Bioaccumulation of Organic Contaminants in Humans: A Multimedia Perspective and the Importance of Biotransformation
Bibliographic record
Abstract
Bioaccumulation is an important component of the exposure hazard assessment and risk assessment of organic chemicals. Screening criteria for chemical hazard used in national and international regulations are based on the paradigm that partitioning properties are the primary chemical determinants of bioaccumulation. We use a holistic multimedia perspective to evaluate the partitioning property paradigm with respect to assessing human bioaccumulation. Multimedia bioaccumulation factors (mmBAFs) for humans were modeled for hypothetical chemicals with a wide range of physical-chemical properties. Varying partitioning properties over 12 orders of magnitude (a plausible range for nonionizing organics) resulted in only modest changes in mmBAFs (a factor of ∼ 10) for all but very volatile or hydrophilic chemicals. In contrast, varying biotransformation rate constants over 6 orders of magnitude resulted in substantial differences in mmBAFs (greater than a factor of 10(9)). Our model results are supported by empirical observations of well characterized pollutants, which demonstrate that chemicals with similar partitioning properties can have very different bioaccumulation behavior. Susceptibility to biotransformation clearly determines bioaccumulation in humans for many chemicals. We conclude that a holistic multimedia perspective for bioaccumulation assessment is necessary to develop regulations, criteria, and policies that are protective of human health and the environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".