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SCREENING OF PESTICIDE AND BIOCIDE PATTERNS AS RISK DRIVERS IN SEDIMENTS OF MAJOR EUROPEAN RIVER MOUTHS: UBIQUITOUS OR RIVER BASIN-SPECIFIC CONTAMINATION? Riccardo Massei, Wibke Busch, Hendrik Wolschke, Lena Schinkel, Maike Bitsch, Tobias Schulze, Martin Krauss, and Werner Brack Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b04355 • Publication Date (Web): 21 Jan 2018 Downloaded from http://pubs.acs.org on January 21, 2018

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SCREENING

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SEDIMENTS OF MAJOR

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BASIN-SPECIFIC CONTAMINATION?

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*Riccardo Massei1,2, Wibke Busch3, Hendrik Wolschke4, Lena Schinkel1,5, Maike Bitsch1, Tobias

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Schulze1, Martin Krauss1, Werner Brack1,2

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1

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15, Leipzig, Germany (email: [email protected])

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2

9

Worringerweg 1, Aachen, Germany

OF PESTICIDE AND BIOCIDE PATTERNS AS RISK DRIVERS IN

EUROPEAN RIVER MOUTHS: UBIQUITOUS

OR RIVER

Department Effect-Directed Analysis, Helmholtz Centre for Environmental Research - UFZ, Permoserstr.

Department of Ecosystem Analyses, RWTH Aachen University, Institute for Environmental Research,

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3

11

Permoserstr. 15, Leipzig, Germany

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4

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Research, Department for Environmental Chemistry, Max-Planck-Strasse 1, 21502 Geesthacht, Germany

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Current address Laboratory for Advanced Analytical Technologies, Swiss Federal Institute for Material

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Science and Technology, Empa, Überlandstrasse 129, 8600 Dübendorf, Switzerland

Department of Bioanalytical Ecotoxicology, Helmholtz Centre for Environmental Research - UFZ,

Helmholtz-Zentrum Geesthacht, Centre for Materials and Coastal Research, Institute of Coastal

:5

16 17 18 19 20 21 22 23


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Abstract

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Pesticides and biocides (PaB) are ubiquitously present in aquatic ecosystems due to their wide-

26

spread application and have been detected in rivers at concentrations that may cause distress

27

to aquatic life. Many of these compounds accumulate in sediments acting as long-term source

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for aquatic ecosystems. However, data on sediment contamination with current-use PaB in

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Europe are scarce. Thus, in this study, we elucidated PaB patterns and associated risks in

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sediments of seven major European rivers focusing on their last stretch as an integrative sink of

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particles transported by these rivers. Sediments were extracted with pressurized liquid

32

extraction (PLE) using a broad-spectrum method recovering many compound classes with a

33

wide range of physico-chemical properties. Altogether 126 compounds were analyzed and 81 of

34

them were detected with LC-HRMS and GC-NCI-MS/MS at least in one of the sediments. The

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highest number of compounds was detected (59) in River Elbe sediments close to Cuxhaven

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with outstanding concentrations ranging from 0.8 to 1691 mg/g organic carbon. Multivariate

37

analysis identified a cluster with 3 ubiquitous compounds (cyhalothrin, carbendazim,

38

fenpropimorph) and three clusters of chemicals with higher variability within and between rivers.

39

Risk assessment indicates an acute toxic risk to benthic crustaceans at all investigated sites

40

with the pyrethroids tefluthrin and cyfluthrin together with the fungicide carbendazim as the main

41

drivers. Risks to algae were driven at most sites almost exclusively by photosynthesis inhibitors

42

with estuary-specific herbicide mixtures, while in the rivers Po and Gironde cell division

43

inhibitors played an important role at some sites. Mixtures of specific concern have been

44

defined and suggested for integration in future monitoring programs.

45 46



47

1. Introduction

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The use and emission of a large variety of anthropogenic chemicals resulted in the

49

contamination of European freshwater resources that may pose a risk for ecosystems and

50

human health1. This holds particularly true for pesticides and biocides (PaB) that are designed

51

to be toxic to organisms. While pesticides are intensively used for plant protection in agriculture,

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biocides have a wide application range including disinfection, preservation of buildings,

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antimicrobial activity in personal care products, and many others2. About 1.5x106 tons of PaB

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were sold within the European market between 2011 and 20143. As a consequence of their

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widespread use, they are present in many water bodies often in concentrations causing distress

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to the aquatic life4-10.

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PaB are emitted to water bodies through different pathways and they distribute among different

58

compartments according to their physico-chemical properties2. Monitoring studies on current-

59

use PaB have focused so far mainly on the water phase1, 11, but these compounds could also be

60

detected in aquatic biota and sediments12-16. In fact, sediments have been shown to be a sink

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for many anthropogenic pollutants including PaB and may also act as a long-term source to the

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water phase and aquatic organisms, particularly if contaminated sediment is resuspended17, 18.

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Although the potential environmental relevance of contaminated sediments is acknowledged,

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there is a general lack of knowledge on the occurrence of and risk from PaB in European

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sediments. Moreover, it is unclear whether contamination and risk patterns are similar in most

66

European river basins or whether basin-specific contamination prevails. Recent studies have

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also shown that pesticides and biocides frequently occur in specific mixtures in European

68

streams that might be of specific concern for risk assessment5, 19. This holds particularly true for

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mixtures of compounds with additive or even synergistic effects such as pesticides with the

70

same mode of action (MoA) or combinations of sterol biosynthesis inhibiting fungicides with

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other pesticides. It is well-known that pesticides may interact synergistically on aquatic


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organisms with cumulative effects that may be larger than predicted from the reference model of

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concentration addition20.

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In the European Union the Ecological Status according to the Water Framework Directive is

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classified on the basis of the Biological Quality Elements (BQEs) phytoplankton and other

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aquatic plants, macro invertebrates and fish21. All three BQEs include benthic species and life

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stages living in close contact to sediments. Thus, risk assessment is often based on the

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calculation of toxic units (TU) according to the model of concentration addition (CA) to standard

79

laboratory test organisms from environmental concentrations as a proxy for toxicity towards

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these BQEs22. Although sediment-toxicity benchmarks for currently used pesticides are

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becoming available23, they are still limited with respect to compounds and BQEs addressed.

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Thus, for screening purposes, estimated corresponding water concentrations can be calculated

83

from equilibrium partitioning estimating organic carbon-water partition coefficients (KOC)24. By

84

normalizing these water concentration estimates to effect concentrations, toxic units (TU) for the

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respective organisms can be calculated9 and summed up according to the concept of

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concentration addition to estimate mixture toxicity5 and to evaluate their cumulative risk6, 9, 25, 26.

