The Biofiltration of Indoor Air: Air Flux and Temperature Influences the Removal of Toluene, Ethylbenzene, and Xylene
Bibliographic record
Abstract
An alternative approach to maintaining indoor air quality may be the biofiltration of air circulated within the space. A biofilter with living botanical matter as the packing medium reduced concentrations of toluene, ethylbenzene, and o-xylene concurrently present at parts per billion (volume) in indoor air. The greatest reduction in concentrations per pass was under the slowest influent air flux (0.025 m s(-1)); however, the maximum amount removed per unit time occurred under the most rapid flux (0.2 m s(-1)). There was little difference between the different compounds with removal capacities of between 1.3 and 2.4 micromol m(-3) biofilter s(-1) (between 0.5 and 0.9 g m(-3) biofilter h(-1)) depending on influent flux and temperature. Contrary to biofilters subjected to higher influent concentrations, the optimal temperatures for removal by this biofilter decreased to less than 20 degrees C at the most rapid flux for all three compounds. Microbial activity was decreased at these cooler temperatures suggesting the biofilter was not microbially limited but rather was limited by the availability of substrate. The cooler temperatures allowed greater partitioning of the VOCs into the water column which had a greater impact on removal than its reduction in microbial activity.
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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 | 0.000 | 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 | 0.001 | 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 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".