# Avoidance behaviour of aquatic macroinvertebrates for real-time detection of micropollutant surge in wastewater effluents

George Ruck, Alexandre Decamps, Jean-Baptiste Aubin, Hervé Quéau, Laura Garnero, T. Cavanna, Jean-Luc Bertrand-Krajewski, Didier Neuzeret, Olivier Geffard, Arnaud Chaumot

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## To cite this version:

George Ruck, Alexandre Decamps, Jean-Baptiste Aubin, Hervé Quéau, Laura Garnero, et al. Avoidance behaviour of aquatic macroinvertebrates for real-time detection of micropollutant surge in wastewater effluents. Water Research, 2023, 242, pp.120228. 10.1016/j.watres.2023.120228. hal-04372023

HAL Id: hal-04372023

https://hal.inrae.fr/hal-04372023v1

Submitted on 9 Jul 2025

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Distributed under a Creative Commons Attribution 4.0 International License
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**Title:** Avoidance behaviour of aquatic macroinvertebrates for real-time detection of micropollutant surge in wastewater effluents

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G Ruck¹,², A Decamps², JB Aubin³, H Quéau¹, L Garnero¹, T Cavanna², JL Bertrand-Krajewski³, D

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Neuzeret², O Geffard¹, A Chaumot¹*

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¹INRAE, UR RiverLy, Laboratoire d'écotoxicologie, F-69625 Villeurbanne, France

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²Viewpoint, 67 rue Copernic, F-01390 Civrieux, France

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³University of Lyon, INSA Lyon, Laboratory DEEP – EA 7429, 11 rue de la physique, F-69621

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Villeurbanne

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*corresponding author: arnaud.chaumot@inrae.fr

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**Abstract**

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Micropollutants are regularly detected at the outlets of wastewater treatment plants (WWTPs).

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Across urban and industrial WWTPs, monitoring directives only require assessment for a handful of chemicals via sampling methods that fail to capture the temporal variability in micropollutant discharge. In this study, we develop a biotest for real-time on-line monitoring of micropollutant discharge dynamics in WWTPs effluents. The selected biomonitoring device ToxMate uses videotracking of invertebrate movement, which was used to deduce avoidance behaviour of the amphipod *Gammarus fossarum*. Organism conditioning was set up to induce a state of minimal locomotor activity in basal conditions to maximise avoidance signal sensitivity to micropollutant spikes. We showed that with a standardised protocol, it was possible to minimise both overall movement and sensitivity to physio-chemical variations typical to WWTP effluents, as well as capture the spikes of two micropollutants upon exposure (copper and methomyl). Spikes in avoidance behaviour were consistently seen for the two chemicals, as well as a strong correlation between avoidance intensity and spiked concentration. A two-year effluent monitoring case study also illustrates how this biomonitoring method is suitable for real-time on-site monitoring, and shows a promising non-targeted approach for characterising complex micropollutant discharge variability at WWTP effluents, which today remains poorly understood.

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**Keywords**

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behaviour, biomonitoring, locomotor activity, videotracking, gammarids, on-line monitoring

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31 Introduction

32 Micro-pollutant discharge from wastewater treatment plants (WWTPs) represents ongoing concern
33 due to the continuous discovery of threats posed to both biodiversity in the aquatic ecosystem and
34 human health (Luo et al., 2014; Rowsell et al., 2010; Schwarzenbach et al., 2006). In urban WWTPs,
35 thousands of chemical compounds may end up in sewer influents ranging from surfactants,
36 household chemicals and pharmaceuticals, as well as non-domestic products originating runoffs such
37 as pesticides and heavy metals (Margot et al., 2015). Various chemical screening studies have shown
38 that WWTP effluents can contain relevant concentrations for hundreds of organic micropollutants in
39 an individual outlet sample (Rogowska et al., 2020; Schymanski et al., 2014). Complementing grab
40 sampling approaches, composite sampling strategy along with passive integrated samplers
41 successfully show the scale of micropollutants in urban effluents, and the importance of such
42 monitoring campaigns. However, difficulties subsist in capturing micropollutant charge dynamics
43 and variability, as favouring averaged concentration measures leads to sharp concentration
44 increases going unnoticed in most monitoring campaigns (INERIS, 2021). There is a highly transient
45 nature associated with micropollutant discharge in effluents, leading to temporary surges in
46 concentration that calls for complementary real-time monitoring approaches, as this would provide
47 novel insight and characterisation of toxic contamination transfer to the aquatic environment
48 (Bownik et al., 2021; Musolff et al., 2009).

