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Record W212507467

Assessment of Heavy Metal Pollution in a Trans-Boundary River: The Case of the Akagera River

2010· article· en· W212507467 on OpenAlexaboutno aff
F. Nshimiyimana, Innocent Nhapi, Umaru Garba Wali, Hermogène Nsengimana, Noble Banadda, I. Nansubuga, Frank Kansiime

Bibliographic record

VenueInternational journal of mathematics and computation · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Biodiversity
Canadian institutionsnot available
Fundersnot available
KeywordsTributaryCadmiumTurbidityManganeseZincPollutionEnvironmental scienceWater qualityChromiumEnvironmental chemistryPopulationCopperHydrology (agriculture)ChemistryGeographyGeologyEcologyBiology
DOInot available

Abstract

fetched live from OpenAlex

Lake Victoria one of the biggest fresh water lakes in the world is faced with serious water quality deterioration as a result of the activities of the riparian population and the uncontrolled pollution of its tributary rivers. The Akagera River drains Burundi, Rwanda, Tanzania and Uganda and is one of the major rivers discharging into Lake Victoria. Although the status of water quality in Lake Victoria has been studied, little is known about actual sources, especially the role of trans-boundary rivers. This study focused on quantifying and monitoring heavy metal levels mainly Cadmium, Chromium, Copper, Iron, Lead, Manganese, Zinc, and Conductivity, pH, Temperature, and Turbidity in the Akagera River. Sampling was conducted monthly from February, 2008 to March, 2009 based on eleven sampling points, four of which are on the tributary rivers, Nyabarongo, Akanyaru and Muvumba (origin Burundi). Cadmium, Chromium and Lead were analyzed using an Atomic Absorption Spectrometer. Copper, Iron, Manganese and Zinc were analyzed using a colorimeter, whilst conductivity, temperature, turbidity and pH were measured in the field using HACH field kits. The results indicate that the levels of Copper and Lead in Akanyaru are higher than those in other rivers, while Muvumba River shows high concentration of Cadmium of about 0.965 mg/L. The mean values observed are 0.965 mg/L for Cadmium, 0.015 mg/L for Chromium, 0.045 mg/L for Lead, 0.415 mg/L for Copper, 0.553 mg/L of Zinc, 14.62 mg/L for Manganese and 0.56 mg/L for Iron. These parameters show that the river contains high values of Cadmium, Lead and Manganese, compared to the WHO Guidelines for Drinking Water Quality and Canadian Water Quality Guidelines for the protection of aquatic life. This presents serious problems to the aquatic life and to the different water users. The high levels in the river were attributed to agricultural, industrial and high erosion levels in the catchment. Since this is a trans-boundary river, it was recommended that the riparian countries come together and develop appropriate measures (including institutions) to identify and control the sources of pollution.

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 score0.227
Threshold uncertainty score0.098

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 communication0.0000.000
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 head0.012
GPT teacher head0.264
Teacher spread0.252 · 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

Citations7
Published2010
Admission routes1
Has abstractyes

Explore more

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