger Gruve could be traced from the pipeline outlet outside of Kirkenes to the island of Reinøya in the middle part of the fjord, which corresponds to the dispersion of MTaffected sediments identified in this study. The height differences between subgroups A, B and C in the HCA combined with the spread in score distribution from the PCA show MT-affected sediments to encounter different sediment characteristics. These differences could be related to varying grain sizes. According to PC2 (Fig. 3), silt is highly uncorrelated to sand expressed by their opposite location in the loading plot. The higher fraction of sand in subgroup A, representing samples from St 2 and 3, is presumably related to the coarsest fractions of mine tailings. Due to selective hydraulic sorting, coarser and hence heavier grains are more easily deposited closer to the source area. Unexpectedly, the highest sand content (51.5%) is found in St 3, instead of St 1 taken closer to the source of discharge, which only consists of 7.97% sand. An important point to address in the dispersion of mine tailing-related sediments and the sediment characteristics observed in the inner fjord is the impact from the freshwater inlet of Langfjorden. Langfjorden was used as deposition for mine tailings from Sydvaranger Gruve until 1971 (Klima- og Forurensningsdirektoratet 2010). Despite the fact that no direct particle transport has been detected between Langfjorden and Bøkfjorden by Berge et al. (2012), a strong tidal current controls water transport between the two fjords and must presumably be capable of

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transporting minor grain sizes. Whether or not grain size distributions in the inner Bøkfjorden can be directly attributed to particle and water exchange from Langfjorden, the hydrography of Bøkfjorden involving the influence of currents plays a major role in the dispersion of mine tailing-affected sediments. Strong tidal currents have been awarded a significant importance for the dispersion of mine tailings in Bøkfjorden to reach a wide deposition area (Klima- og Forurensningsdirektoratet 2010; Berge et al. 2012). With a lack of published studies on currents in Bøkfjorden, it is assumed that the primary current transports particles out of Bøkfjorden. The freshwater input from Korsfjorden, Ropelv and Pasvikelva contributes to the flow and prevents significant accumulation of mine tailing-affected sediments especially in the cores retrieved from St R1 and R2. In addition to natural influences, Bøkfjorden captures a wide variety of anthropogenic activities. Metals in Bøkfjorden can hence originate from ship traffic, the shipyard in Kirkenes and harbour activities in general. Environmental investigations of sediment in Langfjorden exposed to harbour activities from KILA (Kirkenes Industrial Logistics Area) showed elevated concentrations of especially Cu to pose a potential environmental risk on the marine fauna (Norconsult 2010). The affected area is located 1 km from the pipeline outlet of Sydvaranger Gruve (Berge et al. 2012), which complicates a clear separation between elevated Cu concentrations related to harbour activities and mine tailing discharge. As for Cu, Pb concentrations show no clear dependence to distance from the pipeline outlet, since highly elevated Pb concentrations were detected in the outer fjord sediments of St 6 and 7 (Table 2). Even though previous investigations (Skei and Rygg 1989) and the PCA (Fig. 3) associated Pb concentrations to mine tailing discharge, the elevated concentrations observed in the outer fjord (St 6 79.0 mg/kg and St 7 79.5 mg/kg) could originate from other sources. Taking the fjord topography into account, it is also likely that the sill located at the fjord mouth forms a barrier for sediment dispersion. Since sediment dispersion outside of Reinøya occurs as suspension in the lower water column (Berge et al. 2012) and that the fjord increases in depth with distance to the outlet source, hydraulic sorting could accumulate denser heavy metal-associated finer particles in the outer fjord, illustrated by the elevated Pb concentration in St 6 and 7. Nevertheless, Pb showed a less significant loading on PC2 and is hence less characteristic for subgroup B. The weaker loading of Cu and Pb in the PCA (Fig. 3) could be ascribed to disturbances from other

