Predicting indoor concentrations and chemical composition of outdoor-originated particulate matter with a CONTAM building model
Bibliographic record
Abstract
Outdoor-originated aerosols impact indoor air quality. Both concentrations and chemical compositions of outdoor aerosols are modified while transported into indoor environments. Humans spend most of their time indoors, thus understanding this modification is important to understand indoor exposure to ambient pollutants. In this work, the impacts of the variation in outdoor aerosol concentration and chemical composition on indoor aerosol were examined within a high-rise, multi-family building. High-rise multi-family buildings rely on pressurized corridor ventilation systems to bring ambient air indoors. These ventilation systems often do not perform to specifications and could lead to floor-based disparities in distributed ventilation air, especially when indoor–outdoor temperature gradient is pronounced, resulting in variations in thermodynamic partitioning, and subsequently indoor–outdoor ratios of ambient pollutants. Airflow and pollutant simulations were performed with a CONTAM (a multizone indoor air quality analysis computer software) building model to obtain the indoor–outdoor ratio of a nonvolatile, non-reactive inert species. Chemical composition of ambient particulate matter that are smaller than 2.5 micrometer (PM2.5) was reconstructed from regulatory monitoring data based on modified PM2.5 mass reconstruction techniques. Indoor PM2.5 concentrations were computed using a combination of a mechanical particle transport model and composition-dependent scaling factors that account for thermodynamic behavior of semi-volatile particle subcomponents. Indoor–outdoor ratios and by extension concentrations and composition of particulate chemical species showed variation across seasons and by floor due to differences in building ventilation. This work quantifies how thermodynamically-representative speciated exposures to ambient PM vary by both floor and ambient temperature within a single building.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| 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".