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Development of Algal Interspecies Correlation Estimation Models for Chemical Hazard Assessment
Citation:
Brill, J., S. Belanger, J. Chaney, S. Dyer, Sandy Raimondo, M. Barron, AND C. Pittinger. Development of Algal Interspecies Correlation Estimation Models for Chemical Hazard Assessment. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, 35(9):2368-2378, (2016).
Impact/Purpose:
Development and evaluation of ICE models for algal species for inclusion in Web-ICE
Description:
Web-based Interspecies Correlation Estimation (ICE) is an application developed to predict the acute toxicity of a chemical from 1 species to another taxon. Web-ICE models use the acute toxicity value for a surrogate species to predict effect values for other species, thus potentially filling in data gaps for a variety of environmental assessment purposes. Web-ICE has historically been dominated by aquatic and terrestrial animal prediction models. Web-ICE models for algal species were essentially absent and are addressed in the present study. A compilation of public and private sector–held algal toxicity data were compiled and reviewed for quality based on relevant aspects of individual studies. Interspecies correlations were constructed from the most commonly tested algal genera for a broad spectrum of chemicals. The ICE regressions were developed based on acute 72-h and 96-h endpoint values involving 1647 unique studies on 476 unique chemicals encompassing 40 genera and 70 species of green, blue-green, and diatom algae. Acceptance criteria for algal ICE models were established prior to evaluation of individual models and included a minimum sample size of 3, a statistically significant regression slope, and a slope estimation parameter ≥0.65. A total of 186 ICE models were possible at the genus level, with 21 meeting quality criteria; and 264 ICE models were developed at the species level, with 32 meeting quality criteria. Algal ICE models will have broad utility in screening environmental hazard assessments, data gap filling in certain regulatory scenarios, and as supplemental information to derive species sensitivity distributions.
