Personal exposures to PM2.5 in rural Beijing, China: Spatial-temporal variation patterns and modeling approaches
Bibliographic record
Abstract
Background and aims Household air pollution is a leading health risk factor for global morbidity and mortality. However, studies with high quality personal exposure data in settings of household air pollution are very limited. To investigate the levels and patterns of personal exposures to PM₂.₅ in rural Beijing and develop a personal exposure model, we conducted measurements in over 500 households in 50 villages in rural Beijing over two winters. Methods Adults (> 40-year-old) from 10 randomly selected households in each village were recruited to wear a personal exposure sampler (Ultrasonic Personal Aerosol Samplers, UPAS) to measure 24-h exposures. Indoor PM₂.₅ was concurrently monitored in 300 households using calibrated low-cost sensors (Plantower) and outdoor PM₂.₅ was monitored at the community levels using the same sensors. We also administered a survey to collect household socio-demographic information, fuel use patterns, and general physical activity patterns. Mixed-effects models and quantile regression were applied to investigate distributional effects in indoor and outdoor PM₂.₅ and temperature, heating energy use, and other socio-demographic factors on personal exposures. Results Personal PM₂.₅ decreased by 39 (95%CI: 3.8-74) μg/m³ for participants who shifted from solid fuel to clean energy for heating between study years, but the reduction in participants who continued using solid fuel was only 27 (95%CI: 9.9-44) μg/m³. Indoor and outdoor PM₂.₅ explained the largest proportion of personal PM₂.₅ variance. Less wealthy participants who were current smokers and used solid fuels for space heating tended to have higher exposures. In quantile regression, at high quantiles of personal exposure levels, the influences of indoor PM₂.₅ on personal exposures enlarged. Conclusions Transitions to clean energy for space heating improved indoor air quality and personal exposures. The influence of different variables on personal exposures varied by exposure levels, and it is essential to model personal exposures by exposure levels.
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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) | 0.000 | 0.000 |
| 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 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".