Next Article in Journal
Multi-Scale Application of Advanced ANN-MLP Model for Increasing the Large-Scale Improvement of Digital Data Visualisation Due to Anomalous Lithogenic and Anthropogenic Elements Distribution
Next Article in Special Issue
The Features of Distribution of Chemical Elements, including Heavy Metals and Cs-137, in Surface Sediments of the Barents, Kara, Laptev and East Siberian Seas
Previous Article in Journal
Synchrotron-Based HR-Fluorescence and Mineralogical Mapping of the Initial Growth Stages of Polynesian Cultivated Pearls Disprove the ‘Reversed Shell’ Concept
Previous Article in Special Issue
Heavy Metals in Sapropel of Lakes in Suburban Territories of Vilnius (Lithuania): Reflections of Paleoenvironmental Conditions and Anthropogenic Influence
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Minerals
Volume 12
Issue 2
10.3390/min12020173
minerals-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Spatial Variability of Metals in Coastal Sediments of Ełckie Lake (Poland)

by
Elżbieta Skorbiłowicz
*,
Weronika Rogowska
*,
Mirosław Skorbiłowicz
and
Piotr Ofman
Department of Environmental Engineering Technology and Systems, Faculty of Civil Engineering and Environmental Sciences, Białystok University of Technology, 15-351 Bialystok, Poland
*
Authors to whom correspondence should be addressed.
Minerals 2022, 12(2), 173; https://doi.org/10.3390/min12020173
Submission received: 16 December 2021 / Revised: 26 January 2022 / Accepted: 26 January 2022 / Published: 29 January 2022
(This article belongs to the Special Issue Heavy Metals in Marine and Lake Sediments)
Browse Figures
Versions Notes

Abstract

:
This study aimed to determine the content and spatial distribution of metals (Ca, Mg, Fe, Na, K, Mn, Zn, Cr, Cu, Pb, Co) in sediments in the coastal zone of Ełckie Lake located in the area of "Green Lungs of Poland" in the north-eastern part of the country, depending on the land use (urban area, agricultural and forest area, and beaches). The concentration of metals was determined using atomic absorption spectrometry. The average contents of major elements in 28 sediment samples occurred in the following order: Ca > Mg > Fe > Na > K > Mn. The order of these elements in the coastal sediments located within the different parts of the catchment was identical. These elements may originate from natural sources such as the Earth’s crust, soil, and wind-blown dust from unpaved roads. The average contents of potentially toxic elements (PTEs) in the sediments were as follows: Cr > Zn > Pb > Cu > Co in agricultural and forest areas and beaches (the exception was Cu for beach B, which occurred at the end of the series). A different pattern occurred in urbanized areas: Zn > Cr > Cu > Pb > Co. The spatial distribution of heavy metals in the sediments indicated the highest contents in the shoreline adjacent to the urbanized part of the catchment. The primary sources of metals in sediment are transportation, coal burning, sanitary sewage from unsewered developments on the lakeshore, and storm runoff from roads. This was confirmed by positive correlations of Zn with Cu (r = 0.58), Pb (r = 0.90), Fe (r = 0.40). No correlations between the studied metals and organic matter were found, which may indicate its insignificant influence on metal content in the sediments. Pearson correlation coefficients also showed no relationship between sediment pH and the presence of metals. Factor analysis (FA) indicated that lithogenic (geogenic) and anthropogenic factors have almost equal shares in the distribution of most of the metals studied. The analysis of variance (ANOVA) showed that the average contents of Zn, Cu, Co, and Na in the sediments from urbanized areas are statistically significantly higher than the sediments from other areas (rural/forest, beaches).

1. Introduction

The ongoing intensification of human activity and scientific and technological progress have increased the number of processes affecting the environment. These often lead to adverse changes in water, soil, or air quality, thus affecting humans, animals, and plants [1,2]. Aquatic ecosystems located in urbanized areas are the final link in the transport of pollutants such as heavy metals and biogenic elements [3]. Lakes in depressions of the land are natural receivers of pollutants from the catchment area. Lake sediments have a high capacity to retain various pollutants, whether organic, chemical, mineral, or physical, and they also carry these pollutants and can be their secondary source [4,5]. For this reason, they are excellent indicators of the state of the aquatic environment, and they also play a significant role in shaping the chemical composition of surface waters [6]. Heavy metals are among the most common pollutants that adversely affect aquatic ecosystems [7], causing permanent biogeochemical effects [8]. They are not biodegradable, and their mobility and bioavailability depend on many factors—including temperature, pressure, pH, salinity, chemical form, redox conditions, and organic matter content [9]. Considering water pollution, the crucial trace and toxic elements are Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn. Some of these metals (e.g., Co, Cr, Cu, Ni, and Zn) are also essential for the development and functioning of organisms but are toxic at high concentrations [10].
The content of heavy metals in sediments is an excellent tool to estimate changes in catchment areas and allows assessment of the severity of anthropopressure within the reservoir [11]. Contamination of lakes with metals depends considerably on the degree of urbanization of the area, the intensity of reservoir use, agricultural production, and emission of gases and dust, also from natural sources, introduced into the sediments at a point or in a diffuse manner [12]. The most polluted lakes are urban ones. Crucial metal sources in such lakes include vehicle emissions, atmospheric pollution, stormwater runoff [13], coal combustion [14], and accompanying resuspension and surface runoff. Roads and vehicle traffic are likely some of the most significant sources of metals in the environment [15]. Urban dust is more contaminated with metals than highway dust due to different driving conditions (there is a higher frequency of braking in cities, which causes additional contamination of road dust with metals through, among other things, wear of brake pads) [16]. High loading of heavy metals in urban environments notably occurs among impervious surfaces (urban sidewalks, roadways) [17].
The subject of this paper was the study of sediments collected in the coastal zone of Ełckie Lake, located in the north-eastern part of Poland, in the area of the “Green Lungs of Poland”. The human activities associated with tourism and the impact of urban development expose the lake ecosystem. To our knowledge, there are no comprehensive studies on the content of metals in the sediments of this lake. Analyses of the contents of nutrients in water and sediments have already been performed [18]. The pressure from the tourist traffic and the influence of the urbanized part of the catchment may cause degradation of the natural values of the lake.
Due to the unquestionable importance of water resources, the protection and sustainable management of aquatic ecosystems is a priority in environmental programs worldwide. Polish water resources are below the European average, and they are exposed to pollution from agriculture, industry, and tourism; hence, it is essential to protect the existing water reservoirs. The crucial issue in the protection of freshwater reservoirs is the implementation of appropriate protection measures within the catchment area, allowing for a radical reduction in pollution loads introduced not only from point sources but also from non-point sources.
The objectives of the study were to: (1) determine the content and spatial distribution of metals (Ca, Mg, Fe, Na, K, Mn, Zn, Cr, Cu, Pb, Co) in sediments in the coastal zone of Ełckie Lake depending on land use (urbanized area, agricultural–forest area, and beach); (2) determine the potential environmental risk of heavy metals (Zn, Cr, Cu, Pb, Co) based on sediment classification using geochemical indices: geoaccumulation index (Igeo), contamination factor (CF) and pollution load index (PLI); and (3) attempt to identify the sources of metal pollution by multivariate statistical analysis and correlation analysis. This study also compared the metal pollution of Ełckie Lake with other lakes in the world by catchment type. The results provide new information on the heavy metal contents that will allow the identification and classification of the sources of the studied metals within the lake, thus contributing to the knowledge and better management of this region in the future. The study may be applicable to finding a proper solution between economic development, population needs, and protection of the natural values of the lake ecosystem.

