Environ Sci Pollut Res (2014) 21:5007–5022
DOI 10.1007/s11356-013-2429-8
RESEARCH ARTICLE
Longer-term and short-term variability in pollution
of fluvial sediments by dioxin-like and endocrine
disruptive compounds
P. Macikova & T. Kalabova & J. Klanova & P. Kukucka &
J. P. Giesy & K. Hilscherova
Received: 9 September 2013 / Accepted: 3 December 2013 / Published online: 22 December 2013
# Springer-Verlag Berlin Heidelberg 2013
Abstract Changes in pollutant loads in relatively dynamic
river sediments, which contain very complex mixtures of
compounds, can play a crucial role in the fate and effects
of pollutants in fluvial ecosystems. The contamination of
sediments by bioactive substances can be sensitively
assessed by in vitro bioassays. This is the first study that
characterizes detailed short- and long-term changes in
concentrations of contaminants with several modes of
Responsible editor: Ester Heath
Electronic supplementary material The online version of this article
(doi:10.1007/s11356-013-2429-8) contains supplementary material,
which is available to authorized users.
P. Macikova : T. Kalabova : J. Klanova : P. Kukucka :
K. Hilscherova (*)
Research Centre for Toxic Compounds in the Environment
(RECETOX), Faculty of Science, Masaryk University, Kamenice
753/5, 625 00 Brno, Czech Republic
e-mail: hilscherova@recetox.muni.cz
J. P. Giesy
Department of Biomedical Veterinary Sciences and Toxicology
Centre, University of Saskatchewan, Saskatoon, SK, Canada
J. P. Giesy
Zoology Department and Centre for Integrative Toxicology,
Michigan State University, East Lansing, MI 48824, USA
J. P. Giesy
Department of Biology and Chemistry, City University of Hong
Kong, Hong Kong SAR, People’s Republic of China
J. P. Giesy
Zoology Department, College of Science, King Saud University,
P.O. Box 2455, Riyadh 11451, Saudi Arabia
J. P. Giesy
Environmental Science Program, Nanjing University, Nanjing,
China
action in river sediments. One-year long monthly study
described seasonal and spatial variability of contamination
of sediments in a representative industrialized area by
dioxin-like and endocrine disruptive chemicals. There
were significant seasonal changes in both antiandrogenic
and androgenic as well as dioxin-like potential of river
sediments, while there were no general seasonal trends in
estrogenicity. Aryl hydrocarbon receptor-dependent potency
(dioxin-like potency) expressed as biological TCDDequivalents (BIOTEQ) was in the range of 0.5–17.7 ng/g,
dry mass (dm). The greatest BIOTEQ levels in sediments
were observed during winter, particularly at locations downstream of the industrial area. Estrogenicity expressed as estradiol equivalents (EEQ) was in the range of 0.02–3.8 ng/g, dm.
Antiandrogenicity was detected in all samples, while androgenic potency in the range of 0.7–16.8 ng/g, dm dihydrotestosterone equivalents (DHT-EQ) was found in only 30 % of
samples, most often during autumn, when antiandrogenicity
was the least. PAHs were predominant contaminants among
analyzed pollutants, responsible, on average, for 13–21 % of
BIOTEQ. Longer-term changes in concentrations of BIOTEQ
corresponded to seasonal fluctuations, whereas for EEQ, the
inter-annual changes at some locations were greater than
seasonal variability during 1 year. The inter- as well as
intra-annual variability in concentrations of both BIOTEQ
and EEQ at individual sites was greater in spring than in
autumn which was related to hydrological conditions in the
river. This study stresses the importance of river hydrology
and its seasonal variations in the design of effective sampling campaigns, as well as in the interpretation of any
monitoring results.
Keywords Sediments . Seasonality . Monitoring .
Dioxin-like potency . Estrogenicity . Antiandrogenicity
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Introduction
Sediments are considered as an important compartment of
aquatic ecosystems that provide substratum for benthic organisms and represent a deposit of nutrients that can be returned
to the biocycles during natural flooding (Forstner and
Salomons 2010). Association with sediments and particulate
matter also plays a crucial role in the fate and effects of
contaminants in aquatic systems. Sediments serve as a sink
for various hazardous chemicals, especially hydrophobic organic contaminants (HOCs) due to their hydrophobic nature
and low-water solubility. Important parameters for the binding
of organic pollutants to sediments are the specific surface of
particles as well as quantity and quality of organic carbon
(Jaffe 1991). Sediments contain a wide spectrum of compounds, of both natural and anthropogenic origin, that can
affect organisms through different modes of action to cause
additive, supra-additive, or infra-additive effects. Among
HOCs, polycyclic aromatic hydrocarbons (PAHs),
polychlorinated biphenyls (PCBs), organochlorine pesticides
(OCPs) or polychlorinated dibenzo-p-dioxins (PCDDs), and
dibenzofurans (PCDFs) have been detected in sediments
worldwide (Colombo et al. 2006; Hilscherova et al. 2010;
Kannan et al. 2008; Koh et al. 2004). Apart from the traditionally monitored hydrophobic pollutants, other classes of
compounds such as pharmaceuticals and personal care products, polyphenolic compounds, phthalates, or various pesticides may be present in sediments (Brack et al. 2007; Jobling
and Tyler 2003; Vigano et al. 2008). It has also been shown
that sediments can serve as a sink of xenohormones and other
endocrine disrupting compounds (Higley et al. 2012; Peck
et al. 2004; Urbatzka et al. 2007).
To achieve good water quality within the European Union
(EU), the Water Framework Directive (Directive 2000/60/EC)
has been introduced into the EU legislation and limits for
concentration of several hazardous priority substances in surface waters, so-called Environmental Quality Standards (EQS),
have been defined (Directive 2008/105/EC). Recently, the list
of priority substances has been revisited and EQS for more
compounds in surface waters as well as EQS for some compounds in biota have been proposed (European Commission
2012). Contamination of sediments plays a crucial role in the
pollution of aquatic environment. The Water Framework
Directive recommends the monitoring of sediments at an adequate frequency to provide sufficient data for reliable determination of long-term status and trends and to establish limits for
contaminants in sediments according to the local situation in
each country. Specific approaches for sediment quality assessment along with EQS for sediments are under development,
which is one of the remaining challenges for better protection
of aquatic ecosystems. Sediment quality guidelines (SQGs)
developed on the base of ecological and ecotoxicological information for several HOCs as well as metals have been
Environ Sci Pollut Res (2014) 21:5007–5022
introduced in Flanders, Belgium and incorporated into
Flemish legislation in 2010 (de Deckere et al. 2011). Another
approach was previously used in the Netherlands, where limits
for some organic substances and pesticides were derived by use
of the equilibrium partitioning method (Crommentuijn et al.
2000). An SQG for PCBs corresponding to the regulatory fish
consumption limit based on biota-to-sediment accumulation
factor has been derived for the Rhone River basin, France
(Babut et al. 2012). No SQG have been promulgated by the
Czech Republic yet.
Implementation of EQS for priority substances in sediments is a crucial step in better protection of aquatic environments. However, priority pollutants remain to be identified.
Recently, more attention has been driven to “emerging contaminants” in addition to HOCs since they can elicit various
biological responses (Brack et al. 2007; Kaplan 2013). While
quantification of individual contaminants by instrumental
analysis is an important tool to investigate the fate and distribution of known pollutants in the environment, possible biological effects of complex mixture are difficult to predict
solely from chemical analysis. Instrumental analysis of individual, known contaminants does not account for possible
interactions among chemicals or for those compounds that
are not identified or not quantified. Thus, various in vitro
bioassays have been applied to characterize contamination
by bioactive substances in various environmental compartments, such as surface water, sediments, soil, air, or biota
(e.g., Higley et al. 2012; Martinez-Gomez et al. 2013;
Novak et al. 2009; Urbatzka et al. 2007; Wolz et al. 2011).
In vitro bioassays are relatively rapid, cost-effective, and
useful, especially in screening and long-term monitoring of
contamination. Some of these assays are applied to estimate
the potency of individual compounds as well as of complex
mixtures to elicit biological responses mediated through specific nuclear receptors, such as the aryl hydrocarbon receptor
(AhR), estrogen receptor (ER), or androgen receptor (AR).
Activation of the AhR is considered critical in mediating
effects of dioxin-like compounds that have been shown to
cause hepatotoxicity, teratogenicity, carcinogenesis,
immunotoxicity, and other adverse effects (Janosek et al.