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Strictly speaking, CA and thus the TU concept is only valid for similarly acting compounds, while

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dissimilarly acting chemicals follow the Independent Action (IA) model. However, required effect

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data for a calculation of mixture toxicity according to IA are hardly available and it has been

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concluded previously that for the vast majority of pesticide mixtures, CA application predicts

91

toxicity well within a factor of 2 from observations independent of the similarity and dissimilarity

92

of the MoA27. Thus, we follow the suggestion by Backhaus and Faust

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precautious screening approach for the cumulative risk assessment in this study.

94

The present study provides a first overview on the composition and diversity of PaB mixtures in

95

sediments of the lower stretches of seven major European rivers and derives estimates of risk

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patterns for different organism groups. The lower stretches of the rivers (defined as river mouth)

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were selected as typical sedimentation zones at the continent/ocean interface integrating inputs


28

to use CA as a


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from upstream agricultural and industrial activities29. In detail, the objectives of the present study

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where 1) to identify sediment contamination patterns based on a set of 126 frequently applied

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pesticides and biocides, 2) to identify typically co-occurring pesticides and biocides as mixtures

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of specific concern and 3) to assess BQEs and MoA-specific cumulative risks from currently

102

used pesticides and biocides based on equilibrium partitioning and TUs.

2. Materials and Methods

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2.1

Standards and reagents

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A target list of 126 active compounds (33 herbicides, 37 fungicides, 32 insecticides, 17

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transformation products, 7 biocides) was analyzed, of which 102 were used as pesticides, 7 as

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biocides and 15 in both applications. These compounds covered a wide range of hydrophobicity

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(log KOW from -3.5 to 8) were expected to potentially occur in European river sediments

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according to previous screening studies30-35 and a frequent usage.

110

A standard mixture of all compounds excluding pyrethroids (n=120) was prepared at a

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concentration of 1 µg/mL in methanol. A separate standard was prepared including all

112

pyrethroids (n=6) approved for usage in the EU36. A mixture of 19 isotope-labeled internal

113

standards was also prepared in methanol at 1 µg/mL. Details on the target compounds and

114

other chemicals used are provided in the SI (Table SI 1, 2).

115

2.2

Analysis of the environmental samples

116

2.2.1

Sediments sampling

117

Sediment samples were collected in the river mouth of seven major rivers in Europe (Tiber and

118

Po in Italy, Elbe in Germany, Rhine and Scheldt in the Netherlands, Gironde in France and


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Danube in Romania) between 2012 and 2015. For each river, surface sediments (0 - 5 cm) of

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two to eight sites were sampled with a grab sampler, resulting in 30 samples altogether. The

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sites were chosen considering areas upstream and downstream of potential pollution sources

122

(e.g., the city of Rome at the Tiber River). Details about sampling sites are provided in SI (SI 1,

123

Table S3). The samples were stored in aluminum containers, transported on ice to the

124

laboratory and stored at -20°C. Samples were freeze-dried, sieved to ≤ 63 µm and stored in

125

brown glass bottles at -20°C until extraction. Details on the total organic carbon, total inorganic

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carbon and black carbon contents of the sediments are provided in the SI (Table S3).

127

2.2.2

128

Sediments were prepared according to Massei, et al.

129

the amount of total organic carbon (TOC) rather than on dry weight assuming TOC as the

130

predominant phase for the accumulation of chemicals and to achieve comparable matrix effects

131

for LC-HRMS analysis

132

the DIN 1953938 and performed on a LECO RC-612 carbon analyzer. Details on the extraction

133

procedure are given in the SI (SI 1). Further details of the PLE method and the preparation of

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the normal phase for clean-up are given in SI (SI 2, 3, Table S4). Detailed LC, GC, and MS

135

conditions, quantification and internal standards are provided in the SI (SI 4, Tables S5, 6, 7, 8,

136

9). The method detection limits (MDLs) were determined according to the US US EPA

137

guidelines. Additional information is provided in the SI (SI 5).

138

2.3

139

In order to identify specific pollution patterns and address variability, coefficients of variation

140

within (CVin) and between (CVbw) the river basins were calculated for all target compounds

141

according to Eq.1 and Eq.2:

Sediment extraction, clean-up and chemical analysis

37

37

. Extraction and analysis were based on

. The carbon content of sediment samples was analyzed according to

39

Identification of pollution patterns and mixture analysis



142

=

143

=

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∑ {)

!

(Eq.1)

(Eq.2)

144

with SD and AV as the calculated standard deviation and average of the concentration for river

145

basins, respectively. Compounds with concentrations below MDLs were excluded from the

146

analysis. Additionally, compounds detected at only one site were excluded from analysis and

147

considered as site-specific contaminants. Data were centered and log-normalized before

148

analysis. Pollution patterns were then analyzed by k-means clustering as suggested by

149

Kassambara 40. The optimal number of clusters for the k-means clustering was estimated by the

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R package factoextra package.

151

2.4

152

Equilibrium water concentrations (Cew) were calculated from concentrations in organic carbon

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(COC) assuming equilibrium partitioning to organic matter as the dominating binding phase in

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sediments24 by normalizing COC on the organic carbon and water partition coefficient (KOC)

155

(Eq.3):

Toxic Units (TU)

"# =

$% &

%$(Eq.3)

156

KOC values were estimated by the software KOCWIN (EPISUITE)41. As a measure of toxic

157

stress, TUs were calculated for each chemical and BQE by normalizing Cew to the lethal (LC50)

158

or effect concentrations (EC50) causing 50% lethality or growth inhibition, respectively, of a

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representative fish, invertebrate or algal species (Pimephales promelas, Oncorhyncus mykiss,

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Daphnia magna, Americamysis bahia and Pseudokirchneriella subcapitata) according to Eq. 4

161

(Sprague, 1970).


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'( =

)* )* $. +, /,-

(Eq.4)

162

Acute toxicity data were selected in this order: 1) Experimental data retrieved from the EPA

163

ECOTOX database and Malaj et al.10 2) predicted read-across data19 3) predicted ECOSAR

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data41 (baseline toxicity).

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Additional information about sources of acute toxicity data can be found in SI (Table S.10).

166

TU values for individual mixture components were summed up (TUsum) following the concept of

167

concentration addition to calculate the cumulative risk of the sediment contaminants for each

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BQE.

169

2.5

170

Graphs, statistics and general data analyses were performed using Microsoft Excel 2010,

171

Sigma Plot (version 12), RStudio (version 1.0.136) and the R packages cluster, fpc, factor extra

172

and gplot.

