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Enregistrement W2738357398

Effect of clay content on Fe, Mn-adsorption in overbank sediment of Požega valley

2011· article· en· W2738357398 sur OpenAlexaboutno aff
Tamara Marković, Nikolina Ilijanić, Slobodan Miko, Ozren Larva, Željka Brkić

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueHeavy metals in environment
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésClay mineralsChemistryManganeseSoil waterAdsorptionWeatheringEnvironmental chemistryMetalIon exchangeMineralogyInorganic chemistryGeologyGeochemistryIonSoil science
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

In soils and sediments, various elements exist in different forms and are associated with a range of components and minerals. A range of different physico-chemical processes, which dictate their availability and mobility in the soil or sediment system, governs the behaviour and fate of metals. Clays are hydrous aluminosilicates composed of mixtures of fines-grained clay minerals, crystals of other minerals and metal oxides. Clays play an important role in the environment by taking up cations and anions through adsorption or ion exchange. The good adsorption ability comes from their negative charge, which can be neutralised by adsorption of positively charges anions. The trace metals manganese (Mn) and iron (Fe) are ubiquitous in the environment ; they are a minor component of most rock types and present in soils from weathering. Mn and Fe can exist in different oxidation states, but the most widely occurring forms in the environment are soluble Mn (II) and Fe (II) when reduced and insoluble Mn (IV) and Fe (III) when oxidised. Naturally, occurring Mn and Fe are commonly found in drinking water supplies and are essential for human health at low concentrations (Keen and Zidenberg-Cherr, 1994). Excessive Mn and Fe concentrations can result in metallic tasting water, staining of clothes, dishes, and products such as paper or plastics, and reduced water pressure and flow in pipes from accumulation of Mn and Fe oxides (Sly et al., 1990). Although Mn can be removed from water by treatment (Casale et al., 2002), this is expensive and water supply infrastructure prior to treatment, such as pipes, pumps and boreholes, may still be adversely affected by build up of Mn oxides (Sly et al., 1990). Hence, if the occurrence of excessive Mn concentrations can be predicted, it could be avoided when sourcing new water supplies. Several studies in different environmental settings have reported significant associations between exposure to Mn in groundwater- sourced drinking water supplies and adverse human health effects. For example, Wasserman et al., (2006) identified a significant negative relationship between well water Mn concentration (0.004–3.91mg/l) and measures of intellectual function in 142 children of 10 years of age in Bangladesh (notable difference in intellectual function between 1 mg/l group). Mn exposure associated with drinking water was also significantly positively related with hyperactive classroom behaviours in a study of 46 children in Quebec (Bouchard et al., 2007). In adults, exposure to elevated Mn concentrations in drinking water has been associated with manganism, a Parkinson-like disorder. The behaviour and fate of Mn and Fe were studied in the overbank sediments of the river Orljava in the Požega valley, wail the high concentration of Mn and Fe were observed in groundwater of pumping site Zapadno polje. The occurrence and concentration of Mn in groundwater is controlled by many factors, and one of the main ones being rock geochemistry, water chemistry and microbiological activity. The study area is part of the Pannonian basin and it is located in the central part of northern Croatia. The Orljava River and its tributaries are depositing the sediments in the valley from the surrounding mountains – Psunj, Papuk, Krndija, Dilj and Požeska gora. The surrounding mountains consist of magmatic, metamorphic and sedimentary rocks. The valley is build from alluvial, pond and sedimentary deposits. Chemical composition of overbank sediments and water samples were determined on AAS and IC (metal and ion concentrations). Other analysis were obtained on overbank sediments: CEC, pH and organic matter content. X-ray diffraction (XRD) was used to determine the bulk mineralogy and the clay mineral assemblages. The clay mineralogy was determined on oriented preparations of the <2µm fraction after air-drying, heating to 400°C and 550°C and ethylene-glycol solvation. Prior of taking water samples, in situ were measured pH, EC, T and oxygen content of water. XRD analysis showed following mineral composition: quartz, feldspar and calcite as dominant mineral phases. In bulk sediment samples muscovite/illite are dominant. Clay minerals are dominated by smectite, following illite and kaolinite. Fe and Mn-containig phases are non-crystalline. Manganese minerals are often found associated with clays and iron oxides in soils and they are usually poorly crystalline. High concentrations of Fe and Mn are measured in shallow and deep part of the overbank profile of sediments where are present high content of clay minerals. Adsorb on clay minerals and soluble Mn and Fe were infiltrated in the aquifer by Orljava River. High concentrations of Mn occur in water samples of well Z-9. The concentrations vary from 464 to 847 ppb. Iron in compartment to Mn concentrations are low and they vary from <1 to 101 ppb. In the river water the concentrations of Mn vary from 19 to 244 ppb and Fe concentrations from 64 to 2521 ppb. The recharge area of the well and the river water have high dissolved oxygen content. It was determine that the change of pH is influencing the release of Mn into groundwater.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,041
Score d'incertitude au seuil0,082

Scores du classifieur distillé par catégorie (deux têtes)

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

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 Opus0,031
Tête enseignante GPT0,240
Écart entre enseignants0,208 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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
Publié2011
Routes d'admission1
Résumé présentoui

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