Extraction of dust collected in HVAC filters for quantitative filter forensics
Bibliographic record
Abstract
The analysis of dust collected on the filters installed in the heating, ventilation, and air-conditioning (HVAC) systems, filter forensics, is a useful approach to explore concentration, size distribution, and composition of indoor particles. The extraction of dust from filters represents one of the biggest challenges to obtain accurate results from filter forensics. Although vacuuming is one of the most common dust extraction techniques, it is unclear how efficient it is and whether it provides a representative sample in terms of particle size. In this article, we used a high-capacity vacuum sampler to extract dust from 20 filters artificially loaded with well-characterized test dust as well as from 41 filters naturally loaded in residential HVAC systems. After all extractions, we recovered 0.1–5.5 g and 0.02–11.4 g of dust from the artificially and naturally loaded filters, respectively. These ranges were equivalent to 11.3–52.2% and 1.8–72.9% recovery efficiency, the ratio of dust recovered to the dust loaded in the filters. Multiple extractions were found to be an effective strategy to add to the recovery to enable filter forensics for the detection of multiple analytes. The recovered samples were slightly over-representative of particles greater than 10 µm. Therefore, caution should be taken when applying filter forensics for studying contaminants associated with smaller particles.Copyright © 2020 American Association for Aerosol Research
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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 | 0.001 |
| 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) | 0.000 | 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".