The Biofiltration of Indoor Air: Implications for Air Quality
Bibliographic record
Abstract
An alternative method of maintaining indoor air quality may be through the biofiltration of air recirculating within the structure rather than the traditional approach of ventilation. This approach is currently being investigated. Prior to its acceptance for dealing with volatile organic compounds (VOCs) and CO2, efforts were made to determine whether the incorporation of this amount of biomass into the indoor space can have an (negative) impact on indoor air quality. A relatively large ecologically complex biofilter composed of a ca. 10 m2 bioscrubber, 30 m2 of plantings and a 3,500 litre aquarium were established in a 160 m2 'airtight' room in a recently constructed office building in downtown Toronto. This space maintained ca. 0.2 air changes per hour (ACH) compared to the 15 to 20 ACH (with a 30% refresh rate) of other spaces in the same building. Air quality parameters of concern were total VOCs (TVOCs), formaldehyde and aerial spore counts. TVOC and formaldehyde levels in the biofilter room were the same or significantly less than other spaces in the building despite a much slower refresh rate. Aerial spore levels were slightly higher than other indoor spaces but were well within reported values for 'healthy' indoor spaces. Levels appeared to be dependent on horticultural management practices within the space. Most genera of fungal spores present were common indoors and the other genera were associated with living or dead plant material or soil. From these results, the incorporation of a large amount of biomass associated with indoor biofilters does not in itself lower indoor air quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".