49 Behaviour is a sensitive ecotoxicological endpoint, providing quick responses to contaminant
50 exposure at sublethal toxicity doses (Legradi et al., 2018; Robinson et al., 2009; Van der Geest et al.,
51 1999), with behavioural ecotoxicology long being identified as a means for rapid early warning signs
52 in marine and freshwater pollution (Hellou et al., 2011). Avoidance behaviour has been well studied
53 for diverse taxa in response to contamination, such as for macroinvertebrates (Beketov et al., 2008;
54 Gauthier et al., 2015; Nørum et al., 2010), pollutant tracking with bivalves (Castro et al., 2018), soil
55 toxicity assessments with earthworms (Amorim et al., 2005; Hund-Rinke et al., 2001) or chronic
exposure impacts to heavy metal pollution in fish (Faucher et al., 2008; Svecevičius et al., 1999).

Avoidance is observed as a rapid stress reaction upon exposure to micropollutants caused by
sensory interactions such as olfaction (Könemann et al., 2021; Handy et al., 1994; Tierney et al.,
2010), leading to an instinctive desire to escape the contamination. It can lead to instantaneous
reactions to sublethal doses of different classes of micropollutant (Rihel et al., 2010; Svecevičius et
al., 1999), making it a potential suitable choice for a non-targeted approach to monitoring
intermittent surges in micropollutants. Organism avoidance behaviour is thus a good candidate for
in-vivo water quality assessment that could be adapted to continuous surveillance. Nonetheless, if
avoidance is to be used to detect effluent contamination spikes it is essential that the method is
robust and reproducible, given the complexity and ever-changing nature of WWTP outlets. Results
from previous research on avoidance have only shown success in repeatability under controlled
conditions, with biosentinel pollution tracking shown to be highly sensitive to external stimuli, and
locomotor behaviour in amphipods showing inconsistency in baseline activity across independent
assays (Castro et al., 2018; Nørum et al., 2010; Rothe et al., 2022). The high complexity and
sensitivity associated with locomotor behaviour can be attributed to the numerous biological and
chemical variables that influence movement patterns (Goossens et al., 2020). Minimising these
confounding factors and stabilising baseline activity presents a real challenge in the generalisation of
avoidance measures for real-time biomonitoring in an ever changing WWTP environment.

