# Impact of peri-urban landscape on the organic and mineral contamination of pond waters and related risk assessment

Sylvie Nelieu, Isabelle Lamy, Sara Karolak, Ghislaine Delarue, Olivier Crouzet, Claire Barraud, Maya Bimbot, Fatima Allaoui, Christophe Hanot, Arnaud Delorme, et al.

► To cite this version:

Sylvie Nelieu, Isabelle Lamy, Sara Karolak, Ghislaine Delarue, Olivier Crouzet, et al. Impact of peri-urban landscape on the organic and mineral contamination of pond waters and related risk assessment. Environmental Science and Pollution Research, 2021, 28 (42), pp.59256-59267. 10.1007/s11356-020-10355-5. hal-02937913

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# Impact of peri-urban landscape on the organic and mineral contamination of pond waters and related risk assessment

Sylvie Nélieu<sup>a*</sup>, Isabelle Lamy<sup>b</sup>, Sara Karolak<sup>c</sup>, Ghislaine Delarue<sup>a</sup>, Olivier Crouzet<sup>b1</sup>, Claire Barraud<sup>c</sup>, Maya Bimbot<sup>c</sup>, Fatima Allaoui<sup>c</sup>, Christophe Hanot<sup>c</sup>, Arnaud Delorme<sup>d</sup>, Yves Lévi<sup>c</sup>, Florence D. Hulot<sup>c</sup>, Emmanuelle Baudry<sup>c</sup>

a: Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, 78850, Thiverval-Grignon, France

b: Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, 78026, Versailles, France

c: Université Paris-Saclay, CNRS, AgroParisTech, UMR ESE, 91405, Orsay, France

d: CEA, Centre de Saclay, 91191, Gif-sur-Yvette, France

* Corresponding author: Sylvie Nélieu (sylvie.nelieu@inrae.fr)

**Key-words:** Peri-urban; Ponds; Water quality; Endocrine disruptor; Pesticides; Trace metals; Pharmaceuticals; Risk Quotient

## Abstract

Ponds are important for their ecological value and for the ecosystem services they provide to human societies, but they are strongly affected by human activities. Peri-urban development, currently one of the most pervasive processes of land use change in Europe, exposes ponds to both urban and agricultural contaminants, causing a potential combination of adverse effects. This study, focused on 12 ponds located in a peri-urban area, has two main objectives: (1) to link the physico-chemical characteristics of the waters and the nature of their contaminants, either organic or mineral, with the human activities around ponds, and (2) to estimate the environmental risk caused by these contaminants. The ponds were sampled during two consecutive years in both spring and in autumn. Although the ponds were distributed over a limited geographical area, their contamination profiles were different and more correlated with the agricultural than the urban land use. In terms of aptitude for biology, half of the ponds were classified in degraded states due to their physico-chemical parameters, but without correlation with the endocrine disrupting activities and the levels of organic pollutants as indicators. The main quantified organic pollutants, however, were pesticides with sufficiently high levels in certain cases to induce an environmental risk exceeding the classical thresholds of risk quotient.

<footnote n="1">¹ Present address: Office Français de la Biodiversité (OFB), DRAS – UPFSEO, Saint-Benoît, 78610 Auffargis, France</footnote>
**Introduction**

Freshwater ecosystems are of primarily interest for biodiversity and ecosystem services, even in agroecosystems. Although they cover only 2.8 to 3% of the Earth's surface (Downing et al. 2006), they are home to almost 6% of all described species, including 40% of global fish diversity and one quarter of global vertebrate diversity (Dudgeon et al. 2006). Among these freshwater ecosystems, ponds are small permanent or temporary water bodies of natural or man-made origin. In the past, ponds were often neglected by both ecologists and environmental managers as an individual pond is, by definition, small in size and may therefore appear to be of little importance. Yet, taken as a whole, ponds represent an exceptional freshwater habitat and resource. Indeed, although ponds are the smallest water bodies, they are also by far the most numerous since they represent approximately 90% of the estimated 304 millions of world water bodies (Downing et al. 2006). These numbers, which do not include reservoirs and impoundments, suggest that ecological processes in these small ecosystems may be of global significance (Downing et al. 2006).

Due to their high number and the variability of their physico-chemical conditions, ponds constitute a mosaic of varied habitats in a landscape and are home to a vast and diversified fauna and flora (Davies et al. 2008). At the regional level, they are also biodiversity hotspots, collectively supporting more unique species and more scarce species than other types of freshwater bodies (Williams et al. 2003; Davies et al. 2008). Ponds also provide social and economic benefits for human well-being, called ecosystem services (Landuyt et al. 2014; Millennium Ecosystem Assessment 2005). They store rainwater, helping to mitigate flooding and providing water for irrigation and for animals. They also contribute to the removal or storage of nutrients but also organic and metallic contaminants from surface waters (Wu et al. 1996). Thanks to their very significant contribution to the sequestration of organic carbon, they contribute to mitigating the greenhouse gas emissions responsible for climate change (Mendonça et al. 2017). Finally, they participate in cultural ecosystem services (Ghermandi and Fichtman 2015), contributing to the improvement of the physical and mental well-being of the inhabitants, as well as to their awareness of biodiversity and nature conservation. However, despite their dominance as aquatic ecosystems and usefulness for societies, they have been less studied than large and deep lakes (Davies et al. 2008; Moss 2010). Because of their small size, ponds are particularly prone to destruction and those that remain are under increasing pressure due to land use change (Wood et al. 2003). For example, during the 20th century, freshwater ecosystem losses reached 40-90% in a number of countries in north-western Europe (Maltby 2009). These changes consist mainly of the consumption of agricultural and natural areas by urbanization, in parallel with agricultural intensification. Although urban development has slowed considerably since the late 1960s, peri-urbanization is currently one of the most pervasive processes of land use change in Europe, with strong impacts on the environment (Nilsson et al. 2013). Because they are coupled to a watershed, peri-urban ponds and lakes present a complex set of environments and are integrators of the state of the whole landscape. In particular, they are confronted with two different types of contamination: pollutants originating from agricultural activities such as excess of nutriments and pesticides and pollutants linked to urban activities such as trace elements and polycyclic aromatic hydrocarbons (PAH) (Chrétien et al. 2016). Peri-urban landscapes could thus constitute a particularly challenging environment for ponds biodiversity, which need to be documented.

