Spatial and geochemical aspects of heavy metal distribution in lacustrine sediments, using the example of Lake Wigry (Poland)

Chemosphere 240 (2020) 124879

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Spatial and geochemical aspects of heavy metal distribution in lacustrine sediments, using the example of Lake Wigry (Poland) Anna Kostka a, *, Andrzej Le
sniak b a b

Department of Environmental Protection, AGH University of Science and Technology, al. Mickiewicza 30, 30-059, Cracow, Poland Department of Geoinformatics and Applied Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30-059, Cracow, Poland

h i g h l i g h t s
A set of almost 500 geochemical data of lacustrine sediments was used in the study.
Large data collection enabled scrupulous modelling of metals spatial distribution.
Spatial distribution of metals in sediments was associated with the sediment type.
Spatial distribution of heavy metals in sediments was largely related to the depth.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 30 July 2019 Received in revised form 13 September 2019 Accepted 14 September 2019 Available online 17 September 2019

Heavy metals which pollute aquatic environments typically bond with bottom sediments and the analysis of the spatial distribution of metals allows to assess the geochemical purity of deposits and to identify the potential pollution sources. Research carried out on the Wigry Lake involved the collection of almost 500 samples of sediments, and the specification of the depth of their residence (0.2e71.4 m) as well as the level of concentration of three metals: Fe (80.3e32 857 mg kg1), Mn (17.8e1698 mg kg1) and Zn (3.14e632 mg kg1). The geochemical and bathymetric data was interpolated using geostatistical methods and mapped with the consideration of 5 types of sediments: lacustrine chalk, carbonate gyttja, fluvial-lacustrine sediment, organic gyttja and clastic sediment. As a result, a significant increase in the concentration of metals was revealed in deeper zones, at a considerable distance from the lake shore, wherein the respective values of correlation coefficients were as follows: deptheMn 0.77; deptheFe 0.60; deptheZn 0.58. A strong dependency between the concentration of analysed metals and the type of sediment, attributed to the granular and chemical composition of sediments, was also revealed. Correlations between individual metallic pairs (FeeMn 0.77; FeeZn 0.80; MneZn 0.75) indicated that similar factors influence spatial distribution of metals in sediments. The implementation of 3 different geochemical backgrounds allowed to conclude that the Wigry Lake is slightly polluted with the analysed metals, and that the origin of Mn is mainly natural, while in the case of Fe and Zn anthropogenic influence can also be identified. © 2019 Elsevier Ltd. All rights reserved.

Handling Editor: Lena Q. Ma Keywords: Sediment contaminants Metallic pollutants Trace elements Environmental Risk Assessment Spatial analysis Geostatistics

1. Introduction Heavy metals are considered to be among the most problematic environmental pollutants, due to their persistence, nonbiodegradability, toxicity or bioaccumulation, thus influencing both biotic and abiotic components in different ways (Chen et al.,

* Corresponding author. E-mail addresses: [email protected] (A. Le
sniak).

(A.

https://doi.org/10.1016/j.chemosphere.2019.124879 0045-6535/© 2019 Elsevier Ltd. All rights reserved.

Kostka),

[email protected]

2000; Smal et al., 2015; Salam et al., 2019). Their origin may be natural (erosion of rocks and metallic minerals, decay, atmospheric deposition) or anthropogenic (excavation and processing of metallic ores, various branches of industry, urbanization, sewage treatment or fertilization) (Farkas et al., 2001; Li et al., 2008; Ye et al., 2011; Decena et al., 2018; Sibal and Espino, 2018; Salam et al., 2019). Heavy metals which affect aquatic ecosystems usually become trapped in bottom sediments, which act as an important sink for omnifarious pollutants discharged, thus reflecting the quality of specific aquatic environments. Water deposits may also serve as a secondary source of pollution in particular ecosystems

