Environmental Drivers and Health Impacts of Drought-Induced Poor Air Quality in Saskatchewan, Canada
Bibliographic record
Abstract
As greenhouse gas emissions increase worldwide, warmer and drier conditions as well as drought frequency and events such as dust storms and wildfires are expected to increase in Saskatchewan, reducing air quality. The predicted increase in drought conditions (dust and wildfire smoke) and exposures to poor air quality warrants research on the environmental variables that drive drought-induced poor air quality and the impacts of drought on human health in the Canadian context. There is an increasing need for research that focuses on regional-scale climate uncertainties and associated impacts on human health. The study looks to (1) understand the weather conditions that exacerbate air quality as a result of drought to better inform early warning systems and to (2) identify relationships between health impacts and weather conditions associated with droughts to inform public health policy and practice. This study assessed meteorological variables related to droughts as well as human health outcomes over 8 years (2015-2022). It uses a coupled human and environment approach to assess meteorological drivers of drought-induced poor air quality on health. Environmental data include fine particulate matter, nitrogen dioxide, ground level ozone, temperature, specific humidity, and wind speed. Health data include aggregates of health outcomes including stress and mental health, respiratory health, and cardiovascular health. The outcomes of this study are to examine specific weather conditions that exacerbate drought, and therefore air quality, to better inform early warning systems and to identify impacts on health aggregates in Saskatchewan health regions to better inform policy and practice during periods of drought-induced poor air quality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".