2006). Estrogens and androgens are endogenous steroid sex
hormones that control reproduction, development, differentiation, and growth. Functions of these hormones are mainly
mediated by ER and AR, and many compounds have been
shown to disrupt their signaling (Janosek et al. 2006).
Reproductive disorders, such as feminization or masculinization of aquatic vertebrates and invertebrates were observed in
the environment (as reviewed in Sumpter 2005). Exposure to
synthetic estrogens can even lead to collapse of whole fish
populations (Kidd et al. 2007).
River sediments represent a dynamic system and their
potential risks are connected primarily with transport and
deposition of contaminated solids in downstream regions
Environ Sci Pollut Res (2014) 21:5007–5022
(Forstner et al. 2004; Hilscherova et al. 2003). Rivers can
exhibit large differences in hydrodynamic characteristics during an annual cycle. In remobilization processes, pollutants
associated with particles can be resuspended, thus, sediments
can serve as a secondary source of contamination (Brinkmann
et al. 2013; Hilscherova et al. 2007). Strong fluctuations in
concentrations of contaminants can occur upon stronger
floods that have been discussed in recent years in possible
relation to the global climate change (Hunt 2002). Further,
seasonal variability of contamination was observed at some
places (Hilscherova et al. 2010; Zhao et al. 2011). Both
temporal and spatial dynamics should be considered when
assessing contamination of river ecosystems as was documented in a previous study (Hilscherova et al. 2010). Even
though pollution of sediments by compounds with the abovelisted modes of action has been reported from rivers in many
parts of the world, there is a lack of information regarding
long- as well as short-term variations or trends in their concentrations and/or potencies.
The present year-long study was focused on temporal (both
seasonal and long-term) and spatial variability of contaminants
in river sediments of a typical industrial area in the southeastern part of the Czech Republic (Central Europe) that represents a suitable model ecosystem for research on the accumulation and distribution of pollutants on a local and regional
scale. The studied region is a part of Danube River basin,
situated near the city of Zlin. It includes two rivers, the
Morava and its tributary, the Drevnice (Fig. 1). This area has
been affected for many years by industrial and agricultural
activities as well as effluents from wastewater treatment facilities and runoff from urban landscapes. Chemical, boot-andshoe, plastics-and-rubber, food-stuff industry, agricultural
crops and livestock production, as well as transport are among
the most important sources of contamination (Hilscherova
et al. 2007). The goal of this year-long study with monthly
sampling was to characterize seasonal and spatial variability of
contamination of fluvial sediments by compounds with dioxinlike and endocrine disruptive modes of action. Sediments were
sampled monthly at five locations throughout a whole year.
Extracts of sediments were assessed for AhR-, ER-, and ARdependent potencies. Another goal was to address longer-term
trends/variability through comparison of current and previous
results from the region (Hilscherova et al. 2010, 2002). Thus, a
comparison of seasonal as well as inter-annual trends in contamination by bioactive compounds could be conducted.
Materials and methods
Sampling and locations
Sediments were collected, monthly, from July 2007 to July
2008 at five locations in the south-eastern part of the Czech
5009
Republic in the Morava River and its tributary Drevnice River
(Fig. 1). The Malenovice (MA) location is situated on the
Drevnice River and is affected mainly by contamination from
the city of Zlin and its surroundings. The Belov (BE) location
is situated on the Morava River upstream from the confluence
with the Drevnice River, whereas the Spytihnev (SP) location
is downstream on Morava River and integrates contamination
from both rivers. The Certak oxbow lake (CR) is a unique
location that was separated from the active Morava River
channel in the 1930s, but water communication with the river
is provided via underground piping that enables the lake to act
as a trap for suspended sediments from the river (Babek et al.
2008). The Certak (CE) location is situated on the Morava
River near the oxbow lake to better assess differences between
the active and abandoned channel. Samples were taken from
each location in a period of 28 days, in 15 sampling campaigns. A total of 73 samples were collected. Two samples
could not be obtained because of weather conditions. Samples
were clustered according to four hydrologically defined seasons (Table S1): spring (March–May), summer (June–
August), autumn (September–November), and winter
(December–February). Data on river discharge and temperature were obtained from gauging stations in Zlin (representative for location MA), Kromeriz (representative for location
BE), and Spytihnev (representative for locations SP, CE). The
following parameters were used: Q =average discharge over
the 28 days prior to each sampling campaign, T actual =temperature on the day of sampling, T average =time-weighted, average
temperature over the 28 days prior to each sampling campaign. Composite samples of surface sediments were collected
from the top 10-cm layer by use of pre-cleaned trowels. Large
pieces of wood, leaves, and stones were removed manually
and sediments were homogenized and freeze-dried. Dry sediments were sieved (2 mm). Total organic carbon content
(TOC) was determined by use of high-temperature TOC/
TNb Analyzer liquiTOC II (Elementar Analysensysteme,
Hanau, Germany).
Chemical analysis
For quantification of organic pollutants, 10 g of freeze-dried
sediments were extracted with dichloromethane by use of
automated warm Soxhlet extraction (1 h, min. 15 cycles;
Büchi B-811, Büchi, Switzerland). Laboratory blanks and
reference material were analyzed with each set of samples.
Surrogate recovery standards (final amount in each sample
10 ng PCB30, 10 ng PCB185, 333 ng D8-naphthalene, 333 ng
D10-phenanthrene, and 333 ng D12-perylene) and 13C labeled PCDD/Fs standards (800 pg tetra-hexa PCDD/Fs,
1,600 pg hepta-octa PCDD/Fs) were used prior to extraction.
Extracts were cleaned-up on silica column (for PAHs analysis), sulfuric acid-modified silica column was used for analysis of organohalogens. Copper powder was used to remove
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Environ Sci Pollut Res (2014) 21:5007–5022
Fig. 1 Sampling localities on the
Morava and Drevnice Rivers. MA
Malenovice, BE Belov, SP
Spytihnev, CE Certak Morava
river, CR Certak oxbow lake;
arrows indicate the river flow
direction
sulfur. Further fractionation step was needed to analyze
dioxin-like PCBs (dl-PCBs) and PCDD/Fs. Samples were
applied on columns containing charcoal/silica mixture and
eluted with DCM/cyclohexane in fraction 1 (mono-ortho dlPCBs) and with toluene in fraction 2 (PCDD/Fs, non-ortho
dl-PCBs). Terphenyl (200 ng/mL), PCB 121 (200 ng/mL) and
13C-labeled PCDD/Fs (16 ng/mL) were used as injection
standards for quantification of PAHs, PCBs, and PCDD/Fs,
respectively. Samples were analyzed using GC-MS instrument (Agilent 6890N GC–Agilent 5973N MS, Agilent,
USA), separation of individual compounds was achieved
on a DB-5MS (J&W Agilent, USA) for indicator PCBs
(congeners 28, 52, 101, 118, 138, 153, 180), OCPs (dichlorodiphenyltrichloroethane p , p′-DDT and its metabolites p, p′-DDE, p , p′-DDD; hexachlorocyclohexane isomers α-, β-, γ-, δ-HCH; hexachlorobenzene), and 16 US EPA
PAHs. Concentrations of contaminants were quantified using
Pesticide Mix 13 (Dr. Ehrenstorfer GmbH, Germany) and PAH
Mix 27 (Promochem, Germany) standard mixtures.
HRGC/HRMS instrumental analysis for PCDD/Fs and dlPCBs (congeners 77, 81, 105, 114, 123, 126, 156, 157, 167,
169, 189) was performed on Agilent 7890A GC (Agilent,
USA) coupled to AutoSpec Premier MS (Waters, Micromass,
UK). The GC was fitted with a capillary column DB5-MS,
60 m×0.25 mm i.d., 0.25-μm film. MS was operated in EI+
mode at R >10 k (Kukucka et al. 2010).
Total potency of samples to cause AhR-mediated effects,
expressed as 2,3,7,8-tetrachlorodibenzo-p -dioxin (TCDD)equivalents (TEQ), were calculated as the sum of the product
of concentrations of individual AhR-active compounds multiplied by their relative potency (REP) to activate AhRmediated responses in H4IIE-luc cells (Eq. 1).
TEQ ¼
X
ð1Þ
cX *REPX
TEQ for individual groups of pollutants were calculated
(Eqs. 2 and 3).
