Quantitative filter forensics for allergens and semivolatile organic compounds in residential buildings in Toronto, Canada
Bibliographic record
Abstract
Quantitative filter forensics (QFF) is a method for estimating the time-averaged concentration of particle-bound containments using filter metadata and its extracted dust. In this study, QFF was applied to dust samples extracted from four types of HVAC filters deployed in 20 homes from December 2016 to December 2017 in Toronto, Canada. The analysis covered six allergens, eight phthalates, and 12 polycyclic aromatic hydrocarbons (PAHs). The allergen results from four homes showed elevated concentrations of cat and dog allergens (Fel d 1 and Can f 1) in homes with cats or dogs. All eight phthalates were detected, dominated by diisononyl phthalate (DiNP) and bis(2-ethylhexyl) phthalate (DEHP), with median concentrations of 4.27 ng/m³ and 4.20 ng/m³, respectively. All PAHs, except anthracene, were detected in over 50 % of the homes, with benzo[a]pyrene being the most abundant (median concentration of 0.067 ng/m³). Median concentrations of all semivolatile organic compounds (SVOCs) were lower in winter 2016 compared to spring 2017, potentially due to losses of more volatile SVOCs from filters because of higher filtration volumes in winter, in addition to differences in sources and ventilation rates. All phthalate and most PAH concentrations were one to two orders of magnitude lower compared to those found from QFF in apartments in Toronto social housing multi-unit residential buildings (MURB), indicating those with lower socio-economic status (SES) were exposed to higher SVOC concentrations. Occupant density was positively related to most phthalate and specific PAH concentrations, while environmental factors including room temperature, relative humidity, and supply air temperature were not. • Quantitative filter forensics enables long-term mean SVOC concentration estimation. • Large variations in phthalate and PAH concentrations across homes. • Seasonal variations likely caused by sources, ventilation, and sampling artefacts. • SVOC concentrations are not strongly correlated with filter efficiency. • SVOC concentrations were lower in single-family homes than in social housing units.
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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".