The objective of this paper is to employ biotic ligand model (BLM) to link between acute copper (Cu) toxicity and its effect on valve closure behavior of freshwater clam Corbicula fluminea in order to further support for the BLM that potentially offers a rapid and cost-effective method to conduct the acute toxicity tests for freshwater clam exposed to waterborne Cu. Reanalysis of published experimental data of C. fluminea closure daily rhythm and dose-response profiles based on the laboratory-acclimated clams showed that a BLM-based Hill model best described the free Cu(2+)-activity-valve closure response relationships. Our proposed Cu-BLM-Corbicula model shows that free ionic form of waterborne Cu binds specifically to a biotic ligand (i.e., clam gills) and impairs normal valve closure behavior, indicating that a fixed-level of metal accumulation at a biotic ligand is required to elicit specific biological effects. With derived mechanistic-based Cu-BLM-Corbicula model, we show that the site-specific EC50(t) and valve closure behavior at any integrated time can be well predicted, indicating that our model has the potential to develop a biomonitoring system as a bioassay tool to on-line measure waterborne Cu levels in aquatic systems. Our results confirm that BLM can be improved to analytically and rigorously describe the bioavailable fraction of metal causing toxicity to valve closure behavior in freshwater C. fluminea. We suggest that the Cu-BLM-Corbicula model can be used to assist in developing technically defensible site-specific water quality criteria and performing ecological risk assessment and to promote more focused and efficient uses of resources in the regulation and control of metals and the protection of the aquatic ecosystems.

No abstract

No abstract

No abstract

Using a probabilistic risk-based framework, we have developed a simple predictive risk threshold model for protecting the survival of farmed abalone, Haliotis diversicolor supertexta, exposed to waterborne zinc (Zn). Probabilistic techniques using Monte Carlo analysis propagate parameter uncertainty/variability throughout the model, providing decision makers with a credible range of information and increased flexibility in establishing a specific Zn level in aquacultural ecosystems. We coupled a first-order two-compartment bioaccumulation model with a reconstructed dose-response profile based on a three-parameter Hill equation model to form a probabilistic risk model in order to determine the risk quotient associated with a 10% probability of exceeding the abalone 5% effect concentration (EC(5)) at site-specific abalone farms. Sensitivity analysis revealed that waterborne Zn concentration (C(w)) and algae bioconcentration factor (BCF(a)) have a significant effect on Zn levels in abalone. Using multiple nonlinear regression analysis with C(w) and BCF(a) as the parameters, a predictive risk threshold equation that can be used in a variety of site-specific conditions was developed for protecting the survival of farmed abalone. We believe this probabilistic framework provides an effective method for conceptualizing a public policy decision vis-a-vis the establishment of a specific acceptable risk threshold for aquacultural water quality management.