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which is mainly associated with variable redox conditions or pH, while higher heavy metals content in sediments favours their bioaccumulation in food chains and may contribute to the contamination of drinking water (Szefer et al., 1995; Glasby et al., 2004; Selvaraj et al., 2004; Smal et al., 2015; Decena et al., 2018; Salam et al., 2019). Sediments experience and record changes in aquatic ecosystems which are both associated with natural conditions, as well as attributable to human activity, and play an important role in environmental assessment (Maghrebi et al., 2018; Karbassi et al., 2019). The chemical quality assessment of aquatic deposits not only provides information about the ecological state of examined ecosystem, but also allows the development of appropriate environmental management (Smal et al., 2015). Pollution of aquatic sediments with heavy metals is a worldwide phenomenon and affects marine environments (Szefer et al., 1995, 1999; Glasby et al., 2004; Selvaraj et al., 2004; Maghrebi et al., 2018), rivers (Audry et al., 2004; Karbassi et al., 2008; Ciszewski et al., 2012; Singh et al., 2017; Decena et al., 2018; Khan et al., 2018; Salam et al., 2019) as well as lakes (Jackson et al., 1993; Kirby et al., 2001; Lacey et al., 2001; Menounou and Presley, 2003; Liu et al., 2006; Lepane et al., 2007; Li et al., 2011; Raulinaitis et al., 2012; Ji et al., 2019) and dam reservoirs (Loska and Wiechuła, 2003; Li et al., 2008; Karbassi et al., 2011; Akhurst et al., 2012; Wang et al., 2012; Zhao et al., 2013; Zahra et al., 2014; Smal et al., 2015). Among different bodies of water, lakes are a particularly important feature of the Earth's landscape as they provide important sources of fresh water, habitats for various living organisms, as well as recreational opportunities for humans, while being at the same time particularly susceptible to the degradation of their ecological value (Puri et al., 2015). Thus the analysis of the level of concentration of heavy metals in lakes' bottom sediments, with reference to environmental risk assessment, represents one of the most important problems in the field of water science. The geochemical condition of lakes largely depends on their location and development. Bodies of water located far from human settlements, in non-industrialized areas or zones protected by law usually maintain a certain degree of purity, at the level of a geochemical background (KI˛aviss et al., 1995; Ricking and Terytze, 1999). On the other hand the geochemical quality of sediments is poorer in lakes under the influence of anthropopressure, especially those located near industrial areas or under their influence (Jackson et al., 1993; Kirby et al., 2001; Menounou and Presley, 2003). One of the most interesting subjects of geochemical studies is the distinction of metals of anthropogenic and natural origin (Selvaraj et al., 2004), identification of the sources of pollution, as well as the identification of factors affecting the spatial distribution of specific parameters. The most commonly used method is the research of bottom sediment cores, as it is regarded as the best way to investigate the history of a particular body of water and to determine the beginnings of anthropopressure (da Silva et al., 2000; Rognerud et al., 2000; Lacey et al., 2001; Yang and Rose, 2005; Liu et al., 2006; Lepane et al., 2007). On the other hand spatial analysis of the concentration of heavy metals in bottom sediments (Baudo et al., 1989; Li et al., 2011; Raulinaitis et al., 2012; Suresh et al., 2012) is less popular, probably due to the necessity of obtaining a sizable data set, which is usually costly and timeconsuming. Both the geochemical quality of sediments and the sources of contamination can simply be assessed by means of an analysis of the geochemical background, different pollution indices, such as, e.g., Enrichment Factor (EF), the Geoaccumulation Index (Igeo), the Metal Pollution Index (MPI) (Audry et al., 2004; Zahra et al., 2014; Kowalska et al., 2018), Principal Component Analysis (Loska and Wiechuła, 2003; Wang et al., 2012), or a wide spectrum of Geographic Information System (GIS) tools (Goodchild, 2009;

Valjarevi
c et al., 2018; Ji et al., 2019). The Wigry Lake is one of Poland's most valuable lakes and also one of the most interesting in terms of its origin, morphology, bathymetry, and sedimentation. These factors are very complicated, as has been demonstrated below. Therefore the modelling of the spatial distribution of heavy metals in the Wigry Lake entails many technical problems, which can be solved in several ways. Some experimental modelling has been done in the past, the most important of which was the MultiCriteria Evaluation (MCE) application (Kostka et al., 2008; Kostka, 2009). As a GIS tool, the MCE allows to map the spatial distribution of certain variables, i.e. selected spatial factors affecting an analysed parameter. Although good modelling results were achieved, some simplification was necessary and probably some information was lost. As a consequence, in the present study we decided to implement different tools in order to analyse the spatial distribution of Fe, Mn, and Zn in bottom sediments of the Wigry Lake more closely. The main hypotheses investigated in this study were as follows: (1) Whether and how the spatial distribution of heavy metals is associated with different types of bottom sediments; (2) Whether and how the spatial distribution of heavy metals is associated with lake depth; (3) Whether and how the spatial distribution of heavy metals is associated with the anthropogenic development of the area around the lake and its main tributaries; (4) Whether the sources of heavy metals in the lake environment are natural or anthropogenic in origin.

2. Materials and methods 2.1. Study area The Wigry Lake lies in NE Poland and is part of the Lithuanian Lakes District. In 1989 the lake has been incorporated into the Wigry National Park (WNP) and thus enjoys considerable protection (Fig. 1A). In 1998 the lake was “adopted” by the International
ski, 1999). Association of Theoretical and Applied Limnology (Kamin Its geomorphology has been formed by Weichselian (Vistulian, Baltic) glaciation and is clearly bifid in character. The northern part of the WNP features frontal moraines made of glacial till, gravels and rocks, as well as numerous kames and eskers, while the southern part is covered by extensive sandur (Drzymulska et al., 2014). The Wigry Lake is one of 42 lakes in the WNP. It is one of the deepest (max depth 73 m) and at the same time one of the largest (21.2 km2; 336.7 mln m3) in Poland. It is ribbon-shaped with a diversified shoreline, as is evident in the relatively high (4.35) degree of coastline development. The Wigry Lake has a total shoreline of 72.225 km, while the shorelines of its basin and islands are 59.850 km and 12.375 km in length, respectively. The islands cover a total area of 0.68 km2. Wigry is a postglacial, furrow-meltout type lake and assumed its present form between the Younger Dryas period and the Holocene epoch, when blocks of dead ice completely melted. The lake consists of five clearly distinguishable (in terms of both morphology and depth) parts (Figs. 1A and 7). The Zaka˛ towskie and Bryzglowskie Basins are morainic in form, characterized by numerous islands and coastal and offshore shallows. Szyja Basin and Wigierki Bay have furrow type features, while Wigierskie Basin is partly morainic and partly furrow. The waters of
cza and Wiatrołuza _ the lake are supplied mainly by the Czarna Han rivers, which flow into the Wigierskie Basin (Fig. 1A). Other sources include smaller streams and lakes, groundwater, and precipitations. The Wigry Lake is dimictic (with summer and winter stagnation, and spring and autumn mixing) and has an essentially mesotrophic to eutrophic character. The lowest recorded level of water quality in

