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Temporal and spatial variations in the effect-based ecotoxicological assessment of streams

Environmental Sciences Europe volume 36, Article number: 167 (2024) Cite this article

Abstract

Background

Water bodies are affected by chemical contamination, including micropollutants, which is not fully captured by conventional chemical monitoring methods. The inclusion of integrative, effect-based in vivo and in vitro methods in standardized assessment procedures offers the possibility of bridging discrepancies between chemical and biological assessments and has already been proposed in several studies. However, there is a need to develop a comparable ecotoxicological assessment system for surface waters as for chemical and ecological status. This study aims to contribute to this discourse by investigating the temporal and spatial variation of ecotoxicological effects by assessing water grab samples of 15 different sites in central Germany over the course of 1 year using different in vitro assays.

Results

The level of measured estrogenicity and anti-estrogenicity varied between the four measurement campaigns, while baseline toxicity, dioxin-like effects and mutagenicity showed relatively constant detectable effects over the study period. The impact of conventionally treated wastewater appeared to be one of the strongest influencing stressors, as direct comparisons of ecotoxicity upstream and downstream of wastewater treatment plant dischargers showed a significant increase for most of the conducted bioassays. Comparison of the measured estrogenicity with proposed threshold values showed effects within ecotoxicologically relevant ranges.

Conclusions

Bioassays record ecotoxicological effects on the basis of specific modes of action, allowing whole groups of substances to be identified as pollutants. Recording ecotoxicological status in this way is a useful complement to water assessment tools and can contribute to successful water management. Although most of the assays in this study were very consistent in detecting strong anthropogenic influences, possible temporal variations of individual assays should be taken into account when planning sampling strategies to improve the comparability of results.

Background

Today’s surface waters are affected by multiple stressors on a global scale. The most significant threats to biodiversity in these ecosystems are chemical pollution and morphological degradation [1]. But the effects of multiple stressors make it difficult to identify the responsible causes for a deficient ecological status in a given river. Over the last decades, an increased number of emerging contaminants has been detected in the environment, entering aquatic ecosystems from diffuse and point sources [1,2,3,4]. Micropollutants have the potential to pose risks to both the environment [5, 6] and human health [7] as they are often ubiquitous and persistent pollutants [8]. The main routes for micropollutants to enter the aquatic environment include discharges from industrial and wastewater treatment plants (WWTPs), surface runoff from urban and agricultural areas, as well as atmospheric deposition [9, 10].

In Germany, as in the entire European Union, chemical status is assessed on the basis of defined priority substances (2013/39/EU). In addition, several river basin-specific pollutants are measured. However, selective chemical tests are not sufficient for a comprehensive assessment of water pollution because they only cover a fraction of the substances present in the surface water and do not take into account cocktail effects or substance interactions [11]. For effective water management, the relevant pollutants need to be clearly identified. A major advantage of effect-based methods (EBMs) is the possibility of an integrative assessment of the pollutant effects even in complex mixtures as they occur in the environment. They address different modes of action (MoAs) via specific and non-specific toxicity mechanisms, including potential interactions between substances [12]. This holistic approach enables to bridge the gap between ecological condition and chemical pollution, thus providing a better understanding of the status of surface waters. In vitro bioassays, along with in vivo techniques (in situ and ex situ), are a subset of EBMs. Single cell organisms or cell lines can be used to identify both non-specific and specific toxic effects of a water sample. This means that measured effects can be traced back to a specific group of substances [13]. Several studies recommend complementary effect-based monitoring to identify specific pressures as a cost- and time-efficient amendment [14,15,16,17]. There is already a wide range of different test systems in the field of in vitro assessment methods [18, 19]. However, to ensure accurate comparison of rivers and streams in terms of pollution, it is essential that sampling and analysis methods are standardized, as the analytical results of a sample can be strongly influenced by various factors [20]. For example, seasonal variations and the regional land use patterns are known to affect physicochemical factors such as temperature and oxygen levels in surface waters [25]. Water quality degradation is associated with catchment-specific characteristics such as land use, soil and topography, although the explanatory power of these parameters may be seasonally dependent [21, 22]. In particular, urban areas and agricultural land appear to have a generally negative impact on water quality, in contrast to natural land use [23, 24]. Nutrient concentrations but also organic pollutants can be influenced by low water periods in summer as well as catchment-specific pollutant inputs [25, 26], indicating that spatial and seasonal factors have an influence on water quality [27, 28]. Knowing the spatial and temporal variability of pollutants helps to understand the occurrence of pollution, which is important for the chemical status assessment and mitigation measures of the surface water.

This study aims to contribute to a better understanding of the variability of EBMs by repeatedly assessing effects along a pollutant gradient in one catchment area. The ecotoxicological effects of 15 river sites in the southern Hesse (Germany), with varying degrees of anthropogenic influences, were characterized four times a year using seven different in vitro assays. A comparison of the activity at each sampling site at different times of the year was used to show whether the results varied over the course of the year and whether some assays showed greater variability than others. The next step was to analyze the influence of surrounding land use as an indicator of anthropogenic stressors on this variability. A cluster analysis was used to group the sampling sites according to their anthropogenic influence, and then a two-factor analysis was used to analyze the contribution of spatial and temporal factors to the variability of the results. With a particular focus on the spatial and temporal variation of ecotoxicological activities throughout the year, we hypothesized that:

  1. i.

    the level of ecotoxicological effects can be influenced by both temporal variations and anthropogenic stressors, and

  2. ii.

    as their main entry route, increased ecotoxicological effects downstream of continuous point sources such as WWTPs can be measured during each sampling campaign.

