Sediment−Water Distribution of Organic Contaminants in Aquatic Ecosystems: The Role of Organic Carbon Mineralization
Bibliographic record
Abstract
The distribution between sediments and water plays a key role in the food-chain transfer of hydrophobic organic chemicals. Current models and assessment methods of sediment-water distribution predominantly rely on chemical equilibrium partitioning despite several observations reporting an "enrichment" of chemical concentrations in suspended sediments. In this study we propose and derive a fugacity based model of chemical magnification due to organic carbon decomposition throughout the process of sediment diagenesis. We compare the behavior of the model to observations of bottom sediment-water, suspended sediments-water, and plankton-water distribution coefficients of a range of hydrophobic organic chemicals in five Great Lakes. We observe that (i) sediment-water distribution coefficients of organic chemicals between bottom sediments and water and between suspended sediments and water are considerably greaterthan expected from chemical partitioning and that the degree sediment-water disequilibrium appears to follow a relationship with the depth of the lake; (ii) concentrations increase from plankton to suspended sediments to bottom sediments and follow an inverse ratherthan a proportional relationship with the organic carbon content and (iii) the degree of disequilibrium between bottom sediment and water, suspended sediments and water, and plankton and water increases when the octanol-water partition coefficient K(ow) drops. We demonstrate that these observations can be explained by a proposed organic carbon mineralization model. Our findings imply that sediment-water distribution is not solely a chemical partitioning process but is to a large degree controlled by lake specific organic carbon mineralization processes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".