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czan
ska Fig. 1. Study area. (A) Wigry Lake and Wigry National Park: 1 e Wigierskie Basin; 2 e Szyja Basin; 3 e Zaka˛towskie Basin; 4 e Bryzglowskie Basin; 5 e Wigierki Bay; 6 e Han
cza River inflow; 7 e Wiatrołuza _ River inflow; 8 e Postaw Lake/Czarna Han
cza River outflow; 9 e Cieszkinajki Bay; 10 e Krzyza _ n
ska Bay. (B) Sampling points grid. Bay/Czarna Han

the lake was noted in the 1980s (Niewolak, 2001; Pawlyta et al., 2004; Rutkowski et al., 2007). The sediments of the Wigry Lake have already been well documented, since both the lake and its sediments have been the subject of scientific study for over a century. However, the area has become especially well known to researchers in the years since 1997, mainly thanks to geochemical and seismic surveys (Rutkowski et al., 2005). The Wigry Lake is one of a few lakes in Poland with contemporaneous carbonate sedimentation, resulting from the dilution of deposits rich in carbonates (predominantly Paleozoic limestones and Devonian dolomites), and represented mainly by lacustrine chalk and carbonate gyttja. Lacustrine chalk, light in colour and usually coarse-grained, covers about 25e30% of the lakebed and occurs mainly in the littoral zone, as well as offshore or in-lake shallows. Occasionally it can also be found in some deeper zones. It is composed mainly of calcium carbonate (mostly >80% as dry) precipitated chemically and biologically, some organic matter, sand, crushed mussels and water. The profundal zone, encompassing about 60e75% of Wigry Lake's bottom, is primarily covered with fine-grained carbonate gyttja, which is dark in colour and composed of lower calcium carbonate content and higher organic matter content. The fluvial-lacustrine deposit found
cza River (Han
czan
ska Bay) is in the mouth of the Czarna Han almost black in colour and is composed mainly of poorly decomposed organic matter and water, while its CaCO3 content is very low. Similar deposits, although generally finer-grained and a little

lighter in colour, can be found in some isolated bays and eutrophication-exposed lake zones (especially Cieszkinajki and _ n
ska Bays; Fig. 1A). These are called detritus gyttja or organic Krzyza gyttja. Clastic sediments (sands and gravels) appear locally in costal zones in the form of narrow belts. Other deposits (e.g. conchoidal alluvion) cover less than 1% of the lake area and are less important (Rutkowski et al., 2002, 2007, 2008; Aleksander-Kwaterczak et al., 2009). The south and south-west parts of the lake adjoin a number of villages featuring extensive agricultural land (Fig. 2). Another sizeable agricultural area is located around the northern branch of the lake, where it alternates with isolated rural buildings. Forest stretches from the south-west and the west, as well as from the
cza River passes south-east. On the western side, the Czarna Han through the forest land and enters the lake from the west (Figs. 1A and 2). The regional capital of Suwałki (about 70 000 inhabitants) is located close to its shore, about 5 km to the northwest.

2.2. Geochemical analysis Beginning in 1997, sediment samples were collected during the summer months over a period of a dozen consecutive years using a gravitational sampler designed by Rutkowski (2007). The location of each sample was determined by GPS and its depth by echosounder (sometimes, in the case of shallows, by means of a

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Fig. 2. (A) Satellite image of the area around the Wigry Lake taken by a Landsat ETM þ satellite; the photo is a colour composition of the channels (321) i.e. Red, Green and Blue. (B) Land development map prepared by supervised classification on a basis of satellite image: 1 e waters; 2 e forests; 3 e farmlands, grasslands, and pastures; 4 e urbanized areas and roads.

Table 1 Basic statistical parameters of Fe, Mn and Zn concentrations in bottom sediments of the Wigry Lake. Data has been presented for 5 major sediment types typical for the lake environment, as well as for the whole data set. lacustrine chalk

carbonate gyttja

fluvial-lacustrine sediment

n min [mg kg1] max [mg kg1] mean [mg kg1] SD [mg kg1]

200 80.3 5588 983 943

217 484 10 654 3670 2098

21 5863 32 857 18 377 8061

n min [mg kg1] max [mg kg1] mean [mg kg1] SD [mg kg1]

200 17.9 206 94.3 33.3

216 55.5 1698 354 264

21 142 1373 518 370

n min [mg kg1] max [mg kg1] mean [mg kg1] SD [mg kg1]

200 4.63 103 17.8 12.7

213 7.12 119 44.6 17.6

21 84.5 632 339 182

organic gyttja

clastic sediment

all sediments together

9 2542 15 876 7529 5143

12 581 3181 1496 785

459 80.3 32 857 3191 4363

9 86.0 372 230 111

12 27.8 85.2 51.3 19.5

458 17.8 1698 238 244

9 6.19 106 62.7 33.9

12 3.14 60.2 14.7 16.2

455 3.14 632 46.0 77.8

Fe

Mn

Zn

calibrated pole). Over 1200 sediment cores were collected during the research period, of which just under 500 were eventually analysed for metal concentration levels (Fig. 1B, Table 1). In the case of the present study, the top 0e5 cm layer of each core (reflecting recent sediments) was taken. The samples were dried, homogenized, then treated with a 10 ml 65% HNO3/2 ml H2O2 mixture and decomposed in a microwave oven. The level of concentration of metals extracted as above (Fe, Mn, and Zn) was determined using the AAS (Atomic Absorption Spectrometry) method. Reagent blanks and certified reference material (LKSD-4) were used to ensure and evaluate analytical accuracy (Namie
snik and Szefer, 2008), and the reference material was additionally analysed with ICP-MS (Inductively Coupled Plasma Mass Spectrometry) method. On the basis of

Table 2 Comparison of results of an analysis of certified reference material (LKSD-4) using two analytical methods: Atomic Absorption Spectrometry (AAS) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS); p ¼ 95%. metal 1

Mn [mg kg ] Zn [mg kg1]

