Characterizing Freshwater Ecotoxicity of More Than 9000 Chemicals by Combining Different Levels of Available Measured Test Data with In Silico Predictions

Mélanie Douziech*, Susan Anyango Oginah, Laura Golsteijn, Michael Zwicky Hauschild, Olivier Jolliet, Mikołaj Owsianiak, Leo Posthuma, Peter Fantke*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Ecotoxicological impacts of chemicals released into the environment are characterized by combining fate, exposure, and effects. For characterizing effects, species sensitivity distributions (SSDs) estimate toxic pressures of chemicals as the potentially affected fraction of species. Life cycle assessment (LCA) uses SSDs to identify products with lowest ecotoxicological impacts. To reflect ambient concentrations, the Global Life Cycle Impact Assessment Method (GLAM) ecotoxicity task force recently recommended deriving SSDs for LCA based on chronic EC10s (10% effect concentration, for a life-history trait) and using the 20th percentile of an EC10-based SSD as a working point. However, because we lacked measured effect concentrations, impacts of only few chemicals were assessed, underlining data limitations for decision support. The aims of this paper were therefore to derive and validate freshwater SSDs by combining measured effect concentrations with in silico methods. Freshwater effect factors (EFs) and uncertainty estimates for use in GLAM-consistent life cycle impact assessment were then derived by combining three elements: (1) using intraspecies extrapolating effect data to estimate EC10s, (2) using interspecies quantitative structure-activity relationships, or (3) assuming a constant slope of 0.7 to derive SSDs. Species sensitivity distributions, associated EFs, and EF confidence intervals for 9862 chemicals, including data-poor ones, were estimated based on these elements. Intraspecies extrapolations and the fixed slope approach were most often applied. The resulting EFs were consistent with EFs derived from SSD-EC50 models, implying a similar chemical ecotoxicity rank order and method robustness. Our approach is an important step toward considering the potential ecotoxic impacts of chemicals currently neglected in assessment frameworks due to limited test data.
Original languageEnglish
JournalEnvironmental Toxicology and Chemistry
ISSN0730-7268
DOIs
Publication statusAccepted/In press - 2024

Keywords

  • Ecological risk assessment
  • Environmental toxicology
  • Freshwater toxicology
  • Hazard/risk assessment
  • In silico methods
  • Predictive toxicology
  • Uncertainty

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