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Record W2262771847 · doi:10.3390/su8020135

Assessment of Trace Metals Contamination of Surface Water and Sediment: A Case Study of Mvudi River, South Africa

2016· article· en· W2262771847 on OpenAlexaboutno aff
Joshua N. Edokpayi, John O. Odiyo, Oluwaseun Popoola, Titus A.M. Msagati

Bibliographic record

VenueSustainability · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
FundersUniversity of Venda
KeywordsSedimentTurbidityEnvironmental chemistryContaminationWater qualityEnvironmental scienceEstuaryTrace metalSurface waterPollutionAquatic ecosystemHydrology (agriculture)MetalChemistryEnvironmental engineeringOceanographyGeologyEcology

Abstract

fetched live from OpenAlex

Trace metals contamination of rivers and sediments remains a global threat to biodiversity and humans. This study was carried out to assess the variation pattern in trace metals contamination in Mvudi River water and sediments for the period of January–June 2014. Metal concentrations were analyzed using an inductively-coupled plasma optical emission spectrometer after nitric acid digestion. A compliance study for the water samples was performed using the guidelines of the Department of Water Affairs and Forestry (DWAF) of South Africa and the World Health Organization (WHO). The National Oceanic and Atmospheric Administration (NOAA) sediment quality guidelines for marine and estuarine sediments and the Canadian Council of Ministers of the Environment sediment guidelines (CCME) for freshwater sediments were used to determine the possible toxic effects of the metals on aquatic organisms. pH (7.2–7.7) and conductivity (10.5–16.1 mS/m) values complied with DWAF and WHO standards for domestic water use. Turbidity values in nephelometric turbidity units (NTU) were in the range of 1.9–429 and exceeded the guideline values. The monthly average levels of trace metals in the water and sediments of Mvudi River were in the range of: Al (1.01–9.644 mg/L and 4296–5557 mg/kg), Cd (0.0003–0.002 mg/L and from below the detection limit to 2.19 mg/kg), Cr (0.015–0.357 mg/L and 44.23–149.52 mg/kg), Cu (0.024–0.185 mg/L and 13.22–1027 mg/kg), Fe (0.702–2.645 mg/L and 3840–6982 mg/kg), Mn (0.081–0.521 mg/L and 279–1638 mg/kg), Pb (0.002–0.042 mg/L and 1.775-4.157 mg/kg) and Zn (0.031–0.261 mg/L and 14.481–39.88 mg/kg). The average concentrations of Al, Cr, Fe, Mn and Pb in the water samples exceeded the recommended guidelines of DWAF and WHO for domestic water use. High concentrations of Al and Fe were determined in the sediment samples. Generally, the concentrations of Cd, Cr and Cu in the sediments exceeded the corresponding effect range low (ERL) values in the sediment quality guidelines and could have adverse effects on aquatic organisms in Mvudi River.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.013
GPT teacher head0.273
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), 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

Citations152
Published2016
Admission routes1
Has abstractyes

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