Diesel Engine Exhaust Exposure in the Ontario Civil Infrastructure Construction Industry
Bibliographic record
Abstract
OBJECTIVES: Diesel engine exhaust (DEE) is a known lung carcinogen and a common occupational exposure in Canada. The use of diesel-powered equipment in the construction industry is particularly widespread, but little is known about DEE exposures in this work setting. The objective of this study was to determine exposure levels and identify and characterize key determinants of DEE exposure at construction sites in Ontario. METHODS: Elemental carbon (EC, a surrogate of DEE exposure) measurements were collected at seven civil infrastructure construction worksites and one trades training facility in Ontario using NIOSH method 5040. Full-shift personal air samples were collected using a constant-flow pump and SKC aluminium cyclone with quartz fibre filters in a 37-mm cassette. Exposures were compared with published health-based limits, including the Dutch Expert Committee on Occupational Safety (DECOS) limit (1.03 µg m-3 respirable EC) and the Finnish Institute of Occupational Health (FIOH) recommendation (5 µg m-3 respirable EC). Mixed-effects linear regression was used to identify determinants of EC exposure. RESULTS: In total, 149 EC samples were collected, ranging from <0.25 to 52.58 µg m-3 with a geometric mean (GM) of 3.71 µg m-3 [geometric standard deviation (GSD) = 3.32]. Overall, 41.6% of samples exceeded the FIOH limit, mostly within underground worksites (93.5%), and 90.6% exceeded the DECOS limit. Underground workers (GM = 13.20 µg m-3, GSD = 1.83) had exposures approximately four times higher than below grade workers (GM = 3.56 µg m-3, GSD = 1.94) and nine times higher than above ground workers (GM = 1.49 µg m-3, GSD = 1.75). Training facility exposures were similar to above ground workers (GM = 1.86 µg m-3, GSD = 4.12); however, exposures were highly variable. Work setting and enclosed cabins were identified as the key determinants of exposure in the final model (adjusted R2 = 0.72, P < 0.001). The highest DEE exposures were observed in underground workplaces and when using unenclosed cabins. CONCLUSIONS: This study provides data on current DEE exposure in Canadian construction workers. Most exposures were above recommended health-based limits, albeit in other jurisdictions, signifying a need to further reduce DEE levels in construction. These results can inform a hazard reduction strategy including targeted intervention/control measures to reduce DEE exposure and the burden of occupational lung cancer.
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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 | 0.001 | 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 | 0.001 |
| 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 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".