Impacts of Mixing on AcceptableIndoor Air Quality in Homes
Bibliographic record
Abstract
Ventilation reduces occupant exposure to indoor contaminants by diluting or removing them. In a multizone environment, such as a house, every zone will have different dilution rates and contaminant source strengths. The total ventilation rate is the most important factor in determining occupant exposure to given contaminant sources, but the zone-specific distribution of exhaust and supply air and the mixing of ventilation air can play significant roles. Different types of ventilation systems will provide different amounts of mixing depending on several factors, such as air leakage, air distribution system, and contaminant source and occupant locations. Most US and Canadian homes have central forced-air heating, ventilation, and air-conditioning systems, which tend to mix the air; thus, the indoor air in different zones tends to be well mixed for significant fractions of the year. This article reports recent results of investigations to determine the impact of air mixing on exposures of residential occupants to prototypical contaminants of concern. We summarize existing literature and extend past analyses to determine the parameters that affect air mixing as well as the impacts of mixing on occupant exposure, and draw conclusions that are relevant for standards development and for practitioners designing and installing home ventilation systems. The primary conclusion is that mixing will not substantially affect the mean indoor air quality across a broad population of occupants, homes, and ventilation systems, but it can reduce the number of occupants who are exposed to extreme pollutant levels. If the policy objective is to minimize the number of people exposed above a given pollutant threshold, some amount of mixing will be of net benefit even though it does not benefit average exposure. If the policy is to minimize exposure on average, then mixing air in homes is detrimental and should not be encouraged. We also conclude that most homes in the US have adequate mixing already, but that new, high-performance homes may require additional mixing. Also, our results suggest that some differentiation should be made in policies and standards for systems that provide continuous exhaust, thereby reducing relative dose for occupants overall.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".