PAHs−TEQ ¼
nonPAHs−TEQ ¼
X
X
X
cPCBs *REPPCBs þ
X
þ
ð2Þ
cPAHs *REPPAHs
cPCDFs *REPPCDFs
cPCDDs *REPPCDDs
ð3Þ
Environ Sci Pollut Res (2014) 21:5007–5022
REPs derived by Machala et al. (2001) were used for
PAHs, REPs derived by Behnisch et al. (2003) were used for
PCBs and PCDD/Fs (Table S2).
Bioassays
For in vitro testing, 20 g of freeze-dried sediments without any surrogate standards were extracted as described
above (Section Chemical Analysis). Extracts were treated
with copper powder to remove sulfur, enriched under a
gentle stream of nitrogen, and aliquots were transferred
to ethanol (EtOH) and dimethyl sulfoxide (DMSO). Final
concentration of sediment equivalents (SEQ) in the extracts was 20 g/mL. Three different mammalian cell lines
transfected with the luciferase gene under control of
several intracellular receptors were used to determine
potencies of extracts of sediments to interfere with
receptor-mediated responses. The potency to elicit
dioxin-like effects via activation of AhR was quantified
by use of the H4IIE-luc rat hepatocarcinoma cells
(Hilscherova et al. 2001). ER-mediated response was
evaluated by use of MVLN human breast carcinoma cells
(Demirpence et al. 1993). MDA-kb2 human breast cancer cell line was used to assess AR-dependent response
(Wilson et al. 2002).
H4IIE-luc cells were cultured in Dulbecco’s modified
Eagle’s medium (DMEM) containing 10 % (v/v) fetal calf
serum (FCS; both PAA laboratories, Austria) and exposed in
the same medium supplemented with 1 % (v/v) gentamicin to
prevent bacterial contamination. MVLN cells were cultured in
DMEM/F12 medium (Sigma-Aldrich, Czech Republic) supplemented with 10 % (v/v) FCS and exposed in DMEM/F12
supplemented with 5 % (v/v) stripped (dextran/charcoal treated) FCS and 1 % (v /v ) gentamicin. MDA-kb2 cells were
cultured in Leibowitz L-15 medium (Sigma-Aldrich, Czech
Republic) supplemented with 10 % (v/v) FCS and exposed in
Leibowitz L-15 medium supplemented with 5 % (v /v )
stripped FCS and 1 % (v /v ) gentamicin. H4IIE-luc and
MVLN cells were incubated and exposed at 5 % CO2 and
37 °C. MDA-kb2 cells were incubated and exposed at 37 °C
without addition of CO2.
In the first step, test of cytotoxicity of the sediment extracts
was conducted to determine the non-cytotoxic concentrations
for testing of receptor-mediated effects. Upon testing, cells
were seeded into sterile 96-well microplates in exposure medium. After 24-h incubation, cells were exposed to extracts of
sediment samples in several dilutions. The greatest tested
concentration for cytotoxicity assessment was 100 mg SEQ/
mL. Cytotoxicity of the samples was measured using colorimetric Neutral Red (NR) uptake assay (Babich and
Borenfreund 1990). Fifty microliters of NR dissolved in
DMEM (0.5 mg/mL) were added into each well with cells
and exposure medium after 24-h exposure. The mixture was
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incubated with cells for 1 h and then the medium with NR was
removed. An aliquot of 150 μL of lysis solution (water,
ethanol, acetic acid) was added and cells were shaken for
15 min (Orbital Shaker OS-20, BIOSAN, at 150 rpm).
Absorbance was measured using a spectrophotometer
(Tecan-Genios, λ =570 nm). Data from the cytotoxic sample
dilutions were excluded from calculations.
The interference with the receptor signaling was tested at
several dilutions that did not significantly affect the viability
of the cells, in three independent experiments. Cells were
seeded into 96-well microplates and after 24-h incubation,
exposed to extracts of sediment samples and appropriate
standard calibration for agonistic potency along with blanks
and solvent controls (0.5 % v/v). Reference compounds used
for calibration were TCDD (Ultra Scientific, USA; concentration range 0.4–500 pM in EtOH) for H4IIE-luc, 17β-estradiol
(E2; Sigma-Aldrich, Czech Republic; 1.23–100 pM in
DMSO) for MVLN, and dihydrotestosterone (DHT; SigmaAldrich, Czech Republic; 10 pM–10 μM in DMSO) for
MDA-kb2, respectively. For ER- and AR-antagonistic potency assessment, the exposure medium was supplemented with
the reference compound (competing ligand) at approximately
EC50 level, e.g., 33.3 pM E2 (MVLN) and 1 nM DHT
(MDA-kb2), thus solvent concentration was 1 % (v/v). After
24-h exposure, cells were lysed, Promega Steady Glo Kit
(Promega, USA) was added and the intensity of luminescence
was measured by Luminoskan Ascent Microplate
Luminometer (Thermo Scientific).
Data analysis
After subtraction of solvent control response, effects elicited
by extracts of sediments were related to the luminescence
caused by the reference compounds in the transactivation
assay. The dose–response curves were fitted using non-linear
logarithmic regression in GraphPad Prism (GraphPad
Software, USA). AhR-mediated potency was expressed as
TCDD-equivalents (BIOTEQ) calculated as EC50TCDD/
EC50sample. Since many of the active samples did not reach
50 % of E2max induction, to avoid any predictions beyond the
measured responses, estrogenicity was expressed as estradiol
equivalents (EEQ) calculated as EC25 E2 /EC25 sample.
Antiestrogenicity of samples was expressed as the concentration (in sediment equivalents) that caused 25 % inhibition of
luminescence in the presence of the competing ligand E2
(IC25). Androgenicity was expressed as DHT-equivalents
(DHT-EQ) calculated as a point estimate based on the percentage of the luminescence induction caused by the greatest
non-cytotoxic sample concentration because the dose–response curve for most samples did not exceed 20 % induction.
DHT-EQ was calculated as ECX DHT/ECX sample, where X
represents percentage induction of the greatest non-cytotoxic
sample concentration. Antiandrogenicity was expressed as
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percent inhibition of luminescence in the presence of the
competing ligand DHT caused by the greatest concentration
that was non-cytotoxic (as described for DHT-EQ).
The limit of detection (LOD) for each bioassay used
in this study was derived as the ratio of the lowest
amount of standard that elicits statistically significant
response per the greatest tested non-cytotoxic concentration of SEQ. To calculate the LOD, the lowest concentration of reference compound significantly affecting the
receptor-mediated response (lowest observed effect concentration for the receptor-mediated effect; LOEC), and
the greatest non-cytotoxic sample concentration (no observable effects concentration for cytotoxicity; NOEC)
were determined. Responses obtained for reference compounds and sample extracts were compared with solvent
control response using ANOVA followed by Dunnet’s test to
determine significant effects (p < 0.05). Nonparametric
Kruskal-Wallis test was used in case of non-homogenous
variances (as tested by Levene’s test). The LOD was then
calculated as follows: LOD (pg/g, dry mass (dm) of sediment) = LOECstandard ligand (pg/mL)/NOECsample (g SEQ/mL).
Spatial and seasonal variability of dioxin-like toxicity (TEQ,
BIOTEQ), estrogenic potency (EEQ), and antiandrogenic
potency (AA) was tested by nonparametric Kruskal-Wallis
test and visualized using boxplots.
Multivariate variation of bioassays results as well as
chemical and environmental parameters was further
summarized in the principal component analysis
(PCA) as an effective technique simplifying the correlation structure through linear transformation of the
original variables. PCA based on the correlation matrix
was performed to provide component loading vectors
explaining the relationships among the bioassays, pollutants, and other parameters and component score vectors as pair-wise uncorrelated variables that were used
for the final exploratory survey of the data from the
examined locations. Only variables with less than 10 %
values below LOD were used for multivariate analysis.
Values < LOD were replaced by ½ LOD. The variables
with non-normal distribution were transformed by logarithmic transformation before use in PCA and parametric correlation analyses. The most important variables (estimates by eigenvalues) were selected for creating PCA (active variables), some other variables were
visualized in the same ordination space as supplementary variables. Biplot was used as a common graphical
tool representing not only projections on extracted principal components but also the 2-D loadings of original
variables by lines. Additionally, Pearson’s correlation
analysis was used to quantify relationships between
variables. All statistical analyses were performed with
the software STATISTICA for Windows 10 (StatSoft,
Inc. USA).
