Increasing in situ bioremediation effectiveness through field-scale application of molecular biological tools
Bibliographic record
Abstract
Leveraging the capabilities of microorganisms to reduce (degrade or transform) concentrations of pollutants in soil and groundwater can be a cost-effective, natural remedial approach to manage contaminated sites. Traditional design and implementation of bioremediation strategies consist of lab-scale biodegradation studies or collection of field-scale geochemical data to infer associated biological processes. While both lab-scale biodegradation studies and field-scale geochemical data are useful for remedial decision-making, additional insights can be gained through the application of Molecular Biological Tools (MBTs) to directly measure contaminant-degrading microorganisms and associated bioremediation processes. Field-scale application of a standardized framework pairing MBTs with traditional contaminant and geochemical analyses was successfully performed at two contaminated sites. At a site with trichloroethene (TCE) impacted groundwater, framework application informed design of an enhanced bioremediation approach. Baseline abundances of 16S rRNA genes for a genus of obligate organohalide-respiring bacteria (i.e., Dehalococcoides ) were measured at low abundances (10 1 –10 2 cells/mL) within the TCE source and plume areas. In combination with geochemical analyses, these data suggested that intrinsic biodegradation (i.e., reductive dechlorination) may be occurring, but activities were limited by electron donor availability. The framework was utilized to support development of a full-scale enhanced bioremediation design (i.e., electron donor addition) and to monitor remedial performance. Additionally, the framework was applied at a second site with residual petroleum hydrocarbon (PHC) impacted soils and groundwater. MBTs, specifically qPCR and 16S gene amplicon rRNA sequencing, were used to characterize intrinsic bioremediation mechanisms. Functional genes associated with anaerobic biodegradation of diesel components (e.g., naphthyl-2-methyl-succinate synthase, naphthalene carboxylase, alkylsuccinate synthase, and benzoyl coenzyme A reductase) were measured to be 2–3 orders of magnitude greater than unimpacted, background samples. Intrinsic bioremediation mechanisms were determined to be sufficient to achieve groundwater remediation objectives. Nonetheless, the framework was further utilized to assess that an enhanced bioremediation could be a successful remedial alternative or complement to source area treatment. While bioremediation of chlorinated solvents, PHCs, and other contaminants has been demonstrated to successfully reduce environmental risk and reach site goals, the application of field-scale MBT data in combination with contaminant and geochemical data analyses to design, implement, and monitor a site-specific bioremediation approach can result in more consistent remedy effectiveness.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".