Methods

Study area

The Gersprenz catchment is located south-east of Frankfurt am Main, Hesse (Germany), and covers approximately 513 km2. Most rivers in this catchment do not achieve a good ecological status as defined by the European Water Framework Directive (EU-WFD, Directive 2000/60/EC) due to various inputs and structural deficits [29]. We investigated 15 sites in this catchment (Fig. 1) with a variety of different potential entry routes for pollutants (see Table S1). Five of these sampling sites (G1, G2, G3, G5, G15) are located in the river Gersprenz. The other sampling sites (marked with *) are located in tributaries of the middle and lower reaches of the river, including upstream and downstream of the WWTP Reinheim-Spachbrücken (G*6 and G*7), the WWTP Groß-Umstadt-Richen (G*10 and G*11) and the WWTP Eppertshausen (G*13 and G*14). A total of nine WWTPs discharge into the Gersprenz catchment, representing the wastewater load of almost 250,000 connected inhabitants (see Table S2). The selected waters are expected to have a generally high pollution load due to stormwater and combined sewage discharges and intensive agriculture. Sampling site G1 was selected as a field reference in the headwaters of the river system and was, therefore, expected to have a low level of pollution.

Fig. 1
figure 1

Study area. Sampled sites and land use in the Gersprenz catchment (Hesse, Germany)

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Sample collection and processing

Samples were taken once per site during each meteorological season (spring: May 2021, summer: August 2021, autumn: November 2021 and winter: February 2022) (see Table S3). Water was collected as grab samples from a depth of approximately 10 cm at each site and filled into cleaned 1 L amber glass bottles. Samples were stored at 4 °C in the dark and processed within 24 h. Water samples were enriched by solid phase extraction (SPE). First, the water was filtered through glass fibre filters (125 mm diameter, particle retention 1.5 μm, VWR International GmbH). After conditioning, OASIS HLB cartridges [200 mg, Waters; preconditioning: 2 × 4 mL n-heptane, 2 × 4 mL acetone, 2 × 4 mL methanol, 2 × 4 mL ultrapure H2O (max. 15 mL min−1)] were loaded with 1 L of the prepared sample using a vacuum pump. The cartridges were dried under a gentle stream of nitrogen and the adsorbed compounds were eluted with 4 mL methyl tert-butyl ether 4 mL methanol. After the addition of 200 µL dimethylsulfoxide (DMSO), the eluate was reduced under a gentle stream of nitrogen and stored in glass vials (1.5 mL, VWR) at − 25 °C. The resulting extract represented a 5000-fold enrichment compared to the original water sample. In parallel with each sampling, an SPE blank test was prepared as a process control to rule out any possible contamination. For this purpose, 1 L of ultrapure water was extracted in the same way as the water samples.

In vitro assays

Ecotoxicological effects of the 5000-fold enriched water samples were quantified in the laboratory using seven in vitro assays. The test systems (Table 1) were chosen to cover a wide range of MoAs found in the environment. All samples were tested for baseline toxicity, mutagenicity, estrogenicity and dioxin-like activity in three independent runs and for anti-estrogenicity and anti-androgenicity in two independent runs. Compliance with the respective validity criteria was checked and documented in the supplemental information.

Table 1 Effect-based test systems
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Microtox assay

The microtox assay (MT) was used to determine the baseline toxicity of the samples as a non-specific endpoint. The test is based on a standardized test procedure [30] but was modified for 96-well plates [31, 32]. Aliivibrio fischeri is a gram-negative, bioluminescent bacterium. The intensity of the luminescence can be inhibited by toxic substances. Based on the measured luminescence inhibition, the EC50 value was determined in relation to the relative enrichment factor (REF). According to Harth et al. [31], samples that did not show at least a 20% effect at the highest concentration tested were considered non-toxic. Because it was necessary to be able to express non-toxic samples mathematically for further calculations, these samples were set to a common non-toxic limit (NTL) of 300 REF. This value was calculated by taking the mean EC50 value from a historical data set around the non-toxic limit and adding 1.96 times the standard deviation as a safety factor.

Yeast-based reporter-gene assay

Four different yeast-based reporter-gene assays with Saccharomyces cerevisiae were used to detect potential endocrine and dioxin-like effects. The Yeast Estrogen Screen and Yeast Anti-Estrogen Screen [YES and YAES, human estrogen receptor α (hERα)], the Yeast Anti-Androgen Screen [YAAS, human androgen receptor (hAR)] and the Yeast Dioxin Screen [YDS, aryl-hydrocarbon receptor (AhR)] were performed according to Giebner et al. [33]. For the YES and YDS, enriched extracts were used. YAES and YAAS were performed within 48 h with filtered native water samples. These native samples were not enriched by SPE to avoid potential loss of bioactive compounds [20]. In the presence of chemicals acting as agonists at the respective nuclear receptors in the environmental sample, the reporter gene encoding the enzyme β-galactosidase is activated. The β-galactosidase 4-cleaves methylumbelliferyl-β-d-galactopyranoside (MUG), resulting in a measurable fluorescence. The fluorescence is directly correlated to the amount of enzyme formed and therefore to the amount and activity of agonists in the sample. By comparison with the corresponding positive substances, the activities of the samples can be converted into the respective equivalent concentrations (Table 1). The limit of detection (LOD) of each assay was calculated from the mean activity of the negative controls and adding three times the standard deviation.

