Assessment of indoor exposure to outdoor environmental noise and effects on occupant comfort in multi-unit residential buildings
Bibliographic record
Abstract
Outdoor environmental noise is a major source of annoyance in urban areas and exposure to it can increase the risk of severe health issues. Consequently, it has been the focus of research for decades. Even though people spend the majority of their time indoors, most studies use outdoor noise levels and do not include indoor noise measurements to estimate real exposure levels. This study conducted simultaneous indoor and outdoor noise measurements for 24 h in four multi-unit residential buildings to identify the levels and sources of outdoor noise heard indoors and quantify the effects of outdoor noise on indoor levels. The measurements were conducted in unoccupied suites that are most exposed to traffic and other outdoor noise sources. Surveys were administered following building occupancy to collect information regarding perceived acoustic comfort levels due to outdoor noise. The indoor L Aeq,24h in three of the study buildings were above 40 dB(A) and exceeded WHO’s noise level limits. Regression analysis showed that outdoor noise only explains 14%–58% of the variability in indoor noise levels. This is mainly because of heating, ventilation, and air conditioning (HVAC) system noise which resulted in consistently high indoor noise levels despite variations in outdoor noise. Analysis of the survey showed a poor correlation between reported annoyance and measured noise levels. But annoyance strongly depended on other factors such as suite location and noise sensitivity. The findings show that outdoor noise measurements alone may not be good predictors of exposure levels and the effects of outdoor noise on occupants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".