This study focuses on ponds located in a peri-urban area in Ile-de-France region (France) and exposed to both urban and agricultural pollutants. The objectives were twofold: (1) to link the physico-chemical characteristics of the water and the nature of the contaminants present in the water with the activities around ponds, whether urban or agricultural, and (2) to estimate the environmental risk caused by these contaminants. For this purpose, we measured the water quality and contaminants present in the water of 12 ponds with various exposure to urban and agricultural pollutants, including the main trace elements and some pesticides, pharmaceuticals and polycyclic aromatic hydrocarbons (PAH). Bio-assays have also been performed to assess endocrine disrupting effects. The levels of quantified organic pollutants were compared with their environmental toxicity thresholds in order to assess the environmental risk linked to the contamination of the ponds and their environment.

**Material and methods**

**Study area and sampling**

This study was conducted in an 80 km² peri-urban territory located south-west of Paris, France, in the area of the plateau around the town of Saclay (N: 48° 43' 59.99" E: 2° 10' 0.01). This area was selected because it presents a relatively high density of ponds, due to the presence of a shallow layer of low permeability clay, and because it
recently underwent a major urban development, while retaining an agricultural vocation. One criterion of choice was also the increasing interest towards the conservation of biodiversity among the stakeholders of the territory. Within this area, we chose 12 ponds with various exposures to agricultural and urban activities (Figure 1). To estimate exposure to agricultural and urban pollutants, we calculated the proportion of agricultural and urbanized surface around each pond in a 100 m radius buffer. Land use classification was derived from the map of mainland France land cover that was produced by the Theia land data services using Sentinel 2 and Landsat 8 data (Theia 2017). To estimate the proportion of agricultural land, we added two Theia categories: winter crop and summer crop. To estimate the proportion of urbanize land, we added three Theia categories: dense built-up areas, diffuse built-up areas and industrial and commercial areas. The remaining areas consist of forest and grassland. The selected ponds had areas ranging from 64 to 5,108 m² (median [Q1 - Q3]: 566 [224 – 881]) (Supplementary table S1). Water samples were collected twice a year in spring and autumn in 2016 and 2017 at two opposite points of each pond. For laboratory analysis, the water samples were taken approximately one meter from the edge of the ponds using a stainless steel beaker attached to an extendable handle.

<figure><img src="image_2.png" /><figcaption>**Fig. 1** Proportion of agricultural, urbanized and grasslands and forest land use in a 100 m radius buffer around the 12 studied ponds</figcaption></figure>

## Determination of the global quality parameters

Three physico-chemical parameters (pH, conductivity and dissolved oxygen DO) were measured on site using a portable Multimeter pHenomenal® MU 6100 H equipped with a 3IN1 GEL 1M BNC pH electrode, a CO 11 conductivity sensor and a OXY 11 oxygen sensor (VWR, Fontenay sous Bois, France). Turbidity was also measured in situ using a 2100P Portable Turbidimeter (Hach-Lange, Lognes, France). Water was collected on a stainless steel bucket to measure temperature, pH, conductivity and turbidity but dissolved oxygen was measured directly in the water using a pole so that the electrode was located away from the bank.

Others physico-chemical parameters were determined by the Laboratory of Radioanalysis and Environmental Chemistry at CEA-Saclay, (Gif-sur-Yvette, France, COFRAC accredited laboratory) using ISO/NF normative procedures (see supplementary material): Chemical Oxygen Demand (COD), suspended solids (SS), total nitrogen (N) from addition of Kjeldahl nitrogen (Kjeldahl method), nitrates (NO₃⁻) and nitrites (NO₂⁻) and total phosphorus (P). Anions and cations were quantified on 0.45 µm filtered water by normative ionic chromatography methods. The dissolved organic carbon (DOC) in mgC/L was determined on 0.45 µm filtered water samples using a Shimazu TC apparatus in the “Non Purgable Organic Carbon” (NPOC) mode in the Ecosys laboratory, Versailles.

## Endocrine disruptor activities

Endocrine-disrupting effects were assessed using bioluminescent reporter gene assays for quantifying hormonal-dependent luciferase expression in recombinant cells, MELN cells (kindly provided by Dr P. Balaguer, INSERM,
U439, Montpellier, France, Balaguer et al., 1999) and AIZ-AR cells (kindly provided by Dr I. Bartonkova, Bartonkova et al. 2015) for estrogenic and androgenic-disrupting potential respectively. These assays were carried out on methanolic extracts obtained from water samples by solid-phase extraction (SPE) on 500 mg Oasis HLB cartridges (Waters, Guyancourt, France).