Ames fluctuation test

The mutagenicity of the samples was tested using the Ames fluctuation test [34, 35]. The strains YG 1041 and 1042 of the bacterium Salmonella typhimurium were used, which have an additional plasmid (pYG233) with a nitroreductase and acetyltransferase gene in contrast to the standard strains TA 98 and 100. The overexpression of the two enzymes makes them more sensitive to nitrated aromatic hydrocarbons, such as nitroaromatics, nitrosamines and aromatic amines [34]. For some substances, the mutagenic potential is only activated by metabolization. To test this, an S9 mixture consisting of homogenized rat liver extract was also added in a separate run [36]. The strains are only able to grow in histidine-free medium in the presence of specific mutagenic substances, resulting in a pH change that can be detected photometrically. Wells with an optical density less than 0.6 were considered as revertant wells. Samples with an average of more than 20.8% revertants after three independent runs and after subtraction of the historical negative control (see Table S9) were classified as mutagenic.

Anthropogenic stressors

Data on land use in the vicinity of the individual sampling points were used to assess the anthropogenic impact on the rivers. For each site, buffer zones of 5000 m length upstream and a width of 250 m on both sites of the river were created using ArcMap v10.8.1 (Environmental Systems Research Institute (ESRI), Redlands, California, USA). The buffer width was chosen to ensure the inclusion of the riparian zone. The surrounding land use (artificial surface, arable land, pasture, forest) was recorded based on the Corine Land Cover 5 (CLC5) dataset [37]. As an additional anthropogenic stressor, the distance and number of inhabitants connected to each upstream WWTP were recorded for all sampling sites. All datasets used for waterways, catchments, and WWTPs were provided by the Hessian Agency for Nature Conservation, Environment and Geology (HLNUG).

Statistical analyses

To improve the relevance and reliability of this study, a checklist for reporting ecotoxicological studies provided by Moermond et al. [38] was used. Statistical analyses were performed using R v.4.1.1 (R Core Team, Vienna, Austria,) and GraphPad Prism, v.10.0.3 (GraphPad Software Inc., San Diego, CA, USA). To test for differences in mean activities between the sampled seasons, a one-way ANOVA was performed followed by Bonferroni post hoc tests with a significance level of α = 0.05. Significant differences are marked with an asterisk (*p < 0.05). Significant differences in the activity of the individual sampling sites compared to the corresponding reference site (G1) within each measurement campaign were tested using unpaired t-tests, and Welch's correction was applied in the case of unequal variances. To maintain the overall significance level of α = 0.05 and to avoid alpha error accumulation, the significance level for multiple t-tests was lowered to p < 0.01.

The land use and WWTP data were used for cluster analysis to categorize the sampling sites according to their expected anthropogenic impact. Therefore, we used the surrounding land use (artificial surface, arable land, pasture, forest, natural) and information on the size and distance of the sampling site to each upstream located WWTP (see Table S4). When necessary, the data were first log-transformed to downweigh extreme values, afterwards all data values were scaled to a range between zero and one to account for differences in units. The cluster analysis was performed using k-means clustering. The optimal number of clusters was determined using the Average Silhouette method (‘stats’ package, V4.1.1, R Core Team [39]).

To test whether the sampling season (spring, summer, autumn, winter) or anthropogenic factors (cluster) had a greater influence on the measured effects and whether there were interaction effects between the two factors, a two-way ANOVA was performed followed by Bonferroni post-hoc test with a significance level of α = 0.05. In addition, simple linear regression analysis was performed to test for time-dependent changes in the measured effects.

Results

Ecotoxicological effects of the water samples

No anti-androgenic activity of the water samples was detected in the YAAS at any time or sampling site (LOD = 0.77 mg FLU-EQ/L). Averaged over all sampling sites, there were no seasonal variations in baseline toxicity (MT), frequency of mutagenic sites (Ames fluctuation assay) and estrogenic (YES) or dioxin-like activity (YDS). Only the mean anti-estrogenic activity (YAES), averaged over all 15 sampling sites, increased significantly in spring, autumn and winter compared to summer sampling (Fig. 2).

Fig. 2
figure 2

Mean seasonal activities. Measured activities averaged over all sampling sites at different seasons (spring. summer. autumn. winter). a baseline toxicity (REF) as EC50, b dioxin-like activity (βNF-EQ) in µg/L, c estrogenic activity (E-EQ) in ng/L, d anti-estrogenic activity (OHT-EQ) in mg/L, e Proportion of mutagenic samples in %. Non-toxic limits (NTL) or limit of detection (LOD) are indicated for respective assays as dotted horizontal lines. One-way ANOVA was performed followed by Bonferroni post-hoc test with a significance level of α = 0.05. Significant seasonal differences are marked with an asterisk (*p < 0.05; **p < 0.01; ***p < 0.001)

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Baseline toxicity

No baseline toxicity was found during any season at G1, G2, G*4, G5, and G*10 (Fig. 3a). In contrast, toxic effects occurred at G*7, G*11, G*12 and G*14 in all samples. Downstream of the discharger of the WWTP Groß-Umstadt-Richen (G*11) the baseline toxicity increased in all seasons significantly compared to the upstream site G*10. Increased toxicity downstream of WWTP dischargers was also found in summer and autumn in Reinheim-Spachbrücken (G*7 vs. G*6) and in spring, summer and autumn in Eppertshausen (G*14 vs. G*13). The highest baseline toxicity occurred at G*9 (LC50 = 33 REF) in winter.

