Application of adverse outcome pathway-based tools to ambient water quality criteria development
Citation:
Villeneuve, Dan, G. Ankley, C. LaLone, Dave Mount, Joe Tietge, AND D. Hoff. Application of adverse outcome pathway-based tools to ambient water quality criteria development. SETAC North America, Orlando, FL, November 06 - 10, 2016.
Impact/Purpose:
not applicable
Description:
Increasing numbers and diversity of chemical contaminants are being detected in ambient surface waters. States, regions, and communities across the US are faced with the issue of understanding which chemicals may warrant concern and at what concentrations. Integrating new scientific data streams and predictive approaches into the process of numeric water aquatic life criteria development offers the potential to develop more criteria, for more chemicals, more rapidly. For example, well established models of non-polar narcosis provide an effective estimate of “baseline” toxicity. Emerging data streams from high throughput, pathway-based, toxicity testing programs provide broad spectrum screening for more specific modes of action that may result in deviations from “baseline” toxicity predictions (i.e., excess toxicity). The adverse outcome pathway (AOP) framework provides a scientifically credible basis for linking specific pathway-perturbations to predicted hazard and facilitates systematic consideration of taxa and life stages likely to be sensitive. Application of this information can facilitate more targeted and hypothesis-driven approaches to the collection of data for use in numeric criteria development. It can also support interim application of appropriate AOP-based prediction models in cases where empirical data are lacking. While these approaches will be subject to error and uncertainty, they offer the ability to provide a scientifically-based preliminary estimate of levels of concern, which can immediately guide priority setting and programs at the state and regional level while criteria are refined through the gradual application of stronger data sets and more tailored and sophisticated modeling. Examples illustrating how these tools can be integrated to aid rapid numeric criteria derivation will be presented.