2. Materials and Methods

2.1. Study Area

Ełckie Lake (5348′24″ N 2220′58″ E) (Figure 1) is in the central part of the Ełk Lake District, within the Ełk municipal district (Poland). There are three parts of the lake: the upper, the middle, and the southern. An artificial peninsula and a road bridge isolate the upper and middle sections, whereas the middle and southern parts are separated by narrowing and shallows. The reservoir area is 382.4 hectares.
In general, reservoirs with a large surface area are less susceptible to degradation than small ones. Furthermore, the higher the water volume in a lake, the less susceptible it is to degradation. The volume of Ełckie Lake is 57,420.3 thousand m3. In addition, the resistance to biodegradation of a lake increases with the reservoirs’ depth. The average depth of Ełckie Lake is 15.0 m. The length of the shoreline determines the area of contact between the lake and the catchment; the longer the shoreline, the more pollutant migration paths generally connect the catchment and the reservoir. The shoreline length, the total catchment area, and the immediate catchment area of Ełckie Lake are 18,650 m, 979.8 km2, and 315.1 hectares, respectively.
The land-use pattern of the catchment influences the degree of threat to the lake from external factors. The percentage shares of the individual land use forms in the Ełckie Lake catchment area are as follows: urbanized land—37.3%, arable land—31.4%, forests—16.6%, crops and allotments—9.7%, arable land with a high proportion of natural vegetation—3.3%. Farmlands and forests lie on the western side of the lake, and it is clear that agriculture can significantly transform the natural environment. Non-point pollution reaches the water reservoir from cultivated fields along with surface runoff. On the eastern shoreline of the lake sit urbanized areas, i.e., the buildings of the city of Ełk (population 61,523), from which most of the pollution probably originates. Ełk is one of the most prominent tourist centers in the "Green Lungs of Poland" and is an excellent base for trips to surrounding towns. There are two municipal beaches by the lake, in the vicinity of which there are campsites, sports fields, guarded bathing beaches, and water equipment rentals. Ełk is a typical tourist town with only a small industry (wood, meat, and food processing). The eastern side of the lake is polluted mainly by the sanitary sewage from the unsewered residential estates located on the shores of the lake, rain sewage discharges, and traffic pollution (street dust from the abrasion of tires, brake linings, and asphalt, containing heavy metals).
The main climate characteristics are long winters, short springs, a relatively short vegetation season, high humidity (average annual 80%), and frequent fog. The average annual air temperature ranges from 4.8 to 8.3 °C. There is also relatively high precipitation, averaging 600—700 mm per year [19].

2.2. Sample Collection and Preparation

The study subjects were sediments (28 samples) collected in August 2019 in the coastal zone of Ełckie Lake. The littoral zone of urban lakes is the most liable to the migration of pollutants from the catchment. Metals from the topsoil of the catchment are transported to waters by surface runoff and erosion and then accumulated on the sediment surface [20]. The number of samples and their location depended on land use; accessibility and anthropogenic activities around the lake were the main criteria for choosing research points [21]. There are four groups: (1) urbanized areas (points 1–6); (2) agricultural–forest areas (points 7–11), (3) beach B (points 12–18), and (4) beach A (points 19–28) (Figure 1). The points of the first group sit on the eastern shore—within the city, near shoreline outlets of storm drains, roads, and parking lots. The second includes points from the western, less urbanized shore of the lake. There are mainly fields, forested areas, and also single-family dwellings nearby. Most of the samples come from the beaches due to the prominent use of the lake’s tourist potential. Both beaches are infilled and have similar morphodynamics. Beach B is characterized by a much higher number of visitors due to the amenities of a large parking lot, water equipment rentals, playing fields, and changing rooms, which are not available at beach A.
Sediments were sampled in the shoreline area. Ten individual samples were collected from a depth of 0–5 cm, from beneath the water’s surface, at each designated point. The crucial layer where water–sediment interactions occur and biological activity is relatively high is that of surface sediments [22]. The collected material was blended to obtain a representative sample (approximately 1000 g) from each point. After the transportation of the samples to the laboratory, pH in the water was measured by the potentiometric method and then dried in an “air-dry” condition and stored for further analyses. Before determination organic matter content by ignition loss at 550 °C, samples were dried at 105 °C.
The analysis of the granulometric composition of the sediments was performed using the sieve method, which consisted of separating the sample of raw, air-dry sediments into granulometric fractions using an appropriate set of sieves (mesh sizes: 2, 1, 0.2, 0.1, and 0.063 mm) connected to a mechanical shaker. After sieving, the fractions from each sieve were weighed, and the percentage of sample masses from the respective sieves to the total sample mass was calculated.
Due to the lack of binding criteria in Poland, the quality of bottom sediments was assessed based on geochemical criteria and the classification of local sediments made by Bojakowska and Sokołowska [23]. The data they used came from studies of sediments in the <0.2 mm fraction.
One gram of dry sample (fraction <0.2 mm) was placed in Teflon dishes; 8 mL of HNO3 and 2 mL of 30% H2O2 were added, and the sample was digested in a microwave digestion system (Ethos Easy, Milestone, Italy) according to the instructions. The samples were filtered and quantitatively transferred into 50 mL volumetric flasks. All solutions were prepared using ultrapure water. Before starting the procedure, laboratory glassware was drenched in nitric acid (8%), washed with tap water, then rinsed with deionized water. Metal concentrations were determined using flame atomic absorption spectrometry on a Thermo Scientific AAS ICE 3500 spectrometer (Thermo Scientific Portable Analytical Instruments Inc., Tewksbury, MA, USA). All measurements were performed in triplicate. Analysis results were verified using the certified reference material for sediments NCS DC 73317a. The measurement results of the standard reference material showed good agreement with the certified values (Zn = 96%, Cu = 101%, Pb = 95%, Cr = 80%, Mn = 97%, Fe = 98%, Co = 93%, Ca = 99%, Na = 89%, Mg = 97%, K = 95%). Reference material values were measured at the beginning and after each series of analyzes.

2.3. Assessment of Sediment Contamination

The geochemical background of Poland [23], global background [24], and selected indices—geochemical index (Igeo), contamination factor (CF), pollution load index (PLI)—were applied to assess the level of contamination of lake sediments.
The geochemical index (Igeo) is applied to assess pollution levels by individual heavy metals. Igeo has a precise scale to determine the degree of contamination of the matrix under study and is widely used for soil and sediment [25]. The Igeo value was calculated using the formula [26]:
Igeo = log2[Cn / (1.5 × Bn)]
where Cn means the measured content of the analyzed metal (mg∙kg−1), Bn is the geochemical background concentration [23]. The constant 1.5 is the background matrix correlation coefficient due to lithological variability.
A contamination factor (CF) is applied to assess soil and sediment quality, especially concerning the presence of toxic elements. It assesses the difference between the metal concentration in the sample and reference values. The application of this index is simple, and the result is related to a precise scale, which allows determining the contamination of the matrix [25,27].
CF = Cmetal/Cbackground
where Cmetal means metal concentration in the sample and Cbackground is the geochemical background.
The pollution load index (PLI) is a complex index, allowing comprehensive assessment of the contamination of soil/sediment with heavy metals. It combines any number of analyzed elements and allows the comparison of contamination at different locations in the soil [25,28].
PLI = (CF1 × CF2 × … × CFn) 1/n
where n is the number of metals analyzed.
Table 1 presents interpretations of the values of the used indices.
The concentrations of the studied metals (Ca, Mg, Fe, Na, K, Mn, Zn, Cr, Cu, Pb, Co) refers to air-dry sediments. The results were compared with literature data for different lakes worldwide, considering the type of catchment: urbanized, agricultural–forest, and beaches.

2.4. Statistical Analysis

STATISTICA 13.1 (TIBCO Software Inc., Palo Alto, CA, USA) was used to perform all statistical analyses. The Shapiro–Wilk test was applied to assess the distribution normality of the tested variables. When the test showed statistically insignificant values at p > 0.05, the distribution of the variables was considered similar to normal. In all tests, the statistical significance was assumed to be 0.05. Firstly, descriptive statistics were used to determine mean, maximum, and minimum values. The Pearson correlation coefficient was applied to study the relationships between metals in sediments and identify their sources. One-way analysis of variance (ANOVA) showed significant differences between the study groups. Levene’s test was applied to test for homogeneity of variance. Tukey’s post hoc test was used to examine the differences between the means of the groups. The logarithmic version of the Box–Cox transformation was used to transform the data not having a normal distribution to a normal distribution. Then, the Kaiser–Meyer–Olkin (KMO) criterion was calculated, and Bartlett’s test of sphericity was performed. The KMO criterion value (0.6) enabled the use of factor analysis (FA). Factor analysis (FA) is the most common multivariate statistical method applied in environmental studies and is used to extract a small number of latent factors to analyze the relationships between observed variables [29]. FA was applied to identify sources of metals in sediments. This study’s number of factors was based on the Kaiser’s criterion and the scree plot criterion. In order to interpret FA results, it was assumed that the relations of the original variable with the factor are strong when the absolute values of its charges are higher than 0.70. The purpose of FA is usually the interpretation of individual factors’ influence on the variables. Cluster analysis (CA) via Ward’s version was used to investigate possible sources of heavy metals in sediments based on data classification.