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sources. Instead, the target metal of the mining operation presumably serves as a crucial parameter to reveal MTaffected sediments along with Mn and Cd. According to Table 2, the target metal Fe shows similar deviation from trend in concentrations with distance. Here, unexpected, elevated concentrations of Fe in sediments of St R1 (35,650 mg/kg) is the second highest average concentration of cores in the fjord and would be interpreted as MTaffected sediments based on metal concentrations alone. Nevertheless, the PCA highly connects elevated concentrations of Fe with MT-affected sediments, relying on the correlations between parameters. Metal concentrations depended on distance to the outlet of mine tailings have previously served useful in detection of dispersion related to mine tailing discharge (Elberling et al. 2002; Odhiambo et al. 1996). If the dispersion of this study was based on metal concentrations alone, it is assumed that inadequate conclusions could have been drawn. 5.3 Essential Parameters In this study, the categorisation of MT-affected and nonaffected sediments is mostly determined by organic matter, the target metal Fe and metals presumably related to mine tailing discharge from Sydvaranger Gruve: Cd and Mn. These observations were strongly supported by the location of stations in Bøkfjorden (Fig. 1), previous environmental investigations of Bøkfjorden (Skei and Rygg 1989; Skei et al. 1995; Skaare et al. 2007), mine tailings characteristics in general (Jamieson et al. 2015; Lottermoser 2007) and previous cases of mine tailing dispersion in other fjord systems (Pedersen et al. 2017; Riget et al. 1997; Søndergaard et al. 2011). The findings of this study agree with similar studies of source and dispersion identification, where physicochemical sediment characteristics played a vital role along with mine tailing-related metals (Loska and Wiechuła 2003; Pedersen et al. 2015). At the same time, the findings differ from other applications of chemometrics (Filgueiras et al. 2004; Guillén et al. 2012), which determined the source and dispersion identification on metals alone. As suggested previously, this could complicate the evaluation since this study shows that Bøkfjorden is exposed to different metal sources. In addition, investigations of Skei and Rygg (1989) found certain metal concentrations related to mine tailing discharge (Pb, Cd, Ni) not to exceed natural background levels, which would complicate the identification of metal dispersion based on metal


concentrations. In general, metals related to mine tailing discharge depended on the extracted ore which differs in respect to location, target metal and type of ore processing (Jamieson et al. 2015). Therefore, it is generally essential to take each study area into consideration before conducting environmental investigations. The complexity of each study area, i.e. each fjord system, therefore complicates general recommendations. It should also be considered to take other pollutants into account in order to differentiate between mine tailing- and harbour-related sources. Sediment exposed to harbour activities can contain complex mixtures of inorganic heavy metals as well as organic priority pollutants such as polychlorinated biphenyls (PCB), polyaromatic hydrocarbons (PAH) and tributyltin (TBT), recognized to pose severe environmental risk by Norwegian (Norwegian Pollution Control Authorities 2007) and international authorities (OSPAR Commission 2009). The environmental investigations of the harbour area in Kirkenes (KILA) performed by Norconsult (2010) found elevated concentrations of PCB, PAH, TBT and especially the heavy metal Cu to originate from harbour activities. This could explain the weak loading of Cu, representing multiple sources of the heavy metal. Source identification among several pollutants have previously been conducted in harbours by the use of chemometrics (Pedersen et al. 2015; Sprovieri et al. 2007), and including organic priority pollutants in future mine tailing studies could potentially distinguish between mine tailing-affected sediments and sediments affected by other activities in the fjord. It is however important to note that due to the discharge of mine tailings in Bøkfjorden adjacent to the harbour, mixing of tailingaffected sediments and harbour-polluted sediments is expected in this case study. 5.4 Application of Chemometrics In this study, the use of chemometrics proved highly useful for identifying dispersion of mine tailings in a complex fjord system. Assessing multiple variables simultaneously provides a complete overview of the predominating sediment characteristics as the multivariate technique separates sediments with dissimilar variations in characteristics. The study reveals significant differences in MT-affected sediments based on correlations between variables. In a fjord subjected to mine tailing discharge, but with mine tailing-related metal concentrations not deviating from uncontaminated


