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Article

Trace Elements in Lakes Located in an Agricultural-Forest Catchment: A Case Study of Lake Raczyńskie, Poland

by
Katarzyna Wiatrowska
1,*,
Jolanta Kanclerz
2 and
Ewelina Janicka
2
1
Department of Soil Science, Land Reclamation and Geodesy, Poznań University of Life Sciences, Piątkowska 94, 60-649 Poznań, Poland
2
Department of Land Improvement, Environmental Development and Spatial Management, Poznań University of Life Sciences, Piątkowska 94, 60-649 Poznań, Poland
*
Author to whom correspondence should be addressed.
Water 2024, 16(23), 3342; https://doi.org/10.3390/w16233342
Submission received: 30 August 2024 / Revised: 13 November 2024 / Accepted: 18 November 2024 / Published: 21 November 2024
(This article belongs to the Section Water Quality and Contamination)
Browse Figures
Figure 1
<p>Sampling points in the study area with structure of the land use (112—discontinuous urban fabric; 211—non-irrigated arable land; 242—complex cultivation patterns; 243—land principally occupied by agriculture, with significant areas of natural vegetation; 312—coniferous forest; 313—mixed forest; 512—water bodies) (source: Corine land cover (CLC) vector layers for 2018).</p> "> Figure 2
<p>The local relief of Lake Raczyńskie direct catchment area (digital elevation model (DEM) with bathymetric plan of the lake). A DEM was downloaded with a 1 m by 1 m resolution using LIDAR (light detection and ranging). Sampling points: 1–7, 10–16, 18—bank zone (1–4, 6–7, 10—settlement influence zones; 12–16—agriculture influence zones; 5, 18—tourism influence zones); 8, 9—island zone; 17, 19–22—the central profile of the lake. The bathymetric plan was obtained from the resources of the National Inland Fisheries Research Institute, which was vectorized in the ArcGIS program ver. 10.8.1.</p> "> Figure 3
<p>Spatial distribution of trace elements in the surface sediment of Lake Raczyńskie, Poland. (<b>a</b>) Cd content; (<b>b</b>) Cu content; (<b>c</b>) Mn content; (<b>d</b>) Ni content; (<b>e</b>) Pb content; (<b>f</b>) Zn content.</p> "> Figure 3 Cont.
<p>Spatial distribution of trace elements in the surface sediment of Lake Raczyńskie, Poland. (<b>a</b>) Cd content; (<b>b</b>) Cu content; (<b>c</b>) Mn content; (<b>d</b>) Ni content; (<b>e</b>) Pb content; (<b>f</b>) Zn content.</p> "> Figure 4
<p>The values of I<sub>geo</sub> in the bottom sediments of Lake Raczyńskie.</p> "> Figure 5
<p>The values of mean PEL-Q of the bottom sediments of Lake Raczyńskie.</p> "> Figure 6
<p>Loading plot of trace elements in the space defined by PC1 and PC2.</p> ">
Versions Notes

Abstract

:
The enrichment of bottom sediments with trace elements due to anthropogenic factors is of growing concern worldwide. With the development of industry, agriculture, and urbanization, the risk of freshwater contamination with trace elements is increasing. As trace elements are poorly soluble in water, they have a tendency to accumulate in bottom sediments. The study focused on the evaluation of the trace element contents in the surface layer of bottom sediments of Lake Raczyńskie, located in Poland, and assessing the risks posed by these sediments. The pollution of bottom sediments was estimated based on the index of the geo-accumulation (Igeo), enrichment factor (EF), Nemerow multi-factor index (Pn), and pollution load index (PLI). The assessment of environmental risk was evaluated by the potential ecological risk index (RI) and mean PEL quotient method. The results obtained showed that the average contents of Cd, Cu, Ni, Pb, and Zn exceeded the national geochemical background values (Bn), indicating possible enrichment of bottom sediments due to human activity. Only for Mn were the observed contents below the Bn. This evaluation was confirmed by the PLI and Pn values, which indicated sediment pollution from anthropogenic sources. However, the risk assessment by RI and mean PEL showed a low risk of contamination. The results of principal component analysis (PCA) and values of Igeo and EF suggested that Cd, Cu, Mn, and Zn mainly originate from geogenic sources, while Ni and Pb probably come from an unrecognized anthropogenic source. The distribution of Cd, Mn, and Ni depended on the contents of silt and clay fractions. Additionally, organic carbon influenced Cu, Mn, Ni, and Zn contents in bottom sediments.

