Mobility of heavy metals in soils amended with sewage sludge
Bibliographic record
Abstract
Sewage sludges added to arable land can improve soil fertility and physical properties. However, the concentrations of heavy metals commonly found in sludges limits their application to soil. The purpose of this paper is to evaluate the mobility of heavy metals (Cd, Cu, Ni, Pb and Zn) in four soils amended with different rates (0, 80, 60 and 320 t ha–1) of anaerobically stabilized urban sewage sludge. Total metal content in the sewage sludge was Zn >> Cu > Pb > Ni >> Cd. Sludge, soils and sludge-soil mixtures were fractionated by the Tessier sequential extraction procedure. The fractions extracted by H2O2/HNO3 and NH2OH.HCl were the most abundant pools for metals under study. The apparent mobility of metals in the sludge was Zn ≈ Cd ≈ Ni > Pb > Cu. The addition of sewage sludge in soils increased the percentages of metal extracted in non-residual fractions. ANOVA showed that the most significant increases were those of Zn, followed by Cu and Pb; there were no statistical differences (P < 0.05) for Ni and Cd. Exchangeable Zn from sludge was immobilized in basic soils. The other trace metals showed no fraction redistribution. The soils and sludge-treated soil samples were also extracted with EDTA and DTPA. Extraction with EDTA was more sensitive to soil type, whereas extraction with DTPA showed wider variation with metals. Both chelates seemed to be more effective to assess the mobility of metals added with the sludges at low concentrations than the Tessier’s chemical partitioning. Key words: Soils, sewage sludge, heavy metals, mobility
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".