Microbially Mediated Subsurface Calcite Precipitation for Removal of Hazardous Divalent Cations: Microbial Activity, Molecular Biology, and Modeling
Notice bibliographique
Résumé
Current approaches for remediating hazardous divalent cations in aquifers are costly, can generate large volumes of waste, and focus on the small amounts of contaminants in the water rather than the larger reservoir of contamination sorbed to the aquifer matrix. An alternative to waste removal and repackaging is to encourage in situ biogeochemical processes to permanently bind the contaminants in the mineral matrix of an aquifer. Our research involves one such approach in which we accelerate calcite precipitation (an on going geochemical process in arid western aquifers) and the assisted co-precipitation of cationic contaminants like strontium-90 using biologically driven urea hydrolysis to increase aquifer pH and alkalinity. This paper describes progress related to stimulating and measuring indigenous urease activities in aquifer microbes and how these activities can be modeled for application in an aquifer of concern to the U.S. Department of Energy. Experiments using 14 C-labeled urea indicated that microbial communities from the Snake River Plain aquifer (SRPA) of eastern Idaho hydrolyzed urea at rates higher than those measured for a model urea hydrolyzing bacterium (Bacillus pasteurii) under similar conditions, if they were provided a source of organic carbon along with the urea. By using a phylogenetic approach for analyzing urease gene sequences we developed polymerase chain reaction primer pairs that detected the ureC gene in urease positive microbial isolates. In a field test where molasses and urea were added to the SRPA, the ca. 400 base pair ureC fragment was amplified from DNA extracted from aquifer cells. Amplification and sequencing of bacterial 16S rDNA gene fragments from the aquifer before and after the molasses and urea additions indicated measurable changes in the communities as a result of the treatment. Rate constants derived from urease activity experiments were used to simulate the calcite precipitation process in the SRPA. The model predicts that field application would result in three distinct geochemical reaction phases: a condition where urea hydrolysis rates exceed calcite precipitation rates, a condition where calcite precipitation rates exceed urea hydrolysis rates, and finally a condition where the two rates are equal. The model also indicates that most of the metals that are precipitated as carbonates will come from the aquifer matrix, not the groundwater. These two modeling observations suggest that when the rates of calcite precipitation and urea hydrolysis are equal, the entire process can be described by a pseudo-first order kinetic model. In this model the calcite precipitation rate is controlled by the urea hydrolysis rate and is independent of the concentration of calcium in the groundwater. The use of these techniques for determining the response of microbial communities to urea additions, as well as the predictive capabilities of the model, will allow better control and evaluation of pending field experiments to test calcite precipitation as an approach for contaminant removal from aquifers.
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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) | 0,000 | 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 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 ».