Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1016/j.envint.2025.109370 |
Licence  |
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| Title (Primary) | AI-aided chronic mixture risk assessment along a small European river reveals multiple sites at risk and pharmaceuticals being the main risk drivers |
| Author | Weichert, F.G.; Inostroza, P.A.; Ahlheim, J.; Backhaus, T.; Brack, W.; Brauns, M.; Fink, P.
; Krauss, M.
; Svedberg, P.; Hollert, H. |
| Source Titel | Environment International |
| Year | 2025 |
| Department | ASAM; FLOEK; EXPO |
| Volume | 197 |
| Page From | art. 109370 |
| Language | englisch |
| Topic | T5 Future Landscapes T9 Healthy Planet |
| Supplements | https://ars.els-cdn.com/content/image/1-s2.0-S0160412025001217-mmc1.docx |
| Keywords | Chronic mixture risk assessment; Multi-scenario mixture risk assessment; Artificial intelligence-aided hazard assessment; In silico (eco)toxicity predictions; Large-volume solid phase extraction |
| Abstract | The vast amount of registered chemicals leads to a high diversity of substances occurring in the environment and the creation of new substances outpaces chemical risk assessment as well as monitoring strategies. Hence, risk assessment strategies need to be modified ensuring that they remain aligned with the rapid development and marketing of new substances. Here we performed a longitudinal chronic mixture risk assessment considering a real-world case study scenario with diverse anthropogenic impact types characterised by different land uses along a river in Central Germany. We sampled river water using large-volume solid phase extraction at six selected sampling sites. Following chemical analysis using liquid chromatography-high resolution mass spectrometry, we quantified 192 substances. For 34% of them, we obtained empirical chronic effect data for freshwater organisms. Furthermore, we used the open-source artificial intelligence (AI) model TRIDENT to predict chronic toxicity for all substances. A multi-scenario mixture risk assessment was conducted for three taxonomic groups, using the concentration-addition concept and considering various hazard and exposure scenarios. The results showed that the chronic risk estimates for all taxonomic groups were considerably higher when the empirical data was amended with data from in silico modelling. We identified hot spots of chemical pollution and our analysis indicated that fish were the most vulnerable taxonomic group, with pharmaceuticals being the most relevant risk drivers. Our study exemplifies the application of an AI model to predict chronic risk for aquatic organisms in combination with the consideration of multiple risk scenarios, that may complement future risk assessment strategies. |
| Persistent UFZ Identifier | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=29977 |
| Weichert, F.G., Inostroza, P.A., Ahlheim, J., Backhaus, T., Brack, W., Brauns, M., Fink, P., Krauss, M., Svedberg, P., Hollert, H. (2025): AI-aided chronic mixture risk assessment along a small European river reveals multiple sites at risk and pharmaceuticals being the main risk drivers Environ. Int. 197 , art. 109370 10.1016/j.envint.2025.109370 |
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