Fig. 3
figure 3

Heatmap of the activity of the sampling sites in spring, summer, autumn and winter. a Baseline toxicity in REF [EC50], b dioxin-like activity in βNF-EQ (µg/L), c estrogenic activity in E-EQ (ng/L), d anti-estrogenic activity in OHT-EQ (mg/L). Non-toxic samples in the MT were set to the non-toxic limit (NTL), samples below the limit of detection (LOD) were set to ½ LOD, NA = not analyzed due to dry stream. Significant differences compared to reference site G1 were assessed using unpaired t-test and Welch’s correction for unequal variances and coloured orange (significant, p < 0.01) or blue (not significant). In addition, differences between up- and down-stream of WWTP effluents were assessed for G*6 vs. G*7, G*10 vs. G*11 and G*13 vs. G*14 (*p < 0.01, ns = not significant)

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Dioxin-like activity

Most sites showed increased dioxin-like activity in the YDS during at least one measured time point (Fig. 3b). Comparisons between upstream and downstream of the WWTP dischargers Reinheim-Spachbrücken (G*6 vs. G*7), Groß-Umstadt-Richen (G*10 vs. G*11) and Eppertshausen (G*13 vs. G*14) revealed a significant increase in dioxin-like activity in all cases, except for G*7 in spring. Here, an increased activity had already been measured upstream of the WWTP discharger (G*6). In addition, water samples from G*8, G*12, and G*13 showed a significantly higher dioxin-like activity compared to the reference site G1 in each season. Most non-active samples (LOD = 0.02 µg βNF-EQ/L) were located in the headwaters of the catchment, while higher activities occurred in the downstream samples. The highest activity was measured at G*7 (0.29 µg βNF-EQ/L) in summer.

Estrogenic activity

At all sampling sites, a measurable estrogenic activity was found during at least one season (LOD = 0.14 ng E-EQ/L Fig. 3c). The estrogenic activity increased significantly downstream of the WWTP dischargers in Reinheim-Spachbrücken (G*7) and Groß-Umstadt-Richen (G*11) at all times, compared to the respective upstream samples (G*6 and G*10). Downstream of the WWTP Eppertshausen (G*14) this was only in the case in autumn and winter. In spring and summer, the estrogenic activity upstream of the WWTP (G*13) was already so high that it did not increase significantly at G*14. The estrogenic activities in the samples downstream of WWTPs were on average 3.7 times higher than upstream. Samples from G3, G*7, G*8, G*9, G*10 to G15 revealed an evaluated estrogenic activity in every season compared to the reference site. As for dioxin-like activity, we observed a tendency towards increased estrogenic activities in the samples from the lower reaches of the Gersprenz catchment. Unfortunately, this observation could not be tested for statistical significance. The highest activity was found at G*14 (2.5 ng E-EQ/L) in autumn.

Anti-estrogenic activity

Except for G1 and G2, samples from all sites showed an anti-estrogenic activity (LOD = 3.7 mg OHT-EQ/L, Fig. 3d). Samples from G*11, G*12 and G*14 showed significantly higher activities compared to the reference site for each time of sampling. While comparing sites upstream and downstream of WWTP effluents, G*7 showed a significantly increased anti-estrogenic activity during autumn and winter, the sampling site downstream of WWTP Groß-Umstadt-Richen (G*11) only during summer and winter. A significant increase in activity was observed downstream of WWTP Eppertshausen (G*14) during each season. The highest activities were measured in autumn at G*7 (24 mg OHT-EQ/L), followed by G*11 (22 mg OHT-EQ/L) in winter.

Mutagenicity

In the case of positive test results (> 20.8% revertants), samples were classified as mutagenic without further differentiation of the degree of mutagenicity. No mutagenicity was detected at G1, G2, G5, G*6, G*9, G*10, G*13 and G*15 at any time of the year (Fig. 4). Only at the sampling sites located downstream of WWTP dischargers (G*7, G*11, G*14) a mutagenic effect occurred throughout the year, while the respective upstream sampling sites (G*6, G*10, G*13) showed no mutagenic activity. For G*3 and G*4 only the summer samples were mutagenic, for G*8 the summer, autumn and winter samples and for G*12 the summer and winter samples. The highest frequency of mutagenicity occurred during the summer campaign, when 7 sampling sites were active.