3. Results

3.1. Selected Parameters Shaping Metal Content in Coastal Sediment

Metal contamination in sediments is an increasingly global problem [30,31]. Only a fraction of free metal ions is dissolved in water [32], whereas over 90% of the heavy metal load in aquatic systems is associated with suspended particles and sediments [33,34,35]. Thus, the distribution of metals in the coastal sediments of lakes with diverse catchment characteristics may provide evidence of anthropogenic impact on an ecosystem. Various factors, including sediment nature, granulometric composition, pH, and organic matter content, determine the metal concentrations in the sediments.
Sediment pH affects the solubility of metals [36]. High pH values promote adsorption and precipitation, whereas low pH can weaken the binding strength of metals and hinder their retention by sediments [37]. The pH values ranged from 7.8 to 8.3 pH (Table 2). There was no significant variation in the areas analyzed.
Organic matter (OM) strongly impacts the bioavailability and toxicity of metals in sediments [38]. Its presence arises from the decomposition of dead biota under bacterial activity [21,39]. The high OM content may increase the adsorption of pollutants in the aquatic environment [40]. The intensive application of agricultural fertilizers, population growth, and sewage inflow may increase OM content [41]. The highest average amounts of OM were in agricultural and forestry areas (1.14%), slightly lower in urban areas (0.98%), and the least amount was on beaches (A—0.88% and B—0.67%) (Table 2). However, the variation in OM content was minor.
Sediment grain size is a crucial determinant of adsorption and considerably influences metal bioavailability [42]. Particle size is an essential property of sediments that affects their physicochemical properties, erosion, and sedimentation [43]. In general, we suppose that the finer fraction holds the most considerable load of examined metals due to its much higher specific surface area, which causes much greater heavy metals’ adsorption on sediments. However, the role of the coarser sediment fractions should not be excluded [44,45,46]. The surface sediments of Ełckie Lake are mainly sandy fractions (>98%). Results clearly showed that the 0.2–1.0 mm fraction (average 54.2%) prevails in the studied sediments. In addition, in most research points, the following fractions also have a significant share: 0.1–0.2 mm (average 16.2%), >2 mm (average 15.2%) and 1.0–2.0 mm (average 12.1%). The agricultural and forestry areas had a high proportion of fractions >2.0 mm (average 28.8%) compared to the other groups, which potentially reduced the sorption capacity of the sediments. As an example, Lake Guaíba has a similar granulometric composition [47]. The coarse-grained fraction mainly derives from erosion of the rocks present in the catchment area, and only a small part is of anthropogenic origin. It is dominated by the products of physical weathering of the underlying rocks, whereas components derived mainly from chemical weathering prevails in finer fractions [23]. The two most important factors affecting metal enrichment are sediment grain size distribution and organic carbon content [48].

3.2. Concentrations of Major Elements and Five Potentially Toxic Elements (PTE) in Sediments

The study of the coastal sediments of Ełckie Lake based on the average metal contents showed the following order of elements in examined areas (Table 3):
  • urbanized: Ca > Mg > Fe > Na > K > Mn > Zn > Cr > Cu > Pb > Co,
  • agricultural and forest: Ca > Mg > Fe > Na > K > Mn > Cr > Zn > Pb > Cu > Co,
  • beach A: Ca > Mg > Fe > Na > K > Mn > Cr > Zn > Pb > Cu > Co,
  • beach B: Ca > Mg > Fe > Na > K > Mn > Cr > Zn > Pb > Co > Cu.
The average concentrations of major elements were identical in all regions and followed the descending order: Ca > Mg > Fe > Na > K > Mn. These may come from natural sources such as the Earth’s crust, soil, and wind-borne dust from unpaved roads. Natural factors such as rock weathering and leaching processes are of minor importance but can cause locally elevated concentrations of some elements [49].
Elements such as Ca, Mg, K, Na, Fe, and Mn are common in regional soils and mineral sediments. Analysis of coastal sediments in Ełckie Lake showed the highest average Ca content (31,415.4 mg·kg−1), which occurred in agricultural and forestry areas, which may be related to liming of fields by farmers. The highest Mg concentration was on the beaches (A—4410.2 mg·kg−1, B—4865.2 mg·kg−1). In urban areas, the average concentration of Fe was the highest (2609.5 mg·kg−1). Higher Fe contents in cities may be related to pollutants from vehicular transport [50]. For instance, the abrasion of vehicle brake pads constantly used to reduce vehicle acceleration may cause relatively high emissions of this element [51]. In urbanized (560.0 mg·kg−1) and forested areas (523.2 mg·kg−1), average Na concentrations were similar. In sediments close to beaches, the content of Na was slightly lower. The highest concentration of K was found in urbanized areas (302.4 mg·kg−1), whereas the contents were similar on both beaches (A—217.7 mg·kg−1, B—234.6 mg·kg−1). The highest amount of Mn occurred in the urbanized areas (97.8 mg·kg−1), although there was not much variation between the studied localizations. Mn originates from the soil, and significant amounts of this element may also come from vehicle emissions such as tire wear or dust resuspension [52,53]. In addition, Mn content in soil is related to methylcyclopentadienyl manganese tricarbonyl (MMT), which has displaced tetraethyl lead used as an antiknock additive in gasoline and oils [54].
The study focused on PTEs because they pose a risk to the environment and living organisms. PTEs concentrations in sediments from agricultural and forestry areas and beaches follow the order: Cr > Zn > Pb > Cu > Co. The exception was Cu in the case of beach B, which occurred at the end of the series. The order of PTE concentrations in sediments from urbanized areas differs from the above and was Zn > Cr > Cu > Pb > Co. Studies [55] showed the influence of urban activities as a source of heavy metals in lake sediments. The metals most commonly found in the highest concentrations in lake sediments from urbanized catchments are Zn, Cr, and Cu [56], whereas Cu, Pb, and Zn are most commonly found in high concentrations in urban runoff [12]. The same relationships occurred in this study. The average metal contents in Ełckie Lake sediments from urbanized areas are dominated mainly by Zn. The occurrence of heavy traffic in the vicinity of research points 1–6 (Figure 1) is the main source of heavy metal contamination of sediments there. Heavy metals accumulated on paved surfaces due to vehicle emissions are washed away during rain and enter the lake through storm runoff. Moreover, sanitary wastewater from unsewered housing developments located along the lakeshore can be a source of metals. The mean content of Zn (30.4 mg·kg−1) and Pb (9.3 mg·kg−1) in sediments from urbanized areas was about three times higher than in agricultural and forest areas (10.8 and 3.4 mg·kg−1, respectively) and beaches (A—10.4 and 2.7 mg·kg−1, B—11.2 and 3.2 mg·kg−1, respectively). Tire abrasion during vehicle exploitation emits high Zn amounts [15,57]. Tires are believed to contain approximately 1.3–1.7% [58] to 4.3% [59] Zn. Moreover, the presence of Pb in lake sediments may be related to vehicle emissions [60]. Most vehicles use unleaded gasoline, but the element remains in the environment that is especially exposed to heavy traffic [61]. Pb can also enter lake surface sediments from coal combustion and associated dust resuspension. High Cu concentrations (11.1 mg·kg−1) were also recorded in sediments from urbanized areas, about ten times higher than from beaches (A—1.1 mg·kg−1, B—0.7 mg·kg−1) and five times higher than from agricultural–forest areas (2.0 mg·kg−1). The presence of Cu in sediments from urbanized areas may also be related to communication. Cu is common in car bearings, brake linings, and other engine parts [62]. Car exploitation causes wear and tear of the metal, and Cu is released into the environment [57]. The contents of Co (1.2 mg·kg−1) and Cr (13.1 mg·kg−1) in sediments from urbanized areas occurred at only slightly higher levels than in beaches (A—0.7 and 12.4 mg·kg−1, and B—0.9 and 11.8 mg·kg−1, respectively) or agricultural–forest areas (0.6 and 12.7 mg·kg−1, respectively). Cr may be present in road dust in urbanized catchments, which occurs due to the friction of tires and road surfaces, from marking paint and anti-corrosion coatings on vehicles and safety barriers [15]. Analysis indicates that sediments from urbanized localizations were more contaminated with heavy metals than beaches or agricultural–forest areas (Table 3). Urbanization has led to heavy metal contamination of urban soils and surface sediments of urban lakes due to anthropogenic sources such as transportation and coal burning [14].

