Analysis into the viability of pea gravel as a diffusing material for biostimulation systems in petroleum hydrocarbon‐contaminated soils
Bibliographic record
Abstract
Abstract Injecting biostimulatory solutions around underground storage tanks (UGSTs) promotes petroleum hydrocarbon (PHC) bioremediation without the use of intrusive infrastructure. Yet, it is unclear how the interaction between porous bedding material (i.e., pea gravel) and biostimulatory solutions impacts PHC biodegradation rates. Consequently, we assessed whether pea gravel can act as an inert diffusion material that facilitates PHC bioremediation within UGST beds. First, we evaluated how biostimulatory solution elution through pea gravel (i.e., weathered) altered nutrient composition and benzene degradation rates. Suspected microbial activity decreased the proportion (i.e., nutrient concentration after pea gravel elution divided by initial biostimulatory solution concentration) of ammonium (0.45–0.84) and citrate (0.0–0.25) in effluents. In comparison, the proportion of anaerobic electron acceptors sulfate (0.91–1.08) and nitrate (0.78–1.0) were largely unaffected by pea gravel sorption. After 28 days, the weathered biostimulatory solution degraded 25 ± 0.50% of added benzene at rates similar (0.010 ± 0.002 days −1 ) to fresh biostimulatory solution (0.008 ± 0.001 days −1 ). Next, using positron emission tomography (PET) with [ 18 F]‐fluorodeoxyglucose and [ 18 F]‐fluoride, we evaluated the risks of biofilm fouling to gravel beds. In agreement with the reduced nutrient concentration, PET results show that the biostimulatory solution promoted microbial growth on the surface of the pea gravel. Yet, as the pore space occupied by bacteria remained low (between 8.8% and 13%) following biostimulatory solution elution, biofilm formation is not a concern. Therefore, pea gravel environments in tank nest areas can support PHC biostimulatory solution delivery with minimal consequences.
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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) | 0.001 | 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".