Dataset for: A framework for ecological risk assessment of metal mixtures in aquatic systems

Although metal mixture toxicity has been studied relatively intensely, there is no general consensus yet on how to incorporate metal mixture toxicity into aquatic risk assessment. Here, we combined existing data on chronic metal mixture toxicity at the species level with species-sensitivity-distribution (SSD)-based in-silico metal mixture risk predictions at the community-level for mixtures of Ni, Zn, Cu, Cd and Pb, in order to develop a tiered risk assessment scheme for metal mixtures in freshwater. Generally, independent action (IA) predicts chronic metal mixture toxicity at the species level most accurately, while concentration addition (CA) is the most conservative model. Mixture effects are non-interactive in 69% (IA) and 44% (CA) and antagonistic in 15% (IA) and 51% (CA) of the experiments, while synergisms are only observed in 15% (IA) and 5% (CA) of the experiments. At low effect sizes (around 10% mixture effect), CA overestimates metal mixture toxicity at the species-level by 1.2-fold (i.e. the Mixture interaction Factor [MiF]; median). Species, metal presence or number of metals does not significantly affect the MiF. To predict metal mixture risk at the community-level, bioavailability-normalization procedures were combined with CA or IA using SSD-techniques in 4 different methods, which were compared using environmental monitoring data of a European river basin (Dommel, the Netherlands). We found that the most simple method, in which CA is directly applied to the SSD (i.e. CASSD), is also the most conservative method. CASSD has a median Margin of Safety (MoS) of 1.1 and 1.2 for binary mixtures relative to the theoretically more consistent methods of applying CA or IA directly to the dose response curve (i.e. CADRC or IADRC, respectively)[...