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Abstract
Background
Humans and wildlife are continuously exposed to chemical mixtures. These mixtures vary in composition but typically contain hundreds of micropollutants at low concentrations. As it is not feasible to measure the toxicity of all possibly occurring mixtures, there is a need to predict mixture toxicity. Two models, Concentration Addition (CA) and Independent Action (IA), have been applied to estimate mixture toxicity. Here, we compared measured with predicted toxicity of nine mixtures designed from 15 environmentally relevant substances in zebrafish embryos to investigate the usability of these models for predicting phenotypic effects in a whole organism short term acute assay.

Results
In total, we compared 177 toxicity values derived from 31 exposure scenarios with their predicted counterparts. Our results show that mixture toxicity was either correctly estimated (86%) by the prediction window, the concentration-effect space that is spanned between both models, or was underestimated with both models (14%). The CA model correctly predicted the measured mixture toxicity in 100% of cases when a prediction deviation factor of 2.5 was allowed. However, prediction accuracy of mixture toxicity prediction was dependent on exposure duration and mixture potency. The CA model showed highest prediction quality for long-term exposure with highly potent mixtures, respectively, whereas IA proved to be more accurate for short-term exposure with less potent mixtures. Obtained mixture concentration–response curves were steep and indicated the occurrence of remarkable combined effects as mixture constituents were applied at concentrations below their respective individual effect threshold in 90% of all investigated cases.

Conclusions
Experimental factors, such as exposure duration or mixture potency, influence the prediction accuracy of both inspected models. The CA model showed highest prediction accuracy even for a set of diverse mixtures and various exposure conditions. However, the prediction window served as the most robust predicator to estimate mixture toxicity. Overall, our results demonstrate the importance of considering mixture toxicity in risk assessment schemes and give guidance for future experiment design regarding mixture toxicity investigations.

Environmental water quality monitoring aims to provide the data required for safeguarding the environment against adverse biological effects from multiple chemical contamination arising from anthropogenic diffuse emissions and point sources. Here, we integrate the experience of the international EU-funded project SOLUTIONS to shift the focus of water monitoring from a few legacy chemicals to complex chemical mixtures, and to identify relevant drivers of toxic effects. Monitoring serves a range of purposes, from control of chemical and ecological status compliance to safeguarding specific water uses, such as drinking water abstraction. Various water sampling techniques, chemical target, suspect and non-target analyses as well as an array of in vitro, in vivo and in situ bioanalytical methods were advanced to improve monitoring of water contamination. Major improvements for broader applicability include tailored sampling techniques, screening and identification techniques for a broader and more diverse set of chemicals, higher detection sensitivity, standardized protocols for chemical, toxicological, and ecological assessments combined with systematic evidence evaluation techniques. No single method or combination of methods is able to meet all divergent monitoring purposes. Current monitoring approaches tend to emphasize either targeted exposure or effect detection. Here, we argue that, irrespective of the specific purpose, assessment of monitoring results would benefit substantially from obtaining and linking information on the occurrence of both chemicals and potentially adverse biological effects. In this paper, we specify the information required to: (1) identify relevant contaminants, (2) assess the impact of contamination in aquatic ecosystems, or (3) quantify cause–effect relationships between contaminants and adverse effects. Specific strategies to link chemical and bioanalytical information are outlined for each of these distinct goals. These strategies have been developed and explored using case studies in the Danube and Rhine river basins as well as for rivers of the Iberian Peninsula. Current water quality assessment suffers from biases resulting from differences in approaches and associated uncertainty analyses. While exposure approaches tend to ignore data gaps (i.e., missing contaminants), effect-based approaches penalize data gaps with increased uncertainty factors. This integrated work suggests systematic ways to deal with mixture exposures and combined effects in a more balanced way, and thus provides guidance for future tailored environmental monitoring. © 2019, The Author(s). ; Funding text #1 1 UFZ‑Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig, Germany. 2 Institute for Environmental Research, RWTH Aachen University, 52074 Aachen, Germany. 3 Office of Research and Development, Atlantic Ecology Division, United States Environmental Protection Agency, Narragansett, RI, USA. 4 Center for Applied Geoscience, Eberhard Karls Uni‑ versity Tübingen, 72074 Tübingen, Germany. 5 Alterra, Wageningen University and Research Centre, P.O. Box 47, 6700 AA Wageningen, The Netherlands. 6 Environment and Climate Change Canada, Burlington, ON, Canada. 7 Sophus Bauditz Vej 19 B, 2800 Kgs. Lyngby, Denmark. 8 Water and Soil Quality Research Group, Institute of Environmental Assessment and Water Research (IDAEA‑ CSIC), Jordi Girona 18‑26, 08034 Barcelona, Spain. 9 Unité d'Ecotoxicologie Funding text #2 The SOLUTIONS Project is supported by the Seventh Framework Programme (FP7‑ENV‑2013) of the European Union under Grant Agreement No. 603437. G.A. Umbuzeiro thanks FAPESP Projects 2013/16956‑6 and 2015/24758‑5. We like to thank all partners for their continued efforts in making this project a success story. Funding text #3 The SOLUTIONS Project is supported by the Seventh Framework Programme (FP7‑ENV‑704 2013) of the European Union under Grant Agreement No. 603437. G.A. Umbuzeiro thanks 705 FAPESP Projects 2013/16956‑6 and 2015/24758‑5. ; Peer reviewed