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Record W2802220967 · doi:10.4314/jasem.v22i4.3

Assessment of Polychlorinated Biphenyls (PCBs) in water, sediments and biota from e-waste dumpsites in Lagos and Osun States, South-West, Nigeria

2018· article· en· W2802220967 on OpenAlexaboutno aff
JK Igbo, L.O. Chukwu, E.O. Oyewo

Bibliographic record

VenueJournal of Applied Sciences and Environmental Management · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceLeachateSedimentEnvironmental chemistryPollutionContaminationFisheryEcologyBiologyChemistry

Abstract

fetched live from OpenAlex

The levels of PCBs in sediments, water, leachate and aquatic fauna (Tilapia guineensis, Callinectes amnicola and Cardiosoma armatum) found in and around e-waste dumpsites in Lagos and Osun States, South-West, Nigeria were analyzed using Gas Chromatography Electron Capture Detector (GC ECD) Agilent 7820A. All the 28 PCBs congeners studied were detected with the Σhexa-PCBs dominating in Lagos (21%) while the Σtetra-PCBs (24%) enriched the samples from Osun State. The concentrations of Σ- PCBs in decreasing order were Lagos: sediment ˃ fish gill ˃ fish muscles ˃ water ˃ crab ˃ leachate and Osun: fish gill ˃ fish muscle ˃ sediment ˃ crab ˃ water ˃ leachate. The concentrations of total indicator PCBs ( Σ7PCBs) in the sediment from Lagos (4.19 μg/kg) and Osun (8.58 μg/kg) exceeded the Canadian Sediment Quality standard threshold effect level (CSQ TEL) (0.03 μg/kg) and the National Oceanic Atmospheric Administration threshold effect level (NOAA TEL) for fresh and marine sediments.. The calculated toxic equivalent quotient (TEQ) for fish from Lagos and Osun (3.7 and 4.4) respectively further suggests a likely occurrence of adverse effects to humans who consume the fish. This study reveals the high health and ecological risks associated with e waste pollution in the aquatic environment.Keywords: e-waste, polychlorinated Biphenyls, aquatic environment, leachate

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.582

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.007
GPT teacher head0.224
Teacher spread0.217 · 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

Citations21
Published2018
Admission routes1
Has abstractyes

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