A meta-analysis of the carcinogenic effects of particulate matter and polycyclic aromatic hydrocarbons
Bibliographic record
Abstract
Urbanization has numerous benefits to human society, but some aspects of urban environments, such as air pollution, can negatively affect human health. Two major air pollutants, particulate matter (PM) and polycyclic aromatic hydrocarbons (PAH), have been classified as carcinogens by the International Agency for Research on Cancer. Here, we answer two questions: (1) What are the carcinogenic effects of PM and PAH exposure? (2) How does carcinogenic risk vary across geographical regions? We performed a comprehensive literature search of peer-reviewed published studies examining the link between air pollution and human cancer rates. Focusing on studies published since 2014 when the last IARC monograph on air pollution was published, we converted the extracted data into relative risks and performed subgroup analyses. Exposure to PM2.5 (per 10μg/m3) resulted in an 8.5% increase in cancer incidence when all cancer types were combined, and risk for individual cancer types (i.e. lung cancer and adenocarcinoma) was also elevated. PM2.5 was also associated with 2.5% higher mortality due to cancer when all types of cancer were combined, and for individual cancer types (i.e., lung and breast cancer). Exposure to PM2.5 and PM10 posed the greatest risk to lung cancer incidence and mortality in Europe (PM2.5 RR 2.15; PM10 RR 1.26); the risk in Asia and the Americas was also elevated. Exposure to PAH and benzo[a]pyrene significantly increased the pooled risk of cancer incidence (10.8% and 8.0% respectively) at the highest percentile of exposure concentration. Our meta-analyses of studies over the past decade shows that urban air pollution in the form of PM2.5, PM10, and PAH all elevate the incidence and mortality of cancer. We discuss the possible mechanisms of carcinogenesis of PM and PAH. These results support World Health Organization’s conclusion that air pollution poses among the greatest health risks to humans living in cities.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.054 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".