Environ Sci Pollut Res (2014) 21:5007–5022
Results and discussion
This study documents variability of pollution in surface
sediments of the rivers during the year. Sediments contained
all chemically analyzed classes of pollutants (Table 1) at
each location. SP was the most polluted location with the
greatest concentrations of most contaminants and also with
the greatest median of TOC content, whereas CR (oxbow
lake) contained, overall, the least levels of contaminants.
Detailed information about temporal and spatial distribution
of HOCs will be described elsewhere (Prokes et al., in
preparation). Comparing the contamination in assessed areas
with SQGs derived from ecological and ecotoxicological
data by de Deckere et al. (2011) (Table S3), all of the
investigated locations are polluted. The SQGs proposed to
be achieved in a long-term objective (so-called consensus 1
values) were exceeded by concentrations of PAHs, PCBs,
and DDE in all samples up to 6-, 7-, and 30-fold, respectively, especially in winter and spring (PAHs), and in autumn
(PCBs, DDE). In the case of DDD, the SQGs were exceeded
even more than 200-fold in winter samples from location SP.
According to these results, the studied locations are not in a
good ecological sediment status as it was defined in de
Deckere et al. (2011) during the year. The SQGs proposed
to be achieved as a short-term objective (consensus 2 values)
were only slightly exceeded by concentrations of some PAHs
and DDE (1.3-fold), while the concentration of DDD was up to
5-fold greater in winter samples from location SP than the
proposed limits. Consensus 2 values are described as values
above which toxic and in situ effects are most likely to occur
(de Deckere et al. 2011). From this point of view, all investigated sediments are likely to negatively affect biota. Desorption
of contaminants from sediments might enhance their bioavailability, which plays a crucial role in manifestation of toxic
effects on organisms. Results of previous studies indicated that
sediments from the studied area represent a potential source of
PAHs into the water column (Prokes et al. 2012).
Comparison of chemical analysis results to SQGs documents pollution by HOCs. However, compounds other than
those HOCs that were quantified were present in the mixtures
in sediments; therefore, specific biological activities were
assessed in order to estimate the potential effects on organisms. Three transactivation cell lines were used to investigate
specific biological potential of sediment samples. Mixtures
extracted from sediments were cytotoxic; therefore, extracts
were first treated with copper to remove sulfur, which is a
frequent cause of cytotoxicity. Cytotoxicity of treated extracts
was measured by use of the NR assay for each cell line in
order to avoid any interference with specific endpoints measured in this study. Only concentrations of extracts that did not
cause cytotoxicity were included in the evaluation of specific
potencies. MDA-kb2 cells were more sensitive to effects on
viability than were H4IIE-luc and MVLN cells. For MDA-
kb2 cells, the greatest NOEC corresponded to 50 mg SEQ/mL
for most samples, four sediment extracts from location MA
showed a greater cytotoxicity with NOEC of 15 mg SEQ/mL.
For H4IIE-luc and MVLN cells, the cytotoxicity NOEC was
100 mg SEQ/mL for all samples.
Total number of samples (n) with observed activity is noted in brackets in case when significant activity was not detected in all 15 samplings over the year
AhR-mediated potency
a
Abbreviations of sites as in Fig. 1
5013
PAHs polycyclic aromatic hydrocarbons, ind. PCBs indicator polychlorinated biphenyls, dl-PCBs dioxin-like PCBs, PCDDs polychlorinated dibenzo-p-dioxins, PCDFs polychlorinated dibenzofurans,
OCPs organochlorine pesticides, TOC total organic carbon, TEQ TCDD-equivalent (derived based on chemical analysis), BIOTEQ TCDD-equivalent (bioassay-derived), EEQ estradiol-equivalent
(bioassay derived), DHT-EQ dihydrotestosterone-equivalent (bioassay-derived), AA antiandrogenic activity (bioassay-derived)
74 (52–98)
1.9 (0.5–5.3)
3.7 (1.3–15.4)
5.3 (1.2–11.6) 2.9 (2.1–5.9) 0.3 (0.2–0.9) 21 (10–44)
5.4 (2.9–39.5) 1.8 (1.2–2.6) 0.7 (0.4–1.1)
17 (11–24)
13 (11–28)
52 (36–94)
106 (57–268)
7.1 (4.5–20.6) 317 (142–643)
6.2 (0.7–13.7) 290 (188–431)
13 (6–49)
5.2 (3.0–9.0)
2.6 (1.5–8.8)
CE
CR
110 (62–229) 5.4 (0.8-16.8) (n=7) 56 (17–84)
72 (32–98)
143 (45–895) 4.1 (0.7-5.9) (n=7)
6.4 (1.5–17.7)
8.5 (5.3–13.8) 12.0 (6.5–33.4) 300 (127–612) 354 (152–818) 37 (24–109) 25.3 (8.9–58.1) 4.1 (2.9–5.3) 0.9 (0.6–1.9) 17 (8–109)
SP
75 (39–198) (n=12) 1.9 (n=1)
72 (51–84)
68 (17–91)
76 (40–3,753) 2.8 (1.4–8.3) (n=4)
4.9 (1.0–12.6)
3.7 (0.9–14.7) 99 (20–954) (n=13) 3.1 (1.8–3.9) (n=3)
3.3 (0.6–7.7) 0.6 (0.1–1.4)
9.2 (1.6–19)
34 (4–49) 16.7 (0.9–53.1) 3.1 (0.2–5.1) 1.0 (0.1–1.2)
15 (2–23)
38 (8–94)
90 (11–190)
7.5 (1.7–10.5) 238 (105–340)
14 (5–52)
7.5 (0.5–10.7)
15 (6–39)
4.4 (0.6–14.3) 11.6 (3.5–21.4) 399 (179–494)
MA
BE
AA (%
inhibition)
DHT-EQ (ng/g)a
EEQ (pg/g)a
BIOTEQ
(ng/g)
TEQ/
BIOTEQ
(%)
TEQ (ng/g)
TOC (%)
OCPs
(ng/g)
PCDFs
(pg/g)
PCDDs
(pg/g)
Dl-PCBs
(pg/g)
Ind. PCBs
(ng/g)
Sampling PAHs
site
(μg/g)
Table 1 Median and range (in brackets) of concentrations of pollutants, organic carbon, and biological potencies in extracts of sediments from studied localities from all sampling campaigns over a year
(based on sediment dry mass)
Environ Sci Pollut Res (2014) 21:5007–5022
AhR-mediated potency, expressed as BIOTEQ, was found in
extracts of all sediments and was in the range of 0.5–17.7 ng/
g, dm of sediment (LOD=1.3 pg/g, dm). Seasonal changes in
BIOTEQ were obvious at all locations except CR (Figs. 2a
and 3). The greatest dioxin-like potency was detected in
sediments collected during winter. Concentrations of
BIOTEQ in sediments collected during winter were significantly greater than in those collected during summer, which
contained the least concentration of BIOTEQ (p <0.05). The
same trend was observed for content of TOC (Fig. S1), which
is an important parameter in accumulation of hydrophobic
pollutants (Jaffe 1991). The trend of greatest concentrations
in winter was most pronounced in the Morava River below the
confluence with the Drevnice River (locations SP, CE). There
was a trend of increasing concentration of BIOTEQ at SP
compared to upstream locations (MA, BE) in samples collected during the summer and winter (Fig. 3 and S2). However,
this trend was not obvious in spring and autumn, which
indicates that spatial differences can be more pronounced
during some seasons. SP is an integrating location for contamination from both rivers and additional nearby sources of
pollution. Location CR (oxbow lake) was the least contaminated location, with concentrations of BIOTEQ significantly
lesser (p <0.05) than those in sediments from locations BE,
SP, and CE (Fig. 2a, Table 1). CR also exhibited lesser
variability among seasons with AhR-mediated potency only
slightly greater in sediments collected during winter (Fig. 3).
This finding demonstrates the function of the oxbow lake as a
more stable deposit of various HOCs without greater fluctuations in pollution that are obvious in the active channel.
Concentrations of BIOTEQ in sediments from all riverine
locations exhibited least variability during summer (Fig. 3),
which was probably related to the least fluctuations in river
water discharge during this season (Fig. S3). Variability in
concentrations of BIOTEQ was greater among all riverine
locations during winter and spring (Fig. 3).
