Efficiency of the Vehicle Cabin Air Filters for Removing Black Carbon Particles and BTEX from the Air Intake
Bibliographic record
Abstract
A laboratory study was conducted to evaluate 11 vehicular cabin filters (including electrostatic filters) in removing fine particles. Two filters with charcoal were also evaluated to understand their usefulness in removing five common volatile organic compounds, including benzene, toluene, ethylbenzene, and xylene isomers (BTEX). Filters were found to show considerably different particle filtration efficiencies (FE). Electrostatic filters were found to provide 20–60% better FE across all particle diameters (6–520 nm). For 6 nm particles, FE from 78 to 94% were observed (from the worst to the best filters), while at 520 nm, FE varied from 35 to 60%. The best group of filters provided 44–46% FE for capturing the most penetrating particles (100–300 nm), while the worst group of filters provided only 10–11% FE. The filtration behavior of nominal filters was typically stable (with respect to particle number, black carbon, and particulate matter mass) over the course of 1–2 years of usage. The benefits of the electrostatic filters were significant, but such advantages were observed to gradually dissipate over the course of about 1 year; by then, the electrostatic filter becomes no different compared to a nominal filter in terms of filtration behavior. Charcoal filters showed variabilities in removing BTEX, and removal efficiencies varied from 11 to 41%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".