Impacts of Air Pollution on the Respiratory System of Adults in Relation to Socioeconomic Status
Bibliographic record
Abstract
Air pollution has a significant impact on respiratory health, yet comprehensive summaries of specific impacts are limited. This study reviews previous research done on this link, while connecting it to socioeconomic factors. Common air pollutants, such as particulate matter, have a large impact on respiratory health and can exacerbate diseases and medical conditions, such as coughing, wheezing, chronic obstructive pulmonary disease, asthma, and cystic fibrosis. Specifically, PM10 pollutants can deposit in the upper airways, and PM2.5 can reach deep into the lungs, leading to many complications. The efficacy of measures taken to combat these issues are also discussed. In recent years, governments have introduced national climate policies and green-space designs in urban municipalities to reduce negative health outcomes. These measures accomplish this goal to a limited degree but require further investment and development. To outline how socioeconomic status impacts air pollution-related respiratory illnesses, our study discusses five studies investigating socioeconomic disparities in air pollution exposure in various parts of the world. Evidence shows that low-income people typically reside in areas with high air pollution because housing is more affordable. This provides an opportunity for new research in social determinants of health to better understand its connection with human health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".