3.3. Contamination Indices (Igeo, CF, PLI)

Figure 2 shows Igeo, CF, and PLI values calculated for PTEs in coastal sediments. The geoaccumulation index is a well-known parameter used to determine sediment contamination by toxic elements [25,63,64]. The average Igeo values for metals followed the order Cr > Co > Pb > Zn > Cu. The highest average Igeo values occurred for Cr, indicating moderate contamination by this element. According to the standard of the degree of contamination [26], the mean Igeo values suggest that the studied sediments are uncontaminated by Co, Pb, Zn, and Cu. The maximum Igeo values for Pb (0.6, point 6) and Cu (2.5, point 1) show the highest amounts of these metals occurred at these sampling sites. These points sit on the urbanized part of the lake shoreline. This observation is consistent with the metal concentration analysis and suggests that the heavy traffic intensity, high population densities, and sanitary sewage from unsanitized residential areas located on the lakeshore affect the distribution of metals.
The average values of CF followed the order Cr > Cu > Pb > Co > Zn. The index calculated for beaches and agricultural and forestry areas proved low contamination by Zn, Cu, Pb, and Co. Points 1 and 6 (urban area) were moderately contaminated by Zn and Pb; significant contamination by Cu was detected at point 1 (urban area), suggesting significant anthropogenic contributions of these elements at the above sites. In addition, points 5, 6 (urban area), 9, and 10 (agricultural and forestry area) were significantly contaminated by Cr. Contamination caused by this element was moderate in all other points. Cr contamination in the studied sediments may come from geogenic and anthropogenic factors, especially road dust.
PLI is a simple tool to compare the pollution status at different sites. The PLI values assessed from Zn, Cu, Pb, Cr, and Co contents in the sediments ranged from 0.3 to 1.8. The highest values occurring in the urbanized area indicate the most polluted sites in the sediments of Ełckie Lake (points 1 and 6, PLI equal to 1.8 and 1.3, respectively). Both of these points sit close to the outlets of the stormwater drainage system, near high-traffic intersections. A PLI value >1 indicates contamination and suggests an anthropogenic source of pollution. In points in agricultural and forestry areas and beaches, PLI values were low.

3.4. Spatial Distribution of Metals

The spatial distribution of metals (Figure 3) in coastal sediments helps distinguish sites with high metal content and shows the general distribution trend of potentially toxic elements in sediments. The most contaminated sediments are found in point 6 (Zn 61.6 mg·kg−1, Pb 22.2 mg·kg−1, and Cr 15.5 mg·kg−1). This point is located near a street that is one of the main transportation routes of the city. It suggests that the high contents of these elements originate from anthropogenic sources related to transportation and communication. High Cu concentration (51.7 mg·kg−1) was found at point 1. This point is located close to the outlet of the stormwater drainage system, and there is a junction connecting three streets with heavy traffic nearby. In addition, the highest concentration of Co (1.6 mg·kg−1) was at point 5. The study showed the highest amount of Mn (136.4 mg·kg−1—point 9), Na (864.7 mg·kg−1—point 7), Fe (3770.0 mg·kg−1—point 11), and Ca (50,447.3 mg·kg−1—point 11) in the agricultural and forest areas. Typically, higher amounts of Mn and Fe in forest catchments, and Ca and Na in agricultural catchments, originate due to eluting these elements from soils periodically deprived of plant cover. The highest amounts of Mg (8814.0 mg·kg−1) and K (568.5 mg·kg−1) occurred at point 19 (beach A).
The results of the study were compared with metal concentrations in sediments in other lakes around the world (Table 4). Comparing metal concentrations within the beaches, other authors obtained similar or lower results, except for Cr and the macronutrients: Ca, Na, K, and Mg. In this study, these elements occurred in higher amounts. Heavy metal contents in the sediments of lakes with urbanized and agricultural–forest catchments were generally noticeably higher in other studies.

4. Discussion

Identification of Pollution Sources Using Statistical Analyses

Pearson correlation coefficients between elements provide valuable information about the sources of elements in the environment [69,70,71]. Positive correlations of Zn with Cu (r = 0.58), Pb (r = 0.90), and Fe (r = 0.40) were found (Table 5). These relationships mean that these metals tend to accumulate together, and the result is their co-occurrence and interdependence; they come from similar sources and migrate together [72,73,74]. Sources of Cu, Pb, Zn, and Fe can include domestic and industrial wastewater, motor vehicle traffic, and human activities, including tourism [75]. Mn shows weak correlations with other trace metals, indicating its independent variability in the studied sediments [76]. Correlations of Fe with Ca (r = 0.47), Mg (r = 0.37), and K (r = 0.60) were also detected. Increased concentrations of Fe and Mn in the surface layer of sediments may not result as much from pollution as from their accumulation due to redox processes at the sediment–water interface. There were no correlations found between the metals and organic matter, which may indicate its small contribution to shaping the metal content of the sediments. The lack of correlation may also be related to the absence or low amount of fulvic or humic acids in coastal sediments. These compounds have a high metal adsorption capacity [77,78]. Correlation analyses also did not show an association between sediment pH and the presence of metals.
One-way analysis of variance (ANOVA) was applied to test the significance of the relationship between area type and metal distribution in the coastal sediments of Ełckie Lake. Calculations showed a statistically significant (ANOVA) difference in mean contents of Zn, Cu, Co, and Na in sediments from urbanized areas compared to agricultural and forest areas, beach A and beach B. Statistically significant differences in mean contents of Cu, Co, and Mg were found in sediments from agricultural and forest areas compared to other locations. In this study, we analyzed metal contents in sediments from two beaches of the city of Ełk. The ANOVA showed a statistically significant difference in mean Na content in sediments from beach B and beach A. The mean Na content in sediments from beach B was higher than in the sediments from beach A. Infrastructure construction works in the vicinity of the recently established beach B may be the reason for the increased Na content in the coastal sediments. Overall, the results of the ANOVA analysis showed that the type of area bordering Ełckie Lake has a statistically significant relationship with the metal content in coastal sediments.
To identify metal sources, factor analysis with varimax rotation was undertaken. The relationships between metals were analyzed based on three factors (Table 6). Factors with eigenvalues greater than 0.70 were obtained, which explain 62% of the total variance. Factor F1 explains 25% of the total variance. It is related to Mn and geogenic processes in the study areas, which involve natural erosional processes and non-point agricultural sources. Factor F2 explains 23% of the total variance. It is associated with concentrations of Pb, Cu, and Zn. The study shows that Pb, Cu, and Zn may come from sewage, motor vehicle traffic, technical and construction transformations of urbanized areas, and waste management. These activities are particularly intensified during tourist seasons. Factor F3 showed only 14% of the total variance. Soil weathering and modification activities in tourist and recreational areas may increase Na content in sediments. All three factors explain 62% of the total variance, which indicates that lithogenic (geogenic) and anthropogenic factors make almost equal contributions to the distribution of most of the metals studied.

5. Conclusions

The use of multivariate statistical techniques in combination with elemental content analysis is an effective tool for identifying metal sources in the coastal sediments of Ełckie Lake. The average major element contents of the 28 sediment samples occurred in the order Ca > Mg > Fe > Na > K > Mn, and were identical regardless of the kind of adjacent land development (urbanized, agricultural–forest, beaches). These elements may come from natural sources, such as the Earth’s crust, soil, and wind-borne dust from unpaved roads.
The average content of potentially toxic elements (PTEs) in the sediments followed the order Cr > Zn > Pb > Cu > Co in agricultural and forest areas and beaches. The exception was Cu for beach B, which occurred at the end of the series. A different pattern occurred in urbanized areas, i.e., Zn > Cr > Cu > Pb > Co. The spatial distribution of heavy metals in the sediments indicated the highest contents in the shoreline within the urbanized part of the catchment (city of Ełk). Urbanization has led to heavy metal contamination of the coastal sediments of Ełckie Lake due to anthropogenic activities, primarily transport, coal combustion, and sanitary sewage from unsanitary residential areas located on the lakeshore. Metals may also enter the lake through storm runoff from roads. This was confirmed by positive correlations of Zn with Cu (r = 0.58), Pb (r = 0.90), and Fe (r = 0.40). No correlations between the studied metals and organic matter were found, which may indicate its insignificant contribution to metal content in the sediments. Correlation analyses also showed no relationship between sediment pH and metal presence.
Factor analysis indicated that lithogenic (geogenic) and anthropogenic factors have almost equal shares in the distribution of most of the metals studied. Analysis of variance (ANOVA) showed that the mean contents of Zn, Cu, Co, and Na in the sediments from urbanized areas are statistically significantly higher compared to the sediments from other areas (agro-forestry, beaches). Lake Ełckie sits in the area of the Green Lungs of Poland, and its ecosystem is little affected by human activities, despite the development of tourism and the impact of urban development. The PLI for Lake Ełckie due to potentially toxic elements was below 1.0.

