IAQ and energy implications of high efficiency filters in residential buildings: A review (RP-1649)
Bibliographic record
Abstract
Exposure to airborne particulate matter (PM) in residential buildings can cause adverse health risks for building occupants. One approach to mitigate this exposure is the use of filters in HVAC systems. In this article, we present an integrated picture of the overall performance of higher efficiency residential filters by assessing the impacts of their use in residences that incorporate a forced air recirculating HVAC system. We answer two major research questions in this article: (1) What is the effectiveness of higher efficiency filters in reducing residential PM concentrations? (2) What is the impact of higher efficiency filters on system energy use? The results show that a high efficiency filter can be effective in reducing indoor PM concentrations if HVAC system runtime (the fraction of time an HVAC system operates) is high and if particle removal by the filter is greater than deposition and ventilation losses. In addition, higher efficiency filters generally only slightly change system energy use. Cost–benefit analyses of filters in residences show that increasing filter efficiency may not be as fruitful compared to doing so in commercial buildings due to operational differences of residential systems. Overall results suggest caution when assuming that higher efficiency filters perform better in residences when compared to lower efficiency filters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".