Physiological Responses to Diesel Exhaust Exposure Are Modified by Cycling Intensity
Bibliographic record
Abstract
BACKGROUND: Outdoor exercisers are frequently exposed to diesel exhaust (DE) that contains particulate matter (PM) air pollution. How the respiratory and metabolic responses to exercise are affected by DE exposure and how these responses change with exercise intensity are unknown. PURPOSE: This study aimed to determine the respiratory and metabolic responses to low- and high-intensity cycling with DE exposure containing high levels of PM. METHODS: Eighteen males age 24.5 ± 6.2 yr performed 30-min trials of low-intensity (30% of power at V˙O2peak) and high-intensity (60% of power at V˙O2peak) cycling as well as rest. Each trial was performed once while breathing filtered air (FA) and once while breathing DE (300 μg·m of PM2.5) for a total of six trials, each separated by 7 d. During the trials, minute ventilation (V˙E), oxygen consumption (V˙O2), CO2 production (V˙CO2), RER, and perceived exertion for lungs (RPELungs) and legs (RPELegs) were measured. Work of breathing, respiratory muscle V˙O2, ratio of O2 consumption to power output, and gross efficiency were estimated. RESULTS: The RER was significantly lower (0.02 lower, P = 0.008), and the RPELungs (0.9 greater, P = 0.001) and the RPELegs (0.6 greater, P = 0.017) were significantly greater, in DE compared with FA. During low-intensity exercise, V˙E (44.5 ± 8.9 vs 40.5 ± 8.0 L·min, P < 0.001), V˙O2 (27.9 ± 5.4 vs 24.9 ± 4.4 mL·kg·min, P = 0.001), and V˙CO2 (25.9 ± 5.3 vs 23.5 ± 4.5 mL·kg·min, P = 0.006) were significantly greater in DE. This pattern was not seen during high-intensity cycling. CONCLUSIONS: Respiratory and metabolic responses to low-intensity, but not high-intensity, cycling in DE exceed FA. Practically, the greater responses during low-intensity exercise in DE could have implications for individuals with cardiopulmonary disease. Also, the elevated RPE during DE could impair performance in self-paced exercise.
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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 | 0.000 | 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) | 0.002 | 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".