Comparison between personal sampling methodologies for evaluating diesel particulate matter exposures in mines: submicron total carbon corrected for the adsorption of vapor-phase organic carbon vs. respirable total carbon
Bibliographic record
Abstract
In the mining industry, personal measurements of elemental and total carbon are frequently used as surrogates of diesel particulate matter (DPM) exposure, and the respirable or submicron fractions are usually measured. However, vapor-phase organic carbon (OC) can be adsorbed in the filters, interfering with total carbon results. This study presents a comparative evaluation between the submicron fraction of DPM concentrations corrected for the adsorption of the vapor-phase OC (dynamic blank), and the respirable fraction of DPM corrected for a field blank. Respirable and submicron fractions of total carbon (TCR and TC1) and elemental carbon (ECR and EC1) concentrations were sampled in parallel, in the workers’ breathing zone, in an underground gold mine. A total of 20 full-shift personal samples were taken for each size fraction. Field blanks were collected each day for both the submicron and respirable fractions, while dynamic blank correction was also applied for the submicron fraction. TCR presented a larger and statistically different geometric mean concentration compared to TC1 (98 µg/m3 vs. 72 µg/m3; p = 0.01), while the concentrations of ECR and EC1 were not statistically different (58 µg/m3 vs. 54 µg/m3; p = 0.74). Average TCR/ECR ratio was 1.7, while the TC1/EC1 ratio was 1.3. In addition, 93% of EC had an aerodynamic size lower than 1 µm, while the proportion of TC particles in the submicron fraction was lower (73%). Finally, a similar quantity of OC was found when analyzing the dynamic and field blanks of the filters with the submicron fraction selective size (24 µg and 22 µg, respectively). In conclusion, the correction for the vapor phase OC by the dynamic blank was not a significant correction in our study design compared to the field blank samples. This study suggests that the differences in TC may be explained by the different aerodynamic fractions of DPM collected. In addition, elemental carbon measurements did not seem to be extensively affected by the aerodynamic size of the particles collected.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".