LKSD-4

AAS

ICP-MS

420 195

405 ± 12 190 ± 11

412 ± 7.9 202 ± 9.6

the obtained results (Table 2), the accuracy of the analytical method was assessed as very good. The obtained geochemical data was quality tested by way of an analysis of variance with Robust statistics method. It assumes that technical variance (combined sampling and analytical variances) for the geochemical data should not exceed 20% of the total variance (Ramsey et al., 1992; Ramsey, 1993). Technical variance for the Wigry Lake data did not exceed the limit value and amounted to 20% for Fe, 11% for Mn and 16% for Zn. Basic statistics (Table 1), as well as graphs visualizing differences between particular sediment types (Figs. 3Ce5C), were calculated and prepared using Statistica software. 2.3. Preparation of land development map The land development map of the areas surrounding the lake (Fig. 2B) was prepared on the basis of an image taken by a Landsat ETM þ satellite in June 2002 (Fig. 2A), i.e. during the extraction of samples from the lakebed. The photo was a colour composition of the channels (321) i.e. Red, Green and Blue. It provided the basis for a supervised classification that distinguished between four types of land cover: (1) waters; (2) forests; (3) farmlands, grasslands and pastures; (4) urbanized areas and roads. The stable classification of

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Fig. 3. Iron in bottom sediments of the Wigry Lake. (A) Map of spatial distribution of Fe in bottom sediments, prepared with use of ordinary kriging method and mapped against contours of 5 major sediment types characteristic for the Wigry Lake environment, prepared with use of Thiessen Polygons method. (B) Fe in bottom sediments mapped according to different geochemical backgrounds: GB1 e local geochemical background defined as a mean Fe concentration in lacustrine chalk underlying peat dated with 14C method at 7970 ± 70 years BP (344 mg kg1); GB2 e geochemical background in aquatic bottom sediments in Poland (1000 mg kg1); GB3 e geochemical background for carbonate rocks (3800 mg kg1). (C) Graph depicting basic statistics for Fe concentrations in 5 major sediment types of the Wigry Lake, with regard to geochemical backgrounds values.

Fig. 4. Manganese in bottom sediments of the Wigry Lake. (A) Map of spatial distribution of Mn in bottom sediments, prepared with use of ordinary kriging method and mapped against contours of 5 major sediment types characteristic for the Wigry Lake environment, prepared with use of Thiessen Polygons method. (B) Mn in bottom sediments mapped according to different geochemical backgrounds: GB1 e local geochemical background defined as a mean Mn concentration in lacustrine chalk underlying peat dated with 14C method at 7970 ± 70 years BP (82 mg kg1); GB2 e geochemical background in aquatic bottom sediments in Poland (500 mg kg1); GB3 e geochemical background for carbonate rocks (1100 mg kg1). (C) Graph depicting basic statistics for Mn concentrations in 5 major sediment types of the Wigry Lake, with regard to geochemical backgrounds values.

the coverage of all areas shown on the map was achieved by way of implementing learning sets consisting of 25 points for each type of terrain coverage.

2.4. Spatial analysis Information on the different sediment types identified during

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Fig. 5. Zinc in bottom sediments of the Wigry Lake. (A) Map of spatial distribution of Zn in bottom sediments, prepared with use of ordinary kriging method and mapped against contours of 5 major sediment types characteristic for the Wigry Lake environment, prepared with use of Thiessen Polygons method. (B) Zn in bottom sediments mapped according to different geochemical backgrounds: GB1 e local geochemical background defined as a mean Zn concentration in lacustrine chalk underlying peat dated with 14C method at 7970 ± 70 years BP (4 mg kg1); GB2 e geochemical background in aquatic bottom sediments in Poland (48 mg kg1); GB3 e geochemical background for carbonate rocks (20 mg kg1). (C) Graph depicting basic statistics for Zn concentrations in 5 major sediment types of the Wigry Lake, with regard to geochemical backgrounds values.

sampling was used for the purpose of creating a map of bottom sediments. Five such sediment types were identified on the basis of sediment samples (namely: lacustrine chalk, carbonate gyttja, fluvial-lacustrine sediment, organic gyttja and clastic sediment). The map was generated using the Thiessen Polygons method (ArcMap software), and with the assumption that only one type of deposit will be identified in the measurement point located within an area limited by a particular polygon (Figs. 3Ae5A). Maps of the concentration of elements (Fe, Mn and Zn) and the bathymetric map were generated via the interpolation of concentration (or depth) values measured at individual sampling points. The interpolation of the bathymetric data was additionally based on data from acoustic cross-sections (Rutkowski et al., 2005; Osadczuk et al., 2006). The selection of the appropriate interpolation method for the prediction of spatial variables depends on a number of factors, such as the availability of a physical model of the analysed phenomenon, which determines the spatial changes of concentration of elements, the non-zero autocorrelation of a spatial variable, and its spatial correlation with other factors which affect its distribution, or the expected results of the procedure of prediction (Pebesma et al., 2007). An initial analysis of the distribution of the concentration of metals revealed considerable variation in these values in each interpolated data set. It was also found that the metal concentration values measured for bottom sediments were non-zero-autocorrelated variables. One of the best ways to carry out interpolation is by kriging, and an analysis of each of the four datasets revealed ordinary kriging, which is considered to be the best unweighted estimator of unknown function values (Burrough and McDonnell, 1998), to be the most appropriate version of this method for the purposes of this research. The interpolation procedure for each of the analysed quantities was based on the following steps. First of all, the stationarity of the data was checked for each analysed data set. No spatial trend indicating non-stationarity was found for any of the analysed data