1. Introduction

Sub-aquatic landscapes, where the water table is above the land surface (lakes, rivers, and seas), are sites of deposition and accumulation of various substances supplied from adjacent areas as well as those precipitated from the water. These landscapes are characterized by circular matter movement, which favors the accumulation of weakly and poorly mobile substances in them [1,2,3]. Numerous studies have shown that bottom sediments accumulated in the sub-aquatic landscape become an important element of aquatic ecosystems, participating in the geochemical cycling of elements.
Over the last century, increasing anthropopression has led to significant acceleration of matter circulation in the landscape, including increased sediment input to surface waters and a consequent deterioration in water quality [4,5]. According to Tylman et al. [5], the sedimentation rate of the lake sediments in northern Poland has increased from ca 1 mm·year−1 in the Holocene to 2–3 mm·year−1 on average in recent decades. Therefore, the role of bottom sediments as a natural reservoir for poorly mobile elements such as trace elements (an element having an average concentration of less than 0.01% [6]) is increasing. These elements are considered to be one of the most problematic environmental contaminants due to their non-biodegradability, persistence, and toxicity to biota [7]. According to Sojka et al. [7] and Shah et al. [8], trace elements deposited in bottom sediments are mainly of anthropogenic origin. The influx of these elements into the aquatic environment is mainly through the discharge of municipal and industrial wastewater, surface runoff from agricultural fields and urbanized areas, as well as atmospheric deposition of dust [7,9]. The chemical composition of bottom sediments reflects not only the geochemistry of parent materials but also the quantity of substances flowing from the catchment area to the lake, the chemical processes taking place in this reservoir, and additionally the element properties (solubility in water, element speciation) [8,10,11]. The loads transported to lakes depend on land relief, geochemical structure, land use, seasonal variations in weather conditions, plant cover, the degree of catchment urbanization, and atmospheric deposition [12,13].
The contamination of the bottom sediments by trace elements (TE) is one of the greatest threats to the aquatic environment because of their possible biotic toxicity, even at low concentrations, environmental persistence, and accumulation [7,14,15]. Due to the specificity of element migration in sub-aquatic landscapes, the chemistry of the water column is closely linked to the chemistry of the bottom sediments. Over 90% of the trace element content in water reservoirs is found in sediments, highlighting the critical role of the solid phase in the circulation and ecotoxicity of these metals [16]. In the case of trace elements, the sorption capacity of the sediment can contribute to reducing the level of contamination of the waters with them. Gibbs [17] reported that bottom sediments may contain up to five orders of magnitude more trace elements than in the water column above. Three mechanisms are involved in the accumulation of these elements in sediments: physical and chemical adsorption, biological uptake, and physical accumulation [18,19]. Investigations previously carried out indicated that the distribution of trace elements is influenced by sediment texture, mineralogical composition, organic carbon content, and physical transport [11,20,21,22]. Additionally, higher metal contents have been found in sediments collected from the deepest zones of lakes. However, their content in the bottom sediments of a water body depends on the characteristics of the sources of the pollutants. Taking into consideration the non-biodegradability of trace elements, knowledge of their content in bottom sediments allows the long-term anthropopressure impact on lakes to be studied.
Variations in the properties of the overlying water column, such as: redox condition, pH, EC, concentration of organic complexing agents, and pollutant concentrations, may lead to the liberation of trace elements from bottom sediments into the water. In this sense, bottom sediments may become a source of pollution [11,15,23,24,25]. When an element concentration exceeds certain thresholds, it may cause serious environmental problems due to its toxicity, non-biodegradable properties, and widespread distribution [26]. Therefore, determination of trace element contents in sediments is a significant criterion to understand the possible changes caused by human activities. There are numerous results presenting the state of trace element pollution of lake bottom sediments around the globe. However, for the most part, studies are carried out for lakes located in protected areas [4,7,27], situated in catchments close to industrial centers or large urban agglomerations [8,9,10,11,14,15], or for dam reservoirs [22,28]. In the case of lakes located away from major pollution sources with typical forest-agricultural use, there is still insufficient recognition of the pollution status of such lakes. The main objectives of this study were to: (i) evaluate the contents of the trace elements in the surface layer of bottom sediments of Lake Raczyńskie that are affected by moderate anthropopressure [29]. The following trace elements were included in the analysis: Cd, Cu, Zn, Mn, and Ni, whose potential source of contamination could be agriculture (use of plant protection products and fertilizers), and Pb, which was used as an additive for liquid fuels [30,31]. (ii) assess the ecological risks posed by these sediments and (iii) compare different evaluation methods of sediment pollution levels.

2. Materials and Methods

2.1. Study Area

An investigation was carried out on the shallow, polymictic, hypertrophic Lake Raczyńskie (52°08′36″ N, 17°09′56″ E) in western central Poland in the water district of the River Warta. This is the headwater lake, the first lake in the Kórnik gully, and it is fed by several small, seasonal watercourses. The River Głuszynka flows out of the northwestern part of the lake. Lake Raczyńskie covers an area of 84.4 ha with a mean depth of 2.8 m (max. 5.8 m) with an island elevation (Edward Island with an area of 3.1 ha) [32] (Table 1). The direct catchment of Lake Raczyńskie is located within the boundary of the hydrogeological catchment area coded PLGW600060 (Figure S1). The direct catchment of this lake, defined as an area of land from which all surface water is transported to the reservoir, covers 9.65 km2 [33]. This area is dominated by arable land (59%) and forest (27%; CLC 2018). Seasonal holiday resorts, bathing areas, and private recreational plots are located on the banks of the lake, which makes it exposed to tourist pressure (Figure 1). Among the potential anthropogenic pollution of the aquatic environment are fertilizers and plant protection products entering waters through surface runoff, municipal sewage, illegal landfill sites, dust fallout from the combustion of fossil fuels, transportation of substances from the nearby roads, or, in the summer season, tourist traffic. Additionally, as the catchment area does not have a completed sewerage system, old septic tanks still pose a significant threat to the quality of the aquatic environment. Natural sources, on the other hand, include mineral weathering processes and the mineralization of organic matter.
The area studied is located in the Wrzesińska Plain and Śrem Basin mesoregion, the Wielkopolska Lakeland macroregion. In this area, the mean annual temperature is 9.1 °C, and the mean annual precipitation is 580 mm (based on Kórnik meteorological station data from 1994–2022). This area is covered with fluvio-glacial and glacial formations of the North-Polish Glaciation. The oldest sediments are mainly fluvio-glacial sands and gravels and glacial till [34]. The soil cover of the Lake Raczyńskie catchment area consists of Podzols, Luvisols, and Fluvisols (Figure S2).
The local relief of the lake catchment is diversified. The highest elevation is 85 m a.s.l., while the lake itself is located at the lowest point in the catchment area (67.5 m a.s.l.), which makes the reservoir exposed to runoff of nutrients and other elements (Figure 2). The eastern part of the lake catchment is an area where slopes exceed 13°. Additionally, the eastern part of the catchment is characterized by dense land development, and an urban beach and recreation centers are located here that may increase the inflow of pollutants.
Due to the high degree of eutrophication of the lake, the local government decided to conduct reclamation processes on this reservoir. During 1999–2021, the following measures were undertaken: installation of two aerators (D-Flox 600) and application of a few coagulants (MgCl2, PIX 100COP, and Phoslock) [35].

2.2. Sampling and Sediment Treatment

Surface sediment samples (topmost 5 cm of bottom sediments) were collected in November 2022 from 22 points in Lake Raczyńskie using a Nurek and Czapla sampler by a random method [36]. Given the elongated shape of Lake Raczyńskie, sediment samples were taken along the potential flow path of the water. The location of sampling sites is presented in Figure 2. A GPS Garmin Oregon 450 was used to record the coordinates of each sampling point. Composite sediment samples, consisting of two subsamples with a minimum weight of 400 g, were packed into polyethylene bags and transported at 4 °C. Then the sediment samples were air dried, ground in an agate mortar, and sieved through a 200-μm sieve (data about background level from Polish lakes are given for fractions <0.2 mm [37]).

