Ecotoxicology and Environmental Safety 118 (2015) 103–111

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Heavy metal contents in the sediments of astatic ponds: Influence of geomorphology, hydroperiod, water chemistry and vegetation Bartłomiej Gołdyn a, Maria Chudzińska b, Danuta Barałkiewicz b,n, Sofia Celewicz-Gołdyn c a

Faculty of Biology, Department of General Zoology, Adam Mickiewicz University, Umultowska 89, 61-614 Poznań, Poland Faculty of Chemistry, Department of Trace Elements Analysis by Spectroscopic Method, Adam Mickiewicz University, Umultowska 89b, 61-614 Poznań, Poland c Department of Botany, University of Life Sciences, Wojska Polskiego 71C, 60-625 Poznań, Poland b

art ic l e i nf o

a b s t r a c t

Article history: Received 20 January 2015 Received in revised form 17 April 2015 Accepted 17 April 2015

The contents of heavy metals (Cd, Cr, Cu, Ni, Pb, Zn) were analysed in the bottom sediments of 30 small, astatic ponds located in the agricultural landscape of Western Poland. The samples were collected from 118 stations located in patches of four vegetation types. Relationships between the contents of particular elements and four groups of factors (geomorphology, hydroperiod, water quality and vegetation) were tested using Redundancy Analysis (RDA). The most important factors influencing the heavy metal contents were the maximum depth and area of the pond, its hydroperiod, water pH and conductivity values. In general, low quantities of heavy metals were recorded in the sediments of kettle-like ponds (small but located in deep depressions) and high in water bodies of the shore-bursting type (large but shallow). Moreover, quantities of particular elements were influenced by the structure of the vegetation covering the pond. Based on the results, we show which types of astatic ponds are most exposed to contamination and suggest some conservation practices that may reduce the influx of heavy metals. & 2015 Elsevier Inc. All rights reserved.

Keywords: Vernal pool Pothole Kettle hole Temporary waters Heavy metal contamination Impact of agriculture

1. Introduction Small, astatic water bodies of glacial origin, so-called potholes or kettle hole ponds, are typical elements of a young moraine landscape (Kalettka, 1999). They often constitute the last refuges of biodiversity in intensively used farmland. They are islands of diverse, often endangered vegetation as well as refuges and breeding places for animals important for the functioning of farmland ecosystems. Kettle holes are also significant from the hydrological point of view. They function as sinks during storm events and are often the main reservoirs of water during drought, and thus they regulate the subsoil water level (Juszczak and Kędziora, 2007; Surmacki, 1998). Despite their importance, kettle holes in farmland areas are seriously threatened due to human activity. First of all, as a result of changes in the ground water table, they tend to dry off, becoming transformed to temporary marshes and reed-beds and finally become converted into meadows or even arable fields. Furthermore, the more permanent kettle holes are subject to eutrophication and heavy metal pollution due to intensive artificial fertilisation in their catchment areas (Kalettka et al., 2001; Szajdak n

Corresponding author. E-mail address: [email protected] (D. Barałkiewicz).

http://dx.doi.org/10.1016/j.ecoenv.2015.04.016 0147-6513/& 2015 Elsevier Inc. All rights reserved.

et al., 2009). The heavy metal content in the sediments of water bodies in agricultural landscapes is strongly dependant on the use of mineral fertilizers within their catchment (Mansour and Sidky, 2003; Szymanowska et al., 1999), but there is a scarcity of information as to how environmental factors influence the process of heavy metal transport and accumulation within the ponds. Therefore, the aim of our study was to find out how the morphometry of a pond and its catchment, inundation period (hydroperiod), water chemistry and vegetation structure influence the heavy metal contents in the sediments of kettle hole ponds in a farmland landscape.

2. Materials and methods 2.1. Study area and sampling The study area was ca. 30 km2 of farmland, located approximately 20 km west of Poznań, Wielkopolska Province, Western Poland (52°27′ N, 16°57′ E). The geomorphology of this region was shaped during the last glaciation (about 12,000 years ago), and is dominated by forms typical of young moraine landscapes, including small water bodies of glacial origin, so-called kettle hole ponds. About 90 such ponds can be found within the study area, and 30 of them were included in the present study. They were

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distributed randomly and the maximum distance between the most outlying ponds was about 8 km. See Gołdyn et al. (2007) for the detailed geographical coordinates of all the studied ponds (Supplementary Figure). Catchments of all the studied kettle holes (area from 0.75 to 14 ha) consisted of arable lands (large monocultures of cereals and oilseed rape) and meadows exclusively. The farming economy in the catchments of the ponds was similar, all the more since most of them were located in the same fields and all the tillage in the study area was owned or rented by only five farmers. The maximum elevation of the catchment area above the bottom level of the ponds varied from 3 to 11 m. The ponds varied in terms of their permanent/temporary character (assessed on the basis of observations from 10 years) from ephemeral (inundation period: 4–5 months; n ¼3) to permanent (n ¼5) in years of average precipitation. Some of them were connected with ditches (n ¼8) supplying them with water from adjacent fields (there is no water discharge through the ditches except during episodes of water excess, all the ponds are endorheic). The areas of the ponds varied from 135 to 2490 m2 and their maximum depths from 0.3 to 1.5 m. According to Kalettka's (1999) hydrogeomorphological classification (HGM), 10 ponds were typical kettle-like water bodies (relatively small area, situated in deep terrain depressions – with high banks above the water table) and eight were classified as shore-bursting type (large area, shallow terrain depression). The remaining 12 ponds constituted transitional forms between the above mentioned HGM types. Ten of the ponds were surrounded by trees and bushes (mainly poplars Populus sp. and willows Salix sp.). Vegetation consisted mainly of rushes, sedges and reeds (Carex acutiformis, Carex riparia, Schoenoplectus lacustris, Phragmites australis) and other amphibian vegetation (e.g. Sparganium erectum, Agrostis stolonifera, Rorippa amphibia, Polygonum amphibium). Only the most permanent ponds had submerged vegetation (usually Ceratophyllum demersum or C. submersum). Sediment sampling was conducted in July, approximately two weeks after all the ponds had dried off as a result of a severe drought. Within the studied water bodies 2–8 sampling stations were established, depending on the structure of vegetation covering their area (two stations per each vegetation type, 118 in total). Four rough types of vegetation were distinguished: (1) “open water” – parts of a water body free of emergent vegetation; (2) “sedge” – patches covered by C. riparia, C. acutiformis or S. erectum; (3) “reed” – reed-beds with P. australis; (4) “club-rush” – parts of ponds covered by S. lacustris. 2.2. Analytical methods