Bioassays were carried out on 96-well cell-culture plates according to laboratory protocol detailed in supplementary file. Dose-response curves of natural hormone (β-estradiol E2 or dehydrotestosterone DHT) were performed for the quantitative determination of endocrine activity expressed in ng/L as E2 equivalent quantity (EEQ) or DHT equivalent quantity (DHTQ). The MELN assays displayed EC₅₀ concentration for E2 equal to 6 ± 0.6 pM, a limit of detection (LOD) of 0.01 ng/L EEQ (three times RLU value obtained for blank) and a limit of quantification (LOQ) of 0.03 ng/L EEQ. The AIZ-AR assays displayed EC₅₀ concentration for DHT equal to 650 pM, a LOD of 0.8 ± 0.5 ng/L DHTQ and a LOQ of 2.4 ± 1.2 ng/L DHTQ. For the fourth campaign, T47D-KBluc cells (ATCC, LGC standard, Molsheim, France) were used for the evaluation of the estrogenic disrupting effect because the MELN cells did not respond and were no longer available. Consequently, this last campaign will not be compared with the first three, given the variability of response of cellular models.

## Selected trace elements

Determination of the trace metal contents on 0.45 µm filtered waters were carried out by the Laboratory of Radioanalysis and Environmental Chemistry at CEA-Saclay, France, accredited by the French accreditation body, Cofrac (accreditation number 1-0795). This accreditation according to ISO / IEC 17025: 2017 covers "water quality" analyses, including metal assays according to the standard NF ISO 17294-2 using ICP-MS methods. The samples were kept in a cooler and brought to the Saclay laboratory as soon as the samplings were finished. The analyzed elements were: Al, As, B, Be, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Sn, U and Zn. In all the water samples, Be, Cr, Hg and Ni remained below the LOQ (1, 1.7, 0.031 and 8.3 µg/L, respectively). The other LOQ were established in µg/L at 0.19 (Al); 1.6 (As); 0.3 (B); 0.66 (Cd); 1.5 (Cu), 16 (Fe); 19 (Mn), 0.65 (Pb); 0.21 (Sn); 0.007 (U) and 3 (Zn).

## Organic contaminants

Polycyclic aromatic hydrocarbons (PAH) were analyzed in the water from the two first campaigns by a validated method detailed in supplementary material, which was based on stir bar sorptive extraction and high performance liquid chromatography-fluorescence detection (Bourdat-Deschamps et al. 2007). Given the low level detected, the analyses were not continued in the second year (See Results and discussion below).

The pesticides selected to be researched in this study represented a diversity of activities (herbicides, fungicides and insecticides) and uses (cereals, maize, rapeseed, sunflower, orchard, vegetable crops) which aimed to cover the crops in the Saclay area. In addition, some banned pesticides and their main metabolites were also included in the list, as these compounds are known to be persistent and were extensively used in the past. Five pharmaceuticals, compatible with the multi-residue analysis and used for humans or animals (farming or domestic pets) were also sought. Analyses of pesticides and pharmaceuticals were performed, according to laboratory protocol detailed in supplementary file, on an ultra-high performance liquid chromatograph (Acquity UPLC, Waters) coupled through an electrospray interface to a tandem mass spectrometer (TQD, Waters). The injections of a 800-µL sample volume were performed by online SPE using an Oasis HLB cartridge (2.1 mm × 20 mm, 25 µm particle size) from Waters. The chromatographic separation was then carried out on an Acquity BEH C18 column (2.1 mm × 100 mm, 1.7 µm particle size, Waters) at 30°C. The detection was performed under multiple-reaction monitoring mode, under positive or negative ionization mode optimized individually for each compound (table S2). The external calibration was performed with standard mixtures also analyzed by online-SPE-UPLC-MS/MS. The matrix effects were evaluated spiking each sample at 0.1 µg/L and the concentrations were then corrected considering the matrix effects. The LOD and LOQ were globally in the ng/L range (table S3).

## Multivariate statistical analyses

All statistical analyses were performed using RStudio version 1.1.456. Multivariate analysis of the pond water quality data set was performed through principal component analysis (PCA). Briefly, PCA transforms an original data set into new, uncorrelated variables called axes so that the variation in the data can be accounted for as concisely as possible. Here we used PCA to obtain information on the most important parameters, which describe the whole data set and to summarize the statistical correlation among the water quality variables.
## Ecotoxicological risk assessment

The ecotoxicological risks attributable to the pesticides and pharmaceuticals quantified in the samples were evaluated on the basis of the risk quotient (RQ), which indicates the probability of harmful effects of a single pollutant or a mixture. For each pollutant, a risk quotient was calculated by the ratio between the measured concentrations (MEC) and an ecotoxicity threshold. The risk quotients were then summed up for all the analyzed pesticides and pharmaceuticals, to obtain RQsum. The ecotoxicity threshold was either the predicted no effect concentration (PNEC) of the most sensitive trophic level (von der Ohe et al. 2011; Gustavsson et al. 2017), or an ecotoxicity data for one of the trophic levels. The use of PNEC provides a measure of risk across trophic levels, and is feasible even when the data set is incomplete. But this approach is largely discussed because it is based on the addition of effects, independently of the mode of action of each pollutant (Backhaus and Faust, 2012). For this reason, we also considered specific RQs at primary producer and consumer trophic levels (Gustavsson et al. 2017). When the concentrations were between LOD and LOQ, a value corresponding to half the LOQ was used to estimate the MEC (Von der Ohe et al. 2011). To add a safety margin, according to the recommendations of European guidelines (ECB 2003; EMEA 2006; ECHA 2008), the PNEC values were obtained by dividing by an AF assessment factor of 10 to 1000 the value of the ecotoxicity data (determined from chronic or acute tests) found for aquatic organisms in the pesticide properties database (PPDB 2020) and the veterinary substance database (VSDB 2020) and supplemented by the literature (table S4). In the case of trophic-level specific RQs, (i) the limit data (i.e. EC50 given as "greater than" values) have been included if no other data were available and (ii) an EC50 of 10,000,000 µg/L was taken into account when no data was available. In both cases, these choices led to a very low MEC/EC50 ratio and therefore did not change the interpretation of the data.