Norwegian sediments (Skei and Rygg 1989; Skei et al. 1995), this study has proved the importance of assessing multiple variables simultaneously, especially concerning physicochemical parameters. Evaluating MT-affected sediments based on organic matter content alone would have determined the entire fjord to be influenced by mine tailings, since sediments affected by mine tailings contain 0.3–0.7% organic matter according to values defined by Skei and Rygg (1989) and Skei et al. (1995). Chemometrics are capable of assessing parameters relatively, which hence avoid deviations of single parameters. Even though high sand fractions were found in the inner fjord, the HCA and PCA still categorised these sediments as MT-affected. This illustrates the strength of combining multiple variables of metals and physico-chemical properties in contrast to uni- and bivariate statistics. However, the statistical tools of chemometrics are advanced and the final interpretation is highly dependent on the interpreter. As previously stated by Danielsson et al. (1999), chemometrics are highly subjective in regard to HCA linkage method, choice of number of HCA clusters and generally the interpretation of the PCA plot. In this study, the HCA separated two highly different main groups (Fig. 2), but the number of significant subgroups was less clear and could have been interpreted differently. The interpretations of chemometric results were however assessed in combination with information on mine tailing characteristics, previous investigations among others and the interpretations were hence aided by additional info. Despite the subjectivity, the chemometric methods provided clear patterns of the complex relations between MT- and non-affected sediments and were therefore highly useful in the study of Bøkfjorden.

6 Conclusion The application of chemometrics to metal concentrations and physico-chemical sediment characteristics made it possible to distinguish sediment samples in groups of similar characteristics by the use of HCA. The PCA provided information on predominating sediment characteristics for groups determined to represent MT-affected and non-affected sediments. The investigations revealed elevated mine tailing-related metals of especially Fe in the MT-affected sediments whereas the non-affected sediments were characterised by high concentrations of organic matter. This revealed MT-

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affected sediments to be dispersed from the outlet of mine tailings up to the middle part of Bøkfjorden. However, Bøkfjorden captures a variety of other anthropogenic activities potentially involving the release of metals to the fjord system. This could have disturbed the identification of MT-affected sediments if the analyses were based on metals alone. Assessing multiple variables of physico-chemical and metal concentrations simultaneously by the use of chemometric techniques has therefore served highly useful for identifying mine tailing-related dispersion. Acknowledgements The authors would like to thank the captain and crew of R/V Helmer Hanssen as well as the scientific participants for help with coring and core sampling, especially Kari Skirbekk and Noortje Dijkstra from UiT. Malene Grønvold and Ebba Schnell are acknowledged for their assistance in the laboratory of Arctic Technology Centre, DTU. Funding Information The Northern Environmental Waste Management (EWMA) project, funded by the Research Council of Norway through NORDSATSNING (grant number 195160) and EniNorgeAS, is gratefully acknowledged for funding.

References Berge, J. A., Beylich, B., Brooks, S., Jaccard, P. F., Tobiesen, A., & Øxnevad, S. (2012). Overvåking Av Bøkfjorden 2011 Og Giftighetstesting Av Gruvekjemikaliene Magnafloc LT 38 Og Magnafloc 10. NIVA-report, 6310—2012. Berge, J., Schwermer, C., Tobiesen, A., & Vogelsang, C. (2014). Gruveavgang I Bøkfjorden—Utlekking Og Giftighetstesting Av Vannbehandlingskjemikalier. NIVA-report, 6693—2014. Boyd, R., Bjerkgård, T., Ihlen, P. M., Korneliussen, A., Sandstad, J. S., & Schiellerup, H. (2012). Geology for society. NGUreport, 2012.048. Bryan, G. W., & Langston, W. J. (1992). Bioavailability, accumulation and effects of heavy-metals in sediments with special reference to United-Kingdom estuaries—a review. Environmental Pollution, 76(2), 89–131. Burd, B. (2002). Evaluation of mine tailings effects on a benthic marine infaunal community over 29 years. Marine Environmental Research, 53(5), 481–519. Cooke, J., & Johnson, M. (2002). Ecological restoration of land with particular reference to the mining of metals and industrial minerals: a review of theory and practice. Environmental Reviews, 10(1), 41–71. Danielsson, Ê., Cato, I., Carman, R., & Rahm, L. (1999). Spatial clustering of metals in the sediments of the Skagerrak/ Kattegat. Applied Geochemistry, 14(6), 689–706. Dold, B. (2014a). Evolution of acid mine drainage formation in sulphidic mine tailings. Minerals, 4, 621–641. Dold, B. (2014b). Submarine tailings disposal (STD)—a review. Minerals, 4(3), 642–666.

