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Record W2012952503 · doi:10.1080/15320383.2014.808171

A Counter-Current Acid Leaching Process for the Remediation of Contaminated Soils from a Small-Arms Shooting Range

2013· article· en· W2012952503 on OpenAlexafffund
Stéphanie Lafond, Jean‐François Blais, Guy Mercier, Richard Martel

Bibliographic record

VenueSoil and Sediment Contamination An International Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicElectrokinetic Soil Remediation Techniques
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsEnvironmental remediationLeaching (pedology)LeachateSoil waterContaminationSoil contaminationChemistryEnvironmental chemistryMetalEnvironmental scienceEnvironmental engineeringSoil science

Abstract

fetched live from OpenAlex

The objective of this research was to use a counter-current leaching process (CCLP) with leachate treatment to develop a remediation process for contaminated soils at a small-arms shooting range (SASR). The soil contaminant concentrations were 245 mg Cu kg−1, 3,368 mg Pb kg−1, 73 mg Sb kg−1, and 177 mg Zn kg−1. The CCLP includes three acid leaching steps (1M H2SO4 + 4M NaCl, t = 1 h, T = 20°C, soil suspension = 100 g L−1), followed by one water rinsing step (1 h). Seven counter-current remediation cycles were completed, and the average resulting metal removals were 93.2 ± 3.5% of Cu, 91.5 ± 5.7% of Pb, 82.2 ± 10.9% of Sb, and 30.0 ± 11.4% of Zn. The metal leaching performances decreased with the number of completed cycles. Soil treated with the CCLP with leachate treatment process met the USEPA threshold criteria of 5 mg Pb L−1 in the TCLP leachate. The CCLP allows a decrease of the water use by 32.9 m3 t−1 and the chemicals’ consumption by approximately 2,650 kg H2SO4, 6,014 kg NaCl, and 1,150 kg NaOH per ton of treated soil, in comparison to standard leaching processes. This corresponds to 78%, 69%, 83%, and 67% of reduction, respectively.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.526

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 head0.016
GPT teacher head0.267
Teacher spread0.251 · 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 designOther design
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

Citations14
Published2013
Admission routes2
Has abstractyes

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