A wind tunnel and field evaluation of various dust suppressants
Bibliographic record
Abstract
A series of experiments was designed to assess the relative efficacy of various dust suppressants to suppress PM10 emissions from nepheline syenite tailings. The experiments were conducted in the Trent University Environmental Wind Tunnel, Peterborough, Ontario, and on the tailings ponds at a mine near Havelock, Ontario. Treated surfaces were subjected to particle-free airflow, abrasion with blown sand particles, and particle-free airflow after physical disturbance. Emission rates in the wind tunnel tests were calculated from dust concentration measurements obtained in vertical profile with DustTrak™ II aerosol monitors (model 8530); rates in the field were measured using a Portable In-Situ Wind Erosion Laboratory (PI-SWERL). In the particle-free wind tunnel tests, three of the surface treatments performed well, and PM10 emission scaled inversely with crust strength. Light bombardment of each surface by saltating sand grains increased PM10 emission rates by two orders of magnitude. All treated surfaces emitted significantly more PM10 after physical disturbance. In the field study, plots treated with a commercial dust suppressant were found to release more PM10 than either the control or irrigated plots, although it should be noted that the emission rates were similar in magnitude. As in the wind tunnel experiments, all of the field plots became significantly more emissive after physical disturbance. The field results suggest that the site conditions, inclusive of the potential for dust advection and resuspension, must be taken into account when considering the use of a commercial dust suppressant.Implications: Fugitive dust (PM10) emissions from mining and industrial operations worldwide present significant environmental and human health risks, leaving mine operators challenged to find reliable, durable, and cost-effective mitigation options. Commercial dust suppressants boast unique chemical compositions and commensurate particle binding capabilities, although few side-by-side comparisons exist in the literature. The efficacy of four commercial products to suppress PM10 emissions from mine tailings, before and after physical disturbance, was assessed using robust field and wind tunnel experiments. All surfaces emitted significantly more PM10 after physical disturbance but with considerable variability amongst products. Possible reasons for the differences in relative performance are explored.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".