Evaluating an Air Quality Health Index (AQHI) amendment for communities impacted by residential woodsmoke in British Columbia, Canada
Bibliographic record
Abstract
Smoke from burning biomass is an important source of fine particulate matter (PM2.5), but the health risks may not be fully captured by the Canadian Air Quality Health Index (AQHI). In May 2018, the province of British Columbia launched an evidence-based amendment (AQHI-Plus) to improve AQHI performance for wildfire smoke, but the AQHI-Plus was not developed or tested on data from the residential woodsmoke season. This study assesses how the AQHI and AQHI-Plus are associated with acute health outcomes during the cooler seasons of 2010–2017 in British Columbia, Canada. Monthly and daily patterns of temperature and PM2.5 concentrations were used to identify Local Health Areas (LHAs) that were impacted by residential woodsmoke. The effects of the AQHI and AQHI-Plus on five acute health outcomes (including non-accidental mortality, outpatient physician visits, and medical dispensations for cardiopulmonary conditions) were estimated using generalized linear mixed effect models with Poisson distributions adjusted for long- and short-term temperature trends. Values of the Akaike information criterion (AIC) were compared to evaluate whether the AQHI or AQHI-Plus was better fitted to each health outcome. Eleven LHAs were categorized as woodsmoke-impacted. In these LHAs, the AQHI and AQHI-Plus associations with acute health outcomes were sensitive to temperature adjustments. After temperature adjustments, the most consistent associations were observed for the two asthma-specific outcomes where the AQHI-Plus was better fitted than the AQHI. The improved performance of the AQHI-Plus for susceptible populations with asthma is consistent between communities impacted by residential woodsmoke and wildfire smoke. Implications: Canada’s Air Quality Health Index (AQHI) is a three pollutant index used to communicate the short term health impact of degraded air quality. As fine particulate matter (PM2.5) is the lowest weighted pollutant in the AQHI, the index is poorly reflective of woodsmoke impacts. The present analysis found that an AQHI amendment developed for improved sensitivity to PM2.5 during wildfire seasons (AQHI-Plus) is also more predictive of acute asthma-related health outcomes in communities impacted by residential woodsmoke. The BC Ministry of Environment and Climate Change Strategy has piloted the AQHI-Plus year-round. Other jurisdictions should consider whether their air quality indices are reflective of the risks posed by woodsmoke.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".