Results of this study confirmed the role of PAHs as the
predominant contributors to the overall AhR-mediated potency observed in previous studies from the region
(Hilscherova et al. 2001; Vondracek et al. 2001). Total
TEQ calculated from concentrations of individual AhRactive compounds (0.1–1.9 ng/g; Table 1) exhibited similar
seasonal patterns as did concentrations of BIOTEQ
(Fig. S4). However, concentrations of BIOTEQ were greater than concentrations of TEQ in extracts of all sediments
5014
Environ Sci Pollut Res (2014) 21:5007–5022
Fig. 2 Spatial and seasonal
variability of bioassay-derived: a
dioxin-like potency (BIOTEQ,
pg/g, dm of sediment), b estrogenic potency (EEQ, pg/g, dm),
c antiandrogenic potency (AA,
% luminescence inhibition in
competition with DHT caused
by the highest non-cytotoxic
sample concentration) in sediment samples from the 15
sampling campaigns in July
2007–July 2008 (n =73). Middle
line is median, box means quartile range (25–75 %), whisker is
non-outlier range and triangles
are measured values
with a single exception. Generally, only 13–21 % BIOTEQ
(median values “TEQ/BIOTEQ (%)” among locations;
Table 1) could be explained by the presence of known
AhR ligands, namely PAHs. PAHs accounted on average
for 99.4 % of the total TEQ, which is consistent with the results
of previous studies conducted in this area (Hilscherova et al.
2001; Vondracek et al. 2001). The main contributors among
PAHs were benzo[k]fluoranthene and indeno[123cd]perylene. Other source of dioxin-like toxicity might be
azaarenes and oxygenated PAH derivatives (oxy-PAHs) that
were previously detected in sediments from the studied area
(Machala et al. 2001). Comparable levels of pollution with
dioxin-like compounds were found in sediments from rivers
affected by municipal and industrial activities from other areas,
such as sediments from two Chinese rivers (BIOTEQ 0.3–
13.9 ng/g, dm), where greatest potencies were observed in
fractions containing PAHs, OCPs, a portion of PCDD/Fs and
unknown compounds (Song et al. 2006). In sediments from the
Environ Sci Pollut Res (2014) 21:5007–5022
5015
Fig. 3 Seasonal variability of
bioassay derived dioxin-like activity (BIOTEQ, pg/g, dm) at
each sampling site during the 15
sampling campaigns in July
2007–July 2008 (n =73). Middle
line is median, box means quartile range (25–75 %), whisker is
non-outlier range and triangles
are measured values
Netherlands, most AhR-mediated potency was caused by acidlabile compounds, such as PAHs (Houtman et al. 2004).
Alternatively, only 6 % of AhR-mediated potency was attributed to PAHs in sediments from Germany (Brack et al. 2008).
Furthermore, PCDD/Fs were a major source of dioxin-like
potency observed in the sediments in the USA where concentrations were as great as 19.9 and 17.7 ng/g, dm PCDDs and
PCDFs, respectively (Hilscherova et al. 2003), and 46.5 ng/g,
dm for the sum of PCDD/Fs (Kannan et al. 2008). However, in
the study, the results of which are reported here, dl-PCBs and
PCDD/Fs, due to their relatively small concentrations relative
to PAHs, contributed little of the total concentrations of TEQ
(Table 1). Despite their greater potency, the average contribution of PCDDs, PCDFs, and dl-PCBs to the total TEQ was
only 0.2, 0.3, and 0.1 %, respectively.
ER-mediated potency
Results of the bioassay documented the presence of estrogenic
compounds in almost all sediments. Estrogenic potency
expressed as EEQ was detected in 93 % of samples in the
range of 20–3,753 pg/g, dm, but most samples (88 %)
contained 20–300 pg/g, dm (Fig. 2b). Four samples taken in
summer 2007 at locations MA (n =1), BE (n =2), and SP (n =
1) were noted for great values of EEQ that reached 895–
3,753 pg/g, dm. These extreme concentrations are not included in Fig. 2b. Estrogenic potency of 7 % of the sediment
samples (n =5) was less than the limit of detection (LOD=
3.25 pg/g). Median concentration of EEQ among seasons was
greatest in sediments from location SP, similarly to median
concentration of BIOTEQ. Samples from BE and MA were
noted for a very variable estrogenicity among sampling campaigns including extreme EEQ values. When seasonal variability throughout the year was taken into account, statistically
significant difference in estrogenic potency was observed only
between locations SP and CR. But there were more pronounced differences in separate seasons. For example, EEQ
were always greater in SP compared to BE in autumn and
winter samples, while there was no such trend in the other two
seasons. The greatest median estrogenic potency across locations was observed in summer (Fig. 2b), even without the
extreme concentrations of EEQ observed in a few samples.
These extremes could indicate exceptional inputs of
(xeno-)estrogens of unknown origin that occurred during early
summer 2007 at the above mentioned localities. No general
significant seasonal trends in estrogenicity were observed in
this study (Fig. 2b). However, sediments sampled at locations
MA and CE tended to have lesser concentrations of EEQ in
spring compared to autumn. A similar trend was observed
previously in this region (Hilscherova et al. 2010; Table 2).
Alternatively, greater estrogenicity was observed in sediments
collected in spring than in those collected in autumn in a study
where a smaller sample set was compared (Creusot et al.
2013).
A weak antiestrogenic potency in the presence of competing E2 was detected only in two sediment samples taken from
CR in November and December 2007. Concentration of extract causing 25 % inhibition of luminescence (IC25) in competition with E2 was 25.6 and 16.6 mg SEQ/mL, respectively.
These two samples exhibited none and little estrogenic potency, respectively, but the presence of estrogenic pollutants
might be masked by antiestrogenic compounds present in
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Environ Sci Pollut Res (2014) 21:5007–5022
Table 2 Concentrations of AhR-mediated potency (BIOTEQ, pg TCDD/g, dm) and estrogenic potency (EEQ, pg E2/g, dm) of sediments at Malenovice
(MA), Belov (BE) and Spytihnev (SP) determined by bioassays
BIOTEQ (pg TCDD/g)
EEQ (pg E2/g)
MA
BE
SP
MA
BE
SP
October 1996a
6,542
4,223
NA
239
39
NA
October 1997a
6,675
4,449
NA
1,134
93
NA
May 2005b
15,368
8,123
9,867
95
29
186
October 2005b
7,868
1,442
4,611
231
107
175
May 2006b
7,768
914
14,356
<1
<1
90
October 2006b
1,660
764
5,573
442
66
127
October 2007
5,506
6,488
7,779
124
85
178
May 2008
1,333
3,001
3,166
<3
51
130
Summer 2007
1,058–2,302
1,042–2,566
2,098–5,893
141–954
198–3,753
45–895
Autumn 2007
2,139–5,663
2,927–6,488
1,515–7,884
99–124
58–85
136–178
Winter 2007/08
3,708–13,797
3,413–12,624
10,276–17,722
60–167
44–117
122–154
Spring 2008
1,333–14,690
3,001–9,768
3,166–12,317
<3–76
51–212
87–130
Summer 2008
940–1,867
2,062
3,870–6,824
20–61
40
97–131
NA data not available
a
Hilscherova et al. (2001) (2002)
b
Hilscherova et al. (2010)
these samples. There were four samples that elicited neither
estrogenic nor antiestrogenic potency. Antiestrogenic effects
might play an important role in some regions. For example,
81 % of sediments from the Pearl River, China, exhibited
estrogenicity but at the same time, 61 % of all samples were
antiestrogenic meaning that both estrogenic and antiestrogenic
compounds were present (Zhao et al. 2011). Sediments from
the Svratka and Svitava Rivers that flows into the Morava River
downstream from the studied area of Zlin vicinity, elicited only
antiestrogenic potencies (Jalova, personal communication) despite the fact that the region is relatively densely populated. The
estrogenicity detected in sediments from the region around the
city of Zlin indicates that there might be greater inputs of
estrogenic compounds due to less effective wastewater treatment plants (WWTPs) and/or more intensive agriculture.
ER-dependent potency was previously assessed in samples from the studied area. Concentrations of EEQ were in
the range of 5–23 (Vondracek et al. 2001) and 10–1,200 pg/
g, dm in extracts of sediments (Hilscherova et al. 2002).
