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Comparison of four methods for bioavailability‐based risk assessment of mixtures of Cu, Zn, and Ni in freshwater
Author(s) -
Van Regenmortel Tina,
Nys Charlotte,
Janssen Colin R.,
Lofts Stephen,
De Schamphelaere Karel A.C.
Publication year - 2017
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1002/etc.3746
Subject(s) - risk assessment , environmental chemistry , copper , bioavailability , zinc , nickel , chemistry , sensitivity (control systems) , metal , environmental science , biotic ligand model , ecotoxicology , biology , computer science , bioinformatics , computer security , organic chemistry , electronic engineering , engineering
Although chemical risk assessment is still mainly conducted on a substance‐by‐substance basis, organisms in the environment are typically exposed to mixtures of substances. Risk assessment procedures should therefore be adapted to fit these situations. Four mixture risk assessment methodologies were compared for risk estimations of mixtures of copper (Cu), zinc (Zn), and nickel (Ni). The results showed that use of the log‐normal species sensitivity distribution (SSD) instead of the best‐fit distribution and sampling species sensitivities independently for each metal instead of using interspecies correlations in metal sensitivity had little impact on risk estimates. Across 4 different monitoring datasets, between 0% and 52% of the target water samples were estimated to be at risk, but only between 0% and 15% of the target water samples were at risk because of the mixture of metals and not any single metal individually. When a natural baseline database was examined, it was estimated that 10% of the target water samples were at risk because of single metals or their mixtures; when the most conservative method was used (concentration addition [CA] applied directly to the SSD, i.e., CA SSD ). However, the issue of metal mixture risk at geochemical baseline concentrations became relatively small (2% of target water samples) when a theoretically more correct method was used (CA applied to individual dose response curves, i.e., CA DRC ). Finally, across the 4 monitoring datasets, the following order of conservatism for the 4 methods was shown (from most to least conservative, with ranges of median margin of safety [MoS] relative to CA SSD ): CA SSD > CA DRC (MoS = 1.17–1.25) > IA DRC (independent action (IA) applied to individual dose‐response curves; MoS = 1.38–1.60) > IA SSD (MoS = 1.48–1.72). Therefore, it is suggested that these 4 methods can be used in a general tiered scheme for the risk assessment of metal mixtures in a regulatory context. In this scheme, the CA SSD method could serve as a first (conservative) tier to identify situations with likely no potential risk at all, regardless of the method used (the sum toxic unit expressed relative to the 5% hazardous concentration [SumTU HC5 ] < 1) and the IA SSD method to identify situations of potential risk, also regardless of the method used (the multisubstance potentially affected fraction of species using the IA SSD method [msPAF IA,SSD ] > 0.05). The CA DRC and IA DRC methods could be used for site‐specific assessment for situations that fall in between (SumTU HC5 > 1 and msPAF IA,SSD < 0.05). Environ Toxicol Chem 2017;36:2123–2138. © 2017 SETAC