The ability of selected filter materials in removing nutrients, metals, and microplastics from stormwater in biofilter structures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Creative solutions to manage stormwater include ecologically based designs, such as biofilter structures. A laboratory experiment was established to study the ability of biofilters to remove nutrients, metals, total suspended solids (TSS), and total organic C originating from roadside stormwater as melted snow. Special attention was paid to the removal of P. In addition, the fate of microplastics (MPs) in the biofilters was followed. The materials selected for biofilters were (a) crushed light-expanded clay aggregates without biochar or amended with biochar, (b) Filtralite P clay aggregates, (c) crushed concrete, or (d) filter sand. A layer to support grass growth was placed above these materials. Stormwater was rich in TSS with associated P and metals, which were substantially retained by all biofilters. Filtralite and concrete had almost 100% P removal, but the high pH had adverse effects on plants. Light-expanded clay aggregates had lower retention of P, and, when mixed with biochar (30% v/v), the leaching of P increased and N retention was improved. None of the materials was ideal for treating both nutrients and metals, but sand was generally best. Vegetation improved N retention and stormwater infiltration. Plant roots formed preferential pathways for water and associated substances, evidenced by the accumulation of MPs along root channels. No MPs were found in discharge. Given the high loading of suspended solids and associated contaminants in snowmelt from traffic areas and their efficient retention in biofiltration, results of this study suggest the implementation of such stormwater management solutions along road verges.
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.
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 | 0.001 | 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) | 0.001 | 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 it