Applications of Contaminant Fate and Bioaccumulation Models in Assessing Ecological Risks of Chemicals: A Case Study for Gasoline Hydrocarbons
Bibliographic record
Abstract
Mass balance models of chemical fate and transport can be applied in ecological risk assessments for quantitative estimation of concentrations in air, water, soil, and sediment. These concentrations can, in turn, be used to estimate organism exposures and ultimately internal tissue concentrations that can be compared to mode-of-action-based critical body residues that induce toxic effects. From this comparison, risks to the exposed organism can be evaluated. To demonstrate the use of fate models in ecological risk assessment, we combine the EQuilibrium Criterion (EQC) environmental fate model with a simple screening level biouptake model for three representative organisms: a bird, a mammal, and a fish. This effort yields estimates of internal body concentrations that can be compared with levels known to elicit toxic effects. As an illustration, we present an analysis of 24 hydrocarbon components of gasoline that differ in properties but are assumed to elicit toxicity by a common narcotic mode of action. Results demonstrate that differences in chemical properties and mode of entry into the environment lead to profound differences in the efficiency of transport from emission to target biota. We discussthe implications of these results and draw attention to the insights gained about regional fate and ecological risks associated with gasoline. This approach is suitable for assessing single chemicals or mixtures that have similar modes of action. We conclude that the model-based methodologies presented are widely applicable for screening level ecological risk assessments that support effective chemicals management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".