Bibliographic record
Abstract
• We present an in-situ test methodology for portable air cleaners. • This methodology considers interval, speed, data truncation, duration, and environmental mixing. • Effectiveness of a given air cleaner varies over time in the same and different environments. • Variation in clean air delivery rate is not fully reflected in effectiveness. • Some low-cost sensors demonstrate reliability in testing portable air cleaners. Portable air cleaners (PACs) are widely used to reduce indoor airborne particle concentration. However, the performance of an air cleaner fluctuates over time within the same environment and varies across different environments due to factors such as room volume, ventilation, sources of particles, room mixing, background loss rates, and outdoor particle levels. This study presents an in-situ test methodology for PACs to capture the actual performance using low-cost sensors. The testing consisted of switching from air cleaner operation to placebo operation every 2.5 h for two weeks and the effectiveness was calculated from the PM 2.5 concentrations during neighboring placebo/air cleaning conditions. The median PM 2.5 effectiveness of three types of tested PACs varied from 36.3 % to 94.3 % in residential, 0 % to 66.7 % in classroom, and 11.4 % to 33.3 % in office environments owing to the variation of room size, clean air delivery rate (CADR), sources, and background loss rates. Although the CADR of the top performing PAC is approximately 8.8 times higher than the least performing PAC, the median effectiveness only improved by a factor of 2.4. One type of low-cost sensor predicted a similar median effectiveness when compared to a more robust instrument, while another type of low-cost sensor resulted in a lower effectiveness, likely owing to its reduced responsiveness during periods with elevated PM 2.5 concentrations. Overall, this study contributes to the development of an in-situ testing methodology for PACs, which will facilitate the adoption, use, and evaluation of PACs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".