Large role of anthropogenic climate change in driving smoke concentrations across the western United States from 1992 to 2020
Bibliographic record
Abstract
Wildfire activity has increased dramatically in the western United States over the last three decades, having a significant impact on air quality and human health. However, quantifying the drivers of trends in wildfires and subsequent smoke concentrations is challenging, as both natural variability (NV) and anthropogenic climate change (ACC) play important roles. Here, we devise an approach involving observed meteorology and vegetation and a range of models to determine the relative roles of ACC and NV in driving burned area across the western United States. We also examine the influence of ACC on smoke concentrations. We estimate that ACC accounts for 33 to 82% of observed total burned area, depending on the ecoregion, yielding 65% of total fire emissions on average across the western United States from 1992 to 2020. In all ecoregions except Mediterranean California, ACC contributes to a greater percentage of burned area in lightning-ignited wildfires than in human-ignited wildfires. On average, ACC contributes 49% to smoke PM 2.5 concentrations in the western United States from 1997 to 2020, and explains 58% of the increasing trend in smoke PM 2.5 from 2010 to 2020. Northern California and areas in Oregon, Washington, and Idaho experience the greatest smoke concentrations attributable to ACC, averaging 40 to 66% of total PM 2.5 over 2010–2020. Our work highlights the significant role of ACC in degrading air quality in the western United States and identifies those regions most vulnerable to wildfire smoke and thus adverse health impacts.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".