Data analysis

3. Results and Discussion

173

174

3.1

Pesticide and biocide detection frequencies

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Out of 126 PaB analyzed, 81 compounds (32 herbicides, 21 fungicides, 2 biocides and 16

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insecticides and 10 transformation products) were identified in the samples. Ten of the detected

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compounds (four insectides, five fungicides and one herbicide) are also used as biocides.

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Among all the sites, the area of Cuxhaven (Elbe1) showed the highest diversity and

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concentrations of chemicals in sediments with 59 quantified compounds and a cumulative

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concentration of 8.7 µg / mg organic carbon. Figure 1 shows an overview of detected average

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concentrations in sediment organic carbon of each river. Sediment concentrations from the



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River Elbe close to Cuxhaven are presented separately due to the strong qualitative and

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quantitative deviation from the other Elbe sites. To achieve comparable results among the sites,

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all chemical concentrations were normalized by the organic carbon content. Concentrations for

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individual sites and MDLs are provided in the SI (Table S.11, 12).

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While in surface waters herbicides are often found to predominate the PaB mixtures5,

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pyrethroid insecticide concentrations are typically below detection limits5, 7. The present study

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indicates that pyrethroids are widespread in European rivers since they were detected in

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sediments with high frequency (≥90%) due to their high log KOC values of about 3.4 to 5.6. This

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is in agreement with previous findings of high frequency (≥50%) in sediments even at a global

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scale43 and a market share of the world insecticide market of nearly 38%. The fungicide

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carbendazim was also detected nearly at all the sampling sites (detection frequency: 86%),

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although its log KOC value is comparably low (2.1). Despite its ban for agricultural application in

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the EU in 2014, carbendazim is currently used in 15 EU countries as biocide for wood

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preservation and has been broadly used in the past for several agricultural crops

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Moreover, carbendazim is one of the major transformation products of thiophanate and

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thiophanate-methyl, two fungicides that are authorized in the EU market

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triclocarban was detected in nearly 77% of the sampling sites. Triclocarban is one of the most

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widely used bactericides and preservatives in tooth pastes, deodorants, beauty creams and

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shampoos46. Since triclocarban is characterized by a moderate hydrophobicity (log Kow: 4.9)

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and is only partially biodegraded in wastewater treatment plants (WWTPs)47, it is likely to be

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detected in sediments of estuary and deltas. In contrast, the biocide triclosan which is also

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widely used in the EU-market was detected in only 37% of the sampling sites.


7, 42

,

44, 45

.

45

. The biocide

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204

3.2

Pesticide and biocide pollution pattern

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The variability of concentrations between (CVbw) and within (CVin) basins was analyzed and k-

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clustered in four groups (Figure 2). Among the 81 compounds detected in the seven rivers, 27

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compounds were detected above the MDL and in more than one sampling site. We identified

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one pyrethroid insecticide (cyhalothrin) and two fungicides (fenpropimorph, and carbendazim)

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as ubiquitous in European river mouth’s sediments (purple group, LV).

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Additionally, we identified two clusters with medium variability (MV) between and within the

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rivers. The cluster MV1 (high CVin, and low CVbw) contained two more pyrethroids (bifenthrin

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and tefluthrin,) one fungicide (spiroxamine), one herbicides (diuron) and the biocide triclocarban.

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The cluster MV2 (high CVbw, and low CVin) was composed of two additional pyrethroids

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(cypermethrin and cyfluthrin). A third cluster with compounds of highest variability (HV) between

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and within the rivers could be separated including particularly herbicides and fungicides.

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For hierarchical clustering of sediment contamination patterns, the compounds from both MV

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clusters were selected. They were assumed to provide the highest discriminative power

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avoiding compounds with low variation as well as those found at very few sites only. This

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approach resulted in six main clusters (Figure 3) including 1) a cluster with seven samples from

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Po, Danube and the left branch of river Rhine contaminated with all MV compounds except

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cyfluthrin and diuron, 2) a cluster with Cuxhaven and two samples from Scheldt characterized

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by low concentrations of cypermethrin and bifenthrin, 3) a group of samples from Tiber with high

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concentrations of cyfluthrin, cypermethrin and bifenthrin, 4) a spiroxamine and triclocarban

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driven cluster of samples from the right branch of river Rhine, one site from Po and Elbe

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respectively, 5) a cluster of sites from Gironde driven by high concentrations of diuron and 6) a

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cluster with the remaining sites of Gironde, Elbe and Scheldt characterized by low

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concentrations of all the MV compounds.



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Thus, there is a tendency to group sites from the same river together, suggesting indeed river

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basin specific pollution. Additionally, we detected generally higher concentrations near the river

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mouth to the sea. This holds particularly true for sediments of the rivers Elbe, Scheldt, Po and

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Tiber. The contamination profile of River Danube was nearly homogeneous except for the

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higher concentration of triclocarban in its first site (Dan1). Sediments from Dan1 were collected

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in front of the small town of Reni that may be a local source of triclocarban due to incomplete

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removal by wastewater treatment 48. In a similar way wastewater effluents from the city of Rome

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may explain the increase of triclocarban concentrations between site T1 and T2 (from 25 to 596

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ng / mg organic carbon). The higher concentrations of diuron in the brackish area of Gironde

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(Gi 2, 4, 5 and 6) may be also related to local pollution sources (Figure 3, Table S11). Diuron is

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often applied in viniculture49 which is an important agricultural sector in the area of Bordeaux. In

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particular, the left and right shore of the brackish area of Gironde is characterized by an

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intensive wine production with 65 x106 hectares of vines. Higher concentrations of diuron can be

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directly related to potential agricultural surface run-off.

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Interestingly, the two outlets of river Rhine (namely, Waterway and Haring Vliet) were clustered

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separately (Figure 3, Table S11). The Haringvliet channel (Rhi4 and 5) was characterized by

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higher concentrations of PaB than the Waterway branch (Rhi1, 2 and 3). The main difference

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between the two branches of River Rhine is that the Haringvliet channel is a stagnant

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sedimentation area where pollutants may easily accumulate50, 51, which is not the case for the

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Waterway channel. The same situation holds for the area of Cuxhaven, where sediments were

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collected from a tidal flat in the outer Elbe estuary. Higher concentrations of pollutants are

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usually found in mudflats areas characterized by low flow velocities and where sediment-bound

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pollutants accumulate 51.


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3.3

Risk assessment based on toxic units (TUs) and modes of action (MoAs)

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Cumulative TUs per site for all compounds and BQEs ranged from 2.6 x10-4 to 5.9, SI Table S13

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with a maximum value of 3.3 for an individual compound determined for acetochlor in sediments

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from Cuxhaven (Elbe). These numbers slightly exceed TUs measured by Schäfer et al.

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sediments of small streams with TUs from 2.5x10-7 to 0.63.

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For algae, TUs of individual sites ranged from 3.8x10-3 to 5.9 (Figure 4), with photosynthesis

257

inhibition as the dominant MoA accompanied by the inhibition of cell multiplication by acetochlor

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and metolachlor in the rivers Gironde and Po. Detailed cumulative risk plots are available in the

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SI (SI5). While photosynthesis inhibition strongly prevailed, the dominating compounds were

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quite river specific. Diuron drives the risk in Elbe, Gironde and Scheldt (average TUs 0.2, 0.4,

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and 0.2, respectively) and contributes to the risk in the Rhine and Tiber River (TUs 0.08 and 0.1,

262

respectively). Irgarol was the main driver of risk in the first sites of Danube and Tiber rivers (TUs

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0.01and 0.2, respectively), while acetochlor pre-dominated the risk to algae in the river mouth of

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Po and Cuxhaven in river Elbe (average TU 0.3 and 3.3, respectively). The terbutylazine

265

metabolite 2-hydroxy terbutylazine was the main driver of risk in the rivers Po, Rhine and the

266

site of Cuxhaven at the Elbe (average TUs 0.2, 0.07 and 0.8, respectively). Although a definite

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knowledge about of its MoA is lacking, it may be assumed that it is similar to that of the parent

268

compound acting as photosynthesis inhibitor.

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The cumulative risk by sediment-borne PaB to daphnia was remarkably similar over all

270

investigated rivers and spanned with a TUsum of 0.02 to 0.6 only one order of magnitude (Figure

271

4). The predominating MoA in sediments at all sites except the site of Cuxhaven in the River

272

Elbe was neurotoxicity, followed by effects on the cytoskeleton motor protein and sterol

273

biosynthesis inhibition. The latter two MoAs play a prominent role in sediments of the site

274

Cuxhaven. Several studies show that cumulative TUs larger than 0.001 are associated with a

275

decline of the invertebrate community structure52-54, while cumulative TUs larger than 0.1 are


29

in


276

suggested to pose acute risks to invertebrates10. Thus, the present results indicate an acute

277

toxic risk to crustaceans by pesticide- and biocide-contaminated sediments in all investigated

278

European river mouths. This is well in agreement with previous findings in water suggesting that

279

organic chemicals jeopardize the health of freshwater ecosystems on the continental scale10.

280

Pyrethroids and fungicides were the main drivers of risk for daphnia in the studied sediments

281

(Figure 4 and SI5). Tefluthrin (average TU: 0.005 to 0.05) predominated the cumulative risk to

282

daphnia in six of the seven river mouths together with the fungicide carbendazim (average TU:

283

0.0006 to 0.002, Figure 4). In the Tiber river the main driver of risk was cyfluthrin (average TUs:

284

0.14) complemented by tefluthrin. At the site Cuxhaven the risk to daphnia was driven by the

285

fungicides difenoconazole (TU: 0.3) and carbendazim (TU: 0.05), the insecticides picolinafen

286

(TU: 0.003) and pirimicarb (TU: 0.002) and the herbicide transformation product 2-

287

hydroxyterbutylazine (TU: 0.05).

288

Pyrethroids are acting neurotoxic by irreversibly blocking voltage-gated sodium channels in the

289

axonal membranes55 and are known to cause acute toxicity to freshwater invertebrates at

290

environmental concentrations56,

291

sediments toxicity on invertebrate species43. The fungicide carbendazim, as the second driver of

292

risk for daphnia in river mouths sediments inhibits the synthesis of cytoskeleton proteins in

293

fungi, but is also a well-known acetylcholinesterase inhibitor58. Lab experiments showed that the

294

compound was highly toxic to invertebrate species of Gammarus pulex and Gammarus

295

fossarum at environmental concentrations (LC50: 51 µg/L) influencing their reproduction and

296

feeding behavior even bellow LC20 levels59. Moreover, carbendazim is classified as possible

297

carcinogen and endocrine disruptor and highly toxic to the zooplankton community60.

298

The second fungicide playing a prominent role for the risk to daphnia in sediments particularly

299

from Cuxhaven was difeconazole, a sterol biosynthesis inhibitor (SBI). SBIs have also been

57

. Moreover, pyrethroids are known to play a major role in


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300

shown to influence the endocrine signaling molecules in arthropods61 and to disturb different

301

stages of maturation, development, growth and reproduction by long term exposure62. Azole

302

fungicides act also by inhibiting cytochrome P450 enzyme in many organisms, which makes

303

aquatic species more vulnerable to pollutants as the huge enzyme class of CYP catalyzes

304

several metabolic and detoxification processes 63.

305

Cumulative TUs per river mouths for fish were lower than for invertebrates (range: 2.6x10-4 to

306

6.5x10-2) and very diverse with many compounds contributing to risks including the pyrethroids

307

including tefluthrin, cyfluthrin and cypermethrin, the herbicide metabolite 2-hydroxyterbutylazine,

308

the neonicotinoid imidacloprid, the respiration inhibitor picoxystrobin and the herbicide and cell

309

multiplication inhibitor acetochlor (Figure 4 and SI5). Since fish are not among the target

310

organisms of pesticides and biocides and no compound with fish-specific MoAs are applied, the

311

relatively low risk for fish is in agreement with expectations.

312

3.4

313

In order to derive mixtures of specific concern (MPC), individual compounds were identified that

314

explain in mixture at least 90% of the risk of pesticide and biocide mixtures to fish, daphnia or

315

algae. The selection is based on cumulative risk plots given in SI (Figure S.2) and was restricted

316

to those river mouth/BQE pairs with a cumulative TU of more than 0.01. This approach excludes

317

most of the river mouths with respect to risks to fish except Tiber and the site of Cuxhaven. All

318

cumulative TUs for daphnia and algae were above 0.01 and thus considered as relevant.

319

The results suggest that the pyrethroid tefluthrin (sometimes replaced or complemented by

320

cyfluthrin and cypermethrin) and the fungicide carbendazim are an almost ubiquitous sediment-

321

borne MPC driving the pesticide and biocide risk to daphnia. While the pyrethroids are assumed

322

to act according to concentration addition, investigations on joint effects with carbendazim on

323

non-target species are extensively missing. The site of Cuxhaven may be seen as an exception

Identification of mixtures of specific concern



324

with a mixture of five compounds, the fungicide difenoconazole and two metabolites

325

(terbutylazine-2-hydroxy and thiacloprid amide) as main drivers.

326

Other than for crustaceans, no Europe-wide MPCs in sediments driving risks to algae can be

327

identified, but river- and often even site-specific combinations of photosynthesis inhibiting

328

triazine and phenyl urea herbicides. Individual chemicals include terbutylazine and its metabolite

329

2-hydroxy terbutylazine, terbutryn, irgarol, diuron, hexazinone, and simazine. In 19 out of the 30

330

investigated sites photosynthesis inhibitors contribute to more than 80% of the risk to algae. In

331

sediments from the eight sites from the rivers Gironde, Po and Cuxhaven, the cell division

332

inhibitors acetochlor and metolachlor contribute around 35 to 85 % of algal toxicity. In sediments

333

of the remaining three sites from Tiber, Scheldt and Rhine the lipid metabolism and sterol

334

biosynthesis inhibitors triclosan and spiroxamine contribute around 20 to 63% of the total risk.

335

A large number of presently used herbicides with other MoAs such as cell wall stability,

336

inhibition of cell division, carotenoid biosynthesis inhibition and protein biosynthesis inhibition

337

have been analyzed in sediments, but were not found to be relevant drivers of mixture risk to

338

algae. These results suggest that an MPC for algae might be better defined by the common

339

mode of action that can be detected with effect-based monitoring tools64 than via a specific

340

compound selection.

341

The present study revealed a new set of sediment-associated pesticides and biocides with wide

342

distribution over Europe and an acute toxic risk to invertebrates and possibly to algae.

343

Interestingly, only three of the identified drivers of photosynthesis inhibition are listed as Priority

344

Substances (terbutryn, diuron and isoproturon) in the Water Framework Directive65. The main

345

drivers of the risk to invertebrates, tefluthrin, carbendazim and cyfluthrin are neither considered

346

as Priority nor as River Basin Specific Pollutants (RBSPs) in the third implementation report of

347

the WFD summarizing the river basin management plans of 27 EU Member States.


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348

The concentrations of these sediment contaminants are expected to affect the structure of the

349

benthic communities and thus the ecological status at least in the river mouths. At the same

350

time, information on their occurrence and concentrations in the river basins is widely lacking.

351

Thus, a consideration of these compounds for future monitoring is highly recommended. It could

352

be demonstrated that contaminated sediments may contribute to the overall risk in a water body

353

and that compound lists and prioritization may differ from those derived from water

354

concentrations. Particularly pyrethroids are difficult to monitor in water and thus may be easily

355

overlooked if not involving particulate matter or sediments.

356

It should be stressed that water-converted sediment concentrations estimated by EPISUITE is

357

accompanied with significant uncertainty. The use of experimental toxicity data obtained by

358

sediments spiking experiments could improve the reliability of the risk assessments as shown

359

recently23. However, this requires an extension to other organisms such as benthic algae and

360

fish embryos and to a larger selection of compounds. Additionally, our final results should be

361

taken with caution since we analyzed only the sediment fraction below 63 µm. It is possible that

362

the analysis of the fine fraction may lead to an underestimation of the total toxicity by excluding

363

the chemicals which are present in the whole sediments.

364

The present study addressed a large range of relevant PaB and toxicity to three BQEs, but did

365

neither consider other compound groups typically occurring in sediments such as polycyclic

366

aromatic hydrocarbons (PAHs), polychlorinated biphenyls, dibenzo-p-dioxins and dibenzofurans

367

and naphthalene nor sublethal MoAs such as dioxin-like effects and endocrine disruption. These

368

compounds are expected to further increase risks for all BQEs, particularly for fish66.

369

The assessment of sediment contamination from lower stretches of seven European rivers

370

suggests several individual PaB as candidates for prioritization on a European scale such as

371

tefluthrin and carbendazim, while the priority substance diuron could be confirmed as a major



372

driver of risk to algae. However, there is also indication for river basin specific pollution such as

373

acetochlor in the River Po, an active substance that is not approved in the European Union67,

374

and cyfluthrin in the River Tiber. Interestingly, also site-specific contamination may play an

375

important role. This may result from point sources but also from local sediment dynamics that

376

may result in legacy chemicals in upper sediment layers. This might be the explanation of the

377

predominance of the legacy chemical acetochlor as risk driver for algae in sediments from the

378

most downstream Elbe cite at Cuxhaven.

379

Considering contamination as the occurrence of MSC, defined via components or common

380

effects, may be a way to further focus monitoring activities on drivers of risks and effects. This

381

approach may also contribute to the requirement from legislation to define priority mixtures for

382

assessment and monitoring68.

383

Although the present study indicates that BQE-specific MSCs based on individual compounds is

384

a promising approach, it has also clear limitations as shown particularly for algae, where many

385

compounds with the same MoA replace each other in different river basins. Thus, a

386

complementation of chemical analysis with bioanalysis based on specific MoAs may improve

387

risk assessment and allow for a better and more mechanistic understanding of risks on the

388

community level. A combined analytical and bioanalytical approach may also allow for a

389

prioritization of MoAs with respect to toxic pressure and for a targeted investigation of

390

interactions between most important MoAs. This will help bridging the gap between chemical

391

contamination and the ecological status of European surface waters.

392

4. Acknowledgment

393

We thank Melis Muz for critical comments on the draft versions. The sampling campaigns were

394

organized and supported by the following institutions, which we gratefully acknowledge:

395

National Research Center of Milan and Rome, Italy; Brunel University, London, UK; University


Page 18 of 28

Page 19 of 28


396

of Bordeaux, Bordeaux, France; Helmholtz Centre Geesthacht, Germany; Internationl

397

ccommision for the Protection of the Danube (ICPDR), Vienna, Austria; Umeå University, Umeå,

398

Sweden – Institute of Environmental Assessment and Water Research (IDAEA-CSIC),

399

Barcelona, Spain). We especially acknowledge the sampling coordinators: Stefano Polesello,

400

Sara Spedicati, Pierre Labadie, Luisa Patrolecco, Peter Haglund and Christine Gallampois. We

401

acknowledge funding by the SOLUTIONS Project supported by the European Union Seventh

402

Framework

403

agreement no. 603437. Chemaxon (Budapest, Hungary) is gratefully acknowledged for a free

404

academic license of Marvin and JChem for Excel.

Programme

(FP7-ENV-2013-two-stage

Collaborative

project)

under

grant

5. Associated Content

405

406

5.1

Supporting information

407

Detailed information regarding target compounds, sampling sites, LC-HRMS analyses, identified

408

compounds, toxicity data and cumulative risk plots are given in supporting information (SI).



6. Figures

Fig.1: Mean cumulative concentrations of the 81 identified pesticides and transformation products in sediments of European river mouths. The main contributors are highlighted with dashed lines and named. For the seven river basins, average concentrations are shown, excluding the site of Cuxhaven (Elbe), which is shown separately due to the high concentrations. Error bars represent the concentrations standard deviation within the basin for all samples at a given site.


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Page 21 of 28


Fig.2: K-means cluster analysis of 29 detected pesticides, biocides and transformation products (LV: Low variability; MV: Medium variability; HV: High variability). Compounds detected at only one site were excluded from analysis. Data were log normalized and centered before analysis. The –x axis show the variation within basins and y axis the variation between basins (Normal probability ≥ 0.90).



Page 22 of 28

Fig.3: Heat map and hierarchical clustering of identified river basin-specific sediment contaminants vs. single sampling sites (Elbe1 = Cuxhaven). 69

Data were log normalized and scaled before analysis. Cluster similarity was calculated using Euclidean distance and Ward method .


Page 23 of 28


Fig.4: TU contributions in each taxon for each river calculated in each site. TUs were summed according to the concentration addition concept .The main contributors to the risk are shown in different colors. The total sum of toxic unit (TUsum) is represented by the diameter of the pie chart (log-scale). The main contributors to the risk for fish are given in SI (Figure S2).



7. References 1. Loos, R.; Gawlik, B. M.; Locoro, G.; Rimaviciute, E.; Contini, S.; Bidoglio, G., EU-wide survey of polar organic persistent pollutants in European river waters. Environmental pollution 2009, 157, (2), 561568. 2. Schwarzenbach, R. P.; Gschwend, P. M.; Imboden, D. M., Environmental organic chemistry. John Wiley & Sons: 2005. 3. Eurostat, European commission statistics database. http://ec.europa.eu/eurostat 2016. 4. Schwarzenbach, R. P.; Escher, B. I.; Fenner, K.; Hofstetter, T. B.; Johnson, C. A.; von Gunten, U.; Wehrli, B., The Challenge of Micropollutants in Aquatic Systems. Science 2006, 313, (5790), 1072-1077. 5. Schreiner, V. C.; Szöcs, E.; Bhowmik, A. K.; Vijver, M. G.; Schäfer, R. B., Pesticide mixtures in streams of several European countries and the USA. Science of The Total Environment 2016, 573, 680689. 6. Ccanccapa, A.; Masiá, A.; Navarro-Ortega, A.; Picó, Y.; Barceló, D., Pesticides in the Ebro River basin: Occurrence and risk assessment. Environmental pollution 2016, 211, 414-424. 7. Moschet, C.; Wittmer, I.; Simovic, J.; Junghans, M.; Piazzoli, A.; Singer, H.; Stamm, C.; Leu, C.; Hollender, J., How a Complete Pesticide Screening Changes the Assessment of Surface Water Quality. Environmental science & technology 2014, 48, (10), 5423-5432. 8. Vorkamp, K.; Bossi, R.; Bester, K.; Bollmann, U. E.; Boutrup, S., New priority substances of the European Water Framework Directive: biocides, pesticides and brominated flame retardants in the aquatic environment of Denmark. Science of the Total Environment 2014, 470, 459-468. 9. de Castro-Català, N.; Kuzmanovic, M.; Roig, N.; Sierra, J.; Ginebreda, A.; Barceló, D.; Pérez, S.; Petrovic, M.; Picó, Y.; Schuhmacher, M.; Muñoz, I., Ecotoxicity of sediments in rivers: Invertebrate community, toxicity bioassays and the toxic unit approach as complementary assessment tools. Science of The Total Environment 2016, 540, 297-306. 10. Malaj, E.; von der Ohe, P. C.; Grote, M.; Kühne, R.; Mondy, C. P.; Usseglio-Polatera, P.; Brack, W.; Schäfer, R. B., Organic chemicals jeopardize the health of freshwater ecosystems on the continental scale. Proceedings of the National Academy of Sciences 2014, 111, (26), 9549-9554. 11. von der Ohe, P. C.; Dulio, V.; Slobodnik, J.; De Deckere, E.; Kühne, R.; Ebert, R.-U.; Ginebreda, A.; De Cooman, W.; Schüürmann, G.; Brack, W., A new risk assessment approach for the prioritization of 500 classical and emerging organic microcontaminants as potential river basin specific pollutants under the European Water Framework Directive. Science of the Total Environment 2011, 409, (11), 2064-2077. 12. Covaci, A.; Gheorghe, A.; Hulea, O.; Schepens, P., Levels and distribution of organochlorine pesticides, polychlorinated biphenyls and polybrominated diphenyl ethers in sediments and biota from the Danube Delta, Romania. Environmental pollution 2006, 140, (1), 136-149. 13. Feo, M. L.; Ginebreda, A.; Eljarrat, E.; Barceló, D., Presence of pyrethroid pesticides in water and sediments of Ebro River Delta. Journal of Hydrology 2010, 393, (3), 156-162. 14. Masiá, A.; Campo, J.; Vázquez-Roig, P.; Blasco, C.; Picó, Y., Screening of currently used pesticides in water, sediments and biota of the Guadalquivir River Basin (Spain). Journal of hazardous materials 2013, 263, 95-104.


Page 24 of 28

Page 25 of 28


15. Palma, P.; Köck-Schulmeyer, M.; Alvarenga, P.; Ledo, L.; de Alda, M. L.; Barceló, D., Occurrence and potential risk of currently used pesticides in sediments of the Alqueva reservoir (Guadiana Basin). Environ Sci Pollut Res 2015, 22, (10), 7665-7675. 16. Inostroza, P. A.; Massei, R.; Wild, R.; Krauss, M.; Brack, W., Chemical activity and distribution of emerging pollutants: Insights from a multi-compartment analysis of a freshwater system. Environmental pollution 2017, 231, (Pt 1), 339-347. 17. Brils, J., Sediment monitoring and the European water framework directive. Annali dell''Istituto Superiore di Sanita 2008, 44, (3), 218. 18. Camusso, M.; Galassi, S.; Vignati, D., Assessment of river Po sediment quality by micropollutant analysis. Water Research 2002, 36, (10), 2491-2504. 19. Busch, W.; Schmidt, S.; Kühne, R.; Schulze, T.; Krauss, M.; Altenburger, R., Micropollutants in European rivers: A mode of action survey to support the development of effect-based tools for water monitoring. Environmental Toxicology and Chemistry 2016, 35, (8), 1887-1899. 20. Nørgaard, K. B.; Cedergreen, N., Pesticide cocktails can interact synergistically on aquatic crustaceans. Environ Sci Pollut Res 2010, 17, (4), 957-967. 21. European Union, Directive 2000/60/EC of the European Parliament and of the Council, establishing a framework for Community action in the field of water policy. Official Journal of the European Union 2000, L327, 1. 22. Schafer, R. B.; Gerner, N.; Kefford, B. J.; Rasmussen, J. J.; Beketov, M. A.; de Zwart, D.; Liess, M.; von der Ohe, P. C., How to Characterize Chemical Exposure to Predict Ecologic Effects on Aquatic Communities? Environmental Science & Technology 2013, 47, (14), 7996-8004. 23. Nowell, L. H.; Norman, J. E.; Ingersoll, C. G.; Moran, P. W., Development and application of freshwater sediment-toxicity benchmarks for currently used pesticides. Science of the Total Environment 2016, 550, 835-850. 24. Di Toro, D. M., Sediment flux modeling. Wiley-Interscience New York: 2001; Vol. 116. 25. Schäfer, R. B.; Pettigrove, V.; Rose, G.; Allinson, G.; Wightwick, A.; von der Ohe, P. C.; Shimeta, J.; Kühne, R.; Kefford, B. J., Effects of pesticides monitored with three sampling methods in 24 sites on macroinvertebrates and microorganisms. Environmental science & technology 2011, 45, (4), 1665-1672. 26. Schäfer, R. B.; Caquet, T.; Siimes, K.; Mueller, R.; Lagadic, L.; Liess, M., Effects of pesticides on community structure and ecosystem functions in agricultural streams of three biogeographical regions in Europe. Science of the Total Environment 2007, 382, (2), 272-285. 27. Belden, J. B.; Gilliom, R. J.; Lydy, M. J., How well can we predict the toxicity of pesticide mixtures to aquatic life? Integr Environ Assess Manag 2007, 3, 364-372. 28. Backhaus, T.; Faust, M., Predictive environmental risk assessment of chemical mixtures: A conceptual framework. Environmental Science and Technology 2012, 46, (5), 2564-2573. 29. Schäfer, R. B.; von der Ohe, P. C.; Rasmussen, J.; Kefford, B. J.; Beketov, M. A.; Schulz, R.; Liess, M., Thresholds for the effects of pesticides on invertebrate communities and leaf breakdown in stream ecosystems. Environmental science & technology 2012, 46, (9), 5134-5142. 30. Köck-Schulmeyer, M.; Olmos, M.; de Alda, M. L.; Barceló, D., Development of a multiresidue method for analysis of pesticides in sediments based on isotope dilution and liquid chromatographyelectrospray–tandem mass spectrometry. Journal of Chromatography A 2013, 1305, 176-187. 31. Chiaia-Hernandez, A. C.; Krauss, M.; Hollender, J., Screening of lake sediments for emerging contaminants by liquid chromatography atmospheric pressure photoionization and electrospray ionization coupled to high resolution mass spectrometry. Environmental science & technology 2013, 47, (2), 976-86. 32. Chiaia-Hernandez, A. C.; Schymanski, E. L.; Kumar, P.; Singer, H. P.; Hollender, J., Suspect and nontarget screening approaches to identify organic contaminant records in lake sediments. Analytical and bioanalytical chemistry 2014, 406, (28), 7323-7335.



33. Lazartigues, A.; Fratta, C.; Baudot, R.; Wiest, L.; Feidt, C.; Thomas, M.; Cren-Olivé, C., Multiresidue method for the determination of 13 pesticides in three environmental matrices: water, sediments and fish muscle. Talanta 2011, 85, (3), 1500-1507. 34. Moschet, C.; Piazzoli, A.; Singer, H.; Hollender, J., Alleviating the reference standard dilemma using a systematic exact mass suspect screening approach with liquid chromatography-high resolution mass spectrometry. Analytical chemistry 2013, 85, (21), 10312-10320. 35. Kalogridi, E.-C.; Christophoridis, C.; Bizani, E.; Drimaropoulou, G.; Fytianos, K., Part II: temporal and spatial distribution of multiclass pesticide residues in lake sediments of northern Greece: application of an optimized MAE-LC-MS/MS pretreatment and analytical method. Environ Sci Pollut Res 2014, 21, (12), 7252-7262. 36. PPDB, Pesticide Properties Database [online]. http://sitem.herts.ac. uk/aeru/ppdb/en/index.htm 2014/2015. 37. Massei, R.; Byers, H.; Beckers, L.-M.; Prothmann, J.; Brack, W.; Schulze, T.; Krauss, M., A sediment extraction and cleanup method for wide-scope multitarget screening by liquid chromatography–high-resolution mass spectrometry. Analytical and bioanalytical chemistry 2017, 1-12. 38. DIN Deutsches Institut für Normung e. V., U. v. F., Temperaturabhängige Differenzierung des organischen, elementaren und anorganischen Kohlenstoffgehaltes. In 13.080.10, B. V. G., Ed. 2013; Vol. DIN 19539. 39. US EPA, Definition and Procedure for the Determination of the Method Detection Limit. 40 CFR Part 136, Appendix B to Part 136 2011, Revision 1.11. 40. Kassambara, A., Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning. STHDA: 2017; Vol. 1. 41. EPA, U., Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.11. United States Environmental Protection Agency, Washington, DC, USA. 2017. 42. Belden, J. B.; Gilliom, R. J.; Lydy, M. J., How well can we predict the toxicity of pesticide mixtures to aquatic life? Integrated environmental assessment and management 2007, 3, (3), 364-372. 43. Li, H.; Cheng, F.; Wei, Y.; Lydy, M. J.; You, J., Global occurrence of pyrethroid insecticides in sediment and the associated toxicological effects on benthic invertebrates: An overview. Journal of hazardous materials 2017, 324, 258-271. 44. Silva, A. R. R.; Cardoso, D. N.; Cruz, A.; Lourenço, J.; Mendo, S.; Soares, A. M.; Loureiro, S., Ecotoxicity and genotoxicity of a binary combination of triclosan and carbendazim to Daphnia magna. Ecotoxicology and environmental safety 2015, 115, 279-290. 45. PPDB, General Informations on Carbendazim. PPDB: Pesticide Proprieties Database 2017. 46. González-Mariño, I.; Quintana, J. B.; Rodríguez, I.; Cela, R., Simultaneous determination of parabens, triclosan and triclocarban in water by liquid chromatography/electrospray ionisation tandem mass spectrometry. Rapid Communications in Mass Spectrometry 2009, 23, (12), 1756-1766. 47. Luigi, V.; Giuseppe, M.; Claudio, R., Emerging and priority contaminants with endocrine active potentials in sediments and fish from the River Po (Italy). Environ Sci Pollut Res 2015, 22, (18), 1405014066. 48. Harwood, J. J., Molecular markers for identifying municipal, domestic and agricultural sources of organic matter in natural waters. Chemosphere 2014, 95, (Supplement C), 3-8. 49. Jacobson, A. R.; Dousset, S.; Guichard, N.; Baveye, P.; Andreux, F., Diuron mobility through vineyard soils contaminated with copper. Environmental pollution 2005, 138, (2), 250-259. 50. de Boer, J.; Dao, Q. T.; van Leeuwen, S. P. J.; Kotterman, M. J. J.; Schobben, J. H. M., Thirty year monitoring of PCBs, organochlorine pesticides and tetrabromodiphenylether in eel from The Netherlands. Environmental pollution 2010, 158, (5), 1228-1236.


