Comparison of human exposure pathways in an urban brownfield: Reduced risk from paving roads
Bibliographic record
Abstract
Risk assessments often do not quantify the risk associated with soil inhalation. This pathway generally makes a negligible contribution to the cumulative risk, because soil ingestion is typically the dominant exposure pathway. Conditions in northern or rural centers in Canada characterized by large areas of exposed soil, including unpaved roads, favor the resuspension of soil particles, making soil inhalation a relevant risk pathway. The authors determined and compared human exposure to metals and polycyclic aromatic hydrocarbons (PAHs) from soil ingestion and inhalation and analyzed the carcinogenic and noncarcinogenic risks before and after roads were paved in a northern community. To determine the inhalation exposure, three size fractions of airborne particulate matter were collected (total suspended particulates [TSP], particulate matter with an aerodynamic diameter less than 10 µm [PM10], and particulate matter with an aerodynamic diameter less than 2.5 µm [PM2.5]) before and after roads were paved. Road paving reduced the concentration of many airborne contaminants by 25 to 75%, thus reducing risk. For example, before paving, the carcinogenic risk associated with inhalation of Cr was 3.4 excess cancers per 100,000 people exposed, whereas after paving, this risk was reduced to 1.6 in 100,000. Paving roads reduced the concentrations of total suspended particulates (TSP; p < 0.1) and PM10 (p < 0.05) but not PM25. Consequently, the ingestion of inhaled soil particles was substantially reduced. The authors conclude that resuspended soil is likely an important source of risk for many northern communities and that paving roads is an effective method of reducing risk from the inhalation of soil particles.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".