Modeling Exposure to Persistent Chemicals in Hazard and Risk Assessment
Bibliographic record
Abstract
Fate and exposure modeling has not, thus far, been explicitly used in the risk profile documents prepared for evaluating the significant adverse effect of candidate chemicals for either the Stockholm Convention or the Convention on Long-Range Transboundary Air Pollution. However, we believe models have considerable potential to improve the risk profiles. Fate and exposure models are already used routinely in other similar regulatory applications to inform decisions, and they have been instrumental in building our current understanding of the fate of persistent organic pollutants (POP) and persistent, bioaccumulative, and toxic (PBT) chemicals in the environment. The goal of this publication is to motivate the use of fate and exposure models in preparing risk profiles in the POP assessment procedure by providing strategies for incorporating and using models. The ways that fate and exposure models can be used to improve and inform the development of risk profiles include 1) benchmarking the ratio of exposure and emissions of candidate chemicals to the same ratio for known POPs, thereby opening the possibility of combining this ratio with the relative emissions and relative toxicity to arrive at a measure of relative risk; 2) directly estimating the exposure of the environment, biota, and humans to provide information to complement measurements or where measurements are not available or are limited; 3) to identify the key processes and chemical or environmental parameters that determine the exposure, thereby allowing the effective prioritization of research or measurements to improve the risk profile; and 4) forecasting future time trends, including how quickly exposure levels in remote areas would respond to reductions in emissions. Currently there is no standardized consensus model for use in the risk profile context. Therefore, to choose the appropriate model the risk profile developer must evaluate how appropriate an existing model is for a specific setting and whether the assumptions and input data are relevant in the context of the application. It is possible to have confidence in the predictions of many of the existing models because of their fundamental physical and chemical, mechanistic underpinnings and the extensive work already done to compare model predictions and empirical observations. The working group recommends that modeling tools be applied for benchmarking PBT and POPs according to exposure-emissions relationships and that modeling tools be used to interpret emissions and monitoring data. The further development of models that combine fate, long-range transport, and bioaccumulation should be fostered, especially models that will allow time trends to be scientifically addressed in the risk profile.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".