Remediation of a heavy metal and PAH-contaminated sediment by a rhamnolipid foam
Notice bibliographique
Résumé
Industrial contaminants have long term and sometimes irreversible adverse effects on humans, animals and the ecosystem. As a result attention has been focused on the effects of these contaminants. One of the concerning environmental issues these days is contaminated sediments. The presence of aquatic organisms in sediments makes them important in the well-being and health of these organisms. If sediments become contaminated, they can pose a threat to sediment dwelling habitants and through these microorganism that can be spread even to humans. Nowadays a major concern that government regulators and related industry can face is the protection and investigation of the quality of aquatic sediments. A protection that can be done through different pathways includes remediation. \nAn investigation was made into evaluating the capability of a rhamnolipid biosurfactant (JBR425) in the form of a foam for treatment of polycyclic aromatic hydrocarbon (PAH) contaminated fresh water sediments that also have elevated levels of Pb, Zn and Ni. Studies commenced by evaluating foam characteristics followed by performing column tests. To do this, dewatered non-dried sediments were put in a column and the biosurfactant was injected in the form of a foam or a liquid solution. The pressure gradient was monitored during flushing tests to avoid possible problems due to high pressure. Foam quality of the rhamnolipid varied between 85% and 99% with stabilities from 15 to 43 min. PAH and metal removal were then evaluated for sediment samples from sector 103 of the Port of Montreal in Montreal, Quebec with different initial concentrations of target contaminants. Among PAHs, pyrene; benz(a)anthracene and chrysene had concentrations above thresholds according to Quebec Sediment Quality Criteria. Highest removal for PAHs was obtained by a 99% quality foam produced by 0.5% rhamnolipid solution after 20 pore volumes. Removal efficiency (due to mobilization) for the biosurfactant foam was 44.6% of pyrene, 30% of benz(a)anthracene and 37.8% of chrysene while total removal efficiency (mobilization + volatilization) for the biosurfactant foam was 56.4% of pyrene, 41.2% of benz(a)anthracene and 45.9% of chrysene. With biosurfactant liquid solution at the same pH as above mentioned foam (pH 6.8), maximum removal (mobilization) was 31.4% of pyrene, 20.5% of benz(a)anthracene and 27% of chrysene. Here no volatilization of PAHs was observed. Deionized water (DI) did not remove any PAH. For metals, highest removal was achieved using 0.5% rhamnolipid foam (99% quality, pH 10.0). These were 53.3% of Ni, 56.8% of Pb and 55.2% of Zn. Removal efficiencies were reduced between 11% - 13% for metals when a 0.5% rhamnolipid solution was used. DI water removed only 16% for both Pb and Ni individually and 17% of Zn at pH 10.0. From these analyses, lower pH (6.8) rhamnolipid showed higher removal efficiencies for PAHs while it was not as successful for metals. As for metals, pH 10 was proven to be the best. It is concluded that, rhamnolipid foam could be a non-toxic and effective method of remediating PAH and heavy metal contaminated soil/sediments. Further efforts will be required to optimize the performance of the foam.
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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 ».