digestion procedures were carried out in a microwave oven (MARS 5, CEM) according to the instrumental parameters and settings reported above. After digestion the samples were filtered using Whatman. Reagent blank solutions were prepared in the same way. Inductively coupled plasma mass spectrometry, ICP-MS was used to determine six elements in the bottom sediments according to ISO 17294–2 (2003). A standard ICP-MS spectrometer ELAN DRC II (Perkin-Elmer SCIEX, Canada) equipped with a Meinhard concentric nebuliser, cyclonic spray chamber, Pt cones and a quadruple mass analyser was applied for this study. Typical instrument operating conditions for the ICP-MS spectrometer were: RF power  1150 W; plasma Ar flow rate  15 L min  1; nebuliser Ar flow rate  0.89 L min  1 and auxiliary Ar flow rate  1.2 L min  1. All solutions were prepared with double de-ionised water obtained by passing distilled water through a Millipore Milli-Q water purification system (Waters Corporation, Milford, MA, USA). All reagents were of analytical grade unless otherwise stated. The element standard solutions (in the range 0.1 mg L  1–50 mg L  1) were prepared by diluting stock solution (ICP standard CertiPUR) of 1000 mg L  1 Cd, Cr, Cu, Ni, Pb, Zn (Merck, Germany). The same procedure was applied to prepare a mixed solution of Sc45, Y89, Tb159 (10 mg L  1) which was chosen as an internal standard in the mass spectrometric technique in order to effectively correct for temporal variations in signal intensity. The calibration curves for determined elements were linear in the range of calibration standards. The correlation coefficient r exceeded a value of 0.999 and was appropriate for Zn 0.9996, for Cr, Cu, Ni 0.9997 and for Cd, Pb 0.9999. The limits of detection, calculated as three standard deviations of 7 independent replicates of the reagent blank, were respectively (in mg L  1) for Cd – 0.008; Cr – 0.07; Cu – 0.10; Ni – 0.05; Pb – 0.05 and Zn – 4. The following certified reference materials (CRMs) were used for checking accuracy: LKSD-1, LKSD-2, LKSD-3 and LKSD-4 lake sediments (Canadian Certified Reference Materials Project, Ottawa, Ont., Canada). In the course of the study, the control material was run every 10 samples to ensure analytical accuracy. The certified mass concentration (in brackets) and determination value expressed in μg g  1 as mean value 7 standard deviation was appropriate: for LKSD-1Cd (1.2 70.11) and 1.157 0.12; Cu (44 75) and 42 75; Cr (12 72) and 14 72; Ni (167 3) and 16 74; Pb (837 4) and 86 76; Zn (335 718) and 315 721; for LKSD-2Cd (0.8 70.09) and 0.777 0.08; Cu (37 74) and 34 73; Cr (29 73) and 257 4; Ni (26 74) and 23 73; Pb (44 72) and 40 72; Zn (2057 10) and 215 79; for LKSD-3Cd (0.670.07) and 0.56 70.08; Cu (35 73) and 36 74; Cr (51 75) and 5576; Ni (47 75) and 51 76; Pb (29 71) and 25 71; Zn (151 75) and 158 76; for LKSD-4Cd (1.970.15) and 1.85 70.16; Cu (31 74) and 28 73; Cr (21 72) and 18 71; Cu (31 75) and 2875; Cr (917 4) and 877 6; Zn (195716) and 184 717.

Samples for sediment analyses (118 in total) were collected using a core bottom sampler (Kajak et al., 1965). The length of the core was 15 cm, its diameter was 7 cm. All the samples were dried in the laboratory at 105 °C and then carefully ground in an agate mortar to obtain uniform dust after which they were passed through a nylon sieve of a mesh size of 0.2 mm.

2.2.2. Organic matter content The samples were examined for organic matter content by incineration at 550 °C. Organic matter content was calculated as the difference between the mass of a sample before and after incineration (Myślińska, 2001).

