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Assessing the risks posed by mixtures of chemicals in freshwater environments: case study of Lake Geneva, Switzerland

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Aquatic organisms are typically exposed simultaneously to several organic compounds released from human activities like agriculture, industries, or simply from people living in cities. The ecological risk assessment of mixtures of such compounds has therefore to be addressed by scientists. The aims of this paper are (1) to describe the current mixture risk assessment procedures, (2) to apply such approach to a specific case study, Lake Geneva and the River Rhône in Switzerland, and (3) to discuss the outcomes of such an application. Two models, called concentration addition and independent action, are recognized to be robust enough to predict the mixture effect of substances on a given species. They are classically used also to assess the risk of mixtures for the ecosystem, but their use is often limited by the lack of available ecotoxicity data. Adopting a first level assessment, we describe the evolution of the mixture risk for several years of Lake Geneva, and for 2010 for the River Rhône. These first assessments allow identification of the most problematic substances demanding risk reduction measures. Furthermore, again for the two cases studies, we show that the risk levels associated with mixtures of compounds can rapidly exceed critical aquatic thresholds, and therefore, it is the sum of the substances that is problematic, which is more challenging in term of risk management. Further analysis of effects in compound mixtures as well as a better characterization of the overall ecological risk are necessary for the thousands substances co‐occuring at very low concentrations. This article is categorized under: Science of Water > Water Quality
Classification of the different joint actions between two substances described by Hewlett and Plackett and Hewlett. Substance A and B have a similar joint action, but dissimilar from substance C. They act therefore on a same, respectively a different site of action. The model of concentration addition corresponds to the case of simple similar action, and the model of response addition to the case of independent action. Both models assume no interaction between substances in mixture, i.e., one substance will not modified (through the toxico‐kinetics/‐dynamics) the biological action of the other.
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Lake Geneva is located in the South of Switzerland. The map shows the two sampling points for the monitoring of pesticides and pharmaceuticals: SHL2 in the middle of the lake and Porte du Scex just before the River Rhône enters the lake.
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(a) Mixture risk quotient of pesticides measured in 2010 in the River Rhône based on concentration addition (CA) model. Each histogram represents the mixture risk quotient (RQ) for different trophic level. The mixture RQ corresponds to the sum of individual RQ of substances in mixture, being for one substance, the ratio between the concentration and the lowest EC50 (divided by 1000) of the trophic level. Mixture RQ above 1 is considered as an unacceptable risk. (b) Focus on the risk quotients for daphnids of the pesticide mixtures measured in 2010 for the River Rhône, with highlight of the most contributing individual RQ. I: insecticides, h: herbicides, f: fungicides, pgr: plant growth regulator.
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Worst‐case mixture risk quotient over years for Lake Geneva for pesticides and pharmaceuticals based on concentration addition (CA) model and the predicted no‐effect concentration (PNEC) values. Each histogram represents the sum of each individual risk quotient (RQ) measured at the sampling date, which is the ratio between the concentration of the substance and its PNEC Mixture RQ over 1 is considered as an unacceptable risk. (a) Highlight of the main contributors to the risk. (b) Mixture risk quotient without substances having an individual RQ above 1. (c) Mixture risk quotient on October 10, 2011, if all other substances not measured at this date, but previously measured in the lake, are fixed to a concentration well below the detection limit.
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Fluctuations of the concentrations of pesticides measured in 2010 in the River Rhône just before it enters Lake Geneva.
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On the left: Average concentrations of four pharmaceutical substances detected twice a year since 2006 in the middle of Lake Geneva. On the right: Average concentrations of pharmaceuticals detected in the middle of the lake during three different years as an extension of the classical monitoring. The substances that were looked for only on 1 year were excluded.
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On the left: Total tons of pesticides in Lake Geneva over years. The individual tonnage (per pesticide) is calculated with average concentration of each active substance and with the volume of the lake water. On the right: Total tonnage of pesticides and pharmaceuticals on September 9, 2009.
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Illustration of a theoretical comparison between mixture model predictions and comparisons. The mixture effect on a given species is determined experimentally for different mixture concentrations (with a constant mixture ratios between substances). Note that often concentration addition (CA) and independent action (IA) predictions are very similar. (a) In this case, the CA prediction is better than IA for a mixture of substances acting with the same mode of action. The deviation of the experimental data from the prediction for low mixture concentrations could be described as an antagonism effect compared to CA. (b) The IA prediction is better with substances in mixture acting with different mode of action. In this case, the deviation of experimental data could be described as a synergistic effect compared to model IA (or strongly antagonist to CA).
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Dose–response curve of a given substance and a given species. The effect concentrations (EC) 5 or 50% can be calculated from the curve. The NOEC is calculated differently but is placed on the figure to show that it generally corresponds to a higher value than the EC10.
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