Risks of source and species-specific air pollution for COVID-19 incidence and mortality in Los Angeles
Bibliographic record
Abstract
Abstract Growing evidence from ecological studies suggests that chronic exposure to standard air pollutants (PM 2.5 , NO 2 , and ozone) exacerbates risks of coronavirus 2 (COVID-19) incidence and mortality. This study assessed the associations between an expanded list of air pollutants and COVID-19 incidence and mortality in Los Angeles. Annual mean exposure to air pollutants in 2019—including PM 0.1 mass, PM 2.5 mass, PM 2.5 elemental carbon (EC), PM 2.5 tracer from mobile sources, NO 2 , and ozone—were estimated at the ZIP code level in residential areas throughout Los Angeles. Negative binomial models and a spatial model were used to explore associations between health outcomes and exposures in single pollutant and multi-pollutant models. Exposure to PM 0.1 mass, ozone, NO 2 , and PM 2.5 EC were identified as risk factors for COVID-19 incidence and mortality. The results also suggest that PM 2.5 and NO 2 together may have synergistic effects on harmful COVID-19 outcomes. The study provides localized insights into the spatial and temporal associations between species-specific air pollutants and COVID-19 outcomes, highlighting the potential for policy recommendations to mitigate specific aspects of air pollution to protect public health.
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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 | 0.005 | 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 | 0.001 |
| 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".