Fine and Ultrafine Particle Decay Rates in Multiple Homes
Bibliographic record
Abstract
Human exposure to particles depends on particle loss mechanisms such as deposition and filtration. Fine and ultrafine particles (FP and UFP) were measured continuously over seven consecutive days during summer and winter inside 74 homes in Edmonton, Canada. Daily average air exchange rates were also measured. FP were also measured outside each home and both FP and UFP were measured at a central monitoring station. A censoring algorithm was developed to identify indoor-generated concentrations, with the remainder representing particles infiltrating from outdoors. The resulting infiltration factors were employed to determine the continuously changing background of outdoor particles infiltrating the homes. Background-corrected indoor concentrations were then used to determine rates of removal of FP and UFP following peaks due to indoor sources. About 300 FP peaks and 400 UFP peaks had high-quality (median R(2) value >98%) exponential decay rates lasting from 30 min to 10 h. Median (interquartile range (IQR)) decay rates for UFP were 1.26 (0.82-1.83) h(-1); for FP 1.08 (0.62-1.75) h(-1). These total decay rates included, on average, about a 25% contribution from air exchange, suggesting that deposition and filtration accounted for the major portion of particle loss mechanisms in these homes. Models presented here identify and quantify effects of several factors on total decay rates, such as window opening behavior, home age, use of central furnace fans and kitchen and bathroom exhaust fans, use of air cleaners, use of air conditioners, and indoor-outdoor temperature differences. These findings will help identify ways to reduce exposure and risk.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".