Fig. 4
figure 4

Heatmap of the mutagenicity during spring, summer, autumn and winter. Active sampling sites (> 20.8% revertants) are coloured blue, non-active sites (≤ 20.8% revertants) are coloured orange (NA = not analyzed due to dry stream)

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Cluster analysis

Cluster analysis was used to categorize the sampling sites according to their expected anthropogenic impact. Sampling sites were divided into two groups (see Fig. S1). Cluster 1 was mainly characterized by arable land and artificial surfaces and in most cases the presence of an upstream located WWTP. It included sampling sites G3, G5, G*6, G*7, G*8, G*11, G*12 and G15. Cluster 2 included sampling sites G1, G2, G*4, G*9, G*10, G*13 and G*14 which were located in forests and pasture areas and no sampling site except G*14 was influenced by an upstream located WWTP discharger.

Analysis of factors influencing ecotoxicity

We analyzed whether the ecotoxicological effects at a sampling site were influenced by the time of sampling (time) or the anthropogenic impact at the sampling sites (cluster) (Table 2). We also looked for temporal trends in the measured effects using simple regression analysis (see Table S5).

Table 2 Two-way ANOVA analysis of the factors ‘cluster’ and ‘season’
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For baseline toxicity (MT), the identified clusters contributed significantly to the variance of the dataset (7.8%, p = 0.001). No significant effect of ‘time’ or interaction between the two factors was observed. At each time point sampled, higher baseline toxicities were measured in cluster 1 compared to cluster 2 but this effect was not significant (Fig. 5a). For the dioxin-like activity (YDS), a significant influence of the sampling location (cluster) was observed (3.3%, p = 0.01). The post-hoc test shows that in summer significantly higher dioxin-like activities were measured at samples from cluster 1 compared to cluster 2 (Fig. 5b). Temporal differences in sampling or the interaction between ‘cluster’ and ‘time’ had no significant effect on the measured activity. For the estrogenic activity (YES), no significant influence of the sampling location (cluster) or the sampling time (time) was found. The total variances of the dataset cannot be solely explained by the location or the time of sampling. As there was no significant main effect, no post hoc test was conducted. Regression analysis showed a significant increase in estrogenic activity over the course of the year from spring to winter for samples in cluster 1 (Table S5, R2 = 0.09, p = 0.004) but not in cluster 2. Anti-estrogenic activity (YAES) showed a strong significant effect for ‘time’ in the ANOVA (25.5%, p < 0.001). Samples from cluster 1 and 2 showed significantly higher anti-estrogenic activities in autumn and winter compared to summer (Fig. 5c). The difference in anti-estrogenic activity between the clusters and the interaction effects of ‘cluster’ and ‘time’ was not significant. Therefore, the explained variance of the anti-estrogenic activity can be attributed to temporal variations. In addition, the activity of samples in cluster 1 (Table S5, R2 = 0.12, p = 0.01) and cluster 2 (R2 = 0.1, p = 0.02) increased significantly over the course of the year from spring to winter. For mutagenicity, the clusters account for 7.7% of the total variance of the measured activity (AMES fluctuation), which is considered significant (p = 0.04). At each time point, samples from cluster 1 were more frequently mutagenic than from cluster 2 (Fig. 5d) but again, this effect was not significant. The ‘time’ and interaction effect of time and cluster had no significant impact, suggesting that the mutagenicity measured in this assay is due to clustering and thus the anthropogenic impact.

Fig. 5
figure 5

Measured activity related to sampling time and clusters. Differences between sampling time (spring, summer, autumn, winter) within each cluster and differences in the mean activity of both clusters during the same sampling time. a baseline toxicity in REF [EC50], b dioxin-like activity in βNF-EQ (µg/L), c anti-estrogenic activity in OHT-EQ (mg/L), d share of mutagenic samples (%). Bonferroni post-hoc test of the 2-way ANOVA was used, all data are expressed as mean values with standard error of the mean (SEM). Significant effects are marked with an asterisk (*p < 0.05; **p < 0.01; ***p < 0.001)

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Discussion

We analyzed the ecotoxicity of water samples from 15 sampling sites, considering possible variations in activity over the course of a year. Across all assays, only the anti-estrogenic activity showed significant differences within the four grab samples. For most of the other test systems, anthropogenic stressors (clusters) appeared to have a greater influence on the activity levels than the temporal variation. By comparing upstream and downstream sites of major WWTP dischargers, these point sources were identified as a dominant pollution factor throughout the sampling campaigns. To improve the interpretability of the results, chemical monitoring data from the HLNUG (2019–2021) were compared with the measured effects of the individual EBMs. In addition to the mean sum of the chemicals of the individual substance groups (see Table S11), the data were checked for compliance with the EQS according to the Ordinance on the Protection of Surface Waters 2016 (OGewV 2016) on the basis of the annual mean concentration (EQSAA) (see Table S12). Only one such exceedance was recorded at site G*4. At all other sites where chemical measurements could be used for comparison (G*8, G*9, G*10, G*12, G*14 and G15) EQSAA exceedances were measured 14 to 15 times. There was no difference in the amount of detected EQSAA exceedances for cluster 1 and 2.