2.3. Analysis of Sediments

The sediment reaction was measured potentiometrically in a sediment:water suspension 1:5 (v/v) according to PN-EN ISO 10390 [38]. Electrical conductivity was done in a sediment:water ratio of 1:5 (m/v) (EC1:5) [39]. The Scheibler method was used to determine CaCO3 content. The sample was treated with hydrochloric acid, and the released carbon dioxide was collected in a graduated tube. Based on gas volume and sediment mass, the CaCO3 percentage was calculated. The total organic carbon content was determined by dry combustion using Multi N/C 3100 (Jena Analytics, Jena, Germany). As samples contained carbonates before analysis, samples were treated with 0.1 N HCl to remove mineral CO2. The total content of trace elements Fe, Mn, and P were determined according to the Lim and Jackson method [40]. A 0.2 g sample of sediments, previously mineralized in an oven at 500 °C, was digested by 1 mL of aqua regia. After 1 h of digestion, 6 mL of concentrated HF was added to the samples and shaken for 24 h at room temperature. Then, mineralization was continued at 65 °C for 8 h. After cooling down, 10 mL of saturated boric acid was added to the samples and shaken for 24 h. Finally, the mixture was diluted with demineralized water to a volume of 25 mL. Six TE (Cd, Cu, Mn, Ni, Pb, and Zn) and Fe concentrations were determined by atomic absorption spectrophotometry (AAS) using the 249 FSAA Agilent Technologies instrument (Agilent Technologies, Inc., Snata Clara, CA, USA). To ensure the quality of the results, the instrument was calibrated using certified standards, and calibrated standards were measured after 25 samples to ensure that the instrument remained calibrated. The correlation coefficient of the standards was R2 > 0.99. Reagent blanks and an internal reference sample were included for in-house laboratory quality assurance and control. The recovery of TE content was in the range of 90.3–102.8%, indicating that results have good accuracy and precision. All samples were prepared and analyzed in duplicate. The detection limits for Cd, Cu, Mn, Ni, Pb, and Zn were 0.05, 0.3, 0.4, 0.3, 0.4, and 0.5 mg·kg−1, respectively. Phosphorus was determined using the molybdate method [41]. All chemicals used were of analytical grade, and ultrapure deionized water was used during the analyses. The vessels for analyses were pre-cleaned by soaking in 10% HNO3 for 2 h. Additionally, the texture of the bulk sediments was determined using aerometric and sieve methods [42]. The following fractions were separated: clay (≤0.002 mm), silt (0.002–0.05 mm), very fine sand (0.05–0.10 mm), fine sand (0.10–0.25 mm), medium sand (0.25–0.5 mm), coarse sand (0.5–1.0 mm), and very coarse sand (1.0–2.0 mm).

2.4. Pollution Assessment

The potential contamination of lake sediments was estimated on the basis of a few geochemical indices, including the geo-accumulation index (Igeo), enrichment factor (EF), Nemerow’s Pollution Index (Pn), pollution load index (PLI), potential ecological risk index (RI), and mean PEL quotient (PEL-Q).

2.4.1. Geo-Accumulation Index (Igeo)

The geo-accumulation index (Igeo), proposed by Müller [43], enables assessment of trace element contamination by comparing current and pre-industrial metal content [44,45]. The index value was calculated by using the following formula:
I g e o = l o g 2 C n 1.5 B n
where:
Cn—the measured content of the metal analyzed (mg·kg−1)
Bn—the geochemical background value of the element (mg·kg−1). These values for the alluvium of Poland are: <0.5, 6, 500, 5, 10 and 48 mg·kg−1 for Cd, Cu, Mn, Ni, Pb, and Zn, respectively [37,46]
1.5—factor introduced to mitigate the impact of fluctuation in the element content, which may be attributed to the lithogenic effect
Igeo was classified into seven classes on the basis of its value: ≤0—uncontaminated (class 0); ≤1—uncontaminated to moderately contaminated (class 1); ≤2—moderately contaminated (class 2); ≤3—moderately to heavily contaminated (class 3); ≤4—heavily contaminated (class 4); ≤5—heavily to extremely contaminated (class 5); >5—extremely contaminated (class 6).

2.4.2. Enrichment Factor (EF)

The enrichment factor (EF) allows identification of the sources of the elements, which can be natural or anthropogenic. It is a common indicator used to assess the intensity of the anthropopressure of each trace element in sediments [47]. This index is calculated by normalizing the content of an element studied in sediments with a reference element; Al, Mn, Ti, and Fe have been extensively used as a conservative tracer [11,47,48,49]. In this study, Fe was used as the reference element. The EF values were calculated as in Equation (2):
E F n = C n C F e B n B F e
where:
Cn—the measured content of the metal analyzed (mg·kg−1)
Bn—the geochemical background value of the element (mg·kg−1)
CFe—the content of iron at a particular sampling point (mg·kg−1)
BFe—the reference geochemical background value of iron (10,000 mg·kg−1 [37])
EF was classified [50,51] into 6 categories. EF ≤ 1—no enrichment; 1 < EF ≤ 2—minor enrichment; 2 < EF ≤ 5—moderate enrichment; 5 < EF ≤ 20—moderately severe enrichment; 20 < EF ≤ 40—severe enrichment; EF > 40—extremely high enrichment.

2.4.3. Nemerow Multi-Factor Index (Pn)

As both the Igeo and EF allow the assessment of the degree of pollution only for a single trace element, thus it cannot show the overall picture of the alteration of the study area. An evaluation based on a comprehensive index method is necessary. The Nemerow multi-factor index (Pn) is widely used to reflect the overall pollution situation caused by the simultaneous occurrence of several elements [52,53]. This method takes into account not only the average contamination but also combines maximum values. The Pn was calculated as shown below:
P n = C F a v e 2 + C F m a x 2 2 2
where:
Pn—is the Nemerow multi-factor index
CFn—the contamination factor of a particular element (Cn/Bn, where Cn is the content of metal and Bn—the geochemical background value of the element mg·kg−1)
CFmax—is the maximum value of contamination factors of the sample
CFave—is the arithmetic mean of the contamination factors of the sample.
The Pn was classified into four classes: 0—uncontaminated (Pn < 1); 1—slightly contaminated (1 ≤ Pn < 2.5); 2—moderately contaminated (2.5 ≤ p < 7); 3—severely contaminated (Pn ≥ 7).

