Air quality in the U.S. during the 2023 wildfire season
Bibliographic record
Abstract
Abstract During the summer of 2023, Canada was devastated by a series of large and uncontrolled wildfires that set the unprecedented record of over 40 million acres burned and over 120,000 people evacuated. The smoke from the Canadian wildfires reached the U.S. and negatively affected the air quality in several states. The goal of this paper is to document the U.S. air quality in 2023 within the historical and regulatory contexts; more specifically, to compare the concentrations of fine particulate matter (PM2.5) and tropospheric ozone (O3), assessed using statistics as close as possible to those recommended for comparison against the National Ambient Air Quality Standards (NAAQS), in 2023 against those in the past 20 years. We report that the year 2023 was an anomaly, meaning that the well-established trend of decreasing concentrations and improving air quality was suddenly reversed in 2023, with national-median PM2.5 and O3 concentrations exceeding 32 µ $$\text {g}$$ g $$\text {m}^{-3}$$ m - 3 and 75 parts per billion (ppb), respectively. These values had not been reached since 2007 and 2012 for PM2.5 and O3, respectively. Out of 50 states (plus the District of Columbia), 42 and 45 experienced summer-average concentrations in 2023 that were higher than those in 2013–2022 for PM2.5 and O3, respectively. For PM2.5, the states that experienced the highest concentrations in 2023 were: California, Oregon, and North Dakota with 148, 135, and 67µ $$\text {g}$$ g $$\text {m}^{-3}$$ m - 3 ; for O3, they were: California, Utah, Texas, and Illinois with 107, 99, 90, and 86 ppb. In summary, in the year 2023 the air quality in the U.S. was the worst of the past 15 and 10 years in terms of PM2.5 and O3, respectively.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".