sets. The distribution of data was subsequently checked for compatibility with normal distribution. Quantile-Quantile charts (Q-Q plots) were used for this purpose (Gan and Koehler, 1990). The next stage of the analysis involved the estimation of empirical semivariograms (depicting spatial relations between data in a set) for the individual data sets and the estimation of the theoretical semivariogram, which represents its best approximation. The curvilinear regression method was employed for this purpose, and the best theoretical model was chosen by comparing the Root Mean Square Error (RMSE) resulting from the adaptation of the semivariogram model to the empirical semivariogram. In the case of iron concentration values the exponential model represented the closest fit, while in the case of both manganese concentration and zinc concentration values, the stable model was chosen. The Gaussian model represented the optimum fit for the bathymetric data. The next step of the analysis involved the calculation of concentration maps for particular metal concentration values, as well as the calculation of the depth map of the lake, on the basis of the selected semivariogram models and via interpolation using the kriging method. The result of interpolation was affected by the value of the radius of the neighbourhood from which data was taken to estimate the value of the function at a specific point. In order to ensure the quality of interpolation, the cross-validation procedure was used for each tested radius to compare the interpolation results and to select the optimum searching neighbourhood value. Two types of errors were implemented. The first was the Root Mean Square Error (RMSE), while the second was the Average Standard Error (ASE). The best prediction result was accepted as the one which returned the smallest RMSE and was closest to the ASE. The final maps of Fe, Mn and Zn concentrations (Figs. 3Ae5A) and the depth map (Fig. 7) were obtained on the basis of iteration. Maps illustrating the concentration of particular metals

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Fig. 6. Models showing spatial correlations between appropriate pairs of examined metals (FeeMn, FeeZn and MneZn) in bottom sediments of the Wigry Lake. High and positive correlations indicate that similar geochemical factors determine spatial distribution of Fe, Mn and Zn in sediments, and the most important of them are granulation of deposits and organic matter content.

additionally served to define the spatial distribution of correlation coefficients between the distribution of concentration of particular metals (Fig. 6). The correlation coefficient in each point of the regular grid was calculated on the basis of the values read from the map within a 5  5 rectangular area, using the ArcMap Raster Calculator Tool. Similarly, maps depicting the correlation between the depth of sediments and the distribution of concentration of each metal (Fig. 7) were based on the map illustrating the depth of sediments and on the distribution of concentration of particular metals. 3. Results and discussion Geochemical studies of lakes worldwide are often based on a small number of samples (from several to a dozen or so), taken most often from the deepest parts of the lake, which may not necessarily be representative of the lake environment as a whole. Greater emphasis is usually placed on the analysis of sediment cores, which provides an image of the lake's development history. Similar studies have been also carried out on the Wigry Lake and have been helpful in the reconstruction of its history throughout the entire Holocene epoch (Kupryjanowicz, 2007; Piotrowska et al.,
ska, 2007). 2007; Rutkowski et al., 2007; Zawisza and Szeroczyn However, what distinguishes the Wigry Lake from other lakes is the very extensive set of available data, including geochemical data (almost 500 samples), collected over the course of more than ten years of research. This data has enabled scrupulous modelling and interpretation of the spatial distribution of metals in the lake's bottom sediments. Spatial distribution models of Fe (Fig. 3A), Mn (Fig. 4A) and Zn (Fig. 5A) in the bottom sediments of the Wigry Lake indicate that the concentration levels of these metals are: (1) higher in the northern part of the lake than in its southern part (especially in the case of Fe and Mn); (2) lower in the lake's littoral zones and higher in its central part (in particular, Mn and Fe); and (3) clearly elevated
cza flows into the lake near the point where the Czarna Han (especially Zn) and slightly elevated near the mouth of the _ (Fe) (Fig. 1A). Wiatrołuza An analysis of the spatial distribution models for individual metals has revealed the dependence of these concentration levels on the type of bottom sediments present in the lake. Although attention has already been paid to this issue earlier, with reference to the Wigry Lake (e.g. Prosowicz and Helios-Rybicka, 2002; Aleksander-Kwaterczak and Kostka, 2011), as well as to other water
ski and Siepak, 2001), such a bodies (e.g. Zerbe et al., 1999; Sobczyn large-scale quantitative and especially spatial research of this kind has not been previously carried out. Due to the abundant

geochemical data gathered here, the Wigry Lake offers very good material for such analyses. The least iron-rich deposit is lacustrine chalk, while clastic sediment is the most deficient in manganese and zinc (Table 1, Figs. 3Ce5C). Fluvial-lacustrine deposit located in the mouth of the
cza River is by the far the richest in all examined metals. Czarna Han The average concentration of Fe in this sediment type is 19 times higher than in the least polluted lacustrine chalk, while in the case of manganese and zinc, the average concentrations are 10 times and 23 times higher, respectively, than in the least polluted clastic sediment. The main reason for the observed differences is the granular and chemical composition of sediments. Lacustrine chalk and clastic sediment are relatively coarse-grained. Clastic sediment is basically composed of sandy and gravel fractions that can be found in various proportions, which varies between all individual samples. Chalk concededly consists mainly of silt and clay fraction grains (<0.06 mm), but sandy fraction content amounts to 28e59% (39% on average), and even gravel fraction (>2 mm) can sometimes be observed. Sandy and gravel fractions of lacustrine chalk contain calcite tubes of Charophyta origin, and their microscopic observation has revealed that they are made of almost pure CaCO3, which indicates that most of the micro-pollution must be accumulated in the finer fraction (Rutkowski et al., 2002; Rutkowski, 2004). In turn, carbonate gyttja is almost exclusively composed of silt and clay fractions that are known to have the best capability in terms of the €rstner, 1984; Selvaraj et al., sorption of metals (Salomons and Fo 2004), while the admixture of sandy fraction in this type of sediment only amounts to approximately 1% (Rutkowski et al., 2002). Organic matter is also largely responsible for the accumulation of metals, mainly due to its large sorption surface (Coquery and Welbourn, 1995; El Bilali et al., 2002; Migaszewski et al., 2003; Salam et al., 2019). It can be found in high percentage values in carbonate gyttia (8e30%) and organic sediments, in particular in
cza (10e51%), while sediment found in the mouth of the Czarna Han organic matter content in lacustrine chalk amounts to 2e7% (Rutkowski et al., 2002, 2007; Aleksander-Kwaterczak and Prosowicz, 2007; Aleksander-Kwaterczak et al., 2009). Correlation coefficients describing relationships between organic matter content and the levels of heavy metal concentration were calculated; however, relevant data of organic matter content were insufficiently complete, as a part of lithological and geochemical research was performed on separate sets of samples. Thus statistically significant correlation coefficients could only be obtained for Fe and Mn, and in both cases the coefficient value was 0.83. In the course of other studies of the sediments of the Wigry Lake, Migaszewski et al. (2003) reported distinct positive correlation between Zn and total organic carbon (TOC) and clay minerals, while a weak correlation

