Health implications due to exposure to fine and ultra-fine particulate matters: a short review
Bibliographic record
Abstract
Air pollution has caused 40% higher deaths than that of COVID-19, in the past two years; making it a most serious global concern with the exponential increase in health implications and mortality over the last few decades. Air pollution is characterized by fine and ultra-fine particulate matter and gaseous pollutants exhibiting diverse sizes and volatility responsible for various diseases such as respiratory, cardiovascular, hypertension, stroke, and lung cancer. These pollutants are emitted to the atmosphere from numerous anthropogenic sources mainly the combustion of different types of fuels resulting in the exponential enhancement of pollution levels. This manuscript discusses the impact of hazardous pollutants on human health, encompassing different types, levels, sizes, and sources originating from anthropogenic activities. According to the World Health Organization (WHO), more than 72.67% of global deaths are attributed to non-communicable diseases (NCD), predominantly influenced by environmental pollutants. Particulate matter (PM2.5 and below) and other toxic gaseous pollutants are major contributors, responsible for more than 16% of total NCD mortality. Cardiovascular disease, chronic obstructive pulmonary disease, ischaemic heart disease, stroke, lung cancer, and other ailments constitute the majority of these deaths.Highlights Tiny size of particulate matter may accumulate in the lungs and alter cell replication by raising DNA methyltransferase3beta enzymes.DNA methylation may activate oncogenes while repressing tumour suppressors, which is the major cause of cancer.Indoor pollutant contains more than 900 chemicals, biological, and PM which are two to five times more concentrated than outdoor pollutants.Cardiovascular disease attributes to half of the total deaths (around 3.3 million) due to air pollution.The mortality rate increases by 0.7% for every 10 g/m3 increase in PM concentration.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".