AquaSSD

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This web tool is based on the works below:

N. Aurisano, P. F. Albizzati, M. Hauschild, and P. Fantke, "Extrapolation Factors for Characterizing Freshwater Ecotoxicity Effects," Environ Toxicol Chem, vol.38, no.11, pp. 2568-2582, Nov 2019, doi: 10.1002/etc.4564. [ link ]
M. Owsianiak et al., "Ecotoxicity characterization of chemicals: Global recommendations and implementation in USEtox," Chemosphere, vol. 310, p. 136807, 2023. [ link ]
Liang, W.; Li, J.; Wang, X.; Giesy, J. P.; Zhao, X. Machine Learning-Driven Cross-Species Toxicity Prediction for Advancing Ecologically Relevant PFAS Water Quality Criteria. Environ. Sci. Technol. 2025, 59 (48), 25688–25702. [ link ]
Wang, L.; Yang, T.; Liu, X.; Liu, J.; Liu, W. Critical Evaluation and Meta-Analysis of Ecotoxicological Data on Per- and Polyfluoroalkyl Substances (PFAS) in Freshwater Species. Environ. Sci. Technol. 2024, 58 (40), 17555–17566. [ link ]
Olker, J. H.; Elonen, C. M.; Pilli, A.; Anderson, A.; Kinziger, B.; Erickson, S.; Skopinski, M.; Pomplun, A.; LaLone, C. A.; Russom, C. L.; Hoff, D. The ECOTOXicology Knowledgebase: A Curated Database of Ecologically Relevant Toxicity Tests to Support Environmental Research and Risk Assessment. Environmental Toxicology and Chemistry 2022, 41 (6), 1520–1539. [ link ]
Schür, C.; Gasser, L.; Perez-Cruz, F.; Schirmer, K.; Baity-Jesi, M. A Benchmark Dataset for Machine Learning in Ecotoxicology. Sci Data 2023, 10 (1), 718. [ link ]

Reference
Biganzoli, F., Ceriani, L., Crenna, E., Pant, R., Sala, S. et al. (2020). Environmental footprint – Update of Life Cycle Impact Assessment Methods – Ecotoxicity freshwater, human toxicity cancer, and non-cancer. Publications Office. [ link ]

To help practitioners assess the reliability of the data, a quality score (QS) is associated with each HC.

QS = ln(species) × ln(taxonomic groups) × 1 / (1 + extrapolations)^0.1

0 ≤ QS < 1.48 → LOW QUALITY
1.48 ≤ QS < 1.77 → INTERMEDIATE QUALITY
QS ≥ 1.77 → HIGH QUALITY

This work received funding from European Union's Horizon research and innovation programme via the DESIDERATA project under grant agreement number 101178011.