Microbially Mediated Subsurface Calcite Precipitation for Removal of Hazardous Divalent Cations: Microbial Activity, Molecular Biology, and Modeling
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".