2.2.1. Metal analysis For the sample pretreatment (digestion adopted procedure of U.S. EPA (1994, 2007) methods) a microwave oven was used (MARS 5, CEM). Instrumental parameters and settings were: 20 min for 1200 W at 120 °C, 15 min for 1200 W at 170 °C and 20 min vent. Bottom sediment samples were prepared using the following procedure: approximately 0.5 g of each bottom sediment sample was weighed into PTFE vessels and dissolved in 9 mL 65% HNO3 (65% HNO3 suprapur, Merck, Germany) and 1 mL hydrogen peroxide (30% H2O2 pure p.a, Chempur, Poland). The

2.2.3. Water sample analysis Water samples for chemical analyses were taken in May, using 1.5 l plastic containers, just below the water surface. Basic physicochemical variables of water were measured in situ (pH value, temperature, conductivity and dissolved oxygen content), using an HACH portable multiparameter metre Sension-156. The water samples were transported directly to the laboratory, and the contents of mineral forms of nitrogen (N-NH4, N-NO2, N-NO3), soluble reactive phosphorus (SRP) and total phosphorus (TP) were analysed. Ammonium was analysed spectrophotometrically with

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Nessler reagent, nitrite with sulphanilic acid and 1-naphtylamine (APHA, 1992), nitrate with the salicylate method (ISO 7890-3, 1988) and phosphorus using the molybdenate method, with ascorbic acid as a reducer (ISO 6878, 2004). 2.2.4. Morphology data Detailed data covering the morphology of each pond and its catchment were collected. The hydroperiod of the water bodies was estimated on the basis of inspections performed on the occasion of other studies conducted on the same area, over 10 years preceding the study. Maximum areas and depths of the water bodies and heights of their banks above the water table (¼ depth of the depression in which the pond was situated) were measured in situ during spring inspections. Moreover, connectivity with ditches, occurrence of trees and bushes in the ponds' surroundings and subjective characteristics describing the insolation of the water table and its wind exposure were recorded. Areas and mean elevation of the catchments, distances to the nearest farms, roads and areas of meadows were recorded from 1:10 000 scale maps. 2.3. Sediment pollution evaluation Evaluation of the quality of sediments in terms of metal pollution was carried out according to national and worldwide sediment standards. The quality evaluation of sediments was based on: (i) geochemical criteria (Bojakowska, 2001; Bojakowska and Sokołowska, 1998), which were related to the geochemical background of sediments in Poland under natural conditions (Table 1); (ii) classification according to the Polish Geological Institute (PGI) and the State Inspection of Environmental Protection (SIEP) (Table 1); (iii) the criteria for evaluation of the extent of sediment pollution in accordance with the Regulation of the Minister of Environment (2002); (iv) the Sediment Quality Guideline (SQG) criteria, which reflect the ecological and biological effects on organisms (CCME, 1999; MacDonald, 1994; MacDonald et al., 2000; NOAA, 1999). The SQG levels were used to determine whether the concentrations reported are toxic to aquatic organisms. Until 2012 the Regulation of the Minister of Environment (2002) was enforced in Poland, but actually (since 2013) evaluation of the quality of sediments has been predominantly based on geochemical (Bojakowska, 2001; Bojakowska and Sokołowska, 1998) and toxicological criteria (NOAA, 1999). 2.4. Statistical methods Standard statistical methods were used to describe and analyse the data (Jongman et al., 1995) and basic calculations were performed using the Statistica 7.1 software package. To find out to what extent the contents of heavy metals in bottom sediments depend on environmental factors differing for each of the studied ponds, a set of Redundancy Analyses (RDA) was performed using the CANOCO 4.5 software package (Ter Braak and Šmilauer, 2002). It was possible to divide the analysed environmental factors into four major groups: (1) catchment area and pond morphology, (2) hydroperiod of the waterbody, (3) physico-chemical parameters of water and (4) vegetation covering the bottom of the pond (see Table 2 for a detailed list of the factors which were analysed as well as for their basic statistics). Since variables from each of these groups constitute different functional levels and often depend on factors from the other groups, four respective RDA models were created. We considered the variables that describe the morphological character of the catchment and pond to be the most general factors controlling the functioning of the water body, therefore the variables from this group were analysed for their influence on the heavy metal

105

Table 1 Geochemical background and classification of water sediments according to Polish Geological Institute (PGI) and the State Inspection of Environmental Protection (SIEP). I class – unpolluted sediments; II – sediments moderately polluted; III – sediments of medium pollution; IV – sediments heavily polluted (Bojakowska, 2001; Bojakowska and Sokołowska, 1998). Metal [mg kg  1]

Geochemical background

I class II class III class IV class

Cd Cr Cu Ni Pb Zn

0.5 5 6 5 10 48

0.7 50 20 16 30 125

3.5 100 100 40 100 300

6 400 300 50 200 1000

46 4400 4300 4 50 4200 4 1000

Table 2 List of the analysed environmental factors and their basic statistics. Factor

Model no. Mean

SD

Min

Max

Pond area (m2) Depth (m) Volume (m3) Bank height (m) Wind exposure (%) Shadow (%) Overflow (nominal; 0/1) Catchment area (ha) Catchment elevation (m) Trees in the catchment (n) Bushes in the catchment (n) Meadows in the catchment (%) Distance to nearest buildings (km) Distance to nearest road (m) Hydroperiod (%) Organic matter content (%) Water temperature (°C) Water pH value Water O2 content (mg dm  3) Water conductivity (μS cm  1) Water N-NH4 content (mg dm  3) Water N-NO2 content (mg dm  3) Water N-NO3 content (mg dm  3) Water SRP content (mg dm  3) Water TP content (mg dm  3) Open water (nominal; 0/1) Reed (nominal; 0/1) Club-rush (nominal; 0/1) Sedge (nominal; 0/1)

