The Co-occurrence of Wildfire Smoke and Extreme Heat Events in British Columbia, 2010–2022: Evaluating Spatiotemporal Trends and Inequities in Exposure Burden
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Climate change is fueling more frequent and severe wildfire smoke (WFS) and extreme heat events (EHEs), and co-exposure may have synergistic adverse health effects. We evaluated the spatiotemporal trends in population exposure to co-occurring WFS and EHEs (WFS-EHEs) in British Columbia (BC). We calculated the frequency, intensity, and trends in WFS-EHEs in each census dissemination area (DA) in BC between 2010 and 2022. WFS-EHEs were identified using established exceedance thresholds and daily data on fine particulate matter, smoke plumes, and meteorological conditions. Trends were identified using the Mann–Kendall and Theil–Sen approaches. Census data was used to identify the characteristics of the most exposed communities. Over 13 years, there were 276,666 DA-level WFS-EHEs, impacting all BC residents and leading to a cumulative 170.8 million person-days of exposure. Although there was substantial year-to-year variability, the frequency and intensity of WFS-EHEs increased over time, with 60.8% of co-occurrences between 2018 and 2022. 42.5% of DAs (∼1.9 million people) experienced significant increases in exposure. The highest co-exposure burden occurred in rural communities with lower adaptive capacity. Our findings demonstrate the need for public health guidance on these increasingly frequent and intense compound hazards and can inform climate change adaptation and mitigation efforts in BC and elsewhere.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".