Removal of microplastics from agricultural runoff using biochar: a column feasibility study
Bibliographic record
Abstract
Plastics are extensively used in agriculture, but their weathering and degradation generates microplastics (MPs) that can be carried by runoff into water bodies where they can accumulate and impact wildlife. Due to its physicochemical properties, biochar has shown promise in mitigating contaminants in agricultural runoff. However, few studies have examined its effectiveness at removing MPs. In this study, we assessed MP pollution (>30 μm) in runoff from a farm in the Mississippi Delta and examined the effectiveness of biochar (pinewood and sugarcane) to remove MPs from aqueous solutions. Using micro-Fourier Transform Infrared spectroscopy (µ-FTIR), we observed an average of 237 MPs/L (range 27–609) in the runoff, with most particles identified as polyethylene, polyamide, polyvinyl chloride, polyurethane, acrylonitrile butadiene styrene, and polyarylamide. Biochar columns effectively removed MPs from runoff samples with reductions ranging from 86.6% to 92.6%. MPs of different sizes, shapes, and types were stained with Nile red dye (to facilitate observation by fluorescence) and quantified their downward progress with multiple column volumes of water and wet/dry cycles. Smaller MPs penetrated the columns further, but ≥90% of MPs were retained in the ∼20 cm columns regardless of their shape, size, and type. We attribute these results to physical entrapment, hydrophobic behaviors, and electrostatic interactions. Overall, this proof-of-concept work suggests biochar may serve as a cost-effective approach to remove MPs from runoff, and that subsequent field studies are warranted.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".