The objective of this study is to develop a biotest using gammarid hyperactivity avoidance behaviour
for the detection of surges in intermittent micropollutant discharge, suitable for use in WWTP
effluent monitoring. For this, our study introduces the use of the ToxMate technology, a new device
for invertebrate locomotor behavioural videotracking for both in laboratory and on-site application.
The amphipod species *Gammarus fossarum* was selected as the test organism. This sentinel
macroinvertebrate species is widespread in Western Europe's rivers (Wattier et al., 2020) where it
can be found at high densities, and can be easily identified and selected for their biological
characteristics (Chaumot et al., 2015). Continuous movement is not essential for *G. fossarum* as it is
not a filter-feeder, and can go prolonged periods without feeding (Hervant et al., 1997). Therefore it
was deemed appropriate to pursue a strategy where organism conditioning was designed to attain a
minimal activity model, defined whereby average movement is continuously minimal or null during
observation (a state of lethargy). To demonstrate avoidance sensitivity for varied chemicals, both
copper (an inorganic heavy metal) and methomyl (an organic pesticide) were tested. Copper is
estimated to be one of the metals in wastewater that poses the most risk to aquatic environments
(Aemig et al., 2021; Al-Saydeh et al., 2017; Mutzner et al., 2021; Shrivastava et al., 2009). Due to its
high value in industry for uses such as metal finishing, plastics and fungicides, French national
micropollutant campaigns in 2021 showed that almost 98% of the 476 evaluated WWTPs in 2021
detected copper contamination at entry, with almost 50% of treatment plants exceeding allowable
concentration levels, some with average entry concentration over 100µg/L (INERIS, 2021).
Methomyl, a well characterised carbamate historically used as an insecticide that acts as an
acetylcholinesterase (AChE) inhibitor (Barr et al., 2011; Lorenz et al., 2011), was selected for its
relevance as a neurotoxic compound, as neurotoxic insecticides are regularly detected in municipal
wastewater and surface water (Münze et al., 2017; Weston et al., 2013). In addition, studies have
shown amphipod species, such as gammarids, to be particularly sensitive to such chemicals, causing
drift (Beketov et al., 2008; Nørum et al 2010; Švara et al., 2021), a type of avoidance (Araújo et al.,
2020; Johnston et al., 2015).

Our hypotheses for a robust biotest rely firstly on the condition that *G. fossarum* shows an
avoidance reaction in response to micropollutant concentration surge (in this case for the two model
chemicals Cu and methomyl), and secondly that the minimal activity conditions defined ensure
reproducible conditions for avoidance reaction detection. We propose that minimising baseline
activity will both reduce the influence of environmental conditions which could be viewed as
confounding factors, and increase signal resolution for activity increase in the case of contaminant
induced avoidance. To test our hypotheses, this article first presents the methods used to attain
107 minimal activity, and secondly presents results from G. *fossarum* exposure to spikes of the two

108 different micro-pollutants in varying conditions, including long term monitoring at a WWTP effluent.
109 **Materials and Methods**

110 *G. fossarum* collection and conditioning

111 Organisms were collected 3 weeks prior to experiments at a known unpolluted upstream Rhone
112 basin source in the Ain department, France. *G. fossarum* are easily identified at this site, and
113 collected as one of the reference uncontaminated populations for all research at the ecotoxicology
114 INRAE laboratory, Lyon (Alric et al., 2019). This source population was genetically characterized by
115 COI barcoding in previous studies (Cogne et al., 2019). Prior to experimentation, the organisms
116 undergo a 3-week acclimation phase in the regularly controlled drill water-source at the laboratory
117 site. The stabilisation basins are subject to complete darkness and constant temperature (12-13°C),
118 to replicate test conditions and remove the bias from day-night cycle activity variations. This method
119 was developed to induce a state of minimal activity by the time of observation. Organisms are fed
120 regularly over the acclimation to maximise the possible observation time without feeding in
121 individual observation chambers of the ToxMate (> 30 days).

122 **Behavioural measurements in laboratory experiments**

123 Organism tracking is carried out in the ToxMate monitoring device using inbuilt Viewpoint
124 videotracking software. ToxMate, designed for aquatic organism video-tracking, is used here for the
125 individual measurement of invertebrate avoidance during exposure to micropollutants. The
126 apparatus is made up of a compact cubic chamber unit, with three observation panels composed of
127 four columns of four individual chambers allowing for monitoring in continuous water flow
128 conditions (Fig. 1). Observation panels are connected to a water tank by hydraulic pumps, forming a
129 closed 15L water circuit for experiments. The flowrate is 1.4L/min in each of the three panels,
130 meaning organisms are exposed to a flow rate of 0.35L/min in the four-column arrangement, tested
131 to be quasi-laminar across the panel. In the scope of this study, *G. fossarum* was used to measure
132 behavioural response (avoidance) to chemical exposure (spiking). Each panel contains 16 isolated
133 gammarids that are tracked individually by the software (Fig. 1), removing any immobile organisms
(<5% across experiments). Infrared panels and cameras are used to enable total darkness. Output data files were calibrated to give the total displacement of each organism for 20 second time steps. To limit biological variability, homogenously sized males (<10 mm) are taken from pairs of gammarids in amplexus, after the second week of acclimation. The selected males are then left for a third week. This process controls biotic factors and ensures moult in the week prior to exposition (Geffard et al., 2010).

