MétaCan
Menu
← Back to cohort
Record W2738357398

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

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

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsClay mineralsChemistryManganeseSoil waterAdsorptionWeatheringEnvironmental chemistryMetalIon exchangeMineralogyInorganic chemistryGeologyGeochemistryIonSoil science
DOInot available

Abstract

fetched live from 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.

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)0.000
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 score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.240
Teacher spread0.208 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

Explore more

Same topicHeavy metals in environment→French-language works237,207→