The impact of sorption on perceived indoor air quality
Bibliographic record
Abstract
UNLABELLED: Sensory evaluations were used to investigate the impact of sorption processes on indoor air quality. Experiments were carried out in four similar, adjacent, unfurnished offices. Samples of carpet, linoleum, painted gypsum board, and Semia (a specially designed high-sorbing fabric) were tested individually and in combination. Additionally, to investigate the interaction between the pollutants emitted from the building materials and the test room surfaces themselves, air streams polluted by two different building materials were vented into an empty test office. Each experiment lasted for either 1 week (adsorption stage only) or 10 days (adsorption and desorption stages). Untrained panels assessed the air quality at specified times after moving the materials into or out of the rooms. The results showed that, in comparison with air in a room with carpet or linoleum alone, the presence of painted gypsum board improved the perceived air quality. This improvement persisted throughout the 168 h of the 'adsorption stage' of the experiments. A mass balance model was used to quantify the improvement. Calculated results indicate that, for the conditions used in these experiments, pollutant removal via sorption to the relatively inert office surfaces was equivalent to an extra 0.4 air change per hour (ACH) of ventilation air, while sorption to painted gypsum board surfaces was equivalent to an extra 1-7 ACH of ventilation air. In the case of Semia, sorption was equivalent to an extra 16 ACH of ventilation air. During the 'desorption stage' of the experiments, after carpet or linoleum were taken out of a room, approximately 3 days were required before the air in the test office, ventilated at 0.8 ACH, was judged to be free of the sorbed pollutants. PRACTICAL IMPLICATIONS: Ventilation rates in non-industrial buildings are based largely on sensory pollution sources and a desired level of perceived air quality. This study documents that sorptive materials in a room influence the perceived air quality and should be considered when evaluating ventilation requirements. Indeed, it may be possible to deliberately use sorption/desorption to improve indoor air in a manner analogous to the way thermal storage/release is currently used in buildings as a means of conserving energy.
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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 | 0.001 |
| 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) | 0.005 | 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".