Mineral Adsorbents for Removal of Metals in Urban Runoff
Notice bibliographique
Résumé
The aim of this research was to determine the capacity of four different soil minerals to adsorb metals frequently detected in urban runoff. These are low-cost, natural and commercially available soil minerals. Contaminated surface runoff from urban areas is a major cause of concern for water quality and aquatic ecosystems worldwide. Pollution in urban areas is generated by a wide array of non-point sources, including vehicular transportation and building materials. Some of the most frequently detected pollutants in urban runoff are metals. Exhaust gases, tire wear and brake linings are major sources of such metals as Pb, Zn and Cu, while impregnated wood, plastics and galvanized surfaces may release As, Cd, Cr and Zn. Many metals have toxic effects on aquatic plants and animals, depending on metal speciation and bioavailability. The removal efficiency of pollutants in stormwater depends on treatment practices and on the properties the pollutant. The distribution of metals in urban runoff has shown, for example, that Pb is predominantly particle-associated, whereas Zn and Cd are present mainly in dissolved form. Many metals are also attached to colloids, which may act as carriers for contaminants, thereby facilitating their transport through conventional water treatment processes. Filtration of stormwater is one of the most promising techniques for removal of particulates, colloidal and truly dissolved pollutants, provided that effective filtration and adsorption media are used. Filtration and infiltration are used in a wide array of stormwater treatment methods e.g. porous paving, infiltration drains and rain gardens. Several soil minerals were investigated for their potential as stormwater filter materials. Laboratory batch tests were conducted to determine the adsorption capacity of these minerals. A synthetic stormwater was tested, with spiked concentrations corresponding to levels reported in urban runoff, ranging from 50–1,500 µg/L for Zn; 5–250 µg/L for Cu, 2–20 µg/L for Cd and 10–150 µg/L for Ni and Pb. Humic acids were used to imitate natural stormwater contaminated with natural organic matter (NOM). The adsorption kinetics was also investigated through a sequence (10–120 min) of batch tests. By studying the capacity of a range of sorbents in batch tests under identical conditions, the most promising sorbent can be identified. The research is ongoing. Preliminary results will be presented.
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 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 | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 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 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 ».