Heavy metal removal by porous asphalt in cyclical wetting and drying
Bibliographic record
Abstract
Porous asphalt pavement (PAP) is subjected to wetting and drying cycles involving storms of varying intensity and inter-event drying periods to study the impact of continuous weather systems on heavy metal removal in runoff. Four physical models: a complete PAP system, a PAP surface, a geotextile fabric and a reservoir of cobblestone aggregate, were created at the lab-scale and tested with stormwater contaminated with dissolved Mn(Ⅱ), Zn(Ⅱ), Cu(Ⅱ), and Cr(Ⅵ). The models were exposed to repeated wetting and drying cycles spanning over 27 days in triplicate. Wetting involved light, moderate and heavy rainfall intensities. All pavement material designs performed well at removing Cu(Ⅱ) and Cr(Ⅵ). The PAP system had an overall better performance at removing Mn(Ⅱ), Zn(Ⅱ), Cu(Ⅱ), and Cr(Ⅵ) than any of the individual pavement materials but was unable to remove all of the heavy metals simultaneously to sufficiently high levels. The materials used in the filter layer and reservoir structure and their adsorption and desorption capacities should be assessed for potential contamination (leaching) of Zn(Ⅱ) and Mn(Ⅱ), respectively, prior to construction. Wetting and drying cycles had the greatest impacts on the reservoir structure’s performance versus any of the other layers. Leaching of Mn(II) observed from cobblestone aggregate was affected by the solution composition and pH. Removal of selected heavy metals was rapid in first flush and fluctuated with storm event intensity.
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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".