Assessing Diesel Particulate Matter Exposure by a Multi-Metric Approach in Three Different Workplaces in Québec, Canada
Bibliographic record
Abstract
Elemental carbon and total carbon are the surrogates usually used to assess diesel particulate matter (DPM) exposure, but the measurement of different indicators by direct-reading instruments (DRI) is also described and includes advantages and limitations. The aim of this study was to assess and compare several indicators of DPM exposure in three workplaces by using a multi-metric approach.Three workplaces in Quebec (Canada) were evaluated: an underground mine (W1; n=12), a subway tunnel (W2; n=12) and a truck repair workshop (W3; n=12). Ambient particle number concentration (PNC), submicron particulate matter (PM1), and submicron elemental carbon (EC1-DRI) were measured by DRI. Filter-based samples (FBS) were also collected to estimate the respirable and submicron fractions of elemental carbon (ECR and EC1-FBS), as well as the respirable and submicron fractions of total carbon (TCR and TC1).The geometric means of the DRI were: 128,000; 32,800 and 22,700 particles/cm3 for PNC in W1, W2 and W3, respectively; 165, 20.4 and 6.9 µg/m3 for PM1; and 148, 25 and 4.1 µg/m3 for EC1-DRI. W1 also had the highest concentrations of ECR, EC1-FBS, TCR and TC1. EC1-DRI showed the strongest association with EC1-FBS when considering all workplaces (ρ= 0.966; p<0.001), but this association decreased at lower concentrations of DPM. PM1 (ρ= 0.936; p<0.001) and PNC (ρ= 0.871; p<0.001) also showed a strong positive correlation with EC1-FBS. Ratios of 1.05, 1.76 and 1.30 were calculated between TC1/PM1 for W1, W2 and W3, respectively. This study provided solid information about the concentrations, as well as their associations, of several indicators of DPM exposure in three different exposure contexts.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".