## 140 Experimental deployment on WWTP site

The ToxMate has been adapted for industrial use for WWTP effluent on-line monitoring. The interior of the compact chamber unit remains unchanged. There is an added cooling chamber for thermoregulation as well as a filter system, placed in a water control unit upstream of the ToxMate (Fig.1B). Organism conditioning and acclimation periods remain unchanged from laboratory experiments, and new organisms are put in once a month. Designated water inlets and outlets are added to the system to allow for a continuous bypass of the effluent. On-site monitoring and copper spiking tests for this study were carried out in 2021 and 2022 in the effluent of the Saint-Fons WWTP (in partnership with SAUR R&D) in the Lyon agglomeration. This WWTP serves an equivalent population of 1 million habitants and receives a daily average influx of 200,000 m³/day, with a 9% industrial affluent proportion in chemical oxygen demand.

## 151 Chemical preparation and spiking protocol

Both chemicals (Cu and methomyl) were prepared in a highly demineralised water solution. A range of spike concentrations were tested for both substances ranging from 30-300 µgL⁻¹. All concentrations remained sublethal (<LC50), as estimated via preliminary 48-hour acute mortality testing as well as previous laboratory research (Xuereb et al., 2009). In order to avoid an initial artificially high spike in concentration, which can be disproportionately detrimental to organism health (Berghahn et al., 2012, McDonald et al., 2020), a pre-dilution is carried out with 300 mL of water from the circuit to ensure near instantaneous mixing. The solution is systematically introduced
after 96 hours of ToxMate exposure following results from the development of the minimal activity model (see Section 3.1). To evaluate the effects of exposure concentration, samples were taken and analysed at five even log intervals for both chemicals (t = 1 minute, 5 minutes, 20 minutes, 1 hour, 5 hours and 20 hours) to ensure minimal degradation, quick onset of stress (shown to occur in less than one minute) and minimal overshoot in initial exposure (Supplementary Materials Appendix A).

## Statistical analyses

Python and R were used for all data treatment and signal processing of the results. Minimal activity was defined by a 12-hour period whereby the median locomotor activity remained stationary according to an augmented Dickey Fuller test. From the movement distribution ($x_i$) of N=16 gammarids at a given time t corresponding to travelled distance for each individual during 20s, the avoidance response $A(x_i)$ is calculated as the square of quantile $Q_p$ as indicated in Eq. 1, for a quantile probability $p = 0.05$.

$$A(x_i) = Q_{0.05}^2(x_{i=1,2...N}), \quad Q_p(x_i) = (1 - \gamma)x_{i-1} + \gamma x_i, \quad \begin{array}{l} j = \lfloor pN + 1 \rfloor \\ \gamma = pN - \lfloor pN \rfloor \end{array} \quad (\text{Eq. 1})$$

For dose-response curves, evaluation of total avoidance response during a spiking experiment was calculated by taking the integral over time of the avoidance measures. Up to 10 hours post-spike were systematically considered, after ensuring minimal chemical decomposition in the exposure system (Supplementary Materials Appendix A).
176 3. Results and Discussion

177 3.1 Minimal activity model

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179 Control repetitions for *G. fossarum* videotracking showed high initial locomotor activity, which 180 subsequently decreased significantly before reaching a stable minimal activity state (Fig.2). The time 181 required to obtain minimal activity was estimated via both Dickey-Fuller stationarity tests and an 182 intersection estimate between regression fits for the initial activity (0-48 hours) and the final activity 183 (last 24 hours). Both methods showed that a stable baseline activity was consistently reached in 184 around 72 hours across repetitions (Supplementary materials, Appendix B). Given the average time 185 taken to reach minimal activity, chemicals were introduced during testing at least 96 hours after the 186 placement of gammarids in the ToxMate observation chambers.

