Health effects of PM2.5 exposure on short-term international travellers to Thailand
Bibliographic record
Abstract
BACKGROUND: Thailand has increased in popularity as a top travel destination but there are concerns regarding significant seasonal levels of particulate matter 2.5 (PM2.5) that are 2.5 μm or smaller in diameter, which may affect the health of travellers during periods of high air pollution. The aim of this study was to examine the prevalence of short-term health effects and the association between exposure to PM2.5 and adverse health outcomes among international travellers. METHODS: A cross-sectional study was conducted in Bangkok and Chiang Mai, Thailand, between February and May 2024 during a period of high pollution levels. Data were collected via online questionnaires distributed at the international airports in both cities. Travellers aged 20 or older visiting during this polluted season were included, excluding those staying longer than 6 months. PM2.5 levels were obtained from the Thai Pollution Control Department from February to May 2024. RESULTS: Among 617 participants, 63.5% were male, 75.2% (n = 464) visited Bangkok and 66.1% (n = 408) were from North America/Canada. Tourism was the primary purpose of travel for 81.4% (n = 502). Mean PM2.5 levels were 30.5 μg/m3 [standard deviation (SD) = 13.2] in Bangkok and 65.9 μg/m3 (SD 32.0) in Chiang Mai. Travellers who visited Chiang Mai reported significantly more short-term health symptoms than those who visited Bangkok (58.8% vs 0.4%, P < 0.001). Among Chiang Mai visitors, respiratory symptoms were most common (46.4%), followed by eye (20.3%) and skin issues (11.1%). Reporting from participants who reduced outdoor activities during periods of high pollution were associated with a 69% lower level of symptoms [adjusted odds ratio (aOR) = 0.31, 95% confidence interval (CI): 0.1-0.7]. CONCLUSIONS: A majority of travellers reported health symptoms during their visit to Thailand during high-pollution seasons. Limiting outdoor activities appeared to mitigate these effects. Pre-travel advice should highlight the health risks associated with air pollution and emphasize preventive measures for minimizing exposure.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".