Mercury Speciation in Highly Contaminated Soils from Chlor‐Alkali Plants Using Chemical Extractions
Bibliographic record
Abstract
A four-step novel sequential extraction procedure (SEP) was developed to assess Hg fractionation and mobility in three highly contaminated soils from chlor-alkali plants (CAPs). The SEP was validated using a certified reference material (CRM) and pure Hg compounds. Total, volatile, and methyl Hg concentrations were also determined using single extractions. Mercury was separated into four fractions defined as water-soluble (F1), exchangeable (F2) (0.5 M NH4Ac-EDTA and 1 M CaCl2 were tested), organic (F3) (successive extractions with 0.2 M NaOH and CH3COOH 4% [v/v]), and residual (F4) (HNO3 + H2SO4 + HClO4). The soil characterization revealed extremely contaminated (295 +/- 18 to 11 500 +/- 500 mg Hg kg(-1)) coarse-grained sandy soils having an alkaline pH (7.9-9.1), high chloride concentrations (5-35 mg kg(-1)), and very low organic carbon content (0.00-18.2 g kg(-1)). Methyl Hg concentrations were low (0.2-19.3 microg kg(-1)) in all soils. Sequential extractions indicated that the majority of the Hg was associated with the residual fraction (F4). In Soils 1 and 3, however, high percentages (88-98%) of the total Hg were present as volatile Hg. Therefore, in these two soils, a high proportion of volatile Hg was present in the residual fraction. The nonresidual fraction (F1 + F2 + F3) was most abundant in Soil 1 (14-42%), suggesting a higher availability of Hg in this soil. The developed and validated SEP was reproducible and efficient for highly contaminated samples. Recovery ranged between 93 and 98% for the CRM and 70 and 130% for the CAP-contaminated soils.
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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 | 0.001 | 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".