Author Contributions

Conceptualization, E.S. and W.R.; methodology, E.S., W.R. and P.O.; validation, P.O.; formal analysis, M.S. and P.O.; investigation, P.O. and W.R.; resources, E.S. and P.O.; writing—original draft preparation, E.S., W.R. and M.S.; writing—review and editing, E.S., M.S. and W.R.; visualization, W.R.; supervision, E.S.; project administration, E.S.; funding acquisition, E.S. and M.S. All authors have read and agreed to the published version of the manuscript.

Funding

The research was carried out as part of research project No. WZ/WB-IIŚ/2/2021 at Białystok University of Technology and financed from a subsidy provided by the Minister of Science and Higher Education.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Petrosyan, V.; Pirumyan, G.; Perikhanyan, Y. Determination of heavy metal background concentration in bottom sediment and risk assessment of sediment pollution by heavy metals in the Hrazdan River (Armenia). Appl. Water Sci. 2019, 9, 102. [Google Scholar] [CrossRef] [Green Version]
  2. Ramírez-Pérez, A.M.; Álvarez-Vázquez, M.A.; De Uña-Álvarez, E.; de Blas, E. Environmental assessment of trace metals in San Simon bay sediments (NW Iberian Peninsula). Minerals 2020, 10, 826. [Google Scholar] [CrossRef]
  3. Kurilenko, V.V.; Osmolovskaya, N.G. Ecological-biogeochemical role of macrophytes in aquatic ecosystems of urbanized territories (An example of small water bodies of St. Petersburg). Russ. J. Ecol. 2006, 37, 147–151. [Google Scholar] [CrossRef]
  4. Alonso Castillo, M.L.; Sánchez Trujillo, I.; Vereda Alonso, E.; García de Torres, A.; Cano Pavón, J.M. Bioavailability of heavy metals in water and sediments from a typical Mediterranean Bay (Málaga Bay, Region of Andalucía, Southern Spain). Mar. Pollut. Bull. 2013, 76, 427–434. [Google Scholar] [CrossRef] [PubMed]
  5. Superville, P.J.; Prygiel, E.; Magnier, A.; Lesven, L.; Gao, Y.; Baeyens, W.; Ouddane, B.; Dumoulin, D.; Billon, G. Daily variations of Zn and Pb concentrations in the DeÛle River in relation to the resuspension of heavily polluted sediments. Sci. Total Environ. 2014, 470–471, 600–607. [Google Scholar] [CrossRef] [PubMed]
  6. Valiev, V.S.; Ivanov, D.V.; Ziganshin, I.I.; Aleksandrova, A.B.; Khasanov, R.R.; Shamaev, D.E.; Malanin, V.V. Adapted methodology for comprehensive contamination assessment of bottom sediments on the content of various forms of heavy metals. IOP Conf. Ser. Mater. Sci. Eng. 2019, 663, 012059. [Google Scholar] [CrossRef] [Green Version]
  7. Ali, Z.; Malik, R.N.; Qadir, A. Heavy metals distribution and risk assessment in soils affected by tannery effluents. Chem. Ecol. 2013, 29, 676–692. [Google Scholar] [CrossRef]
  8. Wang, J.; Liu, G.; Lu, L.; Zhang, J.; Liu, H. Geochemical normalization and assessment of heavy metals (Cu, Pb, Zn, and Ni) in sediments from the Huaihe River, Anhui, China. Catena 2015, 129, 30–38. [Google Scholar] [CrossRef]
  9. Jernström, J.; Lehto, J.; Dauvalter, V.A.; Hatakka, A.; Leskinen, A.; Paatero, J. Heavy metals in bottom sediments of Lake Umbozero in Murmansk Region, Russia. Environ. Monit. Assess. 2010, 161, 93–105. [Google Scholar] [CrossRef]
  10. Nazeer, S.; Hashmi, M.Z.; Malik, R.N. Heavy metals distribution, risk assessment and water quality characterization by water quality index of the River Soan, Pakistan. Ecol. Indic. 2014, 43, 262–270. [Google Scholar] [CrossRef]
  11. Lone, A.M.; Achyuthan, H.; Shah, R.A.; Sangode, S.J. Environmental Magnetism and Heavy Metal Assemblages in Lake Bottom Sediments, Anchar Lake, Srinagar, NW Himalaya, India. Int. J. Environ. Res. 2018, 12, 489–502. [Google Scholar] [CrossRef]
  12. Landre, A.L.; Winter, J.G.; Helm, P.; Hiriart-Baer, V.; Young, J. Metals in Lake Simcoe sediments and tributaries: Do recent trends indicate changing sources? J. Great Lakes Res. 2011, 37, 124–131. [Google Scholar] [CrossRef]
  13. Baek, Y.-W.; An, Y.-J. Assessment of toxic heavy metals in urban lake sediments as related to urban stressor and bioavailability. Environ. Monit. Assess. 2010, 171, 529–537. [Google Scholar] [CrossRef] [PubMed]
  14. Zhu, Y.G.; Ioannidis, J.P.A.; Li, H.; Jones, K.C.; Martin, F.L. Understanding and harnessing the health effects of rapid urbanization in China. Environ. Sci. Technol. 2011, 45, 5099–5104. [Google Scholar] [CrossRef]
  15. Bhattacharya, T.; Chakraborty, S.; Tuteja, D.; Patel, M. Zinc and Chromium Load in Road Dust, Suspended Particulate Matter and Foliar Dust Deposits of Anand City, Gujarat. Open J. Met. 2013, 3, 42–50. [Google Scholar] [CrossRef] [Green Version]
  16. Adamiec, E.; Jarosz-Krzemińska, E.; Wieszała, R. Heavy metals from non-exhaust vehicle emissions in urban and motorway road dusts. Environ. Monit. Assess. 2016, 188, 369. [Google Scholar] [CrossRef] [Green Version]
  17. Zhao, H.; Li, X.; Wang, X.; Tian, D. Grain size distribution of road-deposited sediment and its contribution to heavy metal pollution in urban runoff in Beijing, China. J. Hazard. Mater. 2010, 183, 203–210. [Google Scholar] [CrossRef]
  18. Dunalska, J.A. Rekultywacja Jezior—Teoria i Praktyka; Polska Akademia Nauk: Warsaw, Poland, 2019; ISBN 978-83-63714-47-5. [Google Scholar]
  19. Górniak, A. Klimat i termika wód powierzchniowych. In Kotlina Biebrzańska i Biebrzański Park Narodowy. Aktualny Stan, Walory, Zagrożenia i Potrzeby Czynnej Ochrony Środowiska; Monografia przyrodnicza; Banaszuk, H., Ed.; Wydawnictwo Ekonomia i Środowisko: Białystok, Poland, 2004; pp. 345–362. ISBN 978-3-030-12122-8. [Google Scholar]
  20. Li, H.B.; Yu, S.; Li, G.L.; Liu, Y.; Bin Yu, G.; Deng, H.; Wu, S.C.; Wong, M.H. Urbanization increased metal levels in lake surface sediment and catchment topsoil of waterscape parks. Sci. Total Environ. 2012, 432, 202–209. [Google Scholar] [CrossRef]
  21. Ekengele, L.N.; Blaise, A.; Jung, M.C. Accumulation of heavy metals in surface sediments of Lere Lake, Chad. Geosci. J. 2017, 21, 305–315. [Google Scholar] [CrossRef]
  22. Unda-Calvo, J.; Ruiz-Romera, E.; Fdez-Ortiz de Vallejuelo, S.; Martínez-Santos, M.; Gredilla, A. Evaluating the role of particle size on urban environmental geochemistry of metals in surface sediments. Sci. Total Environ. 2019, 646, 121–133. [Google Scholar] [CrossRef]
  23. Bojakowska, I.; Sokołowska, G. Geochemiczne klasy czystości osadów wodnych. Prz. Geol. 1998, 46, 49–54. [Google Scholar]
  24. Turekian, K.K.; Wedepohl, K.H. Distribution of the Elements in Some Major Units of the Earth’s Crust. Geol. Soc. Am. Bull. 1961, 72, 175–192. [Google Scholar] [CrossRef]
