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Enregistrement W70334374

Evaluating the Release of Arsenic from Drinking Water Absorbents under Regulatory and Landfill Leaching Conditions Using X-ray Adsorption Fine Structure (XAFS) Spectroscopy

2010· article· en· W70334374 sur OpenAlexfundno aff
Mengling Stuckman

Notice bibliographique

RevueThe Knowledge Bank (The Ohio State University) · 2010
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueArsenic contamination and mitigation
Établissements canadiensnon disponible
Organismes subventionnairesBasic Energy SciencesNatural Sciences and Engineering Research Council of CanadaUniversity of WashingtonOhio State UniversityOffice of ScienceSimon Fraser UniversityU.S. Environmental Protection AgencyU.S. Department of Energy
Mots-clésX-ray absorption fine structureArsenicLeaching (pedology)AdsorptionChemistryEnvironmental chemistrySpectroscopyMaterials scienceMetallurgyEnvironmental scienceSoil scienceOrganic chemistrySoil water
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Due to its known toxic and carcinogenic effects, the presence of arsenic in drinking water is currently regulated in the United States at a level of 10μg/L. In order to meet regulatory needs and protect public health, single-use adsorbents are utilized to remove arsenic in most water systems in the US. Consequently, when saturated with arsenic, an estimated 10,000 tons of arsenic bearing solid residuals (ABSRs) from drinking water treatment systems will be discarded into landfills every year. However, arsenic may be concentrated and released into landfill leachate. If improperly treated, released arsenic may cause secondary pollution to the discharged water bodies, some of which can be drinking water reservoirs, resulting in possible ecological or human health concerns. Most ABSRs have been shown to pass regulatory leaching tests which determine their eligibility to be disposed of into a municipal landfill. However, existing evidence suggests that regulatory leaching tests failed to simulate the actual landfill conditions potentially favorable for arsenic release, such as elevated pH, reducing conditions, presence of competitive anions and complex natural organic matter (NOM) that could dissolve the minerals comprising the adsorbent. Therefore, it is crucial to investigate the mechanism of arsenic release, especially information of surface binding transformation that directly determines As leachability from the ABSRs under different leaching conditions. This research, therefore, represents one of the first studies utilizing both traditional chemical analysis and x-ray adsorption fine structure (XAFS) spectroscopy to evaluate As binding structures in ABSRs from long-term full-scale operations and associated binding strength differed from geographically diverse sources. Furthermore, this study also aims at providing direct binding transformation evidence to investigate leaching conditions favorable for As release. Our results show that the coexistence of weaker As binding structures is usually an indicator of less effective adsorption during drinking water treatment confounded by source water or treatment processes, such as less operation time, manganese coating and insufficient pH adjustment. Higher initial As loading would lead to larger As release in subsequent landfill disposal. Thus, drinking water facilities should consider the balance between the effective As treatment of the media and disposal cost in the future as the legislation tightens. Our results also presented no binding transformation after leaching tests at lower pH and transformation from stronger binding to weaker bindings as pH increases in the leaching condition. This could be utilized to directly support the idea that current leaching tests are not sufficient enough to simulate As release in landfill conditions with respect to elevated pH. TCLP was also shown to misrepresent the potential carbon-promoted iron dissolution or carbon competition with As on iron surface which would facilitate As release. The iron dissolution is found to be one of main contributors to As release in many recent studies.

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 candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,745
Score d'incertitude au seuil0,952

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 technologies0,0010,000
Communication savante0,0000,000
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 Opus0,016
Tête enseignante GPT0,251
Écart entre enseignants0,234 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
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
Publié2010
Routes d'admission1
Résumé présentoui

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