Synoptic weather typing applied to air pollution mortality among the elderly in 10 Canadian cities
Bibliographic record
Abstract
BACKGROUND: Synoptic circulation patterns (large-scale weather systems) affect ambient levels of air pollution, as well as the relationship between air pollution and human health. OBJECTIVE: To investigate the air pollution-mortality relationship within weather types and seasons, and to determine which combination of atmospheric conditions may pose increased health threats in the elderly age categories. METHODS: The relative risk of mortality (RR) due to air pollution was examined using Poisson generalized linear models (GLMs) within specific weather types. Analysis was completed by weather type and age group (all ages, ≤64, 65-74, 75-84, ≥85 years) in ten Canadian cities from 1981 to 1999. RESULTS: There was significant modification of RR by weather type and age. When examining the entire population, weather type was shown to have the greatest modifying effect on the risk of dying due to ozone (O3). This effect was highest on average for the dry tropical (DT) weather type, with the all-age RR of mortality at a population weighted mean (PWM) found to be 1.055 (95% CI 1.026-1.085). All-weather type risk estimates increased with age due to exposure to carbon monoxide (CO), nitrogen dioxide (NO2), and sulphur dioxide (SO2). On average, RR increased by 2.6, 3.8 and 1.5% for the respective pollutants between the ≤64 and ≥85 age categories. Conversely, mean ozone estimates remained relatively consistent with age. Elevated levels of air pollution were found to be detrimental to the health of elderly individuals for all weather types. However, the entire population was negatively effected by air pollution on the hot dry (DT) and hot humid (MT) days. CONCLUSIONS: We identified a significant modification of RR for mortality due to air pollution by age, which is enhanced under specific weather types. Efforts should be targeted at minimizing pollutant exposure to the elderly and/or all age groups with respect to weather type in question.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.012 |
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; both teacher heads agree on what is shown here.
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".