Quantitative filter forensics: Size distribution and particulate matter concentrations in residential buildings
Bibliographic record
Abstract
We applied filter forensics, the analysis of dust from the heating, ventilation, and air-conditioning (HVAC) filters, to measure particle size distribution in 21 residences in Toronto, Canada over a year. Four filters with different nominal efficiencies (Minimum Efficiency Reporting Value (MERV) 8–14 from ASHRAE Standard 52.2) were deployed in each residence each for three months, while the effective filtration volumes (the product of flow rate, runtime, and in-situ filter efficiency) were characterized over each filter lifetime. Using extraction and laser diffraction, we found that approximately 90% of the volumetric distributions were >10 µm and the volume median diameter (VMD) ranged from 23.4 to 75.1 µm. Using quantitative filter forensics (QFF), total suspended particle (TSP) concentrations ranged from 2.9 to 823.7 µg/m3 (median = 89.8 µg/m3) with a moderate correlation with the content of TSP on the filters (in terms of g) and with the TSP effective filtration volume (m3) indicating the importance of both filter forensics and HVAC metadata parameters to QFF concentration estimates. There was no strong correlation between PM10 or PM2.5 concentrations and hourly airborne particle number concentrations measured by low-cost sensors suggesting an evaluation of QFF is warranted, particularly for the exploration of smaller particles.
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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 | 0.000 | 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 | 0.000 |
| 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 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".