1 1 1 1 1 1 1 1 1 1 1 1 1

620.9 0.84 389.5 1.02 51.2 26.2 – 3.67 5.39 3.1 0.6 7.8 1.64

490.66 135 2490 0.335 0.30 1.50 343.09 26 1406 0.859 0.0 3.0 27.6 0 100 30.37 0.0 100 – 0 1 3.046 0.75 14 2.225 3.0 11.0 6.28 0 24 1.51 0 10 11.12 0 30 1.08 0.5 4

1 2 3 3 3 3 3 3

477.9 62.1 18.157 20.27 – 5.18 469.4 2.253

243.83 15.85 5.6233 1.938 – 2.388 432.46 1.5429

10 32 4.65 16.5 6.0 1.3 44 0.90

900 100 32.55 24 8.6 12.0 2237 7.71

3

0.015

0.0240

0.00

0.08

3

0.206

0.8751

0.00

5.89

3 3 4 4 4 4

4.767 1.913 – – – –

2.5741 0.8421 – – – –

0.36 0.27 0 0 0 0

11.68 4.01 1 1 1 1

contents of the sediments as the first. The hydroperiod of the water body is known to be another major factor controlling the functioning of the whole ecosystem (e.g. Williams, 2006), on the other hand this factor is strongly influenced by the variables from the previous group. Therefore a second model, analysing how the contents of heavy metals depend on the permanency of the pond, was created on the residuals from the first model. Since water chemistry depends on the hydroperiod and morphology of the water body and its catchment, a third model, testing the influence of physico-chemical parameters of water on the contents of the analysed elements was created on the residuals from the two previous analyses. To avoid pseudoreplication, the calculations were performed on mean values of the content of particular elements for each sampled pond. Statistical significances of the models created, as well as particular factors included in the analyses were calculated using the Monte Carlo permutation test set for the maximum possible number of permutations (9999). The forward selection of explanatory variables was limited to five

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variables that best explained the observed variance in heavy metal contents. Though geology and land use within the catchments of all the studied ponds was similar, we could not exclude the possibility of an autocorrelation effect in our dataset. Therefore the permutation tests were limited to the blocks of data grouping ponds with respect to their geographical distribution. Preliminary data analysis showed that at least in the case of some elements the variability between samples collected from the same pond was quite large (see Section 3). Therefore one could expect the influence of some local factors, different for each sampling station on the distribution of heavy metals within the area of the water body. We performed another RDA analysis to find out whether the content of the studied elements depends on the structure and type of vegetation recorded at each station. In this model mean values for each of the stations were analysed, and the dataset was grouped with respect to the geographical distribution of ponds as well as to the affinity of sampling stations to the particular waterbody. If it was necessary to obtain the normal distribution, logtransformations of data were performed. We considered po0.05 as the minimum level determining significance.

3. Results Average concentrations of heavy metals were higher by 1.3 (Zn) to 4.5 (Ni) times than the geochemical background for Polish conditions. Nevertheless, the minimum values (except for nickel) were lower than the geochemical background. The basic statistics of the heavy metal contents calculated from the data obtained during the sediment analyses are described in Table 3. Contents of all heavy metals in the ponds were highly positively correlated, with the exception of the pairs Cr–Zn and Cr–Pb (Table 4). Maximum content of lead (49.4 mg kg  1 dm) was recorded in the “sedge” sampling station of a shallow (max. depth 40 cm), semi-permanent, kettle-like pond. The water body was covered by dense emergent vegetation (mainly C. riparia and S. erectum) and its catchment was of an average area (3.4 ha). Cadmium had the lowest content among the studied elements (mean value 1.15 mg kg  1 dm). Its highest value (2.8 mg kg  1 dm) was stated at the “sedge” sampling station of a very shallow (max. 30 cm), semi-permanent shore-bursting type pond. The water body was almost completely covered by C. riparia and S. erectum, its catchment was of an average area (2.8 ha). Copper content was highest (32.5 mg kg  1 dm) at the “open water” sampling station of a permanent pond of an average maximum depth (80 cm) classified to the kettle-like type. The

Table 4 Correlation analysis of the content of heavy metals in all studied ponds. Element

Correlation

Pb

Cd

Cu

Ni

Cr

Zn

Pb

Pearson Correlation Sig. (2-tailed) Pearson Correlation Sig. (2-tailed) Pearson Correlation Sig. (2-tailed) Pearson Correlation Sig. (2-tailed) Pearson Correlation Sig. (2-tailed) Pearson Correlation Sig. (2-tailed)

1 . .374a .006 .529a .000 .525a .000 .097 .496 .503a .000

.374a .006 1 . .495a .000 .584a .000 .514a .000 .361a .009

.529a .000 .495a .000 1 . .611a .000 .434a .001 .531a .000

.525a .000 .584a .000 .611a .000 1 . .506a .000 .448a .001

.097 .496 .514a .000 .434a .001 .506a .000 1 . .261 .062

.503a .000 .361a .009 .531a .000 .448a .001 .261 .062 1 .

