Occupational Monitoring of Particulate Diesel Exhaust by NIOSH Method 5040
Bibliographic record
Abstract
NMAM 5040 is a particulate carbon method based on a thermal-optical analysis technique. The method was evaluated and published as a method for monitoring occupational exposures to particulate diesel exhaust, but it is applicable to particulate carbon aerosols in general, and has been routinely used in both occupational and environmental settings. Both organic and elemental carbon are determined, but EC is a more selective measure of workplace diesel exposure. In previous studies, good agreement between TC results obtained by different methods has been achieved, but the OC-EC results for different methods have been quite variable. Although a reference material is not currently available to test the accuracy of different methods, previous studies indicate that purely thermal methods are subject to positive bias from organic materials that char. Charring and inadequate removal of refractory OC components during the nonoxidative mode (typically 550 degrees C in nitrogen) likely explain the positive bias of thermal methods, as well as the large variability across methods. These interferences may be negligible in some cases (e.g., samples from mines), but they present significant biases in others (e.g., urban air samples, samples containing wood or cigarette smokes). Good interlaboratory agreement was obtained in a round robin comparison between six laboratories that used NMAM 5040, which was not the case with purely thermal methods. Good agreement has also been seen in smaller-scale comparisons conducted for quality assurance purposes. Until a suitable reference material becomes available, such comparisons are recommended as part of a laboratory's QA procedures. At present, five commercial laboratories (4 in the United States and 1 in Canada) perform the 5040 analysis, and over 40 instruments are in use globally for environmental and occupational monitoring.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".