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Information on common toxicity pathways may also provide input for the assessment of mixture effects.
Apparently, the grid-based inventory shows various mixture effects but no over-yielding.
Pesticides followed by pharmaceuticals and personal care products dominated the observed mixture effects.
We compared the observed mixture effects against component-based mixture effect predictions derived from additivity expectations (assumption of non-interaction).
Conventional target analysis of biological samples such as blood limits our ability to understand mixture effects of chemicals.
Based on these concentration-response data, mixture effects were predicted by applying the model of concentration addition.
Earlier, positive mixture effects have been reported by several authors (Mielikäinen 1985; Pukkala et al. 1994; Liang et al. 2005 and Pretzsch et al. 2010).
They cover a range of uncertainties, such as laboratory to field extrapolation or acute to chronic extrapolation[4] but are not sized to account for mixture effects.
Hence a specific mixture assessment factor (MAF) has been discussed in order to safeguard against unwanted mixture effects from multi-component mixtures of partly unidentified composition[5].
Furthermore, these mixture effects occurred in a quite predictable manner.
Here, we have applied this assay to the analysis of multi-component mixture effects.
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