2.4.4. Pollution Load Index (PLI)

The pollution load index (PLI), proposed by Tomlinson et al. [54], is a multi-metal approach for an overall assessment of sediment quality with respect to trace element content. This index shows the number of times when trace element content in the studied medium exceeds the background level. A PLI value of >1 indicates a polluted sediment and a deterioration of the environmental quality; a PLI < 1 means there is no metal pollution in the area, and a PLI value of 1 indicates a baseline level of pollution [54,55]. The PLI was obtained as follows:
PLI = (CF1 × CF2 × …… CFn)1/n
where:
CFn—the contamination factor of a particular element
n—total number of elements studied

2.4.5. Potential Ecological Risk Index (RI)

The pollution indicators presented above did not take into account the effect of particular elements on living organisms. The impact of the individual elements differs from one another due to differences in solubility, ways of entering into living organisms, and the degree of toxicity. Developed by Håkanson [24], the potential ecological risk index (RI) represents the toxicity of trace elements and the response of the environment. The RI was calculated as follows:
R I = ∑ E r n = ∑ T r n × C F n
where:
Ern—the risk factor of a particular TE
Trn—the toxicity factor of trace elements, which include lake sensitivity (given by the BPI- value): Zn = 1·√5/√BPI; Mn =1·√5/√BPI; Cu = 5·√5/√BPI; Ni = 5·√5/√BPI; Pb = 5·√5/√BPI; and Cd = 30·√5/√BPI [24,56]
CFn—the contamination factor for a given metal
Based on the Er value, the following categories of potential risk were identified: Er < 40—low level, 40 ≤ Er < 80—moderate level, 80 ≤ Er < 160—considerable level, 160 ≤ Er < 320—high level, and Er ≥ 320—very high level. For RI values, four classes were distinguished: RI < 150—low ecological risk for the lake; 150 ≤ RI < 300—moderate ecological risk for the lake; 300 ≤ RI < 600—considerably ecological risk for the lake; and RI ≥ 600—very high ecological risk for the lake. The value of BPI (bioproduction index) was calculated on the basis of the concentration of total P in water. The average concentration of this component in the waters of the studied lake from May to October 2022 was 145 μg·dm−3. Therefore, the value of the BPI amounted to 8.0 in this study [57].

2.4.6. Mean PEL Quotient (PEL-Q)

Additionally, estimation of ecological risk based on the Line of Evidence approach was conducted using the mean probable effect level quotient (PEL-Q) method. Generally, the Line of Evidence approach compares sediment quality with conservative sediment quality guidelines. The PEL-Q takes into consideration that elements occur in sediments as complex mixtures. In this study, PEL-Q values were calculated for those elements that have PEL available. The PEL quotient for (Cd, Cu, Ni, Pb, and Zn) was calculated:
P E L − Q = ∑ ( C n P E L n ) n
where Cn is the sediment content of each metal of interest (mg·kg−1), PELn is the PEL for each metal of interest (4.2, 108, 42.8, 112, and 271 mg·kg−1 for Cd, Cu, Ni, Pb, and Zn, respectively), and n is the total number of metals in the sample considered in the calculation of PEL-Q [58,59]. The PEL-Q values were interpreted as follows: <0.1, 0.11–1.5, 1.51–2.3, and >2.3 coincide with 10%, 25%, 50%, and 76% likelihood of toxicity, respectively, giving four relative levels of sediment quality: non-toxic, slightly toxic, medium toxic, and highly toxic [56].

2.5. Data Processing

Statistical methods were applied to process the analytical data in terms of its distribution and correlation with the metal content and the bottom sediment parameters studied. Basic statistical parameters such as mean, median, standard deviation (SD), and coefficient of variation (CV) were calculated. To identify the relationships among trace elements in sediments and their possible sources, Person’s correlation coefficient analysis was computed. Additionally, the principal component analysis (PCA) was used to identify the potential groups of factors influencing TE content in the bottom sediments of Lake Raczyńskie. Statistical analyses were performed using the Statistica 13.3 program (TIBCO Software Inc., Palo Alto, CA, USA). All maps of the study area were created using ArcGIS 10.8.1 (Esri Polska, Warsaw, Poland). The inverse distance weighted (IDW) method was applied to prepare maps of spatial variability in trace element content. This method exploits the phenomenon that geographic objects closer together are more similar than those further apart. The IDW method interpolates the features under study based on the location of points and the distance between them [60]. The method is commonly used to represent environmental data such as contamination of bottom sediments [61].

3. Results

3.1. General Sediment Characteristics

Particle size distribution of sediments was carried out, as accumulation of trace elements depends both on the nature and sediment texture. According to Shepard’s sediment granularity classification, all sediment samples were classified as sand. Considering the content of the gravel fraction, the studied sediments were classified as slightly gravelly sand. However, 45% of samples were categorized as fine-grained sand and 55% as coarse-grained sand [62]. The organic carbon (OC) content varied and ranged from 5.80–129.2 g⸱kg−1 with a median of 21.59 g·kg−1. On the basis of OC content, four sediment samples (points 1–4) were classified as organic. As the lake was classified as hypertrophic, the organic carbon is probably derived from autochthonous sources, which include phytoplankton and macrophytes. However, allochthonous sources of organic carbon compounds, which are washed out from surrounding soils and also derived from domestic sewage, cannot be excluded. Especially during summer months when the lake is under tourist pressure, the significance of this second source increases [63]. The data of pH and electrical conductivity (EC1:5) are presented in Table 2. Lake Raczyńskie is characterized by high variability (CV–56%) of EC values 276–1818 μS·cm−1 and an average of 781 μS·cm−1. The highest values of EC were noted for organic sediments.