## Results and discussion

### Temporal homogeneity between samplings

Waters from 12 peri-urban ponds were examined during spring and autumn in 2016 and 2017 for physico-chemical parameters, trace metals, organic contaminants (PAH, pesticides and pharmaceuticals) and endocrine disrupting effects. Four times, the decrease in the water level led us to sample from one point instead of two, resulting in a total of 92 samples collected instead of the 96 initially planned.

We used Principal Component Analysis (PCA) to summarize the variations in the physico-chemical parameters and in the levels of contaminants (Figure S1). As shown on figure S1a and S1b, extremely low global variation was observed for physico-chemical parameters, taking into account the year or the sampling season. Somewhat different trends are observed for organic contaminants and endocrine disrupting effects, with slightly higher variability in spring than in autumn (figure S1d) and for trace metals with slightly higher variability in autumn than in spring (figure S1f). These results suggest that the physico-chemical parameters and the concentrations of contaminants in pond waters show a limited seasonality among the studied ponds. In the rest of this article, we therefore present the overall results, without separating them according to the time periods.

## Physico-chemical parameters and trace metals: environmental water quality and relationship with land use

### Variation of the physico-chemical parameter values and trace metal concentrations

As shown in table S5, in general mean pH values remained close to 7.5 in the 6.5-8.2 range, except punctual basic pH values for pond B and C during the third campaign (pH 9.2; outside temperature 27°C), and for pond L during the fourth campaign (pH 9.7). Dissolved oxygen values were lower than 3 mg/L with oxygen saturation under 30% for ponds A, D, F, H and I and varied between 0.3 mg/L and 21 mg/L with oxygen saturation between 20% and 80% for the other ponds. An oxygen saturation greater than 100% was observed punctually for ponds B, E, G and L, perhaps due to the proliferation of photosynthetic organisms. The organic loads estimated from the COD were mainly low except for ponds B, E and I where COD values greater than 60 mg/L were observed continuously (pond I) or punctually (ponds B and E). Dissolved organic carbon (DOC) remained rather constant and mainly lower than 10 mg C/L among the four campaigns, with highest values in pond I and lowest in H. The pond B had higher turbidity values (> 200 NTU) than others. However, this parameter varied seasonally with higher values during the warm season, probably due to evaporation of water. Similarly, suspended matter values remained low (below 50 mg/L) for ponds C, H, J and K, and varied widely for other ponds from 10 mg/L to 100 mg/L even
punctually higher, 140 mg/L (pond A campaign 2) and 820 mg/L (pond B campaigns 2 and 3), when the water
level was low. One third of the ponds (A, B, E, I) had occasionally high values of total phosphorus (>1 mg/L)
associated with high values of total nitrogen (until > 10 mg/L) for ponds A and I. However, the values of these
parameters tended to vary from one campaign to another. The observed concentrations of anions and cations
remain low in accordance with satisfactory biological development conditions. Ponds G and J had high
concentrations of sulfates (SO₄²⁻) and calcium (Ca²⁺) relatively to the others. The only high values to note are
phosphates concentrations over 2 mg/L for ponds A and E and ammonium (NH₄⁺) concentrations punctually over
5 mg/L for ponds A and B. Finally, the dissolved metal contents observed are generally low. The main quantified
major or trace elements are iron (min = 300 to max = 13000 µg/L), manganese (430 to 985 µg/L), aluminum
(<LOQ to 150 µg/L), arsenic (2 to 8.4 µg/L), boron (31 to 120 µg/L) and at lower concentration copper (1.3 to 16
µg/L), zinc (1.5 to 14 µg/L) and Pb (<LOQ to 2.2 µg/L) and U (< LOQ to 1.5 µg/L). A high concentration of
mercury has been observed just one time in pond L for the third campaign (720 µg/L). Finally, when taken the
median of the element contents for each campaign, and by comparison with known values for quality of fresh
waters in France (Guide REEE-ESC 2019), only the median for copper contents (around 2 µg/L) exceeds for two
campaigns twice the threshold value of 1µgCu/L. The metallic traces therefore do not seem to have a significant
impact on the environmental quality of the water in the ponds studied.

