Diesel engine exhaust exposure in underground mines: Comparison between different surrogates of particulate exposure
Bibliographic record
Abstract
Exposure to diesel particulate matter (DPM) is frequently assessed by measuring indicators of carbon speciation, but these measurements may be affected by organic carbon (OC) interference. Furthermore, there are still questions regarding the reliability of direct-reading instruments (DRI) for measuring DPM, since these instruments are not specific and may be interfered by other aerosol sources. This study aimed to assess DPM exposure in 2 underground mines by filter-based methods and DRI and to assess the relationship between the measures of elemental carbon (EC) and the DRI to verify the association of these instruments to DPM. Filter-based methods of respirable combustible dust (RCD), EC, and total carbon (TC) were used to measure levels of personal and ambient DPM. For ambient measurements, DRI were used to monitor particle number concentration (PNC; PTrak), particle mass concentration (DustTrak DRX and DustTrak 8520), and the submicron fraction of EC (EC1;Airtec). The association between ambient EC and the DRI was assessed by Spearman correlation. Geometric mean concentrations of RCD, respirable TC (TCR) and respirable elemental EC (ECR) were 170 µg/m3, 148 µg/m3, and 83 µg/m3 for personal samples, and 197 µg/m3, 151 µg/m3, and 100 µg/m3 for ambient samples. Personal measurements had higher TCR:ECR ratios compared to ambient samples (1.8 vs. 1.50) and weaker association between ECR and TCR. Among the DRI, the measures of EC1 by the Airtec (ρ = 0.86; P < 0.001) and the respirable particles by the DustTrak 8520 (ρ = 0.74; P < 0.001) showed the strongest association with EC, while PNC showed a weak and non-significant association with EC. In conclusion, this study provided important information about the concentrations of DPM in underground mines by measuring several indicators using filter-based methods and DRI. Among the DRI, the Airtec proved to be a good tool for estimating EC concentrations and, although the DustTrak showed good association with EC, interferences from other aerosol sources should be considered when using this instrument to assess DPM.
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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 | 0.002 |
| 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 | 0.001 | 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 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".