Linking ecotoxicological effects to anthropogenic stressors

Overall, the effects observed in the in vitro assays can be mostly attributed to anthropogenic pollution. WWTPs may be particularly relevant in this context, as the inadequate removal of micropollutants in conventional treatment processes results in increased inputs to aquatic ecosystems [4, 40, 41] and thus adverse effects on aquatic organisms [15, 31, 42, 43]. In addition to point source inputs such as WWTP discharges, other types of land use in the vicinity of the sampling sites appear to have influenced the measured ecotoxicity in the form of diffuse inputs (e.g., surface runoff from sealed surfaces or agricultural land). Significant ecotoxicological effects were also found at sampling sites without WWTP influence but with a high proportion of agricultural and urban areas in their vicinity. This highlights the complexity of recording and assessing pollutant inputs to aquatic ecosystems and is in line with previous studies using effect-based assessment approaches for ecotoxicological risk assessment of anthropogenic inputs to surface water [44, 45]. The use of a battery of in vitro bioassays representing different MoAs allows conclusions to be drawn about the substances likely to be responsible for the effects.

Baseline toxicity

Cluster 1 was more strongly impacted by WWTP dischargers, urban areas and surrounding agricultural land than cluster 2, coinciding with a higher mean baseline toxicity in each season sampled. We have repeatedly found increased baseline toxicity at sampling sites downstream of WWTP discharges. Such a non-specific effect is caused by a variety of organic substances and is often due to micropollutants from treated wastewater [32, 46]. In the Gersprenz catchment area, low flow rates in the investigated rivers and streams, combined with a high average discharge of conventionally treated wastewater, lead to a high wastewater content downstream of the WWTP discharges. In addition, the intensive agricultural use in the surrounding area and road runoff may also contribute to the elevated toxicity in the water [47,48,49,50]. With the exception of sampling point G*4, exceedances of the EQS of various plant protection products were measured at all sites for which chemical monitoring results were available (see Table S12). In particular, the highest average total concentrations of various pesticides and their metabolites were measured at G*12 (see Table S11). In addition, exceedances of the EQS for hexabromocyclododecane, a halogenated hydrocarbon hazardous to the aquatic environment, were measured at this site.

Dioxin-like activity

A significant increase in dioxin-like activities was found at most sites downstream of WWTP discharges. Such a dioxin-like response, triggered by the activation of the aryl hydrocarbon receptor (AhR), can be caused by Polycyclic aromatic hydrocarbons (PAHs), dioxins, furans and dioxin-like polychlorinated biphenyls (PCBs) [51,52,53]. In addition to the repeatedly measured AhR activity, various PCBs were measured at sites G*9 and G*12 (see Table S11). Benzotriazole UV stabilisers or textile preservatives based on pentachlorophenol (PCP) as well as chloranil-based dyes and pigments can be released from textiles during washing processes and thus end up in municipal wastewater [54, 55]. The highest annual mean concentrations of benzotriazoles were also measured at sites G*8, G*12, G*14 and G15, where significant AhR activity was detected at several measurement times (see Table S11). When not only municipal, but also industrial wastewater is discharged a wide range of AhR agonists can also originate from a variety of industrial chemicals [8]. Dioxins and other AhR agonists have been shown to be harmful to aquatic organisms, for example through reproductive toxicity [15, 56]. In addition to the repeated increase in activity downstream of several WWTPs, we observed a tendency towards an increase in dioxin-like activities along the course of the river. Sites in cluster 1 again tended to show high activities and were characterized by urban land use and an increased proportion of treated wastewater. This could be due to an increase in pollutants entering aquatic systems as the proportion of urban areas and infrastructure increases. Natural components of coal and oil, such as PAHs, are formed by the incomplete combustion of fuels, plastics and biomass [8]. They are also used as plasticisers in rubber in the form of tar oils and can be found in the abrasion of car tyres. Activities of AhR agonists can be detected in extracts from road surfaces and diesel exhaust particles. These diffuse inputs reach aquatic ecosystems via surface runoff from roads and sealed surfaces [57].

Endocrine activities

Endocrine disrupters can interfere with the endocrine system of organisms [58] and thereby cause adverse effects. Xenoandrogens such as tributyltin compounds (TBT) and xenoestrogens such as bisphenol A (BPA) can affect reproduction in aquatic organisms, including invertebrates [59, 60]. BPA was detected at almost all the chemical monitoring sites analyzed here, but did not exceed the acceptable EQSAA (see Table S12). We detected estrogenic (YES) and anti-estrogenic (YAES) activity in most of the samples analysed. In the yeast assay with Saccharomyces cerevisiae, estrogenic and anti-estrogenic substances compete for the same receptor binding site in the test organism. Therefore, masking effects due to antagonistic [61] substances cannot be excluded in this study. Conventional wastewater treatment appears to be insufficient to eliminate all endocrine substances in wastewater [33, 62, 63]. We found a significant increase in both estrogenic and anti-estrogenic activity downstream of WWTPs at several times and in different seasons. Effect-based trigger values (EBTs) are a good way to assess the effects of bioassays in terms of their ecological risk potential, and have already been derived and applied for various assays [64,65,66]. Jarošová et al. [67] consider a concentration of 0.1 to 0.4 ng EEQ/L in municipal WWTP effluents as safe. Sampling sites G*7 (0.41 ng EEQ/L), G*11 (1.1 ng EEQ/L) and G*14 (1.8 ng EEQ/L), which are located downstream of WWTP discharges, exceeded this value on annual average. The estrogenic effects clearly exceeded the proposed EBT [68] as well as the proposed environmental quality standard EQS for 17β-estradiol (0.4 ng/L) [69], even after dilution of the treated wastewater in the receiving water. Synthetic estrogens in can have significant negative effects on various aquatic organisms [58, 62, 70], therefore, such high concentrations indicate a high ecological relevance.

