The Application of Fugacity and Activity to Simulating the Environmental Fate of Organic Contaminants
Bibliographic record
Abstract
The concept of fugacity, which is widely used in chemical processing calculations, has also been successfully applied to a variety of environmental simulations of the fate and transport of organic contaminants. The challenges of estimating fugacities in environmental phases are discussed, especially for ill-defined phases such as soils, sediments, and biota for which activity coefficients and molar volumes cannot be measured. It is shown that by lumping these quantities and a reference fugacity in a single parameter, empirical partition coefficient data can be used to deduce fugacities and thus the relative equilibrium status between phases and directions of diffusive transport. For assessments of substances that display narcosis, chemical activities, which can be readily deduced from fugacities, can provide valuable estimates of the proximity of calculated or measured environmental concentrations to potentially toxic levels. Five illustrations are presented to demonstrate the value of applying the fugacity concept in environmental contexts, namely, the equilibrium distribution of diverse substances, the evaluation of air−water exchange processes, bioconcentration and bioaccumulation in fish, comprehensive risk assessment of regional chemical fate and exposure, and demonstrating the global distribution of chemicals by atmospheric and oceanic transport.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 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".