Coupling River Concentration Simulations with a Toxicokinetic Model Effectively Predicts the Internal Concentrations of Wastewater-Derived Micropollutants in Field Gammarids
Bibliographic record
Abstract
Abstract Although the exposure assessment of wastewater-derived micropollutants via chemical, bioanalytical, and modeling methods in environmental compartments is becoming more frequent, the whole-body burden (i.e., internal concentrations) in nontarget organisms is rarely assessed. An understanding of the internal concentration fluctuation is especially important when exploring the mechanistic linkage between exposure and effects. In this study, we coupled a simple river model with a first-order toxicokinetic (TK) model to predict the concentrations of wastewater-derived micropollutants in freshwater invertebrates (Gammarus spp.). We applied Monte Carlo simulations and conducted laboratory experiments to account for the uncertain input data and the lack of uptake/depuration rate constants required for the TK model. The internal concentrations in field gammarids were predicted well, and the estimates varied only by a factor of 0.1–1.9. Fast equilibrium may also be assumed such that bioconcentration factors (BCFs) are used together with the daily river dilution patterns to predict internal concentrations. While this assumption is suitable for compounds observed in our experiment to reach the steady state within 48 h in gammarids, the model overpredicted the concentrations of substances that reach this condition after longer periods. Nevertheless, this approach provides conservative estimates and simplifies the coupling of models as BCFs are slightly more accessible than the rate constants. However, if one is interested in a more detailed exposure information (e.g., peak concentration and the whole-body burden recovery after a spill), then the nonsteady-state formulation should be employed.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".