Background
This study aims to tackle the lack of freshwater ecotoxicological effect factors (EFs) crucial for determining freshwater ecotoxicity characterization factors (CFs) using the widely accepted scientific consensus USEtox model for ecotoxicity impact characterization. The objectives are: (1) to offer a collection of experimental EFs to support USEtox ecotoxicity characterization factor computations and (2) to contrast ecotoxicity data produced by various quantitative structure–activity relationship (QSAR) models against experimental data.
Results
Experimental ecotoxicity data were gathered from the REACH database and CompTox Version 2.1.1, which includes toxicity information from ToxValDB v9.4. QSAR-driven ecotoxicity data were extracted from ECOSAR v1.11 and T.E.S.T. v5.1.2. The experimental and estimated data underwent a harmonization process to ensure consistency. Subsequently, aquatic ecotoxicological EFs were determined. The merged REACH and CompTox databases list EFs for 11,295 substances, each identified by a unique CAS number. Among these, the USEtox database already catalogs 2426 substances with freshwater ecotoxicological EFs. This study expanded on that by calculating EFs for an additional 8869 substances. Using estimated data, EFs were determined for 6029 chemicals based on ECOSAR data and 6762 chemicals using TEST data.
Conclusions
This study calculated EFs for an additional 8869 substances, thereby broadening their inclusion in LCA evaluations. When integrated with the USEtox EFs database, this research encompasses 11,368 chemicals. The high correlation observed between experimental EFs and those in the USEtox database lends significant confidence to the calculations for chemicals not listed in USEtox. Conversely, the low correlation between estimated EFs and those in USEtox suggests limited confidence in calculations based on estimated data. Furthermore, the disparity in correlations between EFs calculated using ECOSAR and TEST indicates that different QSARs can yield varied results. This discrepancy underscores the need for caution when relying on estimated data. Given that EFs are contingent on data availability, it is imperative to periodically update EFs as new data emerges.