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Enregistrement W2578542882

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

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

Notice bibliographique

RevueUnimas Institutional Repository (Universiti Malaysia Sarawak) · 2015
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueWater Quality and Pollution Assessment
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEnvironmental scienceIrrigationSoil waterPollutionPollutantWater qualityFood chainAgricultureWater scarcityEnvironmental engineeringHydrology (agriculture)Water resource managementAgronomyGeographySoil scienceEcology
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,830
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,016
Tête enseignante GPT0,267
Écart entre enseignants0,251 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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