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Record W2578542882

The Impact of Using Polluted Benue River and Shinko Waters on Irrigated Vegetables at Geriyo, Nigeria

2015· dissertation· en· W2578542882 on OpenAlexaboutno aff
Aliyu Haliru Hong

Bibliographic record

VenueUnimas Institutional Repository (Universiti Malaysia Sarawak) · 2015
Typedissertation
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceIrrigationSoil waterPollutionPollutantWater qualityFood chainAgricultureWater scarcityEnvironmental engineeringHydrology (agriculture)Water resource managementAgronomyGeographySoil scienceEcology
DOInot available

Abstract

fetched live from OpenAlex

Water pollution and scarcity are identified as the major challenge affecting food production through irrigation in a sahelian region of Africa. However, the impact of using polluted water for irrigation on soil and edible crops and the associated risk from heavy metals loaded in polluted water, soil and crops on consumers remains uncertain. A typical case of this is in Yola, Adamawa State, Nigeria where high quality irrigation water is scarce. To close this gap, this PhD research studied the impact of polluted river and lake water characteristics, and heavy metal concentration levels in water, soils and irrigated vegetables. Health risk index for consumption of heavy metals in polluted vegetables were estimated on adult and children through the water – soil – plant food chain transfer pathway from two irrigation sites of Geriyo catchment area using standard methods. The result of water characteristics and heavy metals indicated significantly high level of pollutants with most of the parameters above the threshold levels set by FAO/WHO and FEPA Standards for irrigation water uses. Heavy metal concentration levels in soil across the two sites indicated significant difference in concentration of heavy metals with Shinko Lake site soil higher than River Benue site. Heavy metal concentration levels in soils of the two sites have been impacted due to accumulation of metals in water and soil, with most of the values above the international critical threshold levels set by EU, USA, Canada and UK. The calculated metal pollution index (MPI) of the two soils revealed severe contamination of soil, to severe pollution of soil with heavy metals; with potentials of effecting plant growth and ground water contamination. The evaluated heavy metal transfer factor from soil into vegetables which is a key component of metal exposure was observed to be higher due to high percentage of sand fraction and low soil pH. Vegetables showed evidence of bioaccumulation of heavy metals from both sites; with their maximum values above permissible level of heavy metals in vegetable set out by FAO/WHO (2007) standard. The evaluated food chain transfer of heavy metals via the consumption of contaminated vegetables based on determined daily intake rate of 345g/day and 232g/day for adults above 18 years of age with body weights category of 60, 50, and 40 kg; and children below 18 years of age, with body weights of 32.5, 22.5 and 12.5kg using health risk index tool (HRI) revealed that health risks of heavy metals in vegetables are due to Cu and Pb elements. For adults consumers of vegetables with body weights of 60, 50, and 40 kg; the estimated health risk index range from 1.112 - 1.114, 1.0850 – 1.3358 and 1.0630 – 1.6697 are due to ingestion of Pb and Cu in cabbage, amaranthus and tomatoes. For children with body weights category of 32.5, 22.5 and 12.5kg, estimated health risk index range of 1.1180 – 1. 3830, 1.6218 – 1.9983 and 1.0294 – 3.5969 are due to Pb and Cu in vegetables. The results indicated that children with body weights under 12.5kg are more prone to heavy metal exposure from intake of these vegetables, as their estimated health risk index are higher than other body weights of consumers. The long time toxic effect of food chain transfer and accumulation of Cu and Pb on human body organs, such as liver, kidneys, spleen and lungs to cause defects is a serious source of concern. Urgent integrated health risk management and risk education need to be taken by local authority in the area.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score1.000

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 studies0.0010.001
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 head0.016
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.

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

Citations0
Published2015
Admission routes1
Has abstractyes

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