Concentration-dependent increase in symptoms due to diesel exhaust in a controlled human exposure study
Bibliographic record
Abstract
Abstract Background Traffic-related air pollution (TRAP) exposure causes adverse effects on wellbeing and quality of life, which can be studied non-invasively using self-reported symptoms. However, little is known about the effects of different TRAP concentrations on symptoms following controlled exposures, where acute responses can be studied with limited confounding. We investigated the concentration–response relationship between diesel exhaust (DE) exposure, as a model TRAP, and self-reported symptoms. Methods We recruited 17 healthy non-smokers into a double-blind crossover study where they were exposed to filtered air (FA) and DE standardized to 20, 50, 150 µg/m3 PM2.5 for 4 h, with a ≥ 4-week washout between exposures. Immediately before, and at 4 h and 24 h from the beginning of the exposure, we administered visual analog scale (VAS) questionnaires and grouped responses into chest, constitutional, eye, neurological, and nasal categories. Additionally, we assessed how the symptom response was related to exposure perception and airway function. Results An increase in DE concentration raised total (β ± standard error = 0.05 ± 0.03, P = 0.04), constitutional (0.01 ± 0.01, P = 0.03) and eye (0.02 ± 0.01, P = 0.05) symptoms at 4 h, modified by perception of temperature, noise, and anxiety. These symptoms were also correlated with airway inflammation. Compared to FA, symptoms were significantly increased at 150 µg/m3 for the total (8.45 ± 3.92, P = 0.04) and eye (3.18 ± 1.55, P = 0.05) categories, with trends towards higher values in the constitutional (1.49 ± 0.86, P = 0.09) and nasal (1.71 ± 0.96, P = 0.08) categories. Conclusion DE exposure induced a concentration-dependent increase in symptoms, primarily in the eyes and body, that was modified by environmental perception. These observations emphasize the inflammatory and sensory effects of TRAP, with a potential threshold below 150 µg/m3 PM2.5. We demonstrate VAS questionnaires as a useful tool for health monitoring and provide insight into the TRAP concentration–response at exposure levels relevant to public health policy.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".