The Air Pollution Exposure Laboratory (APEL) for controlled human exposure to diesel exhaust and other inhalants: characterization and comparison to existing facilities
Bibliographic record
Abstract
OBJECTIVE: The Air Pollution Exposure Laboratory (APEL) was designed for the controlled inhalation of human subjects to aged and diluted diesel exhaust (DE) to mimic "real-world" occupational and environmental conditions. METHODS: An EPA Tier 3-compliant, 6.0 kW diesel generator is operated under discrete cyclic loads to simulate diesel on-road emissions. The engine accepts standard ultra-low sulfur diesel or a variety of alternative fuels (such as biodiesel) via a partitioned tank. A portion of raw exhaust is drawn into the primary dilution system and is diluted 9:1 with compressed air at standard temperature (20°C) and humidity (40%) levels. The exhaust is further diluted approximately 25:1 by high efficiency particulate air (HEPA)-filtered air (FA) and then aged for 4 min before entering the 4 × 6 × 7-foot exposure booth. An optional HEPA filter path immediately proximal to the booth can generate a particle-reduced (gas-enriched) exposure. In-booth particulate is read by a nephelometer to provide an instantaneous light scattering coefficient for closed-loop system control. A Scanning Mobility Particle Sizer and multi-stage impactor measures particle size distribution. Filter sampling allows determination of sessional average concentrations of size-fractionated and unfractionated particulate oxidative potential, elemental carbon, organic carbon and trace elements. RESULTS: Approximately 300 μg/m(3) PM(2.5) is routinely achievable at APEL and is well characterized in terms of oxidative potential and elemental components. CONCLUSION: APEL efficiently creates fresh DE, appropriately aged and diluted for human experimentation at safe yet realistic concentrations. Description of exposure characteristics allows comparison to other international efforts to deepen the current evidence base regarding the health effects of DE.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".