206

Page 14 of 15

Dudka, S., & Adriano, D. (1997). Environmental impacts of metal ore mining and processing: a review. Journal of Environmental Quality, 26(3), 590–602. Edraki, M., Baumgartl, T., Manlapig, E., Bradshaw, D., Franks, D. M., & Moran, C. J. (2014). Designing mine tailings for better environmental, social and economic outcomes: a review of alternative approaches. Journal of Cleaner Production, 84(1), 411–420. Eggleton, J., & Thomas, K. (2004). A review of factors affecting the release and bioavailability of contaminants during sediment disturbance events. Environment International, 30(7), 973–980. Elberling, B., Asmund, G., Kunzendorf, H., & Krogstad, E. (2002). Geochemical trends in metal-contaminated fiord sediments near a former lead-zinc mine in West Greenland. Applied Geochemistry, 17(4), 493–502. Ellis, D., & Ellis, K. (1994). Very deep STD. Marine Pollution Bulletin, 28(8), 472–476. Eriksson, L., Trygg, J., & Wold, S. (2014). A chemometrics toolbox based on projections and latent variables. Journal of Chemometrics, 28(5), 332–346. Facchinelli, A., Sacchi, E., & Mallen, L. (2001). Multivariate statistical and GIS-based approach to identify heavy metal sources in soils. Environmental Pollution, 114(3), 313–324. Filgueiras, A. V., Lavilla, I., & Bendicho, C. (2004). Evaluation of distribution, mobility and binding behaviour of heavy metals in surficial sediments of Louro River (Galicia, Spain) using chemometric analysis: a case study. Science of the Total Environment, 330(1–3), 115–129. Guillén, M. T., Delgado, J., Albanese, S., Nieto, J. M., Lima, A., & De Vivo, B. (2012). Heavy metals fractionation and multivariate statistical techniques to evaluate the environmental risk in soils of Huelva township (SW Iberian peninsula). Journal of Geochemical Exploration, 119–120, 32–43. IMO. (1972). Convention on the prevention of marine pollution by dumping of wastes and other matter. IMO website, 1–16. Retrieved http://www.imo.org/blast/blastDataHelper. asp?data_id=21278&filename=LC-LPbrochure.pdf. Jamieson, H. E., Walker, S. R., & Parsons, M. B. (2015). Mineralogical characterization of mine waste. Applied Geochemistry, 57, 85–105. Josefson, A., Hansen, J., Asmund, G., & Johansen, P. (2008). Threshold response of benthic macrofauna integrity to metal contamination in West Greenland. Marine Pollution Bulletin, 56(7), 1265–1274. Klima- og Forurensningsdirektoratet. (2010). Bergverk Og Avgangsdeponering: Status, Miljøutfordringer Og Kunnskapsbehov. TA-2715. Kutti, T., Bannister, R. J., Fosså, J. H., Krogness, C. M., Tjensvoll, I., & Søvik, G. (2015). Metabolic responses of the deep-water sponge Geodia barretti to suspended bottom sediment, simulated mine tailings and drill cuttings. Journal of Experimental Marine Biology and Ecology, 473, 64–72. Kvassness, A., & Iversen, E. (2013). Waste sites from mines in Norwegian fjords. Mineralproduksjon, 3, 27–38. Larsen, T. S., Kristensen, J. A., Asmund, G., & Bjerregaard, P. (2001). Lead and zinc in sediments and biota from Maarmorilik, West Greenland: an assessment of the environmental impact of mining wastes on an Arctic fjord system. Environmental Pollution, 114(2), 275–283.

