Remediation of Heavy Metals with Poplar Trees
Notice bibliographique
Résumé
The increase in population and car fleets has led to a sharp rise in the generation of carwash wastewater (CWW). CWW contains heavy metals, detergents, oil and grease, and suspended solids, and is considered one of the most polluting industries in terms of water consumption and wastewater production. In various parts of the world CWW is not treated but left to drain and impair receiving waters. Numerous jurisdictions are examining how simple green infrastructure like low impact development (LID) technologies, such as rain gardens that treat polluted urban stormwater may be used to help treat the pollutant loads in CWW. Given that many of these green technologies use trees, this thesis examines how poplar trees are impacted by and remediate heavy metals that exist in CWW. The study was conducted at the University of Victoria, BC, Canada, and involved both laboratory work and field work conducted from 2021 to 2022. The research focused on tree health and heavy metal uptake and evaluated the performance of the designed treatment field in removing contaminants from CWW. The proposed methodology is grounded in a low-impact development (LID) approach tailored for wastewater treatment, particularly suited to remote, rural, and underserved areas, including developing nations like India, Malaysia, and other countries in the Middle East. The results demonstrate a decreasing trend in the concentration of zinc, cadmium, nickel, iron, copper, and lead from the point of application (point 1) at the field site to the effluent point (point 4), indicating good performance for removing these heavy metals. The removal rates for zinc, nickel, lead, iron, copper, and cadmium were 78.4%, 61.9%, 82.4%, 86.4%, 78.1%, and 98.95% respectively. \nThe study assessed the uptake of heavy metals by poplar trees by analyzing the concentrations in tree leaves. The data showed variations in heavy metal concentrations between different trees and leaf positions, with some metals exhibiting higher concentrations in the bottom leaves and others showing higher concentrations in the top leaves. The concentrations of heavy metals in the leaves were also influenced by seasonal variations and leaf turnover. Overall, the research findings indicate the impact to poplar trees in systems that use poplar trees for treating carwash wastewater, highlighting the importance of considering seasonal variations and leaf turnover when studying heavy metal uptake in trees. These findings carry significance for enhancing wastewater treatment procedures and encouraging responsible approaches across a range of industrial and environmental contexts.
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Comment cette classification a été obtenuedéplier
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 | 0,001 | 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 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 ».