Insights into microbial and sorptive regulation of chlorpyrifos-bispyribac dissipation in floating treatment wetlands
Why this work is in the frame
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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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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 | 0.000 | 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 it