After major floods in 1997, antiestrogenic potencies became
more apparent in sediments compared to the situation before
floods (Hilscherova et al. 2002). Approximately 10 years
after the floods, regional median concentrations of EEQ in
sediments from the studied area were in the range of 10–
340 pg/g, dm (Hilscherova et al. 2010). Estrogenic compounds (EEQ 21.3–29.9 pg/g, dm) were found in sediments
from both upstream and downstream of WWTPs that are
considered to be an important source of estrogenic compounds in UK; estrone (E1) and E2 were determined as
major estrogenic pollutants (Peck et al. 2004). On the other
hand, EEQ in the range of 3.3–10.6 pg/g, dm was detected
in sediments from downstream locations from WWTPs in
Korea, whereas no potency was observed in upstream locations (Oh et al. 2000). High contamination by estrogenic
compounds was observed in sediment from a river in Italy,
where E1, estriol (E3), and nonylphenol contributed to the
observed estrogenicity; phthalates and octylphenol isomers
were suggested as potential contributors (Vigano et al.
2008). In the area around the city of Zlin, rivers receive
treated effluents from a number of WWTPs as well as
untreated sewage effluents from smaller villages and farms.
Effects of large as well as smaller towns as sources of
estrogenic compounds have been documented (Jarosova
et al. 2012; Vermeirssen et al. 2005). Natural and synthetic
estrogens, such as E1, E2, E3, and ethinyl estradiol, were
not analyzed in our study but they can enter the rivers and
are likely to accumulate in sediments (Luo et al. 2011; Peck
et al. 2004; Streck 2009). Therefore, they could be important contributors to the estrogenic potency of extracts of
sediments. In addition, PAHs have been found to be a
source of estrogenicity in sediments (Hilscherova et al.
2002, 2010; Houtman et al. 2004; Luo et al. 2011). In this
study, concentrations of EEQ in sediments were not correlated with concentrations of measured PAHs. However,
some of their metabolites produced in sediments by microbial degradation such as hydroxylated PAHs could play a
role in the estrogenic effects (e.g., Hayakawa et al. 2007;
Luan et al. 2006).
Environ Sci Pollut Res (2014) 21:5007–5022
5017
AA was more profound in extracts of sediments than androgenic potency. All extracts at non-cytotoxic concentrations
inhibited luminescence in competition with natural ligand
DHT with median inhibition during the year at 56–74 % at
all sites (Table 1). Antiandrogenicity was greater in extracts of
sediments from CR than that from CE (Fig. 2c), namely in
summer and winter. These results suggest that antiandrogenic
compounds could accumulate better in relatively stable sediments of the oxbow lake. This could be also affected by lower
TOC content at CE compared to the other sites, since AA was
shown to correlate with organic carbon level (Fig. 4). The AA
was least in autumn and significantly greater concentrations
were observed in spring.
Androgenic potency expressed as DHT-EQ greater than
LOD (580 pg/g) was detected in 30 % of extracts of sediments. Concentrations of DHT-EQ were 0.7–16.8 ng/g, dm.
Androgenic potency was detected in at least one sampling
period at all locations, but in more than half of samples
from the individual locations there was no detectable
androgenicity (Table 1). Androgenic potency was detected
most frequently in samples from locations SP and CE,
whereas only one sample from CR exhibited androgenic
potency. Androgenicity was detected most often in sediments collected during autumn (73 % of autumn samples),
followed by summer (29 % of summer samples), while
only three samples collected during winter and one during
spring were androgenic.
To our knowledge, this is the first study that documents the
significant seasonal changes in both antiandrogenic and androgenic potential of organic extracts of sediments from a
river. Seasonal changes in both androgenic and antiandrogenic
potencies were in good agreement. The least antiandrogenic
potency, which was observed during autumn, corresponded to
the frequent detection of androgenicity in extracts of sediments collected during autumn. Alternatively, AA was
greatest in spring when only one sample was androgenic.
Previously, AA potency of few sediment samples was shown
to be greater in dry season compared to wet season (Zhao et al.
2011). Furthermore, androgenic potency was observed in 34
of 50 extracts of sediments collected in Germany, but seasonal
trends were not investigated (Galluba and Oehlmann 2012).
Antiandrogenic potency has been frequently detected in
studies of unfractionated extracts of sediments (Hilscherova
et al. 2010; Zhao et al. 2011). In some studies, AA was a
predominant effect in extracts of sediments, whereas androgenic potency was found in only some fractions (Urbatzka
et al. 2007; Weiss et al. 2009). Effect-directed analysis was
previously applied to reveal both AA and androgenic compounds in sediments. PAHs, such as fluoranthene,
benz[a]anthracene, pyrene and phenanthrene, nonylphenol
Fig. 4 Pearson’s correlation coefficient of bioassay-derived dioxin-like
potency (BIOTEQ), estrogenic potency (EEQ), and antiandrogenic potency (AA) with other parameters. Dark bands indicate a significant
correlation (p <0.05). Abbreviations as in Table 1; Σ DDT = sum of
concentrations of dichlorodiphenyltrichloroethane (p, p′-DDT) and its
metabolites p , p ′-DDE, p , p ′-DDD; PAHs -TEQ TCDD-equivalent
calculated based on PAHs concentration, nonPAHs -TEQ TCDDequivalent calculated based on dl-PCBs and PCDD/Fs concentration,
Tactual river water temperature on the day of sampling, Taverage timeweighted, average temperature over the 28 days prior to each sampling
campaign, Q average discharge over the 28 days prior to each sampling
campaign
AR-mediated potency
5018
(Weiss et al. 2009), and the metabolite of DDT, p, p′-DDE
(Urbatzka et al. 2007) were found in antiandrogenic fractions.
Various compounds, including oxygenated PAHs, organophosphates, musks, and steroids, were detected in androgenic
fractions (Weiss et al. 2011). A number of contaminants
analyzed in this study, including some PAHs, PCBs,
PCDD/Fs, and OCPs, have also been reported to be
antiandrogenic (Vinggaard et al. 2008).
Correlation and multivariate analysis
The correlation profiles of bioassay results with environmental parameters and concentrations of measured residues are
displayed as bivariate relationships (Fig. 4). Concentrations of
BIOTEQ were significantly positively correlated with TOC,
clay content, and flow and negatively with temperature even
when the seasonal variability was taken into account. These
correlations document a significant role of abiotic parameters
in accumulation of dioxin-like compounds, which was demonstrated for TOC and clay also in a previous study
(Hilscherova et al. 2010). The fine-grained fraction of sediment particles plays an important role in the accumulation of
HOCs in sediments (Jaffe 1991). BIOTEQ was also correlated
with concentrations of all studied classes of HOCs. The most
significant correlation has been found with PAHs and TEQ
derived from PAHs, which documents their important contribution to BIOTEQ. However, from the comparison of TEQ
and BIOTEQ it was calculated that only a negligible portion
of dioxin-like activity was attributed to dl-PCBs and
PCDD/Fs. The correlation does not imply causal relationship
but rather indicates that compounds with similar properties
like measured HOCs were responsible for the observed AhRpotency of sediments.
There was a significant negative correlation of concentrations of BIOTEQ with actual and average monthly temperature (Fig. 4). This corresponds with the greater concentrations
of BIOTEQ observed during winter, which is probably related
to slower rates of degradation of chemicals as well as greater
PAHs inputs from local combustion during colder periods.
Furthermore, concentrations of EEQ were significantly correlated with actual temperature (Fig. 4). Concentrations of PAHs
might be partially reduced by microbial degradation that is
greater during warmer months. Consequently, this could result
in an increased estrogenic potency of sediments due to the
formation of estrogenic metabolites, such as hydroxylated
PAHs (Hayakawa et al. 2007; Luan et al. 2006; Wang et al.
2012). The opposite trend is observed during winter, because
microbial degradation is lower at lower temperatures. Further,
lesser dilution of (xeno)estrogens can be expected during
warmer months due to the lesser discharge (Sumpter 2005;
Figs. S3 and S5). However, no significant correlation between
concentrations of EEQ and discharge was observed.
Environ Sci Pollut Res (2014) 21:5007–5022
Antiandrogenic potency was significantly correlated with
TOC (Fig. 4), which was also shown in a previous study
(Hilscherova et al. 2010). Thus, relatively hydrophobic compounds are likely to contribute to the AA potency of extracts
of sediments. However, no significant correlation was found
between AA and concentrations of studied HOCs among
locations and seasons (Fig. 4). The only correlations with
AA were found with concentrations of p, p′-DDE at location
MA and with both p, p′-DDE and p, p ′-DDD in sediments
from CE, respectively. These DDT metabolites are considered
as antiandrogenic compounds (Vinggaard et al. 2008).
