Air Quality in First Nations Communities and the Policy Implications for Community-Based Physical Activity Programming
Bibliographic record
Abstract
Background: The health benefits of routine physical activity and exercise are clear; however, increased physical activity can lead to elevated exposure to air pollution that increases the risks for adverse events. Within most Indigenous or rural communities in Canada there is limited monitoring of air quality so community members may unknowingly be at an increased health risk for adverse events while being active. Policy Components: An air quality monitor was placed outdoors in Lytton, BC, a rural First Nations community, from April 2019 – June 2021 and particular matter (PM1.0, PM2.5, PM10.0), inorganic matter (CO2, O3), temperature, pressure, and humidity were recorded during the two-year span. Evaluation: There were significant increases in particular matter (PM1.0, PM10.0) over the two-year sample period (p≤ 0.05). There were significant relationships between the different particular matter sizes (i.e., PM1.0/2.5 (0.976), PM2.5/10.0 (0.999), and PM1.0/10.0 (0.965), respectively (p<0.001)) and also between PM 1.0/O3 (0.200, p<0.05). At the highest PM levels, Environment and Climate Change Canada would recommend the reduction or rescheduling of physical activity/exercise. Conclusions: In a rural and First Nations community in BC, Canada, there were marked levels of air pollution that would affect the ability to safely participate in physical activity. These findings have important policy implications highlighting the need for monitoring of air quality within Indigenous or rural communities, and to consider the effects of air pollution when engaging in physical activity/exercise. Funding: This study was funded by Environment and Climate Change Canada.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".