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Fig. 7. Bathymetry map of Wigry lakebed and models showing correlations between depth and Fe, Mn and Zn concentrations, respectively. High and positive correlations indicate that depth of the lake is the most important spatial factor determining distribution of Fe, Mn and Zn in sediments, which is mainly associated with specific sedimentation conditions in the lake environment: fine-grained deposits (more susceptible to the sorption of metals) accumulate in profundal lake zones, while coarse-grained sediments (less susceptible to the sorption of metals) accumulate in litoral lake zones.

was also observed for FeeTOC. The correlations between specific pairs of metals were subsequently investigated and were found to be as follows: FeeMn 0.77; FeeZn 0.80; MneZn 0.75. The presented interdependencies, also shown in a spatial manner (Fig. 6), indicate that similar geochemical factors define the distribution of metals in sediments of the Wigry Lake. High correlation coefficients for appropriate pairs of metals (MneFe 0.76; FeeZn 0.96; MneZn 0.71) were also reported by Glasby et al. (2004). One of the most important of factors defining the distribution of metals in sediments is the tendency of Fe and Mn to coexist in the form of oxides and hydroxides, and these compounds have a very high sorption capacity in relation to metals (e.g. Zn). Other substances which are favourable to metal sorption are organic matter (discussed above) and clay minerals (Helios-Rybicka and Kyzioł, 1990; Helios-Rybicka, 1992; Kralik, 1999). Fe- and Mn-oxyhydroxides and clay minerals were found to be largely responsible for elevated content of Zn (and other trace elements) in sediments of the southern Baltic Sea, nearby the estuary of Vistula River (Szefer et al., 1995), while in case of Szczecin Lagoon aggregates of clay minerals and organic material were found to concentrate heavy metals (Glasby et al., 2004). Calcium carbonate, as a very important component of Wigry Lake sediments, could also be expected to correlate with the metallic content. The level of CaCO3 content present in particular types of Wigry Lake sediments is varied and amounts to: 52e98%, 94% on average for lacustrine chalk; 54e87%, 70% on average for carbonate gyttja; 7e16%, 8% on average for clastic sediment; and 3e13% for organic sediments (Prosowicz and Helios-Rybicka, 2002;

Rutkowski et al., 2002; Rutkowski, 2004). Nevertheless, the correlation coefficient is quite high only in case of Fe (FeeCaCO3 -0.54), while in case of Mn and Zn the strength of the analysed relationship is rather moderate (MneCaCO3 -0.22; ZneCaCO3 -0.38), which indicates that some other factors also significantly affect the spatial distribution of metals in the examined sediments. Another important factor affecting the spatial distribution of metals in Wigry sediments is the depth of the lake. This has already been pointed out in previous studies (e.g. Prosowicz and HeliosRybicka, 2002; Kostka, 2009), while the results of a more detailed analysis support the conclusion that of all the tested elements, those three metals, especially manganese, are the most strongly associated with depth. The relevant correlation coefficients are as follows: deptheFe 0.60; deptheMn 0.77; deptheZn 0.58. However, the previous study did not analyse the spatial nature of this correlation (because the tools used there did not provide for such a possibility), which was found to be inverse locally (Fig. 7). This is largely due to the specific nature of sedimentation in the Wigry Lake, where sediments in the profundal zone (fine-grained and relatively rich in organic matter) are more susceptible to the accumulation of metals, while lacustrine chalk and clastic sediment with lower sorption properties settle in the littoral zone. Finegrained sediments in water bodies accumulate in sheltered areas €rstner, and in areas not exposed to strong waves (Salomons and Fo 1984), i.e. the deepest parts of the lake, such as the Wigierskie _ n
ska and Szyja Basins, and isolated bays, like Cieszkinajki or Krzyza Bays (Fig. 1A). The strong correlation between concentration levels of Mn (and