3.2. Assessment of Sediment Pollution

The contents (mg⸱kg−1) of trace elements in the surface bottom sediments of Lake Raczyńskie were in the range of 0.08–1.45 (average 0.53) for Cd, 3.23–14.99 (average 7.84) for Cu, 39.42–400.36 (average 159.0) for Mn, 14.35–32.61 (average 19.67) for Ni, 59.04–112.1 (average 83.51) for Pb, and 7.20–97.77 (average 34.95) for Zn (Table 3). The trace elements showed the following order according to their mean contents: Mn < Pb < Zn< Ni < Cu < Cd. Higher contents of trace elements in bottom sediments were found in points 1, 4, 3, 9, 19, 20, 21, and 22 (Figure 3).
The contents of trace elements detected in bottom sediments for almost all metals fit Class I of the State Institute of Geology (PIG) [64] except Pb. In the case of Pb, it was found that all points were beyond Class I and classified sediments into Class II (Table 3). For Cd, only two points (sample No. 1 and 20) and for Ni, sample point No. 20 were classified as Class II. According to the Polish Geological Institute National Research Institute, the background values of Cd, Cu, Mn, Ni, Pb, and Zn were 0.50, 6, 500, 5, 10, and 48, respectively [35,44]. It appeared that the content of Zn in most sampling points was below geochemical background, whereas for Cd (12 points) and Cu (16 points), they were slightly higher than the respective values. In all samples studied, the contents of Ni and Pb were significantly higher than the national geochemical background. This clearly demonstrated an anthropogenic contribution and sediment pollution by trace elements, especially by Ni and Pb. The Spearman correlation analyses were used to indicate the existence of potential relationships among trace elements and basic properties of sediments. The high correlation between metals studied may indicate that the metals come from a similar pollution source and also their common migration [54,65]. The data obtained showed a strong positive correlation between Fe and Mn (r = 0.95, p < 0.01). That could suggest that these metals not only accumulated but also migrated together. Strong positive correlation with Fe and Mn was noted for Ni and Zn (Table 4). Additionally, Mn showed a significant correlation with Cd (r = 0.683, p < 0.01). A strong positive correlation existing between Zn and Cu may suggest that these elements had a similar source or were accumulated by similar physico-chemical processes [56]. The content of OC had an important role in Mn and Fe binding in bottom sediments and, to a lesser extent, for Cu, Zn, and Ni (Table 4), while calcium carbonate was an important factor for Ni, Mn, and Fe accumulation in bottom sediments. This analysis showed a lack of correlation of Pb with the other elements analyzed at the p < 0.01 level. However, at the p < 0.05 level, Pb was correlated only with Ni. This could indicate a different source of this metal in relation to the other elements studied.

3.3. Trace Elements Risk Assessment in Bottom Sediments

The calculated Igeo values of the TEs in Lake Raczyńskie showed variations in relation to particular metals. The Ni and Pb Igeo values indicate moderate to heavily contaminated levels, respectively. For nickel, The Ni-Igeo values range from +0.94 to 2.12, where 90.9% of samples fit the condition of moderate contamination (Igeo ≤ 2). Higher values of Pb-Igeo were observed, ranging from +1.98 to +2.90. As many as 95.45% of samples were classified as heavily contaminated (Igeo ≤ 3). For the rest of the elements studied, lower values of Igeo were observed. In the case of Cd and Cu, only 31.8% and 36.4% of the samples fit the condition of being uncontaminated to moderately contaminated (Igeo ≤ 1), respectively. Whereas the Igeo values for Zn (except for one point) and Mn classified the samples into the uncontaminated level (Figure 4). The average pollution level in the sediments followed the sequence Mn < Zn < Cd < Cu < Ni < Pb. The data obtained implied that this lake is mostly contaminated by Pb and Ni.
The EF of trace elements in the bottom sediments was calculated according to Equation (2); Fe was used as the reference element as the natural source of this element dominates its input [66]. The EF values varied with the studied elements. Results obtained revealed the severe contamination with Pb and Ni, where 50% and 23% of samples, respectively, were classified into moderately severe enrichment. The remaining samples, due to their content, were categorized into a moderate enrichment class. The highest values of EF were recorded at sampling point No. 20, sampled from the deepest part of the lake. In contrast, the EF values for Cd, Cu, Mn, and Zn were within 0.1–1.5, indicating that their contents may be entirely derived from natural weathering processes of minerals.
To comprehensively assess the level of chemical transformation of the bottom sediments of Lake Raczyńskie, the Nemerow multi-factor index was also used. The index values varied from 4.4 to 8.64 and classified the studied sediments as moderately polluted. Only 2 points (1 and 20) were classified as highly polluted (Table 5). This assessment of the sediments was mostly influenced by the lead content.
The second composite geochemical index chosen to assess the quality of the bottom sediments was the PLI (Equation (4)), which indicates the number of times by which the content of a trace element exceeds its natural content in the sediment. According to Muzerengi [67], the PLI values give information about the total level of metal toxicity. Values greater than 1 indicate detrition of the bottom sediment quality. For 95% of the sediment samples, calculated values of PLI varied from 1.09 to 3.02, and only one sample showed a value below 1. This indicated that most sampling sites were contaminated with trace elements, except point 11 (PLI = 0.83). The values of this index for the studied lake were mainly connected to the load introduced by Pb and, to a slightly lesser extent, Ni. Thus, the low quality of the bottom sediments of Lake Raczyńskie was mainly due to their enrichment by lead.
Hakanson’s assessment of the effects of trace elements on the aquatic environment takes into account their mobility in the environment, interactions with solid and solution phases, and potential toxicity of particular elements to living organisms. The RI index also includes the ecosystem’s resistance to the presence of a potential contaminant, depending on the trophic level of the reservoir. Based on the values of the potential ecological risk of each element, an ecological risk index for the ecosystem is calculated. The results of the evaluation on the potential ecological risk factor (Er) and the potential ecological risk index (RI) considering the six metals (Cd, Cu, Mn, Ni, Pb, and Zn) were summarized in Table 5. The RI values indicate a low potential risk (RI 49.8–136.1). The highest contributions to the total value of this index were given by lead, due to its significant enrichment relative to the geochemical background value, and from cadmium, which has a significantly higher toxicity factor than the other elements studied. The sequence of average Er of trace element contribution followed a decreasing sequence Pb > Cd > Ni > Cu > Zn > Mn (33.0, 25.1, 15.6, 5.2, 0.6, and 0.3, respectively).
The assessment of the potential biological effect of the summed trace element contents of the bottom sediments was further estimated using the quotient method. The m-PEL-Q calculated for the sampling’s points (based on Cd, Cu, Ni, Pb, and Zn contents) ranged from 0.21 to 0.50 (on average 0.31), indicating that the mixture of studied elements may have a 25% probability of being toxic. On this basis, the bottom sediments of Lake Raczyńskie were classified as slightly toxic (Figure 5). Additionally, potential acute toxicity of metals in sediments was assessed as a sum of the toxic units ∑(TUs), calculated as the quotient of the measured content of a particular metal to its PEL value [56]. The highest values of TU were observed in points 19–20, located in the deepest part of the lake, and in point 1.

