Characterization of Woodsmoke Generated in the Air Pollution Exposure Lab and Comparison to Diesel Exhaust
Bibliographic record
Abstract
Abstract To address the increasing concern of woodsmoke (WS) and better understand its effects on human health, a woodsmoke generation system was built in the Air Pollution Exposure Laboratory to facilitate future controlled human exposure studies. Two different woodsmoke conditions, flaming (WSFL) and smoldering (WSSM), were generated, and PM 2.5 concentrations of approximately 500 μg/m 3 were achieved. The woodsmoke produced using the system was characterized in this study and compared with diesel exhaust (DE) generated and collected at the same facility. Within the gas phase generated by the pollutants, WS showed slight increases in CO and CO 2 compared to filtered air (FA), while DE contained significantly higher levels of NOx, CO 2 , and total volatile organic compounds compared to FA. The WS aerosols were comprised of approximately 98% organics, 0.6% NH 4 , 0.9% NO 3 , and 0.2% SO 4 . Among the organic species, the CHO1 and CHOgt1 families encompassed around 60%, which was higher than the fraction of oxygenated families in DE aerosols. Moreover, the WS aerosols had higher concentrations of Cd compared to the DE aerosols. Greater oxidative potentials were also observed for WSFL and WSSM compared to DE, with DTT consumption rates normalized to the PM mass being 0.0099 and 0.0090 nmol/min/μg, respectively. The difference in the compositions and properties of WS and DE suggests that it is critical to conduct further studies on how these pollutants can affect health differently.
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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 | 0.001 | 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".