  25. Kowalska, J.B.; Mazurek, R.; Gąsiorek, M.; Zaleski, T. Pollution indices as useful tools for the comprehensive evaluation of the degree of soil contamination—A review. Environ. Geochem. Health 2018, 40, 2395–2420. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Müller, G. Index of geoaccumulation in sediments of the Rhine River. Geol. J. 1969, 2, 108–118. [Google Scholar]
  27. Hakanson, L. An ecological risk index for aquatic pollution control: A sedimentological approach. Water Res. 1980, 14, 975–1001. [Google Scholar] [CrossRef]
  28. Tomlinson, D.L.; Wilson, J.G.; Harris, C.R.; Jeffrey, D.W. Problems in the assessment of heavy-metal levels in estuaries and the formation of a pollution index. Helgoländer Meeresunters. 1980, 33, 566–575. [Google Scholar] [CrossRef] [Green Version]
  29. Caeiro, S.; Costa, M.H.; Ramos, T.B.; Fernandes, F.; Silveira, N.; Coimbra, A.; Medeiros, G.; Painho, M. Assessing heavy metal contamination in Sado Estuary sediment: An index analysis approach. Ecol. Indic. 2005, 5, 151–169. [Google Scholar] [CrossRef]
  30. Fernandes, C.; Fontaínhas-Fernandes, A.; Cabral, D.; Salgado, M.A. Heavy metals in water, sediment and tissues of Liza saliens from Esmoriz-Paramos lagoon, Portugal. Environ. Monit. Assess. 2008, 136, 267–275. [Google Scholar] [CrossRef]
  31. Kucuksezgin, F.; Uluturhan, E.; Batki, H. Distribution of heavy metals in water, particulate matter and sediments of Gediz River (Eastern Aegean). Environ. Monit. Assess. 2008, 141, 213–225. [Google Scholar] [CrossRef]
  32. Huo, S.; Xi, B.; Yu, X.; Su, J.; Zan, F.; Zhao, G. Application of equilibrium partitioning approach to derive sediment quality criteria for heavy metals in a shallow eutrophic lake, Lake Chaohu, China. Environ. Earth Sci. 2013, 69, 2275–2285. [Google Scholar] [CrossRef]
  33. Amin, B.; Ismail, A.; Arshad, A.; Yap, C.K.; Kamarudin, M.S. Anthropogenic impacts on heavy metal concentrations in the coastal sediments of Dumai, Indonesia. Environ. Monit. Assess. 2009, 148, 291–305. [Google Scholar] [CrossRef] [PubMed]
  34. Zahra, A.; Hashmi, M.Z.; Malik, R.N.; Ahmed, Z. Enrichment and geo-accumulation of heavy metals and risk assessment of sediments of the Kurang Nallah-Feeding tributary of the Rawal Lake Reservoir, Pakistan. Sci. Total Environ. 2014, 470–471, 925–933. [Google Scholar] [CrossRef] [PubMed]
  35. Zheng, N.; Wang, Q.; Liang, Z.; Zheng, D. Characterization of heavy metal concentrations in the sediments of three freshwater rivers in Huludao City, Northeast China. Environ. Pollut. 2008, 154, 135–142. [Google Scholar] [CrossRef] [PubMed]
  36. Belzile, N.; Chen, Y.W.; Gunn, J.M.; Dixit, S.S. Sediment trace metal profiles in lakes of Killarney Park, Canada: From regional to continental influence. Environ. Pollut. 2004, 130, 239–248. [Google Scholar] [CrossRef]
  37. Guven, D.E.; Akinci, G. Effect of sediment size on bioleaching of heavy metals from contaminated sediments of Izmir Inner Bay. J. Environ. Sci. 2013, 25, 1784–1794. [Google Scholar] [CrossRef]
  38. Hacısalihoğlu, S.; Karaer, F. Relationships of Heavy Metals in Waterand Surface Sediment with Different ChemicalFractions in Lake Uluabat, Turkey. Pol. J. Environ. Stud. 2016, 25, 1937–1946. [Google Scholar] [CrossRef]
  39. Hong, S.W.; Kim, H.S.; Chung, T.H. Alteration of sediment organic matter in sediment microbial fuel cells. Environ. Pollut. 2010, 158, 185–191. [Google Scholar] [CrossRef]
  40. He, Z.; Li, F.; Dominech, S.; Wen, X.; Yang, S. Heavy metals of surface sediments in the Changjiang (Yangtze River) Estuary: Distribution, speciation and environmental risks. J. Geochem. Explor. 2019, 198, 18–28. [Google Scholar] [CrossRef]
  41. Fernandes, L.; Nayak, G.N.; Ilangovan, D.; Borole, D.V. Accumulation of sediment, organic matter and trace metals with space and time, in a creek along Mumbai coast, India. Estuar. Coast. Shelf Sci. 2011, 91, 388–399. [Google Scholar] [CrossRef]
  42. Sutherland, T.F.; Petersen, S.A.; Levings, C.D.; Martin, A.J. Distinguishing between natural and aquaculture-derived sediment concentrations of heavy metals in the Broughton Archipelago, British Columbia. Mar. Pollut. Bull. 2007, 54, 1451–1460. [Google Scholar] [CrossRef]
  43. Zhang, C.; Yu, Z.; Zeng, G.; Jiang, M.; Yang, Z.; Cui, F.; Zhu, M.; Shen, L.; Hu, L. Effects of sediment geochemical properties on heavy metal bioavailability. Environ. Int. 2014, 73, 270–281. [Google Scholar] [CrossRef] [PubMed]
  44. Skorbiłowicz, E.; Skorbiłowicz, M. Metals in grain fractions of bottom sediments from selected rivers in north-eastern Poland. Phys. Chem. Earth 2011, 36, 567–578. [Google Scholar] [CrossRef]
  45. Skorbiłowicz, E.; Skorbiłowicz, M.; Misztal, W. Distribution of Nickel, Copper and Cobalt in the Grain Fractions of Bottom Sediments of the Sokolda River and its Tributaries (Poland). J. Ecol. Eng. 2020, 21, 88–96. [Google Scholar] [CrossRef]
  46. Förstner, U.; Wittmann, G.T.W. Metal Pollution in the Aquatic Environment; Springer: Berlin/Heidelberg, Germany, 1979. [Google Scholar]
  47. de Andrade, L.C.; Tiecher, T.; de Oliveira, J.S.; Andreazza, R.; Inda, A.V.; de Oliveira Camargo, F.A. Sediment pollution in margins of the Lake Guaíba, Southern Brazil. Environ. Monit. Assess. 2018, 190, 3. [Google Scholar] [CrossRef] [PubMed]
  48. Chen, C.W.; Kao, C.M.; Chen, C.F.; Dong, C.D. Distribution and accumulation of heavy metals in the sediments of Kaohsiung Harbor, Taiwan. Chemosphere 2007, 66, 1431–1440. [Google Scholar] [CrossRef] [PubMed]
  49. Skorbiłowicz, E. Studia nad Rozmieszczeniem Niektórych Metali w Środowisku Wodnym Zlewni Górnej Narwi; Oficyna Wydawnicza Politechniki Białostockiej: Białystok, Poland, 2012. [Google Scholar]
  50. Yongming, H.; Peixuan, D.; Junji, C.; Posmentier, E.S. Multivariate analysis of heavy metal contamination in urban dusts of Xi’an, Central China. Sci. Total Environ. 2006, 355, 176–186. [Google Scholar] [CrossRef] [PubMed]
  51. Bardelli, F.; Cattaruzza, E.; Gonella, F.; Rampazzo, G.; Valotto, G. Characterization of road dust collected in Traforo del San Bernardo highway tunnel: Fe and Mn speciation. Atmos. Environ. 2011, 45, 6459–6468. [Google Scholar] [CrossRef]
  52. Han, N.M.M.; Latif, M.T.; Othman, M.; Dominick, D.; Mohamad, N.; Juahir, H.; Tahir, N.M. Composition of selected heavy metals in road dust from Kuala Lumpur city centre. Environ. Earth Sci. 2014, 72, 849–859. [Google Scholar] [CrossRef]
