Field Evaluation of Rice Husk Biochar and Pine Tree Woodchips for Removal of Tire Wear Particles from Urban Stormwater Runoff in Oxford, Mississippi (USA)
Bibliographic record
Abstract
Tire wear particles (TWPs), a form of microplastics (MPs) pollution, are transported into waterbodies through stormwater runoff, leading to environmental pollution and impacts on associated biota. Here, we investigated the effectiveness of stormwater filter socks filled with rice husk biochar or pine tree woodchips in reducing TWP pollution in urban runoff in Oxford, Mississippi. Triplicate runoff samples were collected upstream and downstream of the biofilters at two sites during two storm events at peak flow within minutes of the start of the storm and after 30 min. Samples were analyzed for TWPs using a combination of stereomicroscopy, micro-attenuated total reflectance Fourier transform infrared spectroscopy (µ-ATR-FTIR), and scanning electron microscopy (SEM) with energy dispersive X-ray spectroscopy (EDX). Concentrations (TWPs/L) upstream of the biofilter were variable but highest at the start of the runoff, dropping from an average of 2811 ± 1700 to 476 ± 63 after 30 min at site 1 and from 2702 ± 353 to 2356 ± 884 at site 2. Biochar was more effective than woodchips (p < 0.05) at removing TWPs, reducing concentrations by an average of 97.6% (first use) and 85.3% (second use) compared to 66.2% and 54.2% for woodchips, respectively. Biochar was particularly effective at removing smaller TWPs (<100 µm). Both materials became less effective with use, suggesting fewer available trapping sites and the need for removal and replacement of the material with time. Overall, this study suggests that biochar and woodchips, alone or in combination, deserve further scrutiny as a potential cost-effective and sustainable method to mitigate the transfer of TWPs to aquatic ecosystems and associated biota.
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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 | 0.000 | 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 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".