Characterization of woodsmoke generated in the air pollution exposure lab and comparison to diesel exhaust
Bibliographic record
Abstract
To address the increasing concern regarding woodsmoke (WS) exposure and better understand its effects on human health, a WS generation system was built in the Air Pollution Exposure Laboratory to facilitate future controlled human exposure studies. Ground lodgepole pine was burned to generate WS, with PM2.5 concentrations of approximately 500 µg/m3 obtained. The WS produced by this system was characterized and directly compared with diesel exhaust (DE) generated and collected at the same facility. For gases, WS showed slight increases in CO and CO2 compared with filtered air (FA), whereas DE had significantly higher levels of NOx, CO, CO2, and total volatile organic compounds than FA. The non-refractory composition of WS aerosols was approximately 98% organics, 0.2% ammonium, 1.3% nitrate, and 0.2% sulfate. Among the organic species, the fraction of oxygenated species was much higher in WS aerosols than in DE aerosols. Moreover, WS aerosols had higher concentrations of Cd compared with DE aerosols. Greater oxidative potential was also observed for WS compared with DE, with dithiothreitol consumption rates of 0.0090 nmol/min/µg. This study established a controlled human exposure platform for WS and described the methods used for analyzing and comparing the concentrations, particulate morphologies, chemical compositions, and oxidative potentials of different lab-generated pollutants. The observed differences between WS and DE in oxidative potential and amounts of gases, organic species, and metals provide a foundation for investigating how specific air pollution components differentially impact human health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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 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".