Air Quality Impacts of the January 2025 Los Angeles Wildfires: Insights from Public Data Sources
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Smoke from the Los Angeles (LA) wildfires that started on January 7, 2025 caused severe air quality impacts across the region. Government agencies released guidance on assessing personal risk, pointing to publicly available data platforms that present information from monitoring networks and smoke plume outlines. Additional satellite-based products provide supporting information during dynamic wildfire smoke events. We evaluate the regional air quality impacts of the fires through publicly available fine particulate matter (PM 2.5 ) and nitrogen dioxide (NO 2 ) observations from regulatory monitoring stations, PurpleAir low-cost sensors, the TEMPO and TROPOMI satellite sensors, and Hazard Mapping System (HMS) Smoke Plumes during this multifire event. The most extreme air quality impacts were observed on January 8–9, particularly in the southern half of LA county, where daily average PM 2.5 concentrations at the downtown LA regulatory monitor reached 101.7 μg/m 3 and 52.3 μg/m 3 in Compton. On January 8th, 12 PurpleAir sensors located closer to burn areas exceeded daily PM 2.5 concentrations of 225 μg/m 3 . While smoke impacts were largely consistent across all data sources, differences in the spatiotemporal, including vertical, resolution of each product may affect interpretability for end users. This study underscores the importance of integrating multiple air quality data sources and improving accessibility to enhance public health messaging during wildfire events.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".