Sustainable porous collectors for Agricultural Runoff Treatment
Bibliographic record
Abstract
Excess nutrient and pollutant loading from agricultural runoff is a key driver of water quality degradation, necessitating low-cost, in-situ treatment solutions. Filtration beds are commonly employed for this purpose, yet the traditional use of sand as the dominant filter medium is increasingly unsustainable due to resource scarcity. Crushed recycled glass is emerging as a promising alternative, though its performance characteristics and potential for surface modification remain underexplored. This study evaluates the filtration efficiency of crushed recycled glass, both unmodified and surface-modified with iron oxyhydroxides, across a range of particle sizes and heating temperatures. A 3 × 4 factorial design was used in column experiments to assess removal efficiency across multiple water quality parameters, including turbidity, total organic carbon, orthophosphates, and metals. Surface-coated media modified at 90 °C significantly outperformed the control for turbidity removal (p <0.05) for the two coarsest size fractions (1190 µm and 2000 µm). No significant differences in surface elemental composition were identified between modified media dehydrated at 90°C and 100°C. Modified media across particle size fractions and temperature treatment did not leach B or Fe > 0.1 ppm. Instead, Fe removal was of at least 76% for all tested modified media. These findings demonstrate that surface-modified recycled glass can effectively enhance contaminant removal in water treatment, while highlighting that particle size distribution and column configuration (L/D ratio) influence overall performance. The results support the potential of modified recycled glass as a practical, low-cost, and environmentally sustainable filter medium.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".