<figure><img src="image_3.png" /><figcaption>**Fig. 2** Principal component analysis of the physico-chemical properties of the water of 12 peri-urban ponds. In black arrows, physico-chemical properties of the water, active variables used to compute the PCA axes. In red arrows, environmental supplementary variables, used to help interpret the PCA axes. Blue and red letters respectively indicate ponds with and without agricultural land use within a 100 m radius buffer around the ponds. PC1 explains 28.4% of variation, which together with PC2 explains 44.5% of the variation</figcaption></figure>

Clustering of ponds according to physico-chemical parameters and relationship with land use

PCA was used to summarize the statistical correlation among the physico-chemical parameters. Given their very
low concentrations, the metallic traces were not included in this analysis. The PCA of physico-chemical parameters
explained 28.4% and 16.1% of the variance on the first and second axes respectively (Figure 2). Some ponds
present almost identical values at each measurement (eg, K, I, H), while others present very variable values
according to the campaigns (eg, A, B, G). Conductivity and major ions (Ca²⁺, Na⁺, Mg⁺, SO₄²⁻, Cl⁻) clustered together and are positively correlated with the first axis. This cluster is associated to F and J ponds and, to a lesser extent, G, D and H ponds. The PCA reveals a second cluster of variables typical of eutrophied water (high COD, turbidity, total phosphorus, total nitrogen, phosphate, ammonium and potassium concentrations) negatively correlated to the first axis. This cluster is associated to B, E and I ponds located in agricultural areas. The two parameter clusters are positively correlated with the second axis. Land uses were added as supplementary variables to help interpret the results of the analysis. The PCA shows a clear negative correlation between agricultural land use and grasslands and forest, the first one being associated to the parameter cluster characteristic of eutrophied ponds (Figure 2). The presence of urbanized areas within a 100 m radius was only weakly correlated with any of the physico-chemical parameters. Apart pond G, ponds associated with high conductivity and ion concentrations are located in areas without agricultural activities while the ponds associated to the parameters typical of eutrophied water are located in agricultural areas.

## Organic contaminants and their link with land use

### Assessment of PAH contamination

PAHs, considered as priority pollutants by US-EPA and other regulations, were analyzed in the water collected during the first two sampling campaigns. Only 20% of the analyses led to quantifiable levels, with concentrations below 21 ng/L (naphthalene) or 8 ng/L (all the others). Naphthalene, fluorene and phenanthrene were the main quantified molecules, in more than 80% of the samples. This observation is consistent, since these 2 and 3 cycle PAHs are among the most water soluble and less hydrophobic according to their Log K<sub>ow</sub>. The low concentrations of all PAHs in water correspond to background levels or slight contaminations and probably did not induce any toxic effects (Bakke et al. 2010). These low concentrations, possibly linked to their overall low solubility in water, do not make it possible to differentiate ponds, according to the possible origins of PAHs such as the combustion of biomass or coal, the combustion of gasoline and diesel, or the spills of liquid fuel (Tobiszewski and Namiesnik 2012). However, the concentrations of PAHs were also measured in the sediments of the ponds where they can potentially accumulate (unpublished data), and it will be interesting to compare their concentrations with those measured in water.

### Assessment of pesticide and pharmaceutical contamination

The sum of the average contents of each analyzed pesticide and pharmaceutical over all the ponds shows higher values in spring (2.82 µg/L campaign 1 and 1.93 µg/L campaign 3) than in autumn (0.72 µg/L campaign 2 and 0.77 µg/L campaign 4). Likewise, calculating the sum of the maximum levels of each compound over all the ponds shows higher values in spring (10.34 µg/L campaign 1 and 8.23 µg/L campaign 3) than in autumn (2.05 µg/L campaign 2 and 2.66 µg/L campaign 4). Thus, the first campaign is characterized by higher concentrations and a greater number of quantified molecules. On the other hand, the results obtained for the two sampling points of the same pond are generally similar.

Over the 33 pesticides and metabolites analyzed in the waters, 30 were observed (Figure 3a). The most commonly observed was tebuconazole, in 93.5% of the samples. This fungicide, like most triazoles, is conventionally used on cereals and has a half-life greater than 80 days in soil and in water (PPDB, 2020). Two other triazoles, metconazole and epiconazole, have often been observed (76.1 and 58.7% of the samples, respectively). Hexaconazole was present in 33.7% of the samples, even though it was banned in 2007 ie 9 years before the first sampling. The most observed herbicides were (S)-metolachlor and isoproturon, in 88 and 68.5% of the samples respectively. They both have leaching capabilities (limited Koc), relatively short half-lives in soil (15-90 and 12-51 days, respectively) but longer in water (365 and 149 days, respectively). However, they differ according to their use on different crops: mainly maize and rapeseed for (S)-metolachlor, and cereals for isoproturon. The high levels of these pesticides could hardly be compared with the literature. Indeed they depend on the agricultural practices in the area, which differ from those of other countries, such as Australia or Canada, even if many contaminants are common to different studies (Allinson et al. 2015; Metcalfe et al. 2019). They also depend on the evolution of the uses: for instance, triazine herbicides (atrazine, simazine, terbuthylazine) were banned in France in 2003-2004, as well as diuron in 2008, and these molecules are no longer quantified as high as they were in a previous study performed in 1998 in rivers close of Saclay area (Blanchoud et al. 2004). The three herbicide metabolites terbuthylazine-desethyl, atrazine-desethyl (derived from terbuthylazine and atrazine, respectively) and DCPMU (1-(3,4-Dichlorophenyl)-3-methylurea, derived from linuron or diuron) were observed in various proportions
compared with their parent, assuming multiple origins from the soil and/or sediments. The most rarely detected pesticides correspond either to banned compounds (simazine and acetochlor, banned 13 and 8 years respectively before first sampling) or to compounds presenting a conjunction of medium or low recommended doses, half-lives and frequencies of use (mesotrione, sulcotrione, chlorsulfuron, picoxystrobin) (PPDB 2020; e-phy 2020).

