Improving conventional household greensand treatment for efficient Mn(II) removal from drinking water
Notice bibliographique
Résumé
Geogenic contaminants pose a global threat to ensuring access to safe drinking water. Manganese (Mn) is a naturally-occurring redox-active element which in its reduced form, Mn(II), is a widespread groundwater contaminant. Prolonged consumption of water containing high levels of Mn has been linked to adverse effects on memory, attention, motor skills, and nervous system function, particularly in vulnerable groups including pregnant individuals and young children. In addition, Mn can lead to aesthetic issues such as altered taste, clogging, and damage to plumbing systems. While Mn has historically been regulated as an aesthetic concern, a mounting body of evidence linking health issues to Mn exposure through drinking water has heightened the challenges of using groundwater to meet drinking water needs. Recently, Health Canada established a health guideline which set a maximum acceptable concentration for total Mn in drinking water of 120 μg/L. Greensand (GS) filters are commonly used in conventional drinking water treatment systems for Mn(II) removal due to their cost-effectiveness and high exchange capacity. However, under certain conditions conventional GS systems may have a low Mn(II) removal efficiency (Galangashi et al. 2021) and encounter challenges related to Mn leaching (Outram et al. 2018), resulting in failure to meet health-related standards. Furthermore, GS filters typically undergo regeneration using potassium permanganate (KMnO 4 ), a mild oxidant that results in the release of additional Mn waste byproducts during the regeneration of the filter. Recent research suggests that Mn-containing materials can effectively activate peroxymonosulfate (PMS) and produce reactive oxygen species (ROS) that facilitate contaminant degradation. This study aims to investigate whether PMS may be used to improve greensand treatment systems and enhance Mn(II) removal from drinking water. Batch experiments were performed to test the Mn(II) removal efficiency across a range of PMS concentrations (0-500 μM) and GS mass (0.1-3 g). Aqueous Mn concentrations were measured over time using inductively coupled plasma optical emission spectrometry (ICP-OES). Solid-phase reaction products were characterized by X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). The mechanisms of Mn oxidation were identified by quenching experiments and electron paramagnetic resonance (EPR) spectroscopy. The activation of PMS by greensand significantly increased the removal efficiency of Mn(II) compared to the conventional method (e.g., 96.83(±3.77)% at PMS = 500 µM vs. 5.77(±11.1)% at PMS = 0 µM). Our results attribute the mechanism underlying increased Mn removal in our improved treatment method to advanced oxidation processes that involve free radicals (e.g., hydroxyl radical (·OH) and sulfate radical (SO 4 · - )) and non-free radical pathways, with Mn oxides as the main oxidation products. This study provides a new treatment method for more efficient Mn(II) removal while simultaneously reducing Mn wastes produced during the regeneration cycle which allows for a more sustainable and holistic management of invaluable groundwater resources.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».