Highway proximity and black carbon from cookstoves as a risk factor for higher blood pressure in rural China
Bibliographic record
Abstract
Air pollution in China and other parts of Asia poses large health risks and is an important contributor to global climate change. Almost half of Chinese homes use biomass and coal fuels for cooking and heating. China's economic growth and infrastructure development has led to increased emissions from coal-fired power plants and an expanding fleet of motor vehicles. Black carbon (BC) from incomplete biomass and fossil fuel combustion is the most strongly light-absorbing component of particulate matter (PM) air pollution and the second most important climate-forcing human emission. PM composition and sources may also be related to its human health impact. We enrolled 280 women living in a rural area of northwestern Yunnan where biomass fuels are commonly used. We measured their blood pressure, distance from major traffic routes, and daily exposure to BC (pyrolytic biomass combustion), water-soluble organic aerosol (organic aerosol from biomass combustion), and, in a subset, hopane markers (motor vehicle emissions) in winter and summer. BC had the strongest association with systolic blood pressure (SBP) (4.3 mmHg; P < 0.001), followed by PM mass and water-soluble organic mass. The effect of BC on SBP was almost three times greater in women living near the highway [6.2 mmHg; 95% confidence interval (CI), 3.6 to 8.9 vs. 2.6 mmHg; 95% CI, 0.1 to 5.2]. Our findings suggest that BC from combustion emissions is more strongly associated with blood pressure than PM mass, and that BC's health effects may be larger among women living near a highway and with greater exposure to motor vehicle emissions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".