Roles of steady-state and dynamic models for regulation of hydrophobic chemicals in aquatic systems: A case study of decamethylcyclopentasiloxane (D5) and PCB-180 in three diverse ecosystems
Bibliographic record
Abstract
We seek to contribute to the improved regulatory use of mass balance models to complement environmental monitoring data by applying the steady-state Quantitative Water Air Sediment Interactive model (QWASI) and a novel unsteady-state QWASI model. A steady-state model can yield not only a useful simulation of chemical fate under near steady-state conditions, but it can provide insights into the likely influences of increasing or decreasing emission rates, temperature changes, and unexpectedly high sensitivities to model parameters that may require additional investigation. We compared the consistency of insights from both types of model, in the expectation that while the dynamic model provides a closer simulation of actual conditions, for many purposes a simple, less computationally demanding, more transparent and less expensive model may be adequate for many regulatory purposes. We investigated the response times of decamethylcyclopentasiloxane (D5) and PCB-180 concentrations in water and sediment under three emission scenarios in three different aquatic systems, namely Lake Ontario, Oslofjord, and Lake Pepin. D5 was predicted to be removed largely by hydrolysis and volatilization in Lake Ontario and Oslofjord whereas it is subject to removal by advective loss in Lake Pepin. The half-times of D5 water concentration to a stepwise reduction in emission were <60 days in all three water bodies. In contrast, the predicted half-times were 0.53, 1.4, and 2.9 years in Lake Pepin, Oslofjord, and Lake Ontario, respectively. We also explored how uncertainties in input parameters propagate into uncertainties of concentrations in water and sediments possibly necessitating more accurate values.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".