  53. Hernández-Pellón, A.; Fernández-Olmo, I. Using multi-site data to apportion PM-bound metal(loid)s: Impact of a manganese alloy plant in an urban area. Sci. Total Environ. 2019, 651, 1476–1488. [Google Scholar] [CrossRef]
  54. Karar, K.; Gupta, A.K.; Kumar, A.; Biswas, A.K. Characterization and identification of the sources of chromium, zinc, lead, cadmium, nickel, manganese and Iron in PM10 particulates at the two sites of Kolkata, India. Environ. Monit. Assess. 2006, 120, 347–360. [Google Scholar] [CrossRef]
  55. Davis, H.T.; Marjorie Aelion, C.; McDermott, S.; Lawson, A.B. Identifying natural and anthropogenic sources of metals in urban and rural soils using GIS-based data, PCA, and spatial interpolation. Environ. Pollut. 2009, 157, 2378–2385. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Swarnalatha, K.; Nair, A.G. Assessment of sediment quality of a tropical lake using sediment quality standards. Lakes Reserv. Res. Manag. 2017, 22, 65–73. [Google Scholar] [CrossRef]
  57. Schauer, J.J.; Lough, G.C.; Shafer, M.M.; Christensen, W.F.; Arndt, M.F.; DeMinter, J.T.; Park, J.S. Characterization of Metals Emitted from Motor Vehicles; Health Effects Institute: Boston, MA, USA, 2006. [Google Scholar]
  58. Ozaki, H.; Watanabe, I.; Kuno, K. Investigation of the heavy metal sources in relation to automobiles. Water. Air. Soil Pollut. 2004, 157, 209–223. [Google Scholar] [CrossRef]
  59. Smolders, E.; Degryse, F. Fate and effect of zinc from tire debris in soil. Environ. Sci. Technol. 2002, 36, 3706–3710. [Google Scholar] [CrossRef] [PubMed]
  60. Chatterjee, M.; Massolo, S.; Sarkar, S.K.; Bhattacharya, A.K.; Bhattacharya, B.D.; Satpathy, K.K.; Saha, S. An assessment of trace element contamination in intertidal sediment cores of Sunderban mangrove wetland, India for evaluating sediment quality guidelines. Environ. Monit. Assess. 2009, 150, 307–322. [Google Scholar] [CrossRef]
  61. Rajaram, B.S.; Suryawanshi, P.V.; Bhanarkar, A.D.; Rao, C.V.C. Heavy metals contamination in road dust in Delhi city, India. Environ. Earth Sci. 2014, 72, 3929–3938. [Google Scholar] [CrossRef]
  62. Adamiec, E. Road environments: Impact of metals on human health in heavily congested cities of Poland. Int. J. Environ. Res. Public Health 2017, 14, 697. [Google Scholar] [CrossRef] [Green Version]
  63. Kahkha, M.R.R.; Bagheri, S.; Noori, R.; Piri, J.; Javan, S. Examining total concentration and sequential extraction of heavy metals in agricultural soil and wheat. Pol. J. Environ. Stud. 2017, 26, 2021–2028. [Google Scholar] [CrossRef]
  64. Wei, B.; Yang, L. A review of heavy metal contaminations in urban soils, urban road dusts and agricultural soils from China. Microchem. J. 2010, 94, 99–107. [Google Scholar] [CrossRef]
  65. Hahladakis, J.; Smaragdaki, E.; Vasilaki, G.; Gidarakos, E. Use of Sediment Quality Guidelines and pollution indicators for the assessment of heavy metal and PAH contamination in Greek surficial sea and lake sediments. Environ. Monit. Assess. 2013, 185, 2843–2853. [Google Scholar] [CrossRef]
  66. Duman, F.; Aksoy, A.; Demirezen, D. Seasonal variability of heavy metals in surface sediment of Lake Sapanca, Turkey. Environ. Monit. Assess. 2007, 133, 277–283. [Google Scholar] [CrossRef] [PubMed]
  67. Rabajczyk, A. Reservoir recultivation versus forms of heavy metals in sediments: The case of the Kielce City Lake. Cent. Eur. J. Chem. 2011, 9, 357–366. [Google Scholar] [CrossRef]
  68. Alemdaroğlu, T.; Onur, E.; Erkakan, F.; Tu, T.; Alemdaro, T.; Glu, G.; Fu, F.; Erkakan, F. Trace Metal Levels In Surficial Sediments Of Lake Manyas, Turkey and Tributary. Int. J. Environ. Stud. 2003, 60, 287–298. [Google Scholar] [CrossRef]
  69. Natesan, U.; Madan Kumar, M.; Deepthi, K. Mangrove sediments a sink for heavy metals? An assessment of Muthupet mangroves of Tamil Nadu, southeast coast of India. Environ. Earth Sci. 2014, 72, 1255–1270. [Google Scholar] [CrossRef]
  70. Gopal, V.; Krishnakumar, S.; Simon Peter, T.; Nethaji, S.; Suresh Kumar, K.; Jayaprakash, M.; Magesh, N.S. Assessment of trace element accumulation in surface sediments off Chennai coast after a major flood event. Mar. Pollut. Bull. 2017, 114, 1063–1071. [Google Scholar] [CrossRef] [PubMed]
  71. Gopal, V.; Kalpana, G.; Nethaji, S.; Jayaprakash, M. Geochemical study of core sediments from Ennore Creek, North of Chennai, Tamil Nadu, India. Arab. J. Geosci. 2016, 9, 141. [Google Scholar] [CrossRef]
  72. Bhuiyan, M.A.H.; Parvez, L.; Islam, M.A.; Dampare, S.B.; Suzuki, S. Heavy metal pollution of coal mine-affected agricultural soils in the northern part of Bangladesh. J. Hazard. Mater. 2010, 173, 384–392. [Google Scholar] [CrossRef] [PubMed]
  73. Robertson, D.J.; Taylor, K.G.; Hoon, S.R. Geochemical and mineral magnetic characterisation of urban sediment particulates, Manchester, UK. Appl. Geochem. 2003, 18, 269–282. [Google Scholar] [CrossRef]
  74. Crosby, C.J.; Booth, C.A.; Worsley, A.T.; Fullen, M.A.; Searle, D.E.; Khatib, J.M.; Winspear, C.M. Application of mineral magnetic concentration measurements as a particle size proxy for urban road deposited sediments. WIT Trans. Ecol. Environ. 2009, 123, 153–162. [Google Scholar] [CrossRef] [Green Version]
  75. Fianko, J.R.; Laar, C.; Osei, J.; Anim, A.K.; Gibrilla, A.; Adomako, D. Evaluation of some heavy metal loading in the Kpeshi lagoon, Ghana. Appl. Water Sci. 2013, 3, 311–319. [Google Scholar] [CrossRef] [Green Version]
  76. Paneer Selvam, A.; Laxmi Priya, S.; Banerjee, K.; Hariharan, G.; Purvaja, R.; Ramesh, R. Heavy metal assessment using geochemical and statistical tools in the surface sediments of Vembanad Lake, Southwest Coast of India. Environ. Monit. Assess. 2012, 184, 5899–5915. [Google Scholar] [CrossRef] [PubMed]
  77. Hlavay, J.; Prohaska, T.; Weisz, M.; Wenzel, W.W.; Stingeder, G.J. Determination of trace elements bound to soils and sediment fractions: (IUPAC Technical Report). Pure Appl. Chem. 2004, 76, 415–442. [Google Scholar] [CrossRef]
  78. Kargar, M.; Jutras, P.; Clark, O.G.; Hendershot, W.H.; Prasher, S.O. Trace Metal Contamination Influenced by Land Use, Soil Age, and Organic Matter in Montreal Tree Pit Soil. J. Environ. Qual. 2013, 42, 1527–1533. [Google Scholar] [CrossRef] [PubMed]
Minerals 12 00173 g001 550
Figure 1. The location of 28 research points on Ełckie Lake, Poland.
Figure 1. The location of 28 research points on Ełckie Lake, Poland.
Minerals 12 00173 g001
Minerals 12 00173 g002 550
