Revisiting Dust and Soil Ingestion Rates Based on Hand-to-Mouth Transfer
Bibliographic record
Abstract
Soil ingestion rates calculated using a tracer-based mass balance approach may carry considerable study errors, insensitivities, and “artefacts” of analysis that result in significant uncertainty. These same soil ingestion rates are often used as surrogates for dust ingestion rates. Therefore, a more direct and mechanistic method was developed to estimate soil and dust ingestion rates. The soil and dust ingestion rates were calculated using measures of: particle loading to indoor surfaces; fraction transferred to the hands; hand surface area; fraction of hand surface area that may be mouthed or contact food; frequency of hand-to-mouth events, amount dissolved by saliva; and exposure time. Adapted specifically for the Canadian context, estimated mean indoor dust ingestion rates range from 2.2 mg/d for teenagers to 41 mg/d for toddlers; mean soil ingestion rates range from 1.2 mg/d for seniors to 23 mg/d for children. Combined soil and dust ingestion rates ranged from 3.8 mg/d for seniors to 61 mg/d for toddlers. These ingestion rates are lower than values adopted by most agencies. These ingestion rates are mechanistic, can be adjusted on a site-specific basis, can be modified into an hourly rate and are presented as a more realistic alternative to traditional mass balance approaches. [Supplementary materials are available for this article. Go to the publisher's online edition of Human and Ecological Risk Assessment to view the free supplementary files.]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".