Exploring persistent organic pollutants (POPs) in the Danube River: An analysis of Polybrominated diphenyl ethers (PBDEs) and Per- and Polyfluorinated Substances (PFAS) using NORMAN Database
Bibliographic record
Abstract
Abstract Polybrominated diphenyl ethers (PBDEs) and per- and polyfluoroalkyl substances (PFAS) are environmental contaminants that have been widely detected in various matrices, including air, water, sediment, and biota, across the globe, but their sources and fate remain poorly understood. This review aims to explore the occurrence of PBDEs and PFAS in the Danube River. The study employs the NORMAN database repository as a source of data pertaining to persistent organic pollutants (POPs). This study compares and evaluates the occurrence patterns of PBDEs and PFAS in various countries along the Danube River basin. The spatial results demonstrate a decreasing trend for PBDEs in surface water and biota, while a significant increase for PFAS is observed. The distributions of PBDE congeners in biota samples mirrored the compositional profiles in the water, which were dominated by BDE-47 and/or BDE-99, while BDE-209 predominated in sediments. In regards to PFAS, PFOA and PFOS are prevalent in surface water. In conclusion, the occurrence of PBDEs and PFAS in Europe is of significant concern, and regulatory policies have been implemented to control their use and release into the environment. The results of this study can be used to assess the health and environmental risks posed by POPs in the Black Sea and can aid in the formulation of future public health policies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".