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Reductive Dechlorination of Groundwater Contaminated with PCE using Biobarrier: Column Study

2007· article· en· W29230485 on OpenAlexaboutno aff
Hyun-Wook HwangBo

Bibliographic record

VenueJournal of Environmental Science International · 2007
Typearticle
Languageen
FieldEngineering
TopicEnvironmental remediation with nanomaterials
Canadian institutionsnot available
Fundersnot available
KeywordsReductive dechlorinationEnvironmental remediationGroundwaterEnvironmental chemistryGroundwater remediationChemistryChlorinated solventsContaminationPeatGeologyOrganic chemistryBiodegradation

Abstract

fetched live from OpenAlex

The applicability of in situ biobarrier or microbial filter technology for the remediation of groundwater contaminated with chlorinated solvent was investigated through column study. In this study, the effect of packing materials on the reductive dechlorination of PCE was investigated using Canadian peat, Pahokee peat, peat moss and vermicompost (or worm casting) as a biobarrier medium. Optimal conditions previously determined from a batch microcosm study was applied in this column study. Lactate/benzoate was amended as electron donors to stimulate reductive dechlorination of PCE. Hydraulic conductivity was approximately <TEX>$6{\times}10^{-5}-8{\times}10^{-5}\;cm/sec$</TEX> and no difference was found among the packing materials. The transport and dispersion coefficients determined from the curve-fitting of the breakthrough curves of <TEX>$Br^-$</TEX> using CXTFIT 2.1 showed no difference between single-region and two-region models. The reductive dechlorination of PCE was efficiently occurred in all columns. Among the columns, especially the column packed with vermicompost exhibited the highest reductive dechlorination efficiency. The results of this study showed the promising potential of in situ biobarrier technology using peat and vermicompost for the remediation of groundwater contaminated with chlorinated solvents.

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)0.001
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 score0.374
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.238
Teacher spread0.229 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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