Evaluating the relevance of seasonal differentiation of human health intake fractions in life cycle assessment
Bibliographic record
Abstract
The intake fraction (iF) is the fraction of an emitted mass of chemical that is ultimately taken in by an entire population, and it is used as an indicator of human health potential impacts related to environmental chemical persistence and bioaccumulation in the food chain. In chemical screening applications, the iF can be predicted using multimedia and multipathway fate and exposure models. One of the sources of iF uncertainty is the natural seasonal variability of the input parameters used in the models, i.e., the physicochemical properties of the pollutant and the landscape and exposure parameters. The objective of this article is to determine the relevance of including seasonal differentiation when assessing iFs in life cycle assessment. This was done by calculating and comparing seasonal iFs with each other and with iFs at 25° C, for both Canadian and global contexts. Two Canadian seasonal models based on the IMPACT 2002 predictive tool, and 2 models for the global context based on the USEtox consensus model were developed to calculate summer and winter iFs. Emissions into air and water and a set of 35 representative organic chemicals were considered. Partition coefficients for seasonal conditions were calculated using an integration of the van't Hoff equation. First-order degradation rate constants were calculated assuming that the rate constant doubles with each 10° C increase in temperature. For Canadian air emissions, results indicated that iFs for winter emissions could be up to 1 to 2 orders of magnitude higher than summer iFs or iFs calculated at 25° C. For Canadian water emissions, results showed that iFs for both summer and winter conditions were, in general, closer to each other with outliers within 1 order of magnitude to iFs calculated at 25° C. Results also indicated that seasonal variability was of lesser importance when assessing iFs within a global context. Because the ranking between chemicals was maintained, it can be concluded that seasonal variability is not relevant within a comparative context. However, this difference might be significant when comparing the magnitude of human toxicity impacts versus other impact categories contributing to human health damages.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".