Comparison of reduction efficiency of woven straw for entrained emissions of particulate matter with diameters less than 10 µm (PM<sub>10</sub>) and less than 2.5 µm (PM<sub>2.5</sub>) from exposed areas at construction sitesA paper submitted to the Journal of Environmental Engineering and Science.
Bibliographic record
Abstract
From an engineering application point of view in reducing fugitive particulate matter emissions, this paper presents experimental data comparing the efficiency of woven straw at reducing entrained emissions of particulate matter with diameters less than 10 µm (PM 10 ) with that for particulate matter with diameters less than 2.5 µm (PM 2.5 ) at construction sites. A series of field-analogous experiments were carried out to continuously measure PM 10 and PM 2.5 using laser particle monitors in an artificial wind tunnel system. It was found that the efficiency of woven straw at reducing either PM 10 or PM 2.5 is significantly dependent on the woven straw coverage ratio. The maximum reduction efficiencies for PM 10 and PM 2.5 are about 40% and 12%, respectively. Note that the reduction efficiencies approach zero as the coverage ratio is less than about 40%. Total elimination of PM 2.5 and PM 10 emissions is not possible, even using full-coverage woven straw. Reduction of PM 2.5 emissions is less efficient than that of PM 10 emissions using woven straw, and this trend is likely more obvious with an increase in the woven straw coverage ratio. The ratio of reduction efficiencies (R) between PM 2.5 and PM 10 for woven straw decreased slightly with an increase in the woven straw coverage ratio. Overall, for wind speeds of 0.5–3.0 m/s, silt contents of 4.8%–21.0%, and soil moisture contents of 10%–30% as used in this work, the average value of R is 0.31. The effects of wind velocity, silt content, and moisture content on the reduction efficiencies are also discussed.
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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 | 0.000 | 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) | 0.000 | 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".