Bioavailability Models for Predicting Copper Toxicity to Freshwater Green Microalgae as a Function of Water Chemistry

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 reinhardtii and 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.