Measuring the fitted filtration efficiency of cloth masks, medical masks and respirators
Bibliographic record
Abstract
Abstract Importance Masks reduce transmission of SARS-CoV2 and other respiratory pathogens. Comparative studies of the fitted filtration efficiency of different types of masks of are few. Objective To describe the fitted filtration efficiency against small aerosols (0.02 – 1 µm) of medical and non-medical masks and respirators when worn, and how this is affected by user modifications (hacks) and by overmasking with a cloth mask. Design We tested a 2-layer woven-cotton cloth mask of a consensus design, ASTM-certified level 1 and level 3 masks, a non-certified mask, KF94s, KN95s, an N95 and a CaN99. Setting Closed rooms with ambient particles supplemented by salt particles. Participants 12 total participants; 21 – 55 years, 68% female, 77% white, NIOSH 1 to 10. Main Outcome and Measure Using standard methods and a PortaCount 8038, we counted 0.02–1µm particles inside and outside masks and respirators, expressing results as the percentage filtered by each mask. We also studied level 1 and level 3 masks with earguards, scrub caps, the knot-and-tuck method, and the effects of braces or overmasking with a cloth mask. Results Filtration efficiency for the cloth mask was 47-55%, for level 1 masks 52-60%, for level 3 masks 60-77%. A non-certified KN95 look-alike, two KF94s, and three KN95s filtered 57-77%, and the N95 and CaN99 97-98% without fit testing. External braces and overmasking with a well-fitting cloth mask increased filtration, but earguards, scrub caps, and the knot-and-tuck method did not. Limitations Limited number of masks of each type sampled; no adjustment for multiple comparisons. Conclusions and Relevance Well-fitting 2-layer cotton masks filter in the same range as level 1 masks when worn: around 50%. Level 3 masks and KN95s/KF94s filter around 70%. External braces or overmasking with a cloth-mask-on-ties produced filtration around 90%. Only N95s and CaN99s, both of which have overhead elastic, performed close to the occupational health and safety standards for fit tested PPE (>99%), filtering at 97-99%, without fit testing. These findings inform public health messaging about relative protection from aerosols from different mask types and increase understanding of findings of studies of implementation of masks and respirators. Key Points Question: How well do medical and non-medical masks filter aerosols when worn? Findings: Well-fitting 2-layer cotton masks, and level 1 medical masks were similar, both filtering around 50% of aerosols. Level 3 masks and KN95/KF94s were similar, filtering around 70%. N95s and CaN99s, without formal fit testing, filtered 97-98%. Meaning: Level 1 medical masks were not better than the well-fitting 2-layer cotton masks we tested. KN95/KF94s are not as efficient, when worn, as N95s and CaN99s. Overmasking and the use of external braces improve filtration: these are potentially useful strategies when N95s are not available. Abstract Figure
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".