Cd Cu Ni Cr Zn

a

Correlation is significant at the 0.01 level (2-tailed).

emergent vegetation was scarce and concentrated near the shores of the water body; its deepest part was overgrown with C. demersum. The area of the catchment was small (1.5 ha). Maximum content of nickel (47.6 mg kg  1 dm) was recorded at the “reed” sampling station of a shallow (max. depth 50 cm), semipermanent pond of intermediate HGM character (between shore bursting and kettle-like type). The water body was almost completely covered by common reed (P. australis) and it was one of the largest of the studied ponds (maximum area 1550 m2). The area of the catchment was average (2.7 ha). Chromium content was highest (31.2 mg kg  1 dm) at the “clubrush” sampling station of a shallow (max. depth 40 cm), temporary pond classified to the shore bursting HGM type. It was one of the smallest water bodies (max. area 180 m2), covered by diverse emergent vegetation (mainly S. lacustris, R. amphibia, P. amphibium, Iris pseudacorus). The area of the catchment was medium in size (3 ha). The amount of zinc was found to predominate among the studied elements (mean¼63.15 mg kg  1 dm). Its content was highest (157.1 mg kg  1 dm) at the “open water” sampling point of a deep (max. 110 cm), permanent pond of intermediate HGM character. The emergent vegetation was concentrated along the shores of the water body, and its inner area was mainly overgrown with C. demersum. The area of catchment was medium in size (3.3 ha). The lowest overall content of almost all the analysed heavy metals (Pb¼4.8; Cu¼ 5.2; Ni¼11.0; Cr¼0.0; Zn¼ 30.1 mg kg  1 dm) was detected at the “club-rush” sampling station of a small (170 m2), deep (150 cm), permanent pond of the kettle-like type, surrounded by dense riparian vegetation and a small catchment (1.3 ha).

Table 3 Basic statistics of heavy metal content in all sediment samples (mg kg  1 dm) and sediment quality standards and guidelines (SQG) for metals (mg kg  1). Metal

Cd Cr Cu Ni Pb Zn a b c

Heavy metal content in all sediment samples

Regulation of the Minister of Environment (2002)

Mean

SD

Min.

Max.

Variance

1.15 8.50 14.30 22.27 20.35 63.44

0.768 5.513 5.876 9.161 8.602 26.432

0.0 0.0 4.0 6.18 2.52 18.7

2.8 31.2 32.5 47.6 49.4 157.1

0.586 30.395 34.358 83.254 73.527 699.313

TEL (Threshold Effect Level). PEL (Probable Effects Level). PEC (Consensus-Based Sediment Quality Guidelines).

7.5 200 150 75 200 1000

SQG (NOAA, 1999)

TELa

PELb

PECc

0.6 37 36 18 35 123

3.5 90 197 36 91 315

4.98 111 149 48.6 128 459

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107

Fig. 1. Diagram of the RDA analysis (A) influence of catchment and pond morphometry on heavy metal contents; (B) influence of hydroperiod on the heavy metal contents in the sediments of astatic ponds; (C) relationship between variables of water chemistry and heavy metal contents in the pond sediments (D) influence of vegetation type on heavy metal content in the sediments of astatic ponds. Explanations: Dashed bold line – the environmental variable does not improve the model significantly; (A) catchement ¼ area of catchment; depth¼ maximum depth of the water body; area¼ maximum area of the water body; bank ¼mean height of the banks above the water table (mean depth of the terrain depression); elevation ¼mean elevation of the catchment area; (B) conductivity¼ water conductivity; O2 ¼ contents of dissolved oxygen in water; organic matter¼ contents of organic matter in the sediments; pH ¼ water pH value; SRP ¼contents of soluble reactive phosphorus in the water; (D) clubrush ¼ patches of S. lacustris; reed: patches of P. australis; sedge¼patches of C. riparia, C. acutiformis or S. erectum; open water¼ parts of the pond devoid of emergent vegetation.

The first RDA model tested for influence of the catchment area and pond morphology on the concentration of the analysed elements (Fig. 1A; Table 5). The results showed that the most important factors were: area of the water body, its maximum depth, height of the banks above the water surface, area of the catchment and its mean elevation. According to the results of the permutation test performed during forward selection of explanatory variables, the variables that significantly improved the model were depth of the water body and its area (p¼ 0.015 and 0.024; F¼3.24 and 3.12, respectively). According to the results, concentrations of all the analysed heavy metals were lowest in sediments of small but deep ponds, surrounded by high banks. Moreover, concentrations of Zn and Pb were higher in ponds with a large catchment area, while its elevation positively influenced Cr and Ni contents. The three last mentioned factors (bank height, area of catchment and its elevation) did not improve the model

significantly (p o0.05), the whole model, however, was statistically significant (significance of the first canonical axis: eigenvalue ¼0.17; F-ratio¼ 5.61; p-value ¼0.0496; significance of all canonical axes: trace¼0.29; F ¼2.23; p ¼0.017). According to the second model, hydroperiod was another factor differentiating the contents of particular elements in the bottom sediments of the sampled ponds (Fig. 1B; Table 5). Permanency of the water body positively influenced Cr concentration most of all, and the relation became gradually weaker in the case of Cd, Ni, Cu and Zn contents, ending with Pb, whose concentration was slightly negatively correlated with the hydroperiod. The model explained 13.7% of variance in the residuals from the previous analysis and was significant at p ¼0.01 level (significance of all canonical axes: trace¼ 0.096; F¼3.97; p ¼0.007; forward selection results for variable “hydroperiod”: F¼ 3.97; p ¼0.0099). The third model (Fig. 1C; Table 5) showed that among water

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Table 5 Summary of the RDA testing of the models: (I) influence of catchment and pond morphometry on the heavy metal contents, (II) influence of hydroperiod on the heavy metal contents, (III) relationship between variables of water quality and heavy metal contents, (IV) influence of vegetation type on the heavy metal contents in bottom sediments. Axes Model I