Figure 2. Igeo, CF and PLI values for the PTEs analyzed.
Figure 2. Igeo, CF and PLI values for the PTEs analyzed.
Minerals 12 00173 g002
Minerals 12 00173 g003 550
Figure 3. Spatial distribution of the analyzed metals (letter C on charts in the bottom right corner means concentration).
Figure 3. Spatial distribution of the analyzed metals (letter C on charts in the bottom right corner means concentration).
Minerals 12 00173 g003
Table
Table 1. Interpretation of values and classes of used indices.
Table 1. Interpretation of values and classes of used indices.
Geoaccumulation Index (Igeo)Contamination Factor (CF)Pollution Load Index (PLI)
ValueSediment QualityValueSediment ContaminationValuePollution Status
Igeo ≤ 0uncontaminatedCF < 1lowPLI = 0denote perfection
0 < Igeo < 1uncontaminated to moderately contaminated
1 < Igeo < 2moderately contaminated1 ≤ CF < 3moderate0 < PLI ≤ 1only baseline levels of pollution
2 < Igeo < 3moderately to strongly contaminated3 ≤ CF< 6considerablePLI > 1deterioration of sediment quality
3 < Igeo < 4strongly contaminatedCF ≥ 6very high
4 < Igeo < 5strongly to extremely contaminated
Igeo ≥ 5extremely contaminated
Table
Table 2. Selected parameters of coastal sediments of Ełckie Lake.
Table 2. Selected parameters of coastal sediments of Ełckie Lake.
Urban Areas
(n = 6)		Rural/Forest Areas
(n =5 )		Beach A
(n = 10)		Beach B
(n = 7)
pH (-)7.9–8.17.8–8.17.9–8.28.0–8.3
OM (%)0.4–2.20.7–1.90.4–1.40.3–1.0
granulometric fractions (%)
>2.0 mm15.9128.8110.0112.23
1.0–2.0 mm 15.766.8113.8210.48
0.2–1.0 mm61.2350.9450.4355.79
0.1–0.2 mm6.1112.0522.1619.22
0.063–0.1 mm 0.721.042.831.67
<0.063 mm0.270.350.750.61
Table
Table 3. Descriptive statistics of metal concentrations in lake sediments including catchment land-use pattern (urbanized, agricultural and forest areas, beaches).
Table 3. Descriptive statistics of metal concentrations in lake sediments including catchment land-use pattern (urbanized, agricultural and forest areas, beaches).
CaMgFeNaKMnZnCrCuPbCo
GGB [24]22,10015,00047,200960026,6008509590452019
LGB [23]n.a.n.a.n.a.n.a.n.a.n.a.4856102
Urban Area
min15,841.81506.91931.8283.1160.968.79.610.10.72.30.8
max47,718.14772.33659.0747.3436.1120.961.615.551.722.21.6
average28,633.12697.42609.5560.0302.497.830.4 a13.111.1 b9.31.2 de
SD11,584.9 f1207.5590.7200.6109.320.220.62.020.09.70.3
Rural/Forest Area
min23,774.7973.11169.4328.092.918.47.58.71.62.30.5
max50,447.34412.83767.0864.7210.0136.416.016.22.63.90.7
average31,415.42292.5 h1922.0523.2132.494.310.812.72.0c3.40.6 d
SD11,123.81383.51055.7237.548.446.43.63.60.50.70.1
Beach A
min21,153.92619.11706.2291.259.760.14.610.00.32.20.4
max42,402.28814.03953.9370.8568.5109.328.114.21.94.01.3
average27,400.3 fg4410.22182.9320.7217.789.910.4 a12.41.12.70.7 e
SD6468.51801.9679.928.2147.615.56.61.10.50.50.3
Beach B
min23,198.53792.32026.8339.6190.947.87.911.30.12.80.4
max35,672.25998.32999.3697.6276.172.814.912.92.34.01.3
average28,946.2 g4865.2 h2457.2468.8234.665.111.211.80.7bc3.20.9
SD4078.8910.2392.9150.733.68.42.30.50.70.40.3
Numbers with the same letter (a–h)—statistically significant difference at p < 0.05 ANOVA. Numbers without letters—no statistical difference ANOVA. GGB—global geochemical background, LGB—local geochemical background, SD—standard deviation, n.a.—not available.
Table
Table 4. Means/ranges of metal concentrations in coastal lake sediments in different areas of the world (dash “-“—not available).
Table 4. Means/ranges of metal concentrations in coastal lake sediments in different areas of the world (dash “-“—not available).
Lake, LocalisationZnCuPbCoCrMnFeCaNaMgKRef.
Rural/Forest Areas
Lere Lake, Chad29.013.55.11.420.5-14,410----[21]
Lake Guaíba, Brazil33.61.94.7-3.460012,200850330560260[47]
Ełckie Lake, Poland30.411.19.31.213.197.82609.528,633.15602697.4302.4this study
Urban Areas
Lake Koumoundourou, Greece91.724.646.1-76.1------[65]
Lake Guaíba, Brazil9.3–34.581.7–10.92.2–14.6-1.5–5.730–2703300–6500160–870220–37090–57070–250[47]
Sapanca Lake, Turkey62.026.715.2-19.1337.8-----[66]
Kielce City Lake, Poland11–981.4–3556–324-3.7–168122–9402500–18,320----[67]
Ełckie Lake, Poland10.82.03.40.612.794.31922.031,415.4523.22292.5132.4this study
Beaches
Lake Manyas, Turkey224.733.6119.1--760.723,419----[68]
Lake Guaíba, Ipanema Beach, Brazil11.91.84.3-2.7603600250300300170[47]
Lake Guaíba, Itapuã Beach, Brazil8.10.93.0-2.1100410029029016060[47]
Ełckie Lake, beach A, Poland10.41.12.70.712.489.92182.927,400.3320.74410.2217.7this study
Ełckie Lake, beach B, Poland11.20.73.20.911.865.12457.128,946.2468.84865.2234.6this study
Table
Table 5. Spearman correlation matrix between heavy metal concentrations and sediment properties.
Table 5. Spearman correlation matrix between heavy metal concentrations and sediment properties.
ZnCuPbCoCrMnFeCaNaMgKOMpH
Zn1.00
Cu0.581.00
Pb0.900.721.00
Co0.190.010.081.00
Cr0.210.090.310.071.00
Mn0.120.020.130.030.351.00
Fe0.400.080.320.310.16−0.571.00
Ca0.03−0.17−0.20−0.03−0.27−0.550.471.00
Na0.170.290.090.07−0.57−0.13−0.090.171.00
Mg−0.21−0.08−0.250.18−0.44−0.540.390.24−0.021.00
K0.330.370.260.53−0.18−0.270.600.150.280.581.00
OM0.060.280.14−0.31−0.120.25−0.34−0.16−0.07−0.04−0.151.00
pH−0.100.020.05−0.04−0.08−0.320.11−0.35−0.120.420.08−0.031.00
Table
Table 6. Spearman correlation matrix between heavy metal concentrations and sediment properties (pH and OM).
Table 6. Spearman correlation matrix between heavy metal concentrations and sediment properties (pH and OM).
Factor 1Factor 2Factor 3
Zn0.010.91−0.07
Cu0.130.810.27
Pb0.140.93−0.10
Co−0.400.27−0.27
Cr0.290.23−0.69
Mn0.770.11−0.20
Fe−0.690.39−0.32
Ca−0.56−0.110.17
Na−0.100.250.74
Mg−0.68−0.140.23
K−0.660.510.13
OM0.460.120.40
pH−0.25−0.05−0.01
% of total variance252314
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Skorbiłowicz, E.; Rogowska, W.; Skorbiłowicz, M.; Ofman, P. Spatial Variability of Metals in Coastal Sediments of Ełckie Lake (Poland). Minerals 2022, 12, 173. https://doi.org/10.3390/min12020173

AMA Style

Skorbiłowicz E, Rogowska W, Skorbiłowicz M, Ofman P. Spatial Variability of Metals in Coastal Sediments of Ełckie Lake (Poland). Minerals. 2022; 12(2):173. https://doi.org/10.3390/min12020173

Chicago/Turabian Style

Skorbiłowicz, Elżbieta, Weronika Rogowska, Mirosław Skorbiłowicz, and Piotr Ofman. 2022. "Spatial Variability of Metals in Coastal Sediments of Ełckie Lake (Poland)" Minerals 12, no. 2: 173. https://doi.org/10.3390/min12020173

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop