# Pesticide toxicity towards microalgae increases with environmental mixture complexity

Caroline Gardia-Parège, Sandra Kim Tiam, Hélène Budzinski, Nicolas

Mazzella, Marie-Hélène Devier, Soizic Morin

► To cite this version:

Caroline Gardia-Parège, Sandra Kim Tiam, Hélène Budzinski, Nicolas Mazzella, Marie-Hélène Devier, et al.. Pesticide toxicity towards microalgae increases with environmental mixture complexity. Environmental Science and Pollution Research, 2022, 29 (20), pp.29368-29381. 10.1007/s11356-021-17811-w. hal-03738581

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Pesticide toxicity towards microalgae increases with environmental
mixture complexity

Running head: EDA of pesticide mixtures using microalgae

Gardia-Parège, Caroline<sup>a1</sup>, Kim Tiam, Sandra<sup>b1</sup>, Budzinski, Hélène<sup>a</sup>, Mazzella, Nicolas<sup>b</sup>, Devier, Marie-Hélène<sup>a</sup>, Morin, Soizic<sup>b*</sup>

<sup>a</sup> Université de Bordeaux, EPOC - LPTC (UMR 5805 CNRS), 351 cours de la Libération, 33405 Talence Cedex, France

<sup>b</sup> INRAE, UR EABX, 50 avenue de Verdun, 33612 Cestas cedex, France

<sup>1</sup> These authors contributed equally to this work.

* Address correspondence to soizic.morin@inrae.fr

ABSTRACT

Effect Directed Analysis (EDA) aims at identifying the compound(s) responsible for toxicity in a complex environmental sample where several dozens of contaminants can be present. In this study, we used an environmental mixture extracted from Polar Organic Chemical Integrated Samplers (POCIS) previously immersed downstream a landfill (River Ponteils, South-West France), to perform an EDA approach using a microalgae bioassay based on the photosynthetic capacities of diatom (Nitzschia
*palea*) cultures. Adverse effects on photosynthetic capacities were recorded when algae were exposed to the entire POCIS extract (>85% inhibition at the highest concentration tested). This result was coherent with the detection of diuron and isoproturon, that were the 2 most concentrated herbicides in the extract. However, the EDA process did not allow pointing out the specific compound(s) responsible for the observed toxicity but rather suggested that multiple compounds were involved in the overall toxicity and caused mixture effects.

Keywords: Effect-Directed Analysis, POCIS, diatoms, bioassays, herbicides, toxicity
fractionation.

# 1. INTRODUCTION

Many chemicals of diverse anthropogenic origins are continuously ending up in the environment, in particular in rivers where they threaten water resources and the aquatic biota. It is in this context that the Water Framework Directive (WFD, 2000/60/EC) has been implemented. This Directive aims to reach good chemical and ecological status for all UE waters. Chemical status is based on targeted chemical analyses, setting strict limits for the concentrations of individual substances in the environment (Directive 2013/39/EU). Ecological status in French rivers is assessed by bioindicators based on diatoms (IBD, AFNOR 2007), invertebrates (IBGN, AFNOR 2004), macrophytes (IBMR, AFNOR 2003) or fishes (IPR, AFNOR 2011) supported by physicochemical and hydromorphological elements. Establishing the link between
the toxic effects on biota (ecological status) and the presence of toxic compounds (chemical status) is not always simple given the numerous unknowns in this complex equation. For example, the detection of toxic compounds in a water sample may be non-representative of the overall contamination in particular because contamination peaks can occur and some toxic compounds can be non-targeted in the chemical analysis (e.g. unknown transformation products; Brack et al. 2019). Moreover, the multiplicity of toxicants present in waters can lead to mixture effects which are not considered in the Water Framework Directive. Mixture effects are increasingly given attention, in particular with the development of more integrative water quality assessment using effect-based monitoring tools (Wernersson et al. 2015) that do not require a priori knowledge of the substances present.

Effect-directed analysis (EDA) is an efficient approach to identify the toxicants in a complex mixture. EDA aims to track the chemicals involved in the toxicity of natural samples, in a tiered approach establishing cause-effect relationships with sequential reductions of mixture complexity by fractionation (Brack 2003). Once a specific fraction has been pointed out as responsible (or highly contributive) for the toxicity observed, non-target analysis and structure elucidation may be necessary to identify and quantify potentially bioactive compounds. Finally, a confirmation step is performed to endorse the candidate substances as causative of the toxicity measured.

To track major toxic compounds, the sampling and the preparation of extracts are crucial steps in EDA approach. They must be the most representative of overall contamination. One strategy is to use the extraction of compounds based on a partitioning process (Brack et al. 2009). In practical, the contaminants can be extracted
from the environment by passive samplers. These devices immersed *in situ* pre-
concentrate the mixture of substances, even at trace levels, over their exposure time
(Vrana et al. 2005). Targeted and unknown chemicals sharing the same affinity for the
receiving phase of the passive sampling device are accumulated. Complex mixtures of
hydrophilic contaminants, including a wide range of herbicides, can be taken up using
Polar Organic Chemical Integrative Samplers (Alvarez et al. 2004).

To characterize the toxicity of a sample and then the toxicity of each fraction obtained
during EDA, sensitive high-throughput toxicity assays are required. *In vitro* bioassays
(reviewed in Brack et al. 2016) generally fulfil those requirements and are rapid,
reliable, sensitive and cost-effective. Numerous kits are now available to easily
perform toxicity tests with varied spectra of response, either specific of toxic modes of
action (e.g. endocrine disruption, mutagenicity) or indicative of global impairment
(cytotoxicity, oxidative stress). They may be combined according to their ability to
screen substances having different modes of action potentially present in a sample and
can allow the detection of agonist or antagonistic effects. The drawback of those bio-
analytical tools, mainly based on the response of cell lines, lies in their low complexity
cellular endpoints disconnected with real impacts at the whole-organism scale (Artigas
et al. 2012; Kim Tiam et al. 2016).

As an alternative to *in vitro* testing, *in vivo* bioassays can be used to measure the
toxicity of environmental samples under standardized laboratory conditions. They are
performed using live organisms and provide more holistic response, integrated at the
scale of the organism. Highly relevant endpoints can be monitored, such as survival or
fitness; environmental realism can be easily emphasized by selecting endpoints
characteristic of major ecosystem functions. Primary producers ensure a basic process of the functioning of aquatic ecosystems, by converting mineral elements and solar energy into biomass available for the higher trophic levels. In streams, diatoms (brown microalgae) are present all over the year and often dominate among primary producers. Moreover, these key aquatic organisms are considered as "biological quality elements" in the implementation of the WFD (Kelly et al. 2009). These unicellular organisms present major advantages for experimental testing: short-time delay of response (minutes to days depending on the endpoint used), microscopic scale allowing for miniaturized toxicity tests. In particular, Escher et al. (2008), Booij et al. (2014) or Kim Tiam et al. (2014) used 96-well plates to simultaneously process several contaminants and concentrations in toxicity testing with microalgae. Some EDA approaches were already performed with algae, to assess the toxicity of several matrices: sediments (Bandow et al. 2009), landfill leachates (Brack and Frank 1997; Brack et al. 1998), marine waters (Booij et al. 2014), freshwaters (Escher et al. 2006)... Growth and photosynthesis have been used as endpoints in these inhibition bioassays. Bioassays using growth as endpoints are generally performed over several days to allow sufficient cellular division of the non-contaminated microalgae (i.e. control condition). Bioassays based on photosynthesis permit to detect earlier toxic effects as responses can be measured within a few hours of exposure. Pulse-Amplitude-Modulation (PAM) fluorimetry (Schreiber 2004) is the most common method to assess photosynthesis inhibition in microalgae caused by compounds with different modes of action; from direct photosystem II inhibitors to toxicants with unknown mode of action on algae (see review in Corcoll et al. 2012).
In this study, we aimed at identifying compound(s) responsible for toxicity by combining *in vivo* bioassays using microalgae with toxicity testing following a fractionation approach of a mixture extracted from POCIS immersed downstream passive landfill discharge. The sampling site was selected based on previous knowledge of contamination by leachates containing polar herbicides (Fauvelle 2012), associated with a significant impairment of microflora (Neury-Ormanni et al. 2016). Given this background, we assumed that the mixture extracted from POCIS would have direct adverse outcomes on microalgae; therefore, we selected photosynthesis inhibition as toxicity endpoint in the bioassays. Following the EDA's assumptions, we hypothesized that the toxicity of the mixture would be explained by one or a few substances dominant in concentration. To test this theory, POCIS extracts were fractionated using high-performance liquid chromatography (HPLC) based on their polarity into 3 fractions (simplified mixtures), corresponding each to 20 lower-complexity subfractions. Toxicity patterns resulting from chemical exposure were derived from iterative dose-response curves of microalgal photosynthesis inhibition.

We expected some decrease in observed toxicity with mixture fractionation; the confrontation between chemical screening and biological responses at the lowest mixture complexity levels (subfractions) were anticipated to highlight the substances responsible for the observed decline of microalgae at this river site.

## 2. METHODS
## 2.1 Chemicals and reagents

Analytical standards were purchased from Sigma Aldrich (Saint Quentin Fallavier, France), Santa Cruz (Heidelberg, Germany) and Dr Ehenstorfer GmbH (Augsburg, Germany). Methanol, ethyl acetate, acetonitrile and formic acid were at least of analytical grade and provided by Atlantic Labo (Bruges, France). Milli-Q grade water was prepared from a Milli-Q water purification system using an EDS-PAK filter (Millipore SA, Guyancourt, France). For bioassays, 96-well plates were purchased from BD Falcon® (Germany).

## 2.2. Chemical sampling in situ

The site studied is located in a small tributary of Arcachon Bay (South-West France), in the watershed of the River Ponteils. More specifically, the sampling site implemented is downstream the landfill of the city of Audenge (44°41'16"N, 1°01'01"W), closed since 2008. Ten "accumulative" POCIS (600 mg of Oasis HLB sorbent, Waters) were deployed for 4 weeks in the river from October 22 to November 19, 2012.

## 2.3. Processing of POCIS

### 2.3.1. Extraction of POCIS

The 600 mg of sorbent were transferred into a 6 mL empty SPE tube with polyethylene (PE) frit and packed under vacuum by using a Visiprep SPE Manifold. Afterwards, another PE frit was added to the top of the SPE cartridge. All the cartridges were dried under a gentle stream of nitrogen for 30 min. Analytes were
eluted with 10 mL of 100% MeOH, and then with 10 mL of MeOH:ethyl acetate 75:25 (v/v) (Lissalde et al. 2011). The eluates were pooled, evaporated to dryness and recovered in 2 mL of 100% acetonitrile.

### 2.3.2. Fractionation

The fractionation protocol was adapted from Creusot et al. (2013). The procedure was validated using a solution of diuron (3-(3,4-dichlorophenyl)-1,1-dimethylurea) as the standard herbicide. Volumes of 100µL of the solution of diuron (12 mg/L) and 100µL of POCIS extract were fractionated using reversed-phase high-performance liquid chromatography (RP-HPLC) performed on an Agilent apparatus (1200 Series, Agilent Technologies), equipped with a diode array detector (1200 Series) and a fraction collector (1260 Infinity Series).

Compounds were separated on a C18 column (Spherisorb ODS2, 5 µm, 4.6 x 250 mm, Waters. The run was performed at 25 °C at a flow rate of 1 mL/min with the following gradient program using ultra-pure water (A) and acetonitrile (B): 0-10 (80:20, A/B), 60 (55:45), 100-120 (0:100, A/B), and 120.01-125 min (80:20, A/B). Sixty subfractions of 2 mL each were collected. The collected fractions were evaporated to dryness using EZ-2+® solvent evaporator system (Genevac, Fisher Scientific) and then reconstituted in 100 µL of acetonitrile.

Each fraction was separated into two aliquots: 10 µL for chemical analysis (2.4) and 90 µL for ecotoxicological tests (2.5.2). All fractions were stored at -20°C until further processing. In parallel to samples, a procedural blank (solvents only) was also fractionated to check the absence of contamination during the whole procedure.
Diuron was injected on HPLC and collected as if it was not fractionated to ensure that the HPLC fractionation procedure did not affect the toxic responses on the bioassay, either positively via contamination (by solvents, glassware, material ...) or negatively via a loss of toxicity during the whole procedure (HPLC, reconcentration...).

## 2.4. Chemical analyses

Target and non-target chemical analyses were performed on POCIS fractions. Due to volume restriction, 60 fractions were recombined by 20 before chemical analysis, to reconstitute three larger fractions which were analyzed.

### 2.4.1 Target chemical analysis

Target chemicals analyses on fractions were performed in Multiple Reaction Monitoring (MRM) mode by using liquid chromatography (Agilent 1290) coupled with a mass spectrometer detector (Agilent 6460 series Triple Quadrupole system). The method was described in Belles et al. (2014). Analytes and instrumental conditions are detailed in Table SI1. Briefly, fifty-seven pesticides were separated on a C18 reversed-phase column (1.7 µm, 100 x 2.1 mm, Kinetex Phenomenex®, Agilent Technologies). The injection volume was set at 5 µL of MeOH. The separation was performed at a flow rate of 0.5 mL/min under gradient elution conditions using ultra-pure water + 0.1 % acetic acid + 5 mM ammonium acetate (A) and methanol (B).

Gradient elution was from 0 % B to 100 % B at 14 min, kept isocratic for 1 minute, then the initial conditions of 0 % B were again reached at the time of 17 min and kept isocratic for 2 min. The acquisition was performed in positive electrospray ionization.
The pesticides were quantified relatively to deuterated pesticides as surrogate standards.

### 2.4.2. Non-target chemical analysis

**Instrumental parameters.** Analyses were carried out on an Agilent Infinity 1290 HPLC system coupled to an Agilent 6540 accurate-mass quadrupole time-of-flight mass spectrometer (Agilent Technologies).

Chemicals were separated on an analytical C18 column (100 x 2.1 mm, 100 Å, 1.7 µm, Kinetex Phenomenex®, Agilent Technologies). The column was kept at 40°C. For each fraction, 2 µL were injected. The mobile phase was a mixture of ultra-pure water (A) and acetonitrile (B) (both with 0.1 % of formic acid for positive ionization mode) at a flow rate of 0.3 mL/min. The elution gradient program was the following: 90 % of A and 10 % of B and attained to 40 % B in 22 min, then increased to 100 % B until 30 min, and held at 100 % B for 2 min, before returning to the initial value at 34 min and kept until 38 min. Fractions were analyzed using positive and negative mode electrospray ionization (ESI) coupled to an Agilent Jet Stream technology which allows focalizing ion spray by the presence of sheath gas increasing detection sensitivity.

The operating conditions for the ESI source were as follows: source temperature 300°C, gas flow rate 8 L/min, capillary voltage 3000 V, nebulizer gas pressure 40 PSI, sheath gas temperature 400°C, nozzle voltage 0 V in positive mode or 1500 V in negative mode and fragmentor 110 V. The instrument was operated in 2 GHz. The data acquisitions were performed using (data-dependent) auto-MS/MS acquisition
mode. This consists, in the same run, to perform a full HR-MS analysis and to select the most abundant precursor ions from a TOF mode scan and then fragmented in MS/MS mode (Q-TOF). Full-MS spectra were acquired with a scan range of 70–1700 m/z. The most abundant precursor ions were isolated in the width of ~1.3 amu and fragmented with three fixed collision energies: 10, 20 and 40 eV. A constant flow of an Agilent TOF reference solution through the reference nebulizer allowed the system to continuously correct for any mass drift by using the reference mass ion HP-921 [hexakis-(1H,1H,3H-tetrafluoropropoxy)-phosphazine] at m/z 922.0098 [M+H]$^+$, 966.0007 [M-H]$^-$.

Before all injections, the stability of mass accuracy was checked with an Agilent standard solution and the system was re-calibrated if values went above 3 ppm error. A standard solution containing 36 compounds was injected before the sample to check the retention time of compounds and the instrumental sensitivity.

**Data analysis and analytical confirmation.** The data were processed using the MassHunter Qualitative software (version B.05.00, Agilent Technologies). A peak picking approach was performed on the total ion chromatogram using the molecular feature extraction (MFE) which is an untargeted feature finding algorithm of the software. Only peaks with intensity > 1500 counts were searched. The elemental formulae of features were generated using the relative isotopic abundance of mass spectra profiles and precursor ion mass. The composition was defined by the Generate Formulas function of the software. The calculation of most probable molecular formula included the common elements C, H, N, O, S, Cl, F, P, score higher than 50 % and an error margin of 5 ppm. Different ionization products were considered: [M+H]$^+$ or [M+NH4]$^+$ in ESI+ and [M-H]$^-$, [M+Cl]$^-$, [M+HCOO]$^-$ or [M+CH3COO]$^-$ in ESI-.
To isolate compounds of interest, the list of features detected in the POCIS fraction was compared to the corresponding blank fraction using MassProfiler Agilent software. This software is a visualization tool allowing the identification of statistically meaningful differences between sample groups. The characterization of features was performed on peak only present in POCIS fractions. The molecular structure was searched using exhaustive spectral libraries namely Metlin, Forensics and Pesticides (PCDL B.04). Only compounds with an MS/MS score higher than 80 % and a mass difference of 5 ppm were accepted.

To confirm the identity of the compound, reference standards were purchased, when available. The purchased molecule was analyzed in the same LC-QTOF-MS method that unknown compounds, whose identity was confirmed by retention time match, accurate mass, isotope pattern and the fragmentation spectra.

## 2.5. Ecotoxicological bioassay

### 2.5.1. Biological material

We used a diatom culture of *Nitzschia palea* (Kützing) W. Smith, cultured in WC medium (Guillard and Lorenzen 1972) in flasks placed in thermoregulated chambers (20°C) with a 12:12h light:dark photoperiod. This species was selected for its relevance in the field: indeed it is cosmopolitan and frequently recorded in contaminated sites (Trobajo et al. 2009), highlighting some tolerance to pollutions.

This makes the species appropriate for toxicity testing based on sublethal effects (e.g. Wang et al. 2020). Toxicity was assessed based on photosynthesis inhibition (see 2.5.2.), as recommended by Brack et al. (2016).
Preliminary assays were performed to assess the photosynthetic response of this culture against model toxicants and confirm its ability to detect herbicide contamination in the short-term. The duration of exposure was optimized to 6h, and the toxicity patterns of the following herbicides were tested: atrazine and its metabolites (deisopropylatrazine -DIA- and deethylatrazine -DEA), diuron, isoproturon, metolachlor. The results of these prior tests revealed that *N. palea* culture was sensitive enough to highlight toxic exposure (Table SI2). Indeed, it followed dose-response patterns consistent with the endpoint assessed and chemical mode of action: photosystem II inhibitors directly impacted photosynthesis and were toxic at lower doses than metolachlor with indirect impact, or herbicide metabolites. As a consequence, the slopes of the different DRC were different, as well as the maximal inhibition reached (Table SI2). The harmlessness of the solvent (acetonitrile) towards the culture was previously checked for a wide range of concentrations, to ensure that the 5% acetonitrile concentration further used in all tests, including the blanks, would not impact photosynthesis. Preliminary trials confirmed that photosynthetic activity of the culture was very reproducible between replicates and sensitive enough to detect significant monotonic dose-related responses with model toxicants. Valuable experimental data were thus expectable for modelling in further toxicity assays.

### 2.5.2. Dose-response curves based on photosynthesis inhibition

**Tested solutions.** The description of the solutions tested for toxicity on *Nitzschia palea* and the modality used for each tested solution are summarized in Table 1. From the 60
subfractions of the original sample (2.3.2), we reconstituted the entire POCIS (Fr.1 x Fr.2 x Fr.3), the 3 main fractions (Fr.1, Fr.2 and Fr.3) and the binary mixtures of the main fractions (Fr.1 x Fr.2, Fr.1 x Fr.3 and Fr.2 x Fr.3). The initial POCIS extract, the reconstituted POCIS, the 3 main fractions and the binary mixtures of the main fractions were evaporated to near dryness and the volume was then brought to 900µL in a solution of 10% (v/v) acetonitrile. The solutions were tested in a toxicity test where dose-response curves (DRC) were established based on six-fold dilution series of the greatest available concentration. Diuron standard was selected as positive control based on the prescreening results (Table SI2). In the negative controls, a 5% acetonitrile (solvent) concentration was used. The toxicity of the subfractions (provided the fraction had an effect) and of binary combinations of the 3 main fractions was only assessed at the highest concentration. All the pesticide solutions (standard and mixtures) used were freshly prepared just before performing the toxicity tests, from stock solutions stored at -20°C so far.

**Toxicity assessment.** The bioassays were performed in 96-well plates with a clear, flat bottom (TC-treated imaging plates; BD Falcon®) after the protocol developed by Kim Tiam et al. (2014). Each well contained 150 µL of an exponentially growing culture (to reach average cell concentrations of 414±40.10³cells/mL, and mortality below 5%) in WC medium and 150 µL of contaminants. The DRCs were established based on the effective quantum yield of photosystem II (ΦPSII) of the *N. palea* cultures. All microplates were incubated at 20°C in illuminated chambers. ΦPSII was assessed *in vivo* through the transparent bottom of the wells using a pulse amplitude modulated fluorometer equipped with an Emitter Detector Fiberoptics Unit (PHYTO-PAM
version EDF, Heinz Walz GmbH) combined with a home-made system for
reproducible direct measurements at a standard distance to the bottom of the wells.

All conditions were run at least in triplicate.

**Timing of measurements.** Due to the elevated number of samples to be processed, 4 microplates were used and the toxicity tests were run over 4 consecutive days. To account for possible between-test variability and standardize the data, semi-logarithmic dilutions of diuron were held systematically in every well-plate and reference (positive control) DRCs were established for each microplate. ΦPSII was measured after 6 hours of pesticide exposure.

## 2.6. Data analyses

### 2.6.1. Dose-response curves modelling

DRCs were fitted to the ΦPSII data with REGTOX (v.7.0.7, © Eric Vindimian, 2001) macro for Microsoft Excel™, using the Hill model, and ECₓ values, i.e. the concentration causing a x% inhibition of photosynthesis were extracted. The quality of DRC fitting was given by the confidence intervals at the 5% level for ECₓ values estimated by 500 bootstrap simulations. ECₓ for single substances were expressed in µg/L, whereas they were expressed as total pesticides concentrations (µg/L) for the initial POCIS extract, the reconstituted POCIS and the 3 main fractions.

### 2.6.2. Toxicity assessment and consistency in the bioassays

Photosynthesis inhibition of the different levels of fractionation at the highest concentration was assessed by ANOVA, to identify significant differences in
photosynthetic efficiency with control values. Significance levels were set to a 0.05 α-level value.

To comply with the key EDA requirements, statistical analyses were performed to monitor the toxicity recovery (i) and the derivation of meaningful toxicity values (ii).

Specifically, two-way ANOVAs were used to detect differences in toxicity patterns:

(i) between chemicals before and after fractionation (diuron and POCIS extract), to assess the impact of the fractionation procedure, and (ii) between wellplates based on the positive control (diuron dose-response curves).

All statistical analyses were performed with R (Ihaka and Gentleman 1996), and data (chemicals concentrations) were log-transformed to achieve normality and homoscedasticity of residuals. Two-way ANOVAs were run to account for contaminant and dose effects, using Type II sums of squares (function Anova from the package car).

### 2.6.3. Benchmarking of toxicity values

The results of the bioassays were analyzed according to the toxic equivalency concept (e.g. Escher et al. 2008). Indeed, we first calculated the Relative Potency (RP, Equation 1) of the different model substances compared to diuron, tested in the preliminary assays (2.5.1).

$$RP_i = \frac{EC_{x}(\text{diuron})}{EC_{x}(\text{substance}_i)} \qquad \text{Equation 1}$$

The RP values were dose-dependent (see details in Supplementary material, Table SI2 and Figure SI1), as the consequence of nonparallel dose-response curves (Villeneuve
et al. 2000). Consequently, we benchmarked the ecotoxicity values based on the diuron-equivalent concentrations (DEq, in µg/L), defined as the concentrations of diuron (used as a standard) yielding similar response in the bioassay as those of the test substances or complex samples. We derived relationships specific of each substance or sample from the fitted DRC to obtain RP functions, and then DEq values, from the ecotoxicity (photosynthetic inhibition) measurements. DEq values were used to predict the toxicity of the complex samples tested assuming additivity, based on their chemical composition and the RPᵢ we already determined, as in Equation 2.

$$ \text{Predicted toxicity} = \sum DEq_i \qquad \text{Equation 2} $$

where DEqᵢ are the diuron-equivalent concentrations causing the level of effect measured for substance i, derived from the correlation (RP function) between DRCs of diuron and model substances.

The accuracy of the prediction was quantified by the model deviation ratio (MDR), as recommended by EFSA Scientific Committee et al. (2019). The MDR was calculated by dividing the predicted toxicity by the observed toxicity, a factor of 5 or more highlighting synergistic/antagonistic mixture effects (ECHA, 2014).

# 3. RESULTS

## 3.1. Mixture composition

### 3.1.1. Identified compounds
Based on the screening of 57 compounds by LC-MS/MS, 22 pesticides were identified (Figure 1). They included 15 herbicides (and two degradation products), two fungicides (and two degradation products) and one insecticide.

These pesticides were collected in three main fractions according to the retention time, correlated to their hydrophobicity (log K<sub>ow</sub>). Fr.1 was constituted of most hydrophilic compounds three herbicides (bentazone, hexazinone and terbuthylazine) and two transformation products of fungicides (DMSA, DMST). In contrast, most hydrophobic pesticides (8 herbicides and one degradation product) eluted in Fr.3. In Fr.2, a wide range of pesticides was detected since 13 compounds were collected in this fraction. Half of them were herbicides, among them: isoproturon and diuron. Others were degradation products of fungicides (DMSA, DMST) or herbicides (DCPMU, 2-hydroxyatrazine) and insecticide (carbofuran). Several compounds were mutual to Fr.2 and Fr.1 (DMSA and DMST) or Fr.2 and Fr.3 (isoproturon, diuron and 2-hydroxyatrazine).

Non-target analyses by LC-QTOF-MS evidenced the presence of around a hundred of features in Fr.1 and in Fr.2, and about 50 features in Fr.3 (Table 2). Correspondences higher than 80% were found in the databases *Forensics*, *Metlin* and *Pesticides* for 9 compounds. They confirmed the presence of pesticides detected by target screening (e.g. isoproturon, prometryn/terbutryn). They also allowed identifying one more herbicide (tebutam) and an insect repellent (DEET) in Fr.2, which were unequivocally confirmed by injection of analytical standards.

### 3.1.2. Concentrations
Figure 1 summarizes the concentrations of pesticides quantified in the fractions, expressed in ng/g of POCIS sorbent. The majority of compounds (16 pesticides) were detected at low concentration, lower than 5ng/g HLB sorbent (Figure 1). Four compounds: bentazone, hexazinone, DMST and diuron, were quantified around 30ng/g HLB sorbent. DMSA and isoproturon were the main chemicals detected in Fr.1 and Fr.2 respectively. Their measured concentrations achieved 215 ng/g HLB sorbent for DMSA and 238 ng/g HLB sorbent for isoproturon.

Based on sampling rates (Belles 2012), the concentration of compounds in river water was estimated from the content of the fractions (Figure 1). Isoproturon was detected at the concentration of 572 ng/L and the concentration of DMSA was 1.4 µg/L. Other pesticides, such as hexazinone, DMST and diuron, ranged in concentrations between 40 ng/L and 30 ng/L, and bentazone concentration reached 270 ng/L.

## 3.2. Toxicity assessment

### 3.2.1. Diuron

No difference was observed in diuron toxicity between the diuron standard before and after HPLC-fractionation (p=0.7216), nor between wellplates (p=0.6916) (data not shown). Total photosynthesis inhibition was always achieved at the highest test concentration (Figure 2a). Diuron toxicity towards *N. palea* during the EDA process was higher than during the preliminary assays, with EC₅₀ of 6.5µg/L [CI=5.7-7.5] (Figure 2a) and 20µg/L [CI=14-28] (Table SI2) during the EDA process and the preliminary assays respectively.

### 3.2.2. Initial POCIS extract and reconstituted POCIS
Fractionation resulted in slight differences in toxicity towards microalgae (Figure 2b and 2c). The reconstituted POCIS caused less photosynthesis inhibition than the initial POCIS extract at the highest concentrations (treatment x dose interaction: p=0.0074) and its dose-response pattern was more scattered. REGTOX modelling was consequently less stringent and failed to properly assess ECₓ values. By using all data (initial POCIS extract and reconstituted POCIS), however, DRCs were fitted. EC₅₀ fell into a [51.6-132.2]µg total pesticides/L confidence interval, with an optimal value of 84.13µg/L.

### 3.2.3. Fractions, subfractions and binary mixtures

DRCs were established for the three fractions. For Fr.1 and Fr.3, no adjustment of the model was possible, and no significant impact was measured even at the highest concentration (Table 3). Marginal toxicity was observable at high doses of Fr.2, as well as in some of its subfractions (see Table S14 in Supplementary material). Photosynthesis inhibition at the highest concentration of Fr.2 tested (Table 3) was only of 12.3±1.7% and REGTOX modelling resulted quite relaxed. The binary mixtures of fractions at the highest concentration had intermediate toxicity, between the fractions and the reconstituted POCIS (Table 3). Photosynthesis was significantly inhibited by all binary mixtures.

### 3.3. Comparisons with predicted toxicity

The MDR values highlighted acceptable deviation between bioassay-derived and instrumentally-based DEqs in all DRCs established: initial POCIS extract (Figure 2b), reconstituted POCIS (Figure 2c) and Fr.2 (Figure 2d). It has to be noticed that DEqs
were available for only a few substances present in the mixtures (Table S12), and that
maximal DEqs for Fr.2 and the reconstituted POCIS were half those of the initial
POCIS extract.

**4. DISCUSSION**

4.1. *Characterization of the chemical mixture and fractionation of POCIS extracts.*

4.1.1. *Use of passive samplers in an effect-based perspective*

Besides the benefits of passive sampling approaches for improvement of *in situ* water
quality assessment, the accumulation in passive sampling devices of known and
unknown compounds offers a panel of perspectives for toxicity testing of contaminant
mixtures (e.g. Kim Tiam et al. 2016), from “black box approaches” to the targeted
identification of substance(s) threatening biodiversity. Passive samplers such as
POCIS accumulate contaminants with variable uptake rates, depending on the
substance, sorbent used, as well as environmental prevailing conditions. When used
for monitoring *in situ* water quality, sorption/desorption rates can be determined for
any targeted substance to achieve quantitative assessment of the time-averaged
contamination. In the case of toxicity assays such as EDA however, the compound-
specific sampling rates make toxicity assessment semi-quantitative. Alternatives for
compounds recovery, such as active sampling followed by preconcentration (requiring
initially high volumes of water) or passive dosing (Jahnke et al. 2016), would clearly
overcome this limitation although they are also associated with some technical
challenges.
A successful effect-directed analysis relies on the non-destructive and non-discriminative treatment of the POCIS extracts. To do that, sequential steps in chemical treatment, i.e. proper extraction, fractionation, storage and analytical procedures (Schymanski et al. 2009) were carefully performed to ensure a good representation of the overall chemical contamination. The interest of the fractionation step was to simplify the procedure of molecular identification, to focus on biologically active compounds and to remove a maximum of no interest molecules (Brack et al. 2016). Moreover, the deconvolution of sample complexity gives information on biological effect induced by the mixture of compounds. In our case, the fractionation process allowed detecting that the inhibition of photosynthesis was due to the mixture of several compounds and the separation of these molecules by the fractionation process involved the loss of effect. The occurrence of biologically active molecules in a complex sample could have masked the effect of others (Hashmi et al. 2020).

### 4.1.2 Composition of the pesticides mixture in river Ponteils

The River Ponteils flows through agricultural parcels (i.e. corn crops and logging), a landfill and an urban area. Its contamination profile reflected its watershed's land-use: in particular, herbicides used as weed killers for the protection of cereal crops (such as bentazone or isoproturon, and several chloroacetanilides -less abundant) were detected in the pesticide mixture. The estimated concentration of isoproturon in river water was higher than the environmental quality standard of 300 ng/L.

In addition to current anthropic impact, past uses of substances were highlighted with the detection of pesticides unauthorized for agricultural uses in France (diuron,
tebutam, metolachlor) and of the transformation products (DCPMU and 2-hydroxyatrazine) of two prohibited pesticides: diuron and atrazine, respectively. These observations stress the need to search transformation products in addition to parent compounds, to perform a relevant assessment of the environmental contamination. In particular, herbicides transformation products may be persistent despite the prohibition of the parent chemicals, and are sometimes more toxic than parent compounds (Kim Tiam et al. 2014). Diuron and atrazine are forbidden for sale since 2008 and 2003, respectively.

Moreover, this river is a small tributary of Arcachon Bay, a semi-enclosed lagoon enduring a high anthropic pressure especially by nautical activities. The detection of the antifouling irgarol and its transformation products, namely DMSA and DMST, in Fr.1 and Fr.2 of POCIS extract, highlighted the influence of upwelling from Arcachon Bay to the river.

Finally, for several compounds, sources were unclear or difficult to define. The detection of hexazinone, which has a wide range of use, could be likely imputed to forest or/and industrial uses in the watershed. The insect repellent DEET was detected by non-target analysis as well as the herbicide, tebutam. DEET is a ubiquitous compound and its occurrence was reported in effluents of sewage treatment plants, in surface water, in groundwater and in drinking water (Merel and Snyder 2016).

## 4.2. *Effect-based toxicity assessment of “environmental” mixtures from river Ponteils.*

In this study, we performed an effect-based methodical approach to identify the compound(s) responsible of algal toxicity downstream the river Ponteils, where water
quality was of ecological concern (Neury-Ormanni et al. 2016). Behind using a tiered
approach of progressive simplification of mixtures and removal of the non or less
toxic fractions, we assumed simple cause/consequence (chemical/impact)
relationships, which is the basis for, e.g. prioritization of compounds of ecological
relevance.

4.2.1. *Standardization of experimental conditions and toxicity assessment in bioassays*

We performed a toxicity test expected to be ecologically relevant, using live
microalgae (*Nitzschia palea*) and assessing photosynthesis inhibition. The organisms
and experimental conditions were standardized to rule out other factors than chemical
toxicity. This approach had the major advantage to favour reproducibility between
tests, and/or benchmarking of toxicity values. The inhibition of photosynthetic
activity, moreover, provides sensitive responses, easily measurable, on a relatively
short time scale (within hours). This makes his endpoint particularly appropriate in the
case of high throughput toxicity screenings. In the case of the river Ponteils, PSII
activity as a global descriptor of microalgal health was also selected based on the
suspected pressures at that site (decline of the microalgal community: Neury-Ormanni
et al. 2016).

Preliminary establishment of DRCs using model substances highlighted the limitations
of using relative potencies based on unique ECₓ estimates (Figure SI1 and Table SI3),
given that the curves did not have parallel slopes nor similar maximal achievable
inhibition (Villeneuve et al. 2000). Toxic equivalents approaches should, thus, be
performed with caution and rather be based on mathematical derivations accounting
for dose-dependency of substances toxicity (Figure SI1). Moreover, diuron toxicity
towards *N. palea* during the EDA process was higher than in the preliminary tests.
Changes in the sensitivity of the culture may be a consequence of culturing under
laboratory conditions, selecting for certain phenotypes. This shift in the culture's
response to diuron exposure over time makes benchmarking relevant.

4.2.2. *Unravelling mixture toxicity of environmental contaminants from river Ponteils*

Using the reconstituted POCIS after fractionation led stepwise to slightly decreased
toxicity compared to the initial POCIS extract (although in the acceptable range of
MDR values) than expected by using DEqs (Figure 2b). The small difference between
predicted and measured toxicity could be attributed to biologically-active substances
not considered in toxicity predictions but present in the extract; likely bentazone and
hexazinone the 2 most concentrated herbicides in the extract after isoproturon and
diuron. Reconstituting complex mixtures (fractions, binary mixtures and reconstituted
POCIS, see Table 1) from the lower fractions tested (subfractions) when performing
EDA analysis is a mean to prevent from falsely attributing a decrease of toxicity to a
loss of synergic effects, and to allow comparisons between fractions in EDA attempts.
Limitations in the prediction for Fr.2 based on its chemical composition may be
explained by diverse independent factors related to experimental manipulations or
specific mixture interactions. Limited solubility of analytes in the mobile phase used
in chromatography, irreversible binding to the stationary phase or losses during
solvent exchange could have occurred during the fractionation step; solvent
evaporation and mixture re-creation from the subfractions, although carefully
performed, may also have contributed to decreased exposure concentrations in the
toxicity assay.

The results of the bioassays revealed that the toxicity of the reconstituted POCIS was
higher than the sum of the toxicity explained by the 3 fractions (Table 3: 54.8% of the
Ctrl for the reconstituted POCIS vs 101.4, 87.7 and 105.5 % of the Ctrl for Fr.1, Fr.2
and Fr.3 respectively). Reducing the complexity of the mixture by removing the less
toxic fractions allowed to identify gradually decreasing toxic effects. As expectable
due to the presence of two PSII inhibitors (isoproturon and diuron), Fr.2 likely
contributed to the effect of the mixture but further fractioning did not point out any
specific subfraction or compound significantly responsible for the extract's initial
toxicity, showing a "something from nothing" effect (Thrupp et al. 2018). Considering
specifically this fraction, EDA combined with toxicity prediction confirmed the
prevailing influence of the herbicides isoproturon and diuron over the others in
causing toxicity towards microalgae. In our case of study, it could be argued that a
targeted analysis of the overall mixture and the pre-existing knowledge of isoproturon
and diuron modes of action and environmental effects would have been enough to
establish satisfactory exposure/effects relationships at this particular river site. In the
same way, the EDA study conducted by Booij et al. (2014) finally pointed out the
predominant contribution in toxicity of atrazine, diuron, isoproturon, irgarol, terbutryn
and terbuthylazine from a broad range of chemicals extracted from passive samplers.
In their work also, large numbers of bioassays were processed to finally identify
bioactive substances that would have been otherwise pointed out by chemical
monitoring, given their status of priority substances (for most of them), and existing
knowledge regarding their mode of action towards autotrophic organisms. In this
sense, both cases demonstrate the interest to use target or suspect analysis as the first
step of bioanalytical approaches, in order to perform a mass balance analysis before to
apply an EDA approach. However, EDA was still interesting because it supported the
assumption that the degree of complexity of the mixture was a major factor in
explaining the overall toxicity of the waters, with increasing impacts from single
fractions to binary, ternary and "environmental" mixtures. Finally, from this study, we
cannot unequivocally discard any of the fractions as specific causes of the decline of
microalgae in the river Ponteils downstream the landfill. This impact could be the
result of multiple chemical and ecological factors.

This EDA approach performed on the mixture collected downstream the passive
landfill highlighted potential toxicity of the POCIS extract. Extrapolation to *in situ* risk
is however tricky, as passive sampling integrates average pesticide contamination over
time. The ecotoxicity test, as performed here, is based on the likely inaccurate
assumption that the pesticides composing the extract occurred coincidentally, though
the timing, coincidence, frequency, duration or peak exposure of pesticides is of
crucial importance in the response of microorganisms. Moreover the environmental
relevance of the results is limited by the fact that the toxicity of the mixture (average
of 4 weeks of exposure) was tested in short-term assays; the contaminants (isoproturon
and diuron) explaining the toxicity at this time-scale are temporary, non-covalent
inhibitors of PSII. Therefore, photosynthesis inhibition by these substances does not
allow accurate prediction of survival, growth and reproduction in the field, as
microalgae can recover quickly after the contamination ceases (e.g. Morin et al. 2018).
## 5. CONCLUSION

In this work, an EDA approach was performed on a complex mixture collected downstream passive landfill where microalgal biodiversity *in situ* is threatened. The difference of toxicity between reconstituted POCIS and the sum of the individual fractions highlighted the cumulated toxicity occurring in the environmental mixture from the Ponteils. An EDA approach should contribute to provide guidance for the identification of causative agents. However, although fractionation helped to highlight some toxicity in some fractions, it did not point out any particular compound explaining the toxicity observed for the environmental mixture (reconstituted POCIS).

As the EDA approach is time-consuming, we advise to preferably perform a mass balance analysis based on target or suspect chemical screenings and biological impacts observed. In case no evident cause/effect relationship can be deduced from these analyses, then EDA could be performed in a second time.

Complex mixture effects as we observed here, are frequently evidenced *in situ*.

Integrative detection tools such as EDA can, indeed, be powerful in highlighting mixture effects. If the identification of one (or few) responsible compound(s) is not achieved, such experimental works call for a deeper focus on mixture toxicity assessment and prediction.

## 6. ACKNOWLEDGEMENTS

This work was supported by the ANR (The French National Research Agency) through its programme CESA (Contaminants and Environments: Metrology, Health,
Adaptability & usages) and the PoToMAC (Potential Toxicity of pesticides in

Continental Aquatic Environments: passive sampling and exposure/impact on

biofilms) programme under the reference ANR-11-CESA-022.

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<figure><img src="image_2.png" /><figcaption>**Figure 1.** Concentrations measured for the pesticides detected by LC-MS/MS in the 3 POCIS fractions (in ng/g of POCIS sorbent, histograms and left Y-axis), and estimated in situ (in ng/L, diamonds and right Y-axis). Note the log-scale on the left Y-axis.</figcaption></figure>
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 2.** Dose-response curves established for diuron (a) and environmental mixtures (b: initial POCIS extract, c: reconstituted POCIS, d: Fraction 2) based on photosynthesis inhibition, as a function of diuron equivalent concentrations (DEq, in µg/L). The diamonds represent the effect measured, the black curves the DRC modelled based on real measures, and the grey curve the DRC predicted based on DEq</figcaption></figure>
values. a) DRC for diuron of the bioassay is predicted from the toxicity results from
the preliminary tests; b-d) DRCs are predicted for the mixtures based on the diuron
DRC of their respective wellplates; MDR for ECₓ values are provided in the tables.
Note the log-scale on the X-axis.
<table><caption>Table 1. Description of the solutions tested for toxicity on *Nitzschia palea*. The modality (entire DRC or only d2 as the highest concentration tested) used for each tested solution is specified, with d for dilution of the mixture. DRC: dose-response curve.

*Subfractions were tested for toxicity only when the fractions had an effect. Diuron was used as a positive control.</caption><thead><tr><th rowspan="2">Name</th><th rowspan="2">Tested solution</th><th rowspan="2">Description</th><th colspan="2">Modality used</th></tr><tr><th>Entire DRC (d1 to d6)</th><th>Only high concn. (d2)</th></tr></thead><tbody><tr><td>Initial POCIS extract</td><td>Initial POCIS extract</td><td>POCIS extract without fractionation</td><td>x</td><td></td></tr><tr><td>Reconstituted POCIS</td><td>Fr.1 x Fr.2 x Fr.3</td><td>Fr.1_1 to Fr.1_20 + Fr.2_1 to Fr.2_20 + Fr.3_1 to Fr.3_20</td><td>x</td><td></td></tr><tr><td rowspan="3">Fractions</td><td>Fr.1</td><td>Fr.1_1 to Fr.1_20</td><td>x</td><td></td></tr><tr><td>Fr.2</td><td>Fr.2_1 to Fr.2_20</td><td>x</td><td></td></tr><tr><td>Fr.3</td><td>Fr.3_1 to Fr.3_20</td><td>x</td><td></td></tr><tr><td rowspan="3">Binary mixtures</td><td>Fr.1 x Fr.2</td><td>Fr.1_1 to Fr.1_20 + Fr.2_1 to Fr.2_20</td><td></td><td>x</td></tr><tr><td>Fr.1 x Fr.3</td><td>Fr.1_1 to Fr.1_20 + Fr.3_1 to Fr.3_20</td><td></td><td>x</td></tr><tr><td>Fr.2 x Fr.3</td><td>Fr.2_1 to Fr.2_20 + Fr.3_1 to Fr.3_20</td><td></td><td>x</td></tr><tr><td rowspan="8">Subfractions</td><td>*Fr.1_1</td><td>Fr.1_1</td><td></td><td></td></tr><tr><td>...</td><td></td><td></td><td></td></tr><tr><td>*Fr.1_20</td><td>Fr.1_20</td><td></td><td></td></tr><tr><td>Fr.2_1</td><td>Fr.2_1</td><td></td><td>x</td></tr><tr><td>...</td><td></td><td></td><td></td></tr><tr><td>Fr.2_20</td><td>Fr.2_20</td><td></td><td></td></tr><tr><td>*Fr.3_1</td><td>Fr.3_1</td><td></td><td>x</td></tr><tr><td>...</td><td></td><td></td><td></td></tr><tr><td>*Fr.3_20</td><td>Fr.3_20</td><td></td><td></td></tr></tbody></table>
<table><caption>Table 2. Number of compounds detected by LC-QTOF and list of the compounds identified in the POCIS fractions, with a >80% correspondence. *based on Schymanski et al. (2014)</caption><thead><tr><th rowspan="2">Fraction</th><th colspan="2">Number of detected compounds</th><th colspan="10">Identified compounds</th></tr><tr><th>Positive ion mode</th><th>Negative ion mode</th><th>Name</th><th>Structure</th><th>Formula</th><th>Tr (min)</th><th>Ion</th><th>Experimental exact mass</th><th>Error mass (ppm)</th><th>Score MSMS</th><th>Confirmation level*</th></tr></thead><tbody><tr><td rowspan="2">Fraction 1</td><td rowspan="2">100</td><td rowspan="2">71</td><td>Astemizole</td><td></td><td>C<sub>28</sub>H<sub>31</sub>FN<sub>4</sub>O</td><td>16.4</td><td>[M+H]+</td><td>458.2459</td><td>-2.04</td><td>92.13</td><td>2</td></tr><tr><td>Dibunate</td><td></td><td>C<sub>18</sub>H<sub>24</sub>O<sub>3</sub>S</td><td>19.0</td><td>[M+H]+</td><td>320.1455</td><td>-2.91</td><td>98.58</td><td>2</td></tr><tr><td rowspan="4">Fraction 2</td><td rowspan="4">81</td><td rowspan="4">92</td><td>Dibunate</td><td></td><td>C<sub>18</sub>H<sub>24</sub>O<sub>3</sub>S</td><td>19.0</td><td>[M+H]+</td><td>320.1455</td><td>-2.91</td><td>95.39</td><td>2</td></tr><tr><td>N,N-Diethyl-3-methylbenzamide (DEET)</td><td></td><td>C<sub>12</sub>H<sub>17</sub>NO</td><td>15.7</td><td>[M+H]+</td><td>191.1316</td><td>-2.92</td><td>99.46</td><td>1</td></tr><tr><td>Tebutam</td><td></td><td>C<sub>15</sub>H<sub>23</sub>NO</td><td>24.6</td><td>[M+H]+</td><td>233.1782</td><td>-0.91</td><td>97.61</td><td>1</td></tr><tr><td>Isoproturon</td><td></td><td>C<sub>12</sub>H<sub>18</sub>N<sub>2</sub>O</td><td>16.1</td><td>[M+H]+</td><td>206.1423</td><td>-1.94</td><td>95.31</td><td>1</td></tr><tr><td rowspan="3">Fraction 3</td><td rowspan="3">36</td><td rowspan="3">50</td><td>Mephentermine</td><td></td><td>C<sub>11</sub>H<sub>17</sub>N</td><td>3.4</td><td>[M+H]+</td><td>163.1366</td><td>-2.83</td><td>90.41</td><td>2</td></tr><tr><td>Benzoic acid</td><td></td><td>C<sub>7</sub>H<sub>6</sub>O<sub>2</sub></td><td>1.2</td><td>[M-H]-</td><td>122.0374</td><td>-4.95</td><td>98.53</td><td>2</td></tr><tr><td>Terbutryn</td><td></td><td>C<sub>10</sub>H<sub>19</sub>N<sub>5</sub>S</td><td>13.7</td><td>[M+H]+</td><td>242.1435</td><td>-0.76</td><td>96.45</td><td>1</td></tr></tbody></table>
<table><caption>Table 3. Potentially bioactive chemicals in the fractions used for bioassays and respective modes of toxic action. The highest concentrations used (in µg/L final concentration) are provided for the fractions and their combinations tested. bdl: below the quantification limit. dp: degradation product. Predicted and effective effects on photosynthesis are shown in per cent of the control. Significant differences in measured photosynthetic activity, with respect to control values after ANOVA: *<0.05, ***<0.001.</caption><thead><tr><th rowspan="2">Potentially<br>bioactive chemicals</th><th rowspan="2">Mode of<br>action</th><th colspan="6">Highest exposure concentration (µg/L)</th><th rowspan="2">Reconstituted<br>POCIS<br>(Fr.1 x Fr.2 x<br>Fr.3)</th><th rowspan="2">Initial<br>POCIS<br>extract</th></tr><tr><th>Fr.1</th><th>Fr.2</th><th>Fr.3</th><th>Fr.1 x Fr.2</th><th>Fr.1 x Fr.3</th><th>Fr.2 x Fr.3</th></tr></thead><tbody><tr><td>Bentazone</td><td>Herbicide</td><td>10.58</td><td>bdl</td><td>Bdl</td><td>5.24</td><td>5.24</td><td>bdl</td><td>10.48</td><td>20.97</td></tr><tr><td>Hexazinone</td><td>Herbicide</td><td>9.05</td><td>bdl</td><td>Bdl</td><td>4.52</td><td>4.52</td><td>bdl</td><td>9.05</td><td>18.10</td></tr><tr><td>Terbuthylazine</td><td>Herbicide</td><td>1.12</td><td>bdl</td><td>Bdl</td><td>0.56</td><td>0.56</td><td>bdl</td><td>1.12</td><td>2.24</td></tr><tr><td>DMSA<sup>dp</sup></td><td>Fungicide</td><td>73.52</td><td>0.13</td><td>Bdl</td><td>36.82</td><td>36.76</td><td>0.06</td><td>73.65</td><td>147.30</td></tr><tr><td>DMST<sup>dp</sup></td><td>Fungicide</td><td>13.14</td><td>0.13</td><td>Bdl</td><td>6.63</td><td>6.57</td><td>0.06</td><td>13.27</td><td>26.53</td></tr><tr><td>Carbendazim</td><td>Fungicide</td><td>bdl</td><td>1.17</td><td>Bdl</td><td>0.58</td><td>Bdl</td><td>0.58</td><td>1.17</td><td>2.33</td></tr><tr><td>Carbofuran</td><td>Insecticide</td><td>bdl</td><td>0.02</td><td>Bdl</td><td>0.01</td><td>Bdl</td><td>0.01</td><td>0.02</td><td>0.04</td></tr><tr><td>Chlorotoluron</td><td>Herbicide</td><td>bdl</td><td>1.13</td><td>Bdl</td><td>0.56</td><td>Bdl</td><td>0.56</td><td>1.13</td><td>2.25</td></tr><tr><td>DCPMU<sup>dp</sup></td><td>Herbicide</td><td>bdl</td><td>1.91</td><td>Bdl</td><td>0.95</td><td>Bdl</td><td>0.95</td><td>1.91</td><td>3.82</td></tr></tbody></table>
<table><tbody><tr><td>Metazachlor</td><td>Herbicide</td><td>bdl</td><td>0.04</td><td>BDL</td><td>0.02</td><td>BDL</td><td>0.02</td><td>0.04</td><td>0.08</td></tr><tr><td>Metolachlor</td><td>Herbicide</td><td>bdl</td><td>0.17</td><td>BDL</td><td>0.08</td><td>BDL</td><td>0.08</td><td>0.17</td><td>0.34</td></tr><tr><td>Propiconazole</td><td>Fungicide</td><td>bdl</td><td>1.51</td><td>BDL</td><td>0.75</td><td>BDL</td><td>0.75</td><td>1.51</td><td>3.02</td></tr><tr><td>Diflufenican</td><td>Herbicide</td><td>bdl</td><td>0.03</td><td>BDL</td><td>0.01</td><td>BDL</td><td>0.01</td><td>0.03</td><td>0.05</td></tr><tr><td>Isoproturon</td><td>Herbicide</td><td>bdl</td><td>82.69</td><td>0.03</td><td>41.34</td><td>0.02</td><td>41.36</td><td>82.72</td><td>165.44</td></tr><tr><td>Diuron</td><td>Herbicide</td><td>bdl</td><td>11.94</td><td>0.02</td><td>5.97</td><td>0.01</td><td>5.98</td><td>11.95</td><td>23.91</td></tr><tr><td>2-hydroxyatrazine<sup>dp</sup></td><td>Herbicide</td><td>bdl</td><td>0.96</td><td>0.08</td><td>0.48</td><td>0.04</td><td>0.52</td><td>1.04</td><td>2.08</td></tr><tr><td>Ametryn</td><td>Herbicide</td><td>bdl</td><td>bdl</td><td>1.37</td><td>bdl</td><td>0.69</td><td>0.69</td><td>1.37</td><td>2.75</td></tr><tr><td>Prometryn</td><td>Herbicide</td><td>bdl</td><td>bdl</td><td>0.06</td><td>bdl</td><td>0.03</td><td>0.03</td><td>0.06</td><td>0.13</td></tr><tr><td>Terbutryn</td><td>Herbicide</td><td>bdl</td><td>bdl</td><td>2.00</td><td>bdl</td><td>1.00</td><td>1.00</td><td>2.00</td><td>4.01</td></tr><tr><td>Irgarol</td><td>Herbicide</td><td>bdl</td><td>bdl</td><td>0.39</td><td>bdl</td><td>0.19</td><td>0.19</td><td>0.39</td><td>0.77</td></tr><tr><td>Quizalofop-ethyl</td><td>Herbicide</td><td>bdl</td><td>bdl</td><td>0.01</td><td>bdl</td><td>&lt;0.01</td><td>&lt;0.01</td><td>0.01</td><td>0.02</td></tr><tr><td>Fluazifop-p-butyl</td><td>Herbicide</td><td>bdl</td><td>bdl</td><td>&lt;0.01</td><td>bdl</td><td>&lt;0.01</td><td>&lt;0.01</td><td>&lt;0.01</td><td>&lt;0.01</td></tr><tr><td><strong>Total concentration</strong></td><td></td><td></td><td><strong>107.31</strong></td><td><strong>101.80</strong></td><td><strong>3.98</strong></td><td><strong>104.56</strong></td><td><strong>55.64</strong></td><td><strong>52.89</strong></td><td><strong>213.09</strong></td><td><strong>426.19</strong></td></tr><tr><td>Measured effect (% of control)</td><td></td><td></td><td>101.4±1.7</td><td>87.7±1.7</td><td>105.5±3.4</td><td>62.0±28.2***</td><td>64.0±4.9***</td><td>76.0±8.8*</td><td>54.8±9.3***</td><td>14.0±4.9***</td></tr></tbody></table>
<table><caption>**Table SI1.** Instrumental conditions for the pesticide analyse by LC-MS/MS</caption><thead><tr><th>Items</th><th>Conditions</th></tr></thead><tbody><tr><td>Source</td><td>Agilent Jet Stream ESI</td></tr><tr><td>Ionisation mode</td><td>Positive</td></tr><tr><td>Gas Temp</td><td>300°C</td></tr><tr><td>Drying gas</td><td>8 L.min<sup>-1</sup></td></tr><tr><td>Nebulizer</td><td>40 psi</td></tr><tr><td>Sheath gas</td><td>400°C</td></tr><tr><td>Sheath gas flow</td><td>11 L.min<sup>-1</sup></td></tr><tr><td>Noozle voltage</td><td>300V</td></tr><tr><td>V Cap</td><td>3000 V</td></tr></tbody></table>

<table><thead><tr><th>Compounds</th><th>MS/MS 1 (m/z)</th><th>MS/MS 2 (m/z)</th><th>Fragmentor (V)</th><th>CE 1 (V)</th><th>CE 2 (V)</th><th>Surrogate standard</th></tr></thead><tbody><tr><td>1,2,4 DCPU</td><td>205>161.9</td><td>205>127</td><td>93</td><td>8</td><td>28</td><td>DIA d5</td></tr><tr><td>1,3,4 DCPU</td><td>205>161.9</td><td>205>127</td><td>93</td><td>8</td><td>28</td><td>DIA d5</td></tr><tr><td>1,3,4,3 DCPMU</td><td>219>161.9</td><td>219>127</td><td>103</td><td>8</td><td>24</td><td>simazine d10</td></tr><tr><td>acetochlor-ESA</td><td>270.1>148.1</td><td>270.1>120.1</td><td>108</td><td>16</td><td>28</td><td>DIA d5</td></tr><tr><td>acetochlor-OA</td><td>220.1>174</td><td>220.1>146</td><td>93</td><td>8</td><td>12</td><td>alachlor d13</td></tr><tr><td>acetochlor</td><td>270.1>224</td><td>270.1>148.1</td><td>83</td><td>4</td><td>4</td><td>alachlor d13</td></tr><tr><td>alachlor</td><td>270.1>238</td><td>270.1>162.1</td><td>83</td><td>4</td><td>12</td><td>alachlor d13</td></tr><tr><td>ametryn</td><td>228.1>186</td><td>228.1>68</td><td>84</td><td>12</td><td>40</td><td>irgarol d9</td></tr><tr><td>atrazine</td><td>216.1>174</td><td>216.1>68</td><td>109</td><td>12</td><td>40</td><td>atrazine d5</td></tr><tr><td>azoxystrobine</td><td>404.1>372.1</td><td>404.1>344.1</td><td>102</td><td>8</td><td>24</td><td>carbofuran d3</td></tr><tr><td>bentazone</td><td>241.1>199</td><td>241.1>107</td><td>79</td><td>4</td><td>24</td><td>DIA d5</td></tr><tr><td>carbendazim</td><td>192.1>160</td><td>192.1>105</td><td>102</td><td>12</td><td>40</td><td>hexazinone d6</td></tr><tr><td>carbetamide</td><td>237.1>192.1</td><td>237.1>118.1</td><td>77</td><td>0</td><td>8</td><td>terbuthylazine d5</td></tr><tr><td>carbofuran</td><td>222.1>165</td><td>222.1>123</td><td>77</td><td>4</td><td>20</td><td>carbofuran d3</td></tr><tr><td>carbosulfan</td><td>381.2>160.1</td><td>381.2>118</td><td>104</td><td>8</td><td>16</td><td>irgarol d9</td></tr><tr><td>chlortoluron</td><td>213.1>72</td><td>213.1>46.1</td><td>98</td><td>16</td><td>12</td><td>atrazine d5</td></tr><tr><td>chlorsulfuron</td><td>358>167</td><td>358>141</td><td>99</td><td>12</td><td>16</td><td>DIA d5</td></tr><tr><td>cyanazine</td><td>241.1>214</td><td>241.1>43</td><td>108</td><td>8</td><td>48</td><td>simazine d10</td></tr><tr><td>cyromazine</td><td>167.1>85</td><td>167.1>48</td><td>108</td><td>16</td><td>44</td><td>simazine d10</td></tr><tr><td>DEA</td><td>188.1>146</td><td>188.1>43.1</td><td>103</td><td>12</td><td>40</td><td>simazine d10</td></tr><tr><td>DIA</td><td>174.1>96</td><td>174.1>68</td><td>119</td><td>16</td><td>32</td><td>DIA d5</td></tr><tr><td>diflufenican</td><td>395.1>266</td><td>395.1>246</td><td>156</td><td>24</td><td>40</td><td>irgarol d9</td></tr><tr><td>dimetachlor</td><td>256.1>224</td><td>256.1>148.1</td><td>93</td><td>20</td><td>12</td><td>hexazinone d6</td></tr><tr><td>diuron</td><td>233>72</td><td>233>46.1</td><td>114</td><td>16</td><td>12</td><td>diuron d6</td></tr><tr><td>DMSA</td><td>201.1>92.1</td><td>201.1>65.1</td><td>77</td><td>12</td><td>36</td><td>atrazine d5</td></tr></tbody></table>
<table><tr><td>DMST</td><td>215.1>106.1</td><td>215.1>77.1</td><td>72</td><td>8</td><td>48</td><td>irgarol d9</td></tr><tr><td>flazasulfuron</td><td>408.1>182</td><td>408.1>83</td><td>104</td><td>16</td><td>50</td><td>simazine d10</td></tr><tr><td>fluazifop-p-butyl</td><td>384.1>328.1</td><td>384.1>282</td><td>139</td><td>12</td><td>20</td><td>irgarol d9</td></tr><tr><td>flusilazole</td><td>316.1>247</td><td>316.1>165</td><td>124</td><td>16</td><td>24</td><td>alachlor d13</td></tr><tr><td>hexazinone</td><td>253.2>171</td><td>253.2>71</td><td>103</td><td>8</td><td>32</td><td>hexazinone d6</td></tr><tr><td>hydroxy-atrazine</td><td>198.1>156</td><td>198.1>86</td><td>132</td><td>12</td><td>20</td><td>atrazine d5</td></tr><tr><td>hydroxy-simazine</td><td>184.1>114</td><td>184.1>69</td><td>127</td><td>16</td><td>32</td><td>atrazine d5</td></tr><tr><td>imidacloprid</td><td>256>209</td><td>256>175</td><td>92</td><td>12</td><td>12</td><td>simazine d10</td></tr><tr><td>irgarol</td><td>254.2>198</td><td>254.2>83</td><td>131</td><td>16</td><td>28</td><td>irgarol d9</td></tr><tr><td>isoproturon</td><td>207.2>72</td><td>207.2>46.1</td><td>103</td><td>12</td><td>12</td><td>diuron d6</td></tr><tr><td>linuron</td><td>249.0>159.9</td><td>149>159.9</td><td>99</td><td>12</td><td>12</td><td>irgarol d9</td></tr><tr><td>metazachlor</td><td>278.1>134.1</td><td>278.1>134.1</td><td>74</td><td>0</td><td>16</td><td>carbofuran d3</td></tr><tr><td>methiocarb</td><td>226.1>169.0</td><td>226.1>121</td><td>67</td><td>4</td><td>16</td><td>irgarol d9</td></tr><tr><td>metolachlor</td><td>284.1>252.0</td><td>284.1>176.1</td><td>97</td><td>12</td><td>24</td><td>alachlor d13</td></tr><tr><td>metolachlor ESA</td><td>352.1>230.1</td><td>352.1>208.1</td><td>124</td><td>12</td><td>12</td><td>DIA d5</td></tr><tr><td>metolachlor OA</td><td>280.2>248.1</td><td>280.2>146.1</td><td>92</td><td>8</td><td>20</td><td>carbofuran d3</td></tr><tr><td>metoxuron</td><td>229.1>72.0</td><td>229.1>46.1</td><td>103</td><td>12</td><td>6</td><td>carbofuran d3</td></tr><tr><td>metsulfuron-methyl</td><td>382.1>167.0</td><td>382.1>77</td><td>97</td><td>12</td><td>50</td><td>atrazine d5</td></tr><tr><td>nicosulfuron</td><td>411.1>182.0</td><td>411.1>182</td><td>151</td><td>12</td><td>20</td><td>simazine d10</td></tr><tr><td>prometryn</td><td>242.2>158.0</td><td>242.2>158</td><td>127</td><td>12</td><td>20</td><td>irgarol d9</td></tr><tr><td>propachlor</td><td>212.1>170.0</td><td>212.1>94</td><td>88</td><td>8</td><td>24</td><td>diuron d6</td></tr><tr><td>propazine</td><td>230.1>188.0</td><td>230.1>146</td><td>122</td><td>12</td><td>20</td><td>terbuthylazine d5</td></tr><tr><td>propiconazole</td><td>342.1>158.9</td><td>342.1>69</td><td>134</td><td>32</td><td>20</td><td>irgarol d9</td></tr><tr><td>prosulfuron</td><td>420.1>167.0</td><td>420.1>141</td><td>134</td><td>16</td><td>20</td><td>simazine d10</td></tr><tr><td>pymethrozine</td><td>218.1>105.0</td><td>218.1>51</td><td>103</td><td>16</td><td>60</td><td>terbuthylazine d5</td></tr><tr><td>quizalofop-ethyl</td><td>373.1>299.0</td><td>373.1>91</td><td>124</td><td>16</td><td>28</td><td>irgarol d9</td></tr><tr><td>quizalofop-p-tertufuryl</td><td>429.1>85.0</td><td>429.1>85</td><td>129</td><td>16</td><td>16</td><td>irgarol d9</td></tr><tr><td>simazine</td><td>202.1>68.0</td><td>202.1>68</td><td>108</td><td>24</td><td>32</td><td>simazine d10</td></tr><tr><td>terbuthylazine</td><td>230.1>174.0</td><td>230.1>68</td><td>103</td><td>12</td><td>40</td><td>terbuthylazine d5</td></tr><tr><td>terbutryn</td><td>242.2>186.0</td><td>242.2>68</td><td>84</td><td>12</td><td>44</td><td>irgarol d9</td></tr><tr><td>terbuthylazine desethyl</td><td>202.1>145.9</td><td>202.1>43.1</td><td>93</td><td>12</td><td>44</td><td>diuron d6</td></tr><tr><td>thiamethoxan</td><td>292.0>211.0</td><td>292>181</td><td>77</td><td>4</td><td>20</td><td>atrazine d5</td></tr><tr><td>alachlor-d13</td><td>283.2>251.1</td><td>-</td><td>98</td><td>4</td><td>-</td><td></td></tr><tr><td>atrazine -d5</td><td>221.1>179</td><td>-</td><td>116</td><td>16</td><td>-</td><td></td></tr><tr><td>carbofuran-d3</td><td>225.1>165.1</td><td>-</td><td>82</td><td>4</td><td>-</td><td></td></tr><tr><td>DIA-d5</td><td>179.1>101</td><td>-</td><td>119</td><td>16</td><td>-</td><td></td></tr><tr><td>diuron-d6</td><td>239.1>78</td><td>-</td><td>109</td><td>24</td><td>-</td><td></td></tr><tr><td>hexazinone-d6</td><td>259.2>177.1</td><td>-</td><td>114</td><td>12</td><td>-</td><td></td></tr><tr><td>irgarol-d9</td><td>263.2>199</td><td>-</td><td>124</td><td>16</td><td>-</td><td></td></tr><tr><td>simazine-d10</td><td>212.1>104.9</td><td>-</td><td>121</td><td>24</td><td>-</td><td></td></tr><tr><td>terbuthylazine-d5</td><td>235>179</td><td>-</td><td>108</td><td>12</td><td>-</td><td></td></tr></table>

