Air Quality and Energy Implications of Filtration System and Operation Strategies in Residential Buildings
Bibliographic record
Abstract
Central forced-air HVAC systems with filters are commonly used in North American homes to reduce particle concentrations and occupant exposure. The filtration performance of these systems is strongly influenced by many system-, filter-, and building-specific parameters including system runtime, cycle on-time, recirculation rate, in-situ filter efficiency, and building air change rate. These parameters vary considerably across homes, and in the same home over time. Thus, characterization of these parameters is essential for filtration system performance evaluation. The goal of this dissertation is to improve the performance of residential filtration systems by advancing the understanding of their influencing parameters. This work first assesses the runtimes in both heating- and cooling-dominated climates. The results show that there were large variations in runtimes and the mean daily runtime was less than 20% in the studied homes. Secondly, this work evaluates the in-situ efficiency of four types of filters in 21 residences in Toronto, Ontario. The results show that the variations for the same type of filters across homes were greater than the differences between filter types. Comparisons between in-situ and lab-tested filter performance further confirm that the lab-tested results of a filter may not well represent its in-situ performance – they generally overestimate the efficiency and underestimate the pressure drops. Lastly, this work examines the range of effectiveness of the HVAC filtration systems with varying influencing parameters through a time-varying mass balance model. The results show that in addition to runtimes, when and how long (i.e., cycle on-time) the system operates also influences its performance. Based on this finding, four filtration operation strategies (concentration, source, pulsed, and concentration pulsed) are evaluated, and the results show that operating the system during periods with high indoor concentrations could achieve a comparable level of effectiveness as continuous operation but at runtimes as low as 80%. This strategy provides opportunities to reduce indoor particle concentration while conserving fan energy use. Overall, this dissertation provides a framework with tools and models to characterize the influencing parameters of residential filtration, evaluate filtration performance, and provide guidance on operation strategies for performance improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".