Bioavailability models for predicting copper toxicity to freshwater green microalgae as a function of water chemistry
De Schamphelaere, K.A.C.; Janssen, C.R. (2006). Bioavailability models for predicting copper toxicity to freshwater green microalgae as a function of water chemistry. Environ. Sci. Technol. 40(14): 4514-4522. https://dx.doi.org/10.1021/es0525051
In: Environmental Science and Technology. American Chemical Society: Easton. ISSN 0013-936X; e-ISSN 1520-5851, meer
We investigated whether an earlier-developed bioavailability model for predicting copper toxicity to growth rate of the freshwater alga Pseudokirchneriella subcapitata could be extrapolated to other species and toxicological effects (endpoints). Hardness and dissolved organic carbon did not significantly affect the toxicity of the free Cu2+ ion to P. subcapitata (earlier study) and Chlorella vulgaris (this study), but a higher pH resulted in an increased toxicity for both species. Regression analysis showed significant linear relationships between EcxpCu (= "effect concentration" that produces x% adverse effect, expressed as pCu = -log of the Cu2+ activity) and pH. By linking these regression models with a geochemical metal speciation model, dissolved copper concentrations that elicit a given adverse effect (Ecxdissolved) can be predicted. Within the pH range investigated (5.5-8.7), slopes of the linear EcxpCu vs pH regression models varied between 1.301 and 1.472 depending on the species and the effect level (10% or 50%) considered. In a statistical sense these slopes were all significantly different from one another (p < 0.05), suggesting that this empirical regression model does not yet capture the full complexity of toxicological copper bioavailability to algae. However, we demonstrated that regression models with an "average" slope of 1.354 had predictive power very similar to those of regression models with species and effect-specific slopes. Additionally, the "average" regression model was further successfully validated for other species (Chlamydomonas reinhardtiiand, Scenedesmus quadricauda) and for different toxicological effects/endpoints (growth rate, biomass yield, and phosphorus uptake rate). For all these toxicity datasets effect concentrations of copper could be predicted with this "average" model by errors of less than a factor of 2 in 94-100% of the cases. The success of this "average" model suggests the possibility that the pH-based linear regression model may form a sound conceptual basis for modeling the toxicological bioavailability of copper to green algae in regulatory assessments, although a full mechanistic understanding is lacking and should be the focus of future studies.
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