Assessment of Air Quality Using Personal Exposure Sensors during the 2023 Dhofar Monsoons Period
Bibliographic record
Abstract
Dhofar, the largest governorate by area in the Sultanate of Oman, is the only area in the Arabian Peninsula that experiences the influence of the southwest monsoon originating from the southern tropical Indian Ocean.Dhofar experienced its highest peak in tourism during the monsoon of 2023, with an 18.4% increase compared to the same period in 2022.Increased tourists' arrivals, active transportation, elevated patronage of restaurants and shops, high engagement of tour operators, expanded participation in recreational activities, staging of cultural and festival events in tourist destinations; collectively impact on the air quality.The aim of this study is to investigate personal exposure to indoor and outdoor air pollutants during the Dhofar monsoon season through real-time air quality monitors equipped with GPS-Enabled weather tracker.Selected air pollutants include PM10, PM2.5, PM1, CO2, CO, VOC, NO2 and O3.The various microenvironments were characterized by differing pollutant levels.PM10 levels were highest at festivals (489 g/m), while PM2.5 and PM1 were most prevalent in homes (207 g/m and 64.4 g/m, respectively).The TAD identified incense burning at festivals and in homes as a significant source of high PM levels.Diesel bikes for children entertainments, tourist vehicles, and diesel generators for outdoor restaurant electricity were major contributors to PM emissions at festivals and on streets.Restaurants had the highest concentration of VOCs (58.7 ppm), while the highest NO2 levels were found in the valleys (10.0 ppm).CO2 concentration was high in restaurants and cars (both at 1.5 x 10 ppm), while CO levels were elevated only in restaurants (3.8 ppm).Except for ozone, which was either undetected or present at low levels, the PE of tourists is concluded to be well above the background concentrations of other pollutants during the measurement campaign.With respect to the relative contributions of specific microenvironments to tourists' timeweighted, integrated exposure to PM2.5, hills were the most significant contributors, followed by markets, valleys, and beaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".