187 Day-night activity cycles along with higher activity variance were notable during the first 48 hours of 188 observation, however both phenomena dissipated by the time minimal activity was reached (Fig. 2), 189 implying that the stabilisation procedure is effective in creating a reproducible environment 190 providing chemical exposure occurs during minimal activity. This result shows that *G. fossarum* 191 minimal activity can be obtained via controlled acclimation conditions (which was then also 192 observed in on-site WWTP effluent conditions in section 3.3). Similar research on behavioural 193 changes in macroinvertebrates by Nørum et al. (2010) successfully demonstrated rapid surges in 194 behavioural activity in response to comparable chemical exposure, but observed that pre-exposure 195 mean activity presented high variability, even between control and test groups. The minimal activity 196 model proposed here is successful in overcoming this obstacle, achieving the stable baseline across 197 testing necessary for robustness and comparison of assays. It does however impose the use of 198 hyperactivity avoidance as a stress indicator, as minimal movement means inhibition cannot be 199 observed.

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201 3.2 Micropollutant Sensitivity

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203 Initially, avoidance behaviour was assessed for Cu spikes, showing immediate increase in activity as
204 well as a significant decrease in activity variance upon chemical exposure (Fig. 3). The immediate
205 changes in response to the presence of the Cu solution at 100 µgL⁻¹ indicate global activity increase
206 and stress avoidance (Gauthier et al., 2016). The measure of a lower quantile (Q₀.₀₅) from the
207 activity distribution was used to channel avoidance (Eq.1), as it remained quasi-zero in control
208 conditions, and only spiked upon chemical exposure, giving a sustained response (Fig. 3). Avoidance
209 is thus defined in the minimal activity model as uniformly high locomotor activity, an extreme event
210 shown not to occur in control conditions which are characterised by the stable null baseline. Even
211 the short upsurge in avoidance after 18 hours was associated with a sustained avoidance reaction, as
212 despite a small spike in the high end of the control distribution, the avoidance signal remains
213 undisturbed and the onset is decorrelated by over an hour. Furthermore, median activity in the Cu
214 sample is unchanged at the time of the avoidance spike. The decreased overall activity obtained
215 thanks to the 96h acclimation period before spiking does not seem to compensate on organism
216 sensitivity to micropollutant spikes, as there is a quick clear avoidance response. The advantage of
217 using an avoidance metrics based on a lower quantile of individual locomotor activity distribution
218 (here Q₀.₀₅) to increase test repeatability and channel sensitivity to micropollutants is clear when
219 compared to the median activity curve for a Cu spike (Fig. 3), where the median values quickly
220 returned to control levels and total variance gives no repeatability in control conditions. Such short
221 response time on the hourly scale is novel in non-lethal bioassay testing and could provide valuable
222 real-time information for micropollutant detection and alerting.
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224 Validating avoidance signal sensitivity to copper and methomyl, results from four independent
225 experimental repetitions showed repeatability in the onset of avoidance patterns for both model
226 compounds (100 µgL⁻¹ and 125 µgL⁻¹ for Cu and methomyl respectively - Fig. 4A). Despite differences
227 in amplitude and total avoidance time, all repetitions showed avoidance for both chemicals.

