Effect of ambient air pollutants PM2.5 and PM10 on COVID-19 incidence and mortality: observational study.
Bibliographic record
Abstract
OBJECTIVE: Environmental pollution is a leading global challenge affecting weather conditions and causing severe environmental, social, and public health problems. This study explores the impact of ambient air pollutants, particular matter (PM), PM2.5 and PM10 on morbidity and mortality of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) globally. MATERIALS AND METHODS: In this study, initially 492 research articles were identified through seven search engines containing Web of Science, Medline, PubMed, EMBASE, Scopus, WHO COVID-19 literature, and Ovid databases by consuming keywords "Environmental pollution, Ambient air pollutants, particulate matter, PM2.5, PM10, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), and COVID-19 pandemic". Finally, 26 original research publications were included for the analysis, and the remaining were excluded. These studies were originated from the United States of America (9), China (5), Italy (3), India (1), worldwide (1), and one study each from England, Spain, Canada, Saudi Arabia, Singapore, Japan, and Iran. RESULTS: The analysis based on a worldwide dataset assembled the information from the global literature from December 2019 to September 30, 2021. The assessment for the various regions to a global extent was based on air pollutants and COVID-19 cases and deaths. Twenty- six studies met the selection criteria representing almost all over the world. Based on the synthesis of scientific studies, it was identified that PM2.5 and PM10 are associated with 15.08%, 11.44 increased COVID-19 cases, and 9.26% and 0.75% COVID-19 deaths, respectively. CONCLUSIONS: The evidence indicates that PM2.5 and PM10 can affect COVID-19 epidemiology in various geographical regions. The findings established an association and a possible causal link between increasing ambient air pollutants, particulate matter PM2.5 and PM10 with increased incidence and mortality of COVID-19. The global health authorities must take strict preventive measures to minimize air pollution and combat such challenging and threatening COVID-19 pandemic globally.
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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 | 0.002 | 0.003 |
| 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".