Nano-Scale Drinking Water Treatment Residuals Affect Arsenic Fractionation and Speciation in Biosolids-Amended Agricultural Soil
Bibliographic record
Abstract
An incubation experiment was conducted to determine the effects of nanoscale drinking water treatment residuals (nWTRs) on arsenic (As) fractionation and speciation in agricultural soil amended with biosolids. The soils were treated with biosolids of 3% (w/w), along with nWTR application rates of 0, 0.25, 0.50, or 1.00% (w/w). The results revealed that the As adsorption rate increased with increasing the As treatment level from 50 to 800 mg/L. The maximum efficiency of As adsorption was 95%–98% in the soil treated with nWTRs of 1%, while the least As adsorption was 53%–91% in the soil treated with nWTRs of 0.25%. The overall As bioavailability in the biosolids-amended soil followed a descending order of nWTRs treatment: (0%) > 0.25% nWTRs, >0.50% nWTRs, and >1% nWTRs. The addition of nWTRs significantly changed As speciation in biosolids-amended soil. The X-ray absorption near-edge structure spectroscopy (XANES) and MINEQL+4.6 analyses showed that most of As was in a oxidized form of As5+ that likely incorporated in As pentoxide, and thus, with low mobility, bioavailability, and toxicity. This study demonstrated that nWTRs were effective in adsorbing and immobilizing As in biosolids-amended agricultural soils by forming stable As-nWTR surface complexes.
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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) | 0.001 | 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".