Field Measurements and Human Perception to Remediate Noise Pollution in the Urban Public Parks in Saudi Arabia
Bibliographic record
Abstract
The deleterious effects of noise pollution on public health have been well documented, with traffic noise being identified as a significant contributor to stress and adverse impacts on the human body and mind. In this study, sound levels at 12 different points in Al-Oqailat Park in Buraydah, Saudi Arabia, were measured using a sound level meter (SLM), with the study’s primary objective being to conduct this measurement. The experimental results were then compared with perception measurements collected from users who frequently visited Al-Oqailat park. Sound measurements were taken in four different zones (A, B, C, and D) during rush hours between 1:30 p.m. and 5:20 p.m. It was found that noise levels at point A1 peaked at 79 dBA at 4:40 p.m., while the lowest level recorded was 41.1 dBA at point D2 at 2:35 p.m. The range of noise levels varied between 79 and 41 dBA, with a rate of decline of 48.10%. Zones A and B seemed to have the highest noise levels during rush hours, since they were located closest to King Fahd Road and Al-Adl Street, while zone D exhibited the lowest noise levels due to its location as a parking lot for Buraydah Court. An intermediate noise level was found in zone C, in the middle of Al-Oqailat park. The people perception results, completed by 84 park visitors, showed that zone A was identified as having exceptionally high noise levels compared to the other zones, with zone D having the lowest levels. These results were consistent with the experimental findings and reflected that the points along King Fahd Road and Al-Adl Street had the highest noise levels. Overall, the research highlighted the dominance of car traffic and horns as the primary sources of noise pollution in and around Al-Oqailat Park, emphasizing the significance of meticulous site selection for parks in urban areas.
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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 | 0.006 | 0.002 |
| 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 | 0.000 |
| 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".