Model II

Model III

Model IV

Eigenvalues Species-environment correlations Cumulative percentage variance of heavy metal data of heavy metal-environment relation Sum of all eigenvalues Sum of all canonical eigenvalues Eigenvalues Species-environment correlations Cumulative percentage variance of heavy metal data of heavy metal-environment relation Sum of all eigenvalues Sum of all canonical eigenvalues Eigenvalues Species-environment correlations Cumulative percentage variance of heavy metal data of heavy metal-environment relation Sum of all eigenvalues Sum of all canonical eigenvalues Eigenvalues Species-environment correlations Cumulative percentage variance of heavy metal data of heavy metal-environment relation Sum of all eigenvalues Sum of all canonical eigenvalues

chemistry characteristics the most important factors were the water pH value (F ¼3.79; p ¼0.009) and its conductivity (2.11 and 0.031, respectively). Other variables included were soluble reactive phosphorus (SRP), oxygen concentration and organic matter content in the sediments, although these factors did not improve the model significantly (p 40.05) and the whole model was statistically significant (significance of the first canonical axis: eigenvalue ¼0.115; F¼4.71; p ¼0.023; significance of all canonical axes: trace¼ 0.187; F ¼1.79; p ¼0.003). According to the results, the concentrations of all the analysed elements were higher in ponds with an alkaline reaction of water. The contents of Zn, Pb and Cu were higher in water bodies with low water conductivity and high concentration of dissolved oxygen, SRP and organic matter. On the other hand, concentrations of Cr and Ni were slightly higher in ponds with high water conductivity. Despite quite large variance in most of the heavy metal contents (Table 3), the Kruskal–Wallis H test showed that the differences in concentrations of particular elements between the studied ponds were significant only in the case of Cu, Ni, and Cr (ChiSquare¼43.59; 49.24; 50.73; p ¼0.040; 0.011; 0.008, respectively). In the case of other elements the variability between samples had to be connected with some local conditions, varying between the sampling stations within the studied ponds. The results of Redundancy Analysis checking for differences in heavy metal contents between sampling stations covered with different types of vegetation are presented in Table 5 and in Fig. 1D. The model showed that those parts of the water bodies devoid of any emergent vegetation (“open water”) were rich in all elements with the exception of Cd. Contents of Cu and Zn were the highest at sampling stations covered by sedges, while patches of reed were rich in Cr and Ni. On the other hand, concentration of Cd was highest at sampling stations covered by club-rush. The model explained 7.3% of the variance in heavy metal contents and was significant on p ¼0.05 level (significance of first canonical axis: eigenvalue ¼0.057; F¼ 3.23; p ¼0.032; significance of all canonical

1

2 0.172 0.606

0.062 0.658

19.6 60.0 0.879 0.287 0.096 0.643

26.6 81.5

13.7 100.0 0.701 0.096 0.115 0.668

56.6 0.0

19.0 61.5 0.605 0.187 0.057 0.324

26.2 84.6

6.3 86.4 0.910 0.066

7.0 95.4

0.300 0.000

0.043 0.611

0.006 0.261

3 0.029 0.506 29.9 91.5

0.098 0.000 70.5 0.0

0.021 0.513 29.6 95.7

0.002 0.161 7.2 98.0

4 0.021 0.544 32.3 98.9

0.079 0.000 81.7 0.0

0.008 0.367 30.9 99.7

0.001 0.113 7.3 100.0

axes: trace¼0.066; F¼ 0.945; p ¼0.048).

4. Discussion 4.1. Sediment pollution evaluation According to the PGI and SIEP criteria (Table 1) the sediments were rated to class III as “medium” polluted. Although the maximum contents of Cd, Cu, Cr, Pb and Zn did not exceed the level of the second class of these criteria, sediments were grouped into the third class because the Ni maximum level exceeded the permitted value of the class II. However, when the mean content of metals was taken into consideration, the sediments could be classified to the second class, as moderately polluted sediments. The metal contamination of sediments compared to the Regulation of the Minister of Environment (2002) criteria concluded that none of the maximum contents of the determined elements exceeded the maximum acceptable levels (Table 3). The comparison of metals with SQGs showed that Cd, Ni, Pb and Zn maximum values of contents in sediment were above TEL values and for Ni the PEL value as well (Table 3). Therefore, toxic effects could be observed for these metals. Nevertheless, the level of Cr and Cu did not exceed any values of the SQG criteria. The maximum concentrations of all heavy metals stated during the present study exceed Polish Standards of soil contamination. Cr exceeds standards for very light soils, Cu and Pb for very light and light soils, while Cd, Ni and Zn exceed the standards for medium soil types (Regulation of the Minister of Environment, 2006). Mean concentration exceeds standards for very light soils only in the case of Cd, Ni, Pb and Zn. Similar quantities of particular elements have been recorded in the sediments of man-made reservoirs in Poland,situated on rivers with agriculturally used catchmentas (Michalec et al., 2014). Similar concentrations have also been reported in most natural lakes in Poland, with the

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exception of nickel, usually significantly lower than in the studied water bodies (Bojakowska and Krasuska, 2014). Only in the lakes of the Leszno Lake District, located in a large complex of meadows, were concentrations of Cr, Cu, Ni and Pb found to be much lower (Szymanowska et al., 1999). Similar differences have been observed in concentrations of heavy metals in the United States in the bottom sediments of both contaminated and uncontaminated lakes (Ikem et al., 2003; Marvin et al., 2004).

elements are supplied to the ponds predominantly during sudden surface runoff events (e.g. heavy rains), when the water supply is significantly greater in ponds surrounded by a steeper catchment. Zn and Pb on the contrary seem to be flushed from the catchment even by less intensive runoff, when the pond recharge rate depends more on the area of the catchment.

