One-Third of Global Population at Cancer Risk due to Elevated Volatile Organic Compounds Levels
Bibliographic record
Abstract
Abstract Outdoor air pollution, particularly volatile organic compounds (VOCs), significantly contributes to the global health burden. Previous analyses of VOC exposure have been confined to regional and national scales, limiting global health burden assessments. Our study employed a global chemistry-climate model to simulate VOC distributions from 2000 to 2019 and estimated the associated cancer risks. Our findings revealed a 10.2% increase in global VOC emissions between 2000 and 2019, with significant increases in China, the Rest of Asia, and Sub-Saharan Africa, but decreases in the U.S and Europe due to transportation and residential sectors reductions. Approximately 36.4-39.7% of the global population was exposed to unhealthy VOC levels, with an extremely high percentage identified in China (82.8-84.3%) versus considerably lower percentage in Europe (1.7-5.8%). The lifetime cancer burden attributable to carcinogenic VOCs exposure was estimated at 0.60 [95% confidence interval (95CI): 0.40-0.81] to 0.85 [95CI: 0.56-1.14] million individuals globally. Open agricultural burning in less-developed regions escalated the associated respiratory risks and cancer burdens. We noted significant disparities in cancer burdens between high- and low-middle-income countries, stemming from disproportionate population expansions and VOC emissions. This finding highlights the amplified health disparity across different income nations, critical for persistently addressing environmental injustice associated with air pollution 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.004 | 0.001 |
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".