Mercury Speciation in Highly Contaminated Soils from Chlor‐Alkali Plants Using Chemical Extractions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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