Inhalation exposure to ambient polycyclic aromatic hydrocarbons and lung cancer risk of Chinese population
Bibliographic record
Abstract
An Euler atmospheric transport model (Canadian Model for Environmental Transport of Organochlorine Pesticides, CanMETOP) was applied and validated to estimate polycyclic aromatic hydrocarbon (PAH) ambient air concentrations at ground level in China based on a high-resolution emission inventory. The results were used to evaluate lung cancer risk for the Chinese population caused by inhalation exposure to PAHs. The uncertainties of the transport model, exposure, and risk analysis were assessed by using Monte Carlo simulation, taking into consideration the variation in PAH emission, aerosol and OH radical concentrations, dry deposition, respiration rate, and genetic susceptibility. The average benzo[a]pyrene equivalent concentration (B[a]P(eq)) was 2.43 [ approximately 1.29-4.50 as interquartile range (IR)] ng/m(3). The population-weighted B[a]P(eq) was 7.64 (IR, approximately 4.05-14.1) ng/m(3) because of the spatial overlap of the emissions and population density. It was estimated that 5.8% (IR, approximately 2.0-11%) of China's land area, where 30% (IR, approximately 17-43%) of the population lives, exceeded the national ambient B[a]P(eq) standard of 10 ng/m(3). Taking into consideration the variation in exposure concentration, respiration rate, and susceptibility, the overall population attributable fraction (PAF) for lung cancer caused by inhalation exposure to PAHs was 1.6% (IR, approximately 0.91-2.6%), corresponding to an excess annual lung cancer incidence rate of 0.65 x 10(-5). Although the spatial variability was high, the lung cancer risk in eastern China was higher than in western China, and populations in major cities had a higher risk of lung cancer than rural areas. An extremely high PAF of >44% was estimated in isolated locations near small-scale coke oven operations.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".