Alternative sorption filter materials effectively remove non-particulate organic pollutants from stormwater
Bibliographic record
Abstract
Urban runoff contains a mixture of both particulate and non-particulate organic pollutants (OPs). Hydrophobic OPs such as higher petroleum hydrocarbons, phthalates, and polycyclic organic hydrocarbons (PAHs) are not exclusively bound to particles, but also present in runoff in colloidal and truly dissolved forms. These hydrophobic compounds can also form nano- and microsized emulsions that may carry pollutants in stormwater. Hence, it is of great importance to develop treatment technologies such as sorption filters that can remove non-particulate OPs from contaminated stormwater. A pilot plant using column bed-filters of sand as a pre-filter, in combination with granulated activated carbon, Sphagnum peat or Pinus sylvestris bark, was used to investigate the removal of non-particulate OPs from urban stormwater. Samples from the filter effluents were collected weekly; during or after rain events; and during stress tests when incoming water was spiked with contaminated sediment and petrol or diesel. All sorption filters showed efficient reduction of aliphatic diesel hydrocarbons C16–C35, benzene, and the PAHs phenanthrene, fluoranthene, and pyrene during most of the operation time, which was 18 months. During the stress test events, all sorption filters showed 100% reduction of PAH-16, petrol and diesel aliphatics C5–C35. All sorption filters released DOC and nanoparticles, which may explain some of the transportation of OPs through the filter beds. The recommendation is to use a combination of sand pre-filtration and all the studied sorption materials in stormwater filters in series, to achieve effective removal of different types of OPs. It is also important to improve the hydraulic conditions to obtain sufficient water flows through the filters.
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 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".