16Evaluation of<scp>MOF</scp>Applications for Groundwater Arsenic Mitigation of the Middle Ganga Plains of Bihar, India
Notice bibliographique
Résumé
In recent years, many parts of the world, including Argentina, Bangladesh, Bolivia, Brazil, Chile, China, Cambodia, Ghana, Greece, Hungary, India, Japan, Korea, Mexico, Mongolia, Nepal, New Zealand, Poland, Taiwan, Vietnam, and the USA, have highlighted the contamination of groundwater by geogenic arsenic (As) as an environmental catastrophe. More than 300 million people worldwide are at risk if the WHO's provisional drinking water guideline of 10 ppb of As is followed. Of these, more than 45 million people, mostly in developing countries in Asia, are at risk of exposure to more than 50 ppb of As, which is the maximum concentration limit in drinking water in the majority of Asian countries. As many as 70 million people could be at risk of chronic arsenic poisoning in South and Southeast Asia, especially in the eastern to north-eastern region of India and neighboring Bangladesh. It is generally known how serious arsenic contamination is in the eastern Indian state of Bihar, which is next to West Bengal. Out of 38 districts, 22 were found to have drinking water with arsenic levels over the WHO's provisional guideline of 10 µg/L. It is estimated that more than 10 million people consume water containing arsenic at levels more than 10µg/L, and 33% of the hand tube-well samples tested for arsenic had levels higher than the WHO's provisional guideline. Arsenicosis, also known as excessive and prolonged exposure to toxic inorganic arsenic, is a term used to describe a range of health issues associated with exposure to arsenic, including abnormalities of the skin; internal cancers of the bladder, kidney, and lung; diseases of the blood vessels in the legs and feet; possible diabetes; high blood pressure; and reproductive issues. Especially when consumed by drinking water, food made with this contaminated water and food crops irrigated with high-arsenic water resources, arsenic exposure is frequently undetected due to its physical properties: no odor, no color, and no flavor. Long-term exposure to arsenic has a number of health hazards that have drawn attention on a worldwide scale. For the removal of arsenic, there are various options. Arsenic short-term solutions, alternative deeper aquifers (those that are hydrogeologically compatible), and surface-water-based supplies may be used. In-situ removal of arsenic is currently being prioritized since it is environmentally favorable because no sludge is created. However, the development of knowledge and ability among the inhabitants of the affected areas is essential to the effectiveness of any arsenic mitigation program. Metal-organic frameworks (MOFs) are the future technologies needed to be developed for decontamination of aquatic arsenic. Few studies clearly indicate that the zirconium MOF (UiO-66) works efficiently to decontaminate aquatic arsenic. As a conclusion, we think there are good chances for the development of affordable and environmentally friendly bio-remediation technologies like MOFs and other absorption techniques, which present fantastic opportunities for the treatment of arsenic in affected areas and in the treatment of industrial wastewater. We are sure that the creation of the necessary arsenic remediation technique is feasible given the most recent developments in bioremediation. The issue of the exposed population will undoubtedly be resolved by all mitigation measures.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».