Here, estrogenicity appeared to be more influenced by anthropogenic factors than anti-estrogenicity. As with dioxin-like activity, an increase in estrogenicity was observed along the course of the river. Samples from sites close to the headwaters showed on average less activity, whereas measured values further downstream were higher. Due to various adverse effects of estrogens at the individual and population level [71], there could be long-term negative effects for the ecosystem. The estrogenic activities could originate from natural estrogens such as 17β-estradiol and estrones, but also from synthetic substances such as 17α-ethinylestradiol [72]. As such synthetic estrogens tend to be much more persistent than natural substances, they pose a greater environmental hazard [73]. A number of pharmaceuticals could have contributed to the antagonistic activity measured in the YAES. Tamoxifen, for example, is used in cancer therapy and can be detected in hospital wastewater [3, 74]. Other pharmaceuticals with antagonistic potential derived from WWTPs include bazedoxifene, clomiphene, fulvestrant and raloxifene [75]. The EQSAA for various fungicides, herbicides and insecticides, some of which have endocrine disrupting effects (e.g., cypermethrin, dichlorovos, dicofol, heptachlor, omethoate), was exceeded at almost all monitoring sites analyzed (see Table S12). The highest concentrations of pesticides were detected in G*12 and G*14 (see Table S11), which enter surface water via agricultural land, WWTPs and surface runoff and can act as endocrine disruptors [76,77,78,79,80].

Mutagenicity

We found a higher mutagenic frequency of sites in cluster 1 compared to cluster 2. Samples downstream of a WWTP were always mutagenic. However, only frame-shift mutations could be detected after metabolic activation by the bacterial strain YG 1041. It can, therefore, be assumed that substances causing this effect did not act directly as mutagens, but require metabolic activation to become mutagenic [81]. PAHs are substances that require metabolic activation, particularly by cytochrome P450 [82, 83], and are almost ubiquitous in the environment [84]. Direct entry pathways for PAHs but also other pollutants with mutagenic potential include WWTPs or industrial discharges, agricultural or urban runoff containing medical and personal care products, pesticides, tyre abrasion particles and many other chemicals [81]. In the Gersprenz catchment, several PAHs were detected at monitoring sites G*4, G*9 and G*12 and benzo(a)pyrene concentrations above the acceptable EQSAA were measured at G*4 and G*12 (see Table S11 and S12). The mutagenic effects detected in water samples from different locations in the catchment must be considered problematic, as mutagenic substances have the potential to alter the genetic integrity of organisms. For example, deleterious changes in the genetic code (e.g., gene mutations or DNA strand breaks) could potentially lead to a loss of fitness and thus an increased risk of extinction for small populations [85]. Especially under chronic exposure, a reduction in overall genetic variability, together with impaired reproduction and increased mortality, may have long-term effects on aquatic populations [86, 87]. EBMs such as the Ames assay are, therefore, a valuable tool for environmental risk assessment because they are able to detect potential mutagenic effects in a composite sample that indicate the presence of specific chemicals of environmental concern.

Temporal variations in ecotoxicological effects

Anti-estrogenicity was the only endpoint that showed clear temporal variation. Samples collected in autumn and winter were the most active, whereas lowest activities were observed in the summer grab samples. Estrogenic activities showed a time-dependent change in activity, but only for samples in cluster 1. As estrogenic and anti-estrogenic substances bind to the same receptors on Saccharomyces cerevisiae in the yeast assay, the two activities cannot be clearly separated in a mixed field sample using this assay. For example, an estrogenic effect in one sample could be masked by a concurrent anti-estrogenic effect. Therefore, a possible temporal variation in estrogenic activity cannot be excluded on the basis of these data. The concentration of certain micropollutants may be influenced by spatial and temporal variability, which should be considered when assessing the occurrence of a substance. Many micropollutants in surface waters show seasonal variations in measured concentrations [88, 89]. In addition to synthetic chemicals, natural compounds can have endocrine disrupting effects by mimicking or antagonising the action of hormones [74]. Such phytoestrogens bind to estrogen receptors and can act as agonists or antagonists, but are weak estrogens compared to estradiol or estrone [90,91,92]. In addition, several pesticides can act as endocrine disruptors or show reproductive or developmental toxicity, especially when used in combination [93]. As both the growing season and the application of different pesticides underlie seasonal variation, these factors may have influenced the results of this study.

Recommendations on effect-based monitoring

In this study, only two of the assays showed temporal variations within the four grab samples taken over 1 year. Most of the assays seem to detect anthropogenic activities, indicating a dominant influence of continuous point sources. Effects detected by MT, Ames fluctuation assay and YDS were relatively constant and effects of WWTP effluents were detected throughout the year. We, therefore, recommend that potential temporal variations, for example, due to seasonality or weather conditions should be considered when assessing ecotoxicity of water samples, especially at sampling sites with many potential diffuse sources of pollution. Ideally, several grab samples per year should be conducted to capture inter- and intra-seasonal variation at a sampling site. If multiple sampling is not feasible, we would recommend at least a consistent sampling period to keep the variability of the results as low as possible and thus to ensure the comparability amongst sites within a year. In addition, event-based monitoring might be more effective when assessing the chemical hazards or impacts of extreme weather events. Heavy rainfall events can lead to short-term high peak concentrations, e.g. due to run-off from roads or sealed surfaces after prolonged dry periods [78, 94, 95].

