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Record W2060685468 · doi:10.1021/es0503581

Remediation of Elemental Mercury Using in Situ Thermal Desorption (ISTD)

2006· article· en· W2060685468 on OpenAlexaboutno aff
Anna M. Kunkel, Jeremy J. Seibert, Lucas J. Elliott, Ricci Kelley, Lynn E. Katz, Gary A. Pope

Bibliographic record

VenueEnvironmental Science & Technology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsnot available
Fundersnot available
KeywordsMercury (programming language)ChemistryEnvironmental remediationThermal desorptionDesorptionSoil waterElemental mercuryOutgassingSoil vapor extractionVolumetric flow rateEnvironmental chemistryChromatographyAnalytical Chemistry (journal)AdsorptionContaminationEnvironmental scienceSoil scienceOrganic chemistry

Abstract

fetched live from OpenAlex

In situ thermal desorption (ISTD) is a soil heating method that simultaneously applies heat and vacuum to the subsurface at temperatures up to 600 degrees C. As the soil is heated, the vapor pressure of the contaminant increases allowing mass transfer to the gas phase and extraction from the soil using vacuum wells. The overall goal of this research is to assess the feasibility of using ISTD to remove elemental mercury from soils. The initial phase of research included design of a laboratory soil column apparatus and preliminary soil column experiments with surrogate nonaqueous phase liquids (perfluorocarbons) to test the apparatus and investigate the effects of air flow rate and temperature on the ISTD process. Following the preliminary experiments, a mercury off-gas treatment system was added and mercury experiments were conducted. Experiments performed using elemental mercury showed greater than 99.8% removal of the mercury from Ottawa sand. These results show that ISTD can remove mercury from soil at temperatures well below its boiling point and that perfluorodecalin can be used as a surrogate for elemental mercury in laboratory experiments. A flow and transport simulator was used to model the results from both the perfluorocarbon and the mercury experiments.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.238
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations51
Published2006
Admission routes1
Has abstractyes

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