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to a lesser extent of Fe) and the depth of deposit (0.77 and 0.60 respectively; see also Fig. 7) may also result from diagenetic processes. During sediment diagenesis, these metals undergo selective dissolution and migration in porewaters in an upwards direction, while this effect is enhanced by the presence of an organic sub€rstner, 1984). Hence the upper layers of stance (Salomons and Fo these sediments are constantly enriched with these elements. Acoustic studies (Osadczuk et al., 2006), have revealed that the deepest parts of the Wigry Lake are filled with the thickest layer of sediments, therefore this deposits experienced the most intensive enrichment. This would mean that higher levels of concentration of Mn and (to a lesser extent) Fe in the bottom sediments of the Wigry Lake are largely natural, while these elements are mainly the result of the weathering of post-glacial rock fragments that are present in abundance in northern Poland (Ber, 1989; Rutkowski, 2004). Another indication of this fact is the fairly high level of concentration of both Mn and Fe in fossil sediments (Migaszewski et al., 2003). Manganese and iron present in the sediments of the Wigry Lake may also come from the groundwater, which is fairly rich in these elements (Krzysztofiak, 2008). The sources of these waters are most likely located within the moraine upland surrounding the Wigry Lake basin to the north-east (Kostka, 2009). Manganese has the strongest correlation with depth (0.77; see also Fig. 7), which may be due to its slower oxidation compared to other metals (Hamilton-Taylor and Davison, 1995). Therefore, despite the considerable presence of this element in sediments
cza mouth (mean ¼ 518 mg kg1), its situated in the Czarna Han highest concentration (1698 mg kg1) has been observed in a sample of carbonate gyttja taken from the deepest part of Szyja Basin (Figs. 1A and 7, Table 1). On the other hand, by far the highest levels of concentration of Fe and Zn have been observed in the fluvial-lacustrine sediment found in the location of the entrance of
cza River into the lake. These sediments are the the Czarna Han most polluted, despite their shallow depth of settlement. Therefore the correlations between the depth and the levels of concentration of the studied metals in this area are opposite to the correlations observed in all other parts of the lake (Fig. 7). The process of formation of fluvial-lacustrine sediment is rather specific, probably as a result of the load discharge conveyed by the river as a result of a rapid change in flow parameters and physico-chemical parameters, which promotes the precipitation of pollutants carried by the
cza at the point where the waters of the river and lake Czarna Han merge (Niewolak, 2001; Zdanowski, 2003; Helios-Rybicka and Kostka, 2007). This observation, clearly visible in the models (Figs. 3Ae5A), especially in the case of Fe and Zn, indicates that the two largest
cza River) are tributaries of the lake (in particular the Czarna Han the main sources of the anthropogenic pool of the analysed elements in the lake. This observation is also confirmed by the ex
cza waters (Migaszewski et al., 2003). amination of the Czarna Han The physico-chemical characteristics of the waters of the Czarna
cza and Wiatrołuza _ rivers are variable both in terms of different Han river sections, as well as different parameters. The condition of these waters has been assessed as good, moderate or bad (Blusiewicz, 2017) and improves downstream of the lake (Zdanowski, 2003), which clearly indicates that the Wigry Lake is the main recipient of pollutants transported by its tributaries. The chemical condition of the waters of the lake has been rated as good in terms of most parameters, although in the case of some of them it has been rated less than good (Dorochowicz and Zega, 2018). Although the lake is situated within a national park, it should be remembered that Wigry receives pollutants from the entire catchment area, also from unprotected areas. The catchment area of the Wigry Lake is 453.7 km2, and the national park area is

9

148.4 km2 in size, 67% of which is covered by forest, 19% by water and 14% by agricultural land. Only 1.15 km2 of woodlands and 3.27 km2 of waters are strictly protected (Kostka, 2009). The geochemical condition of sediments can be evaluated on the basis of various international standards, proposed e.g. by the World Health Organization (WHO), the United States Environmental Protection Agency (USEPA), the Canadian Council of Ministers of the Environment (CCME) or the German Working Group on water issues of the Federal States and the Federal Government represented by the Federal Environment Ministry (LAWA). However, none of them provide a sufficient regulatory scope, due to e.g. local geochemical anomalies, diversified local conditions, different research methodology or diversified granulation of sediments being studied. Moreover, most classifications do not take account of iron and manganese. Various pollution indices may also be used to assess the level of pollution of sediments, and one of the most popular and apparently universal is the Geoaccumulation Index (Igeo) (Kowalska et al., 2018), proposed by Müller (1969). However, Igeo could not be implemented in the course of this study, as metal concentration values in fraction <20 mm are required, and in the case of the Wigry Lake only bulk samples of sediments have been examined. Therefore eventually, in order to assess the degree of pollution affecting the sediments of the Wigry Lake, metal concentration levels were compared with geochemical background (GB) values. Geochemical backgrounds may vary a lot, depending on how they are defined (Gałuszka, 2007). Background values for individual metals are usually defined as: (1) mean values of concentration in shale; (2) mean values of concentration in fossil sediments within a given region; (3) mean values of concentration in modern sediments of unpolluted regions; (4) mean values of concentration within particular sections of sediment cores within a given region €rstner and Wittman, 1979). Three different values of geochem(Fo ical background were implemented in the course of the current study. The local geochemical background (GB1) was defined as mean values of concentration of metals in lacustrine chalk under
l, 1998), lying peat dated with 14C method at 7970 ± 70 years BP (Kro and the corresponding values are as follows: Fe e 344 mg kg1, Mn e 82 mg kg1 and Zn e 4 mg kg1 (Prosowicz and Helios-Rybicka, 2002). The GB2 background was defined as mean values of concentration of metals in aquatic bottom sediments in Poland (Lis and Pasieczna, 1995), and the corresponding values are as follows: Fe e 1000 mg kg1, Mn e 500 mg kg1 and Zn e 48 mg kg1. Finally, GB3 was defined as mean values of concentration in carbonate rocks (as carbonate sedimentation dominate in the Wigry Lake environment), according to Turekian and Wedepohl (1961), and the corresponding values are as follows: Fe e 3800 mg kg1, Mn e 1100 mg kg1 and Zn e 20 mg kg1. It is worth noting that individual values vary greatly, and generally the GB1GB3. One should also bear in mind that the local geochemical background was defined on the basis of the research of lacustrine chalk and therefore may not be appropriate for all types of sediments typical for the lake. The comparison of the values of metal concentration in Wigry sediments with corresponding geochemical background values has been shown on maps (Figs. 3Be5B) and graphs (Figs. 3Ce5C). Most of the samples extracted from the Wigry Lake did not exceed the boundary values of all geochemical backgrounds; around 25% of samples for iron and zinc exceeded the maximum background values (GB3 for Fe and GB2 for Zn), while the corresponding value for manganese was just under 2%. Manganese is also the only metal, in case of which sediments with Mn concentration below GB1 (the lowest one) can be observed at the area of the lake (Figs. 3Be5B); one should bear in mind, however, that the sampling grid was not

