Examining nanoparticle characteristics and removal through direct filtration treatment
Notice bibliographique
Résumé
Water utilities in Nova Scotia face numerous challenges treating low turbidity water and complying with stringent guidelines and treatment standards. Problems associated with the treatment of low-turbidity water are not confined to Nova Scotia; several other provinces, British Columbia, Manitoba and Ontario share similar water characteristics of drinking water sources. The treatment of low turbidity water is a challenge for these utilities as it requires maintaining the appropriate coagulant dosage that will ensure adequate particle and natural organic matter removal, while at the same time not enhancing the formation of disinfection by-products. Another concern associated with the treatment of such water is that when the particle content of the water is very low, charge neutralization will not be effective due to the weak contact between destabilized particles. Currently, nanoparticles are not regulated as water contaminants, and thus it is unclear whether the existing filtration treatment practices are capable of removing them from drinking water. Obtaining in-depth information on nanoparticle characteristics in drinking water sources will provide a valuable resource that can assist in the development of future treatment strategies.\nIn this research, characteristics of four synthetic nanoparticles cerium dioxide (CeO2), ferric oxide (Fe2O3), silicon dioxide (SiO2) and titanium dioxide (TiO2) were investigated in Milli-Q water for particle size, surface area, and surface potential using different characterization techniques. Water samples from Pockwock Lake were also characterized for naturally occurring nanoparticles. After initial testing, titanium dioxide (TiO2) nanoparticles were selected to examine particle removal at bench-scale filtration experiments, under operating conditions similar to those practiced at the J.D. Kline Water Supply Plant, Halifax, NS, Canada. Filter performance for the deposition of TiO2 nanoparticles was evaluated through the calculation of its attachment efficiency and coefficient under various water chemistry conditions. The calculated filter efficiency was then applied to simulate natural nanoparticles removal from water. \nThe results of the research indicate that the investigated nanoparticles behaved similar to natural particles and formed aggregates with larger particle sizes in Milli-Q water. Among the tested nanoparticles, only titanium dioxide could be coagulated with alum, as its negative surface charge and zero point of charge were closer to that of alum. Filtration experiments revealed that TiO2 nanoparticles, when present in water, could successfully be removed by an alum dose of 8 mg/L. Indeed, removal in excess of 99.5% was achieved under the study conditions. Under the investigated water chemistry conditions, very low attachment efficiencies (?) of 0.001, 0.002 and 0.01, and filter coefficients (?) of -0.003, -0.001 and -0.02 were determined for the filters. Based on the calculated attachment efficiencies, and under the studied conditions, natural nanoparticles remain dispersed in the water and would not likely to be removed by direct filtration. The overall research findings represent a major step forward in nanoparticle removal by direct filtration.
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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 | 0,000 | 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 | 0,000 |
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.
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 ».