Conclusion

In line with many previous studies, we confirm that EBMs are well suited to detect ecotoxicological effects in field samples. To test hypothesis (i), we analyzed the variability of four grab samples distributed over the year and the influence of anthropogenic stressors on the measured activity. Despite the use of grab samples, little variability was observed over the measurement period, especially at sites heavily influenced by anthropogenic stressors. Only the activity of the estrogenic and anti-estrogenic effects varied significantly over the measurement period, and we recommend that this be taken into account when planning future sampling strategies. In addition, the assays used clearly showed effects of treated wastewater discharges, probably due to insufficient elimination of pollutants by conventional treatment processes in WWTPs. Even after dilution in receiving waters, some of the effects were in ecotoxicologically relevant ranges. Together with other sources of anthropogenic pollution (e.g., surface run-off), this leads to clearly measurable effects in the water, as assumed in hypothesis (ii). Using different EBMs, a relatively wide range of effects of potential pollutants can be detected with only a few bioassays. EBMs, therefore, provide a good initial risk assessment for surface water and can, therefore, contribute to a better understanding of poor water quality and support management measures for improvement.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files.

Abbreviations

βNF-EQ:

β-Naphthoflavone equivalent

BPA:

Bisphenol A

DDT:

Dichlorodiphenyltrichloroethane

DMSO:

Dimethylsulfoxide

EBM:

Effect-based method

EBT:

Effect-based trigger value

EQS:

Environmental Quality Standard

E-EQ:

17β-Estradiol equivalent

EU-WFD:

European Water Framework Directive

FLU-EQ:

Flutamide equivalent

HLNUG:

Hessian Agency for Nature Conservation, Environment and Geology

LOD:

Limit of detection

MoA:

Mode of action

MT:

Microtox assay

MUG:

4-Methylumbelliferyl-β-d-galactopyranoside

NTL:

Non-toxic limit

OHT-EQ:

4-Hydroxytamoxifen equivalent

PAH:

Polycyclic aromatic hydrocarbon

PCB:

Polychlorinated biphenyl

PCP:

Pentachlorophenol

REF:

Relative enrichment factor

SEM:

Standard error of the mean

SPE:

Solid phase extraction

TBT:

Tributyltin

WWTP:

Wastewater treatment plant

YAAS:

Yeast anti-androgen screen

YAES:

Yeast anti-estrogen screen

YDS:

Yeast dioxin screen

YES:

Yeast estrogen screen

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Acknowledgements

We thank the German Federal Environmental Foundation (DBU) for funding this work. We would also like to thank Andrea Dombrowski, Simon Hornung, Anita Herold, Franziska Steingräber, Stella Jennes and Gustav Glock for their assistance with the field and laboratory work. Furthermore, we would like to thank Andrea Sundermann for her help with the analyses, as well as Peter Ebke for the critical discussions about the study. Finally we would like to thank the Hessian Agency for Nature Conservation, Environment and Geology (HLNUG) and the State of Hesse for the collection and provision of the chemical monitoring data used in this study.

Funding

This study was funded by the German Federal Environmental Foundation (DBU) and was conducted as part of the associated DECIDE project (AZ 35663/01). Open Access funding enabled and organized by Projekt DEAL.

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Authors and Affiliations

  1. Faculty of Biological Sciences, Department Aquatic Ecotoxicology, Goethe University Frankfurt, Max‑von‑Laue‑Str. 13, 60438, Frankfurt Am Main, Germany

    Delia Hof, Jörg Oehlmann & Matthias Oetken

  2. Mesocosm GmbH, Neu-Ulrichstein 5, 35315, Homberg (Ohm), Germany

    Thomas Bing

  3. Department of River Ecology and Conservation, Senckenberg Research Institute and Natural History Museum Frankfurt, Clamecystr. 12, 63571, Gelnhausen, Germany

    Sebastian Heß

  4. Faculty of Biological Sciences, Institute of Ecology, Evolution and Diversity, Goethe University Frankfurt, Max‑von‑Laue‑Str. 13, 60438, Frankfurt Am Main, Germany

    Sebastian Heß

  5. Hessisches Landesamt Für Naturschutz, Umwelt Und Geologie, Rheingaustraße 186, 65203, Wiesbaden, Germany

    Jens Mayer

  6. Kompetenzzentrum Wasser Hessen, Max-Von-Laue-Straße 13, 60438, Frankfurt Am Main, Germany

    Jörg Oehlmann & Matthias Oetken

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  1. Delia Hof
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Contributions

DH: Conceptualization, data curation, methodology, investigation, formal analysis, visualization, writing—original draft, writing—review & editing. TB: conceptualization, writing—review & editing. SH: conceptualization, writing—review & editing. JM: conceptualization, data curation, writing—review & editing. MO: conceptualization, funding acquisition, writing—review & editing. JO: conceptualization, funding acquisition, writing—review & editing.

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Hof, D., Bing, T., Heß, S. et al. Temporal and spatial variations in the effect-based ecotoxicological assessment of streams. Environ Sci Eur 36, 167 (2024). https://doi.org/10.1186/s12302-024-00992-9

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