Future water quality monitoring: improving the balance between exposure and toxicity assessments of real-world pollutant mixtures

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