4.2. Influence of catchment and pond morphometry

The hydroperiod is considered to be the major factor controlling the functioning of whole ecosystems of temporary bodies of water (Williams, 2006) and the conditions in more permanent water bodies are usually more stable than in those with a shorter hydroperiod (Magnusson and Williams, 2006). Furthermore, in the present study the hydroperiod was one of the most important factors influencing the contents of particular elements in the sediments of astatic ponds. In general, the quantities of Cr and Cd were the highest in permanent water bodies, while Pb content was the highest in less permanent ponds. Generally, we could claim that there is a relationship between hydroperiods and metal contents. We observed that during longer hydroperiods the contents of metal in the sediments are higher than for shorter ones as the ponds contain more water at that time in comparison with shorter hydroperiod (less water). Therefore, when there is more water, ipso facto there are more fertilizers, and a higher content of metals.

The range of results obtained during this study was quite large, even though the studied water bodies were close to each other, in a very similar agricultural catchment. The obtained results suggest that among the factors connected with the morphology of the water body and its catchment the area and depth of the pond are the most significant for heavy metal contents in the sediments. The influence of these two variables was roughly antagonistic: the deeper and smaller the pond was, the lower the contents of the elements recorded. This resulted in generally low contents of heavy metals in the sediments of kettle-like ponds and high in the shore-bursting type ones. This relation could be explained by the contrasting the influence of ground-water and surface-water inputs. Shallow ponds with a relatively long shoreline are usually supplied with water by surface runoff which contains a lot of suspensions within which heavy metals and other pollutants are adsorbed (Barałkiewicz et al., 2014). On the contrary deep pools often have a greater influx of ground-water and their inundation period does not rely so much on surface water input, especially when the shoreline is short (Williams, 2006). It does not seem, however, that this explanation holds true in the case of the studied ponds. Detailed biweekly observations on their hydroperiod conducted in the years following the study have shown that all the ponds charge exclusively after events of sudden surface runoff, following spring snow melting or heavy storms in summer. Such a charging event is a very rapid process (it could even take less than one hour to completely fill an average pond, as was observed in the field during spring thawing) and is followed by successive desiccation of the pond. The desiccation rate (and therefore hydroperiod of the pond) depends most of all on the water volume, vegetation and shading of the water table, with the depth of the pool or land depression being far less significant. It is more likely that the relations observed are caused by differences in the immediate surroundings of the ponds. The more kettle-like water bodies, located in deep terrain depressions, are usually surrounded by high and steep banks. Dense herbaceous vegetation covering such banks functions as a buffer strip, filtering suspended solids washed from the fields together with adsorbed heavy metals. The importance of vegetation buffer strips surrounding kettle-hole ponds for limiting the inflow of harmful substances from the catchment was emphasised by Kalettka and Rudat (2000) and Kalettka et al. (2001). Moreover, kettle holes situated in deep and steep depressions are usually surrounded by low, balk-like elevations created during agricultural activities (ploughing), constituting a mechanical barrier for surface runoff. Shore-bursting type ponds on the other hand, are devoid of any protection from substances washed from their catchments. Moreover, during the spring thaws they often flood the adjacent parts of fields, eluting substances from the soil. The catchment area and its elevation in general positively influenced the contents of all the elements indicating that substances washed from an agricultural landscape (like mineral fertilisers) are the major source of heavy metals. However, the impact of these factors was somewhat different: area of the catchment positively mostly influenced Zn and Pb contents, while its elevation increased Ni and Cr. It is possible, that the second mentioned

4.3. Influence of hydroperiod

4.4. Relationship of water quality variables and heavy metals Among the water chemistry parameters tested for relationships with heavy metal contents, the most important were the water conductivity and its pH value. The first parameter was positively correlated with Cr and Ni and negatively with Zn and Pb contents. On the other hand, pH value positively influenced all the elements studied. This is in accordance with well-known relationships between increased solubility of heavy metals in acidic environment (e.g. Miller and Orbock Miller, 2007). Thus, when the water pH value is high, more such elements are present in the sediments. Heavy metals are adsorbed very efficiently on fine soil particles, mainly on clay materials and organic matter, so their content is increased in the case of heavy soils with clay, especially when the pH is alkaline (Crobeddu and Bennis, 2007; Miller and Orbock Miller, 2007). Such soils are intensively treated with mineral fertilizers containing large amounts of heavy metals (Mortvedt and Beaton, 1995). During heavy rain fine particles of soil are eroded and enter water bodies in the form of suspended solids. Heavy metals adsorbed on them then settle downwards to the bottom sediments (Nocoń et al., 2013). In the case of sandy and acidic soils, heavy metal content is lower. They are leached and get mainly into the water column of water bodies (US EPA, 1999). The acidic pH in the water body has a large impact on the growth of the mobility of heavy metals present in sediments. In contrast, alkaline pH does not favour metal mobility, so their concentrations are higher in the sediment (Ciszewski et al., 2013). Such a case was stated in our ponds and the positive relationship of pH and heavy metals in bottom sediments was confirmed by the model (Fig. 1C). The high dependence of both pH and conductivity on the content of heavy metals in sediments was further demonstrated in other studies of this type (e.g. Başyiǧ it and Tekin-Özan, 2013; Radulescu et al., 2015). The conductivity of water bodies in an agricultural landscape is related, among other things with the intensity of mineral fertilisation in the catchment area (Kamiński et al., 2011), and because the intensity of fertilisation is also related to the amount of heavy metals introduced into the fields with fertilizers (Mortvedt and Beaton, 1995), a correlation is observed between the conductivity of the water and the content of heavy metals in the