228 The avoidance triggered by Cu was characterised by an instantaneous reaction and high initial
229 gradient followed by a gradual decline in avoidance, perhaps linked to the experimental setup,
230 whereby organisms cannot escape the contaminated area. Contrary to the Cu response, the
231 methomyl avoidance reactions are more gradual, with the onset of avoidance only peaking beyond
232 around 3 hours of exposure. In general, there was strong resemblance between reactions for each
233 chemical in onset time and initial response gradient. We confirmed via timely chemical analysis that
234 the time required to reach the desired exposure concentration of organisms in the ToxMate system
235 occurs within the first minute post-spiking for both chemicals (Supplementary Materials Appendix
236 A). Previous studies have shown that for fish, immediate response can be explained by olfaction in
237 the case of both metals and insecticides (Könemann et al., 2021; Tierney et al., 2010) implying that
238 this may be the case for Cu, whilst it can be assumed that the more gradual onset methomyl
239 reaction implies no immediate olfactive response in the case of gammarids. The avoidance pathway
240 is likely something different, such as a reaction to the AChE inhibition known to occur at such
241 concentrations for G. fossarum, possibly not appearing toxic before the eventual metabolization of
242 the chemical (Xuereb et al., 2009). In both cases, the biotest offers real-time information on the
243 presence of the micropollutants in the system for a response time ranging from the minute to a few
244 hours, providing novel reaction time amongst comparable bio-assays. The overall difference in
245 response between the two chemicals is particularly interesting, as it suggests that the qualitative
246 differences in the behaviour are caused by the nature of the contamination, and the test may even
247 have potential for chemical class identification. Based on further testing using the defined protocol
248 for screening a large spectrum of chemical compounds, ongoing research into behavioural profiling
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aims to uncover such links between chemical classes and their triggered response, following an approach similar to that previously conducted in biomedical research (Rihel et al., 2010).

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When a range of sublethal concentrations were tested there was a positively correlated dose-response curve for both Cu and methomyl (Fig. 4B), using the integral of the avoidance signal over a 6-hour period, which ensures a period of stable exposure concentration (as supported by sampling analysis in Supplementary Materials Tab. A.1). There is a significant difference in between avoidance at all tested concentrations when compared to control tests. The higher total avoidance measured for Cu samples (Fig. 4B) are a consequence of the sharp initial gradient and quick onset, followed by sustained avoidance. Despite some variance, which can be explained by natural variation in residual movement activity, the correlation demonstrates the biotest potential for quantifying organism exposure to micropollutants, even at low concentrations (30-50 µgL⁻¹) relevant to WWTPs (INERIS, 2021). It also opens the possibility of further study for use within chemical risk assessment procedures, with a sensitive and rapid non-lethal screening of biological impacts (Alcívar et al., 2021). Whilst there is a maintained positive correlation for Cu, there is a plateau in sensitivity for methomyl beyond 67 µgL⁻¹. Because this plateau is an order of magnitude below the maximum recorded avoidance signal for copper, this saturation phenomenon supports the hypothesis that for the neurotoxic insecticide methomyl, beyond a certain concentration, the nerve transmission function is saturated and movement is impeded by AChE inhibition (Xuereb et al., 2009). One could alternatively hypothesise that this is due to toxicokinetics-toxicodynamics saturation, reinforcing the hypothesis that in order to have an impact on avoidance there must be uptake and/or metabolisation of the compound by *G. fossarum*, thus explaining the latency and saturation.

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## 3.3 Potential for real-time micropollutant detection in WWTP effluents

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The last part of the study questioned the possible transposition of the laboratory results to real field conditions. Hence, Fig. 5 shows the *G. fossarum* reaction to copper spiking at 100 µgL⁻¹ in two

