Effects of residential PM2.5 exposures from indoor and outdoor sources on blood pressure and respiratory inflammation in rural and urban Beijing
Bibliographic record
Abstract
Introduction Arising from different activities, indoor-generated (PM2.5_ig) and outdoor-generated residential PM2.5 (PM2.5_og) may have different toxicities and health effects. We aimed to evaluate the effects of PM2.5_ig and PM2.5_og on systolic blood pressure (SBP), diastolic blood pressure (DBP) and respiratory inflammation (represented by exhaled Nitric Oxide (eNO)).Methods 72 subjects participated in the residential monitoring of PM2.5 in urban and rural Beijing during winter 2016 and summer 2017. In total, valid data were captured for 450 measurement days (approx. 3 days per participant per season). Within the same week of exposure monitoring, BP and eNO from participants were measured two times. A classifying algorithm was developed to isolate PM2.5_ig and PM2.5_og from residential and ambient measurements. Linear mixed-effects model was used to examine the associations between residential exposure and health outcomes.Results For all measurements except PM2.5_og (PM2.5_ig, eNO, SBP and DBP), significant differences were observed between rural and urban participants during the two seasons. For all measurements (PM2.5_ig, PM2.5_og, eNO, SBP and DBP), significant differences were observed between seasons in both sites. Overall, an interquartile range (IQR) increase (22.0 ug/m3) in lag 1-day exposure to PM2.5_og was associated with an elevation in SBP by 1.70% (confidence interval [CI]: 0.47%, 2.95%) and eNO by 15.44% (CI: 6.60%, 25.02%); an IQR increase (5.8 ug/m3) in lag 2-day exposure to PM2.5_ig was associated with an elevation in SBP by 1.12% (CI: 0.26%, 2.00%) and an increased DBP by 1.26% (CI: 0.39%, 2.14%). However, PM2.5_ig were negligible (<0.5 ug/m3) in 44% and 54% of measurement days during winter 2016 and summer 2017, which affected the output from the linear mixed-effects model.Conclusion PM2.5_og and PM2.5_ig demonstrated different lag effects and effect sizes in the assessed health metrics. Full investigation with alternative modelling techniques is ongoing to evaluate the relationship in more detail.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".