Diffusion Characteristics of PM2.5 in Rural Dwelling under Different Daily Life Behavior: A Case Study in Rural Shenyang of China
Bibliographic record
Abstract
The highest concentration of PM2.5 in cold rural dwellings of Northeast China is often generated by using mini stoves for cooking and heating, which can directly influence human health. As of yet, little is known about the impact of different daily life behavior on PM2.5 diffusion and residents’ exposure in rural dwellings. In this study, the characteristics of indoor PM2.5 variation and diffusion in rural dwellings was described by measuring some rural dwellings and establishing a multi-zone network model. The calculated results indicated that the relative errors between theoretical calculated results and experimental measured results are within 10%. PM2.5 diffusion in a rural dwelling can be predicted. Furthermore, the impacts of daily life behavior on PM2.5 diffusion and exposure assessment can be analyzed. Through discussion, heating behavior is the most important factor causing high concentrations of PM2.5 in each room, followed by cooking, smoking, and cleaning. Door opening time can lead to different interzonal airflows and PM2.5 diffusion rates. By reducing the inner door opening time to less than 1 min, PM2.5 could decrease to 300 μg/m3. Door closing behavior could decrease risk that PM2.5 diffuses to bedrooms by more than 50%, and exposure of residents in bedrooms could reduce to 100 (μg·h)/m3 effectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".