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 (PM10) with that for particulate matter with diameters less than 2.5 µm (PM2.5) at construction sites. A series of field-analogous experiments were carried out to continuously measure PM10 and PM2.5 using laser particle monitors in an artificial wind tunnel system. It was found that the efficiency of woven straw at reducing either PM10 or PM2.5 is significantly dependent on the woven straw coverage ratio. The maximum reduction efficiencies for PM10 and PM2.5 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 PM2.5 and PM10 emissions is not possible, even using full-coverage woven straw. Reduction of PM2.5 emissions is less efficient than that of PM10 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 PM2.5 and PM10 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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".