3.4. Distriibution Factors

To assess the degree of association among trace elements as well as the basic chemical properties of bottom sediments, principal component analysis (PCA) was applied. PCA is an effective tool used by many authors to provide information on the sources and pathways of trace elements [56,68]. The results of PCA analysis are presented as a diagram (Figure 6) and are summarized in Table 6. In the present study, two main components with eigenvalues greater than 1.0 were taken into account. These components explained 67.48% of the total variance. The first component (PC1), explained 55.21% of total variance and was highly negatively correlated with Mn, Fe, Ni, Zn, Cd, CaCO3, EC, and organic carbon. Fe, Ni, and Mn belong to the moderate and slightly siderophile elements, respectively, and are the main rock-forming metals. As Ni has a similar ion radius as Fe, it is easy for it to enter into iron-magnesium silicate minerals [68,69], whereas Cd and Zn disperse in rock-forming minerals. Therefore, the association of these elements is considered to represent the lithology of the study area and natural input. The parameters EC and OC content also showed a high correlation with the first component, suggesting that this group of metals may be related, i.e., to sediment input from terrestrial areas via surface run-off. The second component explained 12.27% of variance and was mainly connected with Pb (0.66). Pb did not display a strong correlation with other metals, suggesting different sources or pathways. PC1 and PC2 together explained 67.48% of the total variance, indicating that the dominating factor governing metal distribution in bottom sediments is the lithology of the study area.

4. Discussion

Generally, metals classified as trace elements have low solubility in water, get adsorbed, and accumulate on bottom sediments. However, metal bound in sediments may be resuspended and released into a water body, leading to secondary contamination of the water environment. Therefore, sediments act both as a sink and source for elements in an aquatic environment. A spatial survey of trace elements in bottom sediments, followed by a comparison with an unpolluted baseline, is important to understand the mechanism of accumulation and their geochemical distribution in aquatic systems [14,15,17].
In the current study, high contents of Ni and Pb were observed in reference to the geochemical background values. In contrast, the Mn and Zn contents recorded for these sediments were lower than the geochemical background values. The range of trace element contents in bottom sediments obtained in this study are in general in line with those reported by Rabajczyk [10], Tarnawski [70] and Diatta et al. [19] for other Polish regions. It was found that the amounts of Pb measured in this study were higher than in other studies but were similar to the data reported by the Polish Chief Inspectorate for Environmental Protection for this lake (62.0 mgkg−1 in 2001) [29], while the levels of Cd in sediments of Lake Raczyńskie were lower than those found in Niepruszewskie, Tomickie, and Kielce lakes and the data reported by Sojka et al. [7] for lakes located within the Natura 2000 network.
The amount of trace elements in bottom sediments of Lake Raczyńskie were diversified in particular sampling points. Such a variation is probably a result of a few factors: localization of the source of pollutants inflow, shoreline development, bottom shape of the lake, as well as pollution migration. However, maps of spatial distribution of trace elements (Figure 3) indicate higher metal content in the western and northwestern parts of the lake. The western part of the catchment is dominated by agricultural land and housing, which may suggest an inflow of these elements with domestic sewage and surface runoff from agricultural areas. Moreover, the lake investigated is polymictic, which means it is susceptible to hydrochemical fluctuations and also to geochemical changes of the sediments. One of the key factors determining the solubility and accumulation of elements is pH. Under neutral and alkaline reactions, poorly soluble metal salts precipitate out of the water body and enrich the bottom sediments [10,19]. However, in the case of the analyzed sediments, no effect of pH on trace element accumulation was observed. Correlation analysis showed that there was no significant relationship between pH and the content of the elements studied. Probably, the lack of relationship between the content of analyzed trace elements and sediment pH may be due to the low variability of pH observed for Lake Raczyńskie. Additionally, the transportation, deposition, and accumulation of trace elements strongly depend on the behavior of colloids (silt, clay, organic matter, etc.). Their large specific surface area and presence of surface charge allow them to bind cations and later enrich the deposition areas [22,71]. The results obtained have shown that contents of Mn, Ni, and Cd were positively correlated with silt; Mn, Cu, and Ni with clay; and Mn, Zn, Cu, and Ni with OC. The high content of trace elements in bottom sediments sampled from the deepest part of the lake (19–20) and points 1, 3, and 21 may be a consequence of changes in hydrological condition, which leads to faster deposition of colloids [10,11,22].
An Igeo-based assessment of the contamination of the bottom sediments of Lake Raczyńskie showed that the highest pollution concerns Pb and Ni. For Pb and Ni, Igeo values higher than 2 occurred in points 21 and 1, respectively. Observed high values of Igeo for Pb and Ni imply their origin from anthropogenic sources. The PCA analysis showed that the distribution of lead and its content differ from the other analyzed parameters. Therefore, it can be assumed that the content of this metal in bottom sediments has been enriched as a result of human activity. Nevertheless, its source cannot be clearly indicated. High values of the Igeo for Cd and Cu observed at individual sampling points may suggest their anthropogenic origin in these particular sampling points. Additionally, the remediation of the lake, which was carried out several times, undoubtedly had an impact on the distribution of elements in the bottom sediments. During recent remediation works, a floating unit was used for chemical and oxygen stabilization of phosphorus, which could additionally affect the spatial distribution of the analyzed elements. Rabajczyk et al. [10] noted that due to the oxygenation of water and the introduction of the coagulant, precipitation of poorly soluble salts of trace elements occurs, leading to increased content of these elements in bottom sediments.
To assess metal contamination in the bottom sediments, several geochemical indices were calculated. Both single- and multi-element indices were applied, but the evaluations did not entirely coincide. According to the Igeo and EF, sediments were classified as uncontaminated for Cd, Cu, Mn, and Zn, while for Ni, they were moderately contaminated and for Pb, moderately to heavily contaminated. The values of Pn and PLI indicated that most of the sampling sites were classified as moderately contaminated and contaminated, respectively. Trace elements that have accumulated in bottom sediments may have toxic effects on aquatic biota. However, this problem did not exist in the lake studied. The RI values classified sediments into a low-risk class and PEL to slightly toxic. Therefore, the current contents of trace elements in Lake Raczyńskie have little influence on living organisms [24,59]. The results obtained showed that the assessment of the degree of sediment pollution depended on the elements included in the analysis and the method used to assess the degree of transformation. Depending on the applied method (with threshold values (PIG, mean PEL-Q) or with the use of geochemical indices), slightly different assessments of the degree of anthropogenic pollution of the analyzed sediments were obtained. However, all the methods used indicated that the bottom sediments of the studied lake were contaminated, which was mainly due to high Pb and Ni contents.
The results obtained indicate that the assessment of the quality of the bottom sediments may depend on the criteria and indicators adopted by the authors for their assessment. Each of the indicators used in this study has its own merits and limitations, either due to rigid threshold values or lack of consideration of the chemical properties of the environment affecting the mobility of elements and thus their bioavailability.