<figure><img src="image_4.png" /><figcaption>**Fig. 3** Water contamination by pesticides according to concentration classes: a) individual pesticides % of detection in all the 92 samples, b) mean number of detected pesticides per ponds, in all the samples</figcaption></figure>

The pesticides with the concentrations most often above 0.1 µg/L (ie the limit for drinking water in EU) were tebuconazole, (S)-metolachlor and chlorotoluron (22.8, 19.6 and 15.2% of the samples respectively). The highest concentrations were observed for (S)-metolachlor (4.36 µg/L; 1st campaign, pond A), tebuconazole (2.51 µg/L; 3rd campaign, pond G) and the neonicotinoid insecticide imidacloprid (2.22 µg/L; 3rd campaign, pond L).
Considering each pond individually (Figure 3b), ponds G and C had the highest contamination with a mean of 19 pesticides present, with more than 10% of concentrations higher than 0.1 µg/L. A mean of 12 to 17 pesticides were observed in ponds B, E and A with lower concentrations (less than 4% higher than 0.1 µg/L). These five ponds are located in cultivated area (B, C, E) or nearby (A, G). Moreover, ponds G and C are close to farms and can receive wastewater from washing agricultural equipment. Fewer pesticides have been quantified in pond L (8 pesticides in mean), but imidacloprid has been systematically quantified at concentrations higher than 0.1 µg/L (0.13-2.22 µg/L range), much higher concentration than in other ponds where imidacloprid level never exceeded 0.065 µg/L. This divergent result could indicate that imidacloprid does not come from an agricultural activity, where its use has been restricted since 2008. Indeed, this insecticide is used to treat fleas and ticks in dogs, and dogs often bathe in this pond L which is located in a park near a residence. Ponds H and J had the lowest contamination by pesticides, in terms of both the number of pesticides detected (< 4) and of level of concentration (all < 0.01 µg/L). This could be explained by their situation in the forest, far from agricultural areas which do not irrigate to their area.

<figure><img src="image_5.png" /><figcaption>**Fig. 4** Principal component analysis of the organic contaminants within the water of 12 peri-urban ponds. In blue solid arrows, organic contaminants within the water, active variables used to compute the PCA axes. In black arrows, organic contaminants within the water, active variables used to compute the PCA axes. In red arrows, environmental and endocrine disrupting effect supplementary variables, used to help interpret the PCA axes. Blue and red letters respectively indicate ponds with and without agricultural land use within a 100 m radius buffer around the ponds. PC1 explains 35.3% of variation, which together with PC2 explains 53.3% of the variation</figcaption></figure>

Regarding pharmaceutical products, three situations were observed: i) ponds with no detected molecules (A, F, H, I, J, K), ii) ponds with some quinolone antibiotics detected only once, during the 1st campaign (B, L) and iii) ponds with several detections of sulfadiazine and/or carbamazepine (C, D, E, G). Quinolone antibiotics quantified in pond B were flumequine and oxolinic acid (0.015 and 0.12 µg/L, respectively) and in pond L were flumequine, nalidixic acid and oxolinic acid (0.032, 0.26 and 0.02 µg/L, respectively). Flumequine and oxolinic acid are used to treat only animals (cattle, poultry, fishes, pets) while nalidixic acid and sulfadiazine are used to treat humans
and animals. Carbamazepine (0.03 to < 0.001 µg/L) is an anti-epileptic for human use only. Pharmaceuticals could therefore come from several origins. It should be noted that i) the concentrations of pharmaceuticals decreased over time and ii) before the 1st campaign an intense rainy event causing floods occurred, leading to significant runoff which may have caused the contamination of certain ponds with wastewater. All the ponds that contain pharmaceuticals are close to constructed area (farm, university campus, residence). In addition, ponds C and G, where carbamazepine has been mostly observed, are close to a medical centre where it is currently used.

The PCA of the organic contaminants explained 35.3% and 18.0% of the variance on the first and second axis respectively (Figure 4). All the families of the measured organic contaminants project positively on the first axis, which is correlated with agricultural land use. The only exception is imidacloprid: this is consistent with the hypothesis of its presence, mostly in pond L, due to dog antiparasitic treatments. The organic contaminants with the highest projection on the second axis are strobilurins and boscalid, which seem correlated with disruption of androgenic activity. Analysis showed that, on average, organic contaminants in ponds show little variation between seasons or between years (Figures S1c and S1d). However, there are differences between ponds, some with consistently low levels of contaminants while others have higher and more variable levels of contaminants (Figure 4)

Assessment of endocrine disrupting effect

The results of the MELN test show a slight estrogenic activity, mainly between the LOQ and 3 ng/L EEQ, without much difference between campaigns. A higher estrogenic activity was observed for the first campaign and the C pond (8 ng/L EEQ) as well as for the second campaign for the E pond (7 ng/L EEQ). The higher values observed for all ponds for the 4th campaign are related to the bioassay change (Jarošová et al. 2014). For this last campaign, a higher activity is observed for ponds K and especially F. Estrogenic activity values measured with MELN test in the ng/L EEQ range are commonly observed in surface water. In a previous study carried out in the Limours area close to the Saclay plateau, estrogenic activities between 1 and 2 ng/L eq EEQ have been observed in samples collected in September 2007 in two rivers (Miège et al. 2009).