1,2,4-DCPU: 1,2,4-dichlorophenyl urea; 1,3,4-DCPU: 1,3,4-dichlorophenyl urea; 1,3,4,3-DCPMU: 1-(3,4-dichlorophenyl)-3-methylurea; acetochlor-ESA: acetochlor ethanesulfonic acid; acetochlor-OA: acetochlor oxanilic acid; DEA: desethylatrazine; DIA: desisopropyl atrazine; DMSA: N,N-dimethyl-N'-phenylsulfamide; DMST: N,N-dimethyl-N'-p-tolylsulfamide; metolachlor-ESA: metolachlor ethanesulfonic acid; metolachlor-OA: metolachlor oxanilic acid.
<table><caption>**Table SI2.** Toxicity data from preliminary assays established with *N. palea* (photosynthesis inhibition) against model herbicides. dp: degradation product. ECx data are given as optimum concentration in µg/L [confidence interval]. The Relative Potency (RP) function denotes the equation used to derive diuron equivalent concentrations (DEq). In all cases, regression correlations are significant (R²≥0.99, p<0.001).</caption><thead><tr><th></th><th>Atrazine</th><th>DEA</th><th>DIA</th><th>Diuron</th><th>Isoproturon</th><th>Metolachlor</th></tr></thead><tbody><tr><th>Class</th><td>Triazine</td><td>Triazine<sup>dp</sup></td><td>Triazine<sup>dp</sup></td><td>Phenylurea</td><td>Phenylurea</td><td>Chloroacetanilide</td></tr><tr><th>Target</th><td>Photosynthesis</td><td></td><td></td><td>Photosynthesis</td><td>Photosynthesis</td><td>Fatty acids biosynthesis</td></tr><tr><th>logK<sub>ow</sub></th><td>2.7</td><td>1.51</td><td>1.15</td><td>2.87</td><td>2.5</td><td>3.4</td></tr><tr><th>EC<sub>50</sub></th><td>462<br>[358-582]</td><td>4 292<br>[3 829-4 851]</td><td>1 782<br>[1 390-2 388]</td><td>20<br>[14-28]</td><td>58<br>[33-89]</td><td>2 779<br>[1 972-3 672]</td></tr><tr><th>EC<sub>5</sub></th><td>14<br>[6-27]</td><td>2 732<br>[1 704-4 265]</td><td>187<br>[92-394]</td><td>0.3<br>[0.1-0.7]</td><td>6<br>[1-23]</td><td>192<br>[72-418]</td></tr><tr><th>EC<sub>10</sub></th><td>33<br>[18-58]</td><td>3 064<br>[2 145-4 398]</td><td>331<br>[189-603]</td><td>0.8<br>[0.4-1.8]</td><td>11<br>[3-31]</td><td>379<br>[174-713]</td></tr><tr><th>EC<sub>15</sub></th><td>58<br>[34-92]</td><td>3 289<br>[2 465-4 448]</td><td>472<br>[298-803]</td><td>1.6<br>[0.9-3.3]</td><td>15<br>[5-36]</td><td>576<br>[286-986]</td></tr><tr><th>EC<sub>20</sub></th><td>88<br>[55-132]</td><td>3 470<br>[2 733-4 552]</td><td>616<br>[414-976]</td><td>2.7<br>[1.5-5.0]</td><td>20<br>[7-42]</td><td>790<br>[419-1 272]</td></tr><tr><th>EC<sub>25</sub></th><td>124<br>[84-176]</td><td>3 626<br>[2 984-4 606]</td><td>768<br>[541-1 178]</td><td>4.1<br>[2.5-7.1]</td><td>25<br>[10-47]</td><td>1 026<br>[587-1 547]</td></tr><tr><th>RP function</th><td>[ATZ]=<br>38.56×[DEq]<sup>0.83</sup></td><td>[DEA]=<br>2942.44×[DEq]<sup>0.11</sup></td><td>[DIA]=<br>363.63×[DEq]<sup>0.53</sup></td><td></td><td>[IPU]=<br>11.67×[DEq]<sup>0.54</sup></td><td>[MeCl]=<br>422.97×[DEq]<sup>0.63</sup></td></tr></tbody></table>
<table><caption>**Table SI3.** Examples of variability in Relative Potency (RP) values based on different ECₓ, using diuron as the reference toxic substance.</caption><thead><tr><th>Substance/sample</th><th>RP based on:</th><th>EC50</th><th>EC15</th><th>EC5</th></tr></thead><tbody><tr><td>Atrazine</td><td></td><td>0.043</td><td>0.028</td><td>0.021</td></tr><tr><td>DEA</td><td></td><td>0.005</td><td>&lt;0.001</td><td>&lt;0.001</td></tr><tr><td>DIA</td><td></td><td>0.011</td><td>0.003</td><td>0.001</td></tr><tr><td>Isoproturon</td><td></td><td>0.342</td><td>0.108</td><td>0.048</td></tr><tr><td>Metolachlor</td><td></td><td>0.007</td><td>0.003</td><td>0.001</td></tr><tr><td>Initial POCIS extract</td><td></td><td>0.225</td><td>0.609</td><td>1.215</td></tr><tr><td>Fraction 1</td><td></td><td>&lt;0.001</td><td>0.003</td><td>&lt;0.001</td></tr><tr><td>Fraction 2</td><td></td><td>0.061</td><td>0.026</td><td>0.015</td></tr><tr><td>Fraction 3</td><td></td><td>0.009</td><td>0.003</td><td>0.002</td></tr></tbody></table>
<table><caption>**Table SI4.** Photosynthetic activity of *N. palea* (in per cent of their respective controls, mean±SE) exposed to the different fractions at the highest exposure concentration. The ΦPSII is given for the fractions, subfractions of the fraction having some effect (fraction 2, Fr.2), binary and mixtures of fractions at the highest concentration tested. Significant differences in photosynthetic activity compared with their control after ANOVA: *<0.05, ***<0.001.</caption><thead><tr><th>Fraction(s)</th><th>ΦPSII (in % of control)</th><th>p-value</th></tr></thead><tbody><tr><td>Fr.1</td><td>101.4±1.7</td><td>0.886905</td></tr><tr><td><strong>Fr.2</strong></td><td><strong>87.7±1.7</strong></td><td><strong>0.203792</strong></td></tr><tr><td>Fr.2_1</td><td>96.0±5.7</td><td>0.678170</td></tr><tr><td>Fr.2_2</td><td>93.3±4.3</td><td>0.489783</td></tr><tr><td>Fr.2_3</td><td>90.7±4.3</td><td>0.334575</td></tr><tr><td><strong>Fr.2_4</strong></td><td><strong>88.0±2.8</strong></td><td><strong>0.215983</strong></td></tr><tr><td>Fr.2_5</td><td>92.0±2.8</td><td>0.407667</td></tr><tr><td>Fr.2_6</td><td>89.3±1.6</td><td>0.270729</td></tr><tr><td>Fr.2_7</td><td>98.7±4.3</td><td>0.889912</td></tr><tr><td>Fr.2_8</td><td>88.0±7.5</td><td>0.215983</td></tr><tr><td>Fr.2_9</td><td>94.7±5.9</td><td>0.580298</td></tr><tr><td>Fr.2_10</td><td>96.0±13.0</td><td>0.678170</td></tr><tr><td>Fr.2_11</td><td>90.7±4.3</td><td>0.334575</td></tr><tr><td>Fr.2_12</td><td>89.3±4.3</td><td>0.270729</td></tr><tr><td>Fr.2_13</td><td>90.7±4.3</td><td>0.334575</td></tr><tr><td>Fr.2_14</td><td>97.3±7.1</td><td>0.781964</td></tr><tr><td><strong>Fr.2_15</strong></td><td><strong>85.3±1.6</strong></td><td><strong>0.131766</strong></td></tr><tr><td>Fr.2_16</td><td>92.0±5.7</td><td>0.407667</td></tr><tr><td>Fr.2_17</td><td>86.7±5.9</td><td>0.169888</td></tr><tr><td>Fr.2_18</td><td>89.3±8.6</td><td>0.270729</td></tr><tr><td>Fr.2_19</td><td>93.3±3.3</td><td>0.489783</td></tr><tr><td>Fr.2_20</td><td>97.3±4.3</td><td>0.781964</td></tr><tr><td>Fr.3</td><td>105.5±3.4</td><td>0.569959</td></tr><tr><td>Fr.1 x Fr.2</td><td>62.0±28.2***</td><td>0.000212</td></tr><tr><td>Fr.1 x Fr.3</td><td>64.0±4.9***</td><td>0.000415</td></tr><tr><td>Fr.2 x Fr.3</td><td>76.0±8.8*</td><td>0.015265</td></tr><tr><td>Fr.1 x Fr.2 x Fr.3</td><td>54.8±9.3***</td><td>0.000017</td></tr></tbody></table>
<figure><img src="image_7.png" /><figcaption>Figure SI1. Relative potency of the model substances tested in the preliminary test, as a function of diuron equivalent concentrations (µg/L). Note the log-scale on both axes.</figcaption></figure>