Metal uptake by bacteria
Notice bibliographique
Résumé
A process of using the manganese-oxidizing bacterium, Rhizobium etli, for the uptake of several metal ions present in acid mine drainage waters (AMD) such as Mn 2 +,. l n , e , g , u an ions as een mvesttgate .In t e actena manganese oxidation process previously developed in this laboratory (Moy, 1998), it was shown thatRhizobium etli was able to rapidly take up manganese in glucose media.Variation in media composition by increasing glucose concentration had marked effects on manganese uptake and its uptake rate.The addition of excess glucose promoted the production of extracellular polysaccharides (EPS) and subsequently enhanced manganese uptake.The maximum uptake of manganese occurred at the maximum EPS concentration in medium, that was about 1560 mg/L of manganese or 3 8% of cellular dry weight with the average uptake rate of 39 mg/L/h, which occurred in the presence of 7.31 g/L EPS resulting from medium with glucose concentration of 25.8 g/L (C/N=l 0).Characteristics of manganese uptake by Rhizobium et/i indicated active metabolic uptake and sensitive to other metal cations since the presence of Zn 2 +, F e 2 +, Mg 2 + (in high concentration), Cu 2 + and Pb 2 + ions reduced the manganese uptake and uptake rate.The maximal metal uptake in binary mixtures of manganese and these metals was about 1000 mg/L of Mn, 180 mg/L of Zn, 50 mg/L of Mg and 30 mg/L of Fe but the bacterium failed to take up Cu and Pb.The presence of multi metal ions reduced significantly the manganese uptake rates from 12-34 mg/L/h in binary metal mixtures to 5.7 mg/L/h in multi metal mixture, indicating that the presence of multi metal ions resulted in some cumulative inhibitory effect on the manganese uptake rate.With the given initial metal concentrations that mimic the metal composition in a typical AMD (255 mg/L Mn, 20 mg/L Zn, 30 mg/L Fe, 20 mg/L Mg, 40 mg/L Cu and 0.5 mg/L Pb), Rhizobium etli was capable of removing Mn, Zn, Fe and Mg to levels below discharge standard, supporting the idea that manganese-oxidizing bacteria could potentially participate in AMD mitigation.This process has practical potential for removing metals from AMD, particularly for AMD that contains low levels of Pb and Cu.AMD can cause long-term environmental problems at many types of mines if it is not treated appropriately.The treatment of AMD can be a large cost factor.It has been reported that The Canadian Mine Environment Neutral Drainage (MEND) in Canada, for instance, spent C$ l 8 million in 1997 (MEND, 1996), and the US Environmental Protection Agency (EPA) in the United States spent US$50,000 per day on containment and AMD treatment for the Summitville site in 1996 (US EPA, 1996). I. I. 1 Occurrence of metals in acid mine drainage waters and their impact on the environmentAll mining activities particularly base metal mines produce significant tonnages of waste rock as by-product which is exposed to air and water in the environment.This waste rock may consist of sulphide minerals such as pyrite (FeS2), albandite (MnS), sphalerite (ZnS), chalcocite (Cu2S), galena (PbS) and may remain on-site long after mining operations cease.In addition, water enters most mines.The nature of these mine waters varies very considerably from alkaline, moderately or highly saline, to acidic.Understanding these characteristics is important in dealing with the metal discharges of individual mines (Pentreath, 1994).Mine drainage waters can be contaminated by metals via two pnmary mechanisms (Broughton et al., 1992):1. Flushing and leaching of readily soluble metals under neutral or near neutral pH conditions.2. Biological and chemical oxidation, and acid generation resulting in the release of metals and subsequent leaching under acidic conditions.
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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 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 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 | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,026 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».