Water Air Soil Pollut (2018) 229:206 Loska, K., & Wiechuła, D. (2003). Application of principal component analysis for the estimation of source of heavy metal contamination in surface sediments from the Rybnik reservoir. Chemosphere, 51(8), 723–733. Lottermoser, B. G. (2007). Mine wastes—characterization, treatments and environmental impacts. Berlin: Springer. Norconsult. (2010). Kirkenes Industrial Logistics Area (KILA)— Miljøundersøgelser. Norconsult-report, 5012450. Norwegian Pollution Control Authorities. (2007). Revidering av klassifisering av metaller og organiske miljøgifter i vann og sedimenter. TA-2229. Odhiambo, B. K., Macdonald, R. W., O’Brien, M. C., Harper, J. R., & Yunker, M. B. (1996). Transport and fate of mine tailings in a coastal fjord of British Columbia as inferred from the sediment record. Science of the Total Environment, 191(1–2), 77–94. OSPAR Commission. (2007). Convention for the protection of the marine environment of the North-East Atlantic. Retrieved http://www.ospar.org/convention. OSPAR Commission. (2009). Background document on CEMP assessment criteria for the QSR 2010. Monitoring and assessment series. OSPAR. Pedersen, K. B., Lejon, T., Jensen, P. E., & Ottosen, L. M. (2015). Chemometric analysis for pollution source assessment of harbour sediments in Arctic locations. Water, Air, and Soil Pollution, 226, 150. Pedersen, K. B., Lejon, T., Jensen, P. E., & Ottosen, L. M. (2016). Applying multivariate analysis as decision tool for evaluating sediment-specific remediation strategies. Chemosphere, 151, 59–67. Pedersen, K. B., Jensen, P. E., Ottosen, L. M., Sternal, B., & Junttila, J. (2017). Long term dispersion and availability of metals from submarine mine tailings disposal in a fjord in Arctic Norway. Environmental Science and Pollution Research, 1–12. R Core Team. (2018). R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. Ramirez-Llodra, E., Trannum, H. C., Evenset, A., Levin, L. A., Andersson, M., Finne, T. E., Hilario, A., Flem, B., Christensen, G., Schaanning, M., & Vanreusel, A. (2015). Submarine and deep-sea mine tailing placements: a review of current practices, environmental issues, natural analogs and knowledge gaps in Norway and internationally. Marine Pollution Bulletin, 97(1–2), 13–35. Riget, F., Johansen, P., & Asmund, G. (1997). Uptake and release of lead and zinc by blue mussels. Experience from transplantation experiments in Greenland. Marine Pollution Bulletin, 34(10), 805–815. Sima, M., Dold, B., Frei, L., Senila, M., Balteanu, D., & Zobrist, J. (2011). Sulfide oxidation and acid mine drainage formation within two active tailings impoundments in the golden quadrangle of the Apuseni Mountains, Romania. Journal of Hazardous Materials, 189(3), 624–639. Skaare, B., Oug, E., & Nilsson, H. (2007). Miljøundersøkelser I Fjordsystemet Utenfor Kirkenes I Finnmark 2007. Niva-report, 5473—2007. Skei, J., & Rygg, B. (1989). Miljøundersøkelser I Fjordsystemet Utenfor Kirkenes I Finnmark. Niva-report, O-87170. Skei, J., Rygg, B., & Sørensen, K. (1995). Miljøundersøkelser I Fjordsystemet Utenfor Kirkenes I Finnmark. Niva-report, O-94071.

Water Air Soil Pollut (2018) 229:206 Søndergaard, J., Asmund, G., Johansen, P., & Rigét, F. (2011). Longterm response of an Arctic fiord system to lead-zinc mining and submarine disposal of mine waste (Maarmorilik, West Greenland). Marine Environmental Research, 71(5), 331–341. Sprovieri, M., Feo, M. L., Prevedello, L., Manta, D. S., Sammartino, S., Tamburrino, S., & Marsella, E. (2007). Heavy metals, polycyclic aromatic hydrocarbons and polychlorinated biphenyls in surface sediments of the Naples harbour (southern Italy). Chemosphere, 67, 998–1009. Sternal, B., Juntilla, J., Skirbekk, K., Forwick, M., Carroll, J., & Pedersen, K. B. (2017). The impact of submarine copper

Page 15 of 15 206 mine tailing disposal from the 1970s on Repparfjorden, Northern Norway. Marine Pollution Bulletin, 120(1–2), 136–153. Vogt, C. (2013). International assessment of marine and riverine disposal of mine tailings. Final report adopted by the International Maritime Organization. London Convention/ Protocol, IMO. Wilson, B., Lang, B., & Pyatt, F. B. (2005). The dispersion of heavy metals in the vicinity of Britannia Mine, British Columbia, Canada. Ecotoxicology and Environmental Safety, 60(3), 269–276.