The data were further analyzed using multivariate PCA.
Firstly, data from all localities and time points were included
in the PCA. The first and second principal components (PC)
accounted for 54 % of the total variance (40 and 14 %,
respectively), and simplified the multivariate pattern which
allowed the variables and samples to be projected onto a twodimensional space (Fig. 5). Variables with the main influence
were TEQ, BIOTEQ and concentrations of measured HOCs
in the direction of first PC, and EEQ and AA in the direction
of second PC. Secondly, only locations from the active river
channel were assessed and temperature (T actual) and discharge
(Q ) of the river were included as active variables in PCA
(Fig. 6).1 The first and second PC accounted for 52 % of the
total variance (39 and 13 %, respectively). Variables with the
main influence were concentrations of most classes of HOCs
(excluding dl-PCBs) in one direction and T actual and Q in the
other direction (Fig. 6a). The influence of EEQ and AA was
not apparent anymore in this two-dimensional projection. AA
was the dominant parameter associated with PC3, which
explained 10 % of the total variance.
AhR-mediated potency determined in bioassay (expressed
as BIOTEQ) was clearly associated with concentrations of
analyzed HOCs in the first PCA (Fig. 5a). However, if only
locations from the active river channel were included,
BIOTEQ was projected in the very same direction as HOCs
along PC1 but somewhat separated by the direction along PC2
(Fig. 6a). This observation further supported the interpretation
that the observed AhR-mediated potency of sediments cannot
be fully explained by analyzed HOCs and there were other
contaminants with similar properties contributing to the potency. In contrast, analyzed HOCs cannot explain concentrations of EEQ and AA that were projected in a different
direction from concentrations of HOCs in both multivariate
analyses (Figs. 5a and 6a).
When all locations and time points were included in the
analysis, the outcomes of specific bioassays used together
with concentrations of the measured pollutants as active variables did not separate the sediments from different locations
1
Locality CR (oxbow lake) has no water discharge (lentic locality) and
temperature was not measured, therefore, these two variables could not
have been included in Fig. 5.
Environ Sci Pollut Res (2014) 21:5007–5022
5019
Fig. 5 Principal component analysis (PCA) based on the data from all
sampling sites. The ordination diagrams show the relationship among
variables (a) and distribution of samples according to localities (b).
Variables marked by full circles were used for creating PCA (active
variables). Variables marked by empty circles are displayed in the same
ordination space but they were not used for creating PCA (supplementary
variables). Abbreviations as in Table 1 and Fig. 4
(Fig. 5b). Seasonal variability of contamination had a stronger
influence on the distribution of variables and samples in PCA
than the differences among locations. Results of different
samplings from all locations were relatively overlapping and
only individual samples from various locations were outliers.
Only if T actual and Q were included as active variables in PCA,
SP was obviously separated from the other locations in the
direction of greater pollutant concentrations (Fig. 6a, b). In
conclusion, seasonal changes play a dominant role and can be
more important in the studied locations than spatial differences. This finding is consistent with the results of a previous
study, which demonstrated no good separation of samples
from several study regions in autumn compared to spring
(Hilscherova et al. 2010).
Concentrations of dioxin-like and estrogenic potencies measured in fluvial sediments from the three locations (MA, BE,
and SP) (Table 2) during this study were compared to those of
several previous studies (Hilscherova et al. 2010, 2002). Data
from autumn (October) were available from 5 years between
Fig. 6 Principal component analysis (PCA) based on data from sites in
the active river channel (i.e., except CR) including also flow and temperature. The ordination diagrams show the relationship among variables (a)
and distribution of samples according to localities (b). Variables marked
by full circles were used for creating PCA (active variables). Variables
marked by empty circles are displayed in the same ordination space but
they were not used for creating PCA (supplementary variables). Abbreviations as in Table 1 and Fig. 4
Long-term trend analysis
5020
1996 and 2008, while for spring (May) from three different
years (2005–2008), respectively. There was no continuous
trend of changes in concentrations of BIOTEQ or EEQ that
would indicate the decrease or increase of contamination in
time. Rather, the long-term (inter-annual) differences
corresponded well with seasonal fluctuations documented in
the current study. Greater differences in potencies measured in
the bioassays were observed among spring samples from
different years while concentrations were more stable during
autumn. Inter-annual as well as seasonal fluctuations were the
least at location SP; maximally 4- and 2-fold differences were
observed in case of concentrations of BIOTEQ and EEQ,
respectively. This was probably related to the greater overall
discharge and long-term greater contamination at this location.
On the other hand, the greatest differences were found for
location MA on river Drevnice (up to 11-fold for BIOTEQ),
where discharge was relatively small and thus, fluctuations in
discharge could have had larger effects. Both short- and longterm variability in contamination by estrogenic compounds
were substantially greater than in the case of dioxin-like
compounds. Inter-annual variation in concentrations of EEQ
was greater than variation among seasons. As much as 95- and
51-fold difference in EEQ was observed at location MA and
BE, respectively, when comparing situations between May
2005, 2006, and 2008, while 25- and 4-fold difference was
observed within estrogenic potency of sediments from these
two locations in spring 2008, respectively (Table 2). The
greater differences on locations MA and BE are associated
mainly with a strong decrease of EEQ (below limit of detection) in spring 2006, which is a result of local floods that
occurred in the region (Hilscherova et al. 2010). Alternatively,
differences in concentrations of EEQs in extracts of sediments from location SP were only 2-fold among spring and
autumn samples across the studied years. The results of this
1-year study also show that concentrations of both BIOTEQ
and EEQ were more variable in spring compared to autumn.
This is probably related to the hydrology of the studied
rivers. The discharge of the river was relatively less and
stable in autumn 2007 (except for one major rainfall),
whereas greater discharge with stronger fluctuations occurred in spring 2008 which can be linked to a greater
resuspension of sediments (Fig. S3). A similar comparison
of a smaller data set from sediments in a French river
showed 3.6- and 5-fold inter-annual differences in dioxinlike and estrogenic potency, respectively (Creusot et al.
2013). Unlike in this study, lesser fluctuation was found in
spring than in autumn. However, spring was described as
dry season, whereas autumn as wet season in the French
study, which differs from the hydrology situation in our
study region (Fig. S3). This supports the conclusion that
hydrology of the river is a very important parameter that
needs to be taken into account in evaluation of river sediments contamination.
Environ Sci Pollut Res (2014) 21:5007–5022
Conclusions
The characterization of toxic potencies of environmental mixtures of pollutants might be an important step in the risk
assessment of contaminated ecosystems allowing the assessment of potential risks connected with the exposure of organisms, next to comparing concentrations of selected contaminants with quality criteria or EQS. This study documents that
the endocrine disruptive and dioxin-like potencies observed in
sediments were not, respectively only to a minor extent,
associated with routinely monitored hydrophobic organic pollutants. The contribution of PAHs, which were the predominant contaminants in the studied region, to the dioxin-like
potency was 13–21 % across locations (median values).
Despite the correlation between concentrations of dl-PCBs
and PCDD/Fs with BIOTEQ, contribution of these contaminants to the dioxin-like potency was negligible as calculated
based on their concentrations and relative potencies in the
bioassay. Analyzed HOCs could not explain the observed
estrogenic and antiandrogenic activities. The bioassays used
in this study provided important information indicating the
presence of yet unknown pollutants with dioxin-like and
endocrine disruptive potencies in sediments.
This 1-year long study of fluvial sediments also revealed seasonal differences in contamination with dioxinlike AA and androgenic compounds. Further, a long-term
comparison of the unique data set originating from three
locations point to a greater inter-annual fluctuations in
estrogenic than dioxin-like potency. Both short-term and
long-term data documents greater fluctuations in biological
potencies as well as in river water discharge at the individual locations during spring season. Hence, hydrology of
the river and its seasonal differences should be taken into
account both in design and interpretation of any monitoring studies. Locations and time points need to be chosen
carefully to make sure that the variability of contamination
is not overlooked. In addition, to be able to monitor longterm trends in a region, it is necessary to sample in the
same period of the year and under comparable hydrological situation. If this is not possible, the interpretation of
results from long-term monitoring should be corrected to
these factors.