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additional contrasting water types carried out during one laboratory experiment and one on-site WWTP deployment of the ToxMate apparatus. We tested avoidance response to copper spiking in water sampled from the natural habitat of test organisms, and in the wastewater effluent at the Saint-Fons WWTP. The industrial ToxMate unit installed at the Saint-Fons WWTP allowed for the analysis and characterisation of the effluent over two years of data collection, with the copper spike shown zoomed on a several-week extract of the monitoring period (Fig. 5B-C). Testing in these two varied water matrices and under laboratory *versus* on-site deployment conditions showed reproducibility in pre-exposure minimal activity, as well as the onset of avoidance induced by copper spiking (Fig. 5). Furthermore, both reactions are similar in response duration and latency when compared with those observed for copper repetitions from the first tests performed in laboratory controlled water (Fig. 3). When considering varied water matrices, despite higher mean activity in the wastewater, the baseline remains negligible for sustained periods, and the Cu spike was equally prominent, showing the biotest to be robust, and suitable for monitoring in a complex flow such as that of a WWTP effluent. Furthermore, the avoidance signal observed returns to the null baseline sometime after Cu exposure, implying a second episode could potentially be captured in case of repeated events of contamination.

A second data extract from the surveillance study carried out in the Saint-Fons WWTP, corresponding to a summer period in 2021, is presented in Fig. 6. As no controlled spikes were carried out during this period, the data reveals naturally occurring transitory avoidance events induced solely by the on-line exposure to the WWTP effluent (delineated within Fig. 6). In addition, similarly to the copper spiking effluent experiment (Fig. 5B), the activity signal consistently returned to a minimal baseline after the transient avoidance events in Fig. 6, and organisms remained responsive to successive events showing the potential for the biomonitoring strategy over long periods without any need for maintenance or intervention, giving unprecedented insight into the effluent nature. To deduce that these avoidance activity events are possibly linked to transient
unusual discharge in micropollutants in the WWTP effluent, complementary tests should be further undertaken to assess avoidance signal sensitivity to changes in common physio-chemical variations typical to effluents such as temperature, conductivity, nitrate levels and phosphates. We have initiated in laboratory investigation to successfully reject the hypothesis that avoidance occurs after rapid variations in temperature or conductivity (also associated to nitrate, phosphate and chloride changes). Whilst these parameters rarely exhibit such sharp variations in WWTP effluents (especially given the ToxMate thermoregulation), these factors are known to affect locomotor activity, and have been shown to trigger avoidance (Lagerspetz et al., 2006; Nasyrova et al., 2019). Results from the additional experiments with full details of testing conditions and behavioural monitoring records are provided in Supplementary Materials Appendix C. It was concluded from these first investigations that the minimal activity model design ensures diminished sensitivity of the avoidance signal to physico-chemical variations. Hence, these results strongly suggest that while avoidance events in Fig. 6 are almost certainly not triggered by the model micropollutants (Cu and methomyl), transient avoidance events captured during monitoring periods in 2021 in Saint-Fons WWTP can be explained by transitory increases in micropollutant discharge in the effluent. This agrees with the description of a highly transient nature associated with micropollutant effluent discharge, leading to temporary surges in concentration (Musolff et al., 2009). Our results substantiate the identified need for complementary real-time monitoring approaches, as this would provide novel insight and characterisation of toxic contamination transfer to the aquatic environment (Bownik et al., 2021). Our study supports that behaviour change monitoring in sentinel organisms could be added to available biological tools already proposed for water quality assessment via effect-based surveillance. These methods are of particular interest in the context of effluents, as they appear as non-specific to known chemicals and focus on impact evaluation regardless of complexity, going beyond the handful of chemicals required in current effluent monitoring directives (Busch et al., 2016; Wernerssen et al., 2015). Hence, by helping to capture temporal dynamics in effluent contamination, the development of robust real-time biomonitoring such as the one proposed in this
327 study could further help improve waste water management, allowing for short term adjustments to

328 dedicated treatment and potentially offering indications to re-use suitability and tertiary treatment

329 necessity (Angelakis et al., 2015).

