Air pollution exposure, health and performance in elite female soccer players
Bibliographic record
Abstract
Introduction Acute exposure to air pollution negatively affects athletes’ ability to perform at their best, with a more pronounced impact observed in females. Despite this, the representation of female-only cohorts is lacking. This study investigates how exposure to air pollution impacts performance and well-being of an elite female soccer team. Methods Comprehensive data on external, internal, and subjective load variables were combined with the concentrations of three major air pollutants (Particulate Matter >10 µm and Oxidant (Ox = Ozone + Nitrogen Dioxide) during soccer training and matches in the 2022–23 season. Linear mixed-effects models assessed performance parameters such as distance ran, heart rate, rating of perceived exertion (RPE), accounting for confounding factors such as weather and menstrual cycle phase. Results No significant impairments in the physical or physiological demands of athletes were observed with increasing pollution concentrations, nor did athletes’ well-being appear to be disturbed. However, RPE increased by 0.84 units ( p < .001) if athletes played soccer when exposed to Ox levels above 98.3 (µg/m 3 ). Interestingly, this undesirable increase in RPE was mitigated if athletes were also exposed to high levels of Ox in the seven sessions leading up to this event (−0.5 units, p < .001). Discussion While objective performance measures remained largely unaffected by moderate levels of air pollution, players subjectively experienced a heightened sense of effort. Additionally, an acclimation effect was observed regarding preceding Ox exposure, whereby accounting for pre-event exposure levels to Ox appeared to mitigate the negative impact of high Ox levels on RPE.
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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 | 0.000 |
| 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) | 0.003 | 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".