High Concentrations of Ozone Air Pollution on Mount Everest: Health Implications for Sherpa Communities and Mountaineers
Bibliographic record
Abstract
Semple, John L., G.W. Kent Moore, Petros Koutrakis, Jack M. Wolfson, Paolo Cristofanelli, and Paolo Bonasoni. High concentrations of ozone air pollution on Mount Everest: health implications for Sherpa communities and mountaineers. High Alt Med Biol. 17:365-369, 2016.-Introduction: Populations in remote mountain regions are increasingly vulnerable to multiple climate mechanisms that influence levels of air pollution. Few studies have reported on climate-sensitive health outcomes unique to high altitude ecosystems. In this study, we report on the discovery of high-surface ozone concentrations and the potential impact on health outcomes on Mount Everest and the high Himalaya. MATERIALS AND METHODS: Surface ozone measurements were collected during ascending transects in the Mount Everest region of Nepal with passive nitrite-coated Ogawa filter samplers to obtain 8-hour personal exposures (2860-5364 m asl). In addition, the Nepal Climate Observatory-Pyramid, a GAW-WMO Global Station sited in the Khumbu Valley (5079 m asl), collected ozone mixing ratios with photometric gas analyzer. RESULTS: Surface ozone measurements increased with altitude with concentrations that exceed 100 ppb (8-hour exposure). Highest values were during the spring season and the result of diverse contributions: hemispheric background values, the descent of ozone-rich stratospheric air, and the transport of tropospheric pollutants occurring at different spatial scales. DISCUSSION: Multiple climate factors, including descending stratospheric ozone and imported anthropogenic air masses from the Indo-Gangetic Plain, contribute to ambient ozone exposure levels in the vicinity of Mount Everest that are similar to if not higher than those reported in industrialized cities.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".