Androgenic effects sought with the AIZ-AR test were more frequently quantified in spring (100% of samples for campaign 1 and 38% in campaign 3) with activity levels between LOQ and 14 ng/L DHTQ, than in autumn (14% of samples for campaign 2 and 24% in campaign 4) with activity levels close to LOQ. It should be noted that toxic effects on AIZ-AR cells have been observed for pond B and campaigns 3 and 4.

No clear trend appears in the results of these bioassays with the exception of two ponds E and F that showed more consistent but low estrogenic and androgenic activities over the 4 campaigns. Bellet et al. (2012) observed that the estrogenic and androgenic effects observed do not seem to be correlated, as they separate specific estrogen receptor ligands from wastewater extracts using affinity column chromatography and observed estrogenic and androgenic activities in different fractions. A very high value of estrogenic activity observed for pond F and campaign 4 (> 250 ng/L EEQ) remains to be explained.

It appears clearly that the higher the detection frequencies and the concentrations of organic pollutants, the more noticeable the effects, even if they remain for the most part low. This is particularly visible with the results of campaign 1 compared with the other three. On the other hand, it must be taken into account that the detection sensitivity of bioassays is dependent on the molecules: thus, the EC₅₀ value for the MELN test varies by a factor of 10⁶ between the activity of the natural hormone (EC₅₀ ≈ 10⁻¹¹ M) and those of contaminants such as triclosan, diuron and propiconazole or phytoestrogens like genistein (EC₅₀ ≈ 10⁻⁵-10⁻⁶ M) (Serra et al. 2019). Similarly, it has been shown that the triazole derivatives have an androgenic activity but with an EC₅₀ value on the AIZ-AR test approximately 2000 times lower than that of DHT (Lv et al. 2017). However, the samples analyzed corresponded to mixtures of compounds interacting with each other with agonist or antagonist actions.
<figure><img src="image_6.png" /></figure>

**Fig. 5** Box-plots (n=6 to 8) of the sum of risk quotients in water considering the measured concentrations and various ecotoxicity values: a) the most sensitive trophic levels, with assessment factors; b) primary producers; c) primary consumers. The red circle evidence the critical values, according to Gustavsson et al (2017)

## Organic contaminants and potential toxicity estimation

The environmental risks associated to the analyzed pesticides and pharmaceuticals in pond waters were estimated using the RQ approach, and considering the simultaneous presence of various contaminants in mixture. In a first attempt, the sum of MEC/PNEC ratios were considered (RQsum), as recommended by the CA (concentration
addition) concept for the worst case scenario (Backhaus and Faust, 2012). Indeed, a more optimal evaluation taking
into account the mode of action of each compound was not possible due to the lack of data on ecotoxicity,
especially for pharmaceuticals, as shown in table S4. For example, no data were found on fish, invertebrates or
algae for nalidixic acid. Thus, we introduced the prokaryotes (mainly tests on cyanobacteria and Vibrio fisheri) at
the trophic level of primary producers. The RQ approach with additivity of RQ (worst case, considering the most
sensitive organisms) was then used to aggregate the risks of the mixture of contaminants in each pond. As shown
in figure 5a, the RQsum ranged from 0.001 (pond F) to 92 (pond C). Most of the RQsum values were greater than 1
in ponds A, B, C, E, G and L, with medians ranging from 4.1 to 28.6 (ponds A and C, respectively). These ponds
therefore present a high ecological risk, which is consistent with the higher presence and concentration of organic
contaminants as mentioned above. A significant part of the risk is linked to the presence of certain molecules, as
shown by the maximum RQ values presented in Table S6. The highest RQ (MEC/PNEC) were observed for the
herbicides (S)-metolachlor (55), diflufenican (54), nicosulfuron (20) and metsulfuron-methyl (11), and the
insecticide imidacloprid (70). The problem addressed by the herbicides would be explained by their frequency and
concentration in ponds (figure 3), as well as their toxicity with PNEC estimated below 0.1 µg/L. Imidacloprid have
been identified among the most harmful pesticide for aquatic and terrestrial biota (Rondeau et al. 2014; Goulson
2013). Although presenting high concentration, the triazole fungicides did not appear as causing a marked risk:
only tebuconazole caused RQ values higher than 1 (ponds C and G, in spring). Among the pharmaceuticals, the
quinolone antibiotics may cause a risk (RQ 1.2 to 3.5) in pond L and oxolinic acid in pond B (RQ 8.5, first
campaign). Only pond H has a median RQsum value of less than 0.1, and can therefore be considered to present a
very low environmental risk (figure 5a). The other ponds (D, F, I, J and K) present a generally low risk, with
median RQsum values between 0.1 and 1. The highest heterogeneity between samples was observed in pond F,
mostly because of the punctual quantification of sulfonylureas (metsulfuron-methyl and nicosulfuron) and
imidacloprid, in one of the campaign each.

