Global Trends and Hotspots in the Research of the Effects of PM2.5 on Asthma: A Bibliometric and Visualized Analysis
Bibliographic record
Abstract
BACKGROUND: Fine particulate matter (PM2.5) has been identified as a significant environmental and public health challenge, particularly due to its association with respiratory diseases like asthma. With the global rise in urbanization and industrialization, PM2.5-related asthma research has grown substantially over the past two decades. This study aims to provide a comprehensive bibliometric analysis to map global research trends, highlight key contributors, and identify emerging hotspots in the relationship between PM2.5 and asthma. METHODS: We performed a bibliometric analysis using the Web of Science Core Collection database, covering research from January 2004 to September 2024. The selected studies were analyzed using CiteSpace and VOSviewer to assess publication trends, global collaborations, and research hotspots through visualized networks and co-occurrence analyses. RESULTS: A total of 2035 publications were identified, demonstrating a steady increase in research output over the past two decades. The United States and China emerged as dominant contributors, frequently collaborating with countries like Canada, Australia, and South Korea. Key research areas focused on air quality, particulate matter exposure, and asthma exacerbation, with an increasing emphasis on indoor air pollution and long-term exposure risks. Institutional collaborations were led by prominent universities such as the University of California System and Harvard University. Additionally, research on vulnerable populations, particularly children, and the impact of early-life exposure to PM2.5 has gained attention in recent years. CONCLUSIONS: The global research landscape on PM2.5 and asthma has expanded significantly, with growing attention to interdisciplinary approaches that combine environmental science and public health. Future studies should focus on the global burden of air pollution, particularly in low- and middle-income countries, and address the long-term health impacts of PM2.5 exposure, especially in vulnerable populations.
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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 | 0.020 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.024 |
| 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".