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Record W3114750420 · doi:10.11159/ijepr.2020.005

Decontamination of Automobile Workshop Soils containing Heavy Metals and PAHs using Chelating Agents

2020· article· en· W3114750420 on OpenAlexvenueno aff
Ayodele Rotimi Ipeaiyeda, Afolarin O. Ogungbemi

Bibliographic record

VenueInternational Journal of Environmental Pollution and Remediation · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsHuman decontaminationHeavy metalsChelationEnvironmental chemistryEnvironmental scienceSoil waterWaste managementChemistryEngineeringInorganic chemistrySoil science

Abstract

fetched live from OpenAlex

Automobile repair workshops are major anthropological sources of polycyclic aromatic hydrocarbons (PAHs) and heavy metals in Nigerian cities. The extent of contamination of soil from workshops in Ibadan city was evaluated by contamination or pollution index (C/P index) assessment. The C/P index assessment indicated that the soils were categorized from moderately contaminated to severely polluted class with Pb, Cd, Cr, Zn and Mn. The concentration of 16 PAHs in the soil samples ranging from 24521 to 2340025 g/kg were far above the levels in the control samples. Washing of soil from different automobile repair workshops using ethtylenediaminetetraacetic acid (EDTA), diethylenetriaminepentaacetic acid (DTPA) and ethanol was investigated. Since mixed contaminants are usually co-existing in the environment, additional experiments involving a combined solution were conducted to remove both PAHs and heavy metals. The results indicated that the removal efficiencies of the extractants were in the order 0.1M DTPA > 0.1M EDTA > 0.01M DTPA > 0.01M EDTA for the heavy metals removal. However, the combined extractants of EDTA and ethanol had much higher PAHs removal efficiency than ethanol alone. The use of mixed extractants was more effective for PAHs and had very little effect for the removal of heavy metals, especially zinc.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.024
GPT teacher head0.273
Teacher spread0.249 · 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 designObservational
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

Citations4
Published2020
Admission routes1
Has abstractyes

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