Emission factor estimation of ca. 160 emerging organic microcontaminants by inverse modeling in a Mediterranean river basin (Llobregat, NE Spain)
Bibliographic record
Abstract
Starting from measured river concentrations, emission factors of 158 organic compounds out of 199 analyzed belonging to different groups of priority and emerging contaminants [pesticides (25), pharmaceuticals and hormones (81), perfluoroalkyl substances (PFASs) (18), industrial compounds (12), drugs of abuse (8) and personal care products (14)] have been estimated by inverse modeling. The Llobregat river was taken as case study representative of Mediterranean rivers. Industrial compounds and pharmaceuticals are the dominant groups (range of 10(4) mg·1000 inhab(-1)·d(-1)). Personal care products, pesticides, PFASs and illegal drugs showed a load approximately one order of magnitude smaller. Considered on a single compound basis industrial compounds still dominate (range of ca. 10(3) mg·1000 inhab(-1)·d(-1)) over other classes. Generally, the results are within the range when compared to previously published estimations for other river basins. River attenuation expressed as the percentage fraction of microcontaminants eliminated was quantified. On average they were around 60-70% of the amount discharged for all classes, except for PFASs, that are poorly eliminated (ca. 20% on average). Uncertainties associated with the calculated emissions have been estimated by Monte-Carlo methods (15,000 runs) and typically show coefficients of variation of ca. 120%. Sensitivities associated with the various variables involved in the calculations (river discharge, river length, concentration, elimination constant, hydraulic travel time and river velocity) have been assessed as well. For the intervals chosen for the different variables, all show sensitivities exceeding unity (1.14 to 3.43), tending to amplify the variation of the emission. River velocity and basin length showed the highest sensitivity value. Even considering the limitations of the approach used, inverse modeling can provide a useful tool for management purposes facilitating the quantification of release rates of chemicals into the aquatic environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".