Acknowledgments This research was supported by projects
ENVISCREEN (Ministry of Education, Youth and Sports of Czech
Republic No. 2B08036) and CETOCOEN (CZ.1.05/2.1.00/01.0001)
from the European Regional Development Fund. We acknowledge Klara
Komprdova, Roman Prokes, and Ondrej Sanka for their technical assistance. Prof. Giesy was supported by the Canada Research Chair program,
a Visiting Distinguished Professorship in the Department of Biology and
Chemistry and State Key Laboratory in Marine Pollution, City University
of Hong Kong, the 2012 “Great Level Foreign Experts”
(#GDW20123200120) program, funded by the State Administration of
Foreign Experts Affairs, the P.R. China to Nanjing University and the
Einstein Professor Program of the Chinese Academy of Sciences.
Environ Sci Pollut Res (2014) 21:5007–5022
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Fig. S1 Spatial and seasonal variability of total organic carbon (TOC, %) in sediment
samples from the 15 sampling campaigns in July 2007–July 2008 (n=73). Middle line is
median, box means quartile range (25-75%), whisker is non-outlier range, circles are outliers,
stars are extremes and triangles are measured values
Fig. S2 Spatial variability of bioassay derived dioxin-like potency (BIOTEQ, pg/g, dm) in
each season derived from the 15 sampling campaigns in July 2007–July 2008 (n=73). Middle
line is median, box means quartile range (25-75%), whisker is non-outlier range and triangles
are measured values
a
summer
spring
autumn
spring
winter
summer
autumn
Flow (m3.s-1)
300
200
1
Kromeriz
15
Spytihnev
100
1.12.2008
1.11.2008
1.9.2008
1.10.2008
1.8.2008
1.7.2008
1.6.2008
1.5.2008
1.4.2008
1.3.2008
1.2.2008
1.1.2008
1.12.2007
1.11.2007
1.10.2007
1.9.2007
1.8.2007
1.7.2007
1.6.2007
1.5.2007
1.4.2007
1.3.2007
1.2.2007
1.1.2007
0
b
25
spring
autumn
summer
winter
autumn
summer
spring
Flow (m3.s-1)
20
15
1
10
Zlin
15
5
1.12.2008
1.11.2008
1.9.2008
1.10.2008
1.8.2008
1.7.2008
1.6.2008
1.5.2008
1.4.2008
1.3.2008
1.2.2008
1.1.2008
1.12.2007
1.11.2007
1.10.2007
1.9.2007
1.8.2007
1.7.2007
1.6.2007
1.5.2007
1.4.2007
1.3.2007
1.2.2007
1.1.2007
0
Fig. S3 Daily water discharge at gauging stations a Kromeriz (representative for site BE) and
Spytihnev (SP) on Morava River and b Zlin on Drevnice River (representative for site MA).
The numbers 1 and 15 show the dates of first and last (15th) sampling campaigns: 1 – June 20,
2007; 15 – July 16, 2008.
Fig. S4 Spatial and seasonal variability of predicted (chemically-derived) dioxin-like potency
(TEQ, pg/g, dm) in sediment samples derived from the 15 sampling campaigns in July 2007–
July 2008 (n=73).. Middle line is median, box means quartile range (25-75%), whisker is nonoutlier range and triangles are measured values
a
spring
Temperature (°C)
25
summer
autumn
winter
spring
summer
autumn
20
15
1
10
Kromeriz
Spytihnev
15
5
1.12.2008
1.11.2008
1.9.2008
1.10.2008
1.8.2008
1.7.2008
1.6.2008
1.5.2008
1.4.2008
1.3.2008
1.2.2008
1.1.2008
1.12.2007
1.11.2007
1.10.2007
1.9.2007
1.8.2007
1.7.2007
1.6.2007
1.5.2007
1.4.2007
1.3.2007
1.2.2007
1.1.2007
0
b
spring
Temperature (°C)
25
summer
autumn
winter
spring
summer
autumn
20
15
Zlin
10
1
15
5
1.12.2008
1.11.2008
1.9.2008
1.10.2008
1.8.2008
1.7.2008
1.5.2008
1.6.2008
1.4.2008
1.3.2008
1.2.2008
1.1.2008
1.12.2007
1.11.2007
1.10.2007
1.9.2007
1.8.2007
1.7.2007
1.6.2007
1.5.2007
1.4.2007
1.3.2007
1.2.2007
1.1.2007
0
Fig. S5 Average daily water temperature (°C) at gauging stations a Kromeriz (representative
for site BE) and Spytihnev (SP) on Morava River and b Zlin on Drevnice River
(representative for site MA). The numbers 1 and 15 show the dates of first and last (15th)
sampling campaigns: 1 – June 20, 2007; 15 – July 16, 2008.
Table S1 Sampling campaigns in each sampling season (samples were clustered according to
four hydrologically defined seasons)
Campaign
no.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Sampling
date
2007-06-20
2007-07-18
2007-08-15
2007-09-12
2007-10-10
2007-11-07
2007-12-05
2008-01-02
2008-01-30
2008-02-27
2008-03-26
2008-04-23
2008-05-21
2008-06-18
2008-07-16
Season
Summer
Autumn
Winter
Spring
Summer
The sampling times from previous studies are listed in Table 2.
Table S2 Relative potencies (REP) of aryl hydrocarbon receptor (AhR) activation determined
in the test with H4IIE-luc cells (24 h exposure)
Compound
PAHsa
Fluoranthene
Pyrene
Benzo(a)anthracene
Chrysene
Benzo(b)fluoranthene
Benzo(k)fluoranthene
Benzo(a)pyrene
Indeno (123cd)pyrene
Dibenzo(ah)anthracene
PCBsb
PCB 77
PCB 81
PCB 126
PCB 169
PCB 105
PCB 114
PCB 123
PCB 156
PCB 157
PCB 167
PCB 189
a
Machala et al. (2001)
b
Behnisch et al. (2003)
REP
2.27×10-8
1.78×10-6
7.04×10-6
1.01×10-4
3.35×10-5
1.64×10-3
9.01×10-5
2.96×10-4
1.17×10-3
1.30×10-3
4.20×10-3
6.70×10-2
3.40×10-3
1.20×10-5
4.80×10-5
2.40×10-5
2.10×10-4
8.00×10-5
8.20×10-6
6.70×10-6
Compound
REP
PCDDsb
2,3,7,8-TCDD
1,2,3,7,8-PeCDD
1,2,3,4,7,8-HxCDD
1,2,3,6,7,8-HxCDD
1,2,3,7,8,9-HxCDD
1,2,3,4,6,7,8-HpCDD
OCDD
1
0.54
0.30
0.14
6.60×10-2
4.60×10-2
5.00×10-4
PCDFsb
2,3,7,8-TCDF
1,2,3,7,8-PeCDF
2,3,4,7,8-PeCDF
1,2,3,4,7,8-HxCDF
1,2,3,6,7,8-HxCDF
1,2,3,7,8,9-HxCDF
2,3,4,6,7,8-HxCDF
1,2,3,4,6,7,8-HpCDF
1,2,3,4,7,8,9-HpCDF
OCDF
0.32
0.21
0.50
0.13
3.90×10-2
0.11
0.18
2.90×10-2
4.10×10-2
6.50×10-3
Table S3 Sediment quality guidelines derived by de Deckere et al. (2011)
Naphthalene
Acenaphthylene
Acenaphthene
Fluorene
Phenanthrene
Anthracene
Fluoranthene
Pyrene
Benz[a]anthracene
Chrysene
Benzo[b]fluoranthene
Benzo[k]fluoranthene
Benzo[a]pyrene
Indeno[123cd]pyrene
Dibenz[ah]anthracene
Benzo[ghi]perylene
PCB 28
PCB 52
PCB 101
PCB 118
PCB 153
PCB 138
PCB 180
p,p'-DDE
p,p'-DDD
HCB
Consensus 1 value
(µg/kg dry mass of sediment)
200
30
40
40
180
30
250
240
120
150
170
80
140
120
20
110
0.04
0.1
0.54
0.43
1.5
1
0.44
0.31
0.06
0.0004
Consensus 2 value
(µg/kg dry mass of sediment)
6600
5200
3300
260
890
170
1200
940
600
830
660
320
600
480
120
450
2
4.6
6.7
6.9
9.7
7.5
5.5
6.8
3.2
0.72