10

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regular (Fig. 1B). In the littoral zone, which is usually covered with slightly polluted lacustrine chalk, the density of the samples was higher than in the more polluted profundal zones. The data presented above, in combination with the spatial distribution characteristics of Mn in sediments (Fig. 4A), as well as with the lowest (10-fold) difference in mean values of concentration of this element in different sediments, suggests that of all the tested metals, manganese is of the most natural origin. On the other hand, the Fe (Fig. 3A) and Zn (Fig. 5A) models, as well as the 19-fold and the 23-fold difference in the average values of concentration of these metals in different sediment types suggest that their sources are not only natural, but also anthropogenic. Built-up urban areas, especially the town of Suwałki, located to the NE of the Wigry Lake (Figs. 1A and 2), represent the main anthropogenic source of metallic pollution residing in bottom sediments in the Wigry Lake. A sewage treatment plant has been in operation in Suwałki since 1986, and in 1993e1995 it was modernized via the introduction of 3rd degree waste treatment technology. As a result, the quality of water in the Wigry National Park, which was at its worst level in the 1980s (Niewolak, 2001), significantly improved. However, there are still areas outside the town that are not covered by any organized sewage collection system. Rivers flowing into the Wigry Lake also collect wastewater and effluents from the countryside and arable land, mainly concentrated in the northern part of the lake (Fig. 2). Woodland on the other hand performs filtering functions and reduces the quantity of pollutants transported together with the surface runoff. Therefore the southern part of the lake, which is more densely forested, is also less polluted. Trophic state of the Wigry Lake has been constantly rising throughout the Holocene period, mainly due to the changes in climate which gradually became warmer; however, changes in the lake trophic state are considered to be influenced not only by natural, but also by anthropogenic factors visible in the course of the last several dozen years (Zawisza and
ska, 2007), with the maximum level of human-derived Szeroczyn pollution occurring in the 1960se1990s (Rutkowski et al., 2007).

4. Summary and conclusions The impressive set of available data on the geochemistry of the Wigry Lake sediments, along with the implementation of appropriately selected geostatistical tools, enabled the performance of a meticulous analysis of the spatial distribution of Fe, Mn and Zn in these sediments and allowed the authors to obtain answers to the questions posed at the beginning of the study. (1) It has been observed that the spatial distribution of the abovementioned elements is strongly associated with the sediment type, which in turn is a direct result of the physico-chemical properties of the sediment, each with a varying tendency to accumulate metals. It depends mainly on the chemical composition and the granulation of sediment. (2) The second important factor determining the spatial distribution of metals is the depth, which in turn affects the conditions of sedimentation. Manganese is the metal most strongly associated with depth. (3) The important sources of pollution
cza and present in the lake are its two main tributaries: Czarna Han _ rivers. These waterways collect pollution from the Wiatrołuza entire catchment area, and the lake is their main recipient. Another important factor affecting the spatial distribution of metallic pollution in the lake is the development of the surrounding area. (4) Metals present in the environment of the Wigry Lake are of both natural and anthropogenic origin. Spatial and geochemical analyses of the values of metal concentration in sediments led to the conclusion that manganese present in the lake environment is mainly of natural origin, while in case of iron and zinc

anthropogenic factors are also of significance. Spatial and geochemical analyses performed on the sediments in the Wigry Lake, which have a highly complex structure and environment, highlighted several issues that need to be addressed in similar studies. The pollution of sediments should be analysed with the consideration of their composition and properties, which further complicates any analysis if a given body of water contain more than one type of sediment. Another factor that requires attention is the morphology of the lake basin and the depth of this body of water. Local geological, geographic and geomorphological conditions should also be taken into account and the appropriate geochemical background should be selected, in order to ensure a more accurate assessment of the degree of pollution in a given environment. It is also worth noting that the Wigry Lake is unique in terms of the scale of research that has been carried out here over many years. This has been a valuable source of knowledge in many fields and has helped to obtain an interdisciplinary picture of the lake's environment, which is rarely possible due to time-related, financial and organizational limitations. Acknowledgements The study was financially supported by the Faculty of Geology, Geophysics and Environmental Protection at the AGH University of Science and Technology in Cracow, Poland (no. 16.16.140.315). The authors would especially like to thank Professor of AGH, Jacek Rutkowski, whose passion and commitment has made it possible to conduct interdisciplinary research in the Wigry Lake area over many years, as well as his colleagues, including employees and friends of the Wigry National Park. References Akhurst, D.J., Clark, M.W., Reichelt-Brushett, A., Jones, G.B., 2012. Elemental speciation and distribution in sediments of a eutrophied subtropical freshwater reservoir using postextraction normalisation. Water Air Soil Pollut. 223 (7), 4589e4604. https://doi.org/10.1007/s11270-012-1220-7. Aleksander-Kwaterczak, U., Prosowicz, D., 2007. Distribution of Cd, and Pb in the
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