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## 4. Conclusions

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357 Declaration of Competing Interest

358 INRAE and the Viewpoint Company share in co-ownership one patent related to the ToxMate
359 technology (patent #WO2018178585) and one patent related to the use of “minimal activity”
360 approach in behavioural tracking in ecotoxicology (patent #WO2019016273). The authors AD, HQ,
361 DN, OG & AC are listed as co-inventors for these two patents. The Viewpoint Company markets a
362 biomonitoring service based on the ToxMate technology.

363

364 Acknowledgments

365 This research received financial support from Région Auvergne-Rhône-Alpes (projet Pack Ambition
366 Recherche 2020 - ToxPrints), ANRT (Cifre PhD Grant of GR), institut Carnot Eau-Environnement,
367 Agence de l'eau Rhône-Méditerranée-Corse, and European Union (H2020-EIC-SMEInst-2018-2020
368 Grant#881495). The authors thank SAUR R&D, the Greater Lyon Authority and Ecostation operating
369 its WWTP of Saint-Fons for the access to the WWTP monitored during the study. They acknowledge
370 B. Giroud (UMR5280 CNRS ISA-TRACES Villeurbanne-France), L. Dherret and A. Dabrin (INRAE
371 RiverLy-LAMA Villeurbanne-France) for chemical analyses.

372

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**Figure 1** – ToxMate Layout. Laboratory apparatus with view of 1 of 3 observation panels, each one

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composed of 16 individual observation chambers (A) and apparatus deployed on WWTP site (B).

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<figure><img src="image_3.png" /></figure>

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**Figure 2** – Activity distribution for 16 *G. fossarum* (individual travelled distance per 20s), tracked over five days in the ToxMate in control conditions. Linear descent to stable activity is visualised with a linear descent regression with a R coefficient =0.72, here shown to reach minimal activity in 71 hours, as well as augmented Dickey-Fuller Stationarity showing minimal activity is reached after 62 hours. The present-day night cycles (top left) are visualised for the first 48 hours with error residuals from the linear descent regression use in minimal activity estimation.

<figure><img src="image_4.png" /></figure>
**Figure 3** – Activity distribution for 16 gammarids (individual travelled distance per 20s) (blue) and avoidance signal (red) during pre- and post-spiking periods (delimited by the vertical dashed line) for copper exposure (100 µg.L⁻¹) (top panel) and a blank exposure in the control (bottom panel). Spiking was carried out 96h after the placement of gammarids in the Toxmate.

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

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**Figure 4** – Avoidance response to copper and methomyl spiking. (A) Detailed monitoring of avoidance signal during four independent spiking experiments for 100 µgL⁻¹ copper (left) and 125 µgL⁻¹ methomyl (right) (the vertical red line corresponds to the spiking time). (B) Dose-response pattern of total avoidance measures (from the integral of avoidance signal over six hours) for spiking experiments with water control and three sublethal doses of copper and methomyl (the number of independent experiment replications is indicated on the top of each box).

<figure><img src="image_6.png" /></figure>
**Figure 5** – Avoidance response to copper spikes (100 µgL⁻¹) in two varied water matrices (the vertical red solid line corresponds to the spiking time): (A) Laboratory experiment with water sampled from a freshwater source, (B-C) on site experiment during a two-week on-line monitoring of the effluent from the Saint Fons WWTP (B zoomed on the Cu spike period).

<figure><img src="image_7.png" /></figure>
<figure><img src="image_8.png" /><figcaption>**Figure 6** – Avoidance signal recorded during the on-line monitoring of the effluent from the Saint Fons WWTP over a two-week summer period with no controlled spikes added. Pink frames highlight six major avoidance events.</figcaption></figure>
Sensitivity to micropollutants

<figure><img src="image_9.png" /></figure>

Locomotor activity

<figure><img src="image_10.png" /></figure>

Minimal sensitivity

Contaminated Effluents

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

Biomonitoring in WWTPs

<figure><img src="image_15.png" /></figure>

ToxMate

<figure><img src="image_16.png" /></figure>

Real-time alerting