Systematic evaluation of chronic metal-mixture toxicity to three species and implications for risk assessment

Metal contamination of rivers and streams generally occurs as a combination of multiple metals (also called mixtures). However, it is yet unresolved how risks of mixed metal contamination to ecosystems should be evaluated. We collated data from 30 different toxicity tests with metal mixtures and analysed them in a systematic way to derive general conclusions that can be used in risk assessment. We found  cases in which different metals, each individually causing <10% toxicity (relative to uncontaminated water), caused much larger toxicity (up to 66%) when combined. This suggests that the current metal-by-metal approach in risk assessment may not be conservative enough for the environment. We also considered the use of two common mixture toxicity models to predict metal mixture toxicity. In general, the so-called response addition model (which multiplies responses of each metal) was most accurate, while the so-called concentration addition model (which sums concentrations of each metal) was the most conservative for the environment (i.e. overestimates toxicity by 1.4-3.6 fold, depending on the organism considered). Thus, our systematic data analysis provides key information to improve metal mixture risk assessment.
 


Scientific abstract

Metal contamination generally occurs as mixtures. However, it is yet unresolved how to address metal mixtures in risk assessment. Therefore, using consistent methodologies, we have set up experiments to identify which mixture model applies best at low level effects, i.e. the independent action (IA) or concentration addition (CA) reference model. Toxicity of metal mixtures (Ni, Zn, Cu, Cd, and Pb) to Daphnia magna, Ceriodaphnia dubia, and Hordeum vulgare was investigated in different waters or soils, totaling 30 different experiments. Some mixtures of different metals, each individually causing <10% inhibition, yielded much larger inhibition (up to 66%) when dosed in combination. In general, IA was most accurate in predicting mixture toxicity, while CA was most conservative. At low effect levels important in risk assessments, CA overestimated mixture toxicity to daphnids and H. vulgare on average with a factor 1.4 to 3.6. Observed mixture interactions could be related to bioavailability, or by competition interactions either for binding sites of dissolved organic carbon or for biotic ligand sites. Our study suggests that the current metal-by-metal approach in risk evaluations may not be conservative enough for metal mixtures.


Full reference (link)

Nys C, Versieren L, Cordery K, Blust R, Smolders E, De Schamphelaere KAC. 2017. Systematic evaluation of chronic metal-mixture toxicity to three species and implications for risk assessment. Environmental Science & Technology 51 (8), pp 4615-4623.

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