Surface waters can contain a diverse range of organic pollutants, including pesticides, pharmaceuticals and industrial compounds. While bioassays have been used for water quality monitoring, there is limited knowledge regarding the effects of individual micropollutants and their relationship to the overall mixture effect in water samples. In this study, a battery of in vitro bioassays based on human and fish cell lines and whole organism assays using bacteria, algae, daphnids and fish embryos was assembled for use in water quality monitoring. The selection of bioassays was guided by the principles of adverse outcome pathways in order to cover relevant steps in toxicity pathways known to be triggered by environmental water samples. The effects of 34 water pollutants, which were selected based on hazard quotients, available environmental quality standards and mode of action information, were fingerprinted in the bioassay test battery. There was a relatively good agreement between the experimental results and available literature effect data. The majority of the chemicals were active in the assays indicative of apical effects, while fewer chemicals had a response in the specific reporter gene assays, but these effects were typically triggered at lower concentrations. The single chemical effect data were used to improve published mixture toxicity modeling of water samples from the Danube River. While there was a slight increase in the fraction of the bioanalytical equivalents explained for the Danube River samples, for some endpoints less than 1% of the observed effect could be explained by the studied chemicals. The new mixture models essentially confirmed previous findings from many studies monitoring water quality using both chemical analysis and bioanalytical tools. In short, our results indicate that many more chemicals contribute to the biological effect than those that are typically quantified by chemical monitoring programs or those regulated by environmental quality standards. This study not only demonstrates the utility of fingerprinting single chemicals for an improved understanding of the biological effect of pollutants, but also highlights the need to apply bioassays for water quality monitoring in order to prevent underestimation of the overall biological effect.

Surface water can contain countless organic micropollutants, and targeted chemical analysis alone may only detect a small fraction of the chemicals present. Consequently, bioanalytical tools can be applied complementary to chemical analysis to detect the effects of complex chemical mixtures. In this study, bioassays indicative of activation of the aryl hydrocarbon receptor (AhR), activation of the pregnane X receptor (PXR), activation of the estrogen receptor (ER), adaptive stress responses to oxidative stress (Nrf2), genotoxicity (p53) and inflammation (NF-κB) and the fish embryo toxicity test were applied along with chemical analysis to water extracts from the Danube River. Mixture-toxicity modeling was applied to determine the contribution of detected chemicals to the biological effect. Effect concentrations for between 0 to 13 detected chemicals could be found in the literature for the different bioassays. Detected chemicals explained less than 0.2% of the biological effect in the PXR activation, adaptive stress response, and fish embryo toxicity assays, while five chemicals explained up to 80% of ER activation, and three chemicals explained up to 71% of AhR activation. This study highlights the importance of fingerprinting the effects of detected chemicals.

Surface waters can contain a range of micropollutants from point sources, such as wastewater effluent, and diffuse sources, such as agriculture. Characterizing the source of micropollutants is important for reducing their burden and thus mitigating adverse effects on aquatic ecosystems. In this study, chemical analysis and bioanalysis were applied to assess the micropollutant burden during low flow conditions upstream and downstream of three wastewater treatment plants (WWTPs) discharging into small streams in the Swiss Plateau. The upstream sites had no input of wastewater effluent, allowing a direct comparison of the observed effects with and without the contribution of wastewater. Four hundred and five chemicals were analyzed, while the applied bioassays included activation of the aryl hydrocarbon receptor, activation of the androgen receptor, activation of the estrogen receptor, photosystem II inhibition, acetylcholinesterase inhibition and adaptive stress responses for oxidative stress, genotoxicity and inflammation, as well as assays indicative of estrogenic activity and developmental toxicity in zebrafish embryos. Chemical analysis and bioanalysis showed higher chemical concentrations and effects for the effluent samples, with the lowest chemical concentrations and effects in most assays for the upstream sites. Mixture toxicity modeling was applied to assess the contribution of detected chemicals to the observed effect. For most bioassays, very little of the observed effects could be explained by the detected chemicals, with the exception of photosystem II inhibition, where herbicides explained the majority of the effect. This emphasizes the importance of combining bioanalysis with chemical analysis to provide a more complete picture of the micropollutant burden. While the wastewater effluents had a significant contribution to micropollutant burden downstream, both chemical analysis and bioanalysis showed a relevant contribution of diffuse sources from upstream during low flow conditions, suggesting that upgrading WWTPs will not completely reduce the micropollutant burden, but further source control measures will be required.

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Chemicals in the environment occur in mixtures rather than as individual entities. Environmental quality monitoring thus faces the challenge to comprehensively assess a multitude of contaminants and potential adverse effects. Effect-based methods have been suggested as complements to chemical analytical characterisation of complex pollution patterns. The regularly observed discrepancy between chemical and biological assessments of adverse effects due to contaminants in the field may be either due to unidentified contaminants or result from interactions of compounds in mixtures. Here, we present an interlaboratory study where individual compounds and their mixtures were investigated by extensive concentration-effect analysis using 19 different bioassays. The assay panel consisted of 5 whole organism assays measuring apical effects and 14 cell- and organism-based bioassays with more specific effect observations. Twelve organic water pollutants of diverse structure and unique known modes of action were studied individually and as mixtures mirroring exposure scenarios in freshwaters. We compared the observed mixture effects against component-based mixture effect predictions derived from additivity expectations (assumption of non-interaction). Most of the assays detected the mixture response of the active components as predicted even against a background of other inactive contaminants. When none of the mixture components showed any activity by themselves then the mixture also was without effects. The mixture effects observed using apical endpoints fell in the middle of a prediction window defined by the additivity predictions for concentration addition and independent action, reflecting well the diversity of the anticipated modes of action. In one case, an unexpectedly reduced solubility of one of the mixture components led to mixture responses that fell short of the predictions of both additivity mixture models. The majority of the specific cell- and organism-based endpoints produced mixture responses in agreement with the additivity expectation of concentration addition. Exceptionally, expected (additive) mixture response did not occur due to masking effects such as general toxicity from other compounds. Generally, deviations from an additivity expectation could be explained due to experimental factors, specific limitations of the effect endpoint or masking side effects such as cytotoxicity in in vitro assays. The majority of bioassays were able to quantitatively detect the predicted non-interactive, additive combined effect of the specifically bioactive compounds against a background of complex mixture of other chemicals in the sample. This supports the use of a combination of chemical and bioanalytical monitoring tools for the identification of chemicals that drive a specific mixture effect. Furthermore, we demonstrated that a panel of bioassays can provide a diverse profile of effect responses to a complex contaminated sample. This could be extended towards representing mixture adverse outcome pathways. Our findi...