Insights into microbial and sorptive regulation of chlorpyrifos-bispyribac dissipation in floating treatment wetlands
Bibliographic record
Abstract
Agricultural runoff remains one of the most persistent threats to water quality worldwide. Floating wetlands (FWs), when designed appropriately, offer a promising nature-based solution. Here, we show how biologically and sorptively active FWs can remove two agrochemicals—chlorpyrifos (CPF) and bispyribac‑sodium (BIS), under response surface methodology (RSM)‑backed optimal operation (pH 8; 35 °C; 10 mg L⁻¹ each; 1 % glucose). FWs built with Phragmites australis (common reed) were amended with a defined consortium (CB2H, 1 % v/v), plant‑derived biochar (1.5 % w/v), biochar‑immobilized CB2H (1.5 % w/v), and nutrients (N 25 mg L⁻¹, P 25 mg L⁻¹, K 20 mg L⁻¹). CPF and BIS declined exponentially, fitting pseudo‑first‑order kinetics with adsorption component (high S, K d in the immobilized system). CPF disappeared fastest in the consortium‑only and biochar‑immobilized treatments (k = 0.07 and 0.09 day⁻¹), resulting in > 99 % removal (>9.9 mg L⁻¹) by day 20; BIS peaked at > 84 % (8.4 mg L⁻¹) with immobilized cells. FTIR shifts (∼2920–2840, 2800 cm⁻¹) and new C O bands (1600–1800 cm⁻¹) indicated hydrogen bonding in the Phragmites biochar. Approximately 40 % of the CPF loss in the controls was abiotic (sorption/photolysis/hydrolysis). Chemical oxygen demand and ancillary pollutants also declined. Enhanced performance was supported by microbial colonization within biochar pores. The study provides key design constants (k, K d ), positioning engineered FWs as a scalable nature-based technology for pesticide-laden agricultural runoff.
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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".