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sediments. Moreover, the contents of soluble reactive phosphorus (SRP) and organic matter in the sediments were positively correlated with Zn and Pb, indicating that phosphate fertilizers are the most important source of those two elements. The inflow of phosphorus causes rapid growth of planktonic algae in small water bodies, resulting in high dissolved oxygen concentration (Kawecka and Eloranta, 1994). Therefore the positive relationship of Zn and Pb with dissolved oxygen contents detected in our model was of a rather indirect nature. 4.5. Influence of vegetation type Types of vegetation influenced the contents of particular elements in different ways. Large differences in the presence of various heavy metals in sediments sampled within patches of vegetation may be associated with a diverse uptake by various plant species and accumulation in their tissues. Highly efficient bioaccumulators of trace metals in freshwater ecosystems are macroalgae, e.g. freshwater Ulva species (Rybak et al., 2013), which collect metals directly from the water. Also, submerged macrophytes like C. demersum to a large extent uptake metals from the water column instead of bottom sediments (Manny et al., 1991; Keskinkan et al., 2004). Submerged macrophytes were present in only a few of the studied water bodies occupying open water area. They accumulate metals from the water, but after decay they return them to the bottom sediments. Because they occur in open water areas, not covered with emergent vegetation, this may explain the higher concentrations of metals observed in the sediments of these parts of water bodies. Emergent plants mostly take metals from bottom sediments (Lehtonen, 1989). Reeds accumulate most of the collected metals in their roots, and only transfer them to rhizomes and the aboveground parts of the plants to a small degree (Windham et al., 2003). This has little influence on the content of metals in sediment samples collected within the reed community. Club-rushes (S. lacustris) stores metals in their stems to a much greater extent than reeds (Duman et al., 2007). As shown by Szymanowska et al. (1999) in a small Boszkowo Lake Hg content in this species was 5-fold higher than in the reed, Co 4-fold, Cd 3-fold and other metals 1.5-2-fold. Such an intense uptake of metals from sediments and transfer to shoots may cause their depletion in bottom sediments, hence in patches of S. lacustris their concentrations were usually the smallest. After decay in autumn crushed shoots are transferred to the open water column where they sedimentate, increasing the content of heavy metals in the local sediments. Sedges usually occupy the shallowest parts of water bodies, developing ecotone zone, on the external side of rush species. They are exposed to the highest concentrations of heavy metals flowing out from the agricultural catchment. They can take up most heavy metals very intensively, storing them mainly in the roots (Ladislas et al., 2014). Thus in patches of sedge the highest concentrations of Cu and Zn were found. However, they avoid Cd (Ladislas et al., 2014), so it can penetrate further into the rush communities and be accumulated in their patches. Aquatic vegetation is known for its immense potential for the removal/degradation of a variety of contaminants, including heavy metals (e.g. Dhir et al., 2009). The small water bodies are largely overgrown by emergent and sometimes by submerged vegetation which may be very important in the removal and disposal of heavy metals and may therefore affect a reduction of the negative impact of these metals on sensitive aquatic organisms. 4.6. Nature conservation The results obtained during the present study give an insight

into the threats and perspectives for conservation of the vulnerable ecosystems of astatic ponds. Temporary ponds are often exclusive habitats of several endangered animal and plant species. Nine protected amphibians (including Bombina bombina and Triturus cristatus, species of European importance) and five large branchiopod crustacean species occur in the studied ponds, regarded as endangered (or even extinct) in most European countries (Gołdyn et al., 2007). The only way to protect such species is through the proper management of their habitats especially the surroundings of the water bodies, which should be appropriately shaped and have a permanent belt of vegetation in its role of ecotone, providing a barrier against the flow of contamination from overland.

5. Conclusions It has been shown in the present paper that the concentrations of cadmium, nickel, lead and zinc in sediments may have adverse effects on the organisms inhabiting these water bodies. We have also shown that shallow ponds with a large surface area and larger or steeper catchments are more vulnerable to heavy metal contamination. Type of vegetation growing on the water body to a certain extent can modify the metal concentrations in the sediments. Ponds with a shorter inundation period (being exclusive habitats of large branchiopods) are subject to increased contamination with Pb. However, for a longer hydroperiod the contents of most of metals in the sediments are higher than for short ones. Therefore, the best conservation practices aimed at a reduction of heavy metal influx should concentrate most of all on pools characterised by the above mentioned features. As we have shown, such practices could include modelling the nearest surroundings of the pond (creating balk elevations and vegetation strips around the ponds) and appropriate shaping of the distribution and structure of amphibian vegetation along its shore line.

Acknowledgements The first author of the paper was supported by the Polish Ministry of Science and Higher Education Grant no. N N304 3400 33.

Appendix A. Supplementary Information Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.ecoenv.2015.04. 016.

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Heavy metal contents in the sediments of astatic ponds: Influence of geomorphology, hydroperiod, water chemistry and vegetation.

The contents of heavy metals (Cd, Cr, Cu, Ni, Pb, Zn) were analysed in the bottom sediments of 30 small, astatic ponds located in the agricultural lan...
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