5. Conclusions

The content and distribution of trace elements (except Pb and Ni) in bottom sediments of Lake Raczyńskie was mainly a result of natural processes. However, in particular points, higher contents of trace elements were observed as a result of the anthropogenic input. The highest contents of trace elements were noted for sampling points located in the deepest part of the lake or in a small bay, where conditions are favorable for sedimentation processes.
Bottom sediments are not only a deposition site for chemical compounds but can also become a source of them. Thus, the protection of surface water quality requires an understanding of the condition of these sediments, even for water reservoirs not subject to significant anthropogenic pressure. Moreover, a more accurate identification of the pathways of pollutant inputs is needed. In the case of lakes whose catchment area is largely agricultural land, it is necessary to protect against the inflow of sediments from surface erosion processes, for instance, by introducing grass strips, preventing direct contact of arable land with the lake shore zone. This will enable better planning of measures aimed at protecting water quality. Additionally, another factor affecting surface water quality is the extent of sewerage system development in the municipality.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w16233342/s1, Figure S1: Location of the Raczyńskie lake catchment in relation to the hydrogeological catchment area coded PLGW600060 basin. Figure S2: Soil cover of the Raczyńskie lake according to Harmonized World Soil Database v2.0. FV—Fluvisols; GL—Gleysols; LV—Luvisosls; —Podzols.

Author Contributions

Conceptualization, K.W. and J.K.; methodology, K.W. and J.K.; software, E.J. K.W.; validation, K.W.; formal analysis, K.W. and E.J.; investigation, K.W.; resources, K.W.; data curation, K.W. and E.J.; writing—original draft preparation, K.W.; writing—review and editing, K.W.; visualization, K.W. and E.J.; supervision, K.W.; project administration, K.W.; funding acquisition, K.W. All authors have read and agreed to the published version of the manuscript.

Funding

The publication was financed by the Polish Minister of Science and Higher Education as part of the Strategy of the Poznan University of Life Sciences for 2024–2026 in the field of improving scientific research and development work in priority research areas.