Page 26 of 28

Page 27 of 28


51. Wetzel, M. A.; Peter, C.; Manz, W.; Koop, J. H.; Wahrendorf, D.-S., The ecological quality status of the Elbe estuary. A comparative approach on different benthic biotic indices applied to a highly modified estuary. Ecological Indicators 2012, 19, 118-129. 52. Liess, M.; Ohe, P. C. V. D., Analyzing effects of pesticides on invertebrate communities in streams. Environmental Toxicology and Chemistry 2005, 24, (4), 954-965. 53. Castillo, L. E.; Martínez, E.; Ruepert, C.; Savage, C.; Gilek, M.; Pinnock, M.; Solis, E., Water quality and macroinvertebrate community response following pesticide applications in a banana plantation, Limon, Costa Rica. Science of the Total Environment 2006, 367, (1), 418-432. 54. Wijngaarden, R. P. V.; Brock, T. C.; Van Den Brink, P. J., Threshold levels for effects of insecticides in freshwater ecosystems: a review. Ecotoxicology 2005, 14, (3), 355. 55. Clark, J. R.; Goodman, L. R.; Borthwick, P. W.; Patrick, J. M.; Cripe, G. M.; Moody, P. M.; Moore, J. C.; Lores, E. M., Toxicity of pyrethroids to marine invertebrates and fish: A literature review and test results with sediment-sorbed chemicals. Environmental Toxicology and Chemistry 1989, 8, (5), 393-401. 56. Saha, S.; Kaviraj, A., Acute Toxicity of Synthetic Pyrethroid Cypermethrin to Some Freshwater Organisms. Bulletin of environmental contamination and toxicology 2008, 80, (1), 49-52. 57. Shafer, T. J.; Meyer, D. A.; Crofton, K. M., Developmental Neurotoxicity of Pyrethroid Insecticides: Critical Review and Future Research Needs. Environmental Health Perspectives 2005, 113, (2), 123-136. 58. Palanikumar, L.; Kumaraguru, A.; Ramakritinan, C.; Anand, M., Toxicity, biochemical and clastogenic response of chlorpyrifos and carbendazim in milkfish Chanos chanos. International Journal of Environmental Science and Technology 2014, 11, (3), 765-774. 59. Zubrod, J.; Baudy, P.; Schulz, R.; Bundschuh, M., Effects of current-use fungicides and their mixtures on the feeding and survival of the key shredder Gammarus fossarum. Aquatic toxicology 2014, 150, 133-143. 60. Kegley, S. E., Hill, B.R., Orme S., Choi A.H, PAN Pesticide Database. Pesticide Action Network 2016. 61. Mu, X.; LeBlanc, G. A., Environmental antiecdysteroids alter embryo development in the crustacean Daphnia magna. Journal of Experimental Zoology 2002, 292, (3), 287-292. 62. Hassold, E.; Backhaus, T., Chronic toxicity of five structurally diverse demethylase-inhibiting fungicides to the crustacean Daphnia magna: A comparative assessment. Environmental Toxicology and Chemistry 2009, 28, (6), 1218-1226. 63. Rösch, A.; Anliker, S.; Hollender, J., How Biotransformation Influences Toxicokinetics of Azole Fungicides in the Aquatic Invertebrate Gammarus pulex. Environmental science & technology 2016, 50, (13), 7175-7188. 64. Brack, W.; Dulio, V.; Ågerstrand, M.; Allan, I.; Altenburger, R.; Brinkmann, M.; Bunke, D.; Burgess, R. M.; Cousins, I.; Escher, B. I.; Hernández, F. J.; Hewitt, L. M.; Hilscherová, K.; Hollender, J.; Hollert, H.; Kase, R.; Klauer, B.; Lindim, C.; Herráez, D. L.; Miège, C.; Munthe, J.; O'Toole, S.; Posthuma, L.; Rüdel, H.; Schäfer, R. B.; Sengl, M.; Smedes, F.; van de Meent, D.; van den Brink, P. J.; van Gils, J.; van Wezel, A. P.; Vethaak, A. D.; Vermeirssen, E.; von der Ohe, P. C.; Vrana, B., Towards the review of the European Union Water Framework Directive: Recommendations for more efficient assessment and management of chemical contamination in European surface water resources. Science of The Total Environment 2017, 576, 720-737. 65. Union, E., Directive 2008/105/EC of the European Parliament and of the Council on Environmental Quality Standards in the Field ofWater Policy, Amending and Subsequently Repealing Directives 82/176/EEC, 83/513/EEC, 84/156/EEC, 84/ 491/EEC and 86/280/EEC, and amending Directive 2000/60/EC. In p. 84 OJ L 348.24.12.2008, 84., Union, E., Ed. 2008. 66. Sumpter, J. P.; Jobling, S., The occurrence, causes, and consequences of estrogens in the aquatic environment. Environmental Toxicology and Chemistry 2013, 32, (2), 249-251.



67. European Union, COMMISSION IMPLEMENTING REGULATION (EU) No 1372/2011 of 21 December 2011 concerning the non-approval of the active substance acetochlor, in accordance with Regulation (EC) No 1107/2009 of the European Parliament and of the Council concerning the placing of plant protection products on the market, and amending Commission Decision 2008/934/EC. Off. J. Eur Union 2011, L341, 45-46. 68. European Commission, Communication from the Commission to the Council. The combination effects of chemicals. Chemical mixtures. COM(2012)252 final. 2012. 69. Murtagh, F.; Legendre, P., Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion? Journal of Classification 2014, 31, (3), 274-295.


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