The risk assessment at the trophic level of primary producers and consumers was carried out using specific RQ,
namely MEC/EC₅₀, without AF correction, as proposed by Gustavsson et al. (2017). Ponds, A, B, C, E and G
present a sum of MEC/EC₅₀ close to or higher than the critical threshold of 0.1 for primary producers (figure 5b).
These ponds are the most contaminated by herbicides, which systematically induce a higher risk for primary
producers (plants and algae). In addition the pond L, which presents high imidacloprid concentrations, has the
highest sum of MEC/EC₅₀ for primary consumers (figure 5c). It exceeds largely the 0.01 threshold proposed by
Gustavsson et al. (2017) for primary consumers. In a lesser extent, the same MEC/EC₅₀ 0.01 threshold is
overpassed in some determinations in pond G, because of pirimicarb, and mostly lower in ponds A, B and C. It is
worth noting that the risk for primary consumers is evidenced while considering only the two insecticides targeted
by the analysis. It would have certainly been much higher if some other insecticides as pyrethroids have been
targeted.

Comparison of the various risk indicators

The environmental risks of the three main indicators are summarized in table 1. Only the pond J, located in a forest, did not showed any risk according to the various parameters. A low concentration of dissolved oxygen was the only indicator of low quality observed for the ponds D, H and I. It may be related to the fact that these ponds are completely surrounded by deciduous trees, as a high amount of leaf-litter in the water of ponds has been shown to reduce their dissolved oxygen content (Rubbo et al. 2008). On the opposite, J is located in a small clearing in the forest and receives much less leaves litter. The three ponds presenting advanced eutrophication (B, E and G) also presented high risk quotients, associated or not with notable endocrine disruptor effects. The ponds B and E are the only ones where the environmental risks were evidenced by all the three considered factors (physico-chemical parameters, endocrine disruptive effect and risk quotients). All the ponds located inside (B, C, E) or near (A, G) a cultivated area showed a high risk for the aquatic life.
<table><caption>**Table 1 Environmental risks identified in the ponds**</caption><thead><tr><th>Pond</th><th>Physico-chemical parameters</th><th>Endocrine disrupting effect</th><th>Risk quotients</th></tr></thead><tbody><tr><td>A</td><td>Low O<sub>2</sub> content</td><td>-</td><td>High risk according to RQ<sub>sum</sub>; risk for primary producers</td></tr><tr><td>B</td><td>Eutrophized</td><td>Toxic androgenic activity</td><td>High risk according to RQ<sub>sum</sub>; risk for primary producers</td></tr><tr><td>C</td><td>-</td><td>Punctual high estrogenic activity</td><td>High risk according to RQ<sub>sum</sub>; risk for primary producers</td></tr><tr><td>D</td><td>Low O<sub>2</sub> content</td><td>-</td><td>High risk according to RQ<sub>sum</sub>; risk for primary producers</td></tr><tr><td>E</td><td>Eutrophized</td><td>Punctual high estrogenic and androgenic activities</td><td>High risk according to RQ<sub>sum</sub>; risk for primary consumers</td></tr><tr><td>F</td><td>Low O<sub>2</sub> content</td><td>Punctual high estrogenic and androgenic activities</td><td>-</td></tr><tr><td>G</td><td>Eutrophized</td><td>-</td><td>High risk according to RQ<sub>sum</sub>; risk for primary consumers and primary consumers</td></tr><tr><td>H</td><td>Low O<sub>2</sub> content</td><td>-</td><td>-</td></tr><tr><td>I</td><td>Low O<sub>2</sub> content</td><td>-</td><td>-</td></tr><tr><td>J</td><td>-</td><td>-</td><td>-</td></tr><tr><td>K</td><td>-</td><td>Punctual high estrogenic activity</td><td>-</td></tr><tr><td>L</td><td>-</td><td>-</td><td>High risk according to RQ<sub>sum</sub>; risk for primary consumers</td></tr></tbody></table>

**Conclusions**

The various types of measurement allowed assessing some risks for aquatic life, with differences between the ponds. The trace metals and the PAH in solution remained limited in all the ponds and may not degrade the aptitude of water for biology. The general physico-chemical parameters, endocrine disruptive activities and organic contaminant concentrations appeared as complementary indicators to inform for water quality, but are poorly correlated. The water contamination profiles were more correlated with agricultural land use than with urban use, in the case of plant fertilizers (phosphorus, nitrogen) and more significantly in the case of some pesticides. It should be noted, however, that to better detect contamination by urban hydrocarbons, it would be relevant to analyse the sediments of the ponds. Although we recognize that some common contaminants were not screened in this study, the concentration of detected residues in most of the four campaigns indicate that the risk is more likely to be long-term (chronic) than short-term (acute). The overall balance shows a risk for most of the studied ponds, and thus constitutes an alert to preserve the integrity of these aquatic ecosystems. To progress towards a more thorough assessment of the ecological risks, future research should focus on the potential tolerance or resilience of the aquatic organisms. In particular, in order to better assess the possible toxicity of pond water for the biocenosis, it would be interesting to analyse the diversity of the species they contain and to relate this diversity to the various parameters measured.

**Acknowledgements**

Viviane Huteau (CNRS) is thanked for performing extractions and determinations of estrogenic-disrupting activities. Sébastien Breuil and Amélie Trouvé (Ecosys Versailles) are thanked for their help in sampling and packaging during all the campaigns and for DOC analysis. Nathalie Bernet (Ecosys Thiverval-Grignon) is acknowledged for PAH analysis. Philippe Beguinel and Henri Roche (CEA) are thanked for their advice in carrying out the project and their participation in the trace analyses.
**Funding information**

The authors received financial support from the Region Ile-de-France (PSDR 4 IDF program, Project ID "Dynamiques") and the labex BASC.

**Electronic supplementary material**

The online version of this article (https://doi.org/10.1007/s11356-020-10355-5) contains supplementary material, which is available to authorized users.

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