Evaluating the Benefits of Indoor Air Quality Forecasting for Controlling Particle Filter Systems
Bibliographic record
Abstract
Predicting future concentrations of fine particulate matter (PM 2.5 ) and other indoor air pollutants using machine learning is an increasingly frequent topic of research. Although prediction has several proposed applications, the potential benefit has remained largely unevaluated. This study examines whether prediction can improve how particle filter systems such as portable air cleaners are automated by comparing a model predictive control (MPC) strategy with a traditional threshold-based control (TBC) strategy. The MPC controller is designed to optimize the balance between mean reduction in PM 2.5 concentration (i.e., keep concentrations as low as possible following health-based guidance) and reductions in system runtime. These two parameters are compared for both control strategies using 104 simulations of week-long continuous PM 2.5 measurements in occupied apartments. Our findings suggest that there is no meaningful difference in performance between the two control strategies. Additionally, we find only a marginal improvement in performance for MPC controllers that operate using longer prediction horizons. Given the numerous challenges associated with accurately predicting future PM 2.5 concentrations, as well as the additional challenges associated with implementing MPC, controlling particle filter systems may not be an appropriate application for prediction given the lack of benefit over TBC. We would recommend that particle filter systems continue to be controlled using nonpredictive strategies such as TBC, and that prediction tools be used for other applications or adapted to different areas of indoor air quality research and practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".