Data Availability Statement

The datasets used and analyzed during the current study are available from the corresponding authors upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Sampling points in the study area with structure of the land use (112—discontinuous urban fabric; 211—non-irrigated arable land; 242—complex cultivation patterns; 243—land principally occupied by agriculture, with significant areas of natural vegetation; 312—coniferous forest; 313—mixed forest; 512—water bodies) (source: Corine land cover (CLC) vector layers for 2018).
Figure 1. Sampling points in the study area with structure of the land use (112—discontinuous urban fabric; 211—non-irrigated arable land; 242—complex cultivation patterns; 243—land principally occupied by agriculture, with significant areas of natural vegetation; 312—coniferous forest; 313—mixed forest; 512—water bodies) (source: Corine land cover (CLC) vector layers for 2018).
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Figure 2. The local relief of Lake Raczyńskie direct catchment area (digital elevation model (DEM) with bathymetric plan of the lake). A DEM was downloaded with a 1 m by 1 m resolution using LIDAR (light detection and ranging). Sampling points: 1–7, 10–16, 18—bank zone (1–4, 6–7, 10—settlement influence zones; 12–16—agriculture influence zones; 5, 18—tourism influence zones); 8, 9—island zone; 17, 19–22—the central profile of the lake. The bathymetric plan was obtained from the resources of the National Inland Fisheries Research Institute, which was vectorized in the ArcGIS program ver. 10.8.1.
Figure 2. The local relief of Lake Raczyńskie direct catchment area (digital elevation model (DEM) with bathymetric plan of the lake). A DEM was downloaded with a 1 m by 1 m resolution using LIDAR (light detection and ranging). Sampling points: 1–7, 10–16, 18—bank zone (1–4, 6–7, 10—settlement influence zones; 12–16—agriculture influence zones; 5, 18—tourism influence zones); 8, 9—island zone; 17, 19–22—the central profile of the lake. The bathymetric plan was obtained from the resources of the National Inland Fisheries Research Institute, which was vectorized in the ArcGIS program ver. 10.8.1.
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Water 16 03342 g003aWater 16 03342 g003b
Figure 3. Spatial distribution of trace elements in the surface sediment of Lake Raczyńskie, Poland. (a) Cd content; (b) Cu content; (c) Mn content; (d) Ni content; (e) Pb content; (f) Zn content.
Figure 3. Spatial distribution of trace elements in the surface sediment of Lake Raczyńskie, Poland. (a) Cd content; (b) Cu content; (c) Mn content; (d) Ni content; (e) Pb content; (f) Zn content.
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Figure 4. The values of Igeo in the bottom sediments of Lake Raczyńskie.
Figure 4. The values of Igeo in the bottom sediments of Lake Raczyńskie.
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Figure 5. The values of mean PEL-Q of the bottom sediments of Lake Raczyńskie.
Figure 5. The values of mean PEL-Q of the bottom sediments of Lake Raczyńskie.
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Figure 6. Loading plot of trace elements in the space defined by PC1 and PC2.
Figure 6. Loading plot of trace elements in the space defined by PC1 and PC2.
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Table 1. Morphometry of Lake Raczyńskie.
Table 1. Morphometry of Lake Raczyńskie.
ParameterValue
Lake area84.4 ha
Mean depth2.8 m
Maximum depth5.8 m
Direct catchment area9.15 km2
Maximum width of the lake610 m
Maximum length of the lake2190 m
Length of shoreline6225 m
Average water exchange time1.16 [-] *
Note: * Was determined as the ratio of the lake volume and water outflow from the catchment.
Table 2. Selected physical and chemical properties of bottom lake sediments.
Table 2. Selected physical and chemical properties of bottom lake sediments.
Sample No.Particle Size Distribution (g·kg−1)pHH2OEC1:5 CaCO3Organic C
SandSiltClay(μScm−1)(%)(g·kg−1)
192070106.6414769.2957.22
295040106.8914389.2668.82
3850130207.32152612.65109.1
492070107.0318185.87129.2
51000006.582760.845.80
61000006.112960.706.51
71000007.175561.7428.11
897020106.897944.2245.73
91000006.413492.3311.07
101000006.423352.455.97
111000006.244883.3710.15
121000006.264783.2312.75
131000006.886322.4915.56
149900106.727089.3520.16
151000006.305583.2811.47
169505006.9312443.2730.29
171000006.706703.5023.02
181000006.706723.3417.15
191000006.8212578.4168.32
2093060106.9413749.0175.51
2110000106.318092.7817.46
221000006.837843.0431.51
min---6.102760.705.80
max---7.32181812.65129.2
mean---6.588424.7036.40
median---6.716903.3121.59
SD---0.314373.3334.52
CV (%)---4.6854.7070.2694.84
Table 3. Trace elements content (mg⸱kg−1) of Lake Raczyńskie sediments (n = 22).
Table 3. Trace elements content (mg⸱kg−1) of Lake Raczyńskie sediments (n = 22).
MinimumMaximumMeanMedianSDCV (%)PIG Guideline Values
Class IClass II
Cd0.081.450.530.520.3566.381.005.00
Cu3.2314.997.847.373.0538.9720.00100.00
Mn39.42400.4159.0106.6128.981.09-- 1
Ni14.3532.6119.6718.664.2221.4630.0050.00
Pb59.04112.183.5182.8210.5012.5750.00200.00
Zn7.2097.7734.9531.8022.7865.17200.001000.00
Fe7124.412,090.38086.87665.51093.813.53--
1—lack of guidelines for this element.
Table 4. Correlation between trace elements and other bottom sediment properties (n = 22).
Table 4. Correlation between trace elements and other bottom sediment properties (n = 22).
CdCuMnNiPbZnFeH+ECSiltClayOCCaCO3
Cd1.000
Cu0.591 *1.000
Mn0.683 **0.505 *1.000
Ni0.605 *0.1480.822 **1.000
Pb0.2430.1680.0210.441 *1.000
Zn0.4780.667 **0.713 **0.496 *0.0621.000
Fe0.565 *0.3660.948 **0.758 **0.3390.650 **1.000
H+−0.263−0.174−0.273−0.257−0.420−0.110−0.2611.000
EC0.4630.3690.796 **0.642 **0.3140.576 *0.788 **−0.4211.000
Silt0.528 *0.3530.667 **0.624 **0.2400.4540.667 **−0.3690.869 **1.000
Clay0.4220.541 *0.619 *0.515 *0.1860.4780.515−0.2470.747 **0.809 **1.000
OC0.3750.481 *0.730 **0.457 *0.1280.610 *0.729 **−0.3640.911 **0.866 **0.814 **1.000
CaCO30.4440.3030.567 *0.655 **0.2620.3930.491 *−0.3011.0000.756 **0.913 **0.814 **1.000
Levels of significance: * p < 0.05; ** p < 0.01.
Table 5. Evaluation of bottom sediments pollution.
Table 5. Evaluation of bottom sediments pollution.
Sample No.PnPLIPotential Ecological Risk Factor (Er)RIRisk Grade
CdCuMnNiPbZn
17.212.4968.66.40.621.937.80.8136.1low
25.321.6424.54.30.216.228.20.573.9low
36.081.9940.86.30.316.732.20.596.8low
46.021.9819.46.70.515.831.91.175.3low
54.361.0910.03.20.112.923.30.249.8low
65.971.2610.15.00.112.932.20.260.4low
75.661.7732.46.20.111.330.20.580.9low
86.312.0936.49.90.313.233.50.693.8low
95.382.0532.36.00.216.228.21.083.9low
106.040.853.82.10.114.432.70.153.2low
115.521.5938.94.90.113.029.40.386.6low
126.281.3921.54.30.114.533.80.274.4low
136.841.159.93.40.114.337.00.264.9low
146.291.518.55.30.115.033.80.663.4low
155.721.179.93.30.113.330.80.257.7low
166.551.7325.74.30.215.635.10.581.4low
176.001.195.73.20.112.332.40.454.2low
186.081.256.22.80.113.332.80.655.8low
196.982.3538.37.00.618.936.81.0102.7low
208.463.0248.99.70.625.844.31.6130.9low
216.511.9536.04.80.316.834.60.693.2low
226.621.9625.14.30.617.735.20.883.9low
Table 6. Factor loadings on elements in bottom sediment samples (n = 22).
Table 6. Factor loadings on elements in bottom sediment samples (n = 22).
VariablePC1PC2
Cd−0.707−0.124
Cu−0.577−0.478
Fe−0.889−0.013
Mn−0.943−0.082
Ni−0.7990.251
Pb−0.4000.664
Zn−0.740−0.484
H+0.420−0.564
EC−0.8950.095
CaCO3−0.7380.161
OC−0.833−0.114
Eigenvalue6.071.35
% Variance explained55.2112.27
Cumulative % variance55.2167.48
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Wiatrowska, K.; Kanclerz, J.; Janicka, E. Trace Elements in Lakes Located in an Agricultural-Forest Catchment: A Case Study of Lake Raczyńskie, Poland. Water 2024, 16, 3342. https://doi.org/10.3390/w16233342

AMA Style

Wiatrowska K, Kanclerz J, Janicka E. Trace Elements in Lakes Located in an Agricultural-Forest Catchment: A Case Study of Lake Raczyńskie, Poland. Water. 2024; 16(23):3342. https://doi.org/10.3390/w16233342

Chicago/Turabian Style

Wiatrowska, Katarzyna, Jolanta Kanclerz, and Ewelina Janicka. 2024. "Trace Elements in Lakes Located in an Agricultural-Forest Catchment: A Case Study of Lake Raczyńskie, Poland" Water 16, no. 23: 3342. https://doi.org/10.3390/w16233342

APA Style

Wiatrowska, K., Kanclerz, J., & Janicka, E. (2024). Trace Elements in Lakes Located in an Agricultural-Forest Catchment: A Case Study of Lake Raczyńskie, Poland. Water, 16(23), 3342. https://doi.org/10.3390/w16233342

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