Estimating Air Change Rate in Mechanically Ventilated Classrooms Using a Single CO<sub>2</sub> Sensor and Automated Data Segmentation
Bibliographic record
Abstract
With a growing emphasis on indoor air quality (IAQ) in educational environments, CO 2 monitoring in classrooms has become commonplace. CO 2 data can be used to estimate outdoor air change rate (ACH) based on the mass balance principle, which can be further linked to human health, performance, and building energy consumption. This study used a novel machine learning method to automatically segment CO 2 concentration time series data into build-up, equilibrium, and decay periods, and then estimated classroom ACH using the corresponding CO 2 mass balance equations. This method, applied to 40 classrooms in two mechanically ventilated K-6 schools, generated up to ten ACH estimates per day per classroom. A comparison with ACH calculated using the mechanical ventilation rates with 100% outdoor air reported by the building automation system during the study period reveals a slight underestimation by the decay and build-up methods, while the equilibrium method produced closer estimates. These differences may be attributed to uncertainties in occupancy, activity, CO 2 emission rates, and air mixing. This research underscores the potential of leveraging CO 2 data for more comprehensive IAQ assessments and highlights the challenges associated with accurately estimating ACH in real-world settings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".