Causes of Ambient Air Pollution in South Asia and Effects on South Asians’ Cardiovascular Health
Bibliographic record
Abstract
For years, South Asia has been considered a global hotspot for air pollution. The extreme air pollution in South Asia, which stems from various sources, has been proven harmful to many South Asians' health. In major cities, even simply stepping foot outside can trigger a cough, but over time, inhaling the harmful air pollutants in South Asia's air can result in the development of a host of cardiovascular diseases, most of which stem from atherosclerosis, a buildup of plaque on the arteries wall. Numerous researchers have conducted various studies connecting atherosclerosis to ambient air pollution, proving that the air quality is, in fact, harmful and should be taken action upon. This literature review synthesizes information on the sources that contribute to particulate matter in South Asia and the effect major air pollutants have on South Asians' cardiovascular health. This analysis focuses on major air pollutants' contribution to atherosclerosis progression and discusses conditions that stem from the disease, centering hypertension and coronary artery disease. This review synthesizes predominantly peer-reviewed studies which have been published between the years 2000 and 2024. The key findings suggest that particulate matter stemming from vehicular emissions, burning biomass and solid fuels, industrial and agricultural activities, and soil dust contribute to the unhealthy levels of particulate matter in South Asian countries. Studies also suggest that meteorological factors exacerbate particulate matter concentrations; however, research has shown inconsistent findings. Particulate matter, along with various other pollutants, leads to atherosclerosis progression and, subsequently, other cardiovascular conditions, including coronary artery disease and hypertension. The methodologies in conducting this review involved database searches, library catalog searches, and reference list reviews. This review presents findings on the contribution of particulate matter in South Asian countries and its impacts on South Asian health; however, it also presents the need for further research on meteorological exacerbation of existing pollutants, atherosclerosis progression, coronary artery disease, and hypertension in South Asians in relation to particulate matter exposure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".