Risk-Based Prioritisation of Indoor Air Pollution Monitoring Using Computational Fluid Dynamics
Bibliographic record
Abstract
There has been an increasing concern on indoor air quality in recent years due to the possible harmful effects to human health. Indoor air pollution as a result of using natural gas for cooking and heating is a common health threat, particularly for women and young children. Therefore, quantification of the type and emission levels of these pollutants is necessary in order to mitigate and monitor the emissions. Computational fluid dynamics (CFDs) can be used to model airflow and dispersion within buildings of complex geometry and layout. In the present paper, a CFD analysis is performed to determine the concentration of indoor air quality for a typical one-floor building in order to determine the optimal locations of monitoring sensors. According to this study, placing the monitoring sensors based on the maximum concentrations of the individual contaminant does not entirely overcome the problems, as the concentrations of different hazardous pollutants cannot be added. Moreover, high concentration with low duration of exposure is not a good candidate for placing the monitoring system. A risk-based methodology is proposed to determine the optimal location for the monitoring systems. Different risk management strategies are also considered as a part of the methodology to reduce the exposure risk of indoor contaminants.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".