Benefit‐cost analysis of commercially available activated carbon filters for indoor ozone removal in single‐family homes
Bibliographic record
Abstract
This study involved the development of a model for evaluating the potential costs and benefits of ozone control by activated carbon filtration in single-family homes. The modeling effort included the prediction of indoor ozone with and without activated carbon filtration in the HVAC system. As one application, the model was used to predict benefit-to-cost ratios for single-family homes in 12 American cities in five different climate zones. Health benefits were evaluated using disability-adjusted life-years and included city-specific age demographics for each simulation. Costs of commercially available activated carbon filters included capital cost differences when compared to conventional HVAC filters of similar particle removal efficiency, energy penalties due to additional pressure drop, and regional utility rates. The average indoor ozone removal effectiveness ranged from 4 to 20% across the 12 target cities and was largely limited by HVAC system operation time. For the parameters selected in this study, the mean predicted benefit-to-cost ratios for 1-inch filters were >1.0 in 10 of the 12 cities. The benefits of residential activated carbon filters were greatest in cities with high seasonal ozone and HVAC usage, suggesting the importance of targeting such conditions for activated carbon filter applications.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".